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arXiv 2020年

作者： Vishwakarma, Gaurav Sonpal, Aditya Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

arXiv

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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

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2314P Quality and safety of research biopsies (RB) in oncology clinical trials

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Annals of Oncology 2023年 34卷 S1182-S1183页

作者： P.G. Nuciforo F. Ruiz-Pace J. Jimenez R. Comas R. Fasani C. Viaplana C. Sevillano P. Martinez X. Serres Créixams C. Saura Manich M.E. Elez Fernandez A. Oaknin I. Braña E. Muñoz Couselo T. Macarulla J. Carles Galceran J. Tabernero E. Felip E. Garralda R. Dienstmann Molecular Oncology Dept. Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Oncology Data Science (Odyssey Group) Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Radiology Vall d'Hebron University Hospital Barcelona Spain Breast Cancer Program Vall d'Hebron University Hospital Barcelona Spain Medical Oncology Dept. Vall d'Hebron University Hospital Barcelona Spain Medical Oncology Dept. Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Medical Oncology Service (Lung Cancer Unit) Vall d'Hebron University Hospital Barcelona Spain Early Drug Development Group Vall d'Hebron University Hospital Barcelona Spain Oncology Data Science Department Grupo Oncoclinicas São Paulo Brazil

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REDUCING INTER-OBSERVER VARIABILITY IN ENDOMYOCARDIAL BIOPSY ASSESSMENT

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Journal of Pathology Informatics 2022年 13卷 100058-100058页

作者： Lipkova, Jana Chen, Tiffany Y. Lu, Ming Y. Chen, Richard J. Shady, Maha Williams, Mane Wang, Jingwen Noor, Zahra Mitchell, Richard N. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA Broad Institute of Harvard and MIT Cambridge MA USA Data Science Program Dana-Farber Cancer Institute Boston MA USA Harvard-MIT Health Sciences and Technology Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA

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Effects of Mobile Health Care App "Asmile" on Physical Activity of 80,689 Users in Osaka Prefecture, Japan: Longitudinal Observational Study

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Journal of Medical Internet Research 2025年 27卷 e65943页

作者： Oyama, Asuka Taguchi, Kenshiro Seto, Hiroe Kanaya, Reiko Kotoku, Jun'ichi Yamakawa, Miyae Toki, Hiroshi Yamamoto, Ryohei Health and Counseling Center The University of Osaka Toyonaka Japan Graduate Degree Program of Health Data Science Teikyo University Tokyo Japan Graduate School of Human Sciences The University of Osaka Suita Japan Division of Health Sciences Graduate School of Medicine The University of Osaka Suita Japan Graduate School of Medical Care and Technology Teikyo University Tokyo Japan Department of Nephrology Graduate School of Medicine The University of Osaka Suita Japan Laboratory of Behavioral Health Promotion Department of Health Promotion Graduate School of Medicine The University of Osaka Suita Japan Institute for Sports and Global Health The University of Osaka Suita Japan

Background: Lifestyle-related diseases can be controlled by improving individuals’ lifestyles;however, improving and maintaining a healthy lifestyle is difficult. Mobile health (mHealth) applications have recently attracted attention as tools for maintaining and improving health, and their use may also increase physical activity. Objective: This study aimed to verify the effect of registration in Asmile, an mHealth application provided by the Osaka Prefectural Government, on step counts using a Causal Impact approach based on the step count data recorded in the Asmile application. Methods: This observational study included Osaka residents aged 20-79 years, newly registered to Asmile, between the fiscal years 2020 and 2023. Of these, 80,689 participants with step count records for 4 weeks before and after the day they registered to Asmile were included in the analysis. We used daily step counts that were automatically transferred from a standard smartphone health care app into Asmile. We used a Causal Impact model to estimate the increase in step count after registration to Asmile. Results: Of the 80,689 participants analyzed, 38.5% (31,082/80,689) were men, and the mean age was 51.6 (SD 13.2) years. The mean step count before registration was 5923 (SD 4860) steps per day, with the highest proportion of new users registered in spring (38,389/80,689, 47.6%) and in fiscal year 2020 (34,491/80,689, 42.7%). The analysis revealed that the effect of Asmile registration on step counts was 360 steps (95% CI 331-389) per day and 10,041 steps (95% CI 9632-10,450) over 4 weeks. Stratified analysis showed that the impact of increased step count was more pronounced in younger groups and groups with fewer step counts before registration. Conversely, the effect of registration on step count was relatively minor in the groups registered in summer or winter. Conclusions: This study demonstrates increased physical activity among users registered with the Asmile app. These findings sug

