In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. In genetic association studies of complex traits, estimation of the average effects of genetic variants on one or multiple quantitative phenotypic traits such as systolic blood pressure (SBP), diastolic blood pressure (DBP), blood triglycerides level (TG), low density lipoprotein (LDL) and high density lipoprotein (HDL) levels, etc. could be of interest. If two or more of these traits are measured in the same set of individuals, they may be correlated as they could be simultaneously influenced by the same gene(s) (pleiotropic effects) and/or environment  in the same individuals ,2. HenceMultivariate approach could be used to jointly synthesize such correlated end points. . It can exploit the between and/or within-study correlation structure to yield more efficient or precise estimates while univariate approach ignores such correlation structure ,4. It hap, of multiple endpoints increases, estimation problem under multivariate random-effect meta-analysis becomes more complex because the effective number of parameters to be estimated is p(p + 1)/2. For example, when p = 3, a 3-variate meta-analysis requires the estimation of a total of six between study variances and correlation parameters simultaneously while a univariate meta-analysis requires estimation of just one between-study variance parameter at a time. Therefore, even when the end points are highly correlated, the use of multivariate approach can be prohibitive or may offer no clear advantage especially when number of studies is small or between-study variances are smaller compared to within-study variances. Despite advantages in theory, recent studies summarizing the empirical meta-analysis studies found that the improvement on the bias or precision of the pooled estimates is not remarkable from multivariate analysis compared to univariate in most applications [However, there are some practical issues with the use of multivariate approach in meta-analysis. First, for a small meta-analysis or for situation where between-study variation is relatively small compared to within-study variation, the multivariate method often estimates the between-study correlation at the boundary of parameter space (−1 or +1) ,8. This ications ,10,11. Fications .Given the above-discussed promises and issues of multivariate meta-analysis, it is not clear when its application may be preferable  to univariate analysis in the setting of genetic association studies such as candidate genes studies, genome-wide association studies (GWASs) or their replication and validation studies. Minor allele frequency (MAF), and genotypic distribution that maintains Hardy-Weinberg Equilibrium (HWE) are important characteristics of such studies. Also, the effects of the most genetic variants on complex traits are very small to moderate. Another important consideration is potentially high degree of heterogeneity in genetic effects . Besidesp (2-variate and 3-variate end points), 2) degrees of between-study correlation, 3) degrees of within-study correlation, 4) levels of heterogeneity, 5) average size of individual study, 6) size of meta-analysis. Each of these scenarios were analyzed under four different aggregate (summary) data availability scenarios: a) all aggregate data are available, b) all aggregate data except estimates of within-study correlations are available, hence are ignored in the meta-analysis, c) aggregate data for some studies are missing at random for end point 2, and d) aggregate data for some studies are missing informatively for end point 2. We evaluated the performance with respect to mean bias, relative mean bias percentage and root mean square error of the pooled estimate of effect and coverage probability of the 95% confidence interval of the effect for each end point via extensive simulation.In this study, we compared the performance of univariate (separate) vs. multivariate (joint) meta-analysis under random-effects (RE) assumption. When heterogeneity exists  allele in a genotype of a single nucleotide polymorphism (SNP) at a specific DNA locus in an individual, and the effects could be we the average increase/decrease in the traits values per one copy increase in X (under additive model of inheritance). Such a meta-analysis could be performed using univariate or multivariate approach.In a meta-analysis of genetic association studies, suppose we are interested in the estimation of overall (average) effects of some factor ith study , suppose jth phenotype  from the kth subject , and xik is the corresponding value of x. Then their relationship in the original study or in a meta-analysis when individual participant data (IPD) are available can be modeled as (can include other covariates as well)βij is the true effect of x on the jth end point (phenotype) and i. In a univariate random-effects (RE) meta-analysis, we are interested in estimating βj, the average of βij's of x on jth phenotype, from m studies. For the observed effect (estimate), j . If fixed-effect (FE) of x is assumed t is the p-dimensional true effects of x jointly on all p phenotypes and Ψi is the p × p residual covariance matrix in study i. In random-effect multivariate meta-analysis, we are interested in simultaneously estimating the average joint effects vector β = t of x on p phenotypes in overall population from m studies. Let Si be covariance matrix of Yi in study i. Under RE model, let’s assume that βi∼MVNp and its estimates Yi|βi∼MVNpβ in study i. Then, we can use the marginal distribution of the joint estimate in study i is Yi∼MVNpβ, Σ + S(i) to estimate the parameter Σ and then compute the estimate of β. Here, Σ, the between-study covariance matrix represents population variation in the studies’ true underlying effects, while Si, the within-study covariance matrix represents variation in the ith study’s results due to repeated sampling or chance. For instance, in bivariate problem:βi1 and βi2 across studies, and Yi1 and Yi2 within study i, respectively. The between-study correlation, ρb, is the correlation between βi1 and βi2 across studies (or populations) and the within-study correlation, ρwi, is the correlation between Yi1 and Yi2 within study i.Multivariate meta-analysis is the generalization of univariate meta-analysis when Si  is assumed to be known in all studies . However, in real meta-analysis it is typically estimated from individual participant data, if accessible, by fitting the ρwi’s might not be available in the corresponding published studies for the reasons: some of the published studies might report the aggregate data on ρwi’s might have to be inferred or estimated indirectly for multivariate meta-analysis [Σ  is estimated before computing the estimate of β . But, in univariate analysis only one parameter jth endpoint before computing the estimate of βj. Restricted maximum likelihood (REML) method is commonly used for estimation of Σ, assuming multivariate normality of random effects ). REML generally produces smaller variance estimates within the realistic parameter space compared to the method of maximum likelihood [Σ [β and its variance are obtained asΣ = 0 is assumed.In a meta-analysis, the within-study covariance matrix analysis  or diffeemployed . For a Rkelihood ,4. Howevkelihood  or a metihood [Σ . Then thWe first generated the IPD data for a range of scenarios varying the study level parameters such as average sample sizes, number of studies, etc. and estimated the summary data  within a study) in