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arXiv 2023年

作者： Channagiri, Tejasvi Ferrari, Margherita Maria Jeon, Youngkyu Xu, Penghao Storici, Francesca Jonoska, Nataša Department of Mathematics and Statistics University of South Florida TampaFL United States Department of Biostatistics Harvard University United States Department of Mathematics University of Manitoba Winnipeg Canada School of Biological Sciences Georgia Institute of Technology AtlantaGA United States Molecular Targets Program National Cancer Institute National Institutes of Health FrederickMD United States

Double-strand breaks (DSBs) in DNA are naturally occurring destructive events in all organisms that may lead to genome instability. Cells employ various repair methods known as non-homologous end joining (NHEJ), microhomology mediated end joining (MMEJ), and homology-directed recombination (HDR). These repair processes may lead to DNA sequence variations (e.g., nucleotide insertions, deletions, and substitutions) at the location of the break. Studying DNA DSB repair processes often involves the use of high throughput sequencing assays to precisely quantify the sequence variations near the break with software tools. Often methods of assessing and visualizing these data have not taken into account the full complexity of the sequencing data, such as the frequency, type, and position of the sequence variations in a single comprehensive representation. Here we present a method that allows visualization of the overall variation pattern as well as comparison of these patterns among experimental setups. Copyright © 2023, The Authors. All rights reserved.

关键词： Python

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Improving the lifestyle behavior of type 2 diabetes mellitus patients using a mobile application

Improving the lifestyle behavior of type 2 diabetes mellitus...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Andreia Pinto João Viana Diogo Martinho Vítor Crista José Miguel Diniz Joana Reis João Matos David Greer Goreti Marreiros Alberto Freitas Department of Community Medicine Information and Health Decision Sciences (MEDCIDS) Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Institute of Engineering Polytechnic of Porto Porto Portugal Health Data Science PhD Program Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Lisboa Portugal CLOO - Behavioral Insights Unit Faculty of Psychology CICPSI - Research Center for Psychological Science University of Lisbon Lisboa Portugal Behavioral Insights Unit CLOO Porto Portugal Behavioral Insights Unit CLOO Lisboa Portugal

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

关键词： Computers Data analysis Computational modeling User interfaces Diabetes Behavioral sciences Mobile applications

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Miniscope GRIN Lens System for Calcium Imaging of Neuronal Activity from Deep Brain Structures in Behaving Animals

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Current Protocols in Neuroscience 2019年第1期86卷 e56-e56页

作者： Zhang, Lifeng Liang, Bo Barbera, Giovanni Hawes, Sarah Zhang, Yan Stump, Kyle Baum, Ira Yang, Yupeng Li, Yun Lin, Da-Ting Intramural Research Program National Institute on Drug Abuse National Institutes of Health Baltimore MD United States Transgenic Section Laboratory of Neurogenetics National Institute of Aging National Institutes of Health Bethesda MD United States Hefei National Laboratory for Physical Sciences at the Microscale School of Life Sciences University of Science and Technology of China Hefei China Department of Zoology and Physiology University of Wyoming College of Arts and Sciences Laramie WY United States The Solomon H. Snyder Department of Neuroscience Johns Hopkins University School of Medicine Baltimore MD United States

Visualizing neural activity from deep brain regions in freely behaving animals through miniature fluorescent microscope (miniscope) systems is becoming more important for understanding neural encoding mechanisms underlying cognitive functions. Here we present our custom-designed miniscope GRadient INdex (GRIN) lens system that enables simultaneously recording from hundreds of neurons for months. This article includes miniscope design, the surgical procedure for GRIN lens implantation, miniscope mounting on the head of a mouse, and data acquisition and analysis. First, a target brain region is labeled with virus expressing GCaMP6;second, a GRIN lens is implanted above the target brain region;third, following mouse surgical recovery, a miniscope is mounted on the head of the mouse above the GRIN lens;and finally, neural activity is recorded from the freely behaving mouse. This system can be applied to recording the same population of neurons longitudinally, enabling the elucidation of neural mechanisms underlying behavioral control. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

关键词： calcium imaging freely behaving mice GCaMP6 GRadient INdex lens miniscope

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Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

arXiv

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arXiv 2023年

作者： Li, Lianfa Khalili, Roxana Lurmann, Frederick Pavlovic, Nathan Wu, Jun Xu, Yan Liu, Yisi O’Sharkey, Karl Ritz, Beate Oman, Luke Franklin, Meredith Bastain, Theresa Farzan, Shohreh F. Breton, Carrie Habre, Rima Department of Population and Public Health Sciences University of Southern California Los AngelesCA United States State Key Laboratory of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources Chinese Academy of Sciences Beijing China Sonoma Technology Inc. PetalumaCA United States Program in Public Health Susan and Henry Samueli College of Health Sciences University of California IrvineCA United States Departments of Epidemiology and Environmental Health Fielding School of Public Health University of California Los AngelesCA United States Goddard Space Flight Center National Aeronautics and Space Administration GreenbeltMD United States Department of Statistical Sciences University of Toronto TorontoON Canada Spatial Sciences Institute University of Southern California Los AngelesCA United States

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge;however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction. Copyright © 2023, The Authors. All rights reserved.

