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) 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t(e,t,i){return[parseFloat(e[0])*(p.test(e[0])?t/100:1),parseFloat(e[1])*(p.test(e[1])?i/100:1)]}function i(t,i){return parseInt(e.css(t,i),10)||0}function s(t){var i=t[0];return 9===i.nodeType?{width:t.width(),height:t.height(),offset:{top:0,left:0}}:e.isWindow(i)?{width:t.width(),height:t.height(),offset:{top:t.scrollTop(),left:t.scrollLeft()}}:i.preventDefault?{width:0,height:0,offset:{top:i.pageY,left:i.pageX}}:{width:t.outerWidth(),height:t.outerHeight(),offset:t.offset()}}e.ui=e.ui||{};var n,a,o=Math.max,r=Math.abs,h=Math.round,l=/left|center|right/,u=/top|center|bottom/,d=/[\+\-]\d+(\.[\d]+)?%?/,c=/^\w+/,p=/%$/,f=e.fn.position;e.position={scrollbarWidth:function(){if(void 0!==n)return n;var t,i,s=e("<div style='display:block;position:absolute;width:50px;height:50px;overflow:hidden;'><div style='height:100px;width:auto;'></div></div>"),a=s.children()[0];return e("body").append(s),t=a.offsetWidth,s.css("overflow","scroll"),i=a.offsetWidth,t===i&&(i=s[0].clientWidth),s.remove(),n=t-i},getScrollInfo:function(t){var i=t.isWindow||t.isDocument?"":t.element.css("overflow-x"),s=t.isWindow||t.isDocument?"":t.element.css("overflow-y"),n="scroll"===i||"auto"===i&&t.width<t.element[0].scrollWidth,a="scroll"===s||"auto"===s&&t.height<t.element[0].scrollHeight;return{width:a?e.position.scrollbarWidth():0,height:n?e.position.scrollbarWidth():0}},getWithinInfo:function(t){var i=e(t||window),s=e.isWindow(i[0]),n=!!i[0]&&9===i[0].nodeType;return{element:i,isWindow:s,isDocument:n,offset:i.offset()||{left:0,top:0},scrollLeft:i.scrollLeft(),scrollTop:i.scrollTop(),width:s||n?i.width():i.outerWidth(),height:s||n?i.height():i.outerHeight()}}},e.fn.position=function(n){if(!n||!n.of)return f.apply(this,arguments);n=e.extend({},n);var p,m,g,v,y,b,_=e(n.of),x=e.position.getWithinInfo(n.within),w=e.position.getScrollInfo(x),k=(n.collision||"flip").split(" "),T={};return b=s(_),_[0].preventDefault&&(n.at="left top"),m=b.width,g=b.height,v=b.offset,y=e.extend({},v),e.each(["my","at"],function(){var e,t,i=(n[this]||"").split(" ");1===i.length&&(i=l.test(i[0])?i.concat(["center"]):u.test(i[0])?["center"].concat(i):["center","center"]),i[0]=l.test(i[0])?i[0]:"center",i[1]=u.test(i[1])?i[1]:"center",e=d.exec(i[0]),t=d.exec(i[1]),T[this]=[e?e[0]:0,t?t[0]:0],n[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===k.length&&(k[1]=k[0]),"right"===n.at[0]?y.left+=m:"center"===n.at[0]&&(y.left+=m/2),"bottom"===n.at[1]?y.top+=g:"center"===n.at[1]&&(y.top+=g/2),p=t(T.at,m,g),y.left+=p[0],y.top+=p[1],this.each(function(){var s,l,u=e(this),d=u.outerWidth(),c=u.outerHeight(),f=i(this,"marginLeft"),b=i(this,"marginTop"),D=d+f+i(this,"marginRight")+w.width,S=c+b+i(this,"marginBottom")+w.height,N=e.extend({},y),M=t(T.my,u.outerWidth(),u.outerHeight());"right"===n.my[0]?N.left-=d:"center"===n.my[0]&&(N.left-=d/2),"bottom"===n.my[1]?N.top-=c:"center"===n.my[1]&&(N.top-=c/2),N.left+=M[0],N.top+=M[1],a||(N.left=h(N.left),N.top=h(N.top)),s={marginLeft:f,marginTop:b},e.each(["left","top"],function(t,i){e.ui.position[k[t]]&&e.ui.position[k[t]][i](N,{targetWidth:m,targetHeight:g,elemWidth:d,elemHeight:c,collisionPosition:s,collisionWidth:D,collisionHeight:S,offset:[p[0]+M[0],p[1]+M[1]],my:n.my,at:n.at,within:x,elem:u})}),n.using&&(l=function(e){var t=v.left-N.left,i=t+m-d,s=v.top-N.top,a=s+g-c,h={target:{element:_,left:v.left,top:v.top,width:m,height:g},element:{element:u,left:N.left,top:N.top,width:d,height:c},horizontal:0>i?"left":t>0?"right":"center",vertical:0>a?"top":s>0?"bottom":"middle"};d>m&&m>r(t+i)&&(h.horizontal="center"),c>g&&g>r(s+a)&&(h.vertical="middle"),h.important=o(r(t),r(i))>o(r(s),r(a))?"horizontal":"vertical",n.using.call(this,e,h)}),u.offset(e.extend(N,{using:l}))})},e.ui.position={fit:{left:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollLeft:s.offset.left,a=s.width,r=e.left-t.collisionPosition.marginLeft,h=n-r,l=r+t.collisionWidth-a-n;t.collisionWidth>a?h>0&&0>=l?(i=e.left+h+t.collisionWidth-a-n,e.left+=h-i):e.left=l>0&&0>=h?n:h>l?n+a-t.collisionWidth:n:h>0?e.left+=h:l>0?e.left-=l:e.left=o(e.left-r,e.left)},top:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollTop:s.offset.top,a=t.within.height,r=e.top-t.collisionPosition.marginTop,h=n-r,l=r+t.collisionHeight-a-n;t.collisionHeight>a?h>0&&0>=l?(i=e.top+h+t.collisionHeight-a-n,e.top+=h-i):e.top=l>0&&0>=h?n:h>l?n+a-t.collisionHeight:n:h>0?e.top+=h:l>0?e.top-=l:e.top=o(e.top-r,e.top)}},flip:{left:function(e,t){var i,s,n=t.within,a=n.offset.left+n.scrollLeft,o=n.width,h=n.isWindow?n.scrollLeft:n.offset.left,l=e.left-t.collisionPosition.marginLeft,u=l-h,d=l+t.collisionWidth-o-h,c="left"===t.my[0]?-t.elemWidth:"right"===t.my[0]?t.elemWidth:0,p="left"===t.at[0]?t.targetWidth:"right"===t.at[0]?-t.targetWidth:0,f=-2*t.offset[0];0>u?(i=e.left+c+p+f+t.collisionWidth-o-a,(0>i||r(u)>i)&&(e.left+=c+p+f)):d>0&&(s=e.left-t.collisionPosition.marginLeft+c+p+f-h,(s>0||d>r(s))&&(e.left+=c+p+f))},top:function(e,t){var i,s,n=t.within,a=n.offset.top+n.scrollTop,o=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=e.top-t.collisionPosition.marginTop,u=l-h,d=l+t.collisionHeight-o-h,c="top"===t.my[1],p=c?-t.elemHeight:"bottom"===t.my[1]?t.elemHeight:0,f="top"===t.at[1]?t.targetHeight:"bottom"===t.at[1]?-t.targetHeight:0,m=-2*t.offset[1];0>u?(s=e.top+p+f+m+t.collisionHeight-o-a,(0>s||r(u)>s)&&(e.top+=p+f+m)):d>0&&(i=e.top-t.collisionPosition.marginTop+p+f+m-h,(i>0||d>r(i))&&(e.top+=p+f+m))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,i,s,n,o,r=document.getElementsByTagName("body")[0],h=document.createElement("div");t=document.createElement(r?"div":"body"),s={visibility:"hidden",width:0,height:0,border:0,margin:0,background:"none"},r&&e.extend(s,{position:"absolute",left:"-1000px",top:"-1000px"});for(o in s)t.style[o]=s[o];t.appendChild(h),i=r||document.documentElement,i.insertBefore(t,i.firstChild),h.style.cssText="position: absolute; left: 10.7432222px;",n=e(h).offset().left,a=n>10&&11>n,t.innerHTML="",i.removeChild(t)}()}(),e.ui.position,e.widget("ui.draggable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass("ui-draggable"),this.options.disabled&&this.element.addClass("ui-draggable-disabled"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),"handle"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this.element.removeClass("ui-draggable ui-draggable-dragging ui-draggable-disabled"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&"body"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===e(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.handleElement.removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){"y"!==this.options.axis&&"auto"!==this.helper.css("right")&&(this.helper.width(this.helper.width()),this.helper.css("right","auto")),"x"!==this.options.axis&&"auto"!==this.helper.css("bottom")&&(this.helper.height(this.helper.height()),this.helper.css("bottom","auto"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css("position"),top:e.placeholder.css("top"),left:e.placeholder.css("left")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger("deactivate",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n })),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data("ui-sortable-item",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger("toSortable",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger("out",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger("fromSortable",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&"HTML"!==o.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var n,a,o,r,h,l,u,d,c,p,f=s.options,m=f.snapTolerance,g=i.offset.left,v=g+s.helperProportions.width,y=i.offset.top,b=y+s.helperProportions.height;for(c=s.snapElements.length-1;c>=0;c--)h=s.snapElements[c].left-s.margins.left,l=h+s.snapElements[c].width,u=s.snapElements[c].top-s.margins.top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.droppable",{version:"1.11.4",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=e(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(i,{offset:i.element.offset()}),i.options.tolerance,t)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(s)),this.element):!1):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(){function e(e,t,i){return e>=t&&t+i>e}return function(t,i,s,n){if(!i.offset)return!1;var a=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,r=a+t.helperProportions.width,h=o+t.helperProportions.height,l=i.offset.left,u=i.offset.top,d=l+i.proportions().width,c=u+i.proportions().height;switch(s){case"fit":return a>=l&&d>=r&&o>=u&&c>=h;case"intersect":return a+t.helperProportions.width/2>l&&d>r-t.helperProportions.width/2&&o+t.helperProportions.height/2>u&&c>h-t.helperProportions.height/2;case"pointer":return e(n.pageY,u,i.proportions().height)&&e(n.pageX,l,i.proportions().width);case"touch":return(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c)&&(a>=l&&d>=a||r>=l&&d>=r||l>a&&r>d);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance,i),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return e(this).droppable("instance").options.scope===n}),a.length&&(s=e(a[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(t,i){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)}},e.ui.droppable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=e(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("<div class='ui-resizable-handle "+a+"'></div>"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+"px"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css("borderTopWidth"),e.css("borderRightWidth"),e.css("borderBottomWidth"),e.css("borderLeftWidth")],n=[e.css("paddingTop"),e.css("paddingRight"),e.css("paddingBottom"),e.css("paddingLeft")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("<div style='overflow:hidden;'></div>"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable("instance"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css("position"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css("position")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data("ui-resizable-alsoresize"),n={},a=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t,i=e(this).resizable("instance"),s=i.options,n=i.size,a=i.originalSize,o=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,u=h[1]||1,d=Math.round((n.width-a.width)/l)*l,c=Math.round((n.height-a.height)/u)*u,p=a.width+d,f=a.height+c,m=s.maxWidth&&p>s.maxWidth,g=s.maxHeight&&f>s.maxHeight,v=s.minWidth&&s.minWidth>p,y=s.minHeight&&s.minHeight>f;s.grid=h,v&&(p+=l),y&&(f+=u),m&&(p-=l),g&&(f-=u),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=o.top-c):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=o.left-d):((0>=f-u||0>=p-l)&&(t=i._getPaddingPlusBorderDimensions(this)),f-u>0?(i.size.height=f,i.position.top=o.top-c):(f=u-t.height,i.size.height=f,i.position.top=o.top+a.height-f),p-l>0?(i.size.width=p,i.position.left=o.left-d):(p=l-t.width,i.size.width=p,i.position.left=o.left+a.width-p))}}),e.ui.resizable,e.widget("ui.selectable",e.ui.mouse,{version:"1.11.4",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var t,i=this;this.element.addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){t=e(i.options.filter,i.element[0]),t.addClass("ui-selectee"),t.each(function(){var t=e(this),i=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:i.left,top:i.top,right:i.left+t.outerWidth(),bottom:i.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=t.addClass("ui-selectee"),this._mouseInit(),this.helper=e("<div class='ui-selectable-helper'></div>")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger("start",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=e.data(this,"selectable-item");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass("ui-selected"),s.selected=!1,s.$element.addClass("ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,"selectable-item");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass("ui-selected"),n.$element.removeClass(s?"ui-unselecting":"ui-selected").addClass(s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",t,{selecting:n.element}):i._trigger("unselecting",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,"selectable-item"),l=!1; i&&i.element!==s.element[0]&&("touch"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):"fit"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass("ui-selected"),i.selected=!1),i.unselecting&&(i.$element.removeClass("ui-unselecting"),i.unselecting=!1),i.selecting||(i.$element.addClass("ui-selecting"),i.selecting=!0,s._trigger("selecting",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.$element.addClass("ui-selected"),i.selected=!0):(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.startselected&&(i.$element.addClass("ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass("ui-selected"),i.selected=!1,i.$element.addClass("ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(".ui-unselecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",t,{unselected:s.element})}),e(".ui-selecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-selecting").addClass("ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",t,{selected:s.element})}),this._trigger("stop",t),this.helper.remove(),!1}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tbody"===s?t._createTrPlaceholder(t.currentItem.find("tr").eq(0),e("<tr>",t.document[0]).appendTo(n)):"tr"===s?t._createTrPlaceholder(t.currentItem,n):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e("<td> </td>",s.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.accordion",{version:"1.11.4",options:{active:0,animate:{},collapsible:!1,event:"click",header:"> li > :first-child,> :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=e(),this.element.addClass("ui-accordion ui-widget ui-helper-reset").attr("role","tablist"),t.collapsible||t.active!==!1&&null!=t.active||(t.active=0),this._processPanels(),0>t.active&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():e()}},_createIcons:function(){var t=this.options.icons;t&&(e("<span>").addClass("ui-accordion-header-icon ui-icon "+t.header).prependTo(this.headers),this.active.children(".ui-accordion-header-icon").removeClass(t.header).addClass(t.activeHeader),this.headers.addClass("ui-accordion-icons"))},_destroyIcons:function(){this.headers.removeClass("ui-accordion-icons").children(".ui-accordion-header-icon").remove()},_destroy:function(){var e;this.element.removeClass("ui-accordion ui-widget ui-helper-reset").removeAttr("role"),this.headers.removeClass("ui-accordion-header ui-accordion-header-active ui-state-default ui-corner-all ui-state-active ui-state-disabled ui-corner-top").removeAttr("role").removeAttr("aria-expanded").removeAttr("aria-selected").removeAttr("aria-controls").removeAttr("tabIndex").removeUniqueId(),this._destroyIcons(),e=this.headers.next().removeClass("ui-helper-reset ui-widget-content ui-corner-bottom ui-accordion-content ui-accordion-content-active ui-state-disabled").css("display","").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&e.css("height","")},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):("event"===e&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(t)),this._super(e,t),"collapsible"!==e||t||this.options.active!==!1||this._activate(0),"icons"===e&&(this._destroyIcons(),t&&this._createIcons()),"disabled"===e&&(this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this.headers.add(this.headers.next()).toggleClass("ui-state-disabled",!!t)),void 0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var e=this.headers,t=this.panels;this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.panels=this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide(),t&&(this._off(e.not(this.headers)),this._off(t.not(this.panels)))},_refresh:function(){var t,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active).addClass("ui-accordion-header-active ui-state-active ui-corner-top").removeClass("ui-corner-all"),this.active.next().addClass("ui-accordion-content-active").show(),this.headers.attr("role","tab").each(function(){var t=e(this),i=t.uniqueId().attr("id"),s=t.next(),n=s.uniqueId().attr("id");t.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(t=n.height(),this.element.siblings(":visible").each(function(){var i=e(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(t-=i.outerHeight(!0))}),this.headers.each(function(){t-=e(this).outerHeight(!0)}),this.headers.next().each(function(){e(this).height(Math.max(0,t-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===s&&(t=0,this.headers.next().each(function(){t=Math.max(t,e(this).css("height","").height())}).height(t))},_activate:function(t){var i=this._findActive(t)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):e()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n[0]===s[0],o=a&&i.collapsible,r=o?e():n.next(),h=s.next(),l={oldHeader:s,oldPanel:h,newHeader:o?e():n,newPanel:r}; t.preventDefault(),a&&!i.collapsible||this._trigger("beforeActivate",t,l)===!1||(i.active=o?!1:this.headers.index(n),this.active=a?e():n,this._toggle(l),s.removeClass("ui-accordion-header-active ui-state-active"),i.icons&&s.children(".ui-accordion-header-icon").removeClass(i.icons.activeHeader).addClass(i.icons.header),a||(n.removeClass("ui-corner-all").addClass("ui-accordion-header-active ui-state-active ui-corner-top"),i.icons&&n.children(".ui-accordion-header-icon").removeClass(i.icons.header).addClass(i.icons.activeHeader),n.next().addClass("ui-accordion-content-active")))},_toggle:function(t){var i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css("box-sizing"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return"number"==typeof d&&(a=d),"string"==typeof d&&(n=d),n=n||d.easing||u.easing,a=a||d.duration||u.duration,t.length?e.length?(s=e.show().outerHeight(),t.animate(this.hideProps,{duration:a,easing:n,step:function(e,t){t.now=Math.round(e)}}),e.hide().animate(this.showProps,{duration:a,easing:n,complete:c,step:function(e,i){i.now=Math.round(e),"height"!==i.prop?"content-box"===h&&(r+=i.now):"content"!==o.options.heightStyle&&(i.now=Math.round(s-t.outerHeight()-r),r=0)}}),void 0):t.animate(this.hideProps,a,n,c):e.animate(this.showProps,a,n,c)},_toggleComplete:function(e){var t=e.oldPanel;t.removeClass("ui-accordion-content-active").prev().removeClass("ui-corner-top").addClass("ui-corner-all"),t.length&&(t.parent()[0].className=t.parent()[0].className),this._trigger("activate",null,e)}}),e.widget("ui.menu",{version:"1.11.4",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},items:"> *",menus:"ul",position:{my:"left-1 top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().addClass("ui-menu ui-widget ui-widget-content").toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length).attr({role:this.options.role,tabIndex:0}),this.options.disabled&&this.element.addClass("ui-state-disabled").attr("aria-disabled","true"),this._on({"mousedown .ui-menu-item":function(e){e.preventDefault()},"click .ui-menu-item":function(t){var i=e(t.target);!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&e(this.document[0].activeElement).closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(t){if(!this.previousFilter){var i=e(t.currentTarget);i.siblings(".ui-state-active").removeClass("ui-state-active"),this.focus(t,i)}},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-menu-icons ui-front").removeAttr("role").removeAttr("tabIndex").removeAttr("aria-labelledby").removeAttr("aria-expanded").removeAttr("aria-hidden").removeAttr("aria-disabled").removeUniqueId().show(),this.element.find(".ui-menu-item").removeClass("ui-menu-item").removeAttr("role").removeAttr("aria-disabled").removeUniqueId().removeClass("ui-state-hover").removeAttr("tabIndex").removeAttr("role").removeAttr("aria-haspopup").children().each(function(){var t=e(this);t.data("ui-menu-submenu-carat")&&t.remove()}),this.element.find(".ui-menu-divider").removeClass("ui-menu-divider ui-widget-content")},_keydown:function(t){var i,s,n,a,o=!0;switch(t.keyCode){case e.ui.keyCode.PAGE_UP:this.previousPage(t);break;case e.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case e.ui.keyCode.HOME:this._move("first","first",t);break;case e.ui.keyCode.END:this._move("last","last",t);break;case e.ui.keyCode.UP:this.previous(t);break;case e.ui.keyCode.DOWN:this.next(t);break;case e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||"",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("<span>").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, .ui-menu-divider").addClass("ui-menu-item").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(e,t){"icons"===e&&this.element.find(".ui-menu-icon").removeClass(this.options.icons.submenu).addClass(t.submenu),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||e(t.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget("ui.autocomplete",{version:"1.11.4",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move("previousPage",n);break;case a.PAGE_DOWN:t=!0,this._move("nextPage",n);break;case a.UP:t=!0,this._keyEvent("previous",n);break;case a.DOWN:t=!0,this._keyEvent("next",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e("<ul>").addClass("ui-autocomplete ui-front").appendTo(this._appendTo()).menu({role:null}).hide().menu("instance"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur});var i=this.menu.element[0];e(t.target).closest(".ui-menu-item").length||this._delay(function(){var t=this;this.document.one("mousedown",function(s){s.target===t.element[0]||s.target===i||e.contains(i,s.target)||t.close()})})},menufocus:function(t,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type))?(this.menu.blur(),this.document.one("mousemove",function(){e(t.target).trigger(t.originalEvent)}),void 0):(n=i.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:n})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(n.value),s=i.item.attr("aria-label")||n.value,s&&e.trim(s).length&&(this.liveRegion.children().hide(),e("<div>").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,t){var i=t.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e("<span>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass("ui-autocomplete-input").removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),"source"===e&&this._initSource(),"appendTo"===e&&this.menu.element.appendTo(this._appendTo()),"disabled"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:"json",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(":visible"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return e=null!=e?e:this._value(),this.term=this._value(),e.length<this.options.minLength?this.close(t):this._trigger("search",t)!==!1?this._search(e):void 0},_search:function(e){this.pending++,this.element.addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:e},this._response())},_response:function(){var t=++this.requestIndex;return e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass("ui-autocomplete-loading")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger("response",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger("open")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",e))},_change:function(e){this.previous!==this._value()&&this._trigger("change",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return"string"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-autocomplete-item",t)},_renderItem:function(t,i){return e("<li>").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),"i");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget("ui.autocomplete",e.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(e){return e+(e>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e("<div>").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var r,h="ui-button ui-widget ui-state-default ui-corner-all",l="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",u=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},d=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.4",defaultElement:"<button>",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest("form").unbind("reset"+this.eventNamespace).bind("reset"+this.eventNamespace,u),"boolean"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop("disabled"):this.element.prop("disabled",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr("title");var t=this,i=this.options,s="checkbox"===this.type||"radio"===this.type,n=s?"":"ui-state-active";null===i.label&&(i.label="input"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(h).attr("role","button").bind("mouseenter"+this.eventNamespace,function(){i.disabled||this===r&&e(this).addClass("ui-state-active")}).bind("mouseleave"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind("click"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass("ui-state-focus")},blur:function(){this.buttonElement.removeClass("ui-state-focus")}}),s&&this.element.bind("change"+this.eventNamespace,function(){t.refresh()}),"checkbox"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){return i.disabled?!1:void 0}):"radio"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass("ui-state-active"),t.buttonElement.attr("aria-pressed","true");var s=t.element[0];d(s).not(s).map(function(){return e(this).button("widget")[0]}).removeClass("ui-state-active").attr("aria-pressed","false")}):(this.buttonElement.bind("mousedown"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass("ui-state-active"),r=this,t.document.one("mouseup",function(){r=null}),void 0)}).bind("mouseup"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass("ui-state-active"),void 0)}).bind("keydown"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass("ui-state-active"),void 0)}).bind("keyup"+this.eventNamespace+" blur"+this.eventNamespace,function(){e(this).removeClass("ui-state-active")}),this.buttonElement.is("a")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption("disabled",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is("[type=checkbox]")?"checkbox":this.element.is("[type=radio]")?"radio":this.element.is("input")?"input":"button","checkbox"===this.type||"radio"===this.type?(e=this.element.parents().last(),t="label[for='"+this.element.attr("id")+"']",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass("ui-helper-hidden-accessible"),i=this.element.is(":checked"),i&&this.buttonElement.addClass("ui-state-active"),this.buttonElement.prop("aria-pressed",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass("ui-helper-hidden-accessible"),this.buttonElement.removeClass(h+" ui-state-active "+l).removeAttr("role").removeAttr("aria-pressed").html(this.buttonElement.find(".ui-button-text").html()),this.hasTitle||this.buttonElement.removeAttr("title")},_setOption:function(e,t){return this._super(e,t),"disabled"===e?(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),t&&("checkbox"===this.type||"radio"===this.type?this.buttonElement.removeClass("ui-state-focus"):this.buttonElement.removeClass("ui-state-focus ui-state-active")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is("input, button")?this.element.is(":disabled"):this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOption("disabled",t),"radio"===this.type?d(this.element[0]).each(function(){e(this).is(":checked")?e(this).button("widget").addClass("ui-state-active").attr("aria-pressed","true"):e(this).button("widget").removeClass("ui-state-active").attr("aria-pressed","false")}):"checkbox"===this.type&&(this.element.is(":checked")?this.buttonElement.addClass("ui-state-active").attr("aria-pressed","true"):this.buttonElement.removeClass("ui-state-active").attr("aria-pressed","false"))},_resetButton:function(){if("input"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(l),i=e("<span></span>",this.document[0]).addClass("ui-button-text").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push("ui-button-text-icon"+(n?"s":s.primary?"-primary":"-secondary")),s.primary&&t.prepend("<span class='ui-button-icon-primary ui-icon "+s.primary+"'></span>"),s.secondary&&t.append("<span class='ui-button-icon-secondary ui-icon "+s.secondary+"'></span>"),this.options.text||(a.push(n?"ui-button-icons-only":"ui-button-icon-only"),this.hasTitle||t.attr("title",e.trim(i)))):a.push("ui-button-text-only"),t.addClass(a.join(" "))}}),e.widget("ui.buttonset",{version:"1.11.4",options:{items:"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)"},_create:function(){this.element.addClass("ui-buttonset")},_init:function(){this.refresh()},_setOption:function(e,t){"disabled"===e&&this.buttons.button("option",e,t),this._super(e,t)},refresh:function(){var t="rtl"===this.element.css("direction"),i=this.element.find(this.options.items),s=i.filter(":ui-button");i.not(":ui-button").button(),s.button("refresh"),this.buttons=i.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-all ui-corner-left ui-corner-right").filter(":first").addClass(t?"ui-corner-right":"ui-corner-left").end().filter(":last").addClass(t?"ui-corner-left":"ui-corner-right").end().end()},_destroy:function(){this.element.removeClass("ui-buttonset"),this.buttons.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-left ui-corner-right").end().button("destroy")}}),e.ui.button,e.widget("ui.dialog",{version:"1.11.4",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find("[autofocus]")),e.length||(e=this.element.find(":tabbable")),e.length||(e=this.uiDialogButtonPane.find(":tabbable")),e.length||(e=this.uiDialogTitlebarClose.filter(":tabbable")),e.length||(e=this.uiDialog),e.eq(0).focus()},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e("<div>").addClass("ui-dialog ui-widget ui-widget-content ui-corner-all ui-front "+this.options.dialogClass).hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;if(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e("<div>").addClass("ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e("<button type='button'></button>").button({label:this.options.closeText,icons:{primary:"ui-icon-closethick"},text:!1}).addClass("ui-dialog-titlebar-close").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e("<span>").uniqueId().addClass("ui-dialog-title").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(e){this.options.title||e.html(" "),e.text(this.options.title) },_createButtonPane:function(){this.uiDialogButtonPane=e("<div>").addClass("ui-dialog-buttonpane ui-widget-content ui-helper-clearfix"),this.uiButtonSet=e("<div>").addClass("ui-dialog-buttonset").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass("ui-dialog-buttons"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:"button"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e("<button></button>",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass("ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){e(this).addClass("ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,t(n))},drag:function(e,s){i._trigger("drag",e,t(s))},stop:function(n,a){var o=a.offset.left-i.document.scrollLeft(),r=a.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(o>=0?"+":"")+o+" "+"top"+(r>=0?"+":"")+r,of:i.window},e(this).removeClass("ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,t(a))}})},_makeResizable:function(){function t(e){return{originalPosition:e.originalPosition,originalSize:e.originalSize,position:e.position,size:e.size}}var i=this,s=this.options,n=s.resizable,a=this.uiDialog.css("position"),o="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass("ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,t(n))},resize:function(e,s){i._trigger("resize",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},e(this).removeClass("ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,t(a))}}).css("position",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data("ui-dialog-instances");return e||(e=[],this.document.data("ui-dialog-instances",e)),e},_minHeight:function(){var e=this.options;return"auto"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(":visible");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;"dialogClass"===e&&n.removeClass(this.options.dialogClass).addClass(t),"disabled"!==e&&(this._super(e,t),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:""+t}),"draggable"===e&&(i=n.is(":data(ui-draggable)"),i&&!t&&n.draggable("destroy"),!i&&t&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(s=n.is(":data(ui-resizable)"),s&&!t&&n.resizable("destroy"),s&&"string"==typeof t&&n.resizable("option","handles",t),s||t===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):"none","auto"===s.height?this.element.css({minHeight:t,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=e(this);return e("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(".ui-dialog").length?!0:!!e(t.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e("<div>").addClass("ui-widget-overlay ui-front").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data("ui-dialog-overlays")-1;e?this.document.data("ui-dialog-overlays",e):this.document.unbind("focusin").removeData("ui-dialog-overlays"),this.overlay.remove(),this.overlay=null}}}),e.widget("ui.progressbar",{version:"1.11.4",options:{max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.addClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").attr({role:"progressbar","aria-valuemin":this.min}),this.valueDiv=e("<div class='ui-progressbar-value ui-widget-header ui-corner-left'></div>").appendTo(this.element),this._refreshValue()},_destroy:function(){this.element.removeClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.valueDiv.remove()},value:function(e){return void 0===e?this.options.value:(this.options.value=this._constrainedValue(e),this._refreshValue(),void 0)},_constrainedValue:function(e){return void 0===e&&(e=this.options.value),this.indeterminate=e===!1,"number"!=typeof e&&(e=0),this.indeterminate?!1:Math.min(this.options.max,Math.max(this.min,e))},_setOptions:function(e){var t=e.value;delete e.value,this._super(e),this.options.value=this._constrainedValue(t),this._refreshValue()},_setOption:function(e,t){"max"===e&&(t=Math.max(this.min,t)),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,i=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).toggleClass("ui-corner-right",t===this.options.max).width(i.toFixed(0)+"%"),this.element.toggleClass("ui-progressbar-indeterminate",this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=e("<div class='ui-progressbar-overlay'></div>").appendTo(this.valueDiv))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),e.widget("ui.selectmenu",{version:"1.11.4",defaultElement:"<select>",options:{appendTo:null,disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:null,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr("id");this.ids={element:e,button:e+"-button",menu:e+"-menu"},this._drawButton(),this._drawMenu(),this.options.disabled&&this.disable()},_drawButton:function(){var t=this;this.label=e("label[for='"+this.ids.element+"']").attr("for",this.ids.button),this._on(this.label,{click:function(e){this.button.focus(),e.preventDefault()}}),this.element.hide(),this.button=e("<span>",{"class":"ui-selectmenu-button ui-widget ui-state-default ui-corner-all",tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true"}).insertAfter(this.element),e("<span>",{"class":"ui-icon "+this.options.icons.button}).prependTo(this.button),this.buttonText=e("<span>",{"class":"ui-selectmenu-text"}).appendTo(this.button),this._setText(this.buttonText,this.element.find("option:selected").text()),this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){t.menuItems||t._refreshMenu()}),this._hoverable(this.button),this._focusable(this.button)},_drawMenu:function(){var t=this;this.menu=e("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=e("<div>",{"class":"ui-selectmenu-menu ui-front"}).append(this.menu).appendTo(this._appendTo()),this.menuInstance=this.menu.menu({role:"listbox",select:function(e,i){e.preventDefault(),t._setSelection(),t._select(i.item.data("ui-selectmenu-item"),e)},focus:function(e,i){var s=i.item.data("ui-selectmenu-item");null!=t.focusIndex&&s.index!==t.focusIndex&&(t._trigger("focus",e,{item:s}),t.isOpen||t._select(s,e)),t.focusIndex=s.index,t.button.attr("aria-activedescendant",t.menuItems.eq(s.index).attr("id"))}}).menu("instance"),this.menu.addClass("ui-corner-bottom").removeClass("ui-corner-all"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this._setText(this.buttonText,this._getSelectedItem().text()),this.options.width||this._resizeButton()},_refreshMenu:function(){this.menu.empty();var e,t=this.element.find("option");t.length&&(this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup"),e=this._getSelectedItem(),this.menuInstance.focus(null,e),this._setAria(e.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(e){this.options.disabled||(this.menuItems?(this.menu.find(".ui-state-focus").removeClass("ui-state-focus"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",e))},_position:function(){this.menuWrap.position(e.extend({of:this.button},this.options.position))},close:function(e){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",e))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderMenu:function(t,i){var s=this,n="";e.each(i,function(i,a){a.optgroup!==n&&(e("<li>",{"class":"ui-selectmenu-optgroup ui-menu-divider"+(a.element.parent("optgroup").prop("disabled")?" ui-state-disabled":""),text:a.optgroup}).appendTo(t),n=a.optgroup),s._renderItemData(t,a)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-selectmenu-item",t)},_renderItem:function(t,i){var s=e("<li>");return i.disabled&&s.addClass("ui-state-disabled"),this._setText(s,i.label),s.appendTo(t)},_setText:function(e,t){t?e.text(t):e.html(" ")},_move:function(e,t){var i,s,n=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex):(i=this.menuItems.eq(this.element[0].selectedIndex),n+=":not(.ui-state-disabled)"),s="first"===e||"last"===e?i["first"===e?"prevAll":"nextAll"](n).eq(-1):i[e+"All"](n).eq(0),s.length&&this.menuInstance.focus(t,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?"close":"open"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(".ui-selectmenu-menu, #"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move("prev",t);break;case e.ui.keyCode.RIGHT:this._move("next",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move("first",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass("ui-state-disabled")||this._select(t.data("ui-selectmenu-item"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger("select",t,{item:e}),e.index!==i&&this._trigger("change",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(e,t){"icons"===e&&this.button.find("span.ui-icon").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),"appendTo"===e&&this.menuWrap.appendTo(this._appendTo()),"disabled"===e&&(this.menuInstance.option("disabled",t),this.button.toggleClass("ui-state-disabled",t).attr("aria-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)),"width"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass("ui-corner-top",this.isOpen).toggleClass("ui-corner-all",!this.isOpen).attr("aria-expanded",this.isOpen),this.menuWrap.toggleClass("ui-selectmenu-open",this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop("disabled")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var n=e(s),a=n.parent("optgroup");i.push({element:n,index:t,value:n.val(),label:n.text(),optgroup:a.attr("label")||"",disabled:a.prop("disabled")||n.prop("disabled")})}),this.items=i},_destroy:function(){this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.label.attr("for",this.ids.element)}}),e.widget("ui.slider",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"slide",options:{animate:!1,distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this.element.addClass("ui-slider ui-slider-"+this.orientation+" ui-widget"+" ui-widget-content"+" ui-corner-all"),this._refresh(),this._setOption("disabled",this.options.disabled),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,i,s=this.options,n=this.element.find(".ui-slider-handle").addClass("ui-state-default ui-corner-all"),a="<span class='ui-slider-handle ui-state-default ui-corner-all' tabindex='0'></span>",o=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),t=n.length;i>t;t++)o.push(a);this.handles=n.add(e(o.join("")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(t){e(this).data("ui-slider-handle-index",t)})},_createRange:function(){var t=this.options,i="";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=e("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===t.range||"max"===t.range?" ui-slider-range-"+t.range:""))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass("ui-state-active").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css("borderTopWidth"),10)||0)-(parseInt(a.css("borderBottomWidth"),10)||0)+(parseInt(a.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(e){var t,i,s,n,a;return"horizontal"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("start",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger("slide",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger("slide",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("stop",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger("change",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch("range"===t&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),"disabled"===t&&this.element.toggleClass("ui-state-disabled",!!i),this._super(t,i),t){case"orientation":this._detectOrientation(),this.element.removeClass("ui-slider-horizontal ui-slider-vertical").addClass("ui-slider-"+this.orientation),this._refreshValue(),this.handles.css("horizontal"===i?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),s=0;n>s;s+=1)this._change(null,s);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_value:function(){var e=this.options.value;return e=this._trimAlignValue(e)},_values:function(e){var t,i,s;if(arguments.length)return t=this.options.values[e],t=this._trimAlignValue(t);if(this.options.values&&this.options.values.length){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(e){if(this._valueMin()>=e)return this._valueMin();if(e>=this._valueMax())return this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u["horizontal"===h.orientation?"left":"bottom"]=i+"%",e(this).stop(1,1)[l?"animate":"css"](u,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-t+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-t+"%"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](u,r.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===o&&"horizontal"===this.orientation&&this.range[l?"animate":"css"]({width:100-i+"%"},{queue:!1,duration:r.animate}),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===o&&"vertical"===this.orientation&&this.range[l?"animate":"css"]({height:100-i+"%"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var i,s,n,a,o=e(t.target).data("ui-slider-handle-index");switch(t.keyCode){case e.ui.keyCode.HOME:case e.ui.keyCode.END:case e.ui.keyCode.PAGE_UP:case e.ui.keyCode.PAGE_DOWN:case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,e(t.target).addClass("ui-state-active"),i=this._start(t,o),i===!1))return}switch(a=this.options.step,s=n=this.options.values&&this.options.values.length?this.values(o):this.value(),t.keyCode){case e.ui.keyCode.HOME:n=this._valueMin();break;case e.ui.keyCode.END:n=this._valueMax();break;case e.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+a);break;case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-a)}this._slide(t,o,n)},keyup:function(t){var i=e(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,i),this._change(t,i),e(t.target).removeClass("ui-state-active"))}}}),e.widget("ui.spinner",{version:"1.11.4",defaultElement:"<input>",widgetEventPrefix:"spin",options:{culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var t={},i=this.element;return e.each(["min","max","step"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},"mousedown .ui-spinner-button":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){return e(t.currentTarget).hasClass("ui-state-active")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_draw:function(){var e=this.uiSpinner=this.element.addClass("ui-spinner-input").attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml());this.element.attr("role","spinbutton"),this.buttons=e.find(".ui-spinner-button").attr("tabIndex",-1).button().removeClass("ui-corner-all"),this.buttons.height()>Math.ceil(.5*e.height())&&e.height()>0&&e.height(e.height()),this.options.disabled&&this.disable()},_keydown:function(t){var i=this.options,s=e.ui.keyCode;switch(t.keyCode){case s.UP:return this._repeat(null,1,t),!0;case s.DOWN:return this._repeat(null,-1,t),!0;case s.PAGE_UP:return this._repeat(null,i.page,t),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,t),!0}return!1},_uiSpinnerHtml:function(){return"<span class='ui-spinner ui-widget ui-widget-content ui-corner-all'></span>"},_buttonHtml:function(){return"<a class='ui-spinner-button ui-spinner-up ui-corner-tr'><span class='ui-icon "+this.options.icons.up+"'>▲</span>"+"</a>"+"<a class='ui-spinner-button ui-spinner-down ui-corner-br'>"+"<span class='ui-icon "+this.options.icons.down+"'>▼</span>"+"</a>"},_start:function(e){return this.spinning||this._trigger("start",e)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(e,t,i){e=e||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,t,i)},e),this._spin(t*this.options.step,i)},_spin:function(e,t){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+e*this._increment(this.counter)),this.spinning&&this._trigger("spin",t,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(t){var i=this.options.incremental;return i?e.isFunction(i)?i(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_adjustValue:function(e){var t,i,s=this.options;return t=null!==s.min?s.min:0,i=e-t,i=Math.round(i/s.step)*s.step,e=t+i,e=parseFloat(e.toFixed(this._precision())),null!==s.max&&e>s.max?s.max:null!==s.min&&s.min>e?s.min:e},_stop:function(e){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",e))},_setOption:function(e,t){if("culture"===e||"numberFormat"===e){var i=this._parse(this.element.val());return this.options[e]=t,this.element.val(this._format(i)),void 0}("max"===e||"min"===e||"step"===e)&&"string"==typeof t&&(t=this._parse(t)),"icons"===e&&(this.buttons.first().find(".ui-icon").removeClass(this.options.icons.up).addClass(t.up),this.buttons.last().find(".ui-icon").removeClass(this.options.icons.down).addClass(t.down)),this._super(e,t),"disabled"===e&&(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable"))},_setOptions:s(function(e){this._super(e)}),_parse:function(e){return"string"==typeof e&&""!==e&&(e=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(e,10,this.options.culture):+e),""===e||isNaN(e)?null:e },_format:function(e){return""===e?"":window.Globalize&&this.options.numberFormat?Globalize.format(e,this.options.numberFormat,this.options.culture):e},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var e=this.value();return null===e?!1:e===this._adjustValue(e)},_value:function(e,t){var i;""!==e&&(i=this._parse(e),null!==i&&(t||(i=this._adjustValue(i)),e=this._format(i))),this.element.val(e),this._refresh()},_destroy:function(){this.element.removeClass("ui-spinner-input").prop("disabled",!1).removeAttr("autocomplete").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:s(function(e){this._stepUp(e)}),_stepUp:function(e){this._start()&&(this._spin((e||1)*this.options.step),this._stop())},stepDown:s(function(e){this._stepDown(e)}),_stepDown:function(e){this._start()&&(this._spin((e||1)*-this.options.step),this._stop())},pageUp:s(function(e){this._stepUp((e||1)*this.options.page)}),pageDown:s(function(e){this._stepDown((e||1)*this.options.page)}),value:function(e){return arguments.length?(s(this._value).call(this,e),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),e.widget("ui.tabs",{version:"1.11.4",delay:300,options:{active:null,collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var e=/#.*$/;return function(t){var i,s;t=t.cloneNode(!1),i=t.href.replace(e,""),s=location.href.replace(e,"");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return t.hash.length>1&&i===s}}(),_create:function(){var t=this,i=this.options;this.running=!1,this.element.addClass("ui-tabs ui-widget ui-widget-content ui-corner-all").toggleClass("ui-tabs-collapsible",i.collapsible),this._processTabs(),i.active=this._initialActive(),e.isArray(i.disabled)&&(i.disabled=e.unique(i.disabled.concat(e.map(this.tabs.filter(".ui-state-disabled"),function(e){return t.tabs.index(e)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):e(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var t=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===t&&(s&&this.tabs.each(function(i,n){return e(n).attr("aria-controls")===s?(t=i,!1):void 0}),null===t&&(t=this.tabs.index(this.tabs.filter(".ui-tabs-active"))),(null===t||-1===t)&&(t=this.tabs.length?0:!1)),t!==!1&&(t=this.tabs.index(this.tabs.eq(t)),-1===t&&(t=i?!1:0)),!i&&t===!1&&this.anchors.length&&(t=0),t},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):e()}},_tabKeydown:function(t){var i=e(this.document[0].activeElement).closest("li"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:s++;break;case e.ui.keyCode.UP:case e.ui.keyCode.LEFT:n=!1,s--;break;case e.ui.keyCode.END:s=this.anchors.length-1;break;case e.ui.keyCode.HOME:s=0;break;case e.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case e.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}t.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),t.ctrlKey||t.metaKey||(i.attr("aria-selected","false"),this.tabs.eq(s).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",s)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===e.ui.keyCode.UP&&(t.preventDefault(),this.active.focus())},_handlePageNav:function(t){return t.altKey&&t.keyCode===e.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===e.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 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.tocify .tocify-item > a, .tocify .nav-list .nav-header { margin: 0px; } .tocify .tocify-item a, .tocify .list-group-item { padding: 5px; } .tocify .nav-pills > li { float: none; } </style> <script>/* jquery Tocify - v1.9.1 - 2013-10-22 * http://www.gregfranko.com/jquery.tocify.js/ * Copyright (c) 2013 Greg Franko; Licensed MIT */ // Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic. I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE. (function(tocify) { // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/) "use strict"; // Calls the second IIFE and locally passes in the global jQuery, window, and document objects tocify(window.jQuery, window, document); } // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable. The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set. All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript. (function($, window, document, undefined) { // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/) "use strict"; var tocClassName = "tocify", tocClass = "." + tocClassName, tocFocusClassName = "tocify-focus", tocHoverClassName = "tocify-hover", hideTocClassName = "tocify-hide", hideTocClass = "." + hideTocClassName, headerClassName = "tocify-header", headerClass = "." + headerClassName, subheaderClassName = "tocify-subheader", subheaderClass = "." + subheaderClassName, itemClassName = "tocify-item", itemClass = "." + itemClassName, extendPageClassName = "tocify-extend-page", extendPageClass = "." + extendPageClassName; // Calling the jQueryUI Widget Factory Method $.widget("toc.tocify", { //Plugin version version: "1.9.1", // These options will be used as defaults options: { // **context**: Accepts String: Any jQuery selector // The container element that holds all of the elements used to generate the table of contents context: "body", // **ignoreSelector**: Accepts String: Any jQuery selector // A selector to any element that would be matched by selectors that you wish to be ignored ignoreSelector: null, // **selectors**: Accepts an Array of Strings: Any jQuery selectors // The element's used to generate the table of contents. The order is very important since it will determine the table of content's nesting structure selectors: "h1, h2, h3", // **showAndHide**: Accepts a boolean: true or false // Used to determine if elements should be shown and hidden showAndHide: true, // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown" // Used to display any of the table of contents nested items showEffect: "slideDown", // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" // The time duration of the show animation showEffectSpeed: "medium", // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp" // Used to hide any of the table of contents nested items hideEffect: "slideUp", // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" // The time duration of the hide animation hideEffectSpeed: "medium", // **smoothScroll**: Accepts a boolean: true or false // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page smoothScroll: true, // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" // The time duration of the smoothScroll animation smoothScrollSpeed: "medium", // **scrollTo**: Accepts Number (pixels) // The amount of space between the top of page and the selected table of contents item after the page has been scrolled scrollTo: 0, // **showAndHideOnScroll**: Accepts a boolean: true or false // Determines if table of contents nested items should be shown and hidden while scrolling showAndHideOnScroll: true, // **highlightOnScroll**: Accepts a boolean: true or false // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling highlightOnScroll: true, // **highlightOffset**: Accepts a number // The offset distance in pixels to trigger the next active table of contents item highlightOffset: 40, // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none" // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents theme: "bootstrap", // **extendPage**: Accepts a boolean: true or false // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased extendPage: true, // **extendPageOffset**: Accepts a number: pixels // How close to the bottom of the page a user must scroll before the page is extended extendPageOffset: 100, // **history**: Accepts a boolean: true or false // Adds a hash to the page url to maintain history history: true, // **scrollHistory**: Accepts a boolean: true or false // Adds a hash to the page url, to maintain history, when scrolling to a TOC item scrollHistory: false, // **hashGenerator**: How the hash value (the anchor segment of the URL, following the // # character) will be generated. // // "compact" (default) - #CompressesEverythingTogether // "pretty" - #looks-like-a-nice-url-and-is-easily-readable // function(text, element){} - Your own hash generation function that accepts the text as an // argument, and returns the hash value. hashGenerator: "compact", // **highlightDefault**: Accepts a boolean: true or false // Set's the first TOC item as active if no other TOC item is active. highlightDefault: true }, // _Create // ------- // Constructs the plugin. Only called once. _create: function() { var self = this; self.extendPageScroll = true; // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings) self.items = []; // Generates the HTML for the dynamic table of contents self._generateToc(); // Adds CSS classes to the newly generated table of contents HTML self._addCSSClasses(); self.webkit = (function() { for (var prop in window) { if (prop) { if (prop.toLowerCase().indexOf("webkit") !== -1) { return true; } } } return false; }()); // Adds jQuery event handlers to the newly generated table of contents self._setEventHandlers(); // Binding to the Window load event to make sure the correct scrollTop is calculated $(window).load(function() { // Sets the active TOC item self._setActiveElement(true); // Once all animations on the page are complete, this callback function will be called $("html, body").promise().done(function() { setTimeout(function() { self.extendPageScroll = false; }, 0); }); }); }, // _generateToc // ------------ // Generates the HTML for the dynamic table of contents _generateToc: function() { // _Local variables_ // Stores the plugin context in the self variable var self = this, // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above firstElem, // Instantiated variable that will store the top level newly created unordered list DOM element ul, ignoreSelector = self.options.ignoreSelector; // Determine the element to start the toc with // get all the top level selectors firstElem = []; var selectors = this.options.selectors.replace(/ /g, "").split(","); // find the first set that have at least one non-ignored element for(var i = 0; i < selectors.length; i++) { var foundSelectors = $(this.options.context).find(selectors[i]); for (var s = 0; s < foundSelectors.length; s++) { if (!$(foundSelectors[s]).is(ignoreSelector)) { firstElem = foundSelectors; break; } } if (firstElem.length> 0) break; } if (!firstElem.length) { self.element.addClass(hideTocClassName); return; } self.element.addClass(tocClassName); // Loops through each top level selector firstElem.each(function(index) { //If the element matches the ignoreSelector then we skip it if ($(this).is(ignoreSelector)) { return; } // Creates an unordered list HTML element and adds a dynamic ID and standard class name ul = $("<ul/>", { "id": headerClassName + index, "class": headerClassName }). // Appends a top level list item HTML element to the previously created HTML header append(self._nestElements($(this), index)); // Add the created unordered list element to the HTML element calling the plugin self.element.append(ul); // Finds all of the HTML tags between the header and subheader elements $(this).nextUntil(this.nodeName.toLowerCase()).each(function() { // If there are no nested subheader elemements if ($(this).find(self.options.selectors).length === 0) { // Loops through all of the subheader elements $(this).filter(self.options.selectors).each(function() { //If the element matches the ignoreSelector then we skip it if ($(this).is(ignoreSelector)) { return; } self._appendSubheaders.call(this, self, ul); }); } // If there are nested subheader elements else { // Loops through all of the subheader elements $(this).find(self.options.selectors).each(function() { //If the element matches the ignoreSelector then we skip it if ($(this).is(ignoreSelector)) { return; } self._appendSubheaders.call(this, self, ul); }); } }); }); }, _setActiveElement: function(pageload) { var self = this, hash = window.location.hash.substring(1), elem = self.element.find('li[data-unique="' + hash + '"]'); if (hash.length) { // Removes highlighting from all of the list item's self.element.find("." + self.focusClass).removeClass(self.focusClass); // Highlights the current list item that was clicked elem.addClass(self.focusClass); // Triggers the click event on the currently focused TOC item elem.click(); } else { // Removes highlighting from all of the list item's self.element.find("." + self.focusClass).removeClass(self.focusClass); if (!hash.length && pageload && self.options.highlightDefault) { // Highlights the first TOC item if no other items are highlighted self.element.find(itemClass).first().addClass(self.focusClass); } } return self; }, // _nestElements // ------------- // Helps create the table of contents list by appending nested list items _nestElements: function(self, index) { var arr, item, hashValue; arr = $.grep(this.items, function(item) { return item === self.text(); }); // If there is already a duplicate TOC item if (arr.length) { // Adds the current TOC item text and index (for slight randomization) to the internal array this.items.push(self.text() + index); } // If there not a duplicate TOC item else { // Adds the current TOC item text to the internal array this.items.push(self.text()); } hashValue = this._generateHashValue(arr, self, index); // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin item = $("<li/>", { // Sets a common class name to the list item "class": itemClassName, "data-unique": hashValue }); if (this.options.theme !