each study to ensure that we pool realistic summary data typical of genetic association studies. We then pooled them over all studies, thus performing a two-stage IPD meta-analysis. Estimating aggregate data by generating IPD in individual studies (rather than directly sampling aggregate data using some distributions) has another advantage that it also allows us to vary study level parameters such as sizes of individual studies, and MAF of a genetic variant across studies and maintain the Hardy-Weinberg Equilibrium (HWE) within each study, etc. This in turn allows us to assess the impact of such study level parameters on the performance of methods. However, we also compared the performance of two approaches by directly generating (sampling) aggregate data from some reasonable distributions.X , with two traits (p = 2) and three traits (p = 3). We considered the minor allele frequency (MAF), f = 0.20 at the locus. We considered different scenarios with a set of number of studies (m), and total meta-analysis size (N), as m = 5 and N = 5000; m = 5 and N = 10000; m = 10 and N = 10000; m = 15 and N = 20000; m = 30 and N = 30000, with the average study size, n = N/m. To approximate the practical situation where all studies will not be of equal size and the distribution of minor allele will not be the same across all populations, we considered variable study size (Ni) around n and slightly variable MAF (fi) around f across studies. The distribution of X maintained HWE at p-value ≥ 0.001 in HWE test in each study.In the first stage of IPD meta-analysis, we considered a meta-analytic problem of estimating the effect of βi = t were simulated from Np, where β = t for p = 2 and β = t for p = 3. We considered small to modest genetic effect size βj for trait j from a pool = {0.10,0.15,0.20,0.25,0.30,0.40}. For instance, β1 = 0.10 and β2 = 0.10 in a scenario with p = 2, and β1 = 0.20, β2 = 0.30 and β3 = 0.30 in another scenario with p = 3 was considered. Since the vast majority of causal SNPs might contribute only little and only a few of them contribute considerably to heritability of a quantitative complex trait, this pool of βj represents a reasonable spectrum of heritability, f = 0.20 and small to modest effect  of the estimate of βj in a study with the average size n = N/m and MAF distribution strictly under HWE for f = 0.20 (see j) for the between-study heterogeneity, I2 = 25%,50%, and 75%, respectively. Here, Σ are obtained as ρbjj, from a pool {0.20,0.50,0.60,0.70,0.75}.The study-wise effect vector sk model ). For sukth subject  with genotype x = xik  from study i  for p = 2 or p = 3 scenario as in [αi is a p × 1 vector of intercepts (baseline effects on j traits when x = 0), βi is the p × 1 vector of the true effect of x and Ψi is a p × p residual variance matrix with p diagonal elements as the error variances t for p = 2 and αi = t for p = 3 for all x and m, and let x genotypes, p traits and m studies to ensure the identifiability of the model for comparison purpose in our simulation [Ψi was constructed.).In the first stage of IPD meta-analysis, we simulated IPD trait values io as in ,22,23yimulation ,22,23.  were simultaneously obtained by fitting multivariate linear regression of x in each study.The study-wise estimates Simulation methods and scenarios for two-stage IPD meta-analysis are summarized in Yi’s and Si’s across all studies in each scenario performing both multivariate and univariate meta-analyses under random-effects assumption. For each combination of parameters, we considered four scenarios related to the availability of aggregate data: 1) complete data scenario, 2) complete data scenario but complete data scenario, we utilized all summary data including complete data scenario with ignoring , we set j≠j′ pairs) and re-meta-analyzed under MV framework. This allows us to assess the impact of ignoring within-study correlation when they are missing (not reported) in some or all studies and investigators choose to ignore such correlation rather than inferring them indirectly [missing at random (MAR) scenario, we assumed that about 30%  of studies had randomly missing summary data for end point 2 and chose those studies randomly. Random missing is likely in a meta-analysis of genetic studies if investigators in some studies do not consider estimating and reporting the risk of a genetic variant on some trait(s) that are of interest in the meta-analysis. Under sing informatively (MIF) scenario, we assumed that about 30% (m′ = 0.3m) of the studies had informatively missing (for some reason) summary data for end point 2. This is a typical scenario representing ‘publication bias’ in a meta-analysis of genetic studies, where investigators might not report or journal might not publish insignificant genetic association of a variants with some trait in some studies, whereas significant genetic association in any direction (irrespective whether it is protective or risk) is still more likely to be reported and published. Under this scenario, we identified the first m′ smallest m studies for end point 2 and considered them to be missing.In the second stage of IPD meta-analysis, we meta-analyzed the estimated summary data directly  or usingdirectly  in multim′ missing studies , we considered yl2 = 0, yl2j′ = 0 for j′ ≠ 2  using metafor package in R language. The estimates of Σ in MV meta-analysis and j  [R = 5000 times (number of replications).  ,25. We rj . We also compared the percentage of times We compared the performance of MV and UV approaches with respect to of mean bias, relative mean biases percentage (% bias), and RMSE in of each of '|>.9995 ,18. We aYi and Si) in each study . For this, we considered bivariate case with β = t. We used the same Σ  and average I2 = 50% as in two-stage IPD meta-analysis for the similar scenario. To facilitate the direct generation of realistic Si, we relied on the distribution of summary estimates  and Si1, Si2) across studies) observed in the analysis of IPD data. t from its marginal distribution Yi∼N2 provided in Additionally, we compared the performance of multivariate and univariate approaches via simulation in a few specific scenarios by directly generating (sampling) aggregate data  at which multivariate approaches are thought to offer no clear benefit, are also presented.Comparative performance of multivariate and univariate RE meta-analytic methods in certain key scenarios based on estimation of summary data through IPD data generation and analysis are presented on Tables ρb’s were estimated at the parameter boundary m or N or I2 increased , where RMSE of The percentage of times analysis , which id Tables . Also, tρw ≥ 0.3 resulted in the higher percentage of ρw, ignoring βj’s.Ignoring d Tables –5. Also,Mean bias on s Tables . Howeverispersed  Figs resI2 ≤ 50%) by both approaches for m ≥ 10, where UV tended to underestimate more severely and more frequently produced the estimates at the parameter boundary  in individual replicated data sets   were the same.There was no bias on I2 ≥ 50%), MV approach performed similar or better   at p = 2 and I2 = 50%). These figures also show how smaller, similar or larger the m or weaker ρb or greater I2, estimation of ρb or Tables related) –S5 Figs.However, such estimations in the parameter boundaries did not result in higher mean biases or RMSE of pooled estimates in MV than in UV analysis Tables . The avem and moderate to large within- or between- study correlation. For example, for p = 2,m = 15,ρb = 0.75,ρw = 0.75, β1 = β2 = .1, and j = 1,2) under MIF scenario, MV produced much smaller mean bias . Reason might be that MV requires estimating more parameters (including between-study correlation in between-study variance matrix) simultaneously than univariate one. The estimation of between-study correlation at the parameter boundary was quite often for small or moderate m or I2, in which univariate approach much severely and frequently underestimated the between study variances (as seen in m = 10), and consequently produced smaller standard errors of the pooled estimates. Also, there were only 3 studies with randomly missing summary data when m = 10, which might not be sufficient to produce noticeable benefit of MV over UV approach for such moderate sized meta-analysis. UV analysis offering in general similar or slightly higher coverage for randomly missing end point for small or moderate m might be because it relies on fewer available studies, consequently, producing wider confidence interval . However, for larger meta-analysis (m ≥ 15) with moderate to large heterogeneity (I2 ≥ 50%), such estimation problem were minimal and MV estimates were in general similar or better  for the randomly missing end point.We compared the performance of multivariate and univariate approaches to meta-analysis of genetic association studies for the correlated traits via simulation. When all summary data were available from individual studies, MV offered no clear advantage. Also, MV did not offer noticeable advantage even when summary data for some end points were missing randomly  for modm ≥ 15 with I2 ≥ 50%, where the relative mean bias, RMSE and the coverage probability for missing end point were better, confirming the previous finding in clinical studies setting [β2 > 0 (and would be downwardly biased if β2 < 0 was considered) for the missing end point, a phenomenon known as 'publication bias', in univariate analysis. But, multivariate analysis that assigned null effect for missing summary data  might have borrowed the strength of correlation structure to bring otherwise upwardly biased pooled estimate somewhat towards 0, thus decreasing the degree of both mean bias and RMSE, hence somewhat correcting the impact of publication bias. Despite producing wider confidence interval with using fewer studies, UV method might still have lower coverage than MV method for informatively missing end point, perhaps because the pooled estimate in UV analysis was usually much more biased.The biggest advantage of MV method is seen for informatively missing end point for  setting . For infρb ≥ 0.5). However, we noticed that I2 = 50% in complete data scenario as seen in m ≤ 10 or I2 = 25% at which A previous study  suggestem ≥ 10, and preferably m ≥ 15 studies) with large between-study heterogeneity (I2 ≥ 50%) and moderate to large correlations (|ρw| ≥ 0.5 or |ρb| ≥ 0.5) for an end point with missing summary data in some studies (irrespective of whether it was randomly or informatively missing). However, these results are yet to be seen in real genetic data applications. Also, in real meta-analysis of genetic data, IPD data might not be accessible in one or more studies. Therefore, considering additional data (un)availability scenarios might provide further insights about the performance of these approaches in various real data applications. Comparing these as well as other emerging techniques under univariate and multivariate meta-analysis frameworks in various scenarios mimicking real data applications will be even more helpful for genetic and clinical investigators when they are interested in meta-analyzing two or more correlated end points from genetic association studies.Despite the complexities of the model and parameters estimation, multivariate approach in general can be useful in moderate to large meta-analysis Click here for additional data file.S2 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, β2 and τ2, average effect and between-study standard deviation of true study-wise effects for end point 2, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach. aSummary data for end point 2 from 3 least significant studies were missing (either not reported or unpublished).Scenario: (TIFF)Click here for additional data file.S3 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, βj and τj, average effect and between-study standard deviation of true study-wise effects for end point j, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach.Scenario: (TIFF)Click here for additional data file.S4 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, βj and τj, average effect and between-study standard deviation of true study-wise effects for end point j, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach. aSummary data for end point 2 from 3 studies were missing randomly.Scenario: (TIFF)Click here for additional data file.S5 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, βj and τj, average effect and between-study standard deviation of true study-wise effects for end point j, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach. aSummary data for end point 2 from 3 least significant studies were missing (either not reported or unpublished).Scenario: (TIFF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Expression and/or excretion of fibroblast growth factor-23 (FGF23) and its co-receptor Klotho are altered in patients with end-stage renal disease. The possibility that the FGF23/α-Klotho system mediates the aggravated cardiovascular outcome among patients with chronic kidney disease (CKD) has been suggested. We determined whether FGF23 and α-Klotho concentrations are altered among patients with reduced renal function and proteinuria.Serum FGF23 and α-Klotho were measured in cardiology patients who were not undergoing chronic hemodialysis. Estimated glomerular filtration rate (eGFR) was correlated negatively with FGF23 and positively with α-Klotho.2. By stepwise multivariate regression analysis, eGFR was selected as significant predictor for FGF23 or α-Klotho among patients with an estimated GFR of less than 60 mL/min/m2; however, urine albumin/creatinine ratio was not selected as a predictor for FGF23 or α-Klotho irrespective of the eGFR levels. In patients with eGFR of <60 mL/min/1.73 m2, UACR was significantly associated with log(FGF23); but, this association did not remain statistically significant in a multivariate model.The correlation between FGF23 and the renal tubular maximum reabsorption rate of phosphate to the GFR (TmP/GFR) was not significant, but that between FGF23 and serum calcium or inorganic phosphate was significant among patients with an estimated GFR of less than 60 mL/min/mAmong cardiology patients with various stages of CKD, serum concentrations of FGF23 and α-Klotho were associated with renal function, but not with the extent of proteinuria. Fibroblast growth factor-23 (FGF23) plays a crucial role in the regulation of calcium-phosphate metabolism by suppressing the renal tubular reabsorption of phosphate via activation of FGFR-1c in the presence of its co-receptor Klotho, which was originally identified as an anti-aging molecule , which mIn our previous analyses, we found that FGF23 is associated with left ventricular hypertrophy and cardiac systolic dysfunction , 13. As CKD is defined to be present when low glomerular filtration rate (GFR) and/or enhanced urinary protein excretion, both of which conditions has been shown to be associated with cardiac remodeling , 19, exiThe current retrospective study was approved by the Ethics Committee of Osaka Medical College. Between October 2012 and January 2014, 190 cardiac inpatients were recruited who provided written informed consent and for whom sufficient information regarding the data analysis was available. After excluding five patients undergoing chronic hemodialysis, 185 patients were enrolled in the current study.