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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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

Today, evolutionary algorithms are widely used in a variety of fields for problem solving and optimization purposes. The genetic algorithms (GA) entail a number of primary stages which have been optimized many times to date; one of these stages involves the generation of the initial population. Generating a suitable and diverse initial population can prevent the early convergence of the problem and greatly contributes to problem-solving abilities and speed. The proposed method in this study involves generating an initial population in a decentralized manner between a number of processing nods which are generated as subpopulations before being integrated. Carrying out this procedure using the conditions present in any processors increases diversity in the population. The presented method is used based on estimating the parameters of software reliability growth model (SRGMs). Finally, it is shown that the proposed model managed to achieve population diversity, enhanced accuracy, and increased performance. [ABSTRACT FROM AUTHOR]

An interactive artificial ecological optimization algorithm (SIAEO) based on environmental stimulus and a competition mechanism was devised to find the solution to a complex calculation, which can often become bogged down in local optimum because of the sequential execution of consumption and decomposition stages in the artificial ecological optimization algorithm. Firstly, the environmental stimulus defined by population diversity makes the population interactively execute the consumption operator and decomposition operator to abate the inhomogeneity of the algorithm. Secondly, the three different types of predation modes in the consumption stage were regarded as three different tasks, and the task execution mode was determined by the maximum cumulative success rate of each individual task execution. Furthermore, the biological competition operator is recommended to modify the regeneration strategy so that the SIAEO algorithm can provide consideration to the exploitation in the exploration stage, break the equal probability execution mode of the AEO, and promote the competition among operators. Finally, the stochastic mean suppression alternation exploitation problem is introduced in the later exploitation process of the algorithm, which can tremendously heighten the SIAEO algorithm to run away the local optimum. A comparison between SIAEO and other improved algorithms is performed on the CEC2017 and CEC2019 test set. [ABSTRACT FROM AUTHOR]

Simple Summary: This study evaluated 26 sequences of terrapins worldwide through COI DNA barcoding and phylogenetic analysis, which included 12 species and three families. Moreover, 16 haplotypes were found; they were either misidentified, or a potential cryptic species was determined between B. baska and B. affinis affinis. Thus, COI remains an effective barcode marker for the terrapin species. Technological and analytical advances to study evolutionary biology, ecology, and conservation of the Southern River Terrapin (Batagur affinis ssp.) are realised through molecular approaches, including DNA barcoding. We evaluated the use of COI DNA barcodes in Malaysia's Southern River Terrapin population to better understand the species' genetic divergence and other genetic characteristics. We evaluated 26 sequences, including four from field specimens of Southern River Terrapins obtained in Bota Kanan, Perak, Malaysia, and Kuala Berang, Terengganu, Malaysia, as well as 22 sequences from global terrapins previously included in the Barcode of Life Database (BOLD) Systems and GenBank. The species are divided into three families: eight Geoemydidae species (18%), three Emydidae species (6%), and one Pelomedusidae species (2%). The IUCN Red List assigned the 12 species of terrapins sampled for this study to the classifications of critically endangered (CR) for 25% of the samples and endangered (EN) for 8% of the samples. With new haplotypes from the world's terrapins, 16 haplotypes were found. The intraspecific distance values between the COI gene sequences were calculated using the K2P model, which indicated a potential cryptic species between the Northern River Terrapin (Batagur baska) and Southern River Terrapin (Batagur affinis affinis). The Bayesian analysis of the phylogenetic tree also showed both species in the same lineage. The BLASTn search resulted in 100% of the same species of B. affinis as B. baska. The Jalview alignment visualised almost identical sequences between both species. The Southern River Terrapin (B. affinis affinis) from the west coast of Peninsular Malaysia was found to share the same haplotype (Hap_1) as the Northern River Terrapin from India. However, B. affinis edwardmolli from the east coast of Peninsular Malaysia formed Hap_16. The COI analysis found new haplotypes and showed that DNA barcodes are an excellent way to measure the diversity of a population. [ABSTRACT FROM AUTHOR]

