Evolution Of Population Research Articles

Multisample motif discovery and visualization for tandem repeats

Tandem Repeats (TR) occupy a significant portion of the human genome and are the source of polymorphism due to variations in sizes and motif compositions. Some of these variations have been associated with various neuropathological disorders, highlighting the clinical importance of assessing the motif structure of TRs. Moreover, assessing the TR motif variation can offer valuable insights into evolutionary dynamics and population structure. Previously, characterizations of TRs have been limited by short-read sequencing technology, which lacks the ability to accurately capture the full TR sequences. As long-read sequencing becomes more accessible and can capture the full complexity of TRs, there is now also a need for tools to characterize and analyze TRs using long-read data across multiple samples. In this study, we present MotifScope, a novel algorithm for characterization and visualization of TRs based on a de novok-mer approach for motif discovery. Comparative analysis against established tools reveals that MotifScope can identify a greater number of motifs and more accurately represent the underlying repeat sequence. Moreover, MotifScope has been specifically designed to enable motif composition comparisons across assemblies of different individuals, as well as across long-read sequencing reads within an individual, through combined motif discovery and sequence alignment. We showcase potential applications of MotifScope in diverse fields, including population genetics, clinical settings, and forensic analyses.

Information Theory in a Darwinian Evolution Population Dynamics Model

Since Darwin, evolutionary population dynamics has captivated scientists and has applications beyond biology, such as in game theory where economists use it to explore evolution in new ways. This approach has renewed interest in dynamic evolutionary systems. In this paper, we propose an information-theoretic method to estimate trait parameters in a Darwinian model for species with single or multiple traits. Using Fisher information, we assess estimation errors and demonstrate the method through simulations.

On Models of Population Evolution of Three Interacting Species

In this paper, we first analyzed several basic population dynamics models interpreting the relationships between three species. These are the May-Leonard model with three competitors, some prey-predator models of three-species and a prey-predator model with a super-predator. Subsequently, in our work, we proposed a new three-species model consisting of a prey, a predator and a superpredator, including some important assumptions such as competition, self-defense and infected prey. We examined the various equilibrium points of proposed model, and determined the conditions for extinction and survival of species in the long term. Finally, we performed numerical illustrations using Maltlab software to corroborate the theoretical results.

Diachronic evolution in subsistence strategies of ancient humans from the Neolithic Age to the Bronze Age in the Western Liaohe River Basin and its influencing factors

The Western Liaohe River Basin, rich in prehistoric cultures, is one of the significant origins of Chinese civilization. However, what is the subsistence basis that supports the development of prehistoric cultures in this region? More importantly, which role did agriculture play in the development of various prehistoric cultures? Which factors drive the diachronic evolution in subsistence strategies? These issues are worthy of study. Based on this, we systematically collected zooarchaeological, archaeobotanical, and stable isotope data from various prehistoric archeological cultural sites spanning from the Neolithic Age to the Bronze Age in the Western Liaohe River Basin, attempting to explore their economic characteristics, evolution process as well as driving factor. The study found that in the Western Liaohe River Basin, the cultivation of millet crops may have begun as early as the Xiaohexi cultural period, and domestic pigs and dogs may have occurred in the Xinglongwa cultural period (Xionglongwa and Xionglonggou site respectively). However, during Hongshan cultural period, collection and hunting (cervids) remained the main subsistence strategy. In the Lower Xiajiadian cultural period, millet agriculture and domestic pig breeding replaced gathering and hunting, as the predominant subsistence strategy. This subsistence pattern was also adopted by subsequent Upper Xiajiadian cultural populations, and in some areas, animal husbandry developed during this period. We analyze that the evolution of the subsistence strategies of the prehistoric humans in this region may be related with Holocene climate change. Meanwhile, other factors such as improvements in production technology, population pressure and the environmental impacts they brought may also play a role. This study reveals the evolution process of subsistence strategies of prehistoric cultures in the Western Liaohe River Basin, and is of great significance in understanding the relationship between subsistence evolution of ancient populations and environmental changes in northern China.

