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MULTIMODAL AI-BASED ASSESSMENT OF RENAL ALLOGRAFT BIOPSIES

Journal of Pathology Informatics 2022年 13卷 100059-100059页

作者： Sahai, Sharifa Chen, Richard J. Lipkova, Jana Chen, Kuan Chen, Tiffany Y. Williamson, Drew Wang, Judy J. Weins, Astrid Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Broad Institute of Harvard and MIT Cambridge MA United States Data Science Program Dana-Farber Cancer Institute Boston MA United States

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ZokorDB: tissue specific regulatory network annotation for non-coding elements of plateau zokor

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Quantitative Biology 2020年第1期8卷 43-50页

作者： Jingxue Xin Junjun Hao Lang Chen Tao Zhang Lei Li Luonan Chen Wenmin Zhao Xuemei Lu Peng Shi Yong Wang CEMS NCMISMDISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China State Key Laboratory of Genetic Resources and Evolution Laboratory of Evolutionary and Functional GenomicsKunming Institute of ZoologyChinese Academy of SciencesKunming 650223China Key Laboratory of Systems Biology Innovation Center for Cell Signaling NetworkInstitute of Biochemistry and Cell BiologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford UniversityStanfordCA 94305USA University of Chinese Academy of Sciences Beijing 100049China

Background:Plateau zokor inhabits in sealed burrows from 2,000 to 4,200 meters at Qinghai-Tibet *** extreme living env ironment makes it a great model to study animal adaptation to hypoxia,low temperature,and high carbon dioxide ***:We provide an integrated resource,ZokorDB,for tissue specific regulatory network annotation for *** is based on a high-quality draft genome of a plateau zokor at 3,300 m and its transcriptional profiles in brain,heart,liver,kidney,and *** conserved non-coding elements of zokor are annotated by their nearest genes and upstream transcriptional factor motif binding ***:ZokorDB provides a general draft gene regulatory network(GRN),Le?potential transcription factor(TF)binds to non-coding regulatory elements and regulates the expression of target genes(TG).Furthermore,we refined the GRN by incorporating matched RNA-seq and DNase-seq data from mouse ENCODE project and reconstructed five tissue-specific regulatory ***:A web-based,open-access database is developed for easily searching,visualizing,and downloading the annotation and *** pipeline of non-coding region annotation for zokor will be useful for other non-model *** is free available at the website(***/zokordb/).

关键词： tissue specific regulatory network non-coding element plateau zokor non-model species

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Reducing EHR navigation time with an advanced hyperlipidemia management Tool: An evaluation of efficiency

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International Journal of Medical Informatics 2025年 203卷

作者： Chin-Chen Chen Yu-Chen Liu Guan-Ling Lin Annisa Ristya Rahmant Der-Ming Liou Hsuan-Chia Yang Information Technology Department Cardinal Tien Hospital New Taipei City Taiwan Information Technology Department Yonghe Cardinal Tien Hospital New Taipei City Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan School of Nursing College of Medicine National Taiwan University Taipei Taiwan Department Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science Faculty of Science and Technology Middlesex University London UK Master Program in Medical Informatics National Tsing Hua University Hsinchu City Taiwan International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan

Background Effective management of hyperlipidemia is crucial for preventing cardiovascular diseases. However, traditional monitoring systems often lack efficiency, particularly in navigating electronic health records (EHRs) to identify and track at-risk patients. Objective This study aims to evaluate the impact of a Hyperlipidemia Medication Reminder System (HLD-MRS) implemented in a district hospital to streamline EHR navigation and enhance patient management. Methods A pre-post prospective study was conducted at a district hospital. This study designed the HLD-MRS to integrate patient data, guideline recommendations, and risk factor assessments into a single interface, simplifying EHR navigation. This study compared EHR navigation times and patient tracking rates before and after HLD-MRS implementation across the Cardiology, Endocrinology, and Family Medicine departments. Results Among 750 patients with follow-up appointments, 334 were identified as having HLD or being at high-risk status. The HLD-MRS significantly reduced mean EHR navigation time from 83.9 s (SD = 8.6) to 53.8 s (SD = 6.6) across all departments (p < 0.001). The proportion of actively monitored HLD patients increased from 42.5 % to 56.6 %, while cases with unknown or withheld assessment status decreased from 57.5 % to 28.4 %. Conclusion The HLD-MRS improved efficiency in EHR navigation and increased the frequency of active patient monitoring. This guideline-driven decision support system shows promise for enhancing hyperlipidemia care in high-volume outpatient settings.

