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Automatic beam path analysis of laser wakefield particle acceleration data

computational science and Discovery 2009年第1期2卷

作者： Rübel, Oliver Geddes, Cameron G R. Cormier-Michel, estelle Wu, Kesheng Prabhat Weber, Gunther H. Ushizima, Daniela M. Messmer, Peter Hagen, Hans Hamann, Bernd Bethel, Wes Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 One Cyclotron Road United States 'Visualization of Large and Unstructured Data Sets - Applic. in Geospatial Plan. Modeling and Eng. 'Technische Universität Kaiserslautern D-67653 Kaiserslautern Erwin-Schrödinger-Straße Germany Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis CA 95616 Davis One Shields Avenue United States LOASIS Program of Lawrence Berkeley National Laboratory Berkeley CA 94720 One Cyclotron Road United States Tech-X Corporation Boulder CO 80303 5621 Arapahoe Avenue United States

Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high-energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information-derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than has been possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline. © 2009 IOP Publishing Ltd.

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Revisiting the Transit Timing and Atmosphere Characterization of the Neptune-mass Planet HAT-P-26 b

arXiv

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arXiv 2023年

作者： A-Thano, Napaporn Awiphan, Supachai Jiang, Ing-Guey Kerins, eamonn Priyadarshi, Akshay McDonald, Iain Joshi, Yogesh C. Chulikorn, Thansuda Hayes, Joshua J.C. Charles, Stephen Huang, Chung-Kai Rattanamala, Ronnakrit Yeh, Li-Chin Dhillon, Vik S. Department of Physics Institute of Astronomy National Tsing-Hua University Hsinchu30013 Taiwan National Astronomical Research Institute of Thailand 260 Moo 4 Donkaew Mae Rim Chiang Mai50180 Thailand Jodrell Bank Centre for Astrophysics University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Department of Physical Sciences The Open University Walton Hall Milton KeynesMK7 6AA United Kingdom Manora Peak Nainital263001 India Department of Physics Faculty of Science Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok10330 Thailand Institute of Astronomy and Astrophysics Academia Sinica No.1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan PhD Program in Astronomy Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Department of Physics and General Science Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima30000 Thailand Institute of Computational and Modeling Science National Tsing-Hua University Hsinchu30013 Taiwan Department of Physics and Astronomy University of Sheffield SheffieldS3 7RH United Kingdom Instituto de Astrofísica de Canarias Tenerife La LagunaE-38205 Spain

We present the transit timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26 b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST), Transiting exoplanet Survey Satellite (TeSS), and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 ± 0.05 minutes, which could result from the presence of an additional ∼ 0.02 MJup planet at the 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains (formula presented) of H2O with a derived temperature of (formula presented) K. Copyright © 2023, The Authors. All rights reserved.

关键词： Timing circuits

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*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

arXiv

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arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： Data visualization

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26th Annual computational Neuroscience Meeting (CNS*2017) of the Organization for computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NeUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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euclid: early Release Observations – A preview of the euclid era through a galaxy cluster magnifying lens

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Astronomy and Astrophysics 2025年 697卷

作者： Atek, H. Gavazzi, R. Weaver, J.R. Diego, J.M. Schrabback, T. Hatch, N.A. Aghanim, N. Dole, H. Hartley, W.G. Taamoli, S. Congedo, G. Jimenez-Teja, Y. Cuillandre, J.-C. Bañados, e. Belladitta, S. Bowler, R.A.A. Franco, M. Jauzac, M. Mahler, G. Richard, J. Rocci, P.-F. Serjeant, S. Toft, S. Abriola, D. Bergamini, P. Biviano, A. Dimauro, P. ezziati, M. Golden-Marx, J.B. Grillo, C. Hughes, A.C.N. Kang, Y. Kneib, J.-P. Lombardi, M. Mamon, G.A. McPartland, C.J.R. Meneghetti, M. Miyatake, H. Montes, M. Mortlock, D.J. Oesch, P.A. Okabe, N. Rosati, P. Taylor, A.N. Tarsitano, F. Weller, J. Kluge, M. Laureijs, R. Paltani, S. Saifollahi, T. Schirmer, M. Stone, C. Mora, A. Altieri, B. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Balestra, A. Bardelli, S. Basset, A. Bender, R. Bodendorf, C. Bonino, D. Branchini, e. Brescia, M. Brinchmann, J. Camera, S. Candini, G.P. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castander, F.J. Castellano, M. Cavuoti, S. Cimatti, A. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Douspis, M. Dubath, F. Dupac, X. Dusini, S. ealet, A. Farina, M. Farrens, S. Ferriol, S. Fotopoulou, S. Frailis, M. Franceschi, e. Galeotta, S. Gillard, W. Gillis, B. Giocoli, C. Gómez-Alvarez, P. Grazian, A. Grupp, F. Guzzo, L. Hailey, M. Haugan, S.V.H. Hoar, J. Hoekstra, H. Holliman, M.S. Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Aix-Marseille Université CNRS CNES LAM Marseille France Department of Astronomy University of Massachusetts AmherstMA01003 United States Instituto de Física de Cantabria Edificio Juan Jordá Avenida de los Castros Santander39005 Spain Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Department of Astronomy University of Geneva ch. d’Ecogia 16 Versoix1290 Switzerland Physics and Astronomy Department University of California 900 University Ave. RiversideCA92521 United States Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía Granada18080 Spain Observatorio Nacional Rua General Jose Cristino 77-Bairro Imperial de Sao Cristovao Rio de Janeiro20921-400 Brazil Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom The University of Texas at Austin AustinTX78712 United States Department of Physics Centre for Extragalactic Astronomy Durham University South Road DH1 3LE United Kingdom Department of Physics Institute for Computational Cosmology Durham University South Road DH1 3LE United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of

