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Cavity approach for the approximation of spectral density of graphs with heterogeneous structures

Physical Review E 2024年第3期109卷 034303-034303页

作者： Grover E. C. Guzman Peter F. Stadler Andre Fujita Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics School of Excellence in Embedded Composite AI Dresden/Leipzig (SECAI) Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Humboldtstraße 25 04105 Leipzig Germany Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 D-04103 Leipzig Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá 111321 Colombia and The Santa Fe Institute 1399 Hyde Park Road Santa Fe New Mexico 87501 USA Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil and Division of Network AI Statistics Medical Institute of Bioregulation Kyushu University Fukuoka 812-8582 Japan

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|hmax3t) and O(|E|hmax2t), respectively, where |E| is the number of edges, hmax is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

关键词： Network structure Directed networks Monte Carlo methods Network Models Topological tools

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Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

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Genomics, Proteomics & bioinformatics 2020年第1期18卷 52-64页

作者： Fuyi Li Andre Leier Quanzhong Liu Yanan Wang Dongxu Xiang Tatsuya Akutsu Geoffrey IWebb A.Ian Smith Tatiana Marquez-Lago Jian Li Jiangning Song Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash UniversityMelbourneVIC 3800Australia Monash Centre for Data Science Faculty of Information TechnologyMonash UniversityMelbourneVIC 3800Australia School of Medicine University of Alabama at BirminghamBirminghamAL 35233USA College of Information Engineering Northwest A&F UniversityYangling 712100China Bioinformatics Center Institute for Chemical ResearchKyoto UniversityUjiKyoto 611-0011Japan ARC Centre of Excellence in Advanced Molecular Imaging Monash UniversityMelbourneVIC 3800Australia Biomedicine Discovery Institute and Department of Microbiology Monash UniversityMelbourneVIC 3800Australia

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate ***-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological ***,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical ***,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural *** this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural *** features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of *** optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based ***,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent *** is capable of correctly identifying most cleavage sites in the case ***,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different *** is implemented as a webserver and is freely accessible at http://***/.

关键词： Protease Cleavage site prediction Machine learning Conditional random field Structural determinants

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Translation dysregulation in cancer as a source for targetable antigens

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Cancer Cell 2025年第5期43卷 823-840.e18,20页

作者： Weller, Chen Bartok, Osnat McGinnis, Christopher S. Palashati, Heyilimu Chang, Tian-Gen Malko, Dmitry Shmueli, Merav D. Nagao, Asuteka Hayoun, Deborah Murayama, Ayaka Sakaguchi, Yuriko Poulis, Panagiotis Khatib, Aseel Erlanger Avigdor, Bracha Gordon, Sagi Cohen Shvefel, Sapir Zemanek, Marie J. Nielsen, Morten M. Boura-Halfon, Sigalit Sagie, Shira Gumpert, Nofar Yang, Weiwen Alexeev, Dmitry Kyriakidou, Pelgia Yao, Winnie Zerbib, Mirie Greenberg, Polina Benedek, Gil Litchfield, Kevin Petrovich-Kopitman, Ekaterina Nagler, Adi Oren, Roni Ben-Dor, Shifra Levin, Yishai Pilpel, Yitzhak Rodnina, Marina Cox, Jürgen Merbl, Yifat Satpathy, Ansuman T. Carmi, Yaron Erhard, Florian Suzuki, Tsutomu Buskirk, Allen R. Olweus, Johanna Ruppin, Eytan Schlosser, Andreas Samuels, Yardena Department of Molecular Cell Biology Weizmann Institute of Science Rehovot 7610001 Israel Department of Pathology Stanford University Stanford 94305 CA United States Parker Institute for Cancer Immunotherapy San Francisco 94129 CA United States Department of Cancer Immunology Institute for Cancer Research Oslo University Hospital Oslo 0379 Norway Precision Immunotherapy Alliance University of Oslo Oslo Norway Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Department of Systems Immunology Weizmann Institute of Science Rehovot 7610001 Israel Department of Chemistry and Biotechnology of Engineering The University of Tokyo Tokyo 113-8656 Japan Department of Physical Biochemistry Max Planck Institute for Multidisciplinary Sciences Göttingen 37077 Germany Department of Pathology Sackler School of Medicine Tel Aviv University Tel Aviv 69978 Israel Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot 7610001 Israel Computational Systems Biochemistry Research Group Max-Planck Institute of Biochemistry Martinsried 82152 Germany Department of Veterinary Resources Weizmann Institute of Science Rehovot 7610001 Israel Tissue Typing and Immunogenetics Unit Hadassah Hebrew University Hospital Jerusalem 9112102 Israel CRUK Lung Cancer Centre of Excellence University College London Cancer Institute London WC1E 6DD United Kingdom Tumour Immunogenomics and Immunosurveillance Laboratory University College London Cancer Institute London WC1E 6DD United Kingdom Department of Life Science Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel Bioinformatics Unit Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel de Botton Institute for Protein Profiling the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy. © 2025 The Author(s)

