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An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio

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arXiv 2021年

作者： Hepler, Staci A. Kline, David M. Bonny, Andrea McKnight, Erin Waller, Lance A. Department of Mathematics and Statistics Wake Forest University Winston-Salem United States Department of Biostatistics and Data Science Wake Forest School of Medicine Winston-Salem United States Division of Adolescent Medicine Nationwide Children’s Hospital Department of Pediatrics The Ohio State University Columbus United States Department of Biostatistics and Bioinformatics Emory University Atlanta United States

Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the problem is undeniable, estimates of the local prevalence of opioid misuse are lacking, despite their importance to policy-making and resource allocation. This is due, in part, to the challenge of directly measuring opioid misuse at a local level. In this paper, we develop a Bayesian hierarchical spatio-temporal abundance model that integrates indirect county-level data on opioid-related outcomes with state-level survey estimates on prevalence of opioid misuse to estimate the latent county-level prevalence and counts of people who misuse opioids. A simulation study shows that our integrated model accurately recovers the latent counts and prevalence. We apply our model to county-level surveillance data on opioid overdose deaths and treatment admissions from the state of Ohio. Our proposed framework can be applied to other applications of small area estimation for hard to reach populations, which is a common occurrence with many health conditions such as those related to illicit *** Codes 62 © 2021, CC BY.

关键词： Monitoring

Blood Pressure Value Prediction in Hemodialysis Patients Using the CNN+GRU Network Architecture

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Blood Pressure Value Prediction in Hemodialysis Patients Usi...

9th International Conference on Orange Technology, ICOT 2021

作者： Hu, Hsiang-Wei Jheng, Wun-Long Huang, En-Han Chen, Kuan-Yu Liu, Chih-Hao Chi, Hsin-Yin Chin-Yu, Wu Chen, Yu-Cheng Wang, Jian-An Lin, Hsuan-Ming Ghose, Supratip Chen, Che-Wen Wang, Jhing-Fa Department of Biomedical Engineering National Cheng Kung University Tainan Taiwan International Academia of Biomedical Innovation Technology Taipei Taiwan School of Medicine National Defense Medical Center Taipei Taiwan Acusense Biomedical Technology Corp Tainan Taiwan Institute of Biomedical Electronics and Bioinformatics Taipei Taiwan Department of Electrical Engineering Chinese Culture University Taipei Taiwan Department of Electrical Engineering National Cheng Kung University Taiwan Institute of Data Science Department of Management National Cheng-Kung University Tainan Taiwan An Nan Hospital China Medical University Tainan Taiwan

ISBN: (纸本)9781665478427

During clinical operation of Hemodialysis, Blood pressure (BP) variability, affect significant global risks and offshoot complication linked to adverse mortality. However, on this task, a prediction model has not been studied extensively with the advent of big data models. Earlier machine learning models lacked the exploitation of precise modeling regarding temporal context information affecting stratified BP values in the output. Recently deep learning models introduced hierarchical temporal feature engineering and context information for Spatio-Temporal tasks, and thus this study geared to exploit the advantages of deep learning models, specially, in the form of the Convolutional Neural Network (CNN) model, variants of RNN models, and adaptive hybrid networks combining gated recurrent units (GRU) and CNN for the time-series tasks. In addition, a hybrid deep architecture of CNN and LSTM was employed to predict BP value and make it more efficient and effective when applied to health care. The benchmark results on our dataset for the validation of the CNN+GRU hybrid deep architecture model yielded promising contributions on the prediction task with most significant accuracy value of 72.7% corresponding to delta 15 mmHg and 83.7% when 20 mmHg. © 2021 IEEE.

关键词： Long short-term memory

Genome-Wide Association Study of Intraocular Pressure in Population-Based Cohorts in Japan: The Tohoku Medical Megabank Organization Eye Study

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Ophthalmology Science

Ophthalmology science 2025年第5期5卷

作者： Nobuo Fuse Hayato Anzawa Miyuki Sakurai Ikuko N. Motoike Satoshi Nagaie Tomohiro Nakamura Akiko Miyazawa Eiichi N. Kodama Masatsugu Orui Yohei Hamanaka Tomoko Kobayashi Akira Uruno Makiko Taira Ritsuko Shimizu Naoki Nakaya Mami Ishikuro Taku Obara Fuji Nagami Soichi Ogishima Fumiki Katsuoka Masayuki Yamamoto Tohoku Medical Megabank Organization Tohoku University Sendai Japan Department of System Bioinformatics Tohoku University Graduate School of Information Sciences Sendai Japan Department of Data Science Kyoto Women's University Kyoto Japan Tohoku University International Research Institute of Disaster Science Sendai Japan Tohoku University Graduate School of Medicine Sendai Japan Advanced Research Center for Innovations in Next-Generation Medicine Tohoku University Sendai Japan