关键词： app apps Causal Impact Causal Inference exercise healthy lifestyle Japan lifestyle-related disease mHealth mobile health mobile phone observational study Osaka physical activity smartphone step counts

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Comparative Genome Analysis of Three Halobacillus Strains Isolated From Saline Environments Reveal Potential Salt Tolerance and Algicidal Mechanisms

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Environmental microbiology reports 2025年第3期17卷 e70121页

作者： Saru Gurung Chang-Muk Lee Hang-Yeon Weon So-Ra Han Tae-Jin Oh Department of Life Science and Biochemical Engineering SunMoon University Asan Republic of Korea. Bio Big Data-Based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan Republic of Korea. Agricultural Microbiology Division National Institute of Agricultural Sciences Rural Development Administration Jeonju Republic of Korea. Technology Services Division National Institute of Agricultural Sciences Rural Development Administration Jeonju Republic of Korea. Genome-Based BioIT Convergence Institute Asan Republic of Korea. Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan Republic of Korea.

Harmful algal blooms (HABs) pose a significant global threat to water ecosystems, prompting extensive research into their inhibition and control strategies. This study presents genomic and bioinformatic analyses to investigate the algicidal potential and elucidate the survival mechanisms in harsh conditions of newly identified Halobacillus species three strains (SSTM10-2, SSBR10-3, and SSHM10-5) isolated from saline environments. Moreover, genomic and bioinformatic analyses were conducted to elucidate their survival mechanisms in harsh conditions. Moreover, comparative genomic analysis revealed a diverse set of orthologous genes, with a core genome primarily associated with metabolism and information processing. Pangenome analysis highlighted accessory and unique genes potentially involved in environmental adaptation and stress response. Functional annotation using KEGG pathways identified genes linked to xenobiotic compound degradation, stress tolerance, and salt adaptation. Additionally, the study elucidated potential mechanisms underlying algicidal activity, implicating Carbohydrate-Active enZYmes (CAZymes), cytochrome P450 oxidases (CYP), and quorum sensing (QS) systems. Finally, analysis of KEGG pathways related to microcystin degradation suggested the strains' capacity to mitigate HABs. Thus, this research enhances understanding of the genomic diversity, phylogeny, and functional characteristics of Halobacillus species, offering insights into their ecological roles and potential applications in biotechnology and environmental management.

关键词： Halobacillus sp. algicidal activity comparative genome analysis halotolerance stress response

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Abnormal association between neural activity and genetic expressions of impulsivity in attention deficit hyperactivity disorder: an Adolescent Brain Cognitive Development study

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Biological psychiatry. Cognitive neuroscience and neuroimaging 2025年

作者： Soohyun Jeon Jae-Eon Kang Jundong Hwang Vince D Calhoun Jong-Hwan Lee Department of Brain and Cognitive Engineering Korea University Seoul South Korea. Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology and Emory University Atlanta USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA. Department of Brain and Cognitive Engineering Korea University Seoul South Korea Interdisciplinary Program in Precision Public Health Korea University Seoul South Korea McGovern Institute for Brain Research Massachusetts Institute of Technology Boston MA. Electronic address: jonghwan_lee@korea.ac.kr.

BACKGROUND:Impulsivity in highly heritable attention deficit hyperactivity disorder (ADHD) has been studied using neural activity via fMRI or genetic data, but rarely with multivariate methods linking both. We investigated coupled neural activity and gene expression signatures, using parallel independent component analysis (pICA) and Adolescent Brain Cognitive Development data. METHODS:Children with ADHD (n = 394; 63% males) and healthy controls (n = 1,000; 47% males) of European ancestry were included. The subjects were randomly divided into 80% discovery and 20% replication datasets with demographic stratification. We analyzed neural activity and gene expressions from the discovery datasets using pICA and extracted paired independent components (pICs). The loading coefficients of the pICs were utilized to predict behavioral and cognitive data for stop signal task (SST) in replication datasets. RESULTS:We identified three pICs estimated from gene expression in the cortex, cerebellum, and nucleus accumbens. Significant neural activity was mainly localized to the orbital/inferior/middle frontal gyri, rectal gyrus, precuneus, inferior temporal gyrus, inferior parietal lobule, and cerebellum. Significant gene components were associated with immunoglobulin, taste receptor, and immunity-related terms and were overlapped with ADHD-related genes. The extracted fMRI-/Gene-ICs were significantly correlated with mean reaction time, stop signal reaction time of SST, and behavioral inhibition with a large boost in sensitivity when both the paired fMRI-/Gene-ICs and their interaction were used in a multimodal regression analysis. CONCLUSION:We reported biologically plausible pairs of neural activity and gene sets using pICA, which were significantly associated with ADHD impulsivity-related behavioral and cognitive data.