关键词： cancer immunopeptidome immune checkpoint blockade neoantigens non-canonical peptides translation fidelity

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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From mechanism to medicine: The progress and potential of epigenetics in osteoarthritis

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Osteoarthritis and Cartilage Open 2025年第3期7卷 100621-100621页

作者： Roberts, Jack B. Rockel, Jason S. Mulders, Rick Capellini, Terence D. Appleton, C. Thomas Phanstiel, Douglas H. Lories, Rik Geurts, Jeroen Ali, Shabana Amanda Bhutani, Nidhi Stone, Laura Cruz-Almeida, Yenisel Jurisica, Igor Boer, Cindy G. Ramos, Yolande F.M. Rice, Sarah J. Kapoor, Mohit Biosciences Institute Newcastle University Newcastle upon Tyne United Kingdom Division of Orthopedics Osteoarthritis Research Program Schroeder Arthritis Institute University Health Network and Krembil Research Institute University Health Network Toronto ON Canada Department of Medical Biophysics and Computer Science University of Toronto Toronto ON Canada Department of Biomedical Data Sciences Section Molecular Epidemiology Leiden University Medical Center Leiden Netherlands Department of Human Evolutionary Biology Harvard University Cambridge MA United States Bone and Joint Institute and Department of Medicine Schulich School of Medicine and Dentistry Western University London Canada Curriculum in Bioinformatics and Computational Biology University of North Carolina Chapel Hill NC United States Laboratory of Tissue Homeostasis and Disease Skeletal Biology and Engineering Research Centre Department of Development and Regeneration KU Leuven Leuven Belgium Division of Rheumatology University Hospitals Leuven Leuven Belgium Rheumatology Department of Musculoskeletal Medicine Lausanne University Hospital and University of Lausanne (CHUV-UNIL) Lausanne Switzerland Bone and Joint Center Henry Ford Health + Michigan State University Health Sciences Detroit MI United States Department of Orthopaedic Surgery Stanford School of Medicine Stanford CA United States Department of Anesthesiology School of Medicine University of Minnesota Minneapolis MN United States Pain Research and Intervention Center of Excellence (PRICE) at the University of Florida Gainesville FL United States Department of Internal Medicine Genomics Medicine Center Erasmus MC University Medical Center Rotterdam Netherlands Department of Laboratory Medicine and Pathobiology University of Toronto Toronto ON Canada Department of Surgery University of Toronto Toronto ON Canada

Objective: Osteoarthritis (OA) is a chronic, degenerative disease of the articular joints. The disease presents an enormous clinical and economic burden globally, due in part to the lack of disease modifying therapies. For over a decade, OA researchers have been working to determine epigenetic mechanisms underlying the disease to better understand pathology, identify biomarkers of progression, and pinpoint novel targets for therapeutic intervention. Design: This article presents a summary of the 3rd International Workshop on the Epigenetics of Osteoarthritis held in Toronto, Ontario, Canada, in September 2024. The purpose of this meeting was to gather the international community to discuss the status of OA epigenetic research and share expertise on innovative techniques for future. Results: Since the two previous meetings, there has been increasing adoption of advanced single-cell and spatial sequencing technologies and bioinfomatic analyses. Furthermore, investigations of multiple joint tissues has highlighted the shifting paradigm from OA as a cartilage centric disease to the consideration of all joint tissues. Conclusions: The workshop provided a unique opportunity for early-career researchers to expand their network, and for all participants to discuss new or improved approaches to advance the field, including international consortia and data sharing. The highlights and outcomes from this OA epigenetics workshop are described in this report. © 2025 The Authors

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A universal language for finding mass spectrometry data patterns