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed. [ABSTRACT FROM AUTHOR]

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness. [ABSTRACT FROM AUTHOR]

As global temperatures warm, species must adapt to a changing climate or transition to a different location suitable for their survival. Understanding the extent to which species are able to do so, particularly keystone species, is imperative to ensuring the survival of key ecosystems. The ribbed mussel Geukensia demissa is an integral part of salt marshes along the Atlantic coast of North America. Spatial patterns of genomic and phenotypic divergence have been previously documented, although their link with coastal environmental variation is unknown. Here, we study how populations of G. demissa in the northern (Massachusetts) and southern (Georgia) portions of the species range respond to changes in temperature. We combine assays of variation in oxygen consumption and RNA transcriptomic data with genomic divergence analyses to identify how separate populations of G. demissa may vary in distinct thermal environments. Our results show differences in constitutive oxygen consumption between mussels from Georgia and Massachusetts, as well as shared and disparate patterns of gene expression across temperature profiles. We also find that metabolic genes seem to be a strong component of divergence between these two populations. Our analysis highlights the importance of studying integrative patterns of genomic and phenotypic variation in species that are key for particular ecosystems, and how they might respond to further changes in climate. [ABSTRACT FROM AUTHOR]

In order to improve the economy of the multi energy system and the efficiency of energy utilization, the research adopts the non dominated sorting genetic algorithms II (NSGA-II) to expand the population space. The elite strategy is introduced to improve the intelligent algorithm, and then the diversity of the population is retained to improve the optimization accuracy of the algorithm. In addition, the adaptive operator is introduced to improve the NSGA-II algorithm to improve the global search efficiency. The performance test of fast non dominated sorting genetic algorithm shows that the improved algorithm using elite strategy has better performance in coverage index, diversity index and convergence index. For example, in terms of convergence index, the improved NSGA-II algorithm has improved 0.0159, 0.822, 0.0243 and 0.0171 in four ZDT test functions. On the energy optimization operation for the integration of wind and hydrogen, the improved NSGA-II algorithm has obtained lower cost, with a total configuration cost of 606 million yuan, while the total system configuration cost corresponding to the unimproved NSGA-II algorithm is 624 million yuan, so the total system cost after the algorithm improvement has decreased by 18 million yuan. Therefore, this method has a better economy and higher energy efficiency. [ABSTRACT FROM AUTHOR]

After WWII, the demographic transition exhibited features of convergence between developing countries and the forerunner countries with low fertility. Although today fertility is low in the majority of countries, significant differences persist. In this article, I study club convergence of fertility in 190 countries over the period 1950 to 2018. First, I apply a novel econometric method for convergence analysis and club clustering. I find no evidence of global fertility convergence, and I classify the 190 countries into four clubs. I further classify countries into two clubs at the beginning of the period and identify a club of countries transitioning from high to low fertility. Second, I interpret fertility convergence clubs as a feature of the long-term process of economic development and estimate an ordered probit model of the probability that a country enters one of three clubs characterized by high, medium, and low barriers to global fertility convergence. Here, the focus is on ancestral fundamental factors of diversity in economic development. Estimates show a statistically significant inverted U-shaped relationship between interpersonal population diversity and the probability of lower barriers, consistent with the literature on diversity and development. Estimates also highlight that genetic distance to the USA and years since the Neolithic transition to agriculture cause higher barriers to fertility decline. [ABSTRACT FROM AUTHOR]

