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Peta–electron volt gamma-ray emission from the Crab Nebula 期刊论文
Science, 2021
作者:  The LHAASO Collaboration*†;  Zhen Cao;  F. Aharonian;  Q. An;  Axikegu;  L. X. Bai;  Y. X. Bai;  Y. W. Bao;  D. Bastieri;  X. J. Bi;  Y. J. Bi;  H. Cai;  J. T. Cai;  Zhe Cao;  J. Chang;  J. F. Chang;  B. M. Chen;  E. S. Chen;  J. Chen;  Liang Chen;  Liang Chen;  Long Chen;  M. J. Chen;  M. L. Chen;  Q. H. Chen;  S. H. Chen;  S. Z. Chen;  T. L. Chen;  X. L. Chen;  Y. Chen;  N. Cheng;  Y. D. Cheng;  S. W. Cui;  X. H. Cui;  Y. D. Cui;  B. D’Ettorre Piazzoli;  B. Z. Dai;  H. L. Dai;  Z. G. Dai;  Danzengluobu;  D. della Volpe;  X. J. Dong;  K. K. Duan;  J. H. Fan;  Y. Z. Fan;  Z. X. Fan;  J. Fang;  K. Fang;  C. F. Feng;  L. Feng;  S. H. Feng;  Y. L. Feng;  B. Gao;  C. D. Gao;  L. Q. Gao;  Q. Gao;  W. Gao;  M. M. Ge;  L. S. Geng;  G. H. Gong;  Q. B. Gou;  M. H. Gu;  F. L. Guo;  J. G. Guo;  X. L. Guo;  Y. Q. Guo;  Y. Y. Guo;  Y. A. Han;  H. H. He;  H. N. He;  J. C. He;  S. L. He;  X. B. He;  Y. He;  M. Heller;  Y. K. Hor;  C. Hou;  X. Hou;  H. B. Hu;  S. Hu;  S. C. Hu;  X. J. Hu;  D. H. Huang;  Q. L. Huang;  W. H. Huang;  X. T. Huang;  X. Y. Huang;  Z. C. Huang;  F. Ji;  X. L. Ji;  H. Y. Jia;  K. Jiang;  Z. J. Jiang;  C. Jin;  T. Ke;  D. Kuleshov;  K. Levochkin;  B. B. Li;  Cheng Li;  Cong Li;  F. Li;  H. B. Li;  H. C. Li;  H. Y. Li;  Jian Li;  Jie Li;  K. Li;  W. L. Li;  X. R. Li;  Xin Li;  Xin Li;  Y. Li;  Y. Z. Li;  Zhe Li;  Zhuo Li;  E. W. Liang;  Y. F. Liang;  S. J. Lin;  B. Liu;  C. Liu;  D. Liu;  H. Liu;  H. D. Liu;  J. Liu;  J. L. Liu;  J. S. Liu;  J. Y. Liu;  M. Y. Liu;  R. Y. Liu;  S. M. Liu;  W. Liu;  Y. Liu;  Y. N. Liu;  Z. X. Liu;  W. J. Long;  R. Lu;  H. K. Lv;  B. Q. Ma;  L. L. Ma;  X. H. Ma;  J. R. Mao;  A. Masood;  Z. Min;  W. Mitthumsiri;  T. Montaruli;  Y. C. Nan;  B. Y. Pang;  P. Pattarakijwanich;  Z. Y. Pei;  M. Y. Qi;  Y. Q. Qi;  B. Q. Qiao;  J. J. Qin;  D. Ruffolo;  V. Rulev;  A. Saiz;  L. Shao;  O. Shchegolev;  X. D. Sheng;  J. Y. Shi;  H. C. Song;  Yu. V. Stenkin;  V. Stepanov;  Y. Su;  Q. N. Sun;  X. N. Sun;  Z. B. Sun;  P. H. T. Tam;  Z. B. Tang;  W. W. Tian;  B. D. Wang;  C. Wang;  H. Wang;  H. G. Wang;  J. C. Wang;  J. S. Wang;  L. P. Wang;  L. Y. Wang;  R. N. Wang;  Wei Wang;  Wei Wang;  X. G. Wang;  X. J. Wang;  X. Y. Wang;  Y. Wang;  