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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Automatic beam path analysis of laser wakefield particle acceleration data

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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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CellBiAge: Improved single-cell age classification using data binarization

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Cell Reports 2023年第12期42卷 113500页

作者： Yu, Doudou Li, Manlin Linghu, Guanjie Hu, Yihuan Hajdarovic, Kaitlyn H. Wang, An Singh, Ritambhara Webb, Ashley E. Molecular Biology Cell Biology and Biochemistry Graduate Program Brown University Providence 02912 RI United States Data Science Institute Brown University Providence 02912 RI United States Neuroscience Graduate Program Brown University Providence 02912 RI United States Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore 21218 MD United States Department of Computer Science Brown University Providence 02912 RI United States Center for Computational Molecular Biology Brown University Providence 02912 RI United States Department of Molecular Biology Cell Biology and Biochemistry Brown University Providence 02912 RI United States Center on the Biology of Aging Brown University Providence 02912 RI United States Carney Institute for Brain Science Brown University Providence 02912 RI United States Center for Translational Neuroscience Brown University Providence 02912 RI United States

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods. © 2023 The Authors

关键词： aging brain CP: Cell biology hypothalamus machine learning single-cell RNA-seq

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Rare genetic variation in PTPRB is associated with central serous chorioretinopathy, varicose veins and glaucoma

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Nature Communications 2025年第1期16卷 1-13页

作者： Rämö, Joel T. Gorman, Bryan R. Weng, Lu-Chen Jurgens, Sean J. Singhanetr, Panisa Tieger, Marisa G. van Dijk, Elon HC Halladay, Christopher W. Wang, Xin Hauser, Blake M. Kim, Soo Hyun Brinks, Joost Choi, Seung Hoan Luo, Yuyang Pyarajan, Saiju Nealon, Cari L. Gorin, Michael B. Wu, Wen-Chih Anthony, Scott A. Roncone, David P. Sobrin, Lucia Kaarniranta, Kai Yzer, Suzanne Palotie, Aarno Peachey, Neal S. Turunen, Joni A. Boon, Camiel JF Ellinor, Patrick T. Iyengar, Sudha K. Daly, Mark J. Rossin, Elizabeth J. Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Massachusetts Eye and Ear Boston MA United States Cardiovascular Disease Initiative Broad Institute of MIT and Harvard Cambridge MA United States Cardiovascular Research Center Massachusetts General Hospital Boston MA United States Center for Data and Computational Sciences (C-DACS) VA Cooperative Studies Program VA Boston Healthcare System Boston MA United States Booz Allen Hamilton McLean VA United States Department of Experimental Cardiology Amsterdam Cardiovascular Sciences Heart Failure & Arrhythmias Amsterdam UMC University of Amsterdam Amsterdam Netherlands Mettapracharak Eye Institute Mettapracharak (Wat Rai Khing) Hospital Nakhon Pathom Thailand New England Eye Center Tufts Medical Center Boston MA United States Department of Ophthalmology Leiden University Medical Center Leiden Netherlands Center of Innovation in Long Term Services and Supports Providence VA Medical Center Providence RI United States Harvard Medical School Boston MA United States Department of Biostatistics Boston University Boston MA United States VA Cooperative Studies Program VA Boston Healthcare System Boston MA United States Department of Medicine Brigham and Women’s Hospital and Harvard School of Medicine Boston MA United States Eye Clinic VA Northeast Ohio Healthcare System Cleveland OH United States Department of Ophthalmology David Geffen School of Medicine Stein Eye Institute University of California Los Angeles Los Angeles CA United States Department of Human Genetics David Geffen School of Medicine Stein Eye Institute University of California Los Angeles Los Angeles CA United States Section of Cardiology Medical Service VA Providence Healthcare System Providence RI United States Harvard Medical School Department of Ophthalmology Massachusetts Eye and Ear Boston MA United States Department of Ophthalmology Unive

Central serous chorioretinopathy is an eye disease characterized by fluid buildup under the central retina whose etiology is not well understood. Abnormal choroidal veins in central serous chorioretinopathy patients have been shown to have similarities with varicose veins. To identify potential mechanisms, we analyzed genotype data from 1,477 patients and 455,449 controls in FinnGen. We identified an association for a low-frequency (allele frequency = 0.5%) missense variant (rs113791087) in PTPRB, the gene encoding vascular endothelial protein tyrosine phosphatase (odds ratio=2.85, P = 4.5 × 10^-9). This was confirmed in a meta-analysis of 2,452 patients and 865,767 controls from 4 studies (odds ratio=3.06, P = 7.4 × 10^-15). Rs113791087 was associated with a 56% higher prevalence of retinal abnormalities (35.3% vs 22.6%, P = 8.0 × 10^-4) in 708 UK Biobank participants and, surprisingly, with increased risk of varicose veins (odds ratio=1.31, P = 2.3 × 10^-11) and reduced risk of glaucoma (odds ratio=0.82, P = 6.9 × 10^-9). Predicted loss-of-function variants in PTPRB, though rare in number, were associated with central serous chorioretinopathy in All of Us (odds ratio=17.09, P = 0.018). These findings highlight the significance of vascular endothelial protein tyrosine phosphatase in diverse ocular and systemic veno-vascular diseases. © The Author(s) 2025.

