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arXiv 2020年

作者： Behar, Joachim A. Liu, Chengyu Tsutsui, Kenta Corino, Valentina D.A. Singh, Janmajay Pimentel, Marco A.F. Karlen, Walter Warrick, Philip Zaunseder, Sebastian Andreotti, Fernando Osipov1, Maxim McSharry, Patrick E. Kotzen, Kevin Karmakar, Chandan Clifford, Gari D. Faculty of Biomedical Engineering Technion-IIT Haifa Israel State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing Department of Cardiovascular Medicine Saitama International Medical Center Saitama Department of Electronics Information and Bioengineering Milano Italy Independent Researcher Department of Engineering Science University of Oxford Oxford United Kingdom Mobile Health Systems Lab Department of Health Sciences and Technology ETH Zurich Zurich Switzerland PeriGen CaryNC United States Faculty of Information Technology FH Dortmund Dortmund Germany Sensyne Health Oxford United Kingdom Babylon Health London United Kingdom Carnegie Mellon University Africa Kigali Rwanda African Centre of Excellence in Data Science University of Rwanda Kigali Rwanda Oxford Man Institute of Quantitative Finance Oxford University Oxford United Kingdom School of Information Technology Deakin University Geelong Australia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia ranges from mild to critically ill cases and requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g., hotels). Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper we present a short review of remote health monitoring initiatives taken in 19 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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NiFe Hydroxide Lattice Tensile Strain: Enhancement of Adsorption of Oxygenated Intermediates for Efficient Water Oxidation Catalysis

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Angewandte Chemie (International ed. in English) 2019年第3期58卷 736-740页

作者： Daojin Zhou Shiyuan Wang Yin Jia Xuya Xiong Hongbin Yang Song Liu Jialun Tang Junming Zhang Dong Liu Lirong Zheng Yun Kuang Xiaoming Sun Bin Liu State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 China. School of Chemical and Biomedical Engineering Nanyang Technological University Singapore 637459 Singapore) E-mail: Bin Liu. College of Energy Beijing University of Chemical Technology Beijing 100029 China. Institute for Materials Science and Devices Suzhou University of Science and Technology Suzhou 215009 China. State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China. Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of Materials Science & Engineering Beijing Institute of Technology Beijing 100029 China. Beijing Synchrotron Radiation Facility Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China.

The binding strength of reactive intermediates with catalytically active sites plays a crucial role in governing catalytic performance of electrocatalysts. NiFe hydroxide offers efficient oxygen evolution reaction (OER) catalysis in alkaline electrolyte, however weak binding of oxygenated intermediates on NiFe hydroxide still badly limits its catalytic activity. Now, a facile ball-milling method was developed to enhance binding strength of NiFe hydroxide to oxygenated intermediates via generating tensile strain, which reduced the anti-bonding filling states in the d orbital and thus facilitated oxygenated intermediates adsorption. The NiFe hydroxide with tensile strain increasing after ball-milling exhibits an OER onset potential as low as 1.44 V (vs. reversible hydrogen electrode) and requires only a 270 mV overpotential to reach a water oxidation current density of 10 mA cm .

关键词： NiFe catalysts ball-milling oxygen evolution tensile strain

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Cryo-EM structure of human mTOR complex 2

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细胞研究（英文版） 2018年第5期28卷 518-528页

作者： Xizi Chen Mengjie Liu Yuan Tian Jiabei Li Yilun Qi Dan Zhao Zihan Wu Min Huang Catherine C. L. Wong Hong-Wei Wang Jiawei Wang Huirong Yang Yanhui Xu Fudan University Shanghai Cancer Center Institutes of Biomedical Sciences Shanghai Medical College of Fudan University Shanghai 200032 China Key Laboratory of Molecular Medicine Ministry of Education Department of Systems Biology for Medicine School of Basic Medical Sciences Shanghai Medical College of Fudan University Shanghai 200032 China State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and Development School of Life Sciences Fudan University Shanghai 200433 China National Center for Protein Science Shanghai Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 201210 China National Center for Protein Science Shanghai Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 201210 China Center for Precision Medicine Multi-Omics Research (CPMMOR) Peking University Health Science Center Beijing 100871 China State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Science Peking University Beijing 100871 China Ministry of Education Key Laboratory of Protein Sciences Tsinghua-Peking Joint Center for Life Sciences Beijing Advanced Innovation Center for Structural Biology School of Life Sciences Tsinghua University Beijing 100084 China Fudan University Shanghai Cancer Center Institutes of Biomedical Sciences Shanghai Medical College of Fudan University Shanghai 200032 China Key Laboratory of Molecular Medicine Ministry of Education Department of Systems Biology for Medicine School of Basic Medical Sciences Shanghai Medical College of Fudan University Shanghai 200032 China State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and Development School of Life Sciences Fudan University Shanghai 200433 China CAS Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai 200031 China

