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Management of extra-articular manifestations in spondyloarthritis

International journal of rheumatic diseases 2023年第2期26卷 183-186页

作者： Kevin Sheng-Kai Ma Yung-Heng Lee Chia-Jen Lin Po-Cheng Shih James Cheng-Chung Wei Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts USA. Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan. Department of Dermatology Massachusetts General Hospital Boston USA. Department of Orthopedics Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan. Institute of Medical Science and Technology National Sun Yat-sen University Kaohsiung Taiwan. Department of Senior Services Industry Management Minghsin University of Science and Technology Hsinchu Taiwan. Department of Recreation and Sport Management Shu-Te University Kaohsiung Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Division of Allergy Immunology & Rheumatology Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan. Institute of Medicine Chung Shan Medical University Taichung Taiwan. Division of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan. Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan.

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Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer's disease

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Informatics in Medicine Unlocked 2020年 19卷

作者： Chowdhury, Utpala Nanda Islam, M. Babul Ahmad, Shamim Moni, Mohammad Ali Dept. of Computer Science and Engineering University of Rajshahi Rajshahi Bangladesh Department of Electrical and Electronic Engineering University of Rajshahi Bangladesh Pabna University of Science and Technology Pabna Bangladesh WHO Collaborating Centre for eHealth School of Public Health and Community Medicine Faculty of Medicine UNSW Sydney Australia

Alzheimer's disease (AD) is a progressive neurodegenerative disease, the causes of which are poorly understood, although a number of strong risk factors for AD are known. Understanding how these risk factors affect cell pathways that are altered in AD could identify important causal pathways that could be targeted by therapeutics. We thus pro- posed network-based quantitative frameworks to investigate these risk factor-AD relation- ships. We analyzed gene expression microarray datasets from tissues affected by AD as well as by ageing, high alcohol consumption, type II diabetes (T2D), high dietary fat, obesity, high dietary red meat, sedentary lifestyle, and smoking. These datasets derived from studies that compared tissues affected by these factors with control tissues (not exposed to these factors) to identify differentially expressed genes (DEGs) specific to the risk factors. We employed these to develop gene association and diseasome networks based on neighborhood-based benchmarking and multilayer network topology. We identified 484 DEGs from AD brain tissue, of which 27 were also seen in the smoking DEGs gene set. AD data also showed 21 DEGs in common with T2D, and 12 with sedentary lifestyle datasets. AD shared less than ten DEGs with the other factors, but 3 (HLA-DRB4, IGH and IGHA2) were commonly up-regulated among the AD, T2D and high alcohol consumption datasets. IGHD and IGHG1 were up-regulated among AD, T2D, alcohol and sedentary lifestyle datasets. Protein-protein interaction networks identified 10 hub genes: CREBBP, PRKCB, ITGB1, GAD1, GNB5, PPP3CA, CABP1, SMARCA4, SNAP25 and GRIA1. Ontological and pathway analyses identified significant gene ontology and molecular pathways that could enhance our understanding of the mechanisms of AD progression by suggesting new therapeutic approaches to affect the development of AD. We verified genes from ontological and pathway analyses with gold benchmark gene-disease associations databases including Online Mendelia

关键词： Ageing Alzheimer's Lifestyle Network-based Topological Type 2 diabetes

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Using undersampling with ensemble learning to identify factors contributing to preterm birth

arXiv

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arXiv 2020年

作者： Dong, Shi Feric, Zlatan Li, Guangyu Wu, Chieh T. Gu, April Z. Dy, Jennifer Meeker, John Padilla, Ingrid Y. Cordero, Jose Vega, Carmen Velez Rosario, Zaira Alshawabkeh, Akram Kaeli, David Dept. of Electrical and Computer Engineering Northeastern University United States Dept. of Civil and Environmental Engineering Northeastern University United States Department of Civil and Environmental Engineering Cornell University United States University of Michigan United States University of Puerto Rico Mayaguez Puerto Rico University of Georgia Georgia Graduate School of Public Health University of Puerto Rico Medical Campus Puerto Rico

