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DTSTART;TZID=Europe/London:20260616T120000
DTEND;TZID=Europe/London:20260616T130000
DTSTAMP:20260618T232119
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SUMMARY:Welfare webinar: Best practice in research animal anaesthesia
DESCRIPTION:Anaesthesia in an important refinement for research animals\, playing a critical role in minimising pain\, distress and adverse physiological effects\, while also supporting high quality and reliable scientific outputs. Poorly planned or inadequately monitored anaesthesia can compromise both animal welfare and experimental outcomes\, whereas good practice can substantially refine procedures across a wide range of species and research contexts.  \nFor this two-part webinar\, we are joined by veterinary anaesthetists from the Research Animal Anaesthesia Network (RAAN) to share their expert insights exploring principles and practical approaches to improving anaesthetic practice in laboratory animal research. Through expert-led talks and real-world examples\, the sessions will consider common challenges in research animal anaesthesia\, appropriate monitoring strategies\, and the use of injectable and inhalational agents across different species. The series will also highlight the support and resources available from RAAN to researchers and animal care staff. \nTalks will cover: \n\nWhy good anaesthetic practice is a key welfare refinement and essential for robust experimental outcomes.\nPractical approaches to addressing common challenges in research animal anaesthesia.\nWhat to monitor during general anaesthesia (and why)\, with a focus on realistic and appropriate monitoring.\nAppropriate use of injectable agents in laboratory animal anaesthesia\, including when and why they are used.\nHow RAAN supports researchers and veterinarians through advice\, resources and collaborative networks.\n\n\nDr Sarah Morgan\, iRISE\, EATRIS (European Infrastructure for Translational Medicine)\, will walk participants through key practices for effective training.\n\nRegistration and certificates of attendance\n\n\nThe content will be delivered online over two lunchtime sessions. Please register for both parts. \n\nPart one:  Tuesday 16 June (12.00 –  13.00 BST).\nPart two: Thursday 25 June (12.00 – 13.00 BST).\n\n\nPlease register for the event using an institutional email address. You will need to register for part one and part two separately using the links above. Registrations will be reviewed in line with our terms and conditions and policy on attendance at NC3Rs events (PDF). \nRegistration for each session will close 24hrs before the webinar start time.  Information submitted on the registration form will be anonymised and shared with the speakers to help inform session planning. All information submitted through the form will be deleted from our systems on Friday 26 June 2026. \nCertificates of attendance will be issued to all attendees who attend 75%+ of the session. Your certificate will be issued to the email address used to register for this webinar within two weeks of the session. \n\nSpeakers and topics\n\nPart one: Tuesday 16 June\, 12.00 – 13.00\n\nThe importance of good practice in research animal anaesthesia for improved animal welfare and reliable experimental outcomes.\nPolly Taylor\, Independent Specialist in Veterinary Anaesthesia / RAAN.\nPractical approaches to addressing common challenges in research animal anaesthesia.\nEddie Clutton\, University of Edinburgh / RAAN.\nAn introduction to the Research Animal Anaesthesia Network (RAAN) and the resources and support available.\nGabby Musk\, Murdoch University / RAAN.\n\nPart two: Thursday 25 June\, 12.00 – 13.00\n\nWhy and what to monitor during general anaesthesia.\nGabby Musk\, Murdoch University / RAAN.\nInjectable agents in laboratory animal anaesthesia.\nBryony Few\, Charles River Laboratories / RAAN.\n\n\n\n\n\nRegister
URL:https://www.dementiaresearcher.nihr.ac.uk/event/welfare-webinar-best-practice-in-research-animal-anaesthesia/
LOCATION:Online\, United Kingdom
CATEGORIES:Training
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ORGANIZER;CN="NC3Rs":MAILTO:enquiries@nc3rs.org.uk
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DTSTART;TZID=Europe/London:20260616T120000
DTEND;TZID=Europe/London:20260616T130000
DTSTAMP:20260618T232119
CREATED:20260603T160716Z
LAST-MODIFIED:20260604T103345Z
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SUMMARY:An Introduction to Alzheimer's Research UK Grants
DESCRIPTION:Navigate Alzheimer’s Research UK funding with clarity and confidence in this practical\, insight-led webinar for prospective applicants. \nIn this webinar\, hear from Seth Staley\, Senior Research Funding Manager\, and Eve Chapman\, Research Grants Manager\, as they provide an overview of our grants and share actionable tips to support your application. This is also a great opportunity to hear what’s new regarding funding at Alzheimer’s Research UK. \nIdeal for those planning an application this year or simply looking to better understand Alzheimer’s Research UK funding. \n\nRegister
URL:https://www.dementiaresearcher.nihr.ac.uk/event/an-introduction-to-alzheimers-research-uk-grants/
LOCATION:Online\, United Kingdom
CATEGORIES:Training
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260617T130000
DTEND;TZID=Europe/London:20260617T160000
DTSTAMP:20260618T232119
CREATED:20260602T152129Z
LAST-MODIFIED:20260602T152129Z
UID:10002270-1781701200-1781712000@www.dementiaresearcher.nihr.ac.uk
SUMMARY:Interpretable Machine Learning
