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A light in the dark: searching for electromagnetic counterparts to black hole-black hole mergers in LIGO/Virgo O3 with the Zwicky Transient Facility

arXiv

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arXiv 2022年

作者： Graham, Matthew J. McKernan, Barry Ford, K.E. Saavik Stern, Daniel Djorgovski, S.G. Coughlin, Michael Burdge, Kevin B. Bellm, Eric C. Helou, George Mahabal, Ashish A. Masci, Frank J. Purdum, Josiah Rosnet, Philippe Rusholme, Ben California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Department of Science CUNY Borough of Manhattan Community College 199 Chambers Street New YorkNY10007 United States Department of Astrophysics American Museum of Natural History Central Park West New YorkNY10028 United States Physics Program CUNY Graduate Center 365 5th Avenue New YorkNY10016 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States IPAC California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States Université Clermont Auvergne CNRS IN2P3 LPC Clermont-FerrandF-63000 France

The accretion disks of active galactic nuclei (AGN) are promising locations for the merger of compact objects detected by gravitational wave (GW) observatories. Embedded within a baryon-rich, high density environment, mergers within AGN are the only GW channel where an electromagnetic (EM) counterpart must occur (whether detectable or not). Considering AGN with unusual flaring activity observed by the Zwicky Transient Facility (ZTF), we describe a search for candidate EM counterparts to binary black hole (BBH) mergers detected by LIGO/Virgo in O3. After removing probable false positives, we find nine candidate counterparts to BBH mergers during O3 (seven in O3a, two in O3b) with a p-value of 0.0019. Based on ZTF sky coverage, AGN geometry, and merger geometry, we expect ≈ 3(NBBH/83)(fAGN/0.5) potentially detectable EM counterparts from O3, where NBBH is the total number of observed BBH mergers and fAGN is the fraction originating in AGN. Further modeling of breakout and flaring phenomena in AGN disks is required to reduce our false positive rate. Two of the events are also associated with mergers with total masses > 100M☉, which is the expected rate for O3 if hierarchical (large mass) mergers occur in the AGN channel. Candidate EM counterparts in future GW observing runs can be better constrained by coverage of the Southern sky as well as spectral monitoring of unusual AGN flaring events in LIGO/Virgo alert volumes. A future set of reliable AGN EM counterparts to BBH mergers will yield an independent means of measuring cosmic expansion (H0) as a function of redshift. © 2022, CC BY.

关键词： Gravity waves

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Author Correction: A genomic mutational constraint map using variation in 76,156 human genomes

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Nature 2024年第7997期626卷 E1页

作者： Siwei Chen Laurent C Francioli Julia K Goodrich Ryan L Collins Masahiro Kanai Qingbo Wang Jessica Alföldi Nicholas A Watts Christopher Vittal Laura D Gauthier Timothy Poterba Michael W Wilson Yekaterina Tarasova William Phu Riley Grant Mary T Yohannes Zan Koenig Yossi Farjoun Eric Banks Stacey Donnelly Stacey Gabriel Namrata Gupta Steven Ferriera Charlotte Tolonen Sam Novod Louis Bergelson David Roazen Valentin Ruano-Rubio Miguel Covarrubias Christopher Llanwarne Nikelle Petrillo Gordon Wade Thibault Jeandet Ruchi Munshi Kathleen Tibbetts Anne O'Donnell-Luria Matthew Solomonson Cotton Seed Alicia R Martin Michael E Talkowski Heidi L Rehm Mark J Daly Grace Tiao Benjamin M Neale Daniel G MacArthur Konrad J Karczewski Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. siwei@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. siwei@***. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Division of Medical Sciences Harvard Medical School Boston MA USA. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Data Science Platform Broad Institute of MIT and Harvard Cambridge MA USA. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. Division of Genetics and Genomics Boston Children's Hospital Boston MA USA. Richards Lab Lady Davis Institute Montreal Quebec Canada. Broad Institute of MIT and Harvard Cambridge MA USA. Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA. Institute for Molecular Medicine Finland (FIMM) Helsinki Finland. Centre for Population Genomics Garvan Institute of Medical Research and UNSW Sydney Sydney New South Wales Australia. Centre for Population Genomics Murdoch Children's Research Institute Melbourne Victoria Australia. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. konradk@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. konradk@***. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. konradk@***.