== "bootstrap3") { item.append($("<a/>", { "html": self.html() })); } else { item.html(self.html()); } // Adds an HTML anchor tag before the currently traversed HTML element self.before($("<div/>", { // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore) "name": hashValue, "data-unique": hashValue })); return item; }, // _generateHashValue // ------------------ // Generates the hash value that will be used to refer to each item. _generateHashValue: function(arr, self, index) { var hashValue = "", hashGeneratorOption = this.options.hashGenerator; if (hashGeneratorOption === "pretty") { // prettify the text hashValue = self.text().toLowerCase().replace(/\s/g, "-"); // fix double hyphens while (hashValue.indexOf("--") > -1) { hashValue = hashValue.replace(/--/g, "-"); } // fix colon-space instances while (hashValue.indexOf(":-") > -1) { hashValue = hashValue.replace(/:-/g, "-"); } } else if (typeof hashGeneratorOption === "function") { // call the function hashValue = hashGeneratorOption(self.text(), self); } else { // compact - the default hashValue = self.text().replace(/\s/g, ""); } // add the index if we need to if (arr.length) { hashValue += "" + index; } // return the value return hashValue; }, // _appendElements // --------------- // Helps create the table of contents list by appending subheader elements _appendSubheaders: function(self, ul) { // The current element index var index = $(this).index(self.options.selectors), // Finds the previous header DOM element previousHeader = $(self.options.selectors).eq(index - 1), currentTagName = +$(this).prop("tagName").charAt(1), previousTagName = +previousHeader.prop("tagName").charAt(1), lastSubheader; // If the current header DOM element is smaller than the previous header DOM element or the first subheader if (currentTagName < previousTagName) { // Selects the last unordered list HTML found within the HTML element calling the plugin self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index)); } // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element else if (currentTagName === previousTagName) { ul.find(itemClass).last().after(self._nestElements($(this), index)); } else { // Selects the last unordered list HTML found within the HTML element calling the plugin ul.find(itemClass).last(). // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name after($("<ul/>", { "class": subheaderClassName, "data-tag": currentTagName })).next(subheaderClass). // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin append(self._nestElements($(this), index)); } }, // _setEventHandlers // ---------------- // Adds jQuery event handlers to the newly generated table of contents _setEventHandlers: function() { // _Local variables_ // Stores the plugin context in the self variable var self = this, // Instantiates a new variable that will be used to hold a specific element's context $self, // Instantiates a new variable that will be used to determine the smoothScroll animation time duration duration; // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin this.element.on("click.tocify", "li", function(event) { if (self.options.history) { window.location.hash = $(this).attr("data-unique"); } // Removes highlighting from all of the list item's self.element.find("." + self.focusClass).removeClass(self.focusClass); // Highlights the current list item that was clicked $(this).addClass(self.focusClass); // If the showAndHide option is true if (self.options.showAndHide) { var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]'); self._triggerShow(elem); } self._scrollTo($(this)); }); // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin this.element.find("li").on({ // Mouseenter event handler "mouseenter.tocify": function() { // Adds a hover CSS class to the current list item $(this).addClass(self.hoverClass); // Makes sure the cursor is set to the pointer icon $(this).css("cursor", "pointer"); 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class="tocify-header list-group"><li class="tocify-item list-group-item active" data-unique="set_up">Set up</li><ul class="tocify-subheader list-group" data-tag="3"><li class="tocify-item list-group-item" data-unique="packages">Packages</li><li class="tocify-item list-group-item" data-unique="loading_functions_that_are_necessary">Loading functions that are necessary</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="preparation_of_raw_data">Preparation of raw data</li><li class="tocify-item list-group-item" data-unique="functions_for_preparing_the_data_for_meta_analyses">Functions for preparing the data for meta analyses</li></ul><li class="tocify-item list-group-item" data-unique="clean_data">Clean data</li></ul></ul><ul id="tocify-header1" class="tocify-header list-group"><li class="tocify-item list-group-item" data-unique="meta_analysis,_phase_1">Meta analysis, Phase 1</li><ul class="tocify-subheader list-group" data-tag="3"><li class="tocify-item list-group-item" data-unique="preparation">Preparation</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="define_population_variable__add_grouping_variables">Define population variable & add grouping variables</li><li class="tocify-item list-group-item" data-unique="assign_each_unique_parameter_name_(=trait,use_trait_variable)_a_unique_number_(‘id’)">Assign each unique parameter_name (=trait,use trait variable) a unique number (‘id’)</li><li class="tocify-item list-group-item" data-unique="create_a_matrix_to_store_results_for_all_traits">Create a matrix to store results for all traits</li></ul><li class="tocify-item list-group-item" data-unique="loop,_to_run_meta-analysis_on_all_traits">LOOP, to run meta-analysis on all traits</li><li class="tocify-item list-group-item" data-unique="merging_datasets">Merging datasets</li></ul></ul><ul id="tocify-header2" class="tocify-header list-group"><li class="tocify-item list-group-item" data-unique="meta-analysis,_phase_2" style="cursor: pointer;">Meta-analysis, Phase 2</li><ul class="tocify-subheader list-group" data-tag="3"><li class="tocify-item list-group-item" data-unique="dealing_with_correlated_parameters,_preparation">Dealing with Correlated Parameters, preparation</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="collapsing_and_merging_correlated_parameters">Collapsing and merging correlated parameters</li><li class="tocify-item list-group-item" data-unique="count_of_number_of_parameter_names_(correlated_sub-traits)_in_each_parameter_group_(par_group_size)">Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)</li></ul><li class="tocify-item list-group-item" data-unique="perform_meta-analyses_on_correlated_sub-traits,_using_robumeta">Perform meta-analyses on correlated sub-traits, using <code>robumeta</code></li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="extract_and_save_parameter_estimates">Extract and save parameter estimates</li><li class="tocify-item list-group-item" data-unique="clean-up_and_rename">Clean-up and rename</li></ul><li class="tocify-item list-group-item" data-unique="visualisation">Visualisation</li><li class="tocify-item list-group-item" data-unique="overall_results_of_second_order_meta_anlaysis_(figure_4a)">Overall results of second order meta anlaysis (Figure 4a)</li><li class="tocify-item list-group-item" data-unique="heterogeneity">Heterogeneity</li></ul></ul><ul id="tocify-header3" class="tocify-header list-group"><li class="tocify-item list-group-item" data-unique="meta-analysis,_phase_3">Meta-analysis, Phase 3</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="perform_meta-analyses_(3_for_each_of_the_9_grouping_terms:_lncvr,_lnvr,_lnrr)">Perform meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)</li><li class="tocify-item list-group-item" data-unique="prepare_data">Prepare data</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="plot_figure_2_[4_in_ms]_(first-order_meta_analysis_results)">Plot FIGURE 2 [4 in ms] (First-order meta analysis results)</li><li class="tocify-item list-group-item" data-unique="prepare_data_for_traits_with_ci_not_overlapping_0">Prepare data for traits with CI not overlapping 0</li><li class="tocify-item list-group-item" data-unique="create_final_combined_figure_(figure_2)">Create final combined Figure (Figure 2)</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="restructure_data_for_plotting">Restructure data for plotting</li><li class="tocify-item list-group-item" data-unique="plot_figure_4_(second-order_meta_analysis_results)">Plot FIGURE 4 (Second-order meta analysis results)</li><li class="tocify-item list-group-item" data-unique="figure_4b:_prepare_data_for_traits_with_ci_not_overlapping_0">Figure 4B: Prepare data for traits with CI not overlapping 0</li><li class="tocify-item list-group-item" data-unique="female_figure,_significant_traits">Female Figure, significant traits</li><li class="tocify-item list-group-item" data-unique="fig4_c_10%">Fig4 C >10%</li><li class="tocify-item list-group-item" data-unique="male_fig_3__10%_(male_biased_traits)">Male Fig 3 > 10% (male biased traits)</li><li class="tocify-item list-group-item" data-unique="restructure_data_for_plotting37">Restructure data for plotting</li><li class="tocify-item list-group-item" data-unique="plot_fig3_all_plots_combined">Plot Fig3 all plots combined</li><ul class="tocify-subheader list-group" data-tag="4"><li class="tocify-item list-group-item" data-unique="figure_4_(second-order_meta_analysis_on_heterogeneity)">FIGURE 4 (Second-order meta analysis on heterogeneity)</li><li class="tocify-item list-group-item" data-unique="create_matrix_to_store_results_for_all_traits">Create matrix to store results for all traits</li><li class="tocify-item list-group-item" data-unique="loop">LOOP</li><li class="tocify-item list-group-item" data-unique="exclude_traits">Exclude traits</li><li class="tocify-item list-group-item" data-unique="dealing_with_correlated_parameters">Dealing with correlated parameters</li><li class="tocify-item list-group-item" data-unique="merge_the_two_data_sets_(the_new_[robu_all]_and_the_initial_[uncorrelated_sub-traits_with_count_=_1])">Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1])</li><li class="tocify-item list-group-item" data-unique="last_step:_meta-meta-analysis_of_heterogeneity">Last step: meta-meta-analysis of heterogeneity</li><li class="tocify-item list-group-item" data-unique="heterogeneity_plots">Heterogeneity PLOTS</li><li class="tocify-item list-group-item" data-unique="plot_figure_4_(5_in_ms)_(second-order_meta_analysis_on_heterogeneity)">Plot FIGURE 4 (5 in ms) (Second-order meta analysis on heterogeneity)</li></ul></ul></ul></ul></ul></div> </div> <div class="toc-content col-xs-12 col-sm-8 col-md-9"> <div class="fluid-row" id="header"> <div class="btn-group pull-right"> <button type="button" class="btn btn-default btn-xs dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> <ul class="dropdown-menu" style="min-width: 50px;"> <li><a id="rmd-show-all-code" href="file:///Volumes/SZ%20WD%20drive/garvan/Github/IMPC%20sexDiffs/mice_sex_diff/scripts/2019_July_IMPC-variance-and-sex-difference.html#">Show All Code</a></li> <li><a id="rmd-hide-all-code" href="file:///Volumes/SZ%20WD%20drive/garvan/Github/IMPC%20sexDiffs/mice_sex_diff/scripts/2019_July_IMPC-variance-and-sex-difference.html#">Hide All Code</a></li> </ul> </div> <h1 class="title toc-ignore">IMPC Mouse data - Variance in sex differences</h1> <h4 class="author">Susanne & Felix Zajitschek</h4> <h4 class="date">August 2019</h4> </div> <div id="set-up" class="section level2"> <div name="set_up" data-unique="set_up"></div><h2>Set up</h2> <div id="packages" class="section level3"> <div name="packages" data-unique="packages"></div><h3>Packages</h3> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F361" aria-expanded="false" aria-controls="rcode-643E0F361"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F361"><pre class="r"><code class="hljs"><span class="hljs-keyword">library</span>(readr) <span class="hljs-keyword">library</span>(dplyr) <span class="hljs-keyword">library</span>(metafor) <span class="hljs-keyword">library</span>(devtools) <span class="hljs-keyword">library</span>(purrr) <span class="hljs-keyword">library</span>(tidyverse) <span class="hljs-keyword">library</span>(tibble) <span class="hljs-keyword">library</span>(kableExtra) <span class="hljs-keyword">library</span>(robumeta) <span class="hljs-keyword">library</span>(ggpubr) <span class="hljs-keyword">library</span>(ggplot2)</code></pre></div> </div> <div id="loading-functions-that-are-necessary" class="section level3"> <div name="loading_functions_that_are_necessary" data-unique="loading_functions_that_are_necessary"></div><h3>Loading functions that are necessary</h3> <div id="preparation-of-raw-data" class="section level4"> <div name="preparation_of_raw_data" data-unique="preparation_of_raw_data"></div><h4>Preparation of raw data</h4> <ol style="list-style-type: decimal"> <li>Data loading and cleaning of the csv file This step we have already done and provide a cleaned up file which is less computing intensive. However, cvs</li> </ol> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F362" aria-expanded="false" aria-controls="rcode-643E0F362"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F362"><pre class="r"><code class="hljs"><span class="hljs-comment"># loads the raw data, setting some default types for various columns</span> load_raw <- <span class="hljs-keyword">function</span>(filename) { read_csv(filename, col_types = cols( .default = col_character(), project_id = col_character(), id = col_character(), parameter_id = col_character(), age_in_days = col_integer(), date_of_experiment = col_datetime(format = <span class="hljs-string">""</span>), weight = col_double(), phenotyping_center_id = col_character(), production_center_id = col_character(), weight_date = col_datetime(format = <span class="hljs-string">""</span>), date_of_birth = col_datetime(format = <span class="hljs-string">""</span>), procedure_id = col_character(), pipeline_id = col_character(), biological_sample_id = col_character(), biological_model_id = col_character(), weight_days_old = col_integer(), datasource_id = col_character(), experiment_id = col_character(), data_point = col_double(), age_in_weeks = col_integer(), `_version_` = col_character() ) ) } <span class="hljs-comment"># Apply some standard cleaning to the data</span> clean_raw_data <- <span class="hljs-keyword">function</span>(mydata) { mydata %>% <span class="hljs-comment"># Fileter to IMPC source (recommened by Jeremey in email to Susi on 20 Aug 2018)</span> filter(datasource_name == <span class="hljs-string">'IMPC'</span>) %>% <span class="hljs-comment"># standardise trait names</span> mutate(parameter_name = tolower(parameter_name) ) %>% <span class="hljs-comment"># remove extreme ages</span> filter(age_in_days > <span class="hljs-number">0</span> & age_in_days < <span class="hljs-number">500</span>) %>% <span class="hljs-comment"># remove NAs </span> filter(!is.na(data_point)) %>% <span class="hljs-comment"># subset to reasonable set of variables</span> <span class="hljs-comment"># date_of_experiment: Jeremy suggested using as an indicator of batch-level effects</span> select(production_center, strain_name, strain_accession_id, biological_sample_id, pipeline_stable_id, procedure_group, procedure_name, sex, date_of_experiment, age_in_days, weight, parameter_name, data_point) %>% arrange(production_center, biological_sample_id, age_in_days) }</code></pre></div> <ol start="2" style="list-style-type: decimal"> <li>Subsetting the data to choose only one datapoint per individual per trait</li> </ol> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F363" aria-expanded="false" aria-controls="rcode-643E0F363"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F363"><pre class="r"><code class="hljs"><span class="hljs-comment"># this is a necessary step for the loop across all traits </span> data_subset_parameterid_individual_by_age <- <span class="hljs-keyword">function</span>(mydata, parameter, age_min, age_center) { tmp <- mydata %>% filter(age_in_days >= age_min, id == parameter) %>% <span class="hljs-comment"># take results for single individual closest to age_center</span> mutate(age_diff = abs(age_center - age_in_days)) %>% group_by(biological_sample_id) %>% filter(age_diff == min(age_diff)) %>% select(-age_diff) <span class="hljs-comment"># still some individuals with multiple records (because same individual appears under different procedures, so filter to one record)</span> j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id) tmp[j, ] }</code></pre></div> </div> <div id="functions-for-preparing-the-data-for-meta-analyses" class="section level4"> <div name="functions_for_preparing_the_data_for_meta_analyses" data-unique="functions_for_preparing_the_data_for_meta_analyses"></div><h4>Functions for preparing the data for meta analyses</h4> <ol start="3" style="list-style-type: decimal"> <li>“Population statistics”</li> </ol> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F364" aria-expanded="false" aria-controls="rcode-643E0F364"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F364"><pre class="r"><code class="hljs">calculate_population_stats <- <span class="hljs-keyword">function</span>(mydata, min_individuals = <span class="hljs-number">5</span>) { mydata %>% group_by(population, strain_name, production_center, sex) %>% summarise( trait = parameter_name[<span class="hljs-number">1</span>], x_bar = mean(data_point), x_sd = sd(data_point), n_ind = n() ) %>% ungroup() %>% filter(n_ind > min_individuals) %>% <span class="hljs-comment"># Check both sexes present & filter those missing</span> group_by(population) %>% mutate( n_sex = n_distinct(sex) ) %>% ungroup() %>% filter(n_sex ==<span class="hljs-number">2</span>) %>% select(-n_sex) %>% arrange(production_center, strain_name, population, sex) }</code></pre></div> <ol start="4" style="list-style-type: decimal"> <li>Extraction of effect sizes and sample variances</li> </ol> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F365" aria-expanded="false" aria-controls="rcode-643E0F365"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F365"><pre class="r"><code class="hljs">create_meta_analysis_effect_sizes <- <span class="hljs-keyword">function</span>(mydata) { i <- seq(<span class="hljs-number">1</span>, nrow(mydata), by = <span class="hljs-number">2</span>) input <- data.frame( n1i = mydata$n_ind[i], n2i = mydata$n_ind[i + <span class="hljs-number">1</span>], x1i = mydata$x_bar[i], x2i = mydata$x_bar[i + <span class="hljs-number">1</span>], sd1i = mydata$x_sd[i], sd2i = mydata$x_sd[i + <span class="hljs-number">1</span>] ) mydata[i,] %>% select(strain_name, production_center, trait) %>% mutate( effect_size_CVR = Calc.lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_CVR = Calc.var.lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), effect_size_VR = Calc.lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i), sample_variance_VR = Calc.var.lnVR(CN = input$n1i, EN = input$n2i), effect_size_RR = Calc.lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_RR = Calc.var.lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), err = as.factor(seq_len(n())) ) }</code></pre></div> <ol start="5" style="list-style-type: decimal"> <li>Meta Analysis</li> </ol> <p>Function to calculate meta-analysis statistics. Created by A M Senior @ the University of Otago NZ 03/01/2014</p> <p>Below are functions for calculating effect sizes for meta-analysis of variance. All functions take the mean, sd and n from the control and experimental groups.</p> <p>The first function, Cal.lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015.</p> <p>The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes Equal.E.C.Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor’s Law) is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups are calculated separately from one another.</p> <p>Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR (for comparison of means)</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F366" aria-expanded="false" aria-controls="rcode-643E0F366"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F366"><pre class="r"><code class="hljs">Calc.lnCVR <- <span class="hljs-keyword">function</span>(CMean, CSD, CN, EMean, ESD, EN){ log(ESD) - log(EMean) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span>*(EN - <span class="hljs-number">1</span>)) - (log(CSD) - log(CMean) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span>*(CN - <span class="hljs-number">1</span>))) } Calc.var.lnCVR <- <span class="hljs-keyword">function</span>(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr=<span class="hljs-literal">T</span>) { <span class="hljs-keyword">if</span>(Equal.E.C.Corr==<span class="hljs-literal">T</span>){ mvcorr <- <span class="hljs-number">0</span> <span class="hljs-comment">#cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate old, slightly incorrect</span> S2 <- CSD^<span class="hljs-number">2</span> / (CN * (CMean^<span class="hljs-number">2</span>)) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (CN - <span class="hljs-number">1</span>)) - <span class="hljs-number">2</span> * mvcorr * sqrt((CSD^<span class="hljs-number">2</span> / (CN * (CMean^<span class="hljs-number">2</span>))) * (<span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (CN - <span class="hljs-number">1</span>)))) + ESD^<span class="hljs-number">2</span> / (EN * (EMean^<span class="hljs-number">2</span>)) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (EN - <span class="hljs-number">1</span>)) - <span class="hljs-number">2</span> * mvcorr * sqrt((ESD^<span class="hljs-number">2</span> / (EN * (EMean^<span class="hljs-number">2</span>))) * (<span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (EN - <span class="hljs-number">1</span>)))) } <span class="hljs-keyword">else</span>{ Cmvcorr <- cor.test(log(CMean), log(CSD))$estimate Emvcorr <- cor.test(log(EMean), (ESD))$estimate S2 <- CSD^<span class="hljs-number">2</span> / (CN * (CMean^<span class="hljs-number">2</span>)) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (CN - <span class="hljs-number">1</span>)) - <span class="hljs-number">2</span> * Cmvcorr * sqrt((CSD^<span class="hljs-number">2</span> / (CN * (CMean^<span class="hljs-number">2</span>))) * (<span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (CN - <span class="hljs-number">1</span>)))) + ESD^<span class="hljs-number">2</span> / (EN * (EMean^<span class="hljs-number">2</span>)) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (EN - <span class="hljs-number">1</span>)) - <span class="hljs-number">2</span> * Emvcorr * sqrt((ESD^<span class="hljs-number">2</span> / (EN * (EMean^<span class="hljs-number">2</span>))) * (<span class="hljs-number">1</span> / (<span class="hljs-number">2</span> * (EN - <span class="hljs-number">1</span>)))) } S2 } Calc.lnVR <- <span class="hljs-keyword">function</span>(CSD, CN, ESD, EN){ log(ESD) - log(CSD) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span>*(EN - <span class="hljs-number">1</span>)) - <span class="hljs-number">1</span> / (<span class="hljs-number">2</span>*(CN - <span class="hljs-number">1</span>)) } Calc.var.lnVR <- <span class="hljs-keyword">function</span>( CN, EN) { <span class="hljs-number">1</span> / (<span class="hljs-number">2</span>*(EN - <span class="hljs-number">1</span>)) + <span class="hljs-number">1</span> / (<span class="hljs-number">2</span>*(CN - <span class="hljs-number">1</span>)) } Calc.lnRR <- <span class="hljs-keyword">function</span>(CMean, CSD, CN, EMean, ESD, EN) { log(EMean) - log(CMean) } Calc.var.lnRR <- <span class="hljs-keyword">function</span>(CMean, CSD, CN, EMean, ESD, EN) { CSD^<span class="hljs-number">2</span>/(CN * CMean^<span class="hljs-number">2</span>) + ESD^<span class="hljs-number">2</span>/(EN * EMean^<span class="hljs-number">2</span>) }</code></pre></div> <p>Having loaded the necessary functions, we can get started on the dataset.</p> <p>We here provide the cleaned dataset, which we have saved in a folder called “export”, as easy starting point. However, the full dataset can be loaded and cleaned using the data cleaning function (Function 1 above), if “#” signs in the code below are removed (created as that is much smaller than the .csv - which we can still provide for those who absolutely want to start from scratch?)</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F367" aria-expanded="false" aria-controls="rcode-643E0F367"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F367"><pre class="r"><code class="hljs"><span class="hljs-comment">## Load raw data - save cleaned dataset as RDS for reuse</span> <span class="hljs-comment">#data_raw <- load_raw("data/dr7.0_all_control_data.csv") %>%</span> <span class="hljs-comment"># clean_raw_data()</span> <span class="hljs-comment">#dir.create("export", F, F)</span> <span class="hljs-comment">#saveRDS(data_raw, "export/data_clean.rds")</span> getwd()</code></pre></div> <pre><code class="hljs">## [1] "/Volumes/SZ WD drive/garvan/Github/IMPC sexDiffs/mice_sex_diff/scripts"</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F368" aria-expanded="false" aria-controls="rcode-643E0F368"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F368"><pre class="r"><code class="hljs">data1 <- readRDS(<span class="hljs-string">"../export/data_clean.rds"</span>) <span class="hljs-comment">#data1 </span></code></pre></div> </div> </div> <div id="clean-data" class="section level3"> <div name="clean_data" data-unique="clean_data"></div><h3>Clean data</h3> <p>This requires the selection of traits that have been measured in at least 2 centers. Consequently, rare or unusual methods and procedures are being filtered out in this step.</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F369" aria-expanded="false" aria-controls="rcode-643E0F369"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F369"><pre class="r"><code class="hljs">dat1 <- data1 %>% group_by(parameter_name) %>% summarize(center_per_trait = length(unique(production_center, na.rm = <span class="hljs-literal">TRUE</span>))) dat2 <- merge(data1, dat1) dat_moreThan1center <- dat2 %>% filter(center_per_trait >= <span class="hljs-number">2</span>) data2 <- dat_moreThan1center <span class="hljs-comment">#min(data2$center_per_trait) # as a check if there indeed are no single occurences</span></code></pre></div> </div> </div> <div id="meta-analysis-phase-1" class="section level2"> <div name="meta_analysis,_phase_1" data-unique="meta_analysis,_phase_1"></div><h2>Meta analysis, Phase 1</h2> <div id="preparation" class="section level3"> <div name="preparation" data-unique="preparation"></div><h3>Preparation</h3> <div id="define-population-variable-add-grouping-variables" class="section level4"> <div name="define_population_variable__add_grouping_variables" data-unique="define_population_variable__add_grouping_variables"></div><h4>Define population variable & add grouping variables</h4> <p>In this step, a grouping variable is added (found in “Parameter.Grouping.csv”) The grouping variables were decided based on functional groups and procedures</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3610" aria-expanded="false" aria-controls="rcode-643E0F3610"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3610"><pre class="r"><code class="hljs">data3 <- data2 %>% mutate(population = sprintf(<span class="hljs-string">"%s-%s"</span>, production_center, strain_name)) group <- read.csv(<span class="hljs-string">"../export/ParameterGrouping.csv"</span>) data <- data3 data$parameterGroup <- group$parameter[match(data$parameter_name, group$parameter_name)] </code></pre></div> </div> <div id="assign-each-unique-parameter_name-traituse-trait-variable-a-unique-number-id" class="section level4"> <div name="assign_each_unique_parameter_name_(=trait,use_trait_variable)_a_unique_number_(‘id’)" data-unique="assign_each_unique_parameter_name_(=trait,use_trait_variable)_a_unique_number_(‘id’)"></div><h4>Assign each unique parameter_name (=trait,use trait variable) a unique number (‘id’)</h4> <p>We add a new variable, where redundant traits are combined [note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance]</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3611" aria-expanded="false" aria-controls="rcode-643E0F3611"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3611"><pre class="r"><code class="hljs"><span class="hljs-comment">#head(data)</span> names(data)[<span class="hljs-number">16</span>] <- <span class="hljs-string">"parameter_group"</span> data <- transform(data, id = match(parameter_name, unique(parameter_name))) n1 <- length(unique(data$parameter_name)) <span class="hljs-comment">#232</span> n2 <- length(unique(data$parameter_group)) <span class="hljs-comment">#161</span> n3 <- length(unique(data$procedure_name)) <span class="hljs-comment"># 26</span> n <- length(unique(data$id)) <span class="hljs-comment">#n # just to check that the number of traits is 232</span></code></pre></div> </div> <div id="create-a-matrix-to-store-results-for-all-traits" class="section level4"> <div name="create_a_matrix_to_store_results_for_all_traits" data-unique="create_a_matrix_to_store_results_for_all_traits"></div><h4>Create a matrix to store results for all traits</h4> <p>As the current version of this script utilizes a loop instead of tidyR code, it is here necessary to create an empty matrix, in which the returning values will be stored.</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3612" aria-expanded="false" aria-controls="rcode-643E0F3612"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3612"><pre class="r"><code class="hljs">results.alltraits.grouping <- as.data.frame(cbind(c(<span class="hljs-number">1</span>:n), matrix(rep(<span class="hljs-number">0</span>, n*<span class="hljs-number">14</span>), ncol = <span class="hljs-number">14</span>))) <span class="hljs-comment">#number of individual results per trait = 10</span> names(results.alltraits.grouping) <- c(<span class="hljs-string">"id"</span>, <span class="hljs-string">"lnCVR"</span>, <span class="hljs-string">"lnCVR_lower"</span>, <span class="hljs-string">"lnCVR_upper"</span>, <span class="hljs-string">"lnCVR_se"</span>, <span class="hljs-string">"lnVR"</span>, <span class="hljs-string">"lnVR_lower"</span>, <span class="hljs-string">"lnVR_upper"</span>, <span class="hljs-string">"lnVR_se"</span>, <span class="hljs-string">"lnRR"</span>, <span class="hljs-string">"lnRR_lower"</span>, <span class="hljs-string">"lnRR_upper"</span> ,<span class="hljs-string">"lnRR_se"</span> , <span class="hljs-string">"sampleSize"</span>, <span class="hljs-string">"trait"</span>)</code></pre></div> </div> </div> <div id="loop-to-run-meta-analysis-on-all-traits" class="section level3"> <div name="loop,_to_run_meta-analysis_on_all_traits" data-unique="loop,_to_run_meta-analysis_on_all_traits"></div><h3>LOOP, to run meta-analysis on all traits</h3> <p>The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis. Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below.</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3613" aria-expanded="false" aria-controls="rcode-643E0F3613"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3613"><pre class="r"><code class="hljs"><span class="hljs-keyword">for</span>(t <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:n) { <span class="hljs-keyword">tryCatch</span>({ data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = <span class="hljs-number">0</span>, age_center = <span class="hljs-number">100</span>) population_stats <- calculate_population_stats(data_par_age) results <- create_meta_analysis_effect_sizes(population_stats) <span class="hljs-comment">#lnCVR, log repsonse-ratio of the coefficient of variance </span> cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(~<span class="hljs-number">1</span>| strain_name, ~<span class="hljs-number">1</span>|production_center, ~<span class="hljs-number">1</span>|err), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>, data = results) results.alltraits.grouping[t, <span class="hljs-number">2</span>] <- cvr$b results.alltraits.grouping[t, <span class="hljs-number">3</span>] <- cvr$ci.lb results.alltraits.grouping[t, <span class="hljs-number">4</span>] <- cvr$ci.ub results.alltraits.grouping[t, <span class="hljs-number">5</span>] <- cvr$se cvr <span class="hljs-comment">#lnVR, comparison of standard deviations </span> cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(~<span class="hljs-number">1</span>| strain_name, ~<span class="hljs-number">1</span>|production_center,~<span class="hljs-number">1</span>|err), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>, data = results) results.alltraits.grouping[t, <span class="hljs-number">6</span>] <- cv$b results.alltraits.grouping[t, <span class="hljs-number">7</span>] <- cv$ci.lb results.alltraits.grouping[t, <span class="hljs-number">8</span>] <- cv$ci.ub results.alltraits.grouping[t, <span class="hljs-number">9</span>] <- cv$se <span class="hljs-comment"># for means, lnRR</span> means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(~<span class="hljs-number">1</span>| strain_name, ~<span class="hljs-number">1</span>|production_center, ~<span class="hljs-number">1</span>|err), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), data = results) results.alltraits.grouping[t, <span class="hljs-number">10</span>] <- means$b results.alltraits.grouping[t, <span class="hljs-number">11</span>] <- means$ci.lb results.alltraits.grouping[t, <span class="hljs-number">12</span>] <- means$ci.ub results.alltraits.grouping[t, <span class="hljs-number">13</span>] <- means$se results.alltraits.grouping[t, <span class="hljs-number">14</span>] <- means$k results.alltraits.grouping[t, <span class="hljs-number">15</span>] <- unique(results$trait) }, error=<span class="hljs-keyword">function</span>(e){cat(<span class="hljs-string">"ERROR :"</span>,conditionMessage(e), <span class="hljs-string">"\n"</span>)}) }</code></pre></div> <pre><code class="hljs">## ERROR : Optimizer (optim) did not achieve convergence (convergence = 10). ## ERROR : Optimizer (optim) did not achieve convergence (convergence = 10). ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y'</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3614" aria-expanded="false" aria-controls="rcode-643E0F3614"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3614"><pre class="r"><code class="hljs"><span class="hljs-comment"># Now that we have a "results" table with each of the meta-analytic means for all effect sizes of interest, we can use this table as part of the Shiny App, which will then be able to back calculate the percentage differences between males and females for mean, variance and coefficient of variance. We'll export and use this in the Shiny App. **Note that I have not dealt with convergence issues in some of these models, and so, this will need to be done down the road**</span> <span class="hljs-comment">## Note Susi 31/7/2019: This dataset contains dublicated values, plus no info on what the "traits" mean. I will change Dan N's to one further belwo, that have been cleaned up already</span> <span class="hljs-comment">#FILE TO USE: METACOMBO (around line 500)</span> <span class="hljs-comment">#trait_meta_results <- write.csv(results.alltraits.grouping, file = "export/trait_meta_results.csv")</span></code></pre></div> </div> <div id="merging-datasets" class="section level3"> <div name="merging_datasets" data-unique="merging_datasets"></div><h3>Merging datasets</h3> <p>Procedure names, grouping variables etc. are merged back together with the results from the metafor analysis above. This requires loading of another excel sheet, “procedures.csv”</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3615" aria-expanded="false" aria-controls="rcode-643E0F3615"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3615"><pre class="r"><code class="hljs">procedures <- read.csv(<span class="hljs-string">"../export/procedures.csv"</span>) results.alltraits.grouping$parameter_group <- data$parameter_group[match(results.alltraits.grouping$id, data$id)] results.alltraits.grouping$procedure <- data$procedure_name[match(results.alltraits.grouping$id, data$id)] results.alltraits.grouping$GroupingTerm <- procedures$GroupingTerm[match(results.alltraits.grouping$procedure, procedures$procedure)] results.alltraits.grouping$parameter_name <- data$parameter_name[match(results.alltraits.grouping$id, data$id)] meta1 <- results.alltraits.grouping n <- length(unique(meta1$parameter_name)) <span class="hljs-comment"># 232</span></code></pre></div> <p>Removal of traits that did not achieve convergence, are nonsensical for analysis of variance (such as traits that show variation, such as number of ribs, digits, etc). 14 traits from the originally 232 that had been included are removed.</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3616" aria-expanded="false" aria-controls="rcode-643E0F3616"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3616"><pre class="r"><code class="hljs">meta_clean <- meta1[ !(meta1$id %<span class="hljs-keyword">in</span>% c(<span class="hljs-number">84</span>,<span class="hljs-number">144</span>,<span class="hljs-number">158</span>,<span class="hljs-number">160</span>,<span class="hljs-number">161</span>,<span class="hljs-number">162</span>,<span class="hljs-number">163</span>,<span class="hljs-number">165</span>,<span class="hljs-number">166</span>,<span class="hljs-number">167</span>,<span class="hljs-number">168</span>,<span class="hljs-number">221</span>,<span class="hljs-number">222</span>,<span class="hljs-number">231</span>)), ] removed <-length(unique(meta_clean$parameter_name)) <span class="hljs-comment">#218</span></code></pre></div> </div> </div> <div id="meta-analysis-phase-2" class="section level2"> <div name="meta-analysis,_phase_2" data-unique="meta-analysis,_phase_2"></div><h2>Meta-analysis, Phase 2</h2> <div id="dealing-with-correlated-parameters-preparation" class="section level3"> <div name="dealing_with_correlated_parameters,_preparation" data-unique="dealing_with_correlated_parameters,_preparation"></div><h3>Dealing with Correlated Parameters, preparation</h3> <p>This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example, hierarchical parametrization within immunological assays). As those data-points are not independent of each other, we conducted a meta analyses on these correlated parameters to collapse the number of levels.</p> <div id="collapsing-and-merging-correlated-parameters" class="section level4"> <div name="collapsing_and_merging_correlated_parameters" data-unique="collapsing_and_merging_correlated_parameters"></div><h4>Collapsing and merging correlated parameters</h4> <p>Here we double check numbers of trait parameters in the dataset</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3617" aria-expanded="false" aria-controls="rcode-643E0F3617"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3617"><pre class="r"><code class="hljs">meta1 <- meta_clean <span class="hljs-comment"># length(unique(meta1$procedure)) #18</span> <span class="hljs-comment"># length(unique(meta1$GroupingTerm)) #9 </span> <span class="hljs-comment"># length(unique(meta1$parameter_group)) # 148 levels. To be used as grouping factor for meta-meta analysis / collapsing down based on things that are classified identically in "parameter_group" but have different "parameter_name" </span> length(unique(meta1$parameter_name)) <span class="hljs-comment">#218</span></code></pre></div> <pre><code class="hljs">## [1] 218</code></pre> </div> <div id="count-of-number-of-parameter-names-correlated-sub-traits-in-each-parameter-group-par_group_size" class="section level4"> <div name="count_of_number_of_parameter_names_(correlated_sub-traits)_in_each_parameter_group_(par_group_size)" data-unique="count_of_number_of_parameter_names_(correlated_sub-traits)_in_each_parameter_group_(par_group_size)"></div><h4>Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)</h4> <p>This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given “parameter group” equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same parameter group (90 cases), are selected for a “mini-meta analysis”, which removes the issue of correlation.</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3618" aria-expanded="false" aria-controls="rcode-643E0F3618"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3618"><pre class="r"><code class="hljs">kable(cbind (meta1 %>% count(parameter_group) )) %>% kable_styling() %>% scroll_box(width = <span class="hljs-string">"100%"</span>, height = <span class="hljs-string">"200px"</span>)</code></pre></div> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "> <table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> </tr> </tbody> </table> </div> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3619" aria-expanded="false" aria-controls="rcode-643E0F3619"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3619"><pre class="r"><code class="hljs">meta1b <- meta1 %>% group_by(parameter_group) %>% summarize(par_group_size = length(unique(parameter_name, na.rm = <span class="hljs-literal">TRUE</span>))) <span class="hljs-comment">#this gives a summary of number of parameter names in each parameter group, now it neeeds to get merged it back together</span> meta1$par_group_size <- meta1b$par_group_size[match(meta1$parameter_group, meta1b$parameter_group)] <span class="hljs-comment"># Create subsets with > 1 count (par_group_size > 1) </span> meta1_sub<-subset(meta1,par_group_size ><span class="hljs-number">1</span>) <span class="hljs-comment"># 90 observations </span> meta1_sub$sampleSize <- as.numeric(meta1_sub$sampleSize)</code></pre></div> </div> </div> <div id="perform-meta-analyses-on-correlated-sub-traits-using-robumeta" class="section level3"> <div name="perform_meta-analyses_on_correlated_sub-traits,_using_robumeta" data-unique="perform_meta-analyses_on_correlated_sub-traits,_using_robumeta"></div><h3>Perform meta-analyses on correlated sub-traits, using <code>robumeta</code></h3> <p>The subset of the data is prepared (nested), and in this first step the model of the meta analysis effect sizes are calculated</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3620" aria-expanded="false" aria-controls="rcode-643E0F3620"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3620"><pre class="r"><code class="hljs"><span class="hljs-comment"># nesting</span> n_count <- meta1_sub %>% group_by(parameter_group) %>% mutate(raw_N = sum(sampleSize)) %>% nest() model_count <- n_count %>% mutate(model_lnRR = map(data, ~ robu(.x$lnRR ~ <span class="hljs-number">1</span>, data= .x, studynum= .x$id, modelweights = c(<span class="hljs-string">"CORR"</span>), rho = <span class="hljs-number">0.8</span>, small = <span class="hljs-literal">TRUE</span>, var.eff.size= (.x$lnRR_se)^<span class="hljs-number">2</span> )), model_lnVR = map(data, ~ robu(.x$lnVR ~ <span class="hljs-number">1</span>, data= .x, studynum= .x$id, modelweights = c(<span class="hljs-string">"CORR"</span>), rho = <span class="hljs-number">0.8</span>, small = <span class="hljs-literal">TRUE</span>, var.eff.size= (.x$lnVR_se)^<span class="hljs-number">2</span> )), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ <span class="hljs-number">1</span>, data= .x, studynum= .x$id, modelweights = c(<span class="hljs-string">"CORR"</span>), rho = <span class="hljs-number">0.8</span>, small = <span class="hljs-literal">TRUE</span>, var.eff.size= (.x$lnCVR_se)^<span class="hljs-number">2</span> ))) </code></pre></div> <div id="extract-and-save-parameter-estimates" class="section level4"> <div name="extract_and_save_parameter_estimates" data-unique="extract_and_save_parameter_estimates"></div><h4>Extract and save parameter estimates</h4> <p>Function to collect the outcomes of the “mini” meta analysis</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3621" aria-expanded="false" aria-controls="rcode-643E0F3621"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3621"><pre class="r"><code class="hljs">count_fun <- <span class="hljs-keyword">function</span>(mod_sub) <span class="hljs-keyword">return</span>(c(mod_sub$reg_table$b.r, mod_sub$reg_table$CI.L, mod_sub$reg_table$CI.U, mod_sub$reg_table$SE)) <span class="hljs-comment">#estimate, lower ci, upper ci, SE</span></code></pre></div> <p>Extraction of values created during Meta analysis using robu meta</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3622" aria-expanded="false" aria-controls="rcode-643E0F3622"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3622"><pre class="r"><code class="hljs">robusub_RR <- model_count %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c(<span class="hljs-string">"parameter_group"</span>,<span class="hljs-string">"lnRR"</span>,<span class="hljs-string">"lnRR_lower"</span>,<span class="hljs-string">"lnRR_upper"</span>,<span class="hljs-string">"lnRR_se"</span>)) robusub_CVR <- model_count %>% transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c(<span class="hljs-string">"parameter_group"</span>,<span class="hljs-string">"lnCVR"</span>,<span class="hljs-string">"lnCVR_lower"</span>,<span class="hljs-string">"lnCVR_upper"</span>,<span class="hljs-string">"lnCVR_se"</span>)) robusub_VR <- model_count %>% transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c(<span class="hljs-string">"parameter_group"</span>,<span class="hljs-string">"lnVR"</span>,<span class="hljs-string">"lnVR_lower"</span>,<span class="hljs-string">"lnVR_upper"</span>,<span class="hljs-string">"lnVR_se"</span>)) robu_all <- full_join(robusub_CVR, robusub_VR) %>% full_join(., robusub_RR) kable(cbind(robu_all, robu_all)) %>% kable_styling() %>% scroll_box(width = <span class="hljs-string">"100%"</span>, height = <span class="hljs-string">"200px"</span>)</code></pre></div> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "> <table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> </tr> </tbody> </table> </div> <p>Merge the two data sets (the new [robu_all] and the initial [uncorrelated sub-traits with count = 1])</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3623" aria-expanded="false" aria-controls="rcode-643E0F3623"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3623"><pre class="r"><code class="hljs">meta_all <- meta1 %>% filter(par_group_size == <span class="hljs-number">1</span>) %>% as_tibble <span class="hljs-comment">#str(meta_all)</span> <span class="hljs-comment">#str(robu_all)</span> <span class="hljs-comment">#which(is.na(match(names(meta_all),names(robu_all)))) # check</span></code></pre></div> <p>Combine data</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3624" aria-expanded="false" aria-controls="rcode-643E0F3624"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3624"><pre class="r"><code class="hljs"><span class="hljs-comment"># Step1 </span> combinedmeta <- bind_rows(robu_all, meta_all) <span class="hljs-comment">#glimpse(combinedmeta)</span> <span class="hljs-comment"># Steps 2&3</span> metacombo <- combinedmeta metacombo$counts <- meta1$par_group_size[match( metacombo$parameter_group, meta1$parameter_group)] metacombo$procedure2 <-meta1$procedure[match( metacombo$parameter_group, meta1$parameter_group)] metacombo$GroupingTerm2 <-meta1$GroupingTerm[match( metacombo$parameter_group, meta1$parameter_group)] kable(cbind (metacombo, metacombo)) %>% kable_styling() %>% scroll_box(width = <span class="hljs-string">"100%"</span>, height = <span class="hljs-string">"200px"</span>)</code></pre></div> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "> <table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> id </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> sampleSize </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> trait </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_name </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> par_group_size </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> counts </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure2 </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm2 </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> id </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> sampleSize </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> trait </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_name </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> par_group_size </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> counts </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure2 </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm2 </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0538655 </td> <td style="text-align:right;"> -0.0056830 </td> <td style="text-align:right;"> 0.1134139 </td> <td style="text-align:right;"> 0.0303824 </td> <td style="text-align:right;"> 0.0869649 </td> <td style="text-align:right;"> 0.0065802 </td> <td style="text-align:right;"> 0.1673497 </td> <td style="text-align:right;"> 0.0410134 </td> <td style="text-align:right;"> 0.0024851 </td> <td style="text-align:right;"> -0.0214504 </td> <td style="text-align:right;"> 0.0264205 </td> <td style="text-align:right;"> 0.0122122 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0538655 </td> <td style="text-align:right;"> -0.0056830 </td> <td style="text-align:right;"> 0.1134139 </td> <td style="text-align:right;"> 0.0303824 </td> <td style="text-align:right;"> 0.0869649 </td> <td style="text-align:right;"> 0.0065802 </td> <td style="text-align:right;"> 0.1673497 </td> <td style="text-align:right;"> 0.0410134 </td> <td style="text-align:right;"> 0.0024851 </td> <td style="text-align:right;"> -0.0214504 </td> <td style="text-align:right;"> 0.0264205 </td> <td style="text-align:right;"> 0.0122122 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0238241 </td> <td style="text-align:right;"> -0.0331809 </td> <td style="text-align:right;"> 0.0808292 </td> <td style="text-align:right;"> 0.0290848 </td> <td style="text-align:right;"> 0.0250266 </td> <td style="text-align:right;"> -0.0488450 </td> <td style="text-align:right;"> 0.0988982 </td> <td style="text-align:right;"> 0.0376903 </td> <td style="text-align:right;"> -0.0200763 </td> <td style="text-align:right;"> -0.0431508 </td> <td style="text-align:right;"> 0.0029982 </td> <td style="text-align:right;"> 0.0117729 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0238241 </td> <td style="text-align:right;"> -0.0331809 </td> <td style="text-align:right;"> 0.0808292 </td> <td style="text-align:right;"> 0.0290848 </td> <td style="text-align:right;"> 0.0250266 </td> <td style="text-align:right;"> -0.0488450 </td> <td style="text-align:right;"> 0.0988982 </td> <td style="text-align:right;"> 0.0376903 </td> <td style="text-align:right;"> -0.0200763 </td> <td style="text-align:right;"> -0.0431508 </td> <td style="text-align:right;"> 0.0029982 </td> <td style="text-align:right;"> 0.0117729 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0518127 </td> <td style="text-align:right;"> -0.0148242 </td> <td style="text-align:right;"> 0.1184497 </td> <td style="text-align:right;"> 0.0339991 </td> <td style="text-align:right;"> -0.0891510 </td> <td style="text-align:right;"> -0.3321998 </td> <td style="text-align:right;"> 0.1538977 </td> <td style="text-align:right;"> 0.1240067 </td> <td style="text-align:right;"> -0.0224536 </td> <td style="text-align:right;"> -0.0444163 </td> <td style="text-align:right;"> -0.0004910 </td> <td style="text-align:right;"> 0.0112057 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0518127 </td> <td style="text-align:right;"> -0.0148242 </td> <td style="text-align:right;"> 0.1184497 </td> <td style="text-align:right;"> 0.0339991 </td> <td style="text-align:right;"> -0.0891510 </td> <td style="text-align:right;"> -0.3321998 </td> <td style="text-align:right;"> 0.1538977 </td> <td style="text-align:right;"> 0.1240067 </td> <td style="text-align:right;"> -0.0224536 </td> <td style="text-align:right;"> -0.0444163 </td> <td style="text-align:right;"> -0.0004910 </td> <td style="text-align:right;"> 0.0112057 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0170933 </td> <td style="text-align:right;"> -0.0533187 </td> <td style="text-align:right;"> 0.0875053 </td> <td style="text-align:right;"> 0.0359252 </td> <td style="text-align:right;"> -0.0344797 </td> <td style="text-align:right;"> -0.1017901 </td> <td style="text-align:right;"> 0.0328306 </td> <td style="text-align:right;"> 0.0343426 </td> <td style="text-align:right;"> -0.0497874 </td> <td style="text-align:right;"> -0.0748197 </td> <td style="text-align:right;"> -0.0247550 </td> <td style="text-align:right;"> 0.0127718 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 0.0170933 </td> <td style="text-align:right;"> -0.0533187 </td> <td style="text-align:right;"> 0.0875053 </td> <td style="text-align:right;"> 0.0359252 </td> <td style="text-align:right;"> -0.0344797 </td> <td style="text-align:right;"> -0.1017901 </td> <td style="text-align:right;"> 0.0328306 </td> <td style="text-align:right;"> 0.0343426 </td> <td style="text-align:right;"> -0.0497874 </td> <td style="text-align:right;"> -0.0748197 </td> <td style="text-align:right;"> -0.0247550 </td> <td style="text-align:right;"> 0.0127718 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> -0.0077678 </td> <td style="text-align:right;"> -0.0418582 </td> <td style="text-align:right;"> 0.0263226 </td> <td style="text-align:right;"> 0.0173934 </td> <td style="text-align:right;"> 0.0141682 </td> <td style="text-align:right;"> -0.0189973 </td> <td style="text-align:right;"> 0.0473337 </td> <td style="text-align:right;"> 0.0169215 </td> <td style="text-align:right;"> 0.0184043 </td> <td style="text-align:right;"> 0.0056897 </td> <td style="text-align:right;"> 0.0311189 </td> <td style="text-align:right;"> 0.0064872 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> -0.0077678 </td> <td style="text-align:right;"> -0.0418582 </td> <td style="text-align:right;"> 0.0263226 </td> <td style="text-align:right;"> 0.0173934 </td> <td style="text-align:right;"> 0.0141682 </td> <td style="text-align:right;"> -0.0189973 </td> <td style="text-align:right;"> 0.0473337 </td> <td style="text-align:right;"> 0.0169215 </td> <td style="text-align:right;"> 0.0184043 </td> <td style="text-align:right;"> 0.0056897 </td> <td style="text-align:right;"> 0.0311189 </td> <td style="text-align:right;"> 0.0064872 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> -0.0684217 </td> <td style="text-align:right;"> -0.1895020 </td> <td style="text-align:right;"> 0.0526586 </td> <td style="text-align:right;"> 0.0617768 </td> <td style="text-align:right;"> 0.0585179 </td> <td style="text-align:right;"> -0.1322507 </td> <td style="text-align:right;"> 0.2492866 </td> <td style="text-align:right;"> 0.0973327 </td> <td style="text-align:right;"> 0.1069442 </td> <td style="text-align:right;"> 0.0319934 </td> <td style="text-align:right;"> 0.1818950 </td> <td style="text-align:right;"> 0.0382409 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> -0.0684217 </td> <td style="text-align:right;"> -0.1895020 </td> <td style="text-align:right;"> 0.0526586 </td> <td style="text-align:right;"> 0.0617768 </td> <td style="text-align:right;"> 0.0585179 </td> <td style="text-align:right;"> -0.1322507 </td> <td style="text-align:right;"> 0.2492866 </td> <td style="text-align:right;"> 0.0973327 </td> <td style="text-align:right;"> 0.1069442 </td> <td style="text-align:right;"> 0.0319934 </td> <td style="text-align:right;"> 0.1818950 </td> <td style="text-align:right;"> 0.0382409 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 0.1133080 </td> <td style="text-align:right;"> 0.0451475 </td> <td style="text-align:right;"> 0.1814685 </td> <td style="text-align:right;"> 0.0347764 </td> <td style="text-align:right;"> 0.0559995 </td> <td style="text-align:right;"> -0.0080678 </td> <td style="text-align:right;"> 0.1200668 </td> <td style="text-align:right;"> 0.0326880 </td> <td style="text-align:right;"> -0.0567840 </td> <td style="text-align:right;"> -0.0732083 </td> <td style="text-align:right;"> -0.0403597 </td> <td style="text-align:right;"> 0.0083799 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> albumin </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 0.1133080 </td> <td style="text-align:right;"> 0.0451475 </td> <td style="text-align:right;"> 0.1814685 </td> <td style="text-align:right;"> 0.0347764 </td> <td style="text-align:right;"> 0.0559995 </td> <td style="text-align:right;"> -0.0080678 </td> <td style="text-align:right;"> 0.1200668 </td> <td style="text-align:right;"> 0.0326880 </td> <td style="text-align:right;"> -0.0567840 </td> <td style="text-align:right;"> -0.0732083 </td> <td style="text-align:right;"> -0.0403597 </td> <td style="text-align:right;"> 0.0083799 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> albumin </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 0.1043649 </td> <td style="text-align:right;"> 0.0451585 </td> <td style="text-align:right;"> 0.1635713 </td> <td style="text-align:right;"> 0.0302079 </td> <td style="text-align:right;"> -0.3112471 </td> <td style="text-align:right;"> -0.3980164 </td> <td style="text-align:right;"> -0.2244778 </td> <td style="text-align:right;"> 0.0442709 </td> <td style="text-align:right;"> -0.4216032 </td> <td style="text-align:right;"> -0.4694832 </td> <td style="text-align:right;"> -0.3737231 </td> <td style="text-align:right;"> 0.0244290 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 0.1043649 </td> <td style="text-align:right;"> 0.0451585 </td> <td style="text-align:right;"> 0.1635713 </td> <td style="text-align:right;"> 0.0302079 </td> <td style="text-align:right;"> -0.3112471 </td> <td style="text-align:right;"> -0.3980164 </td> <td style="text-align:right;"> -0.2244778 </td> <td style="text-align:right;"> 0.0442709 </td> <td style="text-align:right;"> -0.4216032 </td> <td style="text-align:right;"> -0.4694832 </td> <td style="text-align:right;"> -0.3737231 </td> <td style="text-align:right;"> 0.0244290 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 0.0383407 </td> <td style="text-align:right;"> -0.0423419 </td> <td style="text-align:right;"> 0.1190232 </td> <td style="text-align:right;"> 0.0411653 </td> <td style="text-align:right;"> 0.2795566 </td> <td style="text-align:right;"> 0.1615777 </td> <td style="text-align:right;"> 0.3975355 </td> <td style="text-align:right;"> 0.0601944 </td> <td style="text-align:right;"> 0.2246987 </td> <td style="text-align:right;"> 0.1793151 </td> <td style="text-align:right;"> 0.2700822 </td> <td style="text-align:right;"> 0.0231553 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 0.0383407 </td> <td style="text-align:right;"> -0.0423419 </td> <td style="text-align:right;"> 0.1190232 </td> <td style="text-align:right;"> 0.0411653 </td> <td style="text-align:right;"> 0.2795566 </td> <td style="text-align:right;"> 0.1615777 </td> <td style="text-align:right;"> 0.3975355 </td> <td style="text-align:right;"> 0.0601944 </td> <td style="text-align:right;"> 0.2246987 </td> <td style="text-align:right;"> 0.1793151 </td> <td style="text-align:right;"> 0.2700822 </td> <td style="text-align:right;"> 0.0231553 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> -0.1531723 </td> <td style="text-align:right;"> -0.2210551 </td> <td style="text-align:right;"> -0.0852895 </td> <td style="text-align:right;"> 0.0346347 </td> <td style="text-align:right;"> 0.2748396 </td> <td style="text-align:right;"> 0.1950895 </td> <td style="text-align:right;"> 0.3545898 </td> <td style="text-align:right;"> 0.0406896 </td> <td style="text-align:right;"> 0.4357738 </td> <td style="text-align:right;"> 0.3655882 </td> <td style="text-align:right;"> 0.5059595 </td> <td style="text-align:right;"> 0.0358097 </td> <td style="text-align:right;"> 15 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> -0.1531723 </td> <td style="text-align:right;"> -0.2210551 </td> <td style="text-align:right;"> -0.0852895 </td> <td style="text-align:right;"> 0.0346347 </td> <td style="text-align:right;"> 0.2748396 </td> <td style="text-align:right;"> 0.1950895 </td> <td style="text-align:right;"> 0.3545898 </td> <td style="text-align:right;"> 0.0406896 </td> <td style="text-align:right;"> 0.4357738 </td> <td style="text-align:right;"> 0.3655882 </td> <td style="text-align:right;"> 0.5059595 </td> <td style="text-align:right;"> 0.0358097 </td> <td style="text-align:right;"> 15 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 0.0119165 </td> <td style="text-align:right;"> -0.1228287 </td> <td style="text-align:right;"> 0.1466617 </td> <td style="text-align:right;"> 0.0687488 </td> <td style="text-align:right;"> -0.0566968 </td> <td style="text-align:right;"> -0.2457779 </td> <td style="text-align:right;"> 0.1323843 </td> <td style="text-align:right;"> 0.0964717 </td> <td style="text-align:right;"> -0.0585577 </td> <td style="text-align:right;"> -0.1331777 </td> <td style="text-align:right;"> 0.0160624 </td> <td style="text-align:right;"> 0.0380722 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 0.0119165 </td> <td style="text-align:right;"> -0.1228287 </td> <td style="text-align:right;"> 0.1466617 </td> <td style="text-align:right;"> 0.0687488 </td> <td style="text-align:right;"> -0.0566968 </td> <td style="text-align:right;"> -0.2457779 </td> <td style="text-align:right;"> 0.1323843 </td> <td style="text-align:right;"> 0.0964717 </td> <td style="text-align:right;"> -0.0585577 </td> <td style="text-align:right;"> -0.1331777 </td> <td style="text-align:right;"> 0.0160624 </td> <td style="text-align:right;"> 0.0380722 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> -0.0917931 </td> <td style="text-align:right;"> -0.2022487 </td> <td style="text-align:right;"> 0.0186624 </td> <td style="text-align:right;"> 0.0563559 </td> <td style="text-align:right;"> 0.2031265 </td> <td style="text-align:right;"> -0.0131549 </td> <td style="text-align:right;"> 0.4194079 </td> <td style="text-align:right;"> 0.1103497 </td> <td style="text-align:right;"> 0.2675772 </td> <td style="text-align:right;"> 0.0643028 </td> <td style="text-align:right;"> 0.4708516 </td> <td style="text-align:right;"> 0.1037133 </td> <td style="text-align:right;"> 20 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> -0.0917931 </td> <td style="text-align:right;"> -0.2022487 </td> <td style="text-align:right;"> 0.0186624 </td> <td style="text-align:right;"> 0.0563559 </td> <td style="text-align:right;"> 0.2031265 </td> <td style="text-align:right;"> -0.0131549 </td> <td style="text-align:right;"> 0.4194079 </td> <td style="text-align:right;"> 0.1103497 </td> <td style="text-align:right;"> 0.2675772 </td> <td style="text-align:right;"> 0.0643028 </td> <td style="text-align:right;"> 0.4708516 </td> <td style="text-align:right;"> 0.1037133 </td> <td style="text-align:right;"> 20 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> -0.0934739 </td> <td style="text-align:right;"> -0.1787512 </td> <td style="text-align:right;"> -0.0081966 </td> <td style="text-align:right;"> 0.0435096 </td> <td style="text-align:right;"> -0.0639511 </td> <td style="text-align:right;"> -0.2828066 </td> <td style="text-align:right;"> 0.1549044 </td> <td style="text-align:right;"> 0.1116630 </td> <td style="text-align:right;"> -0.0156339 </td> <td style="text-align:right;"> -0.1102310 </td> <td style="text-align:right;"> 0.0789633 </td> <td style="text-align:right;"> 0.0482647 </td> <td style="text-align:right;"> 21 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> -0.0934739 </td> <td style="text-align:right;"> -0.1787512 </td> <td style="text-align:right;"> -0.0081966 </td> <td style="text-align:right;"> 0.0435096 </td> <td style="text-align:right;"> -0.0639511 </td> <td style="text-align:right;"> -0.2828066 </td> <td style="text-align:right;"> 0.1549044 </td> <td style="text-align:right;"> 0.1116630 </td> <td style="text-align:right;"> -0.0156339 </td> <td style="text-align:right;"> -0.1102310 </td> <td style="text-align:right;"> 0.0789633 </td> <td style="text-align:right;"> 0.0482647 </td> <td style="text-align:right;"> 21 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 0.1314998 </td> <td style="text-align:right;"> 0.0329846 </td> <td style="text-align:right;"> 0.2300151 </td> <td style="text-align:right;"> 0.0502638 </td> <td style="text-align:right;"> -0.0448684 </td> <td style="text-align:right;"> -0.1340146 </td> <td style="text-align:right;"> 0.0442777 </td> <td style="text-align:right;"> 0.0454836 </td> <td style="text-align:right;"> -0.1722378 </td> <td style="text-align:right;"> -0.2207030 </td> <td style="text-align:right;"> -0.1237726 </td> <td style="text-align:right;"> 0.0247276 </td> <td style="text-align:right;"> 22 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 0.1314998 </td> <td style="text-align:right;"> 0.0329846 </td> <td style="text-align:right;"> 0.2300151 </td> <td style="text-align:right;"> 0.0502638 </td> <td style="text-align:right;"> -0.0448684 </td> <td style="text-align:right;"> -0.1340146 </td> <td style="text-align:right;"> 0.0442777 </td> <td style="text-align:right;"> 0.0454836 </td> <td style="text-align:right;"> -0.1722378 </td> <td style="text-align:right;"> -0.2207030 </td> <td style="text-align:right;"> -0.1237726 </td> <td style="text-align:right;"> 0.0247276 </td> <td style="text-align:right;"> 22 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> -0.0347988 </td> <td style="text-align:right;"> -0.0824528 </td> <td style="text-align:right;"> 0.0128552 </td> <td style="text-align:right;"> 0.0243137 </td> <td style="text-align:right;"> -0.0059677 </td> <td style="text-align:right;"> -0.0526221 </td> <td style="text-align:right;"> 0.0406866 </td> <td style="text-align:right;"> 0.0238037 </td> <td style="text-align:right;"> 0.0282722 </td> <td style="text-align:right;"> 0.0233254 </td> <td style="text-align:right;"> 0.0332189 </td> <td style="text-align:right;"> 0.0025239 </td> <td style="text-align:right;"> 23 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> body length </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> -0.0347988 </td> <td style="text-align:right;"> -0.0824528 </td> <td style="text-align:right;"> 0.0128552 </td> <td style="text-align:right;"> 0.0243137 </td> <td style="text-align:right;"> -0.0059677 </td> <td style="text-align:right;"> -0.0526221 </td> <td style="text-align:right;"> 0.0406866 </td> <td style="text-align:right;"> 0.0238037 </td> <td style="text-align:right;"> 0.0282722 </td> <td style="text-align:right;"> 0.0233254 </td> <td style="text-align:right;"> 0.0332189 </td> <td style="text-align:right;"> 0.0025239 </td> <td style="text-align:right;"> 23 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> body length </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> -0.0325368 </td> <td style="text-align:right;"> -0.1066429 </td> <td style="text-align:right;"> 0.0415693 </td> <td style="text-align:right;"> 0.0378099 </td> <td style="text-align:right;"> -0.0303742 </td> <td style="text-align:right;"> -0.1044537 </td> <td style="text-align:right;"> 0.0437054 </td> <td style="text-align:right;"> 0.0377964 </td> <td style="text-align:right;"> 0.0018532 </td> <td style="text-align:right;"> -0.0005002 </td> <td style="text-align:right;"> 0.0042066 </td> <td style="text-align:right;"> 0.0012008 </td> <td style="text-align:right;"> 24 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> body temp </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> -0.0325368 </td> <td style="text-align:right;"> -0.1066429 </td> <td style="text-align:right;"> 0.0415693 </td> <td style="text-align:right;"> 0.0378099 </td> <td style="text-align:right;"> -0.0303742 </td> <td style="text-align:right;"> -0.1044537 </td> <td style="text-align:right;"> 0.0437054 </td> <td style="text-align:right;"> 0.0377964 </td> <td style="text-align:right;"> 0.0018532 </td> <td style="text-align:right;"> -0.0005002 </td> <td style="text-align:right;"> 0.0042066 </td> <td style="text-align:right;"> 0.0012008 </td> <td style="text-align:right;"> 24 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> body temp </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 0.0245675 </td> <td style="text-align:right;"> -0.0420402 </td> <td style="text-align:right;"> 0.0911752 </td> <td style="text-align:right;"> 0.0339841 </td> <td style="text-align:right;"> 0.2335793 </td> <td style="text-align:right;"> 0.1694979 </td> <td style="text-align:right;"> 0.2976607 </td> <td style="text-align:right;"> 0.0326952 </td> <td style="text-align:right;"> 0.2096770 </td> <td style="text-align:right;"> 0.1938727 </td> <td style="text-align:right;"> 0.2254813 </td> <td style="text-align:right;"> 0.0080636 </td> <td style="text-align:right;"> 25 </td> <td style="text-align:right;"> 18 </td> <td style="text-align:left;"> body weight </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 0.0245675 </td> <td style="text-align:right;"> -0.0420402 </td> <td style="text-align:right;"> 0.0911752 </td> <td style="text-align:right;"> 0.0339841 </td> <td style="text-align:right;"> 0.2335793 </td> <td style="text-align:right;"> 0.1694979 </td> <td style="text-align:right;"> 0.2976607 </td> <td style="text-align:right;"> 0.0326952 </td> <td style="text-align:right;"> 0.2096770 </td> <td style="text-align:right;"> 0.1938727 </td> <td style="text-align:right;"> 0.2254813 </td> <td style="text-align:right;"> 0.0080636 </td> <td style="text-align:right;"> 25 </td> <td style="text-align:right;"> 18 </td> <td style="text-align:left;"> body weight </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 0.0853708 </td> <td style="text-align:right;"> 0.0299665 </td> <td style="text-align:right;"> 0.1407751 </td> <td style="text-align:right;"> 0.0282680 </td> <td style="text-align:right;"> 0.2849370 </td> <td style="text-align:right;"> 0.2328875 </td> <td style="text-align:right;"> 0.3369866 </td> <td style="text-align:right;"> 0.0265564 </td> <td style="text-align:right;"> 0.2030973 </td> <td style="text-align:right;"> 0.1864076 </td> <td style="text-align:right;"> 0.2197871 </td> <td style="text-align:right;"> 0.0085153 </td> <td style="text-align:right;"> 26 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 0.0853708 </td> <td style="text-align:right;"> 0.0299665 </td> <td style="text-align:right;"> 0.1407751 </td> <td style="text-align:right;"> 0.0282680 </td> <td style="text-align:right;"> 0.2849370 </td> <td style="text-align:right;"> 0.2328875 </td> <td style="text-align:right;"> 0.3369866 </td> <td style="text-align:right;"> 0.0265564 </td> <td style="text-align:right;"> 0.2030973 </td> <td style="text-align:right;"> 0.1864076 </td> <td style="text-align:right;"> 0.2197871 </td> <td style="text-align:right;"> 0.0085153 </td> <td style="text-align:right;"> 26 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 0.1053511 </td> <td style="text-align:right;"> 0.0412461 </td> <td style="text-align:right;"> 0.1694562 </td> <td style="text-align:right;"> 0.0327073 </td> <td style="text-align:right;"> 0.3038998 </td> <td style="text-align:right;"> 0.2435428 </td> <td style="text-align:right;"> 0.3642568 </td> <td style="text-align:right;"> 0.0307949 </td> <td style="text-align:right;"> 0.2008638 </td> <td style="text-align:right;"> 0.1816362 </td> <td style="text-align:right;"> 0.2200914 </td> <td style="text-align:right;"> 0.0098102 </td> <td style="text-align:right;"> 27 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 0.1053511 </td> <td style="text-align:right;"> 0.0412461 </td> <td style="text-align:right;"> 0.1694562 </td> <td style="text-align:right;"> 0.0327073 </td> <td style="text-align:right;"> 0.3038998 </td> <td style="text-align:right;"> 0.2435428 </td> <td style="text-align:right;"> 0.3642568 </td> <td style="text-align:right;"> 0.0307949 </td> <td style="text-align:right;"> 0.2008638 </td> <td style="text-align:right;"> 0.1816362 </td> <td style="text-align:right;"> 0.2200914 </td> <td style="text-align:right;"> 0.0098102 </td> <td style="text-align:right;"> 27 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 0.0981587 </td> <td style="text-align:right;"> 0.0272824 </td> <td style="text-align:right;"> 0.1690349 </td> <td style="text-align:right;"> 0.0361620 </td> <td style="text-align:right;"> 0.1286546 </td> <td style="text-align:right;"> 0.0533659 </td> <td style="text-align:right;"> 0.2039432 </td> <td style="text-align:right;"> 0.0384133 </td> <td style="text-align:right;"> 0.0315241 </td> <td style="text-align:right;"> 0.0003806 </td> <td style="text-align:right;"> 0.0626676 </td> <td style="text-align:right;"> 0.0158898 </td> <td style="text-align:right;"> 28 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bone area </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 0.0981587 </td> <td style="text-align:right;"> 0.0272824 </td> <td style="text-align:right;"> 0.1690349 </td> <td style="text-align:right;"> 0.0361620 </td> <td style="text-align:right;"> 0.1286546 </td> <td style="text-align:right;"> 0.0533659 </td> <td style="text-align:right;"> 0.2039432 </td> <td style="text-align:right;"> 0.0384133 </td> <td style="text-align:right;"> 0.0315241 </td> <td style="text-align:right;"> 0.0003806 </td> <td style="text-align:right;"> 0.0626676 </td> <td style="text-align:right;"> 0.0158898 </td> <td style="text-align:right;"> 28 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bone area </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 0.1709230 </td> <td style="text-align:right;"> 0.0625642 </td> <td style="text-align:right;"> 0.2792818 </td> <td style="text-align:right;"> 0.0552861 </td> <td style="text-align:right;"> 0.2091372 </td> <td style="text-align:right;"> 0.1015600 </td> <td style="text-align:right;"> 0.3167143 </td> <td style="text-align:right;"> 0.0548873 </td> <td style="text-align:right;"> 0.0372537 </td> <td style="text-align:right;"> -0.0130828 </td> <td style="text-align:right;"> 0.0875902 </td> <td style="text-align:right;"> 0.0256824 </td> <td style="text-align:right;"> 29 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 0.1709230 </td> <td style="text-align:right;"> 0.0625642 </td> <td style="text-align:right;"> 0.2792818 </td> <td style="text-align:right;"> 0.0552861 </td> <td style="text-align:right;"> 0.2091372 </td> <td style="text-align:right;"> 0.1015600 </td> <td style="text-align:right;"> 0.3167143 </td> <td style="text-align:right;"> 0.0548873 </td> <td style="text-align:right;"> 0.0372537 </td> <td style="text-align:right;"> -0.0130828 </td> <td style="text-align:right;"> 0.0875902 </td> <td style="text-align:right;"> 0.0256824 </td> <td style="text-align:right;"> 29 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 0.0542638 </td> <td style="text-align:right;"> -0.0881612 </td> <td style="text-align:right;"> 0.1966887 </td> <td style="text-align:right;"> 0.0726671 </td> <td style="text-align:right;"> 0.0492830 </td> <td style="text-align:right;"> -0.1087868 </td> <td style="text-align:right;"> 0.2073528 </td> <td style="text-align:right;"> 0.0806494 </td> <td style="text-align:right;"> 0.0012286 </td> <td style="text-align:right;"> -0.0187942 </td> <td style="text-align:right;"> 0.0212514 </td> <td style="text-align:right;"> 0.0102159 </td> <td style="text-align:right;"> 30 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 0.0542638 </td> <td style="text-align:right;"> -0.0881612 </td> <td style="text-align:right;"> 0.1966887 </td> <td style="text-align:right;"> 0.0726671 </td> <td style="text-align:right;"> 0.0492830 </td> <td style="text-align:right;"> -0.1087868 </td> <td style="text-align:right;"> 0.2073528 </td> <td style="text-align:right;"> 0.0806494 </td> <td style="text-align:right;"> 0.0012286 </td> <td style="text-align:right;"> -0.0187942 </td> <td style="text-align:right;"> 0.0212514 </td> <td style="text-align:right;"> 0.0102159 </td> <td style="text-align:right;"> 30 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 0.0097946 </td> <td style="text-align:right;"> -0.0464600 </td> <td style="text-align:right;"> 0.0660492 </td> <td style="text-align:right;"> 0.0287018 </td> <td style="text-align:right;"> 0.0135683 </td> <td style="text-align:right;"> -0.0424600 </td> <td style="text-align:right;"> 0.0695966 </td> <td style="text-align:right;"> 0.0285864 </td> <td style="text-align:right;"> 0.0036564 </td> <td style="text-align:right;"> -0.0000609 </td> <td style="text-align:right;"> 0.0073737 </td> <td style="text-align:right;"> 0.0018966 </td> <td style="text-align:right;"> 31 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> calcium </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 0.0097946 </td> <td style="text-align:right;"> -0.0464600 </td> <td style="text-align:right;"> 0.0660492 </td> <td style="text-align:right;"> 0.0287018 </td> <td style="text-align:right;"> 0.0135683 </td> <td style="text-align:right;"> -0.0424600 </td> <td style="text-align:right;"> 0.0695966 </td> <td style="text-align:right;"> 0.0285864 </td> <td style="text-align:right;"> 0.0036564 </td> <td style="text-align:right;"> -0.0000609 </td> <td style="text-align:right;"> 0.0073737 </td> <td style="text-align:right;"> 0.0018966 </td> <td style="text-align:right;"> 31 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> calcium </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 0.0133816 </td> <td style="text-align:right;"> -0.0797535 </td> <td style="text-align:right;"> 0.1065166 </td> <td style="text-align:right;"> 0.0475188 </td> <td style="text-align:right;"> 0.1017991 </td> <td style="text-align:right;"> 0.0206287 </td> <td style="text-align:right;"> 0.1829694 </td> <td style="text-align:right;"> 0.0414142 </td> <td style="text-align:right;"> 0.0934439 </td> <td style="text-align:right;"> 0.0580233 </td> <td style="text-align:right;"> 0.1288645 </td> <td style="text-align:right;"> 0.0180721 </td> <td style="text-align:right;"> 32 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> cardiac output </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 0.0133816 </td> <td style="text-align:right;"> -0.0797535 </td> <td style="text-align:right;"> 0.1065166 </td> <td style="text-align:right;"> 0.0475188 </td> <td style="text-align:right;"> 0.1017991 </td> <td style="text-align:right;"> 0.0206287 </td> <td style="text-align:right;"> 0.1829694 </td> <td style="text-align:right;"> 0.0414142 </td> <td style="text-align:right;"> 0.0934439 </td> <td style="text-align:right;"> 0.0580233 </td> <td style="text-align:right;"> 0.1288645 </td> <td style="text-align:right;"> 0.0180721 </td> <td style="text-align:right;"> 32 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> cardiac output </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 0.0167300 </td> <td style="text-align:right;"> -0.0404735 </td> <td style="text-align:right;"> 0.0739335 </td> <td style="text-align:right;"> 0.0291860 </td> <td style="text-align:right;"> -0.0588515 </td> <td style="text-align:right;"> -0.1004209 </td> <td style="text-align:right;"> -0.0172820 </td> <td style="text-align:right;"> 0.0212093 </td> <td style="text-align:right;"> -0.0724619 </td> <td style="text-align:right;"> -0.1149622 </td> <td style="text-align:right;"> -0.0299616 </td> <td style="text-align:right;"> 0.0216842 </td> <td style="text-align:right;"> 61 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> center average speed </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 0.0167300 </td> <td style="text-align:right;"> -0.0404735 </td> <td style="text-align:right;"> 0.0739335 </td> <td style="text-align:right;"> 0.0291860 </td> <td style="text-align:right;"> -0.0588515 </td> <td style="text-align:right;"> -0.1004209 </td> <td style="text-align:right;"> -0.0172820 </td> <td style="text-align:right;"> 0.0212093 </td> <td style="text-align:right;"> -0.0724619 </td> <td style="text-align:right;"> -0.1149622 </td> <td style="text-align:right;"> -0.0299616 </td> <td style="text-align:right;"> 0.0216842 </td> <td style="text-align:right;"> 61 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> center average speed </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> -0.0162603 </td> <td style="text-align:right;"> -0.0733243 </td> <td style="text-align:right;"> 0.0408038 </td> <td style="text-align:right;"> 0.0291149 </td> <td style="text-align:right;"> -0.1060637 </td> <td style="text-align:right;"> -0.2023343 </td> <td style="text-align:right;"> -0.0097930 </td> <td style="text-align:right;"> 0.0491186 </td> <td style="text-align:right;"> -0.0940204 </td> <td style="text-align:right;"> -0.1945774 </td> <td style="text-align:right;"> 0.0065366 </td> <td style="text-align:right;"> 0.0513055 </td> <td style="text-align:right;"> 62 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> -0.0162603 </td> <td style="text-align:right;"> -0.0733243 </td> <td style="text-align:right;"> 0.0408038 </td> <td style="text-align:right;"> 0.0291149 </td> <td style="text-align:right;"> -0.1060637 </td> <td style="text-align:right;"> -0.2023343 </td> <td style="text-align:right;"> -0.0097930 </td> <td style="text-align:right;"> 0.0491186 </td> <td style="text-align:right;"> -0.0940204 </td> <td style="text-align:right;"> -0.1945774 </td> <td style="text-align:right;"> 0.0065366 </td> <td style="text-align:right;"> 0.0513055 </td> <td style="text-align:right;"> 62 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> -0.0253715 </td> <td style="text-align:right;"> -0.0826435 </td> <td style="text-align:right;"> 0.0319004 </td> <td style="text-align:right;"> 0.0292209 </td> <td style="text-align:right;"> -0.0255734 </td> <td style="text-align:right;"> -0.1014389 </td> <td style="text-align:right;"> 0.0502922 </td> <td style="text-align:right;"> 0.0387076 </td> <td style="text-align:right;"> -0.0035151 </td> <td style="text-align:right;"> -0.0902886 </td> <td style="text-align:right;"> 0.0832585 </td> <td style="text-align:right;"> 0.0442730 </td> <td style="text-align:right;"> 63 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> center permanence time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> -0.0253715 </td> <td style="text-align:right;"> -0.0826435 </td> <td style="text-align:right;"> 0.0319004 </td> <td style="text-align:right;"> 0.0292209 </td> <td style="text-align:right;"> -0.0255734 </td> <td style="text-align:right;"> -0.1014389 </td> <td style="text-align:right;"> 0.0502922 </td> <td style="text-align:right;"> 0.0387076 </td> <td style="text-align:right;"> -0.0035151 </td> <td style="text-align:right;"> -0.0902886 </td> <td style="text-align:right;"> 0.0832585 </td> <td style="text-align:right;"> 0.0442730 </td> <td style="text-align:right;"> 63 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> center permanence time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 0.0244492 </td> <td style="text-align:right;"> -0.0737922 </td> <td style="text-align:right;"> 0.1226906 </td> <td style="text-align:right;"> 0.0501241 </td> <td style="text-align:right;"> -0.0228690 </td> <td style="text-align:right;"> -0.1548339 </td> <td style="text-align:right;"> 0.1090960 </td> <td style="text-align:right;"> 0.0673303 </td> <td style="text-align:right;"> -0.0630751 </td> <td style="text-align:right;"> -0.2215457 </td> <td style="text-align:right;"> 0.0953955 </td> <td style="text-align:right;"> 0.0808538 </td> <td style="text-align:right;"> 64 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> center resting time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 0.0244492 </td> <td style="text-align:right;"> -0.0737922 </td> <td style="text-align:right;"> 0.1226906 </td> <td style="text-align:right;"> 0.0501241 </td> <td style="text-align:right;"> -0.0228690 </td> <td style="text-align:right;"> -0.1548339 </td> <td style="text-align:right;"> 0.1090960 </td> <td style="text-align:right;"> 0.0673303 </td> <td style="text-align:right;"> -0.0630751 </td> <td style="text-align:right;"> -0.2215457 </td> <td style="text-align:right;"> 0.0953955 </td> <td style="text-align:right;"> 0.0808538 </td> <td style="text-align:right;"> 64 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> center resting time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 0.0321555 </td> <td style="text-align:right;"> -0.1270972 </td> <td style="text-align:right;"> 0.1914083 </td> <td style="text-align:right;"> 0.0812529 </td> <td style="text-align:right;"> 0.0241491 </td> <td style="text-align:right;"> -0.1438502 </td> <td style="text-align:right;"> 0.1921485 </td> <td style="text-align:right;"> 0.0857155 </td> <td style="text-align:right;"> -0.0127047 </td> <td style="text-align:right;"> -0.0177349 </td> <td style="text-align:right;"> -0.0076745 </td> <td style="text-align:right;"> 0.0025665 </td> <td style="text-align:right;"> 65 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> chloride </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 0.0321555 </td> <td style="text-align:right;"> -0.1270972 </td> <td style="text-align:right;"> 0.1914083 </td> <td style="text-align:right;"> 0.0812529 </td> <td style="text-align:right;"> 0.0241491 </td> <td style="text-align:right;"> -0.1438502 </td> <td style="text-align:right;"> 0.1921485 </td> <td style="text-align:right;"> 0.0857155 </td> <td style="text-align:right;"> -0.0127047 </td> <td style="text-align:right;"> -0.0177349 </td> <td style="text-align:right;"> -0.0076745 </td> <td style="text-align:right;"> 0.0025665 </td> <td style="text-align:right;"> 65 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> chloride </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> -0.0529450 </td> <td style="text-align:right;"> -0.1534816 </td> <td style="text-align:right;"> 0.0475915 </td> <td style="text-align:right;"> 0.0512951 </td> <td style="text-align:right;"> -0.0561198 </td> <td style="text-align:right;"> -0.1827679 </td> <td style="text-align:right;"> 0.0705282 </td> <td style="text-align:right;"> 0.0646176 </td> <td style="text-align:right;"> -0.0154221 </td> <td style="text-align:right;"> -0.0577200 </td> <td style="text-align:right;"> 0.0268757 </td> <td style="text-align:right;"> 0.0215809 </td> <td style="text-align:right;"> 66 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> -0.0529450 </td> <td style="text-align:right;"> -0.1534816 </td> <td style="text-align:right;"> 0.0475915 </td> <td style="text-align:right;"> 0.0512951 </td> <td style="text-align:right;"> -0.0561198 </td> <td style="text-align:right;"> -0.1827679 </td> <td style="text-align:right;"> 0.0705282 </td> <td style="text-align:right;"> 0.0646176 </td> <td style="text-align:right;"> -0.0154221 </td> <td style="text-align:right;"> -0.0577200 </td> <td style="text-align:right;"> 0.0268757 </td> <td style="text-align:right;"> 0.0215809 </td> <td style="text-align:right;"> 66 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 0.0241232 </td> <td style="text-align:right;"> -0.1071457 </td> <td style="text-align:right;"> 0.1553920 </td> <td style="text-align:right;"> 0.0669751 </td> <td style="text-align:right;"> -0.1318792 </td> <td style="text-align:right;"> -0.3968974 </td> <td style="text-align:right;"> 0.1331390 </td> <td style="text-align:right;"> 0.1352159 </td> <td style="text-align:right;"> -0.1344413 </td> <td style="text-align:right;"> -0.3838303 </td> <td style="text-align:right;"> 0.1149476 </td> <td style="text-align:right;"> 0.1272416 </td> <td style="text-align:right;"> 67 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 0.0241232 </td> <td style="text-align:right;"> -0.1071457 </td> <td style="text-align:right;"> 0.1553920 </td> <td style="text-align:right;"> 0.0669751 </td> <td style="text-align:right;"> -0.1318792 </td> <td style="text-align:right;"> -0.3968974 </td> <td style="text-align:right;"> 0.1331390 </td> <td style="text-align:right;"> 0.1352159 </td> <td style="text-align:right;"> -0.1344413 </td> <td style="text-align:right;"> -0.3838303 </td> <td style="text-align:right;"> 0.1149476 </td> <td style="text-align:right;"> 0.1272416 </td> <td style="text-align:right;"> 67 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 0.0352315 </td> <td style="text-align:right;"> -0.0229205 </td> <td style="text-align:right;"> 0.0933835 </td> <td style="text-align:right;"> 0.0296699 </td> <td style="text-align:right;"> 0.1066373 </td> <td style="text-align:right;"> -0.2200831 </td> <td style="text-align:right;"> 0.4333578 </td> <td style="text-align:right;"> 0.1666972 </td> <td style="text-align:right;"> -0.0844078 </td> <td style="text-align:right;"> -0.1320251 </td> <td style="text-align:right;"> -0.0367905 </td> <td style="text-align:right;"> 0.0242950 </td> <td style="text-align:right;"> 68 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> creatinine </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 0.0352315 </td> <td style="text-align:right;"> -0.0229205 </td> <td style="text-align:right;"> 0.0933835 </td> <td style="text-align:right;"> 0.0296699 </td> <td style="text-align:right;"> 0.1066373 </td> <td style="text-align:right;"> -0.2200831 </td> <td style="text-align:right;"> 0.4333578 </td> <td style="text-align:right;"> 0.1666972 </td> <td style="text-align:right;"> -0.0844078 </td> <td style="text-align:right;"> -0.1320251 </td> <td style="text-align:right;"> -0.0367905 </td> <td style="text-align:right;"> 0.0242950 </td> <td style="text-align:right;"> 68 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> creatinine </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 0.1874544 </td> <td style="text-align:right;"> 0.0716631 </td> <td style="text-align:right;"> 0.3032457 </td> <td style="text-align:right;"> 0.0590783 </td> <td style="text-align:right;"> -0.0895722 </td> <td style="text-align:right;"> -0.2484833 </td> <td style="text-align:right;"> 0.0693388 </td> <td style="text-align:right;"> 0.0810786 </td> <td style="text-align:right;"> -0.2401301 </td> <td style="text-align:right;"> -0.3410322 </td> <td style="text-align:right;"> -0.1392280 </td> <td style="text-align:right;"> 0.0514816 </td> <td style="text-align:right;"> 69 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> cv </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 0.1874544 </td> <td style="text-align:right;"> 0.0716631 </td> <td style="text-align:right;"> 0.3032457 </td> <td style="text-align:right;"> 0.0590783 </td> <td style="text-align:right;"> -0.0895722 </td> <td style="text-align:right;"> -0.2484833 </td> <td style="text-align:right;"> 0.0693388 </td> <td style="text-align:right;"> 0.0810786 </td> <td style="text-align:right;"> -0.2401301 </td> <td style="text-align:right;"> -0.3410322 </td> <td style="text-align:right;"> -0.1392280 </td> <td style="text-align:right;"> 0.0514816 </td> <td style="text-align:right;"> 69 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> cv </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> -0.0187819 </td> <td style="text-align:right;"> -0.0858957 </td> <td style="text-align:right;"> 0.0483318 </td> <td style="text-align:right;"> 0.0342423 </td> <td style="text-align:right;"> -0.1272582 </td> <td style="text-align:right;"> -0.1997426 </td> <td style="text-align:right;"> -0.0547738 </td> <td style="text-align:right;"> 0.0369825 </td> <td style="text-align:right;"> -0.1121373 </td> <td style="text-align:right;"> -0.1816322 </td> <td style="text-align:right;"> -0.0426424 </td> <td style="text-align:right;"> 0.0354572 </td> <td style="text-align:right;"> 70 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> -0.0187819 </td> <td style="text-align:right;"> -0.0858957 </td> <td style="text-align:right;"> 0.0483318 </td> <td style="text-align:right;"> 0.0342423 </td> <td style="text-align:right;"> -0.1272582 </td> <td style="text-align:right;"> -0.1997426 </td> <td style="text-align:right;"> -0.0547738 </td> <td style="text-align:right;"> 0.0369825 </td> <td style="text-align:right;"> -0.1121373 </td> <td style="text-align:right;"> -0.1816322 </td> <td style="text-align:right;"> -0.0426424 </td> <td style="text-align:right;"> 0.0354572 </td> <td style="text-align:right;"> 70 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> -0.0300111 </td> <td style="text-align:right;"> -0.1345066 </td> <td style="text-align:right;"> 0.0744844 </td> <td style="text-align:right;"> 0.0533150 </td> <td style="text-align:right;"> -0.0525735 </td> <td style="text-align:right;"> -0.1483174 </td> <td style="text-align:right;"> 0.0431705 </td> <td style="text-align:right;"> 0.0488499 </td> <td style="text-align:right;"> -0.0284086 </td> <td style="text-align:right;"> -0.0492579 </td> <td style="text-align:right;"> -0.0075592 </td> <td style="text-align:right;"> 0.0106376 </td> <td style="text-align:right;"> 85 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> -0.0300111 </td> <td style="text-align:right;"> -0.1345066 </td> <td style="text-align:right;"> 0.0744844 </td> <td style="text-align:right;"> 0.0533150 </td> <td style="text-align:right;"> -0.0525735 </td> <td style="text-align:right;"> -0.1483174 </td> <td style="text-align:right;"> 0.0431705 </td> <td style="text-align:right;"> 0.0488499 </td> <td style="text-align:right;"> -0.0284086 </td> <td style="text-align:right;"> -0.0492579 </td> <td style="text-align:right;"> -0.0075592 </td> <td style="text-align:right;"> 0.0106376 </td> <td style="text-align:right;"> 85 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 0.1120972 </td> <td style="text-align:right;"> 0.0431489 </td> <td style="text-align:right;"> 0.1810454 </td> <td style="text-align:right;"> 0.0351783 </td> <td style="text-align:right;"> 0.1743929 </td> <td style="text-align:right;"> 0.0875252 </td> <td style="text-align:right;"> 0.2612607 </td> <td style="text-align:right;"> 0.0443211 </td> <td style="text-align:right;"> 0.0600907 </td> <td style="text-align:right;"> 0.0354923 </td> <td style="text-align:right;"> 0.0846891 </td> <td style="text-align:right;"> 0.0125504 </td> <td style="text-align:right;"> 86 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 0.1120972 </td> <td style="text-align:right;"> 0.0431489 </td> <td style="text-align:right;"> 0.1810454 </td> <td style="text-align:right;"> 0.0351783 </td> <td style="text-align:right;"> 0.1743929 </td> <td style="text-align:right;"> 0.0875252 </td> <td style="text-align:right;"> 0.2612607 </td> <td style="text-align:right;"> 0.0443211 </td> <td style="text-align:right;"> 0.0600907 </td> <td style="text-align:right;"> 0.0354923 </td> <td style="text-align:right;"> 0.0846891 </td> <td style="text-align:right;"> 0.0125504 </td> <td style="text-align:right;"> 86 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> -0.0084176 </td> <td style="text-align:right;"> -0.0780811 </td> <td style="text-align:right;"> 0.0612459 </td> <td style="text-align:right;"> 0.0355433 </td> <td style="text-align:right;"> 0.0668966 </td> <td style="text-align:right;"> -0.0016692 </td> <td style="text-align:right;"> 0.1354624 </td> <td style="text-align:right;"> 0.0349832 </td> <td style="text-align:right;"> 0.0763195 </td> <td style="text-align:right;"> 0.0451136 </td> <td style="text-align:right;"> 0.1075254 </td> <td style="text-align:right;"> 0.0159217 </td> <td style="text-align:right;"> 87 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> -0.0084176 </td> <td style="text-align:right;"> -0.0780811 </td> <td style="text-align:right;"> 0.0612459 </td> <td style="text-align:right;"> 0.0355433 </td> <td style="text-align:right;"> 0.0668966 </td> <td style="text-align:right;"> -0.0016692 </td> <td style="text-align:right;"> 0.1354624 </td> <td style="text-align:right;"> 0.0349832 </td> <td style="text-align:right;"> 0.0763195 </td> <td style="text-align:right;"> 0.0451136 </td> <td style="text-align:right;"> 0.1075254 </td> <td style="text-align:right;"> 0.0159217 </td> <td style="text-align:right;"> 87 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> -0.0177245 </td> <td style="text-align:right;"> -0.1256855 </td> <td style="text-align:right;"> 0.0902366 </td> <td style="text-align:right;"> 0.0550832 </td> <td style="text-align:right;"> 0.0702824 </td> <td style="text-align:right;"> -0.0302439 </td> <td style="text-align:right;"> 0.1708087 </td> <td style="text-align:right;"> 0.0512899 </td> <td style="text-align:right;"> 0.0868420 </td> <td style="text-align:right;"> 0.0493007 </td> <td style="text-align:right;"> 0.1243832 </td> <td style="text-align:right;"> 0.0191541 </td> <td style="text-align:right;"> 91 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> -0.0177245 </td> <td style="text-align:right;"> -0.1256855 </td> <td style="text-align:right;"> 0.0902366 </td> <td style="text-align:right;"> 0.0550832 </td> <td style="text-align:right;"> 0.0702824 </td> <td style="text-align:right;"> -0.0302439 </td> <td style="text-align:right;"> 0.1708087 </td> <td style="text-align:right;"> 0.0512899 </td> <td style="text-align:right;"> 0.0868420 </td> <td style="text-align:right;"> 0.0493007 </td> <td style="text-align:right;"> 0.1243832 </td> <td style="text-align:right;"> 0.0191541 </td> <td style="text-align:right;"> 91 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 0.0408799 </td> <td style="text-align:right;"> -0.0430149 </td> <td style="text-align:right;"> 0.1247746 </td> <td style="text-align:right;"> 0.0428042 </td> <td style="text-align:right;"> 0.3714313 </td> <td style="text-align:right;"> 0.2698790 </td> <td style="text-align:right;"> 0.4729837 </td> <td style="text-align:right;"> 0.0518134 </td> <td style="text-align:right;"> 0.3282080 </td> <td style="text-align:right;"> 0.2669032 </td> <td style="text-align:right;"> 0.3895129 </td> <td style="text-align:right;"> 0.0312786 </td> <td style="text-align:right;"> 92 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> fat mass </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 0.0408799 </td> <td style="text-align:right;"> -0.0430149 </td> <td style="text-align:right;"> 0.1247746 </td> <td style="text-align:right;"> 0.0428042 </td> <td style="text-align:right;"> 0.3714313 </td> <td style="text-align:right;"> 0.2698790 </td> <td style="text-align:right;"> 0.4729837 </td> <td style="text-align:right;"> 0.0518134 </td> <td style="text-align:right;"> 0.3282080 </td> <td style="text-align:right;"> 0.2669032 </td> <td style="text-align:right;"> 0.3895129 </td> <td style="text-align:right;"> 0.0312786 </td> <td style="text-align:right;"> 92 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> fat mass </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 0.0777327 </td> <td style="text-align:right;"> -0.0119735 </td> <td style="text-align:right;"> 0.1674390 </td> <td style="text-align:right;"> 0.0457693 </td> <td style="text-align:right;"> 0.2020776 </td> <td style="text-align:right;"> 0.1083557 </td> <td style="text-align:right;"> 0.2957996 </td> <td style="text-align:right;"> 0.0478182 </td> <td style="text-align:right;"> 0.1235292 </td> <td style="text-align:right;"> 0.0638629 </td> <td style="text-align:right;"> 0.1831955 </td> <td style="text-align:right;"> 0.0304425 </td> <td style="text-align:right;"> 93 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 0.0777327 </td> <td style="text-align:right;"> -0.0119735 </td> <td style="text-align:right;"> 0.1674390 </td> <td style="text-align:right;"> 0.0457693 </td> <td style="text-align:right;"> 0.2020776 </td> <td style="text-align:right;"> 0.1083557 </td> <td style="text-align:right;"> 0.2957996 </td> <td style="text-align:right;"> 0.0478182 </td> <td style="text-align:right;"> 0.1235292 </td> <td style="text-align:right;"> 0.0638629 </td> <td style="text-align:right;"> 0.1831955 </td> <td style="text-align:right;"> 0.0304425 </td> <td style="text-align:right;"> 93 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 0.0578158 </td> <td style="text-align:right;"> 0.0039998 </td> <td style="text-align:right;"> 0.1116318 </td> <td style="text-align:right;"> 0.0274577 </td> <td style="text-align:right;"> 0.1145986 </td> <td style="text-align:right;"> 0.0530521 </td> <td style="text-align:right;"> 0.1761451 </td> <td style="text-align:right;"> 0.0314018 </td> <td style="text-align:right;"> 0.0541888 </td> <td style="text-align:right;"> 0.0294838 </td> <td style="text-align:right;"> 0.0788938 </td> <td style="text-align:right;"> 0.0126048 </td> <td style="text-align:right;"> 96 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 0.0578158 </td> <td style="text-align:right;"> 0.0039998 </td> <td style="text-align:right;"> 0.1116318 </td> <td style="text-align:right;"> 0.0274577 </td> <td style="text-align:right;"> 0.1145986 </td> <td style="text-align:right;"> 0.0530521 </td> <td style="text-align:right;"> 0.1761451 </td> <td style="text-align:right;"> 0.0314018 </td> <td style="text-align:right;"> 0.0541888 </td> <td style="text-align:right;"> 0.0294838 </td> <td style="text-align:right;"> 0.0788938 </td> <td style="text-align:right;"> 0.0126048 </td> <td style="text-align:right;"> 96 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 0.0265051 </td> <td style="text-align:right;"> -0.0187240 </td> <td style="text-align:right;"> 0.0717341 </td> <td style="text-align:right;"> 0.0230765 </td> <td style="text-align:right;"> 0.0995076 </td> <td style="text-align:right;"> 0.0539740 </td> <td style="text-align:right;"> 0.1450413 </td> <td style="text-align:right;"> 0.0232319 </td> <td style="text-align:right;"> 0.0697061 </td> <td style="text-align:right;"> 0.0438625 </td> <td style="text-align:right;"> 0.0955496 </td> <td style="text-align:right;"> 0.0131857 </td> <td style="text-align:right;"> 97 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 0.0265051 </td> <td style="text-align:right;"> -0.0187240 </td> <td style="text-align:right;"> 0.0717341 </td> <td style="text-align:right;"> 0.0230765 </td> <td style="text-align:right;"> 0.0995076 </td> <td style="text-align:right;"> 0.0539740 </td> <td style="text-align:right;"> 0.1450413 </td> <td style="text-align:right;"> 0.0232319 </td> <td style="text-align:right;"> 0.0697061 </td> <td style="text-align:right;"> 0.0438625 </td> <td style="text-align:right;"> 0.0955496 </td> <td style="text-align:right;"> 0.0131857 </td> <td style="text-align:right;"> 97 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> -0.0148852 </td> <td style="text-align:right;"> -0.1161666 </td> <td style="text-align:right;"> 0.0863961 </td> <td style="text-align:right;"> 0.0516751 </td> <td style="text-align:right;"> -0.0575326 </td> <td style="text-align:right;"> -0.1558559 </td> <td style="text-align:right;"> 0.0407907 </td> <td style="text-align:right;"> 0.0501659 </td> <td style="text-align:right;"> -0.0413498 </td> <td style="text-align:right;"> -0.0567105 </td> <td style="text-align:right;"> -0.0259891 </td> <td style="text-align:right;"> 0.0078372 </td> <td style="text-align:right;"> 98 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> -0.0148852 </td> <td style="text-align:right;"> -0.1161666 </td> <td style="text-align:right;"> 0.0863961 </td> <td style="text-align:right;"> 0.0516751 </td> <td style="text-align:right;"> -0.0575326 </td> <td style="text-align:right;"> -0.1558559 </td> <td style="text-align:right;"> 0.0407907 </td> <td style="text-align:right;"> 0.0501659 </td> <td style="text-align:right;"> -0.0413498 </td> <td style="text-align:right;"> -0.0567105 </td> <td style="text-align:right;"> -0.0259891 </td> <td style="text-align:right;"> 0.0078372 </td> <td style="text-align:right;"> 98 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 0.0281576 </td> <td style="text-align:right;"> -0.1002531 </td> <td style="text-align:right;"> 0.1565683 </td> <td style="text-align:right;"> 0.0655169 </td> <td style="text-align:right;"> 0.0554109 </td> <td style="text-align:right;"> -0.0736861 </td> <td style="text-align:right;"> 0.1845079 </td> <td style="text-align:right;"> 0.0658670 </td> <td style="text-align:right;"> 0.0193783 </td> <td style="text-align:right;"> -0.0093700 </td> <td style="text-align:right;"> 0.0481266 </td> <td style="text-align:right;"> 0.0146678 </td> <td style="text-align:right;"> 99 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 0.0281576 </td> <td style="text-align:right;"> -0.1002531 </td> <td style="text-align:right;"> 0.1565683 </td> <td style="text-align:right;"> 0.0655169 </td> <td style="text-align:right;"> 0.0554109 </td> <td style="text-align:right;"> -0.0736861 </td> <td style="text-align:right;"> 0.1845079 </td> <td style="text-align:right;"> 0.0658670 </td> <td style="text-align:right;"> 0.0193783 </td> <td style="text-align:right;"> -0.0093700 </td> <td style="text-align:right;"> 0.0481266 </td> <td style="text-align:right;"> 0.0146678 </td> <td style="text-align:right;"> 99 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> -0.0397864 </td> <td style="text-align:right;"> -0.1198801 </td> <td style="text-align:right;"> 0.0403073 </td> <td style="text-align:right;"> 0.0408649 </td> <td style="text-align:right;"> -0.0678231 </td> <td style="text-align:right;"> -0.1513538 </td> <td style="text-align:right;"> 0.0157075 </td> <td style="text-align:right;"> 0.0426184 </td> <td style="text-align:right;"> -0.0283579 </td> <td style="text-align:right;"> -0.0692447 </td> <td style="text-align:right;"> 0.0125289 </td> <td style="text-align:right;"> 0.0208610 </td> <td style="text-align:right;"> 100 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> fructosamine </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> -0.0397864 </td> <td style="text-align:right;"> -0.1198801 </td> <td style="text-align:right;"> 0.0403073 </td> <td style="text-align:right;"> 0.0408649 </td> <td style="text-align:right;"> -0.0678231 </td> <td style="text-align:right;"> -0.1513538 </td> <td style="text-align:right;"> 0.0157075 </td> <td style="text-align:right;"> 0.0426184 </td> <td style="text-align:right;"> -0.0283579 </td> <td style="text-align:right;"> -0.0692447 </td> <td style="text-align:right;"> 0.0125289 </td> <td style="text-align:right;"> 0.0208610 </td> <td style="text-align:right;"> 100 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> fructosamine </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 0.0692601 </td> <td style="text-align:right;"> 0.0184025 </td> <td style="text-align:right;"> 0.1201176 </td> <td style="text-align:right;"> 0.0259482 </td> <td style="text-align:right;"> 0.1279473 </td> <td style="text-align:right;"> 0.0423001 </td> <td style="text-align:right;"> 0.2135946 </td> <td style="text-align:right;"> 0.0436984 </td> <td style="text-align:right;"> 0.0650887 </td> <td style="text-align:right;"> 0.0218496 </td> <td style="text-align:right;"> 0.1083279 </td> <td style="text-align:right;"> 0.0220612 </td> <td style="text-align:right;"> 101 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> glucose </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 0.0692601 </td> <td style="text-align:right;"> 0.0184025 </td> <td style="text-align:right;"> 0.1201176 </td> <td style="text-align:right;"> 0.0259482 </td> <td style="text-align:right;"> 0.1279473 </td> <td style="text-align:right;"> 0.0423001 </td> <td style="text-align:right;"> 0.2135946 </td> <td style="text-align:right;"> 0.0436984 </td> <td style="text-align:right;"> 0.0650887 </td> <td style="text-align:right;"> 0.0218496 </td> <td style="text-align:right;"> 0.1083279 </td> <td style="text-align:right;"> 0.0220612 </td> <td style="text-align:right;"> 101 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> glucose </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> -0.0650177 </td> <td style="text-align:right;"> -0.1255786 </td> <td style="text-align:right;"> -0.0044568 </td> <td style="text-align:right;"> 0.0308990 </td> <td style="text-align:right;"> 0.1724354 </td> <td style="text-align:right;"> 0.0701062 </td> <td style="text-align:right;"> 0.2747646 </td> <td style="text-align:right;"> 0.0522097 </td> <td style="text-align:right;"> 0.2606961 </td> <td style="text-align:right;"> 0.2180421 </td> <td style="text-align:right;"> 0.3033501 </td> <td style="text-align:right;"> 0.0217626 </td> <td style="text-align:right;"> 102 </td> <td style="text-align:right;"> 15 </td> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> -0.0650177 </td> <td style="text-align:right;"> -0.1255786 </td> <td style="text-align:right;"> -0.0044568 </td> <td style="text-align:right;"> 0.0308990 </td> <td style="text-align:right;"> 0.1724354 </td> <td style="text-align:right;"> 0.0701062 </td> <td style="text-align:right;"> 0.2747646 </td> <td style="text-align:right;"> 0.0522097 </td> <td style="text-align:right;"> 0.2606961 </td> <td style="text-align:right;"> 0.2180421 </td> <td style="text-align:right;"> 0.3033501 </td> <td style="text-align:right;"> 0.0217626 </td> <td style="text-align:right;"> 102 </td> <td style="text-align:right;"> 15 </td> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 0.1766832 </td> <td style="text-align:right;"> 0.0672843 </td> <td style="text-align:right;"> 0.2860820 </td> <td style="text-align:right;"> 0.0558168 </td> <td style="text-align:right;"> 0.3651806 </td> <td style="text-align:right;"> 0.2169840 </td> <td style="text-align:right;"> 0.5133772 </td> <td style="text-align:right;"> 0.0756119 </td> <td style="text-align:right;"> 0.1737615 </td> <td style="text-align:right;"> 0.1409037 </td> <td style="text-align:right;"> 0.2066193 </td> <td style="text-align:right;"> 0.0167645 </td> <td style="text-align:right;"> 103 </td> <td style="text-align:right;"> 15 </td> <td style="text-align:left;"> heart weight </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 0.1766832 </td> <td style="text-align:right;"> 0.0672843 </td> <td style="text-align:right;"> 0.2860820 </td> <td style="text-align:right;"> 0.0558168 </td> <td style="text-align:right;"> 0.3651806 </td> <td style="text-align:right;"> 0.2169840 </td> <td style="text-align:right;"> 0.5133772 </td> <td style="text-align:right;"> 0.0756119 </td> <td style="text-align:right;"> 0.1737615 </td> <td style="text-align:right;"> 0.1409037 </td> <td style="text-align:right;"> 0.2066193 </td> <td style="text-align:right;"> 0.0167645 </td> <td style="text-align:right;"> 103 </td> <td style="text-align:right;"> 15 </td> <td style="text-align:left;"> heart weight </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 0.0794303 </td> <td style="text-align:right;"> -0.0060591 </td> <td style="text-align:right;"> 0.1649198 </td> <td style="text-align:right;"> 0.0436179 </td> <td style="text-align:right;"> 0.0355574 </td> <td style="text-align:right;"> -0.0973272 </td> <td style="text-align:right;"> 0.1684419 </td> <td style="text-align:right;"> 0.0677995 </td> <td style="text-align:right;"> -0.0495578 </td> <td style="text-align:right;"> -0.0835809 </td> <td style="text-align:right;"> -0.0155346 </td> <td style="text-align:right;"> 0.0173591 </td> <td style="text-align:right;"> 104 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 0.0794303 </td> <td style="text-align:right;"> -0.0060591 </td> <td style="text-align:right;"> 0.1649198 </td> <td style="text-align:right;"> 0.0436179 </td> <td style="text-align:right;"> 0.0355574 </td> <td style="text-align:right;"> -0.0973272 </td> <td style="text-align:right;"> 0.1684419 </td> <td style="text-align:right;"> 0.0677995 </td> <td style="text-align:right;"> -0.0495578 </td> <td style="text-align:right;"> -0.0835809 </td> <td style="text-align:right;"> -0.0155346 </td> <td style="text-align:right;"> 0.0173591 </td> <td style="text-align:right;"> 104 </td> <td style="text-align:right;"> 14 </td> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 0.0566356 </td> <td style="text-align:right;"> -0.0516862 </td> <td style="text-align:right;"> 0.1649575 </td> <td style="text-align:right;"> 0.0552673 </td> <td style="text-align:right;"> 0.0737071 </td> <td style="text-align:right;"> -0.0328632 </td> <td style="text-align:right;"> 0.1802774 </td> <td style="text-align:right;"> 0.0543736 </td> <td style="text-align:right;"> 0.0173967 </td> <td style="text-align:right;"> 0.0035179 </td> <td style="text-align:right;"> 0.0312754 </td> <td style="text-align:right;"> 0.0070811 </td> <td style="text-align:right;"> 105 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> hematocrit </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 0.0566356 </td> <td style="text-align:right;"> -0.0516862 </td> <td style="text-align:right;"> 0.1649575 </td> <td style="text-align:right;"> 0.0552673 </td> <td style="text-align:right;"> 0.0737071 </td> <td style="text-align:right;"> -0.0328632 </td> <td style="text-align:right;"> 0.1802774 </td> <td style="text-align:right;"> 0.0543736 </td> <td style="text-align:right;"> 0.0173967 </td> <td style="text-align:right;"> 0.0035179 </td> <td style="text-align:right;"> 0.0312754 </td> <td style="text-align:right;"> 0.0070811 </td> <td style="text-align:right;"> 105 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> hematocrit </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 0.0867000 </td> <td style="text-align:right;"> 0.0269936 </td> <td style="text-align:right;"> 0.1464064 </td> <td style="text-align:right;"> 0.0304630 </td> <td style="text-align:right;"> 0.0867345 </td> <td style="text-align:right;"> 0.0194022 </td> <td style="text-align:right;"> 0.1540668 </td> <td style="text-align:right;"> 0.0343538 </td> <td style="text-align:right;"> 0.0051992 </td> <td style="text-align:right;"> -0.0080216 </td> <td style="text-align:right;"> 0.0184199 </td> <td style="text-align:right;"> 0.0067454 </td> <td style="text-align:right;"> 106 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 0.0867000 </td> <td style="text-align:right;"> 0.0269936 </td> <td style="text-align:right;"> 0.1464064 </td> <td style="text-align:right;"> 0.0304630 </td> <td style="text-align:right;"> 0.0867345 </td> <td style="text-align:right;"> 0.0194022 </td> <td style="text-align:right;"> 0.1540668 </td> <td style="text-align:right;"> 0.0343538 </td> <td style="text-align:right;"> 0.0051992 </td> <td style="text-align:right;"> -0.0080216 </td> <td style="text-align:right;"> 0.0184199 </td> <td style="text-align:right;"> 0.0067454 </td> <td style="text-align:right;"> 106 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> -0.0634490 </td> <td style="text-align:right;"> -0.1734699 </td> <td style="text-align:right;"> 0.0465718 </td> <td style="text-align:right;"> 0.0561341 </td> <td style="text-align:right;"> -0.0140315 </td> <td style="text-align:right;"> -0.1488474 </td> <td style="text-align:right;"> 0.1207843 </td> <td style="text-align:right;"> 0.0687849 </td> <td style="text-align:right;"> 0.0406617 </td> <td style="text-align:right;"> -0.0139214 </td> <td style="text-align:right;"> 0.0952448 </td> <td style="text-align:right;"> 0.0278490 </td> <td style="text-align:right;"> 107 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> hr </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> -0.0634490 </td> <td style="text-align:right;"> -0.1734699 </td> <td style="text-align:right;"> 0.0465718 </td> <td style="text-align:right;"> 0.0561341 </td> <td style="text-align:right;"> -0.0140315 </td> <td style="text-align:right;"> -0.1488474 </td> <td style="text-align:right;"> 0.1207843 </td> <td style="text-align:right;"> 0.0687849 </td> <td style="text-align:right;"> 0.0406617 </td> <td style="text-align:right;"> -0.0139214 </td> <td style="text-align:right;"> 0.0952448 </td> <td style="text-align:right;"> 0.0278490 </td> <td style="text-align:right;"> 107 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> hr </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 0.1722593 </td> <td style="text-align:right;"> 0.1094294 </td> <td style="text-align:right;"> 0.2350892 </td> <td style="text-align:right;"> 0.0320567 </td> <td style="text-align:right;"> -0.0813225 </td> <td style="text-align:right;"> -0.2125462 </td> <td style="text-align:right;"> 0.0499011 </td> <td style="text-align:right;"> 0.0669521 </td> <td style="text-align:right;"> -0.2504990 </td> <td style="text-align:right;"> -0.3657436 </td> <td style="text-align:right;"> -0.1352545 </td> <td style="text-align:right;"> 0.0587993 </td> <td style="text-align:right;"> 108 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> hrv </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 0.1722593 </td> <td style="text-align:right;"> 0.1094294 </td> <td style="text-align:right;"> 0.2350892 </td> <td style="text-align:right;"> 0.0320567 </td> <td style="text-align:right;"> -0.0813225 </td> <td style="text-align:right;"> -0.2125462 </td> <td style="text-align:right;"> 0.0499011 </td> <td style="text-align:right;"> 0.0669521 </td> <td style="text-align:right;"> -0.2504990 </td> <td style="text-align:right;"> -0.3657436 </td> <td style="text-align:right;"> -0.1352545 </td> <td style="text-align:right;"> 0.0587993 </td> <td style="text-align:right;"> 108 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> hrv </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> -0.0968821 </td> <td style="text-align:right;"> -0.1503780 </td> <td style="text-align:right;"> -0.0433861 </td> <td style="text-align:right;"> 0.0272943 </td> <td style="text-align:right;"> 0.0429971 </td> <td style="text-align:right;"> 0.0141807 </td> <td style="text-align:right;"> 0.0718136 </td> <td style="text-align:right;"> 0.0147026 </td> <td style="text-align:right;"> 0.1183626 </td> <td style="text-align:right;"> 0.0853242 </td> <td style="text-align:right;"> 0.1514009 </td> <td style="text-align:right;"> 0.0168566 </td> <td style="text-align:right;"> 109 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> -0.0968821 </td> <td style="text-align:right;"> -0.1503780 </td> <td style="text-align:right;"> -0.0433861 </td> <td style="text-align:right;"> 0.0272943 </td> <td style="text-align:right;"> 0.0429971 </td> <td style="text-align:right;"> 0.0141807 </td> <td style="text-align:right;"> 0.0718136 </td> <td style="text-align:right;"> 0.0147026 </td> <td style="text-align:right;"> 0.1183626 </td> <td style="text-align:right;"> 0.0853242 </td> <td style="text-align:right;"> 0.1514009 </td> <td style="text-align:right;"> 0.0168566 </td> <td style="text-align:right;"> 109 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> -0.0993292 </td> <td style="text-align:right;"> -0.3721975 </td> <td style="text-align:right;"> 0.1735391 </td> <td style="text-align:right;"> 0.1392211 </td> <td style="text-align:right;"> 0.1774003 </td> <td style="text-align:right;"> -0.1938091 </td> <td style="text-align:right;"> 0.5486096 </td> <td style="text-align:right;"> 0.1893960 </td> <td style="text-align:right;"> 0.4445455 </td> <td style="text-align:right;"> 0.0944498 </td> <td style="text-align:right;"> 0.7946412 </td> <td style="text-align:right;"> 0.1786236 </td> <td style="text-align:right;"> 110 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> insulin </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> -0.0993292 </td> <td style="text-align:right;"> -0.3721975 </td> <td style="text-align:right;"> 0.1735391 </td> <td style="text-align:right;"> 0.1392211 </td> <td style="text-align:right;"> 0.1774003 </td> <td style="text-align:right;"> -0.1938091 </td> <td style="text-align:right;"> 0.5486096 </td> <td style="text-align:right;"> 0.1893960 </td> <td style="text-align:right;"> 0.4445455 </td> <td style="text-align:right;"> 0.0944498 </td> <td style="text-align:right;"> 0.7946412 </td> <td style="text-align:right;"> 0.1786236 </td> <td style="text-align:right;"> 110 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> insulin </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> -0.0974214 </td> <td style="text-align:right;"> -0.2141737 </td> <td style="text-align:right;"> 0.0193310 </td> <td style="text-align:right;"> 0.0595686 </td> <td style="text-align:right;"> -0.2534898 </td> <td style="text-align:right;"> -0.3963648 </td> <td style="text-align:right;"> -0.1106147 </td> <td style="text-align:right;"> 0.0728968 </td> <td style="text-align:right;"> -0.1527977 </td> <td style="text-align:right;"> -0.1930307 </td> <td style="text-align:right;"> -0.1125646 </td> <td style="text-align:right;"> 0.0205274 </td> <td style="text-align:right;"> 111 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> iron </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> -0.0974214 </td> <td style="text-align:right;"> -0.2141737 </td> <td style="text-align:right;"> 0.0193310 </td> <td style="text-align:right;"> 0.0595686 </td> <td style="text-align:right;"> -0.2534898 </td> <td style="text-align:right;"> -0.3963648 </td> <td style="text-align:right;"> -0.1106147 </td> <td style="text-align:right;"> 0.0728968 </td> <td style="text-align:right;"> -0.1527977 </td> <td style="text-align:right;"> -0.1930307 </td> <td style="text-align:right;"> -0.1125646 </td> <td style="text-align:right;"> 0.0205274 </td> <td style="text-align:right;"> 111 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> iron </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 0.0941249 </td> <td style="text-align:right;"> -0.0214022 </td> <td style="text-align:right;"> 0.2096519 </td> <td style="text-align:right;"> 0.0589435 </td> <td style="text-align:right;"> 0.1409270 </td> <td style="text-align:right;"> -0.0620594 </td> <td style="text-align:right;"> 0.3439133 </td> <td style="text-align:right;"> 0.1035664 </td> <td style="text-align:right;"> 0.0318801 </td> <td style="text-align:right;"> -0.1412218 </td> <td style="text-align:right;"> 0.2049819 </td> <td style="text-align:right;"> 0.0883189 </td> <td style="text-align:right;"> 112 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 0.0941249 </td> <td style="text-align:right;"> -0.0214022 </td> <td style="text-align:right;"> 0.2096519 </td> <td style="text-align:right;"> 0.0589435 </td> <td style="text-align:right;"> 0.1409270 </td> <td style="text-align:right;"> -0.0620594 </td> <td style="text-align:right;"> 0.3439133 </td> <td style="text-align:right;"> 0.1035664 </td> <td style="text-align:right;"> 0.0318801 </td> <td style="text-align:right;"> -0.1412218 </td> <td style="text-align:right;"> 0.2049819 </td> <td style="text-align:right;"> 0.0883189 </td> <td style="text-align:right;"> 112 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 0.1254239 </td> <td style="text-align:right;"> 0.0330185 </td> <td style="text-align:right;"> 0.2178293 </td> <td style="text-align:right;"> 0.0471465 </td> <td style="text-align:right;"> 0.3641221 </td> <td style="text-align:right;"> 0.2056000 </td> <td style="text-align:right;"> 0.5226441 </td> <td style="text-align:right;"> 0.0808801 </td> <td style="text-align:right;"> 0.2734519 </td> <td style="text-align:right;"> 0.0739366 </td> <td style="text-align:right;"> 0.4729672 </td> <td style="text-align:right;"> 0.1017954 </td> <td style="text-align:right;"> 115 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 0.1254239 </td> <td style="text-align:right;"> 0.0330185 </td> <td style="text-align:right;"> 0.2178293 </td> <td style="text-align:right;"> 0.0471465 </td> <td style="text-align:right;"> 0.3641221 </td> <td style="text-align:right;"> 0.2056000 </td> <td style="text-align:right;"> 0.5226441 </td> <td style="text-align:right;"> 0.0808801 </td> <td style="text-align:right;"> 0.2734519 </td> <td style="text-align:right;"> 0.0739366 </td> <td style="text-align:right;"> 0.4729672 </td> <td style="text-align:right;"> 0.1017954 </td> <td style="text-align:right;"> 115 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 0.4231644 </td> <td style="text-align:right;"> 0.1551776 </td> <td style="text-align:right;"> 0.6911512 </td> <td style="text-align:right;"> 0.1367305 </td> <td style="text-align:right;"> 0.2669283 </td> <td style="text-align:right;"> -0.0956833 </td> <td style="text-align:right;"> 0.6295400 </td> <td style="text-align:right;"> 0.1850093 </td> <td style="text-align:right;"> -0.1615499 </td> <td style="text-align:right;"> -0.6010478 </td> <td style="text-align:right;"> 0.2779480 </td> <td style="text-align:right;"> 0.2242378 </td> <td style="text-align:right;"> 116 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 0.4231644 </td> <td style="text-align:right;"> 0.1551776 </td> <td style="text-align:right;"> 0.6911512 </td> <td style="text-align:right;"> 0.1367305 </td> <td style="text-align:right;"> 0.2669283 </td> <td style="text-align:right;"> -0.0956833 </td> <td style="text-align:right;"> 0.6295400 </td> <td style="text-align:right;"> 0.1850093 </td> <td style="text-align:right;"> -0.1615499 </td> <td style="text-align:right;"> -0.6010478 </td> <td style="text-align:right;"> 0.2779480 </td> <td style="text-align:right;"> 0.2242378 </td> <td style="text-align:right;"> 116 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 0.1435756 </td> <td style="text-align:right;"> 0.0759342 </td> <td style="text-align:right;"> 0.2112170 </td> <td style="text-align:right;"> 0.0345115 </td> <td style="text-align:right;"> 0.3382447 </td> <td style="text-align:right;"> 0.2664863 </td> <td style="text-align:right;"> 0.4100031 </td> <td style="text-align:right;"> 0.0366121 </td> <td style="text-align:right;"> 0.1928945 </td> <td style="text-align:right;"> 0.1752425 </td> <td style="text-align:right;"> 0.2105465 </td> <td style="text-align:right;"> 0.0090063 </td> <td style="text-align:right;"> 117 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> lean mass </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 0.1435756 </td> <td style="text-align:right;"> 0.0759342 </td> <td style="text-align:right;"> 0.2112170 </td> <td style="text-align:right;"> 0.0345115 </td> <td style="text-align:right;"> 0.3382447 </td> <td style="text-align:right;"> 0.2664863 </td> <td style="text-align:right;"> 0.4100031 </td> <td style="text-align:right;"> 0.0366121 </td> <td style="text-align:right;"> 0.1928945 </td> <td style="text-align:right;"> 0.1752425 </td> <td style="text-align:right;"> 0.2105465 </td> <td style="text-align:right;"> 0.0090063 </td> <td style="text-align:right;"> 117 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> lean mass </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 0.1953833 </td> <td style="text-align:right;"> 0.0912480 </td> <td style="text-align:right;"> 0.2995186 </td> <td style="text-align:right;"> 0.0531312 </td> <td style="text-align:right;"> 0.1840786 </td> <td style="text-align:right;"> 0.0863764 </td> <td style="text-align:right;"> 0.2817807 </td> <td style="text-align:right;"> 0.0498490 </td> <td style="text-align:right;"> -0.0122785 </td> <td style="text-align:right;"> -0.0257504 </td> <td style="text-align:right;"> 0.0011934 </td> <td style="text-align:right;"> 0.0068736 </td> <td style="text-align:right;"> 118 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 0.1953833 </td> <td style="text-align:right;"> 0.0912480 </td> <td style="text-align:right;"> 0.2995186 </td> <td style="text-align:right;"> 0.0531312 </td> <td style="text-align:right;"> 0.1840786 </td> <td style="text-align:right;"> 0.0863764 </td> <td style="text-align:right;"> 0.2817807 </td> <td style="text-align:right;"> 0.0498490 </td> <td style="text-align:right;"> -0.0122785 </td> <td style="text-align:right;"> -0.0257504 </td> <td style="text-align:right;"> 0.0011934 </td> <td style="text-align:right;"> 0.0068736 </td> <td style="text-align:right;"> 118 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> -0.1854856 </td> <td style="text-align:right;"> -0.4305058 </td> <td style="text-align:right;"> 0.0595347 </td> <td style="text-align:right;"> 0.1250126 </td> <td style="text-align:right;"> -0.1534983 </td> <td style="text-align:right;"> -0.4007283 </td> <td style="text-align:right;"> 0.0937316 </td> <td style="text-align:right;"> 0.1261401 </td> <td style="text-align:right;"> 0.0331746 </td> <td style="text-align:right;"> 0.0284172 </td> <td style="text-align:right;"> 0.0379321 </td> <td style="text-align:right;"> 0.0024273 </td> <td style="text-align:right;"> 119 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> -0.1854856 </td> <td style="text-align:right;"> -0.4305058 </td> <td style="text-align:right;"> 0.0595347 </td> <td style="text-align:right;"> 0.1250126 </td> <td style="text-align:right;"> -0.1534983 </td> <td style="text-align:right;"> -0.4007283 </td> <td style="text-align:right;"> 0.0937316 </td> <td style="text-align:right;"> 0.1261401 </td> <td style="text-align:right;"> 0.0331746 </td> <td style="text-align:right;"> 0.0284172 </td> <td style="text-align:right;"> 0.0379321 </td> <td style="text-align:right;"> 0.0024273 </td> <td style="text-align:right;"> 119 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> -0.1446634 </td> <td style="text-align:right;"> -0.2339950 </td> <td style="text-align:right;"> -0.0553319 </td> <td style="text-align:right;"> 0.0455782 </td> <td style="text-align:right;"> -0.1352252 </td> <td style="text-align:right;"> -0.2234178 </td> <td style="text-align:right;"> -0.0470327 </td> <td style="text-align:right;"> 0.0449970 </td> <td style="text-align:right;"> 0.0075283 </td> <td style="text-align:right;"> -0.0057082 </td> <td style="text-align:right;"> 0.0207648 </td> <td style="text-align:right;"> 0.0067535 </td> <td style="text-align:right;"> 120 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> -0.1446634 </td> <td style="text-align:right;"> -0.2339950 </td> <td style="text-align:right;"> -0.0553319 </td> <td style="text-align:right;"> 0.0455782 </td> <td style="text-align:right;"> -0.1352252 </td> <td style="text-align:right;"> -0.2234178 </td> <td style="text-align:right;"> -0.0470327 </td> <td style="text-align:right;"> 0.0449970 </td> <td style="text-align:right;"> 0.0075283 </td> <td style="text-align:right;"> -0.0057082 </td> <td style="text-align:right;"> 0.0207648 </td> <td style="text-align:right;"> 0.0067535 </td> <td style="text-align:right;"> 120 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 0.0480458 </td> <td style="text-align:right;"> -0.0360706 </td> <td style="text-align:right;"> 0.1321622 </td> <td style="text-align:right;"> 0.0429173 </td> <td style="text-align:right;"> 0.0487217 </td> <td style="text-align:right;"> -0.0347622 </td> <td style="text-align:right;"> 0.1322057 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0006956 </td> <td style="text-align:right;"> -0.0095012 </td> <td style="text-align:right;"> 0.0108923 </td> <td style="text-align:right;"> 0.0052025 </td> <td style="text-align:right;"> 121 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 0.0480458 </td> <td style="text-align:right;"> -0.0360706 </td> <td style="text-align:right;"> 0.1321622 </td> <td style="text-align:right;"> 0.0429173 </td> <td style="text-align:right;"> 0.0487217 </td> <td style="text-align:right;"> -0.0347622 </td> <td style="text-align:right;"> 0.1322057 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0006956 </td> <td style="text-align:right;"> -0.0095012 </td> <td style="text-align:right;"> 0.0108923 </td> <td style="text-align:right;"> 0.0052025 </td> <td style="text-align:right;"> 121 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> -0.0675012 </td> <td style="text-align:right;"> -0.1511666 </td> <td style="text-align:right;"> 0.0161641 </td> <td style="text-align:right;"> 0.0426872 </td> <td style="text-align:right;"> -0.0618025 </td> <td style="text-align:right;"> -0.1452865 </td> <td style="text-align:right;"> 0.0216814 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0063811 </td> <td style="text-align:right;"> 0.0011702 </td> <td style="text-align:right;"> 0.0115921 </td> <td style="text-align:right;"> 0.0026587 </td> <td style="text-align:right;"> 122 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> -0.0675012 </td> <td style="text-align:right;"> -0.1511666 </td> <td style="text-align:right;"> 0.0161641 </td> <td style="text-align:right;"> 0.0426872 </td> <td style="text-align:right;"> -0.0618025 </td> <td style="text-align:right;"> -0.1452865 </td> <td style="text-align:right;"> 0.0216814 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0063811 </td> <td style="text-align:right;"> 0.0011702 </td> <td style="text-align:right;"> 0.0115921 </td> <td style="text-align:right;"> 0.0026587 </td> <td style="text-align:right;"> 122 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> -0.2631046 </td> <td style="text-align:right;"> -0.4734756 </td> <td style="text-align:right;"> -0.0527336 </td> <td style="text-align:right;"> 0.1073341 </td> <td style="text-align:right;"> -0.2687360 </td> <td style="text-align:right;"> -0.4790035 </td> <td style="text-align:right;"> -0.0584686 </td> <td style="text-align:right;"> 0.1072813 </td> <td style="text-align:right;"> -0.0026027 </td> <td style="text-align:right;"> -0.0146655 </td> <td style="text-align:right;"> 0.0094600 </td> <td style="text-align:right;"> 0.0061546 </td> <td style="text-align:right;"> 123 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> -0.2631046 </td> <td style="text-align:right;"> -0.4734756 </td> <td style="text-align:right;"> -0.0527336 </td> <td style="text-align:right;"> 0.1073341 </td> <td style="text-align:right;"> -0.2687360 </td> <td style="text-align:right;"> -0.4790035 </td> <td style="text-align:right;"> -0.0584686 </td> <td style="text-align:right;"> 0.1072813 </td> <td style="text-align:right;"> -0.0026027 </td> <td style="text-align:right;"> -0.0146655 </td> <td style="text-align:right;"> 0.0094600 </td> <td style="text-align:right;"> 0.0061546 </td> <td style="text-align:right;"> 123 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> -0.1975770 </td> <td style="text-align:right;"> -0.4386627 </td> <td style="text-align:right;"> 0.0435087 </td> <td style="text-align:right;"> 0.1230052 </td> <td style="text-align:right;"> -0.1932648 </td> <td style="text-align:right;"> -0.4269751 </td> <td style="text-align:right;"> 0.0404456 </td> <td style="text-align:right;"> 0.1192422 </td> <td style="text-align:right;"> 0.0027995 </td> <td style="text-align:right;"> -0.0034907 </td> <td style="text-align:right;"> 0.0090898 </td> <td style="text-align:right;"> 0.0032094 </td> <td style="text-align:right;"> 124 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> -0.1975770 </td> <td style="text-align:right;"> -0.4386627 </td> <td style="text-align:right;"> 0.0435087 </td> <td style="text-align:right;"> 0.1230052 </td> <td style="text-align:right;"> -0.1932648 </td> <td style="text-align:right;"> -0.4269751 </td> <td style="text-align:right;"> 0.0404456 </td> <td style="text-align:right;"> 0.1192422 </td> <td style="text-align:right;"> 0.0027995 </td> <td style="text-align:right;"> -0.0034907 </td> <td style="text-align:right;"> 0.0090898 </td> <td style="text-align:right;"> 0.0032094 </td> <td style="text-align:right;"> 124 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 0.0960106 </td> <td style="text-align:right;"> 0.0224214 </td> <td style="text-align:right;"> 0.1695997 </td> <td style="text-align:right;"> 0.0375462 </td> <td style="text-align:right;"> -0.0159064 </td> <td style="text-align:right;"> -0.0579694 </td> <td style="text-align:right;"> 0.0261566 </td> <td style="text-align:right;"> 0.0214611 </td> <td style="text-align:right;"> -0.1105803 </td> <td style="text-align:right;"> -0.1761043 </td> <td style="text-align:right;"> -0.0450562 </td> <td style="text-align:right;"> 0.0334313 </td> <td style="text-align:right;"> 125 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 0.0960106 </td> <td style="text-align:right;"> 0.0224214 </td> <td style="text-align:right;"> 0.1695997 </td> <td style="text-align:right;"> 0.0375462 </td> <td style="text-align:right;"> -0.0159064 </td> <td style="text-align:right;"> -0.0579694 </td> <td style="text-align:right;"> 0.0261566 </td> <td style="text-align:right;"> 0.0214611 </td> <td style="text-align:right;"> -0.1105803 </td> <td style="text-align:right;"> -0.1761043 </td> <td style="text-align:right;"> -0.0450562 </td> <td style="text-align:right;"> 0.0334313 </td> <td style="text-align:right;"> 125 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 0.0228924 </td> <td style="text-align:right;"> -0.0247048 </td> <td style="text-align:right;"> 0.0704896 </td> <td style="text-align:right;"> 0.0242847 </td> <td style="text-align:right;"> 0.0454075 </td> <td style="text-align:right;"> -0.0013249 </td> <td style="text-align:right;"> 0.0921399 </td> <td style="text-align:right;"> 0.0238435 </td> <td style="text-align:right;"> 0.0246614 </td> <td style="text-align:right;"> 0.0114095 </td> <td style="text-align:right;"> 0.0379132 </td> <td style="text-align:right;"> 0.0067613 </td> <td style="text-align:right;"> 126 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> lvawd </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 0.0228924 </td> <td style="text-align:right;"> -0.0247048 </td> <td style="text-align:right;"> 0.0704896 </td> <td style="text-align:right;"> 0.0242847 </td> <td style="text-align:right;"> 0.0454075 </td> <td style="text-align:right;"> -0.0013249 </td> <td style="text-align:right;"> 0.0921399 </td> <td style="text-align:right;"> 0.0238435 </td> <td style="text-align:right;"> 0.0246614 </td> <td style="text-align:right;"> 0.0114095 </td> <td style="text-align:right;"> 0.0379132 </td> <td style="text-align:right;"> 0.0067613 </td> <td style="text-align:right;"> 126 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> lvawd </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> -0.0017749 </td> <td style="text-align:right;"> -0.2517581 </td> <td style="text-align:right;"> 0.2482083 </td> <td style="text-align:right;"> 0.1275448 </td> <td style="text-align:right;"> 0.0232601 </td> <td style="text-align:right;"> -0.1776617 </td> <td style="text-align:right;"> 0.2241819 </td> <td style="text-align:right;"> 0.1025130 </td> <td style="text-align:right;"> 0.0112569 </td> <td style="text-align:right;"> -0.0306073 </td> <td style="text-align:right;"> 0.0531211 </td> <td style="text-align:right;"> 0.0213597 </td> <td style="text-align:right;"> 127 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> lvaws </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> -0.0017749 </td> <td style="text-align:right;"> -0.2517581 </td> <td style="text-align:right;"> 0.2482083 </td> <td style="text-align:right;"> 0.1275448 </td> <td style="text-align:right;"> 0.0232601 </td> <td style="text-align:right;"> -0.1776617 </td> <td style="text-align:right;"> 0.2241819 </td> <td style="text-align:right;"> 0.1025130 </td> <td style="text-align:right;"> 0.0112569 </td> <td style="text-align:right;"> -0.0306073 </td> <td style="text-align:right;"> 0.0531211 </td> <td style="text-align:right;"> 0.0213597 </td> <td style="text-align:right;"> 127 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> lvaws </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 0.0453256 </td> <td style="text-align:right;"> -0.0241892 </td> <td style="text-align:right;"> 0.1148405 </td> <td style="text-align:right;"> 0.0354674 </td> <td style="text-align:right;"> 0.0981450 </td> <td style="text-align:right;"> 0.0208146 </td> <td style="text-align:right;"> 0.1754754 </td> <td style="text-align:right;"> 0.0394550 </td> <td style="text-align:right;"> 0.0528053 </td> <td style="text-align:right;"> 0.0378669 </td> <td style="text-align:right;"> 0.0677436 </td> <td style="text-align:right;"> 0.0076218 </td> <td style="text-align:right;"> 128 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> lvidd </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 0.0453256 </td> <td style="text-align:right;"> -0.0241892 </td> <td style="text-align:right;"> 0.1148405 </td> <td style="text-align:right;"> 0.0354674 </td> <td style="text-align:right;"> 0.0981450 </td> <td style="text-align:right;"> 0.0208146 </td> <td style="text-align:right;"> 0.1754754 </td> <td style="text-align:right;"> 0.0394550 </td> <td style="text-align:right;"> 0.0528053 </td> <td style="text-align:right;"> 0.0378669 </td> <td style="text-align:right;"> 0.0677436 </td> <td style="text-align:right;"> 0.0076218 </td> <td style="text-align:right;"> 128 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> lvidd </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> -0.0635228 </td> <td style="text-align:right;"> -0.1990947 </td> <td style="text-align:right;"> 0.0720491 </td> <td style="text-align:right;"> 0.0691706 </td> <td style="text-align:right;"> 0.0083352 </td> <td style="text-align:right;"> -0.1335894 </td> <td style="text-align:right;"> 0.1502598 </td> <td style="text-align:right;"> 0.0724118 </td> <td style="text-align:right;"> 0.0756177 </td> <td style="text-align:right;"> 0.0525777 </td> <td style="text-align:right;"> 0.0986576 </td> <td style="text-align:right;"> 0.0117553 </td> <td style="text-align:right;"> 129 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> lvids </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> -0.0635228 </td> <td style="text-align:right;"> -0.1990947 </td> <td style="text-align:right;"> 0.0720491 </td> <td style="text-align:right;"> 0.0691706 </td> <td style="text-align:right;"> 0.0083352 </td> <td style="text-align:right;"> -0.1335894 </td> <td style="text-align:right;"> 0.1502598 </td> <td style="text-align:right;"> 0.0724118 </td> <td style="text-align:right;"> 0.0756177 </td> <td style="text-align:right;"> 0.0525777 </td> <td style="text-align:right;"> 0.0986576 </td> <td style="text-align:right;"> 0.0117553 </td> <td style="text-align:right;"> 129 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> lvids </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> -0.0317376 </td> <td style="text-align:right;"> -0.1258062 </td> <td style="text-align:right;"> 0.0623311 </td> <td style="text-align:right;"> 0.0479951 </td> <td style="text-align:right;"> -0.0104248 </td> <td style="text-align:right;"> -0.1271922 </td> <td style="text-align:right;"> 0.1063426 </td> <td style="text-align:right;"> 0.0595763 </td> <td style="text-align:right;"> 0.0302674 </td> <td style="text-align:right;"> 0.0131900 </td> <td style="text-align:right;"> 0.0473448 </td> <td style="text-align:right;"> 0.0087131 </td> <td style="text-align:right;"> 130 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> lvpwd </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> -0.0317376 </td> <td style="text-align:right;"> -0.1258062 </td> <td style="text-align:right;"> 0.0623311 </td> <td style="text-align:right;"> 0.0479951 </td> <td style="text-align:right;"> -0.0104248 </td> <td style="text-align:right;"> -0.1271922 </td> <td style="text-align:right;"> 0.1063426 </td> <td style="text-align:right;"> 0.0595763 </td> <td style="text-align:right;"> 0.0302674 </td> <td style="text-align:right;"> 0.0131900 </td> <td style="text-align:right;"> 0.0473448 </td> <td style="text-align:right;"> 0.0087131 </td> <td style="text-align:right;"> 130 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> lvpwd </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> -0.0190522 </td> <td style="text-align:right;"> -0.1014670 </td> <td style="text-align:right;"> 0.0633627 </td> <td style="text-align:right;"> 0.0420492 </td> <td style="text-align:right;"> 0.0089592 </td> <td style="text-align:right;"> -0.0823356 </td> <td style="text-align:right;"> 0.1002540 </td> <td style="text-align:right;"> 0.0465798 </td> <td style="text-align:right;"> 0.0268487 </td> <td style="text-align:right;"> 0.0063146 </td> <td style="text-align:right;"> 0.0473828 </td> <td style="text-align:right;"> 0.0104768 </td> <td style="text-align:right;"> 131 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> lvpws </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> -0.0190522 </td> <td style="text-align:right;"> -0.1014670 </td> <td style="text-align:right;"> 0.0633627 </td> <td style="text-align:right;"> 0.0420492 </td> <td style="text-align:right;"> 0.0089592 </td> <td style="text-align:right;"> -0.0823356 </td> <td style="text-align:right;"> 0.1002540 </td> <td style="text-align:right;"> 0.0465798 </td> <td style="text-align:right;"> 0.0268487 </td> <td style="text-align:right;"> 0.0063146 </td> <td style="text-align:right;"> 0.0473828 </td> <td style="text-align:right;"> 0.0104768 </td> <td style="text-align:right;"> 131 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> lvpws </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 0.0161699 </td> <td style="text-align:right;"> -0.0231196 </td> <td style="text-align:right;"> 0.0554593 </td> <td style="text-align:right;"> 0.0200460 </td> <td style="text-align:right;"> -0.0513056 </td> <td style="text-align:right;"> -0.1167021 </td> <td style="text-align:right;"> 0.0140909 </td> <td style="text-align:right;"> 0.0333662 </td> <td style="text-align:right;"> -0.0413354 </td> <td style="text-align:right;"> -0.1135580 </td> <td style="text-align:right;"> 0.0308871 </td> <td style="text-align:right;"> 0.0368489 </td> <td style="text-align:right;"> 134 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> magnesium </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 0.0161699 </td> <td style="text-align:right;"> -0.0231196 </td> <td style="text-align:right;"> 0.0554593 </td> <td style="text-align:right;"> 0.0200460 </td> <td style="text-align:right;"> -0.0513056 </td> <td style="text-align:right;"> -0.1167021 </td> <td style="text-align:right;"> 0.0140909 </td> <td style="text-align:right;"> 0.0333662 </td> <td style="text-align:right;"> -0.0413354 </td> <td style="text-align:right;"> -0.1135580 </td> <td style="text-align:right;"> 0.0308871 </td> <td style="text-align:right;"> 0.0368489 </td> <td style="text-align:right;"> 134 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> magnesium </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 0.0378015 </td> <td style="text-align:right;"> -0.0880637 </td> <td style="text-align:right;"> 0.1636666 </td> <td style="text-align:right;"> 0.0642181 </td> <td style="text-align:right;"> 0.0253063 </td> <td style="text-align:right;"> -0.1086076 </td> <td style="text-align:right;"> 0.1592202 </td> <td style="text-align:right;"> 0.0683247 </td> <td style="text-align:right;"> -0.0113450 </td> <td style="text-align:right;"> -0.0150702 </td> <td style="text-align:right;"> -0.0076199 </td> <td style="text-align:right;"> 0.0019006 </td> <td style="text-align:right;"> 135 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 0.0378015 </td> <td style="text-align:right;"> -0.0880637 </td> <td style="text-align:right;"> 0.1636666 </td> <td style="text-align:right;"> 0.0642181 </td> <td style="text-align:right;"> 0.0253063 </td> <td style="text-align:right;"> -0.1086076 </td> <td style="text-align:right;"> 0.1592202 </td> <td style="text-align:right;"> 0.0683247 </td> <td style="text-align:right;"> -0.0113450 </td> <td style="text-align:right;"> -0.0150702 </td> <td style="text-align:right;"> -0.0076199 </td> <td style="text-align:right;"> 0.0019006 </td> <td style="text-align:right;"> 135 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 0.0039175 </td> <td style="text-align:right;"> -0.0957495 </td> <td style="text-align:right;"> 0.1035845 </td> <td style="text-align:right;"> 0.0508514 </td> <td style="text-align:right;"> -0.0030447 </td> <td style="text-align:right;"> -0.0961742 </td> <td style="text-align:right;"> 0.0900848 </td> <td style="text-align:right;"> 0.0475159 </td> <td style="text-align:right;"> -0.0063502 </td> <td style="text-align:right;"> -0.0099649 </td> <td style="text-align:right;"> -0.0027355 </td> <td style="text-align:right;"> 0.0018443 </td> <td style="text-align:right;"> 136 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 0.0039175 </td> <td style="text-align:right;"> -0.0957495 </td> <td style="text-align:right;"> 0.1035845 </td> <td style="text-align:right;"> 0.0508514 </td> <td style="text-align:right;"> -0.0030447 </td> <td style="text-align:right;"> -0.0961742 </td> <td style="text-align:right;"> 0.0900848 </td> <td style="text-align:right;"> 0.0475159 </td> <td style="text-align:right;"> -0.0063502 </td> <td style="text-align:right;"> -0.0099649 </td> <td style="text-align:right;"> -0.0027355 </td> <td style="text-align:right;"> 0.0018443 </td> <td style="text-align:right;"> 136 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> -0.0025833 </td> <td style="text-align:right;"> -0.0653065 </td> <td style="text-align:right;"> 0.0601398 </td> <td style="text-align:right;"> 0.0320022 </td> <td style="text-align:right;"> -0.0193465 </td> <td style="text-align:right;"> -0.0824670 </td> <td style="text-align:right;"> 0.0437741 </td> <td style="text-align:right;"> 0.0322049 </td> <td style="text-align:right;"> -0.0169768 </td> <td style="text-align:right;"> -0.0197231 </td> <td style="text-align:right;"> -0.0142305 </td> <td style="text-align:right;"> 0.0014012 </td> <td style="text-align:right;"> 137 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> -0.0025833 </td> <td style="text-align:right;"> -0.0653065 </td> <td style="text-align:right;"> 0.0601398 </td> <td style="text-align:right;"> 0.0320022 </td> <td style="text-align:right;"> -0.0193465 </td> <td style="text-align:right;"> -0.0824670 </td> <td style="text-align:right;"> 0.0437741 </td> <td style="text-align:right;"> 0.0322049 </td> <td style="text-align:right;"> -0.0169768 </td> <td style="text-align:right;"> -0.0197231 </td> <td style="text-align:right;"> -0.0142305 </td> <td style="text-align:right;"> 0.0014012 </td> <td style="text-align:right;"> 137 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 0.0487366 </td> <td style="text-align:right;"> -0.0044688 </td> <td style="text-align:right;"> 0.1019419 </td> <td style="text-align:right;"> 0.0271461 </td> <td style="text-align:right;"> 0.0353913 </td> <td style="text-align:right;"> -0.0210323 </td> <td style="text-align:right;"> 0.0918150 </td> <td style="text-align:right;"> 0.0287881 </td> <td style="text-align:right;"> -0.0174066 </td> <td style="text-align:right;"> -0.0276044 </td> <td style="text-align:right;"> -0.0072089 </td> <td style="text-align:right;"> 0.0052030 </td> <td style="text-align:right;"> 138 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 0.0487366 </td> <td style="text-align:right;"> -0.0044688 </td> <td style="text-align:right;"> 0.1019419 </td> <td style="text-align:right;"> 0.0271461 </td> <td style="text-align:right;"> 0.0353913 </td> <td style="text-align:right;"> -0.0210323 </td> <td style="text-align:right;"> 0.0918150 </td> <td style="text-align:right;"> 0.0287881 </td> <td style="text-align:right;"> -0.0174066 </td> <td style="text-align:right;"> -0.0276044 </td> <td style="text-align:right;"> -0.0072089 </td> <td style="text-align:right;"> 0.0052030 </td> <td style="text-align:right;"> 138 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 0.0084703 </td> <td style="text-align:right;"> -0.0282092 </td> <td style="text-align:right;"> 0.0451499 </td> <td style="text-align:right;"> 0.0187144 </td> <td style="text-align:right;"> -0.0948208 </td> <td style="text-align:right;"> -0.1630495 </td> <td style="text-align:right;"> -0.0265922 </td> <td style="text-align:right;"> 0.0348112 </td> <td style="text-align:right;"> -0.0835612 </td> <td style="text-align:right;"> -0.1503108 </td> <td style="text-align:right;"> -0.0168116 </td> <td style="text-align:right;"> 0.0340565 </td> <td style="text-align:right;"> 139 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 0.0084703 </td> <td style="text-align:right;"> -0.0282092 </td> <td style="text-align:right;"> 0.0451499 </td> <td style="text-align:right;"> 0.0187144 </td> <td style="text-align:right;"> -0.0948208 </td> <td style="text-align:right;"> -0.1630495 </td> <td style="text-align:right;"> -0.0265922 </td> <td style="text-align:right;"> 0.0348112 </td> <td style="text-align:right;"> -0.0835612 </td> <td style="text-align:right;"> -0.1503108 </td> <td style="text-align:right;"> -0.0168116 </td> <td style="text-align:right;"> 0.0340565 </td> <td style="text-align:right;"> 139 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 0.0284617 </td> <td style="text-align:right;"> -0.0131943 </td> <td style="text-align:right;"> 0.0701178 </td> <td style="text-align:right;"> 0.0212535 </td> <td style="text-align:right;"> -0.0876811 </td> <td style="text-align:right;"> -0.1270777 </td> <td style="text-align:right;"> -0.0482845 </td> <td style="text-align:right;"> 0.0201007 </td> <td style="text-align:right;"> -0.1130259 </td> <td style="text-align:right;"> -0.1558048 </td> <td style="text-align:right;"> -0.0702470 </td> <td style="text-align:right;"> 0.0218264 </td> <td style="text-align:right;"> 140 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 0.0284617 </td> <td style="text-align:right;"> -0.0131943 </td> <td style="text-align:right;"> 0.0701178 </td> <td style="text-align:right;"> 0.0212535 </td> <td style="text-align:right;"> -0.0876811 </td> <td style="text-align:right;"> -0.1270777 </td> <td style="text-align:right;"> -0.0482845 </td> <td style="text-align:right;"> 0.0201007 </td> <td style="text-align:right;"> -0.1130259 </td> <td style="text-align:right;"> -0.1558048 </td> <td style="text-align:right;"> -0.0702470 </td> <td style="text-align:right;"> 0.0218264 </td> <td style="text-align:right;"> 140 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 0.0150703 </td> <td style="text-align:right;"> -0.0534907 </td> <td style="text-align:right;"> 0.0836313 </td> <td style="text-align:right;"> 0.0349807 </td> <td style="text-align:right;"> -0.0361259 </td> <td style="text-align:right;"> -0.0952472 </td> <td style="text-align:right;"> 0.0229955 </td> <td style="text-align:right;"> 0.0301645 </td> <td style="text-align:right;"> -0.0588092 </td> <td style="text-align:right;"> -0.1679907 </td> <td style="text-align:right;"> 0.0503723 </td> <td style="text-align:right;"> 0.0557059 </td> <td style="text-align:right;"> 159 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> number of center entries </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 0.0150703 </td> <td style="text-align:right;"> -0.0534907 </td> <td style="text-align:right;"> 0.0836313 </td> <td style="text-align:right;"> 0.0349807 </td> <td style="text-align:right;"> -0.0361259 </td> <td style="text-align:right;"> -0.0952472 </td> <td style="text-align:right;"> 0.0229955 </td> <td style="text-align:right;"> 0.0301645 </td> <td style="text-align:right;"> -0.0588092 </td> <td style="text-align:right;"> -0.1679907 </td> <td style="text-align:right;"> 0.0503723 </td> <td style="text-align:right;"> 0.0557059 </td> <td style="text-align:right;"> 159 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> number of center entries </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> -0.0011326 </td> <td style="text-align:right;"> -0.1141113 </td> <td style="text-align:right;"> 0.1118461 </td> <td style="text-align:right;"> 0.0576432 </td> <td style="text-align:right;"> 0.1869490 </td> <td style="text-align:right;"> -0.0392422 </td> <td style="text-align:right;"> 0.4131402 </td> <td style="text-align:right;"> 0.1154058 </td> <td style="text-align:right;"> 0.1794328 </td> <td style="text-align:right;"> 0.0568682 </td> <td style="text-align:right;"> 0.3019974 </td> <td style="text-align:right;"> 0.0625341 </td> <td style="text-align:right;"> 164 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> -0.0011326 </td> <td style="text-align:right;"> -0.1141113 </td> <td style="text-align:right;"> 0.1118461 </td> <td style="text-align:right;"> 0.0576432 </td> <td style="text-align:right;"> 0.1869490 </td> <td style="text-align:right;"> -0.0392422 </td> <td style="text-align:right;"> 0.4131402 </td> <td style="text-align:right;"> 0.1154058 </td> <td style="text-align:right;"> 0.1794328 </td> <td style="text-align:right;"> 0.0568682 </td> <td style="text-align:right;"> 0.3019974 </td> <td style="text-align:right;"> 0.0625341 </td> <td style="text-align:right;"> 164 </td> <td style="text-align:right;"> 8 </td> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> -0.1684902 </td> <td style="text-align:right;"> -0.2596648 </td> <td style="text-align:right;"> -0.0773156 </td> <td style="text-align:right;"> 0.0465185 </td> <td style="text-align:right;"> -0.1515195 </td> <td style="text-align:right;"> -0.2435956 </td> <td style="text-align:right;"> -0.0594434 </td> <td style="text-align:right;"> 0.0469785 </td> <td style="text-align:right;"> 0.0196158 </td> <td style="text-align:right;"> 0.0049349 </td> <td style="text-align:right;"> 0.0342967 </td> <td style="text-align:right;"> 0.0074904 </td> <td style="text-align:right;"> 169 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> others </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> others </td> <td style="text-align:right;"> -0.1684902 </td> <td style="text-align:right;"> -0.2596648 </td> <td style="text-align:right;"> -0.0773156 </td> <td style="text-align:right;"> 0.0465185 </td> <td style="text-align:right;"> -0.1515195 </td> <td style="text-align:right;"> -0.2435956 </td> <td style="text-align:right;"> -0.0594434 </td> <td style="text-align:right;"> 0.0469785 </td> <td style="text-align:right;"> 0.0196158 </td> <td style="text-align:right;"> 0.0049349 </td> <td style="text-align:right;"> 0.0342967 </td> <td style="text-align:right;"> 0.0074904 </td> <td style="text-align:right;"> 169 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> others </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> -0.1732553 </td> <td style="text-align:right;"> -0.4003845 </td> <td style="text-align:right;"> 0.0538738 </td> <td style="text-align:right;"> 0.1158844 </td> <td style="text-align:right;"> -0.2572491 </td> <td style="text-align:right;"> -0.7186201 </td> <td style="text-align:right;"> 0.2041219 </td> <td style="text-align:right;"> 0.2353977 </td> <td style="text-align:right;"> -0.0915619 </td> <td style="text-align:right;"> -0.2522236 </td> <td style="text-align:right;"> 0.0690997 </td> <td style="text-align:right;"> 0.0819717 </td> <td style="text-align:right;"> 170 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> pdcs </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> -0.1732553 </td> <td style="text-align:right;"> -0.4003845 </td> <td style="text-align:right;"> 0.0538738 </td> <td style="text-align:right;"> 0.1158844 </td> <td style="text-align:right;"> -0.2572491 </td> <td style="text-align:right;"> -0.7186201 </td> <td style="text-align:right;"> 0.2041219 </td> <td style="text-align:right;"> 0.2353977 </td> <td style="text-align:right;"> -0.0915619 </td> <td style="text-align:right;"> -0.2522236 </td> <td style="text-align:right;"> 0.0690997 </td> <td style="text-align:right;"> 0.0819717 </td> <td style="text-align:right;"> 170 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> pdcs </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> -0.0219679 </td> <td style="text-align:right;"> -0.0863184 </td> <td style="text-align:right;"> 0.0423826 </td> <td style="text-align:right;"> 0.0328325 </td> <td style="text-align:right;"> -0.0188907 </td> <td style="text-align:right;"> -0.0912088 </td> <td style="text-align:right;"> 0.0534274 </td> <td style="text-align:right;"> 0.0368977 </td> <td style="text-align:right;"> -0.0061802 </td> <td style="text-align:right;"> -0.0972542 </td> <td style="text-align:right;"> 0.0848938 </td> <td style="text-align:right;"> 0.0464672 </td> <td style="text-align:right;"> 171 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> percentage center time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> -0.0219679 </td> <td style="text-align:right;"> -0.0863184 </td> <td style="text-align:right;"> 0.0423826 </td> <td style="text-align:right;"> 0.0328325 </td> <td style="text-align:right;"> -0.0188907 </td> <td style="text-align:right;"> -0.0912088 </td> <td style="text-align:right;"> 0.0534274 </td> <td style="text-align:right;"> 0.0368977 </td> <td style="text-align:right;"> -0.0061802 </td> <td style="text-align:right;"> -0.0972542 </td> <td style="text-align:right;"> 0.0848938 </td> <td style="text-align:right;"> 0.0464672 </td> <td style="text-align:right;"> 171 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> percentage center time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> -0.0444272 </td> <td style="text-align:right;"> -0.1082870 </td> <td style="text-align:right;"> 0.0194327 </td> <td style="text-align:right;"> 0.0325822 </td> <td style="text-align:right;"> -0.1401304 </td> <td style="text-align:right;"> -0.2117709 </td> <td style="text-align:right;"> -0.0684898 </td> <td style="text-align:right;"> 0.0365520 </td> <td style="text-align:right;"> -0.0963838 </td> <td style="text-align:right;"> -0.1446043 </td> <td style="text-align:right;"> -0.0481633 </td> <td style="text-align:right;"> 0.0246028 </td> <td style="text-align:right;"> 174 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> -0.0444272 </td> <td style="text-align:right;"> -0.1082870 </td> <td style="text-align:right;"> 0.0194327 </td> <td style="text-align:right;"> 0.0325822 </td> <td style="text-align:right;"> -0.1401304 </td> <td style="text-align:right;"> -0.2117709 </td> <td style="text-align:right;"> -0.0684898 </td> <td style="text-align:right;"> 0.0365520 </td> <td style="text-align:right;"> -0.0963838 </td> <td style="text-align:right;"> -0.1446043 </td> <td style="text-align:right;"> -0.0481633 </td> <td style="text-align:right;"> 0.0246028 </td> <td style="text-align:right;"> 174 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> -0.0313217 </td> <td style="text-align:right;"> -0.0918314 </td> <td style="text-align:right;"> 0.0291879 </td> <td style="text-align:right;"> 0.0308728 </td> <td style="text-align:right;"> -0.1342236 </td> <td style="text-align:right;"> -0.1874097 </td> <td style="text-align:right;"> -0.0810376 </td> <td style="text-align:right;"> 0.0271362 </td> <td style="text-align:right;"> -0.1037239 </td> <td style="text-align:right;"> -0.1714836 </td> <td style="text-align:right;"> -0.0359643 </td> <td style="text-align:right;"> 0.0345719 </td> <td style="text-align:right;"> 175 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> -0.0313217 </td> <td style="text-align:right;"> -0.0918314 </td> <td style="text-align:right;"> 0.0291879 </td> <td style="text-align:right;"> 0.0308728 </td> <td style="text-align:right;"> -0.1342236 </td> <td style="text-align:right;"> -0.1874097 </td> <td style="text-align:right;"> -0.0810376 </td> <td style="text-align:right;"> 0.0271362 </td> <td style="text-align:right;"> -0.1037239 </td> <td style="text-align:right;"> -0.1714836 </td> <td style="text-align:right;"> -0.0359643 </td> <td style="text-align:right;"> 0.0345719 </td> <td style="text-align:right;"> 175 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> -0.0369177 </td> <td style="text-align:right;"> -0.1277076 </td> <td style="text-align:right;"> 0.0538721 </td> <td style="text-align:right;"> 0.0463222 </td> <td style="text-align:right;"> -0.0294978 </td> <td style="text-align:right;"> -0.1006346 </td> <td style="text-align:right;"> 0.0416390 </td> <td style="text-align:right;"> 0.0362950 </td> <td style="text-align:right;"> 0.0077038 </td> <td style="text-align:right;"> -0.0137850 </td> <td style="text-align:right;"> 0.0291927 </td> <td style="text-align:right;"> 0.0109639 </td> <td style="text-align:right;"> 176 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> -0.0369177 </td> <td style="text-align:right;"> -0.1277076 </td> <td style="text-align:right;"> 0.0538721 </td> <td style="text-align:right;"> 0.0463222 </td> <td style="text-align:right;"> -0.0294978 </td> <td style="text-align:right;"> -0.1006346 </td> <td style="text-align:right;"> 0.0416390 </td> <td style="text-align:right;"> 0.0362950 </td> <td style="text-align:right;"> 0.0077038 </td> <td style="text-align:right;"> -0.0137850 </td> <td style="text-align:right;"> 0.0291927 </td> <td style="text-align:right;"> 0.0109639 </td> <td style="text-align:right;"> 176 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> -0.0536346 </td> <td style="text-align:right;"> -0.1266045 </td> <td style="text-align:right;"> 0.0193353 </td> <td style="text-align:right;"> 0.0372302 </td> <td style="text-align:right;"> -0.0572459 </td> <td style="text-align:right;"> -0.1071515 </td> <td style="text-align:right;"> -0.0073404 </td> <td style="text-align:right;"> 0.0254625 </td> <td style="text-align:right;"> 0.0026007 </td> <td style="text-align:right;"> -0.0558538 </td> <td style="text-align:right;"> 0.0610552 </td> <td style="text-align:right;"> 0.0298243 </td> <td style="text-align:right;"> 177 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> -0.0536346 </td> <td style="text-align:right;"> -0.1266045 </td> <td style="text-align:right;"> 0.0193353 </td> <td style="text-align:right;"> 0.0372302 </td> <td style="text-align:right;"> -0.0572459 </td> <td style="text-align:right;"> -0.1071515 </td> <td style="text-align:right;"> -0.0073404 </td> <td style="text-align:right;"> 0.0254625 </td> <td style="text-align:right;"> 0.0026007 </td> <td style="text-align:right;"> -0.0558538 </td> <td style="text-align:right;"> 0.0610552 </td> <td style="text-align:right;"> 0.0298243 </td> <td style="text-align:right;"> 177 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> -0.0485897 </td> <td style="text-align:right;"> -0.0839101 </td> <td style="text-align:right;"> -0.0132693 </td> <td style="text-align:right;"> 0.0180209 </td> <td style="text-align:right;"> -0.0826120 </td> <td style="text-align:right;"> -0.1576473 </td> <td style="text-align:right;"> -0.0075767 </td> <td style="text-align:right;"> 0.0382840 </td> <td style="text-align:right;"> -0.0420616 </td> <td style="text-align:right;"> -0.0813582 </td> <td style="text-align:right;"> -0.0027650 </td> <td style="text-align:right;"> 0.0200497 </td> <td style="text-align:right;"> 178 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> phosphorus </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> -0.0485897 </td> <td style="text-align:right;"> -0.0839101 </td> <td style="text-align:right;"> -0.0132693 </td> <td style="text-align:right;"> 0.0180209 </td> <td style="text-align:right;"> -0.0826120 </td> <td style="text-align:right;"> -0.1576473 </td> <td style="text-align:right;"> -0.0075767 </td> <td style="text-align:right;"> 0.0382840 </td> <td style="text-align:right;"> -0.0420616 </td> <td style="text-align:right;"> -0.0813582 </td> <td style="text-align:right;"> -0.0027650 </td> <td style="text-align:right;"> 0.0200497 </td> <td style="text-align:right;"> 178 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> phosphorus </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 0.0737198 </td> <td style="text-align:right;"> 0.0205862 </td> <td style="text-align:right;"> 0.1268534 </td> <td style="text-align:right;"> 0.0271095 </td> <td style="text-align:right;"> 0.2415135 </td> <td style="text-align:right;"> 0.1865330 </td> <td style="text-align:right;"> 0.2964940 </td> <td style="text-align:right;"> 0.0280518 </td> <td style="text-align:right;"> 0.1642192 </td> <td style="text-align:right;"> 0.1369820 </td> <td style="text-align:right;"> 0.1914563 </td> <td style="text-align:right;"> 0.0138968 </td> <td style="text-align:right;"> 179 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> platelet count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 0.0737198 </td> <td style="text-align:right;"> 0.0205862 </td> <td style="text-align:right;"> 0.1268534 </td> <td style="text-align:right;"> 0.0271095 </td> <td style="text-align:right;"> 0.2415135 </td> <td style="text-align:right;"> 0.1865330 </td> <td style="text-align:right;"> 0.2964940 </td> <td style="text-align:right;"> 0.0280518 </td> <td style="text-align:right;"> 0.1642192 </td> <td style="text-align:right;"> 0.1369820 </td> <td style="text-align:right;"> 0.1914563 </td> <td style="text-align:right;"> 0.0138968 </td> <td style="text-align:right;"> 179 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> platelet count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 0.2906905 </td> <td style="text-align:right;"> 0.1716202 </td> <td style="text-align:right;"> 0.4097607 </td> <td style="text-align:right;"> 0.0607512 </td> <td style="text-align:right;"> -0.2926013 </td> <td style="text-align:right;"> -0.5272121 </td> <td style="text-align:right;"> -0.0579905 </td> <td style="text-align:right;"> 0.1197016 </td> <td style="text-align:right;"> -0.6004767 </td> <td style="text-align:right;"> -0.9244113 </td> <td style="text-align:right;"> -0.2765420 </td> <td style="text-align:right;"> 0.1652758 </td> <td style="text-align:right;"> 180 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 0.2906905 </td> <td style="text-align:right;"> 0.1716202 </td> <td style="text-align:right;"> 0.4097607 </td> <td style="text-align:right;"> 0.0607512 </td> <td style="text-align:right;"> -0.2926013 </td> <td style="text-align:right;"> -0.5272121 </td> <td style="text-align:right;"> -0.0579905 </td> <td style="text-align:right;"> 0.1197016 </td> <td style="text-align:right;"> -0.6004767 </td> <td style="text-align:right;"> -0.9244113 </td> <td style="text-align:right;"> -0.2765420 </td> <td style="text-align:right;"> 0.1652758 </td> <td style="text-align:right;"> 180 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> -0.0705522 </td> <td style="text-align:right;"> -0.2214989 </td> <td style="text-align:right;"> 0.0803945 </td> <td style="text-align:right;"> 0.0770150 </td> <td style="text-align:right;"> -0.0074675 </td> <td style="text-align:right;"> -0.1729366 </td> <td style="text-align:right;"> 0.1580015 </td> <td style="text-align:right;"> 0.0844245 </td> <td style="text-align:right;"> 0.0704162 </td> <td style="text-align:right;"> 0.0476647 </td> <td style="text-align:right;"> 0.0931676 </td> <td style="text-align:right;"> 0.0116081 </td> <td style="text-align:right;"> 181 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> potassium </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> -0.0705522 </td> <td style="text-align:right;"> -0.2214989 </td> <td style="text-align:right;"> 0.0803945 </td> <td style="text-align:right;"> 0.0770150 </td> <td style="text-align:right;"> -0.0074675 </td> <td style="text-align:right;"> -0.1729366 </td> <td style="text-align:right;"> 0.1580015 </td> <td style="text-align:right;"> 0.0844245 </td> <td style="text-align:right;"> 0.0704162 </td> <td style="text-align:right;"> 0.0476647 </td> <td style="text-align:right;"> 0.0931676 </td> <td style="text-align:right;"> 0.0116081 </td> <td style="text-align:right;"> 181 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> potassium </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> -0.0650960 </td> <td style="text-align:right;"> -0.1538776 </td> <td style="text-align:right;"> 0.0236857 </td> <td style="text-align:right;"> 0.0452976 </td> <td style="text-align:right;"> -0.0648322 </td> <td style="text-align:right;"> -0.1270688 </td> <td style="text-align:right;"> -0.0025955 </td> <td style="text-align:right;"> 0.0317540 </td> <td style="text-align:right;"> 0.0015656 </td> <td style="text-align:right;"> -0.0259865 </td> <td style="text-align:right;"> 0.0291178 </td> <td style="text-align:right;"> 0.0140575 </td> <td style="text-align:right;"> 182 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> pq </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> -0.0650960 </td> <td style="text-align:right;"> -0.1538776 </td> <td style="text-align:right;"> 0.0236857 </td> <td style="text-align:right;"> 0.0452976 </td> <td style="text-align:right;"> -0.0648322 </td> <td style="text-align:right;"> -0.1270688 </td> <td style="text-align:right;"> -0.0025955 </td> <td style="text-align:right;"> 0.0317540 </td> <td style="text-align:right;"> 0.0015656 </td> <td style="text-align:right;"> -0.0259865 </td> <td style="text-align:right;"> 0.0291178 </td> <td style="text-align:right;"> 0.0140575 </td> <td style="text-align:right;"> 182 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> pq </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> -0.0564860 </td> <td style="text-align:right;"> -0.1048371 </td> <td style="text-align:right;"> -0.0081349 </td> <td style="text-align:right;"> 0.0246694 </td> <td style="text-align:right;"> -0.0754718 </td> <td style="text-align:right;"> -0.1235224 </td> <td style="text-align:right;"> -0.0274213 </td> <td style="text-align:right;"> 0.0245160 </td> <td style="text-align:right;"> -0.0183785 </td> <td style="text-align:right;"> -0.0319887 </td> <td style="text-align:right;"> -0.0047684 </td> <td style="text-align:right;"> 0.0069441 </td> <td style="text-align:right;"> 183 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> pr </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> -0.0564860 </td> <td style="text-align:right;"> -0.1048371 </td> <td style="text-align:right;"> -0.0081349 </td> <td style="text-align:right;"> 0.0246694 </td> <td style="text-align:right;"> -0.0754718 </td> <td style="text-align:right;"> -0.1235224 </td> <td style="text-align:right;"> -0.0274213 </td> <td style="text-align:right;"> 0.0245160 </td> <td style="text-align:right;"> -0.0183785 </td> <td style="text-align:right;"> -0.0319887 </td> <td style="text-align:right;"> -0.0047684 </td> <td style="text-align:right;"> 0.0069441 </td> <td style="text-align:right;"> 183 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> pr </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 0.0725454 </td> <td style="text-align:right;"> 0.0354722 </td> <td style="text-align:right;"> 0.1096185 </td> <td style="text-align:right;"> 0.0189152 </td> <td style="text-align:right;"> 0.0681074 </td> <td style="text-align:right;"> 0.0300869 </td> <td style="text-align:right;"> 0.1061278 </td> <td style="text-align:right;"> 0.0193986 </td> <td style="text-align:right;"> -0.0054233 </td> <td style="text-align:right;"> -0.0154885 </td> <td style="text-align:right;"> 0.0046418 </td> <td style="text-align:right;"> 0.0051354 </td> <td style="text-align:right;"> 184 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> qrs </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 0.0725454 </td> <td style="text-align:right;"> 0.0354722 </td> <td style="text-align:right;"> 0.1096185 </td> <td style="text-align:right;"> 0.0189152 </td> <td style="text-align:right;"> 0.0681074 </td> <td style="text-align:right;"> 0.0300869 </td> <td style="text-align:right;"> 0.1061278 </td> <td style="text-align:right;"> 0.0193986 </td> <td style="text-align:right;"> -0.0054233 </td> <td style="text-align:right;"> -0.0154885 </td> <td style="text-align:right;"> 0.0046418 </td> <td style="text-align:right;"> 0.0051354 </td> <td style="text-align:right;"> 184 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> qrs </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 0.0328106 </td> <td style="text-align:right;"> -0.0101032 </td> <td style="text-align:right;"> 0.0757244 </td> <td style="text-align:right;"> 0.0218952 </td> <td style="text-align:right;"> 0.0310473 </td> <td style="text-align:right;"> -0.0207365 </td> <td style="text-align:right;"> 0.0828310 </td> <td style="text-align:right;"> 0.0264208 </td> <td style="text-align:right;"> -0.0005046 </td> <td style="text-align:right;"> -0.0085696 </td> <td style="text-align:right;"> 0.0075604 </td> <td style="text-align:right;"> 0.0041149 </td> <td style="text-align:right;"> 185 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> qtc </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 0.0328106 </td> <td style="text-align:right;"> -0.0101032 </td> <td style="text-align:right;"> 0.0757244 </td> <td style="text-align:right;"> 0.0218952 </td> <td style="text-align:right;"> 0.0310473 </td> <td style="text-align:right;"> -0.0207365 </td> <td style="text-align:right;"> 0.0828310 </td> <td style="text-align:right;"> 0.0264208 </td> <td style="text-align:right;"> -0.0005046 </td> <td style="text-align:right;"> -0.0085696 </td> <td style="text-align:right;"> 0.0075604 </td> <td style="text-align:right;"> 0.0041149 </td> <td style="text-align:right;"> 185 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> qtc </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 0.0031258 </td> <td style="text-align:right;"> -0.0523919 </td> <td style="text-align:right;"> 0.0586435 </td> <td style="text-align:right;"> 0.0283259 </td> <td style="text-align:right;"> -0.0046501 </td> <td style="text-align:right;"> -0.1060530 </td> <td style="text-align:right;"> 0.0967528 </td> <td style="text-align:right;"> 0.0517371 </td> <td style="text-align:right;"> -0.0077373 </td> <td style="text-align:right;"> -0.0510162 </td> <td style="text-align:right;"> 0.0355416 </td> <td style="text-align:right;"> 0.0220815 </td> <td style="text-align:right;"> 186 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 0.0031258 </td> <td style="text-align:right;"> -0.0523919 </td> <td style="text-align:right;"> 0.0586435 </td> <td style="text-align:right;"> 0.0283259 </td> <td style="text-align:right;"> -0.0046501 </td> <td style="text-align:right;"> -0.1060530 </td> <td style="text-align:right;"> 0.0967528 </td> <td style="text-align:right;"> 0.0517371 </td> <td style="text-align:right;"> -0.0077373 </td> <td style="text-align:right;"> -0.0510162 </td> <td style="text-align:right;"> 0.0355416 </td> <td style="text-align:right;"> 0.0220815 </td> <td style="text-align:right;"> 186 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 0.0773455 </td> <td style="text-align:right;"> 0.0071933 </td> <td style="text-align:right;"> 0.1474977 </td> <td style="text-align:right;"> 0.0357926 </td> <td style="text-align:right;"> 0.0997278 </td> <td style="text-align:right;"> 0.0316996 </td> <td style="text-align:right;"> 0.1677560 </td> <td style="text-align:right;"> 0.0347089 </td> <td style="text-align:right;"> 0.0228493 </td> <td style="text-align:right;"> 0.0088583 </td> <td style="text-align:right;"> 0.0368404 </td> <td style="text-align:right;"> 0.0071384 </td> <td style="text-align:right;"> 187 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 0.0773455 </td> <td style="text-align:right;"> 0.0071933 </td> <td style="text-align:right;"> 0.1474977 </td> <td style="text-align:right;"> 0.0357926 </td> <td style="text-align:right;"> 0.0997278 </td> <td style="text-align:right;"> 0.0316996 </td> <td style="text-align:right;"> 0.1677560 </td> <td style="text-align:right;"> 0.0347089 </td> <td style="text-align:right;"> 0.0228493 </td> <td style="text-align:right;"> 0.0088583 </td> <td style="text-align:right;"> 0.0368404 </td> <td style="text-align:right;"> 0.0071384 </td> <td style="text-align:right;"> 187 </td> <td style="text-align:right;"> 17 </td> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 0.1248464 </td> <td style="text-align:right;"> -0.0035148 </td> <td style="text-align:right;"> 0.2532076 </td> <td style="text-align:right;"> 0.0654916 </td> <td style="text-align:right;"> 0.1353460 </td> <td style="text-align:right;"> -0.0035862 </td> <td style="text-align:right;"> 0.2742782 </td> <td style="text-align:right;"> 0.0708851 </td> <td style="text-align:right;"> 0.0104789 </td> <td style="text-align:right;"> -0.0032056 </td> <td style="text-align:right;"> 0.0241635 </td> <td style="text-align:right;"> 0.0069821 </td> <td style="text-align:right;"> 188 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 0.1248464 </td> <td style="text-align:right;"> -0.0035148 </td> <td style="text-align:right;"> 0.2532076 </td> <td style="text-align:right;"> 0.0654916 </td> <td style="text-align:right;"> 0.1353460 </td> <td style="text-align:right;"> -0.0035862 </td> <td style="text-align:right;"> 0.2742782 </td> <td style="text-align:right;"> 0.0708851 </td> <td style="text-align:right;"> 0.0104789 </td> <td style="text-align:right;"> -0.0032056 </td> <td style="text-align:right;"> 0.0241635 </td> <td style="text-align:right;"> 0.0069821 </td> <td style="text-align:right;"> 188 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> -0.1384843 </td> <td style="text-align:right;"> -0.2178736 </td> <td style="text-align:right;"> -0.0590950 </td> <td style="text-align:right;"> 0.0405055 </td> <td style="text-align:right;"> -0.0703570 </td> <td style="text-align:right;"> -0.1795875 </td> <td style="text-align:right;"> 0.0388735 </td> <td style="text-align:right;"> 0.0557309 </td> <td style="text-align:right;"> 0.0611034 </td> <td style="text-align:right;"> 0.0227141 </td> <td style="text-align:right;"> 0.0994926 </td> <td style="text-align:right;"> 0.0195867 </td> <td style="text-align:right;"> 189 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> respiration rate </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> -0.1384843 </td> <td style="text-align:right;"> -0.2178736 </td> <td style="text-align:right;"> -0.0590950 </td> <td style="text-align:right;"> 0.0405055 </td> <td style="text-align:right;"> -0.0703570 </td> <td style="text-align:right;"> -0.1795875 </td> <td style="text-align:right;"> 0.0388735 </td> <td style="text-align:right;"> 0.0557309 </td> <td style="text-align:right;"> 0.0611034 </td> <td style="text-align:right;"> 0.0227141 </td> <td style="text-align:right;"> 0.0994926 </td> <td style="text-align:right;"> 0.0195867 </td> <td style="text-align:right;"> 189 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> respiration rate </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> -0.0116565 </td> <td style="text-align:right;"> -0.0896490 </td> <td style="text-align:right;"> 0.0663361 </td> <td style="text-align:right;"> 0.0397928 </td> <td style="text-align:right;"> -0.0106530 </td> <td style="text-align:right;"> -0.0878483 </td> <td style="text-align:right;"> 0.0665424 </td> <td style="text-align:right;"> 0.0393861 </td> <td style="text-align:right;"> 0.0017027 </td> <td style="text-align:right;"> -0.0057348 </td> <td style="text-align:right;"> 0.0091402 </td> <td style="text-align:right;"> 0.0037947 </td> <td style="text-align:right;"> 190 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> -0.0116565 </td> <td style="text-align:right;"> -0.0896490 </td> <td style="text-align:right;"> 0.0663361 </td> <td style="text-align:right;"> 0.0397928 </td> <td style="text-align:right;"> -0.0106530 </td> <td style="text-align:right;"> -0.0878483 </td> <td style="text-align:right;"> 0.0665424 </td> <td style="text-align:right;"> 0.0393861 </td> <td style="text-align:right;"> 0.0017027 </td> <td style="text-align:right;"> -0.0057348 </td> <td style="text-align:right;"> 0.0091402 </td> <td style="text-align:right;"> 0.0037947 </td> <td style="text-align:right;"> 190 </td> <td style="text-align:right;"> 9 </td> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> -0.4491432 </td> <td style="text-align:right;"> -1.3293546 </td> <td style="text-align:right;"> 0.4310682 </td> <td style="text-align:right;"> 0.4490957 </td> <td style="text-align:right;"> -0.4157377 </td> <td style="text-align:right;"> -1.2918620 </td> <td style="text-align:right;"> 0.4603867 </td> <td style="text-align:right;"> 0.4470104 </td> <td style="text-align:right;"> 0.0316098 </td> <td style="text-align:right;"> 0.0264512 </td> <td style="text-align:right;"> 0.0367685 </td> <td style="text-align:right;"> 0.0026320 </td> <td style="text-align:right;"> 201 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> -0.4491432 </td> <td style="text-align:right;"> -1.3293546 </td> <td style="text-align:right;"> 0.4310682 </td> <td style="text-align:right;"> 0.4490957 </td> <td style="text-align:right;"> -0.4157377 </td> <td style="text-align:right;"> -1.2918620 </td> <td style="text-align:right;"> 0.4603867 </td> <td style="text-align:right;"> 0.4470104 </td> <td style="text-align:right;"> 0.0316098 </td> <td style="text-align:right;"> 0.0264512 </td> <td style="text-align:right;"> 0.0367685 </td> <td style="text-align:right;"> 0.0026320 </td> <td style="text-align:right;"> 201 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> -0.0355898 </td> <td style="text-align:right;"> -0.2280522 </td> <td style="text-align:right;"> 0.1568726 </td> <td style="text-align:right;"> 0.0981969 </td> <td style="text-align:right;"> -0.0306550 </td> <td style="text-align:right;"> -0.1963692 </td> <td style="text-align:right;"> 0.1350592 </td> <td style="text-align:right;"> 0.0845496 </td> <td style="text-align:right;"> -0.0013855 </td> <td style="text-align:right;"> -0.0237830 </td> <td style="text-align:right;"> 0.0210121 </td> <td style="text-align:right;"> 0.0114275 </td> <td style="text-align:right;"> 202 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> -0.0355898 </td> <td style="text-align:right;"> -0.2280522 </td> <td style="text-align:right;"> 0.1568726 </td> <td style="text-align:right;"> 0.0981969 </td> <td style="text-align:right;"> -0.0306550 </td> <td style="text-align:right;"> -0.1963692 </td> <td style="text-align:right;"> 0.1350592 </td> <td style="text-align:right;"> 0.0845496 </td> <td style="text-align:right;"> -0.0013855 </td> <td style="text-align:right;"> -0.0237830 </td> <td style="text-align:right;"> 0.0210121 </td> <td style="text-align:right;"> 0.0114275 </td> <td style="text-align:right;"> 202 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> -0.2545083 </td> <td style="text-align:right;"> -0.7633116 </td> <td style="text-align:right;"> 0.2542949 </td> <td style="text-align:right;"> 0.2595983 </td> <td style="text-align:right;"> -0.2785114 </td> <td style="text-align:right;"> -0.8373133 </td> <td style="text-align:right;"> 0.2802906 </td> <td style="text-align:right;"> 0.2851083 </td> <td style="text-align:right;"> -0.0175090 </td> <td style="text-align:right;"> -0.0664158 </td> <td style="text-align:right;"> 0.0313978 </td> <td style="text-align:right;"> 0.0249529 </td> <td style="text-align:right;"> 203 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> -0.2545083 </td> <td style="text-align:right;"> -0.7633116 </td> <td style="text-align:right;"> 0.2542949 </td> <td style="text-align:right;"> 0.2595983 </td> <td style="text-align:right;"> -0.2785114 </td> <td style="text-align:right;"> -0.8373133 </td> <td style="text-align:right;"> 0.2802906 </td> <td style="text-align:right;"> 0.2851083 </td> <td style="text-align:right;"> -0.0175090 </td> <td style="text-align:right;"> -0.0664158 </td> <td style="text-align:right;"> 0.0313978 </td> <td style="text-align:right;"> 0.0249529 </td> <td style="text-align:right;"> 203 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 0.0061253 </td> <td style="text-align:right;"> -0.0781241 </td> <td style="text-align:right;"> 0.0903746 </td> <td style="text-align:right;"> 0.0429851 </td> <td style="text-align:right;"> 0.0109098 </td> <td style="text-align:right;"> -0.0731427 </td> <td style="text-align:right;"> 0.0949622 </td> <td style="text-align:right;"> 0.0428847 </td> <td style="text-align:right;"> 0.0055513 </td> <td style="text-align:right;"> 0.0000519 </td> <td style="text-align:right;"> 0.0110508 </td> <td style="text-align:right;"> 0.0028059 </td> <td style="text-align:right;"> 204 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 0.0061253 </td> <td style="text-align:right;"> -0.0781241 </td> <td style="text-align:right;"> 0.0903746 </td> <td style="text-align:right;"> 0.0429851 </td> <td style="text-align:right;"> 0.0109098 </td> <td style="text-align:right;"> -0.0731427 </td> <td style="text-align:right;"> 0.0949622 </td> <td style="text-align:right;"> 0.0428847 </td> <td style="text-align:right;"> 0.0055513 </td> <td style="text-align:right;"> 0.0000519 </td> <td style="text-align:right;"> 0.0110508 </td> <td style="text-align:right;"> 0.0028059 </td> <td style="text-align:right;"> 204 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> -0.0775673 </td> <td style="text-align:right;"> -0.2905688 </td> <td style="text-align:right;"> 0.1354341 </td> <td style="text-align:right;"> 0.1086762 </td> <td style="text-align:right;"> -0.0764571 </td> <td style="text-align:right;"> -0.2893152 </td> <td style="text-align:right;"> 0.1364010 </td> <td style="text-align:right;"> 0.1086031 </td> <td style="text-align:right;"> 0.0071990 </td> <td style="text-align:right;"> -0.0178434 </td> <td style="text-align:right;"> 0.0322413 </td> <td style="text-align:right;"> 0.0127769 </td> <td style="text-align:right;"> 205 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> -0.0775673 </td> <td style="text-align:right;"> -0.2905688 </td> <td style="text-align:right;"> 0.1354341 </td> <td style="text-align:right;"> 0.1086762 </td> <td style="text-align:right;"> -0.0764571 </td> <td style="text-align:right;"> -0.2893152 </td> <td style="text-align:right;"> 0.1364010 </td> <td style="text-align:right;"> 0.1086031 </td> <td style="text-align:right;"> 0.0071990 </td> <td style="text-align:right;"> -0.0178434 </td> <td style="text-align:right;"> 0.0322413 </td> <td style="text-align:right;"> 0.0127769 </td> <td style="text-align:right;"> 205 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> -0.1987993 </td> <td style="text-align:right;"> -0.6457320 </td> <td style="text-align:right;"> 0.2481333 </td> <td style="text-align:right;"> 0.2280310 </td> <td style="text-align:right;"> -0.1925482 </td> <td style="text-align:right;"> -0.6285715 </td> <td style="text-align:right;"> 0.2434750 </td> <td style="text-align:right;"> 0.2224649 </td> <td style="text-align:right;"> 0.0052882 </td> <td style="text-align:right;"> -0.0045957 </td> <td style="text-align:right;"> 0.0151720 </td> <td style="text-align:right;"> 0.0050429 </td> <td style="text-align:right;"> 206 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> -0.1987993 </td> <td style="text-align:right;"> -0.6457320 </td> <td style="text-align:right;"> 0.2481333 </td> <td style="text-align:right;"> 0.2280310 </td> <td style="text-align:right;"> -0.1925482 </td> <td style="text-align:right;"> -0.6285715 </td> <td style="text-align:right;"> 0.2434750 </td> <td style="text-align:right;"> 0.2224649 </td> <td style="text-align:right;"> 0.0052882 </td> <td style="text-align:right;"> -0.0045957 </td> <td style="text-align:right;"> 0.0151720 </td> <td style="text-align:right;"> 0.0050429 </td> <td style="text-align:right;"> 206 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 0.1800273 </td> <td style="text-align:right;"> -0.0882317 </td> <td style="text-align:right;"> 0.4482864 </td> <td style="text-align:right;"> 0.1368694 </td> <td style="text-align:right;"> -0.0161048 </td> <td style="text-align:right;"> -0.4112809 </td> <td style="text-align:right;"> 0.3790712 </td> <td style="text-align:right;"> 0.2016241 </td> <td style="text-align:right;"> -0.1178703 </td> <td style="text-align:right;"> -0.2449843 </td> <td style="text-align:right;"> 0.0092436 </td> <td style="text-align:right;"> 0.0648552 </td> <td style="text-align:right;"> 207 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> rmssd </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 0.1800273 </td> <td style="text-align:right;"> -0.0882317 </td> <td style="text-align:right;"> 0.4482864 </td> <td style="text-align:right;"> 0.1368694 </td> <td style="text-align:right;"> -0.0161048 </td> <td style="text-align:right;"> -0.4112809 </td> <td style="text-align:right;"> 0.3790712 </td> <td style="text-align:right;"> 0.2016241 </td> <td style="text-align:right;"> -0.1178703 </td> <td style="text-align:right;"> -0.2449843 </td> <td style="text-align:right;"> 0.0092436 </td> <td style="text-align:right;"> 0.0648552 </td> <td style="text-align:right;"> 207 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> rmssd </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> -0.0765771 </td> <td style="text-align:right;"> -0.3398075 </td> <td style="text-align:right;"> 0.1866533 </td> <td style="text-align:right;"> 0.1343037 </td> <td style="text-align:right;"> -0.0747691 </td> <td style="text-align:right;"> -0.3351316 </td> <td style="text-align:right;"> 0.1855933 </td> <td style="text-align:right;"> 0.1328404 </td> <td style="text-align:right;"> -0.0746396 </td> <td style="text-align:right;"> -0.2072980 </td> <td style="text-align:right;"> 0.0580188 </td> <td style="text-align:right;"> 0.0676841 </td> <td style="text-align:right;"> 208 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> -0.0765771 </td> <td style="text-align:right;"> -0.3398075 </td> <td style="text-align:right;"> 0.1866533 </td> <td style="text-align:right;"> 0.1343037 </td> <td style="text-align:right;"> -0.0747691 </td> <td style="text-align:right;"> -0.3351316 </td> <td style="text-align:right;"> 0.1855933 </td> <td style="text-align:right;"> 0.1328404 </td> <td style="text-align:right;"> -0.0746396 </td> <td style="text-align:right;"> -0.2072980 </td> <td style="text-align:right;"> 0.0580188 </td> <td style="text-align:right;"> 0.0676841 </td> <td style="text-align:right;"> 208 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> -0.0761505 </td> <td style="text-align:right;"> -0.1876687 </td> <td style="text-align:right;"> 0.0353678 </td> <td style="text-align:right;"> 0.0568981 </td> <td style="text-align:right;"> -0.0896869 </td> <td style="text-align:right;"> -0.2063458 </td> <td style="text-align:right;"> 0.0269721 </td> <td style="text-align:right;"> 0.0595210 </td> <td style="text-align:right;"> -0.0125023 </td> <td style="text-align:right;"> -0.0214082 </td> <td style="text-align:right;"> -0.0035963 </td> <td style="text-align:right;"> 0.0045440 </td> <td style="text-align:right;"> 209 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> rr </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> -0.0761505 </td> <td style="text-align:right;"> -0.1876687 </td> <td style="text-align:right;"> 0.0353678 </td> <td style="text-align:right;"> 0.0568981 </td> <td style="text-align:right;"> -0.0896869 </td> <td style="text-align:right;"> -0.2063458 </td> <td style="text-align:right;"> 0.0269721 </td> <td style="text-align:right;"> 0.0595210 </td> <td style="text-align:right;"> -0.0125023 </td> <td style="text-align:right;"> -0.0214082 </td> <td style="text-align:right;"> -0.0035963 </td> <td style="text-align:right;"> 0.0045440 </td> <td style="text-align:right;"> 209 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> rr </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 0.0262100 </td> <td style="text-align:right;"> -0.1171674 </td> <td style="text-align:right;"> 0.1695873 </td> <td style="text-align:right;"> 0.0731531 </td> <td style="text-align:right;"> 0.0338228 </td> <td style="text-align:right;"> -0.1337162 </td> <td style="text-align:right;"> 0.2013618 </td> <td style="text-align:right;"> 0.0854806 </td> <td style="text-align:right;"> 0.0099680 </td> <td style="text-align:right;"> 0.0065815 </td> <td style="text-align:right;"> 0.0133545 </td> <td style="text-align:right;"> 0.0017278 </td> <td style="text-align:right;"> 210 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> sodium </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 0.0262100 </td> <td style="text-align:right;"> -0.1171674 </td> <td style="text-align:right;"> 0.1695873 </td> <td style="text-align:right;"> 0.0731531 </td> <td style="text-align:right;"> 0.0338228 </td> <td style="text-align:right;"> -0.1337162 </td> <td style="text-align:right;"> 0.2013618 </td> <td style="text-align:right;"> 0.0854806 </td> <td style="text-align:right;"> 0.0099680 </td> <td style="text-align:right;"> 0.0065815 </td> <td style="text-align:right;"> 0.0133545 </td> <td style="text-align:right;"> 0.0017278 </td> <td style="text-align:right;"> 210 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> sodium </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 0.1874259 </td> <td style="text-align:right;"> -0.0500875 </td> <td style="text-align:right;"> 0.4249393 </td> <td style="text-align:right;"> 0.1211825 </td> <td style="text-align:right;"> 0.1133706 </td> <td style="text-align:right;"> -0.1604807 </td> <td style="text-align:right;"> 0.3872220 </td> <td style="text-align:right;"> 0.1397227 </td> <td style="text-align:right;"> -0.1542349 </td> <td style="text-align:right;"> -0.2104415 </td> <td style="text-align:right;"> -0.0980283 </td> <td style="text-align:right;"> 0.0286774 </td> <td style="text-align:right;"> 211 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> spleen weight </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 0.1874259 </td> <td style="text-align:right;"> -0.0500875 </td> <td style="text-align:right;"> 0.4249393 </td> <td style="text-align:right;"> 0.1211825 </td> <td style="text-align:right;"> 0.1133706 </td> <td style="text-align:right;"> -0.1604807 </td> <td style="text-align:right;"> 0.3872220 </td> <td style="text-align:right;"> 0.1397227 </td> <td style="text-align:right;"> -0.1542349 </td> <td style="text-align:right;"> -0.2104415 </td> <td style="text-align:right;"> -0.0980283 </td> <td style="text-align:right;"> 0.0286774 </td> <td style="text-align:right;"> 211 </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> spleen weight </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 0.0032888 </td> <td style="text-align:right;"> -0.0544512 </td> <td style="text-align:right;"> 0.0610288 </td> <td style="text-align:right;"> 0.0294597 </td> <td style="text-align:right;"> -0.0054976 </td> <td style="text-align:right;"> -0.0811810 </td> <td style="text-align:right;"> 0.0701858 </td> <td style="text-align:right;"> 0.0386147 </td> <td style="text-align:right;"> -0.0034902 </td> <td style="text-align:right;"> -0.0175917 </td> <td style="text-align:right;"> 0.0106113 </td> <td style="text-align:right;"> 0.0071948 </td> <td style="text-align:right;"> 212 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> st </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 0.0032888 </td> <td style="text-align:right;"> -0.0544512 </td> <td style="text-align:right;"> 0.0610288 </td> <td style="text-align:right;"> 0.0294597 </td> <td style="text-align:right;"> -0.0054976 </td> <td style="text-align:right;"> -0.0811810 </td> <td style="text-align:right;"> 0.0701858 </td> <td style="text-align:right;"> 0.0386147 </td> <td style="text-align:right;"> -0.0034902 </td> <td style="text-align:right;"> -0.0175917 </td> <td style="text-align:right;"> 0.0106113 </td> <td style="text-align:right;"> 0.0071948 </td> <td style="text-align:right;"> 212 </td> <td style="text-align:right;"> 11 </td> <td style="text-align:left;"> st </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 0.0594276 </td> <td style="text-align:right;"> -0.0782445 </td> <td style="text-align:right;"> 0.1970997 </td> <td style="text-align:right;"> 0.0702422 </td> <td style="text-align:right;"> 0.1574330 </td> <td style="text-align:right;"> 0.0091891 </td> <td style="text-align:right;"> 0.3056769 </td> <td style="text-align:right;"> 0.0756360 </td> <td style="text-align:right;"> 0.0937375 </td> <td style="text-align:right;"> 0.0775587 </td> <td style="text-align:right;"> 0.1099162 </td> <td style="text-align:right;"> 0.0082546 </td> <td style="text-align:right;"> 213 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> stroke volume </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 0.0594276 </td> <td style="text-align:right;"> -0.0782445 </td> <td style="text-align:right;"> 0.1970997 </td> <td style="text-align:right;"> 0.0702422 </td> <td style="text-align:right;"> 0.1574330 </td> <td style="text-align:right;"> 0.0091891 </td> <td style="text-align:right;"> 0.3056769 </td> <td style="text-align:right;"> 0.0756360 </td> <td style="text-align:right;"> 0.0937375 </td> <td style="text-align:right;"> 0.0775587 </td> <td style="text-align:right;"> 0.1099162 </td> <td style="text-align:right;"> 0.0082546 </td> <td style="text-align:right;"> 213 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> stroke volume </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> -0.1475403 </td> <td style="text-align:right;"> -0.4396127 </td> <td style="text-align:right;"> 0.1445320 </td> <td style="text-align:right;"> 0.1490192 </td> <td style="text-align:right;"> -0.1374401 </td> <td style="text-align:right;"> -0.4261352 </td> <td style="text-align:right;"> 0.1512551 </td> <td style="text-align:right;"> 0.1472961 </td> <td style="text-align:right;"> 0.0095199 </td> <td style="text-align:right;"> 0.0059199 </td> <td style="text-align:right;"> 0.0131200 </td> <td style="text-align:right;"> 0.0018368 </td> <td style="text-align:right;"> 217 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> tibia length </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> -0.1475403 </td> <td style="text-align:right;"> -0.4396127 </td> <td style="text-align:right;"> 0.1445320 </td> <td style="text-align:right;"> 0.1490192 </td> <td style="text-align:right;"> -0.1374401 </td> <td style="text-align:right;"> -0.4261352 </td> <td style="text-align:right;"> 0.1512551 </td> <td style="text-align:right;"> 0.1472961 </td> <td style="text-align:right;"> 0.0095199 </td> <td style="text-align:right;"> 0.0059199 </td> <td style="text-align:right;"> 0.0131200 </td> <td style="text-align:right;"> 0.0018368 </td> <td style="text-align:right;"> 217 </td> <td style="text-align:right;"> 12 </td> <td style="text-align:left;"> tibia length </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 0.0605449 </td> <td style="text-align:right;"> -0.0097669 </td> <td style="text-align:right;"> 0.1308567 </td> <td style="text-align:right;"> 0.0358740 </td> <td style="text-align:right;"> 0.0022671 </td> <td style="text-align:right;"> -0.0859910 </td> <td style="text-align:right;"> 0.0905252 </td> <td style="text-align:right;"> 0.0450305 </td> <td style="text-align:right;"> -0.0550333 </td> <td style="text-align:right;"> -0.0979518 </td> <td style="text-align:right;"> -0.0121148 </td> <td style="text-align:right;"> 0.0218976 </td> <td style="text-align:right;"> 218 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 0.0605449 </td> <td style="text-align:right;"> -0.0097669 </td> <td style="text-align:right;"> 0.1308567 </td> <td style="text-align:right;"> 0.0358740 </td> <td style="text-align:right;"> 0.0022671 </td> <td style="text-align:right;"> -0.0859910 </td> <td style="text-align:right;"> 0.0905252 </td> <td style="text-align:right;"> 0.0450305 </td> <td style="text-align:right;"> -0.0550333 </td> <td style="text-align:right;"> -0.0979518 </td> <td style="text-align:right;"> -0.0121148 </td> <td style="text-align:right;"> 0.0218976 </td> <td style="text-align:right;"> 218 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 0.0942595 </td> <td style="text-align:right;"> -0.0751596 </td> <td style="text-align:right;"> 0.2636786 </td> <td style="text-align:right;"> 0.0864399 </td> <td style="text-align:right;"> 0.3142208 </td> <td style="text-align:right;"> 0.1125613 </td> <td style="text-align:right;"> 0.5158803 </td> <td style="text-align:right;"> 0.1028894 </td> <td style="text-align:right;"> 0.2027583 </td> <td style="text-align:right;"> 0.1750477 </td> <td style="text-align:right;"> 0.2304688 </td> <td style="text-align:right;"> 0.0141383 </td> <td style="text-align:right;"> 219 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 0.0942595 </td> <td style="text-align:right;"> -0.0751596 </td> <td style="text-align:right;"> 0.2636786 </td> <td style="text-align:right;"> 0.0864399 </td> <td style="text-align:right;"> 0.3142208 </td> <td style="text-align:right;"> 0.1125613 </td> <td style="text-align:right;"> 0.5158803 </td> <td style="text-align:right;"> 0.1028894 </td> <td style="text-align:right;"> 0.2027583 </td> <td style="text-align:right;"> 0.1750477 </td> <td style="text-align:right;"> 0.2304688 </td> <td style="text-align:right;"> 0.0141383 </td> <td style="text-align:right;"> 219 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> -0.1192293 </td> <td style="text-align:right;"> -0.2542902 </td> <td style="text-align:right;"> 0.0158316 </td> <td style="text-align:right;"> 0.0689099 </td> <td style="text-align:right;"> -0.0964842 </td> <td style="text-align:right;"> -0.2564912 </td> <td style="text-align:right;"> 0.0635228 </td> <td style="text-align:right;"> 0.0816377 </td> <td style="text-align:right;"> 0.0267691 </td> <td style="text-align:right;"> -0.0233285 </td> <td style="text-align:right;"> 0.0768667 </td> <td style="text-align:right;"> 0.0255605 </td> <td style="text-align:right;"> 220 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> total food intake </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> -0.1192293 </td> <td style="text-align:right;"> -0.2542902 </td> <td style="text-align:right;"> 0.0158316 </td> <td style="text-align:right;"> 0.0689099 </td> <td style="text-align:right;"> -0.0964842 </td> <td style="text-align:right;"> -0.2564912 </td> <td style="text-align:right;"> 0.0635228 </td> <td style="text-align:right;"> 0.0816377 </td> <td style="text-align:right;"> 0.0267691 </td> <td style="text-align:right;"> -0.0233285 </td> <td style="text-align:right;"> 0.0768667 </td> <td style="text-align:right;"> 0.0255605 </td> <td style="text-align:right;"> 220 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> total food intake </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> -0.0422347 </td> <td style="text-align:right;"> -0.0623878 </td> <td style="text-align:right;"> -0.0220816 </td> <td style="text-align:right;"> 0.0102824 </td> <td style="text-align:right;"> -0.0355909 </td> <td style="text-align:right;"> -0.0619127 </td> <td style="text-align:right;"> -0.0092692 </td> <td style="text-align:right;"> 0.0134297 </td> <td style="text-align:right;"> 0.0092660 </td> <td style="text-align:right;"> -0.0008158 </td> <td style="text-align:right;"> 0.0193478 </td> <td style="text-align:right;"> 0.0051439 </td> <td style="text-align:right;"> 223 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> total protein </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> -0.0422347 </td> <td style="text-align:right;"> -0.0623878 </td> <td style="text-align:right;"> -0.0220816 </td> <td style="text-align:right;"> 0.0102824 </td> <td style="text-align:right;"> -0.0355909 </td> <td style="text-align:right;"> -0.0619127 </td> <td style="text-align:right;"> -0.0092692 </td> <td style="text-align:right;"> 0.0134297 </td> <td style="text-align:right;"> 0.0092660 </td> <td style="text-align:right;"> -0.0008158 </td> <td style="text-align:right;"> 0.0193478 </td> <td style="text-align:right;"> 0.0051439 </td> <td style="text-align:right;"> 223 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> total protein </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> -0.1457383 </td> <td style="text-align:right;"> -0.2373165 </td> <td style="text-align:right;"> -0.0541601 </td> <td style="text-align:right;"> 0.0467244 </td> <td style="text-align:right;"> -0.2097443 </td> <td style="text-align:right;"> -0.2681948 </td> <td style="text-align:right;"> -0.1512937 </td> <td style="text-align:right;"> 0.0298223 </td> <td style="text-align:right;"> -0.0654284 </td> <td style="text-align:right;"> -0.1374220 </td> <td style="text-align:right;"> 0.0065653 </td> <td style="text-align:right;"> 0.0367321 </td> <td style="text-align:right;"> 224 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> total water intake </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> -0.1457383 </td> <td style="text-align:right;"> -0.2373165 </td> <td style="text-align:right;"> -0.0541601 </td> <td style="text-align:right;"> 0.0467244 </td> <td style="text-align:right;"> -0.2097443 </td> <td style="text-align:right;"> -0.2681948 </td> <td style="text-align:right;"> -0.1512937 </td> <td style="text-align:right;"> 0.0298223 </td> <td style="text-align:right;"> -0.0654284 </td> <td style="text-align:right;"> -0.1374220 </td> <td style="text-align:right;"> 0.0065653 </td> <td style="text-align:right;"> 0.0367321 </td> <td style="text-align:right;"> 224 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> total water intake </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> -0.0320020 </td> <td style="text-align:right;"> -0.1233659 </td> <td style="text-align:right;"> 0.0593619 </td> <td style="text-align:right;"> 0.0466151 </td> <td style="text-align:right;"> 0.3268957 </td> <td style="text-align:right;"> 0.2087111 </td> <td style="text-align:right;"> 0.4450803 </td> <td style="text-align:right;"> 0.0602994 </td> <td style="text-align:right;"> 0.3473552 </td> <td style="text-align:right;"> 0.2592006 </td> <td style="text-align:right;"> 0.4355098 </td> <td style="text-align:right;"> 0.0449777 </td> <td style="text-align:right;"> 226 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> triglycerides </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> -0.0320020 </td> <td style="text-align:right;"> -0.1233659 </td> <td style="text-align:right;"> 0.0593619 </td> <td style="text-align:right;"> 0.0466151 </td> <td style="text-align:right;"> 0.3268957 </td> <td style="text-align:right;"> 0.2087111 </td> <td style="text-align:right;"> 0.4450803 </td> <td style="text-align:right;"> 0.0602994 </td> <td style="text-align:right;"> 0.3473552 </td> <td style="text-align:right;"> 0.2592006 </td> <td style="text-align:right;"> 0.4355098 </td> <td style="text-align:right;"> 0.0449777 </td> <td style="text-align:right;"> 226 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> triglycerides </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> -0.1405306 </td> <td style="text-align:right;"> -0.2664120 </td> <td style="text-align:right;"> -0.0146491 </td> <td style="text-align:right;"> 0.0642264 </td> <td style="text-align:right;"> -0.0950040 </td> <td style="text-align:right;"> -0.2507897 </td> <td style="text-align:right;"> 0.0607817 </td> <td style="text-align:right;"> 0.0794840 </td> <td style="text-align:right;"> 0.0403162 </td> <td style="text-align:right;"> 0.0051883 </td> <td style="text-align:right;"> 0.0754441 </td> <td style="text-align:right;"> 0.0179227 </td> <td style="text-align:right;"> 227 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> -0.1405306 </td> <td style="text-align:right;"> -0.2664120 </td> <td style="text-align:right;"> -0.0146491 </td> <td style="text-align:right;"> 0.0642264 </td> <td style="text-align:right;"> -0.0950040 </td> <td style="text-align:right;"> -0.2507897 </td> <td style="text-align:right;"> 0.0607817 </td> <td style="text-align:right;"> 0.0794840 </td> <td style="text-align:right;"> 0.0403162 </td> <td style="text-align:right;"> 0.0051883 </td> <td style="text-align:right;"> 0.0754441 </td> <td style="text-align:right;"> 0.0179227 </td> <td style="text-align:right;"> 227 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 0.0367062 </td> <td style="text-align:right;"> -0.0660619 </td> <td style="text-align:right;"> 0.1394744 </td> <td style="text-align:right;"> 0.0524337 </td> <td style="text-align:right;"> 0.3626957 </td> <td style="text-align:right;"> 0.0914512 </td> <td style="text-align:right;"> 0.6339402 </td> <td style="text-align:right;"> 0.1383926 </td> <td style="text-align:right;"> 0.4472349 </td> <td style="text-align:right;"> -0.0801891 </td> <td style="text-align:right;"> 0.9746588 </td> <td style="text-align:right;"> 0.2690988 </td> <td style="text-align:right;"> 228 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> uric acid </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 0.0367062 </td> <td style="text-align:right;"> -0.0660619 </td> <td style="text-align:right;"> 0.1394744 </td> <td style="text-align:right;"> 0.0524337 </td> <td style="text-align:right;"> 0.3626957 </td> <td style="text-align:right;"> 0.0914512 </td> <td style="text-align:right;"> 0.6339402 </td> <td style="text-align:right;"> 0.1383926 </td> <td style="text-align:right;"> 0.4472349 </td> <td style="text-align:right;"> -0.0801891 </td> <td style="text-align:right;"> 0.9746588 </td> <td style="text-align:right;"> 0.2690988 </td> <td style="text-align:right;"> 228 </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> uric acid </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> -0.0907957 </td> <td style="text-align:right;"> -0.1703063 </td> <td style="text-align:right;"> -0.0112852 </td> <td style="text-align:right;"> 0.0405673 </td> <td style="text-align:right;"> 0.1168446 </td> <td style="text-align:right;"> -0.0023934 </td> <td style="text-align:right;"> 0.2360826 </td> <td style="text-align:right;"> 0.0608368 </td> <td style="text-align:right;"> 0.1978876 </td> <td style="text-align:right;"> 0.1368305 </td> <td style="text-align:right;"> 0.2589447 </td> <td style="text-align:right;"> 0.0311521 </td> <td style="text-align:right;"> 229 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> -0.0907957 </td> <td style="text-align:right;"> -0.1703063 </td> <td style="text-align:right;"> -0.0112852 </td> <td style="text-align:right;"> 0.0405673 </td> <td style="text-align:right;"> 0.1168446 </td> <td style="text-align:right;"> -0.0023934 </td> <td style="text-align:right;"> 0.2360826 </td> <td style="text-align:right;"> 0.0608368 </td> <td style="text-align:right;"> 0.1978876 </td> <td style="text-align:right;"> 0.1368305 </td> <td style="text-align:right;"> 0.2589447 </td> <td style="text-align:right;"> 0.0311521 </td> <td style="text-align:right;"> 229 </td> <td style="text-align:right;"> 16 </td> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> -0.0156634 </td> <td style="text-align:right;"> -0.0857564 </td> <td style="text-align:right;"> 0.0544296 </td> <td style="text-align:right;"> 0.0357624 </td> <td style="text-align:right;"> -0.1140149 </td> <td style="text-align:right;"> -0.1840029 </td> <td style="text-align:right;"> -0.0440269 </td> <td style="text-align:right;"> 0.0357088 </td> <td style="text-align:right;"> -0.0997437 </td> <td style="text-align:right;"> -0.1519566 </td> <td style="text-align:right;"> -0.0475307 </td> <td style="text-align:right;"> 0.0266397 </td> <td style="text-align:right;"> 230 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> -0.0156634 </td> <td style="text-align:right;"> -0.0857564 </td> <td style="text-align:right;"> 0.0544296 </td> <td style="text-align:right;"> 0.0357624 </td> <td style="text-align:right;"> -0.1140149 </td> <td style="text-align:right;"> -0.1840029 </td> <td style="text-align:right;"> -0.0440269 </td> <td style="text-align:right;"> 0.0357088 </td> <td style="text-align:right;"> -0.0997437 </td> <td style="text-align:right;"> -0.1519566 </td> <td style="text-align:right;"> -0.0475307 </td> <td style="text-align:right;"> 0.0266397 </td> <td style="text-align:right;"> 230 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> -0.0531307 </td> <td style="text-align:right;"> -0.1011672 </td> <td style="text-align:right;"> -0.0050941 </td> <td style="text-align:right;"> 0.0245089 </td> <td style="text-align:right;"> -0.0593672 </td> <td style="text-align:right;"> -0.1076067 </td> <td style="text-align:right;"> -0.0111276 </td> <td