Find the haystacks, then look for needles: The rate of strongly lensed supernovae in galaxy-galaxy strong gravitational lenses

Abstract The time delay between appearances of multiple images of a gravitationally lensed supernova (glSN) is sensitive to the Hubble constant, H0. As well as time delays, a lensed host galaxy is needed to enable precise inference of H0. In this work we investigate the connection between discoverable lensed transients and their host galaxies. We find that the Legacy Survey of Space and Time (LSST) will discover at least 90 glSNe per year, of which 54% will also have a strongly lensed host. The rates are uncertain by approximately 30 % depending primarily on the choice of the unlensed SN population and uncertainties in the redshift evolution of the deflector population, but the fraction of glSNe with a lensed host is consistently around a half. LSST will discover around 20 glSNe per year in systems that could plausibly have been identified by Euclid as galaxy-galaxy lenses before the discovery of the glSN. Such systems have preferentially longer time delays and therefore are well suited for cosmography. We define a golden sample of glSNe Ia with time delays over 10 days, image separations greater than 0.8 arcseconds, and a multiply imaged host. For this golden sample, we find $91\%$ occur in systems that should already be discoverable as galaxy-galaxy lenses in Euclid. For cosmology with glSNe, monitoring Euclid lenses is a plausible alternative to searching the entire LSST alert stream.

Shared environmental similarity between relatives influences heritability of reproductive timing in wild great tits.

Intraspecific variation is necessary for evolutionary change and population resilience, but the extent to which it contributes to either depends on the causes of this variation. Understanding the causes of individual variation in traits involved with reproductive timing is important in the face of environmental change, especially in systems where reproduction must coincide with seasonal resource availability. However, separating the genetic and environmental causes of variation is not straightforward, and there has been limited consideration of how small-scale environmental effects might lead to similarity between individuals that occupy similar environments, potentially biasing estimates of genetic heritability. In ecological systems, environments are often complex in spatial structure, and it may therefore be important to account for similarities in the environments experienced by individuals within a population beyond considering spatial distances alone. Here, we construct multi-matrix quantitative genetic animal models using over 11,000 breeding records (spanning 35 generations) of individually-marked great tits (Parus major) and information about breeding proximity and habitat characteristics to quantify the drivers of variability in two key seasonal reproductive timing traits. We show that the environment experienced by related individuals explains around a fifth of the variation seen in reproductive timing, and accounting for this leads to decreased estimates of heritability. Our results thus demonstrate that environmental sharing between relatives can strongly affect estimates of heritability and therefore alter our expectations of the evolutionary response to selection.

Is the age pension in Australia sustainable and fair? Evidence from forecasting the old-age dependency ratio using the Hamilton-Perry model

The age pension aims to assist eligible elderly Australians who meet specific age and residency criteria in maintaining basic living standards. In designing efficient pension systems, government policymakers seek to satisfy the expectations of the overall aging population in Australia. However, the population’s unique demographic characteristics at the state and territory level are often overlooked due to the lack of available data. We use the Hamilton-Perry model, which requires minimum input, to model and forecast the evolution of age-specific populations at the state and territory level. We also integrate the obtained sub-national demographic information to determine sustainable pension ages up to 2051. We also investigate pension welfare distribution in all states and territories to identify the disadvantaged residents under the current pension system. Using the sub-national mortality data for Australia from 1971 to 2021 obtained from AHMD (2023), we implement the Hamilton-Perry model with the help of functional time series forecasting techniques. With the forecasts of age-specific population sizes for each state and territory, we compute the old age dependency ratio to determine the nationwide sustainable pension age.