In this paper, we propose Ensemble Learning models to identify factors contributing to preterm birth. Our work leverages a rich dataset collected by a NIEHS P42 Center that is trying to identify the dominant factors responsible for the high rate of premature births in northern Puerto Rico. We investigate analytical models addressing two major challenges present in the dataset: 1) the significant amount of incomplete data in the dataset, and 2) class imbalance in the dataset. First, we leverage and compare two types of missing data imputation methods: 1) mean-based and 2) similarity-based, increasing the completeness of this dataset. Second, we propose a feature selection and evaluation model based on using undersampling with Ensemble Learning to address class imbalance present in the dataset. We leverage and compare multiple Ensemble Feature selection methods, including Complete Linear Aggregation (CLA), Weighted Mean Aggregation (WMA), Feature Occurrence Frequency (OFA) and Classification Accuracy Based Aggregation (CAA). To further address missing data present in each feature, we propose two novel methods: 1) Missing Data Rate and Accuracy Based Aggregation (MAA), and 2) Entropy and Accuracy Based Aggregation (EAA). Both proposed models balance the degree of data variance introduced by the missing data handling during the feature selection process, while maintaining model performance. Our results show a 42% improvement in sensitivity versus fallout over previous state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Capacity/cost trade-off for 5G small cell networks in the UHF and SHF bands

Capacity/cost trade-off for 5G small cell networks in the UH...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Emanuel B. Teixeira Anderson R. Ramos Marisa S. Lourenço Fernando J. Velez Jon M. Peha Instituto de Telecomunicacções DEM Universidade da Beira Interior Covilhã Portugal Dept. of Electrical and Computer Engineering Dept. of Engineering & Public Policy Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9781728154190

ISBN: (纸本)9781728154206

To fulfill the demand for high data rates from a wide number of users, higher frequencies can be used to provide the required capacity but with a different cost structure. 5G allows for very high data rates, which needs large bandwidths and requires very high throughput. This paper studies the economic trade-off of small cell networks deployed in Ultra High Frequency (UHF) and Super High Frequency (SHF) bands. Aiming at evaluating the variation of the carrier-to-noise-plus-interference ratio with the cell radius one considers the two-slope urban micro Line-of-Sight (UMiLoS) path loss model (ITU-R 2135 Report). The system capacity is analysed by the implicit function formulation to compute the supported cell throughput through the mapping of the PHY throughput into the step distances that correspond to the minimum carrier-to-interference-plus-noise ratio for each consecutive modulation and coding scheme. The variation of the equivalent supported throughput with the cell size at the 2.6, 3.5 and 5.62 GHz frequency bands facilitates to study the influence of the carrier-to-noise-plus-interference ratio in the dimensioning process. It is possible to conclude that, for the shortest cell radii, the profit is very low, starting to increase at a distance equal to the ratio between the break-point distance and the co-channel reuse factor attaining maxima for values of the cell radius approximately equal to 310, 390 and 740 m, at 2.6, 3.5 and 5.62 GHz, respectively (by assuming that the 5.62 GHz frequency band is unlicensed).

关键词： Throughput 5G mobile communication computer architecture Bandwidth Microprocessors Topology Modulation

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Aberration detection of pertussis from the Mazandaran province, Iran, from 2012 to 2018: Application of discrete wavelet transform

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Journal of Acute Disease 2020年第3期9卷 114-120页

作者： Yousef Alimohamadi Seyed Mohsen Zahraei Manoochehr Karami Mehdi Yaseri MojtabaLotfizad Kourosh Holakouie-Naieni Department of Epidemiology and Biostatistics School of Public HealthTehran University of Medical SciencesTehranIran Center for Communicable Diseases Control Ministry of Health and Medical EducationTehranIran Research Center for Health Sciences Hamadan University of Medical SciencesHamadanIran School of Electrical&Computer Engineering Tarbiat Modares UniversityTehranIran

Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ***:The included cases were composed of the suspectedpertussis patients who came from Mazandaran province andregistered in the Center for Disease Control and Prevention from20 March 2012 to 20 March 2018.A discrete wavelet transformbasedmethod was used to detect the *** analyseswere performed using MATLAB Software version 2018a andExcel ***:A total of 1162 cases were recruited in the study,including 545(46.90%)males and 617(53.10%)females,withmedian age of 1.47(0.22-9.56)*** median age of maleswas 1.18(0.21-8.24)years,while that of females was 1.82(0.21-10.75)*** the level of the alarm threshold,it was1.28 case/d when k=2,while it was 1.34 case/d when k=*** detected aberration days were 123 d and 57 d by consideringk=2 and 3,*** most defined alarm threshold wasrelated to spring(>2 cases/d)and summer(>1 case/d),***:The sensitivity of the surveillance system issubjected to a different ***,determining the level of alarmthreshold periodically using different methods is recommended.

