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Correction to: Authors’ Reply to Letter to the Editor: Continued improvement to genetic diversity indicator for CBD

Conservation Genetics 2021年第4期22卷 537-539页

作者： Laikre, Linda Hohenlohe, Paul A. Allendorf, Fred W. Bertola, Laura D. Breed, Martin F. Bruford, Michael W. Funk, W. Chris Gajardo, Gonzalo González-Rodríguez, Antonio Grueber, Catherine E. Hedrick, Philip W. Heuertz, Myriam Hunter, Margaret E. Johannesson, Kerstin Liggins, Libby MacDonald, Anna J. Mergeay, Joachim Moharrek, Farideh O’Brien, David Ogden, Rob Orozco-terWengel, Pablo Palma-Silva, Clarisse Pierson, Jennifer Paz-Vinas, Ivan Russo, Isa-Rita M. Ryman, Nils Segelbacher, Gernot Sjögren-Gulve, Per Waits, Lisette P. Vernesi, Cristiano Hoban, Sean Division of Population Genetics Department of Zoology Stockholm University Stockholm Sweden Institute for Bioinformatics and Evolutionary Studies Department of Biological Sciences University of Idaho Moscow USA Division of Biological Sciences University of Montana Missoula USA City College of New York New York USA College of Science and Engineering Flinders University Bedford Park Australia School of Biosciences Cardiff University Cardiff Wales UK Graduate Degree Program in Ecology Department of Biology Colorado State University Fort Collins USA Universidad de Los Lagos Lab Genetics Aquaculture & Biodiversity Osorno Chile Instituto de Investigaciones en Ecosistemas y Sustentabilidad Universidad Nacional Autónoma de México Morelia Mexico School of Life and Environmental Sciences Faculty of Science The University of Sydney Sydney Australia School of Life Sciences Arizona State University Tempe USA INRAE Univ. Bordeaux Biogeco Cestas France U.S. Geological Survey Wetland and Aquatic Research Center Gainesville USA Department of Marine Sciences University of Gothenburg Tjärnö Strömstad Sweden School of Natural and Computational Sciences Massey University Auckland New Zealand The John Curtin School of Medical Research/Research School of Biology The Australian National University Acton Australia Research Institute for Nature and Forest Geraardsbergen Belgium Aquatic Ecology Evolution and Conservation KULeuven Leuven Belgium Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Faculty of Biological Sciences Tarbiat Modares University Tehran Iran Scottish Natural Heritage Inverness UK Royal (Dick) School of Veterinary Studies and the Roslin Institute University of Edinburgh Midlothian UK Department of Plant Science Institute of Biology University of Campinas Campinas Brazil Australian Wildlife Conservancy Subiaco East Australia Laboratoire Evolution & Div

A correction to this paper has been published: https://***/10.1007/s10592-021-01376-9

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

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Procedia Computer Science 2018年 135卷 377-384页

作者： Rezzy Eko Caraka Shamarina Shohaimi Isma Dwi Kurniawan Riki Herliansyah Arif Budiarto Shinta Purnama Sari Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Department of Mathematics Kalimantan Institute of Technology (ITK) Kalimantan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave.

关键词： Ecology GLLVM Negative Binomial Show cave Wild Cave

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Protist genomics: key to understanding eukaryotic evolution

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Trends in genetics : TIG 2025年

