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BackgroundStructural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence.ResultsWe present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies.ConclusionsThis SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis -eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2 , VPS37B , DENR , FTCDNL1 , and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2 , MTRFR , and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ. Examining brain eQTLs across African American, European, and East Asian populations reveals significant ancestry-specific genetic variants linked to schizophrenia. The study highlights the importance of genetic diversity in discovering risk genes and improving disease understanding, suggesting that broader ancestral representation enhances the power of genetic analyses. [ABSTRACT FROM AUTHOR]

Over the past decade, genome-wide association studies have identified thousands of variants significantly associated with complex traits. For each locus, gene expression levels are needed to further explore its biological functions. To address this, the PrediXcan algorithm leverages large-scale reference data to impute the gene expression level from single nucleotide polymorphisms, and thus the gene-trait associations can be tested to identify the candidate causal genes. However, a challenge arises due to the fact that most reference data are from subjects of European ancestry, and the accuracy and robustness of predicted gene expression in subjects of East Asian (EAS) ancestry remains unclear. Here, we first simulated a variety of scenarios to explore the impact of the level of population diversity on gene expression. Population differentiated variants were estimated by using the allele frequency information from The Genome Aggregation Database. We found that the weights of a variants was the main factor that affected the gene expression predictions, and that ~70% of variants were significantly population differentiated based on proportion tests. To provide insights into this population effect on gene expression levels, we utilized the allele frequency information to develop a gene expression reference panel, Predict Asian-Population (PredictAP), for EAS ancestry. PredictAP can be viewed as an auxiliary tool for PrediXcan when using genotype data from EAS subjects. [ABSTRACT FROM AUTHOR]

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems. [ABSTRACT FROM AUTHOR]

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Hemp (Cannabis sativa L.) is an annual, and dioecious herb belonging to the Cannabaceae family. This plant is native to Central and Southeast Asia. The wild races of this species are commonly growing in Khyber Pakhtunkhwa and Punjab provinces, as well as in Islamabad, Pakistan. This study provides crucial insights into how environmental variables influence the wild hemp populations, which can be utilized in for conservation and breeding. The present study was aimed at evaluating the effects of key environmental factors such as altitude, geographical location, precipitation, relative humidity, maximum, minimum, and average temperature on 16 morpho-agronomic traits of a wild population of hemp growing in the Potohar Plateau and Lesser Himalayas. Our findings indicated that high relative humidity (> 64%), low average temperature (< 15 °C), intermediate average temperature (19–22 °C), and high average temperature (> 22 °C) played significant roles in determining the distribution pattern of the wild hemp. Correlation analysis demonstrated that average annual temperature contributed a higher percentage of variation in phenotypic diversity than geographic variables. Additionally, cluster analysis indicated three groups for the selected 35 populations. Clustering and Principal Component Analysis (PCA) of the morpho-agronomic traits indicated that group 1 from the Lesser Himalayas showed high relative humidity (> 64%) and low average temperature (< 15 °C). Conversely, Group 2 populations from the Potohar Plateau demonstrated intermediate average temperature (19–22 °C). There is an existence of Group 3 in the Potohar Plateau with a high average temperature (> 22 °C) compared to Group 1 and Group 2. Our examination highlights the complex interplay between ecological factors, and morphological attributes in native landraces of Cannabis sativa, giving significant insight into knowledge for preservation and breeding initiatives. A study of genetic diversity could complement morpho-agronomic traits in future research to learn more about how genetic variation affects environmental adaptation. [ABSTRACT FROM AUTHOR]

Species invading non‐native habitats can cause irreversible environmental damage and economic harm. Yet, how introduced species become widespread invaders remains poorly understood. Adaptation within native‐range habitats and rapid adaptation to new environments may both influence invasion success. Here, we examine these hypotheses using 7058 SNPs from 36 native, 40 introduced and 19 farmed populations of tench, a fish native to Eurasia. We examined genetic structure among these populations and accounted for long‐term evolutionary history within the native range to assess whether introduced populations exhibited lower genetic diversity than native populations. Subsequent to infer genotype–environment correlations within native‐range habitats, we assessed whether adaptation to native environments may have shaped the success of some introduced populations. At the broad scale, two glacial refugia contributed to the ancestry and genomic diversity of tench. However, native, introduced and farmed populations of admixed origin exhibited up to 10‐fold more genetic diversity (i.e., observed heterozygosity, expected heterozygosity and allelic richness) compared to populations with predominantly single‐source ancestry. The effects of introduction to a new location were also apparent as introduced populations exhibited fewer private alleles (mean = 9.9 and 18.9 private alleles in introduced and native populations, respectively) and higher population‐specific Fst compared to native populations, highlighting their distinctiveness relative to the pool of allelic frequencies across tench populations. Finally, introduced populations with varying levels of genetic variation and similar genetic compositions have become established and persisted under strikingly different climatic and ecological conditions. Our results suggest that lack of prior adaptation and low genetic variation may not consistently hinder the success of introduced populations for species with a demonstrated ability to expand their native range. [ABSTRACT FROM AUTHOR]

