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Advances in Intelligent Systems and computing 2019年 834卷 ix页

作者： Pan, Jeng-Shyang Lin, Jerry Chun-Wei Sui, Bixia Tseng, Shih-Pang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian China Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Changzhou College of Information Technology ChangzhouJiangsu China College of Smart Living and Management Tajen University Pingtung Taiwan

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Correction: RPTOR blockade suppresses brain metastases of NSCLC by interfering the ceramide metabolism via hijacking YY1 binding

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Journal of experimental & clinical cancer research : CR 2024年第1期43卷 37页

作者： Ying Lin Yun Wu Qiangzu Zhang Xunwei Tu Sufang Chen Junfan Pan Nengluan Xu Ming Lin Peiwei She Gang Niu Yusheng Chen Hongru Li Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. Department of General Practice Medicine Fujian Provincial Hospital Fuzhou 350001 China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. The Centre for Experimental Research in Clinical Medicine Fujian Provincial Hospital Fuzhou 350001 Fujian China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. g.niu@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. cysktz@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. muzi131122@***. Fujian Provincial Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Shengli Clinical College of Fujian Medical University Fuzhou 350001 Fujian China. muzi131122@***.

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Revealing Interfacial Properties of Inorganic Molecular Crystal Sb2o3 and Monolayer Mos2

SSRN

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SSRN 2023年

作者： Liu, Qin Zuo, Yang He, Jingyu Zeng, Minggang Yang, Tong Zhou, Jun Yang, Yulin Song, Ting Ting Wang, Shijie Yang, Ming College of Physics and Space Science China West Normal University Nanchong637002 China Department of Applied Physics The Hong Kong Polytechnic University Hung Hom Hong Kong Institute of High-Performance Computing A∗STAR 1 Fusionopolis Way Singapore138632 Singapore Institute of Materials Research and Engineering A ∗ STAR 2 Fusionopolis Way Singapore138634 Singapore Fujian Provincial Key Laboratory of Optoelectronic Technology and Devices School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Research Centre on Data Sciences & Artificial Intelligence The Hong Kong Polytechnic University Hung Hom Hong Kong

Inorganic molecule crystal Sb2O3 has been identified as a promising high-k dielectric for direct integration with the two-dimensional semiconductor MoS2. However, a comprehensive understanding of this van der Waals (vdW) interface is still lacking, impeding its applications in high-performance nanoelectronics. In this study, we employ first-principles calculations to elucidate the interfacial interaction and electronic properties of the Sb2O3/monolayer MoS2 interface. We find that a high-performance quasi-vdW interface can be formed between Sb2O3 and monolayer MoS2, as evidenced by weak interfacial interaction, a dangling-bond-free interface, insignificant electron-hole puddle redistribution, and the preserved semiconducting properties of monolayer MoS2. Notably, the interface exhibits a remarkable defect tolerance capability during integration, as Sb2O3 cluster vacancies (the dominant defect in Sb2O3) neither introduce mid-gap states nor significantly affect the interface properties. Besides, our study reveals an asymmetric type-I band alignment at the interface, where the conduction band offset is sufficiently large to minimize electron tunneling. Our work offers a comprehensive understanding of the vdW interface between Sb2O3 and monolayer MoS2, which could potentially advance the development of inorganic molecular crystals as high-k dielectrics for high-performance two-dimensional electronic devices. © 2023, The Authors. All rights reserved.

关键词： Antimony compounds

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CMR×Recon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

arXiv

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arXiv 2023年

作者： Wang, Chengyan Lyu, Jun Wang, Shuo Qin, Chen Guo, Kunyuan Zhang, Xinyu Yu, Xiaotong Li, Yan Wang, Fanwen Jin, Jianhua Shi, Zhang Xu, Ziqiang Tian, Yapeng Hua, Sha Chen, Zhensen Liu, Meng Sun, Mengting Kuang, Xutong Wang, Kang Wang, Haoran Li, Hao Chu, Yinghua Yang, Guang Bai, Wenjia Zhuang, Xiahai Wang, He Qin, Jing Qu, Xiaobo Human Phenome Institute Fudan University Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom 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 Xiamen China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Bioengineering/˜Imperial-X Imperial College London United Kingdom School of Data Science Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Computer Science The University of Texas Dallas United States 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 Simens Healthineers Ltd. China Department of Brain Sciences Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://***/#!Synapse:syn51471091/wiki/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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CMRxRecon2024: A Multi-Modality, Multi-View K-Space dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

