The growing importance of maturity smart cities is currently observed worldwide. The vast majority of smart city models focus on hard domains such as communication and technology infrastructure. Scientists emphasize the need to take into account social capital and the knowledge of residents. The smart cities invest in enhanced openness and transparency data. Mature smart cities use real-time evidences and information to citizens, businesses and visitors. The smart cities are characterized by bottom-down management and civil government. The paper aims to assess the urban smartness of selected European cities based on the ISO 37120 standard. Several research methods including the Multidimensional Statistical Analysis (MSA) were applied. Using the statistical analysis of European smart cities with the implemented ISO 37120 standard, the author tried to fill gaps in the knowledge and to evaluate maturity smart cities. The results of the research have shown that the smart city concept is a viable strategy which contributes to the urban sustainability. The author also found out that urban sustainability frameworks contain a large number of indicators measuring environmental sustainability, the smart city frameworks lack environmental indicators while highlighting social and economic aspects. Cities play an extensive role in the sustainable development of the world. Cities constitute centers of innovation, entrepreneurship and creativity. According to United Nations, 56% of the world’s population resides in cities, whereas forecasts indicate an increase to 69% in 2050. In fact, the world’s 750 largest cities generate 57% of global GDP . On the The main purpose of the article is to attempt the assessment of urban smartness for selected European cities. The manuscript aims to show dependences occurring between the theoretical and practical considerations concerning cities based on the difficult implementation of the urban smartness. Firstly, the paper serves to organize the terminology in the field of sustainability and urban smartness in the smart cities concept and proposes smart city definition. The literature available on the Web of Science, Springer, Scopus, IEEE and Elsevier databases has been reviewed. Secondly, the article is concerned with reflection on sustainable city often confronted with the smart city concept. The empirical part attempts to assess the urban smartness indicators for selected European cities with the implemented ISO 37120 standard through the use of the cluster analysis and the factor analysis. The manuscript is an attempt to answer the research questions: how can we measure the urban smartness and which variables, which cities have the highest level of urban smartness, how does classification of cities present in terms of urban smartness and how are the dependencies between pillars of sustainability?Smart city is a new method of urban management. According to this concept ICT is used to improve the life quality of residents and urban services. Since there is no research on development of urban management, the article focuses on identifying European cities most involved in implementing the concept of smart city. The article attempts to fill this research gap.et al. [et al. [et al. [In world literature there are many definitions of a smart city. Hollands  suggestset al.  approachet al. , the con [et al.  identifi [et al.  distinguFurthermore, Cocchia  preparedWiseman believes that urban management is a complex task because of urbanization and climate change . Cities et al. analysed scientific studies focusing on both environmental sustainability and smart city concepts to understand relationship between these two [Marsal-Llacuna  suggestshese two .In the 2014, International Organization for Standardization published ISO 37120 norm which helps to measure and compare urban performance in terms of urban service and quality of life. It is a tool for uniform reporting of the state city’s development in 17 thematic groups such as: education, energy, environment, finance, fire & emergency response, governance, health, recreation, safety, shelter, solid water, telecommunication & innovation, transportation, urban planning, wastewater, water & sanitation. Urban leaders can effectively manage and plan the sustainability of their cities , 28. Moret al. [et al. [et al. [et al. [Arroyo-Caňada and Gil-Lafuente  suggest et al.  note tha [et al.  investig [et al.  examined [et al.  carried  [et al. .The test procedure consists of several successive stages: (I) date work ; (II) clustering ; (III) results analysis—finding recommendations. The most important stages of the research procedure were visualized in the www.open.dataforcities.org). The following analysis includes only European cities. The selection of urban smartness indicators is a huge challenge because this issue is approached in the scientific literature, international strategic documents and reports of various organizations in so many different ways. Indicators from the ISO 37120 standard were used in the conducted study. The choice of indicators (structure or intensity) was motivate by suggestions from Transforming our World: The 2030 Agenda for Sustainable Development . The avaThe selection of analyzed cities was carried out using three criteria: (i) spatial coverage concerns Europe; (ii) possession of an ISO 37120 certificate at the platinum level; (iii) all mandatory indicators identified. The selection of diagnostic variables to assess sustainable urban development included several stages. It was checked whether the variables fulfill the formal criteria in terms of measurability, completeness and comparability. Regarding statistical premises, the set of variables eliminated those for which the coefficient of variation was below 10%. A further reduction of the variables related to excessive correlation with the analysis of the matrix of coefficients used Pearson correlations. It allowed the identification of diagnostic features that were excessively correlated, which should be removed from further research. Finally, the following indicators were selected for analysis: a share of city's unemployment, a ratio of primary education student to teacher, a amount of fine particulate matter  concentration, a number of firefighters per 100,000 population, a number of total collected municipal solid waste per capita, a number of green area per 100,000 population.1), social  and environmental  as well as an indication of their importance for sustainable development. The research was carried out using STATISTICA version 13.1 program and EXCEL. The evaluation of sustainable development is very complex due to the wide range of factors. Assessment of urban sustainability requires the determination of a set of indicators characterizing key aspects in three dimensions economic , q =  – two points in the Euclidean n-space,The cluster analysis, derived from the modeling classification, was developed by R.C. Tryon in 1939 . It is ad–the distance from a point p to a point q.The research began with computing the basic statistics for urban indicators by measuring position (arithmetic mean) and variability . The most diverse indicator is number of a green areas, while the least is a ratio of primary education student to teacher. The next stage was to determine the eigenvalues of the correlation matrix . The eigThere are several ways to conduct factor analysis for instance unweighted least squares, generalized least squares, maximum likelihood. The interpretability of factors improved through rotation. There are many different types of rotation, but they try make factors each highly responsive to a small subset of items. Rotation works through changing the absolute values of the variables whilst keeping their differential values constant. There are two major categories of rotations as orthogonal and oblique. Orthogonal rotations produce uncorrelated factors, but oblique—correlated factors. Varimax, quartimax and equamax are the variant of orthogonal rotation. The most commonly oblique rotations are Direct Quartimin, Promax and Harris-Kaiser Orthoblique.The next step of investigation was to determine the values of the factor loadings after equamax rotation . Each ofConsequently, it was calculated the projection of each observation on each of the factor. The factor scores gave the location of each observation in the space of the common factors. The The next step of the study involved the identification of outliers based on the configuration of objects in the space of the two factors space. The The next step involved the cluster analysis. Grouping was carried out using agglomeration and k-means methods. In the agglomeration analysis, the Ward method was selected, in which the Euclidean distance was used to compare cities. The agglomeration graph presents information about the binding distance relative to the binding steps. The objects clusters have been identified in the dendrogram—sopel chart . Groups The considerations, carried out in the manuscript, allowed to state that the smart city concept is implemented using sustainability in economic, social and environmental aspects. The application of the factor analysis presented relationships between indicators characterizing sustainability in selected European cities. The evaluation of the implementation of sustainability using cluster methods has allowed to identify similar cities.In the case being analyzed, the impact of input indicators on the sustainability of cities was described through the first two factors . The two6) as well as those that are negatively correlated: a total collected municipal solid waste (X5) and a city's unemployment (X1). The above correlations testify to the association of high values of variables X5, X1 with low value of the number of firefighters (X4) and accordingly, with the increase of the latter the former decrease. The second factor consists of positively correlated variables with a ratio of primary education student to teacher (X2) and a green area (X6) as well as also negatively with a responsible for number of firefighters (X4).The first factor consists of positively correlated variables  with a g1) and a collected municipal solid waste (X5). The lack of correlation is between a fine particulate matter concentration (X3) and a green area (X6) or a collected municipal solid waste (X5) and a ratio of primary education student to teacher (X2). The negatively correlated is between a number of firefighters (X4) related to a ratio of primary education student to teacher (X2).In the analyzed example, most of the information contained in input variables is transferred by the factors . The strThe position of cities shows the graph of objects’ configuration in the two factors space . Aalter The level identifying the cluster number at 13th step corresponds to 6 binding distances On the basis of the agglomeration graph,. The four clusters of cities were identified on the dendrogram . The urbThe clusters’ characteristics were prepared based on the k-means analysis .The cluster 2 is the most numerous group. It consists of eight cities . The number of a green areas is higher , and the remaining indicators’ average values have an medium value.The cluster 3 is the least numerous group with an isolated Aalter. The value of a green areas is the highest  as well as a unemployment (3.3%), the number of firefighters  and the amount of collected municipal solid waste (0.13 t/capita) are the lowest.3) and a green areas  are the lowest.The cluster 4 consists of three cities . The value of a unemployment (18.8%), the ratio of primary education students to teachers (19.1) and the amount of collected municipal solid waste (0.49 t/capita) are the highest. The amount of fine particulate matter concentration (10.8 μg/m3) and the number of firefighters  are the highest. The ratio of primary education student to teacher (12.5) is the lowest. Finally, cluster 1 includes four cities . The amount of fine particulate matter concentration , but the second factor—a ratio of primary education student to teacher (X2). The strong correlation occurs between the city's unemployment (X1) and the collected municipal solid waste (X5). Aalter, Koprivnica and Porto are distinctive from the other cities. The cluster analysis allowed to identify and characterize four groups of similar cities. The fourth cluster  is the weakest set of cities, but the third cluster  is the best set of cities. The results of grouping cities by methods of the cluster analysis and the factor analysis are identical. The conducted research shows that the analysed cities present a huge diversity of the sustainability level.In this study cluster analysis and factor analysis were used to identify significantly variables related to sustainability of selected European cities with the implemented ISO 37120 standard. The method allows replacing the input set of correlated features with a small number of uncorrelated factor which are linear combinations of variables. Two of the extracting factors explain nearly 77% the variability of input data. The first factor justifies 43% of the variability of input data. The second factor explains almost 34% of the variability of data. It can be concluded that the factor analysis is useful in reducing the dimensionality of variables in the description of the problem under considered. The first factor mainly contains the a green area  has the highest level of urban smartness because of the environmental and the economic pillars of sustainability. The main pillar of sustainability is social in the cluster 1. The cluster 4 presents the low urban smartness because of the environmental and the economic pillars of sustainability.The paper fills the gap in the editorial market by reviewing issues related to urban smartness through the use of extensive literature. Another advantage of the article is the application of an original cluster and factor analysis methods to assess the indicators of cities in the area of urban smartness. The further research could be conducted through direct interviews with city managers and players in order to understand their attitude towards the development of smart city projects. However, the research carried out in this manuscript does not fully cover the extensive research topic. Interesting research on the future of urban smartness should include following issues: (i) operationalization of urban smartness measurement; (ii) determinants of the level of urban smartness depending on the size and type of urban units; (iii) conceptualization of the urban smartness model; (iv) using e-Planning tools to build a smart city strategy.