The armyworm, Spodoptera litura Fabricius, 1775 (Noctuidae: Lepidoptera) is a serious and emerging insect pest of sugar beet in India, resulting in significant yield losses. A phylogenetic tree was constructed using the CLUSTAL W and neighbour joining technique, and a neighbour joining haplotype network was formed using PopArt to assess the relationships between S. litura haplotypes. The evolutionary divergence of different strains of Indian-origin S. litura was calculated using the p-distance method in MEGA 11. Neutrality indices, including Tajima's D, Fu, and Li's F, was calculated to test the hypothesis of selective neutrality using DnaSPv6. Larval identification relied on the morphological characteristics, while the molecular characterization utilized the mitochondrial cytochrome oxidase I gene with universal primers (LCO1490 and HCO2198). A DNA fragment of approximately 700 bp from mitochondrial COI revealed two different strains (OP420870 and OP117231) infesting sugar beet crops under Indian subtropical conditions. The amplified barcode sequences exhibited variations in both strains, with genetic divergence ranging from 0.0 to 0.79. The strains OP420870 and OP117231 displayed maximum divergence at 0.74 and 0.73, respectively. Interpopulation nucleotide differences (Kxy) and the average number of nucleotide substitutions per site between populations (Dxy) in different states of India were calculated at 336.42 and 0.61, respectively. The pairwise Fst value was 0.63, with an Nm value of 0.15. One of the identified strains of S. litura in this study was also found to be a haplotype. This study provides valuable insights into the genetic characterization of S. litura infesting sugar beet crops in Indian subtropical conditions, contributing to the understanding of its population structure and diversity. The findings enhance our knowledge of S. litura infestations and can aid in the development of effective strategies for pest management and crop protection in sugar beet. [ABSTRACT FROM AUTHOR]

This research focuses on improving the Harris’ Hawks Optimization algorithm (HHO) by tackling several of its shortcomings, including insufficient population diversity, an imbalance in exploration vs. exploitation, and a lack of thorough exploitation depth. To tackle these shortcomings, it proposes enhancements from three distinct perspectives: an initialization technique for populations grounded in opposition-based learning, a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration, and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators. The effectiveness of the Improved Harris Hawks Optimization algorithm (IHHO) is assessed by comparing it to five leading algorithms across 23 benchmark test functions. Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities. Additionally, this paper introduces a feature selection method leveraging the IHHO algorithm (IHHO-FS) to address challenges such as low efficiency in feature selection and high computational costs (time to find the optimal feature combination and model response time) associated with high-dimensional datasets. Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets. The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality, while also enhancing the efficiency of feature selection. Furthermore, IHHO-FS shows strong competitiveness relative to numerous algorithms. [ABSTRACT FROM AUTHOR]

Abstract Hemp (Cannabis sativa L.) is an annual, and dioecious herb belonging to the Cannabaceae family. This plant is native to Central and Southeast Asia. The wild races of this species are commonly growing in Khyber Pakhtunkhwa and Punjab provinces, as well as in Islamabad, Pakistan. This study provides crucial insights into how environmental variables influence the wild hemp populations, which can be utilized in for conservation and breeding. The present study was aimed at evaluating the effects of key environmental factors such as altitude, geographical location, precipitation, relative humidity, maximum, minimum, and average temperature on 16 morpho-agronomic traits of a wild population of hemp growing in the Potohar Plateau and Lesser Himalayas. Our findings indicated that high relative humidity (> 64%), low average temperature ( 22 °C) played significant roles in determining the distribution pattern of the wild hemp. Correlation analysis demonstrated that average annual temperature contributed a higher percentage of variation in phenotypic diversity than geographic variables. Additionally, cluster analysis indicated three groups for the selected 35 populations. Clustering and Principal Component Analysis (PCA) of the morpho-agronomic traits indicated that group 1 from the Lesser Himalayas showed high relative humidity (> 64%) and low average temperature ( 22 °C) compared to Group 1 and Group 2. Our examination highlights the complex interplay between ecological factors, and morphological attributes in native landraces of Cannabis sativa, giving significant insight into knowledge for preservation and breeding initiatives. A study of genetic diversity could complement morpho-agronomic traits in future research to learn more about how genetic variation affects environmental adaptation.