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model 1

Chemical-Induced Disease Detection Using Invariance-based Pa...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Warikoo, Neha Chang, Yung-Chun Hsu, Wen-Lian Institute of Biomedical Informatics National Yang-Ming University Taipei112 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei115 Taiwan Institute of Information Science Academia Sinica Taipei115 Taiwan Graduate Institute of Data Science Taipei Medical University Taipei106 Taiwan

ISBN: (纸本)9781948087070

In this work, we introduce a novel feature engineering approach named "algebraic invariance" to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning. Preprocessing of the training data classifies the entity pairs as either related or unrelated to build instance types for both inter-sentential and intra-sentential scenarios. An invariant function is proposed to process and optimally cluster similar patterns for both positive and negative instances. The learning model for CDR pairs is based on the SVM tree kernel approach, which generates feature trees and vectors and is modeled on suitable invariance based patterns, bringing brevity, precision and context to the identifier features. Results demonstrate that our method outperformed compared approaches, achieved a high recall rate of 85.08%, and averaged an F1-score of 54.34% without the use of any additional knowledge bases. © 2017 AFNLP

关键词： Forestry

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Global tracking of marine megafauna space use reveals how to achieve conservation targets

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science (New York, N.Y.) 2025年第6751期388卷 1086-1097页

作者： Ana M M Sequeira Jorge P Rodríguez Sarah A Marley Hannah J Calich Mirjam van der Mheen Michelle VanCompernolle Lucy M Arrowsmith Lauren R Peel Nuno Queiroz Marisa Vedor Ivo da Costa Gonzalo Mucientes Ana Couto Nicolas E Humphries Sara Abalo-Morla Francisco J Abascal Debra L Abercrombie Katya Abrantes F Alberto Abreu-Grobois André S Afonso Pedro Afonso Heidi Ahonen Susanne Åkesson Joanna Alfaro-Shigueto Russel D Andrews Frédéric Angelier Marina Antonopoulou Javier A Arata Gonzalo Araujo Randall Arauz José Manuel Arcos Igor Arregui Haritz Arrizabalaga Marie Auger-Méthé Steffen Bach Fred Bailleul Robin W Baird George H Balazs Susan G Barco Adam Barnett Warren Baverstock Alastair M M Baylis Annalea Beard Juan Bécares Eduardo J Belda Ian Bell Ashley Bennison Scott R Benson Diego Bernal Michael L Berumen Sandra Bessudo Natalia P A Bezerra Antonin V Blaison Gabriela S Blanco Barbara A Block Mark Bolton Mark E Bond Ramón Bonfil Camrin D Braun Annette C Broderick Michael de L Brooke Annabelle M L Brooks Edward J Brooks Ignacio M Bruno Jennifer M Burns Michael E Byrne Steven E Campana Hamish A Campbell Richard A Campbell Aaron Carlisle Ruth H Carmichael Gemma Carroll Paolo Casale Filipe R Ceia Demian D Chapman Taylor K Chapple Jean-Benoit Charrassin Andre Chiaradia John Chisholm Christopher R Clarke Thomas A Clay Christophe Cleguer Elizabeth Clingham Eric E G Clua Jesse E M Cochran Rochelle Constantine Robert W Cooper Estelle Crochelet Michelle Cronin Eduardo Cuevas Kayla P DaCosta Laurent Dagorn Ryan Daly Randall W Davis P J Nico de Bruyn Carlos Delgado-Trejo Thomas Dellinger Solène Derville Stella Diamant Andrew DiMatteo Kara L Dodge Philip D Doherty Michael C Double Alistair D M Dove Thomas K Doyle Michael J Drew Lindsay L Dubbs Clinton A J Duffy Peter H Dutton Ewan W J Edwards Luke D Einoder Mark V Erdmann Eduardo Espinoza Nicole Esteban Ana Isabel Fagundes Chris Feare Steven H Ferguson Luciana C Ferreira Francesco Ferretti John Filmalter Brittany Finucci G Chris Fischer Richard J Fitzpatrick Jorge Fontes Angela Formia Division of Ecology and Evolution Research School of Biology The Australian National University Canberra (Australian Capital Territory) Australia. UWA Oceans Institute and the School of Biological Sciences The University of Western Australia Perth (Western Australia) Australia. Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) CSIC-UIB Palma de Mallorca Spain. Instituto Mediterráneo de Estudios Avanzados (IMEDEA) CSIC-UIB Esporles Illes Balears Spain. C.A. Illes Balears Universidad Nacional de Educación a Distancia (UNED) Palma Spain. Delegación Territorial en Illes Balears Agencia Estatal de Meteorología (AEMET) Muelle de Poniente s/n Palma Spain. Scotland's Rural College (SRUC) Craibstone Estate Bucksburn Aberdeen UK. Oceans Graduate School and the UWA Oceans Institute The University of Western Australia Perth (Western Australia) Australia. School of Engineering and the UWA Oceans Institute The University of Western Australia Perth (Western Australia) Australia. Department of Biodiversity Conservation and Attractions Biodiversity and Conservation Science Kensington (Western Australia) Australia. Save Our Seas Foundation Geneva Switzerland. The Manta Trust Catemwood House Norwood Lane Corscombe Dorset UK. Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO) InBIO Laboratório Associado Campus de Vairão Universidade do Porto Vairão Portugal. BIOPOLIS Program in Genomics Biodiversity and Land Planning Campus de Vairao Vairão Portugal. Instituto de Investigaciones Marinas Consejo Superior de Investigaciones Científicas (IIM-CSIC) Vigo Pontevedra Spain. School of Biological Sciences University of Aberdeen Aberdeen UK. Biomathematics and statistics Scotland The kings building Edinburgh Scotland. Marine Biological Association (MBA) The Laboratory Citadel Hill Plymouth UK. Instituto de Investigación para la Gestión Integrada de Zonas Costeras Universitat Politècnica de València Gandia València Spain. Instituto Español de O

