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健康数据科学（英文） 2022年第3期 25-33页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM2.5)on health outcomes related to metabolic disorders and distant *** exposure to ambient PM2.5 leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM2.5 reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-in-difference *** in long-term exposure to ambient PM2.5 were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m3 reduction in PM2.5,a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95％confidence interval[CI]:0.06,0.78)mL/min/1.73m2,-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM2.5 on kidney *** results support a significant nephrotoxicity of PM2.5 based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： association chinese improved function kidney with adults among nationwide quasiexperiment

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Discovery of dual "little red dots" indicates excess clustering on kilo-parsec scales

arXiv

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arXiv 2024年

作者： Tanaka, Takumi S. Silverman, John D. Shimasaku, Kazuhiro Arita, Junya Akins, Hollis B. Inayoshi, Kohei Ding, Xuheng Onoue, Masafusa Liu, Zhaoxuan Casey, Caitlin M. Lambrides, Erini Kokorev, Vasily Jin, Shuowen Faisst, Andreas L. Drakos, Nicole Shen, Yue Li, Junyao Zhuang, Mingyang Fei, Qinyue Ito, Kei Ren, Wenke Matsui, Suin Ando, Makoto Hatano, Shun Fujii, Michiko S. Kartaltepe, Jeyhan S. Koekemoer, Anton M. Liu, Daizhong McCracken, Henry Joy Rhodes, Jason Robertson, Brant E. Franco, Maximilien Andika, Irham T. Cloonan, Aidan P. Fan, Xiaohui Gozaliasl, Ghassem Harish, Santosh Hayward, Christopher C. Huertas-Company, Marc Kakkad, Darshan Kinugawa, Tomoya Roy, Namrata Shuntov, Marko Talia, Margherita Toft, Sune Vijayan, Aswin P. Zhang, Yiyang The University of Tokyo Institutes for Advanced Study The University of Tokyo Kashiwa Chiba277-8583 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan UTIAS The University of Tokyo Kashiwa Chiba277-8583 Japan Center for Astrophysical Sciences Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Research Center for the Early Universe Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Astronomy The University of Texas at Austin 2515 Speedway Boulevard Stop C1400 AustinTX78712 United States Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China School of Physics and Technology Wuhan University Wuhan430072 China Denmark NASA-Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Kapteyn Astronomical Institute University of Groningen Groningen9700 AV Netherlands DTU Space Technical University of Denmark Elektrovej Building 328 Kgs. Lyngby2800 Denmark Caltech/IPAC 1200 E. California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Hawaii Hilo 200 W Kawili St. HiloHI96720 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy School of Physics Peking University Beijing100871 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Osawa 2-21-1 Mitaka Tokyo181-8588 Japan Laboratory for Multiwave

"Little Red Dots" (LRDs) are an abundant high-redshift population newly discovered by the James Webb Space Telescope (JWST). They are characterized by a red color in the rest-frame optical band, compact morphology, and broad Balmer emission lines (FWHM >∼ 1000 km s−1) that suggest an AGN nature. Using a method of pixel-by-pixel color selection and relaxing the compactness criteria, we identify three of the first dual LRD candidates in the COSMOS-Web survey with projected separations of 0.′′2-0.′′4 (1-2 pkpc at their photometric redshifts). A comparison between existing LRD samples and mock data reveals that the projected separations of these dual LRD candidates are unlikely to result from chance projections of objects at different redshifts. In one case (CW-B5-15958), the dual LRD includes two bright sources (mF444W = 24.3 and 24.8) with characteristic V-shape spectral energy distribution (SEDs) and photometric redshifts consistent with each other. We find that CW-B5-15958 has a faint off-centered component and a companion galaxy. In the other two dual systems, the brighter LRD exhibits a V-shape SED, while the fainter LRD (mF444W>∼ 26) is undetected in both F115W and F150W. These discoveries suggest that the angular auto-correlation function (ACF) of LRDs exhibits a significant excess (∼ 3 × 102 times) on sub-arcsec (kilo-parsec) separations compared to the extrapolation of a power-law ACF of JWST-found AGNs measured over 10′′-100′′. Follow-up spectroscopic confirmation of their redshifts and the construction of a larger sample are essential to advance our understanding of the evolution of supermassive black holes and the importance of mergers in the early universe. © 2024, CC BY.