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency. [ABSTRACT FROM AUTHOR]

ABSTRACT Species invading non‐native habitats can cause irreversible environmental damage and economic harm. Yet, how introduced species become widespread invaders remains poorly understood. Adaptation within native‐range habitats and rapid adaptation to new environments may both influence invasion success. Here, we examine these hypotheses using 7058 SNPs from 36 native, 40 introduced and 19 farmed populations of tench, a fish native to Eurasia. We examined genetic structure among these populations and accounted for long‐term evolutionary history within the native range to assess whether introduced populations exhibited lower genetic diversity than native populations. Subsequent to infer genotype–environment correlations within native‐range habitats, we assessed whether adaptation to native environments may have shaped the success of some introduced populations. At the broad scale, two glacial refugia contributed to the ancestry and genomic diversity of tench. However, native, introduced and farmed populations of admixed origin exhibited up to 10‐fold more genetic diversity (i.e., observed heterozygosity, expected heterozygosity and allelic richness) compared to populations with predominantly single‐source ancestry. The effects of introduction to a new location were also apparent as introduced populations exhibited fewer private alleles (mean = 9.9 and 18.9 private alleles in introduced and native populations, respectively) and higher population‐specific Fst compared to native populations, highlighting their distinctiveness relative to the pool of allelic frequencies across tench populations. Finally, introduced populations with varying levels of genetic variation and similar genetic compositions have become established and persisted under strikingly different climatic and ecological conditions. Our results suggest that lack of prior adaptation and low genetic variation may not consistently hinder the success of introduced populations for species with a demonstrated ability to expand their native range.

Abstract Multimodal transport is a challenging, NP-hard problem in combinational optimization and has been solved using evolutionary algorithms, which excel at solving large-scale problems. However, few studies have used evolutionary algorithms, particularly swarm intelligence algorithms, to concurrently handle multiple multimodal transport instances. Ant colony optimization (ACO), which is a population intelligence technique that is adept at identifying the optimal paths in graphs, has been primarily used to address tasks separately rather than concurrently. Therefore, in this study, we introduce a multipopulation-based multitask environment where task-specific populations run in parallel, and ACO serves as the optimizer for each task. A variance-based population diversity measure is then calculated to characterize the distribution differences among individuals. If the population diversity of a specific task falls below a predetermined threshold, the valuable routing traits extracted from other tasks are transferred to the stagnant population. Our method is called population diversity-controlled multitask ACO (PDMTACO). We use multiple benchmark traveling salesman problem (TSP) instances at different scales to validate the efficacy of PDMTACO. Subsequently, we extend PDMTACO to address a series of multimodal transport problems. Our experimental results demonstrate that the use of information transferred by our method significantly reduces its logistics costs and carbon emissions in all multimodal transport tasks.

Abstract Background Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence. Results We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV’s impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies. Conclusions This SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.

In adversarial game environments, the objective agent aims to generate robust game policies, ensuring high returns when facing different opponent policies consistently. Existing self-play-based policy generation methods often overfit to learning against a specific opponent policy, resulting in low robustness and vulnerability to attacks from other opponent policies. Additionally, existing methods that combine deep reinforcement learning and game theory to iteratively generate opponent policies have low convergence efficiency in complex adversarial scenarios with large decision spaces. To address these challenges, a population diversity-based robust policy generation method is proposed. In this method, both adversaries maintain a policy population pool, ensuring diversity within the population to generate a robust target policy. To ensure population diversity, policy diversity is measured from two perspectives: behavioral and quality diversity. Behavioral diversity refers to the differences in state-action trajectories of different policies, while quality diversity refers to the differences in the returns obtained when facing the same opponent. Finally, the robustness of the policies generated based on population diversity is validated in typical adversarial environments with continuous state-action spaces. [ABSTRACT FROM AUTHOR]

In swarm algorithms, the need to measure population diversity arises in various contexts, such as in the adaptation of algorithm parameters, preventing the premature convergence of the algorithm and stopping and restarting it. Measures of population diversity allow the phases of the algorithm, namely, diversification and intensification, to be controlled. The article experimentally investigated six measures of population diversity of the optimization of the swallow swarm algorithm when solving the problem of optimizing the parameters of the membership functions of fuzzy classifiers. The resulting classifiers were tested on publicly available data sets drawn from the KEEL repository. [ABSTRACT FROM AUTHOR]