-1.094 × (age)-0.287 × [serum creatinine])/([serum phosphate] × [urine creatinine]); and percent TRP was calculated by multiplying TRP by 100. The tubular maximum reabsorption of phosphorus per glomerular filtration rate (TmP/GFR [mg/dL]) can be calculated by the following equation : If TRP Baseline characteristics were assessed with standard descriptive statistics. Data were expressed as either mean ± standard deviation or median and interquartile range. For comparisons of differences between groups, analysis of variance (ANOVA) was used for variables with normal distribution, and Kruskal-Wallis test was used when data were not normally distributed. To assess the correlation between two variables, a Pearson’s correlation test was used to assess the correlation between two normally distributed variables; for variables that were not normally distributed, a Spearman rank correlation test was used. For multivariate analysis, multivariate linear regression and multivariate logistic regression analyses were used. Data analysis was performed by SPSS statistics version 22.0 . A value of P < 0.05 was taken to be statistically significant.2) of ≥60, between 30 and 60, and < 30, respectively. In addition, 135 (73%), 40 (22%), and 10 (5%) patients had a UACR (mg/g•cr) of < 30 , between 30 and 300 , and ≥ 300 , respectively. Serum FGF23 levels between patients who were and were not taking ACE inhibitor and/or angiotensin II receptor blocker did not differ significantly (data not shown). By contrast, α-Klotho levels in patients who were taking ACE inhibitor and/or angiotensin II receptor blocker  were significantly lower than those in patients who were not taking either drug  .Among the 185 patients enrolled in the study, 135 were male (73%). More than half of the study subjects were taking ACE inhibitors and/or AT1 receptor blockers . Among the group of patients with CKD, α-Klotho was significantly correlated with parameters related to calcium excretion, but not with those related to phosphate excretion.Log(eGFR) was significantly associated with log(UACR) with a correlation coefficient of −0.190 (P = 0.009). FGF23 was significantly correlated negatively with log(eGFR) and positively with log(UACR) Figure . When paNext, we performed univariate and multivariate analyses for patients using log(FGF23) or log(α-Klotho) as an independent variable Table . eGFR waWe also performed a linear regression analysis after dividing the study population according to their CKD status. None of the parameters tested was selected as a significant predictor for either log(FGF23) or log(α-Klotho) among patients in the no CKD group Table . By univIt was found that serum FGF23 was correlated with both eGFR and UACR in a univariate model Figure . When th2), FGF23 levels were found to be related to eGFR in those with CKD, but not in those without (Table It is increasingly recognized that both a low glomerular filtration rate and increased urinary protein excretion are associated with an adverse cardiac or cardiovascular outcome , 25. BotKlotho is expressed predominantly in the kidney and, to the less extent, in the brain. As described above, previous studies have shown that, in parallel with the reduction of glomerular filtration rate, renal expression of klotho is decreased , 6, whicSeveral experimental , 29 and We also examined the relationship between FGF23/α-Klotho and urine excretion of calcium and phosphate Table . It was On the other hand, Komo et al. reported in an analysis of data from patients referred to their hospital for osteoporosis treatment, that serum FGF23 was not correlated with inorganic phosphate, corrected calcium, or TmP/GFR . They spThe current study has some limitations. First, ACE inhibitors and/or angiotensin II receptor blockers were prescribed for a substantial proportion of the study subjects, and patients who were taking these medications had lower serum α-Klotho than those who were not. Because this was not a randomized interventional study, however, we are not able to draw conclusions about whether or not these medications increase or decrease serum α-Klotho. Second, we excluded patients who were undergoing chronic hemodialysis; therefore, the relationship between FGF23/α-Klotho and renal function may be underestimated .Among cardiology patients, eGFR, but not the extent of proteinuria, was independently associated with serum levels of FGF23 and α-Klotho. In addition to clarifying the prognostic importance of these molecules for the prediction of outcome , 42, the


This experiment investigated the influence of motor expertise on object-based versus egocentric transformations in a chronometric mental rotation task using images of either the own or another person’s body as stimulus material. According to the embodied cognition viewpoint, we hypothesized motor-experts to outperform non-motor experts specifically in the egocentric condition because of higher kinesthetic representation and motor simulations compared to object-based transformations. In line with this, we expected that images of the own body are solved faster than another person’s body stimuli. Results showed a benefit of motor expertise and representations of another person’s body, but only for the object-based transformation task. That is, this other-advantage diminishes in egocentric transformations. Since motor experts did not show any specific expertise in rotational movements, we concluded that using human bodies as stimulus material elicits embodied spatial transformations, which facilitates performance exclusively for egocentric transformations. Regarding stimulus material, the other-advantage ascribed to increased self-awareness-consciousness distracting attention-demanding resources, disappeared in the egocentric condition. This result may be due to the stronger link between the bodily self and motor representations compared to that emerging in object-based transformations. Mental rotation is a specific visuo-spatial ability which involves the process of imagining how a two- or three-dimensional object would look if rotated away from its original upright orientation . In a clIn mental rotation there are two different classes of mental transformation strategies, which seem to represent different cognitive operations: object-based and egocentric transformations . WhereasEvidence from behavioral data confirms the view that object-based and egocentric transformations are implemented by two different processing systems. Regarding response time patterns, there is plentiful literature indicating that perspective transformations are faster and more accurate than object rotation in the ground plane . HoweverBesides, this specific pattern also varies with the view the stimulus is presented from (front vs. back view). Regarding object-based transformations, Even though both egocentric and object-based transformations share the involvement of motor processes, they differ in a crucial point, which is illustrated by the study of It is currently an open question if the performance in egocentric transformations differs when the body of an unknown person or one’s own body is