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Advanced Materials Technologies 2021年第1期6卷

作者： Connor D. Armstrong Noah Todd Abdullah T. Alsharhan David I. Bigio Ryan D. Sochol Department of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30313 USA Department of Mechanical Engineering University of Maryland College Park MD 20742 USA Fischell Department of Bioengineering University of Maryland College Park MD 20742 USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD 20742 USA Maryland Robotics Center University of Maryland College Park MD 20742 USA

Author Correction: Reconciliation of energy use disparities in brick production in India

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Nature Sustainability 2023年 1715页

作者： Kushal Tibrewal Chandra Venkataraman Harish Phuleria Anurag Gupta Pradnya Lokhande Veena Joshi Sameer Maithel Anand Damle Shahadev Rabha Binoy K. Saikia Sayantee Roy Gazala Habib Shubham Rathi Anubha Goel Sakshi Ahlawat Tuhin Kumar Mandal M. Azharuddin Hashmi Asif Qureshi Abisheg Dhandapani Jawed Iqbal Sandeep Devaliya Ramya Sunder Raman Yang Lian Govindan Pandithurai Sudheer Kumar Kuppili M. Shiva Nagendra Sauryadeep Mukherjee Abhijit Chatterjee Tanveer Ahmad Najar Arshid Jehangir Jitender Singh Baerbel Sinha Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai India Pune Maharashtra India Greentech Knowledge Solutions Pvt. Ltd. New Delhi India De Boer Damle (India) Pvt. Ltd. Pune Maharashtra India Coal and Energy Division CSIR-North East Institute of Science and Technology Jorhat India Department of Civil Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur India Center on Global Change Environmental Sciences and Biomedical Metrology Division CSIR-National Physical Laboratory New Delhi India Department of Civil Engineering Indian Institute of Technology Hyderabad Hyderabad India Civil and Environmental Engineering Birla Institute of Technology Ranchi India Earth and Environmental Sciences Indian Institute of Science Education and Research Bhopal Bhopal India Indian Institute of Tropical Meteorology Pune Pune India Department of Civil Engineering Indian Institute of Technology Madras Chennai India Department of Environmental Sciences Bose Institute Kolkata India Department of Environmental Science University of Kashmir Srinagar Jammu and Kashmir India Department of Earth and Environmental Sciences Indian Institute of Science Education and Research Mohali Punjab India

OUTPOST: A comprehensive analysis software for whole-metagenome shotgun sequencing incorporating group stratification

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iMetaOmics

iMetaOmics 2024年第2期1卷

作者： Yihang Zhou Jihong Zheng Wenqi Song Xinyi Yan Li Du Zhonglin Ma Yanbin Fu Zhaohui Ouyang Yuchen Xiao Zhuoqun Liu Feng Tian Jason Wing Hon Wong Jen Hao David Shih Shikang Liang Honglei Tian Liu Liu Ke Wei Chao Zhang Jiangtao Li Xiaozhu Wang Fundamental Research Center Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center) School of Life Sciences and Technology Tongji University Shanghai China State Key Laboratory of Marine Geology School of Ocean and Earth Science Tongji University Shanghai China Department of Biliary-Pancreatic Surgery Shanghai Cancer Institute Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China State Key Laboratory of Cardiology and Medical Innovation Center Shanghai East Hospital Shanghai Key Laboratory of Signaling and Disease Research Frontier Science Center for Stem Cell Research School of Life Sciences and Technology Tongji University Shanghai China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Handan China School of Biomedical Sciences Li Ka Shing Faculty of Medicine The University of Hong Kong Pokfulam Hong Kong SAR China Division of Life Science The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China Shanghai Yuhui Pharmaceutical Technology (Group) Co. Ltd Shanghai China Shanghai Institute of Precision Medicine Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

Publisher Correction: A skin-integrated multimodal haptic interface for immersive tactile feedback

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Nature Electronics 2023年第12期6卷 1041页

作者： Ya Huang Jingkun Zhou Chun Ki Yiu Kuanming Yao Dengfeng Li Jian Li Tsz Hung Wong Yiming Liu Yuyu Gao Xingcan Huang Hu Li Jiyu Li Binbin Zhang Zhenlin Chen Xinge Yu Pingchuan Ke Shaoyu Cai Xingyu Yang Haichen Gao Kening Zhu Xu Guo Zichen Zhao Zhaoqian Xie Yu Zhou Lei Li Huanxi Zheng Zuankai Wang Enming Song Hui Wu Department of Biomedical Engineering City University of Hong Kong Hong Kong China Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Science Park Hong Kong China Shenzhen Research Institute City University of Hong Kong Shenzhen China Department of Sociology Hong Kong Shue Yan University Hong Kong China School of Creative Media City University of Hong Kong Hong Kong China Engineering Design and Innovation Centre College of Design and Engineering National University of Singapore Singapore Singapore State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian China Department of Engineering Mechanics Dalian University of Technology Dalian China Ningbo Institute of Dalian University of Technology Ningbo China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China National Engineering Research Center of Electric Vehicles Beijing Institute of Technology Beijing China Department of Mechanical Engineering Hong Kong Polytechnic University Hong Kong China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing China

