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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Correction: Assessing the usability and reliability of a web-based teledentistry tool for remote diagnosis of oral lesions: a cross-sectional study

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BMC oral health 2024年第1期24卷 1481页

作者： Fatemeh Niknam Maryam Mardani Peivand Bastani Azadeh Bashiri Diep Ha Asma Sookhakian Reza Akbari Roxana Sharifian Department of Health Information Management Iran University of Medical Science Tehran Iran. Oral and Dental Disease Research Center School of Dentistry Shiraz University of Medical Sciences Shiraz Iran. College of Business Government and Law Flinders University Adelaide Bedford Park SA 5042 Australia. Department of Health Information Management Health Human Resources Research Center School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran. School of Dentistry UQ Oral Health Centre The University of Queensland. Brisbane QLD 4006 Australia. Student Research Committee School of Dentistry Shiraz University of Medical Sciences Shiraz Iran. Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran. Department of Health Information Management Health Human Resources Research Center School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran. sharifianroxana@***.

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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A Virtual Reality Four-Square Step Test for Quantifying Dynamic Balance Performance in People with Persistent Postural Perceptual Dizziness

A Virtual Reality Four-Square Step Test for Quantifying Dyna...

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2019 International Conference on Virtual Rehabilitation, ICVR 2019

作者： Aharoni, Moshe M.H. Lubetzky, Anat V. Wang, Zhu Goldman, Maya Krasovsky, Tal Department of Physical Therapy University of Haifa Haifa Israel Department of Physical Therapy Steinhardt School of Culture Education and Human Development New York University New YorkNY United States Computer Science Department Courant Institute of Mathematical Sciences Tandon School of Engineering New York University New YorkNY United States Pediatric Rehabilitation Department Sheba Medical Center Ramat Gan Israel

Persistent-postural perceptual dizziness (PPPD) is a recently-defined diagnosis of chronic vestibular symptoms, which is exacerbated by exposure to moving objects and self-motion but is typically undetectable by clinical tests. The current work evaluates the feasibility of a novel paradigm for evaluation of dynamic balance within complex visual environments in people with PPPD-the Virtual Reality Four Step Square Test (FSST-VR). The FSSTVR measures spatiotemporal head kinematics while subjects perform the FSST pattern of 8 steps in a predefined sequence in a virtual environment of varying levels of visual complexity. Eight healthy individuals and 3 people diagnosed with PPPD were asked to perform the FSST-VR while spatiotemporal head kinematics and heartrate were measured. Additionally, participants reported their anxiety levels and cybersickness. Results indicated that performance of the FSST-VR is feasible and did not aggravate symptoms for people with PPPD. Descriptive statistics further may suggest that people with PPPD move less smoothly and perform smaller steps in anteroposterior direction, corresponding with the visual stimuli flow in the virtual environment. Data collection is ongoing and may provide further evidence as to dynamic balance in people with PPPD within complex virtual environments that mimic visual load in daily living. © 2019 IEEE.

关键词： Virtual reality

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Challenges of Artificial Intelligence in Space Medicine

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空间科学与技术（英文） 2023年第2期3卷 141-147页

作者： Ethan Waisberg Joshua Ong Phani Paladugu Sharif Amit Kamran Nasif Zaman Andrew G.Lee Alireza Tavakkoli University College Dublin School of Medicine BelfieldDublinIreland Michigan Medicine University of MichiganAnn ArborMichiganUSA Brigham and Women's Hospital Harvard Medical SchoolBostonMassachusettsUSA Human-Machine Perception Laboratory Department of Computer Science and EngineeringUniversity of NevadaRenoRenoNevadaUSA Center for Space Medicine Baylor College of MedicineHoustonTexasUSA Department of Ophthalmology Blanton Eye InstituteHouston Methodist HospitalHoustonTexasUSA

The human body undergoes many changes during long-duration spaceflight including musculoskeletal,visual,and behavioral *** of these microgravity-induced effects serve as potential barriers to future exploration *** advent of artificial intelligence(AI)in medicine has progressed rapidly and has many promising applications for maintaining and monitoring astronaut health during ***,the austere environment and unique nature of spaceflight present with challenges in successfully training and deploying successful systems for upholding astronaut health and mission *** this article,the dynamic barriers facing AI development in space medicine are *** diverse challenges range from limited astronaut data for algorithm training to ethical/legal considerations in deploying automated diagnostic systems in the setting of the medically limited space *** to address these challenges is then discussed and future directions for this emerging field of research.

