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NIAGADS: A data repository for Alzheimer's disease and related dementia genomics

Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷

作者： Amanda Kuzma Otto Valladares Emily Greenfest-Allen Heather Nicaretta Maureen Kirsch Youli Ren Zivadin Katanic Heather White Andrew Wilk Lauren Bass Jascha Brettschneider Luke Carter Jeffrey Cifello Wei-Hsuan Chuang Kaylyn Clark Prabhakaran Gangadharan Jacob Haut Pei-Chuan Ho Wenhwai Horng Taha Iqbal Yumi Jin Peter Keskinen Alexis Lerro Rose Michelle K Moon Joseph Manuel Liming Qu Flawless Robbins Naveensri Saravanan Jin Sha Sam Tate Yi Zhao Laura Cantwell Jake Gardner Shin-Yi Chou Jung-Ying Tzeng William Bush Adam Naj Pavel Kuksa Wan-Ping Lee Yuk Yee Leung Gerard Schellenberg Li-San Wang Department of Pathology and Laboratory Medicine Penn Neurodegeneration Genomics Center Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA. Department of Computer and Information Science University of Pennsylvania Philadelphia Pennsylvania USA. Department of Economics Lehigh University Bethlehem Pennsylvania USA. National Bureau of Economic Research Cambridge Massachusetts USA. Bioinformatics Research Center North Carolina State University Raleigh North Carolina USA. Department of Statistics North Carolina State University Raleigh North Carolina USA. Cleveland Institute for Computational Biology Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland Ohio USA.

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is the National Institute on Aging-designated national data repository for human genetics research on Alzheimer's disease and related dementias (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis, including whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project and other genotype/phenotype data, encompassing 211,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a Federal Information Security Modernization Act moderate compliant cloud-based platform that fully supports the National Institutes of Health Genomic Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. As a one-stop access portal for Alzheimer's disease (AD) genetics, NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of AD. HIGHLIGHTS: The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a data repository for the storage of genetics and genomics data. NIAGADS houses data for Alzheimer's disease, related dementias, and healthy aging. NIAGADS offers open and qualified access data and knowledgebases to explore open access data. The Alzheimer's Disease Sequencing Project dataset is the largest Alzheimer's disease and related dementias joint called whole genome sequencing dataset (≈ 58,000 whole genomes).

关键词： Alzheimer's Disease Sequencing Project Alzheimer's disease Alzheimer's disease and related dementias National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site bioinformatics data repository dementia genetics genomics open access qualified access sequencing

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Towards Precise and Accurate Calculations of Neutrinoless Double-Beta Decay: Project Scoping Workshop Report

arXiv

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arXiv 2022年

作者： Cirigliano, V. Davoudi, Z. Engel, J. Furnstahl, R.J. Hagen, G. Heinz, U. Hergert, H. Horoi, M. Johnson, C.W. Lovato, A. Mereghetti, E. Nazarewicz, W. Nicholson, A. Papenbrock, T. Pastore, S. Plumlee, M. Phillips, Daniel R. Shanahan, P.E. Stroberg, S.R. Viens, F. Walker-Loud, A. Wendt, K.A. Wild, S.M. Institute for Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Physics University of North Carolina Chapel HillNC27514 United States Department of Physics Ohio State University ColumbusOH43210 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Frib Laboratory Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Physics Central Michigan University Mount PleasantMI48859 United States Department of Physics San Diego State University San DiegoCA92182 United States Physics Division Argonne National Laboratory LemontIL60439 United States Computational Physics Division Argonne National Laboratory ArgonneIL60439 United States INFN-TIFPA Trento Institute of Fundamental Physics and Applications Trento38123 Italy Theory Division Los Alamos National Laboratory Los AlamosNM87544 United States Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics McDonnell Center for the Space Sciences Washington University in Saint Louis Saint LouisMO63130 United States Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Naise Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Institute of Nuclear and Particle Physics Ohio University AthensOH45701 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Statistics and Probability Michigan State University East LansingMI48824 United States Nuclear and Chemical Sciences Division Lawrence Livermore National Laboratory LivermoreCA94550 Unit

