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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
Black carbon and secondary brown carbon, the dominant light absorption and direct radiative forcing contributors of the atmospheric aerosols over the Tibetan Plateau 期刊论文
Geophysical Research Letters, 2021
作者:  Chong‐;  Shu Zhu;  Yao Qu;  Hong Huang;  Ji Chen;  Wen‐;  Ting Dai;  Ru‐;  Jin Huang;  Jun‐;  Ji Cao
收藏  |  浏览/下载:12/0  |  提交时间:2021/04/12
Estimated contribution of vehicular emissions to carbonaceous aerosols in urban Beijing, China 期刊论文
Atmospheric Research, 2020
作者:  Yang Cui, Wan Cao, Dongsheng Ji, Wenkang Gao, Yuesi Wang
收藏  |  浏览/下载:10/0  |  提交时间:2020/08/09
Economics in the Age of COVID-19 期刊论文
NATURE, 2020, 581 (7809) : 375-377
作者:  Yin, Juan;  Li, Yu-Huai;  Liao, Sheng-Kai;  Yang, Meng;  Cao, Yuan;  Zhang, Liang;  Ren, Ji-Gang;  Cai, Wen-Qi;  Liu, Wei-Yue;  Li, Shuang-Lin;  Shu, Rong;  Huang, Yong-Mei;  Deng, Lei;  Li, Li;  Zhang, Qiang;  Liu, Nai-Le
收藏  |  浏览/下载:25/0  |  提交时间:2020/07/03

Breakneck triage nails many diagnoses, but deeper treatment is needed.


Breakneck triage nails many diagnoses, but deeper treatment is needed.


  
Seasonal variations in the high time-resolved aerosol composition, sources, and chemical process of background submicron particles in North China Plain 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Jiayun Li, Liming Cao, Wenkang Gao, Lingyan He, Yingchao Yan, Dongsheng Ji, Zirui Liu, and Yuesi Wang
收藏  |  浏览/下载:35/0  |  提交时间:2020/05/20
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:15/0  |  提交时间:2020/05/20
Live-animal imaging of native haematopoietic stem and progenitor cells 期刊论文
NATURE, 2020, 578 (7794) : 278-+
作者:  Gerstung, Moritz;  Jolly, Clemency;  Leshchiner, Ignaty;  Dentro, Stefan C.;  Gonzalez, Santiago;  Rosebrock, Daniel;  Mitchell, Thomas J.;  Rubanova, Yulia;  Anur, Pavana;  Yu, Kaixian;  Tarabichi, Maxime;  Deshwar, Amit;  Wintersinger, Jeff;  Kleinheinz, Kortine;  Vazquez-Garcia, Ignacio;  Haase, Kerstin;  Jerman, Lara;  Sengupta, Subhajit;  Macintyre, Geoff;  Malikic, Salem;  Donmez, Nilgun;  Livitz, Dimitri G.;  Cmero, Marek;  Demeulemeester, Jonas;  Schumacher, Steven;  Fan, Yu;  Yao, Xiaotong;  Lee, Juhee;  Schlesner, Matthias;  Boutros, Paul C.;  Bowtell, David D.;  Zhu, Hongtu;  Getz, Gad;  Imielinski, Marcin;  Beroukhim, Rameen;  Sahinalp, S. Cenk;  Ji, Yuan;  Peifer, Martin;  Markowetz, Florian;  Mustonen, Ville;  Yuan, Ke;  Wang, Wenyi;  Morris, Quaid D.;  Spellman, Paul T.;  Wedge, David C.;  Van Loo, Peter;  Deshwar, Amit G.;  Adams, David J.;  Campbell, Peter J.;  Cao, Shaolong;  Christie, Elizabeth L.;  Cun, Yupeng;  Dawson, Kevin J.;  Drews, Ruben M.;  Eils, Roland;  Fittall, Matthew;  Garsed, Dale W.;  Ha, Gavin;  Lee-Six, Henry;  Martincorena, Inigo;  Oesper, Layla;  Peto, Myron;  Raphael, Benjamin J.;  Salcedo, Adriana;  Shi, Ruian;  Shin, Seung Jun;  Spiro, Oliver;  Stein, Lincoln D.;  Vembu, Shankar;  Wheeler, David A.;  Yang, Tsun-Po
收藏  |  浏览/下载:15/0  |  提交时间:2020/07/03

The biology of haematopoietic stem cells (HSCs) has predominantly been studied under transplantation conditions(1,2). It has been particularly challenging to study dynamic HSC behaviour, given that the visualization of HSCs in the native niche in live animals has not, to our knowledge, been achieved. Here we describe a dual genetic strategy in mice that restricts reporter labelling to a subset of the most quiescent long-term HSCs (LT-HSCs) and that is compatible with current intravital imaging approaches in the calvarial bone marrow(3-5). We show that this subset of LT-HSCs resides close to both sinusoidal blood vessels and the endosteal surface. By contrast, multipotent progenitor cells (MPPs) show greater variation in distance from the endosteum and are more likely to be associated with transition zone vessels. LT-HSCs are not found in bone marrow niches with the deepest hypoxia and instead are found in hypoxic environments similar to those of MPPs. In vivo time-lapse imaging revealed that LT-HSCs at steady-state show limited motility. Activated LT-HSCs show heterogeneous responses, with some cells becoming highly motile and a fraction of HSCs expanding clonally within spatially restricted domains. These domains have defined characteristics, as HSC expansion is found almost exclusively in a subset of bone marrow cavities with bone-remodelling activity. By contrast, cavities with low bone-resorbing activity do not harbour expanding HSCs. These findings point to previously unknown heterogeneity within the bone marrow microenvironment, imposed by the stages of bone turnover. Our approach enables the direct visualization of HSC behaviours and dissection of heterogeneity in HSC niches.


A dual genetic strategy enables the labelling and in vivo imaging of native long-term haematopoietic stem cells in the mouse calvarial bone marrow.


  
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).


  
Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2019, 116 (18) : 8657-8666
作者:  An, Zhisheng;  Huang, Ru-Jin;  Zhang, Renyi;  Tie, Xuexi;  Li, Guohui;  Cao, Junji;  Zhou, Weijian;  Shi, Zhengguo;  Han, Yongming;  Gu, Zhaolin;  Ji, Yuemeng
收藏  |  浏览/下载:11/0  |  提交时间:2019/11/27
severe haze  synergetic effects  anthropogenic emission  atmospheric chemistry  climate change  
Waves and Sediment Transport Due to Granular Landslides Impacting Reservoirs 期刊论文
WATER RESOURCES RESEARCH, 2019, 55 (1) : 495-518
作者:  Li, Ji;  Cao, Zhixian;  Liu, Qingquan
收藏  |  浏览/下载:5/0  |  提交时间:2019/04/09
landslide  reservoir  sediment transport  waves  landslide efficiency  momentum transfer