Population annealing is a sequential Monte Carlo scheme well suited to simulating equilibrium states of systems with rough free energy landscapes. Here we use population annealing to study a binary mixture of hard spheres. Population annealing is a parallel version of simulated annealing with an extra resampling step that ensures that a population of replicas of the system represents the equilibrium ensemble at every packing fraction in an annealing schedule. The algorithm and its equilibration properties are described, and results are presented for a glass-forming fluid composed of a 50/50 mixture of hard spheres with diameter ratio of 1.4:1. For this system, we obtain precise results for the equation of state in the glassy regime up to packing fractions φ≈0.60 and study deviations from the Boublik-Mansoori-Carnahan-Starling-Leland equation of state. For higher packing fractions, the algorithm falls out of equilibrium and a free volume fit predicts jamming at packing fraction φ≈0.667. We conclude that population annealing is an effective tool for studying equilibrium glassy fluids and the jamming transition.
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http://dx.doi.org/10.1103/PhysRevE.95.063315 | DOI Listing |
CRISPR J
January 2025
Plant Biotechnology Research Center, Fudan-SJTU-Nottingham Plant Biotechnology R&D Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Minhang, Shanghai, China.
The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 system has revolutionized targeted mutagenesis, but screening for mutations in large sample pools can be time-consuming and costly. We present an efficient and cost-effective polymerase chain reaction (PCR)-based strategy for identifying edited mutants in the T generation. Unlike previous methods, our approach addresses the challenges of large progeny populations by using T generation sequencing results for genotype prediction.
View Article and Find Full Text PDFNucleic Acids Res
January 2025
Department of Convergent Bioscience and Informatics, College of Bioscience and Biotechnology, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea.
Large genetic variants can be generated via homologous recombination (HR), such as polymerase theta-mediated end joining (TMEJ) or single-strand annealing (SSA). Given that these HR-based mechanisms leave specific genomic signatures, we developed GDBr, a genomic signature interpretation tool for DNA double-strand break repair mechanisms using high-quality genome assemblies. We applied GDBr to a draft human pangenome reference.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Physics and Astronomy, Rowan University, Glassboro, NJ 08028, USA.
Biocompatible materials fabricated from natural protein polymers are an attractive alternative to conventional petroleum-based plastics. They offer a green, sustainable fabrication method while also opening new applications in biomedical sciences. Available from several sources in the wild and on domestic farms, silk is a widely used biopolymer and one of the strongest natural materials.
View Article and Find Full Text PDFZoonoses Public Health
January 2025
Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, People's Republic of China.
Introduction: Laboratory animals are widely used in biomedical research. Surveillance of naturally occurring virus in laboratory animals is important to fully understand the results of animal experiment, control laboratory-acquired infections among research personnel and manage viral transmission within laboratory animal populations. This study aimed to investigate the prevalence of multiple RNA viruses in laboratory animals commonly used in China.
View Article and Find Full Text PDFSci Rep
December 2024
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, School of Computer and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability.
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