检索结果-内蒙古大学图书馆

A multipoint guidance mechanism for ß-barrel folding on the SAM complex (vol 30, pg 176, 2023)

NATURE STRUCTURAL & MOLECULAR BIOLOGY 2023年第2期30卷 233-233页

作者： Takeda, Hironori Busto, Jon V. Lindau, Caroline Tsutsumi, Akihisa Tomii, Kentaro Imai, Kenichiro Yamamori, Yu Hirokawa, Takatsugu Motono, Chie Ganesan, Iniyan Wenz, Lena-Sophie Becker, Thomas Kikkawa, Masahide Pfanner, Nikolaus Wiedemann, Nils Endo, Toshiya Faculty of Life Sciences Kyoto Sangyo University Kyoto Japan Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany Department of Cell Biology and Anatomy Graduate School of Medicine The University of Tokyo Tokyo Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Cellular and Molecular Biotechnology Research Institute AIST Tokyo Japan Division of Biomedical Science Faculty of Medicine University of Tsukuba Tsukuba Japan Transborder Medical Research Center University of Tsukuba Tsukuba Japan Computational Bio Big-Data Open Innovation Laboratory AIST Waseda University Tokyo Japan Institute of Biochemistry and Molecular Biology Faculty of Medicine University of Bonn Bonn Germany CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Freiburg Germany BIOSS Centre for Biological Signalling Studies University of Freiburg Freiburg Germany Institute for Protein Dynamics Kyoto Sangyo University Kyoto Japan

Mitochondrial β-barrel proteins are essential for the transport of metabolites, ions and proteins. The sorting and assembly machinery (SAM) mediates their folding and membrane insertion. We report the cryo-electron microscopy structure of the yeast SAM complex carrying an early eukaryotic β-barrel folding intermediate. The lateral gate of Sam50 is wide open and pairs with the last β-strand (β-signal) of the substrate—the 19-β-stranded Tom40 precursor—to form a hybrid barrel in the membrane plane. The Tom40 barrel grows and curves, guided by an extended bridge with Sam50. Tom40’s first β-segment (β1) penetrates into the nascent barrel, interacting with its inner wall. The Tom40 amino-terminal segment then displaces β1 to promote its pairing with Tom40’s last β-strand to complete barrel formation with the assistance of Sam37’s dynamic α-protrusion. Our study thus reveals a multipoint guidance mechanism for mitochondrial β-barrel folding.

关键词： Cryoelectron microscopy Protein transport

来源：评论

学校读者我要写书评

暂无评论

Neural networks for protein structure and function prediction and dynamic analysis

引用

Biophysical reviews 2020年第2期12卷 569-573页

作者： Yuko Tsuchiya Kentaro Tomii Artificial Intelligence Research Center (AIRC) Tokyo Japan. Biotechnology Research Institute for Drug Discovery Tokyo Japan. Artificial Intelligence Research Center (AIRC) Tokyo Japan. k-tomii@aist.go.jp. Biotechnology Research Institute for Drug Discovery Tokyo Japan. k-tomii@aist.go.jp. Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL) National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan. k-tomii@aist.go.jp.

Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.

关键词： Contact prediction Deep learning Neural networks Protein allostery Protein-compound interaction

来源：评论

学校读者我要写书评

暂无评论

Neural multi-objective combinatorial optimization with diversity enhancement 23

Neural multi-objective combinatorial optimization with diver...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain

arXiv

引用

arXiv 2022年

作者： Ren, Pu Rao, Chengping Chen, Su Wang, Jian-Xun Sun, Hao Liu, Yang Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States Key Laboratory of Urban Security and Disaster Engineering The Ministry of Education Beijing University of Technology Beijing100124 China Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China School of Engineering Sciences University of Chinese Academy of Sciences Beijing101408 China

Recently, there has been an increasing interest in leveraging physics-informed neural networks (PINNs) for modeling dynamical systems. However, very limited studies have been conducted along this horizon on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a new PINN model for seismic wave modeling in semi-infinite domain without the need of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. To scale up, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy to account for parametric loading, which estimates the wave propagation in semi-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Wave propagation

