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| http://dx.doi.org/10.1038/s41587-020-0546-8 | DOI Listing |
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Distinct Visual Processing Patterns in Female Elite Athletes: A Comparative Study of Gymnastics, Soccer, and Esports Using Visual P300 Event-Related Potentials.
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College of Sports Science and Technology, Mahidol University, Salaya, Nakhonpathom, THAILAND.
Visual processing is crucial for sports performance, influencing athletes' ability to interpret and respond to visual stimuli. This study investigated distinct visual processing patterns among Thai elite athletes in gymnastics, soccer, and esports, utilizing visual P300 event-related potentials (P300 ERPs). Forty-two female athletes (14 gymnasts, 14 soccer players, and 14 esports athletes) participated.
View Article and Find Full Text PDFSingle-cell sequencing in diabetic retinopathy: progress and prospects.
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Department of Ophthalmology, Renmin Hospital of Wuhan University, Jiefang Road, Wuhan, Hubei, 430060, China.
Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications.
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The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models.
View Article and Find Full Text PDFVideo-based robotic surgical action recognition and skills assessment on porcine models using deep learning.
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Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
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With the increasing availability of high-quality genome assemblies, pangenome graphs emerged as a new paradigm in the genomics field for identifying, encoding, and presenting genomic variation at both population and species levels. However, it remains challenging to truly dissect and interpret pangenome graphs via biologically informative visualization. To facilitate better exploration and understanding of pangenome graphs towards novel biological insights, here we present a web-based interactive Visualization and interpretation framework for linear-Reference-projected Pangenome Graphs (VRPG).
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