关键词： Pixels

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The Impact of Genomic Variation on Function (IGVF) Consortium

arXiv

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arXiv 2023年

作者： Shendure, Jay Ahituv, Nadav Kircher, Martin Agarwal, Vikram Blair, Andrew Chalkiadakis, Theofilos Chardon, Florence M. Dash, Pyaree M. Deng, Chengyu Hamazaki, Nobuhiko Keukeleire, Pia Kubo, Connor Lalanne, Jean-Benoît Maass, Thorben Martin, Beth McDiarmid, Troy Nobuhara, Mai Page, Nicholas F. Regalado, Sam Schubach, Max Sims, Jasmine Ushiki, Aki Zhao, Jingjing Starita, Lea M. Fowler, Douglas M. Best, Sabrina M. Boyle, Gabe Camp, Nathan Casadei, Silvia Da, Estelle Y. Dawood, Moez Dawson, Samantha C. Fayer, Shawn Hamm, Audrey James, Richard G. Jarvik, Gail P. McEwen, Abbye E. Moore, Nick Muffley, Lara A. Pendyala, Sriram Popp, Nicholas A. Post, Mason Rubin, Alan F. Shendure, Jay Smith, Nahum T. Rubin, Alan F. Stone, Jeremy Tejura, Malvika Wang, Ziyu R. Wheelock, Melinda K. Woo, Ivan Zapp, Brendan D. Engreitz, Jesse M. Quertermous, Thomas Amgalan, Dulguun Aradhana, Aradhana Arana, Sophia M. Bassik, Michael C. Bauman, Julia R. Bhattacharya, Asmita Cai, Xiangmeng Shawn Chen, Ziwei Conley, Stephanie Deshpande, Salil Doughty, Benjamin R. Du, Peter P. Gifford, Casey Greenleaf, William J. Gschwind, Andreas R. Guo, Katherine Isobe, Sarasa Jagoda, Evelyn Jain, Nimit Jones, Hank Kang, Helen Y. Kim, Samuel H. Kim, YeEun Klemm, Sandy Kundaje, Anshul Kundu, Soumya Lago-Docampo, Mauro Lee-Yow, Yannick C. Levin-Konigsberg, Roni Li, Daniel Y. Lindenhofer, Dominik Rosa Ma, X. Marinov, Georgi K. Martyn, Gabriella E. Metzl-Raz, Eyal Monteiro, Joao P. Montgomery, Michael T. Mualim, Kristy S. Munger, Chad Munson, Glen Nguyen, Tri C. Nguyen, Trieu Palmisano, Brian T. Pampari, Anusri Park, Chong Y. Rabinovitch, Marlene Ramste, Markus Ray, Judhajeet Roy, Kevin R. Rubio, Oriane M. Schaepe, Julia M. Schnitzler, Gavin Schreiber, Jacob Sharma, Disha Sheth, Maya U. Shi, Huitong Singh, Vasundhara Sinha, Riya Steinmetz, Lars M. Tan, Jason Tan, Anthony Tycko, Josh Valbuena, Raeline C. Pour Amiri, Valeh Valiollah van Kooten, Mariëlle J.F.M. Vaughan-Jackson, Alun Venida, Anthony Weldy, Chad S. Yao, David Zeng, Tony Zhou, Ronghao Vidal, Marc Calderw Department of Genome Sciences University of Washington SeattleWA United States Department of Bioengineering and Therapeutic Sciences Institute for Human Genetics University of California San Francisco San FranciscoCA United States Institute of Human Genetics University Medical Center Schleswig-Holstein University of Lübeck Lübeck23562 Germany Exploratory Diagnostic Sciences Berlin Institute of Health Charité-Universitätsmedizin Berlin Berlin10117 Germany Brotman Baty Institute for Precision Medicine SeattleWA United States Center of immunotherapy and Immunity Seattle Children's Research Institute SeattleWA United States Bioinformatics Division WEHI ParkvilleVIC Australia Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Laboratory Medicine and Pathology University of Washington SeattleWA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Basic Science and Engineering Initiative Stanford Children’s Health Betty Irene Moore Children’s Heart Center StanfordCA United States The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA United States Division of Cardiovascular Medicine School of Medicine Stanford University United States Department of Computer Science Stanford University School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine StanfordCA United States Division of Pediatric Cardiology Cardiovascular Institute Stanford University School of Medicine Stanford University United States Genome Biology Unit Heidelberg Germany Department of Biology Stanford University StanfordCA United States Stanford Genome Techn

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. © 2023, CC BY.