来源：评论

学校读者我要写书评

暂无评论

Capture-seq protocol and TE-reX pipeline guidelines for detection of recombination of repeat elements in short- and long-DNA reads libraries

引用

STAR Protocols 2023年第1期4卷

作者： Pascarella, Giovanni Straniero, Letizia Frith, Martin Carninci, Piero RIKEN Center for Integrative Medical Sciences (IMS) Yokohama 230-0045 Japan Department of Biomedical Sciences Humanitas University Milan 20072 Italy Humanitas Clinical and Research Center IRCCS Milan 20133 Italy Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8562 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) AIST Tokyo 135-0064 Japan

Recombination of repeat elements is an important source of genomic variation in human tissues. Here, we describe steps to prepare libraries enriched for repeat elements starting from the genomic DNA of any species. We also detail the computational post-processing of the TE-reX pipeline output to generate datasets of putative somatic and polymorphic recombination events. For complete details on the use and execution of this protocol, please refer to Pascarella et al. (2022). © 2023 The Authors

关键词： Bioinformatics Genomics Molecular Biology Sequencing

来源：评论

学校读者我要写书评

暂无评论

Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

引用

Engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

来源：评论

学校读者我要写书评

暂无评论

Erratum to “ECMER: Edge-Cloud Collaborative Personalized Multimodal Emotion Recognition Framework in the Internet of Vehicles”

引用

IEEE Network 2024年第3期38卷 292-292页

作者： Puning Zhang Miao Fu Rongjian Zhao Dapeng Wu Hongbin Zhang Zhigang Yang Ruyan Wang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China and the Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Chongqing Innovation Center of Industrial Big-Data Company Ltd. Chongqing China

This addresses errors in [1] . There is incorrect author affiliation information in this article.

关键词： Emotion recognition Collaboration Internet of Vehicles

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins

引用

Briefings in bioinformatics 2021年第6期22卷 bbab287页

作者： Hideki Yamaguchi Yutaka Saito Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba 277-8561 Japan. Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Koto-ku Tokyo 135-0064 Japan. AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) Shinjuku-ku Tokyo 169-8555 Japan.

来源：评论

学校读者我要写书评

暂无评论

Recombination of repeat elements generates somatic complexity in human genomes

引用

四川生理科学杂志 2022年第7期44卷 1291-1291页

作者： Giovanni Pascarella RIKEN Center for Integrative Medical Sciences (IMS) Yokohama 230-0045 Japan Institute for Protein Research Osaka University Osaka 565-0871 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8562 Chiba Japan Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Cell and Molecular Biology Karolinska Institutet Stockholm 171 77 Sweden Stockholm University Frescativägen 114 19 Stockholm Sweden Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm 171 77 Sweden Science for Life Laboratory Tomtebodavägen 23 171 65 Solna Sweden Department of Neuropathology Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology (TMGHIG) Tokyo 173-0015 Japan Central RNA Laboratory and Department of Neuroscience and Brain Technologies Istituto Italiano di Tecnologia (IIT) Genova 16163 Italy Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8562 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) AIST Tokyo 135-0064 Japan Human Technopole Milan 20157 Italy

Non-allelic recombination between homologous repetitive elements contributes to evolution and human genetic ***,we combine short-and long-DNA read sequencing of repeat elements with a new bioinformatics pipeline to show that somatic recombination of Alu and L1 elements is widespread in the human *** analysis uncovers tissue-specific non-allelic homologous recombination hallmarks;moreover,we find that centromeres and cancer-associated genes are enriched for retroelements that may act as recombination *** compare recombination profiles in human-induced pluripotent stem cells and differentiated neurons and find that the neuron-specific recombination of repeat elements accompanies chromatin changes during cell-fate determination.

