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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

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Integration of domain knowledge using medical knowledge graph deep learning for cancer phenotyping

arXiv

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arXiv 2021年

作者： Alawad, Mohammed Gao, Shang Shekar, Mayanka Chandra Hasan, S.M. Shamimul Christian, J. Blair Wu, Xiao-Cheng Durbin, Eric B. Doherty, Jennifer Stroup, Antoinette Coyle, Linda Penberthy, Lynne Tourassi, Georgia Electrical and Computer Engineering Department Wayne State University DetroitMI United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Louisiana Tumor Registry Louisiana State University Health Sciences Center New OrleansLA United States Kentucky Cancer Registry and College of Medicine University of Kentucky LexingtonKY United States Utah Cancer Registry University of Utah School of Medicine Salt Lake CityUT United States New Jersey State Cancer Registry New Jersey Department of Health TrentonNJ United States Information Management Services Inc CalvertonMD United States Surveillance Research Program Division of Cancer Control and Population Sciences National Cancer Institute BethesdaMD United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States

A key component of deep learning (DL) for natural language processing (NLP) is word embeddings. Word embeddings that effectively capture the meaning and context of the word that they represent can significantly improve the performance of downstream DL models for various NLP tasks. Many existing word embeddings techniques capture the context of words based on word co-occurrence in documents and text;however, they often cannot capture broader domain-specific relationships between concepts that may be crucial for the NLP task at hand. In this paper, we propose a method to integrate external knowledge from medical terminology ontologies into the context captured by word embeddings. Specifically, we use a medical knowledge graph, such as the unified medical language system (UMLS), to find connections between clinical terms in cancer pathology reports. This approach aims to minimize the distance between connected clinical concepts. We evaluate the proposed approach using a Multitask Convolutional Neural Network (MT-CNN) to extract six cancer characteristics – site, subsite, laterality, behavior, histology, and grade – from a dataset of 900K cancer pathology reports. The results show that the MT-CNN model which uses our domain informed embeddings outperforms the same MT-CNN using standard word2vec embeddings across all tasks, with an improvement in the overall micro- and macro-F1 scores by 4.97%and 22.5%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Phase transitions in the frustrated Ising ladder with stoquastic and nonstoquastic catalysts

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Physical Review Research 2021年第4期3卷 043013-043013页

作者： Kabuki Takada Shigetoshi Sota Seiji Yunoki Bibek Pokharel Hidetoshi Nishimori Daniel A. Lidar Department of Physics Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Computational Materials Science Research Team RIKEN Center for Computational Science (R-CCS) Kobe Hyogo 650-0047 Japan Computational Condensed Matter Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako Saitama 351-0198 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan Department of Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Institute of Innovative Research Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Graduate School of Information Sciences Tohoku University Sendai Miyagi 980-8579 Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan Department of Electrical & Computer Engineering and Department of Chemistry University of Southern California Los Angeles California 90089 USA

The role of nonstoquasticity in the field of quantum annealing and adiabatic quantum computing is an actively debated topic. We study a strongly-frustrated quasi-one-dimensional quantum Ising model on a two-leg ladder to elucidate how a first-order phase transition with a topological origin is affected by interactions of the ±XX-type. Such interactions are sometimes known as stoquastic (negative sign) and nonstoquastic (positive sign) “catalysts”. Carrying out a symmetry-preserving real-space renormalization group analysis and extensive density-matrix renormalization group computations, we show that the phase diagrams obtained by these two methods are in qualitative agreement with each other and reveal that the first-order quantum phase transition of a topological nature remains stable against the introduction of both XX-type catalysts. This is the first study of the effects of nonstoquasticity on a first-order phase transition between topologically distinct phases. Our results indicate that nonstoquastic catalysts are generally insufficient for removing topological obstacles in quantum annealing and adiabatic quantum computing.