关键词： Clinical decision support system EHR navigation Guideline-directed care Hyperlipidemia management Medication reminder system

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COVID-19 Testing Pipeline: Lesson Learned

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Dian Amirulloh Digdo Sudigyo Arif Budiarto Ika Nurlaila Erlin Listiyaningsih Andrew Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information System Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) in Indonesia is seen to be uncontrollably increasing that urges the government to leverage the capacity for the disease detections. Real-time polymerase chain reaction (RT-PCR), rapid test and computed tomography (CT) scan are the most common methods to determine if one has been infected regardless of whether or not the common symptoms of such Corona Virus Disease 2019 (COVID-19) surface. Among these three, RT-PCR is considered the gold standard for qualitative and quantitative assessment of SARS CoV-2 detection. The present paper aims at elaborating the framework of Roche's RT-PCR machine employed specifically for SARS CoV-2 detection performed by Genetics Indonesia which is deemed to be efficient and relatively quicker than other detection kits. RT-PCR machine detected SARS Cov-2 with RNA amplification curve equals to 10 copies RNA below the cut off value of Crossing point (Cp) positive control. Also elucidated in the paper is the implementations of EAV RNA and LightCycler® 96 RT-PCR System through which analysis time, amounts of individual required sample, as well as the reagents, can be accordingly reduced.

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Machine Learning - Algorithms, Models and Applications

arXiv

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arXiv 2022年

作者： Sen, Jaydip Sen, Rajdeep Dutta, Abhishek Mehtab, Sidra Kherwa, Pooja Singh, Sonali Ahmed, Saheel Berry, Pranay Khurana, Sahil Akinbo, Racheal S. Daramola, Oladunni A. Ost, Kalum J. Anderson, David W. Cadotte, David W. Lainjo, Bongs Department of Data Science Praxis Business School Kolkata India School of Computing and Analytics NASHM Knowledge Campus Kolkata India Maharaja Surajmal Institute of Technology New Delhi India Federal University of Technology Akure Nigeria Hotchkiss Brain Institute CalgaryAB Canada Cumming School of Medicine CalgaryAB Canada Division of Neurosurgery Department of Clinical Neurosciences University of Calgary AB Canada Division of Neurosurgery Department of Radiology University of Calgary AB Canada Combined Orthopedic and Neurosurgery Spine Program University of Calgary AB Canada Cybermatic International MontréalQC Canada

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence. © 2022, CC BY-SA.

关键词： Learning algorithms

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Clustering in Networks Via Kernel-ARMA Modeling and the Grassmannian: The Brain-Network Case

arXiv

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arXiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Patil, Pratik V. Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States Neuroscience Program University at Buffalo BuffaloNY14260 United States Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program University at Buffalo SUNYNY14260 United States Department of Psychology Drexel University Perelman School of Medicine University of Pennsylvania PA19104 United States

This paper introduces a clustering framework for networks with nodes are annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or community detection), and even subnetwork-state-sequence identification/tracking. Via a bottom-up approach, features are first extracted from the raw nodal time-series data by kernel autoregressive-moving-average modeling to reveal non-linear dependencies and low-rank representations, and then mapped onto the Grassmann manifold (Grassmannian). All clustering tasks are performed by leveraging the underlying Riemannian geometry of the Grassmannian in a novel way. To validate the proposed framework, brain-network clustering is considered, where extensive numerical tests on synthetic and real functional Magnetic Resonance Imaging (fMRI) data demonstrate that the advocated learning framework compares favorably versus several state-of-the-art clustering schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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PHARMACOLOGICAL MODULATION OF THE INTEGRATED STRESS RESPONSE ALTERS PATHOLOGY IN AN AMYOTROPHIC LATERAL SCLEROSIS MOUSE MODEL

