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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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Multi-scale simulations of electron and ion dynamics in self-irradiated silicon

arXiv

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arXiv 2020年

作者： Lee, Cheng-Wei Stewart, James A. Foiles, Stephen M. Dingreville, Rémi Schleife, André Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Integrated Nanotechnologies Sandia National Laboratories AlbuquerqueNM87185 United States Computational Materials and Data Science Department Sandia National Laboratories AlbuquerqueNM87185 United States Materials Research Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The interaction of energetic ions with the electronic and ionic system of target materials is an interesting but challenging multi-scale problem and understanding of the early stages after impact of heavy, initially charged ions is particularly poor. At the same time, energy deposition during these early stages determines later formation of damage cascades. We address the multi-scale character by combining real-time time-dependent density functional theory for electron dynamics with molecular dynamics simulations of damage cascades. Our first-principles simulations prove that core electrons affect electronic stopping and have an unexpected influence on the charge state of the projectile. We show that this effect is absent for light projectiles, but dominates the stopping physics for heavy projectiles. By parameterizing an inelastic energy loss friction term in the molecular dynamics simulations using our first-principles results, we also show a qualitative influence of electronic stopping physics on radiation-damage cascades. Copyright © 2020, The Authors. All rights reserved.

关键词： Projectiles

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Vec2SPARQL: Integrating SPARQL queries and knowledge graph embeddings 11

Vec2SPARQL: Integrating SPARQL queries and knowledge graph e...

引用

11th International Conference Semantic Web Applications and Tools for Life sciences, SWAT4LS 2018

作者： Kulmanov, Maxat Kafkas, Senay Karwath, Andreas Malic, Alexander Gkoutos, Georgios V Dumontier, Michel Hoehndorf, Robert Computer Electrical and Mathematical Science and Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Centre for Computational Biology University of Birmingham Birmingham United Kingdom Institute of Data Science Maastricht University Maastricht Netherlands

Recent developments in machine learning have led to a rise of large number of methods for extracting features from structured data. The features are represented as vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://***/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://***/. © 2018 CEUR Workshop Proceedings. All rights reserved.

关键词： Vector spaces

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Pavo: Discovery of a star-forming dwarf galaxy just outside the Local Group

arXiv

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arXiv 2023年

作者： Jones, Michael G. Mutlu-Pakdil, Burçin Sand, David J. Donnerstein, Richard Crnojević, Denija Bennet, Paul Fielder, Catherine E. Karunakaran, Ananthan Spekkens, Kristine Strader, Jay Urquhart, Ryan Zaritsky, Dennis Steward Observatory University of Arizona 933 North Cherry Avenue Rm. N204 TucsonAZ85721-0065 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States University of Tampa 401 West Kennedy Boulevard TampaFL33606 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto TorontoONM5S 3H4 Canada Department of Physics and Space Science Royal Military College of Canada Station Forces P.O. Box 17000 KingstonONK7K 7B4 Canada Department of Physics Engineering Physics and Astronomy Queen’s University KingstonONK7L 3N6 Canada Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States

We report the discovery of Pavo, a faint (MV = −10.0), star-forming, irregular, and extremely isolated dwarf galaxy at D ≈ 2 Mpc. Pavo was identified in Dark Energy Camera Legacy Survey imaging via a novel approach that combines low surface brightness galaxy search algorithms and machine learning candidate classifications. Follow-up imaging with the Inamori-Magellan Areal Camera & Spectrograph on the 6.5 m Magellan Baade telescope revealed a color–magnitude diagram (CMD) with an old stellar population, in addition to the young population that dominates the integrated light, and a tip-of-the-red-giant-branch distance estimate of (formula presented) Mpc. The blue population of stars in the CMD is consistent with the youngest stars having formed no later than 150 Myr ago. We also detected no Hα emission with SOAR telescope imaging, suggesting we may be witnessing a temporary low in Pavo’s star formation. We estimate the total stellar mass of Pavo to be log M∗/M☉ = 5.6 ± 0.2 and measure an upper limit on its H i gas mass of 1.0 × 106 M☉ based on the HIPASS survey. Given these properties, Pavo’s closest analog is Leo P (D = 1.6 Mpc), previously the only known isolated, star-forming, Local Volume dwarf galaxy in this mass range. However, Pavo appears to be even more isolated, with no other known galaxy residing within over 600 kpc. As surveys and search techniques continue to improve, we anticipate an entire population of analogous objects being detected just outside the Local Group. © 2023, CC BY.

