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arXiv 2025年

作者： Huang, Ziyuan Sekhon, Vishaldeep Kaur Guo, Ouyang Newman, Mark Sadeghian, Roozbeh Vaida, Maria L. Jo, Cynthia Ward, Doyle Bucci, Vanni Haran, John P. Department of Microbiology UMass Chan Medical School WorcesterMA01655 United States Department of Geriatric Medicine and Gerontology Johns Hopkins University BaltimoreMD21218 United States Data Sciences Harrisburg University of Science and Technology HarrisburgPA17101 United States DevIS Innovation Labs DevIS LLC ArlingtonVA22201 United States Department of Emergency Medicine UMass Chan Medical School WorcesterMA01655 United States Program in Microbiome Dynamics UMass Chan Medical School WorcesterMA01655 United States

The Alzheimer’s Disease Analysis Model Generation 1 (ADAM-1) is a multi-agent large language model (LLM) framework designed to integrate and analyze multi-modal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and detection of Alzheimer’s disease (AD). By leveraging retrieval-augmented generation (RAG) techniques along with its multi-agent architecture, ADAM-1 synthesizes insights from diverse data sources and contextualizes findings using literature-driven evidence. Comparative evaluation against XGBoost revealed similar mean F1 scores but significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly in small laboratory datasets. While currently tailored for binary classification tasks, future iterations aim to incorporate additional data modalities, such as neuroimaging and biomarkers, to broaden its scalability and applicability for Alzheimer’s research and *** Codes 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Classification (of information)

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Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community

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Global Ecology and Conservation 2025年 60卷

作者： Gilbert, Neil A. Bell, Rayna C. Catenazzi, Alessandro Martins, Renato A. Buttimer, Shannon Neely, Wesley J. Lambertini, Carolina Calderon, Veronica Saenz Haddad, Célio F.B. Becker, C. Guilherme DiRenzo, Graziella V. Department of Integrative Biology Oklahoma State University Stillwater OK United States Department of Herpetology California Academy of Sciences San Francisco CA United States Department of Biological Sciences Florida International University Miami FL United States Program in Fauna Conservation Universidade Federal de São Carlos Rod. Washington Luís Km 235 SP São Carlos 13565-905 Brazil Department of Biology The Pennsylvania State University PA United States Department of Biology Texas State University San Marcos TX United States Departamento de Biodiversidade and Centro de Aquicultura (CAUNESP) Instituto de Biociências Universidade Estadual Paulista SP Rio Claro Brazil One Health Microbiome Center Center for Infectious Disease Dynamics The Huck Institutes of the Life Sciences PA United States US Geological Survey Massachusetts Cooperative Fish and Wildlife Research Unit University of Massachusetts Amherst MA United States

Batrachochytrium dendrobatidis (Bd) has been decimating amphibian populations globally;previous work indicates that infection risk increases with moisture and thermal mismatch from a host's optimum. We hypothesized that, in addition to these abiotic influences, mismatch of hosts from their reproductive habitat heightens infection risk via exposure and/or susceptibility mechanisms. We evaluated this “reproductive habitat mismatch hypothesis” by quantifying the interplay of host breeding mode, habitat, and rainfall on Bd infection dynamics using two years of frog survey data—including swab data for 3427 captures representing 44 species—from Brazil's Atlantic Forest. We modeled infection prevalence, infection intensity, and the number of frogs captured as a function of rainfall, reproductive mode (aquatic or terrestrial), and habitat (aquatic or terrestrial) using hierarchical models. High rainfall was associated with increases in infection prevalence and infection intensity;however, these increases were particularly apparent for species in habitats that were mismatched from the species’ reproductive habitat. Tropical regions experiencing increases in precipitation will likely see higher Bd risk, and our results indicate that such increases in rainfall will be particularly problematic for species that are forced to move from their reproductive habitats by factors such as habitat loss or thermal stress. © 2025

关键词： Atlantic forest Chytridiomycosis Frogs Precipitation Reproductive habitat mismatch hypothesis

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IFNγ regulates NAD⁺ metabolism to promote the respiratory burst in human monocytes

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Blood Advances 2022年第12期6卷 3821-3834页