Y. D. Wang;  Y. J. Wang;  Y. P. Wang;  Z. H. Wang;  Z. X. Wang;  Zhen Wang;  Zheng Wang;  D. M. Wei;  J. J. Wei;  Y. J. Wei;  T. Wen;  C. Y. Wu;  H. R. Wu;  S. Wu;  W. X. Wu;  X. F. Wu;  S. Q. Xi;  J. Xia;  J. J. Xia;  G. M. Xiang;  D. X. Xiao;  G. Xiao;  H. B. Xiao;  G. G. Xin;  Y. L. Xin;  Y. Xing;  D. L. Xu;  R. X. Xu;  L. Xue;  D. H. Yan;  J. Z. Yan;  C. W. Yang;  F. F. Yang;  J. Y. Yang;  L. L. Yang;  M. J. Yang;  R. Z. Yang;  S. B. Yang;  Y. H. Yao;  Z. G. Yao;  Y. M. Ye;  L. Q. Yin;  N. Yin;  X. H. You;  Z. Y. You;  Y. H. Yu;  Q. Yuan;  H. D. Zeng;  T. X. Zeng;  W. Zeng;  Z. K. Zeng;  M. Zha;  X. X. Zhai;  B. B. Zhang;  H. M. Zhang;  H. Y. Zhang;  J. L. Zhang;  J. W. Zhang;  L. X. Zhang;  Li Zhang;  Lu Zhang;  P. F. Zhang;  P. P. Zhang;  R. Zhang;  S. R. Zhang;  S. S. Zhang;  X. Zhang;  X. P. Zhang;  Y. F. Zhang;  Y. L. Zhang;  Yi Zhang;  Yong Zhang;  B. Zhao;  J. Zhao;  L. Zhao;  L. Z. Zhao;  S. P. Zhao;  F. Zheng;  Y. Zheng;  B. Zhou;  H. Zhou;  J. N. Zhou;  P. Zhou;  R. Zhou;  X. X. Zhou;  C. G. Zhu;  F. R. Zhu;  H. Zhu;  K. J. Zhu;  X. Zuo
收藏  |  浏览/下载：14/0  |  提交时间：2021/07/27
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
Structure of the RNA-dependent RNA polymerase from COVID-19 virus 期刊论文
Science, 2020
作者:  Yan Gao;  Liming Yan;  Yucen Huang;  Fengjiang Liu;  Yao Zhao;  Lin Cao;  Tao Wang;  Qianqian Sun;  Zhenhua Ming;  Lianqi Zhang;  Ji Ge;  Litao Zheng;  Ying Zhang;  Haofeng Wang;  Yan Zhu;  Chen Zhu;  Tianyu Hu;  Tian Hua;  Bing Zhang;  Xiuna Yang;  Jun Li;  Haitao Yang;  Zhijie Liu;  Wenqing Xu;  Luke W. Guddat;  Quan Wang;  Zhiyong Lou;  Zihe Rao
收藏  |  浏览/下载：16/0  |  提交时间：2020/05/20
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).


  
Satellite-based entanglement distribution over 1200 kilometers 期刊论文
SCIENCE, 2017, 356 (6343) : 1180-1184
作者:  Yin, Juan;  Cao, Yuan;  Li, Yu-Huai;  Liao, Sheng-Kai;  Zhang, Liang;  Ren, Ji-Gang;  Cai, Wen-Qi;  Liu, Wei-Yue;  Li, Bo;  Dai, Hui;  Li, Guang-Bing;  Lu, Qi-Ming;  Gong, Yun-Hong;  Xu, Yu;  Li, Shuang-Lin;  Li, Feng-Zhi;  Yin, Ya-Yun;  Jiang, Zi-Qing;  Li, Ming;  Jia, Jian-Jun;  Ren, Ge;  He, Dong;  Zhou, Yi-Lin;  Zhang, Xiao-Xiang;  Wang, Na;  Chang, Xiang;  Zhu, Zhen-Cai;  Liu, Nai-Le;  Chen, Yu-Ao;  Lu, Chao-Yang;  Shu, Rong;  Peng, Cheng-Zhi;  Wang, Jian-Yu;  Pan, Jian-Wei
收藏  |  浏览/下载：17/0  |  提交时间：2019/11/27