关键词： Cameras

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Here There Be (Dusty) Monsters: High Redshift AGN are Dustier Than Their Hosts

arXiv

引用

arXiv 2024年

作者： Brooks, Madisyn Simons, Raymond C. Trump, Jonathan R. Taylor, Anthony J. Backhaus, Bren Davis, Kelcey Buat, Véronique Cleri, Nikko J. de la Vega, Alexander Finkelstein, Steven L. Hirschmann, Michaela Holwerda, Benne W. Kocevski, Dale D. Koekemoer, Anton M. Lucas, Ray A. Pacucci, Fabio Seillé, Lise-Marie Department of Physics Unit 3046 University of Connecticut 196A Auditorium Road StorrsCT06269 United States Department of Engineering and Physics Providence College 1 Cunningham Sq ProvidenceRI02918 United States Department of Astronomy The University of Texas at Austin AustinTX United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Aix Marseille Univ CNRS CNES LAM Marseille France Paris France Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics and Astronomy University of California 900 University Ave RiversideCA92521 United States Cosmic Frontier Center The University of Texas at Austin AustinTX United States Observatoire de Sauverny Versoix1290 Switzerland Department of Physics University of Louisville Natural Science Building 102 Louisville40292 KY United States Department of Physics and Astronomy Colby College WatervilleME04901 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Black Hole Initiative Harvard University 20 Garden St CambridgeMA02138 United States

JWST spectroscopy has discovered a population of z 3.5 galaxies with broad Balmer emission lines, and narrow forbidden lines, that are consistent with hosting active galactic nuclei (AGN). Many of these systems, now known as "little red dots" (LRDs), are compact and have unique colors that are very red in the optical/near-infrared and blue in the ultraviolet. The relative contribution of galaxy starlight and AGN to these systems remains uncertain, especially for the galaxies with unusual blue+red spectral energy distributions. In this work, we use Balmer decrements to measure the independent dust attenuation of the broad and narrow emission-line components of a sample of 29 broad-line AGN identified from three public JWST spectroscopy surveys: CEERS, JADES, and RUBIES. Stacking the narrow components from the spectra of 25 sources with broad Hα and no broad Hβ results in a median narrow Hα/Hβ = 2.47+0−0.0505 (consistent with Av = 0) and broad Hα/Hβ > 8.85 (Av > 3.63). The narrow and broad Balmer decrements imply little-to-no attenuation of the narrow emission lines, which are consistent with being powered by star formation and located on larger physical scales. Meanwhile, the lower limit in broad Hα/Hβ decrement, with broad Hβ undetected in the stacked spectrum of 25 broad-Hα AGN, implies significant dust attenuation of the broad-line emitting region that is presumably associated with the central AGN. Our results indicate that these systems, on average, are consistent with heavily dust-attenuated AGN powering the red parts of their SED while their blue UV emission is powered by unattenuated star formation in the host galaxy. © 2024, CC BY.

关键词： Stars

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Discovery and affinity maturation of antibody fragments from an unfavorably enriched phage display selection by deep sequencing and machine learning

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Journal of Bioscience and Bioengineering 2025年

作者： Sakiya Kawada Yoichi Kurumida Tomoyuki Ito Thuy Duong Nguyen Hafumi Nishi Hikaru Nakazawa Yutaka Saito Tomoshi Kameda Koji Tsuda Mitsuo Umetsu Department of Biomolecular Engineering Graduate School of Engineering Tohoku University 6-6-11 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Department of Data Science School of Frontier Engineering Kitasato University 1-15-1 Kitazato Minami-ku Sagamihara Kanagawa 252-0373 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan Department of Applied Information Sciences Graduate School of Information Sciences Tohoku University 6-3-09 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Tohoku Medical Megabank Organization Tohoku University 2-1 Seiryo-machi Aoba-ku Sendai 980-8573 Japan Faculty of Core Research Ochanomizu University 2-1-1 Ohtsuka Bunkyo-ku Tokyo 112-8610 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8561 Japan Center for Advanced Intelligence Project RIKEN 1-4-1 Nihombashi Chuo-ku Tokyo 103-0027 Japan Research and Services Division of Materials Data and Integrated Systems National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan

Phage display selection has been used for directed evolution of antibody fragments. However, variants with binding affinity cannot be always identified due to undesirable enrichment of target-unrelated variants in the biopanning process. Here, our goal was to obtain functional variants by deep sequencing and machine learning from a phage display library where functional variants were not appropriately enriched. Deep sequencing of the previously biopanned pools revealed that amplification bias might have prevented the enrichment of target-binding phages. We performed a sequence similarity search based on the deep sequencing analysis so that the influence of bias was decreased, leading to discovery of a variant with binding affinity, which could not be discovered by a conventional screening method alone. We applied machine learning to the deep sequencing data; the machine learning proposed effective mutations for increasing affinity, allowing us to identify a variant with improved affinity (EC 50 = 3.46 μM). In summary, we present the possibility of obtaining functional variants even from unfavorably enriched phage libraries by using deep sequencing and machine learning.

