Intestelligence: A pharmacological neural network using intestine data

Intestelligence: A pharmacological neural network... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/13-714" }, "headline": "Intestelligence: A pharmacological neural network using intestine data", "datePublished": "2024-06-28T16:24:45", "dateModified": "2024-06-28T16:24:45", "author": [ { "@type": "Person", "name": "Yusuke Watanabe" }, { "@type": "Person", "name": "Hiroshi Ban" }, { "@type": "Person", "name": "Nobuhiro Hagura" }, { "@type": "Person", "name": "Yuji Ikegaya" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background A neural network is a machine learning algorithm that can learn and make predictions by adjusting the strength of the connections between nodes. The sigmoid function is commonly used as an activation function in these nodes. This study explores the potential applicability of biological materials in the development of alternative activation functions. Methods Inspired by the fact that acetylcholine induces intestinal contractions that follow a sigmoid function, we used pharmacological data obtained from guinea pig ilea in a layered neural network for image classification tasks. Results and Conclusions We found that the intestinal data-based neural network with the same structure as a conventional three-layer perceptron achieved an impressive classification accuracy of 85.7% ± 0.6% based on the MNIST handwritten digit dataset (chance = 10%). Additionally, the neural network was trained to determine whether objects in photographs collected from the internet were digestible, achieving an accuracy of 88.5% ± 0.9% (chance = 50%). Our approach highlights the potential applicability of intestine data in neural computations based on pharmacological mechanisms. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-714/v1", "name": "Intestelligence: A pharmacological neural network using intestine..." } } ] } Home Browse Intestelligence: A pharmacological neural network using intestine... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Watanabe Y, Ban H, Hagura N and Ikegaya Y. Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.12688/f1000research.145100.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] Yusuke Watanabe https://orcid.org/0000-0001-9541-6073 1,2 , Hiroshi Ban 3 , Nobuhiro Hagura 3 , Yuji Ikegaya 1,3,4 Yusuke Watanabe https://orcid.org/0000-0001-9541-6073 1,2 , Hiroshi Ban 3 , Nobuhiro Hagura 3 , Yuji Ikegaya 1,3,4 PUBLISHED 28 Jun 2024 Author details Author details 1 Graduate School of Pharmaceutical Sciences, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 1130033, Japan 2 Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, 3010, Australia 3 Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka, 5650871, Japan 4 Institute for AI and Beyond, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 1130033, Japan Yusuke Watanabe Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Hiroshi Ban Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Nobuhiro Hagura Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Yuji Ikegaya Roles: Conceptualization, Funding Acquisition, Investigation, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Background A neural network is a machine learning algorithm that can learn and make predictions by adjusting the strength of the connections between nodes. The sigmoid function is commonly used as an activation function in these nodes. This study explores the potential applicability of biological materials in the development of alternative activation functions. Methods Inspired by the fact that acetylcholine induces intestinal contractions that follow a sigmoid function, we used pharmacological data obtained from guinea pig ilea in a layered neural network for image classification tasks. Results and Conclusions We found that the intestinal data-based neural network with the same structure as a conventional three-layer perceptron achieved an impressive classification accuracy of 85.7% ± 0.6% based on the MNIST handwritten digit dataset (chance = 10%). Additionally, the neural network was trained to determine whether objects in photographs collected from the internet were digestible, achieving an accuracy of 88.5% ± 0.9% (chance = 50%). Our approach highlights the potential applicability of intestine data in neural computations based on pharmacological mechanisms. READ ALL READ LESS Keywords intestine, neural network, activation function Corresponding Author(s) Yusuke Watanabe ( [email protected] ) Yuji Ikegaya ( [email protected] ) Close Corresponding authors: Yusuke Watanabe, Yuji Ikegaya Competing interests: No competing interests were disclosed. Grant information: The Exploratory Research for Advanced Technology by Japan Science and Technology Agency (JST ERATO; Grant Number: JPMJER1801). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 Watanabe Y et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Watanabe Y, Ban H, Hagura N and Ikegaya Y. Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.12688/f1000research.145100.1 ) First published: 28 Jun 2024, 13 :714 ( https://doi.org/10.12688/f1000research.145100.1 ) Latest published: 28 Jun 2024, 13 :714 ( https://doi.org/10.12688/f1000research.145100.1 ) Introduction Neural networks (NNs), which were first introduced by Amari (1967) and later improved by Rumelhart et al. (1986) , are mathematical models that are inspired by the structure and function of the central nervous system (CNS). A critical aspect of NNs is their nonlinearity, which is provided by activation functions that control the nodes’ outputs, similar to biological neurons. Despite their important impact on neural network behavior, many activation functions have been proposed without theoretical justification ( Glorot et al., 2011 ; Hahnloser et al., 2000 ; Jarrett et al., 2009 ; Nair & Hinton, 2010 ; Ramachandran et al., 2017 ; Misra, 2020 ). Therefore, the use of biologically verified activation functions, such as those derived from the enteric nervous system, may improve the performance of existing activation functions. The enteric nervous system shares