Purpose This study was conducted to elucidate the distribution and determinants of ocular biometric parameters and to assess the association between intraocular pressure (IOP) and single nucleotide polymorphisms (SNPs) in the Japanese population–based genome cohort studies. Design Cross-sectional analysis involving genome-wide association studies (GWASs). Participants In total, 22 150 participants aged >18 years from the population cohort (Community-Based Cohort [CommCohort]) and 11 302 participants from the Birth and Three-Generation (BirThree) Cohort of the Tohoku Medical Megabank Organization Eye Study were examined. Methods Participant underwent interviews, ophthalmic and physiological examinations, laboratory tests, and microarray analyses. Genome-wide association studies were conducted in the CommCohort (discovery stage) and the BirThree Cohort (replication stage), followed by a meta-analysis. Associations of SNPs and IOP were evaluated using a genome-wide significance threshold (5 × 10 − 8 ). Main Outcome Measures Association of SNPs with IOP and distributions of IOP by sex and age. Results In the discovery stage, the mean IOP of the right and left eye was 13.95 and 14.02 mmHg, respectively. In the replication stage, the corresponding values were 14.32 and 14.27 mmHg, respectively. A significant age-related reduction in IOP was observed in both stages ( P < 0.001). Genome-wide association studies identified 573 and 2 genome-wide significant SNPs in the discovery and replication stages, respectively. Meta-analysis revealed 1601 significant SNPs across 21 loci on 11 chromosomes (Chrs). Of these loci, 17 were previously known to be associated with IOP or glaucoma, while four—septin-8 ( SEPT8 ; Chr5), aldehyde dehydrogenase 2 ( ALDH2 ; Chr12), collagen type VI alpha 2 chain ( COL6A2 ; Chr21), and Wnt family member 7B ( WNT7B ; Chr22)—were newly identified. Conclusions This large-scale GWAS in a Japanese population identified 21 loci associated with IOP, including

关键词： Intraocular pressure Population-based cohort study Genome-wide association study (GWAS) AL axial length ALDH2 aldehyde dehydrogenase 2 ANAPC1 anaphase promoting complex subunit 1 ARHGEF12 Rho Guanine Nucleotide Exchange Factor 12 ABCA1 ATP binding cassette subfamily A member 1 BirThree Birth and Three-Generation CAMTA2 calmodulin binding transcription activator 2 CAV caveolin CCT central corneal thickness CDKN2B-AS1 CDKN2B and CDKN2A antisense cis and trans regulatory RNA 1 Chr chromosome CI confidence interval CommCohort Community-Based Cohort COL6A2 collagen type VI alpha 2 chain FNDC3B fibronectin type III domain-containing protein 3B FOXC1 Forkhead Box C1 GAS7 growth arrest specific 7 GWAS genome-wide association study IOP intraocular pressure OMIM Online Mendelian Inheritance in Man POAG primary open-angle glaucoma SNP single nucleotide polymorphism SEPT8 septin-8 TMM Tohoku Medical Megabank ToMMo Tohoku Medical Megabank Organization TLCD5 TLC domain containing 5 WNT7B Wnt family member 7B

Protist 10,000 Genomes Project

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The Innovation 2020年第3期1卷 68-69页

作者： Wei Miao Lirong Song Sang Ba Longxian Zhang Guiquan Guan Zhang Zhang Kang Ning National Aquatic Biological Resource Center Institute of HydrobiologyChinese Academy of SciencesWuhanChina Laboratory of Wetland and Catchments Ecology in Tibetan Plateau Faculty of Natural SciencesTibet UniversityLhasaChina College of Veterinary Medicine Henan Agricultural UniversityZhengzhouChina State Key Laboratory of Veterinary Etiological Biology Key Laboratory of Veterinary Parasitology of Gansu ProvinceLanzhou Veterinary Research InstituteChinese Academy of Agricultural SciencesLanzhouChina National Genomics Data Center Beijing Institute of GenomicsChinese Academy of SciencesChina National Center for BioinformationBeijingChina Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingCenter of AI BiologyDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

Protists are a highly diverse group of microscopic eukaryotic organisms that are not fungi,animals,or *** some of the microbes first visualized and described by Anton van Leeuwenhoek using themicroscope in the seventeenth *** that,the description and cataloging of these diverse microbial eukaryotes was pursued by microbiologists throughout the following centuries.1 So far,more than 60,000 protist species have been recorded in the NCBI taxonomy system,but many have yet to be *** have long been considered important models in fundamental biological studies,such as cell biology,genetics,ecology and evolution toxicology,and applied fields,including biofuels,nutritional supplements and aquaculture feed production,environmental monitoring and pollution treatment,protozoan parasitic disease treatment and prevention,as well as agriculture.