Athletes are some of the fittest members of our society, yet paradoxically carry an increased risk of sudden cardiac death (SCD). The athlete's underlying risk of SCD in sports may be increased due to a number of underlying structural, arrhythmic and inherited cardiac conditions (ICCs). There are also physiological adaptations, which occur in the cardiovascular system in athletes as a result of high‐level athletic activity and may be misinterpreted as pathology. Differentiation of “athlete's heart” from heart disease may be challenging due to the effects of exercise on the electrical and structural cardiac remodeling. Features such as prolongation of the QT interval, left ventricular hypertrophy and cavity dilatation, create significant overlap between physiology and inherited channelopathies and cardiomyopathies. Most inherited cardiac conditions have an underlying genetic basis to disease and genetic testing in an athlete can have diagnostic, prognostic and therapeutic implications, including guiding exercise recommendations. Therefore, genetic testing can be a useful diagnostic tool when used carefully and appropriately by a trained cardio‐genetics expert. These adaptations are collectively known as “athlete's heart” and can be considered normal in an athlete, therefore not warranting further investigation. However, differentiation of “athlete's heart” from true underlying heart disease can be challenging for the clinician, as a number of these physiological adaptations such as prolongation of the QT interval, left ventricular hypertrophy (LVH) and left and right ventricular dilatation, are also features of inherited channelopathies and cardiomyopathies which may predispose the athlete to dangerous arrhythmias or SCD. Registries have shown that the majority of SCDs in young <35 years) athletes are due to underlying ICCs including the cardiomyopathies , and the channelopathies . years atWhen evaluating an athlete for potential underlying ICC, the genetic basis to these conditions must be carefully considered.Identification of the causative variant provides benefit to both the athlete under assessment, as well as their family members. Genetic testing has potential utility to assist with diagnosis, prognosis and therapeutics for the athlete, depending on the particular disease in question Table . For somIn order to improve the yield of genetic testing in an athlete, it is integral to ensure the athlete has undergone a comprehensive clinical evaluation first and that the athlete has a clearly defined phenotype. It is also relevant whether the athlete has a known family history of disease, as the yield is much higher in those with a family history, that is, a higher pre‐test probability. Finally, we would recommend only genes, which have strong supporting evidence to be a cause of the phenotype in question be tested, in order to maximize clinically actionable yield and minimize background noise and variants of uncertain significance (VUS), with pre‐ and post‐test genetic counseling.3While genetic testing can be useful when assessing athletes in selected cases, to differentiate physiological adaptation from underling pathology (or ICC), there are some particular conditions where‐by genetic testing has the highest yield and carries the greatest utility. These are highlighted below, and summarized in Figure 3.1Long QT syndrome (LQTS) is an inherited channelopathy with an estimated prevalence of 1:2000.In LQTS, proceeding to genetic testing is an integral part of the diagnostic pathway. In an athlete with a repeatedly prolonged QTc > 480 ms, genetic testing is indicated in most situations, independent of whether the athlete is symptomatic. Athletes tend to exhibit longer QT intervals compared to the general population and an isolated long QT interval in an athlete may represent the effect of increased vagal tone or delayed repolarization as a result of increased left ventricular mass.KCNQ1 the gene encoding for IKs (slow) channel.KCNH2 the gene encoding for the IKr (rapid) channel,SCN5A, the gene encoding for INa.There are currently 17 genes known to cause congenital LQTS, however more than 90% of cases are due to the main 3 genotypes.In addition to diagnostic utility in LQTS, genetic testing assists with prognostic and therapeutic decision‐making. There are specific genotype associated triggers for ventricular arrhythmias including strenuous exercise with particular risk identified with swimming (LQT1), loud noise/alarm clocks (LQT2) and sleep (LQT3).Genetic testing also assists in giving an athlete and their family a personalized prognostic risk. A recent study highlighted that an individual with LQTS can have their arrhythmic risk determined through a composite of the genotype and the QTc of the individual under assessment. This may vary from a low risk individual  to highest risk individuals .3.2Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare, but highly lethal, inherited channelopathy with an estimated prevalence of 1:10000. CPVT is characterized by adrenergically‐stimulated ventricular tachycardia , in the presence of a structurally normal heart.RYR2) which account for up to 60% of cases.CASQ2).As in LQTS, genetic testing can assist in CPVT diagnosis with the most common form due to autosomal dominant mutations in the cardiac ryanodine receptor (3.3Arrhythmogenic cardiomyopathy (ACM) is an umbrella term, which encompasses an arrhythmogenic heart muscle disease not due to valvular, ischaemic or hypertensive heart disease. The underlying causes for ACM include genetic, systemic and inflammatory disorders, however the key feature is the predominance of arrhythmia in the presentation.PKP2, DSC2, DSG, DSP, and JUP.LMNA, PLN, DSP and FLNC.ARVC is typically due to mutations in desmosomal protein genes including It is important to highlight that many high‐level athletes have right ventricular structural changes due to physiological adaptation to exercise. However, there are some key clinical features, which have been shown to be more commonly associated with underlying ARVC rather than athletic adaptation. These include symptoms , family history of SCD, high burden of premature ventricular contractions [PVCs >1000)] or sustained ventricular arrhythmias, abnormal signal‐averaged electrocardiogram (SAECG) and abnormal scar on CMR (cardiac magnetic resonance) imaging.000] or sEvidence suggests that athletic individuals with ARVC develop symptoms earlier and have a higher prevalence of ventricular arrhythmias, compared with non‐athletes.3.4MYBPC3 and MYH7. Athletic individuals may develop significant hypertrophy as part of physiological adaptation to exercise, however a left ventricular wall thickness (LVWT) of >16 mm is very rare, especially in white athletes, and more likely to reflect a diagnosis of HCM. In these individuals, genetic testing should be considered.PRKAG2 should be considered in athletes with LVH and co‐existent pre‐excitation on the ECG.Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular hypertrophy (LVH) in the absence of loading conditions such as aortic stenosis or hypertension and has a prevalence of at least 1 in 500 individuals.The presence of T‐wave inversion on the ECG of an athletic individual can indicate underlying cardiomyopathy, particularly in white athletes with inferolateral t‐wave inversion. However Sheikh et al showed comprehensive clinical evaluation provides significantly greater diagnostic utility (20%) than genetic testing (10%) at a third of the cost. Only athletes with lateral T‐wave inversion were found to have actionable genetic variants with the highest yield of 14% in white athletes with lateral T‐wave inversion. Therefore, the use of genetic testing in this group is usually not indicated.4There are often significant ethical and legal factors for any athlete considering genetic testing that must be carefully considered prior to proceeding. The sporting organization may have specific legal requirements and there may be insurance ramifications for the athlete and their club or organization. However, there are potential benefits in identification of a particular genetic result, including initiating life‐saving medication such as beta‐blockers, which may ultimately facilitate safe “return to play” for the athlete.5Genetic testing in an athlete can provide useful diagnostic, therapeutic and prognostic value provided that the testing is performed and the findings interpreted by a cardiogenetics expert in the setting of a specialized multidisciplinary clinic. Identification of actionable variants in LQTS, ARVC and CPVT genes has important implications for management and prognosis for the athlete, as well as helping to inform exercise prescription. However, it is very important that genetic testing should only proceed in athletes following comprehensive clinical phenotyping, when there is a high index of suspicion of an ICC, or in the presence of a family history. Appropriate pre‐ and post‐test genetic counseling is essential. Before proceeding to genetic testing in any athlete, it is critical that the athlete, their family and their club or sporting organization understand the implications, limitations and difficulties of the testing, so that an informed decision is made in a shared decision‐making model of care.


