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Enzyme formation by immune receptors 期刊论文
Science, 2020
作者:  Lei Tian;  Xin Li
收藏  |  浏览/下载：8/0  |  提交时间：2020/12/07
Increase in Lower Stratospheric Water Vapor in the Past 100 Years Related to Tropical Atlantic Warming 期刊论文
Geophysical Research Letters, 2020
作者:  Fei Xie;  Wenshou Tian;  Xin Zhou;  Jiankai Zhang;  Yan Xia;  Jinping Lu
收藏  |  浏览/下载：13/0  |  提交时间：2020/11/24
SOSTDC1-producing follicular helper T cells promote regulatory follicular T cell differentiation 期刊论文
Science, 2020
作者:  Xin Wu;  Yun Wang;  Rui Huang;  Qujing Gai;  Haofei Liu;  Meimei Shi;  Xiang Zhang;  Yonglin Zuo;  Longjuan Chen;  Qiwen Zhao;  Yu Shi;  Fengchao Wang;  Xiaowei Yan;  Huiping Lu;  Senlin Xu;  Xiaohong Yao;  Lin Chen;  Xia Zhang;  Qiang Tian;  Ziyan Yang;  Bo Zhong;  Chen Dong;  Yan Wang;  Xiu-Wu Bian;  Xindong Liu
收藏  |  浏览/下载：18/0  |  提交时间：2020/08/25
Assessments of the factors controlling latent heat flux and the coupling degree between an alpine wetland and the atmosphere on the Qinghai-Tibetan Plateau in summer 期刊论文
ATMOSPHERIC RESEARCH, 2020, 240
作者:  Chen, Jinlei;  Wen, Jun;  Kang, Shichang;  Meng, Xianhong;  Tian, Hui;  Ma, Xin;  Yuan, Yuan
收藏  |  浏览/下载：10/0  |  提交时间：2020/08/18
Wetlands  Latent heat  Coupling  Control factor  CLM  
Plasmapause surface wave oscillates the magnetosphere and diffuse aurora 期刊论文
Nature, 2020
作者:  Fei He;  Rui-Long Guo;  William R. Dunn;  Zhong-Hua Yao;  Hua-Sen Zhang;  Yi-Xin Hao;  Quan-Qi Shi;  Zhao-Jin Rong;  Jiang Liu;  An-Min Tian;  Xiao-Xin Zhang;  Yong Wei;  Yong-Liang Zhang;  Qiu-Gang Zong;  Zu-Yin Pu;  Wei-Xing Wan
收藏  |  浏览/下载：13/0  |  提交时间：2020/05/13
Anthropogenic Aerosols Significantly Reduce Mesoscale Convective System Occurrences and Precipitation Over Southern China in April 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (6)
作者:  Zhang, Lijuan;  Fu, Tzung-May;  Tian, Heng;  Ma, Yaping;  Chen, Jen-Ping;  Tsai, Tzu-Chin;  Tsai, I-Chun;  Meng, Zhiyong;  Yang, Xin
收藏  |  浏览/下载：13/0  |  提交时间：2020/07/02
anthropogenic aerosols  precipitation  mesoscale convective systems  aerosol-cloud interactions  aerosol-radiation interactions  
A framework for nitrogen futures in the shared socioeconomic pathways 期刊论文
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS, 2020, 61
作者:  Kanter, David R.;  Winiwarter, Wilfried;  Bodirsky, Benjamin L.;  Bouwman, Lex;  Boyer, Elizabeth;  Buckle, Simon;  Compton, Jana E.;  Dalgaard, Tommy;  de Vries, Wim;  Leclere, David;  Leip, Adrian;  Mueller, Christoph;  Popp, Alexander;  Raghuram, Nandula;  Rao, Shilpa;  Sutton, Mark A.;  Tian, Hanqin;  Westhoek, Henk;  Zhang, Xin;  Zurek, Monika
收藏  |  浏览/下载：10/0  |  提交时间：2020/07/02
Scenarios  Nitrogen pollution  Environmental policy  
Improved protein structure prediction using potentials from deep learning 期刊论文
NATURE, 2020, 577 (7792) : 706-+
作者:  Ma, Runze;  Cao, Duanyun;  Zhu, Chongqin;  Tian, Ye;  Peng, Jinbo;  Guo, Jing;  Chen, Ji;  Li, Xin-Zheng;  Francisco, Joseph S.;  Zeng, Xiao Cheng;  Xu, Li-Mei;  Wang, En-Ge;  Jiang, Ying
收藏  |  浏览/下载：142/0  |  提交时间：2020/07/03

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2)  however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures(3). Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force(4) that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores(6) of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined(7).