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Copernican-aged (<200 Ma) Impact Ejecta at the Chang’e-5 Landing Site: Statistical Evidence from Crater Morphology, Morphometry and Degradation Models 期刊论文
Geophysical Research Letters, 2021
作者:  Yuqi Qian;  Long Xiao;  James W. Head;  Christian Wö;  hler;  Roberto Bugiolacchi;  Thorsten Wilhelm;  Stephanie Althoff;  Binlong Ye;  Qi He;  Yuefeng Yuan;  Siyuan Zhao
收藏  |  浏览/下载:12/0  |  提交时间:2021/10/22
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
The association between maternal exposure to fine particulate matter (PM2.5) and gestational diabetes mellitus (GDM): a prospective birth cohort study in China 期刊论文
Environmental Research Letters, 2021
作者:  Guimin Chen;  Xiaoli Sun;  Jiaqi Wang;  Moran Dong;  Yufeng Ye;  Xin Liu;  Jiufeng Sun;  Jianpeng Xiao;  Guanhao He;  Jianxiong Hu;  Lingchuan Guo;  Xing Li;  Zuhua Rong;  Weilin Zeng;  He Zhou;  Dengzhou Chen;  Jiali Li;  Wenjun Ma;  Maksym Bartashevskyy;  Xiaozhong Wen;  Tao Liu
收藏  |  浏览/下载:12/0  |  提交时间:2021/04/20
Role of iodine oxoacids in atmospheric aerosol nucleation 期刊论文
Science, 2021
作者:  Xu-Cheng He;  Yee Jun Tham;  Lubna Dada;  Mingyi Wang;  Henning Finkenzeller;  Dominik Stolzenburg;  Siddharth Iyer;  Mario Simon;  Andreas Kürten;  Jiali Shen;  Birte Rörup;  Matti Rissanen;  Siegfried Schobesberger;  Rima Baalbaki;  Dongyu S. Wang;  Theodore K. Koenig;  Tuija Jokinen;  Nina Sarnela;  Lisa J. Beck;  João Almeida;  Stavros Amanatidis;  António Amorim;  Farnoush Ataei;  Andrea Baccarini;  Barbara Bertozzi;  Federico Bianchi;  Sophia Brilke;  Lucía Caudillo;  Dexian Chen;  Randall Chiu;  Biwu Chu;  António Dias;  Aijun Ding;  Josef Dommen;  Jonathan Duplissy;  Imad El Haddad;  Loïc Gonzalez Carracedo;  Manuel Granzin;  Armin Hansel;  Martin Heinritzi;  Victoria Hofbauer;  Heikki Junninen;  Juha Kangasluoma;  Deniz Kemppainen;  Changhyuk Kim;  Weimeng Kong;  Jordan E. Krechmer;  Aleksander Kvashin;  Totti Laitinen;  Houssni Lamkaddam;  Chuan Ping Lee;  Katrianne Lehtipalo;  Markus Leiminger;  Zijun Li;  Vladimir Makhmutov;  Hanna E. Manninen;  Guillaume Marie;  Ruby Marten;  Serge Mathot;  Roy L. Mauldin;  Bernhard Mentler;  Ottmar Möhler;  Tatjana Müller;  Wei Nie;  Antti Onnela;  Tuukka Petäjä;  Joschka Pfeifer;  Maxim Philippov;  Ananth Ranjithkumar;  Alfonso Saiz-Lopez;  Imre Salma;  Wiebke Scholz;  Simone Schuchmann;  Benjamin Schulze;  Gerhard Steiner;  Yuri Stozhkov;  Christian Tauber;  António Tomé;  Roseline C. Thakur;  Olli Väisänen;  Miguel Vazquez-Pufleau;  Andrea C. Wagner;  Yonghong Wang;  Stefan K. Weber;  Paul M. Winkler;  Yusheng Wu;  Mao Xiao;  Chao Yan;  Qing Ye;  Arttu Ylisirniö;  Marcel Zauner-Wieczorek;  Qiaozhi Zha;  Putian Zhou;  Richard C. Flagan;  Joachim Curtius;  Urs Baltensperger;  Markku Kulmala;  Veli-Matti Kerminen;  Theo Kurtén;  Neil M. Donahue;  Rainer Volkamer;  Jasper Kirkby;  Douglas R. Worsnop;  Mikko Sipilä
收藏  |  浏览/下载:13/0  |  提交时间:2021/02/17
The thermal evolution of Mercury over the past ~ 4.2 Ga as revealed by relaxation states of mantle plugs beneath impact basins 期刊论文
Geophysical Research Letters, 2020
作者:  Qingyun Deng;  Fei Li;  Jianguo Yan;  Zhiyong Xiao;  Mao Ye;  Chi Xiao;  Jean‐;  Pierre Barriot
收藏  |  浏览/下载:9/0  |  提交时间:2020/10/12
Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody 期刊论文
Science, 2020
作者:  Zhe Lv;  Yong-Qiang Deng;  Qing Ye;  Lei Cao;  Chun-Yun Sun;  Changfa Fan;  Weijin Huang;  Shihui Sun;  Yao Sun;  Ling Zhu;  Qi Chen;  Nan Wang;  Jianhui Nie;  Zhen Cui;  Dandan Zhu;  Neil Shaw;  Xiao-Feng Li;  Qianqian Li;  Liangzhi Xie;  Youchun Wang;  Zihe Rao;  Cheng-Feng Qin;  Xiangxi Wang
收藏  |  浏览/下载:17/0  |  提交时间:2020/09/22
Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions 期刊论文
Science, 2020
作者:  Sheikh Taslim Ali;  Lin Wang;  Eric H. Y. Lau;  Xiao-Ke Xu;  Zhanwei Du;  Ye Wu;  Gabriel M. Leung;  Benjamin J. Cowling
收藏  |  浏览/下载:11/0  |  提交时间:2020/09/08
Notch signalling drives synovial fibroblast identity and arthritis pathology 期刊论文
NATURE, 2020, 582 (7811) : 259-+
作者:  Han, Xiaoping;  Zhou, Ziming;  Fei, Lijiang;  Sun, Huiyu;  Wang, Renying;  Chen, Yao;  Chen, Haide;  Wang, Jingjing;  Tang, Huanna;  Ge, Wenhao;  Zhou, Yincong;  Ye, Fang;  Jiang, Mengmeng;  Wu, Junqing;  Xiao, Yanyu;  Jia, Xiaoning;  Zhang, Tingyue;  Ma, Xiaojie;  Zhang, Qi;  Bai, Xueli;  Lai, Shujing;  Yu, Chengxuan;  Zhu, Lijun;  Lin, Rui;  Gao, Yuchi;  Wang, Min;  Wu, Yiqing;  Zhang, Jianming;  Zhan, Renya;  Zhu, Saiyong;  Hu, Hailan;  Wang, Changchun;  Chen, Ming;  Huang, He;  Liang, Tingbo;  Chen, Jianghua;  Wang, Weilin;  Zhang, Dan;  Guo, Guoji
收藏  |  浏览/下载:43/0  |  提交时间:2020/07/03