关键词： determination. elements companies

来源：评论

学校读者我要写书评

暂无评论

The Ninth Visual Object Tracking VOT2021 Challenge Results

The Ninth Visual Object Tracking VOT2021 Challenge Results

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Matej Kristan Jiří Matas Aleš Leonardis Michael Felsberg Roman Pflugfelder Joni-Kristian Kämäräinen Hyung Jin Chang Martin Danelljan Luka Čehovin Zajc Alan Lukežič Ondrej Drbohlav Jani Käpylä Gustav Häger Song Yan Jinyu Yang Zhongqun Zhang Gustavo Fernández Mohamed Abdelpakey Goutam Bhat Llukman Cerkezi Hakan Cevikalp Shengyong Chen Xin Chen Miao Cheng Ziyi Cheng Yu-Chen Chiu Ozgun Cirakman Yutao Cui Kenan Dai Mohana Murali Dasari Qili Deng Xingping Dong Daniel K. Du Matteo Dunnhofer Zhen-Hua Feng Zhiyong Feng Zhihong Fu Shiming Ge Rama Krishna Gorthi Yuzhang Gu Bilge Gunsel Qing Guo Filiz Gurkan Wencheng Han Yanyan Huang Felix Järemo Lawin Shang-Jhih Jhang Rongrong Ji Cheng Jiang Yingjie Jiang Felix Juefei-Xu Yin Jun Xiao Ke Fahad Shahbaz Khan Byeong Hak Kim Josef Kittler Xiangyuan Lan Jun Ha Lee Bastian Leibe Hui Li Jianhua Li Xianxian Li Yuezhou Li Bo Liu Chang Liu Jingen Liu Li Liu Qingjie Liu Huchuan Lu Wei Lu Jonathon Luiten Jie Ma Ziang Ma Niki Martinel Christoph Mayer Alireza Memarmoghadam Christian Micheloni Yuzhen Niu Danda Paudel Houwen Peng Shoumeng Qiu Aravindh Rajiv Muhammad Rana Andreas Robinson Hasan Saribas Ling Shao Mohamed Shehata Furao Shen Jianbing Shen Kristian Simonato Xiaoning Song Zhangyong Tang Radu Timofte Philip Torr Chi-Yi Tsai Bedirhan Uzun Luc Van Gool Paul Voigtlaender Dong Wang Guangting Wang Liangliang Wang Lijun Wang Limin Wang Linyuan Wang Yong Wang Yunhong Wang Chenyan Wu Gangshan Wu Xiao-Jun Wu Fei Xie Tianyang Xu Xiang Xu Wanli Xue Bin Yan Wankou Yang Xiaoyun Yang Yu Ye Jun Yin Chengwei Zhang Chunhui Zhang Haitao Zhang Kaihua Zhang Kangkai Zhang Xiaohan Zhang Xiaolin Zhang Xinyu Zhang Zhibin Zhang Shaochuan Zhao Ming Zhen Bineng Zhong Jiawen Zhu Xue-Feng Zhu University of Ljubljana Slovenia Czech Technical University Czech Republic University of Birmingham United Kingdom Linköping University Sweden Austrian Institute of Technology Austria TU Wien Austria Tampere University Finland ETH Zurich Switzerland University of British Columbia Canada Istanbul Technical University Turkey Eskisehir Osmangazi University Turkey Tianjin University of Technology China Dalian University of Technology China Zhejiang Dahua Technology CO China Kyushu University Japan Tamkang University Taiwan Nanjing University China Indian Institute of Technology Tirupati India ByteDance China Inception Institute of Artificial Intelligence China University of Udine Italy University of Surrey United Kingdom Tianjin University China Beihang University China University of Chinese Academy of Science China SIMIT China Nanyang Technological University Singapore Beijing Institute of Technology China Fuzhou University China Xiamen University China Jiangnan University China Alibaba Group USA Mohamed Bin Zayed University of Artificial Intelligence UAE Korea Institute of Industrial Technology (KITECH) Korea Hong Kong Baptist University China RWTH Aachen University Germany Guangxi Normal University China JD Finance America Corporation USA Shenzhen Research Institute of Big Data China Peng Cheng Laboratory China Huaqiao University China University of Isfahan Iran Microsoft Research Asia China Eskisehir Technical University Turkey University of Oxford United Kingdom University of Science and Technology of China China Sun Yat-sen University China Penn State University USA Southeast University China Remark AI United Kingdom Dalian Maritime University China Nanjing University of Information Science and Technology China

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website 1 .

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：