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  1. Free, publicly-accessible full text available April 1, 2025

    The zebrafish is a widely used model organism for biomedical research due to its ease of maintenance, external fertilization of embryos, rapid embryonic development, and availability of established genetic tools. One notable advantage of using zebrafish is the transparency of the embryos, which enables high-resolution imaging of specific cells, tissues, and structures through the use of transgenic and knock-in lines. However, as the embryo develops, multiple layers of tissue wrap around the lipid-enriched yolk, which can create a challenge to image tissues located deep within the embryo. While various methods are available, such as two-photon imaging, cryosectioning, vibratome sectioning, and micro-surgery, each of these has limitations. In this study, we present a novel deyolking method that allows for high-quality imaging of tissues that are obscured by other tissues and the yolk. Embryos are lightly fixed in 1% PFA to remove the yolk without damaging embryonic tissues and are then refixed in 4% PFA and mounted on custom-made bridged slides. This method offers a simple way to prepare imaging samples that can be subjected to further preparation, such as immunostaining. Furthermore, the bridged slides described in this study can be used for imaging tissue and organ preparations from various model organisms.

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    Free, publicly-accessible full text available July 15, 2024
  3. Pervasive deployment of surveillance cameras today poses enormous scalability challenges to video analytics systems operating over many camera feeds. Currently, there are few indexing tools to organize video feeds beyond what is provided by a standard file system. Recent video analytic systems implement application-specific frame profiling and sampling techniques to reduce the number of raw videos processed, leveraging frame-level redundancy or manually labeled spatial-temporal correlation between cameras. This paper presents Video-zilla, a standalone indexing layer between video query systems and a video store to organize video data. We propose a video data unit abstraction, semantic video stream (SVS), based on a notion of distance between objects in the video. SVS implicitly captures scenes, which is missing from current video content characterization and a middle ground between individual frames and an entire camera feed. We then build a hierarchical index that exposes the semantic similarity both within and across camera feeds, such that Video-zilla can quickly cluster video feeds based on their content semantics without manual labeling. We implement and evaluate Video-zilla in three use cases: object identification queries, clustering for training specialized DNNs, and archival services. In all three cases, Video-zilla reduces the time complexity of inter-camera video analytics from linear with the number of cameras to sublinear, and reduces query resource usage by up to 14x compared to using frame-level or spatial-temporal similarity built into existing query systems. 
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