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Characterizing weibo social media posts from wuhan, china during the early stages of the COVID-19 pandemic: Qualitative content analysis

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JMIR public Health and Surveillance 2020年第4期6卷 e24125页

作者： Xu, Qing Shen, Ziyi Shah, Neal Cuomo, Raphael Cai, Mingxiang Brown, Matthew Li, Jiawei Mackey, Tim Department of Healthcare Research and Policy University of California San Diego - Extension San Diego CA United States Global Health Policy and Data Institute San Diego CA United States S-3 Research LLC San Diego CA United States Masters Program in Computer Science Jacobs School of Engineering University of California San Diego San Diego CA United States Department of Anesthesiology School of Medicine University of California San Diego San Diego CA United States US Embassy National Cancer Institute National Institutes of Health Beijing China Department of Anesthesiology Division of Infectious Diseases and Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. Objective: This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. Methods: We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19–related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. Results: We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak’s seriousness as more information became available. public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. public reaction included both protective and elevated health risk behavior. Conclusions: Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxie

关键词： Attitude Behavior China Content analysis COVID-19 Data mining Infodemic Infodemiology Infoveillance Knowledge Social media Weibo

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A structural haplotype in the 17q21.31 MAPT region is associated with increased risk for chronic traumatic encephalopathy endophenotypes

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Cell Reports Medicine 2025年第5期6卷 102084-102084页

作者： Han, Xudong Zhang, Yichi Petrosky, Jillian N. Bald, Sarah Sherva, Richard M. Labadorf, Adam Cherry, Jonathan D. Chung, Jaeyoon Farrell, Kurt Abdolmohammadi, Bobak Durape, Shruti Martin, Brett M. Palmisano, Joseph N. Farrell, John J. Alvarez, Victor E. Huber, Bertrand R. Dwyer, Brigid Daneshvar, Daniel H. Jun, Gyungah R. Lunetta, Kathryn L. Goldstein, Lee E. Katz, Douglas I. Cantu, Robert C. Shenton, Martha E. Cummings, Jeffrey L. Reiman, Eric M. Stern, Robert A. Alosco, Michael L. Tripodis, Yorghos Farrer, Lindsay A. Stein, Thor D. Crary, John F. McKee, Ann C. Mez, Jesse Bioinformatics Graduate Program Boston University Boston MA United States Section of Biomedical Genetics Department of Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston University Alzheimer's Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston VA Healthcare System Boston MA United States Boston University Chronic Traumatic Encephalopathy Center Boston University Boston MA United States Department of Pathology and Laboratory Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Pathology Neuroscience and Artificial Intelligence & Human Health Neuropathology Brain Bank & Research CoRE Friedman Brain Institute Ronald M. Loeb Center for Alzheimer's Disease Icahn School of Medicine at Mount Sinai New York NY United States Biostatistics and Epidemiology Data Analytics Center Boston University School of Public Health Boston MA United States Bedford VA Healthcare System Bedford MA United States Braintree Rehabilitation Hospital Braintree MA United States Department of Physical Medicine and Rehabilitation Harvard Medical School Boston MA United States Department of Rehabilitation and Human Performance Brain Injury Research Center Icahn School of Medicine at Mount Sinai New York NY United States Department of Neurology Icahn School of Medicine at Mount Sinai New York NY United States Department of Biostatistics Boston University School of Public Health Boston MA United States Department of Ophthalmology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Biomedical Electrical & Computer Engineering Boston University College of Engineering Boston MA United States Departments of Radiology and Psychiatry Boston University Chobanian & Avedisia

Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impact (RHI) exposure. Genetic variation in the 17q21.31 region, containing microtubule-associated protein tau (MAPT), has been implicated in tauopathies but has not been investigated in CTE. The region includes a megabase-long inversion (H1/H2) and copy-number variations, including α, β, and γ segments, which can be characterized as nine segregating structural haplotypes. We leveraged array SNP data and a reference panel across the 17q21.31 region to impute structural haplotypes and test their association with CTE endophenotypes in 447 European ancestry brain donors with RHI exposure. The H1β1γ1 haplotype was significantly associated with dementia and semi-quantitative tau burden in multiple cortical and medial temporal regions commonly affected in CTE. H1β1γ1 differential expression analyses in dorsolateral frontal cortex implicated cis-acting genes and inflammatory pathways. Taken together, the H1β1γ1 haplotype may help explain CTE heterogeneity among those with similar RHI exposure. © 2025 The Authors

关键词： 17q21.31 chronic traumatic encephalopathy dementia differential gene expression immune system MAPT repetitive head impacts structural haplotypes tauopathy traumatic brain injury