We present the first analysis of the euclid early Release Observations (eRO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use imaging data from the Visible instrument (VIS) and the Near-Infrared Spectrometer and Photometer (NISP) to produce photometric catalogs for a total of ∼500 000 objects. The imaging data reach a typical depth of 5 σ in the range 25.1–25.4 AB in the NISP bands and 27.1–27.3 AB in the VIS band. Using the Lyman-break method in combination with photometric redshifts, we searched for high-redshift galaxies. We identified 30 Lyman-break galaxy (LBG) candidates at z > 6 and 139 extremely red sources (eRSs), most of which likely lie at lower redshift. The VIS imaging is deeper than the NISP imaging, which means that we can routinely identify high-redshift Lyman-break galaxies at about a magnitude of 3, which reduces contamination by brown dwarf stars and low-redshift galaxies. The difficulty of spatially resolving most of these sources in 0′′ . 3 pix−1 imaging means that it is difficult to distinguish between galaxies and quasars. Spectroscopic follow-up campaigns of these bright sources will help us to constrain the bright end of the ultraviolet galaxy luminosity function and the quasar luminosity function at z > 6, and it will constrain the physical nature of these objects. Additionally, we performed a combined strong- and weak-lensing analysis of A2390, and we show that euclid will contribute to constraining the virial mass of galaxy clusters better. We also identify optical and near-infrared counterparts of known z > 0.6 clusters in these data. These counterparts exhibit strong-lensing features. This establishes that euclid can characterize high-redshift clusters. Finally, we provide a glimpse of the ability of euclid to map the intracluster light out to larger radii than current facilities, which enables us to understand the cluster assembly history better and to map the dark matter distribution. This ini

关键词： Moon

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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NeUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Revisiting the Transit Timing and Atmosphere Characterization of the Neptune-mass Planet HAT-P-26 b

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Astronomical Journal 2023年第6期166卷 223页

作者： A-Thano, Napaporn Awiphan, Supachai Jiang, Ing-Guey Kerins, eamonn Priyadarshi, Akshay McDonald, Iain Joshi, Yogesh C. Chulikorn, Thansuda Hayes, Joshua J. C. Charles, Stephen Huang, Chung-Kai Rattanamala, Ronnakrit Yeh, Li-Chin Dhillon, Vik S Department of Physics Institute of Astronomy National Tsing-Hua University Hsinchu 30013 Taiwan National Astronomical Research Institute of Thail 260 Moo 4 Donkaew Mae Rim Chiang Mai 50180 Thailand Jodrell Bank Centre for Astrophysics University of Manchester Oxford Road Manchester M13 9PL United Kingdom Department of Physical Sciences The Open University Walton Hall Milton Keynes MK7 6AA United Kingdom Aryabhatta Research Institute of Observational Sciences (ARIES) Manora Peak Nainital 263001 India Department of Physics Faculty of Science Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok 10330 Thailand Institute of Astronomy and Astrophysics Academia Sinica No.1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan PhD Program in Astronomy Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand Department of Physics and General Science Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima 30000 Thailand Institute of Computational and Modeling Science National Tsing-Hua University Hsinchu 30013 Taiwan Department of Physics and Astronomy University of Sheffield Sheffield S3 7RH United Kingdom Instituto de Astrofísica de Canarias La Laguna Tenerife E-38205 Spain

We present a transit-timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26 b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 on the Hubble Space Telescope, the Transiting exoplanet Survey Satellite, and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 ± 0.05 minutes, which could result from the presence of an additional ∼0.02 M _Jup planet at a 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains 2.4 − 1.6 + 2.9 % H₂O with a derived temperature of 590 − 50 + 60 K. © 2023. The Author(s). Published by the American Astronomical Society.

