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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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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Pathogenesis, symptomatology, and transmission of SARS-CoV-2 through analysis of viral genomics and structure

arXiv

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arXiv 2021年

作者： Rando, Halie M. MacLean, Adam L. Lee, Alexandra J. Lordan, Ronan Ray, Sandipan Bansal, Vikas Skelly, Ashwin N. Sell, Elizabeth Dziak, John J. Shinholster, Lamonica D'Agostino McGowan, Lucy Guebila, Marouen Ben Wellhausen, Nils Knyazev, Sergey Boca, Simina M. Capone, Stephen Qi, Yanjun Park, Yoson Sun, Yuchen Mai, David Boerckel, Joel D. Brueffer, Christian Byrd, James Brian Kamil, Jeremy P. Wang, Jinhui Velazquez, Ryan Szeto, Gregory L. Barton, John P. Goel, Rishi Raj Mangul, Serghei Lubiana, Tiago Gitter, Anthony Greene, Casey S. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States University of Colorado School of Medicine AuroraCO United States Department of Quantitative and Computational Biology University of Southern California Los AngelesCA United States Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Biotechnology Indian Institute of Technology Hyderabad Kandi Sangareddy Telangana 502285 India Biomedical Data Science and Machine Learning Group German Center for Neurodegenerative Diseases Tubingen72076 Germany Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Immunology University of Pennsylvania Perelman School of Medicine Philadelphia United States Edna Bennett Pierce Prevention Research Center The Pennsylvania State University University ParkPA United States LSH2126 Mercer University MaconGA United States Department of Mathematics and Statistics Wake Forest University Winston-SalemNC United States Department of Biostatistics Harvard School of Public Health BostonMA United States Georgia State University AtlantaGA United States Innovation Center for Biomedical Informatics Georgetown University Medical Center WashingtonDC United States St. George's University School of Medicine Saint George's Grenada Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Department of Orthopaedic Surgery Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Bioengineering University of Pennsylvania Philadelphia PhiladelphiaPA United States Department of Clinical Sciences L

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. © 2021, CC BY.

关键词： Coronavirus

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Brain modelling as a service: The virtual brain on EBRAINS

arXiv

引用

arXiv 2021年

作者： Schirner, Michael Domide, Lia Perdikis, Dionysios Triebkorn, Paul Stefanovski, Leon Pai, Roopa Prodan, Paula Valean, Bogdan Palmer, Jessica Langford, Chloê Blickensdörfer, André van der Vlag, Michiel Diaz-Pier, Sandra Peyser, Alexander Klijn, Wouter Pleiter, Dirk Nahm, Anne Schmid, Oliver Woodman, Marmaduke Zehl, Lyuba Fousek, Jan Petkoski, Spase Kusch, Lionel Hashemi, Meysam Marinazzo, Daniele Mangin, Jean-François Flöel, Agnes Akintoye, Simisola Stahl, Bernd Carsten Cepic, Michael Johnson, Emily Deco, Gustavo McIntosh, Anthony R. Hilgetag, Claus C. Morgan, Marc Schuller, Bernd Upton, Alex McMurtrie, Colin Dickscheid, Timo Bjaalie, Jan G. Amunts, Katrin Mersmann, Jochen Jirsa, Viktor Ritter, Petra Berlin Institute of Health Charité - Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany Charité - Universitätsmedizin Berlin Freie Universität Berlin Humboldt Universität zu Berlin Department of Neurology with Experimental Neurology Charitéplatz 1 Berlin10117 Germany Bernstein Focus State Dependencies of Learning Bernstein Center for Computational Neuroscience Berlin Germany Einstein Center for Neuroscience Berlin Charitéplatz 1 Berlin10117 Germany Einstein Center Digital Future Wilhelmstraße 67 Berlin10117 Germany Codemart Str. Petofi Sandor Cluj Napoca Romania Institut de Neurosciences des Systèmes Aix Marseille Université Marseille France Institute for Advanced Simulation JARA Forschungszentrum Jülich GmbH Jülich Germany Jülich Supercomputer Centre Forschungszentrum Jülich Jülich Germany Institut für Theoretische Physik Universität Regensburg Regensburg Germany Campus Biotech Geneva Switzerland Research Centre Jülich Jülich Germany Department of Data-Analysis Faculty of Psychology and Educational Sciences Ghent University Belgium San Camillo IRCSS Venice Italy Université Paris-Saclay CEA CNRS Neurospin Baobab Gif-sur-Yvette France Department of Neurology University Medicine Greifswald Greifswald Germany Standort Greifswald Greifswald Germany Leicester De Montfort Law School De Montfort University Leicester United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom Department of Innovation and Digitalisation in Law Faculty of Law University of Vienna Austria Center for Brain and Cognition Computational Neuroscience Group Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institució Catalana de la Recerca i Estudis Avançats Barcelona Spain Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany School of Psychological Sciences Turner Institute for Brain and Mental Health

The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science. It offers services for constructing, simulating and analysing brain network models (BNMs) including the TVB network simulator;magnetic resonance imaging (MRI) processing pipelines to extract structural and functional connectomes;multiscale co-simulation of spiking and large-scale networks;a domain specific language for automatic high-performance code generation from user-specified models;simulation-ready BNMs of patients and healthy volunteers;Bayesian inference of epilepsy spread;data and code for mouse brain simulation;and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability and clinical translation. © 2021, CC BY.

