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Browsing by Author "Wang, Yuxing"

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    An acoustic micro-transmitter enabling tracking of sensitive aquatic species in riverine and estuarine environments
    Deng, Zhiqun Daniel; Li, Huidong; Lu, Jun; Xiao, Jie; Myjak, Mitchell J.; Martinez, Jayson J.; Wang, Yuxing; Zhang, Jiguang (2021-05-19)
    Conservation of aquatic species requires in-depth understanding of their movement and behavior and their interactions with man-made hydraulic structures. Acoustic telemetry is a primary method to remotely track in 3 dimensions (3D) aquatic animals implanted with transmitters. The transmitter's weight and size are the major limiting factors because the transmitter should not affect the animals' natural behavior. Here, we present an acoustic micro-transmitter that weighs 0.08 g in air, only 1/3 that of existing technologies. The transmitter offers a source level of 148 dB (reference: 1 mu Pa at 1 m) and a service life of 30 days at a 5-s transmission rate. Nearly 100% of tagged fish were detected in field studies, demonstrating the viability of this technology for studying species of early life stages. Information resulting from the use of this technology provides valuable insight for ecological and environmental policy making and resource management worldwide.
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    A systematic evaluation of computation methods for cell segmentation
    Wang, Yuxing; Zhao, Junhan; Xu, Hongye; Han, Cheng; Tao, Zhiqiang; Zhao, Dongfang; Zhou, Dawei; Tong, Gang; Liu, Dongfang; Ji, Zhicheng (Cold Spring Harbor Laboratory, 2024-01-31)
    Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.