Bighead carp （Hypophthalmichthys nobilis） is a common freshwater fish species in China. In order to reveal the genetic status of H. nobilis in the middle and lower reaches of the Yangtze River （YR）, 219 individuals were collected from five locations respectively in Shishou （SS）, Changsha （CS）, Ruichang （RC）, Yangzhou （YZ） and Zhangjiagang （ZJG）. The D-loop sequences were tested and analyzed in comparison with those of H. nobilis from the Pearl River （PR, 213 individuals） and Northern America （NA, 33 individuals） downloaded from the NCBI. As a result, 35, 96, and 11 variable sites were observed and 37, 62, and 3 haplotypes were defined in YR, PR, and NA, respectively. Among the YR H. nobilis, high haplotype diversity （Hd, 0. 798-0. 897） and moderate nucleotide diversity （π, 0. 003 38-0. 006 53） were detected, except the pairwise pairs of RC vs. ZJG （FST = 0. 012, P>0. 05） and CS vs. YZ （FST = 0. 018, P> 0. 05）, significant genetic differentiations （FST ranged from 0. 031 to 0. 125, P<0. 05） were found among all the other pairwise stock pairs, and gene flows （Nm ranged from 3. 509 to 39. 993） were generally detected within the five stocks. According to the haplotype network analysis, the D-loop haplotypes contained multiple clades which were obviously shared among different stocks in YR. Meanwhile, the central haplotypes of YR and PR populations were generally mutual-shared, whereas the haplotypes specific to PR were peripherally distributed. The haplotypes sharing situation further evidenced the possible multiple introductions of NA. Based on the neutrality test, it was predicted that only PR went through population expansion in history. Overall, the study revealed an abundance of genetic variations in H. nobilis of YR and PR, laying a solid foundation for the further protection and utilization of the relevant genetic resources. [ABSTRACT FROM AUTHOR]

Phytophthora citrophthora and P. syringae are currently the primary causal organisms of brown rot of citrus fruits in California. To possibly find an explanation for the prevalence of the previously minor species P. syringae, we determined the population structures of both pathogens in California using next-generation sequencing and population genomics analyses. Whole-genome sequencing and aligning with newly assembled reference genomes identified 972,266 variants in 132 isolates of P. citrophthora and 422,208 variants in 154 isolates (including 24 from noncitrus tree crops) of P. syringae originating from three major growing regions. The resulting data sets were visualized using principal component analysis, discriminant analysis of principal components, unweighted pair-group method with arithmetic mean dendrograms, fastStructure, and minimum spanning networks, and we obtained the index of association, diversity summary statistics, and genetic distance statistics values GST, G′′ST, and Jost's D. Subpopulations of both species were mostly defined by their geographic origin indicating restricted dispersal of inoculum. Except for five isolates, the population structure of P. citrophthora (that is heterothallic and unlikely to reproduce sexually) was clonal to semi-clonal, with very little genetic diversity within and among subgroups. In contrast, the population structure of P. syringae was also clonal to semi-clonal, but isolates were placed into four main clusters of much higher diversity. Clonality in both species can be explained by a high level of asexual reproduction. The higher diversity in the homothallic P. syringae is likely due to commonly occurring sexual reproduction. One distinct cluster of P. syringae consisted solely of isolates from noncitrus hosts; therefore, the origin of P. syringae in citrus could not be resolved. [ABSTRACT FROM AUTHOR]

The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to "reward" or "punish" the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems. [ABSTRACT FROM AUTHOR]

Galactic Swarm Optimization (GSO) is an optimization method inspired by the movements of stars and star clusters in the galaxy. This method aims to find the best solution in two phases using other known optimization methods. The first phase explores the search space, while the second phase tries to refine the best solution. In GSO, the population of the best individuals obtained from the first phase is used as the initial population for the second phase. This process is repeated until the stopping criterion is met. Although the knowledge obtained from the first phase is transferred to the second phase in GSO, there is no knowledge transfer from the second phase to the first phase. In this study, we propose a modification where the knowledge obtained in the second phase is transferred back to the first phase. Additionally, the Particle Swarm Optimization (PSO) method, used as the search method in the original study, has a fast convergence problem, which hinders an effective discovery process in the first phase of GSO. To address this, the proposed diversity-guided modification regulates population diversity and enhances exploration. To demonstrate the performance of the proposed method, twenty-six traditional benchmark functions and three engineering design problems were used. The proposed method was compared with the original GSO and six current optimization methods. The results of the experimental study were analyzed using statistical tests. The experimental results and analyses show that the proposed method performs successfully.

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.