Hydrogen medicine: A rising star in gas medicine

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Traditional Medicine and Modern Medicine 2021年第3期3卷 153-161页

作者： Dongyue Du Lin Zhao Meihua Shen Mami Noda Shucun Qin Jiangang Long Xuejun Sun Jiankang Liu Center for Mitochondrial Biology and Medicine The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xi’an Jiaotong University Xi’an 710049 Shaanxi P. R. China Department of Intensive Care Unit Shanghai Provincial Crops Hospital Chinese People’s Armed Police Forces 831 Hongxu Road Shanghai 201103 P. R. China Laboratory of Pathophysiology Graduate School of Pharmaceutical Sciences Kyushu University Fukuoka 812-8582 Japan Taishan Institute for Hydrogen Biomedicine Shandong First Medical University and Shandong Academy of Medical Sciences Tai’an 271000 P. R. China Department of Naval Medicine Second Military Medical University Shanghai P. R. China

Gas medicine, including O 2 , NO, H 2 S, CO, CH 4 , has played important roles in prevention and treatment of diseases for a long time. Molecular hydrogen (H 2 ), the smallest diatomic molecule in nature, has become a rising star in gas medicine in the past decades. Many studies have shown that H 2 has preventive and therapeutic effects on various diseases through its selective antioxidant activity. H 2 , as a non-toxic gas for the human body and convenience to obtain, has provided a great possibility to be used widely. Currently, the main difficulties in hydrogen medicine are lack of definitive clinical evidence and the molecular basis of hydrogen effects. In this paper, the authors have conducted a comprehensive review and analysis of these issues, and also proposed the possibility of developing Hydrogen Biology and Hydrogen Medicine as new disciplines of biology and medicine.

关键词： Molecular hydrogen antioxidant gas medicine hydrogen biology

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

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Journal of Intensive Medicine 2021年第2期1卷 110-116页

作者： Jonathan Montomoli Luca Romeo Sara Moccia Michele Bernardini Lucia Migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux Philippe Guerci Reto Andreas Schüpbach Can Ince Emanuele Frontoni Matthias Peter Hilty RISC-19-ICU Investigators Department of Anaesthesia and Intensive Care Infermi HospitalAUSL della RomagnaRimini 47923Italy Department of Information Engineering UniversitàPolitecnica delle MarcheAncona 60131Italy The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant’AnnaPisa 56127Italy Anesthesia and Intensive Care Unit Azienda Ospedaliero Universitaria Ospedali RiunitiAncona 60126Italy Department of Biomedical Sciences and Public Health UniversitàPolitecnica delle MarcheAncona 60126Italy Department of Anaesthesia and Intensive Care Fondazione Policlinico Universitario A.Gemelli IRCCSUniversitàCattolica del Sacro CuoreRome 00168Italy Institute of Intensive Care Medicine University Hospital of ZurichZurich 8091Switzerland Swiss Society of Intensive Care Medicine Basel 4001Switzerland Department of Anesthesiology and Critical Care Medicine University Hospital of NancyNancy 54511France Department of Intensive Care Erasmus MC University Medical Center RotterdamRotterdam3015 GDNetherlands 不详

Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival *** learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical ***:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)*** applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU *** model was iteratively cross-validated in different subsets of the study ***:The final study population consisted of 675 *** XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)*** area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources.