关键词： intelligence medicine space artificial challenges

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Structured dialogue policy with graph neural networks 27

Structured dialogue policy with graph neural networks

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27th International Conference on Computational Linguistics, COLING 2018

作者： Chen, Lu Tan, Bowen Long, Sishan Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781948087506

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Reinforcement learning

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A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

arXiv

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arXiv 2022年

作者： Casiraghi, Elena Wong, Rachel Hall, Margaret Coleman, Ben Notaro, Marco Evans, Michael D. Tronieri, Jena S. Blau, Hannah Laraway, Bryan Callahan, Tiffany J. Chan, Lauren E. Bramante, Carolyn T. Buse, John B. Moffitt, Richard A. Stürmer, Til Johnson, Steven G. Shao, Yu Raymond Reese, Justin Robinson, Peter N. Paccanaro, Alberto Valentini, Giorgio Huling, Jared D. Wilkins, Kenneth J. Bennet, Tell Chute, Christopher DeWitt, Peter Gersing, Kenneth Girvin, Andrew Haendel, Melissa Harper, Jeremy Hajagos, Janos Hong, Stephanie Pfaff, Emily Reusch, Jane Antoniescu, Corneliu Robaski, Kimberly AnacletoLab Department of Computer Science "Giovanni degli Antoni" Università degli Studi di Milano Milan Italy CINI Infolife National Laboratory Roma Italy Department of Biomedical Informatics Stony Brook University Stony BrookNY United States The Jackson Laboratory for Genomic Medicine Farmington United States Institute for Systems Genomics University of Connecticut FarmingtonCT United States Biostatistical Design and Analysis Center Clinical and Translational Science Institute University of Minnesota MinneapolisMN United States Department of Psychiatry Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States University of Colorado Anschutz Medical Campus AuroraCO United States College of Public Health and Human Sciences Oregon State University Corvallis United States Division of General Internal Medicine University of Minnesota MinneapolisMN United States NC Translational and Clinical Sciences Institute University of North Carolina at Chapel Hill Chapel HillNC United States Division of Endocrinology Department of Medicine University of North Carolina School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel HillNC United States Institute for Health Informatics University of Minnesota MinneapolisMN United States 260 Longwood Ave Boston United States Department of Radiation Oncology UT Southwestern Medical Center Dallas United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States Fundação Getúlio Vargas Rio de Janeiro Brazil Department of Computer Science Royal Holloway University of London Egham United Kingdom Division of Biostatistics School of Public Health University of Minnesota MinneapolisMN United States Biostatistics Program Office of the Director National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of H

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types. © 2022, CC BY-NC-ND.

关键词： Data handling

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Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

arXiv

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arXiv 2020年

作者： Noothout, Julia M.H. de Vos, Bob D. Wolterink, Jelmer M. Postma, Elbrich M. Smeets, Paul A.M. Takx, Richard A.P. Leiner, Tim Viergever, Max A. Išgum, Ivana Image Sciences Institute University Medical Center Utrecht Utrecht Netherlands Amsterdam UMC University of Amsterdam Biomedical Engineering and Physics Amsterdam Netherlands University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands Department of Radiology University Medical Center Utrecht Utrecht Netherlands Division of Human Nutrition and Health Wageningen University Wageningen Netherlands Image Sciences Institute University Medical Center Utrecht Utrecht Netherlands