关键词： Open source software

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A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (vol 14, pg 352, 2018)

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NATURE CHEMICAL BIOLOGY 2018年第9期14卷 901-901页

作者： Piatkevich, Kiryl D. Jung, Erica E. Straub, Christoph Linghu, Changyang Park, Demian Suk, Ho-Jun Hochbaum, Daniel R. Goodwin, Daniel Pnevmatikakis, Eftychios Pak, Nikita Kawashima, Takashi Yang, Chao-Tsung Rhoades, Jeffrey L. Shemesh, Or Asano, Shoh Yoon, Young-Gyu Freifeld, Limor Saulnier, Jessica L. Riegler, Clemens Engert, Florian Hughes, Thom Drobizhev, Mikhail Szabo, Balint Ahrens, Misha B. Flavell, Steven W. Sabatini, Bernardo L. Boyden, Edward S. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA Department of Electrical Engineering and Computer Science MIT Cambridge MA USA Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA Simons Center Data Analysis Simons Foundation New York NY USA Department of Mechanical Engineering MIT Cambridge MA USA Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA Department of Biological Physics Eotvos University Budapest Hungary Department of Biological Engineering MIT Cambridge MA USA MIT Center for Neurobiological Engineering MIT Cambridge MA USA Department of Brain and Cognitive Sciences MIT Cambridge MA USA MIT McGovern Institute for Brain Research MIT Cambridge MA USA

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

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Correction for Rando et al., "Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through analysis of Viral Genomics and Structure"

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mSystems 2022年第1期7卷 e0144721页

作者： Halie M Rando Adam L MacLean Alexandra J Lee Ronan Lordan Sandipan Ray Vikas Bansal Ashwin N Skelly Elizabeth Sell John J Dziak Lamonica Shinholster Lucy D'Agostino McGowan Marouen Ben Guebila Nils Wellhausen Sergey Knyazev Simina M Boca Stephen Capone Yanjun Qi YoSon Park David Mai Yuchen Sun Joel D Boerckel Christian Brueffer James Brian Byrd Jeremy P Kamil Jinhui Wang Ryan Velazquez Gregory L Szeto John P Barton Rishi Raj Goel Serghei Mangul Tiago Lubiana Anthony Gitter Casey S Greene Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia Pennsylvania USA. Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Aurora Colorado USA. Center for Health AI University of Colorado School of Medicine Aurora Colorado USA. Department of Quantitative and Computational Biology University of Southern California Los Angeles California USA. Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Department of Biotechnology Indian Institute of Technology Hyderabad Sangareddy Telangana India. Biomedical Data Science and Machine Learning Group German Center for Neurodegenerative Diseases Tübingen Germany. Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Edna Bennett Pierce Prevention Research Center The Pennsylvania State University University Park Pennsylvania USA. Mercer University Macon Georgia USA. Department of Mathematics and Statistics Wake Forest University Winston-Salem North Carolina USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Georgia State University Atlanta Georgia USA. Innovation Center for Biomedical Informatics Georgetown University Medical Center Washington DC USA. St. George's University School of Medicine St. George's Grenada. Department of Computer Science University of Virginiagrid.27755.32 Charlottesville Virginia USA. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania USA. Department of Orthopaedic Surgery Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Department of Clinical Sciences Lund University Lund Sweden. University of Michigan School of Medicine Ann Arbor Michigan USA. Depar

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Towards radiologist-level cancer risk assessment in CT lung screening using deep learning

arXiv

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arXiv 2018年

作者： Trajanovski, Stojan Mavroeidis, Dimitrios Swisher, Christine Leon Gebre, Binyam Gebrekidan Veeling, Bastiaan S. Wiemker, Rafael Klinder, Tobias Tahmasebi, Amir Regis, Shawn M. Wald, Christoph McKee, Brady J. Flacke, Sebastian MacMahon, Heber Pien, Homer Data Science department Philips Research AE Eindhoven5656 Netherlands Human Longevity Inc. San DiegoCA92121 United States Research was done while C.L.S. was with Philips Research North America Cambridge MA02141 United States Machine Learning lab University of Amsterdam 1090 GH Amsterdam Philips Research AE Eindhoven5656 Netherlands Digital Imaging department Philips Research Hamburg22335 Germany Philips Research North America CambridgeMA02141 United States Lahey Hospital & Medical Center BurlingtonMA01805 United States Department of Radiology University of Chicago ChicagoIL60637 United States

Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Objective: To compare the performance of a deep learning model to state-of-the-art automated algorithms and radiologists as well as assessing the robustness of the algorithm in heterogeneous datasets. Design, Setting, and Participants: Three low-dose CT lung cancer screening datasets from heterogeneous sources were used, including National Lung Screening Trial (NLST, n=3410), Lahey Hospital and Medical center (LHMC, n=3174) data, Kaggle competition data (from both stages, n=1595+505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n=197). Relevant works on automated methods for Lung Cancer malignancy estimation have used significantly less data in size and diversity. At the first stage, our framework employs a nodule detector;while in the second stage, we use both the image area around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets, ensuring there is no patient overlap between the train and validation sets. Exposures: Two stage deep learning algorithms for lung cancer malignancy assessment. Main outcome measures: A binary classifier for malignancy assessment of a low-dose CT lung cancer screening exam at a patient level, evaluated by Area Under receiver operating characteristic Curve (AUC) in the three datasets. Results, Conclusions, and Relevance: The proposed deep learning model: (a) generalizes well across all three data sets, achieving AUC between 86% to 94%;(b) has better performance than the widely accepted PanCan Risk Model, achieving 11% better AUC score;(c

关键词： Computerized tomography

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learning a target sample re-generator for cross-database micro-expression recognition

arXiv

引用

arXiv 2017年

作者： Zong, Yuan Huang, Xiaohua Zheng, Wenming Cui, Zhen Zhao, Guoying Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing210096 China Center for Machine Vision and Signal Analysis Faculty of Information Technology and Electrical Engineering University of Oulu Oulu FI-90014 Finland School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results. Copyright © 2017, The Authors. All rights reserved.

关键词： database systems

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Cosmological constraints from the tomography of DES-Y3 galaxies with CMB lensing from ACT DR4

arXiv

引用

arXiv 2023年

作者： Marques, G.A. Madhavacheril, M.S. Darwish, O. Shaikh, S. Aguena, M. Alves, O. Avila, S. Bacon, D. Baxter, E.J. Bechtol, K. Becker, M.R. Bertin, E. Blazek, J. Bond, J. Richard Brooks, D. Cai, H. Calabrese, E. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Cawthon, R. Crocce, M. da Costa, L.N. Pereira, M.E.S. De Vicente, J. Desai, S. Diehl, H.T. Doel, P. Doux, C. Drlica-Wagner, A. Dunkley, J. Elvin-Poole, J. Everett, S. Ferraro, Simone Ferrero, I. Flaugher, B. Fosalba, P. García-Bellido, J. Gatti, M. Giannini, G. Gluscevic, V. Gruen, D. Gruendl, R.A. Gutierrez, G. Harrison, I. Hill, J. Colin Hinton, S.R. Hollowood, D.L. Honscheid, K. Huterer, D. Jeffrey, N. Kim, J. Kuehn, K. Lahav, O. Lemos, P. Lima, M. Huffenberger, K.M. MacCrann, N. Marshall, J.L. Mena-Fernández, J. Miquel, R. Mohr, J.J. Moodley, K. Muir, J. Naess, S. Nati, F. Page, L.A. Palmese, A. Malagón, A.A. Plazas Porredon, A. Prat, J. Qu, F.J. Raveri, M. Ross, A.J. Rykoff, E.S. Farren, G.S. Samuroff, S. Sanchez, E. Schubnell, M. Sehgal, N. Sevilla-Noarbe, I. Sheldon, E. Sherwin, B.D. Sifón, C. Smith, M. Spergel, D.N. Staggs, S.T. Suchyta, E. Tarle, G. To, C. Van Engelen, A. Weaverdyck, N. Weller, J. Wenzl, L. Wiseman, P. Wollack, E.J. Yanny, B. Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Department of Physics Florida State University TallahasseeFL32306 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Centre for the Universe Perimeter Institute WaterlooONN2L 2Y5 Canada Université de Genève Département de Physique Théorique Centre for Astroparticle Physics 24 quai Ernest-Ansermet Genève 4CH-1211 Switzerland School of Earth and Space Exploration Arizona State University TempeAZ85287 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Department of Physics University of Michigan Ann ArborMI48109 United States The Barcelona Institute of Science and Technology Campus UAB Barcelona Bellaterra08193 Spain Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Institute for Astronomy University of Hawai’i 2680 Woodlawn Drive HonoluluHI96822 United States Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue MadisonWI53706-1390 United States Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France CITA University of Toronto TorontoONM5S 3H8 Canada Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Universidad de La Laguna

We present a measurement of the cross-correlation between the MAGLIM galaxies selected from the Dark Energy Survey (DES) first three years of observations (Y3) and cosmic microwave background (CMB) lensing from the Atacama Cosmology Telescope (ACT) data Release 4 (DR4), reconstructed over ∼ 436 deg2 of the sky. Our galaxy sample, which covers ∼ 4143 deg2, is divided into six redshift bins spanning the redshift range of 0.20 8 compared to results from measurements of CMB anisotropies by the Planck satellite, although at a mild level ( Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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