many features with the central nervous system ( Goyal & Hirano, 1996 ; Rao & Gershon, 2016 ) and may show promise for the development of unconventional neural network models. The intestine, which is sometimes referred to as the “second brain” ( Mayer, 2011 ; Obata & Pachnis, 2016 ; Rao & Gershon, 2016 ; Maniscalco & Rinaman, 2018 ), responds to external stimuli. Its smooth muscle undergoes reversible contractions in response to acetylcholine (ACh) in a dose-dependent manner ( Burgen & Spero, 1997 ; Rao & Gershon, 2016 ). These contractions conform to a dose–response curve that reflects the activation of ACh receptors and subsequent signal pathways; thus, contraction responses are macroscopic expressions of biochemical reactions. Therefore, intestinal contraction data may be a valuable resource for developing novel biologically inspired activation functions. In this study, we developed an intestinal data-based neural network (INN) by incorporating pharmacological data based on intestinal contractions into a neural network model. We hypothesized that the INN model could perform classification tasks as effectively as neural networks based on the central nervous system. Methods Animal ethics The intestinal tissues used in this study were surplus samples in a university student practical course that would otherwise have been discarded. Specifically, retired male breeder guinea pigs weighing 300–400 g ( n = 14; SLC, Shizuoka, Japan) were used in our experiments. Consequently, the sample size was dictated by the enrollment of students in the course. The animals were housed in a temperature-controlled room (22 ± 1°C) with a 12-hour light-dark cycle (light from 07:00 to 19:00) and given ad libitum access to food and water. All animal experiments were performed with the approval of the Animal Experiment Ethics Committee of the University of Tokyo (approval number: P24-1) and in accordance with the University of Tokyo guidelines for the care and use of laboratory animals. Our experimental protocols adhered to the Fundamental Guidelines for Proper Conduct of Animal Experiments and Related Activities in Academic Research Institutions (Ministry of Education, Culture, Sports, Science and Technology, Notice No. 71 of 2006), the Standards for Breeding and Housing of and Pain Alleviation for Experimental Animals (Ministry of the Environment, Notice No. 88 of 2006), and the Guidelines on the Method of Animal Disposal (Prime Minister’s Office, Notice No. 40 of 1995). The primary outcome measure was the variation in ileum tissue length when subjected to different concentrations of acetylcholine (ACh) in a controlled bath setting. Animal identifiers were not identifiable to data analysts. All efforts were made to ameliorate the suffering of animals. Euthanasia methods involved intraperitoneal injection of sodium pentobarbital at a lethal dose of 100 mg/kg body weight to ensure a humane termination of life. Additionally, pre-established criteria for euthanizing animals displaying severe distress or declining health included indicators such as unexplained weight loss of over 20%, labored breathing, and other signs of suffering; however, none of the animals met these criteria. At the conclusion of the educational pharmacology course, all surviving animals were humanely euthanized, adhering to the same ethical standards, and were not used for subsequent experiments or educational activities. Drugs Acetylcholine chloride (C 7 H 16 ClNO 2 ; Wako, Japan) was dissolved in double distilled water at concentrations of 2×10 −7 , 2×10 −6 , 2×10 −5 , 2×10 −4 , 2×10 −3 , 2×10 −2 , and 2×10 −2 M. Intestinal contraction data acquisition To isolate ileum tissue, guinea pigs were deeply anesthetized with 100 mg/kg pentobarbital (i.p.). The chest was opened, and the ileum was removed and washed with nutrient solution (in mM: NaCl 137, KCl 2.7, NaHCO 3 11.9, CaCl 2 1.8, MgCl 2 1.0, D-glucose 5.5) that was bubbled with 5% CO 2 /95% O 2 . The tissue was cut to a length of approximately 3 cm, and mesenteric arteries were removed. Two hooks were used to mount the tissue; one hook was inserted at one end of the tissue and fixed to the tip of an aeration tube, while the other was connected to an isotonic displacement transducer (45347, NEC San-ei Instrument, Tokyo, Japan). The transducer was connected to an amplifier (AS1202, NEC San-ei Instruments) and a flatbed recorder (FBR-251A, TOA Electronics, Tokyo, Japan), which was used to record the ileum tissue responses ( Figure 1A ). Before starting the experiments, the ileum tissue strips were equilibrated, and a stable baseline recording was obtained at a resting tension of 0.5 g. Acetylcholine (ACh) solutions were then sequentially bath-applied to the organ bath at the following concentrations (in M): 1×10 −9 , 3×10 −9 , 1×10 −8 , 3×10 −8 , 1×10 −7 , 3×10 −7 , 1×10 −6 , 3×10 −6 , 1×10 −5 , 3×10 −5 , 1×10 −4 , 3×10 −4 , 1×10 −3 , and 3×10 −3 . Bath application was performed when a local maximum of the contraction reaction at the previous ACh concentration was confirmed. The ACh concentration [M] and intestine length [cm] were recorded, and these steps were repeated 2–3 times. The intestinal contraction rate [%] was calculated by averaging the contraction lengths over different trials and dividing the mean values by the maximum value. We collected the data for the dose–response curves from 512 ileum samples. Sigmoid-curve fitting for the intestine contraction data at different acetylcholine concentrations For each of the 512 data pairs, the ACh concentration was natural-log-transformed: (1) C ′ = ln ( C ) + 4 where C is the ACh concentration in the organ bath. The dose–response curves were plotted as (2) ( x , y ) T = ( C ′ , R i ) T where R i is the i th intestine contraction rate [%] (i = 1, 2, …, 512; Figure 1B ). These curves were fitted by sigmoid functions: (3) ς ( x , α 0 i , α 1 i ) = 1 1 + exp ( − α 0 i x + α 1 i ) where α 0 i and α 1 i are parameters to fit the sigmoid function based on the i th intestine data. The parameters α 0 i and α 1 i were optimized simultaneously using the MATLAB function nlinfit (MathWorks, Natick, MA, USA; https://www.mathworks.com/help/stats/nlinfit.html ). The determined sigmoid functions were shifted by ( x , y ) T = ( − α ¯ 1 α ¯ 0 , 0 ) T as follows: (4) ς shifted ( x , α 0 i , α 1 i ) = ς ( x + α ¯ 1 α ¯ 0 , α 0 i , α 1 i ) = 1 1 + exp { − α 0 i ( x + α ¯ 1 α ¯ 0 ) + α 1 i } = 1 1 + exp { − α 0 i x + ( − α ¯ 1 α ¯ 0 α 0 i + α 1 i ) } = 1 1 + exp ( − β 0 i x + β 1 i ) = ς shifted ( x , β 0 i , β 1 i ) where (5) β 0 i = α 0 i (6) β 1 i = − α ¯ 1 α ¯ 0 α 0 i + α 1 i Thus, β ¯ 0 = α ¯ 0 and β ¯ 1 = 0 ( Figure 1C ). The mean square error (MSE) between the fitted sigmoid curve and the corresponding sample was calculated. Samples that fell outside the range of the mean ± 3 standard deviations (SDs) in the | β 0 i |, | β 1 i |, or MSE value for the i th intestine sample were excluded from further analyses, leaving 503 intestine samples. It is important to emphasize that these criteria were not predetermined. Forward passes of neural networks The three-layer perceptron (control) and INN model had the same structure except for their activation functions. The common structure is written as follows: (7) x ≔ ( x 1 , x 2 , … , x D x ) T (8) a j = ∑ i D x w ji ( h ) x i + b j 0 ( h ) (9) h j = φ j ( h ) ( a j ) (10) a k = ∑ j D h w kj ( o ) h j + b k 0 ( o ) (11) y ̂ k = φ k ( o ) ( a k ) (12) y ̂ ≔ ( y ̂ 1 , y ̂ 2 , … , y ̂ D y ) T where x ∈ ℝ D x : input vector ( D x : dimension of the input vector) w ji ( h ) : the weight between the i th unit in the input layer and the j th unit in the hidden layer (j = 1, 2, …, 100) b j 0 ( h ) : 1, the bias of the j th unit in the hidden layer φ j ( h ) ( · ) : the activation function of the j th unit in the hidden layer w kj ( h ) : the weight between the j th unit in the hidden layer and the k th unit in the output layer (k = 1, 2, …, D y ; D y is dimension of the output vector) b k 0 ( h ) : 1, the bias of the k th unit in the hidden layer φ k ( o ) ( · ) : the activation function of the k th unit in the output layer y ̂ ∈ ℝ D y : output vector. (13) φ Control ( x ) = 1 1 + exp ( − x ) . On the other hand, the activation functions of the INN model, φ j ( h ) ( a j ) and φ k ( o ) ( a k ) , included two random variables B 0 and B 1 as parameters: (14) φ INN ( x ) = 1 1 + exp ( − B 0 x + B 1 ) . Note that Eq. 13 and Eq. 14 are identical when ( B 0 , B 1 ) = (1, 0). For each INN activation function, namely, φ j _ INN ( h ) ( · ) (j = 1, 2, …, 100 ) and φ k _ INN ( o ) ( · ) (k = 1, 2, …, D y ), the random variables ( B 0 , j , B 1 , j ) and ( B 0 , k , B 1 , k ) were sampled from 503 sets of experimentally obtained parameters {( β 0 _ 1 , β 1 _ 1 ), ( β 0 _ 2 , β 1 _ 2 ), …, ( β 0 _ 503 , β 1 _ 503 )} ( Eqs. 1 – 6 ). The source code for the three-layer perceptron and INN is available on our GitHub repository: https://github.com/ywatanabe1989/intestelligence . Cost function The cross-entropy loss was adopted as the cost function, which was calculated as follows: (15) J ( θ ) = 1 M ∑ m = 1 M J m ( θ ) = 1 M ∑ m = 1 M ∑ k = 1 D y [ − y k ( m ) log ( y ̂ k ( m ) ) − ( 1 − y k ( m ) ) log ( 1 − y ̂ k ( m ) ) ] where θ represents all the learnable parameters , M denotes the mini-batch size (the amount of training data used in each training loop), J m ( θ ) represents the cost function derived from the m th sample in the mini-batch, and y k denotes the k th element of y , which is a one-hot encoded vector of the ground-truth label: y ≔ ( y 1 , y 2 , … , y D y ) T y k = { 1 when x ∈ class K 0 otherwise . Backpropagation The backpropagation method ( Rumelhart et al., 1986 ) was adopted to efficiently calculate the gradient of the cost function, ∇ J m ( θ ) . (16) ∂ J m ( θ ) ∂ w kj ( o ) = ∂ J m ( θ ) ∂ a k ∂ a k ∂ w kj ( o ) The following notation was introduced: (17) δ k ≔ ∂ J m ( θ ) ∂ a k . which is analytically defined as: δ k ( m ) = ∂ ∂ a k ( m ) ∑ k = 1 D y [ − y k ( m ) log ( y ̂ k ( m ) ) − ( 1 − y k ( m ) ) log ( 1 − y ̂ k ( m ) ) ] = ∂ ∂ a k ( m ) ∑ k = 1 D y [ − y k ( m ) log { φ k ( o ) ( a k ( m ) ) } − ( 1 − y k ( m ) ) log { 1 − φ k ( o ) ( a k ( m ) ) } ] where: (18) ∂ φ Control ( a ) ∂a = φ Control ( x ) { 1 − φ Control ( a ) } (19) ∂ φ INN ( a ) ∂a = − B 0 exp ( − B 0 a + B 1 ) { 1 + exp ( − B 0 a + B 1 ) } 2 . According to Eq. 10 , we have (20) ∂ a k ∂ w kj ( o ) = h j . Substituting Eqs. 17 and 20 into Eq. 16 , we obtain (21) ∂ J m ( θ ) ∂ w kj ( o ) = δ k h j . For the δ s of the hidden units, the chain rule for partial derivatives is applied to obtain: (22) δ j ≔ ∂ J m ( θ ) ∂ a j = ∑ k ∂ J m ( θ ) ∂ a k ∂ a k ∂ a j . By substituting Eq. 17 into Eq. 22 and using Eqs. 9 and 10 , the following backpropagation formula is obtained: (23) δ j = ∂ φ j ( h ) ( a j ) ∂ a j ∑ k w kj ( o ) δ k . Thus, the gradient of the cost function ∇ J m ( θ ) can be calculated using the backpropagation method. Weight update The model was trained using the stochastic gradient descent (SGD) algorithm. If the error function consists of a sum over data points, J ( θ ) = ∑ n J n ( θ ) , after the presentation of pattern n, the SGD algorithm updates the parameter vector θ using the following formula: (24) θ ( τ + 1 ) = θ ( τ ) − η ∇ J n ( θ ) . Here, τ denotes the iteration number, and η is the learning rate parameter. Classification of handwritten digits The MNIST database of handwritten digits ( LeCun et al., 1998 ) was used for the experiments in this study. The control and INN models were trained based on 60,000 training data points and evaluated based on 10,000 test data points. Each image was 784 pixels (= 28 columns×28 rows), and the images were fed into models after being flattened into a vector. The mini-batch size was set to 100, and the learning rate was set to 0.05. Simulations of INNs The INN models were simulated by sampling the fitting parameters β 0 and β 1 . Dummy fitting parameters β dummy = ( β 0 _ dummy , β 1 _ dummy ) T were sampled from a bimodal Gaussian distribution, as described below. (25) β dummy ∼ N ( μ , Σ ) (26) μ = ( β ¯ 0 β ¯ 1 ) = ( 3.06 0.00 ) (27) Σ = ( σ 2 β 0 0 0 σ 2 β 1 ) ∀ σ 2 β 0 ∈ { 0 , 0.1 , 0.5 , 1.0 , 1.5 , 2.0 , 2.5 , 3.0 } ∀ σ 2 β 1 ∈ { 0 , 0.1 , 0.5 , 1 , 0 , 1.5 , 2.0 , 2.5 , 3.0 , 3.5 , 4.0 , 4.5 , 5.0 , 10 , 30 , 50 } . When the slope at the inflection point ( ( β 1 _ dummy β 0 _ dummy , 1 2 ) T ) of the simulated sigmoid function ( slope: − β 0 _ dummy 4 ) was negative ( β 0 _ dummy > 0), β dummy was resampled. A total of 140 (= 8 × 15) experiments were performed. Classification of edible/inedible images A total of 1,200 images of edible objects and 1,200 images of inedible objects were randomly sampled from https://www.sozaijiten.com . Then, 1,000 images from each class (edible/inedible) were used as training data, and the remaining 200 images from each class were used as test data. The edible images included images of cooked meals and raw foods such as fruits, vegetables, and fish. The inedible images included images of tools, parts, coins, and other objects. The images were converted into feature vectors using a pretrained convolutional neural network model (AlexNet; Krizhevsky et al., 2012 ) based on the Caffe deep learning framework ( Jia et al., 2014 ). Specifically, for every input image, the output of the activation of the fc6 layer (4,096-dimension vector) was extracted. These feature vectors were fed into the INN model instead of the original images to perform the 2-class classification task. The model consists of three layers: an input layer, a hidden layer, and an output layer. The input layer contains 4,097 nodes, including a bias unit. The hidden layer has 101 nodes, also inclusive of a bias unit. The output layer consists of 2 nodes corresponding to two classes: edible or inedible. The learning rate was set to 0.6. Statistical methods To compare classification accuracies of INN and Control, we employed the Mann-Whitney U test due to the absence of any specific parametric hypotheses. The test was conducted using the Python package SciPy ( https://scipy.org/ ). The rank-biserial correlation was used as the effect size measure. Results Ileal sample contraction during acetylcholine bath application After different concentrations of Ach were sequentially applied, the lengths of the isolated ileum tissues decreased. We used an experimental device to measure the isotonic contraction of the intestinal tract ( Figure 1A ) and recorded the dose–response curves of the ilea during bath application of ACh at concentrations ranging from 10 −9 to 10 −3 M ( Figure 1B , n = 512 tissues from 14 guinea pigs). Each dose–response curve was obtained from a single tissue and was normalized and fitted to a sigmoid function ( Figure 1C ) using two fitting parameters, β 0 and β 1 ( Eqs. 1 – 6 ). The MSE between the experimentally recorded data and the fitted sigmoid curve was calculated for all 512 samples. Data samples for which the | β 0 |, | β 1 | or MSE values exceeded the mean ± 3SD were omitted from the following analysis, and the remaining 503 data points were used to construct the INN model. The distributions of β 0 and β 1 are plotted in Figure 1D and 1E . For β 0 , the sample mean β 0 ¯ was 3.06, and the sample variance s 2 β 0 was 1.38; for β 1 , the sample mean β 1 ¯ was 0.00 (due to the adjustment), and the sample variance s 2 β 1 was 3.23). Figure 1. Dose–response curves of ACh-induced contractions of the ileum. (A) Illustration of our experimental devices for measuring ileum contraction. (B) The dose–response curves of 512 data points for contractions in response to bath application of ACh. Each gray line indicates a single data point, whereas the black line indicates the mean. (C) Five examples of sigmoid fitting of the dose–response curves of ACh-induced ileum contraction. Note that β 0 and β 1 are fitting parameters. (D, E) Distributions of the fitting parameters β 0 (D) and β 1 (E). Classification of handwritten digits by the INN model We constructed a three-layer perceptron (Control) and an INN, both with the same structure as illustrated in Figure 2A . These models were used for a 10-class classification task using handwritten digits from the MNIST database ( LeCun et al., 1998 ). During 6,000 rounds of training, the cost of the Control model was lower than that of the INN model ( Figure 2B ; at Loop #6,000, Control: 0.558 ± 0.097; INN: 0.923 ± 0.188; p < 0.001; n s = 40; U = 1,556; Mann–Whitney U test; the rank-biserial correlation = - 0.945). Both models converged, but the sum of the absolute weight update of the Control model was lower than that of the INN model ( Figure 2C ; at Loop #6,000, Control: 1.83 ± 0.86; INN: 2.58 ± 0.94; p < 0.001; n s = 40; U = 1,182; Mann–Whitney U test; the rank-biserial correlation = - 0.478). The accuracy of the Control model was higher than that of the INN model ( Figure 2D ; at Loop #6,000, Control: 92.3 ± 0.4; INN: 85.7 ± 0.6; Chance: 10%; p < 0.001; n s = 40; U = 0; Mann–Whitney U test; the rank-biserial correlation = 1.000). Figure 2. Handwritten number identification using the INN model. (A) A schematic diagram of the three-layer INN model. A conventional perceptron with the same structure was used as the control. (B–D) Output values of test data during training. B, cost function; C, sum of the absolute weight updates; D, classification accuracy. The blue and black lines represent the INN and control model results, respectively. Each line indicates a single data point ( n = 32 tuning trials), whereas the thick line indicates the mean. We also observed the sample variance in the fitting parameters β 0 and β 1 ( Figure 1D and 1E ). These fluctuations might originate from (i) biological noise and (ii) differences in the experimental conditions. We hypothesized that the variations in β 0 and β 1 ( σ 2 β 0 , σ 2 β 0 ) affect the classification accuracy of the model based on the MNIST dataset. To test this hypothesis, we simulated INNs using various values of σ 2 β 0 and σ 2 β 0 . Figure 3 shows the accuracies of the virtually generated INNs in a pseudocolor map. The highest mean accuracy was 92.3% ± 0.3% for σ 2 β 0 = 0.0 and σ 2 β 1 = 0.0, and the lowest mean accuracy was 57.3% ± 5.5% for σ 2 β 0 = 3.0 and σ 2 β 1 = 50. Figure 3. Relationship between INN performance in number identification tasks and parameter variation. Accuracies