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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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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Comprehensive functional annotation of susceptibility variants identifies genetic heterogeneity between lung adenocarcinoma and squamous cell carcinoma

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Frontiers of Medicine 2021年第2期15卷 275-291页

作者： Na Qin Yuancheng Li Cheng Wang Meng Zhu Juncheng Dai Tongtong Hong Demetrius Albanes Stephen Lam Adonina Tardon Chu Chen Gary Goodman Stig EBojesen Maria Teresa Landi Mattias Johansson Angela Risch H-Erich Wichmann Heike Bickeboller Gadi Rennert Susanne Arnold Paul Brennan John KField Sanjay Shete Loic Le Marchand Olle Melander Hans Brunnstrom Geoffrey Liu Rayjean JHung Angeline Andrew Lambertus AKiemeney Shan Zienolddiny Kjell Grankvist Mikael Johansson Neil Caporaso Penella Woll Philip Lazarus Matthew BSchabath Melinda CAldrich Victoria LStevens Guangfu Jin David CChristiani Zhibin Hu Christopher IAmos Hongxia Ma Hongbing Shen Department of Epidemiology Center for Global HealthSchool of Public HealthNanjing Medical UniversityNanjing 211166China State Key Laboratory of Reproductive Medicine Center for Global HealthNanjing Medical UniversityNanjing 211166China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer MedicineNanjing Medical UniversityNanjing 211166China China International Cooperation Center for Environment and Human Health School of Public HealthNanjing Medical UniversityNanjing 211166China Division of Cancer Epidemiology and Genetics National Cancer InstituteBethesdaMD 20892-9304USA Department of Integrative Oncology British Columbia Cancer AgencyVancouverBC V5Z 1L3Canada Faculty of Medicine University of Oviedo and CIBERESPOviedo 33006Spain Program in Epidemiology Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWA 98109-1024USA Public Health Sciences Division Swedish Cancer InstituteSeattleWA 98026USA Department of Clinical Biochemistry Copenhagen University HospitalCopenhagen DK-1017Denmark National Cancer Institute BethesdaMD 20892-9304USA Genetic Epidemiology Group International Agency for Research on CancerLyon 69372France Cancer Center Cluster Salzburg at PLUS Department of Molecular BiologyUniversity of SalzburgHeidelberg 5020Austria Institute of Medical Informatics Biometry and EpidemiologyChair of EpidemiologyLudwig Maximilians UniversityMunichBavaria 80539Germany Department of Genetic Epidemiology University Medical Center GoettingenGoettingen 37075Germany Technion Faculty of Medicine Carmel Medical CenterHaifa 3448516Israel Markey Cancer Center University of KentuckyLexingtonKY 40506-0054USA Department of Molecular and Clinical Cancer Medicine Roy Castle Lung Cancer Research ProgrammeThe University of Liverpool Institute of Translational MedicineLiverpool L697Z

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer(NSCLC)risk,biological mechanisms of these variants remain largely *** integrating a large-scale genotype data of 15581 lung adenocarcinoma(AD)cases,8350 squamous cell carcinoma(SqCC)cases,and 27355 controls,as well as multiple transcriptome and epigenomic databases,we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility *** identified 3064 credible risk variants for NSCLC,which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive *** factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was *** annotation and genebased analysis implicated 894 target genes,including 274 specifics for AD and 123 for SqCC,which were overrepresented in somatic driver genes(ER=1.95,P=0.005).Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways,while SqCC genes were homologous recombination deficiency *** results illustrate the molecular basis of both wellstudied and new susceptibility loci of NSCLC,providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.

关键词： lung cancer genome-wide association study function annotation immune homologous recombination repair deficiency genetic heterogeneity

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning

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Nature 2025年 2025 Apr 22页

作者： Rachel A Silverstein Nahye Kim Ann-Sophie Kroell Russell T Walton Justin Delano Rossano M Butcher Martin Pacesa Blaire K Smith Kathleen A Christie Leillani L Ha Ronald J Meis Aaron B Clark Aviv D Spinner Cicera R Lazzarotto Yichao Li Azusa Matsubara Elizabeth O Urbina Gary A Dahl Bruno E Correia Debora S Marks Shengdar Q Tsai Luca Pinello Suk See De Ravin Qin Liu Benjamin P Kleinstiver PhD Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Department of Pathology Massachusetts General Hospital Boston MA USA. Department of Pathology Harvard Medical School Boston MA USA. Institute of Pharmacy and Molecular Biotechnology (IPMB) Faculty of Engineering Sciences Heidelberg University Heidelberg Germany. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Molecular Pathology Unit Massachusetts General Hospital Charlestown MA USA. Krantz Family Center for Cancer Research Massachusetts General Hospital Charlestown Charlestown MA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Ocular Genomics Institute Massachusetts Eye and Ear Boston MA USA. Department of Ophthalmology Harvard Medical School Boston MA USA. Laboratory of Protein Design and Immunoengineering École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics Lausanne Switzerland. CELLSCRIPTTM Madison WI USA. Department of Systems Biology Harvard Medical School Boston MA USA. Department of Hematology St. Jude Children's Research Hospital Memphis TN USA. Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Harvard Medical School Boston MA USA. bkleinstiver@mgh.harvard.edu.

engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.