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

arXiv

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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

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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Correction to: Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

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Critical care (London, England) 2022年第1期26卷 115页

作者： Lu Ke Jiajia Lin Gordon S Doig Arthur R H van Zanten Yang Wang Juan Xing Zhongheng Zhang Tao Chen Lixin Zhou Dongpo Jiang Qindong Shi Jiandong Lin Jun Liu Aibin Cheng Yafeng Liang Peiyang Gao Junli Sun Wenming Liu Zhenyu Yang Rumin Zhang Wei Xing An Zhang Zhigang Zhou Tingfa Zhou Yang Liu Fei Tong Qiuhui Wang Aijun Pan Xiaobo Huang Chuming Fan Weihua Lu Dongwu Shi Lei Wang Wei Li Liming Gu Yingguang Xie Rongqing Sun Feng Guo Lin Han Lihua Zhou Xiangde Zheng Feng Shan Jianbo Liu Yuhang Ai Yan Qu Liandi Li Hailing Li Zhiguo Pan Donglin Xu Zhiqiang Zou Yan Gao Chunli Yang Qiuye Kou Xijing Zhang Jinglan Wu Chuanyun Qian Weixing Zhang Minjie Zhang Yuan Zong Bingyu Qin Fusen Zhang Zhe Zhai Yun Sun Ping Chang Bo Yu Min Yu Shiying Yuan Yijun Deng Liyun Zhao Bin Zang Yuanfei Li Fachun Zhou Xiaomei Chen Min Shao Weidong Wu Ming Wu Zhaohui Zhang Yimin Li Qiang Guo Zhiyong Wang Yuanqi Gong Yunlin Song Kejian Qian Yongjian Feng Baocai Fu Xueyan Liu Zhiping Li Chuanyong Gong Cheng Sun Jian Yu Zhongzhi Tang Linxi Huang Biao Ma Zhijie He Qingshan Zhou Rongguo Yu Zhihui Tong Weiqin Li Department of Critical Care Medicine Jinling Hospital Medical School of Nanjing University No. 305 Zhongshan East Road Nanjing 210000 Jiangsu Province China. National Institute of Healthcare Data Science Nanjing University Nanjing China. Northern Clinical School Royal North Shore Hospital University of Sydney Sydney Australia. Department of Intensive Care Medicine Gelderse Vallei Hospital Willy Brandtlaan 10 6716 RP Ede The Netherlands. Department of Medical Research and Biometrics Center State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Benq Medical Center Nanjing China. Department of Emergency Medicine Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China. Tropical Clinical Trials Unit Department of Clinical Sciences Liverpool School of Tropical Medicine Liverpool L3 5QA UK. First People's Hospital of Foshan Foshan China. Daping Hospital Army Medical University Chongqing China. First Affiliated Hospital of Xi'an Jiao Tong University Xi'an China. First Affiliated Hospital of Fujian Medical University Fuzhou China. Suzhou Municipal Hospital Suzhou China. North China University of Science and Technology Affiliated Hospital Tangshan China. Qindao University Medical College Affiliated Yantai Yuhuangding Hospital Yantai China. Chengdu University of Traditional Chinese Medicine Affiliated Hospital Chengdu China. Luoyang Central Hospital Affiliated to Zhengzhou University Luoyang China. Changzhou No. 2 People's Hospital Affiliated to Nanjing Medical University Changzhou China. The Second Affiliated Hospital of Harbin Medical University Harbin China. Zibo Central Hospital Zibo China. Department of Intensive Care Medicine The Third Xiangya Hospital of Central South University Changsha China. The Second Affiliated Hospital of Chongqing Medical University Chongqing China. First People's Hospita

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