Predicting the persistence of species under climate change is an increasingly important objective in ecological research and management. However, biotic and abiotic heterogeneity can drive asynchrony in population responses at small spatial scales, complicating species-level assessments. For widely distributed species consisting of many fragmented populations, such as brook trout (Salvelinus fontinalis), understanding the drivers of asynchrony in population dynamics can improve the predictions of range-wide climate impacts. We analyzed the demographic time series from mark-recapture surveys of 11 natural brook trout populations in eastern Canada over 13 years to examine the extent, drivers, and consequences of fine-scale population variation. The focal populations were genetically differentiated, occupied a small area (~25 km2) with few human impacts, and experienced similar climate conditions. Recruitment was highly asynchronous, weakly related to climate variables and showed population-specific relationships with other demographic processes, generating diverse population dynamics. In contrast, individual growth was mostly synchronized among populations and driven by a shared positive relationship with stream temperature. Outputs from population-specific models were unrelated to four of the five hypothesized drivers (recruitment, growth, reproductive success, phylogenetic distance), but variation in groundwater inputs strongly influenced stream temperature regimes and stock-recruitment relationships. Finally, population asynchrony generated a portfolio effect that stabilized regional species abundance. Our results demonstrated that population demographics and habitat diversity at microgeographic scales can play a significant role in moderating species responses to climate change. Moreover, we suggest that the absence of human activities within study streams preserved natural habitat variation and contributed to asynchrony in brook trout abundance, while the small study area eased monitoring and increased the likelihood of detecting asynchrony. Therefore, anthropogenic habitat degradation, landscape context, and spatial scale must be considered when developing management strategies to monitor and maintain populations that are diverse, stable, and resilient to climate change. [ABSTRACT FROM AUTHOR]

This is the first report of the distribution of TPMT and NUDT15 single nucleotide polymorphisms and metabolic phenotypes associated with cytotoxicity of thiopurine drugs, in indigenous groups of Brazilian Amazon: Munduruku, Paiter-Suruí and Yanomami. The minor allele frequency (MAF) of NUDT15 rs116855232 did not differ significantly across the groups; TPMT rs1800462 was absent, while rs1800460 and rs1142345 were in strong linkage disequilibrium, and 10- and 30-fold more common in Paiter-Suruí. Indeed, the MAFs in Paiter-Surui (0.193 and 0.188) are the largest report globally. The distribution of combined NUDT15/TPMT metabolic phenotypes differed significantly (p < 0.0001) and largely (Cramér's V = 0.37) across cohorts. This has important pharmacogenetic implications: the Clinical Pharmacogenetics Implementation Consortium recommendations to reduce or consider reduction of thiopurine dose applies to 4.4% Yanomami, 5.6% Munduruku, versus 41% Paiter-Suruí. The proportion of Paiter-Suruí at risk of thiopurine intolerance is 3- to 4-fold higher than any other population worldwide. [ABSTRACT FROM AUTHOR]

The cashew tree Anacardium occidentale is a globally important food species and in Brazil is a natural component of cerrado and restinga vegetation and has an important role in local economies as a food and medicinal plant. Nevertheless, diversity studies of wild populations—the most important reservoir of natural variation—of this naturally widespread species are urgently needed for conservation strategies because of human impact on tropical American ecosystems where it occurs. Here, we sought to differentiate the previously recognized restinga and cerrado ecotypes using multivariate morphometrics of wild populations in a limited area of northeast Brazil (northern Piauí and northeastern Maranhão states). Thirty individuals were sampled for 47 vegetative and reproductive descriptors from six wild populations (three of each ecotype). Data analysis included principal component analysis and three classification methods (linear discriminant analysis, k-nearest neighbour analysis and classification and regression tree analysis); cross-validation tests were applied to classification results. Univariate statistics obtained for each population and ecotype were found to be consistent with the previously proposed distinction between the two ecotypes. Although principal component analysis showed extensive variation and overlap between populations, cross-validations of classification analyses showed strong differentiation between ecotypes and between most populations; vegetative and reproductive variables were equally important for discrimination. Cerrado populations were more variable and somewhat less distinct than restinga ones. New molecular genetic investigations of wild populations are needed to provide genetic support for the morphological differentiation of the ecotypes reported here. [ABSTRACT FROM AUTHOR]