关键词： Clustering analysis Deep sequencing Galectin-3 Machine learning Phage display VHH

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Roadmap on Electronic Structure Codes in the Exascale Era

arXiv

引用

arXiv 2022年

作者： Gavini, Vikram Baroni, Stefano Blum, Volker Bowler, David R. Buccheri, Alexander Chelikowsky, James R. Das, Sambit Dawson, William Delugas, Pietro Dogan, Mehmet Draxl, Claudia Galli, Giulia Genovese, Luigi Giannozzi, Paolo Giantomassi, Matteo Gonze, Xavier Govoni, Marco Gulans, Andris Gygi, François Herbert, John M. Kokott, Sebastian Kühne, Thomas D. Liou, Kai-Hsin Miyazaki, Tsuyoshi Motamarri, Phani Nakata, Ayako Pask, John E. Plessl, Christian Ratcliff, Laura E. Richard, Ryan M. Rossi, Mariana Schade, Robert Scheffler, Matthias Schütt, Ole Suryanarayana, Phanish Torrent, Marc Truflandier, Lionel Windus, Theresa L. Xu, Qimen Yu, Victor W.-Z. Perez, Danny Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States SISSA ISAS Trieste Trieste34136 Italy IOM-CNR Trieste Trieste34136 Italy Duke University DurhamNC27708 United States London Centre for Nanotechnology LondonWC1H 0AH United Kingdom WPI-MANA National Institute for Materials Science Ibaraki305-0044 Japan Humboldt-Universität zu Berlin Berlin10117 Germany McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States Center for Computational Materials Oden Institute University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at Austin AustinTX78712 United States RIKEN Center for Computational Science Hyogo Kobe650-0047 Japan Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Université Grenoble Alpes MEM L_Sim GrenobleF-38000 France University of Udine Udine33100 Italy IMCN Université catholique de Louvain Louvain-la-neuve Belgium University of Latvia RīgaLV-1586 Latvia University of California DavisCA95616 United States Department of Chemistry & Biochemistry The Ohio State University ColumbusOH43210 United States Berlin14195 Germany Dynamics of Condensed Matter Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University D-33098 Germany Paderborn Center for Parallel Computing Paderborn University D-33098 Germany Department of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru560012 India Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science Paderborn University D-33098 Germany Centre for Computational Chemistry School of Chemistry University of Bristol BristolBS8 1TS United Kingdom Ames National Laboratory AmesIA50011 United States Iowa State University AmesIA50

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic structure

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TOI-2005b: An Eccentric Warm Jupiter in Spin-Orbit Alignment

arXiv

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arXiv 2025年

作者： Bieryla, Allyson Dong, Jiayin Zhou, George Eastman, Jason D. Mayorga, L.C. Latham, David W. Carter, Brad Huang, Chelsea X. Quinn, Samuel N. Collins, Karen A. Abe, Lyu Beletsky, Yuri Brahm, Rafael Colón, Knicole D. Essack, Zahra Guillot, Tristan Henning, Thomas Hobson, Melissa J. Horne, Keith Jenkins, Jon M. Jones, Matías I. Jordán, Andrés Osip, David Ricker, George R. Rodriguez, Joseph E. Schulte, Jack Schwarz, Richard P. Seager, Sara Shporer, Avi Suarez, Olga Tan, Thiam-Guan Ting, Eric B. Triaud, Amaury Vanderburg, Andrew Villaseñor, Jesus Noel Vowell, Noah Watkins, Cristilyn N. Winn, Joshua N. Ziegler, Carl Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States University of Southern Queensland Centre for Astrophysics West Street ToowoombaQLD4350 Australia Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Observatoire de la Côte d’Azur UniCA Laboratoire Lagrange CNRS UMR 7293 CS 34229 06304 Nice cedex 4 France Las Campanas Observatory Carnegie Institution for Science Colina El Pino Casilla La Serena601 Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Santiago Peñalolén Chile Millennium Institute for Astrophysics Chile Data Observatory Foundation Chile GreenbeltMD20771 United States Department of Physics and Astronomy The University of New Mexico 210 Yale Blvd NE AlbuquerqueNM87106 United States Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany SUPA Physics and Astronomy University of St. Andrews Fife ScotlandKY16 9SS United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States European Southern Observatory Alonso de Córdova 3107 Vitacura Casilla Santiago19001 Chile Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics MIT 77 Massachusetts Avenue CambridgeMA02139 United States Observatoire de la Côte d’Azur UniCA CNRS CS 34229 Nice06304 cedex 4

We report the discovery and characterization of TOI-2005 b, a warm Jupiter on an eccentric (e ∼ 0.59), 17.3-day orbit around a Vmag = 9.867 rapidly rotating F-star. The object was detected as a candidate by TESS and the planetary nature of TOI-2005 b was then confirmed via a series of ground-based photometric, spectroscopic, and diffraction-limited imaging observations. The planet was found to reside in a low sky-projected stellar obliquity orbit (λ =4.8+2−235 degrees) via a transit spectroscopic observation using the Magellan MIKE spectrograph. TOI-2005 b is one of a few planets known to have a low-obliquity, high-eccentricity orbit, which may be the result of high-eccentricity coplanar migration. The planet has a periastron equilibrium temperature of ∼ 2100 K, similar to some highly irradiated hot Jupiters where atomic metal species have been detected in transmission spectroscopy, and varies by almost 1000 K during its orbit. Future observations of the atmosphere of TOI-2005b can inform us about its radiative timescales thanks to the rapid heating and cooling of the planet. © 2025, CC BY.

关键词： Photometry

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

引用

arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

引用

arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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