Tibetan pigs are a locally bred domestic pig breed originating from the Tibetan Plateau in China. They can be categorized into four distinct groups based on their geographical locations: Sichuan Tibetan pigs, Tibetan pigs from Tibet, Yunnan Tibetan pigs, and Gansu Tibetan pigs. This study aimed to explore population diversity, genetic structure and selection signals among Tibetan pigs in four Chinese national nature reserves. The results show that there is different observed heterozygosity among Tibetan pig populations (0.1957–0.1978). Ratio of runs of homozygosity (F roh) calculation of four Tibetan pig populations by runs of homozygosity (ROH) revealed the presence of inbreeding within the population (0.0336–0.0378). Analysis of the genetic structure demonstrated distinct population stratification among the four Tibetan pig populations, with each showing relatively independent evolutionary directions. Furthermore, Five methods (FST, Piratio, ROD, Tajima's D, XP-CLR) were used to artificially select evolutionary trajectories. The results mainly involved processes such as DNA repair, immune regulation, muscle fat deposition and adaptation to hypoxia. In conclusion, this study enhances our understanding of the genetic characteristics of Tibetan pig populations and provides a theoretical reference for the conservation of resources across different populations of Tibetan pigs. • Different degrees of inbreeding within Tibetan pig populations. • Large genetic structure differences within populations of Tibetan pigs with different geographical distribution. • Different evolutionary directions in the four Tibetan pig groups. • High altitude habitat is a potential cause of DNA repair, immunity and fat deposition in Tibetan pig populations. [ABSTRACT FROM AUTHOR]

Gaining-sharing knowledge based algorithm (GSK) is a recently emerged meta-heuristic algorithm based on human behavior and has been successfully applied to solve various optimization problems. However, GSK tends to get trapped in local optimum due to the rapid loss of population diversity during the optimization process, resulting in an imbalance between exploration and exploitation. To overcome this deficiency, this paper proposes an enhancing population diversity based GSK (EPD-GSK) framework. The proposed EPD-GSK framework incorporates three components: (1) The utilization of Sobol sequence with low divergence to initialize the population, enhancing the diversity of initial solutions. (2) The integration of the Cauchy mutation strategy in the junior phase to perturb individuals and expand the search space. (3) The application of the reverse learning update mechanism in the senior phase, increasing the likelihood of escaping local optimum. These techniques promote population diversity throughout the exploration and exploitation stages. The proposed EPD-GSK framework was evaluated on CEC2017, CEC2020, and the latest CEC2022 test suites as well as on four constrained real-world engineering design problems. The experimental results demonstrate that EPD-GSK can effectively improve the performance of various existing GSK algorithms. Furthermore, EPD-GSK also exhibits better performance compared with other state-of-the-art algorithms. • Enhancing population diversity based GSK (EPD-GSK) framework is proposed. • EPD-GSK integrates Sobol sequence, Cauchy mutation and reverse learning update. • EPD-GSK significantly improves the performance of various existing GSK algorithms. • EPD-GSK also outperforms other well-known meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

In order to explore the distribution of cyanobacteria in paddy soil under different ecological conditions, the composition, diversity, and environmental drivers of soil cyanobacteria communities in rice fields from six regions of Jilin Province (China) were investigated. The results showed that the 16S amplicon high-throughput sequence detected the existence of cyanobacteria of 16 phyla, 33 orders, 60 families, and 113 genera in the soil of rice fields in Jilin Province. The dominant cyanobacteria populations in Jilin Province paddy soils comprised Cyanobium_PCC-6307, Synechocystis_PCC-6803, Planktothrix_NIVA-CYA_15, and Nodosilinea_PCC-7104. Each soil sample included a significant proportion of nitrogen-fixing filamentous cyanobacteria Anabaena and Nostoc according to microscopic analysis. The structural properties and diversity of cyanobacteria communities differed by geography, with soil pH and SOC being the main environmental drivers of cyanobacteria community structure. The alkaline soils S1, S2, and S5 displayed greater diversity than the acidic soils S3, S4, and S6, with S5 displaying the greatest cyanobacteria diversity. This research has crucial implications for developing and utilizing local cyanobacteria resources. [ABSTRACT FROM AUTHOR]

Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. However, the DE algorithm mainly focuses on strengthening the adaptability of exploitation, which allows the sensitivity of the DE algorithm to be solved in cases where the effects of solving various types of problems are quite different. Moreover, the local search ability and population diversity have not been solved properly. Therefore, an adaptive DE algorithm using fitness distance correlation and a neighbourhood-based strategy (FNADE) is proposed. FNADE introduces the fitness distance correlation (FDC) as the basis for judging the difficulty of the problem, utilises a Voronoi diagram to increase the population diversity for complex multimodal problems and adopts the neighbourhood-based mutation strategy to strengthen the local search capability. FNADE is committed to solving unconstrained single-objective optimisation problems. The proposed algorithm is compared with six advanced DE algorithms in terms of CEC2017 benchmark functions. The experimental results show that the adaptive DE algorithm using FNADE is superior to other DE algorithms with regard to the accuracy and population diversity of the solution. [ABSTRACT FROM AUTHOR]

Genetic factors have long been recognized as important determinants of interindividual variability in drug efficacy and toxicity. However, despite the increasing number of established gene–drug associations, candidate polymorphisms can only explain a fraction of the genetically encoded functional variability in drug disposition. Advancements in genetic profiling methods now allow to analyse the landscape of human pharmacogenetic variations comprehensively, which opens new opportunities to identify novel factors that could explain the "missing heritability." Here, we provide an updated overview of the landscape of pharmacogenomic variability based on recent analyses of population‐scale sequencing projects. We then summarize the current state‐of‐the‐art how the functional consequences of variants with unknown effects can be quantitatively estimated while discussing challenges and peculiarities that are specific to pharmacogenes. In the last sections, we discuss the importance of considering ethnogeographic diversity to provide equitable benefits of pharmacogenomics and summarize current roadblocks for the implementation of sequencing‐based guidance of clinical decision‐making. Based on the current state of the field, we conclude that testing is likely to gradually shift from the interrogation of selected candidate polymorphisms to comprehensive sequencing, which allows to consider the full spectrum of pharmacogenomic variations for a true personalization of genomic prescribing. [ABSTRACT FROM AUTHOR]

Analysis of the ribosomal introns of Plasmodiophora brassicae populations infecting the cruciferous weed Cardamine occulta revealed the complex dynamics of size, intraspecific variability, and distribution. The results showed that P. brassicae populations from the weed have lost multiple introns in the small and large subunits of the ribosomal RNA genes. Moreover, the retained introns, despite a largely mutual share of conserved parts with the cosmopolitan strains, contained numerous novel structures. These structural differences comprise a high level of polymorphisms, such as transversion point mutations occurring at sites involving the intronic splicing sites or insertions/deletions at the binding sites. Two geographical P. brassicae populations from C. occulta carried a lengthy intron-encoded ORF and putative mobile elements established in the large subunit. A few P. brassicae populations from the Brassica crops also harbored polymorphic introns that shared common mutated motifs with the weed-affecting group. The diversity of ribosomal introns observed from those investigated populations demonstrated the genetic distinction of the P. brassicae populations from C. occulta. The genetic variations might play a key role in the adaptability of the weed-infecting populations and are more likely related to the process of pathogenesis for the cosmopolitan P. brassicae infecting the Brassica crops. [ABSTRACT FROM AUTHOR]