A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Feature selection is a crucial data preprocessing technique that effectively reduces the dataset size and enhances the performance of machine learning models. Evolutionary computation (EC) based feature selection has become one of the most important parts of feature selection methods. However, the performance of existing EC methods significantly decrease when dealing with datasets with thousands of dimensions. To address this issue, this paper proposes a novel method called importance-guided particle swarm optimization based on MLP (IGPSO) for feature selection. IGPSO utilizes a two stage trained neural network to learn a feature importance vector, which is then used as a guiding factor for population initialization and evolution. In the two stage of learning, the positive samples are used to learn the importance of useful features while the negative samples are used to identify the invalid features. Then the importance vector is generated combining the two category information. Finally, it is used to replace the acceleration factors and inertia weight in original binary PSO, which makes the individual acceleration factor and social acceleration factor are positively correlated with the importance values, while the inertia weight is negatively correlated with the importance value. Further more, IGPSO uses the flip probability to update the individuals. Experimental results on 24 datasets demonstrate that compared to other state-of-the-art algorithms, IGPSO can significantly reduce the number of features while maintaining satisfactory classification accuracy, thus achieving high-quality feature selection effects. In particular, compared with other state-of-the-art algorithms, there is an average reduction of 0.1 in the fitness value and an average increase of 6.7% in classification accuracy on large-scale datasets.

The miR-31b targets arylsulfatase B to regulate the ovarian development of Bactrocera dorsalis.

Reproduction is the basis of insect population growth and evolution, and encompasses ovarian development, reproductive behavior, and fecundity. Bactrocera dorsalis is a globally significant agricultural pest that is subject to quarantine, with mated females that can lay over 3000 eggs. The post-transcriptional regulation of ovarian development remains unclear. Here, miR-31b is shown to be involved in regulating Bactrocera dorsalis ovarian development. CRISPR/Cas9 was used to generate miR-31b loss-of-function mutations in Bactrocera dorsalis. The removal of miR-31b resulted in severely impaired ovarian development in adults, with phenotypes that included dramatically reduced egg production and hatching rates. The relationship between miR-31b and its target gene arylsulfatase B (ARSB) was subsequently identified using the methods of bioinformatics, transcriptomic sequencing, quantitative polymerase chain reaction (qPCR), RNA pull-down and dual-luciferase reporter assay. Finally, miR-31b was confirmed to bind the target gene arylsulfatase B to affect metabolism and thereby further hindered ovarian development of Bactrocera dorsalis. Overall, these results provide new insights into molecular mechanisms at the post-transcriptional level in regulating ovarian development and insect reproduction, consequently providing potential targets to control arthropod pests through the reproductive strategy. © 2024 Society of Chemical Industry.

Adapting to Individual Differences: An Experimental Study of Language Evolution in Heterogeneous Populations.

Variations in language abilities, use, and production style are ubiquitous within any given population. While research on language evolution has traditionally overlooked the potential importance of such individual differences, these can have an important impact on the trajectory of language evolution and ongoing change. To address this gap, we use a group communication game for studying this mechanism in the lab, in which micro-societies of interacting participants develop and use artificial languages to successfully communicate with each other. Importantly, one participant in the group is assigned a keyboard with a limited inventory of letters (simulating a speech impairment that individuals may encounter in real life), forcing them to communicate differently than the rest. We test how languages evolve in such heterogeneous groups and whether they adapt to accommodate the unique characteristics of individuals with language idiosyncrasies. Our results suggest that language evolves differently in groups where some individuals have distinct language abilities, eliciting more innovative elements at the cost of reduced communicative success and convergence. Furthermore, we observed strong partner-specific accommodation to the minority individual, which carried over to the group level. Importantly, the degree of group-wide adaptation was not uniform and depended on participants' attachment to established language forms. Our findings provide compelling evidence that individual differences can permeate and accumulate within a linguistic community, ultimately driving changes in languages over time. They also underscore the importance of integrating individual differences into future research on language evolution.