of INNs as a function of the variance in the hyperparameters β 0 and β 1 ( σ 2 β 0 and σ 2 β 0 ). The experimentally obtained sample variance ( s 2 β 0 = 1.38; s 2 β 1 = 3.23) is indicated by the black dot. Edible/inedible image classification by the INN model A total of 1,200 images of edible and inedible objects were randomly chosen from the dataset and split into training/test datasets at a ratio of 5:1 ( Figure 4A ). The accuracies of the model based on the test data were evaluated during training. At loop #6,000, the accuracy was 88.5% ± 0.9% ( Figure 4 ; n = 32 cross-validations). Figure 4. Image classification using the INN model. (A) Examples of edible ( left ) and inedible ( right ) images in the dataset. The top images were used for training, and the bottom images were used for testing. (B) The learning curves of the INN prediction results for the test data. Each gray line indicates a single data point ( n = 32 cross-validation), whereas the black line indicates the mean. Discussion In this study, we confirmed that different intestine samples exhibited distinct contraction responses to ACh ( Figure 1B and 1C ). Using the response curves, we built an INN model and demonstrated that the INN model could be used to precisely classify handwritten digits. Furthermore, we artificially generated various INN models and found that the INN model with the average activation function, or the three-layer perceptron, achieved the highest accuracy (92.3% ± 0.3%). Finally, the INN model could be used to classify edible/inedible images, achieving a classification accuracy of 88.5% ± 0.9%. This study presents a novel approach for building neural networks using biological data and demonstrates the potential of the enteric nervous system for developing activation functions for machine learning models. Existing activation functions can be broadly classified into two groups: (i) the sigmoid group, including the logistic sigmoid function and the hyperbolic tangent function, and (ii) the V-shaped group, including the ReLU (Hasnloser et al., 2000; Nair & Hinton, 2010 ), Leaky ReLU (Maas et al., 2013), PReLU (He et al., 2015), Swish ( Ramachandran et al., 2017 ), and Mish (Misra, 2019) activation functions. However, the use of sigmoid functions has recently decreased because sigmoid functions lead to slow weight updates and vanishing gradients ( Bengio et al., 1994 ) in the backpropagation framework ( Rumelhart et al., 1986 ). Despite this disadvantage, sigmoid functions remain important for biologically inspired network models ( Shinozaki, 2021 ; Zhan et al., 2023 ), as backpropagation is unlikely to occur in a real brain ( Bartunov et al., 2018 ). Sigmoid curves are known to emerge as cumulative functions for bell-shaped curves, such as normal distributions. The sigmoid curves observed in our pharmacological experiments are regarded as macroscopic expressions of decision-making processes at the biomolecular level in the enteric nervous system because the ACh signaling pathways via muscarinic receptors trigger contractions in smooth muscles ( Eglen et al., 1996 ). One limitation of our study is the granularity of the model. Our intestine samples were modeled as nodes in an INN, rather than as the network itself. Thus, the developed INN can be viewed as an ensemble model based on intestine samples. This modeling approach was necessitated by the difficulty in recording a large number of individual neurons and receptors. Our model may not necessarily be inaccurate because the central and enteric nervous system exhibit plasticity ( e.g. , updated functional connectivity at synapses; Citri & Malenka, 2008 ) and can be viewed as an ensemble model of different network states at various timepoints. On the other hand, we observed different ACh responsiveness among individual intestine samples (approximately 10 2 scale). This variation may be caused by biological noise, as indicated by the observed parameters β 0 and β 1 , which were located in local maxima. Due to these variations, the INN model may have shown different sensitivity to various input data ( Figure 2B ) and plasticity ( Figure 2C ), which are desirable characteristics in real-world settings, in which the data are often unpredictable and unfixed and balancing the explore-exploit tradeoff is important. To verify this hypothesis, novel datasets and evaluation methods that closely mimic real-world scenarios should be developed to make fair comparisons between in silico and biological models. Conclusion This study has pioneered the development of an INN model based on biological intestine data, offering a novel approach to neural network construction. While the model’s performance in image classification tasks may currently be inferior to traditional three-layer perceptrons, this work focuses on the innovative methodology and its potential for future improvements. The model demonstrates the feasibility and uniqueness of using biological data as a basis for neural network development. Future work could explore the effects of biological diversity among ileum samples in their responsiveness to acetylcholine on the generalization ability of such networks. This could advance our understanding and the practical utility of biologically inspired models. Ethics approval All animal experiments were performed with the approval of the Animal Experiment Ethics Committee of the University of Tokyo (approval number: P24-1) and in accordance with the University of Tokyo guidelines for the care and use of laboratory animals Author contributions Y.I., H.B., and N.H. conceptualized the study; Y.W. performed the experiments and data analysis; Y.W. and Y.I. wrote the original draft; and all authors reviewed and edited the final manuscript. Data availability statement Underlying data Zenodo: Intestelligence: A pharmacological neural network using intestine data, https://zenodo.org/records/11213157 ( Watanabe et al., 2024 ). Data is available under the terms of Creative Commons Zero v1.0 Universal Reporting guidelines Zenode: Checklist for “Intestelligence: A pharmacological neural network using intestine data”, https://zenodo.org/records/11213157 Software availability statement • Source code available from: https://github.com/ywatanabe1989/intestelligence • Archived software available from: https://zenodo.org/records/11213157 • License: Creative Commons Zero v1.0 Universal The source code for this study is available at Zenode ( https://zenodo.org/records/11213157 ). Acknowledgments We thank Dr. Hideyoshi Igata for his technical support. References Amari S: A Theory of Adaptive Pattern Classifiers. IEEE Trans. Electron. Comput. 