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Predicting Solute Transport through Green Stormwater Infrastructure with Unsteady Transit Time Distribution Theory 期刊论文
Water Resources Research, 2020
作者:  E. A. Parker;  S. B. Grant;  Y. Cao;  M. A. Rippy;  K. J. McGuire;  P. A. Holden;  M. Feraud;  S. Avasarala;  H. Liu;  W. C. Hung;  M. Rugh;  J. Jay;  J. Peng;  S. Shao;  D. Li
收藏  |  浏览/下载：11/0  |  提交时间：2020/12/28
Ancient DNA indicates human population shifts and admixture in northern and southern China 期刊论文
Science, 2020
作者:  Melinda A. Yang;  Xuechun Fan;  Bo Sun;  Chungyu Chen;  Jianfeng Lang;  Ying-Chin Ko;  Cheng-hwa Tsang;  Hunglin Chiu;  Tianyi Wang;  Qingchuan Bao;  Xiaohong Wu;  Mateja Hajdinjak;  Albert Min-Shan Ko;  Manyu Ding;  Peng Cao;  Ruowei Yang;  Feng Liu;  Birgit Nickel;  Qingyan Dai;  Xiaotian Feng;  Lizhao Zhang;  Chengkai Sun;  Chao Ning;  Wen Zeng;  Yongsheng Zhao;  Ming Zhang;  Xing Gao;  Yinqiu Cui;  David Reich;  Mark Stoneking;  Qiaomei Fu
收藏  |  浏览/下载：11/0  |  提交时间：2020/07/21
Precise pitch-scaling of carbon nanotube arrays within three-dimensional DNA nanotrenches 期刊论文
Science, 2020
作者:  Wei Sun;  Jie Shen;  Zhao Zhao;  Noel Arellano;  Charles Rettner;  Jianshi Tang;  Tianyang Cao;  Zhiyu Zhou;  Toan Ta;  Jason K. Streit;  Jeffrey A. Fagan;  Thomas Schaus;  Ming Zheng;  Shu-Jen Han;  William M. Shih;  Hareem T. Maune;  Peng Yin
收藏  |  浏览/下载：7/0  |  提交时间：2020/05/25
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).