presented during a mental rotation task. Neuropsychological and neuroimaging studies  revealedThe goal of this study was to investigate the mental rotation performance of egocentric versus object based transformations in motor-experts compared to non-motor experts. In line with mean age = 22.43, SD = 1.9) and 39 non-motor experts referred to as the non-athletic group . The motor experts differed from non-motor experts in the amount of training sessions by practicing more often  than the non-athletic group , F = 206.46, p < 0.001, mean IQA = 117.31, SDD = 17.06, mean IQNA = 111.05, SDNA = 12.23), t(79) = 1.88, n.s., measured by using the Number Connection Test  for presenting the stimuli. Regarding the experimental stimuli, there were four conditions, two object-based and two egocentric ones which were in turn split into two further categories, specifically “self” and “other”: (1) object-based-other, (2) object-based-self, (3) egocentric-other, (4) egocentric-self, presented in four separate blocks, as illustrated in Figure 1, left). The two stimuli were presented pairwise in five different angular disparities of 0°, 45°, 90°, 135° or 180°, in which the right stimulus was obtained by the rotation of left stimulus, the so-called comparison figure. Half of the trials displayed pairs of identical objects and half displayed mirror-reversed objects, resulting in a same–different judgment. In the egocentric condition only one figure was presented in one of the rotation angles mentioned above . This figure raised either the left or right arm and was depicted in both front and back view. Therefore, a left–right decision is required. All stimuli were rotated in the picture plane in a clockwise direction.For the object-based task, two pictures of the same kind of stimuli were presented side-by-side in the centre of the computer screen . The session took place in a controlled setting with constant artificial lighting. Participants were photographed from a fixed distance and in the same position with one outstretched arm (either left or right), and either from a frontal or back perspective. This resulted in a total of four pictures taken of each participant: 2 arm (left/right) × 2 view (front/back). Thereupon, photographs were modified with Adobe Photoshop software to ensure a completely white background. In contrast to this condition, the “other” trials consisted of pictures of another person that was matched in gender and clothes, see Figure 1 (bottom).In the “self” trials, the experimental stimulus consisted of an image of their own body. To this end, we took a picture of each participant wearing standardized clothes, namely blue trousers with black shirt and socks  when the two stimuli were “same” and the right mouse button (right-click) when the two stimuli were “different”. “Same” means that the right stimulus was identical  to the left comparison stimulus, whereas “different” implies that the stimulus on the right side was a mirror version of the left stimulus. The “object-based self” condition contained two pictures of the own body, whereas in the “object-based other” trials, two pictures of another person were presented.In the egocentric conditions (self/other), participants were required to press the right mouse button if the depicted figure raised their right arm or the left mouse button in the case of outstretching their left arm see . AnalogoThe chronometric mental rotation test consisted of four blocks which were presented in randomized order. Ten practice trials preceded each block. During the main experiment, every 20 trials a pause of 15 s was given. Each trial began with a fixation cross for 1 s. After that, the pair of stimuli in the object-based condition or a single figure in the egocentric condition appeared and stayed on the screen until participants answered. Feedback was given only in the practice trials. For correct responses a “+” appeared in the center of the screen and for incorrect responses a “–” appeared. The next trial began after 1500 ms.Each participant performed four blocks of 80 experimental trials, resulting in 320 trials: 2 transformations (object based/egocentric) × 2 stimulus types (self/other) × 5 angular disparities  × 4 repetitions of each combination × 4 stimuli per block (front vs. back view × left vs. right arm raised). Within each block the stimulus presentation order was randomized.Two repeated analyses of variance were conducted, with “reaction time” and “accuracy rate” as dependent variables, and with “angular disparity” , “stimulus type” (self vs. other), “view” (front vs. back), and “group” (motor experts vs. non-motor experts) as between-subject factors. The factors “angular disparity”, “stimulus type,” and “view” were the within-subject factors, “group” served as between-subject factor. We analyzed “object-based,” and “egocentric” transformations separately due to differences in several aspects: visual stimulation  = 27.12, p ≤ 0.001, F = 235.02, p < 0.001, F = 15.50, p = < 0.001, Table 1, mean RTs were higher for the front view  than for the back view , t(80) = 5.19, p ≤ 0.001, confirming the back-view-disadvantage predicted in Hypothesis 2. Regarding the main effect of the factor “angular disparity”, post hoc pair-wise comparisons showed higher RTs for each consecutive angular disparity (p ≤ 0.001), providing overall confirmation for Hypothesis 6. The main effect of “stimulus type” indicates that participants took longer to solve the self-condition  compared to the other-condition, , t(80) = –3.97, p ≤ 0.001, cf. Table 1. Note that this other-advantage is the opposite of the self-advantage we predicted in Hypothesis 4.Regarding RT, the repeated-measures analysis of variance revealed three significant main effects for the factors “view”, Furthermore, there were two two-way interactions:F = 4.61, p = 0.035, Figure 2. Post hoc tests showed that the response time difference between the front and the back view was significantly larger for self-stimuli than for “other” human figures. That is, front view was processed more slowly than the back view for both “other” stimuli  and “self” stimuli . However, this difference was more pronounced when images of the “own” body  were used instead of another person’s body , in the sense of an “own-body-front-view-disadvantage,” t(80) = -2.19, p = 0.032.(1) The interaction between “view,” and “stimulus type” reached significance, F = 2.40, p = 0.050, partial Figure 3. There was a bigger RT difference between angular disparity of 45° and 90° in the front view compared to that in the back view, t(80) = 3.3, p = 0.001. Overall, however, response times show a fairly similar monotonic increase with angular disparity for both front and back view which partly corroborates Hypothesis 8.