关键词： Aberration detection Pertussis Mazandaran Iran Discrete wavelet transform

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Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study ( vol 14, 17380, 2024 )

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kember, Allan J. Zia, Hafsa Elangainesan, Praniya Hsieh, Min-En Adijeh, Ramak Li, Ivan Ritchie, Leah Akbarian, Sina Taati, Babak Hobson, Sebastian R. Dolatabadi, Elham Department of Obstetrics and Gynaecology University of Toronto 123 Edward Street Suite 1200 Toronto ON M5G 0A8 Canada Institute of Health Policy Management and Evaluation University of Toronto 155 College Street Suite 425 Toronto ON M5T 3M6 Canada Maternal-Fetal Medicine Division Department of Obstetrics and Gynaecology Mount Sinai Hospital 600 University Avenue Toronto ON M5G 1X5 Canada School of Health Policy and Management York University Stong College Room 314 4700 Keele Street Toronto ON M3J 1P3 Canada Temerty Faculty of Medicine University of Toronto Medical Sciences Building 1 King’s College Circle Toronto ON M5S 1A8 Canada Electrical Engineering and Computer Science National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Regulatory Affairs Program Northeastern University First Canadian Place 100 King Street West Suite 4620 Toronto ON M5X 1E2 Canada Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada KITE Research Institute Toronto Rehabilitation Institute University Health Network 550 University Avenue #12-165 Toronto ON M5G 2A2 Canada Department of Biological Sciences University of Toronto Scarborough 1265 Military Trail Toronto ON M1C 1A4 Canada Vector Institute 661 University Avenue Suite 710 Toronto ON M5G 1M1 Canada

Sleeping on the back after 28 weeks of pregnancy has recently been associated with giving birth to a small-for-gestational-age infant and late stillbirth, but whether a causal relationship exists is currently unknown and difficult to study prospectively. This study was conducted to build a computer vision model that can automatically detect sleeping position in pregnancy under real-world conditions. Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present.

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Diabetic Retinopathy Identification using Deep Believe Network 3

Diabetic Retinopathy Identification using Deep Believe Netwo...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Syahputra, M.F. Rahmah, M. Jaya, I. Andayani, U. Abdullah, D. Sriadhi, S. Lalusu, E.Y. Tongko, M. Cahya, B.D. Fardian, N. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Public Health Universitas Tompotika Luwuk Banggai Indonesia Department of Nutrition Universitas Malikussaleh Aceh Utara Indonesia

Diabetic retinopathy is an eye disease caused by swelling in blood vessels. If it gets worse the blood vessels will rupture and this is the main factor of blindness. This disease is rarely found in children and symptoms can be seen during puberty. One way to find out this disease is a wide and comprehensive pupil jerky eye examination performed by an ophthalmologist manually. Manual examination takes a long time and can cause misidentification because the similarity of characteristics of diabetic retinopathy is difficult to see directly. Because of this problem a method is needed that can facilitate the eye doctor in identifying diabetic retinopathy by simply inserting a retinal image into the system. The method proposed in this study is Deep Belief Network (DBN). The steps taken before identification are the image of the retina undergoing pre-processing in the form of grayscale, median filter, contrast stretching, morphological close operation and feature extraction using the Grey level Counselling Matrix (GLCM). In this study it was shown that the proposed method was able to identify diabetic retinopathy with 84% accuracy, sensitivity 93%, and specificity 70%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Blood vessels

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Pattern-Based Analysis of Time Series: Estimation

arXiv

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arXiv 2020年

作者： Sabeti, Elyas Song, Peter X.K. Hero, Alfred O. Michigan Institute for Data Science University of Michigan Ann ArborMI48109 United States School of Public Health Department of Biostatistics University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

