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Cheng Qian on Muck Rack

Cheng Qian

Las Vegas, Los Angeles
As seen in: AIChE Journal, Nature, Wiley Online Library, PLOS, Frontiers in Immunology, Future Internet, Journal of Marine Science and Engineering, MDPI, Omega, SAGE Publishing and

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Articles

Geometrically driven reversible solid-liquid phase transition at the atomic scale

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By Peter Orchard, Yeryun Cheon, Alex C. Sternisha, Wenjun Cui, Cheng Qian, Weixiao Lin, Zefan Xue, Zhencui Ge, Wen Zhao, Gustaaf Van Tendeloo, Jinsong Wu, Feng Hua Ding, Xiahan Sang, Zhengyi Fu
| Science Magazine Verified
Editor’s summary Abstract Supplementary Materials References and Notes Information & Authors Metrics & Citations Check Access References Figures Tables Media Share Editor’s summary Controlling how materials transition between liquid and solid states is fundamental to materials science, but the atomic-scale mechanisms remain poorly understood, especially in confined spaces where surfaces influence nucleation. Cui et al.
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Age-associated impairment of humoral and cellular immune responses to SARS-CoV-2 in a large community cohort with hybrid immunity

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By Jinqiu Ran, Liguo Zhu, Mingzhe Ning, Wanying Zhang, Wen Zhang, Pei Liu, Shengxia Yin, Juan Zhang, Yuanfang Qin, Ye Wei, Qun Cai, Chentao Guo, Cheng Qian, Jianfei Gao, Fang Zhang, Xin Tong, Chao Wu, Rui Huang, Yuxin Chen, Jinjin Chu, Wenwen Pu
| Journal of Infection Verified
Keywords Aging SARS-CoV-2 hybrid immunity memory B cells circulating T follicular helper cells TEMRA cells CD8⁺ T cells Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has transitioned from a pandemic threat to an endemic respiratory pathogen, with global immunity now shaped by repeated vaccination, natural infection, or their combination.
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We pre-registered, ran, and verified the macro ablation: information per joule, measured

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By Cheng Qian
| DEV Community
Maker disclosure: I build Macrokit (Apache-2.0, fully open). This is the data, not a pitch — links and the raw runs at the end. The multi-model benchmark answered: can off-the-shelf local models do real GitHub-maintainer work? (Yes — four of them, 74–82.5% on a pre-registered 100-task corpus.) It didn't answer the more interesting question: why is moving the reasoning to design-time the efficient move, not just a trick? So we ran a direct test — the macro ablation. Pre-registered and frozen.
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