More intense forms of management include monocultures, which are agroindustrialized systems developed in more recent times and characterized by the establishment of high densities of A. maximiliana plants in deforested areas and abandoned agricultural lands. We compared monocultures, forestry systems and wild populations  in order to evaluate whether these practices have had an effect on intraspecific morphological and genetic variation and divergence. We also tested whether divergence has a positive relationship with environmental and geographic distance. We analyzed 16 phenotypic traits in 17 populations of A. maximiliana, and 14 populations were further examined by amplifying 9 SSR loci. We employed multivariate methods and analyses of variance in phenotypic and genetic traits to test whether clusters and the percentage of variation contained in the managed and wild categories can be identified. Tests of isolation by environment (IBE) and distance (IBD) were performed to detect the magnitude of divergence explained by climatic and geographic variables. We found that forestry systems are effective as reservoirs of morphological and genetic diversity, since they maintain levels similar to those of wild populations. Moreover, the monocultures showed similar levels, reflecting their recent emergence. While the species showed high morphological diversity , it had low to intermediate genetic diversity . Similar morphological and genetic divergences were found among populations, but these were not correlated with each other in population pairs. Non-significant morphological differentiation was found among categories. Only IBE was significant in the genetic structure , while neither IBE nor IBD was detected in the morphological differentiation. We discuss the implications of these results in the context of the weaknesses and strengths of A. maximiliana in the face of the socio-ecological changes predicted for the study area in the short term.Forestry systems in Mexico are examples of traditional management of land and biodiversity that integrates the use, conservation and restoration of forest elements. Current  Agave L. are used for mescal production  and Agroforestry (AFS) Systems maintained in Mexico. The terms FS and AFS refer to a land utilization type with several criteria of classification  in the coastal region of Jalisco. The historical impact of this activity on A. maximiliana populations has never been evaluated, and no landraces or ecotypes have been documented.In this study, we explored the case of hat time . The spiAgave plants against frost and pests. This action is a common element of Agave management in Mexico, especially on species managed at elevations of 800–2,500 m.a.s.l , where frosts, are light and fleeting, but where some species such as A. maximiliana are frequently damaged  has occurred in wild populations in the region in the last 30 years . Finally, we assume that the sexual reproductive system and probably chiropterophilous pollination system of A. maximiliana are biological strengths that have acted to maintain the connectivity of populations through gene flow.In summary, we consider that a recent but dynamic management exists in A. maximiliana populations have had an effect on their intraspecific diversity; and (2) whether isolation by environment  and by environment (IBE). This is to say, the greater the geographical distance and the environmental differentiation, the greater the genetic and morphological divergence. Our purpose was to delimit and define the state of diversity and intraspecific differentiation of A. maximiliana growing in western Mexico in order to identify biological, ecological and management weaknesses and strengths in the face of imminent socioecological changes in the region.In the context of these observations and assumptions, we aim to determine: (1) whether the methods and intensity of the management practices realized in ent IBE,  and by dAgave maximiliana, known locally as “Lechuguilla,” is endemic to Mexico. It commonly inhabits rocky slopes in the states of Jalisco, Sinaloa, Durango, Nayarit, Colima and Zacatecas, at 900–2,000 m.a.s.l, and requires an annual precipitation of 750–1,000 mm (A. maximiliana var. maximiliana and A. maximiliana var. katherinae (A. Berger) Gentry. The latter differs from the typical variety by its larger rosettes, greener leaves with more undulate repand margins with numerous variable interstitial teeth, and a deeper flower tube. However, A. maximiliana var. katherinae as synonymous of A. maximiliana sensu stricto. Recently, A. maximiliana var. katherinae could occupy the northern windward zone of the Sierra Madre Occidental (SMO), while A. maximiliana var. maximiliana could be found in the southern leeward zone of the SMO, but also in the Sierra Madre del Sur (SMS) and Transmexican Volcanic Belt (TVB)  . We percsensuAgave rows are planted, almost always in lines following the slope. Agrochemicals are added to fertilize the plants or to eliminate weeds. Some farmers also apply chemicals on the leaves to combat pests. Monocultures have a high density of plants compared to plantations managed under the forest, and are characterized by a drastic change in land use. The seeds used for monocultures are collected from plants in the forest, domestic cultivation and in vitro propagation. This latter propagation type is currently carried out by a single producer only, who obtains 2–10 seedlings from each seed. This strategy is a response to the scarce availability of seeds and plants in recent years  was used following the criteria of 2 (NT10) were excluded from the analysis because they presented multicollinearity 2, in which pi is the proportion of the total number of individual plants sampled in a population showing the ith state of a morphological trait (categorized with the Sturges rule) and s is the number of states of that character  was estimated. For this, the traits with the highest eigenvalues in the LDA (explained below) were selected in order to utilize those that better explained morphological diversity. Individual plant values of each trait from all populations were assigned to different morphological states according to the Sturges rule . This prharacter . In thisharacter . The exiharacter .0 = (Y–a)/b); where Y0 is the standardized value, Y is the observed value of the state of a character, a is its mean and b is its standard deviation  was performed. For this, the database was standardized with the expression Yeviation . From theviation . All anaeviation .In order to determine whether the existing set of phenotypes differs significantly among management categories, a nested two-way permutational multivariate analysis of variance (PERMANOVA) was conducted with a type III (mixed effects) model: a fixed factor (condition) with three levels  and a random factor (populations) with 17 levels. A PERMANOVA was performed with the records obtained from twelve non-correlated traits in Primer v6.1.11/PERMANOVA+ v1.0.1 . First, A. tequilana. Only nine microsatellites were reproducible and polymorphic (® (Qiagen) in a final reaction volume of 11 μL. Each reaction contained 120 ng of DNA, 4 μL of Multiplex, 0.8 μL of each primer (10 mM) and 2.4 μL of molecular grade water (MiliQ). The amplification conditions were: 15 min at 95°C, followed by 40 cycles of 30 s at 94 °C, 30 s at the annealing temperature of each primer , observed heterozygosity (HO), expected heterozygosity (HE) under Hardy-Weinberg equilibrium (HWE). Also, we performed HWE analysis based on the X2 test , inbreeding (f) and genotyping errors (b) on the values of homozygosity using the complete model (nfb) and its comparison with the model excluding the effect of the null alleles (fb), through the Deviance Information Criterion (DIC) between the two models. The analysis was run with 500,000 MCMC iterations and 50,000 burn-in cycles. Where the nfb model obtains the lowest DIC, this indicates that the null alleles have an important weight in the calculation of inbreeding. The null alleles also have a potential effect on genetic differentiation (FST). To evaluate this, the FreeNA software was used to perform this estimation by detecting the frequency of null alleles for each locus and population based on the Expectation-Maximization (EM) algorithm (ST) was evaluated excluding null alleles, i.e., the ENA correction method , mean number of alleles per locus (A), mean number of effective alleles per locus  was performed   in R plaST/1−FST. The geographic distance matrix was generated using the geosphere package version 1.5.10 , were obtained. To reduce multicollinearity among environmental variables, those that presented a correlation ≥0.8  were excluded. Separately, for each environmental variable, a simple regression analysis between the environmental distance matrix and the genetic distances was performed using the MRM method. In order to capture the most informative variables, those that exhibited a significant association with genetic distances were selected. Finally, Principal Component Analysis (PCA) was used to reduce the variation of these data, and the two first Principal Components  were used to calculate the Euclidean similarity distance matrix  using the geographical and environmental matrices generated with the procedure explained above. The morphological distance matrix was obtained based on the 12 morphological traits and the same pretreatment and Euclidian distance used in the PCoA and PERMANOVA (Primer v6.1.11.).p = 0.453), suggesting that the plants were morphologically diverse regardless of management condition or the population to which they belong. However, variation tended to be lower among cultivated populations (SD ± 0.03), compared to the managed (SD ± 0.09) and wild populations (SD ± 0.1)  reflected a general mean of 0.638 ± 0.07  .E, HO and HE). On average, close to half of the loci (49.2%) were found to be in Hardy-Weinberg equilibrium, with slight variation presented in each category . Another 38.09% of the loci, on average, were found to present a deficit of heterozygotes, also with slight variation among categories . A total of 7.14% of the loci presented greater heterozygosity than expected in equilibrium: the highest average (13.89%) was found in the cultivated plants, followed by the managed (5.56%) and wild (2.78%) plants. The inbreeding coefficient was FIS = 0.273 ± 0.134, only five of the nine loci were significant . The loci that did not present local inbreeding corresponded to the primers 12, 1763, 28 and 20. A frequency of null alleles was found in a range of 0–0.392. Inbreeding corrected for null alleles was of Avg (Fi) = 0.1529, with a density interval after 95% of 0.0024–0.3179. The model with the lowest DIC value was nfb (DIC: 10216), compared to the model fb (DIC: 10424), indicating that the null alleles had an important effect on the calculation of the inbreeding.The mean percentage of polymorphic loci for all of the populations was 94.75%, the mean number of alleles per locus was 2.37 and the mean number of effective alleles was 1.87. There was an observed heterozygosity of 0.295 and an expected heterozygosity of 0.418 . Mean vaa priori (p < 0.05) with a non-significant 19.8% variation found among a priori categories  explained 70% of the morphological variation. The maximum leaf width (MxLW) had the highest value in the LDA1 and traits related to plant thorniness  and plant size (MxD) had the highest values in the LDA2. This analysis did not define a congruent grouping with the management categories established a priori . The croa priori . Moreovetegories . Likewistegories  three grtegories . Even thtegories , constitCT = 0.38, p = 0.001), which is similar to that found with the morphological data, while 1% corresponded to the variation among management categories . Population differentiation, corrected for null alleles, was FST = 0.43 with ± 0.34–0.51 confidence intervals. Finally, the DAPC showed no genetic structure congruent with the pre-established categories . The general model for IBD and IBE in terms of genetic data was not significant ; however, we detected a significant effect of environmental variables on the genetic distance matrix . These variables were annual precipitation , precipitation