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Atmospheric reactivity and oxidation capacity during summer at a suburban site between Beijing and Tianjin 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2020, 20 (13) : 8181-8200
作者:  Yang, Yuan;  Wang, Yonghong;  Zhou, Putian;  Yao, Dan;  Ji, Dongsheng;  Sun, Jie;  Wang, Yinghong;  Zhao, Shuman;  Huang, Wei;  Yang, Shuanghong;  Chen, Dean;  Gao, Wenkang;  Liu, Zirui;  Hu, Bo;  Zhang, Renjian;  Zeng, Limin;  Ge, Maofa;  Petaja, Tuukka;  Kerminen, Veli-Matti;  Kulmala, Markku;  Wang, Yuesi
收藏  |  浏览/下载：17/0  |  提交时间：2020/08/18
Fast sulfate formation from oxidation of SO2 by NO2 and HONO observed in Beijing haze 期刊论文
NATURE COMMUNICATIONS, 2020, 11 (1)
作者:  Wang, Junfeng;  Li, Jingyi;  Ye, Jianhuai;  Zhao, Jian;  Wu, Yangzhou;  Hu, Jianlin;  Liu, Dantong;  Nie, Dongyang;  Shen, Fuzhen;  Huang, Xiangpeng;  Huang, Dan Dan;  Ji, Dongsheng;  Sun, Xu;  Xu, Weiqi;  Guo, Jianping;  Song, Shaojie;  Qin, Yiming;  Liu, Pengfei;  Turner, Jay R.;  Lee, Hyun Chul;  Hwang, Sungwoo;  Liao, Hong;  Martin, Scot T.;  Zhang, Qi;  Chen, Mindong;  Sun, Yele;  Ge, Xinlei;  Jacob, Daniel J.
收藏  |  浏览/下载：18/0  |  提交时间：2020/06/09
Carbenium ion-mediated oligomerization of methylglyoxal for secondary organic aerosol formation 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (24) : 13294-13299
作者:  Ji, Yuemen;  Shi, Qiuju;  Li, Yixin;  An, Taicheng;  Zheng, Jun;  Peng, Jianfei;  Gao, Yanpeng;  Chen, Jiangyao;  Li, Guiying;  Wang, Yuan;  Zhang, Fang;  Zhang, Annie L.;  Zhao, Jiayun;  Molina, Mario J.;  Zhang, Renyi
收藏  |  浏览/下载：15/0  |  提交时间：2020/06/09
secondary organic aerosol  aqueous  oligomerization  brown carbon  cationic  
A hybrid method for PM2.5 source apportionment through WRF-Chem simulations and an assessment of emission-reduction measures in western China 期刊论文
ATMOSPHERIC RESEARCH, 2020, 236
作者:  Yang, Junhua;  Kang, Shichang;  Ji, Zhenming;  Chen, Xintong;  Yang, Sixiao;  Lee, Shao-Yi;  de Foy, Benjamin;  Chen, Deliang
收藏  |  浏览/下载：14/0  |  提交时间：2020/07/02
PM2.5 source  Hybrid source apportionment  Seasonal difference  Western China  Control strategies  
Generation of the 105-100 Ma Dagze volcanic rocks in the north Lhasa Terrane by lower crustal melting at different temperature and depth: Implications for tectonic transition 期刊论文
GEOLOGICAL SOCIETY OF AMERICA BULLETIN, 2020, 132 (5-6) : 1257-1272
作者:  Zeng, Yun-Chuan;  Xu, Ji-Feng;  Huang, Feng;  Li, Ming-Jian;  Chen, Qin
收藏  |  浏览/下载：8/0  |  提交时间：2020/07/02
Nitrifier adaptation to low energy flux controls inventory of reduced nitrogen in the dark ocean 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (9) : 4823-4830
作者:  Zhang, Yao;  Qin, Wei;  Hou, Lei;  Zakem, Emily J.;  Wan, Xianhui;  Zhao, Zihao;  Liu, Li;  Hunt, Kristopher A.;  Jiao, Nianzhi;  Kao, Shuh-Ji;  Tang, Kai;  Xie, Xiabing;  Shen, Jiaming;  Li, Yufang;  Chen, Mingming;  Dai, Xiaofeng;  Liu, Chang;  Deng, Wenchao;  Dai, Minhan;  Ingalls, Anitra E.;  Stahl, David A.;  Herndl, Gerhard J.
收藏  |  浏览/下载：14/0  |  提交时间：2020/05/13
nitrification  dark ocean  nitrogen flux  carbon fixation  homeostasis  
An unexpected catalyst dominates formation and radiative forcing of regional haze 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (8) : 3960-3966
作者:  Zhang, Fang;  Wang, Yuan;  Peng, Jianfei;  Chen, Lu;  Sun, Yele;  Duan, Lian;  Ge, Xinlei;  Li, Yixin;  Zhao, Jiayun;  Liu, Chao;  Zhang, Xiaochun;  Zhang, Gen;  Pan, Yuepeng;  Wang, Yuesi;  Zhang, Annie L.;  Ji, Yuemeng;  Wang, Gehui;  Hu, Min;  Molina, Mario J.;  Zhang, Renyi
收藏  |  浏览/下载：15/0  |  提交时间：2020/05/13
black carbon  air pollution  climate  multiphase chemistry  haze  
Differences in the destructiveness of tropical cyclones over the western North Pacific between slow- and rapid-transforming El Nino years 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (2)
作者:  Tu, Shifei;  Xu, Jianjun;  Xu, Feng;  Liang, Mei;  Ji, Qianqian;  Chen, Siqi
收藏  |  浏览/下载：10/0  |  提交时间：2020/07/02
tropical cyclones  destructive potential  slow-transforming El Nino years  rapid-transforming El Nino years  
Global warming accelerates uptake of atmospheric mercury in regions experiencing glacier retreat 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (4) : 2049-2055
作者:  Wang, Xun;  Luo, Ji;  Yuan, Wei;  Lin, Che-Jen;  Wang, Feiyue;  Liu, Chen;  Wang, Genxu;  Feng, Xinbin
收藏  |  浏览/下载：7/0  |  提交时间：2020/05/13
global warming  glacier retreat  atmospheric mercury deposition  
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).