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arXiv 2025年

作者： Ford, Colby T. Ullah, Samee Antunes, Dinler Amaral Ferreira, Tarsis Gesteira CharlotteNC United States University of North Carolina at Charlotte Department of Bioinformatics and Genomics CharlotteNC United States University of North Carolina at Charlotte School of Data Science CharlotteNC United States Tuple LLC CharlotteNC United States Islamabad45320 Pakistan University of Houston Departments of Biology and Biochemistry HoustonTX77204 United States College of Optometry University of Houston 4401 Martin Luther King Boulevard HoustonTX77204-2020 United States

PyMOLfold is a flexible and open-source plugin designed to seamlessly integrate AI-based protein structure prediction and visualization within the widely used PyMOL molecular graphics system. By leveraging state-of-the-art protein folding models such as ESM3, Boltz-1, and Chai-1, PyMOLfold allows researchers to directly predict protein tertiary structures from amino acid sequences without requiring external tools or complex workflows. Furthermore, with certain models, users can provide a SMILES string of a ligand and have the small molecule placed in the protein structure. This unique capability bridges the gap between computational folding and structural visualization, enabling users to input a primary sequence, perform a folding prediction, and immediately explore the resulting 3D structure within the same intuitive platform. © 2025, CC BY-SA.

关键词： Prediction models

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GISAID’s Role in Pandemic Response

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China CDC weekly 2021年第49期3卷 1049-1051页

作者： Shruti Khare Céline Gurry Lucas Freitas Mark B Schultz Gunter Bach Amadou Diallo Nancy Akite Joses Ho Raphael TC Lee Winston Yeo GISAID Core Curation Team Sebastian Maurer-Stroh GISAID Global Data Science Initiative(GISAID) MunichGermany Bioinformatics Institute Agency for Science Technology and ResearchSingapore Oswaldo Cruz Foundation(FIOCRUZ) Rio de JaneiroBrazil Institut Pasteur de Dakar DakarSenegal National Institutes of Biotechnology Malaysia SelangorMalaysia Smorodintsev Research Institute of Influenza St.PetersburgRussia Genome Institute of Singapore Agency for Science Technology and ResearchSingapore China National GeneBank ShenzhenChina A*STAR Infectious Disease Labs(ID Labs) Singapore National Public Health Laboratory National Centre for Infectious DiseasesMinistry of HealthSingapore Department of Biological Sciences National University of SingaporeSingapore.

GISAID is a global data science initiative and the primary source of genomic and associated metadata of all influenza viruses,Respiratory Syncytial Virus(RSV)and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pandemic coronavirus causing coronavirus disease 2019(COVID-19).GISAID’s publicly accessible data sharing platform enables collaboration of over 42,000 participating researchers from 198 nations and data generators from over 3,500 institutions across the *** the first wholegenome sequences were made available by China CDC through GISAID on January 10.

关键词： acute respiratory AID

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Characterizing Emergency department Care for Patients With Histories of Incarceration

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JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

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Count time series modelling of Twitter data topic modelling: A case of Indonesia flood events

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. A. Hidayat B Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Twitter data provide rich and powerful information to leverage the dynamics of public perception to establish situational awareness and disaster mitigation strategies during critical times. In this paper, we perform topic modeling via Latent Dirichlet Allocation to extract topics from a collection of tweets related to Indonesia flood events in February 2021 with the query: “banjir”. The extracted topics are used as one of the features to build a generalized linear count time series model with Negative Binomial distribution. We find seven major topics from the model in which tweets containing a topic about the government’s role in handling the situation dominate the conversation. Taking into account a simple intervention analysis, we demonstrate a statistically significant change in the users’ behavior before and after the severe Jakarta flood on 20 February 2021. Moreover, a metric evaluation demonstrates that a covariate that describes the turning point of the Jakarta flood event is convenient to build a more accurate count time series model of the tweets.

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Nesting of Touching Polygons

arXiv

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arXiv 2024年

作者： Seemann, Carsten R. Stadler, Peter F. Hellmuth, Marc Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Polygons are cycles embedded into the plane;their vertices are associated with x- and y-coordinates and the edges are straight lines. Here, we consider a set of polygons with pairwise non-overlapping interior that may touch along their boundaries. Ideas of the sweep line algorithm by Bajaj and Dey for non-touching polygons are adapted to accommodate polygons that share boundary points. The algorithms established here achieves a running time of O(n + N log N), where n is the total number of vertices and N MSC Codes 05C10 Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Killer cell immunoglobulin receptor diversity and its relevance in the human host’s response to HIV infection in African populations

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Translational Medicine Communications 2023年第1期8卷 1-9页

作者： John Mukisa Marion Amujal Obondo J. Sande Moses L. Joloba David P. Kateete Daudi Jjingo Graeme Mardon Neil Hanchard Mogomotsi Matshaba Jill A. Hollenbach Department of Immunology and Molecular Biology College of Health Sciences Makerere University P.O.BOX 7072 Kampala Uganda College of Computing and Information Sciences Makerere University Kampala Uganda African Center of Excellence in Bioinformatics and Data Science Infectious Diseases Institute (IDI) Makerere University Kampala Uganda Department of Molecular and Human Genetics and Department of Pathology and Immunology Baylor College of Medicine Houston TX USA National Human Genome Research Institute Bethesda MD USA Botswana-Baylor Children’s Clinical Centre of Excellence P/Bag BR 129 Gaborone Botswana Department of Neurology and Department of Epidemiology and Biostatistics University of California San Francisco San Francisco California 94158 USA

