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2篇 您的检索式：作者名="Zherui Han"

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		题名	作者	年代	出处	被引量
	1	MEF2C promotes M1 macrophage polarization and Th1 responses显示文摘The polarization of macrophages to the M1 or M2 phenotype has a pivotal role in inflammation and host defense;however,the underlying molecular mechanism remains unclear.Here,we show that myocyte enhancer factor 2 C(MEF2C)is essential for regulating M1 macrophage polarization in response to infection and inflammation.Global gene expression analysis demonstrated that MEF2C deficiency in macrophages downregulated the expression of M1 phenotypic markers and upregulated the expression of M2 phenotypic markers.MEF2C significantly promoted the expression of interleukin-12 p35 subunit(Il12a)and interleukin-12 p40 subunit(Il12b).Myeloid-specific Mef2c-knockout mice showed reduced IL-12 production and impaired Th1 responses,which led to susceptibility to Listeria monocytogenes infection and protected against DSS-induced IBD in vivo.Mechanistically,we showed that MEF2C directly activated the transcription of Il12a and Il12b.These findings reveal a new function of MEF2C in macrophage polarization and Th1 responses and identify MEF2C as a potential target for therapeutic intervention in inflammatory and autoimmune diseases.	Xibao Zhao Qianqian Di Han Liu Jiazheng Quan Jing Ling Zizhao Zhao Yue Xiao Han Wu Zherui Wu Wengang Song Huazhang An Weilin Chen	2022	Cellular & Molecular Immunology2022，19，4：	4
	2	Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity显示文摘Lattice thermal conductivity is important for many applications,but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable.Machine learning approaches that can achieve similar accuracy have been a long-standing open question.Despite recent progress,machine learning models using structural information as descriptors fall short of experimental or first principles accuracy.This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy.The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance.Transfer learning between different orders of phonon scattering can further improve the model performance.Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.	Ziqi Guo Prabudhya Roy Chowdhury Zherui Han Yixuan Sun Dudong Feng Guang Lin Xiulin Ruan	2023	npj Computational Materials2023，，1：	0

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