关键词： Adiabatic quantum optimization Phase transitions by order Quantum spin models Density matrix renormalization group Renormalization group

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Global trends and biases in biodiversity conservation research

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Cell Reports Sustainability 2024年第5期1卷

作者： Caldwell, Iain R. Hobbs, Jean-Paul A. Bowen, Brian W. Cowman, Peter F. DiBattista, Joseph D. Whitney, Jon L. Ahti, Pauliina A. Belderok, Roy Canfield, Sean Coleman, Richard R. Iacchei, Matthew Johnston, Erika C. Knapp, Ingrid Nalley, Eileen M. Staeudle, Timo M. Láruson, Áki Jarl College of Arts Society and Education James Cook University 1 James Cook Drive Douglas 4811 QLD Australia School of Biological Sciences The University of Queensland Brisbane 4069 QLD Australia Hawaiʻi Institute of Marine Biology University of Hawai‘i at Mānoa Kāne‘ohe 96744 HI United States Biodiversity and Geosciences Program Queensland Museum Tropics Townsville 4810 QLD Australia College of Science and Engineering James Cook University Townsville 4811 QLD Australia Australian Museum Research Institute Australian Museum Sydney 2010 NSW Australia National Oceanic and Atmospheric Administration Pacific Islands Fisheries Science Center Honolulu 96818 HI United States Division of Integrated Science and Engineering California Department of Water Resources Sacramento 95814 CA United States Department of Marine Biology and Ecology Rosenstiel School of Marine Atmospheric and Earth Science University of Miami Miami 33149 FL United States College of Natural and Computational Sciences Hawaiʻi Pacific University Makapuʻu Campus 41-202 Kalanianaʻole Hwy Waimānalo 96795 HI United States University of Hawaiʻi Sea Grant College Program University of Hawaiʻi at Mānoa Honolulu 96822 HI United States Aquaculture and Fisheries Group Wageningen University & Research Wageningen 6708 Netherlands Institute of Life and Environmental Sciences University of Iceland Reykjavík 102 Iceland Marine and Freshwater Research Institute Hafnarfjörður 200 Iceland

Efforts to conserve biodiversity have been hampered by long-standing biases, including a disproportionate focus on particular taxa and ecosystems with minimal attention to underlying genetic diversity. We assessed whether these biases have persisted over the past four decades by analyzing trends in 17,502 research articles published in four top conservation-focused journals. Overall, we found that historical biases in conservation biology research remain entrenched. Despite increasing numbers of conservation articles published each decade from 1980 to 2020, research effort has increasingly focused on the same suite of taxa. Surprisingly, some of the most-studied species in these conservation articles had low conservation risk, including several domesticated animals. Animals and terrestrial ecosystems are consistently over-represented while plants, fungi, and freshwater ecosystems remain under-represented. Strategically funding investigations of understudied species and ecosystems will ensure more effective conservation effort across multiple levels of biodiversity, alleviate impediments to biodiversity targets, and ultimately prevent further extinctions. © 2024 The Author(s)

关键词： bibliometry biodiversity conservation GBIF historical trends IUCN research bias threatened species

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle

arXiv

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arXiv 2020年

作者： Yu, Haijun Tian, Xinyuan Weinan, E. Li, Qianxiao NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics National University of Singapore Singapore119077 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a systematic method for learning stable and physically interpretable dynamical models using sampled trajectory data from physical processes based on a generalized Onsager principle. The learned dynamics are autonomous ordinary differential equations parameterized by neural networks that retain clear physical structure information, such as free energy, diffusion, conservative motion and external forces. For high dimensional problems with a low dimensional slow manifold, an autoencoder with metric preserving regularization is introduced to find the low dimensional generalized coordinates on which we learn the generalized Onsager dynamics. Our method exhibits clear advantages over existing methods on benchmark problems for learning ordinary differential equations. We further apply this method to study Rayleigh-Bénard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics. This forms a general approach to building reduced order models for forced dissipative *** Codes 76E30, 34D20, 68T05/07, 82C35 Copyright © 2020, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Genome and clonal hematopoiesis stability contrasts with immune,cfDNA,mitochondrial,and telomere length changes during short duration spaceflight

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Precision Clinical Medicine 2024年第1期7卷 1-13页