style="text-align:right;"> 0.0246125 </td> <td style="text-align:right;"> 0.0045878 </td> <td style="text-align:right;"> -0.0513396 </td> <td style="text-align:right;"> 0.0605152 </td> <td style="text-align:right;"> 0.0285349 </td> <td style="text-align:right;"> 232 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> -0.0531307 </td> <td style="text-align:right;"> -0.1011672 </td> <td style="text-align:right;"> -0.0050941 </td> <td style="text-align:right;"> 0.0245089 </td> <td style="text-align:right;"> -0.0593672 </td> <td style="text-align:right;"> -0.1076067 </td> <td style="text-align:right;"> -0.0111276 </td> <td style="text-align:right;"> 0.0246125 </td> <td style="text-align:right;"> 0.0045878 </td> <td style="text-align:right;"> -0.0513396 </td> <td style="text-align:right;"> 0.0605152 </td> <td style="text-align:right;"> 0.0285349 </td> <td style="text-align:right;"> 232 </td> <td style="text-align:right;"> 13 </td> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> </tr> </tbody> </table> </div> </div> <div id="clean-up-and-rename" class="section level4"> <div name="clean-up_and_rename" data-unique="clean-up_and_rename"></div><h4>Clean-up and rename</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3625" aria-expanded="false" aria-controls="rcode-643E0F3625"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3625"><pre class="r"><code class="hljs">metacombo <-metacombo[, c(<span class="hljs-number">1</span>, <span class="hljs-number">21</span>:<span class="hljs-number">23</span>, <span class="hljs-number">2</span>:<span class="hljs-number">13</span>)] names(metacombo)[<span class="hljs-number">3</span>] <- <span class="hljs-string">"procedure"</span> names(metacombo)[<span class="hljs-number">4</span>] <- <span class="hljs-string">"GroupingTerm"</span> <span class="hljs-comment"># Quick pre-check before doing plots </span> compare <- metacombo %>% group_by(GroupingTerm) %>% dplyr::summarize(MeanCVR = mean(lnCVR),MeanVR = mean(lnVR), MeanRR = mean(lnRR) ) compare</code></pre></div> <pre><code class="hljs">## # A tibble: 9 x 4 ## GroupingTerm MeanCVR MeanVR MeanRR ## <fct> <dbl> <dbl> <dbl> ## 1 Behaviour 0.000871 -0.00727 -0.00869 ## 2 Eye -0.152 -0.146 0.00656 ## 3 Hearing 0.0143 -0.00893 -0.0145 ## 4 Heart 0.0225 -0.0126 -0.0312 ## 5 Hematology 0.0295 0.106 0.0665 ## 6 Immunology -0.0723 -0.107 -0.0528 ## 7 Metabolism -0.0503 0.0931 0.161 ## 8 Morphology 0.0730 0.143 0.0684 ## 9 Physiology 0.0236 0.0480 0.0227</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3626" aria-expanded="false" aria-controls="rcode-643E0F3626"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3626"><pre class="r"><code class="hljs"><span class="hljs-comment"># SHINY APP # Now that we have a corrected "results" table with each of the meta-analytic means for all effect sizes of interest, we can use this table as part of the Shiny App, which will then be able to back calculate the percentage differences between males and females for mean, variance and coefficient of variance. We'll export and use this in the Shiny App. **Note that I have not dealt with convergence issues in some of these models, and so, this will need to be done down the road**</span> <span class="hljs-comment">## Note Susi 31/7/2019: This has been cleaned up already</span> <span class="hljs-comment">#FILE TO USE: METACOMBO </span> <span class="hljs-comment">### note: to use</span> <span class="hljs-comment">#trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv")</span></code></pre></div> </div> </div> </div> <div id="meta-analysis-phase-3" class="section level2"> <div name="meta-analysis,_phase_3" data-unique="meta-analysis,_phase_3"></div><h2>Meta-analysis, Phase 3</h2> <div id="perform-meta-analyses-3-for-each-of-the-9-grouping-terms-lncvr-lnvr-lnrr" class="section level4"> <div name="perform_meta-analyses_(3_for_each_of_the_9_grouping_terms:_lncvr,_lnvr,_lnrr)" data-unique="perform_meta-analyses_(3_for_each_of_the_9_grouping_terms:_lncvr,_lnvr,_lnrr)"></div><h4>Perform meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)</h4> <p>This is the full result dataset</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3627" aria-expanded="false" aria-controls="rcode-643E0F3627"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3627"><pre class="r"><code class="hljs">kable(cbind (metacombo,metacombo)) %>% kable_styling() %>% scroll_box(width = <span class="hljs-string">"100%"</span>, height = <span class="hljs-string">"200px"</span>)</code></pre></div> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "> <table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> counts </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> counts </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> </tr> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0538655 </td> <td style="text-align:right;"> -0.0056830 </td> <td style="text-align:right;"> 0.1134139 </td> <td style="text-align:right;"> 0.0303824 </td> <td style="text-align:right;"> 0.0869649 </td> <td style="text-align:right;"> 0.0065802 </td> <td style="text-align:right;"> 0.1673497 </td> <td style="text-align:right;"> 0.0410134 </td> <td style="text-align:right;"> 0.0024851 </td> <td style="text-align:right;"> -0.0214504 </td> <td style="text-align:right;"> 0.0264205 </td> <td style="text-align:right;"> 0.0122122 </td> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0538655 </td> <td style="text-align:right;"> -0.0056830 </td> <td style="text-align:right;"> 0.1134139 </td> <td style="text-align:right;"> 0.0303824 </td> <td style="text-align:right;"> 0.0869649 </td> <td style="text-align:right;"> 0.0065802 </td> <td style="text-align:right;"> 0.1673497 </td> <td style="text-align:right;"> 0.0410134 </td> <td style="text-align:right;"> 0.0024851 </td> <td style="text-align:right;"> -0.0214504 </td> <td style="text-align:right;"> 0.0264205 </td> <td style="text-align:right;"> 0.0122122 </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0238241 </td> <td style="text-align:right;"> -0.0331809 </td> <td style="text-align:right;"> 0.0808292 </td> <td style="text-align:right;"> 0.0290848 </td> <td style="text-align:right;"> 0.0250266 </td> <td style="text-align:right;"> -0.0488450 </td> <td style="text-align:right;"> 0.0988982 </td> <td style="text-align:right;"> 0.0376903 </td> <td style="text-align:right;"> -0.0200763 </td> <td style="text-align:right;"> -0.0431508 </td> <td style="text-align:right;"> 0.0029982 </td> <td style="text-align:right;"> 0.0117729 </td> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0238241 </td> <td style="text-align:right;"> -0.0331809 </td> <td style="text-align:right;"> 0.0808292 </td> <td style="text-align:right;"> 0.0290848 </td> <td style="text-align:right;"> 0.0250266 </td> <td style="text-align:right;"> -0.0488450 </td> <td style="text-align:right;"> 0.0988982 </td> <td style="text-align:right;"> 0.0376903 </td> <td style="text-align:right;"> -0.0200763 </td> <td style="text-align:right;"> -0.0431508 </td> <td style="text-align:right;"> 0.0029982 </td> <td style="text-align:right;"> 0.0117729 </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0518127 </td> <td style="text-align:right;"> -0.0148242 </td> <td style="text-align:right;"> 0.1184497 </td> <td style="text-align:right;"> 0.0339991 </td> <td style="text-align:right;"> -0.0891510 </td> <td style="text-align:right;"> -0.3321998 </td> <td style="text-align:right;"> 0.1538977 </td> <td style="text-align:right;"> 0.1240067 </td> <td style="text-align:right;"> -0.0224536 </td> <td style="text-align:right;"> -0.0444163 </td> <td style="text-align:right;"> -0.0004910 </td> <td style="text-align:right;"> 0.0112057 </td> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0518127 </td> <td style="text-align:right;"> -0.0148242 </td> <td style="text-align:right;"> 0.1184497 </td> <td style="text-align:right;"> 0.0339991 </td> <td style="text-align:right;"> -0.0891510 </td> <td style="text-align:right;"> -0.3321998 </td> <td style="text-align:right;"> 0.1538977 </td> <td style="text-align:right;"> 0.1240067 </td> <td style="text-align:right;"> -0.0224536 </td> <td style="text-align:right;"> -0.0444163 </td> <td style="text-align:right;"> -0.0004910 </td> <td style="text-align:right;"> 0.0112057 </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0170933 </td> <td style="text-align:right;"> -0.0533187 </td> <td style="text-align:right;"> 0.0875053 </td> <td style="text-align:right;"> 0.0359252 </td> <td style="text-align:right;"> -0.0344797 </td> <td style="text-align:right;"> -0.1017901 </td> <td style="text-align:right;"> 0.0328306 </td> <td style="text-align:right;"> 0.0343426 </td> <td style="text-align:right;"> -0.0497874 </td> <td style="text-align:right;"> -0.0748197 </td> <td style="text-align:right;"> -0.0247550 </td> <td style="text-align:right;"> 0.0127718 </td> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0170933 </td> <td style="text-align:right;"> -0.0533187 </td> <td style="text-align:right;"> 0.0875053 </td> <td style="text-align:right;"> 0.0359252 </td> <td style="text-align:right;"> -0.0344797 </td> <td style="text-align:right;"> -0.1017901 </td> <td style="text-align:right;"> 0.0328306 </td> <td style="text-align:right;"> 0.0343426 </td> <td style="text-align:right;"> -0.0497874 </td> <td style="text-align:right;"> -0.0748197 </td> <td style="text-align:right;"> -0.0247550 </td> <td style="text-align:right;"> 0.0127718 </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0077678 </td> <td style="text-align:right;"> -0.0418582 </td> <td style="text-align:right;"> 0.0263226 </td> <td style="text-align:right;"> 0.0173934 </td> <td style="text-align:right;"> 0.0141682 </td> <td style="text-align:right;"> -0.0189973 </td> <td style="text-align:right;"> 0.0473337 </td> <td style="text-align:right;"> 0.0169215 </td> <td style="text-align:right;"> 0.0184043 </td> <td style="text-align:right;"> 0.0056897 </td> <td style="text-align:right;"> 0.0311189 </td> <td style="text-align:right;"> 0.0064872 </td> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0077678 </td> <td style="text-align:right;"> -0.0418582 </td> <td style="text-align:right;"> 0.0263226 </td> <td style="text-align:right;"> 0.0173934 </td> <td style="text-align:right;"> 0.0141682 </td> <td style="text-align:right;"> -0.0189973 </td> <td style="text-align:right;"> 0.0473337 </td> <td style="text-align:right;"> 0.0169215 </td> <td style="text-align:right;"> 0.0184043 </td> <td style="text-align:right;"> 0.0056897 </td> <td style="text-align:right;"> 0.0311189 </td> <td style="text-align:right;"> 0.0064872 </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0684217 </td> <td style="text-align:right;"> -0.1895020 </td> <td style="text-align:right;"> 0.0526586 </td> <td style="text-align:right;"> 0.0617768 </td> <td style="text-align:right;"> 0.0585179 </td> <td style="text-align:right;"> -0.1322507 </td> <td style="text-align:right;"> 0.2492866 </td> <td style="text-align:right;"> 0.0973327 </td> <td style="text-align:right;"> 0.1069442 </td> <td style="text-align:right;"> 0.0319934 </td> <td style="text-align:right;"> 0.1818950 </td> <td style="text-align:right;"> 0.0382409 </td> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0684217 </td> <td style="text-align:right;"> -0.1895020 </td> <td style="text-align:right;"> 0.0526586 </td> <td style="text-align:right;"> 0.0617768 </td> <td style="text-align:right;"> 0.0585179 </td> <td style="text-align:right;"> -0.1322507 </td> <td style="text-align:right;"> 0.2492866 </td> <td style="text-align:right;"> 0.0973327 </td> <td style="text-align:right;"> 0.1069442 </td> <td style="text-align:right;"> 0.0319934 </td> <td style="text-align:right;"> 0.1818950 </td> <td style="text-align:right;"> 0.0382409 </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1133080 </td> <td style="text-align:right;"> 0.0451475 </td> <td style="text-align:right;"> 0.1814685 </td> <td style="text-align:right;"> 0.0347764 </td> <td style="text-align:right;"> 0.0559995 </td> <td style="text-align:right;"> -0.0080678 </td> <td style="text-align:right;"> 0.1200668 </td> <td style="text-align:right;"> 0.0326880 </td> <td style="text-align:right;"> -0.0567840 </td> <td style="text-align:right;"> -0.0732083 </td> <td style="text-align:right;"> -0.0403597 </td> <td style="text-align:right;"> 0.0083799 </td> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1133080 </td> <td style="text-align:right;"> 0.0451475 </td> <td style="text-align:right;"> 0.1814685 </td> <td style="text-align:right;"> 0.0347764 </td> <td style="text-align:right;"> 0.0559995 </td> <td style="text-align:right;"> -0.0080678 </td> <td style="text-align:right;"> 0.1200668 </td> <td style="text-align:right;"> 0.0326880 </td> <td style="text-align:right;"> -0.0567840 </td> <td style="text-align:right;"> -0.0732083 </td> <td style="text-align:right;"> -0.0403597 </td> <td style="text-align:right;"> 0.0083799 </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1043649 </td> <td style="text-align:right;"> 0.0451585 </td> <td style="text-align:right;"> 0.1635713 </td> <td style="text-align:right;"> 0.0302079 </td> <td style="text-align:right;"> -0.3112471 </td> <td style="text-align:right;"> -0.3980164 </td> <td style="text-align:right;"> -0.2244778 </td> <td style="text-align:right;"> 0.0442709 </td> <td style="text-align:right;"> -0.4216032 </td> <td style="text-align:right;"> -0.4694832 </td> <td style="text-align:right;"> -0.3737231 </td> <td style="text-align:right;"> 0.0244290 </td> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1043649 </td> <td style="text-align:right;"> 0.0451585 </td> <td style="text-align:right;"> 0.1635713 </td> <td style="text-align:right;"> 0.0302079 </td> <td style="text-align:right;"> -0.3112471 </td> <td style="text-align:right;"> -0.3980164 </td> <td style="text-align:right;"> -0.2244778 </td> <td style="text-align:right;"> 0.0442709 </td> <td style="text-align:right;"> -0.4216032 </td> <td style="text-align:right;"> -0.4694832 </td> <td style="text-align:right;"> -0.3737231 </td> <td style="text-align:right;"> 0.0244290 </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0383407 </td> <td style="text-align:right;"> -0.0423419 </td> <td style="text-align:right;"> 0.1190232 </td> <td style="text-align:right;"> 0.0411653 </td> <td style="text-align:right;"> 0.2795566 </td> <td style="text-align:right;"> 0.1615777 </td> <td style="text-align:right;"> 0.3975355 </td> <td style="text-align:right;"> 0.0601944 </td> <td style="text-align:right;"> 0.2246987 </td> <td style="text-align:right;"> 0.1793151 </td> <td style="text-align:right;"> 0.2700822 </td> <td style="text-align:right;"> 0.0231553 </td> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0383407 </td> <td style="text-align:right;"> -0.0423419 </td> <td style="text-align:right;"> 0.1190232 </td> <td style="text-align:right;"> 0.0411653 </td> <td style="text-align:right;"> 0.2795566 </td> <td style="text-align:right;"> 0.1615777 </td> <td style="text-align:right;"> 0.3975355 </td> <td style="text-align:right;"> 0.0601944 </td> <td style="text-align:right;"> 0.2246987 </td> <td style="text-align:right;"> 0.1793151 </td> <td style="text-align:right;"> 0.2700822 </td> <td style="text-align:right;"> 0.0231553 </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1531723 </td> <td style="text-align:right;"> -0.2210551 </td> <td style="text-align:right;"> -0.0852895 </td> <td style="text-align:right;"> 0.0346347 </td> <td style="text-align:right;"> 0.2748396 </td> <td style="text-align:right;"> 0.1950895 </td> <td style="text-align:right;"> 0.3545898 </td> <td style="text-align:right;"> 0.0406896 </td> <td style="text-align:right;"> 0.4357738 </td> <td style="text-align:right;"> 0.3655882 </td> <td style="text-align:right;"> 0.5059595 </td> <td style="text-align:right;"> 0.0358097 </td> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1531723 </td> <td style="text-align:right;"> -0.2210551 </td> <td style="text-align:right;"> -0.0852895 </td> <td style="text-align:right;"> 0.0346347 </td> <td style="text-align:right;"> 0.2748396 </td> <td style="text-align:right;"> 0.1950895 </td> <td style="text-align:right;"> 0.3545898 </td> <td style="text-align:right;"> 0.0406896 </td> <td style="text-align:right;"> 0.4357738 </td> <td style="text-align:right;"> 0.3655882 </td> <td style="text-align:right;"> 0.5059595 </td> <td style="text-align:right;"> 0.0358097 </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0119165 </td> <td style="text-align:right;"> -0.1228287 </td> <td style="text-align:right;"> 0.1466617 </td> <td style="text-align:right;"> 0.0687488 </td> <td style="text-align:right;"> -0.0566968 </td> <td style="text-align:right;"> -0.2457779 </td> <td style="text-align:right;"> 0.1323843 </td> <td style="text-align:right;"> 0.0964717 </td> <td style="text-align:right;"> -0.0585577 </td> <td style="text-align:right;"> -0.1331777 </td> <td style="text-align:right;"> 0.0160624 </td> <td style="text-align:right;"> 0.0380722 </td> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0119165 </td> <td style="text-align:right;"> -0.1228287 </td> <td style="text-align:right;"> 0.1466617 </td> <td style="text-align:right;"> 0.0687488 </td> <td style="text-align:right;"> -0.0566968 </td> <td style="text-align:right;"> -0.2457779 </td> <td style="text-align:right;"> 0.1323843 </td> <td style="text-align:right;"> 0.0964717 </td> <td style="text-align:right;"> -0.0585577 </td> <td style="text-align:right;"> -0.1331777 </td> <td style="text-align:right;"> 0.0160624 </td> <td style="text-align:right;"> 0.0380722 </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0917931 </td> <td style="text-align:right;"> -0.2022487 </td> <td style="text-align:right;"> 0.0186624 </td> <td style="text-align:right;"> 0.0563559 </td> <td style="text-align:right;"> 0.2031265 </td> <td style="text-align:right;"> -0.0131549 </td> <td style="text-align:right;"> 0.4194079 </td> <td style="text-align:right;"> 0.1103497 </td> <td style="text-align:right;"> 0.2675772 </td> <td style="text-align:right;"> 0.0643028 </td> <td style="text-align:right;"> 0.4708516 </td> <td style="text-align:right;"> 0.1037133 </td> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0917931 </td> <td style="text-align:right;"> -0.2022487 </td> <td style="text-align:right;"> 0.0186624 </td> <td style="text-align:right;"> 0.0563559 </td> <td style="text-align:right;"> 0.2031265 </td> <td style="text-align:right;"> -0.0131549 </td> <td style="text-align:right;"> 0.4194079 </td> <td style="text-align:right;"> 0.1103497 </td> <td style="text-align:right;"> 0.2675772 </td> <td style="text-align:right;"> 0.0643028 </td> <td style="text-align:right;"> 0.4708516 </td> <td style="text-align:right;"> 0.1037133 </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0934739 </td> <td style="text-align:right;"> -0.1787512 </td> <td style="text-align:right;"> -0.0081966 </td> <td style="text-align:right;"> 0.0435096 </td> <td style="text-align:right;"> -0.0639511 </td> <td style="text-align:right;"> -0.2828066 </td> <td style="text-align:right;"> 0.1549044 </td> <td style="text-align:right;"> 0.1116630 </td> <td style="text-align:right;"> -0.0156339 </td> <td style="text-align:right;"> -0.1102310 </td> <td style="text-align:right;"> 0.0789633 </td> <td style="text-align:right;"> 0.0482647 </td> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0934739 </td> <td style="text-align:right;"> -0.1787512 </td> <td style="text-align:right;"> -0.0081966 </td> <td style="text-align:right;"> 0.0435096 </td> <td style="text-align:right;"> -0.0639511 </td> <td style="text-align:right;"> -0.2828066 </td> <td style="text-align:right;"> 0.1549044 </td> <td style="text-align:right;"> 0.1116630 </td> <td style="text-align:right;"> -0.0156339 </td> <td style="text-align:right;"> -0.1102310 </td> <td style="text-align:right;"> 0.0789633 </td> <td style="text-align:right;"> 0.0482647 </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1314998 </td> <td style="text-align:right;"> 0.0329846 </td> <td style="text-align:right;"> 0.2300151 </td> <td style="text-align:right;"> 0.0502638 </td> <td style="text-align:right;"> -0.0448684 </td> <td style="text-align:right;"> -0.1340146 </td> <td style="text-align:right;"> 0.0442777 </td> <td style="text-align:right;"> 0.0454836 </td> <td style="text-align:right;"> -0.1722378 </td> <td style="text-align:right;"> -0.2207030 </td> <td style="text-align:right;"> -0.1237726 </td> <td style="text-align:right;"> 0.0247276 </td> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1314998 </td> <td style="text-align:right;"> 0.0329846 </td> <td style="text-align:right;"> 0.2300151 </td> <td style="text-align:right;"> 0.0502638 </td> <td style="text-align:right;"> -0.0448684 </td> <td style="text-align:right;"> -0.1340146 </td> <td style="text-align:right;"> 0.0442777 </td> <td style="text-align:right;"> 0.0454836 </td> <td style="text-align:right;"> -0.1722378 </td> <td style="text-align:right;"> -0.2207030 </td> <td style="text-align:right;"> -0.1237726 </td> <td style="text-align:right;"> 0.0247276 </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.0347988 </td> <td style="text-align:right;"> -0.0824528 </td> <td style="text-align:right;"> 0.0128552 </td> <td style="text-align:right;"> 0.0243137 </td> <td style="text-align:right;"> -0.0059677 </td> <td style="text-align:right;"> -0.0526221 </td> <td style="text-align:right;"> 0.0406866 </td> <td style="text-align:right;"> 0.0238037 </td> <td style="text-align:right;"> 0.0282722 </td> <td style="text-align:right;"> 0.0233254 </td> <td style="text-align:right;"> 0.0332189 </td> <td style="text-align:right;"> 0.0025239 </td> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.0347988 </td> <td style="text-align:right;"> -0.0824528 </td> <td style="text-align:right;"> 0.0128552 </td> <td style="text-align:right;"> 0.0243137 </td> <td style="text-align:right;"> -0.0059677 </td> <td style="text-align:right;"> -0.0526221 </td> <td style="text-align:right;"> 0.0406866 </td> <td style="text-align:right;"> 0.0238037 </td> <td style="text-align:right;"> 0.0282722 </td> <td style="text-align:right;"> 0.0233254 </td> <td style="text-align:right;"> 0.0332189 </td> <td style="text-align:right;"> 0.0025239 </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0325368 </td> <td style="text-align:right;"> -0.1066429 </td> <td style="text-align:right;"> 0.0415693 </td> <td style="text-align:right;"> 0.0378099 </td> <td style="text-align:right;"> -0.0303742 </td> <td style="text-align:right;"> -0.1044537 </td> <td style="text-align:right;"> 0.0437054 </td> <td style="text-align:right;"> 0.0377964 </td> <td style="text-align:right;"> 0.0018532 </td> <td style="text-align:right;"> -0.0005002 </td> <td style="text-align:right;"> 0.0042066 </td> <td style="text-align:right;"> 0.0012008 </td> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0325368 </td> <td style="text-align:right;"> -0.1066429 </td> <td style="text-align:right;"> 0.0415693 </td> <td style="text-align:right;"> 0.0378099 </td> <td style="text-align:right;"> -0.0303742 </td> <td style="text-align:right;"> -0.1044537 </td> <td style="text-align:right;"> 0.0437054 </td> <td style="text-align:right;"> 0.0377964 </td> <td style="text-align:right;"> 0.0018532 </td> <td style="text-align:right;"> -0.0005002 </td> <td style="text-align:right;"> 0.0042066 </td> <td style="text-align:right;"> 0.0012008 </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0245675 </td> <td style="text-align:right;"> -0.0420402 </td> <td style="text-align:right;"> 0.0911752 </td> <td style="text-align:right;"> 0.0339841 </td> <td style="text-align:right;"> 0.2335793 </td> <td style="text-align:right;"> 0.1694979 </td> <td style="text-align:right;"> 0.2976607 </td> <td style="text-align:right;"> 0.0326952 </td> <td style="text-align:right;"> 0.2096770 </td> <td style="text-align:right;"> 0.1938727 </td> <td style="text-align:right;"> 0.2254813 </td> <td style="text-align:right;"> 0.0080636 </td> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0245675 </td> <td style="text-align:right;"> -0.0420402 </td> <td style="text-align:right;"> 0.0911752 </td> <td style="text-align:right;"> 0.0339841 </td> <td style="text-align:right;"> 0.2335793 </td> <td style="text-align:right;"> 0.1694979 </td> <td style="text-align:right;"> 0.2976607 </td> <td style="text-align:right;"> 0.0326952 </td> <td style="text-align:right;"> 0.2096770 </td> <td style="text-align:right;"> 0.1938727 </td> <td style="text-align:right;"> 0.2254813 </td> <td style="text-align:right;"> 0.0080636 </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.0853708 </td> <td style="text-align:right;"> 0.0299665 </td> <td style="text-align:right;"> 0.1407751 </td> <td style="text-align:right;"> 0.0282680 </td> <td style="text-align:right;"> 0.2849370 </td> <td style="text-align:right;"> 0.2328875 </td> <td style="text-align:right;"> 0.3369866 </td> <td style="text-align:right;"> 0.0265564 </td> <td style="text-align:right;"> 0.2030973 </td> <td style="text-align:right;"> 0.1864076 </td> <td style="text-align:right;"> 0.2197871 </td> <td style="text-align:right;"> 0.0085153 </td> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.0853708 </td> <td style="text-align:right;"> 0.0299665 </td> <td style="text-align:right;"> 0.1407751 </td> <td style="text-align:right;"> 0.0282680 </td> <td style="text-align:right;"> 0.2849370 </td> <td style="text-align:right;"> 0.2328875 </td> <td style="text-align:right;"> 0.3369866 </td> <td style="text-align:right;"> 0.0265564 </td> <td style="text-align:right;"> 0.2030973 </td> <td style="text-align:right;"> 0.1864076 </td> <td style="text-align:right;"> 0.2197871 </td> <td style="text-align:right;"> 0.0085153 </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.1053511 </td> <td style="text-align:right;"> 0.0412461 </td> <td style="text-align:right;"> 0.1694562 </td> <td style="text-align:right;"> 0.0327073 </td> <td style="text-align:right;"> 0.3038998 </td> <td style="text-align:right;"> 0.2435428 </td> <td style="text-align:right;"> 0.3642568 </td> <td style="text-align:right;"> 0.0307949 </td> <td style="text-align:right;"> 0.2008638 </td> <td style="text-align:right;"> 0.1816362 </td> <td style="text-align:right;"> 0.2200914 </td> <td style="text-align:right;"> 0.0098102 </td> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.1053511 </td> <td style="text-align:right;"> 0.0412461 </td> <td style="text-align:right;"> 0.1694562 </td> <td style="text-align:right;"> 0.0327073 </td> <td style="text-align:right;"> 0.3038998 </td> <td style="text-align:right;"> 0.2435428 </td> <td style="text-align:right;"> 0.3642568 </td> <td style="text-align:right;"> 0.0307949 </td> <td style="text-align:right;"> 0.2008638 </td> <td style="text-align:right;"> 0.1816362 </td> <td style="text-align:right;"> 0.2200914 </td> <td style="text-align:right;"> 0.0098102 </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0981587 </td> <td style="text-align:right;"> 0.0272824 </td> <td style="text-align:right;"> 0.1690349 </td> <td style="text-align:right;"> 0.0361620 </td> <td style="text-align:right;"> 0.1286546 </td> <td style="text-align:right;"> 0.0533659 </td> <td style="text-align:right;"> 0.2039432 </td> <td style="text-align:right;"> 0.0384133 </td> <td style="text-align:right;"> 0.0315241 </td> <td style="text-align:right;"> 0.0003806 </td> <td style="text-align:right;"> 0.0626676 </td> <td style="text-align:right;"> 0.0158898 </td> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0981587 </td> <td style="text-align:right;"> 0.0272824 </td> <td style="text-align:right;"> 0.1690349 </td> <td style="text-align:right;"> 0.0361620 </td> <td style="text-align:right;"> 0.1286546 </td> <td style="text-align:right;"> 0.0533659 </td> <td style="text-align:right;"> 0.2039432 </td> <td style="text-align:right;"> 0.0384133 </td> <td style="text-align:right;"> 0.0315241 </td> <td style="text-align:right;"> 0.0003806 </td> <td style="text-align:right;"> 0.0626676 </td> <td style="text-align:right;"> 0.0158898 </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1709230 </td> <td style="text-align:right;"> 0.0625642 </td> <td style="text-align:right;"> 0.2792818 </td> <td style="text-align:right;"> 0.0552861 </td> <td style="text-align:right;"> 0.2091372 </td> <td style="text-align:right;"> 0.1015600 </td> <td style="text-align:right;"> 0.3167143 </td> <td style="text-align:right;"> 0.0548873 </td> <td style="text-align:right;"> 0.0372537 </td> <td style="text-align:right;"> -0.0130828 </td> <td style="text-align:right;"> 0.0875902 </td> <td style="text-align:right;"> 0.0256824 </td> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1709230 </td> <td style="text-align:right;"> 0.0625642 </td> <td style="text-align:right;"> 0.2792818 </td> <td style="text-align:right;"> 0.0552861 </td> <td style="text-align:right;"> 0.2091372 </td> <td style="text-align:right;"> 0.1015600 </td> <td style="text-align:right;"> 0.3167143 </td> <td style="text-align:right;"> 0.0548873 </td> <td style="text-align:right;"> 0.0372537 </td> <td style="text-align:right;"> -0.0130828 </td> <td style="text-align:right;"> 0.0875902 </td> <td style="text-align:right;"> 0.0256824 </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0542638 </td> <td style="text-align:right;"> -0.0881612 </td> <td style="text-align:right;"> 0.1966887 </td> <td style="text-align:right;"> 0.0726671 </td> <td style="text-align:right;"> 0.0492830 </td> <td style="text-align:right;"> -0.1087868 </td> <td style="text-align:right;"> 0.2073528 </td> <td style="text-align:right;"> 0.0806494 </td> <td style="text-align:right;"> 0.0012286 </td> <td style="text-align:right;"> -0.0187942 </td> <td style="text-align:right;"> 0.0212514 </td> <td style="text-align:right;"> 0.0102159 </td> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0542638 </td> <td style="text-align:right;"> -0.0881612 </td> <td style="text-align:right;"> 0.1966887 </td> <td style="text-align:right;"> 0.0726671 </td> <td style="text-align:right;"> 0.0492830 </td> <td style="text-align:right;"> -0.1087868 </td> <td style="text-align:right;"> 0.2073528 </td> <td style="text-align:right;"> 0.0806494 </td> <td style="text-align:right;"> 0.0012286 </td> <td style="text-align:right;"> -0.0187942 </td> <td style="text-align:right;"> 0.0212514 </td> <td style="text-align:right;"> 0.0102159 </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0097946 </td> <td style="text-align:right;"> -0.0464600 </td> <td style="text-align:right;"> 0.0660492 </td> <td style="text-align:right;"> 0.0287018 </td> <td style="text-align:right;"> 0.0135683 </td> <td style="text-align:right;"> -0.0424600 </td> <td style="text-align:right;"> 0.0695966 </td> <td style="text-align:right;"> 0.0285864 </td> <td style="text-align:right;"> 0.0036564 </td> <td style="text-align:right;"> -0.0000609 </td> <td style="text-align:right;"> 0.0073737 </td> <td style="text-align:right;"> 0.0018966 </td> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0097946 </td> <td style="text-align:right;"> -0.0464600 </td> <td style="text-align:right;"> 0.0660492 </td> <td style="text-align:right;"> 0.0287018 </td> <td style="text-align:right;"> 0.0135683 </td> <td style="text-align:right;"> -0.0424600 </td> <td style="text-align:right;"> 0.0695966 </td> <td style="text-align:right;"> 0.0285864 </td> <td style="text-align:right;"> 0.0036564 </td> <td style="text-align:right;"> -0.0000609 </td> <td style="text-align:right;"> 0.0073737 </td> <td style="text-align:right;"> 0.0018966 </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0133816 </td> <td style="text-align:right;"> -0.0797535 </td> <td style="text-align:right;"> 0.1065166 </td> <td style="text-align:right;"> 0.0475188 </td> <td style="text-align:right;"> 0.1017991 </td> <td style="text-align:right;"> 0.0206287 </td> <td style="text-align:right;"> 0.1829694 </td> <td style="text-align:right;"> 0.0414142 </td> <td style="text-align:right;"> 0.0934439 </td> <td style="text-align:right;"> 0.0580233 </td> <td style="text-align:right;"> 0.1288645 </td> <td style="text-align:right;"> 0.0180721 </td> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0133816 </td> <td style="text-align:right;"> -0.0797535 </td> <td style="text-align:right;"> 0.1065166 </td> <td style="text-align:right;"> 0.0475188 </td> <td style="text-align:right;"> 0.1017991 </td> <td style="text-align:right;"> 0.0206287 </td> <td style="text-align:right;"> 0.1829694 </td> <td style="text-align:right;"> 0.0414142 </td> <td style="text-align:right;"> 0.0934439 </td> <td style="text-align:right;"> 0.0580233 </td> <td style="text-align:right;"> 0.1288645 </td> <td style="text-align:right;"> 0.0180721 </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0167300 </td> <td style="text-align:right;"> -0.0404735 </td> <td style="text-align:right;"> 0.0739335 </td> <td style="text-align:right;"> 0.0291860 </td> <td style="text-align:right;"> -0.0588515 </td> <td style="text-align:right;"> -0.1004209 </td> <td style="text-align:right;"> -0.0172820 </td> <td style="text-align:right;"> 0.0212093 </td> <td style="text-align:right;"> -0.0724619 </td> <td style="text-align:right;"> -0.1149622 </td> <td style="text-align:right;"> -0.0299616 </td> <td style="text-align:right;"> 0.0216842 </td> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0167300 </td> <td style="text-align:right;"> -0.0404735 </td> <td style="text-align:right;"> 0.0739335 </td> <td style="text-align:right;"> 0.0291860 </td> <td style="text-align:right;"> -0.0588515 </td> <td style="text-align:right;"> -0.1004209 </td> <td style="text-align:right;"> -0.0172820 </td> <td style="text-align:right;"> 0.0212093 </td> <td style="text-align:right;"> -0.0724619 </td> <td style="text-align:right;"> -0.1149622 </td> <td style="text-align:right;"> -0.0299616 </td> <td style="text-align:right;"> 0.0216842 </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0162603 </td> <td style="text-align:right;"> -0.0733243 </td> <td style="text-align:right;"> 0.0408038 </td> <td style="text-align:right;"> 0.0291149 </td> <td style="text-align:right;"> -0.1060637 </td> <td style="text-align:right;"> -0.2023343 </td> <td style="text-align:right;"> -0.0097930 </td> <td style="text-align:right;"> 0.0491186 </td> <td style="text-align:right;"> -0.0940204 </td> <td style="text-align:right;"> -0.1945774 </td> <td style="text-align:right;"> 0.0065366 </td> <td style="text-align:right;"> 0.0513055 </td> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0162603 </td> <td style="text-align:right;"> -0.0733243 </td> <td style="text-align:right;"> 0.0408038 </td> <td style="text-align:right;"> 0.0291149 </td> <td style="text-align:right;"> -0.1060637 </td> <td style="text-align:right;"> -0.2023343 </td> <td style="text-align:right;"> -0.0097930 </td> <td style="text-align:right;"> 0.0491186 </td> <td style="text-align:right;"> -0.0940204 </td> <td style="text-align:right;"> -0.1945774 </td> <td style="text-align:right;"> 0.0065366 </td> <td style="text-align:right;"> 0.0513055 </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0253715 </td> <td style="text-align:right;"> -0.0826435 </td> <td style="text-align:right;"> 0.0319004 </td> <td style="text-align:right;"> 0.0292209 </td> <td style="text-align:right;"> -0.0255734 </td> <td style="text-align:right;"> -0.1014389 </td> <td style="text-align:right;"> 0.0502922 </td> <td style="text-align:right;"> 0.0387076 </td> <td style="text-align:right;"> -0.0035151 </td> <td style="text-align:right;"> -0.0902886 </td> <td style="text-align:right;"> 0.0832585 </td> <td style="text-align:right;"> 0.0442730 </td> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0253715 </td> <td style="text-align:right;"> -0.0826435 </td> <td style="text-align:right;"> 0.0319004 </td> <td style="text-align:right;"> 0.0292209 </td> <td style="text-align:right;"> -0.0255734 </td> <td style="text-align:right;"> -0.1014389 </td> <td style="text-align:right;"> 0.0502922 </td> <td style="text-align:right;"> 0.0387076 </td> <td style="text-align:right;"> -0.0035151 </td> <td style="text-align:right;"> -0.0902886 </td> <td style="text-align:right;"> 0.0832585 </td> <td style="text-align:right;"> 0.0442730 </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0244492 </td> <td style="text-align:right;"> -0.0737922 </td> <td style="text-align:right;"> 0.1226906 </td> <td style="text-align:right;"> 0.0501241 </td> <td style="text-align:right;"> -0.0228690 </td> <td style="text-align:right;"> -0.1548339 </td> <td style="text-align:right;"> 0.1090960 </td> <td style="text-align:right;"> 0.0673303 </td> <td style="text-align:right;"> -0.0630751 </td> <td style="text-align:right;"> -0.2215457 </td> <td style="text-align:right;"> 0.0953955 </td> <td style="text-align:right;"> 0.0808538 </td> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0244492 </td> <td style="text-align:right;"> -0.0737922 </td> <td style="text-align:right;"> 0.1226906 </td> <td style="text-align:right;"> 0.0501241 </td> <td style="text-align:right;"> -0.0228690 </td> <td style="text-align:right;"> -0.1548339 </td> <td style="text-align:right;"> 0.1090960 </td> <td style="text-align:right;"> 0.0673303 </td> <td style="text-align:right;"> -0.0630751 </td> <td style="text-align:right;"> -0.2215457 </td> <td style="text-align:right;"> 0.0953955 </td> <td style="text-align:right;"> 0.0808538 </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0321555 </td> <td style="text-align:right;"> -0.1270972 </td> <td style="text-align:right;"> 0.1914083 </td> <td style="text-align:right;"> 0.0812529 </td> <td style="text-align:right;"> 0.0241491 </td> <td style="text-align:right;"> -0.1438502 </td> <td style="text-align:right;"> 0.1921485 </td> <td style="text-align:right;"> 0.0857155 </td> <td style="text-align:right;"> -0.0127047 </td> <td style="text-align:right;"> -0.0177349 </td> <td style="text-align:right;"> -0.0076745 </td> <td style="text-align:right;"> 0.0025665 </td> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0321555 </td> <td style="text-align:right;"> -0.1270972 </td> <td style="text-align:right;"> 0.1914083 </td> <td style="text-align:right;"> 0.0812529 </td> <td style="text-align:right;"> 0.0241491 </td> <td style="text-align:right;"> -0.1438502 </td> <td style="text-align:right;"> 0.1921485 </td> <td style="text-align:right;"> 0.0857155 </td> <td style="text-align:right;"> -0.0127047 </td> <td style="text-align:right;"> -0.0177349 </td> <td style="text-align:right;"> -0.0076745 </td> <td style="text-align:right;"> 0.0025665 </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0529450 </td> <td style="text-align:right;"> -0.1534816 </td> <td style="text-align:right;"> 0.0475915 </td> <td style="text-align:right;"> 0.0512951 </td> <td style="text-align:right;"> -0.0561198 </td> <td style="text-align:right;"> -0.1827679 </td> <td style="text-align:right;"> 0.0705282 </td> <td style="text-align:right;"> 0.0646176 </td> <td style="text-align:right;"> -0.0154221 </td> <td style="text-align:right;"> -0.0577200 </td> <td style="text-align:right;"> 0.0268757 </td> <td style="text-align:right;"> 0.0215809 </td> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0529450 </td> <td style="text-align:right;"> -0.1534816 </td> <td style="text-align:right;"> 0.0475915 </td> <td style="text-align:right;"> 0.0512951 </td> <td style="text-align:right;"> -0.0561198 </td> <td style="text-align:right;"> -0.1827679 </td> <td style="text-align:right;"> 0.0705282 </td> <td style="text-align:right;"> 0.0646176 </td> <td style="text-align:right;"> -0.0154221 </td> <td style="text-align:right;"> -0.0577200 </td> <td style="text-align:right;"> 0.0268757 </td> <td style="text-align:right;"> 0.0215809 </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0241232 </td> <td style="text-align:right;"> -0.1071457 </td> <td style="text-align:right;"> 0.1553920 </td> <td style="text-align:right;"> 0.0669751 </td> <td style="text-align:right;"> -0.1318792 </td> <td style="text-align:right;"> -0.3968974 </td> <td style="text-align:right;"> 0.1331390 </td> <td style="text-align:right;"> 0.1352159 </td> <td style="text-align:right;"> -0.1344413 </td> <td style="text-align:right;"> -0.3838303 </td> <td style="text-align:right;"> 0.1149476 </td> <td style="text-align:right;"> 0.1272416 </td> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0241232 </td> <td style="text-align:right;"> -0.1071457 </td> <td style="text-align:right;"> 0.1553920 </td> <td style="text-align:right;"> 0.0669751 </td> <td style="text-align:right;"> -0.1318792 </td> <td style="text-align:right;"> -0.3968974 </td> <td style="text-align:right;"> 0.1331390 </td> <td style="text-align:right;"> 0.1352159 </td> <td style="text-align:right;"> -0.1344413 </td> <td style="text-align:right;"> -0.3838303 </td> <td style="text-align:right;"> 0.1149476 </td> <td style="text-align:right;"> 0.1272416 </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0352315 </td> <td style="text-align:right;"> -0.0229205 </td> <td style="text-align:right;"> 0.0933835 </td> <td style="text-align:right;"> 0.0296699 </td> <td style="text-align:right;"> 0.1066373 </td> <td style="text-align:right;"> -0.2200831 </td> <td style="text-align:right;"> 0.4333578 </td> <td style="text-align:right;"> 0.1666972 </td> <td style="text-align:right;"> -0.0844078 </td> <td style="text-align:right;"> -0.1320251 </td> <td style="text-align:right;"> -0.0367905 </td> <td style="text-align:right;"> 0.0242950 </td> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0352315 </td> <td style="text-align:right;"> -0.0229205 </td> <td style="text-align:right;"> 0.0933835 </td> <td style="text-align:right;"> 0.0296699 </td> <td style="text-align:right;"> 0.1066373 </td> <td style="text-align:right;"> -0.2200831 </td> <td style="text-align:right;"> 0.4333578 </td> <td style="text-align:right;"> 0.1666972 </td> <td style="text-align:right;"> -0.0844078 </td> <td style="text-align:right;"> -0.1320251 </td> <td style="text-align:right;"> -0.0367905 </td> <td style="text-align:right;"> 0.0242950 </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1874544 </td> <td style="text-align:right;"> 0.0716631 </td> <td style="text-align:right;"> 0.3032457 </td> <td style="text-align:right;"> 0.0590783 </td> <td style="text-align:right;"> -0.0895722 </td> <td style="text-align:right;"> -0.2484833 </td> <td style="text-align:right;"> 0.0693388 </td> <td style="text-align:right;"> 0.0810786 </td> <td style="text-align:right;"> -0.2401301 </td> <td style="text-align:right;"> -0.3410322 </td> <td style="text-align:right;"> -0.1392280 </td> <td style="text-align:right;"> 0.0514816 </td> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1874544 </td> <td style="text-align:right;"> 0.0716631 </td> <td style="text-align:right;"> 0.3032457 </td> <td style="text-align:right;"> 0.0590783 </td> <td style="text-align:right;"> -0.0895722 </td> <td style="text-align:right;"> -0.2484833 </td> <td style="text-align:right;"> 0.0693388 </td> <td style="text-align:right;"> 0.0810786 </td> <td style="text-align:right;"> -0.2401301 </td> <td style="text-align:right;"> -0.3410322 </td> <td style="text-align:right;"> -0.1392280 </td> <td style="text-align:right;"> 0.0514816 </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0187819 </td> <td style="text-align:right;"> -0.0858957 </td> <td style="text-align:right;"> 0.0483318 </td> <td style="text-align:right;"> 0.0342423 </td> <td style="text-align:right;"> -0.1272582 </td> <td style="text-align:right;"> -0.1997426 </td> <td style="text-align:right;"> -0.0547738 </td> <td style="text-align:right;"> 0.0369825 </td> <td style="text-align:right;"> -0.1121373 </td> <td style="text-align:right;"> -0.1816322 </td> <td style="text-align:right;"> -0.0426424 </td> <td style="text-align:right;"> 0.0354572 </td> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0187819 </td> <td style="text-align:right;"> -0.0858957 </td> <td style="text-align:right;"> 0.0483318 </td> <td style="text-align:right;"> 0.0342423 </td> <td style="text-align:right;"> -0.1272582 </td> <td style="text-align:right;"> -0.1997426 </td> <td style="text-align:right;"> -0.0547738 </td> <td style="text-align:right;"> 0.0369825 </td> <td style="text-align:right;"> -0.1121373 </td> <td style="text-align:right;"> -0.1816322 </td> <td style="text-align:right;"> -0.0426424 </td> <td style="text-align:right;"> 0.0354572 </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0300111 </td> <td style="text-align:right;"> -0.1345066 </td> <td style="text-align:right;"> 0.0744844 </td> <td style="text-align:right;"> 0.0533150 </td> <td style="text-align:right;"> -0.0525735 </td> <td style="text-align:right;"> -0.1483174 </td> <td style="text-align:right;"> 0.0431705 </td> <td style="text-align:right;"> 0.0488499 </td> <td style="text-align:right;"> -0.0284086 </td> <td style="text-align:right;"> -0.0492579 </td> <td style="text-align:right;"> -0.0075592 </td> <td style="text-align:right;"> 0.0106376 </td> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0300111 </td> <td style="text-align:right;"> -0.1345066 </td> <td style="text-align:right;"> 0.0744844 </td> <td style="text-align:right;"> 0.0533150 </td> <td style="text-align:right;"> -0.0525735 </td> <td style="text-align:right;"> -0.1483174 </td> <td style="text-align:right;"> 0.0431705 </td> <td style="text-align:right;"> 0.0488499 </td> <td style="text-align:right;"> -0.0284086 </td> <td style="text-align:right;"> -0.0492579 </td> <td style="text-align:right;"> -0.0075592 </td> <td style="text-align:right;"> 0.0106376 </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1120972 </td> <td style="text-align:right;"> 0.0431489 </td> <td style="text-align:right;"> 0.1810454 </td> <td style="text-align:right;"> 0.0351783 </td> <td style="text-align:right;"> 0.1743929 </td> <td style="text-align:right;"> 0.0875252 </td> <td style="text-align:right;"> 0.2612607 </td> <td style="text-align:right;"> 0.0443211 </td> <td style="text-align:right;"> 0.0600907 </td> <td style="text-align:right;"> 0.0354923 </td> <td style="text-align:right;"> 0.0846891 </td> <td style="text-align:right;"> 0.0125504 </td> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1120972 </td> <td style="text-align:right;"> 0.0431489 </td> <td style="text-align:right;"> 0.1810454 </td> <td style="text-align:right;"> 0.0351783 </td> <td style="text-align:right;"> 0.1743929 </td> <td style="text-align:right;"> 0.0875252 </td> <td style="text-align:right;"> 0.2612607 </td> <td style="text-align:right;"> 0.0443211 </td> <td style="text-align:right;"> 0.0600907 </td> <td style="text-align:right;"> 0.0354923 </td> <td style="text-align:right;"> 0.0846891 </td> <td style="text-align:right;"> 0.0125504 </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0084176 </td> <td style="text-align:right;"> -0.0780811 </td> <td style="text-align:right;"> 0.0612459 </td> <td style="text-align:right;"> 0.0355433 </td> <td style="text-align:right;"> 0.0668966 </td> <td style="text-align:right;"> -0.0016692 </td> <td style="text-align:right;"> 0.1354624 </td> <td style="text-align:right;"> 0.0349832 </td> <td style="text-align:right;"> 0.0763195 </td> <td style="text-align:right;"> 0.0451136 </td> <td style="text-align:right;"> 0.1075254 </td> <td style="text-align:right;"> 0.0159217 </td> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0084176 </td> <td style="text-align:right;"> -0.0780811 </td> <td style="text-align:right;"> 0.0612459 </td> <td style="text-align:right;"> 0.0355433 </td> <td style="text-align:right;"> 0.0668966 </td> <td style="text-align:right;"> -0.0016692 </td> <td style="text-align:right;"> 0.1354624 </td> <td style="text-align:right;"> 0.0349832 </td> <td style="text-align:right;"> 0.0763195 </td> <td style="text-align:right;"> 0.0451136 </td> <td style="text-align:right;"> 0.1075254 </td> <td style="text-align:right;"> 0.0159217 </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0177245 </td> <td style="text-align:right;"> -0.1256855 </td> <td style="text-align:right;"> 0.0902366 </td> <td style="text-align:right;"> 0.0550832 </td> <td style="text-align:right;"> 0.0702824 </td> <td style="text-align:right;"> -0.0302439 </td> <td style="text-align:right;"> 0.1708087 </td> <td style="text-align:right;"> 0.0512899 </td> <td style="text-align:right;"> 0.0868420 </td> <td style="text-align:right;"> 0.0493007 </td> <td style="text-align:right;"> 0.1243832 </td> <td style="text-align:right;"> 0.0191541 </td> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0177245 </td> <td style="text-align:right;"> -0.1256855 </td> <td style="text-align:right;"> 0.0902366 </td> <td style="text-align:right;"> 0.0550832 </td> <td style="text-align:right;"> 0.0702824 </td> <td style="text-align:right;"> -0.0302439 </td> <td style="text-align:right;"> 0.1708087 </td> <td style="text-align:right;"> 0.0512899 </td> <td style="text-align:right;"> 0.0868420 </td> <td style="text-align:right;"> 0.0493007 </td> <td style="text-align:right;"> 0.1243832 </td> <td style="text-align:right;"> 0.0191541 </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0408799 </td> <td style="text-align:right;"> -0.0430149 </td> <td style="text-align:right;"> 0.1247746 </td> <td style="text-align:right;"> 0.0428042 </td> <td style="text-align:right;"> 0.3714313 </td> <td style="text-align:right;"> 0.2698790 </td> <td style="text-align:right;"> 0.4729837 </td> <td style="text-align:right;"> 0.0518134 </td> <td style="text-align:right;"> 0.3282080 </td> <td style="text-align:right;"> 0.2669032 </td> <td style="text-align:right;"> 0.3895129 </td> <td style="text-align:right;"> 0.0312786 </td> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0408799 </td> <td style="text-align:right;"> -0.0430149 </td> <td style="text-align:right;"> 0.1247746 </td> <td style="text-align:right;"> 0.0428042 </td> <td style="text-align:right;"> 0.3714313 </td> <td style="text-align:right;"> 0.2698790 </td> <td style="text-align:right;"> 0.4729837 </td> <td style="text-align:right;"> 0.0518134 </td> <td style="text-align:right;"> 0.3282080 </td> <td style="text-align:right;"> 0.2669032 </td> <td style="text-align:right;"> 0.3895129 </td> <td style="text-align:right;"> 0.0312786 </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0777327 </td> <td style="text-align:right;"> -0.0119735 </td> <td style="text-align:right;"> 0.1674390 </td> <td style="text-align:right;"> 0.0457693 </td> <td style="text-align:right;"> 0.2020776 </td> <td style="text-align:right;"> 0.1083557 </td> <td style="text-align:right;"> 0.2957996 </td> <td style="text-align:right;"> 0.0478182 </td> <td style="text-align:right;"> 0.1235292 </td> <td style="text-align:right;"> 0.0638629 </td> <td style="text-align:right;"> 0.1831955 </td> <td style="text-align:right;"> 0.0304425 </td> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0777327 </td> <td style="text-align:right;"> -0.0119735 </td> <td style="text-align:right;"> 0.1674390 </td> <td style="text-align:right;"> 0.0457693 </td> <td style="text-align:right;"> 0.2020776 </td> <td style="text-align:right;"> 0.1083557 </td> <td style="text-align:right;"> 0.2957996 </td> <td style="text-align:right;"> 0.0478182 </td> <td style="text-align:right;"> 0.1235292 </td> <td style="text-align:right;"> 0.0638629 </td> <td style="text-align:right;"> 0.1831955 </td> <td style="text-align:right;"> 0.0304425 </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0578158 </td> <td style="text-align:right;"> 0.0039998 </td> <td style="text-align:right;"> 0.1116318 </td> <td style="text-align:right;"> 0.0274577 </td> <td style="text-align:right;"> 0.1145986 </td> <td style="text-align:right;"> 0.0530521 </td> <td style="text-align:right;"> 0.1761451 </td> <td style="text-align:right;"> 0.0314018 </td> <td style="text-align:right;"> 0.0541888 </td> <td style="text-align:right;"> 0.0294838 </td> <td style="text-align:right;"> 0.0788938 </td> <td style="text-align:right;"> 0.0126048 </td> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0578158 </td> <td style="text-align:right;"> 0.0039998 </td> <td style="text-align:right;"> 0.1116318 </td> <td style="text-align:right;"> 0.0274577 </td> <td style="text-align:right;"> 0.1145986 </td> <td style="text-align:right;"> 0.0530521 </td> <td style="text-align:right;"> 0.1761451 </td> <td style="text-align:right;"> 0.0314018 </td> <td style="text-align:right;"> 0.0541888 </td> <td style="text-align:right;"> 0.0294838 </td> <td style="text-align:right;"> 0.0788938 </td> <td style="text-align:right;"> 0.0126048 </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0265051 </td> <td style="text-align:right;"> -0.0187240 </td> <td style="text-align:right;"> 0.0717341 </td> <td style="text-align:right;"> 0.0230765 </td> <td style="text-align:right;"> 0.0995076 </td> <td style="text-align:right;"> 0.0539740 </td> <td style="text-align:right;"> 0.1450413 </td> <td style="text-align:right;"> 0.0232319 </td> <td style="text-align:right;"> 0.0697061 </td> <td style="text-align:right;"> 0.0438625 </td> <td style="text-align:right;"> 0.0955496 </td> <td style="text-align:right;"> 0.0131857 </td> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0265051 </td> <td style="text-align:right;"> -0.0187240 </td> <td style="text-align:right;"> 0.0717341 </td> <td style="text-align:right;"> 0.0230765 </td> <td style="text-align:right;"> 0.0995076 </td> <td style="text-align:right;"> 0.0539740 </td> <td style="text-align:right;"> 0.1450413 </td> <td style="text-align:right;"> 0.0232319 </td> <td style="text-align:right;"> 0.0697061 </td> <td style="text-align:right;"> 0.0438625 </td> <td style="text-align:right;"> 0.0955496 </td> <td style="text-align:right;"> 0.0131857 </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0148852 </td> <td style="text-align:right;"> -0.1161666 </td> <td style="text-align:right;"> 0.0863961 </td> <td style="text-align:right;"> 0.0516751 </td> <td style="text-align:right;"> -0.0575326 </td> <td style="text-align:right;"> -0.1558559 </td> <td style="text-align:right;"> 0.0407907 </td> <td style="text-align:right;"> 0.0501659 </td> <td style="text-align:right;"> -0.0413498 </td> <td style="text-align:right;"> -0.0567105 </td> <td style="text-align:right;"> -0.0259891 </td> <td style="text-align:right;"> 0.0078372 </td> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0148852 </td> <td style="text-align:right;"> -0.1161666 </td> <td style="text-align:right;"> 0.0863961 </td> <td style="text-align:right;"> 0.0516751 </td> <td style="text-align:right;"> -0.0575326 </td> <td style="text-align:right;"> -0.1558559 </td> <td style="text-align:right;"> 0.0407907 </td> <td style="text-align:right;"> 0.0501659 </td> <td style="text-align:right;"> -0.0413498 </td> <td style="text-align:right;"> -0.0567105 </td> <td style="text-align:right;"> -0.0259891 </td> <td style="text-align:right;"> 0.0078372 </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0281576 </td> <td style="text-align:right;"> -0.1002531 </td> <td style="text-align:right;"> 0.1565683 </td> <td style="text-align:right;"> 0.0655169 </td> <td style="text-align:right;"> 0.0554109 </td> <td style="text-align:right;"> -0.0736861 </td> <td style="text-align:right;"> 0.1845079 </td> <td style="text-align:right;"> 0.0658670 </td> <td style="text-align:right;"> 0.0193783 </td> <td style="text-align:right;"> -0.0093700 </td> <td style="text-align:right;"> 0.0481266 </td> <td style="text-align:right;"> 0.0146678 </td> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0281576 </td> <td style="text-align:right;"> -0.1002531 </td> <td style="text-align:right;"> 0.1565683 </td> <td style="text-align:right;"> 0.0655169 </td> <td style="text-align:right;"> 0.0554109 </td> <td style="text-align:right;"> -0.0736861 </td> <td style="text-align:right;"> 0.1845079 </td> <td style="text-align:right;"> 0.0658670 </td> <td style="text-align:right;"> 0.0193783 </td> <td style="text-align:right;"> -0.0093700 </td> <td style="text-align:right;"> 0.0481266 </td> <td style="text-align:right;"> 0.0146678 </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0397864 </td> <td style="text-align:right;"> -0.1198801 </td> <td style="text-align:right;"> 0.0403073 </td> <td style="text-align:right;"> 0.0408649 </td> <td style="text-align:right;"> -0.0678231 </td> <td style="text-align:right;"> -0.1513538 </td> <td style="text-align:right;"> 0.0157075 </td> <td style="text-align:right;"> 0.0426184 </td> <td style="text-align:right;"> -0.0283579 </td> <td style="text-align:right;"> -0.0692447 </td> <td style="text-align:right;"> 0.0125289 </td> <td style="text-align:right;"> 0.0208610 </td> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0397864 </td> <td style="text-align:right;"> -0.1198801 </td> <td style="text-align:right;"> 0.0403073 </td> <td style="text-align:right;"> 0.0408649 </td> <td style="text-align:right;"> -0.0678231 </td> <td style="text-align:right;"> -0.1513538 </td> <td style="text-align:right;"> 0.0157075 </td> <td style="text-align:right;"> 0.0426184 </td> <td style="text-align:right;"> -0.0283579 </td> <td style="text-align:right;"> -0.0692447 </td> <td style="text-align:right;"> 0.0125289 </td> <td style="text-align:right;"> 0.0208610 </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0692601 </td> <td style="text-align:right;"> 0.0184025 </td> <td style="text-align:right;"> 0.1201176 </td> <td style="text-align:right;"> 0.0259482 </td> <td style="text-align:right;"> 0.1279473 </td> <td style="text-align:right;"> 0.0423001 </td> <td style="text-align:right;"> 0.2135946 </td> <td style="text-align:right;"> 0.0436984 </td> <td style="text-align:right;"> 0.0650887 </td> <td style="text-align:right;"> 0.0218496 </td> <td style="text-align:right;"> 0.1083279 </td> <td style="text-align:right;"> 0.0220612 </td> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0692601 </td> <td style="text-align:right;"> 0.0184025 </td> <td style="text-align:right;"> 0.1201176 </td> <td style="text-align:right;"> 0.0259482 </td> <td style="text-align:right;"> 0.1279473 </td> <td style="text-align:right;"> 0.0423001 </td> <td style="text-align:right;"> 0.2135946 </td> <td style="text-align:right;"> 0.0436984 </td> <td style="text-align:right;"> 0.0650887 </td> <td style="text-align:right;"> 0.0218496 </td> <td style="text-align:right;"> 0.1083279 </td> <td style="text-align:right;"> 0.0220612 </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0650177 </td> <td style="text-align:right;"> -0.1255786 </td> <td style="text-align:right;"> -0.0044568 </td> <td style="text-align:right;"> 0.0308990 </td> <td style="text-align:right;"> 0.1724354 </td> <td style="text-align:right;"> 0.0701062 </td> <td style="text-align:right;"> 0.2747646 </td> <td style="text-align:right;"> 0.0522097 </td> <td style="text-align:right;"> 0.2606961 </td> <td style="text-align:right;"> 0.2180421 </td> <td style="text-align:right;"> 0.3033501 </td> <td style="text-align:right;"> 0.0217626 </td> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0650177 </td> <td style="text-align:right;"> -0.1255786 </td> <td style="text-align:right;"> -0.0044568 </td> <td style="text-align:right;"> 0.0308990 </td> <td style="text-align:right;"> 0.1724354 </td> <td style="text-align:right;"> 0.0701062 </td> <td style="text-align:right;"> 0.2747646 </td> <td style="text-align:right;"> 0.0522097 </td> <td style="text-align:right;"> 0.2606961 </td> <td style="text-align:right;"> 0.2180421 </td> <td style="text-align:right;"> 0.3033501 </td> <td style="text-align:right;"> 0.0217626 </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1766832 </td> <td style="text-align:right;"> 0.0672843 </td> <td style="text-align:right;"> 0.2860820 </td> <td style="text-align:right;"> 0.0558168 </td> <td style="text-align:right;"> 0.3651806 </td> <td style="text-align:right;"> 0.2169840 </td> <td style="text-align:right;"> 0.5133772 </td> <td style="text-align:right;"> 0.0756119 </td> <td style="text-align:right;"> 0.1737615 </td> <td style="text-align:right;"> 0.1409037 </td> <td style="text-align:right;"> 0.2066193 </td> <td style="text-align:right;"> 0.0167645 </td> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1766832 </td> <td style="text-align:right;"> 0.0672843 </td> <td style="text-align:right;"> 0.2860820 </td> <td style="text-align:right;"> 0.0558168 </td> <td style="text-align:right;"> 0.3651806 </td> <td style="text-align:right;"> 0.2169840 </td> <td style="text-align:right;"> 0.5133772 </td> <td style="text-align:right;"> 0.0756119 </td> <td style="text-align:right;"> 0.1737615 </td> <td style="text-align:right;"> 0.1409037 </td> <td style="text-align:right;"> 0.2066193 </td> <td style="text-align:right;"> 0.0167645 </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0794303 </td> <td style="text-align:right;"> -0.0060591 </td> <td style="text-align:right;"> 0.1649198 </td> <td style="text-align:right;"> 0.0436179 </td> <td style="text-align:right;"> 0.0355574 </td> <td style="text-align:right;"> -0.0973272 </td> <td style="text-align:right;"> 0.1684419 </td> <td style="text-align:right;"> 0.0677995 </td> <td style="text-align:right;"> -0.0495578 </td> <td style="text-align:right;"> -0.0835809 </td> <td style="text-align:right;"> -0.0155346 </td> <td style="text-align:right;"> 0.0173591 </td> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0794303 </td> <td style="text-align:right;"> -0.0060591 </td> <td style="text-align:right;"> 0.1649198 </td> <td style="text-align:right;"> 0.0436179 </td> <td style="text-align:right;"> 0.0355574 </td> <td style="text-align:right;"> -0.0973272 </td> <td style="text-align:right;"> 0.1684419 </td> <td style="text-align:right;"> 0.0677995 </td> <td style="text-align:right;"> -0.0495578 </td> <td style="text-align:right;"> -0.0835809 </td> <td style="text-align:right;"> -0.0155346 </td> <td style="text-align:right;"> 0.0173591 </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0566356 </td> <td style="text-align:right;"> -0.0516862 </td> <td style="text-align:right;"> 0.1649575 </td> <td style="text-align:right;"> 0.0552673 </td> <td style="text-align:right;"> 0.0737071 </td> <td style="text-align:right;"> -0.0328632 </td> <td style="text-align:right;"> 0.1802774 </td> <td style="text-align:right;"> 0.0543736 </td> <td style="text-align:right;"> 0.0173967 </td> <td style="text-align:right;"> 0.0035179 </td> <td style="text-align:right;"> 0.0312754 </td> <td style="text-align:right;"> 0.0070811 </td> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0566356 </td> <td style="text-align:right;"> -0.0516862 </td> <td style="text-align:right;"> 0.1649575 </td> <td style="text-align:right;"> 0.0552673 </td> <td style="text-align:right;"> 0.0737071 </td> <td style="text-align:right;"> -0.0328632 </td> <td style="text-align:right;"> 0.1802774 </td> <td style="text-align:right;"> 0.0543736 </td> <td style="text-align:right;"> 0.0173967 </td> <td style="text-align:right;"> 0.0035179 </td> <td style="text-align:right;"> 0.0312754 </td> <td style="text-align:right;"> 0.0070811 </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0867000 </td> <td style="text-align:right;"> 0.0269936 </td> <td style="text-align:right;"> 0.1464064 </td> <td style="text-align:right;"> 0.0304630 </td> <td style="text-align:right;"> 0.0867345 </td> <td style="text-align:right;"> 0.0194022 </td> <td style="text-align:right;"> 0.1540668 </td> <td style="text-align:right;"> 0.0343538 </td> <td style="text-align:right;"> 0.0051992 </td> <td style="text-align:right;"> -0.0080216 </td> <td style="text-align:right;"> 0.0184199 </td> <td style="text-align:right;"> 0.0067454 </td> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0867000 </td> <td style="text-align:right;"> 0.0269936 </td> <td style="text-align:right;"> 0.1464064 </td> <td style="text-align:right;"> 0.0304630 </td> <td style="text-align:right;"> 0.0867345 </td> <td style="text-align:right;"> 0.0194022 </td> <td style="text-align:right;"> 0.1540668 </td> <td style="text-align:right;"> 0.0343538 </td> <td style="text-align:right;"> 0.0051992 </td> <td style="text-align:right;"> -0.0080216 </td> <td style="text-align:right;"> 0.0184199 </td> <td style="text-align:right;"> 0.0067454 </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0634490 </td> <td style="text-align:right;"> -0.1734699 </td> <td style="text-align:right;"> 0.0465718 </td> <td style="text-align:right;"> 0.0561341 </td> <td style="text-align:right;"> -0.0140315 </td> <td style="text-align:right;"> -0.1488474 </td> <td style="text-align:right;"> 0.1207843 </td> <td style="text-align:right;"> 0.0687849 </td> <td style="text-align:right;"> 0.0406617 </td> <td style="text-align:right;"> -0.0139214 </td> <td style="text-align:right;"> 0.0952448 </td> <td style="text-align:right;"> 0.0278490 </td> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0634490 </td> <td style="text-align:right;"> -0.1734699 </td> <td style="text-align:right;"> 0.0465718 </td> <td style="text-align:right;"> 0.0561341 </td> <td style="text-align:right;"> -0.0140315 </td> <td style="text-align:right;"> -0.1488474 </td> <td style="text-align:right;"> 0.1207843 </td> <td style="text-align:right;"> 0.0687849 </td> <td style="text-align:right;"> 0.0406617 </td> <td style="text-align:right;"> -0.0139214 </td> <td style="text-align:right;"> 0.0952448 </td> <td style="text-align:right;"> 0.0278490 </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1722593 </td> <td style="text-align:right;"> 0.1094294 </td> <td style="text-align:right;"> 0.2350892 </td> <td style="text-align:right;"> 0.0320567 </td> <td style="text-align:right;"> -0.0813225 </td> <td style="text-align:right;"> -0.2125462 </td> <td style="text-align:right;"> 0.0499011 </td> <td style="text-align:right;"> 0.0669521 </td> <td style="text-align:right;"> -0.2504990 </td> <td style="text-align:right;"> -0.3657436 </td> <td style="text-align:right;"> -0.1352545 </td> <td style="text-align:right;"> 0.0587993 </td> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1722593 </td> <td style="text-align:right;"> 0.1094294 </td> <td style="text-align:right;"> 0.2350892 </td> <td style="text-align:right;"> 0.0320567 </td> <td style="text-align:right;"> -0.0813225 </td> <td style="text-align:right;"> -0.2125462 </td> <td style="text-align:right;"> 0.0499011 </td> <td style="text-align:right;"> 0.0669521 </td> <td style="text-align:right;"> -0.2504990 </td> <td style="text-align:right;"> -0.3657436 </td> <td style="text-align:right;"> -0.1352545 </td> <td style="text-align:right;"> 0.0587993 </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0968821 </td> <td style="text-align:right;"> -0.1503780 </td> <td style="text-align:right;"> -0.0433861 </td> <td style="text-align:right;"> 0.0272943 </td> <td style="text-align:right;"> 0.0429971 </td> <td style="text-align:right;"> 0.0141807 </td> <td style="text-align:right;"> 0.0718136 </td> <td style="text-align:right;"> 0.0147026 </td> <td style="text-align:right;"> 0.1183626 </td> <td style="text-align:right;"> 0.0853242 </td> <td style="text-align:right;"> 0.1514009 </td> <td style="text-align:right;"> 0.0168566 </td> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0968821 </td> <td style="text-align:right;"> -0.1503780 </td> <td style="text-align:right;"> -0.0433861 </td> <td style="text-align:right;"> 0.0272943 </td> <td style="text-align:right;"> 0.0429971 </td> <td style="text-align:right;"> 0.0141807 </td> <td style="text-align:right;"> 0.0718136 </td> <td style="text-align:right;"> 0.0147026 </td> <td style="text-align:right;"> 0.1183626 </td> <td style="text-align:right;"> 0.0853242 </td> <td style="text-align:right;"> 0.1514009 </td> <td style="text-align:right;"> 0.0168566 </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0993292 </td> <td style="text-align:right;"> -0.3721975 </td> <td style="text-align:right;"> 0.1735391 </td> <td style="text-align:right;"> 0.1392211 </td> <td style="text-align:right;"> 0.1774003 </td> <td style="text-align:right;"> -0.1938091 </td> <td style="text-align:right;"> 0.5486096 </td> <td style="text-align:right;"> 0.1893960 </td> <td style="text-align:right;"> 0.4445455 </td> <td style="text-align:right;"> 0.0944498 </td> <td style="text-align:right;"> 0.7946412 </td> <td style="text-align:right;"> 0.1786236 </td> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0993292 </td> <td style="text-align:right;"> -0.3721975 </td> <td style="text-align:right;"> 0.1735391 </td> <td style="text-align:right;"> 0.1392211 </td> <td style="text-align:right;"> 0.1774003 </td> <td