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IBRO Neuroscience Reports 2023年 15卷 S472-S472页

作者： Nuria Peregrín Pastor Elias Marlin Miguel Valencia Roberto Ferrero Maria Jesus Nicolás Apesteguía Rodrigo Vinueza-Gavilanes Julio Artieda Monserrat Arrasate Tomás Aragón Center for Applied Medical Research (CIMA) Gene Therapy And Regulation Of Gene Expression Program Pamplona Spain Center for Applied Medical Research (CIMA) Neuroscience Program Pamplona Spain IdiSNA Navarra Institute For Health Research Pamplona Spain University of Navarra Institute Of Data Science And Artificial Intelligence (datai) Pamplona Spain Clínica Universidad de Navarra Department Of Neurology Pamplona Spain University of Navarra School Of Medicine Pamplona Spain

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ANTARCTICA AND THE SOUTHERN OCEAN

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2023年第9期104卷 S322-S365页

作者： Clem, Kyle R. Raphael, Marilyn N. School of Geography Environment and Earth Sciences Victoria University of Wellington Wellington New Zealand Scripps Institution of Oceanography University of California San Diego La Jolla California Department of Atmospheric and Oceanic Sciences University of Colorado Boulder Boulder Colorado Earth Science Observation Center Cooperative Institute for Research in Environmental Sciences (ESOC/CIRES) University of Colorado Boulder Boulder Colorado Argentine Naval Hydrographic Service Buenos Aires Argentina Department of Earth and Environmental Science Temple University Philadelphia Pennsylvania Department of Meteorology University of Valparaíso Valparaíso Chile Center for Climate and Resilience Research (CR)2 Santiago Chile British Antarctic Survey Cambridge United Kingdom Science Systems and Applications Inc. Lanham Maryland NASA Goddard Space Flight Center Greenbelt Maryland Royal Netherlands Meteorological Institute (KNMI) DeBilt The Netherlands Department of Marine Sciences University of Gothenburg Gothenburg Sweden Department of Geography Ohio University Athens Ohio NASA Jet Propulsion Laboratory Pasadena California NOAA/OAR Earth System Research Laboratory Global Monitoring Division Boulder Colorado University of Colorado Boulder Boulder Colorado National Oceanography Centre Southampton United Kingdom. Antarctic Meteorological Research and Data Center Space Science and Engineering Center University of Wisconsin-Madison Madison Wisconsin Department of Physical Sciences School of Arts and Sciences Madison Area Technical College Madison Wisconsin Australian Bureau of Meteorology and Institute for Marine and Antarctic Studies (IMAS) University of Tasmania Hobart Australia Earth Science and Observation Center CIRES University of Colorado Boulder Colorado University of St. Andrews St Andrews United Kingdom Princeton University Princeton New Jersey Australian Antarctic Division Australian Antarctic Program Partnership (AAPP) and Australian Centre fo

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Principles for data analysis workflows

arXiv

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arXiv 2020年

作者： Stoudt, Sara Vásquez, Váleri N. Martinez, Ciera C. Berkeley Institute for Data Science University of California Berkeley BerkeleyCA United States Statistical & Data Sciences Program Smith College NorthamptonMA United States Energy and Resources Group University of California Berkeley BerkeleyCA United States Department of Molecular and Cellular Biology University of California Berkeley BerkeleyCA United States

Traditional data science education often omits training on research workflows: the process that moves a scientific investigation from raw data to coherent research question to insightful contribution. In this paper, we elaborate basic principles of a reproducible data analysis workflow by defining three phases: the Exploratory, Refinement, and Polishing Phases. Each workflow phase is roughly centered around the audience to whom research decisions, methodologies, and results are being immediately communicated. Importantly, each phase can also give rise to a number of research products beyond traditional academic publications. Where relevant, we draw analogies between principles for data-intensive research workflows and established practice in software development. The guidance provided here is not intended to be a strict rulebook;rather, the suggestions for practices and tools to advance reproducible, sound data-intensive analysis may furnish support for both students and current professionals. Copyright © 2020, The Authors. All rights reserved.

关键词： data handling

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