XueBin Zhang on Muck Rack

XueBin Zhang

Hobart, Winterville
As seen in: Color Research & Application, Nature, Wiley, Wiley Online Library, Journal of Automated Methods and Management in Chemistry, MDPI, PNAS, Taylor & Francis Online, The Conversation, Phys.org and
Ocean & Climate Scientist working @CSIRO, with interests in sea-level rise, climate change & variability, ocean/climate modelling. @UW Alum. Views are my own.
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Articles

Projections of Heat Stress and its Impact on Labour Capacity in North America

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By Guilong Li, XueBin Zhang, Megan C. Kirchmeier-Young
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Global warming intensifies heat stress, which can exceed the human body's coping capacity, negatively impacting health and reducing outdoor labour productivity.
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Machine learning helps to strongly reduce future warming uncertainty - Nature Communications

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By Junhao Wu, Zihang Wang, Francis Zwiers, XueBin Zhang, Xi Chen
| Nature
Abstract The observed warming of global mean surface temperature has been used to reduce uncertainty in future climate change and impact projections, but the information embedded in the spatial pattern of warming remains largely untapped. Here, we use machine learning to uncover spatially resolved emergent constraint relationships between 1971-2020 warming trends at individual grid cells and future global mean warming in a large collection of climate model simulations.
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TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis - Nature Communications

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By Xiao Wang, Chao Yun Jiang, Caroline Johnson, Peng Gao, XueBin Zhang
| Nature
Abstract Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations.
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