Mechanistic target of rapamycin (mTOR) complex 2 (mTORC2) plays an essential role in regulating cell proliferation through phosphorylating AGC protein kinase family members, including AKT, PKC and SGK1. The functional core complex consists of mTOR, mLST8, and two mTORC2-specific components, Rictor and mSinl. Here we investigated the intermolecular interactions within mTORC2 complex and determined its cryo-electron microscopy structure at 4.9 A resolution. The structure reveals a hollow rhombohedral fold with a 2-fold symmetry. The dimerized mTOR serves as a scaffold for the complex assembly. The N-terminal half of Rictor is composed of helical repeat clusters and binds to mTOR through multiple contacts. mSinl is located close to the FRB domain and catalytic cavity of mTOR. Rictor and mSinl together generate steric hindrance to inhibit binding of FKBP12-rapamycin to mTOR, revealing the mechanism for rapamycin insensitivity of mTORC2. The mTOR dimer in mTORC2 shows more compact conformation than that of mTORC1 (rapamycin sensitive), which might result from the interaction between mTOR and Rictor-mSinl. Structural comparison shows that binding of Rictor and Raptor (mTORC1-specific component) to mTOR is mutually exclusive. Our study provides a basis for understanding the assembly of mTORC2 and a framework to further characterize the regulatory mechanism of mTORC2 pathway.

关键词： Cryoelectron microscopy TOR signalling

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Determination of Polynomial Degree in the Regression of Drug Combinations

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 41-47页

作者： Boqian Wang Xianting Ding Fei-Yue Wang School of Biomedical Engineering Shanghai Jiao Tong University IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science (SKL-MCCS CASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations. © 2014 Chinese Association of Automation.

关键词： Acoustics Random errors Regression analysis

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MYC amplification at diagnosis drives therapy-induced hypermutation of recurrent glioma

Research Square

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Research Square 2021年

作者： Mu, Quanhua Chai, Ruichao Liu, Hanjie Yang, Yingxi Zhao, Zheng Lui, Ming Hong Bao, Zhaoshi Song, Dong Jiang, Biaobin Sa, Jason K. Cho, Hee Jin Chang, Yuzhou Chan, Kaitlin Hao Yi Loi, Danson Shek Chun Tam, Sindy Sing Ting Chan, Aden Ka Yin Wu, Angela Ruohao Poon, Wai Sang Ng, Ho Keung Chan, Danny Tat Ming Iavarone, Antonio Nam, Do-Hyun Jiang, Tao Wang, Jiguang Division of Life Science Department of Chemical and Biological Engineering Center of Systems Biology and Human Health State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong Beijing Neurosurgical Institute Capital Medical University Beijing China Division of Life Science Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Institute for Refractory Cancer Research Samsung Medical Center Seoul Korea Republic of Department of Biomedical Sciences Korea University College of Medicine Seoul Korea Republic of CUHK Otto Wong Brain Tumour Centre Department of Surgery Prince of Wales Hospital The Chinese University of Hong Kong Hong Kong Department of Anatomical and Cellular Pathology Prince of Wales Hospital The Chinese University of Hong Kong Hong Kong Institute for Cancer Genetics Columbia University New YorkNY United States Department of Neurosurgery Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea Republic of Department of Health Science & Technology Samsung Advanced Institute for Health Sciences & Technology Sungkyunkwan University School of Medicine Seoul Korea Republic of Research Networks China

Clonal evolution drives cancer progression and therapeutic resistance1,2. Recent longitudinal analyses revealed divergent clonal dynamics in adult diffuse gliomas3–11. However, the early genomic and epigenomic factors that steer post-treatment molecular trajectories remain unknown. To track evolutionary predictors, we analyzed sequencing and clinical data of matched initial-recurrent tumor pairs from 511 adult diffuse glioma patients. Using machine learning we developed methods capable of predicting grade progression and hypermutation from tumor characteristics at diagnosis. Strikingly, MYC copy number gain in initial tumors emerged as a key factor predicting development of hypermutation under temozolomide (TMZ) treatment. The driving role of MYC in TMZ-associated hypermutagenesis has been experimentally validated in a model of TMZ-induced hypermutator using both patient-derived gliomaspheres and established glioma cell lines. Subsequent studies showed that c-Myc binding to open chromatin and transcriptionally active regions increases the vulnerability of genomic regions to TMZ-induced mutagenesis. Consequently, MYC target genes, including the key mismatch repair genes, develop loss-of-function mutations, thus triggering the hypermutation process. This study reveals MYC as an early predictor of cancer evolution and provides a machine learning platform for predicting cancer dynamics to improve patient management. © 2021, CC BY.

关键词： Tumors

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Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

Hand Gesture Recognition with Generalized Hough Transform an...

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International Conference on Information Science and Technology (ICIST)

作者： Bo Liao Jing Li Zhaojie Ju Gaoxiang Ouyang School of Information Engineering Nanchang University Nanchang Jiangxi China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth Portsmouth U.K. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

Hand gesture recognition plays an important role in human-computer interaction. With the development of depth cameras, color images combined with depth images can provide richer information for hand gesture recognition. In this paper, we propose a hand gesture recognition system based on the data captured by Intel RealSense Front-Facing Camera SR300. Considering that the pixels in depth images collected by RealSense are not one-to-one to those in color images, the recognition system maps depth images to color images based on generalized Hough transform in order to segment hand from a complex background in color images using the depth information. Then, it recognizes different hand gestures by a novel double-channel convolutional neural network containing two input channels which are color images and depth images. Moreover, we built a hand gesture database of 24 different kinds of hand gestures representing 24 letters in the English alphabet. It contains a total of 168,000 images which are 84,000 RGB images and 84,000 depth images. Experimental results on our newly collected hand gesture database demonstrate the effectiveness of the proposed approach, and the recognition accuracy is 99.4%.