作者： Alexandra Schoenle Ore Francis John M Archibald Fabien Burki Jan de Vries Kenneth Dumack Laura Eme Isabelle Florent Elisabeth Hehenberger Tarja T Hoffmeyer Iker Irisarri Enrique Lara Michelle M Leger Julius Lukeš Ramon Massana Varsha Mathur Frank Nitsche Jürgen F H Strassert Alexandra Z Worden Vyacheslav Yurchenko Javier Del Campo Ann-Marie Waldvogel Ecological Genomics Department of Biology Institute of Zoology Biocenter Cologne University of Cologne Cologne Germany Global Change Limnology School of Life Sciences Technical University of Munich Munich Germany. Electronic address: a.schoenle@tum.de. Wellcome Sanger Institute Cambridge UK. Department of Biochemistry & Molecular Biology Dalhousie University Halifax Canada Institute for Comparative Genomics Dalhousie University Halifax Canada. Department of Organismal Biology Uppsala University Uppsala Sweden. University of Göttingen Institute of Microbiology and Genetics Göttingen Center for Molecular Biosciences (GZMB) Campus Institute Data Science (CIDAS) Dept. of Applied Bioinformatics 37077 Göttingen Germany. Terrestrial Ecology Department of Biology Institute of Zoology Biocenter Cologne University of Cologne Cologne Germany. Unité d'Ecologie Systématique et Evolution CNRS AgroParisTech Université Paris-Saclay Gif Sur Yvette France. UMR7245 National Museum of Natural History and CNRS Paris France. Institute of Parasitology Biology Centre Czech Academy of Sciences České Budějovice Czechia. Department of Biology Institute of Zoology Biocenter Cologne University of Cologne Cologne Germany. Department of Biodiversity and Evolutionary Biology Museo Nacional de Ciencias Naturales (MNCN-CSIC) c/ José Gutiérrez Abascal 2 28006 Madrid Spain. Department of Mycology Royal Botanical Garden-CSIC Madrid Spain. Okinawa Institute of Science and Technology Graduate University Okinawa Japan. Institute of Parasitology Biology Centre Czech Academy of Sciences České Budějovice Czechia Faculty of Science University of South Bohemia České Budějovice Czechia. Department of Marine Biology and Oceanography Institut de Ciències del Mar (ICM-CSIC) Barcelona Catalonia Spain. Faculty of Life Sciences University of Vienna Vienna Austria. General Ecology Department of Biology Institute of Zoology Biocenter Cologne University of Cologne Cologne Germany. Departme

All eukaryotes other than animals, plants, and fungi are protists. Protists are highly diverse and found in nearly all environments, with key roles in planetary health and biogeochemical cycles. They represent the majority of eukaryotic diversity, making them essential for understanding eukaryotic evolution. However, these mainly unicellular, microscopic organisms are understudied and the generation of protist genomes lags far behind most multicellular lineages. Current genomic methods, which are primarily designed for animals and plants, are poorly suited for protists. Advancing protist genome research requires reevaluating plant- and animal-centric genomic standards. Future efforts must leverage emerging technologies and bioinformatics tools, ultimately enhancing our understanding of eukaryotic molecular and cell biology, ecology, and evolution.

关键词： metagenomics protists reference genome standards single-cell sequencing transcriptomics

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AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens

Research Square

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Research Square 2020年

作者： Li, Chenkai Sutherland, Darcy Hammond, S. Austin Yang, Chen Taho, Figali Bergman, Lauren Houston, Simon Warren, René L. Wong, Titus Hoang, Linda M.N. Cameron, Caroline E. Helbing, Caren C. Birol, Inanc BC Cancer Genome Sciences Centre Canada Canada’s Michael Smith Genome Sciences Centre BC Cancer Agency VancouverBCV5Z 4S6 Canada Bioinformatics Graduate Program University of British Columbia VancouverBCV6T 1Z4 Canada Public Health Laboratory British Columbia Centre for Disease Control VancouverBCV5Z 4R4 Canada Department of Pathology and Laboratory Medicine University of British Columbia VancouverBCV6T 1Z4 Canada Department of Biochemistry and Microbiology University of Victoria VictoriaBCV8P 5C3 Canada Medical Microbiology Laboratory Vancouver General Hospital VancouverBCV5Z 1M9 Canada Division of Infectious Diseases Department of Medicine University of Washington SeattleWA98195 United States Department of Medical Genetics University of British Columbia VancouverBCV6H 3N1 Canada

Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging therapeutic agents with promising utility in this domain and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can filter through large volumes of candidate sequences and reduce lab screening costs. Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s "priority pathogens" list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli, demonstrating the utility of tools like AMPlify in our fight against antibiotic resistance. © 2020, CC BY.

关键词： Peptides

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Harsawardana Reza Rahutomo Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Teddy Suparyanto Don Bosco Surya Atmaja Bayu Samoedro Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data.

