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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

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Genomic profiling in advanced stage non-small-cell lung cancer patients with platinum-based chemotherapy identifies germline variants with prognostic value in SMYD2

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Cancer Treatment and Research Communications 2018年 15卷 21-31页

作者： Galván-Femenía, Iván Guindo, Marta Duran, Xavier Calabuig-Fariñas, Sílvia Mercader, Josep Maria Ramirez, Jose Luis Rosell, Rafael Torrents, David Carreras, Anna Kohno, Takashi Jantus-Lewintre, Eloisa Camps, Carlos Perucho, Manuel Sumoy, Lauro Yokota, Jun de Cid, Rafael Genomes For life-GCAT Lab. Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain Barcelona Supercomputing Center (BSC-CNS) Joint BSC-CRG-IRB Research Program in Computational Biology Carrer de Jordi Girona 29-31 Barcelona 08034 Spain Department of Medical Oncology Hospital General Universitario de Valencia Avenida Tres Cruces 2 València 46014 Spain Molecular Oncology Laboratory Fundación Hospital General Universitario de Valencia Avda. Tres Cruces s/n València 46014 Spain Department of Pathology Universitat de València Av. de Blasco Ibáñez 13 València 46010 Spain Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain ICREA Catalan Institution for Research and Advanced Studies Spain Division of Genome Biology National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo 104-0045 Japan Molecular Oncology Laboratory Fundación Hospital General Universitario de València Avda. Tres Cruces s/n València 46014 Spain Department of Biotechnology Universitat Politècnica de València Camí de Vera s/n València 46022 Spain Department of Medicine Universitat de València Av. de Blasco Ibáñez 13 València 46010 Spain Genomics and Bioinformatics. Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts United States Diabetes Unit and Center for Human Genetic Research Massachusetts General Hospital Boston Massachuse

Objective: The aim of the study was to investigate the relationship between germline variations as a prognosis biomarker in patients with advanced Non-Small-Cell-Lung-Cancer (NSCLC) subjected to first-line platinum-based treatment. Materials and Methods: We carried out a two-stage genome-wide-association study in non-small-cell lung cancer patients with platinum-based chemotherapy in an exploratory sample of 181 NSCLC patients from Caucasian origin, followed by a validation on 356 NSCLC patients from the same ancestry (Valencia, Spain). Results: We identified germline variants in SMYD2 as a prognostic factor for survival in patients with advanced NSCLC receiving chemotherapy. SMYD2 alleles are associated to a decreased overall survival and with a reduced Time to Progression. In addition, enrichment pathway analysis identified 361 variants in 40 genes to be involved in poorer outcome in advanced-stage NSCLC patients. Conclusion: Germline SMYD2 alleles are associated with bad clinical outcome of first-line platinum-based treatment in advanced NSCLC patients. This result supports the role of SMYD2 in the carcinogenic process, and might be used as prognostic signature directing patient stratification and the choice of therapy. A two-Stage Genome wide association study in Caucasian population reveals germline genetic variation in SMYD2 associated to progression disease in first-line platinum-based treatment in advanced NSCLC patients. SMYD2 profiling might have prognostic / predictive value directing choice of therapy and enlighten current knowledge on pathways involved in human carcinogenesis as well in resistance to chemotherapy. © 2018 The Authors

关键词： Advanced stage Genome-Wide-Association Studies Lung cancer NSCLC Prognostic factors

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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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Buparlisib and ponatinib inhibit aggressiveness of cholangiocarcinoma cells via suppression of IRS1-related pathway by targeting oxidative stress resistance

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Biomedicine and Pharmacotherapy 2024年 180卷 117569-117569页

作者： Kaewlert, Waleeporn Sakonsinsiri, Chadamas Lert-itthiporn, Worachart Mahalapbutr, Panupong Ali, Saba Rungrotmongkol, Thanyada Jusakul, Apinya Armartmuntree, Napat Pairojkul, Chawalit Feng, Guofei Ma, Ning Pinlaor, Somchai Murata, Mariko Thanan, Raynoo Department of Biochemistry Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Cholangiocarcinoma Research Institute Khon Kaen University Khon Kaen 40002 Thailand Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Program in Bioinformatics and Computational Biology Graduated School Chulalongkorn University Bangkok 10330 Thailand The Center for Research and Development of Medical Diagnostic Laboratories Faculty of Associated Medical Sciences Khon Kaen University Khon Kaen 40002 Thailand Department of Medical Science Amnatcharoen Campus Mahidol University Amnat Charoen 37000 Thailand Department of Pathology Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Department of Environmental and Molecular Medicine Mie University Graduate School of Medicine Mie 514-8507 Japan Graduate School of Health Science Suzuka University of Medical Science Suzuka Mie 510-0226 Japan Department of Parasitology Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand

Cholangiocarcinoma (CCA) is an oxidative stress-driven liver cancer with bile duct epithelial cell phenotypes and currently lacks effective treatments, making targeted drug therapy urgently needed. Oxidative stress plays a critical role in CCA carcinogenesis, involving cells with oxidative stress resistance via upregulation of the PI3K and MEKK3 signaling pathways. In this study, we investigated the antineoplastic efficacy of a PI3K inhibitor (buparlisib) and a multi-tyrosine kinase inhibitor (ponatinib) on CCA. The cytotoxicity of the drug combination was studied in vitro using CCA cell lines and in vivo using CCA xenograft models. It was found that the drug combination suppressed growth, colony formation, and migration abilities of CCA cells and induced oxidative damage, cell cycle arrest, and autophagy by suppressing MEKK3 and YAP1 through inhibition of insulin receptor substrate 1 (IRS1) signaling. Moreover, the drugs would potentially bind to the IRS1 protein, significanly decreasing IRS1 phosphorylation. Additionally, the drug combination significantly diminished the expression of YAP1, the cell proliferation marker and an antioxidant regulator, and increased oxidative stress-responsive markers in the xenograft model. In conclusion, targeting oxidative stress resistance with combined buparlisib and ponatinib suppressed tumor growth and migration by repressing IRS1-related pathways and ultimately inducing oxidative damage, suggesting the potential for targeted therapy and clinical trials in CCA patients over the use of a single drug. © 2024 The Authors

关键词： Anti-cancer Bile duct cancer Buparlisib Insulin receptor substrate 1 Ponatinib Targeted therapy

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Resisting efficiency’s overreach

引用

Science 2023年第6663期381卷 1162页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies and Harry Radzyner Law School Reichman University Herzliya Israel. The reviewer is at the Department of Molecular Biophysics and Biochemistry Yale University New Haven CT USA. The reviewer is at the Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT USA.

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Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

SSRN

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SSRN 2024年

作者： Johansen, Nelson J. Kempynck, Niklas Zemke, Nathan R. Somasundaram, Saroja Winter, Seppe De Hooper, Marcus Dwivedi, Deepanjali Lohia, Ruchi Wehbe, Fabien Li, Bocheng Abaffyová, Darina Armand, Ethan J. Man, Julie De Eksi, Eren Can Hecker, Nikolai Hulselmans, Gert Konstantakos, Vasilis Mauduit, David Mich, John K. Partel, Gabriele Daigle, Tanya L. Levi, Boaz P. Zhang, Kai Tanaka, Yoshiaki Gillis, Jesse Ting, Jonathan T. Ben-Simon, Yoav Miller, Jeremy Ecker, Joseph R. Ren, Bing Aerts, Stein Lein, Ed S. Tasic, Bosiljka Bakken, Trygve E. Allen Institute for Brain Science SeattleWA98109 United States VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research KU Leuven Department of Human Genetics Leuven Belgium Center for Epigenomics Department of Cellular and Molecular Medicine University of California La Jolla San DiegoCA92093 United States Physiology Department Donnelly Centre for Cellular and Biomolecular Research University of Toronto TorontoON Canada Maisonneuve-Rosemont Hospital Research Centre University of Montreal MontrealQC Canada School of Life Sciences Westlake University Zhejiang Hangzhou China Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA92093 United States Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Physiology and Biophysics University of Washington SeattleWA98195 United States Salk Institute for Biological Studies La Jolla CA92037 United States

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi- Omics" to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases. © 2024, The Authors. All rights reserved.