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Nature Methods 2025年 1-8页

作者： Damiani, Tito Jarmusch, Alan K. Aron, Allegra T. Petras, Daniel Phelan, Vanessa V. Zhao, Haoqi Nina Bittremieux, Wout Acharya, Deepa D. Ahmed, Mohammed M. A. Bauermeister, Anelize Bertin, Matthew J. Boudreau, Paul D. Borges, Ricardo M. Bowen, Benjamin P. Brown, Christopher J. Chagas, Fernanda O. Clevenger, Kenneth D. Correia, Mario S. P. Crandall, William J. Crüsemann, Max Fahy, Eoin Fiehn, Oliver Garg, Neha Gerwick, William H. Gilbert, Jeffrey R. Globisch, Daniel Gomes, Paulo Wender P. Heuckeroth, Steffen James, C. Andrew Jarmusch, Scott A. Kakhkhorov, Sarvar A. Kang, Kyo Bin Kessler, Nikolas Kersten, Roland D. Kim, Hyunwoo Kirk, Riley D. Kohlbacher, Oliver Kontou, Eftychia E. Liu, Ken Lizama-Chamu, Itzel Luu, Gordon T. Luzzatto Knaan, Tal Mannochio-Russo, Helena Marty, Michael T. Matsuzawa, Yuki McAvoy, Andrew C. McCall, Laura-Isobel Mohamed, Osama G. Nahor, Omri Neuweger, Heiko Niedermeyer, Timo H. J. Nishida, Kozo Northen, Trent R. Overdahl, Kirsten E. Rainer, Johannes Reher, Raphael Rodriguez, Elys Sachsenberg, Timo T. Sanchez, Laura M. Schmid, Robin Stevens, Cole Subramaniam, Shankar Tian, Zhenyu Tripathi, Ashootosh Tsugawa, Hiroshi van der Hooft, Justin J. J. Vicini, Andrea Walter, Axel Weber, Tilmann Xiong, Quanbo Xu, Tao Pluskal, Tomáš Dorrestein, Pieter C. Wang, Mingxun Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic Metabolomics Core Facility Immunity Inflammation and Disease Laboratory Division of Intramural Research National Institute of Environmental Health Sciences National Institutes of Health Research Triangle Park NC United States Department of Chemistry and Biochemistry University of Denver Denver CO United States Functional Metabolomics Lab CMFI Cluster of Excellence University of Tuebingen Tuebingen Germany Department of Biochemistry University of California Riverside Riverside CA United States Department of Pharmaceutical Sciences Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora CO United States Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA United States Biologicals and Natural Products Discovery Crop Protection R & D Corteva Agrisciences Indianapolis IN United States BioMolecular Sciences School of Pharmacy University of Mississippi Oxford MS United States Pharmacognosy Faculty of Pharmacy Al-Azhar University Nasr City Egypt Department of Fundamental Chemistry Institute of Chemistry University of São Paulo São Paulo Brazil Department of Chemistry Case Western Reserve University Cleveland OH United States Walter Mors Institute of Research on Natural Products Federal University of Rio de Janeiro Rio de Janeiro Brazil Environmental Genomics and Systems Biology Division Lawrence Berkeley National Lab Berkeley CA United States The Joint Genome Institute Lawrence Berkeley National Lab Berkeley CA United States Mass Spectrometry Center of Expertise Regulatory and Stewardship Corteva Agrisciences Indianapolis IN United States Biologicals and Natural Products Crop Protection R & D Corteva Agrisciences Indianapolis IN United States Department of Chemistry – BMC Science for Life Laborat

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized;most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Trends and determinants of health facility childbirth service utilization among mothers in urban slums of Nairobi, Kenya

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Global Epidemiology 2020年 2卷

作者： Atahigwa, Catherine Kadengye, Damazo T. Iddi, Samuel Abrams, Steven Van Rie, Annelies University of Antwerp Global Health Institute Campus Drie Eiken Universiteitsplein 1 Wilrijk 2610 Belgium African Population and Health Research Center APHRC Campus Manga Close Off Kirawa Road P.O. Box 10787-00100 Nairobi Kenya Center for Statistics Interuniversity Institute for Biostatistics and statistical Bioinformatics Data Science Institute UHasselt Agoralaan Gebouw D Diepenbeek Belgium Department of Statistics and Actuarial Science Accra Ghana

High maternal mortality remains a challenge for the attainment of the third Sustainable Development Goal in Sub-Saharan Africa. In Kenya, maternal mortality ratio remains high at 362 deaths per 100,000 live births. Utilization of health facility childbirth services ensures safe birth and is vital for the reduction of maternal mortality. However, this can be greatly affected by socioeconomic and geographical inequalities. In this study, we assess the trends and determinants of health facility childbirth service utilization among women giving birth in the urban slums of Nairobi, Kenya. data were obtained from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) comprising 19,469 births observed between 2003 and [19]. A logistic regression model, with parameter estimation using a generalized estimating equations (GEE) approach, was used to assess factors associated with health facility childbirth. About 81% of the births occurred at health facilities while 19% were occurring at home or outside a health facility. The results further indicated that, education, parity, and relationship to head of households were associated with health facility childbirth. Increasing awareness of the mothers about the benefits of health facility childbirth service utilization and the risks of home childbirth should be given extra attention by health practitioners during antenatal care visits. © 2020 The Authors

