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Correction: Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

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Trees 2024年第2期38卷 549-549页

作者： Arseniou, Georgios MacFarlane, David W. Calders, Kim Baker, Matthew Department of Forestry Michigan State University East Lansing USA CAVElab-Computational & Applied Vegetation Ecology Laboratory Department of Environment Faculty of Bioscience Engineering Ghent University Ghent Belgium Department of Geography and Environmental Systems University of Maryland-Baltimore County Baltimore USA

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Guidelines for public database submission of uncultivated virus genome sequences for taxonomic classification (vol 41, pg 898, 2023)

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NATURE BIOTECHNOLOGY 2023年第9期41卷 1346-1346页

作者： Adriaenssens, Evelien M. M. Roux, Simon Brister, J. Rodney Karsch-Mizrachi, Ilene Kuhn, Jens H. Varsani, Arvind Yigang, Tong Reyes, Alejandro Lood, Cedric Lefkowitz, Elliot J. Sullivan, Matthew B. B. Edwards, Robert A. A. Simmonds, Peter Rubino, Luisa Sabanadzovic, Sead Krupovic, Mart Dutilh, Bas E. E. Quadram Institute Bioscience Norwich Research Park Rosalind Franklin Road Norwich UK United States Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley CA USA National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD USA Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Fort Detrick Frederick MD USA The Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA Structural Biology Research Unit Department of Clinical Laboratory Sciences University of Cape Town Cape Town South Africa Beijing Advanced Innovation Center for Soft Matter Science and Engineering College of Life Science and Technology Beijing University of Chemical Technology Beijing China Max Planck Tandem Group in Computational Biology Department of Biological Sciences Universidad de los Andes Bogotá Colombia Centre of Microbial and Plant Genetics Department of Microbial and Molecular Systems KU Leuven Leuven Belgium Laboratory of Gene Technology Department of Biosystems KU Leuven Leuven Belgium Institute of Biodiversity Faculty of Biological Sciences Cluster of Excellence Balance of the Microverse Friedrich Schiller University Jena Jena Germany Theoretical Biology and Bioinformatics Department of Biology Science for Life Utrecht University CH Utrecht the Netherlands Department of Microbiology University of Alabama at Birmingham Birmingham AL USA Department of Microbiology The Ohio State University Columbus OH USA Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USA Center of Microbiome Science The Ohio State University Columbus OH USA College of Science and Engineering Flinders University Bedford Park South Australia Australia Nuffield Department of Medicine University of Oxford South Parks Road Oxford UK Consiglio Na

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White spot syndrome virus (WSSV) modulates lipid metabolism in white shrimp

Communications Biology

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Communications Biology 2023年第1期6卷 1-11页

作者： Ng, Yen Siong Cheng, Cheng-Shun Ando, Masahiro Tseng, Yi-Ting He, Shu-Ting Li, Chun-Yuan Cheng, Shu-Wen Chen, Yi-Min Kumar, Ramya Liu, Chun-Hung Takeyama, Haruko Hirono, Ikuo Wang, Han-Ching Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan Research Organization for Nano and Life Innovations Waseda University Tokyo Japan International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan Department of Aquaculture National Pingtung University of Science and Technology Pingtung Taiwan Department of Life Science and Medical Bioscience Waseda University Tokyo Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Tokyo Japan Department of Marine Biosciences Tokyo University of Marine Science and Technology Tokyo Japan

In addition to the Warburg effect, which increases the availability of energy and biosynthetic building blocks in WSSV-infected shrimp, WSSV also induces both lipolysis at the viral genome replication stage (12 hpi) to provide material and energy for the virus replication, and lipogenesis at the viral late stage (24 hpi) to complete virus morphogenesis by supplying particular species of long-chain fatty acids (LCFAs). Here, we further show that WSSV causes a reduction in lipid droplets (LDs) in hemocytes at the viral genome replication stage, and an increase in LDs in the nuclei of WSSV-infected hemocytes at the viral late stage. In the hepatopancreas, lipolysis is triggered by WSSV infection, and this leads to fatty acids being released into the hemolymph. β-oxidation inhibition experiment reveals that the fatty acids generated by WSSV-induced lipolysis can be diverted into β-oxidation for energy production. At the viral late stage, WSSV infection leads to lipogenesis in both the stomach and hepatopancreas, suggesting that fatty acids are in high demand at this stage for virion morphogenesis. Our results demonstrate that WSSV modulates lipid metabolism specifically at different stages to facilitate its replication. © 2023, The Author(s).

