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Ying Shen

As seen in: Nature, Wiley Online Library, arXiv, PLOS, BMJ Open, Cisco Blogs, Frontiers, Frontiers in Immunology, Frontiers in Public Health, Gut Journal and
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Articles

Metabolic control of smooth muscle cell phenotype switching in atherosclerosis

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By Mads Peter Heide-Jørgensen, Fangbing Chen, Jeric Da-Anoy, Xiaolong Zhu, Hosung Bae, Jiasheng Zhang, Yanming Li, Pei-Yu Chen, Ying Shen, George Tellides, Nathaniel Snyder, Cholsoon Jang, Martin Schwartz, Zoltan Arany, Michael A Simons, Rong-Mo Zhang
| Science Magazine Verified
Abstract INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS Acknowledgments Supplementary Materials REFERENCES Information & Authors Metrics & Citations View Options References Figures Tables Media Share This article has 0 eLetter . Abstract The loss of smooth muscle cell (SMC) contractile phenotype contributes to various diseases including atherosclerosis. However, its metabolic basis is not entirely elucidated.
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Association Between Cardiovascular Health and Pelvic Inflammatory Disease among US Adults: A Cross-Sectional Study From NHANES 2013-2023

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By Guiyue Wang, Ying Shen, Hong Tang
| medRxiv
The authors have declared no competing interest. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study utilized de-identified, publicly available data from the National Health and Nutrition Examination Survey (NHANES).
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Computer Science > Machine Learning

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By Zhenqing Ling, Yiyang Zhao, Ying Shen
| arXiv
Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or misspecified rewards. We identify a failure mode we call directional inconsistency: within a batch, a small set of high-reward rollouts induces representation-space preference directions that sharply disagree with the batch majority, resulting in high-variance and destabilizing updates.
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