作者： McCann, Katelyn J. Christensen, Stephen M. Colby, Devon H. McGuire, Peter J. Myles, Ian A. Zerbe, Christa S. Dalgard, Clifton L. Sukumar, Gauthaman Leonard, Warren J. McCormick, Beth A. Holland, Steven M. Immunopathogenesis Section Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD United States Department of Microbiology and Physiological Systems and Program in Microbiome Dynamics University of Massachusetts Medical School Worcester MA United States Laboratory of Molecular Immunology The Immunology Center National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Metabolism Infection and Immunity Section National Human Genome Research Institute National Institutes of Health Bethesda MD United States Epithelial Therapeutics Unit Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD United States The American Genome Center Uniformed Services University of the Health Sciences Bethesda MD United States Department of Anatomy Physiology & Genetics Uniformed Services University of the Health Sciences Bethesda MD United States Henry M. Jackson Foundation for the Advancement of Military Medicine Bethesda MD United States

Interferon γ (IFNγ) is an essential and pleiotropic activator of human monocytes, but little is known about the changes in cellular metabolism required for IFNγ-induced activation. We sought to elucidate the mechanisms by which IFNγ reprograms monocyte metabolism to support its immunologic activities. We found that IFNγ increased oxygen consumption rates (OCR) in monocytes, indicative of reactive oxygen species generation by both mitochondria and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. Transcriptional profiling revealed that this oxidative phenotype was driven by IFNγ-induced reprogramming of NAD⁺ metabolism, which is dependent on nicotinamide phosphoribosyltransferase (NAMPT)-mediated NAD⁺ salvage to generate NADH and NADPH for oxidation by mitochondrial complex I and NADPH oxidase, respectively. Consistent with this pathway, monocytes from patients with gain-of-function mutations in STAT1 demonstrated higher-than-normal OCR, whereas chemical or genetic disruption of mitochondrial complex I (rotenone treatment or Leigh syndrome patient monocytes) or NADPH oxidase (diphenyleneiodonium treatment or chronic granulomatous disease [CGD] patient monocytes) reduced OCR. Interestingly, inhibition of NAMPT in healthy monocytes completely abrogated the IFNγ-induced oxygen consumption, comparable to levels observed in CGD monocytes. These data identify an IFNγ-induced, NAMPT-dependent, NAD⁺ salvage pathway that is critical for IFNγ activation of human monocytes. © 2022 American Society of Hematology. All rights reserved.

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A decision theoretic approach to model evaluation in computational drug discovery

arXiv

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arXiv 2018年

作者： Watson, Oliver Cortes-Ciriano, Isidro Taylor, Aimee Watson, James A. Evariste Technologies Ltd Goring on Thames United Kingdom Centre for Molecular Science Informatics Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom Center for Communicable Disease Dynamics Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Infectious Disease Microbiome Program Broad Institute CambridgeMA United States Centre for Tropical Medicine and Global Health Nuffield Department of Medicine University of Oxford United Kingdom Mahidol-Oxford Tropical Medicine Research Unit Faculty of Tropical Medicine Mahidol University Thailand

Background Artificial intelligence, trained via machine learning (e.g. neural nets and random forests) or computational statistics algorithms (e.g. support vector machines and ridge regression), holds much promise for the improvement of small molecule drug discovery. However, structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. Controlling over-fitting remains a major concern. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. Methods 25 publicly available molecular datasets were extracted from ChEMBL. Neural nets, random forests, support vector machines (regression) and ridge regression were then fitted to the structure-activity data. A new validation method, based on quantile splits on the activity distribution function, is proposed for the construction of training and testing sets. This is compared to standard random partitioning of the data. Out-of-sample performance was evaluated using mean squared error and new two rank-based loss functions which penalize only the predicted ranks of high activity molecules. Results Model validation based on random partitioning of available data favours models which overfit and 'memorize' the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes models which can extrapolate onto structurally different molecules outside of the training data. This approach favours more constrained models, namely ridge regression and support vector regression. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. Conclusions Model performance should be evaluated from a decision theoretic perspective with subjective loss functions, i.e. loss functions that

关键词： Decision trees

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Correction to: Mapping malaria by combining parasite genomic and epidemiologic data

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BMC medicine 2018年第1期16卷 241页