of wettest month , precipitation of driest month , precipitation seasonality , precipitation of warmest quarter , precipitation of coldest quarter . This result supports an IBE process, while the geographical distances matrix did not present a significant association with the genetic distances matrix  and therefore does not support an IBD process.Morphological and genetic distances were not related to each other , and neither were the regression coefficients for the geographic and environmental distance matrices .On the other hand, the MRM general model used to explore the influence of geographical and environmental variables on the morphological distance matrix was not statistically significant , with values that are comparable to those reported for columnar cacti under in situ management categories, with mainly sexual (Myrtillocactus schenckii (J.A. Purpuss) Britton & Rose, IMD 0.652–0.703, Stenocereus pruinosus (Otto ex Pfeiff.) Buxb. IMD 0.647–0.677, Stenocereus stellatus (Pfeiff.) Riccob. with asexual reproduction  and from shoots  and even equal or inferior to some landraces of domesticated species such as A. angustifolia, A. rhodacantha and A. tequilana (IS = 0.273), although it should be noted that the inbreeding corrected for null alleles was lower (Fi = 0.153). This value must therefore be treated with some caution. Assuming that A. maximiliana has an outcrossing reproductive system, this would depend to a large extent on the pollinators. The low genetic diversity and presence of inbreeding in this species could be the consequence of low visit rates or inefficient pollinator behavior.In contrast, the low genetic diversity found in tegories , suggestotatorum . It is bequilana . MoreoveA. maximiliana . In some of the sample populations, there is also a high incidence of plants with poorly developed inflorescences; i.e., of small size and with few flowers, and with evidence of infection by fungi and bacteria, (pers. obs.). Low levels of genetic diversity are generally associated with inbreeding and this, in turn, can trigger a reduction in the average adaptation of the population, in a process known as inbreeding depression  H. E. Moore and Stearn, in situ management that not only include the extraction of plants, but also manual dispersion of seeds, tolerance of some individuals and the protection and promotion of plants with characteristics that are desired by humans, among others. These are practices that have been widely documented for agaves in central  to 90%  of individuals in monocultures can be morphologically identified as belonging to other in situ managed populations  and not geographic distance is one of the mechanisms that structure the populations of A. maximiliana. This means that, to some extent, populations are genetically isolated as a result of different precipitation conditions , followed by a reduction in the Last Glacial Maximum period . Thus, past extension-contraction cycles in the demographic history of A. maximiliana could result in decreases in the levels of genetic diversity and increases in genetic structure  have historically affected the spatial structure of both types of variation e.g., . AnotherA. maximiliana was not clearly reflected in our data. The two northern populations collected for this study  of A. maximiliana are locally adapted. In addition, adoption of new approaches regarding developmental plasticity, variation in quantitative trait loci related to natural and artificial selection or niche construction theory (versus domestication contexts (in situ contexts of management practices are still poorly documented, and that this study constitutes a valuable base on which to establish more specific research questions to further the understanding of the origin of domestication processes.Our research approach had restrictions related to the identification of local adaptation processes and phenotypic plasticity. As a result, we cannot make direct conclusions about the extent to which the wild and managed populations (either n theory  could cocontexts . DespiteFinally, we recognize in this case study that, beyond the improvement of technical options to multiply the number of plants available per year, it would be more meaningful to consider broader aspects of sustainability and conservation. This would generate benefits not only for the raicilla production chain, but also for the range of environmental goods and services their habitat provides.http://dx.doi.org/10.17632/3379nnccmy.4.The datasets generated and analyzed for this study can be found in the Mendeley Data, DC-T and OV-P conceived and designed the study, partially organized fieldwork and carried out some final statistical analysis. SA-R and LV-S carried out the collection and first analysis of phenotypic and genetic data, respectively, as part of their bachelor theses. JP-A gave input on the study design and carried out some final statistical analysis and design of figures. JM-S led laboratory work. OH-G described the management practices which were the basis of the hypothesis of this study, he also partially organized fieldwork. All authors reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Balanites aegyptiaca L. natural populations distributed across the Sahelian-Saharan zone of Mauritania using 16 polymorphic ISSR primers. These generated 505 polymorphic bands. Polymorphism information content (PIC) varied from (0.13–0.29) with an average 0.23, marker index (MI) averaged 7.3 (range 3.3–10.3) and resolving power (RP) ranged from (4.53–14.6) with an average 9.9. The number of observed alleles (Na) ranged from (0.62–1.39), Effective number of alleles (Ne) varied from (1.26–1.37), Shannon’s information index (I) ranged from (0.25–0.36). AMOVA analysis showed that 80% of the genetic variation was fined within populations, which is supported by a low level of genetic differentiation between population (GST = 0.21) and an overall estimate of gene flow among populations (Nm = 1.9). The dendrogram based on Jaccard's similarity coefficient and the structure analysis divided the seven populations into two main clusters in which two populations from the Saharan zone were grouped. Our results provide baseline data for genetic conservation programs of this Sahelian neglected crop and with an important econ-ecological role.Drought and desertification are the major environmental constraints facing the Sahelian agro-ecosystems for decades. Assessing genetic diversity of native tree species is critical to assist ecosystems restoration efforts. Here we describe genetic diversity and structure of seven  Balanites aegyptiaca Del. belongs to Zygophyllaceae family, commonly known as desert date tree, is a long-lived dicotyledonous spiny tree up to 10 m tall that is widely distributed in arid and semi-arid regions in Africa and South Asia3. In Mauritania, B. aegyptiaca is widespread throughout the country. It is a xerophytic tree distributed in Sahelian as well as the Saharan zones. The Desert date is a multipurpose tree offering protection against desertification, provides food for human and animals in addition to many medicinal uses3. For instance, fruit of B. aegyptiaca  is used in traditional medicine to treat diabetes, asthma, epilepsy, malaria, etc. Moreover, the seed or Kernel of B. aegyptiaca fruit is very rich in oil (46.0–54.7%) particularly unsaturated fatty acid  and protein (26.1–34.3%)4. Some authors successfully tested B. aegyptiaca oil for biodiesel production and showed that it can be an alternate diesel6.B. aegyptiaca has recently identified as one of the native plants for restoring of degraded Sahelian ecosystem in the so called Great Green Well (GGW) project7. This project aims to create a 15-km- wide (north–south) green belt of trees south of the Saharan desert across more than 11 African countries 9.Due to its extreme resistance to drought as well as its diverse set of natural regeneration strategies through seeds, suckers and rejuvenation, With two-third of its surface areas lying in the African great Sahara Desert, Mauritania like many other Sahelian countries was faced during the 1970s–1980s with the severe drought resulting in huge socio-economic and environmental impacts . The limited biodiversity that naturally characterizes arid and desert regions was significantly reduced due to that environmental condition.Studying genetic variability is of great relevance in plant genetic resource management programs. Through information it provides, it is possible to identify genotypes of interest and use them in the establishment of effective conservation strategies.10. A vast array of molecular markers including restriction fragment length polymorphism (RFLP), randomly amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), inter simple sequence repeats (ISSR) and microsatellites or simple sequence repeats (SSR) have been used to assess genetic diversity in plant species from different parts of the world15. ISSRs are segments of DNA that are flanked at both side by shorts DNA motifs of 2–5 nucleotide long repeated multiple time called microsatellite16. PCR-ISSR is a quick, cost-effective and high reproducibility molecular technique based on PCR amplification of such multilocus inter-microsatellite sequences ISSRs are found to be useful in the analysis of genetic variation below the species level, mainly in studying population structure and differentiation17. Data on the genetic diversity of B. aegyptiaca germplasm worldwide are very limited. Domyati et al.29 assessed genetic diversity in 7 medicinal plants, including B. aegyptiaca using ISSR, RAPD, and AFLP. They showed that ISSR markers revealed high genetic diversity in B. aegyptiaca compared to other markers . Moreover, AFLP markers have been successfully used to assess genetic diversity amongst B. aegyptiaca collected from different geographical regions18.Today, molecular markers are by far more suitable to analyze the genetic diversity than morphological and biochemical traits because they segregate as a single gene and they are not affected by the environmentB. aegyptiaca populations collected from different bioclimatic zones in Mauritania using ISSR markers. The main objective of this study was to provide baseline molecular information for this economically and ecologically important neglected crop for a best conservation and restoration program of Sahelian ecosystem.In the present study, we evaluate genetic diversity among and within 8TC] produced the lowest number of bands (21) whereas the primer BTH2[(AG)8T] showed the highest number (42) with an average of 31.5 fragments per primer (Table The 16 ISSR primers used generated 505 polymorphic bands ranged in size between 40 and 3369 bp. The primer BTH3, where p is the proportion of the genotypes containing the fragment.The binary data matrix was analyzed using GelCompar II software . Only clear and sharp bands were considered as ISSR markers. They were then coded as 1 for the presence and 0 for the absence. From this binary presence/absence matrix, we calculated the polymorphism information content (PIC) according to the formula, described by Roldán-Ruiz et al.24 including the number of alleles (Na), the effective alleles (Ne), the private alleles (Pa), the Shannon’s information index (I) and the percentage of polymorphic loci (%P).To estimate genetic diversity, five parameters were calculated using GenAlex v6.525 was used to compute the coefficient of gene differentiation (Gst) and the gene flow (Nm). Principal coordinate analysis (PCoA) and Molecular Analysis of Variance (AMOVA) were also calculated using the GenAlex v6.5 program.POPGENE version 1.3226 The genetic structure pattern was analyzed using a Bayesian algorithm implemented in the STRUCTURE 2.3.4 software which to infer the number of genetically distinct clusters (K) without a priori group designation. Twenty independent simulations were carried out for each simulated value of K (the number of populations). The range of possible clusters was set from 1 to 10. Analysis parameters included a burn-in period of 30,000 iterations and 50,000 Markov Chain Monte Carlo (MCMC) repetitions with the selection of admixture and correlated allele frequencies models. The credible number of populations was assessed using the Evanno’s ΔK method27 based of maximum value of ΔK with online tool Structure Harvester28.The data were analyzed using the SIMQUAL  method to generate Jaccard similarity coefficients. These similarity coefficients were used to construct dendrogram using the Unweighted Pair-Group Method with Arithmetic mean (UPGMA) employing the SAHN  routine from NTSYS-PC v. 2.02 program