1967; EC-16 : 299–307. Publisher Full Text Bartunov S, et al. : Assessing the scalability of biologically-motivated deep learning algorithms and architectures. Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18. Curran Associates Inc.; 2018; pp. 9390–9400. Bengio Y, Simard P, Frasconi P: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 1994; 5 : 157–166. PubMed Abstract | Publisher Full Text Burgen AS, Spero L: The action of acetylcholine and other drugs on the efflux of potassium and rubidium from smooth muscle of the guinea-pig intestine. Br. J. Pharmacol. 1997; 120 : 317–333. discussion 315-316. 1968. PubMed Abstract | Publisher Full Text | Free Full Text Citri A, Malenka RC: Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms. Neuropsychopharmacology. 2008; 33 : 18–41. Publisher Full Text Eglen RM, Hegde SS, Watson N: Muscarinic receptor subtypes and smooth muscle function. Pharmacol. Rev. 1996; 48 : 531–565. PubMed Abstract Glorot X, Bordes A, Bengio Y: Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, (JMLR Workshop and Conference Proceedings. 2011; pp. 315–323. Goyal RK, Hirano I: The enteric nervous system. N. Engl. J. Med. 1996; 334 (17): 1106–1115. Erratum in: N Engl J Med 1996 Jul 18;335(3):215. PubMed Abstract | Publisher Full Text Hahnloser RHR, Sarpeshkar R, Mahowald MA, et al. : Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature. 2000; 405 : 947–951. PubMed Abstract | Publisher Full Text Jarrett K, Kavukcuoglu K, Ranzato M, et al. : What is the best multi-stage architecture for object recognition? 2009 IEEE 12th International Conference on Computer Vision. 2009; pp. 2146–2153. Jia Y, et al. : Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the 22nd ACM International Conference on Multimedia, MM’14. (Association for Computing Machinery). 2014; pp. 675–678. Krizhevsky A, Sutskever I, Hinton GE: ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems. Curran Associates, Inc.; 2012. Lecun Y, Bottou L, Bengio Y, et al. : Gradient-based learning applied to document recognition. Proc. IEEE. 1998; 86 : 2278–2324. Publisher Full Text Maniscalco JW, Rinaman L: Vagal Interoceptive Modulation of Motivated Behavior. Physiology. 2018; 33 : 151–167. PubMed Abstract | Publisher Full Text | Free Full Text Mayer EA: Gut feelings: the emerging biology of gut–brain communication. Nat. Rev. Neurosci. 2011; 12 : 453–466. PubMed Abstract | Publisher Full Text | Free Full Text Misra D: Mish: A Self Regularized Non-Monotonic Activation Function.2020. (October 22, 2022). Publisher Full Text Nair V, Hinton GE: Rectified Linear Units Improve Restricted Boltzmann Machines.2010; 8 . Obata Y, Pachnis V: The Effect of Microbiota and the Immune System on the Development and Organization of the Enteric Nervous System. Gastroenterology. 2016; 151 : 836–844. PubMed Abstract | Publisher Full Text | Free Full Text Ramachandran P, Zoph B, Le QV: Searching for Activation Functions.2017. (October 22, 2022). Publisher Full Text Rao M, Gershon MD: The bowel and beyond: the enteric nervous system in neurological disorders. Nat. Rev. Gastroenterol. Hepatol. 2016; 13 : 517–528. PubMed Abstract | Publisher Full Text | Free Full Text Rumelhart DE, Hinton GE, Williams RJ: Learning representations by back-propagating errors. Nature. 1986; 323 : 533–536. Publisher Full Text Shinozaki T: Biologically motivated learning method for deep neural networks using hierarchical competitive learning. Neural Netw. 2021; 144 : 271–278. PubMed Abstract | Publisher Full Text Watanabe Y, et al. : Intestelligence: A pharmacological neural network using intestine data (v1.0). Zenodo. 2024. Publisher Full Text Zhan Q, Liu G, Xie X, et al. : Bio-inspired Active Learning method in spiking neural network. Knowl.-Based Syst. 2023; 261 : 110193. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 28 Jun 2024 ADD YOUR COMMENT Comment Author details Author details 1 Graduate School of Pharmaceutical Sciences, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 1130033, Japan 2 Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, 3010, Australia 3 Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka, 5650871, Japan 4 Institute for AI and Beyond, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 1130033, Japan Yusuke Watanabe Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Hiroshi Ban Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Nobuhiro Hagura Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Yuji Ikegaya Roles: Conceptualization, Funding Acquisition, Investigation, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The Exploratory Research for Advanced Technology by Japan Science and Technology Agency (JST ERATO; Grant Number: JPMJER1801). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 28 Jun 2024, 13:714 https://doi.org/10.12688/f1000research.145100.1 Copyright © 2024 Watanabe Y et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Watanabe Y, Ban H, Hagura N and Ikegaya Y. Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.12688/f1000research.145100.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 28 Jun 2024 Views 0 Cite How to cite this report: Siddiqui S. Reviewer Report For: Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.5256/f1000research.158989.r422553 ) The direct URL for this report is: https://f1000research.com/articles/13-714/v1#referee-response-422553 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 14 Nov 2025 Samra Siddiqui , HITEC University, Taxila, Pakistan; COMSATS University Islamabad - Wah Campus, Wah Cantt., Punjab, Pakistan Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.158989.r422553 1. The paper presents a novel concept of integrating neural architecture with biological data, but some points need to be considered explicitly. 