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InteBOMB:Integrating generic object tracking and segmentation with pose estimation for animal behavior analysis
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作者 Hao Zhai Hai-Yang Yan +5 位作者 Jing-Yuan Zhou Jing Liu Qi-Wei Xie Li-Jun Shen Xi Chen Hua Han 《Zoological Research》 2025年第2期355-369,共15页
Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-b... Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis. 展开更多
关键词 Generic object tracking Pose estimation Behavior analysis Background subtraction Online learning selective labeling Joint latent space
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Femoral nerve regeneration and its accuracy under different injury mechanisms 被引量:1
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作者 Aikeremujiang.Muheremu Qiang Ao +2 位作者 Yu Wang Peng Cao Jiang Peng 《Neural Regeneration Research》 SCIE CAS CSCD 2015年第10期1669-1673,共5页
Surgical accuracy has greatly improved with the advent of microsurgical techniques. However, complete functional recovery after peripheral nerve injury has not been achieved to date. The mechanisms hindering accurate ... Surgical accuracy has greatly improved with the advent of microsurgical techniques. However, complete functional recovery after peripheral nerve injury has not been achieved to date. The mechanisms hindering accurate regeneration of damaged axons after peripheral nerve injury are in urgent need of exploration. The present study was designed to explore the mechanisms of peripheral nerve regeneration after different types of injury. Femoral nerves of rats were injured by crushing or freezing. At 2, 3, 6, and 12 weeks after injury, axons were retrogradely labeled using 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate(Dil) and True Blue, and motor and sensory axons that had regenerated at the site of injury were counted. The number and percentage of Dil-labeled neurons in the anterior horn of the spinal cord increased over time. No significant differences were found in the number of labeled neurons between the freeze and crush injury groups at any time point. Our results confirmed that the accuracy of peripheral nerve regeneration increased with time, after both crush and freeze injury, and indicated that axonal regeneration accuracy was still satisfactory after freezing, despite the prolonged damage. 展开更多
关键词 nerve regeneration peripheral nerve injury chemotactic regeneration retrograde labeling selective nerve regeneration functional recovery NSFC grant neural regeneration
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