DESCRIPTION:Interpretable Machine Learning: Visualization\, Sparse Models\, and Neural Networks – Online Course\nThis three-part lecture covers three pillars of interpretable machine learning: dimension reduction for data visualization\, sparse models for tabular data\, and interpretable neural networks for computer vision. These are essential topics for any researcher working in high-stakes machine learning applications—and genuinely useful ones. \nPart 1: Dimension Reduction for Data Visualization \nDimension reduction (DR) for data visualization provides unique insights into the structure of high-dimensional data. DR offers a bird’s-eye view of a dataset\, revealing clusters and their relationships\, manifolds\, branching patterns\, and even potential errors in the data. It is extremely effective for scientific discovery and hypothesis generation. We will discuss key elements of DR algorithms leading to the derivation of the PaCMAP algorithm\, with applications in bioinformatics\, name-ethnicity classification\, finance\, and neurology. \nPart 2: Sparse Models and Rashomon Sets for Tabular Data \nWhile the trend in machine learning has moved toward increasingly complex black-box models\, such models have shown no performance advantage for many real-world tabular datasets. For these datasets\, simpler models—sometimes small enough to fit on an index card—can be just as accurate and far easier to use. The challenge is that designing interpretable models is difficult due to the “interaction bottleneck\,” which arises when domain experts must work closely with machine learning algorithms. We’ll review two families of interpretable models—optimal sparse decision trees and sparse generalized additive models—and introduce the Rashomon set framework as a principled approach to managing the interaction bottleneck\, with examples from finance and criminal justice. \nPart 3: Interpretable Neural Networks for Computer Vision \nPrototype neural networks are among the most popular inherently interpretable architectures for computer vision and signal processing. These models make predictions by comparing parts of an input image to parts of prototypical images\, assigning a score to each comparison and summing those scores to form the final prediction. We will discuss the ProtoPNet algorithm and its extension\, ProtoConcept\, in which a cluster of images defines a “concept prototype\,” making comparisons richer and more informative. An application to ICU neurology is also included. \nThe lecture concludes with an application to computer-aided mammography\, in which an interpretable neural network led to a scientific discovery: subtle left-right asymmetries in mammograms can predict breast cancer up to five years in advance. Our AsymMirai algorithm was the fifth most-viewed paper in Radiology in 2024. \nSee also: \nCynthia Rudin\, Chaofan Chen\, Zhi Chen\, Haiyang Huang\, Lesia Semenova\, and Chudi Zhong. “Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges.” Statistics Surveys\, 2022. https://arxiv.org/abs/2103.11251 \nThis Distinguished Speaker Series seminar will consist of three hours of lecture and Q&A\, held live* via the free video-conferencing software Zoom. \n*The video recording of the seminar will be made available to registrants within 24 hours and will be accessible for four weeks thereafter. That means that you can watch all of the class content and discussion even if you cannot participate synchronously. \nClosed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish\, Korean\, and Italian. For more information\, click here. \nIf you’re looking for a comprehensive introduction to interpretable machine learning\, check out our livestream seminar Interpretable Machine Learning on June 9-12.
URL:https://www.dementiaresearcher.nihr.ac.uk/event/interpretable-machine-learning/
LOCATION:Online\, United Kingdom
CATEGORIES:Training
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DTSTART;VALUE=DATE:20260618
DTEND;VALUE=DATE:20260620
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SUMMARY:Using Conditional Transgenic Models
DESCRIPTION:This course is designed for anyone working with conditional mouse models\, including the Cre-Lox system and alternative recombinase platforms such as Flp\, Dre\, and Vika. These technologies provide researchers with the ability to manipulate gene expression in a spatially and/or temporally controlled manner\, enabling precise investigation of biological processes that cannot be interrogated using traditional constitutive knockout models. \nBy allowing genes to be selectively turned on or off in specific tissues\, cell types\, or developmental stages\, conditional systems offer powerful means to dissect the roles of individual genes within complex physiological networks. Moreover\, combining multiple recombinase systems expands the experimental possibilities even further – for example\, enabling the controlled exchange of a wild type exon for a mutant variant at a defined point in time to model disease progression with exceptional precision. \nHowever\, the intricate mechanisms underlying conditional systems make them particularly vulnerable to errors and misinterpretation. \nWhile the biology behind these approaches is elegant\, in practice there are numerous pitfalls that can compromise experimental outcomes. Many of these issues are not immediately apparent from the literature on conditional models\, nor are they always obvious within an experiment unless you know what to look for. Consequently\, researchers may unknowingly draw inaccurate conclusions\, with errors that can propagate and compromise downstream studies. \nThis course aims to equip trainees with a clear understanding of how these models function biologically\, as well as the practical ways in which they can fail. We will cover how to anticipate and control for confounding effects\, how to troubleshoot unexpected results\, and how to proceed when a model does not behave as expected. \nWho is this for? \nAnyone who is using or planning to use conditional models\, that has previous knowledge of advanced mouse genetics\, including researchers\, PhD students and colony managers. \nAfter this course\, you will be able to: \n\nUnderstand the basic principles of conditional transgenesis\nUnderstand how cre expressing and floxed alleles are produced and the potential impact on the experimental outcome\nIdentify the advantages and challenges of these systems\nAnalyse recombinase (Cre) expression\nUnderstand how to establish colonies for conditional transgenesis and the importance of background strain within this\nPlan breeding schemes with consideration of control strategies and cohort numbers\n\n\nRegister
URL:https://www.dementiaresearcher.nihr.ac.uk/event/using-conditional-transgenic-models/
LOCATION:MRC Harwell\, Becquerel Avenue\, Harwell\, Didcot\, Oxfordshire\, OX11 0RD\, United Kingdom
CATEGORIES:Training
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