Tricorder Array Technologies Animal Welfare Sensor

项目总结 Tricorder阵列技术有限责任公司(Tricorder)将与加州大学合作开发 阿拉巴马州伯明翰分校(UAB)，SIDECARE™(三级阵列技术动物福利公司 传感器)。SIDECARE™将在笼子层面上改善大规模的研究动物护理 通过自动收集相关数据来增强一致性和 动物模型的重复性。SIDECARe™将彻底改变对健康状况的监控 通过第一个对小鼠超声波发声(MUSV)进行分类而成为小鼠的。在这 第二阶段应用，AIM 1完成了连续MUSV监测的集成 解决方案集成到SIDECARe™设备中。AIM 2扩展SIDECARe™人工智能(AI) 支持分类功能，以涵盖导致研究中断的主要原因。目标3 通过在UAB Varium工作流程中进行测试版部署，优化SIDECARe™系统。最后， AIM 4在外部测试地点部署Beta系统进行售前测试。三分音器的创新使用 使传感器阵列能够拾取老鼠的发声并使用它们 解读笼子里的健康和社交活动。 SIDECARe™利用分布式方法部署所需的AI算法 有效地分割和解释多个USVs。当前人工智能算法的重点是 SIDECARe™是检测条件，如老鼠幼崽在笼子里，笼子被水淹，打架 动物和溃疡性皮炎。MUSV数据的连续采集 然而，笼子的数量代表了未来可用的大量未开发数据 开发新的算法和对老鼠行为和幸福感的新理解。的 对于SIDECARE™系统来说，特别重要的是集成连续的机器学习 算法，不断进化以更好地识别其他因素和趋势 导致发病率和死亡率上升。记录的特定环境的存在 条件(笼子照明水平、温度变化、湿度、环境噪声水平) 与鼠标行为相关，有助于研究研究成果和/或 再现性。

PROJECT SUMMARY Tricorder Array Technologies, LLC (Tricorder), will develop, in partnership with the University of Alabama at Birmingham (UAB), SIDECARe™ (Tricorder Array Technologies Animal Welfare Sensor). SIDECARe™ is set to improve research animal care at the cage level on a massive cost-effective scale by automating the collection of relevant data to enhance consistency and reproducibility of animal models. SIDECARe™ will revolutionize monitoring the health and well- being of mice by being the first to categorize mouse ultrasonic vocalizations (mUSV). In this Phase-II application, aim 1 completes the integration of the continuous mUSV monitoring solution into SIDECARe™ devices. Aim 2 expands SIDECARe™ Artificial Intelligence (AI) enabled classification capabilities to cover the top causes of research disruption. Aim 3 optimizes the SIDECARe™ system via beta deployments in the UAB vivarium workflow. Finally, aim 4 deploys beta systems at external test sites for pre-sales trials. Tricorder’s innovative use of ultrasonic sensors allows the sensor array to pick up mouse vocalizations and use them to interpret health and social events in the cage. SIDECARe™ utilizes a distributed approach to deployment of the AI algorithms required for effective segmentation and interpretation of mUSVs. The focus of the current AI algorithms for SIDECARe™ is to detect conditions such as mouse pups in a cage, flooded cages, fighting animals, and ulcerative dermatitis. The continuous acquisition of mUSV data across increasing numbers of cages, however, represents a wealth of unexplored data available for future development of novel algorithms and new understandings of mouse behavior and well-being. Of particular importance to the SIDECARe™ system is to integrate continuous machine-learning algorithms, which constantly evolve to better identify additional factors and trends that contribute to increased rates of morbidity and mortality. The recorded presence of specific environmental conditions (cage lighting levels, temperature variations, humidity, ambient noise levels) correlated with mouse behaviors are useful for studying research outcomes and/or reproducibility.