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Quantum computational advantage using photons 期刊论文
Science, 2020
作者:  Han-Sen Zhong;  Hui Wang;  Yu-Hao Deng;  Ming-Cheng Chen;  Li-Chao Peng;  Yi-Han Luo;  Jian Qin;  Dian Wu;  Xing Ding;  Yi Hu;  Peng Hu;  Xiao-Yan Yang;  Wei-Jun Zhang;  Hao Li;  Yuxuan Li;  Xiao Jiang;  Lin Gan;  Guangwen Yang;  Lixing You;  Zhen Wang;  Li Li;  Nai-Le Liu;  Chao-Yang Lu;  Jian-Wei Pan
收藏  |  浏览/下载:29/0  |  提交时间:2020/12/22
How green is the “Belt and Road Initiative”? – Evidence from Chinese OFDI in the energy sector 期刊论文
Energy Policy, 2020
作者:  Haiyue Liu, Yile Wang, Jie Jiang, Peng Wu
收藏  |  浏览/下载:7/0  |  提交时间:2020/07/14
Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease 期刊论文
Science, 2020
作者:  Wenhao Dai;  Bing Zhang;  Xia-Ming Jiang;  Haixia Su;  Jian Li;  Yao Zhao;  Xiong Xie;  Zhenming Jin;  Jingjing Peng;  Fengjiang Liu;  Chunpu Li;  You Li;  Fang Bai;  Haofeng Wang;  Xi Cheng;  Xiaobo Cen;  Shulei Hu;  Xiuna Yang;  Jiang Wang;  Xiang Liu;  Gengfu Xiao;  Hualiang Jiang;  Zihe Rao;  Lei-Ke Zhang;  Yechun Xu;  Haitao Yang;  Hong Liu
收藏  |  浏览/下载:18/0  |  提交时间:2020/06/22
Plastic pollution in croplands threatens long‐term food security 期刊论文
Global Change Biology, 2020
作者:  Dan Zhang;  Ee Ling Ng;  Wanli Hu;  Hongyuan Wang;  Pablo Galaviz;  Hude Yang;  Wentao Sun;  Chongxiao Li;  Xingwang Ma;  Bin Fu;  Peiyi Zhao;  Fulin Zhang;  Shuqin Jin;  Mingdong Zhou;  Lianfeng Du;  Chang Peng;  Xuejun Zhang;  Zhiyu Xu;  Bin Xi;  Xiaoxia Liu;  Shiyou Sun;  Zhenhua Cheng;  Lihua Jiang;  Yufeng Wang;  Liang Gong;  Changlin Kou;  Yan Li;  Youhua Ma;  Dongfeng Huang;  Jian Zhu;  Jianwu Yao;  Chaowen Lin;  Song Qin;  Liuqiang Zhou;  Binghui He;  Deli Chen;  Huanchun Li;  Limei Zhai;  Qiuliang Lei;  Shuxia Wu;  Yitao Zhang;  Junting Pan;  Baojing Gu;  Hongbin Liu
收藏  |  浏览/下载:13/0  |  提交时间:2020/05/13
Electromechanical coupling in the hyperpolarization-activated K+ channel KAT1 期刊论文
NATURE, 2020, 583 (7814) : 145-+
作者:  Jin, Zhenming;  Du, Xiaoyu;  Xu, Yechun;  Deng, Yongqiang;  Liu, Meiqin;  Zhao, Yao;  Zhang, Bing;  Li, Xiaofeng;  Zhang, Leike;  Peng, Chao;  Duan, Yinkai;  Yu, Jing;  Wang, Lin;  Yang, Kailin;  Liu, Fengjiang;  Jiang, Rendi;  Yang, Xinglou;  You, Tian;  Liu, Xiaoce
收藏  |  浏览/下载:27/0  |  提交时间:2020/07/03

Voltage-gated potassium (K-v) channels coordinate electrical signalling and control cell volume by gating in response to membrane depolarization or hyperpolarization. However, although voltage-sensing domains transduce transmembrane electric field changes by a common mechanism involving the outward or inward translocation of gating charges(1-3), the general determinants of channel gating polarity remain poorly understood(4). Here we suggest a molecular mechanism for electromechanical coupling and gating polarity in non-domain-swapped K-v channels on the basis of the cryo-electron microscopy structure of KAT1, the hyperpolarization-activated K-v channel from Arabidopsis thaliana. KAT1 displays a depolarized voltage sensor, which interacts with a closed pore domain directly via two interfaces and indirectly via an intercalated phospholipid. Functional evaluation of KAT1 structure-guided mutants at the sensor-pore interfaces suggests a mechanism in which direct interaction between the sensor and the C-linker hairpin in the adjacent pore subunit is the primary determinant of gating polarity. We suggest that an inward motion of the S4 sensor helix of approximately 5-7 angstrom can underlie a direct-coupling mechanism, driving a conformational reorientation of the C-linker and ultimately opening the activation gate formed by the S6 intracellular bundle. This direct-coupling mechanism contrasts with allosteric mechanisms proposed for hyperpolarization-activated cyclic nucleotide-gated channels(5), and may represent an unexpected link between depolarization- and hyperpolarization-activated channels.


The cryo-electron microscopy structure of the hyperpolarization-activated K+ channel KAT1 points to a direct-coupling mechanism between S4 movement and the reorientation of the C-linker.


  
Radiance-based NIRv as a proxy for GPP of corn and soybean 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (3)
作者:  Wu, Genghong;  Guan, Kaiyu;  Jiang, Chongya;  Peng, Bin;  Kimm, Hyungsuk;  Chen, Min;  Yang, Xi;  Wang, Sheng;  Suyker, Andrew E.;  Bernacchi, Carl J.;  Moore, Caitlin E.;  Zeng, Yelu;  Berry, Joseph A.;  Pilar Cendrero-Mateo, M.
收藏  |  浏览/下载:15/0  |  提交时间:2020/07/02
photosynthesis  gross primary production  NIRv  near-infrared radiance of vegetation  
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).