关键词： Information science Transforms Convolutional neural networks Databases

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Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis (vol 18, pg 1304, 2021)

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NATURE METHODS 2022年第1期19卷 130-130页

作者： Kawahara, Rebeca Chernykh, Anastasia Alagesan, Kathirvel Bern, Marshall Cao, Weiqian Chalkley, Robert J. Cheng, Kai Choo, Matthew S. Edwards, Nathan Goldman, Radoslav Hoffmann, Marcus Hu, Yingwei Huang, Yifan Kim, Jin Young Kletter, Doron Liquet, Benoit Liu, Mingqi Mechref, Yehia Meng, Bo Neelamegham, Sriram Nguyen-Khuong, Terry Nilsson, Jonas Pap, Adam Park, Gun Wook Parker, Benjamin L. Pegg, Cassandra L. Penninger, Josef M. Phung, Toan K. Pioch, Markus Rapp, Erdmann Sakalli, Enes Sanda, Miloslav Schulz, Benjamin L. Scott, Nichollas E. Sofronov, Georgy Stadlmann, Johannes Vakhrushev, Sergey Y. Woo, Christina M. Wu, Hung-Yi Yang, Pengyuan Ying, Wantao Zhang, Hui Zhang, Yong Zhao, Jingfu Zaia, Joseph Haslam, Stuart M. Palmisano, Giuseppe Yoo, Jong Shin Larson, Goran Khoo, Kai-Hooi Medzihradszky, Katalin F. Kolarich, Daniel Packer, Nicolle H. Thaysen-Andersen, Morten Department of Molecular Sciences Macquarie University Sydney NSW Australia Institute for Glycomics Griffith University Gold Coast Campus Southport QLD Australia Biomolecular Discovery Research Centre Macquarie University Sydney NSW Australia Protein Metrics Inc. Cupertino CA USA Institutes of Biomedical Sciences and the NHC Key Laboratory of Glycoconjugates Research Fudan University Shanghai China UCSF School of Pharmacy Department of Pharmaceutical Chemistry San Francisco CA USA BRC Laboratory of Proteomics Research Szeged Hungary State University of New York Buffalo NY USA Analytics Group Bioprocessing Technology Institute Agency for Science Technology and Research Singapore Singapore Clinical and Translational Glycoscience Research Center (CTGRC) Georgetown University Washington DC USA Department of Biochemistry and Molecular & Cellular Biology Georgetown University Washington DC USA Department of Oncology Georgetown University Washington DC USA Max Planck Institute for Dynamics of Complex Technical Systems Bioprocess Engineering Magdeburg Germany glyXera GmbH Magdeburg Germany Department of Pathology The Johns Hopkins University Baltimore MD USA Department of Chemistry and Biochemistry Texas Tech University Lubbock TX USA Research Center of Bioconvergence Analysis Korea Basic Science Institute Daejeon Republic of Korea Graduate School of Analytical Science and Technology Chungnam National University Daejeon Republic of Korea Department of Mathematics and Statistics Macquarie University Sydney NSW Australia CNRS Laboratoire de Mathématiques et de leurs Applications de PAU E2S-UPPA Pau France State Key Laboratory of Proteomics Beijing Institute of Lifeomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing China Proteomics Core Facility Sahlgrenska academy University of Gothenburg Gothenburg Sweden Doctoral School in Biology Faculty of Science and Informatics University of Szeged Szeged H

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Understanding adversarial attacks on deep learning based medical image analysis systems

arXiv

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arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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3D Pose Estimation for Robotic Grasping Using Deep Convolution Neural Network

3D Pose Estimation for Robotic Grasping Using Deep Convoluti...

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IEEE International Conference on robotics and Biomimetics

作者： Yao Wang Ying Xu Xiaohui Zhang Zhen Sun Yafang Zhang Guoli Song Junchen Wang School of Mechanical Engineering and Automation Beihang University Beijing China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China

ISBN: (纸本)9781728103785;9781728103778

With the progress of artificial intelligence, robots begin to enter family service. Autonomous object grasping in a cluttered scene is the most frequent operation of a service robot in daily life while it is still a challenging problem in the field of robotics. In this paper, we develop a robot system using a deep convolution neural network for 3D object grasping. The system is composed by a color camera, and a robot arm with a gripper. The color camera provides robotic vision about surrounding environments; the deep neural network performs an end-to-end mapping from vision images to the 3D poses of the object of interest; the robot arm with the gripper is then driven to grasp the object. In addition, we also present an automatic data labeling method for the training of the convolution neural network. Preliminary experiments were performed to evaluate our robot system and the results have confirmed its effectiveness.

关键词： Conferences Robots Biomimetics

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