(2) The “view” × “angular disparity” interaction was significant, F = 178.53, p ≤ 0.001, F = 8.45, p ≤ 0.001, F = 267.97, p < 0.001, Table 1. Participants took longer to solve the front view  than for the back view , t(80) = 12.87, p ≤ 0.001, confirming the front-view-disadvantage also found in object-based transformations. Interestingly, for the object-based conditions the effect size for comparing front vs. back view was r = 0.62 (d = 1.57), whereas the effect size for the factor “view” in egocentric transformations was much stronger with r = 0.96 (d = 0.62). The main effect of “group” indicates that motor-experts  solved egocentric transformations faster than non-motor experts , t(80) = –2.91, p = 0.005. Note that this result corroborates the prediction of Hypothesis 1. Regarding the main effect of the factor “angular disparity” , RTs in the egocentric condition did not differ between angular disparities of 45° and 90°, t(80) = –1.29, p = 0.119. Furthermore, by trend RT in the egocentric transformation condition surprisingly decreased between the angular disparity of 0° and 45°. That is, whereas RTs in the object-based condition roughly increased linearly with increasing disparity as expected, they showed a U-shaped pattern for the egocentric transformation condition. Increasing disparity in the egocentric task only led to higher response times for disparities larger than 90°. All other effects did not reach significance at the 0.05 level. In comparison with the roughly linear increase of response time with “angular disparity” in object-based transformations, the observed U-shaped pattern for egocentric transformations provides support for Hypothesis 7, which predicted that the small-angle-advantage should be more pronounced for the object-based transformations.Regarding RT, the repeated-measures analysis of variance revealed three significant main effects for the factors “view,” Furthermore, there were two two-way interactions:F = 6.16, p = 0.015, Figure 5. Post hoc tests showed that the difference between the front and the back view is significant larger for non-motor experts  than for motor experts , t(80) = –2.48, p = 0.015. That is, front view was processed slower than the back view in both motor experts  and non-motor experts , but this difference between front and back view is reduced in motor experts. Since this interaction is restricted to egocentric transformations, this result is in line with Hypothesis 3, where we predicted that the front-view-disadvantage should be diminished in motor-experts compared to non-motor experts, especially for egocentric transformations, which are assumed to be more embodied and therefore motor simulation is required to a higher extent compared to object-based transformations.(1) The interaction between “view” and “group” reached significance, F = 30.62, p < 0.001, Figure 6. Regarding the back view, post hoc tests showed a linear increase of RTs (p < 0.001) for angular disparities larger than 45°. The RT difference between angular disparity of 0° and 45° was not significant, t(80) = –1.06, p = 0.291. Interestingly, RT in the front view surprisingly decreased between the angular disparity of 0° and 45°, t(80) = 8.14, p < 0.001. RTs for angular disparities of 45° and 90° did not differ, t(80) = –1.29, p = 0.199. From angular disparity of 90° on there is a linear increase between successive increasing angular disparities (p < 0.001), resulting in an U-shaped pattern. This result provides some support for Hypothesis 8.(2) The “view” × “angular disparity” interaction was significant, F = 8.94, p < 0.001, Figure 7. Bonferroni corrected t-tests revealed a significant decline in accuracy between angular disparities of 90° and 135°, t(80) = 3.38, p = 0.001. None of the other differences between consecutive angular disparities reach significance, though. Thus, the accuracy data provided only some support for the small-angle-advantage predicted in Hypothesis 6. All other effects failed to reach significance at the 0.05 level. Hence, the accuracy data provided no support for Hypotheses 1, 2, 3, 4, 5, 7, or 8.The repeated-measures analysis of variance score showed one main effect of the factor “angular disparity” on accuracy rates, F = 20.87, p ≤ 0.001, F = 9.47, p < 0.001, M = 91.1%, SD = 1.2) compared to the performance in the back view condition , t(80) = –4.54, p < 0.001, confirming the front-view-disadvantage predicted in Hypothesis 2. Regarding the main effect of “angular disparity” , Bonferroni corrected t-tests revealed that accuracy significantly declines between angular disparities of 135° and 180°, t(80) = 4.08, p < 0.001. This result in partly corroborates Hypothesis 6.The repeated-measures analysis of variance revealed two significant main effects for the factors “view”, other-advantage diminishes in egocentric transformations, this finding provides some support for the effect predicted in Hypothesis 5. Furthermore, increasing angular disparities overall lead to higher RTs which confirms Hypothesis 6. Additionally, this effect was dependent on the kind of transformation and more prominent for object-based transformations. This corroborates Hypothesis 7 and is in line with In the context of the embodied cognition approach, we compared the performance of motor experts and non-motor experts in object-based versus egocentric transformations with a specific focus on the distinction between “self”- and “other” body stimuli. Our main results were that motor experts outperformed non-motor experts regarding RT, but only in the egocentric condition. This result corroborates Hypothesis 1. With respect to view, we observed a front-view-disadvantage which is expressed by higher RTs for stimuli facing the participant for both object-based and egocentric transformations. Regarding accuracy rate, front view elicited more errors than the back view in the egocentric transformation task. These findings provide evidence for Hypothesis 2. We do not refer this decreased front view performance to a conflict in response-stimulus mapping because it occurred in both object-based and egocentric transformations. Otherwise, only egocentric transformations where laterality judgments are required should have been affected. Interestingly, the front-view-disadvantage was reduced in motor-experts, but only for egocentric transformations as predicted in Hypothesis 3. Concerning stimulus type, for object-based transformation tasks another person’s body stimuli seem to be transformed and processed faster than those of own body stimuli, which is contradictory to Hypothesis 4. However, this other-advantage was not observed for egocentric transformation tasks. This finding is in line with further analyzes comparing “self”-stimuli in both transformations. Since the While motor experts performed faster than non-motor experts in the egocentric transformation condition, no such advantage was found for the object-based transformation condition. This seemed to be in contrast to the positive influence of long-term physical activity on object-based transformations reported by Based on the notion that egocentric transformations evoke the simulation of one’s body, a better proprioceptive integration of motor experts compared to non-motor experts could be a further explanation of why they showed performance differences only for egocentric transformations. It still remains unclear, though, whether a general increased expertise in motor imagery of motor experts leads to advantages in perspective transformations. Motor imagery is a widespread technique in motor experts attempting to improve their performance . It is qThese considerations of the embodied viewpoint approach could also provide evidence for the finding of a diminished front-view-disadvantage of motor experts solely in egocentric transformations. Adapting the front view requires an additional in depth-rotation and therefore higher RTs cf. . We concNext to these routes for increasing involvement of body-related representations during egocentric transformations, individual differences should also be taken into account. Note that next to motor and proprioceptive signals, vestibular information is also playing an important role in egocentric transformations. The work of Interestingly, the