This white paper is an outgrowth of a National Science Foundation (NSF) Project Scoping Workshop, the purpose of which was to assess the current status of calculations for the nuclear matrix elements governing neutrinoless doublebeta decay and determine if more work on them is required. The recent effort to define the United States' role in ton-scale experiments, together with the conclusion in 2021 of the Department of Energy (DOE) Topical Collaboration on neutrinoless double-beta decay, made such an exercise extremely timely. The main conclusions of the workshop can be summarized as follows: • Neutrinoless double-beta decay of nuclei is an important window into the physics of neutrinos. It could be the first lepton-number violating process ever observed. As such, it would provide key insights into physics beyond the Standard Model-in particular how the matter-anti-matter asymmetry of the universe arose. • Much progress on the theory of neutrinoless double-beta decay of nuclei has been made over the last five years. An end-to-end set of effective field theories (EFTs) that shows how to evolve the physics of the decay from the scale at which lepton number is violated (possibly much larger than TeV) down to the scales relevant for nuclei has been developed. Lattice quantum chromodynamics (LQCD) calculations of the process in the two-nucleon (NN) system are being set up, and the first ab initio many-body calculations of neutrinoless double-beta decay matrix elements M0νhave been carried out. • Much remains to be done before theory can successfully complement the large experimental effort to observe neutrinoless double-beta decay and measure its rate. Both the accuracy and precision of LQCD and ab initio nuclear many-body calculations need to be improved if crisp statements about experimental observation or non-observation are to be made. The uncertainty in nuclear many-body calculations remains largely unquantified, making it difficult to interpret the significant dif

关键词： Calculations

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How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv

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arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Spatially Perturbed Collision Sounds Attenuate Perceived Causality in 3D Launching Events

Spatially Perturbed Collision Sounds Attenuate Perceived Cau...

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IEEE Annual International Symposium Virtual Reality

作者： Duotun Wang James Kubrlcht Yixin Zhu Wei Lianq Song-Chun Zhu Chenfanfu Jiang Hongjing Lu Laboratory of Intelligent Information Technology Beijing Institute of Technology UCLA Computational Vision and Learning Laboratory Cognition Center for Vision Computer Graphics Group UPenn

ISBN: (纸本)9781538633663

When a moving object collides with an object at rest, people immediately perceive a causal event: i.e., the first object has launched the second object forwards. However, when the second object's motion is delayed, or is accompanied by a collision sound, causal impressions attenuate and strengthen. Despite a rich literature on causal perception, researchers have exclusively utilized 2D visual displays to examine the launching effect. It remains unclear whether people are equally sensitive to the spatiotemporal properties of observed collisions in the real world. The present study first examined whether previous findings in causal perception with audiovisual inputs can be extended to immersive 3D virtual environments. We then investigated whether perceived causality is influenced by variations in the spatial position of an auditory collision indicator. We found that people are able to localize sound positions based on auditory inputs in VR environments, and spatial discrepancy between the estimated position of the collision sound and the visually observed impact location attenuates perceived causality.

关键词： Three-dimensional displays Visualization Solid modeling Delays Spatiotemporal phenomena Virtual environments

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The Atacama Cosmology Telescope: Semi-Analytic Covariance Matrices for the DR6 CMB Power Spectra

arXiv

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arXiv 2024年

作者： Atkins, Zachary Li, Zack Alonso, David Bond, J. Richard Calabrese, Erminia Duivenvoorden, Adriaan J. Dunkley, Jo Giardiello, Serena Hervías-Caimapo, Carlos Hill, J. Colin Jense, Hidde T. Kim, Joshua Niemack, Michael D. Page, Lyman Posta, Adrien La Louis, Thibaut Moodley, Kavilan Morris, Thomas W. Naess, Sigurd Sifón, Cristóbal Wollack, Edward J. Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 United States Canadian Institute for Theoretical Astrophysics University of Toronto McLennan Labs TorontoONM5S 3H8 Canada Department of Physics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom School of Physics and Astronomy Cardiff University The Parade Wales CardiffCF24 3AA United Kingdom Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Str. 1 Garching85748 Germany Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Department of Astrophysical Sciences Peyton Hall Princeton University PrincetonNJ08544 United States Instituto de Astrofísica Centro de Astro-Ingeniería Facultad de Física Pontificia Universidad Católica de Chile Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Department of Physics Columbia University New YorkNY10027 United States Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Department of Physics Cornell University IthacaNY14853 United States Department of Astronomy Cornell University IthacaNY14853 United States Université Paris-Saclay CNRS IN2P3 IJCLab Orsay91405 France Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Department of Physics Yale University New HavenCT06511 United States National Synchrotron Light Source II Brookhaven National Laboratory UptonNY11973 United States Institute of Theoretical Astrophysics University of Oslo Norway Instituto de Física Pontificia Universidad Católica de Valparaíso

