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Neuroscience Applied 2025年 4卷

作者： Khuntia, Adyasha Buciuman, Madalina-Octavia Fanning, John Stolicyn, Aleks Vetter, Clara Armio, Reetta-Liina From, Tiina Goffi, Federica Hahn, Lisa Kaufmann, Tobias Laurikainen, Heikki Maggioni, Eleonora Martinez-Zalacain, Ignacio Ruef, Anne Dong, Mark Sen Schwarz, Emanuel Squarcina, Letizia Andreassen, Ole Bellani, Marcella Brambilla, Paolo Haren, Neeltje van Hietala, Jarmo Lawrie, Stephen M. Soriano-Mas, Carles Whalley, Heather Taquet, Maxime Meisenzahl, Eva Falkai, Peter Wiegand, Ariane Koutsouleris, Nikolaos Department of Psychiatry and Psychotherapy University Hospital Ludwig-Maximilian University Munich Germany International Max-Planck School for Translational Psychiatry Germany Max-Planck Institute of Psychiatry Munich Munich Germany Max Planck Institute of Psychiatry Munich Germany German Center for Mental Health Partner Site Munich Augsburg Germany Division of Psychiatry Centre for Clinical Brain Sciences University of Edinburgh Scotland Edinburgh United Kingdom Munich Center of Machine Learning Munich Germany Helmholtz Association - Munich School for Data Science Munich Germany Department of Psychiatry University of Turku Turku Finland Turku PET Centre University of Turku Turku Finland Turku University Hospital Turku Finland Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Neurosciences and Mental Health Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico University of Milan Milan Italy NORMENT Centre Division of Mental Health and Addiction Oslo University Hospital Oslo Norway Department of Psychiatry and Psychotherapy University of Tübingen Germany German Center for Mental Health Partner Site Tübingen Germany Bellvitge Biomedical Research Institute-IDIBELL Psychiatry Department Bellvitge University Hospital CIBERSAM and Department of Psychobiology and Methodology of Health Sciences Universitat Autònoma de Barcelona Spain Department of Radiology Bellvitge University Hospital Barcelona Spain Hector Institute for Artificial Intelligence in Psychiatry Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany Department of Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany German Center for Mental Health Partner Site Mannheim-Heidelberg-Ulm Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Section of Psychiatry Dept of Neuroscience

The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative cohorts are required for building robust and generalizable models. The European College of Neuropsychopharmacology Neuroimaging Network Accessible data Repository (ECNP-NNADR) addresses this need by collating multi-site, multi-modal, multi-diagnosis datasets that enable collaborative research. The newly established ECNP-NNADR includes 4829 participants across 21 cohorts and 11 distinct psychiatric diagnoses, available via the Virtual Pooling and Analysis of Research data (ViPAR) software. The repository includes demographic and clinical information, including diagnosis and questionnaires evaluating psychiatric symptomatology, as well as multi-atlas grey matter volume regions of interest (ROI). To illustrate the opportunities offered by the repository, two proof-of-concept analyses were performed: (1) multivariate classification of 498 patients with schizophrenia (SZ) and 498 matched healthy control (HC) individuals, and (2) normative age prediction using 1170 HC individuals with subsequent application of this model to study abnormal brain maturational processes in patients with SZ. In the SZ classification task, we observed varying balanced accuracies, reaching a maximum of 71.13% across sites and atlases. The normative-age model demonstrated a mean absolute error (MAE) of 6.95 years [coefficient of determination (R²) = 0.77, P < .001] across sites and atlases. The model demonstrated robust generalization on a separate HC left-out sample achieving a MAE of 7.16 years [R² = 0.74,P < .001]. When applied to the SZ group, the model exhibited a MAE of 7.79 years [R² = 0.79, P < .001], with patients displaying accelerated brain-aging with a brain age gap (BrainAGE) of 4.49 (8.90) years. Conclusively, this novel multi-site, mu

关键词： data repository machine learning Multi-site data Neuroimaging Psychiatry Structural MRI Transdiagnostic

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Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

arXiv

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arXiv 2024年

作者： LaBella, Dominic Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia Velichko, Yury Wang, Chunhao Kirkpatrick, John Floyd, Scott Reitman, Zachary J. Mullikin, Trey Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Haider, Muhammad Ammar Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Coley, Aaron Huang, Christina Ghanta, Siddharth Schneider, Alex Sharp, Conrad Saluja, Rachit Kofler, Florian Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiation Oncology SUNY Upstate Medical University SyracuseNY United States San FranciscoCA United States Department of Neurosurgery King’s College Hospital London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Guy’s and St Thomas’ NHS Foundation Trust United Kingdom Department of Radiation Oncology University of Washington SeattleWA United States Department of Radiology Northwestern University EvanstonIL United States Department of Radiation Oncology Northwestern University EvanstonIL United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiation Oncology Indiana University IndianapolisIN United States Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States CMH Lahore Medical College Lahore Pakistan Duke University Medical Center School of Medicine DurhamNC United States Sage Bionetworks United States University of Zürich Zürich Switzerland Artificial Intelligence Lab Department of Radiology Mayo Clinic RochesterMN United States MLCommons United States Factored AI United States Center For Federated Learning in Medicine Indiana University IndianapolisIN United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States Intel United States Duke Universi

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. © 2024, CC BY.