strength of embodiment is dependent on two further modulating variables: gender and culture. According to the embodied viewpoint, egocentric transformations considered as emulations of a body rotation should emerge especially when actual mental rotation is performed. This is increasingly likely when task difficulty rises, that is when angular disparities are increasing. Interestingly, regarding this specific pattern, there is some evidence that object–based transformations show a linear increase of RTs with angular disparity  due to tbody image includes beliefs, attitudes, and perceptions about the own body. There is evidence that an experimental stimulus like a mirror increases attention to aspects of the self (In contrast to the self . AccordiInterestingly, the other-advantage disappears in egocentric transformations, which could be interpreted as kind of a self-advantage in egocentric transformations. This is in turn in line with the findings of body schema. In contrast to a similar, but distinct consciousness-related concept, specifically the body image, the body schema is not only sensory-motor but also motor-related and outside of intentional awareness. According to Further evidence is provided by the work of Since our results are not in line with previous studies emphasizing the meaning of specific expertise, the notion that not only motor expertise, but also visual experience plays an important role could be crucial. For further studies, an additional control group who is not familiar with these movements could be helpful for a more detailed understanding. In addition, to clarify to which extent both specific expertise and stimulus type influence spatial transformations, a further study might be conducted comparing rotational motor experts, general motor experts, and non-athletes using our stimulus material. However, showing that egocentric transformations are more embodied than object-based ones by using a group-effect of motor expertise is a much more indirect way than manipulating the body’s posture. Thus, manipulating the body’s posture is better approach, and might be a more promising avenue for further research. Regarding the influence of the self on object-based and egocentric transformations, brain imaging studies suggest that the body stimuli used in our study are not directly comparable with hand stimuli, as cortical area in the visual cortex can be selective to the processing human bodies (“extrastriate body area”, cf. In conclusion, our results show that the link between the bodily self and motor representations according to the embodied cognition viewpoint not only occurs in visual recognition but also in higher cognitive processes such as mental rotation. Embodied mental rotation thus proved to be an interesting research topic that deserves further attention, especially in the context of the bodily self.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.


AbstractSalmokottelatisp. n., is described from Alakır Stream (Mediterranean basin) in Turkey. It is distinguished from other Anatolian Salmo species by a combination of the following characters (none unique to the species): general body colour greenish to silvery in life; 7–9 parr marks along lateral line; four dark bands on flank absent in both sexes; black ocellated spots few, present only on upper part of flank in individuals smaller than 160 mm SL but in larger both males and females black spots numerous and located on back and middle and upper part of flank; red spots few to numerous, scattered on median, and half of lower and upper part of flank; head long ; mouth large ; maxilla long ; 105–113 lateral line scales; 24–29 scale rows between lateral line and dorsal-fin origin, 17–19 scale rows between lateral line and anal-fin origin; 13–15 scales between lateral line and adipose-fin insertion. Salmotrutta Linnaeus, 1758 has long been considered to be a polymorphic species widely distributed throughout Europe and the Middle East reaching in south and south-east to the Atlas Range  and to the upper Amu-Darya drainage in Afghanistan .hanistan . Since mSalmotrutta distributed in Europe and Asia were resurrected to the species level by Some forms or subspecies of Salmotrutta: Salmotruttalabrax Pallas, 1814 from the Çoruh River (Black Sea basin), Lake Çıldır in the Kura drainage (Caspian Sea basin), and Uludağ mountains (Marmara Sea basin); Salmotruttacaspius Kessler, 1877 from the Kura River (Caspian Sea basin); Salmotruttamacrostigma  from the Çoruh River (Black Sea basin), and the Çatak Stream (the Tigris system) and described Salmotruttaabanticus Tortonese, 1955, as a new subspecies, from Lake Abant (a closed lake in northwest Anatolia). Distribution area of Salmotruttamacrostigma was later considered as including, besides the Çoruh River, the Aegean Sea basin, the Marmara Sea basin, the Trace region, the Mediterranean basin, and the Tigris-Euphrates drainage , Salmotruttamacrostigma (Çoruh River drainage), and Salmotruttaabanticus (the outlet of the Abant Lake in the Bolu Province). Salmolabrax from the Ulu-Uzen River in Crimea  and a sample from the Khosta River in Krasnodar Krai in Russia (north-eastern Black Sea coast). The comparison revealed that the Salmo populations from the Black Sea coast of Anatolia are different from Salmolabrax from the Crimea Peninsula and the Caucasian coast in Russia. Salmotruttaabanticus was evaluated at species level; the two other populations were asserted morphologically distinct, diagnosable, and described as new species: Salmocoruhensis Turan, Kottelat & Engin, 2010 and Salmorizeensis Turan, Kottelat & Engin, 2010. They also suggested that both species in several streams are present in sympatry, although rarely in syntopy. An opinion was proposed by Salmotruttalabrax and Salmotruttamacrostigma from the Çoruh drainage. Salmocoruhensis and Salmorizeensis respectively. The two species were also recorded from other streams and rivers draining to south-eastern Black Sea coast  but the exact border between the ranges of Salmolabrax and Salmocoruhensis is still not known and the trout from rivers in Georgia still needs a taxonomic study. As to Salmocoruhensis, it is distributed in the drainages of south-eastern Black Sea coast from the Çoruh River in the north to the Kızılırmak River.oast see : 336  , Salmolabecula Turan, Kottelat & Engin, 2012 , Kartoz and Zindan streams , Salmoopimus Turan, Kottelat & Engin, 2012  and Alara Stream , Salmochilo Turan, Kottelat & Engin, 2012 (Akdere Stream in the upper Ceyhan River), Salmookumusi Turan, Kottelat & Engin, 2014 (western Euphrates drainage), and Salmoeuphrataeus Turan, Kottelat & Engin, 2014 (northeastern Euphrates drainage).Distribution of  Algeria . The othainages) . In TurkSalmo populations found in the southern Marmara Sea coast and the Trace Region will be discuss in forthcoming papers.The taxonomic status of Salmokottelati.The present paper reports our data on the identity of the resident trout inhabiting Alakır, a small stream draining to the Mediterranean Sea. We conclude that it is an unnamed species. Here, it is described as a new species , Zoology Museum of the Faculty of Fisheries, Recep Tayyip Erdoğan University, RizeFFR, and , the collection of Maurice Kottelat, CornolCMK. Measurements and counts were all obtained on wild caught specimens, well preserved, in a straight position. SpeciPageBreakmens not fixed straight or damaged were excluded. Most samples include both sexes, juveniles and mature specimens. Most Salmo