关键词： Nitrogen oxides

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TScan-II:A genome-scale platform for the de novo identification of CD4^(+) T cell epitopes

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四川生理科学杂志 2023年第11期45卷 2164-2164页

作者： Mohammad H Dezfuian Division of Genetics Department of Medicine Howard Hughes Medical Institute Brigham and Women's Hospital Boston MA USA Department of Genetics Harvard Medical School Boston MA USA Adeno-Associated Virus Biology Section National Institute of Dental and Craniofacial Research National Institutes of Health Bethesda MD USA Broad Institute of Harvard and Massachusetts Institute of Technology Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Medicine Harvard Medical School Boston MA USA Genes and Human Disease Program Oklahoma Medical Research Foundation Oklahoma City OK USA Salivary Disorders Unit National Institute of Dental and Craniofacial Research National Institutes of Health Bethesda MD USA Arthritis and Clinical Immunology Program Oklahoma Medical Research Foundation Oklahoma City OK USA Departmentment of Microbiology and Immunology University of Oklahoma Health Sciences Center Oklahoma City OK USA Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK USA Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA

CD4+T cells play fundamental roles in orchestrating immune responses and tissue ***,our inability to associate peptide human leukocyte antigen class-II(HLA-II)complexes with their cognate T cell receptors(TCRs)in an unbiased manner has hampered our understanding of CD4^(+)T cell function and role in ***,we introduce TScan-II,a highly sensitive genome-scale CD4^(+) antigen discovery *** platform seamlessly integrates the endogenous HLA-II antigen-processing machinery in synthetic antigen-presenting cells and TCR signaling in T cells,enabling the simultaneous screening of multiple HLAs and *** genome-scale human,virome,and epitope mutagenesis libraries,TScan-II facilitates de novo antigen discovery and deep exploration of TCR *** demonstrate TScan-II's potential for basic and translational research by identifying a non-canonical antigen for a cancer-reactive CD4^(+)T cell ***,we identified two antigens for clonally expanded CD4^(+)T cells in Sjögren's disease,which bind distinct HLAs and are expressed in HLA-II-positive ductal cells within affected salivary glands.

关键词： endogenous specificity homeostasis

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Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer’s disease

Nature Aging

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Nature Aging 2025年 1-18页

作者： Heo, Gyujin Xu, Ying Wang, Erming Ali, Muhammad Oh, Hamilton Se-Hwee Moran-Losada, Patricia Anastasi, Federica González Escalante, Armand Puerta, Raquel Song, Soomin Timsina, Jigyasha Liu, Menghan Western, Daniel Gong, Katherine Chen, Yike Kohlfeld, Pat Flynn, Allison Thomas, Alvin G. Lowery, Joseph Morris, John C. Holtzman, David M. Perlmutter, Joel S. Schindler, Suzanne E. Vilor-Tejedor, Natalia Suárez-Calvet, Marc García-González, Pablo Marquié, Marta Fernández, Maria Victoria Boada, Mercè Cano, Amanda Ruiz, Agustín Zhang, Bin Bennett, David A. Benzinger, Tammie Wyss-Coray, Tony Ibanez, Laura Sung, Yun Ju Cruchaga, Carlos Department of Psychiatry Washington University School of Medicine St. Louis MO United States NeuroGenomics and Informatics Center Washington University St. Louis MO United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY United States Mount Sinai Center for Transformative Disease Modeling Icahn School of Medicine at Mount Sinai New York NY United States Graduate Program in Stem Cell and Regenerative Medicine Stanford University Stanford CA United States The Phil and Penny Knight Initiative for Brain Resilience Stanford University Stanford CA United States Wu Tsai Neurosciences Institute Stanford University Stanford CA United States Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford CA United States Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain Hospital del Mar Research Institute Barcelona Spain Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology (BIST) Barcelona Spain Department of Medicine and Life Sciences Universitat Pompeu Fabra Barcelona Spain Ace Alzheimer Center Barcelona Universitat Internacional de Catalunya Barcelona Spain PhD Program in Biotechnology Faculty of Pharmacy and Food Sciences University of Barcelona Barcelona Spain Department of Neurology Washington University School of Medicine St. Louis MO United States Hope Center for Neurologic Diseases Washington University St. Louis MO United States Knight Alzheimer’s Disease Research Center Washington University St. Louis MO United States Department of Radiology Washington University School of Medicine St. Louis MO United States Department of Genetics Radboud UMC Nijmegen Netherlands Servei de Neurologia Hospital del Mar Barcelona Spain Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED) National Institute of Health Carlos III Madrid Spain Glenn Biggs Institute for Alzheimer’s & Ne

Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer’s disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more than 3,300 well-characterized individuals to identify new proteins, pathways and predictive models for AD. We identified 416 proteins (294 new) associated with clinical AD status and validated the findings in two external datasets representing more than 7,000 samples. AD-related proteins reflected blood–brain barrier disruption and other processes implicated in AD, such as lipid dysregulation or immune responses. A machine learning model was used to identify a set of seven proteins that were highly predictive of both clinical AD (area under the curve (AUC) of >0.72) and biomarker-defined AD status (AUC of >0.88), which were replicated in multiple external cohorts and orthogonal platforms. These findings underscore the potential of using plasma proteins as biomarkers for the early detection and monitoring of AD and for guiding treatment decisions. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Climate and Dengue Mortality: A Machine Learning Based Early Warning System and Future Projections for Pune, India

SSRN

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SSRN 2023年

作者： Yacob, Sophia Roxy, Mathew Koll Murtugudde, Raghu Karipot, Anand Sapkota, Amir Dasgupta, Panini Baliwant, Kalpana Saunik, Sujata Tiwari, Abhiyant Chattopadhyay, Rajib Phalkey, Revati K. Centre for Climate Change Research Indian Institute of Tropical Meteorology Ministry of Earth Sciences Pune India Department of Atmospheric and Space Sciences Savitribai Phule Pune University Pune India DOAS University of Maryland College ParkMD United States Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Powai Mumbai400076 India Department of Epidemiology and Biostatistics School of Public Health University of Maryland College ParkMD United States School of Earth and Environmental Sciences Seoul National University Korea Republic of Health Department Pune Municipal Corporation Pune India Harvard TH Chan School of Public Health BostonMA United States Natural Resources Defense Council India Heidelberg Institute of Global Health University of Heidelberg Germany Division of Epidemiology and Public Health University of Nottingham Nottingham United Kingdom

Background: India accounts for a third of the global dengue disease burden, out of an annual global incidence of 100–400 million infections. The non-linearity and regional heterogeneity inherent in climate-dengue relationship and the lack of consistent data makes it difficult for preparing predictions suited for effective disease prevention. The current study investigates the climate-dengue links in India, and presents a model framework for both near-term and future dengue predictions. Methods: Statistical techniques and machine learning methods are used to evaluate the climate-dengue links and prepare the dengue model framework. Mortality data for a dengue hotspot region, Pune, from 2004 to 2015 is analyzed along with the meteorological conditions, considering the non-linearity and the time-lag between them. The future climate simulations from the Coupled Model Intercomparison Project phase6 models are used as inputs in the dengue model for generating dengue future projections up to 2100, under different future emission pathways. Findings: The current study finds that temperature, rainfall, and relative humidity impact dengue mortality in Pune at a lag of 2–5 months. Moderate rains spread in time leads to an increase in dengue mortality, whereas heavy rains reduce it through the flushing effect. Our dengue model provides a reliable forecast accuracy at a lead time of two months. In a changing climate, dengue mortalities over Pune are projected to increase by 13% in the near-term (2021–2040) and 30–112% in the long-term (2081–2100) under different emission scenarios in response to the changes in rainfall patterns and increase in temperature and relative humidity. Interpretation: The dengue model framework used in this study introduces a comprehensive approach for near-term predictions and future projections, and provides a template for other regions with similar tropical climate. Regional dengue models incorporating the lag in climate-dengue associations can provide

关键词： Climate change

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Fundamental immuneoncogenicity trade-offs define driver mutation fitness (vol 606, pg 172, 2022)

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NATURE 2022年第7914期606卷 E5-E5页