作者： J.Sebastian Garcia-Medina Karolina Sienkiewicz S.Anand Narayanan Eliah G.Overbey Kirill Grigorev Krista A.Ryon Marissa Burke Jacqueline Proszynski Braden Tierney Caleb M.Schmidt Nuria Mencia-Trinchant Remi Klotz Veronica Ortiz Jonathan Foox Christopher Chin Deena Najjar Irina Matei Irenaeus Chan Carlos Cruchaga Ashley Kleinman JangKeun Kim Alexander Lucaci Conor Loy Omary Mzava Iwijn De Vlaminck Anvita Singaraju Lynn E.Taylor Julian C.Schmidt Michael A.Schmidt Kelly Blease Juan Moreno Andrew Boddicker Junhua Zhao Bryan Lajoie Andrew Altomare Semyon Kruglyak Shawn Levy Min Yu Duane C.Hassane Susan M.Bailey Kelly Bolton Jaime Mateus Christopher E.Mason Department of Physiology and Biophysics Weill Cornell MedicineCornell UniversityNew YorkNY 10021USA The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell MedicineNew YorkNY 10021USA Department of Immunology Weill Cornell MedicineCornell UniversityNew YorkNY 10021USA Department of Stem Cell Biology and Regenerative Medicine Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCA 90033USA Space Exploration Technologies Corporation HawthomeCA 90250USA Sovaris Aerospace BoulderCO 80302USA Advanced Pattern Analysis&Human Performance Group BoulderCO 80302USA Children's Cancer and Blood Foundation Laboratories Departments of Pediatrics and Cell and Developmental BiologyDrukier Institute for Children's HealthMeyer Cancer CenterWeill Cornell MedicineNew YorkNY 10021USA Meyer Cancer Center Weill Cornell MedicineNew YorkNY 10065USA Department of Nutrition and Integrative Physiology Florida State UniversityTallahasseeFL 32306USA Department of Systems Engineering Colorado State UniversityFort CollinsCO 80523USA Element Biosciences San DiegoCA 10055USA Department of Environmental and Radiological Health Sciences Colorado State UniversityFort CollinsCO 80523USA Cell and Molecular Biology Program Colorado State UniversityFort CollinsCO 80523USA BioAstra Inc New YorkNYUSA Washington University St Louis Oncology Division St.LouisMO 63100USA Meinig School of Biomedical Engineering Cornell UniversityIthacaNY 14853USA The Feil Family Brain and Mind Research Institute Weill Cornell MedicineNY 10021USA WorldQuant Initiative for Quantitative Prediction Weill Cornell MedicineNew YorkNY 10021USA

Background The Inspiration4(I4)mission,the first all-civilian orbital flight mission,investigated the physiological effects of short-duration spaceflight through a multi-omic *** advances,there remains much to learn about human adaptation to spaceflight's unique challenges,including microgravity,immune system perturbations,and radiation *** To provide a detailed genetics analysis of the mission,we collected dried blood spots pre-,during,and post-flight for DNA *** length was measured by quantitative PCR,while whole genome and cfDNA sequencing provided insight into genomic stability and immune adaptations.A robust bioinformatic pipeline was used for data analysis,including variant calling to assess mutational *** Telomere elongation occurred during spaceflight and shortened after return to ***-free DNA analysis revealed increased immune cell signatures *** significant clonal hematopoiesis of indeterminate potential(CHIP)or whole-genome instability was *** long-term gene expression changes across immune cells suggested cellular adaptations to the space environment persisting months *** Our findings provide valuable insights into the physiological consequences of short-duration spaceflight,with telomere dynamics and immune cell gene expression adapting to spaceflight and persisting after return to *** sequencing data will serve as a reference point for studying the early development of CHIP in astronauts,an understudied phenomenon as previous studies have focused on career *** study will serve as a reference point for future commercial and non-commercial spaceflight,low Earth orbit(LEO)missions,and deep-space exploration.