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Differential regulation of JAK1 expression by ETS1 associated with predisposition to primary biliary cholangitis

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Journal of Genetics and Genomics 2023年第10期50卷 807-812页

作者： Peng Jiang Chan Wang Mingming Zhang Ye Tian Weifeng Zhao Junyi Xin Yexi Huang Zhibin Zhao Wenjuan Sun Jie Long Ruqi Tang Fang Qiu Xingjuan Shi Yi Zhao Li Zhu Na Dai Lei Liu Xudong Wu Jinshan Nie Bo Jiang Youlin Shao Yueqiu Gao Jianjiang Yu Zhigang Hu Zhidong Zang Yuhua Gong Yaping Dai Lan Wang Ningling Ding Ping Xu Sufang Chen Lu Wang Jing Xu Luyao Zhang Junyan Hong Ruonan Qian Hu Li Xuan Jiang Congwei Chen Wenyan Tian Jian Wu Yuzhang Jiang Chongxu Han Kui Zhang Hong Qiu Li Li Hong Fan Liming Chen Jianqiong Zhang Zhongsheng Sun Xiao Han Zhenhua Dai Erguang Li M.Eric Gershwin Zhexiong Lian Xiong Ma Michael F.Seldin Weichang Chen Meilin Wang Xiangdong Liu Key Laboratory of Developmental Genes and Human Diseases School of Life Science and TechnologySoutheast UniversityNanjingJiangsu 210096China Institute of Translational Medicine Yangzhou University Medical CollegeYangzhouJiangsu 225009China Department of Radiology&Oncology The Second Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215004China The First Affiliated Hospital of Soochow University SuzhouJiangsu 215006China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjingJiangsu 210029China Author links open overlay panelYexi Huang Guangdong Provincial People's Hospital Guangdong Academy of Medical SciencesGuangzhouGuangdong 510080China Department of Gastroenterology and Hepatology Shanghai Institute of Digestive DiseasesShanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShanghai 200001China Department of Laboratory Medicine The Fourth Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210031China Department of Gastrointestinal Endoscopy Eastern Hepatobiliary Surgery HospitalShanghai 201800China The Fifth People's Hospital Soochow UniversitySuzhouJiangsu 215007China Department of Gastroenterology Jiangsu University Affiliated Kunshan HospitalKunshanJiangsu 215300China Department of Gastroenterology Yixing People's HospitalYixinJiangsu 214200China Department of Gastroenterology Yancheng First People's HospitalYanchengJiangsu 224005China Department of Gastroenterology Taicang First People's HospitalSoochow UniversityTaicangJiangsu 215400China Department of Hepatology Jingjiang Second People's HospitalJingjiangJiangsu 214500China Department of Hepatology The Third People's Hospital of ChangzhouChangzhouJiangsu 213001China Department of Liver Diseases Shuguang HospitalShanghai University of Traditional Chinese MedicineShanghai 201203China Department of Laboratory Medicine Jiangyin People's HospitalSoutheast UniversityJiangyinJiangsu 214400China Department of Labora

Primary biliary cholangitis(PBC)is an autoimmune liver disease characterized by the destruction of intrahepatic small bile ducts and progressive cholestasis,eventually leading to liver cirrhosis and hepatic failure wi... 详细信息

关键词： cholangitis biliary

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Toward the data-driven 'smart' and 'green' hospital-care 9

Toward the data-driven 'smart' and 'green' hospital-care

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9th ITU Kaleidoscope Academic Conference: Challenges for a Data-Driven Society, ITU K 2017

作者： Spyropoulos, B. Alexandropoulos, A. Boci, N. Chatziapostolou, E. Frappa, E. Georgiadou, E. Louts, I. Pantelakis, I. Poultsaki, M. Xenaki, M. Department of Biomedical Engineering Technological Education Institute of Athens Athens Greece Graduate Program 'Information Technologies in Medicine and Biology' Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9789261242916

The Hospital is the most complex and representative establishment of the society and nowadays, among the most costly ones. ICTs may rationalize personnel-efforts and reduce energy and material-wasting, to enable health-care coverage, of unprivileged social-groups. The aim of the paper is to present the most effective and efficient means and tools, reducing unnecessary cost, as Mobile IP-network, Service-oriented architecture, provided to other components, through a communication protocol, over a network, Ubiquitous-computing, Femtocells, small, low-power cellular base-stations, typically designed for use in a hospital-department, ward-room or unit, Wireless mesh-networks, made-up of radio-nodes, organized in a mesh-topology, supporting intra-hospital data-exchange, training Multimedia-sharing over wireless networks, for real-time or compressed data-streaming over IP/wireless-networks for communication or archiving. Green-computing in wireless-networks, are limiting people and material intrahospital 'circulation' and, thus, they are enabling the necessary environmentally friendly and smooth procedures. Standardization, policies and regulations for green communications and computing, in 'green' Hospitals are indispensable and ICTs enable procedures optimization, solving energy- and material-waste problems, reducing the overall operational-cost, in the emerging 'smart, green and networked' Hospital, in favor of the people in need. © 2017 ITU.

关键词： Standardization

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Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

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arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

引用

arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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