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arXiv 2019年

作者： van der Wijst, M.G. de Vries, D.H. Groot, H.E. Trynka, G. Hon, C.C. Nawijn, M.C. Idaghdour, Y. van der Harst, P. Ye, C.J. Powell, J. Theis, F.J. Mahfouz, A. Heinig, M. Franke, L. Department of Genetics Oncode Institute University of Groningen University Medical Center Groningen Groningen Netherlands Department of Cardiology University of Groningen University Medical Center Groningen Groningen Netherlands Wellcome Trust Sanger Institute Open Targets Wellcome Genome Campus Hinxton Cambridge United Kingdom RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Pathology and Medical Biology GRIAC Research institute University of Groningen University Medical Center Groningen Netherlands Program in Biology Public Health Research Center New York University Abu Dhabi United Arab Emirates Institute for Human Genetics Bakar Computational Health Sciences Institute Bakar ImmunoX Initiative Division of Rheumatology Department of Medicine Department of Bioengineering and Therapeutic Sciences Department of Epidemiology and Biostatistics Chan Zuckerberg Biohub University of California San Francisco San Francisco United States Garvan‐Weizmann Centre for Cellular Genomics Garvan Institute Sydney Australia UNSW Cellular Genomics Futures Institute University of New South Wales Sydney Australia Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Mathematics Technical University of Munich Garching bei München Germany Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Netherlands Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Informatics Technical University of Munich Garching bei München Germany

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease‐associated genetic risk factors through so‐called expression quantitative trait locus (eQTL) analysis. Single‐cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. The enormous increase in throughput and reduction in cost per cell now allow this technology to be applied to large‐scale population genetics studies. Therefore, we have founded the single‐cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing disease‐causing genetic variants and identifying the cellular contexts in which they affect gene expression. Ultimately, this information can enable development of personalized medicine. Here, we outline the goals, approach, potential utility and early proofs‐of-concept of the sc‐eQTLGen consortium. We also provide a set of study design considerations for future single‐cell eQTL studies. Copyright © 2019, The Authors. All rights reserved.

关键词： RNA

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Geometry-based data generation

arXiv

引用

arXiv 2018年

作者： Lindenbaum, Ofir Stanley, Jay S. Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology & Bioinformatics Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States

We propose a new type of generative model of high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, and improves several downstream data analysis tasks such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations and reveal intrinsic patterns and mutual-information relationships between genes on a single cell dataset of hematopoiesis. Copyright © 2018, The Authors. All rights reserved.

关键词： Clustering algorithms

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Author Correction: Differential peripheral immune signatures elicited by vegan versus ketogenic diets in humans

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Nature medicine 2024年第6期30卷 1785页

作者： Verena M Link Poorani Subramanian Foo Cheung Kyu Lee Han Apollo Stacy Liang Chi Brian A Sellers Galina Koroleva Amber B Courville Shreni Mistry Andrew Burns Richard Apps Kevin D Hall Yasmine Belkaid Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. verena.link@nih.gov. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. verena.link@nih.gov. Bioinformatics and Computational Biosciences Branch Office of Cyber Infrastructure and Computational Biology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. Center for Cellular Engineering Department of Transfusion Medicine Clinical Center National Institutes of Health Bethesda MD USA. Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Department of Cardiovascular and Metabolic Sciences Lerner Research Institute Cleveland Clinic Cleveland OH USA. National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD USA. NIAID Microbiome Program National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD USA. kevinh@niddk.nih.gov. Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. ybelkaid@niaid.nih.gov. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. ybelkaid@niaid.nih.gov.

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Association between known Alzheimer’s disease risk loci andin vivobrain amyloid pathology in a Korean Cohort

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Alzheimer's & Dementia 2023年第S24期19卷

作者： Hyejin Ahn Min Soo Byun Dahyun Yi Gijung Jung Yen-Ning Huang Anthony J. Griswold Margaret A. A Pericak-Vance Yu Kyeong Kim Yun-Sang Lee Chul-Ho Sohn Koung Mi Kang Jun-Young Lee Andrew J. Saykin Kwangsik Nho Dong Young Lee Interdisciplinary program of cognitive science Seoul National University College of Humanities Seoul Korea Republic of (South) HyejinAhn Interdisciplinary program of cognitive science Seoul National University College of Humanities Seoul Korea Republic of (South). Department of Psychiatry Seoul National University College of Medicine Seoul Korea Republic of (South) Seoul National University Dementia Research Center Seoul Korea Republic of (South) Department of Neuropsychiatry Seoul National University Hospital Seoul Korea Republic of (South) Institute of Human Behavioral Medicine Medical Research Center Seoul National University Seoul Korea Republic of (South) Center for Neuroimaging Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Indiana Alzheimer Disease Research Center Indiana University School of Medicine Indianapolis IN USA University of Miami Miller School of Medicine Miami FL USA Department of Nuclear Medicine SMG-SNU Boramae Medical Center Seoul Korea Republic of (South) Department of Nuclear Medicine Seoul National University College of Medicine Seoul Korea Republic of (South) Department of Radiology Seoul National University Hospital Seoul Korea Republic of (South) Department of Neuropsychiatry SMG-SNU Boramae Medical Center Seoul Korea Republic of (South) Indiana Alzheimer’s Disease Research Center Indiana University School of Medicine Indianapolis IN USA Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN USA