A Systematic Analysis of Star Cluster Disruption by Tidal Shocks. II. Predicting Star Cluster Dissolution Rates from a Time-series Analysis of Their Tidal Histories

Most of the dynamical mass loss from star clusters is thought to be caused by the time variability of the tidal field (“tidal shocks”). Systematic studies of tidal shocks have been hampered by the fact that each tidal history is unique, implying both a reproducibility and a generalization problem. Here we address these issues by investigating how star cluster evolution depends on the statistical properties of its tidal history. We run a large suite of direct N-body simulations of clusters with tidal histories generated from power spectra of a given slope and with different normalizations, which determine the timescales and amplitudes of the shocks, respectively. At fixed normalization (i.e., the same median tidal field strength), the dissolution timescale is nearly independent of the power spectrum slope. However, the dispersion in dissolution timescales, obtained by repeating simulations for different realizations of statistically identical tidal histories, increases with the power spectrum slope. This result means that clusters experiencing high-frequency shocks have more similar mass-loss histories than clusters experiencing low-frequency shocks. The density–mass relationship of the simulated clusters follows a power law with slope between 1.08 and 1.45, except for the lowest normalizations (for which clusters effectively evolve in a static tidal field). Our findings suggest that star cluster evolution can be described statistically from a time-series analysis of its tidal history, which is an important simplification for describing the evolution of the star cluster population during galaxy formation and evolution.

Population Genetic Dissection of HLA-DPB1 Amino Acid Polymorphism to Infer Selection

Although allele frequency data for most HLA loci provide strong evidence for balancing selection at the allele level, the DPB1 locus is a notable exception, with allele frequencies compatible with neutral evolution (genetic drift) or directional selection in most populations. This discrepancy is especially interesting as evidence for balancing selection has been seen at the nucleotide and amino acid (AA) sequence levels for DPB1. We describe methods used to examine the global distribution of DPB1 alleles and their constituent AA sequences. These methods allow investigation of the influence of natural selection in shaping DPβ diversity in a hierarchical fashion for DPB1 alleles, all polymorphic DPB1 exon 2-encoded AA positions, as well as all pairs and trios of these AA positions. In addition, we describe how asymmetric linkage disequilibrium for all DPB1 exon 2-encoded AA pairs can be used to complement other methods. Application of these methods provides strong evidence for the operation of balancing selection on AA positions 56, 85–87, 36, 55 and 84 (listed in decreasing order of the strength of selection), but no evidence for balancing selection on DPB1 alleles.

Femtosecond laser-induced ultrafast electron dynamics and band gap renormalization in InSb

Studying ultrafast dynamical processes is essential for understanding light-matter coupling, which is crucial for the optoelectronic and photonic applications of semiconductor materials. Herein, we investigate the evolution of electron population near the conduction band minimum (CBM) and band gap in InSb (100) crystal under different laser fluences using time- and angle-resolved photoelectron spectra (TrARPES). In contrast to the pump-fluence-independent fast growth process (FP) of the electron population, the slow process (SP) is fluence sensitive, which is similar to the temperature-dependent behavior observed in InSb (110). In addition, we observe a band gap enlargement with increasing the photoexcitation flux. Comprehensive analysis reveals that these observed diverse results are attributed to the laser-tunable dielectric function on the surface of InSb. Our findings not only enhance the understanding of laser-semiconductor interactions but also broaden the potential applications in the field of optoelectronic and photonic devices.

Spatiotemporal Evolution and Characteristics of Population and Ecosystem Service Value at the Township Level on the Tibetan Plateau

Spatiotemporal Evolution and Characteristics of Population and Ecosystem Service Value at the Township Level on the Tibetan Plateau

Cardiovascular Disease Pathogenicity Predictor (CVD-PP): A Tissue-Specific In Silico Tool for Discriminating Pathogenicity of Variants of Unknown Significance in Cardiovascular Disease Genes.