关键词： Genome

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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International Society for Therapeutic Ultrasound Conference 2016

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Journal of Therapeutic Ultrasound 2017年第1期5卷 1-114页

作者： Brian Fowlkes Pejman Ghanouni Narendra Sanghvi Constantin Coussios Paul C. Lyon Michael Gray Christophoros Mannaris Marie de Saint Victor Eleanor Stride Robin Cleveland Robert Carlisle Feng Wu Mark Middleton Fergus Gleeson Jean-Franҫois Aubry Kim Butts Pauly Chrit Moonen Jacob Vortman Shirley Sharabi Dianne Daniels David Last David Guez Yoav Levy Alexander Volovick Javier Grinfeld Itay Rachmilevich Talia Amar Zion Zibly Yael Mardor Sagi Harnof Michael Plaksin Yoni Weissler Shy Shoham Eitan Kimmel Omer Naor Nairouz Farah Dong-Guk Paeng Zhiyuan Xu John Snell Anders H. Quigg Matthew Eames Changzhu Jin Ashli C. Everstine Jason P. Sheehan Beatriz S. Lopes Neal Kassell Thomas Looi Vera Khokhlova Charles Mougenot Kullervo Hynynen James Drake Michael Slayton Richard C. Amodei Keegan Compton Ashley McNelly Daniel Latt John Kearney David Melodelima Aurelien Dupre Yao Chen David Perol Jeremy Vincenot Jean-Yves Chapelon Michel Rivoire Wei Guo Guoxin Ren Guofeng Shen Michael Neidrauer Leonid Zubkov Michael S. Weingarten David J. Margolis Peter A. Lewin Nathan McDannold Jonathan Sutton Natalia Vykhodtseva Margaret Livingstone Thiele Kobus Yong-Zhi Zhang Michael Schwartz Yuexi Huang Nir Lipsman Jennifer Jain Martin Chapman Tejas Sankar Andres Lozano Robert Yeung Christakis Damianou Nikolaos Papadopoulos Omer Brokman Eyal Zadicario Ori Brenner David Castel Shih-Ying Wu Julien Grondin Wenlan Zheng Marc Heidmann Maria Eleni Karakatsani Carlos J. Sierra Sánchez Vincent Ferrera Elisa E. Konofagou Marinos Yiannakou HongSeok Cho Hwayoun Lee Mun Han Jong-Ryul Choi Taekwan Lee Sanghyun Ahn Yongmin Chang Juyoung Park Nicholas Ellens Ari Partanen Keyvan Farahani Raag Airan Alexandre Carpentier Michael Canney Alexandre Vignot Cyril Lafon Jean-yves Delattre Ahmed Idbaih Henrik Odéen Bradley Bolster Eun Kee Jeong Dennis L. Parker Pooja Gaur Xue Feng Samuel Fielden Craig Meyer Beat Werner William Grissom Michael Marx Hans Weber Valentina Taviani Brian Hargreaves Jun Tanaka Kentaro Kikuchi Ayumu Ishijima Takashi Azuma Kosuke Minamihata Satoshi Basic Radiological Sciences Division Department of Radiology University of Michigan Ann Arbor USA Stanford School of Medicine Stanford USA SonaCare Medical Indianapolis USA Institute of Biomedical Engineering University of Oxford Oxford UK HIFU Unit Churchill Hospital Oxford UK Department of Oncology University of Oxford Oxford UK Department of Radiology Churchill Hospital Oxford UK Institut Langevin Paris France Center for Imaging Sciences Imaging Division University Medical Center Utrecht The Netherlands InSighTec Haifa Israel Sheba Medical Center Ramat-Gan Israel Tel-Aviv University Tel-Aviv Israel Faculty of Biomedical Engineering & Russell Berrie Nanotechnology Institute Technion – Israel Institute of Technology Haifa Israel ELSC Center for Brain Sciences Hebrew University Jerusalem Israel Biomedical Engineering Technion- Israel Institute of Technology Haifa Israel Faculty of Life Sciences Bar Ilan University Ramat Gan Israel Focused Ultrasound Foundation Charlottesville USA Ocean System Engineering Jeju National University Jeju Republic of Korea Neurosurgery University of Virginia Charlottesville USA Biology University of Virginia Charlottesville USA Pathology University of Virginia Charlottesville USA Hospital for Sick Children Toronto Canada University of Toronto Toronto Canada University of Washington Seattle USA Philips Healthcare Markham Canada Sunnybrook Research Institute Toronto Canada Guided Therapy Systems LLC Mesa USA School of Medicine University of Arizona Tucson USA The CORE Institute Phoenix USA LabTAU - U1032 INSERM Lyon France Centre Leon Berard Lyon France Department of Oral Maxillofacial and Head Neck Oncology Shanghai 9th People’s Hospital Shanghai China The School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia USA Department of Surgery College of Medicine Drexel University Philadelphia USA Biostatistics and Epidemiology University of Pennsylvania Philadelphi

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

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

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

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FFG-61 PROTOTYPE DIGITAL TECHNICAL LIBRARY

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NAVAL ENGINEERS JOURNAL 1992年第2期104卷 58-68页

