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Systematic literature review on physical distancing policy for mass gathering events

AIP Conference Proceedings 2022年第1期2472卷

作者： Asmahany Ramely Mohd Kamal Mohd Nawawi Ruzelan Khalid Antoni Wibowo Amalia Zahra 1School of Tourism Hospitality and Event Management Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia 2Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia 3Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The explosive spread of Corona Virus Disease-19 (COVID-19) in late December 2019, requires the health authorities worldwide to enforced stricter standard operating procedures (SOP) for mass gathering events, which eventually caused postponement and cancellation. This has led to a great loss and bankruptcy for most event organizers. As an effort to offer solution to this matter, a professional touch producing “soft landing” such as “Mathematical Modelling of Physical Distancing Policy for Mass Gathering Event Organizer” was proposed. Therefore, a holistic understanding about the issues related to physical distancing in mass gathering event is required. This systematic review paper summarizes current practice of physical distancing among mass gathering event organizers. This study reviewed thirteen articles using two leading databases namely Scopus and Google Scholar. Based on thematic analysis, this review finalized four themes: 1) physical distancing method; 2) the importance of physical distancing; 3) challenges in physical distancing enforcement and 4) approach to monitor the compliance of physical distancing.

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Sharma et al. Reply:

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Physical Review Letters 2022年第21期128卷 219602-219602页

作者： Keshav Sharma Nathalia L. Costa Yoong Ahm Kim Hiroyuki Muramatsu Newton M. Barbosa Neto Luiz G. P. Martins Jing Kong Alexandre Rocha Paschoal Paulo T. Araujo Department of Physics and Astronomy University of Alabama Tuscaloosa 35487 Alabama USA Department of Physics Federal University of Ceara Fortaleza 60455-760 Ceara Brazil Department of Polymer Engineering School of Polymer Science and Engineering and Alan G. MacDiarmid Energy Research Institute Chonnam National University 77 Yongbong-ro Buk-gu Gwangju 61186 Republic of Korea Research Institute for Supra-Materials Shinshu University 4-17-1 Wakasato 380-8553 Nagano Japan Institute of Natural Sciences Graduate Program in Physics Federal University of Para Belem 66075-110 Brazil Department of Physics Massachusetts Institute of Technology Cambridge 02139 Massachusetts USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 Massachusetts USA

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology Informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Pooled analysis of 3,741 stool metagenomes from 18 cohorts for cross-stage and strain-level reproducible microbial biomarkers of colorectal cancer

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Nature medicine 2025年 2025 Jun 3页

