. * * window.aa needs to exist and be usable by ANY module's script, * however early it runs - main-nav's script, for one, runs near * the top of

91探花

, well before the real search-insights.js * so the download * begins as early as before. Repeat calls share the same downloads, a * failed load is retried on the next call, and the versions / SRI * hashes live only here. * * Algolia Insights is unaffected: window.aa above is still created in * on every page and queues events until search-insights loads. * * window.loadAlgoliaLibs({ priority: 'high' }) (Oct 2026): used by the * /search page, where the first results wait on these libraries. Chrome * gives async scripts low priority, behind images and fonts; this asks * for high instead (fetchpriority). Only the first call for a library * sets its priority - later calls share that download. */ (function (win, doc) { var LIBS = [ { ready: function () { return !!win['algoliasearch/lite']; }, src: 'https://cdn.jsdelivr.net/npm/algoliasearch@5.52.1/dist/lite/builds/browser.umd.js', integrity: 'sha256-I2oYUIvhx1g6l4lTP+yoERbzLIHKzw4AKaBRQPwIeDY=' }, { ready: function () { return !!win.instantsearch; }, src: 'https://cdn.jsdelivr.net/npm/instantsearch.js@4.98.0/dist/instantsearch.production.min.js', integrity: 'sha256-ngmqZk4WBxUw257aMYZayyNBd983INngagv0jI6Ecfs=' } ]; function loadOne(lib, options) { if (lib.ready()) return Promise.resolve(); // One download per library, shared by every caller; reset on // failure so the next call retries just the library that failed. if (lib.pending) return lib.pending; lib.pending = new Promise(function (resolve, reject) { var script = doc.createElement('script'); script.src = lib.src; script.integrity = lib.integrity; script.crossOrigin = 'anonymous'; script.async = true; if (options && options.priority === 'high') script.setAttribute('fetchpriority', 'high'); script.onload = function () { lib.ready() ? resolve() : reject(new Error('Loaded but not available: ' + lib.src)); }; script.onerror = function () { script.remove(); reject(new Error('Could not load ' + lib.src)); }; doc.head.appendChild(script); }); lib.pending.catch(function () { lib.pending = null; }); return lib.pending; } win.loadAlgoliaLibs = function (options) { return Promise.all(LIBS.map(function (lib) { return loadOne(lib, options); })); }; })(window, document); . */ window.hsConversationsSettings = window.hsConversationsSettings || {}; window.hsConversationsSettings.loadImmediately = false; (function (win) { var EVENTS = ['pointerdown', 'keydown', 'touchstart', 'scroll', 'mousemove', 'wheel']; var done = false; /* 19 Oct 2026 - chat widget layout shift (field CLS). Phones fail Core Web Vitals on CLS (p75 ~0.2 site-wide in CrUX for months). Biggest cause found: HubSpot's chat container is fixed to the bottom-right corner and changes size - 100x96 launcher to 282x253 when the welcome message pops up (~5 s after load), and to full screen when a visitor opens the chat. Its top-left corner moves, so Chrome scores it as a layout shift: 0.088 per page view at 360x640 (0.044 at 412x850), on every page. Taps happen inside the chat iframe, so the parent page never sees the input and the open/close shifts count too. Fix: whenever the container's size changes it is made transparent for 400 ms (Chrome doesn't score shifts of invisible elements), then shown again at its new size. Tested on live io1001 at 360x640 in a cookie-less frame: the 0.088 shift no longer appears. The chat itself is unchanged - it just appears 0.4 s later at each resize. Relies on HubSpot's container id; if that changes this does nothing (no error). */ function cloakChatResizes() { var ID = 'hubspot-messages-iframe-container'; function watch(c) { if (c.getAttribute('data-bb-cloak')) return; c.setAttribute('data-bb-cloak', '1'); var last = c.offsetWidth + 'x' + c.offsetHeight, timer = null; new MutationObserver(function () { var size = c.offsetWidth + 'x' + c.offsetHeight; if (size === last) return; last = size; c.style.setProperty('opacity', '0', 'important'); clearTimeout(timer); timer = setTimeout(function () { c.style.removeProperty('opacity'); }, 400); }).observe(c, { attributes: true, attributeFilter: ['style', 'class'], subtree: true, childList: true }); } var found = document.getElementById(ID); if (found) { watch(found); return; } if (!document.body || !('MutationObserver' in win)) return; var mo = new MutationObserver(function () { var c = document.getElementById(ID); if (c) { mo.disconnect(); watch(c); } }); mo.observe(document.body, { childList: true }); setTimeout(function () { mo.disconnect(); }, 30000); } function loadWidget() { var api = win.HubSpotConversations; if (api && api.widget && api.widget.status && api.widget.status().loaded) return; try { cloakChatResizes(); } catch (e) {} if (api && api.widget) api.widget.load(); } function onFirstInteraction() { if (done) return; done = true; EVENTS.forEach(function (type) { win.removeEventListener(type, onFirstInteraction, { passive: true }); }); var start = function () { if (win.HubSpotConversations) loadWidget(); else (win.hsConversationsOnReady = win.hsConversationsOnReady || []).push(loadWidget); }; if ('requestIdleCallback' in win) win.requestIdleCallback(start, { timeout: 1500 }); else setTimeout(start, 200); } EVENTS.forEach(function (type) { win.addEventListener(type, onFirstInteraction, { passive: true }); }); })(window);

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Phenotypic characterisation of iPSC-derived ALS disease models by high-throughput MEA
application note

Phenotypic characterisation of iPSC-derived ALS disease models by high-throughput MEA

Functional phenotypic characterisation of precision reprogrammed human iPSC-derived disease models for ALS using high-throughput microelectrode arrays In this Application Note, Charles River Laboratories characterised an ALS-relevant phenotype in 91探花鈥檚 ioGlutamatergic Neurons TDP-43 M337V using the Maestro Pro multiwell microelectrode array platform from Axion BioSystems.
Phenotypic characterisation of iPSC-derived ALS disease models by high-throughput MEA
Functional phenotypic characterisation of precision reprogrammed human iPSC-derived disease models for ALS using high-throughput microelectrode arrays In this Application Note, Charles River Laboratories characterised an ALS-relevant phenotype in 91探花鈥檚 ioGlutamatergic Neurons TDP-43 M337V using the Maestro Pro multiwell microelectrode array platform from Axion BioSystems.

In this Application Note, Charles River Laboratories (Cambridge, UK) functionally characterised ioGlutamatergic Neurons TDP-43 M337V/WT, ioGlutamatergic Neurons TDP-43 M337V/M337V and wild-type ioGlutamatergic Neurons developed by 91探花 (Cambridge, UK). Together, these cells form a genetically matched disease model for the investigation of ALS in vitro. For this purpose, they used the Axion Maestro Pro multiwell microelectrode array (MEA) platform to assess electrophysiological differences between the disease model cells and the wild-type control, observing decreased neural activity in the homozygous disease model.

In this application note you will learn: 

  • How Charles River Laboratories utilised the Maestro Pro multiwell MEA platform from Axion BioSystems to functionally characterise ioGlutamatergic Neurons TDP-43 M337V from 91探花.
  • How ioGluamatergic Neurons TDP-43 M337V and ioGlutamatergic Neurons form a genetically matched model system that allows scientists to attribute experimental outcomes directly to mutations in the TDP-43 gene associated with ALS.
  • How using the Axion MEA platform, Charles River generated phenotypic data showing 91探花's ALS disease models exhibit reduced neuronal activity and network formation compared to a genetically matched control.

V1

2023

91探花 | Axion BioSystems | Charles River Laboratories

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