In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

arXiv

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arXiv 2023年

作者： Kwon, Kangkyu Lee, Yoon Jae Jung, Yeongju Soltis, Ira Choi, Chanyeong Na, Yewon Romero, Lissette Kim, Myung Chul Rodeheaver, Nathan Kim, Hodam Lloyd, Michael S. Zhuang, Ziqing King, William Xu, Susan Ko, Seung-Hwan Lee, Jinwoo Yeo, Woon-Hong School of Electrical and Computer Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States Center for Human-Centric Interfaces and Nanoengineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States Applied Nano and Thermal Science Lab Department of Mechanical Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States Division of Cardiology Emory University School of Medicine AtlantaGA30322 United States National Institute for Occupational Safety and Health National Personal Protective Technology Laboratory PittsburghPA15236 United States Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Mechanical Robotics and Energy Engineering Dongguk University 30 Pildongro 1-gil Jung-gu Seoul04620 Korea Republic of Wallace H. Coulter Department of Biomedical Engineering Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the user’s face, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the *** Codes 92C55 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

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Single-cell transcriptomics reveals that human milk feeding shapes neonatal immune cell interleukin signaling pathways in a nonrandomized clinical trial

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American Journal of Clinical Nutrition 2025年

作者： Salinas, Michael L Mulakala, Bharath Kumar Davidson, Laurie A Cai, James J Donovan, Sharon M Chapkin, Robert S Yeruva, Laxmi Department of Nutrition Texas A&M University College Station TX United States CPRIT Single Cell Data Science Core Texas A&M University College Station TX United States Texas A&M AgriLife Institute for Advancing Health Through Agriculture College Station TX United States Microbiome and Metabolism Research Unit USDA-ARS SEA Little Rock AR United States Arkansas Children's Nutrition Center Little Rock AR United States Department of Veterinary Integrative Biosciences Texas A&M University College Station TX United States Department of Electrical and Computer Engineering Texas A&M University College Station TX United States Department of Food Science and Human Nutrition Personalized Nutrition Initiative University of Illinois Urbana IL United States

Background: Several studies have indicated the benefits of human milk feeding to infants however, mechanisms behind positive health outcomes have not been investigated. Objectives: The study aimed to characterize circulating immune cell subpopulation gene expression in human milk-fed (HMF) compared with cow milk formula-fed (FF) infants using single-cell transcriptomics. Methods: Peripheral blood mononuclear cells (PBMCs) were isolated from healthy HMF (n = 6), and FF (n = 3) infants who were 3–3.5 mo old and enrolled in a nonrandomized clinical trial. Single-cell RNA sequencing was used to generate a PBMC atlas and evaluate gene expression in immune cell subsets. Differential expression analysis was performed on each cell type independently after clustering the cells by similar marker gene expression using the scGEAToolbox. Differentially expressed genes were subjected to pathway analyses using an online functional enrichment analysis program. Results: The relative abundance (%) of T and B lymphocytes, natural killer (NK) cells, and plasmacytoid dendritic cells were similar, whereas monocytes were higher in FF infants than in HMF infants (22.6 ± 10.7 compared with 8.3 ± 5.6;P = 0.0314). In addition, innate and adaptive immune cells from FF infants exhibited a higher activation state compared with HMF infants. We identified 16 distinct cell subsets from the major immune cell types: 3 monocyte subsets, 4 NK subsets, 2 B cell subsets, and 7 T cell subsets. Transcriptional profiles of each peripheral innate and adaptive immune cell subtype varied between HMF and FF infants. Pathway enrichment analysis of cell-specific transcriptional changes within subsets of major cell types revealed that the interleukin (IL)-4/IL-13 signaling pathways were upregulated in FF infants relative to HMF infants. Conclusions: These findings suggest that human milk downregulates peripheral immune cell cytokine transcriptional signatures linked to allergic inflammation and infection relative

关键词： allergy human milk infant formula inflammation peripheral blood mononuclear cells

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