2. While the conceptual innovation is clear, the paper would benefit from a more detailed quantitative comparison ... Continue reading READ ALL 1. The paper presents a novel concept of integrating neural architecture with biological data, but some points need to be considered explicitly. 2. While the conceptual innovation is clear, the paper would benefit from a more detailed quantitative comparison with standard architectures. Including accuracy metrics, loss curves, or ablation studies would help assess the model’s performance more objectively. Some metrics, like accuracy, are stated but do not give promising results. 3. The biological rationale for mapping intestinal data to network parameters could be further elaborated. Why just the ileum is selected as part of experimentation? Explaining how the chosen biological signals correspond to specific neural network behaviors would strengthen the study’s interpretability. 4. More information on the preprocessing of biological data, model training procedures, and parameter settings is needed to ensure reproducibility and clarity. 5. The manuscript could discuss potential limitations, for example, how scalable the proposed INN framework is for larger datasets or different modalities beyond image classification. 6. Some conceptual transitions (from biological modeling to computational implementation) could be made clearer for readers unfamiliar with both fields. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning, Digital Image processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Siddiqui S. Reviewer Report For: Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.5256/f1000research.158989.r422553 ) The direct URL for this report is: https://f1000research.com/articles/13-714/v1#referee-response-422553 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ziaul karim D. Reviewer Report For: Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.5256/f1000research.158989.r422554 ) The direct URL for this report is: https://f1000research.com/articles/13-714/v1#referee-response-422554 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Nov 2025 Dewan Ziaul karim , Brac University, Dhaka, Bangladesh Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.158989.r422554 The study is technically detailed, ethically transparent, and supported by a reproducible technique (including available GitHub code). However, some essential areas require clarity, quantitative rigor, and broader contextualization to fulfill the standards of a high-impact indexing. The ... Continue reading READ ALL The study is technically detailed, ethically transparent, and supported by a reproducible technique (including available GitHub code). However, some essential areas require clarity, quantitative rigor, and broader contextualization to fulfill the standards of a high-impact indexing. The research does not adequately explain why gut contraction behavior is a useful model for neuron activation. While the theory is fascinating and based on the concept of the “second brain,” there has to be a stronger scientific link between how smooth muscle contractions relate to how neurons function. The biological reasoning should be expanded to illustrate why this analogy makes sense in computational terms. The INN model achieved 85.7% accuracy on MNIST, which is lower than many basic neural networks. The authors state that the purpose is to highlight the method rather than performance, however it would still benefit to compare INN with alternative activation functions such as Swish, GELU, or other biologically inspired models. The research should also clarify why the accuracy is reduced - is it because of biological noise, the variability of the activation slopes, or limits of the sigmoid shape? The research does not properly explain what the parameter values (β₀ and β₁) signify biologically or how they affect the network’s learning. A simple analysis relating changes in slope or curve form to learning behavior or stability would make the results more accessible and useful. Although the ethical approval process is presented effectively, the study can clearly clarify why it was important to use real animal tissue. Since equivalent data might be created synthetically, the authors should define why live samples were required and how the advantages justified the ethical and methodological cost. The studies only employ two simple datasets - MNIST and edible/inedible image categorization. These are great for an initial demonstration, but adding one more test with a biologically related dataset (such as biomedical or physiological data) would make the work stronger and prove that the INN may be effective in real-world scientific situations. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Neural Network, Medical Image processing. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ziaul karim D. Reviewer Report For: Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.5256/f1000research.158989.r422554 ) The direct URL for this report is: https://f1000research.com/articles/13-714/v1#referee-response-422554 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 28 Jun 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 28 Jun 24 read read Dewan Ziaul karim , Brac University, Dhaka, Bangladesh Samra Siddiqui , HITEC University, Taxila, Pakistan; COMSATS University Islamabad - Wah Campus, Wah Cantt., Pakistan Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Siddiqui S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 Nov 2025 | for Version 1 Samra Siddiqui , HITEC University, Taxila, Pakistan; COMSATS University Islamabad - Wah Campus, Wah Cantt., Punjab, Pakistan 0 Views copyright © 2025 Siddiqui S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. The paper presents a novel concept of integrating neural architecture with biological data, but some points need to be considered explicitly. 