style="text-align:right;"> -0.1938091 </td> <td style="text-align:right;"> 0.5486096 </td> <td style="text-align:right;"> 0.1893960 </td> <td style="text-align:right;"> 0.4445455 </td> <td style="text-align:right;"> 0.0944498 </td> <td style="text-align:right;"> 0.7946412 </td> <td style="text-align:right;"> 0.1786236 </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0974214 </td> <td style="text-align:right;"> -0.2141737 </td> <td style="text-align:right;"> 0.0193310 </td> <td style="text-align:right;"> 0.0595686 </td> <td style="text-align:right;"> -0.2534898 </td> <td style="text-align:right;"> -0.3963648 </td> <td style="text-align:right;"> -0.1106147 </td> <td style="text-align:right;"> 0.0728968 </td> <td style="text-align:right;"> -0.1527977 </td> <td style="text-align:right;"> -0.1930307 </td> <td style="text-align:right;"> -0.1125646 </td> <td style="text-align:right;"> 0.0205274 </td> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0974214 </td> <td style="text-align:right;"> -0.2141737 </td> <td style="text-align:right;"> 0.0193310 </td> <td style="text-align:right;"> 0.0595686 </td> <td style="text-align:right;"> -0.2534898 </td> <td style="text-align:right;"> -0.3963648 </td> <td style="text-align:right;"> -0.1106147 </td> <td style="text-align:right;"> 0.0728968 </td> <td style="text-align:right;"> -0.1527977 </td> <td style="text-align:right;"> -0.1930307 </td> <td style="text-align:right;"> -0.1125646 </td> <td style="text-align:right;"> 0.0205274 </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0941249 </td> <td style="text-align:right;"> -0.0214022 </td> <td style="text-align:right;"> 0.2096519 </td> <td style="text-align:right;"> 0.0589435 </td> <td style="text-align:right;"> 0.1409270 </td> <td style="text-align:right;"> -0.0620594 </td> <td style="text-align:right;"> 0.3439133 </td> <td style="text-align:right;"> 0.1035664 </td> <td style="text-align:right;"> 0.0318801 </td> <td style="text-align:right;"> -0.1412218 </td> <td style="text-align:right;"> 0.2049819 </td> <td style="text-align:right;"> 0.0883189 </td> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0941249 </td> <td style="text-align:right;"> -0.0214022 </td> <td style="text-align:right;"> 0.2096519 </td> <td style="text-align:right;"> 0.0589435 </td> <td style="text-align:right;"> 0.1409270 </td> <td style="text-align:right;"> -0.0620594 </td> <td style="text-align:right;"> 0.3439133 </td> <td style="text-align:right;"> 0.1035664 </td> <td style="text-align:right;"> 0.0318801 </td> <td style="text-align:right;"> -0.1412218 </td> <td style="text-align:right;"> 0.2049819 </td> <td style="text-align:right;"> 0.0883189 </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.1254239 </td> <td style="text-align:right;"> 0.0330185 </td> <td style="text-align:right;"> 0.2178293 </td> <td style="text-align:right;"> 0.0471465 </td> <td style="text-align:right;"> 0.3641221 </td> <td style="text-align:right;"> 0.2056000 </td> <td style="text-align:right;"> 0.5226441 </td> <td style="text-align:right;"> 0.0808801 </td> <td style="text-align:right;"> 0.2734519 </td> <td style="text-align:right;"> 0.0739366 </td> <td style="text-align:right;"> 0.4729672 </td> <td style="text-align:right;"> 0.1017954 </td> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.1254239 </td> <td style="text-align:right;"> 0.0330185 </td> <td style="text-align:right;"> 0.2178293 </td> <td style="text-align:right;"> 0.0471465 </td> <td style="text-align:right;"> 0.3641221 </td> <td style="text-align:right;"> 0.2056000 </td> <td style="text-align:right;"> 0.5226441 </td> <td style="text-align:right;"> 0.0808801 </td> <td style="text-align:right;"> 0.2734519 </td> <td style="text-align:right;"> 0.0739366 </td> <td style="text-align:right;"> 0.4729672 </td> <td style="text-align:right;"> 0.1017954 </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.4231644 </td> <td style="text-align:right;"> 0.1551776 </td> <td style="text-align:right;"> 0.6911512 </td> <td style="text-align:right;"> 0.1367305 </td> <td style="text-align:right;"> 0.2669283 </td> <td style="text-align:right;"> -0.0956833 </td> <td style="text-align:right;"> 0.6295400 </td> <td style="text-align:right;"> 0.1850093 </td> <td style="text-align:right;"> -0.1615499 </td> <td style="text-align:right;"> -0.6010478 </td> <td style="text-align:right;"> 0.2779480 </td> <td style="text-align:right;"> 0.2242378 </td> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.4231644 </td> <td style="text-align:right;"> 0.1551776 </td> <td style="text-align:right;"> 0.6911512 </td> <td style="text-align:right;"> 0.1367305 </td> <td style="text-align:right;"> 0.2669283 </td> <td style="text-align:right;"> -0.0956833 </td> <td style="text-align:right;"> 0.6295400 </td> <td style="text-align:right;"> 0.1850093 </td> <td style="text-align:right;"> -0.1615499 </td> <td style="text-align:right;"> -0.6010478 </td> <td style="text-align:right;"> 0.2779480 </td> <td style="text-align:right;"> 0.2242378 </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1435756 </td> <td style="text-align:right;"> 0.0759342 </td> <td style="text-align:right;"> 0.2112170 </td> <td style="text-align:right;"> 0.0345115 </td> <td style="text-align:right;"> 0.3382447 </td> <td style="text-align:right;"> 0.2664863 </td> <td style="text-align:right;"> 0.4100031 </td> <td style="text-align:right;"> 0.0366121 </td> <td style="text-align:right;"> 0.1928945 </td> <td style="text-align:right;"> 0.1752425 </td> <td style="text-align:right;"> 0.2105465 </td> <td style="text-align:right;"> 0.0090063 </td> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1435756 </td> <td style="text-align:right;"> 0.0759342 </td> <td style="text-align:right;"> 0.2112170 </td> <td style="text-align:right;"> 0.0345115 </td> <td style="text-align:right;"> 0.3382447 </td> <td style="text-align:right;"> 0.2664863 </td> <td style="text-align:right;"> 0.4100031 </td> <td style="text-align:right;"> 0.0366121 </td> <td style="text-align:right;"> 0.1928945 </td> <td style="text-align:right;"> 0.1752425 </td> <td style="text-align:right;"> 0.2105465 </td> <td style="text-align:right;"> 0.0090063 </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1953833 </td> <td style="text-align:right;"> 0.0912480 </td> <td style="text-align:right;"> 0.2995186 </td> <td style="text-align:right;"> 0.0531312 </td> <td style="text-align:right;"> 0.1840786 </td> <td style="text-align:right;"> 0.0863764 </td> <td style="text-align:right;"> 0.2817807 </td> <td style="text-align:right;"> 0.0498490 </td> <td style="text-align:right;"> -0.0122785 </td> <td style="text-align:right;"> -0.0257504 </td> <td style="text-align:right;"> 0.0011934 </td> <td style="text-align:right;"> 0.0068736 </td> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1953833 </td> <td style="text-align:right;"> 0.0912480 </td> <td style="text-align:right;"> 0.2995186 </td> <td style="text-align:right;"> 0.0531312 </td> <td style="text-align:right;"> 0.1840786 </td> <td style="text-align:right;"> 0.0863764 </td> <td style="text-align:right;"> 0.2817807 </td> <td style="text-align:right;"> 0.0498490 </td> <td style="text-align:right;"> -0.0122785 </td> <td style="text-align:right;"> -0.0257504 </td> <td style="text-align:right;"> 0.0011934 </td> <td style="text-align:right;"> 0.0068736 </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1854856 </td> <td style="text-align:right;"> -0.4305058 </td> <td style="text-align:right;"> 0.0595347 </td> <td style="text-align:right;"> 0.1250126 </td> <td style="text-align:right;"> -0.1534983 </td> <td style="text-align:right;"> -0.4007283 </td> <td style="text-align:right;"> 0.0937316 </td> <td style="text-align:right;"> 0.1261401 </td> <td style="text-align:right;"> 0.0331746 </td> <td style="text-align:right;"> 0.0284172 </td> <td style="text-align:right;"> 0.0379321 </td> <td style="text-align:right;"> 0.0024273 </td> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1854856 </td> <td style="text-align:right;"> -0.4305058 </td> <td style="text-align:right;"> 0.0595347 </td> <td style="text-align:right;"> 0.1250126 </td> <td style="text-align:right;"> -0.1534983 </td> <td style="text-align:right;"> -0.4007283 </td> <td style="text-align:right;"> 0.0937316 </td> <td style="text-align:right;"> 0.1261401 </td> <td style="text-align:right;"> 0.0331746 </td> <td style="text-align:right;"> 0.0284172 </td> <td style="text-align:right;"> 0.0379321 </td> <td style="text-align:right;"> 0.0024273 </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1446634 </td> <td style="text-align:right;"> -0.2339950 </td> <td style="text-align:right;"> -0.0553319 </td> <td style="text-align:right;"> 0.0455782 </td> <td style="text-align:right;"> -0.1352252 </td> <td style="text-align:right;"> -0.2234178 </td> <td style="text-align:right;"> -0.0470327 </td> <td style="text-align:right;"> 0.0449970 </td> <td style="text-align:right;"> 0.0075283 </td> <td style="text-align:right;"> -0.0057082 </td> <td style="text-align:right;"> 0.0207648 </td> <td style="text-align:right;"> 0.0067535 </td> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1446634 </td> <td style="text-align:right;"> -0.2339950 </td> <td style="text-align:right;"> -0.0553319 </td> <td style="text-align:right;"> 0.0455782 </td> <td style="text-align:right;"> -0.1352252 </td> <td style="text-align:right;"> -0.2234178 </td> <td style="text-align:right;"> -0.0470327 </td> <td style="text-align:right;"> 0.0449970 </td> <td style="text-align:right;"> 0.0075283 </td> <td style="text-align:right;"> -0.0057082 </td> <td style="text-align:right;"> 0.0207648 </td> <td style="text-align:right;"> 0.0067535 </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0480458 </td> <td style="text-align:right;"> -0.0360706 </td> <td style="text-align:right;"> 0.1321622 </td> <td style="text-align:right;"> 0.0429173 </td> <td style="text-align:right;"> 0.0487217 </td> <td style="text-align:right;"> -0.0347622 </td> <td style="text-align:right;"> 0.1322057 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0006956 </td> <td style="text-align:right;"> -0.0095012 </td> <td style="text-align:right;"> 0.0108923 </td> <td style="text-align:right;"> 0.0052025 </td> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0480458 </td> <td style="text-align:right;"> -0.0360706 </td> <td style="text-align:right;"> 0.1321622 </td> <td style="text-align:right;"> 0.0429173 </td> <td style="text-align:right;"> 0.0487217 </td> <td style="text-align:right;"> -0.0347622 </td> <td style="text-align:right;"> 0.1322057 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0006956 </td> <td style="text-align:right;"> -0.0095012 </td> <td style="text-align:right;"> 0.0108923 </td> <td style="text-align:right;"> 0.0052025 </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0675012 </td> <td style="text-align:right;"> -0.1511666 </td> <td style="text-align:right;"> 0.0161641 </td> <td style="text-align:right;"> 0.0426872 </td> <td style="text-align:right;"> -0.0618025 </td> <td style="text-align:right;"> -0.1452865 </td> <td style="text-align:right;"> 0.0216814 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0063811 </td> <td style="text-align:right;"> 0.0011702 </td> <td style="text-align:right;"> 0.0115921 </td> <td style="text-align:right;"> 0.0026587 </td> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0675012 </td> <td style="text-align:right;"> -0.1511666 </td> <td style="text-align:right;"> 0.0161641 </td> <td style="text-align:right;"> 0.0426872 </td> <td style="text-align:right;"> -0.0618025 </td> <td style="text-align:right;"> -0.1452865 </td> <td style="text-align:right;"> 0.0216814 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0063811 </td> <td style="text-align:right;"> 0.0011702 </td> <td style="text-align:right;"> 0.0115921 </td> <td style="text-align:right;"> 0.0026587 </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2631046 </td> <td style="text-align:right;"> -0.4734756 </td> <td style="text-align:right;"> -0.0527336 </td> <td style="text-align:right;"> 0.1073341 </td> <td style="text-align:right;"> -0.2687360 </td> <td style="text-align:right;"> -0.4790035 </td> <td style="text-align:right;"> -0.0584686 </td> <td style="text-align:right;"> 0.1072813 </td> <td style="text-align:right;"> -0.0026027 </td> <td style="text-align:right;"> -0.0146655 </td> <td style="text-align:right;"> 0.0094600 </td> <td style="text-align:right;"> 0.0061546 </td> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2631046 </td> <td style="text-align:right;"> -0.4734756 </td> <td style="text-align:right;"> -0.0527336 </td> <td style="text-align:right;"> 0.1073341 </td> <td style="text-align:right;"> -0.2687360 </td> <td style="text-align:right;"> -0.4790035 </td> <td style="text-align:right;"> -0.0584686 </td> <td style="text-align:right;"> 0.1072813 </td> <td style="text-align:right;"> -0.0026027 </td> <td style="text-align:right;"> -0.0146655 </td> <td style="text-align:right;"> 0.0094600 </td> <td style="text-align:right;"> 0.0061546 </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1975770 </td> <td style="text-align:right;"> -0.4386627 </td> <td style="text-align:right;"> 0.0435087 </td> <td style="text-align:right;"> 0.1230052 </td> <td style="text-align:right;"> -0.1932648 </td> <td style="text-align:right;"> -0.4269751 </td> <td style="text-align:right;"> 0.0404456 </td> <td style="text-align:right;"> 0.1192422 </td> <td style="text-align:right;"> 0.0027995 </td> <td style="text-align:right;"> -0.0034907 </td> <td style="text-align:right;"> 0.0090898 </td> <td style="text-align:right;"> 0.0032094 </td> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1975770 </td> <td style="text-align:right;"> -0.4386627 </td> <td style="text-align:right;"> 0.0435087 </td> <td style="text-align:right;"> 0.1230052 </td> <td style="text-align:right;"> -0.1932648 </td> <td style="text-align:right;"> -0.4269751 </td> <td style="text-align:right;"> 0.0404456 </td> <td style="text-align:right;"> 0.1192422 </td> <td style="text-align:right;"> 0.0027995 </td> <td style="text-align:right;"> -0.0034907 </td> <td style="text-align:right;"> 0.0090898 </td> <td style="text-align:right;"> 0.0032094 </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0960106 </td> <td style="text-align:right;"> 0.0224214 </td> <td style="text-align:right;"> 0.1695997 </td> <td style="text-align:right;"> 0.0375462 </td> <td style="text-align:right;"> -0.0159064 </td> <td style="text-align:right;"> -0.0579694 </td> <td style="text-align:right;"> 0.0261566 </td> <td style="text-align:right;"> 0.0214611 </td> <td style="text-align:right;"> -0.1105803 </td> <td style="text-align:right;"> -0.1761043 </td> <td style="text-align:right;"> -0.0450562 </td> <td style="text-align:right;"> 0.0334313 </td> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0960106 </td> <td style="text-align:right;"> 0.0224214 </td> <td style="text-align:right;"> 0.1695997 </td> <td style="text-align:right;"> 0.0375462 </td> <td style="text-align:right;"> -0.0159064 </td> <td style="text-align:right;"> -0.0579694 </td> <td style="text-align:right;"> 0.0261566 </td> <td style="text-align:right;"> 0.0214611 </td> <td style="text-align:right;"> -0.1105803 </td> <td style="text-align:right;"> -0.1761043 </td> <td style="text-align:right;"> -0.0450562 </td> <td style="text-align:right;"> 0.0334313 </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0228924 </td> <td style="text-align:right;"> -0.0247048 </td> <td style="text-align:right;"> 0.0704896 </td> <td style="text-align:right;"> 0.0242847 </td> <td style="text-align:right;"> 0.0454075 </td> <td style="text-align:right;"> -0.0013249 </td> <td style="text-align:right;"> 0.0921399 </td> <td style="text-align:right;"> 0.0238435 </td> <td style="text-align:right;"> 0.0246614 </td> <td style="text-align:right;"> 0.0114095 </td> <td style="text-align:right;"> 0.0379132 </td> <td style="text-align:right;"> 0.0067613 </td> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0228924 </td> <td style="text-align:right;"> -0.0247048 </td> <td style="text-align:right;"> 0.0704896 </td> <td style="text-align:right;"> 0.0242847 </td> <td style="text-align:right;"> 0.0454075 </td> <td style="text-align:right;"> -0.0013249 </td> <td style="text-align:right;"> 0.0921399 </td> <td style="text-align:right;"> 0.0238435 </td> <td style="text-align:right;"> 0.0246614 </td> <td style="text-align:right;"> 0.0114095 </td> <td style="text-align:right;"> 0.0379132 </td> <td style="text-align:right;"> 0.0067613 </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0017749 </td> <td style="text-align:right;"> -0.2517581 </td> <td style="text-align:right;"> 0.2482083 </td> <td style="text-align:right;"> 0.1275448 </td> <td style="text-align:right;"> 0.0232601 </td> <td style="text-align:right;"> -0.1776617 </td> <td style="text-align:right;"> 0.2241819 </td> <td style="text-align:right;"> 0.1025130 </td> <td style="text-align:right;"> 0.0112569 </td> <td style="text-align:right;"> -0.0306073 </td> <td style="text-align:right;"> 0.0531211 </td> <td style="text-align:right;"> 0.0213597 </td> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0017749 </td> <td style="text-align:right;"> -0.2517581 </td> <td style="text-align:right;"> 0.2482083 </td> <td style="text-align:right;"> 0.1275448 </td> <td style="text-align:right;"> 0.0232601 </td> <td style="text-align:right;"> -0.1776617 </td> <td style="text-align:right;"> 0.2241819 </td> <td style="text-align:right;"> 0.1025130 </td> <td style="text-align:right;"> 0.0112569 </td> <td style="text-align:right;"> -0.0306073 </td> <td style="text-align:right;"> 0.0531211 </td> <td style="text-align:right;"> 0.0213597 </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0453256 </td> <td style="text-align:right;"> -0.0241892 </td> <td style="text-align:right;"> 0.1148405 </td> <td style="text-align:right;"> 0.0354674 </td> <td style="text-align:right;"> 0.0981450 </td> <td style="text-align:right;"> 0.0208146 </td> <td style="text-align:right;"> 0.1754754 </td> <td style="text-align:right;"> 0.0394550 </td> <td style="text-align:right;"> 0.0528053 </td> <td style="text-align:right;"> 0.0378669 </td> <td style="text-align:right;"> 0.0677436 </td> <td style="text-align:right;"> 0.0076218 </td> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0453256 </td> <td style="text-align:right;"> -0.0241892 </td> <td style="text-align:right;"> 0.1148405 </td> <td style="text-align:right;"> 0.0354674 </td> <td style="text-align:right;"> 0.0981450 </td> <td style="text-align:right;"> 0.0208146 </td> <td style="text-align:right;"> 0.1754754 </td> <td style="text-align:right;"> 0.0394550 </td> <td style="text-align:right;"> 0.0528053 </td> <td style="text-align:right;"> 0.0378669 </td> <td style="text-align:right;"> 0.0677436 </td> <td style="text-align:right;"> 0.0076218 </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0635228 </td> <td style="text-align:right;"> -0.1990947 </td> <td style="text-align:right;"> 0.0720491 </td> <td style="text-align:right;"> 0.0691706 </td> <td style="text-align:right;"> 0.0083352 </td> <td style="text-align:right;"> -0.1335894 </td> <td style="text-align:right;"> 0.1502598 </td> <td style="text-align:right;"> 0.0724118 </td> <td style="text-align:right;"> 0.0756177 </td> <td style="text-align:right;"> 0.0525777 </td> <td style="text-align:right;"> 0.0986576 </td> <td style="text-align:right;"> 0.0117553 </td> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0635228 </td> <td style="text-align:right;"> -0.1990947 </td> <td style="text-align:right;"> 0.0720491 </td> <td style="text-align:right;"> 0.0691706 </td> <td style="text-align:right;"> 0.0083352 </td> <td style="text-align:right;"> -0.1335894 </td> <td style="text-align:right;"> 0.1502598 </td> <td style="text-align:right;"> 0.0724118 </td> <td style="text-align:right;"> 0.0756177 </td> <td style="text-align:right;"> 0.0525777 </td> <td style="text-align:right;"> 0.0986576 </td> <td style="text-align:right;"> 0.0117553 </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0317376 </td> <td style="text-align:right;"> -0.1258062 </td> <td style="text-align:right;"> 0.0623311 </td> <td style="text-align:right;"> 0.0479951 </td> <td style="text-align:right;"> -0.0104248 </td> <td style="text-align:right;"> -0.1271922 </td> <td style="text-align:right;"> 0.1063426 </td> <td style="text-align:right;"> 0.0595763 </td> <td style="text-align:right;"> 0.0302674 </td> <td style="text-align:right;"> 0.0131900 </td> <td style="text-align:right;"> 0.0473448 </td> <td style="text-align:right;"> 0.0087131 </td> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0317376 </td> <td style="text-align:right;"> -0.1258062 </td> <td style="text-align:right;"> 0.0623311 </td> <td style="text-align:right;"> 0.0479951 </td> <td style="text-align:right;"> -0.0104248 </td> <td style="text-align:right;"> -0.1271922 </td> <td style="text-align:right;"> 0.1063426 </td> <td style="text-align:right;"> 0.0595763 </td> <td style="text-align:right;"> 0.0302674 </td> <td style="text-align:right;"> 0.0131900 </td> <td style="text-align:right;"> 0.0473448 </td> <td style="text-align:right;"> 0.0087131 </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0190522 </td> <td style="text-align:right;"> -0.1014670 </td> <td style="text-align:right;"> 0.0633627 </td> <td style="text-align:right;"> 0.0420492 </td> <td style="text-align:right;"> 0.0089592 </td> <td style="text-align:right;"> -0.0823356 </td> <td style="text-align:right;"> 0.1002540 </td> <td style="text-align:right;"> 0.0465798 </td> <td style="text-align:right;"> 0.0268487 </td> <td style="text-align:right;"> 0.0063146 </td> <td style="text-align:right;"> 0.0473828 </td> <td style="text-align:right;"> 0.0104768 </td> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0190522 </td> <td style="text-align:right;"> -0.1014670 </td> <td style="text-align:right;"> 0.0633627 </td> <td style="text-align:right;"> 0.0420492 </td> <td style="text-align:right;"> 0.0089592 </td> <td style="text-align:right;"> -0.0823356 </td> <td style="text-align:right;"> 0.1002540 </td> <td style="text-align:right;"> 0.0465798 </td> <td style="text-align:right;"> 0.0268487 </td> <td style="text-align:right;"> 0.0063146 </td> <td style="text-align:right;"> 0.0473828 </td> <td style="text-align:right;"> 0.0104768 </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0161699 </td> <td style="text-align:right;"> -0.0231196 </td> <td style="text-align:right;"> 0.0554593 </td> <td style="text-align:right;"> 0.0200460 </td> <td style="text-align:right;"> -0.0513056 </td> <td style="text-align:right;"> -0.1167021 </td> <td style="text-align:right;"> 0.0140909 </td> <td style="text-align:right;"> 0.0333662 </td> <td style="text-align:right;"> -0.0413354 </td> <td style="text-align:right;"> -0.1135580 </td> <td style="text-align:right;"> 0.0308871 </td> <td style="text-align:right;"> 0.0368489 </td> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0161699 </td> <td style="text-align:right;"> -0.0231196 </td> <td style="text-align:right;"> 0.0554593 </td> <td style="text-align:right;"> 0.0200460 </td> <td style="text-align:right;"> -0.0513056 </td> <td style="text-align:right;"> -0.1167021 </td> <td style="text-align:right;"> 0.0140909 </td> <td style="text-align:right;"> 0.0333662 </td> <td style="text-align:right;"> -0.0413354 </td> <td style="text-align:right;"> -0.1135580 </td> <td style="text-align:right;"> 0.0308871 </td> <td style="text-align:right;"> 0.0368489 </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0378015 </td> <td style="text-align:right;"> -0.0880637 </td> <td style="text-align:right;"> 0.1636666 </td> <td style="text-align:right;"> 0.0642181 </td> <td style="text-align:right;"> 0.0253063 </td> <td style="text-align:right;"> -0.1086076 </td> <td style="text-align:right;"> 0.1592202 </td> <td style="text-align:right;"> 0.0683247 </td> <td style="text-align:right;"> -0.0113450 </td> <td style="text-align:right;"> -0.0150702 </td> <td style="text-align:right;"> -0.0076199 </td> <td style="text-align:right;"> 0.0019006 </td> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0378015 </td> <td style="text-align:right;"> -0.0880637 </td> <td style="text-align:right;"> 0.1636666 </td> <td style="text-align:right;"> 0.0642181 </td> <td style="text-align:right;"> 0.0253063 </td> <td style="text-align:right;"> -0.1086076 </td> <td style="text-align:right;"> 0.1592202 </td> <td style="text-align:right;"> 0.0683247 </td> <td style="text-align:right;"> -0.0113450 </td> <td style="text-align:right;"> -0.0150702 </td> <td style="text-align:right;"> -0.0076199 </td> <td style="text-align:right;"> 0.0019006 </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0039175 </td> <td style="text-align:right;"> -0.0957495 </td> <td style="text-align:right;"> 0.1035845 </td> <td style="text-align:right;"> 0.0508514 </td> <td style="text-align:right;"> -0.0030447 </td> <td style="text-align:right;"> -0.0961742 </td> <td style="text-align:right;"> 0.0900848 </td> <td style="text-align:right;"> 0.0475159 </td> <td style="text-align:right;"> -0.0063502 </td> <td style="text-align:right;"> -0.0099649 </td> <td style="text-align:right;"> -0.0027355 </td> <td style="text-align:right;"> 0.0018443 </td> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0039175 </td> <td style="text-align:right;"> -0.0957495 </td> <td style="text-align:right;"> 0.1035845 </td> <td style="text-align:right;"> 0.0508514 </td> <td style="text-align:right;"> -0.0030447 </td> <td style="text-align:right;"> -0.0961742 </td> <td style="text-align:right;"> 0.0900848 </td> <td style="text-align:right;"> 0.0475159 </td> <td style="text-align:right;"> -0.0063502 </td> <td style="text-align:right;"> -0.0099649 </td> <td style="text-align:right;"> -0.0027355 </td> <td style="text-align:right;"> 0.0018443 </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0025833 </td> <td style="text-align:right;"> -0.0653065 </td> <td style="text-align:right;"> 0.0601398 </td> <td style="text-align:right;"> 0.0320022 </td> <td style="text-align:right;"> -0.0193465 </td> <td style="text-align:right;"> -0.0824670 </td> <td style="text-align:right;"> 0.0437741 </td> <td style="text-align:right;"> 0.0322049 </td> <td style="text-align:right;"> -0.0169768 </td> <td style="text-align:right;"> -0.0197231 </td> <td style="text-align:right;"> -0.0142305 </td> <td style="text-align:right;"> 0.0014012 </td> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0025833 </td> <td style="text-align:right;"> -0.0653065 </td> <td style="text-align:right;"> 0.0601398 </td> <td style="text-align:right;"> 0.0320022 </td> <td style="text-align:right;"> -0.0193465 </td> <td style="text-align:right;"> -0.0824670 </td> <td style="text-align:right;"> 0.0437741 </td> <td style="text-align:right;"> 0.0322049 </td> <td style="text-align:right;"> -0.0169768 </td> <td style="text-align:right;"> -0.0197231 </td> <td style="text-align:right;"> -0.0142305 </td> <td style="text-align:right;"> 0.0014012 </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0487366 </td> <td style="text-align:right;"> -0.0044688 </td> <td style="text-align:right;"> 0.1019419 </td> <td style="text-align:right;"> 0.0271461 </td> <td style="text-align:right;"> 0.0353913 </td> <td style="text-align:right;"> -0.0210323 </td> <td style="text-align:right;"> 0.0918150 </td> <td style="text-align:right;"> 0.0287881 </td> <td style="text-align:right;"> -0.0174066 </td> <td style="text-align:right;"> -0.0276044 </td> <td style="text-align:right;"> -0.0072089 </td> <td style="text-align:right;"> 0.0052030 </td> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0487366 </td> <td style="text-align:right;"> -0.0044688 </td> <td style="text-align:right;"> 0.1019419 </td> <td style="text-align:right;"> 0.0271461 </td> <td style="text-align:right;"> 0.0353913 </td> <td style="text-align:right;"> -0.0210323 </td> <td style="text-align:right;"> 0.0918150 </td> <td style="text-align:right;"> 0.0287881 </td> <td style="text-align:right;"> -0.0174066 </td> <td style="text-align:right;"> -0.0276044 </td> <td style="text-align:right;"> -0.0072089 </td> <td style="text-align:right;"> 0.0052030 </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0084703 </td> <td style="text-align:right;"> -0.0282092 </td> <td style="text-align:right;"> 0.0451499 </td> <td style="text-align:right;"> 0.0187144 </td> <td style="text-align:right;"> -0.0948208 </td> <td style="text-align:right;"> -0.1630495 </td> <td style="text-align:right;"> -0.0265922 </td> <td style="text-align:right;"> 0.0348112 </td> <td style="text-align:right;"> -0.0835612 </td> <td style="text-align:right;"> -0.1503108 </td> <td style="text-align:right;"> -0.0168116 </td> <td style="text-align:right;"> 0.0340565 </td> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0084703 </td> <td style="text-align:right;"> -0.0282092 </td> <td style="text-align:right;"> 0.0451499 </td> <td style="text-align:right;"> 0.0187144 </td> <td style="text-align:right;"> -0.0948208 </td> <td style="text-align:right;"> -0.1630495 </td> <td style="text-align:right;"> -0.0265922 </td> <td style="text-align:right;"> 0.0348112 </td> <td style="text-align:right;"> -0.0835612 </td> <td style="text-align:right;"> -0.1503108 </td> <td style="text-align:right;"> -0.0168116 </td> <td style="text-align:right;"> 0.0340565 </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0284617 </td> <td style="text-align:right;"> -0.0131943 </td> <td style="text-align:right;"> 0.0701178 </td> <td style="text-align:right;"> 0.0212535 </td> <td style="text-align:right;"> -0.0876811 </td> <td style="text-align:right;"> -0.1270777 </td> <td style="text-align:right;"> -0.0482845 </td> <td style="text-align:right;"> 0.0201007 </td> <td style="text-align:right;"> -0.1130259 </td> <td style="text-align:right;"> -0.1558048 </td> <td style="text-align:right;"> -0.0702470 </td> <td style="text-align:right;"> 0.0218264 </td> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0284617 </td> <td style="text-align:right;"> -0.0131943 </td> <td style="text-align:right;"> 0.0701178 </td> <td style="text-align:right;"> 0.0212535 </td> <td style="text-align:right;"> -0.0876811 </td> <td style="text-align:right;"> -0.1270777 </td> <td style="text-align:right;"> -0.0482845 </td> <td style="text-align:right;"> 0.0201007 </td> <td style="text-align:right;"> -0.1130259 </td> <td style="text-align:right;"> -0.1558048 </td> <td style="text-align:right;"> -0.0702470 </td> <td style="text-align:right;"> 0.0218264 </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0150703 </td> <td style="text-align:right;"> -0.0534907 </td> <td style="text-align:right;"> 0.0836313 </td> <td style="text-align:right;"> 0.0349807 </td> <td style="text-align:right;"> -0.0361259 </td> <td style="text-align:right;"> -0.0952472 </td> <td style="text-align:right;"> 0.0229955 </td> <td style="text-align:right;"> 0.0301645 </td> <td style="text-align:right;"> -0.0588092 </td> <td style="text-align:right;"> -0.1679907 </td> <td style="text-align:right;"> 0.0503723 </td> <td style="text-align:right;"> 0.0557059 </td> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0150703 </td> <td style="text-align:right;"> -0.0534907 </td> <td style="text-align:right;"> 0.0836313 </td> <td style="text-align:right;"> 0.0349807 </td> <td style="text-align:right;"> -0.0361259 </td> <td style="text-align:right;"> -0.0952472 </td> <td style="text-align:right;"> 0.0229955 </td> <td style="text-align:right;"> 0.0301645 </td> <td style="text-align:right;"> -0.0588092 </td> <td style="text-align:right;"> -0.1679907 </td> <td style="text-align:right;"> 0.0503723 </td> <td style="text-align:right;"> 0.0557059 </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0011326 </td> <td style="text-align:right;"> -0.1141113 </td> <td style="text-align:right;"> 0.1118461 </td> <td style="text-align:right;"> 0.0576432 </td> <td style="text-align:right;"> 0.1869490 </td> <td style="text-align:right;"> -0.0392422 </td> <td style="text-align:right;"> 0.4131402 </td> <td style="text-align:right;"> 0.1154058 </td> <td style="text-align:right;"> 0.1794328 </td> <td style="text-align:right;"> 0.0568682 </td> <td style="text-align:right;"> 0.3019974 </td> <td style="text-align:right;"> 0.0625341 </td> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0011326 </td> <td style="text-align:right;"> -0.1141113 </td> <td style="text-align:right;"> 0.1118461 </td> <td style="text-align:right;"> 0.0576432 </td> <td style="text-align:right;"> 0.1869490 </td> <td style="text-align:right;"> -0.0392422 </td> <td style="text-align:right;"> 0.4131402 </td> <td style="text-align:right;"> 0.1154058 </td> <td style="text-align:right;"> 0.1794328 </td> <td style="text-align:right;"> 0.0568682 </td> <td style="text-align:right;"> 0.3019974 </td> <td style="text-align:right;"> 0.0625341 </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1684902 </td> <td style="text-align:right;"> -0.2596648 </td> <td style="text-align:right;"> -0.0773156 </td> <td style="text-align:right;"> 0.0465185 </td> <td style="text-align:right;"> -0.1515195 </td> <td style="text-align:right;"> -0.2435956 </td> <td style="text-align:right;"> -0.0594434 </td> <td style="text-align:right;"> 0.0469785 </td> <td style="text-align:right;"> 0.0196158 </td> <td style="text-align:right;"> 0.0049349 </td> <td style="text-align:right;"> 0.0342967 </td> <td style="text-align:right;"> 0.0074904 </td> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1684902 </td> <td style="text-align:right;"> -0.2596648 </td> <td style="text-align:right;"> -0.0773156 </td> <td style="text-align:right;"> 0.0465185 </td> <td style="text-align:right;"> -0.1515195 </td> <td style="text-align:right;"> -0.2435956 </td> <td style="text-align:right;"> -0.0594434 </td> <td style="text-align:right;"> 0.0469785 </td> <td style="text-align:right;"> 0.0196158 </td> <td style="text-align:right;"> 0.0049349 </td> <td style="text-align:right;"> 0.0342967 </td> <td style="text-align:right;"> 0.0074904 </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1732553 </td> <td style="text-align:right;"> -0.4003845 </td> <td style="text-align:right;"> 0.0538738 </td> <td style="text-align:right;"> 0.1158844 </td> <td style="text-align:right;"> -0.2572491 </td> <td style="text-align:right;"> -0.7186201 </td> <td style="text-align:right;"> 0.2041219 </td> <td style="text-align:right;"> 0.2353977 </td> <td style="text-align:right;"> -0.0915619 </td> <td style="text-align:right;"> -0.2522236 </td> <td style="text-align:right;"> 0.0690997 </td> <td style="text-align:right;"> 0.0819717 </td> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1732553 </td> <td style="text-align:right;"> -0.4003845 </td> <td style="text-align:right;"> 0.0538738 </td> <td style="text-align:right;"> 0.1158844 </td> <td style="text-align:right;"> -0.2572491 </td> <td style="text-align:right;"> -0.7186201 </td> <td style="text-align:right;"> 0.2041219 </td> <td style="text-align:right;"> 0.2353977 </td> <td style="text-align:right;"> -0.0915619 </td> <td style="text-align:right;"> -0.2522236 </td> <td style="text-align:right;"> 0.0690997 </td> <td style="text-align:right;"> 0.0819717 </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0219679 </td> <td style="text-align:right;"> -0.0863184 </td> <td style="text-align:right;"> 0.0423826 </td> <td style="text-align:right;"> 0.0328325 </td> <td style="text-align:right;"> -0.0188907 </td> <td style="text-align:right;"> -0.0912088 </td> <td style="text-align:right;"> 0.0534274 </td> <td style="text-align:right;"> 0.0368977 </td> <td style="text-align:right;"> -0.0061802 </td> <td style="text-align:right;"> -0.0972542 </td> <td style="text-align:right;"> 0.0848938 </td> <td style="text-align:right;"> 0.0464672 </td> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0219679 </td> <td style="text-align:right;"> -0.0863184 </td> <td style="text-align:right;"> 0.0423826 </td> <td style="text-align:right;"> 0.0328325 </td> <td style="text-align:right;"> -0.0188907 </td> <td style="text-align:right;"> -0.0912088 </td> <td style="text-align:right;"> 0.0534274 </td> <td style="text-align:right;"> 0.0368977 </td> <td style="text-align:right;"> -0.0061802 </td> <td style="text-align:right;"> -0.0972542 </td> <td style="text-align:right;"> 0.0848938 </td> <td style="text-align:right;"> 0.0464672 </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0444272 </td> <td style="text-align:right;"> -0.1082870 </td> <td style="text-align:right;"> 0.0194327 </td> <td style="text-align:right;"> 0.0325822 </td> <td style="text-align:right;"> -0.1401304 </td> <td style="text-align:right;"> -0.2117709 </td> <td style="text-align:right;"> -0.0684898 </td> <td style="text-align:right;"> 0.0365520 </td> <td style="text-align:right;"> -0.0963838 </td> <td style="text-align:right;"> -0.1446043 </td> <td style="text-align:right;"> -0.0481633 </td> <td style="text-align:right;"> 0.0246028 </td> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0444272 </td> <td style="text-align:right;"> -0.1082870 </td> <td style="text-align:right;"> 0.0194327 </td> <td style="text-align:right;"> 0.0325822 </td> <td style="text-align:right;"> -0.1401304 </td> <td style="text-align:right;"> -0.2117709 </td> <td style="text-align:right;"> -0.0684898 </td> <td style="text-align:right;"> 0.0365520 </td> <td style="text-align:right;"> -0.0963838 </td> <td style="text-align:right;"> -0.1446043 </td> <td style="text-align:right;"> -0.0481633 </td> <td style="text-align:right;"> 0.0246028 </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0313217 </td> <td style="text-align:right;"> -0.0918314 </td> <td style="text-align:right;"> 0.0291879 </td> <td style="text-align:right;"> 0.0308728 </td> <td style="text-align:right;"> -0.1342236 </td> <td style="text-align:right;"> -0.1874097 </td> <td style="text-align:right;"> -0.0810376 </td> <td style="text-align:right;"> 0.0271362 </td> <td style="text-align:right;"> -0.1037239 </td> <td style="text-align:right;"> -0.1714836 </td> <td style="text-align:right;"> -0.0359643 </td> <td style="text-align:right;"> 0.0345719 </td> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0313217 </td> <td style="text-align:right;"> -0.0918314 </td> <td style="text-align:right;"> 0.0291879 </td> <td style="text-align:right;"> 0.0308728 </td> <td style="text-align:right;"> -0.1342236 </td> <td style="text-align:right;"> -0.1874097 </td> <td style="text-align:right;"> -0.0810376 </td> <td style="text-align:right;"> 0.0271362 </td> <td style="text-align:right;"> -0.1037239 </td> <td style="text-align:right;"> -0.1714836 </td> <td style="text-align:right;"> -0.0359643 </td> <td style="text-align:right;"> 0.0345719 </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0369177 </td> <td style="text-align:right;"> -0.1277076 </td> <td style="text-align:right;"> 0.0538721 </td> <td style="text-align:right;"> 0.0463222 </td> <td style="text-align:right;"> -0.0294978 </td> <td style="text-align:right;"> -0.1006346 </td> <td style="text-align:right;"> 0.0416390 </td> <td style="text-align:right;"> 0.0362950 </td> <td style="text-align:right;"> 0.0077038 </td> <td style="text-align:right;"> -0.0137850 </td> <td style="text-align:right;"> 0.0291927 </td> <td style="text-align:right;"> 0.0109639 </td> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0369177 </td> <td style="text-align:right;"> -0.1277076 </td> <td style="text-align:right;"> 0.0538721 </td> <td style="text-align:right;"> 0.0463222 </td> <td style="text-align:right;"> -0.0294978 </td> <td style="text-align:right;"> -0.1006346 </td> <td style="text-align:right;"> 0.0416390 </td> <td style="text-align:right;"> 0.0362950 </td> <td style="text-align:right;"> 0.0077038 </td> <td style="text-align:right;"> -0.0137850 </td> <td style="text-align:right;"> 0.0291927 </td> <td style="text-align:right;"> 0.0109639 </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0536346 </td> <td style="text-align:right;"> -0.1266045 </td> <td style="text-align:right;"> 0.0193353 </td> <td style="text-align:right;"> 0.0372302 </td> <td style="text-align:right;"> -0.0572459 </td> <td style="text-align:right;"> -0.1071515 </td> <td style="text-align:right;"> -0.0073404 </td> <td style="text-align:right;"> 0.0254625 </td> <td style="text-align:right;"> 0.0026007 </td> <td style="text-align:right;"> -0.0558538 </td> <td style="text-align:right;"> 0.0610552 </td> <td style="text-align:right;"> 0.0298243 </td> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0536346 </td> <td style="text-align:right;"> -0.1266045 </td> <td style="text-align:right;"> 0.0193353 </td> <td style="text-align:right;"> 0.0372302 </td> <td style="text-align:right;"> -0.0572459 </td> <td style="text-align:right;"> -0.1071515 </td> <td style="text-align:right;"> -0.0073404 </td> <td style="text-align:right;"> 0.0254625 </td> <td style="text-align:right;"> 0.0026007 </td> <td style="text-align:right;"> -0.0558538 </td> <td style="text-align:right;"> 0.0610552 </td> <td style="text-align:right;"> 0.0298243 </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0485897 </td> <td style="text-align:right;"> -0.0839101 </td> <td style="text-align:right;"> -0.0132693 </td> <td style="text-align:right;"> 0.0180209 </td> <td style="text-align:right;"> -0.0826120 </td> <td style="text-align:right;"> -0.1576473 </td> <td style="text-align:right;"> -0.0075767 </td> <td style="text-align:right;"> 0.0382840 </td> <td style="text-align:right;"> -0.0420616 </td> <td style="text-align:right;"> -0.0813582 </td> <td style="text-align:right;"> -0.0027650 </td> <td style="text-align:right;"> 0.0200497 </td> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0485897 </td> <td style="text-align:right;"> -0.0839101 </td> <td style="text-align:right;"> -0.0132693 </td> <td style="text-align:right;"> 0.0180209 </td> <td style="text-align:right;"> -0.0826120 </td> <td style="text-align:right;"> -0.1576473 </td> <td style="text-align:right;"> -0.0075767 </td> <td style="text-align:right;"> 0.0382840 </td> <td style="text-align:right;"> -0.0420616 </td> <td style="text-align:right;"> -0.0813582 </td> <td style="text-align:right;"> -0.0027650 </td> <td style="text-align:right;"> 0.0200497 </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0737198 </td> <td style="text-align:right;"> 0.0205862 </td> <td style="text-align:right;"> 0.1268534 </td> <td style="text-align:right;"> 0.0271095 </td> <td style="text-align:right;"> 0.2415135 </td> <td style="text-align:right;"> 0.1865330 </td> <td style="text-align:right;"> 0.2964940 </td> <td style="text-align:right;"> 0.0280518 </td> <td style="text-align:right;"> 0.1642192 </td> <td style="text-align:right;"> 0.1369820 </td> <td style="text-align:right;"> 0.1914563 </td> <td style="text-align:right;"> 0.0138968 </td> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0737198 </td> <td style="text-align:right;"> 0.0205862 </td> <td style="text-align:right;"> 0.1268534 </td> <td style="text-align:right;"> 0.0271095 </td> <td style="text-align:right;"> 0.2415135 </td> <td style="text-align:right;"> 0.1865330 </td> <td style="text-align:right;"> 0.2964940 </td> <td style="text-align:right;"> 0.0280518 </td> <td style="text-align:right;"> 0.1642192 </td> <td style="text-align:right;"> 0.1369820 </td> <td style="text-align:right;"> 0.1914563 </td> <td style="text-align:right;"> 0.0138968 </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.2906905 </td> <td style="text-align:right;"> 0.1716202 </td> <td style="text-align:right;"> 0.4097607 </td> <td style="text-align:right;"> 0.0607512 </td> <td style="text-align:right;"> -0.2926013 </td> <td style="text-align:right;"> -0.5272121 </td> <td style="text-align:right;"> -0.0579905 </td> <td style="text-align:right;"> 0.1197016 </td> <td style="text-align:right;"> -0.6004767 </td> <td style="text-align:right;"> -0.9244113 </td> <td style="text-align:right;"> -0.2765420 </td> <td style="text-align:right;"> 0.1652758 </td> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.2906905 </td> <td style="text-align:right;"> 0.1716202 </td> <td style="text-align:right;"> 0.4097607 </td> <td style="text-align:right;"> 0.0607512 </td> <td style="text-align:right;"> -0.2926013 </td> <td style="text-align:right;"> -0.5272121 </td> <td style="text-align:right;"> -0.0579905 </td> <td style="text-align:right;"> 0.1197016 </td> <td style="text-align:right;"> -0.6004767 </td> <td style="text-align:right;"> -0.9244113 </td> <td style="text-align:right;"> -0.2765420 </td> <td style="text-align:right;"> 0.1652758 </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0705522 </td> <td style="text-align:right;"> -0.2214989 </td> <td style="text-align:right;"> 0.0803945 </td> <td style="text-align:right;"> 0.0770150 </td> <td style="text-align:right;"> -0.0074675 </td> <td style="text-align:right;"> -0.1729366 </td> <td style="text-align:right;"> 0.1580015 </td> <td style="text-align:right;"> 0.0844245 </td> <td style="text-align:right;"> 0.0704162 </td> <td style="text-align:right;"> 0.0476647 </td> <td style="text-align:right;"> 0.0931676 </td> <td style="text-align:right;"> 0.0116081 </td> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0705522 </td> <td style="text-align:right;"> -0.2214989 </td> <td style="text-align:right;"> 0.0803945 </td> <td style="text-align:right;"> 0.0770150 </td> <td style="text-align:right;"> -0.0074675 </td> <td style="text-align:right;"> -0.1729366 </td> <td style="text-align:right;"> 0.1580015 </td> <td style="text-align:right;"> 0.0844245 </td> <td style="text-align:right;"> 0.0704162 </td> <td style="text-align:right;"> 0.0476647 </td> <td style="text-align:right;"> 0.0931676 </td> <td style="text-align:right;"> 0.0116081 </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0650960 </td> <td style="text-align:right;"> -0.1538776 </td> <td style="text-align:right;"> 0.0236857 </td> <td style="text-align:right;"> 0.0452976 </td> <td style="text-align:right;"> -0.0648322 </td> <td style="text-align:right;"> -0.1270688 </td> <td style="text-align:right;"> -0.0025955 </td> <td style="text-align:right;"> 0.0317540 </td> <td style="text-align:right;"> 0.0015656 </td> <td style="text-align:right;"> -0.0259865 </td> <td style="text-align:right;"> 0.0291178 </td> <td style="text-align:right;"> 0.0140575 </td> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0650960 </td> <td style="text-align:right;"> -0.1538776 </td> <td style="text-align:right;"> 0.0236857 </td> <td style="text-align:right;"> 0.0452976 </td> <td style="text-align:right;"> -0.0648322 </td> <td style="text-align:right;"> -0.1270688 </td> <td style="text-align:right;"> -0.0025955 </td> <td style="text-align:right;"> 0.0317540 </td> <td style="text-align:right;"> 0.0015656 </td> <td style="text-align:right;"> -0.0259865 </td> <td style="text-align:right;"> 0.0291178 </td> <td style="text-align:right;"> 0.0140575 </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0564860 </td> <td style="text-align:right;"> -0.1048371 </td> <td style="text-align:right;"> -0.0081349 </td> <td style="text-align:right;"> 0.0246694 </td> <td style="text-align:right;"> -0.0754718 </td> <td style="text-align:right;"> -0.1235224 </td> <td style="text-align:right;"> -0.0274213 </td> <td style="text-align:right;"> 0.0245160 </td> <td style="text-align:right;"> -0.0183785 </td> <td style="text-align:right;"> -0.0319887 </td> <td style="text-align:right;"> -0.0047684 </td> <td style="text-align:right;"> 0.0069441 </td> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0564860 </td> <td style="text-align:right;"> -0.1048371 </td> <td style="text-align:right;"> -0.0081349 </td> <td style="text-align:right;"> 0.0246694 </td> <td style="text-align:right;"> -0.0754718 </td> <td style="text-align:right;"> -0.1235224 </td> <td style="text-align:right;"> -0.0274213 </td> <td style="text-align:right;"> 0.0245160 </td> <td style="text-align:right;"> -0.0183785 </td> <td style="text-align:right;"> -0.0319887 </td> <td style="text-align:right;"> -0.0047684 </td> <td style="text-align:right;"> 0.0069441 </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0725454 </td> <td style="text-align:right;"> 0.0354722 </td> <td style="text-align:right;"> 0.1096185 </td> <td style="text-align:right;"> 0.0189152 </td> <td style="text-align:right;"> 0.0681074 </td> <td style="text-align:right;"> 0.0300869 </td> <td style="text-align:right;"> 0.1061278 </td> <td style="text-align:right;"> 0.0193986 </td> <td style="text-align:right;"> -0.0054233 </td> <td style="text-align:right;"> -0.0154885 </td> <td style="text-align:right;"> 0.0046418 </td> <td style="text-align:right;"> 0.0051354 </td> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0725454 </td> <td style="text-align:right;"> 0.0354722 </td> <td style="text-align:right;"> 0.1096185 </td> <td style="text-align:right;"> 0.0189152 </td> <td style="text-align:right;"> 0.0681074 </td> <td style="text-align:right;"> 0.0300869 </td> <td style="text-align:right;"> 0.1061278 </td> <td style="text-align:right;"> 0.0193986 </td> <td style="text-align:right;"> -0.0054233 </td> <td style="text-align:right;"> -0.0154885 </td> <td style="text-align:right;"> 0.0046418 </td> <td style="text-align:right;"> 0.0051354 </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0328106 </td> <td style="text-align:right;"> -0.0101032 </td> <td style="text-align:right;"> 0.0757244 </td> <td style="text-align:right;"> 0.0218952 </td> <td style="text-align:right;"> 0.0310473 </td> <td style="text-align:right;"> -0.0207365 </td> <td style="text-align:right;"> 0.0828310 </td> <td style="text-align:right;"> 0.0264208 </td> <td style="text-align:right;"> -0.0005046 </td> <td style="text-align:right;"> -0.0085696 </td> <td style="text-align:right;"> 0.0075604 </td> <td style="text-align:right;"> 0.0041149 </td> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0328106 </td> <td style="text-align:right;"> -0.0101032 </td> <td style="text-align:right;"> 0.0757244 </td> <td style="text-align:right;"> 0.0218952 </td> <td style="text-align:right;"> 0.0310473 </td> <td style="text-align:right;"> -0.0207365 </td> <td style="text-align:right;"> 0.0828310 </td> <td style="text-align:right;"> 0.0264208 </td> <td style="text-align:right;"> -0.0005046 </td> <td style="text-align:right;"> -0.0085696 </td> <td style="text-align:right;"> 0.0075604 </td> <td style="text-align:right;"> 0.0041149 </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0031258 </td> <td style="text-align:right;"> -0.0523919 </td> <td style="text-align:right;"> 0.0586435 </td> <td style="text-align:right;"> 0.0283259 </td> <td style="text-align:right;"> -0.0046501 </td> <td style="text-align:right;"> -0.1060530 </td> <td style="text-align:right;"> 0.0967528 </td> <td style="text-align:right;"> 0.0517371 </td> <td style="text-align:right;"> -0.0077373 </td> <td style="text-align:right;"> -0.0510162 </td> <td style="text-align:right;"> 0.0355416 </td> <td style="text-align:right;"> 0.0220815 </td> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0031258 </td> <td style="text-align:right;"> -0.0523919 </td> <td style="text-align:right;"> 0.0586435 </td> <td style="text-align:right;"> 0.0283259 </td> <td style="text-align:right;"> -0.0046501 </td> <td style="text-align:right;"> -0.1060530 </td> <td style="text-align:right;"> 0.0967528 </td> <td style="text-align:right;"> 0.0517371 </td> <td style="text-align:right;"> -0.0077373 </td> <td style="text-align:right;"> -0.0510162 </td> <td style="text-align:right;"> 0.0355416 </td> <td style="text-align:right;"> 0.0220815 </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0773455 </td> <td style="text-align:right;"> 0.0071933 </td> <td style="text-align:right;"> 0.1474977 </td> <td style="text-align:right;"> 0.0357926 </td> <td style="text-align:right;"> 0.0997278 </td> <td style="text-align:right;"> 0.0316996 </td> <td style="text-align:right;"> 0.1677560 </td> <td style="text-align:right;"> 0.0347089 </td> <td style="text-align:right;"> 0.0228493 </td> <td style="text-align:right;"> 0.0088583 </td> <td style="text-align:right;"> 0.0368404 </td> <td style="text-align:right;"> 0.0071384 </td> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0773455 </td> <td style="text-align:right;"> 0.0071933 </td> <td style="text-align:right;"> 0.1474977 </td> <td style="text-align:right;"> 0.0357926 </td> <td style="text-align:right;"> 0.0997278 </td> <td style="text-align:right;"> 0.0316996 </td> <td style="text-align:right;"> 0.1677560 </td> <td style="text-align:right;"> 0.0347089 </td> <td style="text-align:right;"> 0.0228493 </td> <td style="text-align:right;"> 0.0088583 </td> <td style="text-align:right;"> 0.0368404 </td> <td style="text-align:right;"> 0.0071384 </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.1248464 </td> <td style="text-align:right;"> -0.0035148 </td> <td style="text-align:right;"> 0.2532076 </td> <td style="text-align:right;"> 0.0654916 </td> <td style="text-align:right;"> 0.1353460 </td> <td style="text-align:right;"> -0.0035862 </td> <td style="text-align:right;"> 0.2742782 </td> <td style="text-align:right;"> 0.0708851 </td> <td style="text-align:right;"> 0.0104789 </td> <td style="text-align:right;"> -0.0032056 </td> <td style="text-align:right;"> 0.0241635 </td> <td style="text-align:right;"> 0.0069821 </td> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.1248464 </td> <td style="text-align:right;"> -0.0035148 </td> <td style="text-align:right;"> 0.2532076 </td> <td style="text-align:right;"> 0.0654916 </td> <td style="text-align:right;"> 0.1353460 </td> <td style="text-align:right;"> -0.0035862 </td> <td style="text-align:right;"> 0.2742782 </td> <td style="text-align:right;"> 0.0708851 </td> <td style="text-align:right;"> 0.0104789 </td> <td style="text-align:right;"> -0.0032056 </td> <td style="text-align:right;"> 0.0241635 </td> <td style="text-align:right;"> 0.0069821 </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.1384843 </td> <td style="text-align:right;"> -0.2178736 </td> <td style="text-align:right;"> -0.0590950 </td> <td style="text-align:right;"> 0.0405055 </td> <td style="text-align:right;"> -0.0703570 </td> <td style="text-align:right;"> -0.1795875 </td> <td style="text-align:right;"> 0.0388735 </td> <td style="text-align:right;"> 0.0557309 </td> <td style="text-align:right;"> 0.0611034 </td> <td style="text-align:right;"> 0.0227141 </td> <td style="text-align:right;"> 0.0994926 </td> <td style="text-align:right;"> 0.0195867 </td> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.1384843 </td> <td style="text-align:right;"> -0.2178736 </td> <td style="text-align:right;"> -0.0590950 </td> <td style="text-align:right;"> 0.0405055 </td> <td style="text-align:right;"> -0.0703570 </td> <td style="text-align:right;"> -0.1795875 </td> <td style="text-align:right;"> 0.0388735 </td> <td style="text-align:right;"> 0.0557309 </td> <td style="text-align:right;"> 0.0611034 </td> <td style="text-align:right;"> 0.0227141 </td> <td style="text-align:right;"> 0.0994926 </td> <td style="text-align:right;"> 0.0195867 </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0116565 </td> <td style="text-align:right;"> -0.0896490 </td> <td style="text-align:right;"> 0.0663361 </td> <td style="text-align:right;"> 0.0397928 </td> <td style="text-align:right;"> -0.0106530 </td> <td style="text-align:right;"> -0.0878483 </td> <td style="text-align:right;"> 0.0665424 </td> <td style="text-align:right;"> 0.0393861 </td> <td style="text-align:right;"> 0.0017027 </td> <td style="text-align:right;"> -0.0057348 </td> <td style="text-align:right;"> 0.0091402 </td> <td style="text-align:right;"> 0.0037947 </td> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0116565 </td> <td style="text-align:right;"> -0.0896490 </td> <td style="text-align:right;"> 0.0663361 </td> <td style="text-align:right;"> 0.0397928 </td> <td style="text-align:right;"> -0.0106530 </td> <td style="text-align:right;"> -0.0878483 </td> <td style="text-align:right;"> 0.0665424 </td> <td style="text-align:right;"> 0.0393861 </td> <td style="text-align:right;"> 0.0017027 </td> <td style="text-align:right;"> -0.0057348 </td> <td style="text-align:right;"> 0.0091402 </td> <td style="text-align:right;"> 0.0037947 </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.4491432 </td> <td style="text-align:right;"> -1.3293546 </td> <td style="text-align:right;"> 0.4310682 </td> <td style="text-align:right;"> 0.4490957 </td> <td style="text-align:right;"> -0.4157377 </td> <td style="text-align:right;"> -1.2918620 </td> <td style="text-align:right;"> 0.4603867 </td> <td style="text-align:right;"> 0.4470104 </td> <td style="text-align:right;"> 0.0316098 </td> <td style="text-align:right;"> 0.0264512 </td> <td style="text-align:right;"> 0.0367685 </td> <td style="text-align:right;"> 0.0026320 </td> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.4491432 </td> <td style="text-align:right;"> -1.3293546 </td> <td style="text-align:right;"> 0.4310682 </td> <td style="text-align:right;"> 0.4490957 </td> <td style="text-align:right;"> -0.4157377 </td> <td style="text-align:right;"> -1.2918620 </td> <td style="text-align:right;"> 0.4603867 </td> <td style="text-align:right;"> 0.4470104 </td> <td style="text-align:right;"> 0.0316098 </td> <td style="text-align:right;"> 0.0264512 </td> <td style="text-align:right;"> 0.0367685 </td> <td style="text-align:right;"> 0.0026320 </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0355898 </td> <td style="text-align:right;"> -0.2280522 </td> <td style="text-align:right;"> 0.1568726 </td> <td style="text-align:right;"> 0.0981969 </td> <td style="text-align:right;"> -0.0306550 </td> <td style="text-align:right;"> -0.1963692 </td> <td style="text-align:right;"> 0.1350592 </td> <td style="text-align:right;"> 0.0845496 </td> <td style="text-align:right;"> -0.0013855 </td> <td style="text-align:right;"> -0.0237830 </td> <td style="text-align:right;"> 0.0210121 </td> <td style="text-align:right;"> 0.0114275 </td> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0355898 </td> <td style="text-align:right;"> -0.2280522 </td> <td style="text-align:right;"> 0.1568726 </td> <td style="text-align:right;"> 0.0981969 </td> <td style="text-align:right;"> -0.0306550 </td> <td style="text-align:right;"> -0.1963692 </td> <td style="text-align:right;"> 0.1350592 </td> <td style="text-align:right;"> 0.0845496 </td> <td style="text-align:right;"> -0.0013855 </td> <td style="text-align:right;"> -0.0237830 </td> <td style="text-align:right;"> 0.0210121 </td> <td style="text-align:right;"> 0.0114275 </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2545083 </td> <td style="text-align:right;"> -0.7633116 </td> <td style="text-align:right;"> 0.2542949 </td> <td style="text-align:right;"> 0.2595983 </td> <td style="text-align:right;"> -0.2785114 </td> <td style="text-align:right;"> -0.8373133 </td> <td style="text-align:right;"> 0.2802906 </td> <td style="text-align:right;"> 0.2851083 </td> <td style="text-align:right;"> -0.0175090 </td> <td style="text-align:right;"> -0.0664158 </td> <td style="text-align:right;"> 0.0313978 </td> <td style="text-align:right;"> 0.0249529 </td> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2545083 </td> <td style="text-align:right;"> -0.7633116 </td> <td style="text-align:right;"> 0.2542949 </td> <td style="text-align:right;"> 0.2595983 </td> <td style="text-align:right;"> -0.2785114 </td> <td style="text-align:right;"> -0.8373133 </td> <td style="text-align:right;"> 0.2802906 </td> <td style="text-align:right;"> 0.2851083 </td> <td style="text-align:right;"> -0.0175090 </td> <td style="text-align:right;"> -0.0664158 </td> <td style="text-align:right;"> 0.0313978 </td> <td style="text-align:right;"> 0.0249529 </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0061253 </td> <td style="text-align:right;"> -0.0781241 </td> <td style="text-align:right;"> 0.0903746 </td> <td style="text-align:right;"> 0.0429851 </td> <td style="text-align:right;"> 0.0109098 </td> <td style="text-align:right;"> -0.0731427 </td> <td style="text-align:right;"> 0.0949622 </td> <td style="text-align:right;"> 0.0428847 </td> <td style="text-align:right;"> 0.0055513 </td> <td style="text-align:right;"> 0.0000519 </td> <td style="text-align:right;"> 0.0110508 </td> <td style="text-align:right;"> 0.0028059 </td> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0061253 </td> <td style="text-align:right;"> -0.0781241 </td> <td style="text-align:right;"> 0.0903746 </td> <td style="text-align:right;"> 0.0429851 </td> <td style="text-align:right;"> 0.0109098 </td> <td style="text-align:right;"> -0.0731427 </td> <td style="text-align:right;"> 0.0949622 </td> <td style="text-align:right;"> 0.0428847 </td> <td style="text-align:right;"> 0.0055513 </td> <td style="text-align:right;"> 0.0000519 </td> <td style="text-align:right;"> 0.0110508 </td> <td style="text-align:right;"> 0.0028059 </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0775673 </td> <td style="text-align:right;"> -0.2905688 </td> <td style="text-align:right;"> 0.1354341 </td> <td style="text-align:right;"> 0.1086762 </td> <td style="text-align:right;"> -0.0764571 </td> <td style="text-align:right;"> -0.2893152 </td> <td style="text-align:right;"> 0.1364010 </td> <td style="text-align:right;"> 0.1086031 </td> <td style="text-align:right;"> 0.0071990 </td> <td style="text-align:right;"> -0.0178434 </td> <td style="text-align:right;"> 0.0322413 </td> <td style="text-align:right;"> 0.0127769 </td> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0775673 </td> <td style="text-align:right;"> -0.2905688 </td> <td style="text-align:right;"> 0.1354341 </td> <td style="text-align:right;"> 0.1086762 </td> <td style="text-align:right;"> -0.0764571 </td> <td style="text-align:right;"> -0.2893152 </td> <td style="text-align:right;"> 0.1364010 </td> <td style="text-align:right;"> 0.1086031 </td> <td style="text-align:right;"> 0.0071990 </td> <td style="text-align:right;"> -0.0178434 </td> <td style="text-align:right;"> 0.0322413 </td> <td style="text-align:right;"> 0.0127769 </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1987993 </td> <td style="text-align:right;"> -0.6457320 </td> <td style="text-align:right;"> 0.2481333 </td> <td style="text-align:right;"> 0.2280310 </td> <td style="text-align:right;"> -0.1925482 </td> <td style="text-align:right;"> -0.6285715 </td> <td style="text-align:right;"> 0.2434750 </td> <td style="text-align:right;"> 0.2224649 </td> <td style="text-align:right;"> 0.0052882 </td> <td style="text-align:right;"> -0.0045957 </td> <td style="text-align:right;"> 0.0151720 </td> <td style="text-align:right;"> 0.0050429 </td> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1987993 </td> <td style="text-align:right;"> -0.6457320 </td> <td style="text-align:right;"> 0.2481333 </td> <td style="text-align:right;"> 0.2280310 </td> <td style="text-align:right;"> -0.1925482 </td> <td style="text-align:right;"> -0.6285715 </td> <td style="text-align:right;"> 0.2434750 </td> <td style="text-align:right;"> 0.2224649 </td> <td style="text-align:right;"> 0.0052882 </td> <td style="text-align:right;"> -0.0045957 </td> <td style="text-align:right;"> 0.0151720 </td> <td style="text-align:right;"> 0.0050429 </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1800273 </td> <td style="text-align:right;"> -0.0882317 </td> <td style="text-align:right;"> 0.4482864 </td> <td style="text-align:right;"> 0.1368694 </td> <td style="text-align:right;"> -0.0161048 </td> <td style="text-align:right;"> -0.4112809 </td> <td style="text-align:right;"> 0.3790712 </td> <td style="text-align:right;"> 0.2016241 </td> <td style="text-align:right;"> -0.1178703 </td> <td style="text-align:right;"> -0.2449843 </td> <td style="text-align:right;"> 0.0092436 </td> <td style="text-align:right;"> 0.0648552 </td> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1800273 </td> <td style="text-align:right;"> -0.0882317 </td> <td style="text-align:right;"> 0.4482864 </td> <td style="text-align:right;"> 0.1368694 </td> <td style="text-align:right;"> -0.0161048 </td> <td style="text-align:right;"> -0.4112809 </td> <td style="text-align:right;"> 0.3790712 </td> <td style="text-align:right;"> 0.2016241 </td> <td style="text-align:right;"> -0.1178703 </td> <td style="text-align:right;"> -0.2449843 </td> <td style="text-align:right;"> 0.0092436 </td> <td style="text-align:right;"> 0.0648552 </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0765771 </td> <td style="text-align:right;"> -0.3398075 </td> <td style="text-align:right;"> 0.1866533 </td> <td style="text-align:right;"> 0.1343037 </td> <td style="text-align:right;"> -0.0747691 </td> <td style="text-align:right;"> -0.3351316 </td> <td style="text-align:right;"> 0.1855933 </td> <td style="text-align:right;"> 0.1328404 </td> <td style="text-align:right;"> -0.0746396 </td> <td style="text-align:right;"> -0.2072980 </td> <td style="text-align:right;"> 0.0580188 </td> <td style="text-align:right;"> 0.0676841 </td> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0765771 </td> <td style="text-align:right;"> -0.3398075 </td> <td style="text-align:right;"> 0.1866533 </td> <td style="text-align:right;"> 0.1343037 </td> <td style="text-align:right;"> -0.0747691 </td> <td style="text-align:right;"> -0.3351316 </td> <td style="text-align:right;"> 0.1855933 </td> <td style="text-align:right;"> 0.1328404 </td> <td style="text-align:right;"> -0.0746396 </td> <td style="text-align:right;"> -0.2072980 </td> <td style="text-align:right;"> 0.0580188 </td> <td style="text-align:right;"> 0.0676841 </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0761505 </td> <td style="text-align:right;"> -0.1876687 </td> <td style="text-align:right;"> 0.0353678 </td> <td style="text-align:right;"> 0.0568981 </td> <td style="text-align:right;"> -0.0896869 </td> <td style="text-align:right;"> -0.2063458 </td> <td style="text-align:right;"> 0.0269721 </td> <td style="text-align:right;"> 0.0595210 </td> <td style="text-align:right;"> -0.0125023 </td> <td style="text-align:right;"> -0.0214082 </td> <td style="text-align:right;"> -0.0035963 </td> <td style="text-align:right;"> 0.0045440 </td> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0761505 </td> <td style="text-align:right;"> -0.1876687 </td> <td style="text-align:right;"> 0.0353678 </td> <td style="text-align:right;"> 0.0568981 </td> <td style="text-align:right;"> -0.0896869 </td> <td style="text-align:right;"> -0.2063458 </td> <td style="text-align:right;"> 0.0269721 </td> <td style="text-align:right;"> 0.0595210 </td> <td style="text-align:right;"> -0.0125023 </td> <td style="text-align:right;"> -0.0214082 </td> <td style="text-align:right;"> -0.0035963 </td> <td style="text-align:right;"> 0.0045440 </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0262100 </td> <td style="text-align:right;"> -0.1171674 </td> <td style="text-align:right;"> 0.1695873 </td> <td style="text-align:right;"> 0.0731531 </td> <td style="text-align:right;"> 0.0338228 </td> <td style="text-align:right;"> -0.1337162 </td> <td style="text-align:right;"> 0.2013618 </td> <td style="text-align:right;"> 0.0854806 </td> <td style="text-align:right;"> 0.0099680 </td> <td style="text-align:right;"> 0.0065815 </td> <td style="text-align:right;"> 0.0133545 </td> <td style="text-align:right;"> 0.0017278 </td> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0262100 </td> <td style="text-align:right;"> -0.1171674 </td> <td style="text-align:right;"> 0.1695873 </td> <td style="text-align:right;"> 0.0731531 </td> <td style="text-align:right;"> 0.0338228 </td> <td style="text-align:right;"> -0.1337162 </td> <td style="text-align:right;"> 0.2013618 </td> <td style="text-align:right;"> 0.0854806 </td> <td style="text-align:right;"> 0.0099680 </td> <td style="text-align:right;"> 0.0065815 </td> <td style="text-align:right;"> 0.0133545 </td> <td style="text-align:right;"> 0.0017278 </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.1874259 </td> <td style="text-align:right;"> -0.0500875 </td> <td style="text-align:right;"> 0.4249393 </td> <td style="text-align:right;"> 0.1211825 </td> <td style="text-align:right;"> 0.1133706 </td> <td style="text-align:right;"> -0.1604807 </td> <td style="text-align:right;"> 0.3872220 </td> <td style="text-align:right;"> 0.1397227 </td> <td style="text-align:right;"> -0.1542349 </td> <td style="text-align:right;"> -0.2104415 </td> <td style="text-align:right;"> -0.0980283 </td> <td style="text-align:right;"> 0.0286774 </td> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.1874259 </td> <td style="text-align:right;"> -0.0500875 </td> <td style="text-align:right;"> 0.4249393 </td> <td style="text-align:right;"> 0.1211825 </td> <td style="text-align:right;"> 0.1133706 </td> <td style="text-align:right;"> -0.1604807 </td> <td style="text-align:right;"> 0.3872220 </td> <td style="text-align:right;"> 0.1397227 </td> <td style="text-align:right;"> -0.1542349 </td> <td style="text-align:right;"> -0.2104415 </td> <td style="text-align:right;"> -0.0980283 </td> <td style="text-align:right;"> 0.0286774 </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0032888 </td> <td style="text-align:right;"> -0.0544512 </td> <td style="text-align:right;"> 0.0610288 </td> <td style="text-align:right;"> 0.0294597 </td> <td style="text-align:right;"> -0.0054976 </td> <td style="text-align:right;"> -0.0811810 </td> <td style="text-align:right;"> 0.0701858 </td> <td style="text-align:right;"> 0.0386147 </td> <td style="text-align:right;"> -0.0034902 </td> <td style="text-align:right;"> -0.0175917 </td> <td style="text-align:right;"> 0.0106113 </td> <td style="text-align:right;"> 0.0071948 </td> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0032888 </td> <td style="text-align:right;"> -0.0544512 </td> <td style="text-align:right;"> 0.0610288 </td> <td style="text-align:right;"> 0.0294597 </td> <td style="text-align:right;"> -0.0054976 </td> <td style="text-align:right;"> -0.0811810 </td> <td style="text-align:right;"> 0.0701858 </td> <td style="text-align:right;"> 0.0386147 </td> <td style="text-align:right;"> -0.0034902 </td> <td style="text-align:right;"> -0.0175917 </td> <td style="text-align:right;"> 0.0106113 </td> <td style="text-align:right;"> 0.0071948 </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0594276 </td> <td style="text-align:right;"> -0.0782445 </td> <td style="text-align:right;"> 0.1970997 </td> <td style="text-align:right;"> 0.0702422 </td> <td style="text-align:right;"> 0.1574330 </td> <td style="text-align:right;"> 0.0091891 </td> <td style="text-align:right;"> 0.3056769 </td> <td style="text-align:right;"> 0.0756360 </td> <td style="text-align:right;"> 0.0937375 </td> <td style="text-align:right;"> 0.0775587 </td> <td style="text-align:right;"> 0.1099162 </td> <td style="text-align:right;"> 0.0082546 </td> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0594276 </td> <td style="text-align:right;"> -0.0782445 </td> <td style="text-align:right;"> 0.1970997 </td> <td style="text-align:right;"> 0.0702422 </td> <td style="text-align:right;"> 0.1574330 </td> <td style="text-align:right;"> 0.0091891 </td> <td style="text-align:right;"> 0.3056769 </td> <td style="text-align:right;"> 0.0756360 </td> <td style="text-align:right;"> 0.0937375 </td> <td style="text-align:right;"> 0.0775587 </td> <td style="text-align:right;"> 0.1099162 </td> <td style="text-align:right;"> 0.0082546 </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.1475403 </td> <td style="text-align:right;"> -0.4396127 </td> <td style="text-align:right;"> 0.1445320 </td> <td style="text-align:right;"> 0.1490192 </td> <td style="text-align:right;"> -0.1374401 </td> <td style="text-align:right;"> -0.4261352 </td> <td style="text-align:right;"> 0.1512551 </td> <td style="text-align:right;"> 0.1472961 </td> <td style="text-align:right;"> 0.0095199 </td> <td style="text-align:right;"> 0.0059199 </td> <td style="text-align:right;"> 0.0131200 </td> <td style="text-align:right;"> 0.0018368 </td> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.1475403 </td> <td style="text-align:right;"> -0.4396127 </td> <td style="text-align:right;"> 0.1445320 </td> <td style="text-align:right;"> 0.1490192 </td> <td style="text-align:right;"> -0.1374401 </td> <td style="text-align:right;"> -0.4261352 </td> <td style="text-align:right;"> 0.1512551 </td> <td style="text-align:right;"> 0.1472961 </td> <td style="text-align:right;"> 0.0095199 </td> <td style="text-align:right;"> 0.0059199 </td> <td style="text-align:right;"> 0.0131200 </td> <td style="text-align:right;"> 0.0018368 </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0605449 </td> <td style="text-align:right;"> -0.0097669 </td> <td style="text-align:right;"> 0.1308567 </td> <td style="text-align:right;"> 0.0358740 </td> <td style="text-align:right;"> 0.0022671 </td> <td style="text-align:right;"> -0.0859910 </td> <td style="text-align:right;"> 0.0905252 </td> <td style="text-align:right;"> 0.0450305 </td> <td style="text-align:right;"> -0.0550333 </td> <td style="text-align:right;"> -0.0979518 </td> <td style="text-align:right;"> -0.0121148 </td> <td style="text-align:right;"> 0.0218976 </td> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0605449 </td> <td style="text-align:right;"> -0.0097669 </td> <td style="text-align:right;"> 0.1308567 </td> <td style="text-align:right;"> 0.0358740 </td> <td style="text-align:right;"> 0.0022671 </td> <td style="text-align:right;"> -0.0859910 </td> <td style="text-align:right;"> 0.0905252 </td> <td style="text-align:right;"> 0.0450305 </td> <td style="text-align:right;"> -0.0550333 </td> <td style="text-align:right;"> -0.0979518 </td> <td style="text-align:right;"> -0.0121148 </td> <td style="text-align:right;"> 0.0218976 </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0942595 </td> <td style="text-align:right;"> -0.0751596 </td> <td style="text-align:right;"> 0.2636786 </td> <td style="text-align:right;"> 0.0864399 </td> <td style="text-align:right;"> 0.3142208 </td> <td style="text-align:right;"> 0.1125613 </td> <td style="text-align:right;"> 0.5158803 </td> <td style="text-align:right;"> 0.1028894 </td> <td style="text-align:right;"> 0.2027583 </td> <td style="text-align:right;"> 0.1750477 </td> <td style="text-align:right;"> 0.2304688 </td> <td style="text-align:right;"> 0.0141383 </td> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0942595 </td> <td style="text-align:right;"> -0.0751596 </td> <td style="text-align:right;"> 0.2636786 </td> <td style="text-align:right;"> 0.0864399 </td> <td style="text-align:right;"> 0.3142208 </td> <td style="text-align:right;"> 0.1125613 </td> <td style="text-align:right;"> 0.5158803 </td> <td style="text-align:right;"> 0.1028894 </td> <td style="text-align:right;"> 0.2027583 </td> <td style="text-align:right;"> 0.1750477 </td> <td style="text-align:right;"> 0.2304688 </td> <td style="text-align:right;"> 0.0141383 </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1192293 </td> <td style="text-align:right;"> -0.2542902 </td> <td style="text-align:right;"> 0.0158316 </td> <td style="text-align:right;"> 0.0689099 </td> <td style="text-align:right;"> -0.0964842 </td> <td style="text-align:right;"> -0.2564912 </td> <td style="text-align:right;"> 0.0635228 </td> <td style="text-align:right;"> 0.0816377 </td> <td style="text-align:right;"> 0.0267691 </td> <td style="text-align:right;"> -0.0233285 </td> <td style="text-align:right;"> 0.0768667 </td> <td style="text-align:right;"> 0.0255605 </td> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1192293 </td> <td style="text-align:right;"> -0.2542902 </td> <td style="text-align:right;"> 0.0158316 </td> <td style="text-align:right;"> 0.0689099 </td> <td style="text-align:right;"> -0.0964842 </td> <td style="text-align:right;"> -0.2564912 </td> <td style="text-align:right;"> 0.0635228 </td> <td style="text-align:right;"> 0.0816377 </td> <td style="text-align:right;"> 0.0267691 </td> <td style="text-align:right;"> -0.0233285 </td> <td style="text-align:right;"> 0.0768667 </td> <td style="text-align:right;"> 0.0255605 </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0422347 </td> <td style="text-align:right;"> -0.0623878 </td> <td style="text-align:right;"> -0.0220816 </td> <td style="text-align:right;"> 0.0102824 </td> <td style="text-align:right;"> -0.0355909 </td> <td style="text-align:right;"> -0.0619127 </td> <td style="text-align:right;"> -0.0092692 </td> <td style="text-align:right;"> 0.0134297 </td> <td style="text-align:right;"> 0.0092660 </td> <td style="text-align:right;"> -0.0008158 </td> <td style="text-align:right;"> 0.0193478 </td> <td style="text-align:right;"> 0.0051439 </td> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0422347 </td> <td style="text-align:right;"> -0.0623878 </td> <td style="text-align:right;"> -0.0220816 </td> <td style="text-align:right;"> 0.0102824 </td> <td style="text-align:right;"> -0.0355909 </td> <td style="text-align:right;"> -0.0619127 </td> <td style="text-align:right;"> -0.0092692 </td> <td style="text-align:right;"> 0.0134297 </td> <td style="text-align:right;"> 0.0092660 </td> <td style="text-align:right;"> -0.0008158 </td> <td style="text-align:right;"> 0.0193478 </td> <td style="text-align:right;"> 0.0051439 </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1457383 </td> <td style="text-align:right;"> -0.2373165 </td> <td style="text-align:right;"> -0.0541601 </td> <td style="text-align:right;"> 0.0467244 </td> <td style="text-align:right;"> -0.2097443 </td> <td style="text-align:right;"> -0.2681948 </td> <td style="text-align:right;"> -0.1512937 </td> <td style="text-align:right;"> 0.0298223 </td> <td style="text-align:right;"> -0.0654284 </td> <td style="text-align:right;"> -0.1374220 </td> <td style="text-align:right;"> 0.0065653 </td> <td style="text-align:right;"> 0.0367321 </td> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1457383 </td> <td style="text-align:right;"> -0.2373165 </td> <td style="text-align:right;"> -0.0541601 </td> <td style="text-align:right;"> 0.0467244 </td> <td style="text-align:right;"> -0.2097443 </td> <td style="text-align:right;"> -0.2681948 </td> <td style="text-align:right;"> -0.1512937 </td> <td style="text-align:right;"> 0.0298223 </td> <td style="text-align:right;"> -0.0654284 </td> <td style="text-align:right;"> -0.1374220 </td> <td style="text-align:right;"> 0.0065653 </td> <td style="text-align:right;"> 0.0367321 </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0320020 </td> <td style="text-align:right;"> -0.1233659 </td> <td style="text-align:right;"> 0.0593619 </td> <td style="text-align:right;"> 0.0466151 </td> <td style="text-align:right;"> 0.3268957 </td> <td style="text-align:right;"> 0.2087111 </td> <td style="text-align:right;"> 0.4450803 </td> <td style="text-align:right;"> 0.0602994 </td> <td style="text-align:right;"> 0.3473552 </td> <td style="text-align:right;"> 0.2592006 </td> <td style="text-align:right;"> 0.4355098 </td> <td style="text-align:right;"> 0.0449777 </td> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0320020 </td> <td style="text-align:right;"> -0.1233659 </td> <td style="text-align:right;"> 0.0593619 </td> <td style="text-align:right;"> 0.0466151 </td> <td style="text-align:right;"> 0.3268957 </td> <td style="text-align:right;"> 0.2087111 </td> <td style="text-align:right;"> 0.4450803 </td> <td style="text-align:right;"> 0.0602994 </td> <td style="text-align:right;"> 0.3473552 </td> <td style="text-align:right;"> 0.2592006 </td> <td style="text-align:right;"> 0.4355098 </td> <td style="text-align:right;"> 0.0449777 </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.1405306 </td> <td style="text-align:right;"> -0.2664120 </td> <td style="text-align:right;"> -0.0146491 </td> <td style="text-align:right;"> 0.0642264 </td> <td style="text-align:right;"> -0.0950040 </td> <td style="text-align:right;"> -0.2507897 </td> <td style="text-align:right;"> 0.0607817 </td> <td style="text-align:right;"> 0.0794840 </td> <td style="text-align:right;"> 0.0403162 </td> <td style="text-align:right;"> 0.0051883 </td> <td style="text-align:right;"> 0.0754441 </td> <td style="text-align:right;"> 0.0179227 </td> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.1405306 </td> <td style="text-align:right;"> -0.2664120 </td> <td style="text-align:right;"> -0.0146491 </td> <td style="text-align:right;"> 0.0642264 </td> <td style="text-align:right;"> -0.0950040 </td> <td style="text-align:right;"> -0.2507897 </td> <td style="text-align:right;"> 0.0607817 </td> <td style="text-align:right;"> 0.0794840 </td> <td style="text-align:right;"> 0.0403162 </td> <td style="text-align:right;"> 0.0051883 </td> <td style="text-align:right;"> 0.0754441 </td> <td style="text-align:right;"> 0.0179227 </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0367062 </td> <td style="text-align:right;"> -0.0660619 </td> <td style="text-align:right;"> 0.1394744 </td> <td style="text-align:right;"> 0.0524337 </td> <td style="text-align:right;"> 0.3626957 </td> <td style="text-align:right;"> 0.0914512 </td> <td style="text-align:right;"> 0.6339402 </td> <td style="text-align:right;"> 0.1383926 </td> <td style="text-align:right;"> 0.4472349 </td> <td style="text-align:right;"> -0.0801891 </td> <td style="text-align:right;"> 0.9746588 </td> <td style="text-align:right;"> 0.2690988 </td> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0367062 </td> <td style="text-align:right;"> -0.0660619 </td> <td style="text-align:right;"> 0.1394744 </td> <td style="text-align:right;"> 0.0524337 </td> <td style="text-align:right;"> 0.3626957 </td> <td style="text-align:right;"> 0.0914512 </td> <td style="text-align:right;"> 0.6339402 </td> <td style="text-align:right;"> 0.1383926 </td> <td style="text-align:right;"> 0.4472349 </td> <td style="text-align:right;"> -0.0801891 </td> <td style="text-align:right;"> 0.9746588 </td> <td style="text-align:right;"> 0.2690988 </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0907957 </td> <td style="text-align:right;"> -0.1703063 </td> <td style="text-align:right;"> -0.0112852 </td> <td style="text-align:right;"> 0.0405673 </td> <td style="text-align:right;"> 0.1168446 </td> <td style="text-align:right;"> -0.0023934 </td> <td style="text-align:right;"> 0.2360826 </td> <td style="text-align:right;"> 0.0608368 </td> <td style="text-align:right;"> 0.1978876 </td> <td style="text-align:right;"> 0.1368305 </td> <td style="text-align:right;"> 0.2589447 </td> <td style="text-align:right;"> 0.0311521 </td> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0907957 </td> <td style="text-align:right;"> -0.1703063 </td> <td style="text-align:right;"> -0.0112852 </td> <td style="text-align:right;"> 0.0405673 </td> <td style="text-align:right;"> 0.1168446 </td> <td style="text-align:right;"> -0.0023934 </td> <td style="text-align:right;"> 0.2360826 </td> <td style="text-align:right;"> 0.0608368 </td> <td style="text-align:right;"> 0.1978876 </td> <td style="text-align:right;"> 0.1368305 </td> <td style="text-align:right;"> 0.2589447 </td> <td style="text-align:right;"> 0.0311521 </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0156634 </td> <td style="text-align:right;"> -0.0857564 </td> <td style="text-align:right;"> 0.0544296 </td> <td style="text-align:right;"> 0.0357624 </td> <td style="text-align:right;"> -0.1140149 </td> <td style="text-align:right;"> -0.1840029 </td> <td style="text-align:right;"> -0.0440269 </td> <td style="text-align:right;"> 0.0357088 </td> <td style="text-align:right;"> -0.0997437 </td> <td style="text-align:right;"> -0.1519566 </td> <td style="text-align:right;"> -0.0475307 </td> <td style="text-align:right;"> 0.0266397 </td> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0156634 </td> <td style="text-align:right;"> -0.0857564 </td> <td style="text-align:right;"> 0.0544296 </td> <td style="text-align:right;"> 0.0357624 </td> <td style="text-align:right;"> -0.1140149 </td> <td style="text-align:right;"> -0.1840029 </td> <td style="text-align:right;"> -0.0440269 </td> <td style="text-align:right;"> 0.0357088 </td> <td style="text-align:right;"> -0.0997437 </td> <td style="text-align:right;"> -0.1519566 </td> <td style="text-align:right;"> -0.0475307 </td> <td style="text-align:right;"> 0.0266397 </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0531307 </td> <td style="text-align:right;"> -0.1011672 </td> <td style="text-align:right;"> -0.0050941 </td> <td style="text-align:right;"> 0.0245089 </td> <td style="text-align:right;"> -0.0593672 </td> <td style="text-align:right;"> -0.1076067 </td> <td style="text-align:right;"> -0.0111276 </td> <td style="text-align:right;"> 0.0246125 </td> <td style="text-align:right;"> 0.0045878 </td> <td style="text-align:right;"> -0.0513396 </td> <td style="text-align:right;"> 0.0605152 </td> <td style="text-align:right;"> 0.0285349 </td> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0531307 </td> <td style="text-align:right;"> -0.1011672 </td> <td style="text-align:right;"> -0.0050941 </td> <td style="text-align:right;"> 0.0245089 </td> <td style="text-align:right;"> -0.0593672 </td> <td style="text-align:right;"> -0.1076067 </td> <td style="text-align:right;"> -0.0111276 </td> <td style="text-align:right;"> 0.0246125 </td> <td style="text-align:right;"> 0.0045878 </td> <td style="text-align:right;"> -0.0513396 </td> <td style="text-align:right;"> 0.0605152 </td> <td style="text-align:right;"> 0.0285349 </td> </tr> </tbody> </table> </div> </div> <div id="prepare-data" class="section level4"> <div name="prepare_data" data-unique="prepare_data"></div><h4>Prepare data</h4> <p>Nesting, calculating the number of parameters within each grouping term, and running the meta-analysis</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3628" aria-expanded="false" aria-controls="rcode-643E0F3628"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3628"><pre class="r"><code class="hljs">metacombo_final <- metacombo %>% group_by(GroupingTerm) %>% nest() <span class="hljs-comment"># **Calculate number of parameters per grouping term </span> metacombo_final <- metacombo_final %>% mutate(para_per_GroupingTerm = map_dbl(data, nrow)) <span class="hljs-comment"># For all grouping terms</span> metacombo_final_all <- metacombo %>% nest() <span class="hljs-comment"># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate </span> overall1 <- metacombo_final %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)), model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)), model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>))) <span class="hljs-comment"># **Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate </span> overall_all1 <- metacombo_final_all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)), model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)), model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>))) </code></pre></div> <p>Re-structure data for each grouping term; delete unused variables</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3629" aria-expanded="false" aria-controls="rcode-643E0F3629"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3629"><pre class="r"><code class="hljs">Behaviour <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Behaviour"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Immunology <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Immunology"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Hematology <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Hematology"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Hearing <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Hearing"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Physiology <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Physiology"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Metabolism <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Metabolism"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Morphology <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Morphology"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Heart <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Heart"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] Eye <- as.data.frame(overall1 %>% filter(., GroupingTerm == <span class="hljs-string">"Eye"</span>) %>% mutate(lnCVR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se, lnVR=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">5</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">6</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">7</span>:<span class="hljs-number">18</span>)] All <- as.data.frame(overall_all1 %>% mutate(lnCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, lnCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se,lnVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, lnVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, lnRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, lnRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, lnRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, lnRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) )[, c(<span class="hljs-number">5</span>:<span class="hljs-number">16</span>)] All$lnCVR <- as.numeric(All$lnCVR) All$lnVR <- as.numeric(All$lnVR) All$lnRR <- as.numeric(All$lnRR) All <- All %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) overall2 <- bind_rows(Behaviour, Morphology, Metabolism, Physiology, Immunology, Hematology, Heart, Hearing, Eye, All)</code></pre></div> </div> <div id="visualisation" class="section level3"> <div name="visualisation" data-unique="visualisation"></div><h3>Visualisation</h3> <div id="plot-figure-2-4-in-ms-first-order-meta-analysis-results" class="section level4"> <div name="plot_figure_2_[4_in_ms]_(first-order_meta_analysis_results)" data-unique="plot_figure_2_[4_in_ms]_(first-order_meta_analysis_results)"></div><h4>Plot FIGURE 2 [4 in ms] (First-order meta analysis results)</h4> <p>Re-order grouping terms</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3630" aria-expanded="false" aria-controls="rcode-643E0F3630"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3630"><pre class="r"><code class="hljs">meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>) ) meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, rev(levels(meta_clean$GroupingTerm))) <span class="hljs-comment"># *Prepare data for all traits</span> meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels =c(<span class="hljs-string">"lnCVR"</span>,<span class="hljs-string">"lnVR"</span>,<span class="hljs-string">"lnRR"</span>) ) meta.plot2.all.c <- meta.plot2.all.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise(malebias = sum(value > <span class="hljs-number">0</span>), femalebias = sum(value<= <span class="hljs-number">0</span>), total= malebias + femalebias, malepercent = malebias*<span class="hljs-number">100</span>/total, femalepercent = femalebias*<span class="hljs-number">100</span>/total) meta.plot2.all.c$label <- <span class="hljs-string">"All traits"</span> <span class="hljs-comment"># restructure to create stacked bar plots</span> meta.plot2.all.d <- as.data.frame(meta.plot2.all.c) meta.plot2.all.e <- gather(meta.plot2.all.d, key = sex, value = percent, malepercent:femalepercent, factor_key = <span class="hljs-literal">TRUE</span>) <span class="hljs-comment"># create new sample size variable</span> meta.plot2.all.e$samplesize <- with(meta.plot2.all.e, ifelse(sex == <span class="hljs-string">"malepercent"</span>, malebias, femalebias) ) malebias_Fig2_alltraits <- ggplot(meta.plot2.all.e) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = <span class="hljs-number">50</span>, linetype = <span class="hljs-string">"dashed"</span>, color = <span class="hljs-string">"gray40"</span>) + geom_text(data = subset(meta.plot2.all.e, samplesize != <span class="hljs-number">0</span>), aes(label = samplesize), position = position_stack(vjust = <span class="hljs-number">.5</span>), color = <span class="hljs-string">"white"</span>, size = <span class="hljs-number">3.5</span>) + facet_grid(cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">18</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>) + scale_fill_brewer(palette = <span class="hljs-string">"Set2"</span>) + theme_bw(base_size = <span class="hljs-number">18</span>) + theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_text(size = <span class="hljs-number">12</span>), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>,linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size=<span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = <span class="hljs-string">"none"</span>, axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() <span class="hljs-comment">#malebias_Fig2_alltraits</span></code></pre></div> </div> <div id="prepare-data-for-traits-with-ci-not-overlapping-0" class="section level4"> <div name="prepare_data_for_traits_with_ci_not_overlapping_0" data-unique="prepare_data_for_traits_with_ci_not_overlapping_0"></div><h4>Prepare data for traits with CI not overlapping 0</h4> <p>create column with 1= different from zero, 0= zero included in CI</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3631" aria-expanded="false" aria-controls="rcode-643E0F3631"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3631"><pre class="r"><code class="hljs">meta.plot2.sig <- meta_clean %>% mutate(lnCVRsig = ifelse(lnCVR_lower*lnCVR_upper ><span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), lnVRsig = ifelse(lnVR_lower*lnVR_upper ><span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), lnRRsig = ifelse(lnRR_lower*lnRR_upper > <span class="hljs-number">0</span>, <span class="hljs-number">1</span>,<span class="hljs-number">0</span>)) meta.plot2.sig.b <- meta.plot2.sig[, c(<span class="hljs-string">"lnCVR"</span>, <span class="hljs-string">"lnVR"</span>, <span class="hljs-string">"lnRR"</span>, <span class="hljs-string">"lnCVRsig"</span>, <span class="hljs-string">"lnVRsig"</span>, <span class="hljs-string">"lnRRsig"</span>, <span class="hljs-string">"GroupingTerm"</span>)] meta.plot2.sig.c <- gather(meta.plot2.sig.b, trait, value, lnCVR:lnRR) meta.plot2.sig.c$sig <- <span class="hljs-string">"placeholder"</span> meta.plot2.sig.c$trait <- factor(meta.plot2.sig.c$trait, levels =c(<span class="hljs-string">"lnCVR"</span>,<span class="hljs-string">"lnVR"</span>,<span class="hljs-string">"lnRR"</span>) ) meta.plot2.sig.c$sig <- ifelse(meta.plot2.sig.c$trait == <span class="hljs-string">"lnCVR"</span>, meta.plot2.sig.c$lnCVRsig, ifelse(meta.plot2.sig.c$trait == <span class="hljs-string">"lnVR"</span>, meta.plot2.sig.c$lnVRsig, meta.plot2.sig.c$lnRRsig)) <span class="hljs-comment">#choosing sex biased ln-ratios significantly larger than 0</span> meta.plot2.sig.malebias <- meta.plot2.sig.c %>% group_by_at(vars(trait, GroupingTerm)) %>% filter(sig== <span class="hljs-number">1</span>) %>% summarise(male_sig = sum(value > <span class="hljs-number">0</span>), female_sig = sum(value < <span class="hljs-number">0</span>), total = male_sig + female_sig) meta.plot2.sig.malebias <- ungroup(meta.plot2.sig.malebias) %>% add_row(trait = <span class="hljs-string">"lnCVR"</span>, GroupingTerm = <span class="hljs-string">"Hearing"</span>, male_sig = <span class="hljs-number">0</span>, female_sig = <span class="hljs-number">0</span>, .before = <span class="hljs-number">4</span>) %>% <span class="hljs-comment">#add "Hearing" for lnCVR (not filtered as only zeros)</span> mutate(malepercent = male_sig*<span class="hljs-number">100</span> / total, femalepercent = female_sig*<span class="hljs-number">100</span> / total) meta.plot2.sig.malebias$label <- <span class="hljs-string">"CI not overlapping zero"</span> <span class="hljs-comment"># restructure to create stacked bar plots</span> meta.plot2.sig.bothsexes <- as.data.frame(meta.plot2.sig.malebias) meta.plot2.sig.bothsexes.b <- gather(meta.plot2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = <span class="hljs-literal">TRUE</span>) <span class="hljs-comment"># create new sample size variable</span> meta.plot2.sig.bothsexes.b$samplesize <- with(meta.plot2.sig.bothsexes.b, ifelse(sex == <span class="hljs-string">"malepercent"</span>, male_sig, female_sig) ) <span class="hljs-comment"># *Plot Fig2 all significant results (CI not overlapping zero): </span> <span class="hljs-comment"># no sig. lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no sig. male-biased lnVR for 'Eye'</span> malebias_Fig2_sigtraits <- ggplot(meta.plot2.sig.bothsexes.b) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = <span class="hljs-number">50</span>, linetype = <span class="hljs-string">"dashed"</span>, color = <span class="hljs-string">"gray40"</span>) + geom_text(data = subset(meta.plot2.sig.bothsexes.b, samplesize != <span class="hljs-number">0</span>), aes(label = samplesize), position = position_stack(vjust = <span class="hljs-number">.5</span>), color = <span class="hljs-string">"white"</span>, size = <span class="hljs-number">3.5</span>) + facet_grid(cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">18</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>) + scale_fill_brewer(palette = <span class="hljs-string">"Set2"</span>) + theme_bw(base_size = <span class="hljs-number">18</span>) + theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_blank(), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>,linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size=<span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = <span class="hljs-string">"none"</span>, axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip()</code></pre></div> <p>Prepare data for traits with effect size ratios > 10% larger in males</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3632" aria-expanded="false" aria-controls="rcode-643E0F3632"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3632"><pre class="r"><code class="hljs">meta.plot2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels =c(<span class="hljs-string">"lnCVR"</span>,<span class="hljs-string">"lnVR"</span>,<span class="hljs-string">"lnRR"</span>) ) meta.plot2.over10.c <- meta.plot2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise(malebias = sum(value > log(<span class="hljs-number">11</span>/<span class="hljs-number">10</span>)), femalebias = sum(value < log(<span class="hljs-number">9</span>/<span class="hljs-number">10</span>)), total= malebias + femalebias, malepercent = malebias*<span class="hljs-number">100</span>/total, femalepercent = femalebias*<span class="hljs-number">100</span>/total) meta.plot2.over10.c$label <- <span class="hljs-string">"Sex difference in m/f ratios > 10%"</span> <span class="hljs-comment"># restructure to create stacked bar plots</span> meta.plot2.over10.c <- as.data.frame(meta.plot2.over10.c) meta.plot2.over10.d <- gather(meta.plot2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = <span class="hljs-literal">TRUE</span>) <span class="hljs-comment"># create new sample size variable</span> meta.plot2.over10.d$samplesize <- with(meta.plot2.over10.d, ifelse(sex == <span class="hljs-string">"malepercent"</span>, malebias, femalebias) ) <span class="hljs-comment"># *Plot Fig2 Sex difference in m/f ratio > 10%</span> malebias_Fig2_over10 <- ggplot(meta.plot2.over10.d) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = <span class="hljs-number">50</span>, linetype = <span class="hljs-string">"dashed"</span>, color = <span class="hljs-string">"gray40"</span>) + geom_text(data = subset(meta.plot2.over10.d, samplesize != <span class="hljs-number">0</span>), aes(label = samplesize), position = position_stack(vjust = <span class="hljs-number">.5</span>), color = <span class="hljs-string">"white"</span>, size = <span class="hljs-number">3.5</span>) + facet_grid(cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">18</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>) + scale_fill_brewer(palette = <span class="hljs-string">"Set2"</span>) + theme_bw(base_size = <span class="hljs-number">18</span>) + theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_blank(), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>,linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size=<span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = <span class="hljs-string">"none"</span>, axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() <span class="hljs-comment"># malebias_Fig2_over10</span></code></pre></div> </div> <div id="create-final-combined-figure-figure-2" class="section level4"> <div name="create_final_combined_figure_(figure_2)" data-unique="create_final_combined_figure_(figure_2)"></div><h4>Create final combined Figure (Figure 2)</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3633" aria-expanded="false" aria-controls="rcode-643E0F3633"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3633"><pre class="r"><code class="hljs">Fig2 <- ggarrange(malebias_Fig2_alltraits, malebias_Fig2_sigtraits,malebias_Fig2_over10, nrow = <span class="hljs-number">3</span>, align = <span class="hljs-string">"v"</span>, heights = c(<span class="hljs-number">1.22</span>,<span class="hljs-number">1</span>,<span class="hljs-number">1</span>), labels = c(<span class="hljs-string">"A"</span>, <span class="hljs-string">"B"</span>, <span class="hljs-string">"C"</span>)) Fig2</code></pre></div> <p><img src="data:image/png;base64,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" width="672"></p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3634" aria-expanded="false" aria-controls="rcode-643E0F3634"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3634"><pre class="r"><code class="hljs">ggsave(<span class="hljs-string">"Fig2.pdf"</span>, plot = Fig2, width = <span class="hljs-number">6</span>, height = <span class="hljs-number">5</span>) </code></pre></div> </div> </div> <div id="overall-results-of-second-order-meta-anlaysis-figure-4a" class="section level3"> <div name="overall_results_of_second_order_meta_anlaysis_(figure_4a)" data-unique="overall_results_of_second_order_meta_anlaysis_(figure_4a)"></div><h3>Overall results of second order meta anlaysis (Figure 4a)</h3> <div id="restructure-data-for-plotting" class="section level4"> <div name="restructure_data_for_plotting" data-unique="restructure_data_for_plotting"></div><h4>Restructure data for plotting</h4> <p>Data are restructured, and grouping terms are being re-ordered</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3635" aria-expanded="false" aria-controls="rcode-643E0F3635"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3635"><pre class="r"><code class="hljs">overall3 <- gather(overall2, parameter, value, c(lnCVR, lnVR, lnRR), factor_key= <span class="hljs-literal">TRUE</span>) lnCVR.ci <- overall3 %>% filter(parameter == <span class="hljs-string">"lnCVR"</span>) %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3 %>% filter(parameter == <span class="hljs-string">"lnVR"</span>) %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3 %>% filter(parameter == <span class="hljs-string">"lnRR"</span>) %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4 <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) <span class="hljs-comment"># re-order Grouping Terms</span> overall4$GroupingTerm <- factor(overall4$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>, <span class="hljs-string">"All"</span>) ) overall4$GroupingTerm <- factor(overall4$GroupingTerm, rev(levels(overall4$GroupingTerm))) overall4$label <- <span class="hljs-string">"All traits"</span> kable(cbind(overall4, overall4)) %>% kable_styling() %>% scroll_box(width = <span class="hljs-string">"100%"</span>, height = <span class="hljs-string">"200px"</span>)</code></pre></div> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "> <table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> value </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.low </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.high </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> label </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> value </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.low </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.high </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> label </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0035049 </td> <td style="text-align:right;"> -0.0240688 </td> <td style="text-align:right;"> 0.0170591 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0035049 </td> <td style="text-align:right;"> -0.0240688 </td> <td style="text-align:right;"> 0.0170591 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0774453 </td> <td style="text-align:right;"> 0.0414171 </td> <td style="text-align:right;"> 0.1134734 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0774453 </td> <td style="text-align:right;"> 0.0414171 </td> <td style="text-align:right;"> 0.1134734 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0430831 </td> <td style="text-align:right;"> -0.1125945 </td> <td style="text-align:right;"> 0.0264283 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0430831 </td> <td style="text-align:right;"> -0.1125945 </td> <td style="text-align:right;"> 0.0264283 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0126792 </td> <td style="text-align:right;"> -0.0140094 </td> <td style="text-align:right;"> 0.0393678 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0126792 </td> <td style="text-align:right;"> -0.0140094 </td> <td style="text-align:right;"> 0.0393678 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0681817 </td> <td style="text-align:right;"> -0.0980135 </td> <td style="text-align:right;"> -0.0383499 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0681817 </td> <td style="text-align:right;"> -0.0980135 </td> <td style="text-align:right;"> -0.0383499 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0217865 </td> <td style="text-align:right;"> -0.0165045 </td> <td style="text-align:right;"> 0.0600776 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0217865 </td> <td style="text-align:right;"> -0.0165045 </td> <td style="text-align:right;"> 0.0600776 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0183839 </td> <td style="text-align:right;"> -0.0128375 </td> <td style="text-align:right;"> 0.0496053 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0183839 </td> <td style="text-align:right;"> -0.0128375 </td> <td style="text-align:right;"> 0.0496053 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0157302 </td> <td style="text-align:right;"> -0.0111999 </td> <td style="text-align:right;"> 0.0426603 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0157302 </td> <td style="text-align:right;"> -0.0111999 </td> <td style="text-align:right;"> 0.0426603 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0817932 </td> <td style="text-align:right;"> -0.1476821 </td> <td style="text-align:right;"> -0.0159043 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0817932 </td> <td style="text-align:right;"> -0.1476821 </td> <td style="text-align:right;"> -0.0159043 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0046553 </td> <td style="text-align:right;"> -0.0086242 </td> <td style="text-align:right;"> 0.0179348 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0046553 </td> <td style="text-align:right;"> -0.0086242 </td> <td style="text-align:right;"> 0.0179348 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.0178345 </td> <td style="text-align:right;"> -0.0739862 </td> <td style="text-align:right;"> 0.0383172 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.0178345 </td> <td style="text-align:right;"> -0.0739862 </td> <td style="text-align:right;"> 0.0383172 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.1514171 </td> <td style="text-align:right;"> 0.0818826 </td> <td style="text-align:right;"> 0.2209516 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.1514171 </td> <td style="text-align:right;"> 0.0818826 </td> <td style="text-align:right;"> 0.2209516 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0910609 </td> <td style="text-align:right;"> -0.0337688 </td> <td style="text-align:right;"> 0.2158905 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0910609 </td> <td style="text-align:right;"> -0.0337688 </td> <td style="text-align:right;"> 0.2158905 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0359821 </td> <td style="text-align:right;"> -0.0277944 </td> <td style="text-align:right;"> 0.0997585 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0359821 </td> <td style="text-align:right;"> -0.0277944 </td> <td style="text-align:right;"> 0.0997585 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.1112382 </td> <td style="text-align:right;"> -0.1622150 </td> <td style="text-align:right;"> -0.0602615 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.1112382 </td> <td style="text-align:right;"> -0.1622150 </td> <td style="text-align:right;"> -0.0602615 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0802111 </td> <td style="text-align:right;"> 0.0315390 </td> <td style="text-align:right;"> 0.1288831 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0802111 </td> <td style="text-align:right;"> 0.0315390 </td> <td style="text-align:right;"> 0.1288831 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.0050810 </td> <td style="text-align:right;"> -0.0357003 </td> <td style="text-align:right;"> 0.0255383 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.0050810 </td> <td style="text-align:right;"> -0.0357003 </td> <td style="text-align:right;"> 0.0255383 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0106858 </td> <td style="text-align:right;"> -0.0230440 </td> <td style="text-align:right;"> 0.0444155 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0106858 </td> <td style="text-align:right;"> -0.0230440 </td> <td style="text-align:right;"> 0.0444155 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.0744497 </td> <td style="text-align:right;"> -0.1381380 </td> <td style="text-align:right;"> -0.0107614 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> -0.0744497 </td> <td style="text-align:right;"> -0.1381380 </td> <td style="text-align:right;"> -0.0107614 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0156634 </td> <td style="text-align:right;"> -0.0077457 </td> <td style="text-align:right;"> 0.0390726 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnVR </td> <td style="text-align:right;"> 0.0156634 </td> <td style="text-align:right;"> -0.0077457 </td> <td style="text-align:right;"> 0.0390726 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0199206 </td> <td style="text-align:right;"> -0.0634388 </td> <td style="text-align:right;"> 0.0235976 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0199206 </td> <td style="text-align:right;"> -0.0634388 </td> <td style="text-align:right;"> 0.0235976 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0678160 </td> <td style="text-align:right;"> 0.0072225 </td> <td style="text-align:right;"> 0.1284095 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0678160 </td> <td style="text-align:right;"> 0.0072225 </td> <td style="text-align:right;"> 0.1284095 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.1422577 </td> <td style="text-align:right;"> 0.0364352 </td> <td style="text-align:right;"> 0.2480801 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.1422577 </td> <td style="text-align:right;"> 0.0364352 </td> <td style="text-align:right;"> 0.2480801 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0163695 </td> <td style="text-align:right;"> -0.0443364 </td> <td style="text-align:right;"> 0.0770753 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0163695 </td> <td style="text-align:right;"> -0.0443364 </td> <td style="text-align:right;"> 0.0770753 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0574840 </td> <td style="text-align:right;"> -0.1074213 </td> <td style="text-align:right;"> -0.0075466 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0574840 </td> <td style="text-align:right;"> -0.1074213 </td> <td style="text-align:right;"> -0.0075466 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0388537 </td> <td style="text-align:right;"> -0.0024274 </td> <td style="text-align:right;"> 0.0801348 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0388537 </td> <td style="text-align:right;"> -0.0024274 </td> <td style="text-align:right;"> 0.0801348 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0048933 </td> <td style="text-align:right;"> -0.0324240 </td> <td style="text-align:right;"> 0.0226374 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0048933 </td> <td style="text-align:right;"> -0.0324240 </td> <td style="text-align:right;"> 0.0226374 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0132366 </td> <td style="text-align:right;"> -0.0335982 </td> <td style="text-align:right;"> 0.0071251 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0132366 </td> <td style="text-align:right;"> -0.0335982 </td> <td style="text-align:right;"> 0.0071251 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0091186 </td> <td style="text-align:right;"> 0.0012071 </td> <td style="text-align:right;"> 0.0170302 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0091186 </td> <td style="text-align:right;"> 0.0012071 </td> <td style="text-align:right;"> 0.0170302 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0124332 </td> <td style="text-align:right;"> -0.0061474 </td> <td style="text-align:right;"> 0.0310138 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0124332 </td> <td style="text-align:right;"> -0.0061474 </td> <td style="text-align:right;"> 0.0310138 </td> <td style="text-align:left;"> All traits </td> </tr> </tbody> </table> </div> </div> <div id="plot-figure-4-second-order-meta-analysis-results" class="section level4"> <div name="plot_figure_4_(second-order_meta_analysis_results)" data-unique="plot_figure_4_(second-order_meta_analysis_results)"></div><h4>Plot FIGURE 4 (Second-order meta analysis results)</h4> <p>Preparation: Sub-Plot for Figure 3: all traits</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3636" aria-expanded="false" aria-controls="rcode-643E0F3636"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3636"><pre class="r"><code class="hljs">Metameta_Fig3_alltraits <- overall4 %>% ggplot(aes(y= GroupingTerm, x= value)) + geom_errorbarh(aes(xmin = ci.low, xmax = ci.high), height = <span class="hljs-number">0.1</span>, show.legend = <span class="hljs-literal">FALSE</span>) + geom_point(aes(shape = parameter), fill = <span class="hljs-string">'black'</span>, color = <span class="hljs-string">'black'</span>, size = <span class="hljs-number">2.2</span>, show.legend = <span class="hljs-literal">FALSE</span>) + scale_x_continuous(limits=c(-<span class="hljs-number">0.24</span>, <span class="hljs-number">0.25</span>), breaks = c(-<span class="hljs-number">0.2</span>, -<span class="hljs-number">0.1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0.1</span>, <span class="hljs-number">0.2</span>), name=<span class="hljs-string">'Effect size'</span>) + geom_vline(xintercept=<span class="hljs-number">0</span>, color=<span class="hljs-string">'black'</span>, linetype=<span class="hljs-string">'dashed'</span>)+ facet_grid(cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">23</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>)+ theme_bw()+ theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_text(size = <span class="hljs-number">12</span>), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>, linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size = <span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) <span class="hljs-comment">#Metameta_Fig3_alltraits </span></code></pre></div> <div id="section" class="section level8"> <p></p> </div> </div> <div id="figure-4b-prepare-data-for-traits-with-ci-not-overlapping-0" class="section level4"> <div name="figure_4b:_prepare_data_for_traits_with_ci_not_overlapping_0" data-unique="figure_4b:_prepare_data_for_traits_with_ci_not_overlapping_0"></div><h4>Figure 4B: Prepare data for traits with CI not overlapping 0</h4> <p>create column with 1= different from zero, 0= zero included in CI Male-biased (significant) traits</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3637" aria-expanded="false" aria-controls="rcode-643E0F3637"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3637"><pre class="r"><code class="hljs">meta.male.plot3.sig <- metacombo %>% mutate(sigCVR = ifelse(lnCVR_lower > <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), sigVR = ifelse(lnVR_lower > <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), sigRR = ifelse(lnRR_lower > <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>)) <span class="hljs-comment">#Significant subset for lnCVR</span> metacombo_male.plot3.CVR <- meta.male.plot3.sig %>% filter(sigCVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.all <- meta.male.plot3.sig %>% filter(sigCVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnVR</span> metacombo_male.plot3.VR <- meta.male.plot3.sig %>% filter(sigVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.all <- meta.male.plot3.sig %>% filter(sigVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnRR</span> metacombo_male.plot3.RR <- meta.male.plot3.sig %>% filter(sigRR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.all <- meta.male.plot3.sig %>% filter(sigRR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment"># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate </span> plot3.male.meta.CVR <- metacombo_male.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.VR <- metacombo_male.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.RR <- metacombo_male.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) <span class="hljs-comment"># Across all grouping terms #</span> plot3.male.meta.CVR.all <- metacombo_male.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.CVR.all <- plot3.male.meta.CVR.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.male.meta.VR.all <- metacombo_male.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.VR.all <- plot3.male.meta.VR.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.male.meta.RR.all <- metacombo_male.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.RR.all <- plot3.male.meta.RR.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) <span class="hljs-comment"># Combine with separate grouping term results</span> plot3.male.meta.CVR <- bind_rows(plot3.male.meta.CVR, plot3.male.meta.CVR.all) plot3.male.meta.VR <- bind_rows(plot3.male.meta.VR, plot3.male.meta.VR.all) plot3.male.meta.RR <- bind_rows(plot3.male.meta.RR, plot3.male.meta.RR.all) <span class="hljs-comment"># **Re-structure data for each grouping term; delete un-used variables</span> plot3.male.meta.CVR.b <- as.data.frame(plot3.male.meta.CVR %>% group_by(GroupingTerm) %>% mutate(lnCVR = map_dbl(model_lnCVR, pluck(<span class="hljs-number">2</span>)), lnCVR_lower = map_dbl(model_lnCVR, pluck(<span class="hljs-number">6</span>)), lnCVR_upper =map_dbl(model_lnCVR, pluck(<span class="hljs-number">7</span>)), lnCVR_se =map_dbl(model_lnCVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.male.meta.CVR.b)) plot3.male.meta.CVR.b <- bind_rows(plot3.male.meta.CVR.b, add.row.hearing) plot3.male.meta.CVR.b <- plot3.male.meta.CVR.b[order(plot3.male.meta.CVR.b$GroupingTerm),] plot3.male.meta.VR.b <- as.data.frame(plot3.male.meta.VR %>% group_by(GroupingTerm) %>% mutate(lnVR = map_dbl(model_lnVR, pluck(<span class="hljs-number">2</span>)), lnVR_lower = map_dbl(model_lnVR, pluck(<span class="hljs-number">6</span>)), lnVR_upper =map_dbl(model_lnVR, pluck(<span class="hljs-number">7</span>)), lnVR_se =map_dbl(model_lnVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] plot3.male.meta.VR.b <- plot3.male.meta.VR.b[order(plot3.male.meta.VR.b$GroupingTerm),] plot3.male.meta.RR.b <- as.data.frame(plot3.male.meta.RR %>% group_by(GroupingTerm) %>% mutate(lnRR = map_dbl(model_lnRR, pluck(<span class="hljs-number">2</span>)), lnRR_lower = map_dbl(model_lnRR, pluck(<span class="hljs-number">6</span>)), lnRR_upper =map_dbl(model_lnRR, pluck(<span class="hljs-number">7</span>)), lnRR_se =map_dbl(model_lnRR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] plot3.male.meta.RR.b <- plot3.male.meta.RR.b[order(plot3.male.meta.RR.b$GroupingTerm),] overall.male.plot3 <- inner_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b) overall.male.plot3 <- inner_join(overall.male.plot3, plot3.male.meta.RR.b) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>, <span class="hljs-string">"All"</span>) ) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) <span class="hljs-comment">#add missing GroupingTerms for plot</span> overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = <span class="hljs-string">"Behaviour"</span>) overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = <span class="hljs-string">"Immunology"</span>) overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = <span class="hljs-string">"Eye"</span>) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>, <span class="hljs-string">"All"</span>) ) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm)))</code></pre></div> <p>Restructure MALE data for plotting</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3638" aria-expanded="false" aria-controls="rcode-643E0F3638"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3638"><pre class="r"><code class="hljs">overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key= <span class="hljs-literal">TRUE</span>) lnCVR.ci <- overall3.male.sig %>% filter(parameter == <span class="hljs-string">"lnCVR"</span>) %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sig %>% filter(parameter == <span class="hljs-string">"lnVR"</span>) %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sig %>% filter(parameter == <span class="hljs-string">"lnRR"</span>) %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sig <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sig$label <- <span class="hljs-string">"CI not overlapping zero"</span></code></pre></div> <p>Plot Fig3 all significant results (CI not overlapping zero) for males</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3639" aria-expanded="false" aria-controls="rcode-643E0F3639"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3639"><pre class="r"><code class="hljs"><span class="hljs-comment">######</span> Metameta_Fig3_male.sig <- overall4.male.sig %>% ggplot(aes(y= GroupingTerm, x= value)) + geom_errorbarh(aes(xmin = ci.low, xmax = ci.high), height = <span class="hljs-number">0.1</span>, show.legend = <span class="hljs-literal">FALSE</span>) + geom_point(aes(shape = parameter), fill = <span class="hljs-string">'mediumaquamarine'</span>, color = <span class="hljs-string">'mediumaquamarine'</span>, size = <span class="hljs-number">2.2</span>, show.legend = <span class="hljs-literal">FALSE</span>) + scale_x_continuous(limits=c(<span class="hljs-number">0</span>, <span class="hljs-number">0.4</span>), breaks = c(<span class="hljs-number">0</span>, <span class="hljs-number">0.3</span>), name=<span class="hljs-string">'Effect size'</span>) + geom_vline(xintercept=<span class="hljs-number">0</span>, color=<span class="hljs-string">'black'</span>, linetype=<span class="hljs-string">'dashed'</span>)+ facet_grid(cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">23</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>)+ theme_bw()+ theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_text(size = <span class="hljs-number">12</span>), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>, linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size = <span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) <span class="hljs-comment">#Metameta_Fig3_male.sig</span></code></pre></div> <div id="section-1" class="section level23"> <p></p> </div> </div> <div id="female-figure-significant-traits" class="section level4"> <div name="female_figure,_significant_traits" data-unique="female_figure,_significant_traits"></div><h4>Female Figure, significant traits</h4> <p>Female Fig3 sig</p> <p>Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3640" aria-expanded="false" aria-controls="rcode-643E0F3640"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3640"><pre class="r"><code class="hljs"><span class="hljs-comment"># female-biased traits</span> meta.female.plot3.sig <- metacombo %>% mutate(sigCVR = ifelse(lnCVR_upper < <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), sigVR = ifelse(lnVR_upper < <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), sigRR = ifelse(lnRR_upper < <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>)) <span class="hljs-comment">#Significant subset for lnCVR</span> metacombo_female.plot3.CVR <- meta.female.plot3.sig %>% filter(sigCVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.CVR.all <- meta.female.plot3.sig %>% filter(sigCVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnVR</span> metacombo_female.plot3.VR <- meta.female.plot3.sig %>% filter(sigVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.VR.all <- meta.female.plot3.sig %>% filter(sigVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnRR</span> metacombo_female.plot3.RR <- meta.female.plot3.sig %>% filter(sigRR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.RR.all <- meta.female.plot3.sig %>% filter(sigRR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment"># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate</span> plot3.female.meta.CVR <- metacombo_female.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.female.meta.VR <- metacombo_female.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.female.meta.RR <- metacombo_female.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) <span class="hljs-comment"># Across all grouping terms #</span> plot3.female.meta.CVR.all <- metacombo_female.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.female.meta.CVR.all <- plot3.female.meta.CVR.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.female.meta.VR.all <- metacombo_female.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.female.meta.VR.all <- plot3.female.meta.VR.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.female.meta.RR.all <- metacombo_female.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.female.meta.RR.all <- plot3.female.meta.RR.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) <span class="hljs-comment"># Combine with separate grouping term results</span> plot3.female.meta.CVR <- bind_rows(plot3.female.meta.CVR, plot3.female.meta.CVR.all) plot3.female.meta.VR <- bind_rows(plot3.female.meta.VR, plot3.female.meta.VR.all) plot3.female.meta.RR <- bind_rows(plot3.female.meta.RR, plot3.female.meta.RR.all) <span class="hljs-comment"># **Re-structure data for each grouping term; delete un-used variables</span> plot3.female.meta.CVR.b <- as.data.frame(plot3.female.meta.CVR %>% group_by(GroupingTerm) %>% mutate(lnCVR = map_dbl(model_lnCVR, pluck(<span class="hljs-number">2</span>)), lnCVR_lower = map_dbl(model_lnCVR, pluck(<span class="hljs-number">6</span>)), lnCVR_upper =map_dbl(model_lnCVR, pluck(<span class="hljs-number">7</span>)), lnCVR_se =map_dbl(model_lnCVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.female.meta.CVR.b)) plot3.female.meta.CVR.b <- bind_rows(plot3.female.meta.CVR.b, add.row.hearing) plot3.female.meta.CVR.b <- plot3.female.meta.CVR.b[order(plot3.female.meta.CVR.b$GroupingTerm),] plot3.female.meta.VR.b <- as.data.frame(plot3.female.meta.VR %>% group_by(GroupingTerm) %>% mutate(lnVR = map_dbl(model_lnVR, pluck(<span class="hljs-number">2</span>)), lnVR_lower = map_dbl(model_lnVR, pluck(<span class="hljs-number">6</span>)), lnVR_upper =map_dbl(model_lnVR, pluck(<span class="hljs-number">7</span>)), lnVR_se =map_dbl(model_lnVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] plot3.female.meta.VR.b <- plot3.female.meta.VR.b[order(plot3.female.meta.VR.b$GroupingTerm),] plot3.female.meta.RR.b <- as.data.frame(plot3.female.meta.RR %>% group_by(GroupingTerm) %>% mutate(lnRR = map_dbl(model_lnRR, pluck(<span class="hljs-number">2</span>)), lnRR_lower = map_dbl(model_lnRR, pluck(<span class="hljs-number">6</span>)), lnRR_upper =map_dbl(model_lnRR, pluck(<span class="hljs-number">7</span>)), lnRR_se =map_dbl(model_lnRR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] plot3.female.meta.RR.b <- plot3.female.meta.RR.b[order(plot3.female.meta.RR.b$GroupingTerm),] overall.female.plot3 <- full_join(plot3.female.meta.CVR.b, plot3.female.meta.VR.b) overall.female.plot3 <- full_join(overall.female.plot3, plot3.female.meta.RR.b) overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>, <span class="hljs-string">"All"</span>) ) overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm))) <span class="hljs-comment">#add missing GroupingTerms for plot POTENTIALLY DELETE</span> <span class="hljs-comment">#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Morphology")</span> <span class="hljs-comment">#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Metabolism")</span> <span class="hljs-comment">#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Hematology")</span> <span class="hljs-comment">#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Hearing")</span> <span class="hljs-comment">#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Eye")</span> <span class="hljs-comment">#overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels =c("Behaviour","Morphology","Metabolism","Physiology","Immunology","Hematology","Heart","Hearing","Eye", "All") )</span> <span class="hljs-comment">#overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm))) </span></code></pre></div> <p>Restructure data for plotting</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3641" aria-expanded="false" aria-controls="rcode-643E0F3641"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3641"><pre class="r"><code class="hljs">overall3.female.sig <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key= <span class="hljs-literal">TRUE</span>) lnCVR.ci <- overall3.female.sig %>% filter(parameter == <span class="hljs-string">"lnCVR"</span>) %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.female.sig %>% filter(parameter == <span class="hljs-string">"lnVR"</span>) %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.female.sig %>% filter(parameter == <span class="hljs-string">"lnRR"</span>) %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.female.sig <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.female.sig$label <- <span class="hljs-string">"CI not overlapping zero"</span></code></pre></div> <p>Plot Fig3 all significant results (CI not overlapping zero, female )</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3642" aria-expanded="false" aria-controls="rcode-643E0F3642"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3642"><pre class="r"><code class="hljs">Metameta_Fig3_female.sig <- overall4.female.sig %>% ggplot(aes(y= GroupingTerm, x= value)) + geom_errorbarh(aes(xmin = ci.low, xmax = ci.high), height = <span class="hljs-number">0.1</span>, show.legend = <span class="hljs-literal">FALSE</span>) + geom_point(aes(shape = parameter), fill = <span class="hljs-string">'salmon1'</span>, color = <span class="hljs-string">'salmon1'</span>, size = <span class="hljs-number">2.2</span>, show.legend = <span class="hljs-literal">FALSE</span>) + scale_x_continuous(limits=c(-<span class="hljs-number">0.4</span>, <span class="hljs-number">0</span>), breaks = c(-<span class="hljs-number">0.3</span> ,<span class="hljs-number">0</span>), name=<span class="hljs-string">'Effect size'</span>) + geom_vline(xintercept=<span class="hljs-number">0</span>, color=<span class="hljs-string">'black'</span>, linetype=<span class="hljs-string">'dashed'</span>)+ facet_grid(cols = vars(parameter), <span class="hljs-comment">#rows = vars(label),</span> <span class="hljs-comment">#labeller = label_wrap_gen(width = 23),</span> scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>)+ theme_bw()+ theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_text(size = <span class="hljs-number">12</span>), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>, linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size = <span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) <span class="hljs-comment">#Metameta_Fig3_female.sig</span></code></pre></div> <div id="section-2" class="section level23"> <p></p> </div> </div> <div id="fig4-c-10" class="section level4"> <div name="fig4_c_10%" data-unique="fig4_c_10%"></div><h4>Fig4 C >10%</h4> <p>Prepare data for traits with m/f difference > 10%</p> <p>create column with 1= larger, 0= diff not larger than 10%</p> </div> <div id="male-fig-3-10-male-biased-traits" class="section level4"> <div name="male_fig_3__10%_(male_biased_traits)" data-unique="male_fig_3__10%_(male_biased_traits)"></div><h4>Male Fig 3 > 10% (male biased traits)</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3643" aria-expanded="false" aria-controls="rcode-643E0F3643"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3643"><pre class="r"><code class="hljs">meta.male.plot3.perc <- metacombo %>% mutate(percCVR = ifelse(lnCVR > log (<span class="hljs-number">11</span>/<span class="hljs-number">10</span>), <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), percVR = ifelse(lnVR > log (<span class="hljs-number">11</span>/<span class="hljs-number">10</span>), <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), percRR = ifelse(lnRR > log (<span class="hljs-number">11</span>/<span class="hljs-number">10</span>), <span class="hljs-number">1</span>, <span class="hljs-number">0</span>)) <span class="hljs-comment">#Significant subset for lnCVR</span> metacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>% filter(percCVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>% filter(percCVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnVR</span> metacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>% filter(percVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>% filter(percVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnRR</span> metacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>% filter(percRR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>% filter(percRR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment"># **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate </span> plot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) <span class="hljs-comment"># Across all grouping terms #</span> plot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) <span class="hljs-comment"># Combine with separate grouping term results</span> plot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all) plot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all) plot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all) <span class="hljs-comment"># **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters"</span> plot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate(lnCVR = map_dbl(model_lnCVR, pluck(<span class="hljs-number">2</span>)), lnCVR_lower = map_dbl(model_lnCVR, pluck(<span class="hljs-number">6</span>)), lnCVR_upper =map_dbl(model_lnCVR, pluck(<span class="hljs-number">7</span>)), lnCVR_se =map_dbl(model_lnCVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.male.meta.CVR.perc.b)) plot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing) plot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm),] plot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate(lnVR = map_dbl(model_lnVR, pluck(<span class="hljs-number">2</span>)), lnVR_lower = map_dbl(model_lnVR, pluck(<span class="hljs-number">6</span>)), lnVR_upper =map_dbl(model_lnVR, pluck(<span class="hljs-number">7</span>)), lnVR_se =map_dbl(model_lnVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.male.meta.VR.perc.b)) plot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing) plot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm),] plot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate(lnRR = map_dbl(model_lnRR, pluck(<span class="hljs-number">2</span>)), lnRR_lower = map_dbl(model_lnRR, pluck(<span class="hljs-number">6</span>)), lnRR_upper =map_dbl(model_lnRR, pluck(<span class="hljs-number">7</span>)), lnRR_se =map_dbl(model_lnRR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing) add.row.eye <- as.data.frame(t(c(<span class="hljs-string">"Eye"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye) plot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm),] overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b) overall.male.plot3.perc <- full_join(overall.male.plot3.perc, plot3.male.meta.RR.perc.b) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>, <span class="hljs-string">"All"</span>) ) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm)))</code></pre></div> <p>Restructure data for plotting : Male biased, 10% difference</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3644" aria-expanded="false" aria-controls="rcode-643E0F3644"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3644"><pre class="r"><code class="hljs">overall3.