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71 Genomic regions flanking e-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape

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Journal of Biomolecular Structure and Dynamics 2013年第SUPP 1期31卷 45-46页

作者： Raluca Gordan[a][b][*] Ning Shen[c] Iris Dror[d] Tianyin Zhou[d] John Horton[b] Remo Rohs[d] & Martha L. Bulyk[e][f] [a] Departments of Biostatistics and Bioinformatics Computer Science and Molecular Genetics and Microbiology Duke University Durham NC USA [b] Institute for Genome Sciences and Policy Duke University Durham NC 27708 USA Phone: Phone: +1 (919) 667-8673 Fax: Phone: +1 (919) 667-8673 [c] Department of Pharmacology and Cancer Biology Duke University Durham NC 27708 USA [d] Molecular and Computational Biology Program Departments of Biological Sciences Chemistry and Physics and Astronomy University of Southern California 1050 Childs Way Los Angeles CA 90089 USA [e] Division of Genetics Departments of Medicine and Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA 02115 USA [f] Harvard-MIT Division of Health Sciences and Technology (HST) Harvard Medical School Boston MA 02115 USA

Transcriptional regulation of gene expression is enacted mainly through binding of transcription factors (TFs) to specific, short DNA sites in cis-regulatory regions of genes. Most TFs are members of protein families that share a common DNA-binding domain and thus recognize similar DNA-binding sequences. It is not well understood why paralogous TFs often bind different genomic target sitesin vivoto effect different regulatory programs, despite apparently recognizing the same sequence motifs. Here, we designed custom protein-binding microarrays (PBMs) to analyze the DNA-binding specificities of twoSaccharomyces cerevisiaebasic helix-loop-helix (bHLH) proteins, Tye7 and Cbf1, as a model system. Our data reveal that e-box DNA-binding sequences (CAnnTG), when tested in the context of their native genomic flanking sequences, are bound differently by Cbf1 and Tye7. computational models of the PBM data indicate that DNA sequence features located in the genomic sequences outside the e-box contribute to DNA-binding specificityin vitro. Our analyses suggest that these flanking regions affect DNA-binding specificity indirectly by influencing the three-dimensional structure of the e-box binding sites. Finally, we show that these subtle differences in intrinsic sequence preferences of Cbf1 and Tye7in vitrohelp to explain their differential DNA-binding preferencesin vivo. Our results provide further evidence that the local shape of DNA-binding sites may be an important feature in distinguishing the DNA-binding preferences among paralogous TFs and thus may play a widespread role in determining how transcriptional regulatory specificity within TF families is achieved.

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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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The impact of AI on engineering design procedures for dynamical systems

arXiv

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arXiv 2024年

作者： de Payrebrune, Kristin M. Flaßkamp, Kathrin Ströhla, Tom Sattel, Thomas Bestle, Dieter Röder, Benedict eberhard, Peter Peitz, Sebastian Stoffel, Marcus Rutwik, Gulakala Aditya, Borse Wohlleben, Meike Sextro, Walter Raff, Maximilian Remy, C. David Yadav, Manish Stender, Merten van Delden, Jan Lüddecke, Timo Langer, Sabine C. Schultz, Julius Blech, Christopher RPTU Kaiserslautern-Landau Institute for Computational Physics in Engneering Gottlieb-Daimler-Straße 74 Kaiserslautern67663 Germany Saarland University Systems Engineering Systems Modeling and Simulation Campus Saarbrücken66123 Germany TU Ilmenau Mechatronics group Max-Planck-Ring 12 Ilmenau98693 Germany Brandenburg University of Technology Cottbus-Senftenberg Chair of Engineering Mechanics and Vehicle Dynamics Siemens-Halske-Ring 14 Cottbus03046 Germany University of Stuttgart Institute of Engineering and Computational Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany TU Dortmund Department of Computer Science & Lamarr Institute Joseph-von-Fraunhofer-Str. 25 Dortmund44227 Germany RWTH Aachen University Institute of General Mechanics Eilfschornsteinstraße 18 Aachen52062 Germany Paderborn University Faculty of Mechanical Engineering Dynamics and Mechatronics Warburger Str. 100 Paderborn33098 Germany University of Stuttgart Institute for Nonlinear Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany Technische Universität Berlin Chair of Cyber-Physical Systems in Mechanical Engineering Straße des 17. Juni 135 Berlin10623 Germany University of Göttingen Institute of Informatics Goldschmidtstr. 7 Göttingen37077 Germany Technische Universität Braunschweig Institute for Acoustics and Dynamics Langer Kamp 19 Braunschweig38106 Germany

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority program SPP 2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics. © 2024, CC BY.

关键词： Mechatronics

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