关键词： Healthcare childbirth Maternal mortality Nairobi NUHDSS Slums

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Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information (vol 91, pg 626, 2022)

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BIOLOGICAL PSYCHIATRY 2022年第7期91卷 690-690页

作者： Maihofer, Adam X. Vinkers, Christiaan H. Department of Psychiatry University of California San Diego La Jolla California Department of Family Medicine and Public Health University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California. Electronic address: amaihofer@health.ucsd.edu. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Psychiatry Massachusetts General Hospital Boston Massachusetts. Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom National Institute of Health Research Maudsley Biomedical Research Centre King's College London London United Kingdom. Department of Psychiatry Harvard Medical School Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts Center of Excellence in Depression and Anxiety Disorders Belmont Massachusetts. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts. Department of Psychiatry University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California Research Service Veterans Affairs Healthcare System San Diego California. Duke Molecular Physiology Institute Duke University School of Medicine Durham North Carolina. Department of Psychiatry Veterans Affairs Connecticut Healthcare System West Haven Connecticut Department of Psychiatry Yale University School of Medicine New Haven Connecticut. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Epidemiology Columbia University Mailman School of Public Health New York. Division of Mental Health Atlanta Veterans Affairs Medical Center

BACKGROUND: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. RESULTS: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. CONCLUSIONS: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.

关键词： GWAS Genetics Heritability PTSD PheWAS Trauma

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A global overview of marine heatwaves in a changing climate (vol 5, 701, 2024)

COMMUNICATIONS EARTH & ENVIRONMENT

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COMMUNICATIONS EARTH & ENVIRONMENT 2024年第1期5卷 701页

作者： Capotondi, Antonietta Rodrigues, Regina R. Sen Gupta, Alex Benthuysen, Jessica A. Deser, Clara Froelicher, Thomas L. Lovenduski, Nicole S. Amaya, Dillon J. Le Grix, Natacha Xu, Tongtong Hermes, Juliet Holbrook, Neil J. Martinez-Villalobos, Cristian Masina, Simona Roxy, Mathew Koll Schaeffer, Amandine Schlegel, Robert W. Smith, Kathryn E. Wang, Chunzai University of Colorado Boulder Cooperative Institute for Research in Environmental Sciences Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) Federal University of Santa Catarina Florianópolis Brazil (GRID:grid.411237.2) (ISNI:0000 0001 2188 7235) University of New South Wales Climate Change Research Centre and Centre for Marine Science and Innovation Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) University of New South Wales Australian Research Council Centre of Excellence for Climate Extremes Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) Australian Institute of Marine Science Crawley Australia (GRID:grid.1046.3) (ISNI:0000 0001 0328 1619) National Center for Atmospheric Research Climate and Global Dynamics Laboratory Boulder USA (GRID:grid.57828.30) (ISNI:0000 0004 0637 9680) University of Bern Climate and Environmental Physics Physics Institute Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Bern Oeschger Centre for Climate Change Research Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Colorado Boulder Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) South African Environmental Observation Network Cape Town South Africa (GRID:grid.507758.8) (ISNI:0000 0004 0499 441X) University of Tasmania Institute for Marine and Antarctic Studies Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) University of Tasmania ARC Centre of Excellence for Climate Extremes Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) Universidad Adolfo Ibáñez Faculty of Engineering and Science Santiago Chile (GRID:grid.440617.0) (ISNI:0000 0001 2162 5606) ANID Technology Center No. DO210001 Data Observatory Foundation Santiago Chile (GRID:grid.440617.0) CMCC Foundation—Euro-M

Marine heatwaves have profoundly impacted marine ecosystems over large areas of the world oceans, calling for improved understanding of their dynamics and predictability. Here, we critically review the recent substantial advances in this active area of research, including the exploration of the three-dimensional structure and evolution of these extremes, their drivers, their connection with other extremes in the ocean and over land, future projections, and assessment of their predictability and current prediction skill. To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring platforms that can be rapidly deployed, are essential to achieve comprehensive event characterizations while also chronicling the evolving nature of these extremes and their impacts in our changing *** understanding of marine heatwave predictability and impacts requires analysis of these extremes at full ocean depth, using models and observations capturing their key drivers at the relevant scales, according to a broad literature review.

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