作者： Gianmarco Piccinno Kelsey N Thompson Paolo Manghi Andrew R Ghazi Andrew Maltez Thomas Aitor Blanco-Míguez Francesco Asnicar Katarina Mladenovic Federica Pinto Federica Armanini Michal Punčochář Elisa Piperni Vitor Heidrich Gloria Fackelmann Giulio Ferrero Sonia Tarallo Long H Nguyen Yan Yan Nazim A Keles Bilge G Tuna Veronika Vymetalkova Mario Trompetto Vaclav Liska Tomas Hucl Pavel Vodicka Beatrix Bencsiková Martina Čarnogurská Vlad Popovici Federica Marmorino Chiara Cremolini Barbara Pardini Francesca Cordero Mingyang Song Andrew T Chan Lisa Derosa Laurence Zitvogel Curtis Huttenhower Alessio Naccarati Eva Budinska Nicola Segata Department CIBIO University of Trento Trento Italy. Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA USA. Harvard Chan Microbiome in Public Health Center Harvard T.H. Chan School of Public Health Boston MA USA. Infectious Disease and Microbiome Program Broad Institute of MIT and Harvard Cambridge MA USA. IEO European Institute of Oncology IRCCS Milan Italy. Department of Clinical and Biological Sciences University of Torino Torino Italy. Department of Computer Science University of Torino Torino Italy. Italian Institute for Genomic Medicine (IIGM) c/o IRCCS Candiolo Candiolo Italy. Candiolo Cancer Institute FPO-IRCCS Candiolo Italy. Clinical and Translational Epidemiology Unit Massachusetts General Hospital Boston MA USA. Department of Medical Biology School of Medicine Yeditepe University Istanbul Turkey. Graduate School of Natural and Applied Sciences Yeditepe University Istanbul Turkey. Department of Biophysics School of Medicine Yeditepe University Istanbul Turkey. Department of Molecular Biology of Cancer Institute of Experimental Medicine of the Czech Academy of Sciences Prague Czech Republic. Institute of Biology and Medical Genetics 1st Medical Faculty Charles University Prague Czech Republic. Biomedical Center Faculty of Medicine in Pilsen Charles University Prague Czech Republic. Department of Colorectal Surgery Clinica S. Rita Vercelli Italy. Department of Surgery University Hospital and Faculty of Medicine in Pilsen Charles University Prague Czech Republic. Department of Hepatogastroenterology Institute for Clinical and Experimental Medicine Prague Czech Republic. Masaryk Memorial Cancer Institute Brno Czech Republic. RECETOX Faculty of Science Masaryk University Brno Czech Republic. Department of Translational Research and New Technologies in Medicine and Surgery University of Pisa Pisa Italy. Departments of Epidemiology and Nutrition Harvard T.H. Chan School of Public Health Boston MA USA.

Associations between the gut microbiome and colorectal cancer (CRC) have been uncovered, but larger and more diverse studies are needed to assess their potential clinical use. We expanded upon 12 metagenomic datasets of patients with CRC (n = 930), adenomas (n = 210) and healthy control individuals (n = 976; total n = 2,116) with 6 new cohorts (n = 1,625) providing granular information on cancer stage and the anatomic location of tumors. We improved CRC prediction accuracy based solely on gut metagenomics (average area under the curve = 0.85) and highlighted the contribution of 19 newly profiled species and distinct Fusobacterium nucleatum clades. Specific gut species distinguish left-sided versus right-sided CRC (area under the curve = 0.66) with an enrichment of oral-typical microbes. We identified strain-specific CRC signatures with the commensal Ruminococcus bicirculans and Faecalibacterium prausnitzii showing subclades associated with late-stage CRC. Our analysis confirms that the microbiome can be a clinical target for CRC screening and characterizes it as a biomarker for CRC progression.

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Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

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BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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Correction to: Early-life exposure to PM2.5 and risk of acute asthma clinical encounters among children in Massachusetts: a case-crossover analysis

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Environmental health : a global access science source 2018年第1期17卷 25页

作者： Roxana Khalili Scott M Bartell Xuefei Hu Yang Liu Howard H Chang Candice Belanoff Matthew J Strickland Verónica M Vieira Environmental Health Sciences Graduate Program Susan and Henry Samueli College of Health Sciences University of California Irvine CA USA. Program in Public Health Susan and Henry Samueli College of Health Sciences University of California Irvine 653 E. Peltason Dr. AIRB 2042 Irvine CA 92697-3957 USA. Department of Statistics Donald Bren School of Information and Computer Sciences University of California Irvine CA USA. Department of Epidemiology School of Medicine Susan and Henry Samueli College of Health Sciences University of California Irvine CA USA. Department of Environmental Health Emory University Rollins School of Public Health Atlanta GA USA. Department of Community Health Sciences Boston University School of Public Health Boston MA USA. School of Community Health Sciences University of Nevada Reno NV USA. Environmental Health Sciences Graduate Program Susan and Henry Samueli College of Health Sciences University of California Irvine CA USA. vvieira@uci.edu. Program in Public Health Susan and Henry Samueli College of Health Sciences University of California Irvine 653 E. Peltason Dr. AIRB 2042 Irvine CA 92697-3957 USA. vvieira@uci.edu.