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key= <span class="hljs-literal">TRUE</span>) lnCVR.ci <- overall3.perc %>% filter(parameter == <span class="hljs-string">"lnCVR"</span>) %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == <span class="hljs-string">"lnVR"</span>) %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == <span class="hljs-string">"lnRR"</span>) %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.perc$label <- <span class="hljs-string">"Sex difference in m/f ratios > 10%"</span> overall4.male.perc$value <- as.numeric(overall4.male.perc$value) overall4.male.perc$ci.low <- as.numeric(overall4.male.perc$ci.low) overall4.male.perc$ci.high <- as.numeric(overall4.male.perc$ci.high)</code></pre></div> <p>Plot Fig3 all >10% difference (male bias)</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3645" aria-expanded="false" aria-controls="rcode-643E0F3645"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3645"><pre class="r"><code class="hljs">Metameta_Fig3_male.perc <- overall4.male.perc %>% <span class="hljs-comment">#filter(., GroupingTerm != "Hearing") %>%</span> ggplot(aes(y= GroupingTerm, x= value)) + geom_errorbarh(aes(xmin = ci.low, xmax = ci.high), height = <span class="hljs-number">0.1</span>, show.legend = <span class="hljs-literal">FALSE</span>) + geom_point(aes(shape = parameter, fill = parameter), color = <span class="hljs-string">'mediumaquamarine'</span>, size = <span class="hljs-number">2.2</span>, show.legend = <span class="hljs-literal">FALSE</span>) + scale_x_continuous(limits=c(-<span class="hljs-number">0.2</span>, <span class="hljs-number">0.62</span>), breaks = c(<span class="hljs-number">0</span>, <span class="hljs-number">0.3</span>), name=<span class="hljs-string">'Effect size'</span>) + geom_vline(xintercept=<span class="hljs-number">0</span>, color=<span class="hljs-string">'black'</span>, linetype=<span class="hljs-string">'dashed'</span>)+ facet_grid(cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">23</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>)+ theme_bw()+ theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_blank(), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>, linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size = <span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = <span class="hljs-number">0.5</span>, size = <span class="hljs-number">14</span>), axis.title.y = element_blank()) <span class="hljs-comment"># Metameta_Fig3_male.perc</span></code></pre></div> <div id="section-3" class="section level18"> <p></p> <p>Female Fig 3 >10%</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3646" aria-expanded="false" aria-controls="rcode-643E0F3646"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3646"><pre class="r"><code class="hljs">meta.plot3.perc <- metacombo %>% mutate(percCVR = ifelse(lnCVR < log (<span class="hljs-number">9</span>/<span class="hljs-number">10</span>), <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), percVR = ifelse(lnVR < log (<span class="hljs-number">9</span>/<span class="hljs-number">10</span>), <span class="hljs-number">1</span>, <span class="hljs-number">0</span>), percRR = ifelse(lnRR < log (<span class="hljs-number">9</span>/<span class="hljs-number">10</span>), <span class="hljs-number">1</span>, <span class="hljs-number">0</span>)) <span class="hljs-comment">#Significant subset for lnCVR</span> metacombo_plot3.CVR.perc <- meta.plot3.perc %>% filter(percCVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.CVR.perc.all <- meta.plot3.perc %>% filter(percCVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnVR</span> metacombo_plot3.VR.perc <- meta.plot3.perc %>% filter(percVR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.VR.perc.all <- meta.plot3.perc %>% filter(percVR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment">#Significant subset for lnRR</span> metacombo_plot3.RR.perc <- meta.plot3.perc %>% filter(percRR == <span class="hljs-number">1</span>) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.RR.perc.all <- meta.plot3.perc %>% filter(percRR == <span class="hljs-number">1</span>) %>% nest() <span class="hljs-comment"># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate </span> plot3.meta.CVR.perc <- metacombo_plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.meta.VR.perc <- metacombo_plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.meta.RR.perc <- metacombo_plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) <span class="hljs-comment"># Across all grouping terms #</span> plot3.meta.CVR.perc.all <- metacombo_plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni(yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.meta.CVR.perc.all <- plot3.meta.CVR.perc.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.meta.VR.perc.all <- metacombo_plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni(yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.meta.VR.perc.all <- plot3.meta.VR.perc.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) plot3.meta.RR.perc.all <- metacombo_plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni(yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower)/ (<span class="hljs-number">2</span>*<span class="hljs-number">1.96</span>), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), verbose=<span class="hljs-literal">F</span>)) ) plot3.meta.RR.perc.all <- plot3.meta.RR.perc.all %>% mutate(GroupingTerm = <span class="hljs-string">"All"</span>) <span class="hljs-comment"># Combine with separate grouping term results</span> plot3.meta.CVR.perc <- bind_rows(plot3.meta.CVR.perc, plot3.meta.CVR.perc.all) plot3.meta.VR.perc <- bind_rows(plot3.meta.VR.perc, plot3.meta.VR.perc.all) plot3.meta.RR.perc <- bind_rows(plot3.meta.RR.perc, plot3.meta.RR.perc.all) <span class="hljs-comment"># **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters"</span> plot3.meta.CVR.perc.b <- as.data.frame(plot3.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate(lnCVR = map_dbl(model_lnCVR, pluck(<span class="hljs-number">2</span>)), lnCVR_lower = map_dbl(model_lnCVR, pluck(<span class="hljs-number">6</span>)), lnCVR_upper =map_dbl(model_lnCVR, pluck(<span class="hljs-number">7</span>)), lnCVR_se =map_dbl(model_lnCVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.meta.CVR.perc.b)) plot3.meta.CVR.perc.b <- rbind(plot3.meta.CVR.perc.b, add.row.hearing) plot3.meta.CVR.perc.b <- plot3.meta.CVR.perc.b[order(plot3.meta.CVR.perc.b$GroupingTerm),] plot3.meta.VR.perc.b <- as.data.frame(plot3.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate(lnVR = map_dbl(model_lnVR, pluck(<span class="hljs-number">2</span>)), lnVR_lower = map_dbl(model_lnVR, pluck(<span class="hljs-number">6</span>)), lnVR_upper =map_dbl(model_lnVR, pluck(<span class="hljs-number">7</span>)), lnVR_se =map_dbl(model_lnVR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.meta.VR.perc.b)) plot3.meta.VR.perc.b <- rbind(plot3.meta.VR.perc.b, add.row.hearing) plot3.meta.VR.perc.b <- plot3.meta.VR.perc.b[order(plot3.meta.VR.perc.b$GroupingTerm),] plot3.meta.RR.perc.b <- as.data.frame(plot3.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate(lnRR = map_dbl(model_lnRR, pluck(<span class="hljs-number">2</span>)), lnRR_lower = map_dbl(model_lnRR, pluck(<span class="hljs-number">6</span>)), lnRR_upper =map_dbl(model_lnRR, pluck(<span class="hljs-number">7</span>)), lnRR_se =map_dbl(model_lnRR, pluck(<span class="hljs-number">3</span>))) )[, c(<span class="hljs-number">1</span>,<span class="hljs-number">4</span>:<span class="hljs-number">7</span>)] add.row.hearing <- as.data.frame(t(c(<span class="hljs-string">"Hearing"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hearing) add.row.hematology <- as.data.frame(t(c(<span class="hljs-string">"Hematology"</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>, <span class="hljs-literal">NA</span>))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hematology) plot3.meta.RR.perc.b <- plot3.meta.RR.perc.b[order(plot3.meta.RR.perc.b$GroupingTerm),] overall.plot3.perc <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b) overall.plot3.perc <- full_join(overall.plot3.perc, plot3.meta.RR.perc.b) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>, <span class="hljs-string">"All"</span>) ) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, rev(levels(overall.plot3.perc$GroupingTerm)))</code></pre></div> </div> </div> <div id="restructure-data-for-plotting-1" class="section level4"> <div name="restructure_data_for_plotting37" data-unique="restructure_data_for_plotting37"></div><h4>Restructure data for plotting</h4> <p>Female bias, 10 percent difference</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3647" aria-expanded="false" aria-controls="rcode-643E0F3647"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3647"><pre class="r"><code class="hljs">overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key= <span class="hljs-literal">TRUE</span>) lnCVR.ci <- overall3.perc %>% filter(parameter == <span class="hljs-string">"lnCVR"</span>) %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == <span class="hljs-string">"lnVR"</span>) %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == <span class="hljs-string">"lnRR"</span>) %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.perc$label <- <span class="hljs-string">"Sex difference in m/f ratios > 10%"</span> overall4.perc$value <- as.numeric(overall4.perc$value) overall4.perc$ci.low <- as.numeric(overall4.perc$ci.low) overall4.perc$ci.high <- as.numeric(overall4.perc$ci.high)</code></pre></div> <p>Plot Fig3 all >10% difference (female)</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3648" aria-expanded="false" aria-controls="rcode-643E0F3648"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3648"><pre class="r"><code class="hljs">Metameta_Fig3_female.perc <- overall4.perc %>% ggplot(aes(y= GroupingTerm, x= value)) + geom_errorbarh(aes(xmin = ci.low, xmax = ci.high), height = <span class="hljs-number">0.1</span>, show.legend = <span class="hljs-literal">FALSE</span>) + geom_point(aes(shape = parameter), fill = <span class="hljs-string">'salmon1'</span>, color = <span class="hljs-string">'salmon1'</span>, size = <span class="hljs-number">2.2</span>, show.legend = <span class="hljs-literal">FALSE</span>) + <span class="hljs-comment">#scale_shape_manual(values = </span> scale_x_continuous(limits=c(-<span class="hljs-number">0.53</span>, <span class="hljs-number">0.2</span>), breaks = c(-<span class="hljs-number">0.3</span>, <span class="hljs-number">0</span>), name=<span class="hljs-string">'Effect size'</span>) + geom_vline(xintercept=<span class="hljs-number">0</span>, color=<span class="hljs-string">'black'</span>, linetype=<span class="hljs-string">'dashed'</span>)+ facet_grid(cols = vars(parameter), <span class="hljs-comment">#rows = vars(label),</span> <span class="hljs-comment">#labeller = label_wrap_gen(width = 23),</span> scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>)+ theme_bw()+ theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_blank(), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>, linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size = <span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = <span class="hljs-number">0.5</span>, size = <span class="hljs-number">14</span>), axis.title.y = element_blank()) <span class="hljs-comment">#Metameta_Fig3_female.perc</span></code></pre></div> </div> <div id="plot-fig3-all-plots-combined" class="section level4"> <div name="plot_fig3_all_plots_combined" data-unique="plot_fig3_all_plots_combined"></div><h4>Plot Fig3 all plots combined</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3649" aria-expanded="false" aria-controls="rcode-643E0F3649"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3649"><pre class="r"><code class="hljs"><span class="hljs-keyword">library</span>(ggpubr) Fig3.bottom <- ggarrange(Metameta_Fig3_female.sig, Metameta_Fig3_male.sig, Metameta_Fig3_female.perc, Metameta_Fig3_male.perc, ncol = <span class="hljs-number">2</span>, nrow = <span class="hljs-number">2</span>, widths = c(<span class="hljs-number">1</span>, <span class="hljs-number">1.20</span>), heights = c(<span class="hljs-number">1</span>, <span class="hljs-number">1</span>)) Fig3 <- ggarrange(Metameta_Fig3_alltraits, Fig3.bottom, ncol = <span class="hljs-number">1</span>, nrow = <span class="hljs-number">2</span>, heights = c(<span class="hljs-number">1.4</span>, <span class="hljs-number">2.5</span>)) Fig3</code></pre></div> <p><img 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" width="672"></p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3650" aria-expanded="false" aria-controls="rcode-643E0F3650"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3650"><pre class="r"><code class="hljs"><span class="hljs-comment">#ggsave("Fig3.pdf", plot = Fig3, width = 9, height = 6) </span></code></pre></div> </div> </div> <div id="heterogeneity" class="section level3"> <div name="heterogeneity" data-unique="heterogeneity"></div><h3>Heterogeneity</h3> <div id="figure-4-second-order-meta-analysis-on-heterogeneity" class="section level4"> <div name="figure_4_(second-order_meta_analysis_on_heterogeneity)" data-unique="figure_4_(second-order_meta_analysis_on_heterogeneity)"></div><h4>FIGURE 4 (Second-order meta analysis on heterogeneity)</h4> </div> <div id="create-matrix-to-store-results-for-all-traits" class="section level4"> <div name="create_matrix_to_store_results_for_all_traits" data-unique="create_matrix_to_store_results_for_all_traits"></div><h4>Create matrix to store results for all traits</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3651" aria-expanded="false" aria-controls="rcode-643E0F3651"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3651"><pre class="r"><code class="hljs">results.allhetero.grouping <- as.data.frame(cbind(c(<span class="hljs-number">1</span>:n), matrix(rep(<span class="hljs-number">0</span>, n*<span class="hljs-number">30</span>), ncol = <span class="hljs-number">30</span>))) names(results.allhetero.grouping) <- c(<span class="hljs-string">"id"</span>, <span class="hljs-string">"sigma2_strain.CVR"</span>, <span class="hljs-string">"sigma2_center.CVR"</span>, <span class="hljs-string">"sigma2_error.CVR"</span>, <span class="hljs-string">"s.nlevels.strain.CVR"</span>, <span class="hljs-string">"s.nlevels.center.CVR"</span>, <span class="hljs-string">"s.nlevels.error.CVR"</span>, <span class="hljs-string">"sigma2_strain.VR"</span>, <span class="hljs-string">"sigma2_center.VR"</span>, <span class="hljs-string">"sigma2_error.VR"</span>, <span class="hljs-string">"s.nlevels.strain.VR"</span>, <span class="hljs-string">"s.nlevels.center.VR"</span>, <span class="hljs-string">"s.nlevels.error.VR"</span>, <span class="hljs-string">"sigma2_strain.RR"</span>, <span class="hljs-string">"sigma2_center.RR"</span>, <span class="hljs-string">"sigma2_error.RR"</span>, <span class="hljs-string">"s.nlevels.strain.RR"</span>, <span class="hljs-string">"s.nlevels.center.RR"</span>, <span class="hljs-string">"s.nlevels.error.RR"</span>, <span class="hljs-string">"lnCVR"</span>, <span class="hljs-string">"lnCVR_lower"</span>, <span class="hljs-string">"lnCVR_upper"</span>, <span class="hljs-string">"lnCVR_se"</span>, <span class="hljs-string">"lnVR"</span>, <span class="hljs-string">"lnVR_lower"</span>, <span class="hljs-string">"lnVR_upper"</span>, <span class="hljs-string">"lnVR_se"</span>, <span class="hljs-string">"lnRR"</span>, <span class="hljs-string">"lnRR_lower"</span>, <span class="hljs-string">"lnRR_upper"</span> ,<span class="hljs-string">"lnRR_se"</span>)</code></pre></div> </div> <div id="loop" class="section level4"> <div name="loop" data-unique="loop"></div><h4>LOOP</h4> <p>Parameters to extract from metafor (sigma2’s, s.nlevels)</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3652" aria-expanded="false" aria-controls="rcode-643E0F3652"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3652"><pre class="r"><code class="hljs"><span class="hljs-keyword">for</span>(t <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:n) { <span class="hljs-keyword">tryCatch</span>({ data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = <span class="hljs-number">0</span>, age_center = <span class="hljs-number">100</span>) population_stats <- calculate_population_stats(data_par_age) results <- create_meta_analysis_effect_sizes(population_stats) <span class="hljs-comment"># lnCVR, logaritm of the ratio of male and female coefficients of variance </span> cvr. <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(~<span class="hljs-number">1</span>| strain_name, ~<span class="hljs-number">1</span>|production_center, ~<span class="hljs-number">1</span>|err), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), data = results) results.allhetero.grouping[t, <span class="hljs-number">2</span>] <- cvr.$sigma2[<span class="hljs-number">1</span>] results.allhetero.grouping[t, <span class="hljs-number">3</span>] <- cvr.$sigma2[<span class="hljs-number">2</span>] results.allhetero.grouping[t, <span class="hljs-number">4</span>] <- cvr.$sigma2[<span class="hljs-number">3</span>] results.allhetero.grouping[t, <span class="hljs-number">5</span>] <- cvr.$s.nlevels[<span class="hljs-number">1</span>] results.allhetero.grouping[t, <span class="hljs-number">6</span>] <- cvr.$s.nlevels[<span class="hljs-number">2</span>] results.allhetero.grouping[t, <span class="hljs-number">7</span>] <- cvr.$s.nlevels[<span class="hljs-number">3</span>] results.allhetero.grouping[t, <span class="hljs-number">20</span>] <- cvr.$b results.allhetero.grouping[t, <span class="hljs-number">21</span>] <- cvr.$ci.lb results.allhetero.grouping[t, <span class="hljs-number">22</span>] <- cvr.$ci.ub results.allhetero.grouping[t, <span class="hljs-number">23</span>] <- cvr.$se <span class="hljs-comment"># lnVR, male to female variability ratio (logarithm of male and female standard deviations)</span> vr. <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(~<span class="hljs-number">1</span>| strain_name, ~<span class="hljs-number">1</span>|production_center, ~<span class="hljs-number">1</span>|err), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), data = results) results.allhetero.grouping[t, <span class="hljs-number">8</span>] <- vr.$sigma2[<span class="hljs-number">1</span>] results.allhetero.grouping[t, <span class="hljs-number">9</span>] <- vr.$sigma2[<span class="hljs-number">2</span>] results.allhetero.grouping[t, <span class="hljs-number">10</span>] <- vr.$sigma2[<span class="hljs-number">3</span>] results.allhetero.grouping[t, <span class="hljs-number">11</span>] <- vr.$s.nlevels[<span class="hljs-number">1</span>] results.allhetero.grouping[t, <span class="hljs-number">12</span>] <- vr.$s.nlevels[<span class="hljs-number">2</span>] results.allhetero.grouping[t, <span class="hljs-number">13</span>] <- vr.$s.nlevels[<span class="hljs-number">3</span>] results.allhetero.grouping[t, <span class="hljs-number">24</span>] <- vr.$b results.allhetero.grouping[t, <span class="hljs-number">25</span>] <- vr.$ci.lb results.allhetero.grouping[t, <span class="hljs-number">26</span>] <- vr.$ci.ub results.allhetero.grouping[t, <span class="hljs-number">27</span>] <- vr.$se <span class="hljs-comment"># lnRR, response ratio (logarithm of male and female means)</span> rr. <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(~<span class="hljs-number">1</span>| strain_name, ~<span class="hljs-number">1</span>|production_center, ~<span class="hljs-number">1</span>|err), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">1000</span>), data = results) results.allhetero.grouping[t, <span class="hljs-number">14</span>] <- rr.$sigma2[<span class="hljs-number">1</span>] results.allhetero.grouping[t, <span class="hljs-number">15</span>] <- rr.$sigma2[<span class="hljs-number">2</span>] results.allhetero.grouping[t, <span class="hljs-number">16</span>] <- rr.$sigma2[<span class="hljs-number">3</span>] results.allhetero.grouping[t, <span class="hljs-number">17</span>] <- rr.$s.nlevels[<span class="hljs-number">1</span>] results.allhetero.grouping[t, <span class="hljs-number">18</span>] <- rr.$s.nlevels[<span class="hljs-number">2</span>] results.allhetero.grouping[t, <span class="hljs-number">19</span>] <- rr.$s.nlevels[<span class="hljs-number">3</span>] results.allhetero.grouping[t, <span class="hljs-number">28</span>] <- rr.$b results.allhetero.grouping[t, <span class="hljs-number">29</span>] <- rr.$ci.lb results.allhetero.grouping[t, <span class="hljs-number">30</span>] <- rr.$ci.ub results.allhetero.grouping[t, <span class="hljs-number">31</span>] <- rr.$se }, error=<span class="hljs-keyword">function</span>(e){cat(<span class="hljs-string">"ERROR :"</span>,conditionMessage(e), <span class="hljs-string">"\n"</span>)}) }</code></pre></div> <pre><code class="hljs">## ERROR : Optimizer (optim) did not achieve convergence (convergence = 10). ## ERROR : Optimizer (optim) did not achieve convergence (convergence = 10). ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y' ## ERROR : NA/NaN/Inf in 'y'</code></pre> </div> <div id="exclude-traits" class="section level4"> <div name="exclude_traits" data-unique="exclude_traits"></div><h4>Exclude traits</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3653" aria-expanded="false" aria-controls="rcode-643E0F3653"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3653"><pre class="r"><code class="hljs">results.allhetero.grouping2 <- results.allhetero.grouping[results.allhetero.grouping$s.nlevels.strain.VR != <span class="hljs-number">0</span>, ] nrow(results.allhetero.grouping2) <span class="hljs-comment">#218</span></code></pre></div> <pre><code class="hljs">## [1] 223</code></pre> <p>Merge data sets containing metafor results with procedure etc. names</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3654" aria-expanded="false" aria-controls="rcode-643E0F3654"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3654"><pre class="r"><code class="hljs"><span class="hljs-comment">#procedures <- read.csv("../procedures.csv")</span> results.allhetero.grouping2$parameter_group <- data$parameter_group[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$procedure <- data$procedure_name[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$GroupingTerm <- procedures$GroupingTerm[match(results.allhetero.grouping2$procedure, procedures$procedure)] results.allhetero.grouping2$parameter_name <- data$parameter_name[match(results.allhetero.grouping2$id, data$id)]</code></pre></div> </div> <div id="dealing-with-correlated-parameters" class="section level4"> <div name="dealing_with_correlated_parameters" data-unique="dealing_with_correlated_parameters"></div><h4>Dealing with correlated parameters</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3655" aria-expanded="false" aria-controls="rcode-643E0F3655"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3655"><pre class="r"><code class="hljs">metahetero1 <- results.allhetero.grouping2 length(unique(metahetero1$procedure)) <span class="hljs-comment">#18</span></code></pre></div> <pre><code class="hljs">## [1] 19</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3656" aria-expanded="false" aria-controls="rcode-643E0F3656"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3656"><pre class="r"><code class="hljs">length(unique(metahetero1$GroupingTerm)) <span class="hljs-comment">#9 </span></code></pre></div> <pre><code class="hljs">## [1] 9</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3657" aria-expanded="false" aria-controls="rcode-643E0F3657"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3657"><pre class="r"><code class="hljs">length(unique(metahetero1$parameter_group)) <span class="hljs-comment"># 149 levels: one more tha in effect size alanlysis, see above; CHECK!</span></code></pre></div> <pre><code class="hljs">## [1] 152</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3658" aria-expanded="false" aria-controls="rcode-643E0F3658"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3658"><pre class="r"><code class="hljs">length(unique(metahetero1$parameter_name)) <span class="hljs-comment">#218</span></code></pre></div> <pre><code class="hljs">## [1] 223</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3659" aria-expanded="false" aria-controls="rcode-643E0F3659"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3659"><pre class="r"><code class="hljs"><span class="hljs-comment"># Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) </span> metahetero1b <- metahetero1 %>% group_by(parameter_group) %>% mutate(par_group_size = n_distinct(parameter_name)) metahetero1$par_group_size <- metahetero1b$par_group_size[match(metahetero1$parameter_group, metahetero1b$parameter_group)] <span class="hljs-comment">#Create subsets with > 1 count (par_group_size > 1) </span> metahetero1_sub <- subset(metahetero1, par_group_size > <span class="hljs-number">1</span>) <span class="hljs-comment"># 90 observations </span> str(metahetero1_sub)</code></pre></div> <pre><code class="hljs">## 'data.frame': 92 obs. of 36 variables: ## $ id : num 1 2 3 4 5 17 18 19 33 34 ... ## $ sigma2_strain.CVR : num 3.83e-03 1.09e-14 6.56e-03 4.85e-03 1.55e-10 ... ## $ sigma2_center.CVR : num 6.59e-15 6.78e-03 6.02e-09 2.60e-09 2.14e-02 ... ## $ sigma2_error.CVR : num 4.07e-11 8.57e-15 4.23e-03 1.46e-02 1.54e-03 ... ## $ s.nlevels.strain.CVR: num 6 6 6 6 6 5 5 5 5 6 ... ## $ s.nlevels.center.CVR: num 7 10 10 10 7 4 4 4 4 5 ... ## $ s.nlevels.error.CVR : num 9 13 13 13 9 6 6 6 6 7 ... ## $ sigma2_strain.VR : num 7.60e-04 3.96e-12 3.55e-03 2.48e-03 1.06e-09 ... ## $ sigma2_center.VR : num 9.22e-11 1.64e-10 3.42e-10 3.26e-10 1.53e-02 ... ## $ sigma2_error.VR : num 0 0.006805 0.005798 0.016756 0.000245 ... ## $ s.nlevels.strain.VR : num 6 6 6 6 6 5 5 5 5 6 ... ## $ s.nlevels.center.VR : num 7 10 10 10 7 4 4 4 4 5 ... ## $ s.nlevels.error.VR : num 9 13 13 13 9 6 6 6 6 7 ... ## $ sigma2_strain.RR : num 1.50e-03 1.07e-11 1.92e-03 6.74e-04 0.00 ... ## $ sigma2_center.RR : num 6.67e-12 6.85e-03 1.38e-11 1.37e-04 9.43e-04 ... ## $ sigma2_error.RR : num 1.25e-13 3.00e-14 0.00 5.04e-11 1.12e-11 ... ## $ s.nlevels.strain.RR : num 6 6 6 6 6 5 5 5 5 6 ... ## $ s.nlevels.center.RR : num 7 10 10 10 7 4 4 4 4 5 ... ## $ s.nlevels.error.RR : num 9 13 13 13 9 6 6 6 6 7 ... ## $ lnCVR : num 0.0161 0.1353 0.066 -0.0517 -0.0781 ... ## $ lnCVR_lower : num -0.0479 0.0551 -0.0181 -0.1464 -0.1949 ... ## $ lnCVR_upper : num 0.0801 0.2155 0.15 0.0429 0.0387 ... ## $ lnCVR_se : num 0.0327 0.0409 0.0429 0.0483 0.0596 ... ## $ lnVR : num 0.011 0.0449 0.023 -0.0487 -0.0523 ... ## $ lnVR_lower : num -0.02836 -0.00751 -0.04854 -0.13647 -0.15025 ... ## $ lnVR_upper : num 0.0504 0.0972 0.0945 0.0391 0.0456 ... ## $ lnVR_se : num 0.0201 0.0267 0.0365 0.0448 0.05 ... ## $ lnRR : num -0.00732 -0.06406 -0.03473 0.00518 0.02602 ... ## $ lnRR_lower : num -0.0425 -0.1388 -0.0743 -0.0195 0.0012 ... ## $ lnRR_upper : num 0.02783 0.01063 0.00484 0.02983 0.05085 ... ## $ lnRR_se : num 0.0179 0.0381 0.0202 0.0126 0.0127 ... ## $ parameter_group : Factor w/ 161 levels "12khz-evoked abr threshold",..: 126 126 126 126 126 12 12 12 26 27 ... ## $ procedure : chr "Acoustic Startle and Pre-pulse Inhibition (PPI)" "Acoustic Startle and Pre-pulse Inhibition (PPI)" "Acoustic Startle and Pre-pulse Inhibition (PPI)" "Acoustic Startle and Pre-pulse Inhibition (PPI)" ... ## $ GroupingTerm : Factor w/ 9 levels "Behaviour","Eye",..: 1 1 1 1 1 6 6 6 6 6 ... ## $ parameter_name : chr "% pre-pulse inhibition - global" "% pre-pulse inhibition - ppi1" "% pre-pulse inhibition - ppi2" "% pre-pulse inhibition - ppi3" ... ## $ par_group_size : int 5 5 5 5 5 4 4 4 6 7 ...</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3660" aria-expanded="false" aria-controls="rcode-643E0F3660"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3660"><pre class="r"><code class="hljs"><span class="hljs-comment"># metahetero1_sub$sampleSize <- as.numeric(metahetero1_sub$sampleSize) #from previous analysis? don't think is used: : delete in final version</span> <span class="hljs-comment"># Nest data</span> n_count. <- metahetero1_sub %>% group_by(parameter_group) %>% <span class="hljs-comment">#mutate(raw_N = sum(sampleSize)) %>% #don't think is necessary: delete in final version</span> nest() <span class="hljs-comment"># meta-analysis preparation</span> model_count. <- n_count. %>% mutate(model_lnRR = map(data, ~ robu(.x$lnRR ~ <span class="hljs-number">1</span>, data= .x, studynum= .x$id, modelweights = c(<span class="hljs-string">"CORR"</span>), rho = <span class="hljs-number">0.8</span>, small = <span class="hljs-literal">TRUE</span>, var.eff.size= (.x$lnRR_se)^<span class="hljs-number">2</span> )), model_lnVR = map(data, ~ robu(.x$lnVR ~ <span class="hljs-number">1</span>, data= .x, studynum= .x$id, modelweights = c(<span class="hljs-string">"CORR"</span>), rho = <span class="hljs-number">0.8</span>, small = <span class="hljs-literal">TRUE</span>, var.eff.size= (.x$lnVR_se)^<span class="hljs-number">2</span> )), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ <span class="hljs-number">1</span>, data= .x, studynum= .x$id, modelweights = c(<span class="hljs-string">"CORR"</span>), rho = <span class="hljs-number">0.8</span>, small = <span class="hljs-literal">TRUE</span>, var.eff.size= (.x$lnCVR_se)^<span class="hljs-number">2</span> ))) <span class="hljs-comment">#Robumeta object details:</span> <span class="hljs-comment">#str(model_count.$model_lnCVR[[1]])</span> <span class="hljs-comment">## *Perform meta-analyses on correlated sub-traits, using robumeta</span> <span class="hljs-comment"># Shinichi: We think we want to use these for further analyses:</span> <span class="hljs-comment"># residual variance: as.numeric(robu_fit$mod_info$term1) (same as 'mod_info$tau.sq')</span> <span class="hljs-comment"># sample size: robu_fit$N</span> <span class="hljs-comment">## **Extract and save parameter estimates </span> count_fun. <- <span class="hljs-keyword">function</span>(mod_sub) <span class="hljs-keyword">return</span>(c(as.numeric(mod_sub$mod_info$term1), mod_sub$N) ) robusub_RR. <- model_count. %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun.)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c(<span class="hljs-string">"parameter_group"</span>,<span class="hljs-string">"var.RR"</span>,<span class="hljs-string">"N.RR"</span>)) robusub_CVR. <- model_count. %>% transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun.)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c(<span class="hljs-string">"parameter_group"</span>,<span class="hljs-string">"var.CVR"</span>,<span class="hljs-string">"N.CVR"</span>)) robusub_VR. <- model_count. %>% transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun.)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c(<span class="hljs-string">"parameter_group"</span>,<span class="hljs-string">"var.VR"</span>,<span class="hljs-string">"N.VR"</span>)) robu_all. <- full_join(robusub_CVR., robusub_VR.) %>% full_join(., robusub_RR.)</code></pre></div> </div> <div id="merge-the-two-data-sets-the-new-robu_all.-and-the-initial-uncorrelated-sub-traits-with-count-1" class="section level4"> <div name="merge_the_two_data_sets_(the_new_[robu_all]_and_the_initial_[uncorrelated_sub-traits_with_count_=_1])" data-unique="merge_the_two_data_sets_(the_new_[robu_all]_and_the_initial_[uncorrelated_sub-traits_with_count_=_1])"></div><h4>Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1])</h4> <p>In this step, we<br> 1) merge the N from robumeta and the N from metafor (s.nlevels.error) together into the same columns (N.RR, N.VR, N.CVR) 2) calculate the total variance for metafor models as the sum of random effect variances and the residual error, then add in the same columns together with the residual variances from robumeta</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3661" aria-expanded="false" aria-controls="rcode-643E0F3661"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3661"><pre class="r"><code class="hljs">metahetero_all <- metahetero1 %>% filter(par_group_size == <span class="hljs-number">1</span>) %>% as_tibble metahetero_all$N.RR <- metahetero_all$s.nlevels.error.RR metahetero_all$N.CVR <- metahetero_all$s.nlevels.error.CVR metahetero_all$N.VR <- metahetero_all$s.nlevels.error.VR metahetero_all$var.RR <- log(sqrt(metahetero_all$sigma2_strain.RR + metahetero_all$sigma2_center.RR + metahetero_all$sigma2_error.RR)) metahetero_all$var.VR <- log(sqrt(metahetero_all$sigma2_strain.VR + metahetero_all$sigma2_center.VR + metahetero_all$sigma2_error.VR)) metahetero_all$var.CVR <- log(sqrt(metahetero_all$sigma2_strain.CVR + metahetero_all$sigma2_center.CVR + metahetero_all$sigma2_error.CVR)) <span class="hljs-comment">#str(metahetero_all)</span> <span class="hljs-comment">#str(robu_all.)</span> metahetero_all <- metahetero_all %>% mutate(var.RR = if_else(var.RR == -<span class="hljs-literal">Inf</span>, -<span class="hljs-number">7</span> ,var.RR), var.VR = if_else(var.VR == -<span class="hljs-literal">Inf</span>, -<span class="hljs-number">5</span> ,var.VR), var.CVR = if_else(var.CVR == -<span class="hljs-literal">Inf</span>, -<span class="hljs-number">6</span> ,var.CVR)) <span class="hljs-comment"># **Combine data </span> <span class="hljs-comment">## Step1 </span> combinedmetahetero <- bind_rows(robu_all., metahetero_all) <span class="hljs-comment">#glimpse(combinedmetahetero)</span> <span class="hljs-comment"># Steps 2&3</span> metacombohetero <- combinedmetahetero metacombohetero$counts <- metahetero1$par_group_size[match( metacombohetero$parameter_group, metahetero1$parameter_group)] metacombohetero$procedure2 <-metahetero1$procedure[match( metacombohetero$parameter_group, metahetero1$parameter_group)] metacombohetero$GroupingTerm2 <-metahetero1$GroupingTerm[match( metacombohetero$parameter_group, metahetero1$parameter_group)] <span class="hljs-comment"># **Clean-up and rename </span> metacombohetero <- metacombohetero[, c(<span class="hljs-number">1</span>:<span class="hljs-number">7</span>, <span class="hljs-number">43</span>:<span class="hljs-number">45</span>)] names(metacombohetero)[<span class="hljs-number">9</span>] <- <span class="hljs-string">"procedure"</span> names(metacombohetero)[<span class="hljs-number">10</span>] <- <span class="hljs-string">"GroupingTerm"</span> </code></pre></div> </div> <div id="last-step-meta-meta-analysis-of-heterogeneity" class="section level4"> <div name="last_step:_meta-meta-analysis_of_heterogeneity" data-unique="last_step:_meta-meta-analysis_of_heterogeneity"></div><h4>Last step: meta-meta-analysis of heterogeneity</h4> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3662" aria-expanded="false" aria-controls="rcode-643E0F3662"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3662"><pre class="r"><code class="hljs"><span class="hljs-comment">## *Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR) </span> metacombohetero_final <- metacombohetero %>% group_by(GroupingTerm) %>% nest() <span class="hljs-comment"># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate </span> metacombohetero$var.CVR</code></pre></div> <pre><code class="hljs">## [1] 0.0044991186 0.0000642816 -0.0144265163 0.0050872554 -0.0077958010 ## [6] 0.0035156022 -0.0016450671 -0.0008696892 0.0052878381 0.0001600556 ## [11] -0.0022444358 -0.0004257810 0.0589805219 0.0019663188 -0.0101574334 ## [16] -0.0001192722 -0.0004167067 -0.0065819186 0.0025887091 0.0011066115 ## [21] 0.0007330397 -2.6597488778 -2.7109304575 -2.4666374444 -2.4190625440 ## [26] -6.0000000000 -1.6765385765 -2.2211382571 -2.4803820009 -2.3163922698 ## [31] -2.1462407635 -1.4870666914 -2.4398886718 -2.6594232970 -1.8709366522 ## [36] -2.7492091777 -6.0000000000 -2.2096017872 -2.8372243141 -2.5966411739 ## [41] -2.0372635699 -1.7993498392 -1.3237500184 -2.4470323335 -2.6705595776 ## [46] -2.5640560844 -2.7773725290 -2.8522607171 -2.4179073905 -1.6170447612 ## [51] -2.3170574175 -2.1378338336 -2.3582684091 -2.1601625056 -2.3122643035 ## [56] -2.4773261880 -6.0000000000 -6.0000000000 -1.9777983292 -2.0066354936 ## [61] -1.9181629170 -2.3654858439 -2.6013619833 -2.4904490520 -2.2446924631 ## [66] -2.8284106787 -2.5460751840 -2.2939104814 -1.9775239093 -2.1993504724 ## [71] -1.5779079404 -2.4494899888 -2.0902610847 -3.0974151169 -2.8422362261 ## [76] -1.1383433892 -1.9342984119 -2.6060756341 -2.2943035742 -1.4466019118 ## [81] -2.2190572693 -1.7189444245 -1.8633043362 -6.0000000000 -6.0000000000 ## [86] -6.0000000000 -6.0000000000 -1.6067160546 -2.3012113787 -6.0000000000 ## [91] -1.6270233655 -2.7634753695 -2.1370173646 -2.5779218865 -2.7175087198 ## [96] -6.0000000000 -1.4037959533 -1.7018930341 -2.3344943000 -2.7273106400 ## [101] -6.0000000000 -4.1046049569 -2.1822373277 -1.3326681362 -2.5871679179 ## [106] -2.0174660672 -3.3272866024 -3.2077188205 -1.6370100682 -2.7261740396 ## [111] -2.4227226560 -2.4766881954 -1.8894986680 -2.4175072222 -3.1804162368 ## [116] -2.5174764339 -6.0000000000 -1.7884303244 -2.3647153111 -2.8157523923 ## [121] -3.2648326014 -3.1758796266 -2.9972690061 -2.2693356188 -1.5123342000 ## [126] -2.9715994935 -2.3388729551 -0.4645348140 -2.1908600686 -1.0349121159 ## [131] -6.0000000000 -6.0000000000 -0.9481380899 -1.2489473343 -1.3160742631 ## [136] -2.0331918626 -1.6265101179 -1.3064359147 -2.8298766900 -2.1791320199 ## [141] -0.8300329578 -2.1426880967 -1.3016303060 -1.9384089540 -6.0000000000 ## [146] -2.8430641339 -2.1164664090 -1.6584679154 -6.0000000000 -2.1808169436 ## [151] -2.2525802925 -2.7297180428</code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3663" aria-expanded="false" aria-controls="rcode-643E0F3663"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3663"><pre class="r"><code class="hljs">heterog1 <- metacombohetero_final %>% mutate(model_heteroCVR = map(data, ~ metafor::rma.uni(yi = .x$var.CVR, sei = sqrt(<span class="hljs-number">1</span> / <span class="hljs-number">2</span>*(.x$N.CVR - <span class="hljs-number">1</span>)), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">10000</span>, stepadj=<span class="hljs-number">0.5</span>), verbose=<span class="hljs-literal">F</span>)), model_heteroVR = map(data, ~ metafor::rma.uni(yi = .x$var.VR, sei = sqrt(<span class="hljs-number">1</span> / <span class="hljs-number">2</span>*(.x$N.VR - <span class="hljs-number">1</span>)), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">10000</span>, stepadj=<span class="hljs-number">0.5</span>), verbose=<span class="hljs-literal">F</span>)), model_heteroRR = map(data, ~ metafor::rma.uni(yi = .x$var.RR, sei = sqrt(<span class="hljs-number">1</span> / <span class="hljs-number">2</span>*(.x$N.RR - <span class="hljs-number">1</span>)), control=list(optimizer=<span class="hljs-string">"optim"</span>, optmethod=<span class="hljs-string">"Nelder-Mead"</span>, maxit= <span class="hljs-number">10000</span>, stepadj=<span class="hljs-number">0.5</span>), verbose=<span class="hljs-literal">F</span>))) heterog1</code></pre></div> <pre><code class="hljs">## # A tibble: 9 x 5 ## GroupingTerm data model_heteroCVR model_heteroVR model_heteroRR ## <fct> <list> <list> <list> <list> ## 1 Behaviour <tibble [18 ×… <rma.uni> <rma.uni> <rma.uni> ## 2 Immunology <tibble [19 ×… <rma.uni> <rma.uni> <rma.uni> ## 3 Hematology <tibble [17 ×… <rma.uni> <rma.uni> <rma.uni> ## 4 Hearing <tibble [6 × … <rma.uni> <rma.uni> <rma.uni> ## 5 Physiology <tibble [26 ×… <rma.uni> <rma.uni> <rma.uni> ## 6 Metabolism <tibble [9 × … <rma.uni> <rma.uni> <rma.uni> ## 7 Morphology <tibble [16 ×… <rma.uni> <rma.uni> <rma.uni> ## 8 Heart <tibble [29 ×… <rma.uni> <rma.uni> <rma.uni> ## 9 Eye <tibble [12 ×… <rma.uni> <rma.uni> <rma.uni></code></pre> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3664" aria-expanded="false" aria-controls="rcode-643E0F3664"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3664"><pre class="r"><code class="hljs"><span class="hljs-comment"># **Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables</span> Behaviour. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Behaviour"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Immunology. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Immunology"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Hematology. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Hematology"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Hearing. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Hearing"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Physiology. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Physiology"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Metabolism. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Metabolism"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Morphology. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Morphology"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Heart. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Heart"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Eye. <- heterog1 %>% filter(., GroupingTerm == <span class="hljs-string">"Eye"</span>) %>% select(., -data) %>% mutate(heteroCVR=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$b, heteroCVR_lower=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroCVR_upper=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroCVR_se=.[[<span class="hljs-number">2</span>]][[<span class="hljs-number">1</span>]]$se, heteroVR=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$b, heteroVR_lower=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroVR_upper=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroVR_se=.[[<span class="hljs-number">3</span>]][[<span class="hljs-number">1</span>]]$se, heteroRR=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$b, heteroRR_lower=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.lb, heteroRR_upper=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$ci.ub, heteroRR_se=.[[<span class="hljs-number">4</span>]][[<span class="hljs-number">1</span>]]$se) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) heterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye.) <span class="hljs-comment">#str(heterog2)</span></code></pre></div> </div> <div id="heterogeneity-plots" class="section level4"> <div name="heterogeneity_plots" data-unique="heterogeneity_plots"></div><h4>Heterogeneity PLOTS</h4> <p>Restructure data for plotting</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3665" aria-expanded="false" aria-controls="rcode-643E0F3665"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3665"><pre class="r"><code class="hljs">heterog3 <- gather(heterog2, parameter, value, c(heteroCVR, heteroVR, heteroRR), factor_key= <span class="hljs-literal">TRUE</span>) heteroCVR.ci <- heterog3 %>% filter(parameter == <span class="hljs-string">"heteroCVR"</span>) %>% mutate(ci.low = heteroCVR_lower, ci.high = heteroCVR_upper) heteroVR.ci <- heterog3 %>% filter(parameter == <span class="hljs-string">"heteroVR"</span>) %>% mutate(ci.low = heteroVR_lower, ci.high = heteroVR_upper) heteroRR.ci <- heterog3 %>% filter(parameter == <span class="hljs-string">"heteroRR"</span>) %>% mutate(ci.low = heteroRR_lower, ci.high = heteroRR_upper) heterog4 <- bind_rows(heteroCVR.ci, heteroVR.ci, heteroRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) <span class="hljs-comment"># **Re-order grouping terms </span> heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, levels =c(<span class="hljs-string">"Behaviour"</span>,<span class="hljs-string">"Morphology"</span>,<span class="hljs-string">"Metabolism"</span>,<span class="hljs-string">"Physiology"</span>,<span class="hljs-string">"Immunology"</span>,<span class="hljs-string">"Hematology"</span>,<span class="hljs-string">"Heart"</span>,<span class="hljs-string">"Hearing"</span>,<span class="hljs-string">"Eye"</span>) ) heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, rev(levels(heterog4$GroupingTerm))) heterog4$label <- <span class="hljs-string">"All traits"</span> <span class="hljs-comment">#write.csv(heterog4, "heterog4.csv")</span></code></pre></div> </div> <div id="plot-figure-4-5-in-ms-second-order-meta-analysis-on-heterogeneity" class="section level4"> <div name="plot_figure_4_(5_in_ms)_(second-order_meta_analysis_on_heterogeneity)" data-unique="plot_figure_4_(5_in_ms)_(second-order_meta_analysis_on_heterogeneity)"></div><h4>Plot FIGURE 4 (5 in ms) (Second-order meta analysis on heterogeneity)</h4> <p>Plot Fig4 all traits</p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3666" aria-expanded="false" aria-controls="rcode-643E0F3666"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3666"><pre class="r"><code class="hljs">Metameta_Fig4_alltraits <- heterog4 %>% ggplot(aes(y= GroupingTerm, x= value)) + geom_errorbarh(aes(xmin = ci.low, xmax = ci.high), height = <span class="hljs-number">0.1</span>, show.legend = <span class="hljs-literal">FALSE</span>) + geom_point(aes(shape = parameter, fill = parameter, color = parameter), size = <span class="hljs-number">2.2</span>, show.legend = <span class="hljs-literal">FALSE</span>) + scale_x_continuous(limits=c(-<span class="hljs-number">7.0</span>, <span class="hljs-number">1</span>), <span class="hljs-comment">#breaks = c(-2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0), </span> name=<span class="hljs-string">'Effect size'</span>) + facet_grid(cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = <span class="hljs-number">23</span>), scales= <span class="hljs-string">'free'</span>, space=<span class="hljs-string">'free'</span>)+ theme_bw()+ theme(strip.text.y = element_text(angle = <span class="hljs-number">270</span>, size = <span class="hljs-number">10</span>, margin = margin(t=<span class="hljs-number">15</span>, r=<span class="hljs-number">15</span>, b=<span class="hljs-number">15</span>, l=<span class="hljs-number">15</span>)), strip.text.x = element_text(size = <span class="hljs-number">12</span>), strip.background = element_rect(colour = <span class="hljs-literal">NULL</span>, linetype = <span class="hljs-string">"blank"</span>, fill = <span class="hljs-string">"gray90"</span>), text = element_text(size = <span class="hljs-number">14</span>), panel.spacing = unit(<span class="hljs-number">0.5</span>, <span class="hljs-string">"lines"</span>), panel.border= element_blank(), axis.line=element_line(), panel.grid.major.x = element_line(linetype = <span class="hljs-string">"solid"</span>, colour = <span class="hljs-string">"gray95"</span>), panel.grid.major.y = element_line(linetype = <span class="hljs-string">"solid"</span>, color = <span class="hljs-string">"gray95"</span>), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) Metameta_Fig4_alltraits </code></pre></div> <p><img 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VYUaWmki4+SWkDuueeezmO17nSTApBu68e6deta9+7dnby///67qXWsvrn54osv7IwzzjDlVWtXv0mtSi+77DKnxae22WOPPRyjRo0aOeVXq00Fou+55x7H45prrim1a7V01TL5KunCedBBB1mLFi2cFrejR4+21157zcaPH19qOz2RsepVgVcFTxVMVsvQWEktX5Xkoha1JAQQQMCvQPHoty3y7ZHf7E6+4tf+Z9W6H2KptqxP6qBkRgABBPJcYGnxMrv72yEJz/Kf0x+wY1ocYXUL6yTMSwYEEEAAAQQQQCCeQMYCoN9//73dfffd8Y5barmCauUltfZzg59qfXjKKaeU+iB6+umn24033ugE3tTV/fDDD7dtt9025i4VnFPgVPts0qRJNI+689x///22OjITsMrdvHlzp8WpAnNuUgue66+/3nmqVp7eAKiCg27w84ILLrC//OUv7mbR32o1qQCjgqAvvvhiqQCoWoTq2G4AdPfdd7dLLrkkuq0eqBWlvrlWUtfvO+64o1S3/zPPPNP5VnvIkCHOvu69917Tj9+k1pSqiyqRGZBVzhNOOKHUpvrG/4YbbnCCn6+++qrT2lbldNMbb7wRDX7qmyPl3WqrrdzVTr1de+21TsA5utDz4KijjnICoAoyK6jtDRC72RScVgtQJQWnva1o3Tzub3U3lWnZpMCqlqsedCxSZgS8LZr1mOBRZpzL7lWvazfl22tcf/8aLiSV1P2Tj6x+kjsoWfK7vfbwQ7asXvlb6nri+levXr3Ua15fTLlJfw+5dO3RdVbvC+lI8tEwJqTMCejLSDfpcS691txy5+pv9+9f5c83d+/f7XfffZfytVhGr9QcbUtqLNPDctNvxb/bOa9cakev7R4zn66p6u2lpOtV1chkdvGShnZyk84pyPWk625597nuefj9HeRz9XsOQc/nXgP0G+/Kqy3v+56uB249lC2Bt06WLFmSlutY2WPEej5ziRrs7BhrVbnL1MDpqafeKDdPulZqeEA3+b02utddbcf9hqvH76ALZCwAqhaZCpKlmnST9b///c/ZjVpd9unTZ4tdqpWlukwrMKou6w899JCpJWe8pG7u3uCn8mksI22ni6aSgo/e4KeWaZxRtcxUa1A32KnlSi+//LLzW11p+vbt6zwu+59aVCrQqlaOanWabNKYJbqg68ZO3cxjjXl68sknO+VTV321CFVgVm6JklrAusMAKIBcNvip7RUUvvnmm00BZ13wNCSBxil1A1vuOKvqzt6/f/9SwU9tr6C0litAHCupNauC0OpGr/LHCoCqnG4dqft8eUkBYnXZKpvkpoC6LtSxAqRl8/M8dQG9kXo/PKW+R/bgRyDfXuM///yzc/3xc+7x8izse7JZYfJvff+JfDH28g/z4u02qeW6Ac+la4+u6ekMgObSuSdVsQHMrPdqvLNTMfnm7h2nXkMuucMuVVS3aqsa1vj+dlYQ+ecnfVD1U3vp2mdtw8/p++JanxmCXE/qrZbOAGiQz9XPayDX8uCdnRpTj8p4ybtOc3XoM21lpD16P2ZtOicfAFW84PRrKqeMXgdd75N9/erzufsZ3bsvHiMQNIH0NOmIcVYK0tWrV8/Xjz5cxUvqLu1+q6OxM+MldYf+85//7KxWIM/dpmz+1q1bO0HIsst1k+EGRVV2d19l82nAZCUFeL3pwQcftP/+979Oi8vyPii6QUvvN1De/cR7rJaZbtBUAcryxuXwjmfiBjXj7dddrkmb3FTeEALqUu+Os6pWvq6DAtULFixwdtG7d29T9/xYadddd7V99tkn1ionYOq2qlVLXbfVrzezAqNKO+ywgzOMgHcdjxFAAIFEAlWr+PuwXXY/1Sq4Xdn98BwBBBBAwKxuv2ZWUNX/9bigWoHVPacZdAgggAACCCCAQIUFkm8G4/NQGl9TLQT9JAXS4n0Do29n3KSgobqBx0tud2vlU/As1mzlCoDGSzVr1nRWKUgZr/uMujUqlQ2wKr/Guiyb9O2JzkHnqKCeO9O5u4/ygqXefXm77Oy8887eVVs8VktNd+zRefP8tVhy969Aciw370F0fM3wrqT9KyjszqipZRrftLykVrRqnRorqdWngpxq6aoxQ71DCehY7mRMiVp/at8aB1VdZssmtRbWN1tquerWedk8PE9dwPtNoFowpLMVQ+qly9896G/H/YZbr3F9uZMvScOXxGtB7vccV/0832quT74L9sEnnGg71tj0HhHvWGpt53a7Kvt619Ad7izwhYWFOXXt8fs+Fc/Fu5zrrlcjM491f+J+yaq6c+9bMnM09uoV0LXXvQbk2/2F93Wk3kzxJsP0esR7/EODn230jpsmH42XJ9byGp3q2hl39bOWy7YptVqvefe+XPfjbs+kUpn+eKKhptx7Vr0/Brme4n0WiXVefpYF+Vz9lD8X8nhbSuNdeTWmXmZud2zdY8X72/FeG/T59dxzz62UQi6ovqctqcCRmkViEu7wexXYPKlNXn/99egs8Lre+3n9ytzt4Sdz2ZMQCLpAxgKg6TpxbwD0yiuv9L1bdX2OFcgrr7Wpu/N4rRe13nvhdPN7fytIpwuIJmdS0FMTC6Ujua0/ta9Y51X2GArGavIlvwFQd/8KniZK3jzav1p0aowSN3nXu8u8v8tbr2ECtF5dXd95551SAVC39acusD169PDuMubjeEH1q666ymmirw+Gfi7uMXfOwoQC+iDodoXQG2I+BeISnnwWM+hDoBsAzbfXuIYY0RAmqaSiZ560de9saknuez+RcYMvGDgo0lop/rhy2pda6rs34OoB4Q0camzpXA2A+nbykZEAqA+kFLPoNegGQPV+yftciqBJbC73fA2Aet/Dda9W0Wtx8cZ1duDYY8xWJQHryTpn70U29KBHrFqVzR9h1JXdfd9TYwhvsNazqfNQ49zlSgC0bNlTfc61IFXBxNu7X4LwXpfYKp05dO/r3n/p7z9eIM77OV6fpyt6HUu27I9E2g29s2mO4KQ2bR75PP+Ps1O77/V7wIULF0YDoH6/HPIOccb9hl9p8mVbYPPdQ7ZLEuf4GqBYSR8k480KHmvTeBMrxbsgxtpHMst00R00aFC0ZaR321q1aln79u2dGcs1EcZXX33lXe3rsbclox8H9yZHN4V+krt/txVtedvofNzk7t/95t1dXt7vsi2jvHn1xqTWncOHDzd1q1cgWXb6QOEGQLt27eq0cPVux2MEEEDAj0BhjyNs3Zh3LXKn7Ce7k6f64UcmDH763hkZEUAAgRALPP7dUzZ31ebeXclSzFr5nT0xd6Sdu/1pyW5KfgQQQAABBBAIuUDgA6Bua0C1pnnttdcCW12abd3tFq5umkcccYSpq7i6g2vyH/cbpzlz5kTPwW0lEF1QzgNv9/pFixbZbrvtVk5uM+VRUld4P0n7Vzd4d7vytvHmcffvHZNUww9orM94Sd8wlZfcAKjyvPvuu04AdNKkSdGy+en+Xt7+WYcAArklcOaZZzoFLq/1uN8zqhLp8lT9xJOt+L8jfW1SpU1bKzyyp6+85WXSNds9D41hTEIAAQRySUDj5LvXMH0RXZH069rf7N6Zj1Vk01Lb3DPjETuh5dHWqLq/e1zvxgcddFB0ks5GjRp5V/EYAQTyXECtFN3rWNnJjvP81BOenuY/cVvRunOWJNyIDAjkoEDgA6C6OH355ZdOd25NuKMukEFLaj3pzlSv8g4ZMsTi3VSpW7qb1GpSF2I/yXuRVvfw8pIuXm6Q0p3Yqbz8Wqf9uwFQlcvbdbPstt7jK9ir5A2AeoctKLutnv/000+xFkeXqUuCxpBVa9kPPvjAGfNPk2EpKeC63377RfPyAAEE8l/giSeeSOtJVj+ql0X6S1rxKy+Vu98q27ezmldcbQV/jP1cbuYEK/WlmH5ICCCAQC4KtGnTxlK9Fo//baIduHV67uE+jOzrmBaHJ0154YUXmn5ICCAQPgH1Qkz1OpavameddZbph4RAvgsEPgDqnbRI46edcsopcevk1ltvNc1mrgDazTffHJ3VPe4GaVqh1olu0gzp8YKfGhfm22+/dbNGB2x3F7itRPW8bOtQBRgVLFVwU13BTz311LjBU00e5I69GG/GdfeY7m/d2CrIqPGrxowZE3eMTe33rbfecjbTGCsdO3Z0Hrdt29bx1pinCgb37ds35vgrah2qoGaidNRRRzkBUI3lqrFUP/zwQ2cTjf1ZXhf6RPtlPQIIICCB6sedYFV3/ZMTBN0wbaouulGYgq2bWmGPw6zwkB5WELlZJiGAAAIIpC5wfMueph8SAggggAACCCCQDYHAf7JTIOzpp592JtnRuJAHH3ywM+t4WSzNDq/u0koKQPpt+Vh2PxV57m0tqcHVYyUFDm+66SZzx8xUHg0c7B1U3vvYm095NabnSSedZM8995xpwiINB9C7d2+tKpVWrVpV6put7t27l1of78nxxx9v//d//+eUb9iwYaZm8LHGA1VwU0FJJXWBcvMoGKpZ1wcPHmwKcmof559/frTrv/IrAKyhAtzZ4rQsXurWrZuzr9WrV9uTTz5pCxYscLLq9UBCAAEE0iFQdccOVuvv/a0kct3cuCgyNEfkC6CCRo1N3eRJCCCAAAIIIIAAAgikKtCrk1mXDsnvpVb15LdhCwQQKF+gSvmrs79WE/64XVUUDOvXr5+NGzcu2sJR40kqcHfdddc5hVUryspuvr3TTjtFu4yrFaVaqq5du9Ypj7qTz5gxwwl+TpgwoRTo8uXLSz33TvSkc9SYop988om5ExTpvNwu5wo0/vvf/3ZmHdZOdJzJkyc7Pm6w8OSTT3bGIC11kDhPFDB23dRFXc5qTav9KmlSqUcffdTuu+8+53mdOnXskksucR67//Xq1cvUklTpmWeesWuuucZprapyvfrqq05A1G3J6WQq5z8Fgw899FAnhxvY1mRI+iEhgAAC6RQoiLzPVI10d6+6084EP9MJy74QQAABBBBAAIGQC7RqYrbn9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dLEeWPUm6PGLyOlR8Ab1JgwYYLpJ1fSKW3b2LEHbGplnEyZq69YbldffXUymwQmr27Mef1nrjrWrl2buZ1X8p51XVYwPR2J1106FP3vI5Pvc26gb2Vk8rggXAerNK5mTR7ZwQrM32t17lYLrP9zN1vxZ8l/+eW/BpLPqWsH1+bk3dwtvPci7rJc/a0x8tMZAM3G68pbH++9957pJ92px9+Os9qNNn+m8Lv/994ba9PevtZv9grly4Z5hQqaRxt5X3N5dFqBPxV9zuf1HvhqooARAX93iVAh4EMgUTf4zz77zBkUXLvydn/XBVOTHikpaNq+fXvncaz/3LGD9MHrk08+iZWFZQgggAACCCAQMoE6Z2xjBTWSu62te04zs2pb9lYJGR2niwACCCCAAAIIhEKAFqChqObEJ6nA4yWXXJI44x85GjduvEXeFi1aOAFMdX8eO3asXXHFFebtAvb222872zRo0MC6dOkS3V7d39VlSalhw4bRmVCjGco80Lfhyh+rdac36+WXX25qmVI2XXrppaYue5pISWONktIj4K1rzQJ/wgknlOoCn64WZOkpbem9NF0ZGSphxrTSC308K65X3+6//34fOSs3i9vlTC2zy7Ye0d+Fktbx+k9vvairm9vtqmbNmnnTBT6df7u87tL7mou1N70G3W6Xqju1IstEUl0q1alTx+64445MHML3PufXWGhPNn/Vd343Y7XmNazv42dZl+W7u4tS+u3tfq2hI/ymYcOGRe99dO3g2uxXblM+vee595G6FwnykDvJnFm6zyMbryvvvaFmgT/66KOTIfCV9+PVjWzt5hFofG2jTIceeoidf1R67+H0WrzzzjujDT6yYe4bII8yqtWnGtQo5dM1IOhVJHO3xa2uV96/96CXPVH5yk7InCg/63NHgABo7tRVRktau3Zt06zQqSa1AlUAVIFHTVjUrVs3Z5cKOLqtPNWV3RuU0ezhbnr33XdNP35SogDoMcccE3M3mnleN8r68JapD4YxD5znC71veh07drQLL7ww2hVCN4BBti6JBAxWXXqhRV64SdVSs0N72GUnnpzUNpnOrOCHO/6Ubkbq1q1b6pBuADSTgZFSBwzRE/cmUKesv4d0Bg7zhZHrbuZrUh9I3ACorgGZuva6AdCtttrKLrvsssyfWJwjqEfIEeP6mC2LkyHB4k+bTrH7+wyyrWs2SZAz8Wpde90vQfRlr9/0wQcfRAOgunZkqs78lifX8sncDYDq/tJ7P5Jr55LJ8mbjdeX9IqBTp04ZuVZMfdhsbQX+/vfaa287o/veaSVftmyZaQguza3w/+3dDfQUVf3H8fvj+VmeQR4CHxIR8CFTFLSi8oCmf7M0/yGimB464AETxTQFydDUlOQoGhIcpZNBPpdYf1M6GQgkJKkpYD8JUEwhRVRUHvzzuTXrsE+zs7+duzM773sO/HZ3Hu6d1529O/vdO/fyeVdR2qI70+eeFwDVOec/74puyMIGCSjg7137Rnm90aBCsjECWQLh7hXK2pinCGQLfOlLX8r0XPB6fGod9Qj1Gkj/7e9a5p/RXYEyBWxK+eftT/sgIdAQgbq9vaSanXJquF00a26anjQ83DasjQACCCBQUYEFGx8xq7e9WPY+39v1vrnh5Zllb8+GCCCAAAIIIIAAAskQoAdoMuopMaVUAHPYsGF2kiL1AFVPUN0e5wVDNb5n9hif/tnev/vd79rJjRJzwBQ0I9CnTx8zefJk+/z444/PvJ6UB02Hn2x2PfdXs2ftmpKK3Py8MaaRbyKvkjaKwUpeHXXp0iUGpaEICCCQVAH1JteEB/qMr1ZS8HL6SzManP19Gx4y5/f9X3N4+8MavK9ydnDGGWdkro00USMJgVoR0GSl3nWHd1dYrRxboeM4//zzbecObn8vJMTrCCCAQPUECIBWz75mc9ZERZqlXbcjKQg6ePBgo1nhlfwTJXkAvXv39h6adevWZR7zIFkCBx10kLnxxhszhfZuwcy8EPMHdXtvm2t5ySTz4R0zze4XXyhcWt1Seu75pukJJxZeJ8ZL/HUU42JSNAQQiLnAtddeW/USPr/tJXNy969UpBwvvPty1QKgo0aNqsgxsBME4iYwYMCAfa4N41a+KMqjH4d0+/t+CfyRPAoP9okAAgjESYAAaJxqo0bKovEfe/XqZScpevrpp+2YLBqfSWODaAD07KTJiHTL+/bt223AVOOFakyxfKm+vt5O1tStWzejsUS//e1v51uN1xAoS6Bu71i4LS67wux6ZonZ+Yf/M3v2nm+Z1KKlafK5z5lm//N102j/HpmXeYAAAgggUB2B4zt93ugfCQEEEEAAAQQQQACBIAECoEFCLC9LQON83n333WbZsmWZyRiGDBliZ3nPt0P1frjzzjvN1q1bjWZD1Uzt2UlB1FmzZtlAqYKlY8eOzV6F5wg0WEDjgTYdeqL998mOD8wnb7+tGW1MXYeOpm5vEJ+EAAIIIIAAAgggEA+Bq/fORblzV/iytG8dfhu2QAABBBBItgAB0GTXX8VK/89//tPMnj071P7Uc/PMM8/Mu82IESPMnDlz7PhgS5cutevotULpW9/6lnnsscfMhg0bzMKFC+3YOePGjTMaC0u30q9atco8/PDDZvny5XYXAwcOtLfWF9ofryNQCYG6lq2M/pEQQAABBBBAAAEE4ifQm2Fz41cplAgBBBCIqQAB0JhWjOtibdq0ycyfPz9UthrzsVAAtGvXruaYY44xK1assPts3769UQ/QQqnJ3vEXp02bZjSmmIKxTzzxhP3XqVMnG0TVbfFe6tu3rx1PSOPrkBBAAAEEEEAAAQQQQAABBBBAAAEEECgm0KjYQpYh0BAB/4RHGvtTQc5iSbPD6/Z3jevZrl07u6puifeCnxon9Dvf+Y656667MsuL7Y9lCCCAAAIIIIAAAggggAACCCCAAAIIFI9I4VPTAj179jSapCiq1LRp08yuNTN8Kal58+ZGt77rn4Kfr776qp1EqUePHmb//fcPDKKWkgfrIIAAAggggAACCCCAAAIIIIAAAgikR4AAaHrq2vmR/u53v7N59uvXz6h3Z9ik29/1j4QAAggggAACCCCAAAIIIIAAAggggEC5AtwCX64c2xUVqK+vN0uWLLHrnH766UXXZSECCCCAAAIIIIAAAggggAACCCCAAAJRCdADNCrZFO5Xs7hr7E7N5P6rX/3K7Nmzx/bgHD58eAo1OGQEEEAAAQQQQAABBBBAAAEEEEAAgTgIEACNQy3USBkWLlxo1PPTSxoD9LrrrjPNmjXzXuIvAggggAACCCCAAAIIIIAAAggggAACTgW4Bd4pd21n1r1798wBduvWzVx99dVm0KBBmdd4gAACCCCAAAIIIIAAAggggAACCCCAgGsBeoC6Fq/h/KZPn27Wr19v1POzT58+NXykHBoCCCCAAAIIIIAAAggggAACCCCAQFIECIAmpaYSUM4mTZqUNdt7Ag6NIiKAAAIIIIAAAggggAACCCCAAAIIJFSAW+ATWnEUGwEEEEAAAQQQQAABBBBAAAEEEEAAAQSCBQiABhuxBgIIIIAAAggggAACCCCAAAIIIIAAAggkVIAAaEIrjmIjgAACCCCAAAIIIIAAAggggAACCCCAQLAAAdBgI9ZAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYQKEABNaMVRbAQQQAABBBBAAAEEEEAAAQQQQAABBBAIFiAAGmzEGggggAACCCCAAAIIIIAAAggggAACCCCQUAECoAmtOIqNAAIIIIAAAggggAACCCCAAAIIIIAAAsECBECDjVgDAQQQQAABBBBAAAEEEEAAAQQQQAABBBIqQAA0oRVHsRFAAAEEEEAAAQQQQAABBBBAAAEEEEAgWIAAaLARayCAAAIIIIAAAggggAACCCCAAAIIIIBAQgUIgCa04ig2AggggAACCCCAAAIIIIAAAggggAACCAQLEAANNmINBBBAAAEEEEAAAQQQQAABBBBAAAEEEEioAAHQhFYcxUYAAQQQQAABBBBAAAEEEEAAAQQQQACBYAECoMFGrIEAAggggAACCCCAAAIIIIAAAggggAACCRUgAJrQiqPYCCCAAAIIIIAAAggggAACCCCAAAIIIBAsQAA02Ig1EEAAAQQQQAABBBBAAAEEEEAAAQQQQCChAgRAE1pxFBsBBBBAAAEEEEAAAQQQQAABBBBAAAEEggUIgAYbsQYCCCCAAAIIIIAAAggggAACCCCAAAIIJFSAAGhCK45iI4AAAggggAACCCCAAAIIIIAAAggggECwAAHQYCPWQAABBBBAAAEEEEAAAQQQQAABBBBAAIGEChAATWjFUWwEEEAAAQQQQAABBBBAAAEEEEAAAQQQCBYgABpsxBoIIIAAAggggAACCCCAAAIIIIAAAgggkFABAqAJrTiKjQACCCCAAAIIIIAAAggggAACCCCAAALBAgRAg41YAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQSKkAANKEVR7ERQAABBBBAAAEEEEAAAQQQQAABBBBAIFiAAGiwEWsggAACCCCAAAIIIIAAAggggAACCCCAQEIFCIAmtOIoNgIIIIAAAggggAACCCCAAAIIIIAAAggECxAADTZiDQQQQAABBBBAAAEEEEAAAQQQQAABBBBIqAAB0IRWHMVGAAEEEEAAAQQQQAABBBBAAAEEEEAAgWABAqDBRqyBAAIIIIAAAggggAACCCCAAAIIIIAAAgkVIACa0Iqj2AgggAACCCCAAAIIIIAAAggggAACCCAQLEAANNiINRBAAAEEEEAAAQQQQAABBBBAAAEEEEAgoQIEQBNacRQbAQQQQAABBBBAAAEEEEAAAQQQQAABBIIFCIAGG7EGAggggAACCCCAAAIIIIAAAggggAACCCRUoElCy02xEWiwwIYNG0y/fv0avB92kF9gz5495pNPPrEL6+rqTKNG/N6SX6qyr8pc9kq4V9Y2aG/+c17nu/xrIU2cONGMGzeuIofy5JNPVmxfFSlQDe6ENqB6lbp79+5M5o0bN8485kG0ArXa9g4ZMsTMmzevInjvvfeeOfrooyuyL3ZSWIA2oLBNlEtqtQ2I0qwS+67V6w19fi9atKgSROwjhgIEQGNYKRQpWoGLL77YLF682KxevdqsXbs22sxSvPdmzZqZVq1aWYEPPvjAfPzxxynWcHfoCrrtt99+NsNdu3YZfekhuRFo06aNadLkPx+r27Zty/wA4Cb36HLZunVrxXau85F2t2KceXek4Hu7du3sMrW7an9JbgTatm1rvMDnO++84yZTcjEtWrSw/0Tx/vvvm507d9aESu/evSt2HArM0fZWjLPgjnT9peswBeTefffdguuxoLICLVu2NM2bN7c71XWGrn9J0QvomlfXvkofffSR2bFjR/SZOshBn+P6PCfVpgAB0NqsV46qiMCMGTPMpZdeav72t78VWYtFDRVQ8LNr1652N2+++WbNfCFpqEvU2+tD23NX4ENfBkluBDp06GB0Ea6kC3B/TxA3JYgml0r2ZNW+Krm/aI442Xtt2rRppg3Yvn17zXwhSUKtdOrUyejHPyX9CEJyI9C6dWsje6XNmzfXTPCjkm0lba+bc7Fz5872RxB9/qv9JbkR0I9+3o//+uGvVq6/3OiVn4s+77zvHPrR78MPPyx/ZzHaUu2l95kSo2JRlAoJ1O3tuvyfe1QrtEN2gwACCEjgF7/4hbnuuussxjXXXGNGjRoFjAOBLVu2mKFDh9qcjjnmGFsPDrIli70CI0eOuTNe0wAAGmJJREFUNCtXrrQWzzzzjOnYsSMuCDgXUC+v0047zeY7fPhwM3PmTOdlSGuGJ598sqmvr7eHv2bNmrQyOD/u2267zcyaNcvm+9Of/tSoHkgIVEPg2GOPtT9+6AfRZcuWVaMIqcxzypQpZsGCBfbY7733XjN48OBUOrg+6KVLl5oxY8bYbHUNPHXqVNdFID8EQgswKF9oMjZAAAEEEEAAAQQQQAABBBBAAAEEEEAAgaQIEABNSk1RTgQQQAABBBBAAAEEEEAAAQQQQAABBBAILUAANDQZGyCAAAIIIIAAAggggAACCCCAAAIIIIBAUgQIgCalpignAggggAACCCCAAAIIIIAAAggggAACCIQWYBb40GRsgAACpQi0adPG9OjRw66qxyQ3Ao0aNcq4d+nSxU2m5GIF5O2d86oHEgLVENAs8N55yERcbmugW7duNTMLrlu5huXWtm3bzDnfqlWrhu2MrRFogED37t1N69atMzOSN2BXbBpCoH379pk2oHnz5iG2ZNWGCLRo0SLjrjogIZAEAWaBT0ItUUYEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEuALiplsbERAggggAACCCCAAAIIIIAAAggggAACCCRBgABoEmqJMiKAAAIIIIAAAggggAACCCCAAAIIIIBAWQIEQMtiYyMEEEAAAQQQQAABBBBAAAEEEEAAAQQQSIIAAdAk1BJlRAABBBBAAAEEEEAAAQQQQAABBBBAAIGyBAiAlsXGRggggAACCCCAAAIIIIAAAggggAACCCCQBIEmSSgkZUQAgXgI7Nq1y2zfvr2kwrRq1co0b968pHXzrbRnzx7z8ssvm40bN5qPP/7YDBw40PTt29fU1dXlWz01r23evNls2rTJ/mvZsqXp06eP/SfvhiSXdduQcrrYdsuWLeaVV14xr7/+uunRo4cZMGCAadu2bUWzdpFHRQvMzqoq4PL9Sdubv6qjanuVmz5XVcdBSZ9/7du3D1otsctdnHsu8khsBVDwvAIu359cG+RWwY4dO8yGDRvs94Ft27aZnj17ms985jNm//33b/B3Apd1m3tk8XnFRbvoIo/4iFKSOAvUfbI3xbmAlA0BBOIjsHjxYjNlypSSCjRp0iTz9a9/vaR1s1davny5uf3228369ev3WdSmTRtz1llnmQsuuGCf19PwRAG5O+64wzz77LM5h9usWTMzatQoc8455xg9Lie5qttyyuZqm/fee8/MmTPHPPzww2b37t37ZHvIIYeYa6+91vTu3Xuf18M+cZFH2DKxfvwFXL0/aXtzz4Wo217leO655+Z83uWWxJgOHTqYRx99NN+ixL/m4txzkUfiK4IDyBFw8f7k2iCH3ezcudM88MAD5p577jHyyU79+/c3+q7Rr1+/7EUlP3dRtyUXpkorumgXXeRRJT6yTaAAt8AnsNIoMgLVEli3bl3kWf/5z382kydPznwZVA88Xdw0atTIXgDNmzfP3HLLLUa/JKYlLVmyxAZ9veDnfvvtZw4//HBzwAEHmCZNmtgesnPnzjUXXnih0S/l5SQXdVtOuVxt89FHH9nzThfbCn4qkKyen15vq7Vr15rx48fbnqHllslFHuWWje3iLeDi/Unbm3sOuGh71S6od1Oak4tzz0Ueaa7DWj12F+9Prg1yzx71iJ8wYYL94V/Bz8aNG9u7nY444gj7Q5C2eOmll8xFF11kFi1alLuDEl5xUbclFKOqq7hoF13kUVVEMk+cALfAJ67KKDAC1RNQEEipU6dOZvTo0UULogBd2PTGG2+YqVOn2uCmerrcdNNN5tBDD7W70W0q06ZNM/oVUT30unXrZns9hs0jaeu/9dZb5vrrrzfqrK/bsCdOnGiGDx+eOYytW7eaW2+91fzpT38yr776qu05e/nll2eWl/og6rottRzVWk+9a59//nmb/emnn24uueQSG1yWuy7e1Pvz7bfftv7333+/0fADYZOLPMKWifWTIRD1+5O2N/c8cNX2/uMf/8j8oHfqqaeaz372s7mF+e8rDRlWpuBOq7zAxbnnIo8qM5J9RAIu3p9cG+RWnu7GeeGFF+yCoUOHmssuu8x07tw5s+If/vAHe+2r7wYzZswwgwYNCn2Hjou6zRQ4hg9ctIsu8oghLUWKuQC3wMe8gigeAnES0C3tCrgNGzbM/PCHP6x40XQR8+CDD9oxfdSj8eCDD94nD/0i/L3vfc8899xzpkuXLmbhwoU2SLXPSjX2REMBLFiwwB6VzGWfneQybtw4+2u4lv3617823bt3z16t6POo67Zo5lVeqDG3NLSCHL/whS+Y6dOn55RoxYoV9lYrLdCFuIKkYZKLPMKUh3WTJRD1+5O2N/d8cNX26pb2m2++2RZg/vz5dqzr3NLU7isuzj0XedRuDaX7yKJ+f3JtkHt+qcfnKaecYn/4P+igg+zQRLrbKTuph/73v/99+7LWv/LKK7NXKfo86rotmnkMFrpoF13kEQNKipAwAW6BT1iFUVwEqiWg3m8Kfip5vTIrWRbdivLb3/7W7vL444/PCX5qgS6AvPE/1TtHFz+1nhTsVerVq1fe4KeWyeUb3/iGHtrk9Rbzngf9jbpug/Kv9nJdBCv4qVSoZ/Oxxx5rjjzySLuOgvRhk4s8wpaJ9ZMhEPX7k7Y3/3ngou1Vzt7wBupVrok90pRcnHsu8khTnaXtWKN+f3JtkHtGrV692gY/tURj2+cLfmqZeoZqIiSlNWvW2L9h/ou6bsOUxfW6LtpFF3m4diO/2hAgAFob9chRIBC5gHehoIwaMuB4oYLq4kWzvSsNHjy40Gr2Nhfv9uPHH3+84Hq1sEBBOY1F2bVrV6NJeIolf4/P1157rdiqOcuirtucDGP2gnfru4ZdKOZ83HHH2ZLX19ebsEFmF3nEjJXiVEgg6vcnbW9uRblqe5WzV7+69V1jXacpuTj3XOSRpjpL27FG/f7k2iD3jHrnnXdsT3gN+3TggQfmruB7xbv2DXvdq11EXbe+YsbuoYt20UUesYOlQIkQyO1PnohiU0gEEHAt4F0oKF8vSKSJiN58802jSXm8oGS55fr73/+e2bRYgFW/BOuC6MUXX8xcvGQ2rLEHOtZZs2aVdFTr16/PrNe3b9/M41IeRF23pZShWutojE8NpK+k87qurq5gUfxj88nMex8U3OC/C1zkEVQGlidXIOr3J21v7rnhqu3VZ6jGoVPyf+598MEHRmPbaazrWk4uzj0XedRyHaX52KJ+f3JtkP/s+trXvmb0r5TkXfuGve6Num5LKXs113HRLrrIo5qG5J1cAQKgya07So6AUwHvS3jPnj3tDO2ajV23qajXpoJGukVbt65rJvJygqGbNm3KHI9mfi+W9KVQAVAFX3WLRS1ODFHs+LOX6UJOt1F5KewQBVHXrVeuOP7V7cXvv/++LZp3K1WhcvqDERs3biy0Ws7rLvLIyZQXakYg6vcnbW/5p0pD217Zf/jhh7YAuv39nnvusW25PtuUWrdubSdF0tAvRx11lH2tlv5zce65yKOW6oRj+VQg6vcn1wafWpfzSJN/ylCpf//+oXYRdd2GKkwVVnbRLrrIowp0ZFkDAgRAa6ASOQQEXAh4X8L/9a9/2Ql3/HnqV2wFhPRPM2ZrJvfDDjvMv0rgYy8IpRXVo7RY8i9XL5m0B0AfeeSRTG/Yk046yehW7jAp6roNUxbX62qwfS+1b9/ee5j3b/Z5l3elPC+6yCNPtrxUIwJRvz9pe8s/USrV9qoEt912W2YsYq9EqhuNRTphwgRz5plnmvHjxxccD8/bJkl/XZx7LvJIkjllLV3Aa3u1RRTvT64NSq+L7DXVS967Q6pp06bmjDPOyF6l6POo67Zo5jFY6KJddJFHDCgpQgIFCIAmsNIoMgKuBdRDxevxprHRNAO7Am3qkaIxenQLn2Ye160or7/+urnqqquMZrPVslKT90GpC5mgcdA0LqaXvN4z3vO0/V22bJm9MNdxd+zY0VxyySWhCFzUbagCOV5ZF9FeCgqk+8+7HTt2eJsF/nWRR2AhWCGRAi7en7S95Z0aDW17lav/S7g+W0844QSjCdf0A6Im+lu5cqXRpGvqaXr//febzp0720lByitx/LZyce65yCN+spSoEgJRvz+5Niivlnbu3GlnfPfG/RwzZow54IADQu0s6roNVZgqrOyiXXSRRxXoyLIGBAiA1kAlcggIRC2wYcMGG5TcvXu30Tg7M2bMsF/EvHwHDBhgRowYYa6//nrz5JNP2tnib7/9dnuB4q0T9NcLKBWa7dG/vYKkXtKFUFqTegZdffXVRvWioPEVV1xh2rVrF4rDRd2GKpDjlb3zTtkGnXv+807BilKTizxKLQvrJUvAxfvTOz+Dzn/J+d8DtL0Na3vl6b9F8OKLLzZnn322XrZJY4IqIPrVr37V9gDVcDNz5841J554Ys3MFu/i3HORh1dn/K0tgajfn965KbWg9tff9oa5/qitGjFGnzvTpk0zq1atsoc2aNAgM3LkyNCHGXXdhi6Q4w28cy/ovFOx/OdemM99F3k4ZiO7GhEgAFojFclhIFCOgPfrXKFtNf6YkiZ7UWBT45KpV2ebNm1yNlHvuEmTJtkeK5rBUTO0X3rppSXfnu71vivlws7/AVzOeKM5hXf8gr7I+o8hO3tdbPh7G2Yv1/OnnnrK/OhHP7L7UfDzBz/4gRkyZEi+VYu+5qJuixagygu9807FCDr3/HXWokWLkkvuIo+SC8OKsRAotQ1w8f70zs+g819w/vcAbe9O+8NTuW2vPNWG6zbYbdu2GY2vnS/pB8bzzjvP3H333XbMbX22jh07Nt+qiXvNxbnnIo/Ewae4wKW2vSKK+v3pnZvKK6j99be9Ya4/tO+4pFK/cxQqr4a8Unv717/+1a6invI33XSTady4caFNCr4edd0WzDgmC7xzL+i8U3H9516Yz30XecSEk2IkTIAAaMIqjOIiUCkB3XqjXpvF0h//+MfMhYUuMIImiVFwVL1TfvOb3xiNC6pb4v0z2xbLq1WrVnaxPmi1bbHZuHUB6yUvSOs9T8Lf2bNnmwULFhQs6ujRo81FF11UcPl99923z9hH11xzjRk2bFjB9YMWRF23QflXc7n/Ys5/XuUrkybc8lKY885FHl65+JsMgTBtQNTvT9reT88Z122vctYPivl+VPy0VMaccsopNgCq1+rr6/2LEv3YxbnnIo9EV0LKCh+m7RVNlO/PNF0bhP3OkX1aav6Byy67zH6v0LIjjzzS3HDDDYFtZ/Z+/M+jrFt/PnF87KJddJFHHG0pU/wFGsW/iJQQAQSSJKCZbL2kAGipyT8BjXqQFkverI9ax/uALbZ+rSzTre633nprJvipgLOGI2hI8DOMTbl1GyYP1+v6zzv/eZWvHP7zMihg4d/eRR7+/HicToFy35/+89N/judT9L9HaHvdtb0a+9PzDvO5mq8O4/Sai3PPRR5xMqUs7gXKfX/6z01/25rvCPxtc5jrj3z7Stpra9eutb3evbZPw4LoWtiFQ7l1G3dj/7nnP7fyldt/bnqfQ/nWy37NRR7ZefIcgVIE6AFaihLrIFCDAhr3JWjWxKDJiIJYwlycaGxRL73xxhtFZzLXL8FKvXv3zvRQ9bZNwl+NWVSst6Fu68lOmgxl6tSpZunSpXaReuPq1h+/W/Y2UT4PU7dRlqOh++7QoYPR7O66BVXnXbHknXdaxx9sKraNlrnII6gMLI+XQDltQJgjCPP+9LchtL3xbXt1Z4RSmMkFw5wz1VjXxbnnIo9q2JFneQJRtb3lvD/TdG1Q7ncOTTY3ZcoU440nec4559hgaLG7xMo7MwpvVU7dFt5bPJa4aBdd5BEPTUqRNAECoEmrMcqLQIUENMakxugsJWmG97/85S9GvwL++Mc/Np06dSq4mSbt8FKfPn28h4F/DzzwwMw6L7/8sunfv3/muf+BAoeadV4pX6DQv25cH3/xi180+ldq0jFfeeWV5tlnn7Wb6LhVD7p4bmhyUbcNLWPU2+vc05hSa9asKZrVSy+9lFmucfnCJBd5hCkP61ZXoNQ2wMX7k7a38LkQZdurMbU1WaA+V4866ihzwQUXFCzIli1bMgGAMD++FNxhTBa4OPdc5BETTopRgkCpba+r92darg3CfOfwqlHBT137apxKDQWjW+BPPfVUb3HZf13VbdkFdLChi3bRRR4OqMiiBgW4Bb4GK5VDQqDSAvoS+MwzzxgFJnVBUiipl6K3XLc+BI0Z6t/PwIEDMzOYe70c/cu9xwoCqjxKGgMoDUnBTi/4OXjwYDNz5syKBD9l56Ju415HQ4cOtUVUIMIf5Mwu95IlS+xLXbt2NT169MheXPS5izyKFoCFiRRw8f6k7S18akTZ9nbs2NEsX77cPPfcc+axxx4rXIi9SxYvXpxZntQf/jIH4Hvg4txzkYfvkHhYIwKu3p9cG+Q/YdTRQRMeKfip4Kna4koEP5Wbq7rNf2TxeNVFu+gij3hoUoqkCRAATVqNUV4EqiCgoJuX7r33XqPBzPOlefPmmbfeessuGjNmTKjb03V7zFe+8hW7rYKo6nGanZTvz3/+c/uyAqwnnXRS9io191wOTzzxhD0u9YrVoO/ezIqVOFgXdVuJcka5jy9/+cuZc/Wuu+7aZ8ZLL1/VgddD9KyzzvJeLvmvizxKLgwrJkbAxfuTtjf/6RB12yv3o48+2mauHkkPPvhg3oKo9+fcuXPtMv34ctppp+VdL4kvujj3XOSRRHvKXFzA1fuTa4PcetAt57fcckums4OGfzruuONyVyzzFVd1W2bxnGzmol10kYcTLDKpOYHG1+5NNXdUHBACCFRUQL+W/vvf/7YBoO3bt5vVq1fb28+9W7DVc06T8Tz00EM2X437csUVV5jsMUTHjh1rbr75ZqNA6ZAhQ4wGF/cnzRj/6KOP2gCUerz07NkzM87na6+9Zq666irbC1XbXHjhheaII47wb15zj3fu3Gkd3333XXts+rK8bt06s3LlyqL/9It5r169Mh4KKI8cOdK6K4inAeS9VKm69faXxL+a0V29l59//nmzefNm2wtUt6Tqdc38vmjRInPjjTcaXZRr+Aedh02bNs051GLnd6XyyMmUF2paoFLvz2JtgABpe/c9jVy0vcrxkEMOse2L2uxVq1bZH7cOPfRQ+9mpSe/UQ3Ty5Ml2jGKtP2nSJLuNHtdKqsS5V6ztlVMl8qgVb46jdIFKvD+D2l6uDXLr4/HHHzcPPPCAXdC9e3c7TnvQda+W6zuB/3tHsXahEnWbW/JkvVKJdjHo/K5EHslSpbRJEGAM0CTUEmVEIAYCEyZMMPX19TZIpEDR6NGjbTBIv/D5J4f5/Oc/b/S7il7PTgogeYOJZy/Tc33Znz59uh3wXIHWadOmmRYtWthb49VDxksjRowwZ599tve0Zv8+/fTTZuPGjZnj+/3vf595XOyBvjj7e45pXc/d++vfvhJ1699fEh+rx7KCn0899ZRZsWKF+eY3v2mHcNi6dWumF4Jmv/zJT36SmY05+ziDzu9K5JGdJ89rX6BS70/vve/99cvR9vo1jHHV9moiP93mqc86DXdwxx13mNmzZ9u2RxNSecO9qNe/flSsxbseKnHuBbW9lchj3zOEZ2kQqNT702tzvb/Zdlwb7Cvyy1/+MvOC2sH58+dnnhd7cN555+3z3aNYu1Cpui1Wnrgvq1S76J3X3l//cVcqD/8+eYxAQwW4Bb6hgmyPQEoENAaPJmy4/PLLjW4/V1JwyAt+duvWzZx77rk2QKRZtctNCqDOmTPHHH744faXXPXM84KfynfixIn2i2C5+0/Sdt5kT1GX2VXdRn0cDdm/AgwKQowfP972NtC+FBD1AhDqsfyzn/3MHHzwwWVn4yKPsgvHhrEVcPX+pO399BRw1fYqR03Koi/4J5xwgi2Aep9qMkG1Pap7zVp955131mTw0xN3ce65yMM7Hv7WjoCL9yfXBp+eL2r3/D/8f7qk8o9c1G3lS13ZPbpoF13kUVkV9lbrAnV7o/Wf1PpBcnwIIFBZATUbCkrqS5p6G+qWPS8oWsmcdPvxK6+8YgOtuqVb//SFkBSdgKu6je4IKrNn9TrQuafb09RTIHu4hkrk4iKPSpSTfcRHwNX7k7a3OnW+Y8cO++VfbYMmWtNwMvnupqhO6dzk6uLcc5GHGy1ycSng6v3JtYHLWv1PXq7q1v2RlZ6ji3bRRR6lHzFrplWAAGhaa57jRgABBBBAAAEEEEAAAQQQQAABBBBAIAUC3AKfgkrmEBFAAAEEEEAAAQQQQAABBBBAAAEEEEirAAHQtNY8x40AAggggAACCCCAAAIIIIAAAggggEAKBAiApqCSOUQEEEAAAQQQQAABBBBAAAEEEEAAAQTSKkAANK01z3EjgAACCCCAAAIIIIAAAggggAACCCCQAgECoCmoZA4RAQQQQAABBBBAAAEEEEAAAQQQQACBtAoQAE1rzXPcCCCAAAIIIIAAAggggAACCCCAAAIIpECAAGgKKplDRAABBBBAAAEEEEAAAQQQQAABBBBAIK0CBEDTWvMcNwIIIIAAAggggAACCCCAAAIIIIAAAikQIACagkrmEBFAAAEEEEAAAQQQQAABBBBAAAEEEEirAAHQtNY8x40AAggggAACCCCAAAIIIIAAAggggEAKBAiApqCSOUQEEEAAAQQQQAABBBBAAAEEEEAAAQTSKkAANK01z3EjgAACCCCAAAIIIIAAAggggAACCCCQAgECoCmoZA4RAQQQQAABBBBAAAEEEEAAAQQQQACBtAoQAE1rzXPcCCCAAAIIIIAAAggggAACCCCAAAIIpECAAGgKKplDRAABBBBAAAEEEEAAAQQQQAABBBBAIK0CBEDTWvMcNwIIIIAAAggggAACCCCAAAIIIIAAAikQIACagkrmEBFAAAEEEEAAAQQQQAABBBBAAAEEEEirAAHQtNY8x40AAggggAACCCCAAAIIIIAAAggggEAKBP4fp9WzKfick44AAAAASUVORK5CYII=" width="672"></p> <div class="row"><div class="col-md-12"><button type="button" class="btn btn-default btn-xs code-folding-btn pull-right" data-toggle="collapse" data-target="#rcode-643E0F3667" aria-expanded="false" aria-controls="rcode-643E0F3667"><span>Code</span></button></div></div><div class="collapse r-code-collapse" id="rcode-643E0F3667"><pre class="r"><code class="hljs"><span class="hljs-comment">#ggsave("Fig4.pdf", plot = Metameta_Fig4_alltraits, width = 7, height = 6) </span></code></pre></div> </div> </div> </div> </div> </div> </div> <script> // add bootstrap table styles to pandoc tables function bootstrapStylePandocTables() { $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); } $(document).ready(function () { bootstrapStylePandocTables(); }); </script> <!-- tabsets --> <script> $(document).ready(function () { window.buildTabsets("TOC"); }); $(document).ready(function () { $('.tabset-dropdown > .nav-tabs > li').click(function () { $(this).parent().toggleClass('nav-tabs-open') }); }); </script> <!-- code folding --> <script> $(document).ready(function () { window.initializeCodeFolding("hide" === "show"); }); </script> <script> $(document).ready(function () { // move toc-ignore selectors from section div to header $('div.section.toc-ignore') .removeClass('toc-ignore') .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); // establish options var options = { selectors: "h1,h2,h3,h4", theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); }, ignoreSelector: ".toc-ignore", scrollTo: 0 }; options.showAndHide = true; options.smoothScroll = true; // tocify var toc = $("#TOC").tocify(options).data("toc-tocify"); }); </script> <!-- dynamically load mathjax for compatibility with self-contained --> <script> (function () { var script = document.createElement("script"); script.type = "text/javascript"; script.src = 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