Tupaia longipes, that inhabits forest fragments of the Lower Kinabatangan Wildlife Sanctuary, Sabah. Furthermore, we used these markers to assess dispersal regimes in male and female T. longipes.Constraints in migratory capabilities, such as the disruption of gene flow and genetic connectivity caused by habitat fragmentation, are known to affect genetic diversity and the long-term persistence of populations. Although negative population trends due to ongoing forest loss are widespread, the consequence of habitat fragmentation on genetic diversity, gene flow and genetic structure has rarely been investigated in Bornean small mammals. To fill this gap in knowledge, we used nuclear and mitochondrial DNA markers to assess genetic diversity, gene flow and the genetic structure in the Bornean tree shrew, T. longipes. Clear sex-biased dispersal was not detected based on relatedness, assignment tests, and haplotype diversity.In addition to the Kinabatangan River, a known barrier for dispersal in tree shrews, the heterogeneous landscape along the riverbanks affected the genetic structure in this species. Specifically, while in larger connected forest fragments along the northern riverbank genetic connectivity was relatively undisturbed, patterns of genetic differentiation and the distribution of mitochondrial haplotypes in a local scale indicated reduced migration on the strongly fragmented southern riverside. Especially, oil palm plantations seem to negatively affect dispersal in This study revealed the importance of landscape connectivity to maintain migration and gene flow between fragmented populations, and to ensure the long-term persistence of species in anthropogenically disturbed landscapes. Deforestation of tropical rainforests causes severe problems for the maintenance of biodiversity and ecological functions worldwide –3. AgricOne of the highest rates of deforestation in the tropics has occurred on Borneo . Much ofAlong the Kinabatangan River, habitat connectivity differs considerably on both riversides. While forest connectivity is high on the northern riverbank, it is disrupted  on the southern side. This setting provides a heterogeneous landscape highly suitable for estimating and comparing migratory activities and genetic structures under different habitat configurations.Tupaia longipes Thomas 1893, Scandentia, Tupaiidae) is endemic to Borneo, is widespread throughout the island and is known to inhabit fragmented forest patches such as those along the Kinabatangan River  and the Sabah Biodiversity Centre [Export-Licence No.: JKM/MBS.1000–2/3(38)].T. longipes tissue biopsy samples from 116 individuals following a HotSHOT extraction protocol [msat) primers described by Munshi-South and Wilkinson [msat loci were applicable on the T. longipes samples used in this study  locus to support the msat-analyses. For the sequencing, an optimised primer (L14841tupaia) together with the primer MVZ16 was used as described in Brunke et al. [cyt b haplotype sequences were deposited in GenBank under the accession number MK111987-MK111997 and MT013304-MT013306  and the unbiased expected heterozygosity (He), and the fixation coefficient (FIS) as an indicator for inbreeding. These estimates were obtained for all loci, for each site, and for each side of the river  from the same riverside and from different riversides were assessed and compared to the overall set of pairwise comparisons. Conversely, interindividual relatedness was used as a measure of genetic similarity between forest sites. For this, the relatedness coefficient (r) of Queller and Goodnight [n ≥ 10. Significances between riversides (α = 5%) was tested for genetic differentiation and relatedness with a Mann-Whitney U Test implemented in R v 3.5.0 [Pairwise genetic differentiation between forest sites was explored with Wright’s F-statistic according to Weir and Cockerham  implemenoodnight  was calcoodnight  for all  v 3.5.0 .T. longipes along the Kinabatangan River, Bayesian clustering was used to assign individuals to population clusters based on their genotypes without prior information on their sampling sites. The analysis was performed in STRUCTURE v 2.3.4 [k values ranging from 2 to 8. Each value of k was tested 20 independent times, under an admixture model with correlated allele frequencies between clusters and with each STRUCTURE run lasting 500,000 iterations of the Markov chain Monte Carlo (MCMC) algorithm discarding the first 20% steps as burn-in. To determine the most probable number of population clusters, the likelihood of the data (LnPr(k) [k [In order to investigate population genetic structure in  v 2.3.4 , 75 with(LnPr(k) ;) and itPr(k) [k ;) were iT. longipes was assessed by identifying mitochondrial haplotypes and by estimating haplotype (h) and nucleotide (π) diversity using DnaSP v 5.10.1 [Genetic diversity within v 5.10.1 . Haplotyv 5.10.1 .To estimate the recent migration rates between riversides and between forest sites within each riverside, a MCMC analysis was run for each dataset using the software BayesASS v 3 . For eacr) of Queller and Goodnight [Animal Movement extension in ArcView GIS 3.3 .A possible sex-bias in dispersal was assessed by comparing male and female relatedness together with differences in male and female population assignment indices. The relatedness coefficient  of Goudet et al. [AIc values indicate a genotype less likely than average to occur in the sampled population and characterises possible immigrants, a positive AIc value indicates probable residents. Therefore, the dispersing sex should be characterised by a lower mean AIc with a higher variance compared to the more philopatric sex [AIcs were calculated using the R package hierfstat v 0.04–22 [AIc (mAIc) and variances (vAIc) were analysed for each sex separately. The two riversides were inspected separately to infer possible physical barriers on movements. A Mann-Whitney U Test was used to detect significant sex differences in relatedness and mAIc, and Levene’s test to detect differences between sexes in vAIc. All tests were performed using basic packages in R v 3.5.0 [The relatedness analysis was complemented with corrected assignment index , represent thus signals of immigration, and should be higher in the dispersing sex. Therefore, mitochondrial haplotype diversity was compared between males and females to infer sex-biased dispersal in T. longipes according to Brunke et al. [Finally,  mitochondrial markers represent another tool to infer sex-biased dispersal. Maternally related males and females share the same haplotypes, differences in haplotype diversity, the presence of sex-specific haplotypes and unique haplotypes within sampling locations  and multiplex (M) reactions and the respective microsatellite loci used in T. longipes. Table S2 Upper table: Genetic  and geographic (Euclidean) distances  among forest sites. Lower table: Mean relatedness  and geographic (Euclidean) distances  among and within forest sites. Table S3 Results from MWU test (Observed (Ho) and expected heterozygosity (He), inbreeding index (FIS), allelic richness, haplotype (h) and nucleotide (π) diversity, relatedness (r), on the northern and southern riverside, and relatedness (r), interindividual distances of related dyads (r ≥ 0.25), mAIc in males and females) or Levene’s test . Tests were conducted for the whole sets and for within and among forest site/sampling location comparisons. Significant differences are in bold. Table S4 Relative proportion of individuals per site and riverside, respectively, which were assigned to one of the clusters under k = 2 (LnP(D): − 3112.2), with a probability of q > 80%.Additional file 2 Table S5Cytochrome b haplotypes in T. longipes (n = 60) and their spatial distribution (upstream → downstream) along the northern (NA–ND) and southern (SE–SI) riverside. Figure S1: Haplotype network based on cyt b sequences.Additional file 3 Table S6 Home range area, the maximum (D max) and minimum (D min) home range diameter for the two T. longipes observed with the triangulation method. Figure S2 Landscape changes along the Kinabatangan River between 1985 and time of study (2013). Study sites are marked with green lines .


Signal transducer and activator of transcription 3 (STAT3) is one of seven STAT family members involved with the regulation of cellular growth, differentiation and survival. STAT proteins are conserved among eukaryotes and are important for biological functions of embryogenesis, immunity, haematopoiesis and cell migration. STAT3 is widely expressed and located in the cytoplasm in an inactive form. STAT3 is rapidly and transiently activated by tyrosine phosphorylation by a range of signalling pathways, including cytokines from the IL‐6 family and growth factors, such as EGF and PDGF. STAT3 activation and subsequent dimer formation initiates nuclear translocation of STAT3 for the regulation of target gene transcription. Four STAT3 isoforms have been identified, which have distinct biological functions. STAT3 is considered a proto‐oncogene and constitutive activation of STAT3 is implicated in the development of various cancers, including multiple myeloma, leukaemia and lymphomas. In this review, we focus on recent progress on STAT3 and osteosarcoma (OS). Notably, STAT3 is overexpressed and associated with the poor prognosis of OS. Constitutive activation of STAT3 in OS appears to upregulate the expression of target oncogenes, leading to OS cell transformation, proliferation, tumour formation, invasion, metastasis, immune evasion and drug resistance. Taken together, STAT3 is a target for cancer therapy, and STAT3 inhibitors represent potential therapeutic candidates for the treatment of OS. Signal transducer and activator of transcription 3 (STAT3) is a member of the STAT protein family, vitally important for eukaryotic cells. We review the molecular structure and function of STAT3 and its isoforms, highlighting signalling pathways for the regulation of gene transcription. A critical appraisal of STAT3 in cancers, such as osteosarcoma, is provided emphasizing potential therapeutic approaches targeting STAT3 and its inhibitors DDR1discoid domain receptor 1GM‐CSFgranulocyte‐macrophage colony‐stimulating factorGSEAgene set enrichment analysislncRNAlong non‐coding RNAmiRNAmicro‐RNAMMPmatrix metalloproteinasePDGF(R)platelet‐derived growth factor (receptor)PI3Kphosphatidylinositol‐4,5‐bisphosphate 3‐kinaseSTK35serine/threonine kinase 35VEGF(R)vascular endothelial growth factor  proteins are latent cytoplasmic transcription factors that are activated by cytokines and growth factors.22.1The human STAT3 gene is located on chromosome 17 17q21.2) and has 24 exons.1.2 and h2.2+ T cells, T helper Th17 cells, Th1 and Th2 cells and the STAT3α isoform might interact with proteins, such as Prohibitin 1, for modulation of pathological immune responses.STAT3 is widely expressed and plays a vital role during mammalian development.STAT3 activation is regulated by a complex network of signalling pathways, and its specificity is incompletely understood. STAT3 is activated by tyrosine kinase phosphorylation in response to cytokines and growth factors, such as IL‐6, EGF and PDGF.STAT3 appears to play an important role in mediating cellular differentiation via its isoforms.3STATs are important mediators of cell signalling for a wide range of biological functions including cell growth, differentiation and survival events of immunity and inflammation. STATs may be activated by oncoproteins and contribute to the process of malignant transformation by promoting cell proliferation and preventing apoptosis.3.1STAT3 is implicated in the onset and progression of human cancers including multiple myeloma, leukaemia, lymphomas and solid tumours.3.2STAT3 signalling is implicated in the development and progression of OS Figure .9 STAT4Constitutively activated STAT3 is associated