canaryTank: an in-tank husbandry behavior tracking system for health of fish facilities

项目摘要/摘要 斑马鱼参与的研究项目在2019年总计5亿美元的NIH资金，是速度最快的 在今天的医学研究中，人类疾病的动物模型越来越多。因此，医疗人员的健康和福祉 研究FISH是至关重要的，因为实验数据的质量和稳健性取决于它。创新 斑马鱼养殖系统是至关重要的，因为目前最先进的非侵入性系统 跟踪水质指标。没有一个商业养殖业系统采用自动监测鱼类 使用视频的行为，尽管在评估鱼类健康和福祉方面大多数关键因素，如 饥饿、焦虑或光线变化会产生众所周知的行为特征，计算机视觉系统 可以检测到。因为许多这样的签名需要高时间分辨率，所以在 在逃生行动中，由于需要处理大量数据，视频监控一直受到阻碍。 在这里，拟议的工作旨在构建一个概念验证系统，以视频监控10个客户的网络 代号为“CanaryTanks”的坦克，在战略上将斑马鱼种群置于 现有的养鱼设施。每个坦克将配备一个定制照明系统和一个摄像头，将使用 捕获和处理比标准数据少几个数量级的原创“剩余成像”范例 视频，同时保留瞬间、毫秒级的运动签名。对鱼类健康的威胁--没有一个 被当前的商业系统跟踪-将通过运动的实时分析来检测 CanaryTank捕获的图像流中的签名，可通过浏览器远程访问 通过我们基于云的软件基础设施。第一个目标是证明这样的系统可以产生 及时、准确地每日报告鱼类的活动和睡眠(活动测量)、摄食时间和房间变化 仅基于处理由 金丝雀丹克斯。第二个目标是演示系统生成实时警报通知的能力 使用可自动检测一系列斑马鱼行为特征的定制算法。一系列测试， 包括破坏光周期、暴露在噪音/振动中以及逐渐增加到超出范围 温度，将验证这一概念验证。未来的愿景是CanaryTank技术设置了新的 标准，并提供革命性的、无处不在的水箱内记录功能，可以 利用畜牧业以外的杠杆收集实验数据。

Project Summary/Abstract Zebrafish, involved in research projects totalling half a billion dollars of NIH funding in 2019, are the fastest growing animal model of human disease in medical research today. Thus, the health and wellbeing of medical research fish is paramount as the quality and robustness of experimental data depends on it. Innovation in zebrafish husbandry systems is crucially needed because current state-of-the-art non intrusive systems only track water quality measures. No commercial husbandry system has adopted automatic monitoring of fish behavior using video, despite the fact that most factors crucial in assessing fish health and well being, such as hunger, anxiety, or light changes, produce well known behavioral signatures that a computer vision system could detect. Because many such signatures require high temporal resolution, millisecond in the case of escape movements, video monitoring has been hampered by the need to process enormous data volumes. Here, the proposed effort aims at building a proof-of-concept system to video monitor a network of 10 custom tanks, codenamed “canaryTanks”, containing sentinel zebrafish populations strategically located in the racks of an existing fish facility. Each tank will be equipped with a custom illumination system and a camera that will use an original "Remanent Imaging" paradigm to capture and process orders of magnitude less data than standard video while preserving transient, millisecond-scale motion signatures. Threats to fish health—none of which are tracked by current commercial systems—will be detected through the real time analysis of motion signatures in the image streams captured by the canaryTanks and will be remotely accessible via browser through our cloud-based software infrastructure. The first aim is to show that such a system can generate timely and accurate daily reports of fish activity and sleep (actimetry), feeding times, and changes in room illumination based solely on processing the behavioral data in the images captured continuously by the canaryTanks. The second aim is to demonstrate the system’s ability to generate real-time alert notifications using custom algorithms that automatically detect a range of zebrafish behavioral signatures. A battery of tests, including disruption of the light cycle, exposure to noise/vibration and gradual increase to out-of-range temperatures, will validate this proof-of-concept. The future vision is that the canaryTank technology sets new standards in fish husbandry and provides revolutionary, ubiquitous in-tank recording capabilities that can be leveraged beyond husbandry for collection of experimental data.