Puccinia triticina, the causal agent of wheat leaf rust, is a dynamic pathogen causing significant yield losses worldwide. In this study, we analysed the virulence and genetic structure of 98 P. triticina isolates collected in Canada between 2018 and 2020. Isolates from Manitoba and Saskatchewan were found to be highly related for virulence, as were isolates from Quebec and Ontario. Isolates from Alberta had a virulence profile more similar to those from Manitoba and Saskatchewan than to Ontario and Quebec. To study the genetic structure of P. triticina populations, we used the restriction site‐associated DNA (RAD) genotyping‐by‐sequencing method to identify single‐nucleotide polymorphisms (SNPs). The Puccinia DNA sequences were aligned against the reference genome of P. triticina race BBBD and 1898 SNPs were identified. The phylogenetic analysis using SNP markers grouped these P. triticina into three genetic clades. The separation of P. triticina into three genetic clades was also supported by principal component analysis. Isolates from Clade 1 and Clade 2 were found throughout Canada, whereas Clade 3 isolates were only found in Ontario and Quebec. There were differences in virulence profiles among P. triticina isolates from the three genetic clades and a general correlation between virulence phenotypes and SNP genotypes was observed. These results indicate that the SNPs derived from RAD genotyping‐by‐sequencing could be useful in tracking the genetic and virulence dynamics of this pathogen in Canadian wheat. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus is the main etiological agent of bovine mastitis worldwide and knowledge about its diversity and virulence factors is vital in controlling infections caused by this pathogen. The present study aimed to perform molecular characterization of a population of S. aureus (n=153 strains isolated from 1994 to 2014 in seven Brazilian states) by pulsed-field gel electrophoresis (PFGE) and evaluate their virulence profiles via polymerase chain reaction (PCR). PFGE identified 93 pulsotypes, with the isolates organized into 26 clusters and 20 unique pulsotypes. Predominant pulsotypes were observed, with variations according to the years of isolation and geographic origin of the isolates. Based on the PCR results for the genes encoding agglutination factors (ClfA and ClfB), binding proteins (fibronectin binding protein - FnBPA, elastin binding protein - Ebps, collagen binding protein - Cna), and toxins (Hla, Hlb and Luk-ED), 40 virulence profiles were detected. The frequency of virulence genes ranged from 58 to 98% (clfA:84.3%; clfB and hlb both 81%; hla:71.2%; fnBA:82.3%; Can:94.7%; ebps:58%; and lukED:98%). The existence of prevalent genotypes in some of the Brazilian states and the time period studied suggests that these genotypes are better adapted, with favorable characteristics in host/pathogen relationships. Genes of proven importance for S. aureus pathogenesis in bovine mastitis were widely distributed in genetically divergent populations, suggesting that most of these genes may be interesting candidates in the development of vaccines to control bovine mastitis in Brazil.

Abstract The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to “reward” or “punish” the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems.

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.

Abstract Manta ray foraging optimization (MRFO) tends to get trapped in local optima as it relies on the direction provided by the previous individual and the best individual as guidance to search for the optimal solution. As enriching population diversity can effectively solve this problem, in this paper, we introduce a hierarchical structure and weighted fitness-distance balance selection to improve the population diversity of the algorithm. The hierarchical structure allows individuals in different groups of the population to search for optimal solutions in different places, expanding the diversity of solutions. In MRFO, greedy selection based solely on fitness can lead to local solutions. We innovatively incorporate a distance metric into the selection strategy to increase selection diversity and find better solutions. A hierarchical manta ray foraging optimization with weighted fitness-distance balance selection (HMRFO) is proposed. Experimental results on IEEE Congress on Evolutionary Computation 2017 (CEC2017) functions show the effectiveness of the proposed method compared to seven competitive algorithms, and the proposed method has little effect on the algorithm complexity of MRFO. The application of HMRFO to optimize real-world problems with large dimensions has also obtained good results, and the computational time is very short, making it a powerful alternative for very high-dimensional problems. Finally, the effectiveness of this method is further verified by analyzing the population diversity of HMRFO.

This is the first report of the distribution of TPMT and NUDT15 single nucleotide polymorphisms and metabolic phenotypes associated with cytotoxicity of thiopurine drugs, in indigenous groups of Brazilian Amazon: Munduruku, Paiter-Suruí and Yanomami. The minor allele frequency (MAF) of NUDT15 rs116855232 did not differ significantly across the groups; TPMT rs1800462 was absent, while rs1800460 and rs1142345 were in strong linkage disequilibrium, and 10- and 30-fold more common in Paiter-Suruí. Indeed, the MAFs in Paiter-Surui (0.193 and 0.188) are the largest report globally. The distribution of combined NUDT15/TPMT metabolic phenotypes differed significantly (p < 0.0001) and largely (Cramér´s V = 0.37) across cohorts. This has important pharmacogenetic implications: the Clinical Pharmacogenetics Implementation Consortium recommendations to reduce or consider reduction of thiopurine dose applies to 4.4% Yanomami, 5.6% Munduruku, versus 41% Paiter-Suruí. The proportion of Paiter-Suruí at risk of thiopurine intolerance is 3- to 4-fold higher than any other population worldwide.

