Hasil untuk "Evolution"

B. Paxton, L. Bildsten, A. Dotter et al.

Stellar physics and evolution calculations enable a broad range of research in astrophysics. Modules for Experiments in Stellar Astrophysics (MESA) is a suite of open source, robust, efficient, thread-safe libraries for a wide range of applications in computational stellar astrophysics. A one-dimensional stellar evolution module, MESAstar, combines many of the numerical and physics modules for simulations of a wide range of stellar evolution scenarios ranging from very low mass to massive stars, including advanced evolutionary phases. MESAstar solves the fully coupled structure and composition equations simultaneously. It uses adaptive mesh refinement and sophisticated timestep controls, and supports shared memory parallelism based on OpenMP. State-of-the-art modules provide equation of state, opacity, nuclear reaction rates, element diffusion data, and atmosphere boundary conditions. Each module is constructed as a separate Fortran 95 library with its own explicitly defined public interface to facilitate independent development. Several detailed examples indicate the extensive verification and testing that is continuously performed and demonstrate the wide range of capabilities that MESA possesses. These examples include evolutionary tracks of very low mass stars, brown dwarfs, and gas giant planets to very old ages; the complete evolutionary track of a 1 M☉ star from the pre-main sequence (PMS) to a cooling white dwarf; the solar sound speed profile; the evolution of intermediate-mass stars through the He-core burning phase and thermal pulses on the He-shell burning asymptotic giant branch phase; the interior structure of slowly pulsating B Stars and Beta Cepheids; the complete evolutionary tracks of massive stars from the PMS to the onset of core collapse; mass transfer from stars undergoing Roche lobe overflow; and the evolution of helium accretion onto a neutron star. MESA can be downloaded from the project Web site (http://mesa.sourceforge.net/).

A. Kudo, Y. Miseki

B. Bourdin, G. Francfort, J. Marigo

C. Ghalambor, J. McKay, Scott P Carroll et al.

L. Cavalli-Sforza, A. Edwards

J. Mallet

A. Hofmann

M. Kimura

J. Odling-Smee, K. Laland, M. Feldman

R. Lewontin

Abstract The shortcomings of present population genetic theory are discussed as they pertain to problems of speciation, extinction and the evolution of genetic systems. It is suggested that the modern theory of games may be useful in finding exact answers to problems of evolution not covered by the theory of population genetics. An outline of relevant topics in the theory of games is given. It is suggested that the most pertinent utility measure for a population is its one-generation probability of survival and that a strategy or a mixture of strategies corresponding to a maximin strategy will be found in natural populations. These notions are applied to a population segregating for two alleles with different norms of reaction in different environments. For the model chosen the optimal strategy is found to be homozygosis for different alleles in different populations due either to inbreeding or genetic isolation. A segregating polymorphism in such populations would be a detriment to the species, although the heterozygotes are more constant in fitness.

H. Tanabe

W. A. Searcy, M. Andersson

Lucas Bickmann, Matias Rodriguez, Xiaoyi Jiang et al.

Transposable elements (TEs) constitute a significant portion of eukaryotic genomes and play crucial roles in genome evolution, yet their diverse and complex sequences pose challenges for accurate classification. Existing tools often lack reliability in TE classification, limiting genomic analyses. Here, we present TEclass2, a software employing a deep learning approach based on a linear transformer architecture with k-mer tokenization and sequence-specific adaptations to classify TE consensus sequences into sixteen superfamilies. TEclass2 demonstrates improved classification performance and offers flexible model training on custom datasets. Accessible via a web interface with pre-trained models, TEclass2 facilitates rapid and reliable TE classification. These advancements provide a foundation for enhanced genomic annotation and support further bioinformatics research involving transposable elements.

Yingxin Tu, Can Wang, Wenbing Zhu et al.

