The population annealing algorithm is a population-based equilibrium version of simulated annealing. It can sample thermodynamic systems with rough free-energy landscapes more efficiently than standard Markov chain Monte Carlo alone. A number of parameters can be fine-tuned to improve the performance of the population annealing algorithm. While there is some numerical and theoretical work on most of these parameters, there appears to be a gap in the literature concerning the role of resampling in population annealing which this work attempts to close. The two-dimensional Ising model is used as a benchmarking system for this study. At first various resampling methods are implemented and numerically compared. In a second part the exact solution of the Ising model is utilized to create an artificial population annealing setting with effectively infinite Monte Carlo updates at each temperature. This limit is first performed on finite population sizes and subsequently extended to infinite populations. This allows us to look at resampling isolated from other parameters. Many results are expected to generalize to other systems.
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http://dx.doi.org/10.1103/PhysRevE.108.065309 | DOI Listing |
Nucleic 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.
View Article and Find Full Text PDFMol Ecol Resour
December 2024
Institute of Zoology, Zoological Society of London, London, UK.
In this computer note I introduce software, PopCluster, that implements a new likelihood method for unsupervised population structure analysis from marker data. To infer a coarse population structure, it assumes the mixture model and adopts a simulated annealing algorithm to make a maximum likelihood clustering analysis, partitioning the sampled individuals into a predefined number of clusters. To deduce a fine population structure, it further assumes the admixture model and employs an expectation maximisation algorithm to estimate individual admixture proportions.
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