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| 1 | A draft sequence of the rice (Oryza sativa ssp. indica) genome显示文摘The sequence of the rice genome holds fundamental information for its biology, including physiology, genetics, development, and evolution, as well as information on many beneficial phenotypes of economic significance. Using a 'whole genome shotgun' approach, we have pro-duced a draft rice genome sequence of Oryza sativa ssp. in-dica, the major crop rice subspecies in China and many other regions of Asia. The draft genome sequence is constructed from over 4.3 million successful sequencing traces with an accumulative total length of 2214.9 Mb. The initial assembly of the non-redundant sequences reached 409.76 Mb in length, based on 3.30 million successful sequencing traces with a total length of 1797.4 Mb from an indica variant cultivar 93-11, giving an estimated coverage of 95.29% of the rice genome with an average base accuracy of higher than 99%. The coverage of the draft sequence, the randomness of the sequence distribution, and the consistency of BIG-ASSEM-BLER, a custom-designed software package | YU Jun, HU Songnian, WANG Jun,LI Songgang WONG Ka-Shu Gane, LIU Bin,DENG Yajun, DAI Li, ZHOU Yan,ZHANG Xiuqing, CAO Mengliang, LIU Jing,SUN Jiandong , TANG Jiabin, CHEN Yanjiong,HUANG Xiaobing, LIN Wei, YE Chen, TONG Wei,CONG Lijuan, GENG Jianing, HAN Yujun, LI Lin,LI Wei, HU Guangqiang, HUANG Xiangang,LI Wenjie, LI Jian, LIU Zhanwei, LI Long,LIU Jianping, Ql Qiuhui, LIU Jinsong, LI Li,WANG Xuegang, LU Hong, WU Tingling,ZHU Miao, Nl Peixiang, HAN Hua, DONG Wei,REN Xiaoyu, FENG Xiaoli, GUI Peng,LI Xianran, WANG Hao, XU Xin, ZHAI Wenxue,XU Zhao, ZHANG Jinsong, HE Sijie,ZHANG Jianguo, XU Jichen, ZHANG Kunlin,ZHENG Xianwu, DONG Jianhai, ZENG Wanyong,TAO Lin, CHEN Xuewei, HE Jun, LIU Daofeng,TIAN Wei, TIAN Chaoguang, XIA Hongai,LI Gang, GAO Hui, LI Ping, CHEN Wei ,WANG Xudong, ZHANG Yong, HU Jianfei,WANG Jing, LIU Song, YANG Jian,ZHANG Guangyu, XIONG Yuqing, LI Zhijie,MAO Long, ZHOU Chengshu, ZHU Zhen,CHEN Runsheng, HAO Bailin,ZHENG Weimou, CHEN Shouyi, QUO Wei,LI Guojie, LIU Siqi, HUANG Guyang,TAO Ming, WANG Jian, ZHU Lihuang,YUAN Longping& YANG HuanmingBeijing Genomics Institute/Center of Genomics & Bioinformatics, Chinese Academy of Sciences, Beijing 101300, China Hangzhou Genomics Institute/Institute of Bioinformatics of Zhejiang University/Key Laboratory of Bioinformatics of Zhejiang Province, Hangzhou 310007, China Institute of Genetics, Chinese Academy of Sciences, Beijing 100101, China National Hybrid Rice R & D Center, Changsha 410125, China Laboratory of Bioinformatics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China College of Life Sciences, Peking University, Beijing 100871, China Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 1Q0080, China Digital China Ltd., Beijing 100080, China Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China Medical College, Xi’an Jiaotong University, Xi’an 710061, ChinaThese authors contributed equally to this work.Corresponding author.Corresponden | 2001 | Chinese Science Bulletin2001，46，23： | 6 |
| 2 | The RppC-AvrRppC NLR-effector interaction mediates the resistance to southern corn rust in maize显示文摘Southern corn rust(SCR),caused by the fungal pathogen Puccinia polysora,is a major threat to maize pro-duction worldwide.Efficient breeding and deployment of resistant hybrids are key to achieving durable control of SCR.Here,we report the molecular cloning and characterization of RppC,which encodes an NLR-type immune receptor and is responsible for a major SCR resistance quantitative trait locus.Further-more,we identified the corresponding avirulence effector,AvrRppC,which is secreted by P.polysora and triggers RppC-mediated resistance.Allelic variation of AvrRppC directly determines the effectiveness of RppC-mediated resistance,indicating that monitoring of AvrRppC variants in the field can guide the rational deployment of RppC-containing hybrids in maize production.Currently,RppC is the most frequently deployed SCR resistance gene in China,and a better understanding of its mode of action is crit-ical for extending its durability. | Ce Deng April Leonard James Cahill Meng Lv Yurong Li Shawn Thatcher Xueying Li Xiaodi Zhao Wenjie Du Zheng Li Huimin Li Victor Llaca Kevin Fengler Lisa Marshall Charlotte Harris Girma Tabor Zhimin Li Zhiqiang Tian Qinghua Yang Yanhui Chen Jihua Tang Xintao Wang Junjie Hao Jianbing Yan Zhibing Lai Xiaohong Fei Weibin Song Jinsheng Lai Xuecai Zhang Guoping Shu Yibo Wang Yuxiao Chang Weiling Zhu Wei Xiong Juan Sun Bailin Li Junqiang Ding | 2022 | Molecular Plant2022，15，5： | 6 |