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The Dynamic Multisite Interactions between Two Intrinsically Disordered Proteins

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Angewandte Chemie 2017年第26期129卷

作者： Shaowen Wu Dongdong Wang Jin Liu Yitao Feng Jingwei Weng Yu Li Xin Gao Jianwei Liu Wenning Wang Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Department of Chemistry and Institutes of Biomedical Sciences Fudan University Shanghai 200433 China King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Center (CBRC) Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division Thuwal 23955 Saudi Arabia

Protein interactions involving intrinsically disordered proteins (IDPs) comprise a variety of binding modes, from the well‐characterized folding upon binding to dynamic fuzzy complexes. To date, most studies concern the binding of an IDP to a structured protein, while the interaction between two IDPs is poorly understood. In this study, NMR, smFRET, and molecular dynamics (MD) simulation are combined to characterize the interaction between two IDPs, the C‐terminal domain (CTD) of protein 4.1G and the nuclear mitotic apparatus (NuMA) protein. It is revealed that CTD and NuMA form a fuzzy complex with remaining structural disorder. Multiple binding sites on both proteins were identified by molecular dynamics and mutagenesis studies. This study provides an atomic scenario in which two IDPs bearing multiple binding sites interact with each other in dynamic equilibrium. The combined approach employed here could be widely applicable for investigating IDPs and their dynamic interactions.

关键词： FRET Moleküldynamik NMR-Spektroskopie Proteinstrukturen

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Towards a complete North American Anabaptist genealogy: A systematic approach to merging partially overlapping genealogy resources†

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American Journal of Medical Genetics Part A 1999年第2期86卷

作者： Richa Agarwala Leslie G. Biesecker James F. Tomlin Alejandro A. Schäffer Inherited Diseases Research Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland Genetic Diseases Research Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland Center for Information Technology Computational Bioscience and Engineering Laboratory National Institutes of Health Bethesda Maryland

Genetic studies on closed populations benefit from comprehensive, searchable genealogy resources. To support studies of Anabaptists, a computerized database has been developed that merges two large genealogy books, the “Fisher Family History” and the “Amish and Amish Mennonite Genealogies.” The former is more current but the latter is more comprehensive for the 18th and 19th centuries. Therefore, the merger of the two books is significantly more useful than either book alone. We demonstrate the utility of the merged database with two results: an increase in inbreeding coefficients and the identification of parent–child relationships that do not exist in either book. Am. J. Med. Genet. 86:156–161, 1999. Published 1999 Wiley-Liss, Inc.

关键词： autosomal recessive inheritance Amish Mennonite consanguinity inbreeding

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A bioinformatics tool to mine sequences for microarray studies of mouse models of oncogenesis

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Nature Genetics 1999年第3期23卷 43-43页

作者： Mary E. Edgerton Ronald Taylor John I. Powell Richard Simon Edison T. Liu Department of Pathology and Laboratory Medicine Hospital of the University of Pennsylvania Philadelphia USA Division of Cancer Treatment and Diagnosis Biometric Research Branch Cancer Therapy Evaluation Program National Cancer Institute Bethesda USA Bioinformatics and Molecular Analysis Section Computational Bioscience and Engineering Laboratory Center for Information Technology National Institutes of Health Bethesda USA Division of Clinical Sciences National Cancer Institute Bethesda USA

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Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest

Plant-Environment Interactions

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Plant-Environment Interactions 2024年第2期5卷 e10136页

作者： Kearsley, Elizabeth Verbeeck, Hans Stoffelen, Piet Janssens, Steven B. Yakusu, Emmanuel Kasongo Kosmala, Margaret De Mil, Tom Bauters, Marijn Kitima, Elasi Ramanzani Ndiapo, José Mbifo Chuda, Adelard Lonema Richardson, Andrew D. Wingate, Lisa Ilondea, Bhély Angoboy Beeckman, Hans van den Bulcke, Jan Boeckx, Pascal Hufkens, Koen Computational and Applied Vegetation Ecology Lab Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium BlueGreen Labs Melsele Belgium Meise Botanic Garden Meise Belgium Department of Biology Leuven Plant Institute KULeuven Leuven Belgium UGent-Woodlab (Laboratory of Wood Technology) Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium Service of Wood Biology Royal Museum for Central Africa Tervuren Belgium Faculté de gestion des ressources naturelles renouvelables Université de Kisangani Kisangani Democratic Republic Congo Department of Organismic and Evolutionary Biology Harvard University Cambridge MA United States CIBO Technologies Cambridge MA United States Forest is Life TERRA Teaching and Research Centre Gembloux Agro Bio-Tech University of Liège Gembloux Belgium Isotope Bioscience Laboratory - ISOFYS Department of Green Chemistry and Technology Ghent University Gent Belgium Research Group of Plants and Ecosystems (PLECO) Department of Biology University of Antwerp Wilrijk Belgium Institut National pour l'Etude et la Recherche Agronomiques-INERA Yangambi Democratic Republic Congo Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ United States INRAE UMR ISPA Villenave d'Ornon France Institut National pour l'Étude et la Recherche Agronomiques Kinshasa Democratic Republic Congo

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937–1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals. © 2024 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： leaf senescence leaf turnover phenology scaling tropical forest

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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