Background Most genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have been conducted in populations of European ancestry. It is important to determine if known AD-related variants from these studies are associated with AD biomarkers. Therefore, we investigated the association between known AD risk loci and in vivo brain Aβ deposition in older Korean adults. Method Participants (n = 503) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE) were analyzed. Older adults who were cognitively normal (CN), had mild cognitive impairment (MCI) or AD dementia (264 CN, 148 MCI, 91 AD) underwent comprehensive assessments including 11 C-PiB-PET/MRI and blood sampling. Among 76 AD-related target single nucleotide polymorphisms (SNPs) with genetic evidence compiled by the AD Sequencing Project (ADSP), 38 SNPs with minor allele frequency (MAF)>1% were extracted from TOPMed-based imputed GWAS genotyping data in KBASE. We employed exploratory univariable logistic regression analysis with Aβ positivity as the dependent variable for each SNP. Then, SNPs with a significance level of p<0.05 were subjected to confirmatory multivariable logistic regression analysis, adjusting for age and sex, with Bonferroni correction. Result In the first step of exploratory analyses, we discovered significant differences in Aβ positivity rates between carriers and noncarriers of minor allele of several loci, including rs16824536 in MME , rs11218343 in SORL1 , rs4985556 in IL34 , rs4277405 in ACE and rs3851179, located in the intergenic region between EED and PICALM . The subsequent confirmatory analysis found an association between these variants and Aβ positivity after adjusting age and sex (Table 1). Notably, the negative association of rs3851179 with Aβ positivity remained significant after correcting for multiple comparisons and after controlling for APOE4 carrier status, age, and sex (OR 0.529, 95% CI 0.345–0.813). Conclusion We id

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The anatomic determinants of conductive hearing loss secondary to tympanic membrane perforation

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Journal of Otology 2017年第3期12卷 125-131页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery & Communication Sciences Department of Surgery Duke University Medical Center Durham NC USA Computational Biology & Bioinformatics PhD Program Duke University Durham NC USA Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

Objectives: Recent studies have introduced middle ear volume(MEV) as a novel determinant of perforation-induced conductive hearing loss(CHL) in a mechanism driven by trans-tympanic membrane pressure differences. The primary aims of this preliminary report are to: 1) correlate CHL with perforation size; 2) describe the relationship between CHL and MEV; and 3) compare CHL across a range of cholesteatoma ***: A retrospective pilot study was performed in 31 subjects with audiometry indicative of conductive hearing loss, temporal bone CT scans,and no prior middle ear surgery. Perforation size and MEV were analyzed with respect to CHL in a cohort of 10 perforated ears with no cholesteatoma. CHLs were compared in 3 groups defined by extent of cholesteatoma ***: Ears with large and small perforations showed mean ABG values of 32.0 ± 15.7 dB and 16.0 ± 16.4 dB, respectively. A direct relationship was observed between MEV and CHL for ears with large perforations across all frequencies, whereas this relationship for small perforations was frequency-dependent. Finally, a statistically significant increase in CHL was found across ears with increasing cholesteatoma involvement at 1000 Hz(X^2(2) = 9.786, p = 0.008),2000 Hz(x^2(2) = 8.455, p = 0.015),and 4000 Hz(x^2(2)= 8.253, p = 0.016).Conclusions: These pilot data suggest that greater perforation-induced conductive hearing losses may be associated with larger perforation sizes and cholesteatoma. The correlation between MEV and CHL may require additional study.

关键词： Perforation-induced hearing loss Conductive hearing loss Middle ear volume Three-dimensional volume reconstruction

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Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes

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Nature communications 2022年第1期13卷 7566页

作者： Matthew A Reyna David Haan Marta Paczkowska Lieven P C Verbeke Miguel Vazquez Abdullah Kahraman Sergio Pulido-Tamayo Jonathan Barenboim Lina Wadi Priyanka Dhingra Raunak Shrestha Gad Getz Michael S Lawrence Jakob Skou Pedersen Mark A Rubin David A Wheeler Søren Brunak Jose M G Izarzugaza Ekta Khurana Kathleen Marchal Christian von Mering S Cenk Sahinalp Alfonso Valencia Jüri Reimand Joshua M Stuart Benjamin J Raphael Department of Computer Science Princeton University Princeton NJ 08540 USA. Department of Biomedical Informatics Emory University Atlanta GA 30322 USA. Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute University of California Santa Cruz Santa Cruz CA 95060 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto Ontario Canada. Department of Information Technology IDLab Ghent University IMEC Ghent the Netherlands. Department of Plant Biotechnology and Bioinformatics Ghent University Ghent the Netherlands. Barcelona Supercomputing Center (BSC) Barcelona 08034 Spain. Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway. Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics University of Zurich CH-8057 Zurich Switzerland. Department of Pathology and Molecular Pathology University Hospital Zurich CH-8091 Zurich Switzerland. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10065 USA. Vancouver Prostate Centre 2660 Oak Street Vancouver BC V6H 3Z6 Canada. The Broad Institute of MIT and Harvard Cambridge MA 02124 USA. Massachusetts General Hospital Center for Cancer Research Charlestown MA 02129 USA. Harvard Medical School 250 Longwood Avenue Boston MA 02115 USA. Massachusetts General Hospital Department of Pathology Boston MA 02114 USA. Department of Molecular Medicine (MOMA) Aarhus University Hospital Aarhus Denmark. Bioinformatics Research Centre (BiRC) Aarhus University Aarhus Denmark. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. DTU Bioinformatics Department of Bio and Health Informatics Technical University of Denmark Kemitorvet 2800 Kongens Lyngby Denmark. Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen 2200 Copenhagen Denmark. De

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Disagreement in middle ear volume estimation between tympanometry and three-dimensional volume reconstruction in the context of tympanic membrane perforation

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Journal of Otology 2017年第2期12卷 74-79页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery&Communication Sciences Department of SurgeryDuke University Medical CenterDurhamNCUSA Computational Biology&Bioinformatics PhD Program Duke UniversityDurhamNCUSA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNCUSA

Introduction: Middle ear volume(MEV) is a clinically relevant parameter across middle ear diseases. MEV values between these techniques have never before been tested for agreement in ears with perforated tympanic membranes(TMs).Methods: Middle ears were identified from 36 patients ranging 18-89 years of age with TM perforations who underwent tympanometry and temporal bone computed tomography(CT) between 2005 and 2015. MEVs calculated by both tympanometry and three-dimensional volume reconstruction(3DVR) were analyzed for agreement using Bland Altman plots. The differences between tympanometric and 3DVR MEV values for each given middle ear were characterized across MEV quartiles(1= smallest; 4= largest) and across increasing states of middle ear disease using Kruskale Wallis and Wilcoxon testing with Bonferroni ***: Bland Altman plots demonstrated significant disagreement between MEV measurement techniques. Differences between tympanometric(T) and 3DVR MEV values were significantly greater with increasing average(i.e.(Tt3DVR)/2)) MEV per linear regression(p < 0.0001). Significance was demonstrated between fourth and first average MEV quartiles(p= 0.0024), fourth and second quartiles(p= 0.0024), third and first quartiles(p= 0.0048), and third and second quartiles(p= 0.048). Absolute MEV difference was not significantly different across varying states of middle ear disease(p= 0.44).Conclusion: Statistically and clinically significant disagreement was demonstrated between tympanometric and 3DVR MEV values. Studies that vary in MEV estimation techniques may be expected to demonstrate significantly different results. These preliminary results suggest that clinicians should endeavor to seek further confirmation when interpreting high tympanometric MEV values.

关键词： Middle ear volume Tympanometry Three-dimensional volume reconstruction Tympanic membrane perforation

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Technology dictates algorithms: Recent developments in read alignment

arXiv

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arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin D. Balliu, Brunilda Koslicki, David Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： bioinformatics

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Jammed packings of 3D superellipsoids with tunable packing fraction, contact number, and ordering

arXiv

引用

arXiv 2019年

作者： Yuan, Ye VanderWerf, Kyle Shattuck, Mark D. O’Hern, Corey S. Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

We carry out numerical studies of static packings of frictionless superellipsoidal particles in three spatial dimensions. We consider more than 200 different particle shapes by varying the three shape parameters that define superellipsoids. We characterize the structural and mechanical properties of both disordered and ordered packings using two packing-generation protocols. We perform athermal quasi-static compression simulations starting from either random, dilute configurations (Protocol 1) or thermalized, dense configurations (protocol 2), which allows us to tune the orientational order of the packings. In general, we find that the contact numbers at jamming onset for superellipsoid packings are hypostatic, with zJ f and df = 5 or 6 depending on whether the particles are axi-symmetric or not. Over the full range of orientational order, we find that the number of quartic modes of the dynamical matrix for the packings always matches the number of missing contacts relative to the isostatic value. This result suggests that there are no mechanically redundant contacts for ordered, yet hypostatic packings of superellipsoidal particles. Additionally, we find that the packing fraction at jamming onset for diordered packings of superellipsoidal particles can be collapsed using two particle shape parameters, e.g. the asphericity A and reduced aspect ratio β of the particles. Copyright © 2019, The Authors. All rights reserved.

关键词： Jamming

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