with tumorigenesis and progression of many cancers, such as haematopoietic tumours  and solid tumours .4.1Dysregulated activation of STAT3 appears to promote the proliferation of malignant tumours, including OS.4.2STAT3 is a central signalling mediator for multiple pathways, such as Src, EGF and GM‐CSF, regulating angiogenesis via the upregulation of VEGF.4.3Dysregulated STAT3 activation has been shown to confer resistance to apoptosis in human cancers, including OS.4.4STAT3 signalling might affect the cellular process of autophagy, with subcellular localization patterns indicative of its regulatory activity.4.5STAT3 activation is attributed to both the progression and chemotherapeutic resistance of cancers, including OS.5STAT3 is constitutively activated in tumour cells and immune cells of the tumour microenvironment in a range of cancers, such as OS.5.1Inflammation is associated with the onset and progression of cancers, including OS.5.2STAT3 activity appears to mediate immune evasion by tumours, such as OS, by suppressing the tumour‐specific expression of pro‐inflammatory mediators.6OS is a primary malignant bone tumour exhibiting aggressive growth and metastasis, leading to high mortality. The five‐year survival rate of metastatic OS is estimated to be 20%‐30% and has not improved significantly in the previous decades.6.16.1.1Synthetic pharmacological agents play a leading role in the search for safe and effective chemotherapeutic cancer treatments. Synthetic analogues of naturally occurring compounds with anti‐tumour properties may be designed in order to deliver the level of bioactivity required for chemotherapeutic effect.Direct inhibition of STAT3 activation may include blocking the phosphorylation of STAT3, preventing STAT3 dimerization and nuclear translocation, and disrupting DNA binding by STAT3 in the nucleus. Direct STAT3 inhibitors are designed to target structural elements of the STAT3 protein important for its function, such as the SH2 domain, N‐terminal domain and C‐terminus transcriptional activation domain.Indirect inhibition of STAT3 may be achieved by blocking upstream regulators of STAT3‐signalling, such as IL‐6 and EGF.6.1.2Natural compounds are potentially effective for the treatment of OS and may be developed as synthetic analogues for OS therapy. Natural compounds present an alternative to chemotherapy using agents, such as cisplatin, doxorubicin and methotrexate, and may offer hope to sufferers of OS with poor prognosis. Natural compounds appear to target signalling pathways involved in OS, such as JAK/STAT, PI3K/AKT, Notch and Wnt.Resveratrol is a natural polyphenolic compound, which may have potential for the treatment of OS.Flavonoids, such as ginkgetin, appear to have potential for the treatment of OS.Alkaloids, such as CEP, coptisine, sinomenine and columbamine, appear to have therapeutic potential against OS via inhibition of STAT3‐signalling pathways, and require further investigation.Terpenoids, such as toosendanin, cucurbitacin B and I, RDA, glaucocalyxin A and catalpol, appear to have anti‐OS potential via modulation of STAT3 signalling.4‐methoxydalbergione (4‐MD), extracted from Dalbergia odorifera, belongs to the quinones group of compounds and possesses anti‐oxidant, anti‐inflammatory, as well as anti‐tumour properties. 4‐MD demonstrated the inhibition of OS cell proliferation and induced OS cell apoptosis in vitro by downregulating JAK2/STAT3 signalling. 4‐MD inhibited OS tumour growth in a murine xenograft in vivo model by decreasing the expression of STAT3 and anti‐apoptosis gene, surviving.7STAT3 is a cytoplasmic transcription factor of the STAT family and is activated by tyrosine phosphorylation by numerous signalling pathways. STAT3 is activated by receptor tyrosine kinases, including cytokines of the IL‐6 family signalling by the gp130 subunit, and growth factors, such as EGF, and non‐receptor tyrosine kinases, such as Src and Abl. STAT3 may also be activated as a downstream effector of G protein signalling. STAT3 is a convergent intracellular signalling mediator for a range of pathways. Activated STAT3 is translocated to the nucleus for the transcriptional regulation of target genes involved with vital biological processes, including embryogenesis, immunity, haematopoiesis and apoptosis. The activation of STAT3 is rapid, transient and tightly regulated under physiological conditions. Four STAT3 isoforms have been identified, which have distinct biological functions. The role of STAT3 isoforms in cancers, including OS, is largely unknown and requires further investigation. Constitutive activation of STAT3 appears to lead to the onset and progression of OS. STAT3 is considered a proto‐oncogene, and evidence suggests that dysregulated expression of STAT3 plays an oncogenic role in OS by promoting processes including cellular transformation, tumour growth, invasion, metastasis, resistance to chemotherapy and immune evasion. Oncogenic STAT3 target genes include cyclin D1, Bcl‐xL, survivin, Mcl‐1 and MMPs. STAT3 is a potential target for OS therapy, and STAT3 inhibitors appear to be potentially effective for the treatment of OS. STAT3 inhibitors may act directly or indirectly to downregulate the expression of target genes involved with OS. Further research is needed to develop the therapeutic potential of STAT3 inhibitors from synthetic sources and natural compounds, including possibilities for improved chemotherapeutic efficacy by a combined drug approach.All authors declare that they have no conflict of interest., and Jiake Xu supervised the study and revised the manuscript.Yun Liu and Shijie Liao conducted research and drafted the manuscript. Samuel Bennett, Haijun Tang, and Dezhi Song aided in the process of drafting manuscript and protein structural analysis. Jiake Xu performed the bioinformatics analysis of gene expression. David Wood, Xinli Zhan


Glossina pallidipes is the main vector of animal African trypanosomiasis and a potential vector of human African trypanosomiasis in eastern Africa where it poses a large economic burden and public health threat. Vector control efforts have succeeded in reducing infection rates, but recent resurgence in tsetse fly population density raises concerns that vector control programs require improved strategic planning over larger geographic and temporal scales. Detailed knowledge of population structure and dispersal patterns can provide the required information to improve planning. To this end, we investigated the phylogeography and population structure of G. pallidipes over a large spatial scale in Kenya and northern Tanzania using 11 microsatellite loci genotyped in 600 individuals. Our results indicate distinct genetic clusters east and west of the Great Rift Valley, and less distinct clustering of the northwest separate from the southwest (Serengeti ecosystem). Estimates of genetic differentiation and first-generation migration indicated high genetic connectivity within genetic clusters even across large geographic distances of more than 300 km in the east, but only occasional migration among clusters. Patterns of connectivity suggest isolation by distance across genetic breaks but not within genetic clusters, and imply a major role for river basins in facilitating gene flow in G. pallidipes. Effective population size (Ne) estimates and results from Approximate Bayesian Computation further support that there has been recent G. pallidipes population size fluctuations in the Serengeti ecosystem and the northwest during the last century, but also suggest that the full extent of differences in genetic diversity and population dynamics between the east and the west was established over evolutionary time periods (tentatively on the order of millions of years). Findings provide further support that the Serengeti ecosystem and northwestern Kenya represent independent tsetse populations. Additionally, we present evidence that three previously recognized populations  act as a single population and should be considered as a single unit in vector control. Glossina pallidipes, plays an important role in control of both HAT and AAT. Understanding the population structure of G. pallidipes is important in designing effective fly control strategies. In this study, we determined the spatial genetic structuring of G. pallidipes on a broad spatial scale with genotype data from 11 microsatellite loci and 21 sampling sites in Kenya and northern Tanzania. Results indicate a strong regional separation of tsetse fly populations into three major genetic clusters, with divergence east and west of the Great Rift Valley in central Kenya and between the Serengeti ecosystem and other western sites. Findings from ABC simulations suggest that the east/west divergence reflects the biogeographic break across the Great Rift Valley, and the northwest/southwest divergence reflects the biogeographic break between low elevation savannah and the Kenyan highlands, both biogeographic breaks previously observed in savannah animals with similar ranges. We found evidence of high genetic connectivity and migration rates within each of the three genetic clusters, and only occasional migration between clusters. These patterns of genetic connectivity and migration suggest that, following local eradication, the risk of reinvasion from within genetic clusters is very high even across vast geographic distances of more than 300 km, and that the risk of reinvasion from different genetic clusters is much lower, but still of concern. We argue that these findings call for tsetse control strategies that are coordinated for each genetic cluster, and monitoring schemes that are specifically designed to detect migration events across the geographic boundaries that demarcate genetic clusters. Although coordination of control within and monitoring between genetic clusters will be challenging because of the large spatial extent of the east genetic cluster and the international scale of the Serengeti ecosystem, we argue it is necessary to prevent reinvasions from both proximal and distant localities.Tsetse flies are responsible for transmission of trypanosomes that cause human African trypanosomiasis (HAT) and animal African Trypanosomiasis (AAT) in sub-Saharan Africa, and are thus of economic importance in the regions they inhabit. In Kenya and Tanzania, control of the main vector species,  Glossina) are restricted to Sub-Saharan Africa and inhabit patchy and discontinuous habitat within their distribution [Glossina pallidipes is the most widely-distributed vector of trypanosomes that cause Animal African Trypanosomiasis (AAT), and to a lesser degree, has also been involved in Human African Trypanosomiasis (HAT) transmission [pallidipes runs from Ethiopia to Kenya, Uganda, Tanzania, Democratic Republic of the Congo, Mozambique and Zambia, [Tsetse flies  prediction models have been used to show areas of tsetse abundance and expansion ,15, and G. pallidipes across multiple spatial scales, and to understand the evolutionary forces that have shaped and maintained them. We used samples collected from 21 sites in Kenya and the Serengeti National Park in Tanzania  of the Great Rift Valley ‘Ethiopian rift’, which is a biogeographic boundary that marks a genetic break in G. pallidipes [2) of well-connected natural savannah habitat in the world [The goal of this study was to evaluate patterns and levels of genetic connectivity of Tanzania  and screllidipes –30. At tllidipes . At the he world . The Serhe world . The ecoField collections of tsetse flies were conducted under permit number NACOSTI/P/18/28381/22226 granted by the Kenya National Commission for Science, Technology and Innovation.Glossina pallidipes samples were collected from 21 sites across Kenya and northern Tanzania . We used the following cycling conditions: 95°C for five minutes, 12 touch-down cycles , 40 additional cycles , and a final extension of 72°C for 20 minutes. PCR products were multiplexed in groups of two or three loci in the same way as previously published [http://dna-analysis.yale.edu/). Alleles were scored using the program GENEMARKER v2.4.0 . To ensure replication of genotype calls, automatically generated peaks were visually inspected twice independently using agreed upon criteria for each locus  or the Qiagen DNAeasy blood and tissue  extraction kits, following manufacturers’ protocol. We used fluorescent labelled  forward primers for 11 microsatellite loci  using pullidipes . Brieflyublished , and gench locus , and onlet al [http://popart.otago.ac.nz/index.shtm).For approximate Bayesian computation analysis exploring potential causes of population structure, we sequenced a 439 bp fragment of mitochondrial DNA (mtDNA) from the cytochrome oxidase I gene was PCR-amplified in 24 individuals using primers designed by Simon et al   were calculated using FSTAT v2.9.3.2 [We checked for presence of null alleles using Micro-Checker v2.2.3  and lociv3.5.2.2  to deterv2.9.3.2 .e), which can be considered a proxy for the amount of variation present in the population, and tests for recent population bottlenecks. These parameters inform the numbers of breeding individuals in a region, the effective dispersal ability, the potential strength of selection for resistance to vector control manipulations  [e to 4Ne generations before sampling [We assessed population dynamics with estimates of effective population size  –49. Thuse males) , implemee males) . We teste males) , a progrsampling ,53. We tsampling  under thsampling ). The TPsampling . We alsov2.3.4 [We used the Bayesian clustering method implemented in BAPS v 6 ,56 to inv2.3.4 ,36 with v2.3.4  to alignTo further visualize patterns of genetic structure, we also performed principal components analysis (PCA) and discriminant analysis of principal components (DAPC)  in the "ST using Arlequin with Wright’s statistics [ST values and geographic distances generated by using the web based Geographic Distance Matrix Generator v1.2.3  were used to test for isolation by distance with following Rousset 1997 [ST/(1- FST) and log transformed geographic distance in Km as implemented in the “isolation by distance” web service v3.23 [We measured genetic divergence among sampling localities by computing pairwise Fatistics  and testatistics , computeset 1997  using FSce v3.23  with a Mce v3.23 .Relatedness between individuals within genetic clusters was tested using the program ML-Relate  to deteret al., 2004 [h , Lmax , and their ratio (Lh/Lmax) to identify migrants. We used the Bayesian method of Rannala and Mountain, 1997 [To test for individual migrants between geographically neighboring sampling sites, we used two methods. We used GENECLASS v2.0 –73 to del., 2004  with 100in, 1997  to detecin, 1997 ).Population structure can have multiple causes including the slow accumulation of genetic differences across geographic space because of prolonged migration-drift equilibrium, or by genetic divergence across a geographic break (vicariance). Since the cause of structure remains unclear and has distinct implications on vector control, we explored the cause by modeling the timing of divergence of the major genetic clusters identified in BAPS with Approximate Bayesian Computation (ABC) in DIYABC  v2.0.4 AG. pallidipes and savannah species from east Africa , while the maximum He was observed in SHT (0.72). All sample sites revealed positive and significant FIS values (p < 0.05). The lowest FIS value (0.04) was observed in MRT while the highest (0.48) was observed in SHT. Estimates of Ho, He, and FIS indicate a small heterozygote deficit compared to what is expected under random mating.Diversity statistics are shown in o and He values , as observed using the complete data set. The highest Ho and He values 0.59 and 0.76 were observed in HND and SHT respectively. FIS estimates for this subset data ranged from 0.06 in RUM to 0.45 in SHT  ranged from 1.87 in RUM to 2.62 in SHT  and refl5 in SHT , and reme estimates ranged widely from 2.7  in KAP to 3,507  in HND (e for these sampling sites.N) in HND . Some esResults from the TPM model did not show a significant reduction in effective population size in any of the sample sites , while tMaximum likelihood tests for relatedness indicate that the majority of individuals were unrelated >70%; . The perBayesian analysis of population structure using BAPS indicated three major genetic clusters  that corResults from the PCA fully supported BAPS, with strong separation between the west (northwest/southwest) and east apparent across PC 1 and 2 , and separation between the northwest and southwest apparent along PC 4  to 0.312 (SHT vs. RUM) and were significant in 79% of pairs  than among sites in the southwest or east clusters  separated by ~155 km with a significant Fof 0.123 , and cou ~111 km . 62% of ST (KLM and MSN: FST = -0.005) were separated by a full 69.6 km, and the pair of sampling sites with the highest FST (MRT and MRB: FST = 0.121) were separated by only 14.4 km (ST (KIB and HND: FST = 0.003) were separated by a full 303.6 km, and the pair of sampling sites with the highest FST (KIN and NGU: FST = 0.175) were separated by 400 km, a distance shorter than the maximum of 509.3 km separating SHT and MNP , and across the northwest/southwest genetic break (west: p = 0.0361). In contrast, there was no significant isolation by distance within any of the genetic clusters . Indeed, 14.4 km . In the  and MNP .versus 40 males; With the relatively conservative p-value cutoff (0.05) designed to identify all potential first-generation migrants, GENECLASS identified 83 migrants, with zero migrants within the northwest, 38 migrants within the southwest, and 38 migrants within the east . The souAnalysis 1 . Furthermore, it is likely that the microsatellite dataset could not provide accurate estimates of time of divergence  because microsatellites generally have fast mutation rates that make them inappropriate to estimate timing of splits on the order of millions of years that was indicated in the mtDNA analysis  were supported over scenarios with constant Ne . Differences in anthropogenic disturbance likely played a role in shaping patterns of genetic diversity. Urbanization, habitat destruction, agricultural activity, high grazing pressures, and a history of tsetse control measures  in westeouthwest  and indiIS values and suggest that individuals in our study may be related , and this was over much larger average geographic distances , with G. pallidipes genotypes fitting expectations of Hardy-Weinberg equilibrium only when separated into these three groups. Along with significant population differentiation at multiple scales and lack of isolation by distance within genetic clusters, these results suggest that the major population dynamics such as the population density, the distance of average dispersal, and disease transmission dynamics will be unique to each genetic cluster. Even if the genetic break between the northwest and southwest was caused by isolation by distance rather than a geographic barrier, these regions are ecologically and epidemiologically different because of the conservation status of the Serengeti ecosystem  and so should be treated differently during tsetse fly control campaigns.Our findings provide an understanding on the levels and patterns of genetic diversity, differentiation, gene flow, and population dynamics among and within G. pallidipes and should be considered as a single G. pallidipes population. The results imply that in eastern Kenya for all three KENTTEC terminology fly belts , G. pallidipes eradication may likely never be feasible, and that suppression rather than eradication would be a more realistic target. Results also indicate evidence of infrequent migration between the clusters, which could pose a reinvasion threat after local eradication, if it were to be successful in the northwest or southwest.Using KENTTEC recommended terminology, these results indicate that the Lake Victoria tsetse belt and the Narok-Kajiado fly belt are in separate genetic clusters, but that the three tsetse belts in the east  have high genetic connectivity in e estimates and ABC results indicated that the northwest and southwest have gone through a recent population size reduction and currently have lower Ne and less genetic variation than populations in the east. Results also indicated relatively small Ne (<100) in a subset of G. pallidipes , suggesting that novel vector control methods may be feasible in these regions. There is evidence from disease transmission models that novel control methods such as inundation and replacement of natural populations with sterile males, or genetically/endosymbiont modified flies .Nbachnick , or replbachnick ) are morbachnick ,103. On e, such as those found in the northwest. Furthermore, our data suggest that infrequent long-range migration events do occur even between distinct populations separated by more than 200 km , while there is potential for success of novel vector control methods that require inundation and replacement of natural populations in geographically isolated populations with small Nn 200 km , underscS1 Fig(a) without fluctuating population sizes , considered in Analysis 1 designed to identify the most likely ancestral lineage, and (b) with fluctuating population sizes in the northwest and southwest  considered in Analysis 2 to further refine estimates of timing and Ne for each of the genetic clusters. Priors were based on published estimates and the geologic record (Alternative scenarios c record .(DOCX)Click here for additional data file.S2 FigResults of the principal components analysis conducted with the "adegenet" package v2.1.1  in R Studio v1.1.383, showing the variance found in the three principal components that display separation among the major clusters detected in BAPS v 6 ,56. Thes(DOCX)Click here for additional data file.S3 FigSTRUCTURE results for K = 1–10. Each bar represents a single fly with the proportion of colors representing the Bayesian probability of assignment  of an individual. Black lines separate sampling sites.(DOCX)Click here for additional data file.S4 FigG. pallidipes microsatellite data for 21 sampling sites, completed in the R (R Development core team) using the “adegenet” package [DAPC based on  package  with 40  package ,56 analy(DOCX)Click here for additional data file.S5 FigTCS haplotype network where haplotypes are represented by circles that are sized proportionally to frequency and shaded with the genetic cluster they were chosen to represent in the ABC analysis. Hashes along the branches of the network represent a single nucleotide change (one inferred mutation), and black dots represent unsampled haplotypes.(DOCX)Click here for additional data file.S6 Fig(a) mtDNA under scenarios without fluctuating population sizes , (b) mtDNA based results under scenarios with fluctuating population sizes , (c) microsatellites based results under scenarios without fluctuating population sizes , (b) microsatellite based results under scenarios with fluctuating population sizes . Results from different scenarios are colored as indicated in the legend. ABC analyses was performed in DIYABC v2.0.4 [Principal components analysis (PCA) from C v2.0.4 .(DOCX)Click here for additional data file.S7 FigMode of DIYABC v2.0.4 [Analysis 2  in green, red, blue, and pink, respectively, including estimates of (a) population size from the mtDNA analysis (b) timing of simulated events from the mtDNA analysis, (c) population size from the microsatellite analysis, and (d) timing of simulated events from the microsatellite analysis plotted on a log scale to make all estimates visible in a single image. Population size estimates are presented for the northwest after a population bottleneck (NWb), the southwest after a population bottleneck (SWb), ancestral northwest (NW), ancestral southwest (SW), and the east (E). Timing estimates are presented for the date of bottleneck for the northwest (dbNW), date of bottleneck for the southwest (dbSW), the population split between the northwest and southwest (t1), and the population split between the west and east (t2).C v2.0.4  paramete(DOCX)Click here for additional data file.S1 TableG. pallidipes.Site name, site ID, county, latitude, longitude and sampling data of the 14 locations that did not have any flies during field collections despite past collection records that indicated the presence of (DOCX)Click here for additional data file.S2 TableThe table shows loci names in the first column followed by fluorescent dye used for each locus, DNA sequences of the forward and reverse primers, size range of alleles in base pairs (bp), repeat motif length in base pairs and publication reference for primer design.