The South African population consists of four ethnic groups, i.e., Blacks, Coloreds, Indians, and Whites, and is considered the most diverse conglomeration of humans. In addition to autosomal short tandem repeat (STR) variation, an important tool to study population diversity is Y-chromosome (Y)-STR analysis. Y-STRs aid in forensic investigations and provide essential data about paternal lineage origins. Y-STR kits consisting of an array of stable and rapidly mutating markers offer crucial information on a given population's genetic and haplotype diversity. This review discusses the development of Y-STR kits over the years and highlights some prominent Y-STR studies conducted on the South African population. The earliest Y-STR kit developed was the Y-PLEX™6, with the most recent being the UniQTyper™ Y-10 Multiplex. The South African population studies show varying data, with the "minimal haplotype" having low discrimination capacity among the ethnic groups and the UniQTyper™ Y-10 showing high genetic diversity among the ethnic groups of the country. There is a dearth of Y-STR studies on the South African population. With the advent of new Y-STR kits with increased discriminatory markers, additional studies are required to represent the South African population in the Y-STR databases. Considering the diversity of the South African population, establishment of a local/regional population database would be beneficial. In addition, data on the origins and prevalence of mutations and silent alleles should be obtained from STR datasets generated during kinship investigations (specifically, parentage tests) so that detailed information about the frequencies of mutations, silent alleles, and uniparental disomy in the South African population at Y STR loci can be estimated. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) is proposed. Unlike the original SMA where population size is fixed in every epoch, FP-SMA will adaptively change population size in order to effectively balance exploitation and exploration characteristics of SMA's different phases. Experimental results on 13 standard and 30 IEEE CEC2014 benchmark functions have shown that FP-SMA can achieve significant reduction in run time while maintaining good solution quality when compared to the original SMA. Typical saving in terms of function evaluations for all benchmarks was between 20 and 30% on average with a maximum being as high as 60% in some cases. Therefore, with its higher computation efficiency, FP-SMA is much more favorable choice as compared to SMA in time stringent applications. [ABSTRACT FROM AUTHOR]

In order to provide food and nutritional security for the world's rapidly expanding population, fruit crop researchers have identified two critical priorities: increasing production and preserving fruit quality during the pre- and post-harvest periods. The genetic basis of these complex, commercially important fruit traits which are uniquely regulated by polygenes or multi-allelic genes that interact with one another and the environment can be analyzed with the aid of trait mapping tools. The most interesting trait mapping approach that offers the genetic level investigation for marker-trait associations (MTAs) for these complex fruit traits, without the development of mapping population, is association mapping. This approach was used during the genetic improvement program, emphasizing the obstacles (breeding strategies adopted, generation interval, and their genomic status) pertaining to perennial fruit crops. This method of studying population diversity and linkage disequilibrium in perennial fruit crops has been made possible by recent developments in genotyping, phenotyping, and statistical analysis. Thus, the purpose of this review is to provide an overview of different trait mapping techniques, with a focus on association mapping (method, essential components, viability, constraints, and future perspective) and its advantages, disadvantages, and possibilities for breeding perennial fruit crops. [Display omitted] • Association mapping (AM) - a better alternative to classical breeding in perennial fruit crops. • Linkage disequilibrium, marker density, genomic status, germplasm diversity, and population structure are the key considerations in AM. • Genome-wide association studies (GWAS) and AM possibility has discussed by targeting various bottlenecks. • Softwares used in GWAS along with explanations to avoid misinterpretation has discussed in details. [ABSTRACT FROM AUTHOR]

Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the face of complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, many researchers are working on improving the performance of algorithms for solving various optimization problems in machine learning. The equilibrium optimizer (EO) is a member of evolutionary computation and is inspired by the mass balance model in environmental engineering. Using particles and their concentrations as search agents, it simulates the process of finding equilibrium states for optimization. In this paper, we propose an improved equilibrium optimizer (IEO) based on a decreasing equilibrium pool. IEO provides more sources of information for particle updates and maintains a higher population diversity. It can discard some exploration in later stages to enhance exploitation, thus achieving a better search balance. The performance of IEO is verified using 29 benchmark functions from IEEE CEC2017, a dynamic economic dispatch problem, a spacecraft trajectory optimization problem, and an artificial neural network model training problem. In addition, the changes in population diversity and computational complexity brought by the proposed method are analyzed. [ABSTRACT FROM AUTHOR]