作者： Hoyos, David Zappasodi, Roberta Schulze, Isabell Sethna, Zachary de Andrade, Kelvin Cesar Bajorin, Dean F. Bandlamudi, Chaitanya Callahan, Margaret K. Funt, Samuel A. Hadrup, Sine R. Holm, Jeppe S. Rosenberg, Jonathan E. Shah, Sohrab P. Vazquez-Garcia, Ignacio Weigelt, Britta Wu, Michelle Zamarin, Dmitriy Campitelli, Laura F. Osborne, Edward J. Klinger, Mark Robins, Harlan S. Khincha, Payal P. Savage, Sharon A. Balachandran, Vinod P. Wolchok, Jedd D. Hellmann, Matthew D. Merghoub, Taha Levine, Arnold J. Luksza, Marta Greenbaum, Benjamin D. Computational Oncology Department of Epidemiology & Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Swim Across America Laboratory and Ludwig Collaborative Immunology Program Parker Institute for Cancer Immunotherapy Memorial Sloan Kettering Cancer Center New York NY USA Department of Medicine Weill Cornell Medical College New York NY USA Parker Institute for Cancer Immunotherapy Memorial Sloan Kettering Cancer Center New York NY USA Immunology and Microbial Pathogenesis Program Weill Cornell Graduate School of Medical Sciences New York NY USA Hepatopancreatobiliary Service Department of Surgery Memorial Sloan Kettering Cancer Center New York NY USA Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA Division of Cancer Epidemiology and Genetics Clinical Genetics Branch National Cancer Institute National Institutes of Health Rockville MD USA Department of Pathology and Laboratory Medicine Memorial Sloan Kettering Cancer Center New York NY USA Kravis Center for Molecular Oncology Memorial Sloan Kettering Cancer Center New York NY USA Experimental and Translational Immunology Health Technology Technical University of Denmark Lyngby Denmark Physiology Biophysics & Systems Biology Weill Cornell Medicine Weill Cornell Medical College New York NY USA Department of Surgery Memorial Sloan Kettering Cancer Center New York NY USA Adaptive Biotechnologies Seattle WA USA David M. Rubenstein Center for Pancreatic Cancer Research Memorial Sloan Kettering Cancer Center New York NY USA Human Oncology and Pathogenesis Program Memorial Sloan Kettering Cancer Center New York NY USA Thoracic Oncology Service Memorial Sloan Kettering Cancer Center New York NY USA Simons Center for Systems Biology Institute for Advanced Study Princeton NJ USA Department of Oncological Sciences Tisch Cancer Institute Icahn School of Medicine at Mount Sinai New York NY USA

Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3,4,5,6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical ‘free fitness’ framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.

关键词： Computational models Evolutionary theory Tumour immunology Tumour-suppressor proteins

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Personal health records - Potential one-stop approach in polypharmacy

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Computer Methods and programs in Biomedicine Update 2022年 2卷

作者： Chen, Ya-Lin Satria, Fauzi Budi Jamshed, Shazia Babar, Zaheer-Ud-Din Mukherjee, Nilesh Yang, Hsuan-Chia Li, Yu-Chuan Iqbal, Usman Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan Department of Pharmacy Practice Faculty of Pharmacy UniSZA Kuala Terengganu Malaysia Qualitative Research-Methodological Application in Health Sciences Research Group Kulliyyah of Pharmacy International Islamic University Malaysia Kuantan Malaysia Centre for Pharmaceutical Policy and Practice Research Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Master of Science in Systems Biology Manipal School of Life Sciences Manipal Academy of Higher Education Manipal India International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan

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Thymine DNA glycosylase as a novel target for melanoma (vol 38, pg 3710, 2019)

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ONCOGENE 2022年第23期41卷 3300-3301页

作者： Mancuso, Pietro Tricarico, Rossella Bhattacharjee, Vikram Cosentino, Laura Kadariya, Yuwaraj Jelinek, Jaroslav Nicolas, Emmanuelle Einarson, Margret Beeharry, Neil Devarajan, Karthik Katz, Richard A. Dorjsuren, Dorjbal G. Sun, Hongmao Simeonov, Anton Giordano, Antonio Testa, Joseph R. Davidson, Guillaume Davidson, Irwin Larue, Lionel Sobol, Robert W. Yen, Timothy J. Bellacosa, Alfonso Cancer Epigenetics Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA Department of Medical Biotechnologies Universita’ degli Studi di Siena Siena Italy Institut Curie PSL Research University INSERM U1021 Normal and Pathological Development of Melanocytes 91405 Orsay France Sbarro Institute for Cancer Research and Molecular Medicine Department of Biology College of Science and Technology Temple University Philadelphia PA 19122 USA Equipe Labellisée Ligue Contre le Cancer Orsay France University Paris-Sud University Paris-Saclay CNRS UMR3347 Orsay France Cancer Biology Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA Fels Institute for Cancer and Molecular Biology Temple University School of Medicine Philadelphia PA 19140 USA Department of Biostatistics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA Division of Preclinical Innovation National Center for Advancing Translational Sciences National Institutes of Health Rockville MD 20850 USA Institut de Génétique et de Biologie Moléculaire et Cellulaire CNRS/INSERM/ULP 67404 Illkirch France Department of Oncologic Sciences Mitchell Cancer Institute University of South Alabama Mobile AL 36604 USA

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