关键词： genomes clonal hematopoiesis stability immune mitochondria ribosomes spaceflight

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Multigoal-oriented error estimation and mesh adaptivity for fluid-structure interaction

arXiv

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arXiv 2021年

作者： Ahuja, K. Endtmayer, B. Steinbach, M.C. Wick, Thomas Discipline of Computer Science and Engineering Indian Institute of Technology Indore India Leibniz Universität Hannover Institut für Angewandte Mathematik Welfengarten 1 Hannover30167 Germany Leibniz Universität Hannover Germany Université Paris-Saclay ENS Paris-Saclay LMT - Laboratoire de Mécanique et Technologie Gif-sur-Yvette91190 France Johannes Kepler University Linz Doctoral Program on Computational Mathematics Altenbergerstr. 69 LinzA-4040 Austria Austrian Academy of Sciences Johann Radon Institute for Computational and Applied Mathematics Altenbergerstr. 69 LinzA-4040 Austria

In this work, we consider multigoal-oriented error estimation for stationary fluid-structure interaction. The problem is formulated within a variational-monolithic setting using arbitrary Lagrangian-Eulerian coordinates. Employing the dual-weighted residual method for goal-oriented a posteriori error estimation, adjoint sensitivities are required. For multigoal-oriented error estimation, a combined functional is formulated such that several quantities of interest are controlled simultaneously. As localization technique for mesh refinement we employ a partition-of-unity. Our algorithmic developments are substantiated with several numerical tests such as an elastic lid-driven cavity with two goal functionals, an elastic bar in a chamber with two goal functionals, and the FSI-1 benchmark with three goal *** Codes 65N30, 65N50, 74F10 © 2021, CC BY-NC-ND.

关键词： Fluid structure interaction

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On the particle approximation of lagged Feynman–Kac formulae

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Stochastic Processes and their Applications 2025年 188卷

作者： Awadelkarim, Elsiddig Caffarel, Michel Del Moral, Pierre Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman–Kac marginals associated with a positive operator iterated l∈N times;a lagged Feynman–Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) are commonly used in the physics literature. In short, DMC is an iterative algorithm involving N∈N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman–Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for κ>0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman–Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2025 Elsevier B.V.

关键词： Iterative methods

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Preventing bad behavior in academia

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四川生理科学杂志 2024年第7期46卷 1615-1615页

作者： Rayane da Cruz Albino Faculty of Pharmacy Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil. Department of Host-Microbe Interactions St. Jude Children’s Research Hospital Memphis TN USA. School of the Environment The University of Queensland Whiterock QLD Australia. KU Leuven Ghent Belgium. Laboratory for Computational Social Systems Indraprastha Institute of Information Technology Delhi New Delhi India. University Medical College Shantou Guangdong China. Department of Ecoscience Aarhus Universitet Aarhus Denmark. Institute of Environment and Sustainable Development Banaras Hindu University Varanasi Uttar Pradesh India. Department of Medical Parasitology Faculty of Medicine and Health Sciences Sana’a University Sana’a Yemen and Tropical Disease Research Center Faculty of Medicine and Health Sciences University of Science and Technology Sana’a Yemen. Department of Medicine University of Montreal Montreal QC Canada. Electricity Infrastructure and Buildings Division Pacific Northwest National Laboratory Portland OR USA. Department of Biological Sciences University of New Brunswick Saint John Saint John NB Canada. Department of Biomedical Engineering The University of Melbourne Melbourne VIC Australia. Vagelos Molecular Life Sciences Program University of Pennsylvania Philadelphia PA USA. Doctoral Program in Neurobiology and Behavior Columbia University New York NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Engineering University of Cambridge Cambridge Cambridgeshire UK. Department of Geography Liverpool John Moores University Liverpool UK. Department of Biology Stanford University Stanford CA USA. Department of Psychology University of Nicosia Nicosia Cyprus. Department of Global Ecology Carnegie Institution for Science Stanford CA USA. Research and Support Division Stellenbosch University Cape Town South Africa.

We gave young scientists this prompt:Describe one change to scientific policy or culture that would substantially decrease incidents of scientific misconduct or other unethical behavior.

关键词： behavior. prompt culture

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