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Transitions of biological tissues between solid‐like and liquid‐like phases have been of great recent interest. Here, the first successful cell‐by‐cell evaluation of tissue viscoelastic transition is presented. An in situ micro‐mechanical perturbation is applied to a microtissue, and the resulting volumetric deformation is evaluated using 3D light‐sheet microscopy and digital image correlation (DIC), quantifying both solid‐like, well‐aligned displacement and liquid‐like swirling motion between individual cells. The viscoelastic transition of fibroblasts is crucial in fundamental physiological events, such as placentation, cancer dissemination, and wound healing. This study investigates 3D organoid systems modeling maternal‐fetal and tumor‐stroma interfaces, demonstrating established molecular and structural parallels. The analysis visualizes individual cells in stromal‐epithelial interactions and how they collectively alter tissue viscoelastic properties. It also enables in‐silico microdissection, linking single‐cell viscoelasticity with multi‐channel fluorescence. RNAseq analysis of endometrial stromal fibroblasts shows that decidualization activates mechano‐transcriptional regulators, including myocardin‐related transcription factors (MRTFs), associated with increased cellular contractility and actomyosin mobilization. Knocking down MRTFA in cancer‐associated fibroblasts in the tumor‐fibroblast co‐culture 3D model induces significant changes in fibroblast properties, mirroring those observed in the maternal‐fetal interface model, highlighting parallels between placentation and cancer invasion. This analysis confirms existing beliefs and discovers new insights broadly applicable to studying organoids, embryos, tumors, and other tissues. 

Automatic recognition of bird behavior from long-term, un controlled outdoor imagery can contribute to conservation efforts by enabling large-scale monitoring of bird populations. Current techniques in AI-based wildlife monitoring have focused on short-term tracking and monitoring birds individually rather than in species-rich flocks. We present Bird-Collect, a comprehensive benchmark dataset for monitoring dense bird flock attributes. It includes a unique collection of more than 6,000 high-resolution images of Demoiselle Cranes (Anthropoides virgo) feeding and nesting in the vicinity of Khichan region of Rajasthan. Particularly, each image contains an average of 190 individual birds, illustrating the complex dynamics of densely populated bird flocks on a scale that has not previously been studied. In addition, a total of 433 distinct pictures captured at Keoladeo National Park, Bharatpur provide a comprehensive representation of 34 distinct bird species belonging to various taxonomic groups. These images offer details into the diversity and the behaviour of birds in vital natural ecosystem along the migratory flyways. Additionally, we provide a set of 2,500 point-annotated samples which serve as ground truth for benchmarking various computer vision tasks like crowd counting, density estimation, segmentation, and species classification. The benchmark performance for these tasks highlight the need for tailored approaches for specific wildlife applications, which include varied conditions including views, illumination, and resolutions. With around 46.2 GBs in size encompassing data collected from two distinct nesting ground sets, it is the largest birds dataset containing detailed annotations, showcasing a substantial leap in bird research possibilities. We intend to publicly release the dataset to the research community. The database is available at: https://iab-rubric.org/resources/wildlife-dataset/birdcollect