• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

小鼠与伙伴：使用跨笼单模型对群体中小鼠行为进行自动分类与建模

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages.

作者信息

Camilleri Michael P J, Bains Rasneer S, Williams Christopher K I

机构信息

School of Informatics, University of Edinburgh, Edinburgh, UK.

Mary Lyon Centre, MRC Harwell, Oxfordshire, UK.

出版信息

Int J Comput Vis. 2024;132(12):5491-5513. doi: 10.1007/s11263-024-02118-3. Epub 2024 Jun 17.

DOI:10.1007/s11263-024-02118-3
PMID:39554493
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11568001/
Abstract

UNLABELLED

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual's behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11263-024-02118-3.

摘要

未标注

行为实验通常在专门的场地进行，但这可能会混淆分析结果。为解决此问题，我们提供了在家笼环境中研究小鼠的工具，使生物学家能够在最少人为干预的情况下捕捉个体行为的时间维度，并对同笼伙伴之间的相互作用和相互依存关系进行建模。我们的主要贡献是新颖的全局行为模型（GBM），该模型使用置换矩阵将每个笼子中小鼠的身份与模型进行匹配，从而总结跨笼子小鼠群体的联合行为。为支持上述内容，我们还（a）开发了活动标记模块（ALM）以从视频中自动分类小鼠行为，以及（b）发布了两个数据集，即用于训练行为分类器的ABODe和用于行为建模的IMADGE。

补充信息

在线版本包含可在10.1007/s11263-024-02118-3获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/4890e0b3227e/11263_2024_2118_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/999572f26fc9/11263_2024_2118_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/4810e6eab359/11263_2024_2118_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/ccce38a72e12/11263_2024_2118_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/f36c007933e7/11263_2024_2118_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/907a076363bf/11263_2024_2118_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/1f3adedb6d87/11263_2024_2118_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/d000f008ad9c/11263_2024_2118_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/3c585ba99759/11263_2024_2118_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/173cc5add04b/11263_2024_2118_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/eb69350d724a/11263_2024_2118_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/3020a077f134/11263_2024_2118_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/cfbef415f937/11263_2024_2118_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/f236e5470ab0/11263_2024_2118_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/4890e0b3227e/11263_2024_2118_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/999572f26fc9/11263_2024_2118_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/4810e6eab359/11263_2024_2118_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/ccce38a72e12/11263_2024_2118_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/f36c007933e7/11263_2024_2118_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/907a076363bf/11263_2024_2118_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/1f3adedb6d87/11263_2024_2118_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/d000f008ad9c/11263_2024_2118_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/3c585ba99759/11263_2024_2118_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/173cc5add04b/11263_2024_2118_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/eb69350d724a/11263_2024_2118_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/3020a077f134/11263_2024_2118_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/cfbef415f937/11263_2024_2118_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/f236e5470ab0/11263_2024_2118_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92a6/11568001/4890e0b3227e/11263_2024_2118_Fig14_HTML.jpg