作者： VINROOT, CA ORNER, JG USN Capt. Charles A. Vinroot USN (Ret.)retired from the U.S. Navy in September 1991 following over 27 years of active duty as an engineering duty officer. He holds a BSEE from North Carolina State University and a MSEE and professional degree from the U.S. Naval Postgraduate School. During his naval career he served on USSIndependence (CVA-62) and USSLuce (DLC-7/DDC-38). He also served at Supship Quincy Mass. and Hunters Point Naval Shipyard. He was stationed in Washington D.C. with assignments at CNO (OP 98) ASN (S&L) and the Naval Sea Systems Command. Captain Vinroot was technical director of the Battleship Reactivation Program (PMS 378) technical director of the Destroyer Acquisition Program (PMS 389) and deputy program manager of the Amphibious Warfare and Strategic Sealift Program (PMS 377). Most recently he served as program manager for Gas Turbine Surface Combatants (PMS 314) and Surface Combatants (PMS 330). Captain Vinroot is now employed by PRC Inc. and serves as technical director for the Advanced Technology Division in Crystal City Va. Jeffery G. Ornergraduated from Wittenberg University in Springfield Ohio in 1979 with a bachelor of arts degree in political science and earned a master's of science degree in business from The American University in Washington D.C. in 1982. He has ten years of professional experience with the Naval Sea Systems Command in positions with responsibilities for logistic support planning policy and delivery computer-aided acquisition and logistic support and Fleet Modernization Program (FMP) and ship construction issues. He was a key player in establishing the current FMP integrated logistic support (ILS) process and in implementing of the Ships' Configuration and Logistic Support Information System (SCLSIS). His current position as Fleet Logistic Support Branch head for the Surface Combatant Program includes responsibility for logistic support and management of ship configuration and logistic data for all surface combatant ships (except for Aegis ships). In

USS Ingraham (FFG-61) is the prototype ship for NavSea's Advanced Technical Information system (ATIS). ATIS is a digital technical library, which holds on optical disks the ship's 2,000 technical manuals and 73,000 drawing sheets. It contains a detailed ship's configuration index (derived from SCLSIS) to lead the user to the proper drawing or manual, and it replaces the ship's aperture cards and the second (library) copy of the technical manuals. ATIS, and the data standards established and tested through ATIS development, will be the technical library portion of micro-SNAP and SNAP III. It also forms an important part of NavSea's plans to utilize EDMICS data. This paper describes the goals and technical concepts behind the development of ATIS. Problems encountered, solutions developed, and lessons learned are detailed. Special attention was paid to the application of the computer Aided Acquisition and Logistic Support (CALS) standards, problems caused by conflicts and ambiguities in those standards, the standards. Original program goals are compared with actual operational experiences. Plans for future expansion are outlined, including applications of this technology in the availability planning and execution process. A comparison is developed among the various methods of optical imaging and their costs and benefits.

关键词： Information Retrieval systems

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Expert system to Diagnosing Heart Attack Disease Using Certainty Factor Method

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Journal of Physics: Conference Series 2019年第1期1363卷

作者： Muhammad Iqbal Nurul Fatia Sara Surya Evi Susilawati Parwito Zefri Azharman Anita Dewi Moelyaningrum Rinandita Wikansari Andri Nugraha Arfriani Maifizar Nur Mukarromah Abdurrozzaq Hasibuan Dian Ade Kurnia I Ketut Sudarsana Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Pharmacy University of Dharma Andalas Padang Indonesia Faculty of Education and Teaching Training Universitas Islam Sumatera Utara MedanIndonesia Departement of Agrotechnology Universitas Ratu Samban Bengkulu Indonesia Department of Industrial Engineering University of Putera Batam Batam Indonesia Departement of Environmental Health and Ocupational Health and safety School of Public Health University of Jember Indonesia Department of International Trade for ASEAN and China Region Politeknik APP Jakarta Indonesia Department of Nursing STIKes Karsa Husada Garut Indonesia Department of Sociology Faculty of Social and Political Sciences Universitas Teuku Umar Meulaboh Indonesia Faculty of Health Science Universitas Muhammadiyah of Surabaya Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Departement Of Information Technology STMIK IKMI Cirebon Jawa Barat Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

Coronary Heart Disease is one of many diseases that cause death and still being a health problem for both developed and developing countries. It is estimated to double in the next two decades, making it the biggest leading cause of death in 2020. The causes of coronary heart disease contain age and sex, with the incidence of males is more than in females but the incidence of females will increase after menopause (the end of the menstrual cycle) around the age of 50 years. This is due to estrogen (steroid compounds) have a protective effect on the onset of atherosclerosis. In this study the author wants to create a system that can detect heart disease by users before ensuring himself to the doctor to diagnose heart disease which is very dangerous. The system that the author will design used the certainty factor method to provide an assessment of the symptoms of user diagnosis. The application was designed with the desktop-based visual studio software assistance that is easy to use by everyone because it is familiar with the windows display design.

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