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作者： Xiao, Kexin Xia, Bin Chen, Zhiyong Xiao, Baicen Chen, Dageng Ma, Shaodan Shanghai Institute for Advanced Communication and Data Science Institute of Wireless Communication Technologies Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Wireless Communication Technologies Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering University of Washington SeattleWA98105 United States Cooperative Medianent Innovation Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China Communications Technology Laboratory Huawei Technologies Co. Ltd. Shanghai201206 China Department of Electrical and Computer Engineering University of Macau C-Macau999078 China

Sparse code multiple access (SCMA) is a promising non-orthogonal air-interface technology for its ability to support massive connections. In this paper, we design the multiuser codebook and the advanced decoding from the perspective of the theoretical capacity and the system feasibility. First, different from the lattice-based constellation in point-to-point channels, we propose a novel codebook for maximizing the constellation constrained capacity. We optimize a series of 1-D superimposed constellations to construct multi-dimensional codewords. An effective dimensional permutation switching algorithm is proposed to further obtain the capacity gain. Consequently, it shows that the performance of the proposed codebook approaches the Shannon limit and achieves significant gains over the other existing ones. Furthermore, we provide a symbol-based extrinsic information transfer tool to analyze the convergence of SCMA iterative detection, where the complex codewords are considered in modeling the a priori probabilities instead of assuming the binary inputs in previous literature. Finally, to approach the capacity, we develop a low-density parity-check code-based SCMA receiver. Most importantly, by utilizing the EXIT charts, we propose an iterative joint detection and decoding scheme with only partial inner iterations, which exhibits significant performance gain over the traditional one with separate detection and decoding. © 2002-2012 IEEE.

关键词： Iterative decoding

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

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Journal of Healthcare Engineering 2020年第1期2020卷

作者： Yilin Xie Zhuoyue Wu Xin Han Hongyu Wang Yifan Wu Lei Cui Jun Feng Zhaohui Zhu Zhongyuanlong Chen Department of Information Science and Technology Northwest University Xi’an Shaanxi 710127 *** School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an 710127 Shaanxi *** Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC Urumqi Xinjiang Uygur Autonomous Region 830049 China

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Dense matter with eXTP

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Science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 28-44页

作者： Anna L.Watts WenFei Yu Juri Poutanen Shu Zhang Sudip Bhattacharyya Slavko Bogdanov Long Ji Alessandro Patruno Thomas E.Riley Pavel Bakala Altan Baykal Federico Bernardini Ignazio Bombaci Edward Brown Yuri Cavecchi Deepto Chakrabarty Jér?me Chenevez Nathalie Degenaar Melania Del Santo Tiziana Di Salvo Victor Doroshenko Maurizio Falanga Robert D.Ferdman Marco Feroci Angelo F.Gambino MingYu Ge Svenja K.Greif Sebastien Guillot Can Gungor Dieter H.Hartmann Kai Hebeler Alexander Heger Jeroen Homan Rosario Iaria Jean in 't Zand Oleg Kargaltsev Aleksi KurkelaTheoretical Physics department CERN XiaoYu Lai Ang Li XiangDong Li ZhaoSheng Li Manuel Linares FangJun Lu Simin Mahmoodifar Mariano Méndez M.Coleman Miller Sharon Morsink Joonas N?ttil? Andrea Possenti Chanda Prescod-Weinstein JinLu Qu Alessandro Riggio Tuomo Salmi Andrea Sanna Andrea Santangelo Hendrik Schatz Achim Schwenk LiMing Song Eva?rámková Benjamin Stappers Holger Stiele Tod Strohmayer Ingo Tews Laura Tolos Gabriel T?r?k David Tsang Martin Urbanec Andrea Vacchi RenXin Xu YuPeng Xu Silvia Zane GuoBao Zhang ShuangNan Zhang WenDa Zhang ShiJie Zheng Xia Zhou Anton Pannekoek Institute for Astronomy University of Amsterdam Shanghai Astronomical Observatory Tuorla Observatory Department of Physics and Astronomy University of Turku Nordita KTH Royal Institute of Technology and Stockholm University Institute of High Energy Physics Chinese Academy Sciences Tata Institute of Fundamental Research Columbia Astrophysics Laboratory Columbia University Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen Leiden Observatory Leiden University Research Center for Computational Physics and Data Processing Silesian University in Opava Physics Department Middle East Technical University INAF Osservatorio Astronomico di Roma New York University Abu Dhabi Dipartimento di Fisica Enrico Fermi University of Pisa INFN Italian National Institute for Nuclear Physics Department of Physics and Astronomy Michigan State University Department of Astrophysical Sciences Princeton University Mathematical Sciences and STAG Research Centre University of Southampton MIT Kavli Institute for Astrophysics and Space Research DTU Space Technical University of Denmark INAF/IASF Palermo via Ugo La Malfa 153 Universita degli Studi di Palermo Dipartimento di Fisica e Chimica International Space Science Institute (ISSI) Faculty of Science University of East Anglia INAF Istituto di Astrofisica e Planetologie Spaziali Institut für Kernphysik Technische Universit?t Darmstadt ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Instituto de Astrof'?sica Pontificia Universidad Católica de Chile Department of Physics & Astronomy Kinard Lab of Physics Clemson University School of Physics and Astronomy Monash University SRON Netherlands Institute for Space Research Department of Physics The George Washington University Faculty of Science and Technology University of Stavanger School of Physics and Mechanical & Electrical Engineering Hubei University of Education Department of Astronomy Xiamen University (Haiyun Campus) School of Astronomy and Space