Pearlspot (Etroplus suratensis) is one of the most commercially important brackish water fish species widely found along the coastal regions of peninsular India and Sri Lanka. Pearlspot is known for its tender flesh, delectable taste, culinary tourism and high-yielding market value. Information on the genetic makeup of stocks/populations is extremely vital as it forms the basis for future genetic studies. For this, we utilized ATPase6/8 genes of mtDNA of pearlspot populations collected from nine different locations ranging from Ratnagiri in Maharashtra state on the west coast to Chilika in Odisha on the east coast. Sequence analyses of these genes revealed 33 polymorphic sites, which include 17 singleton and 16 parsimony informative sites. Pair-wise genetic differentiation study (FST = 0.75) indicated significant (P < 0.001) differences among all the pairs of stocks except those from Chilika and Nagayalanka. The spatial analysis of molecular variance (SAMOVA) significantly delineated the population into four groups (FCT = 0.69, P = 0.0001), namely northwest (Ratnagiri and Goa); southwest (Mangalore and lakes at Vembanad, Ashtamudi and Vellayani in Kerala); southeast (Pulicat in Tamil Nadu) and northeast (Chilika in Odisha and Nagayalanka in Andhra Pradesh). The above delineation is supported by clades of the phylogenetic tree and also the clusters of median joining haplotype network. The high haplotype diversity (0.84), low nucleotide diversity (0.003), and negative values of Tajima's D (–1.47) and Fu's Fs statistic (–14.89) are characteristic of populations having recently undergone demographic expansion. Mantel test revealed significant isolation by distance. The study identifies highly delineated structured populations with restricted gene flow. If such a stock is overfished, it is highly unlikely that it would recover through migration. For any future breeding programme in this species, it would be desirable to form a base population which incorporates the genetic material from all the locations so that we get a wide gene pool to select from. [ABSTRACT FROM AUTHOR]

Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. However, the DE algorithm mainly focuses on strengthening the adaptability of exploitation, which allows the sensitivity of the DE algorithm to be solved in cases where the effects of solving various types of problems are quite different. Moreover, the local search ability and population diversity have not been solved properly. Therefore, an adaptive DE algorithm using fitness distance correlation and a neighbourhood-based strategy (FNADE) is proposed. FNADE introduces the fitness distance correlation (FDC) as the basis for judging the difficulty of the problem, utilises a Voronoi diagram to increase the population diversity for complex multimodal problems and adopts the neighbourhood-based mutation strategy to strengthen the local search capability. FNADE is committed to solving unconstrained single-objective optimisation problems. The proposed algorithm is compared with six advanced DE algorithms in terms of CEC2017 benchmark functions. The experimental results show that the adaptive DE algorithm using FNADE is superior to other DE algorithms with regard to the accuracy and population diversity of the solution. [ABSTRACT FROM AUTHOR]