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Liquid-EM goes viral - visualizing structure and dynamics

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Current opinion in structural biology 2022年 75卷 102426页

作者： Deborah F Kelly Liza-Anastasia DiCecco G M Jonaid William J Dearnaley Michael S Spilman Jennifer L Gray Madeline J Dressel-Dukes Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA Center for Structural Oncology Pennsylvania State University University Park PA 16802 USA Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Electronic address: Debkelly@psu.edu. Department of Materials Science and Engineering McMaster University Hamilton ON L8S 4L7 Canada. Electronic address: https://***/LizaDiCecco. Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Electronic address: https://***/PennStateMRI. Direct Electron LP San Diego CA 92128 USA. Electronic address: https://***/DirectElectron. Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Protochips Inc. Morrisville NC 27560 USA. Electronic address: https://***/Protochips.

Liquid-electron microscopy (EM), the room temperature correlate to cryo-EM, is an exciting new technique delivering real-time data of dynamic reactions in solution. Here, we explain how liquid-EM gained popularity in recent years by examining key experiments conducted on viral assemblies and host-pathogen interactions. We describe developing workflows for specimen preparation, data collection, and computing processes that led to the first high-resolution virus structures in a liquid environment. Equally important, we review why liquid-electron tomography may become the next big thing in biomedical research due to its ability to monitor live viruses entering cells within seconds. Taken together, we pose the idea that liquid-EM can serve as a dynamic complement to current cryo-EM methods, inspiring the "real-time revolution" in nanoscale imaging.

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Author Correction: Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes

Nature cancer

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Nature cancer 2023年 2023 May 19页

作者： Do Young Hyeon Dowoon Nam Youngmin Han Duk Ki Kim Gibeom Kim Daeun Kim Jingi Bae Seunghoon Back Dong-Gi Mun Inamul Hasan Madar Hangyeore Lee Su-Jin Kim Hokeun Kim Sangyeop Hyun Chang Rok Kim Seon Ah Choi Yong Ryoul Kim Juhee Jeong Suwan Jeon Yeon Woong Choo Kyung Bun Lee Wooil Kwon Seunghyuk Choi Taewan Goo Taesung Park Young-Ah Suh Hongbeom Kim Ja-Lok Ku Min-Sik Kim Eunok Paek Daechan Park Keehoon Jung Sung Hee Baek Jin-Young Jang Daehee Hwang Sang-Won Lee School of Biological Sciences Seoul National University Seoul Republic of Korea. Department of Chemistry and Center for Proteogenome Research Korea University Seoul Republic of Korea. Department of Surgery and Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Seoul Republic of Korea. Creative Research Initiatives Center for Epigenetic Code and Diseases Seoul National University Seoul Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. Department of Pathology Seoul National University College of Medicine Seoul Korea. Department of Computer Science Hanyang University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Department of Statistics Seoul National University Seoul Republic of Korea. Korean Cell Line Bank Laboratory of Cell Biology Cancer Research Institute Seoul National University College of Medicine Seoul Korea. Department of New Biology Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. dpark@ajou.ac.kr. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. keehoon.jung@snu.ac.kr. Institute of Allergy and Clinical Immunology Seoul National University Medical Research Center Seoul Republic of Korea. keehoon.jung@snu.ac.kr. School of Biological Sciences Seoul National University Seoul Republic of

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The mutational constraint spectrum quantified from variation in 141,456 humans (vol 581, pg 434, 2020)

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NATURE 2021年第7846期590卷 E53-E53页