Interpretation of variants of uncertain significance (VUSs) remains a challenge in the care of patients with inherited cardiovascular diseases (CVDs); 56% of variants within CVD risk genes are VUS, and machine learning algorithms trained upon large data resources can stratify VUS into higher versus lower probability of contributing to a CVD phenotype. We used ClinVar pathogenic/likely pathogenic and benign/likely benign variants from 47 CVD genes to build a predictive model of variant pathogenicity utilizing measures of evolutionary constraint, deleteriousness, splicogenicity, local pathogenicity, cardiac-specific expression, and population allele frequency. Performance was validated using variants for which the ClinVar pathogenicity assignment changed. Functional validation was assessed using prior studies in >900 identified VUS. The model utility was demonstrated using the Catheterization Genetics cohort. We identified a top-ranked model that accurately prioritized variants for which ClinVar clinical significance had changed (n=663; precision-recall area under the curve, 0.97) and performed well compared with conventional in silico methods. This model (CVD pathogenicity predictor) also had high accuracy in prioritizing VUS with functional effects in vivo (precision-recall area under the curve, 0.58). In Catheterization Genetics, there was a greater burden of higher CVD pathogenicity predictor scored VUS in individuals with dilated cardiomyopathy compared with controls (P=8.2×10-15). Of individuals in Catheterization Genetics who harbored highly ranked CVD pathogenicity predictor VUS meeting clinical pathogenicity criteria, 27.6% had clinical evidence of disease. Variant prioritization using this model increased genetic diagnosis in Catheterization Genetics participants with a known clinical diagnosis of hypertrophic cardiomyopathy (7.8%-27.2%). We present a cardiac-specific model for prioritizing variants underlying CVD syndromes with high performance in discriminating the pathogenicity of VUS in CVD genes. Variant review and phenotyping of individuals carrying VUS of pathogenic interest support the clinical utility of this model. This model could also have utility in filtering variants as part of large-scale genomic sequencing studies.

Confirmation of glyphosate-resistant Palmer amaranth (Amaranthus palmeri) populations in New York and responses to alternative chemistries

Abstract Palmer amaranth (Amaranthus palmeri S. Watson, AMAPA) is one of the most troublesome weeds in North America due to its rapid growth rate, substantial seed production, competitiveness and the evolution of herbicide-resistant populations. Though frequently encountered in the South, Midwest, and Mid-Atlantic regions of the United States, A. palmeri was recently identified in soybean [Glycine max (L.) Merr.] fields in Genesee, Orange, and Steuben counties, NY, where glyphosate was the primary herbicide for in-crop weed control. This research, conducted in 2023, aimed to (1) describe the dose response of three putative resistant NY A. palmeri populations to glyphosate, (2) determine their mechanisms of resistance, and (3) assess their sensitivity to other postemergence herbicides commonly used in NY crop production systems. Based on the effective dose necessary to reduce aboveground biomass by 50% (ED50), the NY populations were 42 to 67 times more resistant to glyphosate compared with a glyphosate-susceptible population. Additionally, the NY populations had elevated EPSPS gene copy numbers ranging from 25 to 135 located within extrachromosomal circular DNA (eccDNA). Label rate applications of Weed Science Society of America (WSSA) Group 2 herbicides killed up to 42% of the NY populations of A. palmeri. Some variability was observed among populations in response to WSSA Group 5 and 27 herbicides. All populations were effectively controlled by labeled rates of herbicides belonging to WSSA Groups 4, 10, 14, and 22. Additional research is warranted to confirm whether NY populations have evolved multiple resistance to herbicides within other WSSA groups and to develop effective A. palmeri management strategies suitable for NY crop production.