Host response to the human immune deficiency virus (HIV) involves both the innate and adaptive immune systems. As part of the innate immune system, the killer cell immunoglobulin-like receptors (KIRs) found on natural killer cells and some T-lymphocytes are genetically diverse and play key functions in the host response against viral pathogens. In the last decade, there has been substantial growth in sequencing technologies and bioinformatics capacity to understand human host genetics, including KIR. However, there is limited literature on how the KIR diversity informs the perspectives on HIV disease states in understudied African settings. A better understanding of the effects of KIRs on the host’s immune response to HIV in African settings is essential to inform strategies to develop more effective therapies and vaccines to improve health among people living with HIV (PLWH). In this paper, we review KIR diversity, the role of KIR immunogenetic variation in the human host response to HIV, discuss current perspectives on the studies to assess the relationship between KIR diversity and the HIV disease continuum, and suggest future research prospects.

关键词： Killer cell immunoglobulin-like receptors ( ) Diversity Human immune deficiency virus (HIV) bioinformatics

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Federated Learning for Clinical Structured data: A Benchmark Comparison of Engineering and Statistical Approaches

arXiv

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arXiv 2023年

作者： Li, Siqi Miao, Di Wu, Qiming Hong, Chuan D’Agostino, Danny Li, Xin Ning, Yilin Shang, Yuqing Fu, Huazhu Ong, Marcus Eng Hock Haddadi, Hamed Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Computing Imperial College London London United Kingdom Institute of Data Science National University of Singapore Singapore Singapore

Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. Statistical FL algorithms, however, remain considerably less recognized than their engineering counterparts. Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains. We evaluated five FL frameworks using both simulated and real-world data. The results indicate that statistical FL algorithms yield less biased point estimates for model coefficients and offer convenient confidence interval estimations. In contrast, engineering-based methods tend to generate more accurate predictions, sometimes surpassing central pooled and statistical FL models. This study underscores the relative strengths and weaknesses of both types of methods, emphasizing the need for increased awareness and their integration in future FL applications. © 2023, CC BY.

关键词： Decision making

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covSTATIS: a multi-table technique for network neuroscience

arXiv

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arXiv 2024年

作者： Baracchini, Giulia Yu, Ju-Chi Rieck, Jenny Beaton, Derek Guillemot, Vincent Grady, Cheryl Abdi, Herve Spreng, R. Nathan Montreal Neurological Institute Department of Neurology and Neurosurgery McGill University Montreal Canada Campbell Family Mental Health Institute Centre for Addiction and Mental Health Toronto Canada Rotman Research Institute at Baycrest Toronto Canada Data Science & Advanced Analytics Unity Health Toronto Toronto Canada Institut Pasteur Universite Paris Cite Bioinformatics and Biostatistics Hub Paris France Departments of Psychiatry and Psychology University of Toronto Toronto Canada School of Behavioral and Brain Sciences The University of Texas at Dallas Richardson United States

Similarity analyses between multiple correlation or covariance tables constitute the cornerstone of network neuroscience. Here, we introduce covSTATIS, a versatile, linear, unsupervised multi-table method designed to identify structured patterns in multi-table data, and allow for the simultaneous extraction and interpretation of both individual and group-level features. With covSTATIS, multiple similarity tables can now be easily integrated, without requiring a priori data simplification, complex black-box implementations, user-dependent specifications, or supervised frameworks. Applications of covSTATIS, a tutorial with Open data and source code are provided. CovSTATIS offers a promising avenue for advancing the theoretical and analytic landscape of network neuroscience. © 2024, CC BY-NC-ND.

关键词： Open data

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Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

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Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

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How can mathematics be used to improve burn care?

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Burns Open 2025年 9卷 100390-100390页

作者： Egberts, Ginger Vermolen, Fred Peng, Qiyao Korkmaz, H.Ibrahim van Zuijlen, Paul Computational Mathematics Group Department of Mathematics and Statistics University of Hasselt Diepenbeek Belgium Dutch Burn Foundation Beverwijk Netherlands Data Science Institute (DSI) University of Hasselt Hasselt Belgium Mathematical Institute University of Leiden Leiden Netherlands Burn Center Department of Plastic Reconstructive and Hand Surgery Red Cross Hospital Beverwijk Netherlands Department of Plastic Reconstructive and Hand Surgery Amsterdam UMC Location VUmc Amsterdam Movement Sciences Amsterdam Netherlands Department of Molecular Cell Biology and Immunology Amsterdam UMC Location VUmc Amsterdam Netherlands Pediatric Surgical Centre Emma Children's Hospital Amsterdam UMC Location AMC and VUmc Amsterdam Netherlands Bioinformatics Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

Severe second-degree ‘partial thickness’ and third-degree ‘full thickness’ burns are characterized by damage to the dermal layer of the skin. In the dermis, specialized cells called fibroblasts play a crucial role in wound healing. These cells produce collagen, a protein that provides strength and structure to the skin. After burn injury, fibroblasts migrate to the injured area and start producing and depositing collagen to help repair the damaged tissue. While contraction is essential for closing the wound, it can also result in scar contraction (contractures), especially in more severe burns. This contraction creates stresses on the skin, which can deteriorate the mobility of joints near the burn. This article overviews the most recent research results in computer simulations of scar contraction after burns. © 2024 The Authors

关键词： Agent-based models Artificial intelligence Continuum models Mathematical models Mathematics in the clinic Parameter uncertainty Post-burn contraction

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