While Internet of Things (IoT) devices and sensors create continuous streams of information, Big Data infrastructures are deemed to handle the influx of data in real-time. One type of such a continuous stream of information is time series data. Due to the richness of information in time series and inadequacy of summary statistics to encapsulate structures and patterns in such data, development of new approaches to learn time series is of interest. In this paper, we propose a novel method, called pattern tree, to learn patterns in the times-series using a binary-structured tree. While a pattern tree can be used for many purposes such as lossless compression, prediction and anomaly detection, in this paper we focus on its application in time series estimation and forecasting. In comparison to other methods, our proposed pattern tree method improves the mean squared error of estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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Predicting Transitions from Mild Cognitive Impairment to Dementia within 5-years

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Alzheimer's & Dementia 2023年第S18期19卷

作者： Samad Amini Boran Hao Jingmei Yang Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA College of Arts & Sciences Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Early detection of individuals at high risk of future progression toward dementia is in great need, particularly in the era of emerging treatments for Alzheimer’s disease. Given the easier administration of neuropsychological (NP) tests over invasive and expensive biomarker acquisition, we seek to predict the future cognitive status of an individual based on digital voice recordings of NP exams. Method We utilized automatic speech recognition and natural language processing techniques to create an automated tool that predicts if an individual with mild cognitive impairment (MCI) will develop dementia in a fixed follow-up window (5 years). We characterize the predictive accuracy of this approach using data from the Framingham Heart Study. First, the automated transcription of the digital voice recordings were classified into 8 main NP sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using a large language model, only the participants’ sentences were encoded into quantitative data. These data and the participants’ characteristics, such as age, sex, ApoE4 (+/-) genotype, and education were employed to train and test a binary classifier, predicting dementia withing 5 years after a positive MCI diagnosis. We also provide a comparison between our approach and alternative models using demographics and ApoE genotype or the Mini-Mental State Examination (MMSE) score. Result We evaluated the performance of the classification task on the data containing 111 stable MCI and 103 progressive MCI subjects. Our method achieved an average accuracy of 75.5% on the held-out test data while a baseline model using age, sex, education, and ApoE has an average accuracy of 71.8%. The sensitivity and specificity of the proposed method versus the baseline model are 78.2% vs. 81.8% and 72.7% vs. 61.8%, respectively. Our approach also significantly outperforms the model based on the MMSE score by a margin of 14% in accuracy. Co

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A Multistate Study on Housing Factors Influential to Heat-Related Illness in the United States

SSRN

引用

SSRN 2022年

作者： Hu, Ming Zhang, Kai Nguyen, Quynh Tasdizen, Tolga Krusche, Krupali Uplekar School of Architecture Planning and Preservation University of Maryland 3835 Campus Drive College ParkMD20742 United States Department of Environmental Health Sciences School of Public Health University at Albany State University of New York One University Place | Room 157 | RensselaerNY12144 United States Epidemiology and Biostatistics University of Maryland School of Public Health Atlantic Bldg. #224 Room 1340K 4254 Stadium Dr College ParkMD20742 United States Electrical and Computer Engineering University of Utah 72 S Central Campus Drive 3750 WEB Salt Lake CityUT84112 United States School of Architecture University of Notre Dame 14 Walsh Family Hall of Architecture Notre DameIN46556 United States

As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related causes worldwide, including in developed and developing countries. In this empirical study, three housing characteristic categories (i.e., general housing conditions, living conditions, and housing thermal resistance) and eight variables were analyzed in twenty-seven states in the United States using multivariable regression models. Three variables—housing age (HA), housing crowding ratio (HCR), and roof condition (RC)—were found to be correlated with the risk of heat-related illness (HRI) indexes. Then, a logistic regression model was generated using the three variables to predict the risk of heat-related emergency department visits (EDV) and heat-related mortality (MORD) on a state level. The results indicate that the proposed logistic regression model correctly predicted 100% of the high-risk states for MORD for the eight states tested. Overall, this analysis provides additional evidence about the housing character variables that influence HRI. The outcomes also demonstrate that the built environment, especially housing, should be considered in techniques for mitigating climate change-exacerbated health conditions. © 2022, The Authors. All rights reserved.

关键词： Heat resistance

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