The recent Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals but no clear pathway for how zero loss of important biodiversity areas and halting human-induced extinction of threatened species will be achieved. We assembled a multi-taxa tracking dataset (11 million geopositions from 15,845 tracked individuals across 121 species) to provide a global assessment of space use of highly mobile marine megafauna, showing that 63% of the area that they cover is used 80% of the time as important migratory corridors or residence areas. The GBF 30% threshold (Target 3) will be insufficient for marine megafauna's effective conservation, leaving important areas exposed to major anthropogenic threats. Coupling area protection with mitigation strategies (e.g., fishing regulation, wildlife-traffic separation) will be essential to reach international goals and conserve biodiversity.

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Metatranscriptomics uncovers diurnal functional shifts in bacterial transgenes with profound metabolic effects

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Cell Host & Microbe 2025年

作者： Stephany Flores Ramos Nicole Siguenza Wuling Zhong Ipsita Mohanty Amulya Lingaraju R. Alexander Richter Smruthi Karthikeyan April L. Lukowski Qiyun Zhu Wilhan D.G. Nunes Jasmine Zemlin Zhenjiang Zech Xu Jeff Hasty Pieter C. Dorrestein Satchidananda Panda Rob Knight Amir Zarrinpar Division of Gastroenterology University of California San Diego La Jolla CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Department of Pediatrics University of California San Diego La Jolla CA USA Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA Division of Engineering and Applied Science California Institute of Technology Pasadena CA USA Scripps Institution of Oceanography University of California San Diego La Jolla CA USA School of Life Sciences Arizona State University Tempe AZ USA Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA Key Laboratory of Food Science and Technology Nanchang University Nanchang China Shu Chien-Gene Lay Department of Bioengineering University of California San Diego La Jolla CA USA Center for Microbiome Innovation University of California San Diego La Jolla CA USA Synthetic Biology Institute University of California San Diego La Jolla CA USA Salk Institute for Biological Studies La Jolla CA USA Department of Computer Science and Engineering University of California San Diego La Jolla CA USA Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Moores Cancer Center University of California San Diego La Jolla CA USA Division of Gastroenterology Jennifer Moreno Department of Veterans Affairs Medical Center La Jolla CA USA Institute of Diabetes and Metabolic Health University of California San Diego La Jolla CA USA

Diurnal rhythmicity in the gut maintains gut integrity, circadian rhythms, and metabolic homeostasis. However, existing studies focus on microbial composition rather than transcriptional activity. To understand microbial functional dynamics, we characterize diurnal fluctuations in the mouse cecal metatranscriptome and metagenome under high-fat diet and time-restricted feeding (TRF). We show that metatranscriptomics uncovers TRF-induced time-dependent microbial functional shifts that are undetectable with metagenomics alone. We also found bile salt hydrolase ( bsh ) from Dubosiella newyorkensis exhibits diurnal expression in the TRF group. Engineering this bsh , along with other candidates, into a native E. coli chassis reveals distinct differences in deconjugation and amidation activities, underscoring functional specificity. In vivo , a D. newyorkensis bsh improves insulin sensitivity, glucose tolerance, and body composition, suggesting a direct role in TRF metabolic benefits. This study highlights how coupling metatranscriptomics with engineered bacterial systems is a powerful approach for uncovering time-dependent bacterial functions related to health and disease.

