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Xin Zhou

Researcher at St. Jude Children's Research Hospital

Publications -  262
Citations -  17637

Xin Zhou is an academic researcher from St. Jude Children's Research Hospital. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 36, co-authored 71 publications receiving 13444 citations. Previous affiliations of Xin Zhou include Rice University & Washington University in St. Louis.

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ACSL4 promotes microglia-mediated neuroinflammation by regulating lipid metabolism and VGLL4 expression

TL;DR: Acyl-CoA synthetase long-chain family member 4 (ACSL4) is an important isozyme in polyunsaturated fatty acid (PUFA) metabolism as mentioned in this paper .
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Dimerization of sub-nanoscale molecular clusters affords broadly tuneable viscoelasticity above the glass transition temperature

TL;DR: In this paper , the authors showed that polyhedral oligomeric silsesquioxane (POSS) is a glassy material with glass transition temperatures (Tgs) lower than room temperature, and that its viscoelasticity can be tuned through the simple tailoring of the linker structures above their Tgs.
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Data Access and Interactive Visualization of Whole Genome Sequence of Sickle Cell Patients within the St. Jude Cloud

TL;DR: The St. Jude Cloud is expanded to sickle cell disease data through the Sickle Genome Project (SGP) Data Portal to allow instantaneous raw data access (following data access committee approval), as well as visualization of genotype calls at individual level in a novel genome.
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Modeling Small-Granularity Expressway Traffic Volumes With Quantum Walks

TL;DR: A small granularity simulation model named Small-Granularity Expressway Traffic Volumes with Quantum Walks (SGETV-QW), which adopts quantum walks to generate probability patterns of the exiting time of drivers from the expressway improves the simulation accuracy at small granularities.
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Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

TL;DR: In this article , a dual latent variable enhancement is proposed to improve the performance of bidirectional image rescaling models, which can improve image upscaling accuracy consistently without sacrificing image quality in downscaled LR images.