Background: global trade in living plants and plant material has significantly increased the geographic distribution of many plant pathogens. As a consequence, several pathogens have been first found and described in their introduced range where they may cause severe damage on naïve host species. Knowing the center of origin and the pathways of spread of a pathogen is of importance for several reasons, including identifying natural enemies and reducing further spread. Several Phytophthora species are well-known invasive pathogens of natural ecosystems, including Phytophthora multivora. Following the description of P. multivora from dying native vegetation in Australia in 2009, the species was subsequently found to be common in South Africa where it does not cause any remarkable disease. There are now reports of P. multivora from many other countries worldwide, but not as a commonly encountered species in natural environments. Results: a global collection of 335 isolates from North America, Europe, Africa, Australia, the Canary Islands, and New Zealand was used to unravel the worldwide invasion history of P. multivora, using 10 microsatellite markers for all isolates and sequence data from five loci from 94 representative isolates. Our population genetic analysis revealed an extremely low heterozygosity, significant non-random association of loci and substantial genotypic diversity suggesting the spread of P. multivora readily by both asexual and sexual propagules. The P. multivora populations in South Africa, Australia, and New Zealand show the most complex genetic structure, are well established and evolutionary older than those in Europe, North America and the Canary Islands. Conclusions: according to the conducted analyses, the world invasion of P. multivora most likely commenced from South Africa, which can be considered the center of origin of the species. The pathogen was then introduced to Australia, which acted as bridgehead population for Europe and North America. Our study highlights a complex global invasion pattern of P. multivora, including both direct introductions from the native population and secondary spread/introductions from bridgehead populations. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a novel active disturbance rejection attitude controller for quadrotors and propose a controller parameters identification approach to obtain better control results. Design/methodology/approach: Aiming at the problem that quadrotor is susceptible to disturbance in outdoor flight, the improved active disturbance rejection control (IADRC) is applied to design attitude controller. To overcome the difficulty that adjusting the parameters of IADRC controller manually is complex, paired coevolution pigeon-inspired optimization (PCPIO) algorithm is used to optimize the control parameters. Findings: The IADRC, where nonlinear state error feedback control law is replaced by non-singular fast terminal sliding mode control law and a third-order tracking differentiator is designed for second derivative of the state, has higher control accuracy and better robustness than ADRC. The improved PIO algorithm based on evolutionary mechanism, named PCPIO, is proposed. The optimal parameters of ADRC controller are found by PCPIO with the optimization criterion of integral of time-weighted absolute value of the error. The effectiveness of the proposed method is verified by a series of simulation experiments. Practical implications: IADRC can improve the accuracy of attitude control of quadrotor and resist external interference more effectively. The proposed PCPIO algorithm can be easily applied to practice and can help the design of the quadrotor control system. Originality/value: An improved active disturbance rejection controller is designed for quadrotor attitude control, and a hybrid model of PIO and evolution mechanism is proposed for parameters identification of the controller. [ABSTRACT FROM AUTHOR]

A two-phase evolutionary algorithm is developed to find multiple solutions of a nonlinear equations system. It transforms a nonlinear equations system into a multimodal optimization problem. In phase one of the proposed algorithm, a strategy combines a multiobjective optimization technique and a niching technique to maintain the population diversity. Phase two consists of a detection method and a local search method for encouraging the convergence. The detection method finds several promising subregions and the local search method locates the corresponding optimal solutions in each promising subregion. The experiments on a set of 30 nonlinear equation systems demonstrate that the proposed algorithm is better than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Predicting student performance for educational organizations such as universities, community colleges, schools, and training centers will enhance the overall results of these organizations. Big data can be extracted from the internal systems of these organizations, such as exam records, statistics about virtual courses, and e-learning systems. Finding meaningful knowledge from extracted data is a challenging task. In this paper, we proposed a modified version of Harris Hawks Optimization (HHO) algorithm by controlling the population diversity to overcome the early convergence problem and prevent trapping in a local optimum. The proposed approach is employed as a feature selection algorithm to discover the most valuable features for student performance prediction problem. A dynamic controller that controls the population diversity by observing the performance of HHO using the k-nearest neighbors (kNN) algorithm as a clustering approach. Once all solutions belong to one cluster, an injection process is employed to redistribute the solutions over the search space. A set of machine learning classifiers such as kNN, Layered recurrent neural network (LRNN), Naïve Bayes, and Artificial Neural Network are used to evaluate the overall prediction system. A real dataset obtained from UCI machine learning repository is adopted in this paper. The obtained results show the importance of predicting students' performance at an earlier stage to avoid students' failure and improve the overall performance of the educational organization. Moreover, the reported results show that the combination between the enhanced HHO and LRNN can outperform other classifiers with accuracy equal to 92 % , since LRNN is a deep learning algorithm that is able to learn from previous and current input values. [ABSTRACT FROM AUTHOR]