The development of information technology is accelerating the development of network multimedia-assisted teaching modes. In this regard, this paper studies the influence of multimedia-assisted sports education on the teaching of tennis classes in colleges and universities. The first step is to analyze the applicable fields and characteristics of network multimedia CAI. Then, the TLBO algorithm focuses on analyzing the “teaching” and “learning” phases of tennis teaching and finds that the TLBO algorithm has defects. To overcome the local optimal situation of the TLBO algorithm, a grouping mechanism is implemented to optimize the TLBO algorithm and maintain the diversity of the population. Finally, the influence of multimedia-assisted teaching was studied in terms of learning motivation, physical fitness, and learning effect. From the perspective of liking tennis sport, the number of experimental classes is 20, which is 15 more than the number of control classes. The mean score of the experimental class in the forehand hitting spinning ball test was 19 points higher than that of the control class.

Explaining the decisions made by population‐based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that increasingly require end‐user input and confirmation, the need to explain the internal decisions being made has grown. In this article, we present our approach to the extraction of explanation supporting features using trajectory mining. This is achieved through the application of principal components analysis techniques to identify new methods of tracking population diversity changes post‐runtime. The algorithm search trajectories were generated by solving a set of benchmark problems with a genetic algorithm and a univariate estimation of distribution algorithm and retaining all visited candidate solutions which were then projected to a lower dimensional sub‐space. We also varied the selection pressure placed on high fitness solutions by altering the selection operators. Our results show that metrics derived from the projected sub‐space algorithm search trajectories are capable of capturing key learning steps and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients. A comparative study of variable importance rankings derived from a surrogate model built on the same dataset was also performed. The results show that both approaches are capable of identifying key features regarding variable interactions and their influence on fitness in a complimentary fashion. [ABSTRACT FROM AUTHOR]

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) stands out as a straightforward yet remarkably powerful evolutionary algorithm employed for real-world problem-solving purposes. In the DE algorithm, few parameters are used, and the population is evolved by applying various operations. It is difficult in evolutionary computation algorithms to maintain population diversity. The main issue is the sub-population of the DE algorithm that helps improve convergence speed and escape from the local optimum. Evolving sub-populations by maintaining diversity is an important issue in the literature that is considered in this research. A solution is proposed that uses sub-populations to promote greater diversity within the population and improve the algorithm performance. DE, heterogeneous distributed differential evolution (HDDE), multi-population ensemble differential evolution (MPEDE), and the proposed improved multi-population ensemble differential evolution (IMPEDE) are implemented using parameter settings; population sizes of 100 NP, 150 NP, and 200 NP; and dimensions of 10D, 30D, and 50D for performance comparison. Different combinations of mutations are used to generate the simulated results. The simulation results are generated using 1000, 3000, and 5000 iterations. Experimental outcomes show the superior results of the proposed IMPEDE over existing algorithms. The non-parametric significance Friedman test confirms that there is a significant difference in the performance of the proposed algorithm and other algorithms used in this study by considering a 0.05 level of significance using six benchmark functions. [ABSTRACT FROM AUTHOR]

Assembly shop scheduling and energy-efficient scheduling have attracted much attention in the past decades; however, energy consumption is often ignored in assembly hybrid flow shop scheduling. Neglecting energy consumption will greatly diminish the progress of sustainable manufacturing. In this study, an assembly hybrid flow shop scheduling problem considering energy consumption (EAHFSP) is investigated, and a population diversity-based artificial bee colony algorithm (DABC) is proposed to minimize the makespan and total energy consumption (TEC) simultaneously. Diversified search strategies based on rank value are introduced to the employed bee phase; a novel probability selection method in the onlooker bee phase is designed to control the selection pressure; moreover, a diversity control strategy is applied to improve the diversity of food sources and avoid falling into stagnation. A number of experiments based on 44 extended benchmark instances from the literature and a real case are conducted to test the performance of the DABC algorithm. The statistical results show that the DABC algorithm is superior to the other four state-of-the-art algorithms on over 70% of the instances corresponding to metrics I G D and c , which means that the DABC algorithm is effective and competitive in solving the considered EAHFSP. [ABSTRACT FROM AUTHOR]