关键词： Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score

Characterization and Control of the Run-and-Tumble Dynamics of Escherichia Coli

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arXiv 2022年

作者： Kurzthaler, Christina Zhao, Yongfeng Zhou, Nan Schwarz-Linek, Jana Devailly, Clemence Arlt, Jochen Huang, Jian-Dong Poon, Wilson C.K. Franosch, Thomas Tailleur, Julien Martinez, Vincent A. Max Planck Institute for the Physics of Complex Systems Dresden01187 Germany Center for Systems Biology Dresden Dresden01307 Germany Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States Institut für Theoretische Physik Universität Innsbruck Technikerstraße 21A InnsbruckA-6020 Austria Center for Soft Condensed Matter Physics and Interdisciplinary Research School of Physical Science and Technology Soochow University Suzhou215006 China School of Biomedical Sciences Li Ka Shing Faculty of Medicine University of Hong Kong Pok Fu Lam Hong Kong Université de Paris MSC UMR 7057 CNRS Paris75205 France School of Physics and Astronomy Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou311200 China School of Physics and Astronomy University of Edinburgh James Clerk Maxwell Building Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

We characterize the full spatiotemporal gait of populations of swimming Escherichia coli using renewal processes to analyze the measurements of intermediate scattering functions. This allows us to demonstrate quantitatively how the persistence length of an engineered strain can be controlled by a chemical inducer and to report a controlled transition from perpetual tumbling to smooth swimming. For wild-type E. coli, we measure simultaneously the microscopic motility parameters and the large-scale effective diffusivity, hence quantitatively bridging for the first time small-scale directed swimming and macroscopic diffusion. Copyright © 2022, The Authors. All rights reserved.

关键词： Escherichia coli

Quantitative characterization of run-and-tumble statistics in bulk bacterial suspensions

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arXiv 2022年

作者： Zhao, Yongfeng Kurzthaler, Christina Zhou, Nan Schwarz-Linek, Jana Devailly, Clemence Arlt, Jochen Huang, Jian-Dong Poon, Wilson C.K. Franosch, Thomas Martinez, Vincent A. Tailleur, Julien Center for Soft Condensed Matter Physics and Interdisciplinary Research School of Physical Science and Technology Soochow University Suzhou215006 China School of Physics and Astronomy Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Sciences Li Ka Shing Faculty of Medicine University of Hong Kong Pok Fu Lam Hong Kong Université de Paris MSC UMR 7057 CNRS Paris75205 France Max Planck Institute for the Physics of Complex Systems Dresden01187 Germany Center for Systems Biology Dresden Dresden01307 Germany Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States Institut für Theoretische Physik Universität Innsbruck Technikerstraße 21A InnsbruckA-6020 Austria ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou311200 China School of Physics and Astronomy University of Edinburgh James Clerk Maxwell Building Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

We introduce a numerical method to extract the parameters of run-and-tumble dynamics from experimental measurements of the intermediate scattering function. We show that proceeding in Laplace space is unpractical and employ instead renewal processes to work directly in real time. We first validate our approach against data produced using agent-based simulations. This allows us to identify the length and time scales required for an accurate measurement of the motility parameters, including tumbling frequency and swim speed. We compare different models for the run-and-tumble dynamics by accounting for speed variability at the single-cell and population level, respectively. Finally, we apply our approach to experimental data on wild-type Escherichia coli obtained using differential dynamic microscopy. © 2022, CC BY.

关键词： Numerical methods

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

Characterizing and detecting freezing of gait using multi-modal physiological signals

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arXiv 2020年

作者： Wang, Ying Beuving, Floris Nonnekes, Jorik Cohen, Mike X. Long, Xi Aarts, Ronald M. van Wezel, Richard Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Academic Center for Epileptology Kempenhaeghe Heeze Netherlands Donders Institute for Brain Cognition and Behaviour Department of Rehabilitation Radboud University Medical Centre Nijmegen Netherlands Department of Rehabilitation Sint Maartenskliniek Nijmegen Netherlands Philips Research Eindhoven Netherlands Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Freezing-of-gait a mysterious symptom of Parkinson’s disease and defined as a sudden loss of ability to move forward. Common treatments of freezing episodes are currently of moderate efficacy and can likely be improved through a reliable freezing evaluation. Basic-science studies about the characterization of freezing episodes and a 24/7 evidence-support freezing detection system can contribute to the reliability of the evaluation in daily life. In this study, we analyzed multi-modal features from brain, eye, heart, motion, and gait activity from 15 participants with idiopathic Parkinson’s disease and 551 freezing episodes induced by turning in place. Statistical analysis was first applied on 248 of the 551 to determine which multi-modal features were associated with freezing episodes. Features significantly associated with freezing episodes were ranked and used for the freezing detection. We found that eye-stabilization speed during turning and lower-body trembling measure significantly associated with freezing episodes and used for freezing detection. Using a leave-one-subject-out cross-validation, we obtained a sensitivity of 97% ± 3%, a specificity of 96% ± 7%, a precision of 73% ± 21%, a Matthews correlation coefficient of 0.82 ± 0.15, and an area under the Precision-Recall curve of 0.94 ± 0.05. According to the Precision-Recall curves, the proposed freezing detection method using the multi-modal features performed better than using single-modal features. Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors