Starting from the hypothesis that the different pedoclimatic environments in which Solina lines were conserved may have shaped the population, some phenotypic characteristics were studied in the Solina core collection. Growth habit, low-temperature resistance, allelic variations at major loci involved in vernalization response, and sensitivity to photoperiod were evaluated, together with seed morphologies, grain colour, and hardness. The two Solina groups showed different responses to low temperatures and to photoperiod-specific allelic variations as well as the different morphology and technological characteristics of the grain. In conclusion, the long-term in situ conservation of Solina in environments sited at different altitudes has had an impact on the evolution of this landrace which, despite its high genetic diversity, remains clearly identifiable and distinct so as to be included in conservation varieties.Solina is an example of a bread wheat landrace that has been conserved in situ for centuries in Central Italy. A core collection of Solina lines sampled in areas at different altitudes and climatic conditions was obtained and genotyped. A clustering analysis based on a wide SNP dataset generated from DArTseq analysis outlined the existence of two main groups, which, after F Solina bread wheat is an ancient landrace which, until recently, was cultivated extensively in Abruzzo, a Central Italian region mainly mountainous (65%) and hilly (34%), with a plain (1%) consisting only of a narrow coastal strip. Starting from the mid-twentieth century, with the spread of highly productive modern varieties, the Solina cultivation area shrank, remaining mainly in marginal areas of high hills and mountains and in areas characterized by low fertility soils. Although its cultivation has been drastically reduced, Solina wheat has continued to be present in the Abruzzo region, ensuring its “in situ” conservation. Such conservation is of particular value for crop genetic resources as the accessions are maintained in a state of continuous interaction with the environment. In “ex-situ” conservation, ensured by germplasm banks, the accessions, preserved as a cyclically renewed seed, are fixed and crystallized with respect to the time of their collection, while in the case of “in situ” conservation, the accessions are in constant evolution.Solina d’Abruzzo is an exemplary case of “in situ” conservation, thanks to the local farmers who have continued its cultivation over time and thanks to the local demand for traditional products, which specifically require the technological characteristics of Solina flour . MoreoveThe peculiarities of the history and of the agronomic and qualitative characteristics of this wheat have been valorised by the Abruzzo region, which has supported Solina’s registration in the Italian National Register of Conservation Varieties and has promoted various dissemination and research actions.Landraces are classically made up of a set of lines, with a highly variable level of genetic diversity, depending on the population considered. In this regard, Solina wheat, which was included in a European study aimed at the conservation, use, and study of landraces significant for European cereal farming , was fouIn the work of Khan et al., the characterization of genetic diversity within Solina was accomplished using a small number of single nucleotide polymorphism (SNP) markers and a small number of samples . In a dest) between the two clusters was carried out to identify highly differentiated genomic regions and possibly link them to known adaptation-related loci.The two genetic groups brought together lines sampled in areas with different climatic conditions, including, for instance, a significant difference in average altitudes. Starting from the hypothesis that the in-situ conservation of Solina in different pedoclimatic environments may have shaped its adaptation, in the present work, Solina lines were characterized for two key adaptive traits, such as growth habitus and resistance to low temperatures and grain quality traits. Allelic variation in the main genes controlling adaptation-related traits has been investigated. In addition, an evaluation of genetic differentiation . The plants were then grown in greenhouses in accordance with the protocols routinely applied in terms of irrigation and fertilization. The heading dates were determined, and, at maturity some agronomic parameters were measured .p < 0.001) between-cluster difference.In autumn sowing, all accessions reached the heading stage after 162–164 days, whereas no accession flowered when sown in late spring sowing (4 June), as shown in At the end of the life cycle, some agronomic characteristics were assessed, i.e., plant height, above-ground biomass, the number of spikes, and thousand kernel weight (TKW). The results are reported in p < 0.001) between cluster difference.The mean plant heights were 163 ± 7.8 cm in autumn (10 November) sown plants, 108 ± 21.2 cm in early spring (18 February) sown plants, and 59 ± 8.8 cm in late spring (4 June) sown plants . The staThe mean biomass production differed significantly between populations for all sowing dates  and was The mean number of spikes was 22 ± 3.1 in November sown plants and 7.9 ± 5.2 in February sown plants, as shown in p < 0.001) between cluster difference.The mean TKW of 44 ± 4.2 g was found in autumn sown plants, and 13 ± 14.3 g was found for the February sown ones C. High vOut of 15 imbibed seeds, ten or more germinated until the days after imbibition (DAI) 11 for all populations. The average Dualex chlorophyll  and flavonoid index values , as well as the nitrogen balance index , were measured at DAI 21 and DAI 48, respectively, and did not significantly differ between accessions. Epidermal flavonoid content, indicated by the flavonoid index, increased between the first measurement at DAI 21, before cold acclimation, and DAI 48, seventeen days after the change to acclimation temperatures of 3/1 °C . At DAI v/Fm measured on dark-adapted leaves at DAI 41, 66, 76, and 91  did not significantly differ between the populations. Population median values were always above 0.73, indicating healthy, photosynthetically competent leaves. The grand mean varied in the course of time between 0.785, 0.786, 0.765, and 0.777 at DAI 41, 66, 76, and 91, respectively.The maximal quantum yield of photosystem II (PSII), Fv/Fm re-measured after freezing stress and recovery indicated only a generally slight to moderate impairment of PSII  should be close to −15 °C. Relative ranks of Fv/Fm were calculated experiment-wise in order to combine the results of the three experiments. In the two experiments on cut leaves, the population in the red genetic cluster, together with Solina 21, had significantly higher frost tolerance than the accessions in the blue group (p < 0.02). Considering that the relative ranks of all three experiments together (p < 0.001), Solina 21 was not statistically different from both. Solina accession 17 resulted significantly more susceptible compared to Solina 4, 23, and 24.The statistical power of the first two experiments (n = 3) was relatively low, resulting in some or no statistically significant differences between accessions except for the first experiment, where accession six was significantly more damaged than accessions 14, 21, and 24. The experiment on whole plants (8 < n < 13) better discriminated between more and less susceptible plants. No plant mortality was observed up to six days after the end of the freezing treatment. The relatively high frost tolerance after freezing stress of −14 °C was in accordance with what could be expected for the species together  resultedp < 0.001). Additionally, for the six accessions measured at DAI 52, the width of leaf 2 did not differ significantly between populations, as well as the Haun stage, which was 3.5 on average, i.e., the fourth leaf was expanding. At this time, the accessions demonstrated between 1.4 and 1.8 visible tillers. At DAI 69 and 76, no significant difference in leaf length was found between the genetic clusters. The same held true for the leaf width measured at DAI 76. In conclusion, the accessions and genetic groups exhibited similar phenology and leaf dimensions during the acclimation treatment. Under hardening conditions, the plants still grew slowly.At the start of the hardening treatment (DAI 23), the second leaf was still expanding. The leaf length of leaves 1 and 2 measured at DAI 31 did not differ significantly between accessions nor between the red and blue genetic clusters for any of the two leaf ranks. The length of the first leaf was significantly correlated with the length of the second leaf  based assays reported in the Material and Methods section, have been exploited to explore allelic diversity in Solina accessions . Two majVrn-A1 sequence was coupled with the assay developed by Morcia et al. to target the Pinb-D1 single-copy gene as an internal positive control  interval and were generally non-normally distributed [Between population differences in frost, tolerance were assessed with a Kruskal–Wallis test with Bonferroni correction for multiple comparisons  . This notributed . For othThe statistical analyses of quality parameters were performed using XLSTAT 2021 software . Mixed nested ANOVA was carried out on data from the 24 accessions of PLS-DA was computed using all the morphological and quality parameters  of the Solina d’Abruzzo is a local population of common wheat that has been cultivated for a very long time in an environment that, although restricted, has peculiar environmental and climatic variations. It is a case of long-term in situ conservation on-farm, able to drive microevolution of the crop under a dynamic management of its diversity ,37,38. BGenome-wide genetic analysis has outlined how the Solina d’Abruzzo landrace can be split into two clusters that correspond to areas at different altitudes and climates. A deeper genetic and phenotypic characterization of the two clusters showed diversity for key adaptive traits that are able to modulate the spreading of a crop ,13,39,40


The stages were: select the concept; elaborate analytical questions; comprehensively review the literature; and determine concept structural components. Sixty-one documents were analyzed after a search carried out until October 2019.four temporal periods of contextual changes have occurred since the emergence of ideas of a systematization in the 1960s. This first lasted until 1990. It was followed by those from 1990 to 2002, from 2002 to 2009 and from 2009 onwards. Partial conceptual maturity was identified, operationalization over the years, based on multiple definitions, and, currently, a concept of Systematization of Nursing Care with managerial and organizational attributes.the concept of Systematization of Nursing Care is partially mature, presents multiple definitions, being operationalized in uncertain connections with other concepts. Such interpretations raise the growing interest of professionals and academics for the conceptual clarification of the term. However, debates and publications existing today tend not to precisely explain the SNC ontology or offer operational definitions, except for some contributions already presented in manuscript in pre-print format.Inaccurate conceptions of the Systematization of Nursing Care (SNC) term can lead to mistaken or limited interpretations, influencing its conceptual applicationAssociação Brasileira de Enfermagem), a scientific body in the area, has for some years been including, in its documents, goals to encourage the debate on the SNC and the NP. The Federal Council of Nursing , the body that regulates professional nursing practice in Brazil, established a working group to update the norm that regulates NP and SNC implementation in health units.In the contemporary professional context, nursing class entities and a network of researchers have used strategic actions to promote the SNC’s and the Nursing Process’ (NP) conceptual design. The Brazilian Association of Nursing (ABEn - -10. Nursing Process Research Network (REPPE - Rede de Pesquisa em Processo de Enfermagem) researchers, when reflecting on the NP concept in Brazil, expressed the need to expand the SNC concept’s conceptual and operational definitions so that there is no overlap between its definition and that of other concepts.In the academic context, for more than a decade, Brazilian researchers have shown the existence of different currents of thought for the mentioned terms, with the permanence of conceptual inaccuracies and terminological overlap in recent years,9-10,12, the temporal markers of conceptual evolution and SNC concept components still remain uncertain. Moreover, available research is insufficient to delineate or clarify the concepts and identify systematization meanings contained in original elements of nursing production, particularly between the 1970s and 1990s, a period prior to the first COFEN Resolution 272 of 2002 on the NCS.The issue that still represents a barrier to the advancement of knowledge on the subject refers to non-exploration of temporal origin and dynamics of overlapping SNC concept attributes, by most of available publications. Although there are already reflexive gains in the literature from the aggregation of evidence by reviewing the literature and qualitative research.There is a tendency to recognize that the SNC is a Brazilian construct, which leads researchers to investigate the concept fundamentally in Brazilian literature. Despite this, there are potentially related constructs in other countries, such as the Professional Practice Model, which, to a certain extent, would be a possible model of professional practice with convergent attributes to that of the SNC. Thus, the challenge of advancing knowledge about the SNC construct and producing extrapolations and comparisons with other international concepts depends on concept analysis.In this article, we present advances in the original findings of a pragmatic utility concept analysis, from doctoral research, and those brought in a pre-print article, with a view to reflecting changes that improve conceptual knowledge and facilitate the theoretical construction and verification of specific SNC attributes. Because of this, this article expands the interpretation of results currently available in the literature on the subject, investigating the process of conceptual maturation and being relevant for future research decisions on the subject.Findings from a pragmatic concept analysis can guide the future research decision, from qualitative approaches, for better description, clarification and design or indicate the possibility of advancing to quantitative studies, theory development and applicationTo analyze the SNC conceptual maturation from the perspective of pragmatic utility.. The results and preliminary analyzes were deposited on the platform in pre-print format, by the same team of authors, without having received comments until the time of submission of manuscript to this journal. However, this manuscript is substantially different in content from most sections, incorporating blind reviewer recommendations and supporting our SNC concept’s interpretative evolution.Ethical issues related to research with human beings are not applicable, however, the principle of authorship rights was respected, with due source citation and indication,18-19, with writing guided by the Standards for Reporting Qualitative Research (SRQR) checklist.This is a concept analysis study using the pragmatic utility method. In it, epistemological, pragmatic, linguistic and logical principles are applied. The epistemological guide the assessment of the concept’s internal structure, which must be clear, distinct, internally consistent and differentiated from others. The pragmatist points out that the concept must be applicable to the world or operable. The linguistic indicates the appropriate use of the concept in context. The logician postulates conceptual coherence and the relationship with other concepts. These last three principles contribute to assessing the concept’s external structure.Pragmatic utility is used to develop, clarify, delineate, compare, or correct concepts.The present study analyzes the SNC concept’s internal structure through definitions, attributes, boundaries, preconditions and outcomes developed over time. In external assessment, existing limits with the competing concept are investigated: the NP,18. In the first stage, the SNC concept was chosen, based on the diffuse scope of definitions and contextual changes over time already pointed out in the literature.We applied the method’s four operational stages: select the concept; elaborate analytical questions; comprehensively review the literature; and describe concept use in the scientific contextIn the second stage, analysts constructed two analytical questions: what changes occurred in the SNC concept definition in nursing literature in Brazil over the years, indicating the conceptual design? What conceptual and contextual evidence indicated the concept maturation process?. The aforementioned trail was obtained by combining a generic search using a search tool, access to literature data and selection of publications cited in the retrieved materials.The third stage was to comprehensively review the literature. The method employed requires tracing the concept back as far as possible to what would presumably be its origins® tool, until October 2019, using the term “SNC”, without establishing a time limit. After adding the words, not descriptors, in English and Spanish, “Sistematización de Enfermería”, “Systematization of Nursing Care”, the authors checked 15,209 materials.Initially, we performed a generic search with the Google Scholar. Concept analysis methods are not literature reviews per se, although they do review publications. They dispense with most of literature synthesis and summarization techniques required in empiricist-based reviews, and do not apply the same quality criteria as the bibliographic sources of systematized reviews, especially when one considers that a concept is built evolutionarily by maturing and in the multiplicity of opinions, such as pragmatic utility. Thus, in this research, Critical Theory’s philosophical foundation for material collection and selection for pragmatic utility analysis did not emphasize concept contextualism, as opposed to the observable evidence and universalization of the language of empiricist science.The inclusive extension of this search is compatible with the analytical principle of concept analysis methods, since it operates in a logic contrary to that of most literature review studies that tend towards synthesis and aggregationFrom the search to the preliminary analysis, 31 manuscripts that actually dealt with SNC were selected, excluding NP. Of these, 09 manuscripts were in the category regulations and professional consensus documents, namely: COFEN professional resolutions and synthesis documents of symposiums and congresses organized by ABEn. This procedure was useful to obtain old publications that could not be included in the literature databases and of other nature, essential to conceptual maturity analysis.Later, in 2021, the researchers carried out a second search, which was structured in Latin American and Caribbean Literature in Health Sciences (LILACS), through the Virtual Health Library (VHL) portal.sistematizacao OR organizacao OR organizacion OR sistematizacion)) AND ) AND ) AND  AND (db:(“LILACS”)).Aligned descriptors were chosen due to the absence of the descriptor “Systematization of Nursing Care”, namely: . Thus, the SNC, in its organizational perspective, will tend to support its own decisions to achieve a PTS and suffer particular interference arising from this multidisciplinary strategy.It seems essential to us to recognize the SNC as a construct specific to the organizational type and different from the NP, given the existence of multidisciplinary care systems that do not primarily use the NP, such as the Singular Therapeutic Project (PTS - . However, the organizational meaning of nursing practice involves situations that do not identify and are exclusive to the subject in health care, which makes it impossible to circumscribe all professional action to the NP. This issue demands an advance of reflections for the field of nursing and health management and invites to this debate researchers who are not exclusively those interested in NP and standardized language systems.Relating the SNC to the NP is the central aspect constructed for decades by the area, especially in the perspective that the NP confers scientific rationality to highlight the specificity of subject/profession’s know-how.Thus, the philosophical foundations and methodological procedures of pragmatic utility analysis seem to have been adequate to verify conceptual maturation concepts over the more than 40 years since the introduction of original ideas of a “systematization” for nursing care. However, the fragile consensus on definitions and structural elements of SNC reflects a process that is still in partial maturityThe authors recognize a study limitation in offering objectifiable elements that are essential to build an operational definition for the SNC.It is possible to understand the substantial challenges of currently producing research that validates SNC strategies in the sense of an organizational or management system, before resolving conceptual confusions. Also, concept elements are under construction in the implantation and implementation in different health units in the country, influenced by the absence of referential, attributes and robust constitutive definitions about the concept. Based on this, the authors recommend two guidelines for future studies: the first in relation to developing conceptual and empirical models for the SNC that would serve as a reference structure; the second in relation to exploratory and analytical studies that provide evidence of the application of individualized initiatives and of targeted interactions with the NP.This analysis contributes to the field of nursing, by providing evidence on different periods of SNC concept maturation, verifying its current point of partial maturity and proposing a general definition. In this regard, this study enables a better understanding of the phenomenon that the SNC concept represents in Brazilian nursing practice.The study presents a conceptual outline and clarification without abandoning the historical or temporal context in which the concept was developed. The findings of this research, in addition to contributing to current scientific debate and entities representing the professional category of nursing, give room for a possible and necessary adequacy of language and operationalization in the training and assistance spaces. The study can also serve as a reference for changes in policies and regulations that deal with and regulate the SNC.The SNC concept is still a partially mature concept, presenting multiple definitions, being operationalized in uncertain links with other concepts, implying the current state of knowledge in an elusive and confusing concept.Analysis by the pragmatic utility method was useful to verify the SNC concept maturation process in four time periods. It also helped in verifying the process’ conceptual maturation and characteristics and in the outlining of a definition that could be useful for its operationalization in the context.


Myodes rufocanus was preserved in an intensive farming region in Japan, where voles inhabited isolated windbreak forests along the borders of plowed lands. Genetic structure was examined in 673 vole samples  collected at 34 windbreak forests located 0.35–20 km apart. A part of the control region (425 bp) of mitochondrial DNA (mtDNA) was sequenced in 673 voles, yielding 76 haplotypes. Genetic differentiation of maternally inherited mtDNA among trapping sites was markedly lower in males than in females in both seasons, indicating strong male‐biased dispersal. Genotypes at six microsatellite DNA loci were determined in 494 voles  from 18 trapping sites, and loci harbored 16–24 alleles. The mean number of alleles per locus  at trapping sites was positively correlated with the number of examined individuals (density) in both seasons, and the relationship was very similar to that of a previous study performed in much less fragmented populations. The genetic differentiation of microsatellite DNA among trapping sites decreased considerably from spring to fall. In a STRUCTURE analysis with a most probable cluster number of two, closer trapping sites showed more similar mean values of cluster admixture proportions. The present findings indicate that gene flow among isolated windbreak forests, which occurred mainly by dispersal of males, was not restrained in this intensive farming region. Furthermore, the results suggest that genetic diversity in the study population was preserved as well as in less fragmented populations.Individual dispersal plays an important role in preserving genetic diversity in density‐fluctuating populations of arvicoline rodents. When habitats are fragmented and dispersal between habitats is severely constrained, genetic diversity can be lost. Here, I investigated whether genetic diversity in the gray‐sided vole  In the STRUCTURE analysis, admixture cluster proportions were spatially heterogeneous, and closer trapping sites showed more similar mean values of cluster admixture. Females and males were consistent in terms of the directions of changes in the mean admixture proportions between spring (left) and fall (right) at each trapping site, although the degree of change in males was greater than in females at most sites. Fragmentation results in an altered configuration of habitats with decreasing size and increasing average distance between habitats. For animals with low dispersal ability, even separation of a short distance may interfere with individual dispersal between fragmented habitats. Without individual dispersal, a loss of genetic variation (genetic erosion) could occur by genetic drift and inbreeding in small, isolated subpopulations, and the diminished genetic variation could reduce the opportunity for adaptive responses to varying local environmental conditions  and 32.7 ± 12.1  for bred females and males, respectively  and 103.5 m , respectively, from the estimated birthplace  herbivorous rodent with a short life span  and biparentally inherited microsatellite DNA. First, the genetic structures of mtDNA were compared between sexes to clarify sexual differences in dispersal patterns. Second, to evaluate whether individual dispersal works well in preserving neutral nuclear genetic variations, genetic structure at microsatellite DNA loci was compared between seasons. Subsequently, genetic diversity was compared with data from a previous study performed in much less fragmented populations. In addition, cluster admixture proportions were explored using Bayesian clustering analysis. By comparing cluster admixture proportions of individuals between seasons and between sexes, I estimated how genetic admixture progressed within and among windbreak forests in association with individual dispersal. To my knowledge, it is not yet known how admixture of different clusters progresses by individual dispersal at the population level during density fluctuations. Clustering analysis has been used to evaluate the spatial genetic structure and admixture of lineages  and September 5 to October 20  in 2007, without sacrificing individuals. The trapping sessions were conducted at the same position in all windbreak forests. During a trapping session, 30 Sherman‐type live traps were set in a 3 × 10 grid pattern at an interval of 10 m (~0.3 ha) with a handful of oats in each trap for two nights; that is, a total of 60 trap nights per session. Upon capture, three toes (one per foot) of each vole were clipped, sex was determined, and the vole was released. Clipped toes were stored in plastic bags at −10°C during a trapping session and at −80°C in the laboratory until DNA extraction.2.3Genomic DNA was extracted from samples  using the conventional phenol‐chloroform method  selection was performed using the glm function (family = Poisson) in R software ; Slatkin, FST, was calculated for all pairs of trapping sites with five or more samples with GenAlEx 6.51b2  using GENEPOP 4.7.5  selection was performed for the mean individual heterozygosity and mean number of different alleles at trapping sites (response variables) using the lm function in R. The best model was identified as the GLM analysis mentioned previously. As predictors, the number of individuals, seasons, and area of windbreak forests were considered in the LM analysis.FST values were calculated for all pairs of trapping sites with five or more samples using GenAlEx. Because the sample size at each trapping site was not very large, males and females were not separated in the analysis. Mantel tests were performed with GenAlEx to examine correlations between geographic distance and linFST in each season.To compare spatial genetic structure between seasons, lin2.6K) considered varied from 1 to 10. Each run, replicated 100 times, comprised a burn‐in period of 104 and 104 iterations under default settings except option LOCPRIOR, for which trapping sites of individuals were used as prior information. The most likely number of clusters was inferred based on ad hoc ΔK statistics . The density at trapping sites ranged from low to medium . Based on the reproductive activity of gray‐sided voles in Hokkaido, Japan .The sequence of part of the mtDNA control region 425 bp) was determined for 673 individuals. There were 41 variable sites among the sequences, defining 76 haplotypes Appendix . These h5 bp was 3.3FST values were calculated among trapping sites with five or more samples for each sex . In both sexes, no correlation was observed between linFST and geographic distance between trapping sites in either season . In females, although variance in spring was significantly greater than in fall , mean values were not different between seasons . In males, spring samples showed marked variability because linFST values >0.5 were observed in all combinations between trapping site #24 and the other 16 sites  were similar between seasons in males . In addition, the mean and variance in males were significantly smaller than in females in both seasons .Lins Figure , because3.4p > .05/6 for all). FIS values did not differ from zero , suggesting that no inbreeding occurred in windbreak forests. Linkage disequilibrium was not found among the loci . Micro‐Checker analysis suggested the presence of null alleles at three loci at three trapping sites  because of homozygote excess  from 18 trapping sites where 17 or more voles were sampled. In total, 16–24 different alleles were detected at those loci Appendix . Hardy–WAppendix . At eachρ = .557, p = .016; fall, ρ = .670, p = .003). The relationships between the number of individuals and the mean number of alleles were similar between seasons . The spatial genetic structure of microsatellite DNA loci decreased considerably from spring to fall  were significantly smaller in fall . No significant correlation was detected between geographic distance and linFST in either season , although the significance was marginal in spring.Linl Figure . The mea3.6K) was 2 for 494 individuals from 18 trapping sites, based on ΔK statistics , the trapping sites also had varying proportions of three clusters within individuals  for spring samples and 0.543 ± 0.270 for fall samples, and the difference between seasons was not significant . However, a weak negative correlation was observed between the mean admixture proportion of cluster I in spring and change in mean admixture proportion of cluster I between seasons, although the significance was marginal , and the average absolute value in males was 1.65‐fold greater than that in females (0.147 vs. 0.089).When the admixture proportions were analyzed for each sex separately, no significant difference was detected between seasons in the mean admixture proportion of cluster I or its variance in either sex . In these forests, relatively low peaks were observed in 2004–2005; a major decrease occurred in 2006; and the next peak was not observed until 2008 Appendix . Given pFST values among trapping sites were markedly lower in males than in females in both seasons , was found in microsatellite DNA in spring Figure . Until tIn the STRUCTURE analysis, admixture proportions of clusters were spatially heterogeneous Figure . Closer K = 2, the mean admixture proportion of one cluster (and its variance) did not differ between seasons in either sex ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing .None declared.Appendix S1–S9.Click here for additional data file.


Differentiation of closely related biological species using molecular genetic analysis is important for breeding farm animals, creating hybrid lines, maintaining the genetic purity of breeds, lines and layering. Bos grunniens and Bos taurus differentiation based on STR locus polymorphism will help maintain the genetic isolation of these species and identify hybrid individuals. The aim of this study is to assess the differentiating potential of 15 microsatellite loci to distinguish between domestic yak (B. grunniens) bred in the Kalmak-Ashuu highland region  and cattle (B. taurus) of three breeds  using molecular genetic analysis. The samples were genotyped at 15 microsatellite loci . Twelve of the loci were from the standard markers panel recommended by ISAG. Statistical analysis was performed using GenAlEx v.6.503, Structure v.2.3.4, PAST v.4.03, and POPHELPER v1.0.10. The analysis of the samples’ subpopulation structure using the Structure v.2.3.4 and 15 STR locus genotyping showed that the accuracy of assigning a sample to B. taurus was 99.6 ± 0.4 %, whereas the accuracy of assigning a sample to B. grunniens was 99.2 ± 2.6 %. Of the 15 STRs, the greatest potential to differentiate B. grunniens and B. taurus was found in those with the maximal calculated FST values, including BM1818 (0.056), BM1824 (0.041), BM2113 (0.030), CSSM66 (0.034) and ILSTS006 (0.063). The classification accuracy of B. grunniens using only these five microsatellite loci was 98.8 ± 3.4 %, similar for B. taurus, 99.1 ± 1.2 %. The proposed approach, based on the molecular genetic analysis of 5 STR loci, can be used as an express test in Kyrgyzstan breeding and reproduction programs for B. grunniens Kyrgyzstan is characterized by a variety of natural and climaticconditions, therefore, animal husbandry may vary a lotbetween locations. Thus, breeding yaks at high altitude makesmuch sense given that the natural conditions are favorablefor the species. In contrast, breeding cattle develops more atlow and middle altitudes. Compared to cattle, yaks use lowgrowthpasture feed better, and in winter they extract it fromunder a snow cover 10–15 cm thick. Yak meat is not inferiorto beef and is rich in proteins, as well as trace elements vitalfor humans. Although yak milk output is low, their milk isknown for the high content of fat (5.5–8.6 %), phosphorus(0.28 %) and calcium (0.30 %) . The yaknot only produces milk, meat, skin and wool, but is also usedfor transport by people in the highlands of Asia . Yak is a very strong alternative for domesticcattle, easy to breed at high altitude with a very harshand cold climate. Yaks have thick subcutaneous fat, coveredwith thick long hair, as well as sharp “steel” hooves that allowthem to move along very steep, rocky trails, unattainable forany other livestock.Unlike the common cattle, which is currently bred on allcontinents, domestic yak has a very small geographical distributionarea, which is limited to the mountainous regions ofCentral Asia . The reason for this, accordingto , is the animal itself. Domestic yak, aswell as its wild relative, the Tibetan yak, is perfectly adaptedto the conditions of high altitude and mountain plateaus . They both live in the harsh climate of the highlands,where the annual temperature is close to zero for more thaneight months a year, and the minimal temperature can dropto –50 °C. In such harsh conditions, yaks live all year roundin the open air on pastureOne of the ways to further intensify yak breeding as anindependent branch of animal husbandry is to improvebreeding technology, yak breeding and productive qualities,expand knowledge on their biology, as well as increase meatproductivity. The study of yak genetic characteristics allowingthem to live in the harsh climate of the highlands is of greatpractical interestCurrently, the most convenient genetic markers describinggenetic structure of different animal species, including yaksand cattle, are polymorphic microsatellite DNA loci , which have a codominant nature ofinheritance and serve as an indispensable tool to study geneticdifferences not only between animals, but also populations ofthe same breeds, as well as between breeds.The aim of this study is to evaluate the differentiating potentialof 15 STR loci  todistinguish individuals of Bos grunniens and Bos taurus.The biological material for molecular genetic research wasblood samples taken from adult livestock, including 55 domesticyaks (B. grunniens) bred in the Kalmak-Ashuu highlandregion ,which comprised a sample called YAK, as well as blood DNAsamples taken from an adult herd of 145 cows (B. taurus)of three breeds, including Aberdeen-Angus , Holstein  and Alatau . All applicable international, national and/orinstitutional principles for the care and use of animals havebeen observed.DNA isolation was carried out by phenol-chloroformextraction . The samples weregenotyped by 15 microsatellite loci. Of the analyzed STRloci, 12 were the standard markers panel recommended by theInternational Society of Animal Genetics (ISAG), includingETH3, INRA023, TGLA227, TGLA126, TGLA122, SPS115,ETH225, TGLA53, BM2113, BM1824, ETH10 and BM1818.Microsatellite loci CSSM66, ILSTS006 and CSRM60 wereanalyzed additionally. Oligonucleotides sequence is shownin Table 1.PCR was analyzed using capillary electrophoresis viaan automatic genetic analyzer with a laser-induced fluorescencedetection Applied Biosystems 3500 . Samples validated using the COrDIS Cattle kit  were used as a reference forallelic calculationStatistical analysis was carried out using GenAlEx v.6.503, Structure v.2.3.4 , PAST v.4.03  and POPHELPERv1.0.10 . GenAlEx v.6.503 was used to estimategenetic distances using the AMOVA  method; Structure v.2.3.4 was used to calculatethe Q criterion, which characterizes the attribution of eachindividual to the corresponding cluster (subgroup within thegroup); POPHELPER v1.0.10 web application was utilized for graphical interpretation of results obtained in Structurev.2.3.4, whereas PAST v.4.03 was used to plot the main componentsbased on the calculation of genetic distances usingthe AMOVA method.The analysis of the subpopulation structure of B. grunniens andB. taurus using the Structure v.2.3.4 program on the genotypingdata of 15 STR loci, as well as the graphical representationshowing the assignment of individuals to a specific group,produced by the POPHELPER v1.0.10 web application, isshown in Figure 1.As a result of the simulation repetitions after the burn-in 50,000, 10 iterations), we foundfour distinct clusters . Structure v.2.3.4,according to the method of J.K. Pritchard , allowed to compute the Q criterion, which characterizedthe assignment of each individual to a group (species) forfour samples, including ABR, HOL, ALA and YAK. Q ≥ 75 %in the ABR sample was found in 88.9 % (40/45) individuals,accuracy 94.4 ± 5.7 %; HOL, in 82.0 % (41/50), accuracy95.8 ± 3.3 %; ALA, in 90.0 % (45/50), accuracy 96.3 ± 4.3 %;and YAK, in 98.2 % (54/55), accuracy 98.3 ± 3.2 %. Whencombining three B. taurus samples into one (COW) and analyzingonly two groups – COW and YAK, Q ≥ 75 % was identifiedin 100 % (145/145) individuals, accuracy 99.6 ± 0.4 % inthe first group and in 98.2 % (54/55), accuracy 99.2 ± 2.6 %in the second groupBased on the genetic distances analysis calculated usingthe AMOVA algorithm, we constructed a graph of principalcomponent analysis (PCA) . COW and YAK groupson the graph are spaced relative to each other and form twonon-overlapping arrays.Of all 15 STR loci analyzed in the study, including ETH3,INRA023, TGLA227, TGLA126, TGLA122, SPS115,ETH225, TGLA53, BM2113, BM1824, ETH10, BM1818,CSSM66, ILSTS006 and CSRM60, those with the highestcalculated FST had the greatest potential to differentiateB. grunniens and B. taurus (Table 2).A similar approach aimed to develop an algorithm to differentiateevolutionarily close animals using STR loci wasdescribed in .The highest calculated FST values are shown for BM1818,BM1824, BM2113, CSSM66 and ILSTS006 STR loci. Table 3summarizes the allelic diversity and allele frequency for theSTR loci listed above.As a result, the representation of major alleles was verydifferent between COW and YAK groups. In particular, ‘256’,‘258’ and ‘262’ were the major alleles for BM1818 in the COW group, whereas‘262’ (occurrence 56.4 %) was major for the YAK group. ForBM1824, the difference in the frequency of ‘195’ allele intwo groups was 24.0 % , and22.0 %  for ‘187’ allele. Themost common alleles for the BM2113 STR locus in the YAKgroup were ‘128’ (23.6 %) and ‘130’ (27.3 %), while totalfrequency of these alleles in the COW group was only 17.2 %.A similar trend was observed for the CSSM66 locus, andthere was a significant difference in the frequency of ‘172’,‘178’, ‘180’, ‘184’ and ‘190’ alleles. ‘294’ (42.7 %) was themost common allele in the ILSTS006 locus for the YAK group whereas ‘286’ (11.4 %), ‘290’ (12.8 %), ‘272’ (19.3 %) and‘292’ (23.8 %) were the most prevalent in COW. Table 4 summarizesprivate alleles for these STR loci.Based on the data obtained, a repeated analysis of the subpopulationstructure was completed using Structure v.2.3.4only for 5 out of 15 STR loci, as a result of genotyping analysis(Table 5). Table 3 presents allelic diversity and allele prevalencefor these STR loci.As a result of the simulation with Structure v.2.3.4 , we found four distinctclusters . Structure v.2.3.4, according tothe method of J.K. Pritchard , allowedto compute the Q criterion, which characterized the assignmentof each individual to a group (species) for four samples, includingABR, HOL, ALA and YAK. Q ≥ 75 % in the ABR samplewas found in 88.9 % (40/45) of the individuals, accuracy92.6 ± 5.8 %; HOL, in 68.0 % (34/50), accuracy 92.4 ± 6.2 %;ALA, in 82.0 % (41/50), accuracy 93.2 ± 5.6 %; and YAK,in 96.4 % (53/55), accuracy 97.7 ± 3.4 %. To improve the accuracyof individuals differentiation in the Holstein breed, thelist of analyzed STR loci should be further expanded, startingwith ETH3, TGLA126 and TGLA122.In total, differentiation accuracy based on BM1818,BM1824, BM2113, CSSM66 and ILSTS006 STR loci in YAK(B. grunniens) was 98.8 ± 3.4 %, and 99.1 ± 1.2 % in COW(B. taurus). Thus, differentiation accuracy was not lost evenwhen 5 STR loci out of 15 were analyzed.Earlier, Inter Simple Sequence Repeats of yak-cattle hybridswere studied at the Institute of General Genetics RAS, anda species-specific pattern of eight ISSR fragments for yak wasfound in yak and F1 hybrids populations . Also, the allele depository of yaks and their hybridswith B. taurus was assessed earlier using microsatellite analysis,yielding high genetic diversity for F1 hybrids in comparisonwith the original species . Our study didnot confirm hybrid individuals of B. grunniens and B. taurus.This study assessed the differentiating potential of 15 STR loci,including ETH3, INRA023, TGLA227, TGLA126, TGLA122,SPS115, ETH225, TGLA53, BM2113, BM1824, ETH10,BM1818, CSSM66, ILSTS006 and CSRM60 for B. grunniensand B. taurus individuals, as well as attempted to identifyhybrids of these speciesAccording to the subpopulation structure analysis, followinggenotyping of 15 STR loci, the classification accuracy ofB. grunniens individuals was 99.1 ± 1.2 %, and 99.6 ± 0.4 %for B. taurus. When the number of STI loci used for decisionwas limited to five, including BM1818, BM1824, BM2113,CSSM66 and ILSTS006, the differentiating potential ofwhich, according to FST, was the greatest and varied from0.030 to 0.063, the classification accuracy for B. grunnienswas 98.8 ± 3.4 %, and 99.1 ± 1.2 % for B. taurusThus, we conclude that the analysis of even a small numberof STR loci allows to ascertain differentiation of domesticyak and three breeds of cattle  bred in Kyrgyzstan. At the same time, furtherresearch is needed in the longer run to more accurately classifydifferentiation potential for selected loci.The authors declare no conflict of interest.Abdykerimov A. Theory and Practice of Raising Yaks in Kyrgyzstan.Bishkek, 2001. (in Russian)Al-Kaisy T.V. Comparative study of the allele pool of yaks and theirhybrids with cattle using microsatellites. Cand. Biol. Sci. Diss. Dubrovitsy,2011. (in Russian)Barendse W., Armitage S.M., Kossarek L.M., Shalom A., KirkpatrickB.W., Ryan A.M., Clayton D., Li L., Neibergs H.L.,Zhang N., Grosse W.M., Weiss J., Creighton P., McCarthy F.,Ron M., Teale A.J., Fries R., McGraw R.A., Moore S.S., GeorgesM., Soller M., WomackJ.E., Hetzel D.J.S. A genetic linkage mapof the bovine genome.Nat. Genet. 1994;6(3):227-235. DOI 10.1038/ng0394-227.Bishop M.D., Kappes S.M., Keele J.W., Stone R.T., Sunden S.L.F.,Hawkins G.A., Toldo S.S., Fries R., Grosz M.D., Yoo J., Beattie C.W.A genetic linkage map for cattle. Genetics. 1994;136(2):619-639.DOI 10.1093/genetics/136.2.619.Bovine Genome Project. Baylor College of Medicine Human GenomeSequencing Center. Genome Bovine Whole Genome Assembly releaseBtau_3.1. URL: http://www.hgsc.bcm.tmc.edu/projects/bovine/(Application 25.07.2022).Brezinsky L., Kemp S.J., Teale A.J. ILSTS006: a polymorphic bovinemicrosatellite. Anim. Genet. 1993;24(1):73. DOI 10.1111/j.1365-2052.1993.tb00933.x.Chertkiev Sh.Ch., Chortonbaev T.J. Scientific Basis for the Formationof Meat Productivity of Yaks in Ontogeny. Bishkek, 2007. (in Russian)Francis R.M. POPHELPER: an R package and web app to analyse andvisualise population structure. Mol. Ecol. Resour. 2016;17(1):27-32.DOI 10.1111/1755-0998.12509.Georges M., Massey J. Polymorphic DNA markers in Bovidae. Patentapplication WO PUBL NO 92/13102. World Intellectual PropertyOrganization. Geneva, 1992.Hammer Ø., Harper D.A.T., Ryan P.D. Past: Paleontological statisticssoftware package for education and data analysis. Palaeontol. Electron.2001;4(1):4.Jacques G., d’Alpoim Guedes J., Zhang S. Yak domestication: a reviewof linguistic, archaeological, and genetic evidence. Ethnobiol. Lett.2021;12(1):103-114. DOI 10.14237/ebl.12.1.2021.1755.Luz Ya.Ya. (Ed.) Domestic Animals of Mongolia. Proceedings of thelivestock detachment of the Mongolian expedition of the Academyof Sciences of the USSR in 1931. Moscow–Leningrad: PublishingHouse of the USSR Academy of Sciences, 1936. (in Russian)Nosova A.Yu., Kipen V.N., Tsar A.I., Lemesh V.A. Differentiation ofhybrid progeny of silver carp and bighead carp (H. nobilis Rich.) based on microsatellite polymorphism.Russ. J. Genet. 2020;56(3):317-323. DOI 10.1134/S1022795420030126.Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel. Populationgenetic software for teaching and research – an update. Bioinformatics.2012;28(19):2537-2539. DOI 10.1093/bioinformatics/bts460.Pritchard J.K., Stephens M., Donnelly P. Inference of population structureusing multilocus genotype data. Genetics. 2000;155(2):945-959. DOI 10.1093/genetics/155.2.945Rębała K., Rabtsava A.A., Kotova S.A., Kipen V.N., Zhurina N.V.,Gandzha A.I., Tsybovsky I.S. STR profiling for discriminationbetween wild and domestic swine specimens and between main breeds of domestic pigs reared in Belarus. PLoS One. 2016;11(11):e0166563. DOI 10.1371/journal.pone.0166563Sambrook J., Russell D.W. Molecular Cloning: A Laboratory Manual.New York: Cold Spring Harbor Laboratory Press, 2001.Steffen P., Eggen A., Dietz A.B., Womack J.E., Stranzinger G., Fries R.Isolation and mapping of polymorphic microsatellites in cattle.Anim. Genet. 1993;24(2):121-124. DOI 10.1111/j.1365-2052.1993.tb00252.x.Stolpovsky Yu.A., Kol N.V., Evsyukov A.N., Nesteruk L.V.,Dorzhu Ch.M., Tsendsuren Ts., Sulimova G.E. Comparative analysisof ISSR marker polymorphism in population of yak (Bos mutus)and in F1 hybrids between yak and cattle in the Sayan-Altai region.Russ. J. Genet. 2014;50(10):1163-1176. DOI 10.1134/S1022795414100135.Toldo S., Fries R., Steffen P., Neibergs H.L., Barendse W., WomackJ.E.,Hetzel D.J., Stranzinger G. Physically mapped, cosmid-derived microsatellitemarkers as anchor loci on bovine chromosomes. Mamm.Genome. 1993;4(12):720-727. DOI 10.1007/BF00357796.Vaiman D., Mercier D., Moazami-Goudarzi K., Eggen A., CiampoliniR., Lepingle A., Velmala R., Kaukinen J., Varvio S.L., Martin P.,Leveziel H., Guerin G. A set of 99 cattle microsatellites: characterization,synteny mapping, and polymorphism. Mamm. Genome.1994;5(5):288-297. DOI 10.1007/BF00389543.


In this paper, an extended Intuitionistic Fuzzy Synthetic Measure (IFSM) with intuitionistic fuzzy (IF) entropy-based weights is presented. This method can be implemented in a ranking problem where the assessments of the criteria are expressed in the form of intuitionistic fuzzy values and the information about the importance criteria is unknown. One example of such a problem is measuring the subjective quality of life in cities. We join the debate on the determination of weights for the analysis of the quality of life problem using multi-criteria methods. To handle this problem, four different IF entropy-based weight methods were applied. Their performances were compared and analyzed based on the questionnaires from the survey concerning the quality of life in European cities. The studies show very similar weighting systems obtained by different IF entropy-based approaches, resulting in almost the same city rankings acquired through IFSM by using those weights. The differences in rankings obtained through the IFSM measure (and only by one position) concern the six cities included in the analysis. Our results support the assumption of the equal importance of the criteria in measuring this complex phenomenon. Global environmental challenges—especially those related to climate change—and social and economic problems of the modern world force decision makers, organizations, communities, and individuals to take action toward sustainable development. These activities can be individual  or collective, integrating the efforts of businesses, non-governmental organizations, or EU and/or UN governments. The Europe 2020 Strategy is the EU’s agenda adopted for growth and jobs that emphasizes mutually reinforcing the three priorities of smart, sustainable, and inclusive growth . FurtherIn this paper, we concentrate on sustainable development in the area of subjective quality of life in cities. According to Agenda 2030, goal 11 assumes that by 2030 cities and human settlements should be inclusive, safe, resilient, and sustainable. Cities must therefore meet the urbanization challenges related to the management and optimal involvement of local resources and the social, environmental, and economic potential of a given territorial unit. Paraphrasing the definition of sustainable development from the Brundtland Report )A; The numbers If the universe ue (IFV) ,47.A lot of distance measures between IFSs have been proposed in the literature ,52,53,54Definition 2Let us considerwith membership functions,and non-membership functions,, respectively.. Let usThen, the weighted Euclidean distance between A and B is calculated as follows:and the weighted Hamming distance between A and B is defined as follows:where The membership degree, non-membership degree, and hesitancy degree together allow for a more precise and complete expression of the decision-making context. The hesitancy degree describes the lack of information in the decision-making process.The entropy concept is used for measuring uncertain information. Shannon  proposedBurillo and Bustince  proposedWe listed here four intuitionistic entropy measures that will be used later to determine the weights of the criteria ,44,58,59Definition 3.Let be the set of IFS defined on the universe of discourse Then, the entropy measure (for ) is defined as follows ,59:where is the distance  from A to the nearer point  and , andamong points  is the distance  from A to the farther point  and ;among points where: constant which assures Let us notice that using Euclidean distance, we have :(9)E2E as:A  hasIn this paper, we extended the IFSM  method bi-th object in terms of the j-th criterion is expressed in the form of an intuitionistic fuzzy value i-th object is represented by the vector:Let Step 1. Determination of the Intuitionistic Fuzzy Decision Matrix.i-th object in terms of the j-th criterion (he study .Step 2. Determination of the vector of weights.The stage of determining IF entropy-based weights  consistsStep 2a: Transformation of the matrix D (see Formula (15)) to IF entropy matrix ED:D calculated using one of the Formulas (5)–(10), Step 2b: Aggregation and normalization of the IF entropy values using the formula:Step 2c: Calculation of the criteria weights using the formula:Finally, the weighs vector has the form Step 3. Determination of the intuitionistic fuzzy pattern object i-th object with respect to the j-th criterion and Step 4 Calculation of the distance measures between the objects and the intuitionistic fuzzy pattern object using either Formula (3) or (4).The distance measure from the pattern object takes the form of:Step 5. Calculation of the value of the Intuitionistic Fuzzy Synthetic Measure.IFSM) coefficient is defined as follows:The Intuitionistic Fuzzy Synthetic Measure , based on the Euclidean entropy measure. Four IF entropy-based systems of weights (E1–E4) were compared with equal weights (E0).In step 2, we assign weights for individual criteria according to Formulas (5) and (7)–(9), applying four IF entropy-based weight techniques. The results are presented in From To compare the systems of weights, Garuti’s G compatibility index ,93 was uThe index The results of applying Garuti’s compatibility index for systems of weights are presented in In step 3, the intuitionistic fuzzy pattern object has been determined see .IFSM and the rankings of cities obtained by using systems of weights, as presented in i for the coefficient obtained using a system of weights Ei (In step 4, the values of distance measures between the objects and the intuitionistic fuzzy pattern object IFSM1 measure when compared with others. Let us note that the weights system in the While analyzing the positions of cities in the overall rankings obtained using different synthetic measures, one may observe that the rankings of six cities change (highlighted in grey in e survey ,35.Further, the differences in ficients . In bothBasic descriptive statistics for all Intuitionist Fuzzy Synthetic Measures are presented in IFSM values, it is noticeable that the IFSM values are extremely similar. The differences in the IFSM values of the cities are about 0.9. The mean value is about 0.5, with a SD of about 0.25.When analyzing the box plots of the IFSM measures of cities were very diverse and were within the entire allowable range. An interesting case of the city of Palermo was also observed, for which some of the values of the IFSM measure were negative. This is a rare but possible case when the value of the IFSM measure may go beyond the  range. In such a situation, the Double Intuitionistic Fuzzy Synthetic Measure [In summary, the application of the criteria weighting method based on different formulas for the IF entropy measure did not significantly affect the IFSM measure values and the cities’ positions in the rankings. In each of the rankings obtained, the cities with the highest levels of subjective quality of life were, in order, Luxembourg, Wienna and Helsinki. The values of the IFSM measure of each of these cities exceeded the 0.8. The lowest-ranked cities were, in order, Palermo, Athina, and Skopje. The values of the  Measure  can be cIn this paper, three main scientific goals have been achieved. The first goal was to extend IFSM for IF entropy-based weights, which would allow handles to lack knowledge about importance criteria when they are evaluated using intuitionistic fuzzy values. Let us emphasize that the major advantage of entropy-based weight methods is simple and uncomplicated calculations using only information provided by the criteria and an intuitive interpretation of the entropy measure.The second goal of the paper was to verify the proposed framework by implementing it to measure the subjective quality of life. The IFSM method was used in the analysis of the results from the fifth survey on quality of life in selected European cities.The final goal was to compare the performance of different IF entropy-based weight methods in the problem of measuring the importance of individual criteria of subjective quality of life. We also compared the rankings obtained by means of IFSM, with different IF entropy-based weight methods.An analysis conducted on quality of life measurement results confirmed and illustrated the effectiveness of the proposed method. The application of the weighting method based on entropy measures did not have a major impact on the results of measuring the subjective quality of life in selected European capitals using the IFSM. This confirms the validity of the approach adopted by the authors in previous works  regardinA certain weakness of the article is the lack of comparisons of the adopted weighting method based on entropy measures with other weighting methods. Therefore, future research will focus on several areas: The first will concern the assessment of the impact of other objective weighting methods on the results of measuring the subjective quality of life using IFSM. Carrying out comparative studies is also being planned; these studies will compare the IFSM method with other methods based on reference points, such as intuitionistic fuzzy VIKOR or intuitionistic fuzzy TOPSIS, in the context of their sensitivity to the choice of weighting method. Furthermore, the theoretical framework of the IFSM with different IF entropy-based weights needs to be investigated. In addition, it should be emphasized that the subject of the article was only objective weights. Comparing them with the subjective weights obtained on the basis of the opinions of European city dwellers could provide additional guidance on the necessity and ways of determining the importance of the criteria. Finally, the proposed method should be utilized in other applications for further verification of its effectiveness.


Genetic map is a linear arrangement of the relative positions of sites in the chromosome or genome based on the recombination frequency between genetic markers. It is the important basis for genetic analysis. Several kinds of software have been designed for genetic mapping, but all these tools require users to write or edit code, making it time-costing and difficult for researchers without programming skills to handle with. Here, MG2C, a new online tool was designed, based on PERL and SVG languages.Users can get a standard genetic map, only by providing the location of genes (or quantitative trait loci) and the length of the chromosome, without writing additional code. The operation interface of MG2C contains three sections: data input, data output and parameters. There are 33 attribute parameters in MG2C, which are further divided into 8 modules. Values of the parameters can be changed according to the users’ requirements. The information submitted by users will be transformed into the genetic map in SVG file, which can be further modified by other image processing tools.MG2C is a user-friendly and time-saving online tool for drawing genetic maps, especially for those without programming skills. The tool has been running smoothly since 2015, and updated to version 2.1. It significantly lowers the technical barriers for the users, and provides great convenience for the researchers. Genetic map, also known as linkage map, illustrates the arrangement and physical or genetic distance between molecular markers (genes or DNA markers) on chromosomes , and submit the corresponding information into the input section in operation interface, a standard genetic map will be generated. The map can be downloaded and saved in SVG format for further editing and beautification. In this report, the workflow, main parameters and usage of MG2C are mainly introduced.In view of this, MG2C, an online visualization tool for genetic map was developed, based on PERL and SVG languages. The tool can be used on any operating system, and the latest version is MG2C2.1. After preparing the information including gene location and chromosome length, according to the EXCEL template, the users only need to open an internet browser that supports SVG such as Google Chrome or FireFox that support SVG display, enter the URL  Th Th1A) fiIn this paper, all the parameters are divided into 8 modules: SVG container, single chromosome (chr) container, chromosome, gene line, gene ID, connection and scale. Each module corresponds to its own attribute parameters Table  with a tUsers are required to prepare the input data for MG2C, according to an excel template provided on the website. The input data are divided into two parts. One is gene location (input1), and the other is chromosome length (input2). The former contains 5 fields: gene_ID, gene_start, gene_end, chr_ID and gene_color. Gene_color is an optional field used to customize color of the gene ID, with the default in black. The latter contains two fields: chr_ID and chr_length. The delimiter between the fields is “TAB”. Users copy and paste the input data into the corresponding textbox, left-click the “DRAW” button and a genetic map will be generated by MG2C. In addition, frequently asked questions (FAQ) in English and Chinese version separately are provided on the website.ExamplesMYB gene family members in Arabidopsis thaliana was obtained (Dubos et al. MYB gene family on chromosomes and the corresponding position of 27 important gene members.Here, chromosome information of 163 MG2C is a simple and user-friendly online tool for visualizing genetic maps. To draw a genetic map, users only copy-paste the data of gene location and chromosome length into input1 and input2 textbox of MG2C, respectively; and left-click the “DRAW” button to get a standard genetic map quickly. The output result can also be saved as a SVG file for further re-editing. In addition, users can change the value of all parameters of MG2C based on their preference. Briefly, MG2C is well-designed software, avoiding users writing or editing code. It not only saves the analysis time of users but also meets the needs of those without adequate programming skills. Since 2015, the tool has been running smoothly for about 5 years, and will be updated with a constant functional improvement and free service in future. By the end of March 2021, the number of unique access IP was over 28,000.


Assessing the genetic diversity and population structure of cultivated sorghum is important for heterotic grouping, breeding population development, marker-assisted cultivar development, and release. The objectives of the present study were to assess the genetic diversity and deduce the population structure of 200 sorghum accessions using diversity arrays technology (DArT)-derived single nucleotide polymorphism (SNP) markers. The expected heterozygosity values ranged from 0.10 to 0.50 with an average of 0.32, while the average observed heterozygosity (0.15) was relatively low, which is a typical value for autogamous crops species like sorghum. Moderate polymorphic information content (PIC) values were identified with a mean of 0.26, which indicates the informativeness of the chosen SNP markers. The population structure and cluster analyses revealed four main clusters with a high level of genetic diversity among the accessions studied. The variation within populations (41.5%) was significantly higher than that among populations (30.8%) and between samples within the structure (27.7%). The study identified distantly related sorghum accessions such as SAMSORG 48, KAURA RED GLUME; Gadam, AS 152; CSRO1, ICNSL2014-062; and YALAI, KAFI MORI. The accessions exhibited wide genetic diversity that will be useful in developing new gene pools and novel genotypes for West Africa sorghum breeding programs. Sorghum bicolor (L.) Moench, 2n = 2x = 20] is the fifth most important cereal crop in the world after maize, wheat, rice, and barley. The global production area of sorghum is close to 50 million hectares, with production levels exceeding 60 million tons per annum [−1, while the productivity of sorghum in Nigeria is trailing at 1.2 t ha−1. A similar trend has existed for the past three decades in West Africa, where the area planted with sorghum has increased by 50% but yields an average of <1 t ha−1 [Sorghum [er annum . Sorghumer annum . Nigeriaer annum . However1 t ha−1 . The far1 t ha−1 .Striga, and stem borer disease, limit sorghum productivity in Africa [ex-situ sorghum germplasm collection among the National Agricultural Research Systems (NARS) in West Africa. The germplasm collection is a priceless genetic resource for various breeding programs, nationally and internationally. To fully utilize these genetic resources in gene banks, breeders should explore the genetic variation within and among the collections and accessions. This is necessary for efficient use of genetic resources and resource allocation to breeding projects and to minimize handling of duplicated accessions or closely related accessions. At present, the genetic diversity and genetic structure of IAR’s conserved sorghum germplasm have not been fully documented. Thus, there is a need to assess the underlying genetic diversity and structure in the germplasm to devise optimal breeding strategies for sorghum at IAR.Several constraints, including drought, low soil fertility, n Africa . FortunaGenetic diversity in germplasm collections is routinely assessed using different phenotypic and molecular markers. Molecular markers have been extensively used in genetic diversity studies because they are not affected by changes in environmental factors. Several molecular marker technologies have been developed, including proteins , random Diversity Array Technology (DArT) was developed in early 2000 to minimize a bottleneck inherent to other marker platforms. The DArT platform utilizes a microarray hybridization method to produce thousands of polymorphic loci in a single assay. The platform is fast becoming a marker of choice because it provides a cost-effective sequencing that is independent of prior sequence information with ultra-high-throughput marker systems. DArT markers have been used successfully in population genetic studies of sorghum ; but alsConversely, Lasky et al.  reportedSeeds of 200 sorghum accessions from an existing collection  were obthttps://www.diversityarrays.com/ (accessed on 22 February 2022) of the Integrated Genotype Service and Support (IGSS) platform in Nairobi, Kenya. The GBS was performed by using a combination of DArT complexity reduction methods and next generation sequencing following protocols described in [https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_SbicolorRio_er (accessed on 5 April 2022). Each allele was scored in a binary fashion  while heterozygotes were scored as 1/1 .The genotypes were grown in a plant growth chamber  at the Biosciences eastern and central Africa-International Livestock Research Institute (BecA-ILRI) hub using cell trays. Three seeds of each genotype were sown per tray. Three-week-old leaf sample was collected from the three seedlings and the pooled leaf samples were frozen in liquid nitrogen and stored at −80 °C for later use. Genomic DNA (gDNA) was extracted from the frozen tissue according to the CTAB protocol, with some modifications . The quaribed in ,24,25. MGenetic diversity parameters: polymorphic information content (PIC), minor allele frequency (MAF), major allele frequency (MaF), observed heterozygosity (Ho), and expected heterozygosity or gene diversity (GD) were estimated for each defined group with R software .Two complementary methods were employed to deduce the population structure of the 200 African sorghum accessions. The first method involved using a Bayesian model-based clustering algorithm called STRUCTURE software ,27. The The number of subpopulations determined with STRUCTURE was used for analysis of molecular variance (AMOVA) and the calculation of Nei’s genetic distance  using poThe phylogenetic relationships of the subpopulations were generated based on pair-wise fixation indexes using the StAMPP package  and neigIS) of 0.53 was moderate. From the DArT SNP markers, The summary statistics of the 7516 SNP markers are presented in The population structure of the 200 sorghum accessions revealed four distinct subpopulations A,B. The Based on the pairwise genetic distance matrix among all the 200 accessions, the principal coordinate analysis (PCoA) revealed four clustered groups in accordance with the STRUCTURE results . The totThe genetic distance among the population is represented by the neighbor-joining phylogenetic tree A. The neST) and the number of migrants per generation (Nm)  . The AMOST values between different groups of accessions, sources of collection and biological races are presented in ST value G3 and G4 (0.23), and G1 and G4 had the lowest (0.13). For the sources of collection FST values, the highest was recorded between IAR and ACCI (0.11) and the lowest between ACCI and ICRISAT (0.07). The highest FST values ranged from 0.04 for Caudatum and Durra and 0.01 for Guinea and bicolor. Overall, the FST estimates, averaged 0.18, 0.09 and 0.02 for structure groups, sources of collection and races respectively indicated that there is moderate genetic differentiation. The average FST value (0.18) among the structure group was less than 0.25 which revealed the possibility of migration among the accessions. The result was confirmed by the Nm average value of 1.14 which revealed that there was enough gene flow and no clear partitioning of levels of genetic exchange according to structure group. Similar results were obtained for groupings among the sources of collections (FST = 0.09 and Nm = 2.60) and the biological race (FST = 0.02 and Nm = 16.58).Pairwise population FCrop improvement depends on access to new sources of genetic variation. The most notable sources of genetic variation are landraces, wild or semi-wild relatives of cultivated crop species. In Africa, smallholder farmers produce the bulk of sorghum crops, mostly using unimproved landrace varieties and this enhance their resilience to climate variability. Landraces are valued for their beneficial genetic traits as local farmers have favored them for their ability to adapt to various environmental challenges . To effeThe current study examined the genetic diversity present among 200 sorghum accessions, including landraces obtained from Nigeria using DArT markers. The polymorphic information content (PIC) provides an estimate of the information content of a marker. In this study, the highest PIC value observed was 0.38, indicating the presence of alleles in approximately 14.1% of the population. The average PIC value of 0.26 is similar to the findings of Afolayan et al.  and EnyeHeterozygosity is a fundamental measure of genetic variation in a population. The gene diversity (GD) of a locus, also known as its expected heterozygosity (He) describes the expected proportion of heterozygous genotypes under Hardy-Weinberg equilibrium . In thisST value (0.68). The SNP data showed that the test accessions had high ancestry membership coefficients of more than 0.60. The grouping of accessions from various collection areas together, despite their diverse origins, indicates a strong genetic association. These findings suggest that sorghum landrace genotypes are likely exchanged among regions by farmers, possibly through multiple routes. This aligns with the hypothesis of seed mixing, exchanging, and trade among small-scale farmers, highlighting the dynamic nature of genetic interactions and seed movement in agricultural communities.The population structure analysis provides insights into the genetic diversity among sorghum genotypes and is useful in controlling false-positive associations between marker loci and traits of interest . The resSorghum breeding efforts in West Africa were initiated in 1966 through the introduction of exotic lines. Subsequent pedigree breeding programs utilized local and exotic crosses, resulting in the release of improved pure line varieties and hybrids in the region . AnalysiST) quantifies the extent of genetic diversity resulting from allele frequency variations among populations, thereby reflecting population structure [ST value (0.23) was observed between subpopulations 3 and 4 (m = 0.994) was low suggesting that a low genetic exchange might occur which led to a high genetic differentiation (0.201) between the subpopulations  was higher than that among populations (30.8%) and between samples within the structure (27.7%). In self-pollinating species like sorghum, the usual pattern is to maintain genetic variation within populations, while genetic variation tends to be lower among populations. This observation is consistent with previous studies that investigated genetic diversity using SNP markers  and SSR tructure . Values tructure . In our  3 and 4 , indicatulations . AccessiThe present study reports genetic diversity studies and population structure analysis on a panel of 200 sorghum germplasm collections of West African origin, using DArT-Seq derived SNP markers, as a basis for future breeding. The SNP markers employed in this study exhibited a considerable degree of polymorphism, effectively revealing the genetic differences between and within the sorghum populations. Approximately half of the SNP markers were highly informative, making them valuable candidates for future genetics studies. Interestingly, a notable proportion of loci displayed an excess of heterozygosity. Exploring these loci further by studying genotypes with different alleles could provide insights into their relevance and importance in terms of desirable traits. The significant genetic differentiation observed among the sorghum accessions stemming from diverse germplasm collections across Africa will be valuable for sorghum breeders in identifying and selecting desirable parent plants for effective hybrid breeding strategies. Therefore, future sorghum improvement should focus on genetic improvement using the landraces. The formation of four distinct clusters among the sorghum accessions highlights the potential for crossbreeding genotypes from different clusters to assess their progenies for desirable traits. The study identified distantly related sorghum accessions such as SAMSORG 48, ICNSL2014-024-2, KAURA RED GLUME (Cluster I); Gadam, AS 152, AS 1, MACIA (Cluster 2); CSRO1, CAPARLASG2015002, ICNSL2014-062 (Cluster 3); and YALAI, KAFI MORI, FARA DOGON DAWA (Cluster 4). The distantly related sorghum accessions will be used in creating new gene pools and novel genotypes for sorghum breeding programs in Nigeria and similar agro-ecologies in Africa. Although the SNP markers used in this study adequately discriminated between the accessions, it is important to conduct phenotypic evaluations to fully elucidate the genetic basis of phenotypic variation for crop improvement.


ST, coefficient of genetic differentiation GST), and three different methods of structure assessment . The mean values of Na (11.25), He (0.69), Ho (0.66), FST (0.012), and GST (0.012) estimated in the indigenous Beninese goat population highlighted great genetic diversity. STRUCTURE and SOM results showed the existence of two distinct goat groups  with high crossbreeding effects. Furthermore, DAPC distinguished four clusters within the goat population descending from the two ancestry groups. Clusters 1 and 3  respectively showed a mean Djallonké ancestry proportion of 73.79% and 71.18%, whereas cluster 4 (mainly of goats from SZ and some goats of GSZ) showed a mean Sahelian ancestry proportion of 78.65%. Cluster 2, which grouped almost all animals from the three zones, was also of Sahelian ancestry but with a high level of interbreeding, as shown by the mean membership proportion of only 62.73%. It is therefore urgent to develop community management programs and selection schemes for the main goat types to ensure the sustainability of goat production in Benin.Improved knowledge of the diversity within and among local animal populations is increasingly necessary for their sustainable management. Accordingly, this study assessed the genetic diversity and structure of the indigenous goat population of Benin. Nine hundred and fifty-four goats were sampled across the three vegetation zones of Benin  and genotyped with 12 multiplexed microsatellite markers. The genetic diversity and structure of the indigenous goat population of Benin were examined using the usual genetic indices (number of alleles Na, expected and observed heterozygosities He and Ho, Fixation index F West African countries are characterized in general by high variability in farm animal genetic resources . ConcernHence, this study investigated the genetic diversity and structure of the indigenous goat population in Benin using microsatellite markers to allow a clear identification of breed groups or genetic types and to confirm or refute the phenotypic diversity aforementioned.n = 954) randomly sampled goat hair from the three vegetation zones of Benin and used in a previous morphological characterization study . These hair samples were selected from unrelated animals using the information provided by goat farmers on their animals. Some characteristics of these vegetation zones i.e., humidity index, soil characteristics, and predominant vegetation, can be found in To address the genetic base and structuring of the indigenous goat population of Benin, nine hundred and fifty-four .The genotyping analysis was performed with 15 μL of template DNA using the multiplex kit of 12 microsatellite markers and the PCR protocol developed by n = 954) using the Cervus software v 3.0  (ST), and Nei’s genetic distance , the effective number of alleles (Nae), observed (Ho) and expected (He) heterozygosities, and Polymorphic Information Content (PIC) of each microsatellite marker were first estimated from the dataset  , the coedistance  were thedistance  to assesAdditionally, an analysis of molecular variance (AMOVA) was performed to assess the partition of genetic variation between (inter-) and within (intra-) the goat groups .Three methods were used to estimate the genetic clustering of the goat samples of Benin and their genetic relationship.First, the genetic structure of the indigenous goat population was analyzed using the Bayesian admixture approach in the STRUCTURE software 2.3.4 . The ancSecond, the non-linear relationships of the genotypic data were estimated using the Self-Organizing Map (SOM) method ; 2001 unfind.clusters” function of the R package adegenet version 2.1.1   was applon 2.1.1  to estimon 2.1.1 . The resFinally, the genetic variation existing within and among the inferred goat groups from genetic clustering with DAPC was estimated using the genetic parameters previously calculated in the first section of Statistic analysis .IS, FST, FIT, and GST estimated from the Beninese goat dataset are presented in ST, FIT, FIS, and GST were 0.012, 0.047, 0.035, and 0.012 respectively.The different genetic indices Na, He, Ho, PIC, FIS values . The highest pairwise FST (0.021) and Nei’s genetic distance (0.047) were recorded between GCZ and SZ, whereas the lowest FST (0.006) and Nei’s genetic distance (0.013) were observed between GSZ and SZ  based on the highest delta K value (53.17) resulting from the data  was plotted against K, the average log-likelihood of the data Ln P(D) increased up to to K = 9 . The runor K = 4  indicateor K = 4 .SOM analysis showed the neural assignment of individuals on the network . The strK = 2 and K = 8 where they reach the lowest value of BIC  showed many overlaps and representation of four probable goat groups in the dataset  of PCA and two discriminant eigenvalues were retained. The resulting scatterplot  and 0.03 (C4) with a mean value of 0.04. Considering the FST values recorded between the inferred DAPC clusters, the highest FST value (0.06) was estimated between C3 and C4. A similar FST value (0.04) was recorded between the pairs . Additionally, a low Nei’s genetic distance was recorded between C1 and C3, whereas a high distance was estimated between C3 and C4, but smaller than that recorded between C1 and C3.IS (0.035) and FIT (0.047) values recorded suggest some inbreeding events in this population  reported by IS = 0.105, FIT = 0.129) by IS = 0.035, FIT = 0.063) by IS = 0.057, FIT = 0.102) reported by This study constitutes the first one performed on the genetic diversity within the goat population of Benin. All the microsatellite loci used in this study were informative because they recorded at least 4 alleles  and mostpulation . Indeed,ilibrium . NeverthST (0.012) obtained in this study was inferior to 0.05, indicating a very low genetic differentiation in the goat population of Benin. The coefficient of gene differentiation (GST) obtained with a mean value of 0.012 confirmed the limited genetic differentiation between vegetation zones. The result of the AMOVA applied to the dataset using vegetation zones as a like-effect of variation also confirmed this limited genetic differentiation. Therefore, the genetic differentiation of the Beninese goat population is intraspecific diversity, thus mainly due to the diversity between individuals within vegetation zones. The lack of genetic differentiation observed between vegetation zones is probably due to different factors including the proximity of production areas, similar extensive breeding practices in the different vegetation zones, but especially the gene flow that occurred between individuals of the main goat groups in the past. A similar finding has been reported by ST over loci (0.012) estimated in the Beninese goat population is lower than the value obtained in goat populations of Burkina Faso (0.035)  , Nigeria (0.035)  and (0.0 (0.035) , and Alg (0.035) . TherefoIS obtained in GSZ and SZ goat subpopulations when measuring the genetic diversity existing within and among the vegetation zones, underline that the goats of these vegetation zones are very diverse, but some individuals from these zones are also inbred. In a similar study, IS, and concluded a high genetic diversity within these subpopulations or breed groups. Furthermore, the highest values of pairwise FST and Nei’s genetic distance recorded between GCZ and SZ confirm that goats from these two vegetation zones are genetically different. In contrast, the lowest values of FST and Nei’s genetic distance obtained between GSZ and SZ suggest that goats from these zones are genetically close. However, some goats from GSZ would be also genetically closer to GCZ individuals, and their genetic proximity seems similar to that observed between GSZ and SZ, as shown by their near similarity between the indices of genetic differentiation and the genetic distance of Nei’s  in Benin according to both vegetation and phytogeographic zones, confirm the geographic distribution of goat types in Benin as previously defined based on morphology  confirmed the aforementioned results. First, the STRUCTURE results confirmed the widely accepted existence of two existing ancestral populations of goats in Benin  with genrphology . These rrphology . ConsideThis study provides valuable data on the genetic diversity and structure of the indigenous goat population of Benin and fairly confirms the phenotypic diversity observed within this population. Indeed, the results highlighted the presence of two ancestral genetic groups of goats in Benin with a high level of interbreeding, particularly in GSZ. However, although the indigenous goat population of Benin is highly diverse, the pressure of poorly planned and controlled crossbreeding might threaten the sustainability of goat farming systems. With the current pressure of climate and societal changes, any threat to local goat resources should be prevented more than ever. Measures for the conservation and sustainable management of indigenous goat resources need to be taken involving the farmers who are the owners of these animal genetic resources. For instance, sensitization and training sessions could be organized to raise the awareness of farmers on the need to maintain farm animal genetic resources, to show them the importance and necessity of monitoring and organizing reproduction in their herds, and to remind them or strengthen their knowledge of the qualities of local breeds such as the trypanotolerance and prolificacy of the Djallonké goats with a view to establishing purebred breeding. In addition, the Beninese government should, in the long term, introduce breeding laws and policies to control the movement of animals both at the borders and within Beninese localities. Finally, conservation programs for the local breeds should be urgently set up.