作者： Amy Wesolowski Aimee R Taylor Hsiao-Han Chang Robert Verity Sofonias Tessema Jeffrey A Bailey T Alex Perkins Daniel E Neafsey Bryan Greenhouse Caroline O Buckee Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. awesolowski@jhu.edu. Department of Epidemiology Harvard TH Chan School of Public Health Boston MA USA. Center for Communicable Disease Dynamics Harvard TH Chan School of Public Health Boston MA USA. Infectious Disease and Microbiome Program Broad Institute Cambridge MA USA. Department of Infectious Disease Epidemiology Medical Research Council Centre for Global Infectious Disease Analysis Imperial College London UK. Department of Medicine University of California-San Francisco San Francisco CA USA. Program in Bioinformatics and Integrative Biology University of Massachusetts Worcester MA USA. Division of Transfusion Medicine Department of Medicine University of Massachusetts Worcester MA USA. Department of Biological Sciences and Eck Institute for Global Health University of Notre Dame Notre Dame IN USA. Department of Immunology and Infectious Diseases Harvard TH Chan School of Public Health Boston MA USA. Chan Zuckerberg Biohub San Francisco CA 94158 USA.

The original article [1] contained an error in the presentation of Figure 1; this error has now been rectified and Figure 1 is now presented correctly.

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Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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Nature biotechnology 2020年第4期38卷 504页

作者： Christian Lieven Moritz E Beber Brett G Olivier Frank T Bergmann Meric Ataman Parizad Babaei Jennifer A Bartell Lars M Blank Siddharth Chauhan Kevin Correia Christian Diener Andreas Dräger Birgitta E Ebert Janaka N Edirisinghe José P Faria Adam M Feist Georgios Fengos Ronan M T Fleming Beatriz García-Jiménez Vassily Hatzimanikatis Wout van Helvoirt Christopher S Henry Henning Hermjakob Markus J Herrgård Ali Kaafarani Hyun Uk Kim Zachary King Steffen Klamt Edda Klipp Jasper J Koehorst Matthias König Meiyappan Lakshmanan Dong-Yup Lee Sang Yup Lee Sunjae Lee Nathan E Lewis Filipe Liu Hongwu Ma Daniel Machado Radhakrishnan Mahadevan Paulo Maia Adil Mardinoglu Gregory L Medlock Jonathan M Monk Jens Nielsen Lars Keld Nielsen Juan Nogales Intawat Nookaew Bernhard O Palsson Jason A Papin Kiran R Patil Mark Poolman Nathan D Price Osbaldo Resendis-Antonio Anne Richelle Isabel Rocha Benjamín J Sánchez Peter J Schaap Rahuman S Malik Sheriff Saeed Shoaie Nikolaus Sonnenschein Bas Teusink Paulo Vilaça Jon Olav Vik Judith A H Wodke Joana C Xavier Qianqian Yuan Maksim Zakhartsev Cheng Zhang Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark. Systems Biology Lab Amsterdam Institute of Molecular and Life Sciences (AIMMS) Vrije Universiteit Amsterdam Amsterdam the Netherlands. BioQUANT/COS Heidelberg University Heidelberg Germany. Ecole Polytechnique Fédérale de Lausanne Laboratory of Computational Systems Biotechnology Lausanne Switzerland. iAMB-Institute of Applied Microbiology ABBt-Aachen Biology and Biotechnology RWTH Aachen University Aachen Germany. Department of Bioengineering University of California La Jolla CA USA. Department of Chemical Engineering and Applied Chemistry University of Toronto Toronto Ontario Canada. Human Systems Biology Laboratory Instituto Nacional de Medicina Genomica & Coordinación de la Investigación Científica-Red de Apoyo a la Investigación UNAM Mexico City Mexico. Institute for Systems Biology Seattle WA USA. Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens Institute for Biomedical Informatics (IBMI) Tübingen Germany. Department of Computer Science University of Tübingen Tübingen Germany. German Center for Infection Research (DZIF) partner site Tübingen Tübingen Germany. Australian Institute for Bioengineering and Nanotechnology The University of Queensland Brisbane Queensland Australia. Argonne National Laboratory Lemont IL USA. Analytical Biosciences Division of Systems Biomedicine and Pharmacology Leiden Academic Centre for Drug Research Leiden University Leiden the Netherlands. Department of Systems Biology Centro Nacional de Biotecnología Consejo Superior de Investigaciones Científicas (CNB-CSIC) Madrid Spain. Centro de Biotecnología y Genómica de Plantas (CBGP UPM-INIA) Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Madrid Spain. Department of Animal and Aquacultural Sciences Faculty of Biosciences Norwegian University of Life Sciences Oslo Norway

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Biochemical networks Computational models Software

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