NOTCH3 signalling is shown to be the underlying driver of the differentiation and expansion of a subset of synovial fibroblasts implicated in the pathogenesis of rheumatoid arthritis.


The synovium is a mesenchymal tissue composed mainly of fibroblasts, with a lining and sublining that surround the joints. In rheumatoid arthritis the synovial tissue undergoes marked hyperplasia, becomes inflamed and invasive, and destroys the joint(1,2). It has recently been shown that a subset of fibroblasts in the sublining undergoes a major expansion in rheumatoid arthritis that is linked to disease activity(3-5)  however, the molecular mechanism by which these fibroblasts differentiate and expand is unknown. Here we identify a critical role for NOTCH3 signalling in the differentiation of perivascular and sublining fibroblasts that express CD90 (encoded by THY1). Using single-cell RNA sequencing and synovial tissue organoids, we found that NOTCH3 signalling drives both transcriptional and spatial gradients-emanating from vascular endothelial cells outwards-in fibroblasts. In active rheumatoid arthritis, NOTCH3 and Notch target genes are markedly upregulated in synovial fibroblasts. In mice, the genetic deletion of Notch3 or the blockade of NOTCH3 signalling attenuates inflammation and prevents joint damage in inflammatory arthritis. Our results indicate that synovial fibroblasts exhibit a positional identity that is regulated by endothelium-derived Notch signalling, and that this stromal crosstalk pathway underlies inflammation and pathology in inflammatory arthritis.


  
Nanoplasma-enabled picosecond switches for ultrafast electronics (vol 579, pg 534, 2020) 期刊论文
NATURE, 2020, 580 (7803) : E8-E8
作者:  Li, Jing;  Xu, Chuanliang;  Lee, Hyung Joo;  Ren, Shancheng;  Zi, Xiaoyuan;  Zhang, Zhiming;  Wang, Haifeng;  Yu, Yongwei;  Yang, Chenghua;  Gao, Xiaofeng;  Hou, Jianguo;  Wang, Linhui;  Yang, Bo;  Yang, Qing;  Ye, Huamao;  Zhou, Tie;  Lu, Xin;  Wang, Yan;  Qu, Min;  Yang, Qingsong;  Zhang, Wenhui;  Shah, Nakul M.;  Pehrsson, Erica C.;  Wang, Shuo;  Wang, Zengjun;  Jiang, Jun;  Zhu, Yan;  Chen, Rui;  Chen, Huan;  Zhu, Feng;  Lian, Bijun;  Li, Xiaoyun;  Zhang, Yun;  Wang, Chao;  Wang, Yue;  Xiao, Guangan;  Jiang, Junfeng;  Yang, Yue;  Liang, Chaozhao;  Hou, Jianquan;  Han, Conghui;  Chen, Ming;  Jiang, Ning;  Zhang, Dahong;  Wu, Song;  Yang, Jinjian;  Wang, Tao;  Chen, Yongliang;  Cai, Jiantong;  Yang, Wenzeng;  Xu, Jun;  Wang, Shaogang;  Gao, Xu;  Wang, Ting;  Sun, Yinghao
收藏  |  浏览/下载:25/0  |  提交时间:2020/07/03
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:143/0  |  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).