关键词： Attention deficit hyperactivity disorder functional magnetic resonance imaging gene expression imaging genetics parallel independent component analysis stop signal task

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Molecular profiling of adult C. elegans glia across sexes by single-nuclear RNA-seq

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Developmental cell 2025年

作者： Maria D Purice Elgene J A Quitevis R Sean Manning Liza J Severs Nina-Tuyen Tran Violet Sorrentino Connor Finkbeiner Feinan Wu Michael Zager Manu Setty Aakanksha Singhvi Division of Basic Sciences Fred Hutchinson Cancer Center Seattle WA 98109 USA. Division of Basic Sciences Fred Hutchinson Cancer Center Seattle WA 98109 USA Herbold Computational Biology Program Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA 98109 USA. Molecular and Cell Biology Graduate Program Fred Hutchinson Cancer Center and University of Washington Seattle WA 98109 USA. Herbold Computational Biology Program Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA 98109 USA Department of Genome Sciences University of Washington Seattle WA 98109 USA. Genomics and Bioinformatics Shared Resource Fred Hutchinson Cancer Center Seattle WA 98109 USA. Data Visualization Core Fred Hutchinson Cancer Center Seattle WA 98109 USA. Translational Data Science IRC Fred Hutchinson Cancer Center Seattle WA 98109 USA. Electronic address: msetty@***. Department of Neurobiology and Biophysics University of Washington School of Medicine Seattle WA 98195 USA. Electronic address: asinghvi@***.

A comprehensive understanding of nervous system function requires molecular insight into the diversity and sex dimorphism of both its component cell types, glia and neurons. Here, we present a single-nuclear RNA sequencing (RNA-seq) census of all neuroectoderm-derived glia in the adult C. elegans nervous system, across sexes. By iteratively coupling computational modeling and custom analytics with in vivo validations, we uncovered molecular markers for all glia, as well as class-specific and pan-glial molecular signatures. These identified that each glia is functionally heterogeneous across the nervous system and variably sex dimorphic between sexes. Thus, this glial transcriptome (***) offers deep mechanistic insights into glial biology brain wide. Complementing the existing C. elegans neuronal transcriptome and mapped connectome, it also enables single-cell and molecular resolution insight into the entire nervous system of an adult metazoan.

关键词： C. elegans glia cell-type classification cellular heterogeneity dense core vesicles glia glial transcriptome sexual dimorphism snRNA-seq

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IoT Environmental Analyzer using Sensors and Machine Learning for Migraine Occurrence Prevention

IoT Environmental Analyzer using Sensors and Machine Learnin...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Rosemarie J. Day Hassan Salehi Mahsa Javadi Data Science Program Worcester Polytechnic Institute Worcester MA U.S.A California State University Chico CA USA University of Connecticut Health Center Farmington CT USA

Everyday migraines are affecting more than one billion people worldwide. This headache disorder is classified as the sixth most disabling disease in the world. Migraines are just one chronic illness affected by environmental triggers due to changes that occur inside the home. Migraines share this characteristic with sinus headaches and thus are often misdiagnosed. In this research work, an iOS-based environmental analyzer was designed, implemented and evaluated for migraine sufferers with the use of sensors. After the data collection and cleaning, five machine learning model were used to estimate prediction accuracy of migraines in terms of the environment. The data was evaluated against the models using K-Fold cross validation. The algorithm accuracy comparison showed that Linear Discriminant Analysis (LDA) produced highest accuracy for the testing data at a mean of 0.938. Preliminary results demonstrate the feasibility of using machine learning algorithms to perform the automated recognition of migraine trigger areas in the environment.

关键词： Sensors Cloud computing Humidity Machine learning Standards Photonics databases

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Correction to: Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization

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Health care management science 2023年第4期26卷 747页

作者： Shima Azizi Özge Aygül Brenton Faber Sharon Johnson Renata Konrad Andrew C Trapp Business Analytics and Information Systems Peter J. Tobin College of Business St. John's University Queens NY USA. azizis@stjohns.edu. Data Science Program Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. Biomedical Engineering Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. The Business School Worcester Polytechnic Institute 100 Institute Road Worcester MA USA.

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