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Direct links between hygroscopicity and mixing state of ambient aerosols: estimating particle hygroscopicity from their single-particle mass spectra 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2020, 20 (11) : 6273-6290
作者:  Wang, Xinning;  Ye, Xingnan;  Chen, Jian-Min;  Wang, Xiaofei;  Yang, Xin;  Fu, Tzung-May;  Zhu, Lei;  Liu, Chongxuan
收藏  |  浏览/下载:6/0  |  提交时间:2020/06/09
Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (6)
作者:  Kang, Yanghui;  Ozdogan, Mutlu;  Zhu, Xiaojin;  Ye, Zhiwei;  Hain, Christopher;  Anderson, Martha
收藏  |  浏览/下载:15/0  |  提交时间:2020/07/02
crop yields  climate impact  machine learning  deep learning  data-driven  
The Great Oxidation Event expanded the genetic repertoire of arsenic metabolism and cycling 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (19) : 10414-10421
作者:  Chen, Song-Can;  Sun, Guo-Xin;  Yan, Yu;  Konstantinidis, Konstantinos T.;  Zhang, Si-Yu;  Deng, Ye;  Li, Xiao-Min;  Cui, Hui-Ling;  Musat, Florin;  Popp, Denny;  Rosen, Barry P.;  Zhu, Yong-Guan
收藏  |  浏览/下载:21/0  |  提交时间:2020/05/13
arsenic  detoxification  evolution  oxygen  biogeochemistry  
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:19/0  |  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: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).


  
Growing-season temperature and precipitation are independent drivers of global variation in xylem hydraulic conductivity 期刊论文
GLOBAL CHANGE BIOLOGY, 2019
作者:  He, Pengcheng;  Gleason, Sean M.;  Wright, Ian J.;  Weng, Ensheng;  Liu, Hui;  Zhu, Shidan;  Lu, Mingzhen;  Luo, Qi;  Li, Ronghua;  Wu, Guilin;  Yan, Enrong;  Song, Yanjun;  Mi, Xiangcheng;  Hao, Guangyou;  Reich, Peter B.;  Wang, Yingping;  Ellsworth, David S.;  Ye, Qing
收藏  |  浏览/下载:12/0  |  提交时间:2020/02/17
biome  climate  functional types  hydraulic diversity  species distribution  water transport  
Using wavelet transform to analyse on-road mobile measurements of air pollutants: a case study to evaluate vehicle emission control policies during the 2014 APEC summit 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2019, 19 (22) : 13841-13857
作者:  Li, Yingruo;  Tan, Ziqiang;  Ye, Chunxiang;  Wang, Junxia;  Wang, Yanwen;  Zhu, Yi;  Liang, Pengfei;  Chen, Xi;  Fang, Yanhua;  Han, Yiqun;  Wang, Qi;  He, Di;  Wang, Yao;  Zhu, Tong
收藏  |  浏览/下载:12/0  |  提交时间:2020/02/17
Multi-strategic RNA-seq analysis reveals a high-resolution transcriptional landscape in cotton 期刊论文
NATURE COMMUNICATIONS, 2019, 10
作者:  Wang, Kun;  Wang, Dehe;  Zheng, Xiaomin;  Qin, Ai;  Zhou, Jie;  Guo, Boyu;  Chen, Yanjun;  Wen, Xingpeng;  Ye, Wen;  Zhou, Yu;  Zhu, Yuxian
收藏  |  浏览/下载:12/0  |  提交时间:2019/11/27
A genome-wide association study identifies six novel risk loci for primary biliary cholangitis 期刊论文
NATURE COMMUNICATIONS, 2017, 8
作者:  Qiu, Fang;  Tang, Ruqi;  Zuo, Xianbo;  Shi, Xingjuan;  Wei, Yiran;  Zheng, Xiaodong;  Dai, Yaping;  Gong, Yuhua;  Wang, Lan;  Xu, Ping;  Zhu, Xiang;  Wu, Jian;  Han, Chongxu;  Gao, Yueqiu;  Zhang, Kui;  Jiang, Yuzhang;  Zhou, Jianbo;  Shao, Youlin;  Hu, Zhigang;  Tian, Ye;  Zhang, Haiyan;  Dai, Na;  Liu, Lei;  Wu, Xudong;  Zhao, Weifeng;  Zhang, Xiaomin;  Zang, Zhidong;  Nie, Jinshan;  Sun, Weihao;  Zhao, Yi;  Mao, Yuan;  Jiang, Po;  Ji, Hualiang;  Dong, Qing;  Li, Junming;  Li, Zhenzhong;  Bai, Xinli;  Li, Li;  Lin, Maosong;  Dong, Ming;  Li, Jinxin;  Zhu, Ping;  Wang, Chan;  Zhang, Yanqiu;  Jiang, Peng;  Wang, Yujue;  Jawed, Rohil;  Xu, Jing;  Zhang, Yu;  Wang, Qixia;  Yang, Yue;  Yang, Fan;  Lian, Min;  Jiang, Xiang;  Xiao, Xiao;  Li, Yanmei;  Fang, Jingyuan;  Qiu, Dekai;  Zhu, Zhen;  Qiu, Hong;  Zhang, Jianqiong;  Tian, Wenyan;  Chen, Sufang;  Jiang, Ling;  Ji, Bing;  Li, Ping;  Chen, Guochang;  Wu, Tianxue;  Sun, Yan;  Yu, Jianjiang;  Tang, Huijun;  He, Michun;  Xia, Min;  Pei, Hao;  Huang, Lihua;  Qing, Zhuye;  Wu, Jianfang;  Huang, Qinghai;  Han, Junhai;  Xie, Wei;  Sun, Zhongsheng;  Guo, Jian;  He, Gengsheng;  Gershwin, M. Eric;  Lian, Zhexiong;  Liu, Xiang;  Seldin, Michael F.;  Liu, Xiangdong;  Chen, Weichang;  Ma, Xiong
收藏  |  浏览/下载:18/0  |  提交时间:2019/11/27