Population and life‐history diversity can buffer species from environmental variability and contribute to long‐term stability through differing responses to varying conditions akin to the stabilizing effect of asset diversity on financial portfolios. While it is well known that many salmon populations have declined in abundance over the last century, we understand less about how different dimensions of diversity may have shifted. Specifically, how has diminished wild abundance and increased artificial production (i.e. enhancement) changed portfolios of salmon populations, and how might such change influence fisheries and ecosystems?We apply modern genetic tools to century‐old sockeye salmon Oncorhynchus nerka scales from Canada's Skeena River watershed to (a) reconstruct historical abundance and age‐trait data for 1913–1947 to compare with recent information, (b) quantify changes in population and life‐history diversity and the role of enhancement in population dynamics, and (c) quantify the risk to fisheries and local ecosystems resulting from observed changes in diversity and enhancement.The total number of wild sockeye returning to the Skeena River during the modern era is 69% lower than during the historical era; all wild populations have declined, several by more than 90%. However, enhancement of a single population has offset declines in wild populations such that aggregate abundances now are similar to historical levels.Population diversity has declined by 70%, and life‐history diversity has shifted: populations are migrating from freshwater at an earlier age, and spending more time in the ocean. There also has been a contraction in abundance throughout the watershed, which likely has decreased the spatial extent of salmon provisions to Indigenous fisheries and local ecosystems. Despite the erosion of portfolio strength that this salmon complex hosted a century ago, total returns now are no more variable than they were historically perhaps in part due to the stabilizing effect of artificial production.Policy implications. Our study provides a rare example of the extent of erosion of within‐species biodiversity over the last century of human influence. Rebuilding a diversity of abundant wild populations—that is, maintaining functioning portfolios—may help ensure that watershed complexes like the Skeena are robust to global change. [ABSTRACT FROM AUTHOR]

The aboriginal form of the Prussian carp in lakes and rivers in the south of Western Siberia is currently being replaced by the Amur forms of the same species introduced into the water bodies of the region in the late 1970s. We analyze the genetic diversity of this species in Southern Siberia based on the polymorphism of a D-loop fragment of mitochondrial DNA (460 bp) and reveal the presence of two haplogroups, one of which (B) is extremely rare in South East Asia and is likely an indicator of the autochthonous forms of the Prussian carp, and the other (A) is similar to haplotypes typical of the Far Eastern populations. [ABSTRACT FROM AUTHOR]

The performance of micro-population teaching-learning-based optimization(TLBO) algorithm is mainly depended on the population diversity. In order to further improve its performance, this paper proposed a micro-population teaching-learningbased optimization based on gene level diversity(MTLBO-GLD) . In the proposed algorithm, the pop ulation diversity was monitored at the genetic level, and the chaos search and the cosine function were used to improve the population diversity in different evolutionary stages. The proposed algorithm was compared with eight meta-heuristic algorithms ( including four micro-population algorithms and four non-micro-population algorithms) on 13 test functions . The experimental results show that the overall performance of the MTLBO-GLD is significantly better than that of compared algorithms. [ABSTRACT FROM AUTHOR]

Keywords: Mammary gland; ScRNAseq; Human breast cell atlas; International consortium; Cell types; Population diversity EN Mammary gland ScRNAseq Human breast cell atlas International consortium Cell types Population diversity 67 70 4 06/23/21 20210301 NES 210301 With the human genome project out of the way [[1]] and the task of identifying all functional elements in our DNA bestowed upon the ENCODE consortium [[3]], scientists must have felt they were ready for the next big challenge: that of drafting a I magna carta i of the human body - and with single cell resolution at that. Histologic findings in normal breast tissues: comparison to reduction mammaplasty and benign breast disease tissues. A Single Cell Atlas of the Healthy Breast Tissues Reveal Clinically Relevant Clusters of Breast Epithelial Cells. bioRxiv. Once this is achieved, however, the Human Breast Cell Atlas should serve as a reference for the whole research community to understand how cells, cell niches, cell communities and cell-cell communication become deregulated to result in breast cancer and disease. [Extracted from the article]