In immigrant countries like America, Canada and Australia, heritage language retention and language shift (to the language of the receiving society) have long been associated with classical spatial theory, of initial segregation into inner city ethnic enclaves and subsequent intra-urban migration into majority 'white' or 'mainstream' residential suburbia, respectively. First through third generation spatial dynamics of shift and retention in Sydney are analysed for the ten largest post-1945 labour workforce immigrant streams from Europe and the ten post-1960s mainly skilled immigrant streams from the Middle East and Asia. Quartile and diversity analyses show that the association of intergenerational heritage language retention with spatial concentration and language shift with spatial dispersion has been superceded by a new set of spatial dynamics. Instead, patterns of retention and shift are responding to high levels of population diversity and minority majority suburbs where culturally hegemonic mainstream and minority cultural groups are intermixed across 80 per cent of Sydney's suburbs. [ABSTRACT FROM AUTHOR]

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an "exploitation" property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the "exploration" property is bad. Here, "exploitation" refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, "exploration" denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population. [ABSTRACT FROM AUTHOR]

To fully exploit the economic value of the Chinese endemic species Pteroceltis tatarinowii and provide new resources for forage production, the forage nutritional value of P. tatarinowii leaves from different populations was analyzed and evaluated. The results were as follows: 1) There were significant differences in the forage nutrient indices of leaves from different populations. The crude protein content was 10.77%-18.65%, with an average of 14.58%, and the SDJN population had the highest crude protein content. The average crude fat, crude fiber content was 7.62%; the average neutral detergent fiber content was 25.33%; and the average acid detergent fiber contents were 6.79%, 7.62%, 25.33%, and 17.52%, respectively. The average phosphorus and calcium content in the leaves was 0.785 g·kg-1 and 58.01 g·kg-1, respectively. The tannin content was much lower than the antifeedant standard, at an average of 4.97 g·kg-1. The average total amounts of hydrolyzed and free amino acids in the leaves were 108.20 mg·g-1 and 47.87 mg·g-1, respectively. Thus, P. tatarinowii leaves have high crude protein, crude fat, and calcium contents, and low fiber, tannin contents, and are protein-rich. These results provide evidence that this species can be developed into an excellent woody forage tree. 2) There were significant differences in the forage quality evaluation indices among the populations. The forage indices of NDP, ADP, DMI, DDM, and RFV of 21 populations all met the super standard of the American Grass and Grassland Association (AFGC) for hay, two crude protein indices met the grade 1 standard, and 12 crude protein indices met the grade 2 standard. Four high-protein and high-RFV forage populations (SDJN, SDZZ, SXLQ, and AHXX) were selected. 3) The results of the correlation analysis showed that there was no significant correlation between the forage characteristics of P. tatarinowii leaves and latitude and longitude, indicating no significant geographical variation. However, the forage characteristics were strongly correlated with elevation, average annual temperature, and annual precipitation. Thus, high elevation, low temperatures, and rainy weather can improve the forage value of the leaves. P. tatarinowii can be planted to provide leaf forage in cold and wet areas at a specific elevation. Moreover, the forage value of P. tatarinowii leaves can be further improved by increasing nitrogen fertilizer and reducing K and Ca fertilizers during cultivation. 4) Cluster analysis revealed obvious regionalism. Taking the Yangtze River Basin as the limit, cluster analysis divided the species into four population groups: the Yangtze River Basin and northern, southwestern, and eastern coastal populations. [ABSTRACT FROM AUTHOR]

Apple scab instigated by Venturia inaequalis impels remarkable losses to apple fruit production. In an effort to comprehend the key mechanisms of evolutionary potential defining V. inaequalis population, 132 isolates of V. inaequalis from five commercial apple orchards were collected and assayed using 14 microsatellite markers. The average diversity was observed within the individuals of populations based on the Shannon-Wieners index (I) and observed heterozygosity (Ho) was average but considerably lower than expected heterozygosity (He). The genetic differentiation based on FST values was revealed as an average measure of divergence between populations and had varying proportions of gene flow and migration among themselves. Analysis of Molecular Variance (AMOVA) revealed that variance (94%) was dispersed across individuals with a significant (6%) variation between populations from different regions. To examine host specialization within the V. inaequalis population, the assignment approach based on K-means of clustering (an unsupervised machine learning approach), revealed that the clustering method supported three clusters at (K = 3) and three major clusters were also observed in Principle Component Analysis (PCA). Additionally, Nei's genetic distance values, pairwise estimates of genetic differentiation, dendrogram using the neighbor-joining and PCoA revealed the random distribution of V. Inaequalis isolates that depicted a high proportion of genotypic diversity within populations and population genetic structure. [ABSTRACT FROM AUTHOR]