关键词： Mammals

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Comprehensive analysis of the pseudogenes of glycolytic enzymes in vertebrates: The anomalously high number of GAPDH pseudogenes highlights a recent burst of retrotrans-positional activity

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BMC Genomics 2009年第1期10卷 480-480页

作者： Liu, Yuen-Jong Zheng, Deyou Balasubramanian, Suganthi Carriero, Nicholas Khurana, Ekta Robilotto, Rebecca Gerstein, Mark B. Department of Surgery Beth Israel Deaconess Medical Center Harvard Medical School Boston MA 110 Francis Street United States Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 P.O. Box 208114 United States Albert Einstein College of Medicine of Yeshiva University Department of Neurology Rose F. Kennedy Center Bronx NY 10461 1410 Pelham Parkway South United States Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 Bass 432 266 Whitney Avenue United States

Background: Pseudogenes provide a record of the molecular evolution of genes. As glycolysis is such a highly conserved and fundamental metabolic pathway, the pseudogenes of glycolytic enzymes comprise a standardized genomic measuring stick and an ideal platform for studying molecular evolution. One of the glycolytic enzymes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), has already been noted to have one of the largest numbers of associated pseudogenes, among all proteins. Results: We assembled the first comprehensive catalog of the processed and duplicated pseudogenes of glycolytic enzymes in many vertebrate model-organism genomes, including human, chimpanzee, mouse, rat, chicken, zebrafish, pufferfish, fruitfly, and worm (available at http://***/glycolysis/). We found that glycolytic pseudogenes are predominantly processed, i.e. retrotransposed from the mRNA of their parent genes. Although each glycolytic enzyme plays a unique role, GAPDH has by far the most pseudogenes, perhaps reflecting its large number of non-glycolytic functions or its possession of a particularly retrotranspositionally active sub-sequence. Furthermore, the number of GAPDH pseudogenes varies significantly among the genomes we studied: none in zebrafish, pufferfish, fruitfly, and worm, 1 in chicken, 50 in chimpanzee, 62 in human, 331 in mouse, and 364 in rat. Next, we developed a simple method of identifying conserved syntenic blocks (consistently applicable to the wide range of organisms in the study) by using orthologous genes as anchors delimiting a conserved block between a pair of genomes. This approach showed that few glycolytic pseudogenes are shared between primate and rodent lineages. Finally, by estimating pseudogene ages using Kimura's two-parameter model of nucleotide substitution, we found evidence for bursts of retrotranspositional activity approximately 42, 36, and 26 million years ago in the human, mouse, and rat lineages, respectively. Conclusion: Overall, we pe

关键词： Glycolytic Enzyme Segmental Duplication Chimpanzee Genome Assembly Version ENSEMBL Release

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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The role of deformability in determining the structural and mechanical properties of bubbles and emulsions

arXiv

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arXiv 2019年

作者： Boromand, Arman Signoriello, Alexandra Lowensohn, Janna Orellana, Carlos S. Weeks, Eric R. Ye, Fangfu Shattuck, Mark D. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Beijing National Laboratory for Condensed Matter Physics CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States School of Physical Sciences University of Chinese Academy of Sciences Beijing China Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

We perform computational studies of jammed particle packings in two dimensions undergoing isotropic compression using the well-characterized soft particle (SP) model and deformable particle (DP) model that we developed for bubbles and emulsions. In the SP model, circular particles are allowed to overlap, generating purely repulsive forces. In the DP model, particles minimize their perimeter, while deforming at fixed area to avoid overlap during compression. We compare the structural and mechanical properties of jammed packings generated using the SP and DP models as a function of the packing fraction ρ, instead of the reduced number density φ. We show that near jamming onset the excess contact number ∆z = z−zJ and shear modulus G scale as ∆ρ0.5 in the large system limit for both models, where ∆ρ = ρ−ρJ and zJ ≈4 and ρJ ≈0.842 are the values at jamming onset. ∆z and G for the SP and DP models begin to differ for ρ & 0.88. In this regime, ∆z∼G can be described by a sum of two power-laws in ∆ρ, i.e. ∆z∼G ∼C0∆ρ0.5+C1∆ρ1.0 to lowest order. We show that the ratio C1/C0 is much larger for the DP model compared to to that for the SP model. We also characterize the void space in jammed packings as a function of ρ. We find that the DP model can describe the formation of Plateau borders as ρ → 1. We further show that the results for z and the shape factor A versus ρ for the DP model agree with recent experimental studies of foams and emulsions. Copyright © 2019, The Authors. All rights reserved.

关键词： Jamming

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