Dynamics of a Discrete Population Model

In order to explore the influences of the strong Allee effect on population evolution in two-patch environments, this paper constructs a population model with discrete diffusion and analyzes its dynamics. Using the center manifold theorem, we discuss the codimension-one bifurcation, such as flip bifurcation, of the model. The qualitative structures and stability of the model at the degenerate fixed point are obtained from the approximation of flow. Based on the theoretical analysis results, we investigate the influences of the strong Allee effect and diffusion on population evolution. The qualitative structures of degenerate fixed point and numerical simulation show that the strong Allee effect will make the population tend to a stable size when the two initial population patch sizes are equal and exceed a threshold. But the stable size of population does not exceed the carrying capacity, depending on the constant Allee effect and carrying capacity, which differ from the conclusion of logistic model that population size tends to the carrying capacity. However, if the two initial population patches are equal in size and below this threshold, or if the sizes of the initial population patches are unequal, the population size will decrease. The fixed point becomes degenerate with respect to the strong Allee effect causing the population tend to a stable size below carrying capacity, which exacerbates the complexity of population evolution.

Genetic analysis of preaxial polydactyly: identification of novel variants and the role of ZRS duplications in a Chinese cohort of 102 cases.

Preaxial polydactyly (PPD) is a congenital limb malformation, previously reported to be caused primarily by variants in the ZRS and upstream preZRS regions. This study investigated genetic variations associated with PPD, focusing on point variants and copy number variations (CNVs) in the ZRS and preZRS regions. Comprehensive genetic analyses were conducted on 102 patients with PPD, including detailed clinical examinations and Sanger sequencing of the ZRS and preZRS regions. Additionally, real-time quantitative PCR (qPCR) was used to detect CNVs in the ZRS region. The evolutionary conservation and population frequencies of identified variants were also evaluated. Six point variants were identified, among which four are likely pathogenic novel variants: 93G > T (g.156584477G > T), 106G > A (g.156584464G > A), 278G > A (g.156584292G > A), and 409A > C (g.156585378A > C). Additionally, qPCR analysis revealed that 66.67% of patients exhibited ZRS duplications. Notably, these duplications were also present in cases with newly identified potential pathogenic point variants. These findings suggest the possible interaction of point variants in ZRS and preZRS through a common pathogenic mechanism, leading jointly to PPD. The findings expand the variant spectrum associated with non-syndromic polydactyly and highlight that, despite different classifications, anterior polydactyly caused by variants in ZRS and nearby regions may share common pathogenic mechanisms. The incorporation of various variant types in genetic screening can effectively enhance the rate of pathogenic variant detection and contribute to the cost-effectiveness of genetic testing for limb developmental defects, thereby promoting healthy births.

Counting stars from the integrated spectra of galaxies

Over the last few decades, evolutionary population synthesis models have powered an unmatched leap forward in our understanding of galaxies. From dating the age of the first galaxies in the Universe to providing detailed measurements of the chemical composition of nearby galaxies, the success of this approach built upon simple stellar population (SSP) spectro-photometric models is unquestionable. However, the internal constraints inherent to the construction of SSP models can hinder our ability to analyze the integrated spectra of galaxies in situations where the SSP assumption does not sufficiently hold. Thus, here we revisit the possibilities of fitting galaxy spectra as a linear combination of stellar templates without assuming any a priori knowledge on stellar evolution. We showcase the sensitivity of this alternative approach to changes in the stellar population properties, in particular the direct connection to variations in the stellar initial mass function, as well as its advantages when dealing with noncanonical integrated populations and semi-resolved observations. Furthermore, our analysis demonstrates that the absorption spectra of galaxies can be used to independently constrain stellar evolution theory beyond the limited conditions of the solar neighborhood.

Stability of Certain Non-Autonomous Cooperative Systems of Difference Equations with the Application to Evolutionary Dynamics

This paper investigates the dynamics of non-autonomous cooperative systems of difference equations with asymptotically constant coefficients. We are mainly interested in global attractivity results for such systems and the application of such results to evolutionary population cooperation models. We use two methods to extend the global attractivity results for autonomous cooperative systems to related non-autonomous cooperative systems which appear in recent problems in evolutionary dynamics.