Ticks are the second most important vectors of human diseases after mosquitoes. Hard ticks are more abundant and widespread than soft ticks, resulting in their greater involvement in diverse diseases. Consequently, most research on tick-borne pathogens has focused on hard ticks. In contrast, soft ticks, which comprise fewer species, have received less research attention. In this study, we identified a novel single-stranded RNA virus (tentatively named Gagosa Mountain virus) in Ornithodoros lahorensis ticks from the Shigatse region of Tibet. We collected 80 engorged soft ticks from Tibetan sheep, placing each in a separate tube for pathogen analysis. Quantitative real-time PCR (qRT-PCR) and nested PCR techniques were employed to confirm the presence of Gagosa Mountain virus, and subsequent analyses focused on elucidating its genomic features and phylogenetic relationships. Our results demonstrated that Gagosa Mountain virus was present in 15 out of 80 ticks, corresponding to a positivity rate of 19%. The 13,133-nucleotide single-stranded negative-sense RNA genome contained six open reading frames (ORFs) encoding the N protein, RdRp, and four hypothetical proteins. Pairwise distance analysis showed high nucleotide sequence identity among the viral sequences. Phylogenetic analysis indicated that Gagosa Mountain virus is most closely related to Lhasa Rhabd tick virus 1, which is an unclassified member of the family Rhabdoviridae. Further analyses demonstrated that Gagosa Mountain virus represents a novel member of the genus Betanemrhavirus within the family Rhabdoviridae.

Dhaker Kroumi

Cooperation often depends on individuals avoiding exploitation and interacting preferentially with other cooperators. We explore how context-dependent migration influences the evolution of cooperation in spatially structured populations. Individuals interact in small groups through public goods games and reproduce with possible dispersal. Cooperators migrate more frequently when surrounded by defectors, while defectors disperse uniformly. This behavioral asymmetry reflects realistic differences in mobility and social responsiveness. Our results show that conditional migration can promote cooperation by enabling cooperators to escape defector-rich environments and cluster together. The effectiveness of this mechanism depends on baseline migration rates, group size, and the sensitivity of cooperators to local conditions. We identify parameter ranges where cooperation is favored even under conditions that would typically hinder its evolution. These findings highlight how behavioral plasticity and dispersal strategies can interact with population structure to support the emergence of cooperation.

Dae Gyu Choi, Ju Hye Baek, Dong Min Han et al.

Abstract Background Enterococcus faecium and E. lactis are phylogenetically closely related lactic acid bacteria that are ubiquitous in nature and are known to be beneficial or pathogenic. Despite their considerable industrial and clinical importance, comprehensive studies on their evolutionary relationships and genomic, metabolic, and pathogenic traits are still lacking. Therefore, we conducted comparative pangenome analyses using all available dereplicated genomes of these species. Results E. faecium was divided into two subclades: subclade I, comprising strains derived from humans, animals, and food, and the more recent phylogenetic subclade II, consisting exclusively of human-derived strains. In contrast, E. lactis strains, isolated from diverse sources including foods, humans, animals, and the environment, did not display distinct clustering based on their isolation sources. Despite having similar metabolic features, noticeable genomic differences were observed between E. faecium subclades I and II, as well as E. lactis. Notably, E. faecium subclade II strains exhibited significantly larger genome sizes and higher gene counts compared to both E. faecium subclade I and E. lactis strains. Furthermore, they carried a higher abundance of antibiotic resistance, virulence, bacteriocin, and mobile element genes. Phylogenetic analysis of antibiotic resistance and virulence genes suggests that E. faecium subclade II strains likely acquired these genes through horizontal gene transfer, facilitating their effective adaptation in response to antibiotic use in humans. Conclusions Our study offers valuable insights into the adaptive evolution of E. faecium strains, enabling their survival as pathogens in the human environment through horizontal gene acquisitions.

Hassan Al Garni, Arunachalam Sundaram, Anjali Awasthi et al.

A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide.

Ravi Nandan, Hiroki Nara, Ho Ngoc Nam et al.