相似文献

1
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages.小鼠与伙伴：使用跨笼单模型对群体中小鼠行为进行自动分类与建模
Int J Comput Vis. 2024;132(12):5491-5513. doi: 10.1007/s11263-024-02118-3. Epub 2024 Jun 17.
2
Behavioural characterization of C57BL/6N and BALB/c female mice in social home cage - Effect of mixed housing in complex environment.C57BL/6N和BALB/c雌性小鼠在社交笼舍中的行为特征——复杂环境下混养的影响
Physiol Behav. 2018 May 1;188:32-41. doi: 10.1016/j.physbeh.2018.01.024. Epub 2018 Jan 31.
3
Evaluation of the effects of cage height and stocking density on the behaviour of laying hens in furnished cages.评估笼具高度和饲养密度对配备栖架蛋鸡行为的影响。
Br Poult Sci. 2007 Feb;48(1):1-11. doi: 10.1080/00071660601156479.
4
Low-cost solution for rodent home-cage behaviour monitoring.低成本的啮齿动物笼内行为监测解决方案。
PLoS One. 2019 Aug 2;14(8):e0220751. doi: 10.1371/journal.pone.0220751. eCollection 2019.
5
Mice Recognise Mice in Neighbouring Rearing Cages and Change Their Social Behaviour.老鼠能识别相邻饲养笼中的老鼠，并改变其社交行为。
Behav Neurol. 2024 Jan 16;2024:9215607. doi: 10.1155/2024/9215607. eCollection 2024.
6
The effect of preweaning and postweaning housing on the behaviour of the laboratory mouse (Mus musculus).断奶前和断奶后的饲养环境对实验小鼠（小家鼠）行为的影响。
Lab Anim. 2007 Jan;41(1):92-102. doi: 10.1258/002367707779399482.
7
Automated home-cage behavioural phenotyping of mice.自动化小鼠笼内行为表型分析。
Nat Commun. 2010 Sep 7;1:68. doi: 10.1038/ncomms1064.
8
Altered exploratory behaviour and increased food intake in the spiny mouse before menstruation: a unique pre-clinical model for examining premenstrual syndrome.经前期综合征的独特临床前模型：在月经前，棘鼠的探索行为改变和食物摄入量增加。
Hum Reprod. 2019 Feb 1;34(2):308-322. doi: 10.1093/humrep/dey360.
9
AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data.AHCODA-DB：一个带有基于网络挖掘工具的数据存储库，用于分析自动化高内涵小鼠表型组学数据。
BMC Bioinformatics. 2017 Apr 4;18(1):200. doi: 10.1186/s12859-017-1612-1.
10
Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools.使用自动化笼内分析工具评估小鼠在光照/黑暗周期中的行为。
J Neurosci Methods. 2018 Apr 15;300:37-47. doi: 10.1016/j.jneumeth.2017.04.014. Epub 2017 Apr 26.

本文引用的文献

1
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions.多智能体行为数据集：小鼠二元社交互动
Adv Neural Inf Process Syst. 2021 Dec;2021(DB1):1-15.
2
Persistent animal identification leveraging non-visual markers.利用非视觉标记的持久动物识别
Mach Vis Appl. 2023;34(4):68. doi: 10.1007/s00138-023-01414-1. Epub 2023 Jul 13.
3
Longitudinal home-cage automated assessment of climbing behavior shows sexual dimorphism and aging-related decrease in C57BL/6J healthy mice and allows early detection of motor impairment in the N171-82Q mouse model of Huntington's disease.
对C57BL/6J健康小鼠攀爬行为的纵向笼内自动评估显示出性别差异以及与衰老相关的下降，并能在亨廷顿舞蹈病的N171-82Q小鼠模型中早期检测到运动障碍。
Front Behav Neurosci. 2023 Mar 22;17:1148172. doi: 10.3389/fnbeh.2023.1148172. eCollection 2023.
4
Inference and Learning for Generative Capsule Models.生成式胶囊模型的推理与学习
Neural Comput. 2023 Mar 18;35(4):727-761. doi: 10.1162/neco_a_01564.
5
Multi-animal pose estimation, identification and tracking with DeepLabCut.多动物姿态估计、识别和跟踪的 DeepLabCut 方法
Nat Methods. 2022 Apr;19(4):496-504. doi: 10.1038/s41592-022-01443-0. Epub 2022 Apr 12.
6
Detecting and Tracking of Multiple Mice Using Part Proposal Networks.基于部分提议网络的多只老鼠检测与跟踪。
IEEE Trans Neural Netw Learn Syst. 2023 Dec;34(12):9806-9820. doi: 10.1109/TNNLS.2022.3160800. Epub 2023 Nov 30.
7
The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice.鼠标动作识别系统（MARS）软件流水线，用于自动分析小鼠的社交行为。
Elife. 2021 Nov 30;10:e63720. doi: 10.7554/eLife.63720.
8
Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model.基于深度图形模型的多视图鼠标社交行为识别
IEEE Trans Image Process. 2021;30:5490-5504. doi: 10.1109/TIP.2021.3083079. Epub 2021 Jun 11.
9
Action detection using a neural network elucidates the genetics of mouse grooming behavior.使用神经网络进行动作检测揭示了小鼠梳理行为的遗传学机制。
Elife. 2021 Mar 17;10:e63207. doi: 10.7554/eLife.63207.
10
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation.通过零参考深度曲线估计学习增强低光图像
IEEE Trans Pattern Anal Mach Intell. 2022 Aug;44(8):4225-4238. doi: 10.1109/TPAMI.2021.3063604. Epub 2022 Jul 1.