2. While the conceptual innovation is clear, the paper would benefit from a more detailed quantitative comparison with standard architectures. Including accuracy metrics, loss curves, or ablation studies would help assess the model’s performance more objectively. Some metrics, like accuracy, are stated but do not give promising results. 3. The biological rationale for mapping intestinal data to network parameters could be further elaborated. Why just the ileum is selected as part of experimentation? Explaining how the chosen biological signals correspond to specific neural network behaviors would strengthen the study’s interpretability. 4. More information on the preprocessing of biological data, model training procedures, and parameter settings is needed to ensure reproducibility and clarity. 5. The manuscript could discuss potential limitations, for example, how scalable the proposed INN framework is for larger datasets or different modalities beyond image classification. 6. Some conceptual transitions (from biological modeling to computational implementation) could be made clearer for readers unfamiliar with both fields. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning, Digital Image processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Siddiqui S. Peer Review Report For: Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.5256/f1000research.158989.r422553) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-714/v1#referee-response-422553 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Ziaul karim D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Nov 2025 | for Version 1 Dewan Ziaul karim , Brac University, Dhaka, Bangladesh 0 Views copyright © 2025 Ziaul karim D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study is technically detailed, ethically transparent, and supported by a reproducible technique (including available GitHub code). However, some essential areas require clarity, quantitative rigor, and broader contextualization to fulfill the standards of a high-impact indexing. The research does not adequately explain why gut contraction behavior is a useful model for neuron activation. While the theory is fascinating and based on the concept of the “second brain,” there has to be a stronger scientific link between how smooth muscle contractions relate to how neurons function. The biological reasoning should be expanded to illustrate why this analogy makes sense in computational terms. The INN model achieved 85.7% accuracy on MNIST, which is lower than many basic neural networks. The authors state that the purpose is to highlight the method rather than performance, however it would still benefit to compare INN with alternative activation functions such as Swish, GELU, or other biologically inspired models. The research should also clarify why the accuracy is reduced - is it because of biological noise, the variability of the activation slopes, or limits of the sigmoid shape? The research does not properly explain what the parameter values (β₀ and β₁) signify biologically or how they affect the network’s learning. A simple analysis relating changes in slope or curve form to learning behavior or stability would make the results more accessible and useful. Although the ethical approval process is presented effectively, the study can clearly clarify why it was important to use real animal tissue. Since equivalent data might be created synthetically, the authors should define why live samples were required and how the advantages justified the ethical and methodological cost. The studies only employ two simple datasets - MNIST and edible/inedible image categorization. These are great for an initial demonstration, but adding one more test with a biologically related dataset (such as biomedical or physiological data) would make the work stronger and prove that the INN may be effective in real-world scientific situations. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Neural Network, Medical Image processing. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Ziaul karim D. Peer Review Report For: Intestelligence: A pharmacological neural network using intestine data [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :714 ( https://doi.org/10.5256/f1000research.158989.r422554) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-714/v1#referee-response-422554 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Intestelligence: A pharmacological neural...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-714/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-714/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-714/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Watanabe Y et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/13-714/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/13-714", templates : { twitter : "Intestelligence: A pharmacological neural network using intestine.... Watanabe Y et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/13-714/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/145100/158989") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "158989"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "315404": 0, "315405": 0, "315406": 0, "315407": 0, "315412": 0, "422550": 0, "315413": 0, "422551": 0, "315408": 0, "315409": 0, "315410": 0, "315411": 0, "335645": 0, "422558": 0, "335644": 0, "422559": 0, "422556": 0, "422557": 0, "422554": 3, "422555": 0, "335643": 0, "422552": 0, "422553": 1, "298924": 0, "298925": 0, "298926": 0, "298927": 0, "304175": 0, "298921": 0, "298922": 0, "298923": 0, "304180": 0, "304181": 0, "304182": 0, "304183": 0, "298928": 0, "304176": 0, "298929": 0, "304177": 0, "298930": 0, "304178": 0, "304179": 0, "304184": 0, "312260": 0, "312261": 0, "312262": 0, "312263": 0, "312258": 0, "312259": 0, "312264": 0, "312265": 0, "312266": 0, "312267": 0, "318684": 0, "318680": 0, "318681": 0, "318682": 0, "318683": 0, "308453": 0, "308454": 0, "308455": 0, "308460": 0, "308461": 0, "308462": 0, "308456": 0, "308457": 0, "308458": 0, "308459": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "7abe09b2-7d8b-4b88-9aa4-33a4b5bc5485"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });