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您的检索式：作者名="Mingfeng Zhan"
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| 1 | The prognostic value of combined NT-pro-BNP levels and NIHSS scores in pa- tients with acute ischemic stroke显示文摘 | Xingyong Chen Xu Zhan Mingfeng Chen | 2012 | Internal medicine (Tokoy Ja- pan)2012，51，10： | 1 |
| 2 | Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects显示文摘Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects.This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic(2014),the stable balance weighting procedure by Zubizarreta(2015),the calibration balance weighting approach by Chan,et al.(2016),and the integrated propensity score technique by Sant’Anna,et al.(2020).Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods.Simulation results show that in general,the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes.Finally,the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income. | ZHAN Mingfeng FANG Ying LIN Ming | 2022 | Journal of Systems Science & Complexity2022，35，6： | 0 |
| 3 | Near-threshold-voltage operation in flash-based high-precision computing-in-memory to implement Poisson image editing显示文摘We propose a NOR flash-based computing-in-memory(CIM)to implement high-precision(32-bit)Poisson image editing,including the gradient operations and Laplace operation.To meet the requirements of image processing,CIM operation schemes and reliabilities are carefully studied and optimized,showing that the power consumption at the near-threshold-voltage(NTV)region can be as low as 40 fJ/bit,which is two orders lower than working at the saturation region.High-temperature retention,as well as various disturbs,are also analyzed.The proposed CIM scheme can be applied as an energy-efficient approach to construct the high-precision image processing accelerator. | Yang FENG Bing CHEN Mingfeng TANG Yueran QI Maoying BAI Chengcheng WANG Hai WANG Xuepeng ZHAN Junyu ZHANG Jing LIU Jixuan WU Jiezhi CHEN | 2023 | Science China(Information Sciences)2023，66，12： | 0 |
| 4 | The S100 calcium binding protein A11 promotes liver fibrogenesis by targeting TGF-βsignaling显示文摘Liver fibrosis is a key transformation stage and also a reversible pathological process in various types of chronic liver diseases.However,the pathogenesis of liver fibrosis still remains elusive.Here,we report that the calcium binding protein A11(S100A11)is consistently upregulated in the integrated data from GSE liver fibrosis and tree shrew liver proteomics.S100A11 is also experimentally activated in liver fibrosis in mouse,rat,tree shrew,and human with liver fibrosis.While overexpression of S100A11 in vivo and in vitro exacerbates liver fibrosis,the inhibition of S100A11 improves liver fibrosis.Mechanistically,S100A11 activates hepatic stellate cells(HSCs)and the fibrogenesis process via the regulation of the deacetylation of Smad3 in the TGF-βsignaling pathway.S100A11 physically interacts with SIRT6,a deacetylase of Smad2/3,which may competitively inhibit the interaction between SIRT6 and Smad2/3.The subsequent release and activation of Smad2/3 promote the activation of HSCs and fibrogenesis.Additionally,a significant elevation of S100A11 in serum is observed in clinical patients.Our study uncovers S100A11 as a novel profibrogenic factor in liver fibrosis,which may represent both a potential biomarker and a promising therapy target for treating liver fibrosis and fibrosis-related liver diseases. | Tingting Zhu Linqiang Zhang Chengbin Li Xiaoqiong Tan Jing Liu Huiqin Li Qijing Fan Zhiguo Zhang Mingfeng Zhan Lin Fu Jinbo Luo Jiawei Geng Yingjie Wu Xiaoju Zou Bin Liang | 2022 | Journal of Genetics and Genomics2022，49，4： | 0 |
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