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

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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.