作者： Karczewski, Konrad J. Francioli, Laurent C. Tiao, Grace Cummings, Beryl B. Alfoldi, Jessica Wang, Qingbo Collins, Ryan L. Laricchia, Kristen M. Ganna, Andrea Birnbaum, Daniel P. Gauthier, Laura D. Brand, Harrison Solomonson, Matthew Watts, Nicholas A. Rhodes, Daniel Singer-Berk, Moriel England, Eleina M. Seaby, Eleanor G. Kosmicki, Jack A. Walters, Raymond K. Tashman, Katherine Farjoun, Yossi Banks, Eric Poterba, Timothy Wang, Arcturus Seed, Cotton Whiffin, Nicola Chong, Jessica X. Samocha, Kaitlin E. Pierce-Hoffman, Emma Zappala, Zachary O'Donnell-Luria, Anne H. Minikel, Eric Vallabh Weisburd, Ben Lek, Monkol Ware, James S. Vittal, Christopher Armean, Irina M. Bergelson, Louis Cibulskis, Kristian Connolly, Kristen M. Covarrubias, Miguel Donnelly, Stacey Ferriera, Steven Gabriel, Stacey Gentry, Jeff Gupta, Namrata Jeandet, Thibault Kaplan, Diane Llanwarne, Christopher Munshi, Ruchi Novod, Sam Petrillo, Nikelle Roazen, David Ruano-Rubio, Valentin Saltzman, Andrea Schleicher, Molly Soto, Jose Tibbetts, Kathleen Tolonen, Charlotte Wade, Gordon Talkowski, Michael E. Aggregation, Genome Neale, Benjamin M. Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine Finland Helsinki Finland Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Vertex Pharmaceuticals Inc Boston MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA Department of Neurology Harvard Medical School Boston MA USA Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Diabetes Unit Massachusetts General Hospital Boston MA USA Program in Metabolism Broad Institute of MIT and Harvard Cambridge MA USA Department of Medicine Harvard Medical School Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Cardiovascular Disease Initiative and Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Genomics Diabetes and Endocrinology Lund University Lund Sweden Folkhälsan Institute of Genetics Folkhälsan Research C

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The mutational constraint spectrum quantified from variation in 141,456 humans (vol 581, pg 434, 2020)

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NATURE 2021年第7874期597卷 E3-E4页

作者： Gudmundsson, Sanna Karczewski, Konrad J. Francioli, Laurent C. Tiao, Grace Cummings, Beryl B. Alfoldi, Jessica Wang, Qingbo Collins, Ryan L. Laricchia, Kristen M. Ganna, Andrea Birnbaum, Daniel P. Gauthier, Laura D. Brand, Harrison Solomonson, Matthew Watts, Nicholas A. Rhodes, Daniel Singer-Berk, Moriel England, Eleina M. Seaby, Eleanor G. Kosmicki, Jack A. Walters, Raymond K. Tashman, Katherine Farjoun, Yossi Banks, Eric Poterba, Timothy Wang, Arcturus Seed, Cotton Whiffin, Nicola Chong, Jessica X. Samocha, Kaitlin E. Pierce-Hoffman, Emma Zappala, Zachary O'Donnell-Luria, Anne H. Minikel, Eric Vallabh Weisburd, Ben Lek, Monkol Ware, James S. Vittal, Christopher Armean, Irina M. Bergelson, Louis Cibulskis, Kristian Connolly, Kristen M. Covarrubias, Miguel Donnelly, Stacey Ferriera, Steven Gabriel, Stacey Gentry, Jeff Gupta, Namrata Jeandet, Thibault Kaplan, Diane Llanwarne, Christopher Munshi, Ruchi Novod, Sam Petrillo, Nikelle Roazen, David Ruano-Rubio, Valentin Saltzman, Andrea Schleicher, Molly Soto, Jose Tibbetts, Kathleen Tolonen, Charlotte Wade, Gordon Talkowski, Michael E. Neale, Benjamin M. Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine Finland Helsinki Finland Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Vertex Pharmaceuticals Inc Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA Department of Neurology Harvard Medical School Boston MA USA Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Diabetes Unit Massachusetts General Hospital Boston MA USA Program in Metabolism Broad Institute of MIT and Harvard Cambridge MA USA Department of Medicine Harvard Medical School Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Cardiovascular Disease Initiative and Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Genomics Diabetes and Endocrinology Lund University Lund Sweden Folkhälsan Institute of Genetics Folkhälsan Research C

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Hypostatic jammed packings of frictionless nonspherical particles

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Physical Review E 2018年第1期97卷 012909-012909页

作者： Kyle VanderWerf Weiwei Jin Mark D. Shattuck Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520 USA Department of Mechanics and Engineering Science Peking University Beijing 100871 China Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naive constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and cir-polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a masterlike form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of “quartic” modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable.

关键词： Granular materials Granular packing Jamming

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