(DOCX)Click here for additional data file.S3 Table(a) displays the prior minimum (min) and maximum (max) values of the priors used in the simulations and the scenarios these priors applied to. Panel (b) and (c) display results for the mtDNA and microsatellite based ABC analyses, respectively. Results displayed include the relative posterior probability of the scenario tested using the weighted logistic regression method described by [ABC modeling was done using DIYABC v2.0.4 . Panel Click here for additional data file.S4 TableIS values were considered significant after Benjamini-Hochberg correction for multiple testing.Bolded F(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 TableProbability of assignment  for individuals to each of the four clusters identified in BAPS v 6 ,56. Admi(DOCX)Click here for additional data file.S8 Table(a) pairwise genetic differentiation (FST) and (b) geographic distance (in km) among pairs that were not included in ST was computed in Arlequin [This table shows Arlequin  based onArlequin . Signifi(DOCX)Click here for additional data file.S9 Table(a) within the southwest, (b) within the east, (c) between the northwest and southwest, and (d) between the southwest and east. Marginally significant after Benjamini-Hochberg correction for multiple testing  are marked with *.Home site, sample ID, sex of the migrant, inferred origin of the migrant, and p-value of the test of migrants detected using GENECLASS  (a) with(DOCX)Click here for additional data file.S1 Filehttp://dna-analysis.yale.edu/). Instead of relying on these binning rules, we recommend that researchers request and use DNA from the same individuals as controls to calibrate future studies carried out on different equipment.We strongly caution that these rules may only be valid for PCR amplifications and genotype calls made with samples processed on the same Mastercycler Pro Thermocycler  and ABI 3730xL Automated Sequencer  at the DNA Analysis Facility on Science Hill at Yale University ((TXT)Click here for additional data file.S2 FileMethods of the mitochondrial DNA sequencing done for ABC analysis, and the ABC scenarios used are described. Basic results of the mitochondrial data included, and ABC analysis are reported.(DOCX)Click here for additional data file.S3 File(a)Analysis 1 comparing Scenarios 1a, 2a, 3a, and 4a, (b)Analysis 2 comparing Scenarios 1a and 1b, (c)Analysis 2 comparing Scenarios 2a and 2b, (d)Analysis 2 comparing Scenarios 3a and 3b, and (e)Analysis 2 comparing Scenarios 4a and 4b. Output includes prior checking, posterior probability comparison of scenarios, and parameter estimation. The details of the scenarios are in DIYABC v2.0.4  output f(TXT)Click here for additional data file.S4 File(a)Analysis 1 comparing Scenarios 1a, 2a, 3a, and 4a, (b)Analysis 2 comparing Scenarios 1a and 1b, (c)Analysis 2 comparing Scenarios 2a and 2b, (d)Analysis 2 comparing Scenarios 3a and 3b, and (e)Analysis 2 comparing Scenarios 4a and 4b. Output includes prior checking, posterior probability comparison of scenarios, and parameter estimation. The details of the scenarios are in DIYABC v2.0.4  output f(TXT)Click here for additional data file.


Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ( KnowledFollowing Equation (1) illustrates that the value of i.e., additive plus non-additive genetic values). In the absence of GxE-interaction, the genotypic correlation between both populations is equal to one irrespective of the presence of GxG-interactions, because the genotypic value of a genotype  is the same in both populations. The additive genetic correlation Note that quencies .A deeper understanding of the relationship between non-additive genetic effects, allele frequencies and i.e., biological) sense means that the genotypic values of individuals depend on interactions between alleles or genotypes at different loci  of the individual loci , In summary, the i.e., genetic model) and the magnitude of non-additive effects (We aimed to investigate the relationship between non-additive effects and the additive genetic correlation between populations ( effects .AA and EM), and scenarios with all types of non-additive variance (EC and EDD). For each genetic model, we considered three magnitudes of non-additive effects, labeled as small, intermediate, and large.We considered six genetic models; a basic model with additive effects only (A), which served as a basis for comparison, and five alternative models with non-additive effects: one with only dominance effects (D), and four with only epistatic effects. With epistasis, we simulated interactions between pairs of loci that followed one of the configurations presented in We simulated genotypes of two livestock populations (1 and 2) that diverged for 50 generations . For divWe simulated a historical population with QMSim  by randoneration .After simulating the historical population, we simulated two current populations (1 and 2). We randomly sampled 100 males and 500 females from the last historical generation to become founders of population 1. The remaining 100 males and 500 females were the founders of population 2. We will refer to the generation of founders as generation 0. Within each population, simulation continued for 50 generations by randomly mating 100 selected males with 500 selected females. Each mating resulted in 5 offspring, resulting in a total of 2,500 offspring  in each generation. Generations were non-overlapping, meaning that in each generation, the parents were selected from the previous generation only. In the drift scenario, animals in both populations were randomly selected to become parents of the next generation. Effective population size , we randomly sampled the allele frequencies of loci from a uniform distribution. Mutation rate was 2.5*10Additive effects /5≈0.16 .We simulated dominance and epistasis by introducing functional dominance and epistatic effects that are independent of allele and genotype frequencies. Our interest, however, is in statistical average, dominance and epistatic effects of QTL, which do depend on genotype frequencies . We willAA), dominance x dominance (EDD), complementary (EC), or multiplicative (EM)  model  for eachive (EM) . The coni.e., statistical substitution effect) to refer to We repeated this procedure of partitioning functional epistatic effects into statistical genetic effects for all pairwise interactions between QTL. Each QTL was involved in 5 epistatic interactions and therefore has 5 terms that contribute to its average effect. Following this reasoning, the average effect of QTL In our simulations, we needed to compute phenotypes of selection candidates in each generation, for which we needed the statistical dominance effect  the genetic correlation between the trait in population 1 and the trait in population 2 . For each of those replicates, we computed i.e., genetic model and magnitude). We ran the simulations with both selection and drift for each scenario separately, because the selection of parents in population 1 depended on the genetic model. To limit computation time, we used 20 replicates for each scenario with selection.We ran the simulation with drift 50 times, resulting in 50 sets of genotypes  in population 1 across generations, to illustrate how population 1 evolved over time. Second, we report realized fractions of additive, dominance and epistatic variance in generation 1 and 50. Third, for scenarios with small non-additive effects, we show the effects of the genetic model and of applying selection on the additive genetic correlation  decreased due to selection with all scenarios .The additive genetic variance in population 1 (VA) and epistatic variance (VI), VA accounted for the largest, and VI for the smallest fraction of the total genetic variation , followed by EDD (max. 0.033), EC (max. 0.024) and EM (max. 0.017). The largest fraction of dominance variance (VD) was realized with model EDD (max. 0.364), followed by D (max. 0.298) and EC (max. 0.105). With genetic models D, EDD and EC, the fraction VD increased and VA decreased across generations, especially with intermediate or large effects , followed by EAA (0.75), D (0.83), EC (0.83) and finally EM (0.94). There was a tendency that scenarios with the largest non-additive variance in generation 1 had the smallest For all genetic models and small non-additive effects, ift only . With drAs expected, ft 0.05; . With seFor all genetic models and with selection,  effects . With geDD , the effect of an increase in magnitude on For all genetic models with selection, the average absolute difference in allele frequency between lines  . After tse in rg .DD showed a smaller M, EAA, and EC.With all scenarios, the average absolute difference in allele frequency between lines . Empirical studies on livestock  than estimates from an empirical study on litter size in pigs (about 26%) . NeverthDD with small effects, and scenarios EAA, EC and EM are probably most realistic, because these scenarios always resulted in little dominance (max. 7%) and epistatic (max. 5%) variance.In summary, among the scenario’s we studied here, scenarios D and Ei.e., 0.5% for small effects, 16% for intermediate effects, and 51% for large effects). Furthermore, our simulations resulted in U-shaped distributions of allele frequencies in the last generation of the historical population, which agrees with expectations based on neutral theory (For the dominance model (D), we observed that  results . These dl theory . After tIn another simulation study, where the fraction of loci showing overdominance was 12%, realized i.e., configuration). In addition, there was a tendency for configurations that resulted in large initial non-additive variance to result in smaller values of With epistasis, AA and EM interactions only affect the DD and EC interactions affect the AA and EM interactions create a dependency of DD and EC interactions also create a dependency of To our knowledge, the relationship between the nature of epistasis and AA, using non-additive effects that were multiplied by 100 for all magnitudes. Results from those simulations showed that the difference in AA.As expected, an increase in magnitude of dominance effects resulted in a lower i.e., type of inter-allelic interaction) and by the difference in allele frequencies between populations.To show the mechanism behind this result, we again consider a two-locus model where, like before, both loci have an additive effect  will promote the selection of favorable alleles, whereas negative interactions  will suppress the selection of favorable alleles   . Our resEscherichia coli have shown that additive genetic variation (VA) can be maintained for at least 100 generations , resulting in larger differences in allele frequencies than simulated here. Hence, In our simulations, there was selection in only one of the populations, while the other population was unselected. In reality, populations may have been divergently selected , so that e.g., i.e., in the importance of non-additive effects). Although this may be the case, differences in Estimated genetic correlations between two populations (The results in this study may be relevant for the prediction of additive genetic values across populations using genomic information. In this strategy, termed across-population genomic prediction, average effects at markers are estimated in one population, and used to compute additive genetic values in another population (Similar to across populations, genomic prediction from current to future generations may be inefficient because of changes in allele frequencies, and the subsequent changes in average effects at QTL. In other words, two different generations can be considered as two populations that have a genetic correlation between them that may be lower than unity. The results of this study may therefore partly explain the need for frequent retraining of genomic prediction models to achieve constant accuracy across generations (Our findings show that the genetic correlation between populations (