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to be launched in the mid 2020 s.

关键词： neutron X-rays dense matter equation of state

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Brain-computer interface with corrupted EEG data: A tensor completion approach

arXiv

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arXiv 2018年

作者： Solé-Casals, J. Caiafa, C.F. Zhao, Q. Cichocki, A. University of Vic Central University of Catalonia Data and Signal Processing Research Group Vic Catalonia Spain - CCT-La Plata CONICET CICPBA Villa Elisa Buenos Aires Argentina Department of Psychological and Brain Sciences Indiana University BloomingtonIN United States Tensor Learning Unit RIKEN Center for Advanced Intelligence Tokyo Japan School of Automation Guangdong University of Technology China Skolkovo Institute of Science and Technology Moscow Russia Department of Informatics Nicolaus Copernicus University Torun87-100 Poland College of Computer Science Hangzhou Dianzi University Hangzhou310018 China

Background / Introduction: One of the current issues in Brain-computer Interface (BCI) is how to deal with noisy Electroencephalography (EEG) measurements organized as multidimensional datasets (tensors). On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements (electrode misconnections, subject movements, etc.) are considered as unknowns (missing samples) that are inferred from a tensor decomposition model (tensor completion). Methods: We evaluate the performance of four recently proposed tensor completion algorithms, CP-WOPT (Acar et al, 2011), 3DPB-TC (Caiafa et al, 2013), BCPF (Zhao et al, 2015) and HaLRT (Liu et al. (2013), plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI applications. Results: We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery (MI), however, not at the same level as clean data. Summarizing, compared to the interpolation case, all tensor completion algorithms succeed to increase the classification performance by

关键词： Electroencephalography

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Risk-aversemodel predictive control

arXiv

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arXiv 2017年

作者： Sopasakis, Pantelis Herceg, Domagoj Bemporad, Alberto Patrinos, Panagiotis University of Cyprus Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks 1 Panepistimiou Avenue Aglantzia Nicosia2109 IMT School for Advanced Studies Lucca Piazza San Francesco 19 Lucca55100 Italy STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Kasteelpark Arenberg 10 Leuven3001 Belgium

Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for *** Codes 93E20 Copyright © 2017, The Authors. All rights reserved.

关键词： Model predictive control

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

引用

Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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