Abstract High Entropy Alloys (HEAs) are a versatile material with unique properties, tailored for various applications. They enable pH‐sensitive electrocatalytic transformations like hydrogen evolution reaction (HER) and hydrogen oxidation reactions (HOR) in alkaline media. Mesoporous nanostructures with high surface area are preferred for these electrochemical reactions, but designing mesoporous HEA sis challenging. To overcome this challenge, a low‐temperature triblock copolymer‐assisted wet‐chemical approach is developed to produce mesoporous HEA nanospheres composed of PtPdRuMoNi systems with sufficient entropic mixing. Owing to active sites with inherent entropic effect, mesoporous features, and increased accessibility, optimized HEA nanospheres promote strong HER/HOR performance in alkaline medium. At 30 mV nominal overpotential, it exhibits a mass activity of ≈167 (HER) and 151 A gPt−1 (HOR), far exceeding commercial Pt‐C electrocatalysts (34 and 48 A gPt−1) and many recently reported various alloys. The Mott‐Schottky analysis reveals HEA nanospheres inherit high charge carrier density, positive flat band potential, and smaller charge transfer barrier, resulting in better activity and faster kinetics. This micelle‐assisted synthetic enable the exploration of the compositional and configurational spaces of HEAs at relatively low temperature, while simultaneously facilitating the introduction of mesoporous nanostructures for a wide range of catalytic applications.

Masaaki Inaba, Eizo Akiyama

The evolutionary process that led to the emergence of modern human behaviors during the Middle Stone Age in Africa remains enigmatic. While various hypotheses have been proposed, we offer a new perspective that integrates the variability selection hypothesis (VSH) with the evolution of cooperation among human groups. The VSH suggests that human adaptability to fluctuating environments was a primary force driving the development of key evolutionary traits. However, the mechanisms by which environmental variability (EV) influenced human evolution, particularly the emergence of large-scale and complex cooperative behaviors, are not yet fully understood. To explore the connection between intensified EV and the evolution of intergroup cooperation, we analyzed three stochastic models of EV: (i) Regional Variability (RV), where resource-rich areas shift while overall resource levels remain stable; (ii) Universal Variability (UV), where overall resource levels fluctuate but resource-rich areas remain stable; and (iii) Combined Variability (CV), where both resource-rich areas shift and overall resource levels fluctuate. Our results show that RV strongly promotes cooperation, while UV has a comparatively weaker effect. Additionally, our findings indicate that the coevolution of cooperation and network structures is crucial for EVs to effectively promote cooperation. This study proposes a novel causal link between EV and the evolution of cooperation, potentially setting a new direction for both theoretical and empirical research in this field.

Saverio Rossi, Leonardo Di Bari, Martin Weigt et al.

Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct sites and times. To quantify these correlations, we examine the role of various fluctuation sources in protein evolution, simulated using a data-driven energy landscape as a proxy for protein fitness. By applying spatio-temporal correlation functions developed in the context of disordered physical systems, we disentangle fluctuations originating from the initial condition, i.e. the ancestral sequence from which the evolutionary process originated, from those driven by stochastic mutations along independent evolutionary paths. Our analysis shows that, in diverse protein families, fluctuations from the ancestral sequence predominate at shorter time scales. This allows us to identify a time scale over which ancestral sequence information persists, enabling its reconstruction. We link this persistence to the strength of epistatic interactions: ancestral sequences with stronger epistatic signatures impact evolutionary trajectories over extended periods. At longer time scales, however, ancestral influence fades as epistatically constrained sites evolve collectively. To confirm this idea, we apply a standard ancestral sequence reconstruction algorithm and verify that the time-dependent recovery error is influenced by the properties of the ancestor itself. Overall, our results reveal that the properties of ancestral sequences - particularly their epistatic constraints - influence the initial evolutionary dynamics and the performance of standard ancestral sequence reconstruction algorithms.