After publication of the article [1], it was brought to our attention that a number in Table 1 is incorrect.

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Waters network's potential to transform environmental engineering education

Waters network's potential to transform environmental engine...

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114th Annual ASEE Conference and Exposition, 2007

作者： Eschenbach, Elizabeth Montgomery, Jami Johnson, James Brus, Chris Carlson, Patricia Giammar, Dan Grauer, Bette Hotaling, Liesl Oguntimein, Gbekeloluwa Safferman, Steven Wentling, Tim Humboldt State University United States Department of Environmental Resources Engineering Humboldt State University United States WATERS Network - CLEANER Project Office Network Howard University United States Department of Environmental Engineering College of Engineering Architecture and Computer Sciences Howard University United States American Academy of Environmental Engineers United States University of Iowa United States Program University of Iowa United States Rose-Hulman Institute of Technology United States Department of Rhetoric Rose-Hulman Institute of Technology United States National Research Council U. S. Air Force United States NASA's Classroom of the Future Wheeling WV United States Washington University United States Department of Energy Environmental and Chemical Engineering Washington University St. Louis United States Environmental Studies Program Center for Materials Innovation United States McPherson High School United States McPherson High School McPherson KS United States Stevens Institute of Technology United States Stevens Institute of Technology United States Morgan State University United States Michigan State University United States Biosystems and Agricultural Engineering Department Michigan State University United States University of Dayton United States Education Planning Committee National Center for Supercomputing Applications United States Department of Information Science Graduate School of Library and Information Science National Center for Supercomputing Applications University or Illinois United States Knowledge and Learning Systems Group NCSA United States

The WATERS Network (WATer and Environmental Research Systems Network) will be an integrated real-time distributed observing system which will enable academic and government scientists, engineers, educators, and practitioners to advance effective management of our nation's water resources by understanding human interactions with water and the natural and built environment. WATERS will provide easily accessible real time environmental data as well as analysis tools to engineers, scientists, educators, K- graduate students and policymakers so they can better understand how water quantity, quality and related components of the hydrologic cycle are impacted by natural and human influences. The WATERS strategic plan will be completed by July 2007 and requires constituent input. This paper describes the draft Conceptual Design and the Education Plan of WATERS Network. This paper will highlight the potential impact of WATERS on undergraduate and graduate environmental engineering education in order to elicit input from the environmental engineering education community on how WATERS Network could better meet the future needs of undergraduate and graduate students and educators. More information can be found at ***. © American Society for Engineering Education, 2007.

关键词： Water resources

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Correction: Advancements in additive manufacturing for video laryngoscopes: a comprehensive scoping and technological review

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Systematic reviews 2024年第1期13卷 31页

作者： Ana Cristina Beitia Kraemer Moraes Chiara das Dores do Nascimento Everton Granemann Souza Mauricio Beitia Kraemer Mauricio Moraes Neftali Lenin Villarreal Carreno Evandro Piva Rafael Guerra Lund Pelotas Dental School Graduate Program in Dentistry Federal University of Pelotas Pelotas RS 96010‑560 Brazil. Master's Degree in Electronic and Computer Engineering Center for Social and Technological Sciences Catholic University of Pelotas Pelotas RS 96015‑560 Brazil. Faculty of Medicine São Francisco University Bragança Paulista SP 12916‑900 Brazil. Faculty of Medicine Federal University of Pelotas Pelotas RS 96010‑560 Brazil. Graduate Program in Materials Science and Engineering Technological Development Center Federal University of Pelotas Pelotas RS 96010‑610 Brazil. Pelotas Dental School Graduate Program in Dentistry Federal University of Pelotas Pelotas RS 96010‑560 Brazil. rafael.lund@***. Graduate Program in Materials Science and Engineering Technological Development Center Federal University of Pelotas Pelotas RS 96010‑610 Brazil. rafael.lund@***.

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