Protocol for non-invasive assessment of spontaneous movements of group-housed animals using remote video monitoring.
Protocol for non-invasive assessment of spontaneous movements of group-housed animals using remote video monitoring.
复制标题
使用远程视频监测对分组饲养动物自发运动进行无创评估的方案。
DOI：
10.1016/j.xpro.2022.101326
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发表时间：
2022-06-17
期刊：
影响因子：
--
通讯作者：
Jordt, Sven-Eric
中科院分区：
文献类型：
--
作者：
Marcus, Alan David;Achanta, Satyanarayana;Jordt, Sven-Eric
对实验动物的频繁监测是确保动物福利和实验数据收集的关键。为了最大限度地减少当前监控协议和人员在场带来的不利和混乱影响，我们开发了一种低成本、非侵入性、可远程访问、可扩展的红外视频监控系统。该协议描述了系统的结构和操作，然后应用深度学习神经网络来跟踪群养的、未标记的小鼠，以进行客观的行为量化。该系统可以适应各种家庭笼养环境和物种。安全、低成本、非侵入性的远程动物红外视频监控跟踪群养的、未标记的小鼠，以量化行为并评估药效可扩展，不需要高级技术或编程技能频繁监控实验室动物对于确保动物福利和实验数据收集至关重要。为了最大限度地减少当前监控协议和人员在场带来的不利和混乱影响，我们开发了一种低成本、非侵入性、可远程访问、可扩展的红外视频监控系统。该协议描述了系统的结构和操作，然后应用深度学习神经网络来跟踪群养的、未标记的小鼠，以进行客观的行为量化。该系统可以适应各种家庭笼养环境和物种。
Frequent monitoring of laboratory animals is critical for ensuring animal welfare and experimental data collection. To minimize the adverse and confounding effects caused by current monitoring protocols and human presence, we developed a low-cost, non-invasive, remotely accessible, extensible infrared video monitoring system. This protocol describes the construction and operation of the system, followed by applying deep-learning neural networks to track group-housed, unmarked mice for objective behavioral quantification. This system can be adapted to a variety of home-cage environments and species. Secure, low-cost, non-invasive, remote infrared video monitoring of animals Tracks group-housed, unmarked mice to quantify behavior and assess drug efficacy Extensible and requires no advanced technological or programming skills Frequent monitoring of laboratory animals is critical for ensuring animal welfare and experimental data collection. To minimize the adverse and confounding effects caused by current monitoring protocols and human presence, we developed a low-cost, non-invasive, remotely accessible, extensible infrared video monitoring system. This protocol describes the construction and operation of the system, followed by applying deep-learning neural networks to track group-housed, unmarked mice for objective behavioral quantification. This system can be adapted to a variety of home-cage environments and species.
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影响因子：
48
作者：
Pereira, Talmo D.;Tabris, Nathaniel;Matsliah, Arie;Turner, David M.;Li, Junyu;Ravindranath, Shruthi;Papadoyannis, Eleni S.;Normand, Edna;Deutsch, David S.;Wang, Z. Yan;McKenzie-Smith, Grace C.;Mitelut, Catalin C.;Castro, Marielisa Diez;D'Uva, John;Kislin, Mikhail;Sanes, Dan H.;Kocher, Sarah D.;Wang, Samuel S-H;Falkner, Annegret L.;Shaevitz, Joshua W.;Murthy, Mala
通讯作者：
Murthy, Mala
影响因子：
3
作者：
Noorshams, Omid;Boyd, Jamie D.;Murphy, Timothy H.
通讯作者：
Murphy, Timothy H.
影响因子：
3.3
作者：
Filippidis, Aristotelis S.;Zarogiannis, Sotirios G.;Matalon, Sadis
通讯作者：
Matalon, Sadis
影响因子：
5.2
作者：
Achanta S;Jordt SE
通讯作者：
Jordt SE
影响因子：
3
作者：
Voikar V;Gaburro S
通讯作者：
Gaburro S