关键词： prevention taxonomy pollution

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos

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arXiv 2023年

作者： Turishcheva, Polina Fahey, Paul G. Hansel, Laura Froebe, Rachel Ponder, Kayla Vystrčilová, Michaela Willeke, Konstantin F. Bashiri, Mohammad Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Qiur, Yongrong Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://***/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and

关键词： Mammals

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

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Pharmacological Research 2022年 175卷 105996-105996页

作者： ElHarouni, Dina Berker, Yannick Peterziel, Heike Gopisetty, Apurva Turunen, Laura Kreth, Sina Stainczyk, Sabine A. Oehme, Ina Pietiäinen, Vilja Jäger, Natalie Witt, Olaf Schlesner, Matthias Oppermann, Sina Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Neuroblastoma Genomics German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://*** © 2021 The Authors

关键词： Asymmetric sensitivity scoring Differential combination sensitivity scoring Drug target networks Interactive drug screen analysis Personalized medicine Therapy response profiling and exploration

Antimicrobial activity of flower extracts against wound pathogens and fungi

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Current Research in Green and Sustainable Chemistry 2021年 4卷

作者： Abirami, S. Jabesta, J. Renitta, R. Emilin Anand, D. Alex Samrot, Antony V. Department of Microbiology Kamaraj College of Arts and Science Thoothukudi Tamil Nadu India Department of Food Processing Technology Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore 641 114 Tamil Nadu India Department of Bioinformatics and the Centre for Molecular Data Science and Systems Biology Sathyabama Institute of Science and Technology Chennai 600119 India School of Bioscience Faculty of Medicine Bioscience and Nursing MAHSA University Jenjarom 42610 Selangor Malaysia

Since ages, the use of flowers as medicine throughout world is well known. This paper deals the antimicrobial screening of flowers of some medicinal plants against a few post-operative wound pathogens. Aqueous, ethanol and butyl alcohol extracts of flowers were subjected for antibacterial activity against the pathogens. Among the flower extracts tested, ethanol flower extract of Pedalium murex L. was having the highest antibacterial activity against pathogens. Thus, ethanol extract of P. murex (L) flower was subjected for further characterization study including GC-MS analysis. Ethanol flower extract of P. murex of 100 μg/ml showed 67.47 % free radical scavenging activity. © 2021 The Author(s)

关键词： Antimicrobial activity Extraction Pedalium murex Postoperative wound pathogens

Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death Processes

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arXiv 2022年

作者： Huynh, Linh Scott, Jacob G. Thomas, Peter J. Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Department of Translational Hematology and Oncology Research Cleveland Clinic ClevelandOH44106 United States Department of Systems Biology and Bioinformatics School of Medicine Case Western Reserve University ClevelandOH44106 United States Department of Biology Case Western Reserve University ClevelandOH44106 United States Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth processes, death processes, or both. Therefore, we utilize the mean and variance of cell number fluctuations to separately identify birth and death rates from time series that follow stochastic birth-death processes with logistic growth. Our method provides a novel perspective on stochastic parameter identifiability, which we validate by analyzing the accuracy in terms of the discretization bin size. We apply our method to the scenario where a homogeneous cell population goes through three stages: (1) grows naturally to its carrying capacity, (2) is treated with a drug that reduces its carrying capacity, and (3) overcomes the drug effect to restore its original carrying capacity. In each stage, we disambiguate whether it happens through the birth process, death process, or some combination of the two, which contributes to understanding drug resistance mechanisms. In the case of limited data sets, we provide an alternative method based on maximum likelihood and solve a constrained nonlinear optimization problem to identify the most likely density dependence parameter for a given cell number time series. Our methods can be applied to other biological systems at different scales to disambiguate density-dependent mechanisms underlying the same net growth rate. © 2022, CC BY.

关键词： Stochastic systems

Breast Cancer Immunohistochemical Image Generation: a Benchmark dataset and Challenge Review

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arXiv 2023年

作者： Zhu, Chuang Liu, Shengjie Yu, Zekuan Xu, Feng Aggarwal, Arpit Corredor, Germán Madabhushi, Anant Qu, Qixun Fan, Hongwei Li, Fangda Li, Yueheng Guan, Xianchao Zhang, Yongbing Singh, Vivek Kumar Akram, Farhan Sarker, Md Mostafa Kamal Shi, Zhongyue Jin, Mulan The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Academy for Engineering and Technology Fudan University Shanghai China Beijing Chaoyang Hospital Capital Medical University Beijing China Biomedical Engineering Department Georgia Tech and Emory University United States The Atlanta Veterans Affairs Medical Center United States Biomedical Engineering Department Emory University United States Data Science Institute Imperial College London United Kingdom The School of Computer and Electrical Engineering Purdue University United States The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The Department of Computer Engineering and Mathematics Rovira I Virgili University Spain The Department of Pathology and Clinical Bioinformatics Erasmus Medical Center Netherlands The Institute of Biomedical Engineering University of Oxford Oxford United Kingdom

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning