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

项目总结/摘要 斑马鱼参与的研究项目在2019年总共获得了NIH 5亿美元的资金， 在当今的医学研究中，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 鱼类研究是至关重要的，因为实验数据的质量和稳健性取决于它。 斑马鱼饲养系统是至关重要的，因为目前最先进的非侵入式系统只 跟踪水质措施。没有任何商业化的畜牧业系统采用自动监测鱼类 行为使用视频，尽管事实上，大多数因素在评估鱼类健康和福祉，如 饥饿、焦虑或光线变化，都会产生众所周知的行为特征， 可以检测。因为许多这样的签名需要高的时间分辨率，在 由于需要处理大量数据，视频监控受到了阻碍。 在这里，所提出的努力旨在建立一个概念验证系统，以视频监控10个定制的网络。 坦克，代号为“金丝雀坦克”，载有哨兵斑马鱼种群的战略位置在机架的 现有的渔业设施。每个坦克将配备一个定制的照明系统和一个摄像头，将使用 独创的“剩余成像”模式，可捕获和处理比标准低几个数量级的数据 视频，同时保留瞬态，毫秒级的运动签名。对鱼类健康的威胁， 将通过运动的真实的时间分析来检测 CanaryTanks捕获的图像流中的签名，并将通过浏览器远程访问 通过我们基于云的软件基础设施。第一个目的是证明这样一个系统可以产生 及时准确地每日报告鱼的活动和睡眠（活动测定法），喂食时间和房间的变化 在一个实施例中，所述照明仅基于处理由所述照明设备连续捕获的图像中的行为数据来实现。 金丝雀坦克第二个目的是展示系统生成实时警报通知的能力 使用自定义算法自动检测一系列斑马鱼行为特征。一系列的测试， 包括光周期中断、暴露于噪声/振动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技术将为 标准，并提供革命性的，无处不在的水箱记录功能，可以 用于收集实验数据。

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.