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Author Correction: Widespread exposure to SARS-CoV-2 in wildlife communities

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Nature communications 2024年第1期15卷 6940页

作者： Amanda R Goldberg Kate E Langwig Katherine L Brown Jeffrey M Marano Pallavi Rai Kelsie M King Amanda K Sharp Alessandro Ceci Christopher D Kailing Macy J Kailing Russell Briggs Matthew G Urbano Clinton Roby Anne M Brown James Weger-Lucarelli Carla V Finkielstein Joseph R Hoyt Department of Biological Sciences Virginia Tech Blacksburg VA USA. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. Department of Biomedical Sciences and Pathobiology Virginia Tech Blacksburg VA USA. Translational Biology Medicine and Health Graduate Program Virginia Tech Roanoke VA USA. Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg VA USA. Department of Biochemistry Virginia Tech Blacksburg VA USA. Data Services University Libraries Virginia Tech Blacksburg VA USA. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. Academy of Integrated Science Virginia Tech Blacksburg VA USA. Department of Biological Sciences Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. finkielc@vt.edu. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. finkielc@vt.edu. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Academy of Integrated Science Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Department of Biological Sciences Virginia Tech Blacksburg VA USA. hoytjosephr@***.

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

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Procedia Computer science 2018年 135卷 377-384页

作者： Rezzy Eko Caraka Shamarina Shohaimi Isma Dwi Kurniawan Riki Herliansyah Arif Budiarto Shinta Purnama Sari Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Department of Mathematics Kalimantan Institute of Technology (ITK) Kalimantan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave.

关键词： Ecology GLLVM Negative Binomial Show cave Wild Cave

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Adherence to an anti-inflammatory diet is associated with lower Alzheimer's disease mortality: A modifiable risk factor in a national cohort

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The journal of prevention of Alzheimer's disease 2025年 100221页

作者： Ching-Chi Hsu Shiow-Ing Wang Sebastian Yu Eric S Lin James Cheng-Chung Wei Board of Directors Wizcare Medical Corporation Aggregate Taichung 404022 Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu 300044 Taiwan. Electronic address: chi7hsu@***. Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung 402306 Taiwan Department of Health Policy and Management College of Health Care and Management Chung Shan Medical University Taichung 402306 Taiwan. Electronic address: shiowing0107@***. Department of Dermatology College of Medicine Kaohsiung Medical University Kaohsiung 807378 Taiwan Department of Dermatology Kaohsiung Medical University Gangshan Hospital Kaohsiung 820111 Taiwan Department of Dermatology Kaohsiung Medical University Hospital Kaohsiung 807377 Taiwan Master of Public Health Program College of Public Health National Taiwan University Taipei 100025 Taiwan Neuroscience Research Center Kaohsiung Medical University Kaohsiung 807378 Taiwan. Electronic address: epor324@***. Department of Economics National Tsing Hua University Hsinchu 300044 Taiwan EMBA/MBA/MFB/MPM/HBA Programs National Tsing Hua University Hsinchu 300044 Taiwan. Electronic address: slin@mx.nthu.edu.tw. Institute of Medicine Chung Shan Medical University Taichung 402306 Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung 402306 Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung 406040 Taiwan. Electronic address: jccwei@***.

BACKGROUND:Chronic neuroinflammation contributes to Alzheimer's disease (AD) pathogenesis, and diet is a modifiable factor influencing inflammation. The impact of an anti-inflammatory diet on AD-specific mortality remains unclear. OBJECTIVES:To examine the association between adherence to an anti-inflammatory diet (measured as the percentage of dietary energy from anti-inflammatory foods) and AD-specific mortality, as well as all-cause mortality, in a large national cohort, and to determine whether associations differ by sex or race/ethnicity. METHODS:We analyzed 18,795 U.S. adults (≥18 years) from the 2007-2014 National Health and Nutrition Examination Survey. Anti-inflammatory diet adherence was defined as the percentage of total energy intake from anti-inflammatory foods, categorized as 0 %, <5 %, 5-9.99 %, or ≥10 %. Outcomes were AD-specific mortality and all-cause mortality ascertained via the National Death Index. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs) for mortality across intake categories, adjusting for demographic, lifestyle, and health factors. Analyses were stratified by sex, race/ethnicity, and age (≥45 years for AD mortality). RESULTS:Participants with 0 % anti-inflammatory intake had a higher all-cause mortality risk (HR 3.82, 95 % CI 1.18-12.33) compared to those with ≥10 % intake. In the overall analysis, 0 % anti-inflammatory intake showed a trend of reduced AD-specific mortality although its did not reach statistical significance after full adjustment (HR 3.04, 95 % CI 0.74-12.46 vs. ≥10 % intake; p>0.05). Notably, the inverse association between anti-inflammatory diet and AD mortality emerged in subgroup analyses. Male participants and non-Hispanic White participants with 0 % intake had the highest AD mortality hazards (HR 12.83 and 3.77, respectively, vs. ≥10 % intake), indicating significant risk reductions with anti-inflammatory diet in these groups. In contrast, no signific

关键词： Brain-gut axis Gut dysbiosis Ketogenic diet Neurodegeneration Neuroinflammation

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