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Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195]

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Asia-Pacific journal of oncology nursing 2024年第5期11卷 100437页

作者： Shuo-Chen Chien Chun-You Chen Chia-Hui Chien Usman Iqbal Hsuan-Chia Yang Huei-Chia Hsueh Shuen-Fu Weng Wen-Shan Jian Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Artificial Intelligence Research and Development Center Wan Fang Hospital Taipei Medical University Taipei Taiwan. International Center for Health Information and Technology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Radiation Oncology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Office of Public Affairs Taipei Medical University Taipei Taiwan. Health ICT Department of Health Tasmania Australia. Global Health and Health Security Department College of Public Health Taipei Medical University Taipei Taiwan. Department of Artificial Intelligence in Medicine Professional Master Program Taipei Medical University Taipei Taiwan. Department of Pharmacy Taipei Veterans General Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan. Graduate Institute of Data Science Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1016/***.2023.100195.].

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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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Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

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Journal of Medical Virology 2024年第5期96卷 e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

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Characterization of K2-167 b and CALM, a new stellar activity mitigation method

arXiv

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arXiv 2024年

作者： de Beurs, Zoë L. Vanderburg, Andrew Thygesen, Erica Rodriguez, Joseph E. Dumusque, Xavier Mortier, Annelies Malavolta, Luca Buchhave, Lars A. Shallue, Christopher J. Zieba, Sebastian Kreidberg, Laura Livingston, John H. Haywood, R.D. Latham, David W. López-Morales, Mercedes Silva, André M. Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States NSF Graduate Research Fellow MIT Presidential Fellow United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Observatoire de Genève Université de Genève 51 chemin des Maillettes Versoix1290 Switzerland School of Physics & Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Dipartimento di Fisica e Astronomia "Galileo Galilei" Universitá di Padova Vicolo del l’Osservatorio 3 PadovaI-35122 Italy INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 Padova35122 Italy DTU Space National Space Institute Technical University of Denmark Elektrovej 328 Kgs. LyngbyDK-2800 Denmark Center for Astrophysics | Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Max-Planck-Institut für Astronomie Königstuhl 17 HeidelbergD-69117 Germany Leiden Observatory Leiden University Niels Bohrweg 2 Leiden2333CA Netherlands Astrobiology Center 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Astrophysics Group University of Exeter ExeterEX4 2QL United Kingdom STFC Ernest Rutherford Fellow United Kingdom Departamento de Fisica e Astronomia Faculdade de Ciencias Universidade do Porto Rua do Campo Alegre Porto4169-007 Portugal Instituto de Astrofisica e Ciencias do Espaco Universidade do Porto CAUP Rua das Estrelas Porto4150-762 Portugal Quad Fellow

We report precise radial velocity (RV) observations of HD 212657 (= K2-167), a star shown by K2 to host a transiting sub-Neptune-sized planet in a 10 day orbit. Using Transiting Exoplanet Survey Satellite (TESS) photometry, we refined the planet parameters, especially the orbital period. We collected 74 precise RVs with the HARPS-N spectrograph between August 2015 and October 2016. Although this planet was first found to transit in 2015 and validated in 2018, excess RV scatter originally limited mass measurements. Here, we measure a mass by taking advantage of reductions in scatter from updates to the HARPS-N data Reduction System (2.3.5) and our new activity mitigation method called CCF Activity Linear Model (CALM), which uses activity-induced line shape changes in the spectra without requiring timing information. Using the CALM framework, we performed a joint fit with RVs and transits using EXOFASTv2 and find Mp = 6.3+1.4−1.4 M◦ and Rp = 2.33+0.17−0.15 R◦, which places K2-167 b at the upper edge of the radius valley. We also find hints of a secondary companion at a ∼ 22 day period, but confirmation requires additional RVs. Although characterizing lower-mass planets like K2-167 b is often impeded by stellar variability, these systems especially help probe the formation physics (i.e. photoevaporation, core-powered mass loss) of the radius valley. In the future, CALM or similar techniques could be widely applied to FGK-type stars, help characterize a population of exoplanets surrounding the radius valley, and further our understanding of their formation. © 2024, CC BY.

关键词： Stars

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Advances of machine learning in molecular modeling and simulation

arXiv

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arXiv 2019年

作者： Haghighatlari, Mojtaba Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter

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Procedia Computer science 2018年 135卷 579-586页

作者： Kahlil Muchtar Faris Rahman Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges.

关键词： change detection foreground segmentation texture features

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Efficient construction method for phase diagrams using uncertainty sampling

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Physical Review Materials 2019年第3期3卷 033802-033802页

作者： Kei Terayama Ryo Tamura Yoshitaro Nose Hidenori Hiramatsu Hideo Hosono Yasushi Okuno Koji Tsuda RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan Medical Sciences Innovation Hub Program RIKEN Cluster for Science Technology and Innovation Hub Kanagawa 230-0045 Japan Graduate School of Medicine Kyoto University Kyoto 606-8507 Japan International Center for Materials Nanoarchitectonics (WPI-MANA) National Institute for Materials Science Ibaraki 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki 305-0047 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8568 Japan Department of Materials Science and Engineering Kyoto University Kyoto 606-8501 Japan Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology Yokohama 226-8503 Japan

We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phases can be rapidly found, and smaller numbers of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.

关键词： Phase diagrams

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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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