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Towards A Task Taxonomy of Visual Analysis of Electronic Health or Medical Record Data

Towards A Task Taxonomy of Visual Analysis of Electronic Hea...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Xiaohui Bian Hadi Kharrazi Jesus J. Caban Gaoqi He Zhiquan Feng Jian Chen Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Department of Health Policy and Management The Johns Hopkins School of Public Health Baltimore USA The National Intrepid Center of Excellence Walter Reed National Military Medical Center Bethesda USA Department of Computer Science and Software Engineering East China Normal University Shanghai China School of Information Science and Engineering University of Jinan Jinan China Department of Computer Science and Engineering The Ohio State University Columbus USA

ISBN: (纸本)9781538676738;9781538676721

We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques.

关键词： Task analysis Data visualization Medical diagnostic imaging Taxonomy History Data mining Visualization

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Ultra-Low Power Subthreshold Quasi Floating Gate CMOS Logic Family for Energy Harvesting

Ultra-Low Power Subthreshold Quasi Floating Gate CMOS Logic ...

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International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS)

作者： M. Pilar Garde Antonio Lopez-Martin Daniel Orradre Jaime Ramírez-Angulo Institute of Smart Cities Public University of Navarra Pamplona Spain Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA

ISBN: (纸本)9781538663660

Subthreshold digital circuits are suitable for application niches where high performance is not required, but extremely low power consumption is a must. One of the most relevant applications of this kind nowadays is energy harvesting, where self-powered devices experience severe energy constraints. In this paper, a subthreshold CMOS logic family is proposed based on the Quasi-Floating Gate transistor, aimed to the design of different subcircuits required in energy harvesting. Measurement results of a test chip prototype confirm the feasibility of this approach.

关键词： Logic gates Inverters MOSFET Energy harvesting Threshold voltage Capacitance

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1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic

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Global Epidemiology 2020年 2卷 100023页

作者： Koczkodaj, W.W. Mansournia, M.A. Pedrycz, W. Wolny-Dominiak, A. Zabrodskii, P.F. Strzałka, D. Armstrong, T. Zolfaghari, A.H. Dębski, M. Mazurek, J. Computer Science Laurentian University Sudbury P3E 2C6 ON Canada Department of Epidemiology and Biostatistics School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Electrical & Computer Engineering Alberta University Edmonton AB Canada University of Economics in Katowice 1 Maja 47 Katowice 40-287 Poland Saratov Medical University Reaviz Saratov Russian Federation Rzeszów University of Technology Powstańców Warszawy 12 Rzeszów 35-959 Poland Department of Sociology University of Gdańsk Bażyńskiego 8 Gdańsk 80-309 Poland Silesian University in Opava Opava Czech Republic

We forecast 1,000,000 COVID-19 cases outside of China by March 31st, 2020 based on a heuristic and WHO situation reports. We do not model the COVID-19 pandemic;we model only the number of cases. The proposed heuristic is based on a simple observation that the plot of the given data is well approximated by an exponential curve. The exponential curve is used for forecasting the growth of new cases. It has been tested for the last situation report of the last day. Its accuracy has been 1.29% for the last day added and predicted by the 57 previous WHO situation reports (the date 18 March 2020). Prediction, forecast, pandemic, COVID-19, coronavirus, exponential growth curve parameter, heuristic, epidemiology, extrapolation, abductive reasoning, WHO situation report. © 2020

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Author Correction: Battery-free, wireless soft sensors for continuous multi-site measurements of pressure and temperature from patients at risk for pressure injuries

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Nature communications 2021年第1期12卷 6827页

作者： Yong Suk Oh Jae-Hwan Kim Zhaoqian Xie Seokjoo Cho Hyeonseok Han Sung Woo Jeon Minsu Park Myeong Namkoong Raudel Avila Zhen Song Sung-Uk Lee Kabseok Ko Jungyup Lee Je-Sang Lee Weon Gi Min Byeong-Ju Lee Myungwoo Choi Ha Uk Chung Jongwon Kim Mengdi Han Jahyun Koo Yeon Sik Choi Sung Soo Kwak Sung Bong Kim Jeonghyun Kim Jungil Choi Chang-Mo Kang Jong Uk Kim Kyeongha Kwon Sang Min Won Janice Mihyun Baek Yujin Lee So Young Kim Wei Lu Abraham Vazquez-Guardado Hyoyoung Jeong Hanjun Ryu Geumbee Lee Kyuyoung Kim Seunghwan Kim Min Seong Kim Jungrak Choi Dong Yun Choi Quansan Yang Hangbo Zhao Wubin Bai Hokyung Jang Yongjoon Yu Jaeman Lim Xu Guo Bong Hoon Kim Seokwoo Jeon Charles Davies Anthony Banks Hyung Jin Sung Yonggang Huang Inkyu Park John A Rogers Center for Bio-Integrated Electronics Northwestern University Evanston IL USA. Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA. Department of Materials Science and Engineering University of Illinois at Urbana Champaign Urbana IL USA. Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL USA. State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian People's Republic of China. Ningbo Institute of Dalian University of Technology Ningbo People's Republic of China. Department of Biomedical Engineering Texas A&M University College Station TX USA. Department of Mechanical Engineering Northwestern University Evanston IL USA. Advanced 3D Printing Technology Development Division Korea Atomic Energy Research Institute Daejeon Republic of Korea. Qualcomm Institute La Jolla CA USA. Sibel Health Inc Niles IL USA. Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Planning and Development Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Rehabilitation Medicine Pusan national university hospital Busan Republic of Korea. Department of Materials Science and Engineering KAIST Institute for The Nanocentury (KINC) Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Materials Science and Engineering Northwestern University Evanston IL USA. Department of Mechanical Engineering Kyung Hee University Yongin Republic of Korea. Department of Biomedical Engineering College of Future Technology Peking University Beijing People's Republic of China. School of Biomedical Engineering Korea University Seoul Republic of Korea. Interdisci

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OC12.08: Observer-reality agreement for tumour spread and prediction non-resectability in ovarian cancer (ISAAC study, imaging study on advanced ovarian cancer)

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Ultrasound in Obstetrics & Gynecology 2023年第S1期62卷

作者： D. Fischerová M. Pesta M. Blazko M. Moruzzi K. Hundarova U. Scovazzi N. de Sousa M. Ambrosio A. C. Testa D. Franchi V. Chiappa J. Alcazar P. Pinto D. Cibula Obstetrics and Gynecology First Faculty of Medicine Charles University and General University Hospital Prague Czechia Faculty of Mathematics and Physics Charles University Prague Czechia Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czechia Women Children and Public Health Sciences Policlinico Universitario Agostino Gemelli IRCSS Rome Italy Gynecology Coimbra Hospital and University Center Coimbra Portugal Ospedale Policlinico San Martino and University of Genoa Genoa Italy Gynecology and Obstetrics Braga Hospital Braga Portugal Obstetrics and Gynecology Sant'Orsola Malpighi Hospital University of Bologna Bologna Italy Obstetrics and Gynecology Catholic University of the Sacred Heart Rome Italy European Institute of Oncology Milan Italy Gynecologic Oncology National Cancer Institute of Milan Milan Italy Obstetrics and Gynecology University of Navarra Pamplona Spain Gynecology Portuguese Institute of Oncology Francisco Gentil Lisbon Portugal

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New method for the stability analysis of neutral systems with time-varying structured uncertainties

New method for the stability analysis of neutral systems wit...

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第37届中国控制会议

作者： Liming Ding Dajiang He Xianwu Mi Jun Shu Leiping Chen Fazhong Hou Dengfeng Xiao College of Electrical and Information Engineering Huaihua University Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Teaching and Research Section for Computer in Department of Public Courses Huaihua University of Medicine School of Information and Electrical Engineering Hunan University of Science and Technology

This study investigates the delay-dependent stability problem for neutral systems with mixed *** from previous works,this paper employs the free-weighting matrix(FWM) approach and integral inequality approach to estimate the derivative of LKF ***,a new integral inequality based the Wirtinger inequality is *** improved inequality is right used to cope with the terms ∫x(s)ds which are generated by using the free-weighting matrix ***,by integrating the newly integral inequality with free-weighting matrix approach,some less conservative stability criteria are *** examples demonstrate the effectiveness of the method and its advantage over existing methods.

关键词： free-weighting matrix（FWM） integral inequality Stabilization Linear matrix inequality（LMI） neutral systems

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Repurposing an Energy System Optimization Model for Seasonal Power Generation Planning

arXiv

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arXiv 2019年

作者： de Queiroz, A.R. Mulcahy, D. Sankarasubramanian, A. Deane, J.P. Mahinthakumar, G. Lu, N. DeCarolis, J.F. Department of Decision Sciences School of Business NC Central University 1801 Fayetteville Rd DurhamNC27707 United States Department of Civil Construction and Environmental Engineering NC State University 2501 Stinson Drive Box 7908 RaleighNC27695 Department of Electrical and Computer Engineering NC State University 890 Oval Drive RaleighNC27606 United States Energy Policy and Modelling Group University College Cork College Road CorkT12 K8AF Ireland

Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty. Copyright © 2019, The Authors. All rights reserved.

关键词： Mathematical programming

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