| 3 | Removing local irregularities of triangular meshes with highlight line models显示文摘The highlight line model is a powerful tool in assessing the quality of a surface. It increases the ffexibility of an interactive design environment. In this paper, a method to generate a highlight line model on an arbitrary triangular mesh is presented. Based on the highlight line model, a technique to remove local shape irregularities of a triangular mesh is then presented. The shape modification is done by solving a minimization problem and performing an iterative procedure. The new technique improves not only the shape quality of the mesh surface, but also the shape of the highlight line model. It provides an intuitive and yet suitable method for locally optimizing the shape of a triangular mesh. | YONG JunHai DENG BaiLin CHENG FuHua WANG Bin WU Kun GU HeJin | 2009 | Science in China(Series F)2009，52，3： | 5 |
| 4 | Differentiable Deformation Graph-Based Neural Non-rigid Registration显示文摘The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target surface.Among the pipeline,the correspondence construction and iterative manner are key to the results,while existing strategies might result in local optima.In this paper,we adopt the widely used deformation graph-based representation,while replacing some key modules with neural learning-based strategies.Specifically,we design a neural network to predict the correspondence and its reliability confidence rather than the strategies like nearest neighbor search and pair rejection.Besides,we adopt the GRU-based recurrent network for iterative refinement,which is more robust than the traditional strategy.The model is trained in a self-supervised manner and thus can be used for arbitrary datasets without ground-truth.Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin. | Wanquan Feng Hongrui Cai Junhui Hou Bailin Deng Juyong Zhang | 2023 | Communications in Mathematics and Statistics2023，11，1： | 0 |
| 5 | Real-time face view correction for front-facing cameras显示文摘Face views are particularly important in person-to-person communication.Differenes between the camera location and the face orientation can result in undesirable facial appearances of the participants during video conferencing.This phenomenon is particularly noticeable when using devices where the frontfacing camera is placed in unconventional locations such as below the display or within the keyboard.In this paper,we take a video stream from a single RGB camera as input,and generate a video stream that emulates the view from a virtual camera at a designated location.The most challenging issue in this problem is that the corrected view often needs out-of-plane head rotations.To address this challenge,we reconstruct the 3D face shape and re-render it into synthesized frames according to the virtual camera location.To output the corrected video stream with natural appearance in real time,we propose several novel techniques including accurate eyebrow reconstruction,high-quality blending between the corrected face image and background,and template-based 3D reconstruction of glasses.Our system works well for different lighting conditions and skin tones,and can handle users wearing glasses.Extensive experiments and user studies demonstrate that our method provides high-quality results. | Yudong Guo Juyong Zhang Yihua Chen Hongrui Cai Zhangjin Huang Bailin Deng | 2021 | Computational Visual Media2021，7，4： | 0 |
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