关键词： Radiotherapy

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Local White Matter Architecture Defines Functional Brain Dynamics

Local White Matter Architecture Defines Functional Brain Dyn...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yo Joong Choe Sivaraman Balakrishnan Aarti Singh Jean M. Vettel Timothy Verstynen Dept. of Statistics and Data Science Carnegie Mellon University Pittsburgh PA USA Kakao Seongnam-si South Korea Machine Learning Dept. Carnegie Mellon University Pittsburgh PA USA U.S. Army Research Laboratory MD USA Dept. of Psychology and CNBC Carnegie Mellon University Pittsburgh PA USA

Large bundles of myelinated axons, called white matter, anatomically connect disparate brain regions together and compose the structural core of the human connectome. We recently proposed a method of measuring the local integrity along the length of each white matter fascicle, termed the local connectome [1]. If communication efficiency is fundamentally constrained by the integrity along the entire length of a white matter bundle [2], then variability in the functional dynamics of brain networks should be associated with variability in the local connectome. We test this prediction using two statistical approaches that are capable of handling the high dimensionality of data. First, by performing statistical inference on distance-based correlations, we show that similarity in the local connectome between individuals is significantly correlated with similarity in their patterns of functional connectivity. Second, by employing variable selection using sparse canonical correlation analysis and cross-validation, we show that segments of the local connectome are predictive of certain patterns of functional brain dynamics. These results are consistent with the hypothesis that structural variability along axon bundles constrains communication between disparate brain regions.

关键词： Correlation White matter Functional magnetic resonance imaging Euclidean distance Image reconstruction Axons

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Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease

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Journal of neuroengineering and rehabilitation 2019年第1期16卷 98页

作者： An Nguyen Nils Roth Nooshin Haji Ghassemi Julius Hannink Thomas Seel Jochen Klucken Heiko Gassner Bjoern M Eskofier Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Carl-Thiersch-Straße 2b 91052 Erlangen Germany. an.nguyen@fau.de. Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-University Erlangen-Nürnberg (FAU) Carl-Thiersch-Straße 2b 91052 Erlangen Germany. Control Systems Group Department of Electrical Engineering and Computer Science Technische Universität Berlin (TUB) Einsteinufer 17 10587 Berlin Germany. Department of Molecular Neurology University Hospital Erlangen Friedrich-Alexander University Erlangen-Nürnberg (FAU) Schwabachanlage 6 91054 Erlangen Germany.

The original article [1] contained an error whereby Fig. 6 contained a minor shading glitch affecting its presentation. This has now been corrected.

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Knowing what you know in brain segmentation using Bayesian deep neural networks

arXiv

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arXiv 2018年

作者： McClure, Patrick Rho, Nao Lee, John A. Kaczmarzyk, Jakub R. Zheng, Charles Ghosh, Satrajit S. Nielson, Dylan M. Thomas, Adam G. Bandettini, Peter Pereira, Francisco Machine Learning Team National Institute of Mental Health BethesdaMD United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD United States Data Sharing and Science Team National Institute of Mental Health BethesdaMD United States McGovern Institute for Brain Research Massachusetts Institute of Technology CambridgeMA United States

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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Reply to chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests

arXiv

引用

arXiv 2018年

作者： Eklund, Anders Knutsson, Hans Nichols, Thomas E. Department of Biomedical Engineering Division of Medical Informatics Linkoping University Linkoping Sweden Department of Computer and Information Science Division of Statistics and Machine Learning Linkoping University Linkoping Sweden Linkoping University Linkoping Sweden Big Data Institute University of Oxford Oxford United Kingdom United Kingdom University of Oxford Oxford United Kingdom Department of Statistics University of Warwick Coventry United Kingdom

One-sided t-tests are commonly used in the neuroimaging field, but two-sided tests should be the default unless a researcher has a strong reason for using a one-sided test. Here we extend our previous work on cluster false positive rates, which used one-sided tests, to two-sided tests. Briey, we found that parametric methods perform worse for two-sided t-tests, and that non-parametric methods perform equally well for one-sided and two-sided tests. Copyright © 2018, The Authors. All rights reserved.