The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complicated ACT map properties. We use recent advances in the literature to handle sharp features in the signal and noise power spectra, and account for the effect of map-level anisotropies on the covariance matrix. In including inhomogeneous survey depth information, the resulting covariance matrix prescription is structurally similar to that used in the Planck Cosmic Microwave Background (CMB) analysis. We quantify the performance of our prescription using comparisons to Monte Carlo simulations, finding better than 3% agreement. This represents an improvement from a simpler, pre-existing prescription, which differs from simulations by ∼ 16%. We develop a new method to correct the analytic covariance matrix using simulations, after which both prescriptions achieve better than 1% agreement. This correction method outperforms a commonly used alternative, where the analytic correlation matrix is assumed to be accurate when correcting the covariance. Beyond its use for ACT, this framework should be applicable for future high resolution CMB experiments including the Simons Observatory (SO). Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Zapaishchykova, Anna Pavaine, Julija Shah, Lubdha M. Jones, Blaise V. Sheth, Nakul Prabhu, Sanjay P. McAllister, Aaron S. Tu, Wenxin Nandolia, Khanak K. Rodriguez, Andres F. Shaikh, Ibraheem Salman Sanchez-Montano, Mariana Lai, Hollie Anne Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anderson, Hannah Anwar, Syed Muhammed Aristizabal, Alejandro Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Eddy, James Ezhov, Ivan Familiar, Ariana M. Farahani, Keyvan Gandhi, Deep Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Elaine, Elaine Karargyris, Alexandros Kassem, Hasan Khalili, Neda Kofler, Florian LaBella, Dominic Van Leemput, Koen Li, Hongwei B. Maleki, Nazanin Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Pati, Sarthak Piraud, Marie Poussaint, Tina Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Sheller, MIcah Shinohara, Russell Takeshi Viswanathan, Karthik Wang, Chunhao Wiestler, Benedikt Wiggins, Walter F. Davatzikos, Christos Storm, Phillip B. Bornhorst, Miriam Packer, Roger Hummel, Trent de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Ware, Jeffrey B. Kann, Benjamin H. Rood, Brian Resnick, Adam Bakas, Spyridon Vossough, Arastoo Linguraru, Marius George The Children’s Hospital of Philadelphia PA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Dana-Farber Brigham Cancer Center Boston Children’s Hospital BostonMA United States Department of Paediatric Radiology Royal Manchester Children’s Hospital Manchester University Hospitals NHS Foundation Trust University of Manchester Manchester United Kingdom University of Utah UT United States Cincinnati Children’s Hospital Medical Center OH United States Department of Radiology The Children’s Hospital of Philadelphia PA United States Division of Neuroradiology Department of Radiology Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Radiology Nationwide Children’s Hospital OH United States College of Arts and Sciences University of Pennsylvania PA United States Department of Diagnostic and Interventional Radiology All India Institute of Medical Sciences Rishikesh India Department of Diagnostic & Interventional Imaging Neuroradiology Section The University of Texas Health HoustonTX United States Beth Israel Deaconess Medical Center Harvard Medical School MA United States Unidad de Patologia Clinica:Guadalajara Jalisco Mexico Department of Radiology Children’s Health Orange County CA United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States MLCommons United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors. © 2024, CC BY.

关键词： Pediatrics

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Uncertainty quantification in stochastic economic dispatch using Gaussian process emulation

arXiv

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arXiv 2019年

作者： Hu, Zhixiong Xu, Yijun Korkali, Mert Chen, Xiao Mili, Lamine Tong, Charles H. Department of Statistics University of California-Santa Cruz Santa CruzCA95064 United States Department of Electrical and Computer Engineering Virginia Tech Northern Virginia Center Falls ChurchVA22043 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Loève expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric load dispatching

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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

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Database 2023年第2023期2023卷 baad005-baad005页

作者： Leaman, Robert Islamaj, Rezarta Adams, Virginia Alliheedi, Mohammed A. Almeida, João Rafael Antunes, Rui Bevan, Robert Chang, Yung-Chun Erdengasileng, Arslan Hodgskiss, Matthew Ida, Ryuki Kim, Hyunjae Li, Keqiao Mercer, Robert E. Mertová, Lukrécia Mobasher, Ghadeer Shin, Hoo-Chang Sung, Mujeen Tsujimura, Tomoki Yeh, Wen-Chao Lu, Zhiyong National Center for Biotechnology Information (NCBI) National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda 20894 MD United States Nvidia 2788 San Tomas Expressway Santa Clara 95051 CA United States Department of Computer Science Al Baha University 4781 King Fahd Rd Al Aqiq 65779 Saudi Arabia Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Information and Communications Technologies University of A Coruña Camiño Do Lagar de Castro A Coruña 15008 Spain Informatics Department Medicines Discovery Catapult Alderley Park Block 35 Mereside Macclesfield SK10 4ZF United Kingdom Graduate Institute of Data Science Taipei Medical University No. 172-1 Section 2 Keelung Rd Da’an District Taipei City 106 Taiwan Department of Statistics Florida State University 117 N. Woodward Ave Tallahassee 32306 FL United States Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Aichi 468-8511 Japan Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea Department of Computer Science The University of Western Ontario Middlesex College Room 355 London N6A 5B7 ON Canada Scientific Databases and Visualization Group Heidelberg Institute for Theoretical Studies (HITS GGmbH) Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Institute of Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg 69120 Germany Institute of Information Systems and Applications National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti

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