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Biases in inverse Ising estimates of near-critical behavior

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Physical Review E 2023年第1期108卷 014109-014109页

作者： Maximilian B. Kloucek Thomas Machon Shogo Kajimura C. Patrick Royall Naoki Masuda Francesco Turci School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Bristol Centre for Functional Nanomaterials HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto 606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL 75005 Paris France Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

关键词： Critical phenomena Cortical networks Spin glasses Functional magnetic resonance imaging Ising model

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Utilising ASC Speck Assay to interrogate the pathogenicity of MEFV variants

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Pathology 2025年 57卷 S28-S29页

作者： Dai, Pei Skinner, Oliver Lin, Xufeng Telfser, Aiden Ruiz-Diaz, Stephanie Saldanha, Rohit G. Frith, Katie Lin, Ming-Wei Preece, Kahn Gray, Paul E. Pinzo, Alberto Sullivan, Anna Adelstein, Stephen Tong, Winnie W.Y. Parker, Matthew J.S. Girgis, Laila Wainstein, Brynn Ojami, Samar Deenick, Elissa K. Goldstein, Leonard D. Rogers, Michael J. Phan, Tri Giang Precision Immunology Program Garvan Institute of Medical Research Sydney NSW Australia St Vincent’s Healthcare Clinical Campus School of Clinical Medicine Faculty of Medicine and Health UNSW Sydney Sydney NSW Australia Department of Immunology Nepean Hospital Sydney NSW Australia Immune Biotherapies Program Garvan Institute of Medical Research Sydney NSW Australia Department of Microbiology and Immunology The University of Melbourne at The Peter Doherty Institute for Infection and Immunity Melbourne Vic Australia Computational Biology Group Data Science Platform Garvan Institute of Medical Research Sydney NSW Australia Department of General Paediatrics Sydney Children’s Hospital Sydney NSW Australia Department of Paediatrics Bankstown-Lidcombe Hospital Sydney NSW Australia Randwick Clinical Campus School of Clinical Medicine Faculty of Medicine and Health UNSW Sydney Sydney NSW Australia Department of Immunology Sydney Children’s Hospital Sydney NSW Australia Department of Clinical Immunology Westmead Hospital Sydney NSW Australia Department of Immunopathology ICPMR Westmead Hospital Sydney NSW Australia Centre of Immunology and Allergy Research Westmead Institute of Medical Research Sydney NSW Australia Western Clinical School Faculty of Medicine and Health University of Sydney Sydney NSW Australia Paediatric Immunology Department John Hunter Children’s Hospital Newcastle NSW Australia Western Sydney University School of Medicine Sydney NSW Australia Queensland Children’s Hospital Brisbane Qld Australia Department of Clinical Immunology and Allergy and Institute of Academic Medicine Royal Prince Alfred Hospital Sydney NSW Australia Central Clinical School Faculty of Medicine and Health University of Sydney Sydney NSW Australia HIV and Immunology Department St Vincent’s Hospital Sydney NSW Australia Department of Rheumatology Royal Prince Alfred Hospital Sydney NSW Australia Department of Rheumatology St Vincent’s Hospital

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Early Hyperlipidemia Triggers Metabolomic Reprogramming with Increased Sah, Increased Acetyl-Coa-Cholesterol Synthesis, and Decreased Glycolysis

SSRN

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SSRN 2023年

作者： Xu, Keman Saaoud, Fatma Shao, Ying Lu, Yifan Wu, Sheng Zhao, Huaqing Chen, Kaifu Vazquez-Padron, Roberto Jiang, Xiaohua Wang, Hong Yang, Xiaofeng Centers of Cardiovascular Research Temple Unoiversity Lewis Katz School of Medicine PhiladelphiaPA19140 United States Metabolic Disease Research Thrombosis Research Departments of Cardiovascular Sciences Temple University Lewis Katz School of Medicine PhiladelphiaPA19140 United States Medical Education and Data Science Temple University Lewis Katz School of Medicine PhiladelphiaPA19140 United States Computational Biology Program Department of Cardiology Boston Children’s Hospital Harvard Medical School BostonMA02115 United States DeWitt Daughtry Family Department of Surgery Leonard M. Miller School of Medicine University of Miami MiamiFL33125 United States

To identify metabolomic reprogramming in early hyperlipidemia, unbiased metabolome was screened in four tissues from ApoE-/- mice fed with high fat diet (HFD) for 3 weeks. 30, 122, 67, and 97 metabolites in the aorta, heart, liver, and plasma, respectively, were upregulated. 9 upregulated metabolites were uremic toxins, and 13 metabolites, including palmitate, promoted a trained immunity with increased syntheses of acetyl-CoA and cholesterol, increased S-adenosylhomocysteine (SAH) and hypomethylation and decreased glycolysis. The cross-omics analysis found upregulation of 11 metabolite synthetases in ApoE ¯ / ¯ aorta, which promote ROS, cholesterol biosynthesis, and inflammation. Statistical correlation of 12 upregulated metabolites with 37 gene upregulations in ApoE ¯ / ¯ aorta indicated 9 upregulated new metabolites to be proatherogenic. Antioxidant transcription factor NRF2-/- transcriptome analysis indicated that NRF2 suppresses trained immunity-metabolomic reprogramming. Our results have provided novel insights on metabolomic reprogramming in multiple tissues in early hyperlipidemia oriented toward three co-existed new types of trained immunity. © 2023, The Authors. All rights reserved.

关键词： Metabolites

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Recurrence quantification analysis of dynamic brain networks

arXiv

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arXiv 2020年

作者： Lopes, Marinho A. Zhang, Jiaxiang Krzemiński, Dominik Hamandi, Khalid Chen, Qi Livi, Lorenzo Masuda, Naoki Department of Engineering Mathematics University of Bristol BS8 1UB United Kingdom Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University CardiffCF24 4HQ United Kingdom Center for Studies of Psychological Application School of Psychology South China Normal University Guangzhou510631 China Departments of Computer Science and Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom Department of Mathematics University at Buffalo State University of New York United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York United States

Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in brain function in health and other neurological disorders. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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