关键词： Neuroimaging

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Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy

arXiv

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arXiv 2023年

作者： Spilker, Justin S. Phadke, Kedar A. Aravena, Manuel Archipley, Melanie Bayliss, Matthew B. Birkin, Jack E. Béthermin, Matthieu Burgoyne, James Cathey, Jared Chapman, Scott C. Dahle, Håkon Gonzalez, Anthony H. Gururajan, Gayathri Hayward, Christopher C. Hezaveh, Yashar D. Hill, Ryley Hutchison, Taylor A. Kim, Keunho J. Kim, Seonwoo Law, David Legin, Ronan Malkan, Matthew A. Marrone, Daniel P. Murphy, Eric J. Narayanan, Desika Navarre, Alex Olivier, Grace M. Rich, Jeffrey A. Rigby, Jane R. Reuter, Cassie Rhoads, James E. Sharon, Keren Smith, J.D.T. Solimano, Manuel Sulzenauer, Nikolaus Vieira, Joaquin D. Weiß, Axel Whitaker, Katherine E. Department of Physics and Astronomy George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy Texas A&M University 4242 TAMU College StationTX77843-4242 United States Department of Astronomy University of Illinois 1002 West Green St. UrbanaIL61801 United States Center for AstroPhysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Núcleo de Astronomía de la Facultad de Ingeniería y Ciencias Universidad Diego Portales Av. Ejército Libertador 441 Santiago Chile Department of Physics University of Cincinnati CincinnatiOH45221 United States Aix Marseille Univ. CNRS CNES LAM Marseille France Department of Physics and Astronomy University of British Columbia 6225 Agricultural Rd. VancouverV6T 1Z1 Canada Department of Astronomy University of Florida 211 Bryant Space Sciences Center GainesvilleFL32611 United States National Research Council Herzberg Astronomy and Astrophysics 5701 West Saanich Rd. VictoriaV9E 2E7 Canada Department of Physics and Atmospheric Science Dalhousie University HalifaxNS Canada Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern OsloNO-0315 Norway University of Bologna Via Gobetti 93/2 BolognaI-40129 Italy INAF - Osservatorio di Astrofisica e Scienza dello Spazio Via Gobetti 93/3 BolognaI-40129 Italy Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Département de Physique Université de Montréal MontréalQCH3T 1J4 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Mila - Québec Artificial Intelligence Institute Montréal Canada Observational Cosmology Lab NASA Goddard Space Flight Center Code 665 8800 Greenbelt Rd. GreenbeltMD20771 United States Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Department of Physics and Astronomy University of California

Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths.1-3 Polycyclic aromatic hydrocarbons (PAHs) are large organic molecules that trace millimeter-size dust grains and regulate the cooling of the interstellar gas within galaxies.4,5 Observations of PAH features in very distant galaxies have been difficult due to the limited sensitivity and wavelength coverage of previous infrared telescopes.6,7 Here we present JWST observations that detect the 3.3μm PAH feature in a galaxy observed less than 1.5 billion years after the Big Bang. The high equivalent width of the PAH feature indicates that star formation, rather than black hole accretion, dominates the infrared emission throughout the galaxy. The light from PAH molecules, large dust grains, and stars and hot dust are spatially distinct from one another, leading to order-of-magnitude variations in the PAH equivalent width and the ratio of PAH to total infrared luminosity across the galaxy. The spatial variations we observe suggest either a physical offset between the PAHs and large dust grains or wide variations in the local ultraviolet radiation field. Our observations demonstrate that differences in the emission from PAH molecules and large dust grains are a complex result of localized processes within early galaxies. Copyright © 2023, The Authors. All rights reserved.

关键词： Polycyclic aromatic hydrocarbons

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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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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Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes

引用

JMIR Research Protocols 2018年第7期7卷 e10961页

作者： Suominen, Hanna Kelly, Liadh Goeuriot, Lorraine Research School of Computer Science College of Engineering and Computer Science The Australian National University Canberra ACT Australia Machine Learning Research Group Data61 Commonwealth Scientific and Industrial Research Organisation Canberra ACT Australia Faculty of Science and Technology University of Canberra Canberra ACT Australia Department of Future Technologies Faculty of Science and Engineering University of Turku Turku Finland Department of Computer Science Maynooth University Maynooth Co Kildare Ireland Grenoble Informatics Laboratory Université Grenoble Alpes Grenoble France

Background: The eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development challenges and tasks, benchmark datasets, and software for processing and evaluation. In 2012, it ran as a scientific workshop with the aim of establishing an evaluation lab, and since 2013, this annual workshop has been supplemented with 3 or more preceding labs each year. An evaluation lab is an activity where the participating individuals or teams’ goal is to solve the same problem, typically using the same dataset in a given time frame. The overall purpose of this initiative is to support patients, their next of kin, clinical staff, health scientists, and health care policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. In the CLEF eHealth 2013 to 2017 installations, the aim was to address patient-centric text processing. From 2015, the scope was also extended to aid both patients’ understanding and clinicians’ authoring of various types of medical content. CLEF eHealth 2017 introduced a new pilot task on technology-assisted reviews (TARs) in empirical medicine in order to support health scientists and health care policymakers’ information access. Objectives: This original research paper reports on the outcomes of the first 6 installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analyzing the scholarly influence by reviewing CLEF eHealth papers and their citations. Methods: A review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions were conducted. Citation content analysis was used for the publications and their citations collected from Google Scholar. Results: As many as 718 teams registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. A total of 184 papers using CLEF eHealth data gen

关键词： Evaluation studies as topic Health records Information extraction Information storage and retrieval Information visualization Patient education as topic Speech recognition Systematic reviews Test-set generation Text classification

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