populations are small, geographically restricted and under great threat because of overfishing and habitat destruction, and it is not advisable to collect and preserve large series of individuals. Colour pattern and variation in shape were observed in the field on additional individuals which were not preserved.Fish were caught with pulsed DC electro fishing equipment. Material is deposited in: bands to refer to the broad blackish vertical marks on the body, typically positioned behind the gill opening, below the dorsal fin, above the anal fin, and on the caudal peduncle. In some species these bands are known in well preserved specimens only, or in stressed individuals only, and in other species they are visible in situ in undisturbed individuals. A spot is called ocellated when surrounded by a white or very pale ring.Measurements were taken with digital calipers (0.1 mm accuracy). Counts and measurements follow Salmocoruhensis, Salmorizeensis, Salmoabanticus, Salmocaspius, Salmotigridis, Salmoplatycephalus, Salmolabecula, Salmoopimus, Salmochilo, Salmookumusi, and Salmoeuphrataeus are from The morphometric and meristic data for Taxon classificationAnimaliaSalmoniformesSalmonidaehttp://zoobank.org/A1CED992-9A64-4369-B88C-918DFB12BBDC40°35.32'N, 40°51.50'E); D. Turan, E. Doğan and C. Kaya. 21 September 2014.FFR 03180, 205 mm SL, male; Turkey: Antalya Province: Altınyaka village; Alakır Stream ; M. Kanyılmaz, 15 September 2008.FFR 03181, 21, 98–210 mm SL; same data as holotype. FFR 03182, 16, 98–176 mm SL; CMK 22405, 4, 97–143 mm SL; Turkey: Antalya Province: Altınyaka village; Alakır Stream  in males larger than about 160 mm SL, and females between about 160−190 mm SL; in males and females smaller than about 160 mm SL, black spots few, present only on upper part of flank; few to numerous ocellated red spots on back and half of upper and lower flank; number of both black or red spots commonly increasing with size and age in males while number of both black and red spots decreasing with size and age in females; head long ; mouth large , slightly subterminal; maxilla long , reaching beyond eye in males longer than about 120 mm SL and in females longer than about 170 mm SL; 105–113 lateral-line scales ; 24–29 scale rows between lateral line and dorsal-fin origin; 17–19 scale rows between lateral line and anal-fin origin; 13–15 scale rows between lateral line and adipose-fin insertion; gill rakers 18–20 on outer side of first gill arch.General appearance shown in Fig. PageBreakconvex anteriorly and convex posteriorly or large, almost reaching caudal-fin base in males larger than about 190−200 mm SL, upper margin markedly convex. In females, adipose fin small to medium, upper edge markedly convex both anteriorly and posteriorly. Pectoral fin long, with 1 simple, 10–13 branched rays, outer margin slightly concave. Pelvic fin with 1 simple and 7–9 branched rays, outer margin slightly convex. PageBreakPageBreakAnal fin with 3 simple and 6½–8½ branched rays, outer margin straight or slightly convex anteriorly, concave posteriorly. Caudal fin long and emarginate in small to large size specimens, lobes slightly pointed. Gill rakers 6–7 + 12–13 = 18–20 on outer side of first gill arch. Total vertebrae 55(2), 56(12), 57(5), and 58(1); predorsal vertebrae 13–15 with mode of 14; number of abdominal vertebrae 33–35 with mode of 34, and that of caudal vertebrae 21–23 with mode of 22. Abdominal region longer than caudal region, rarely regions equal, and difference between abdominal and caudal counts 11–14; most common vertebral formula 34+22.Lateral line with 105–113 scales; 24–29 scale rows between lateral line and dorsal-fin origin; 17–19 scale rows between lateral line and anal-fin origin; 13–15 scale rows between lateral line and adipose-fin insertion  by having fewer parr marks on the flank . It is further distinguished from Salmoplatycephalus, Salmoopimus, Salmochilo, and Salmolabecula by the absence of four dark bands on the flank in males and females (vs. presence).Salmokottelati is distinguished from Salmoplatycephalus by the presence of red spots on the flank in individuals of all sizes (vs. absence in specimens larger than about 70 mm SL) and the presence of black spots in individuals of all sizes (vs. absence in specimens larger than about 170 mm SL). The new species has fewer gill rakers on the outer side of the first gill arch , a longer head in males (29–33% SL vs. 27–29), a deeper head , and a longer maxilla .Besides the above differences, Salmokottelati differs from Salmoopimus in having fewer lateral-line scales  and by the number and location of the black and red spots on the body in males and in females larger than about 160 mm SL. In Salmokottelati, the black spots are numerous in males larger than about 160 mm SL and in females between about 160–190 mm SL, present on median, and half of upper and lower of part flank, and the number of both red and black spots increases with increasing size and age in males. In Salmoopimus, black spots are few (less than 50), present on back and upper part of flank, and their number does not increase with increasing size and age in males. There are also other differences between males of Salmokottelati and Salmoopimus: males of Salmokottelati have a deeper head , a longer maxilla , and a greater mouth gape .Besides the differences listed above, PageBreakSalmokottelati is distinguished from Salmochilo by the following characters: a slightly convex dorsal profile of head (vs. strongly convex), a pointed snout (vs. blunt), a slightly subterminal mouth , non-flesh maxilla and lower lip (vs. flesh), length of the maxilla markedly sexually dimorphic ; black spots on the body roundish (vs. irregularly shaped). The new species also differs from Salmochilo by having more numerous scale rows between the lateral line and the anal-fin origin . When compared to males of Salmochilo, males of Salmokottelati have a deeper head , and a longer maxilla .In addition to the differences mentioned above, Salmokottelati differs from Salmolabecula by the presence of red spots on the flank (vs. absence in specimens larger than about 70 mm SL) and the black spots on body roundish (vs. irregularly shaped). Salmokottelati has more numerous scale rows between the lateral line and the anal-fin origin  and fewer gill rakers . It also differs from Salmolabecula by having a greater predorsal distance . Moreover, Salmokottelati has a longer head in males  and a deeper head in males .Besides the differences mentioned above, Salmokottelati is further distinguished from Salmotigridis by the number and locations of black spots on the body in specimens larger than about 160 mm SL. In Salmokottelati, black spots are numerous in males larger than about 160 mm SL and in females between about 160–190 mm SL, present on median and half of upper and lower part of flank, and the number of both red and black spots increases with increasing size and age in males. In Salmotigridis, black spots are few (less than 50), present on back and the upper part of flank and their number does not increase with increasing size and age. The new species also differs from Salmotigridis by having fewer scale rows between the lateral line and the dorsal-fin origin , fewer scale rows between the lateral line and the anal-fin origin , and fewer scale rows between the adipose fin insertion and the lateral line . Besides the above listed differences, Salmokottelati males are distinguished from Salmotigridis males by a longer  and deeper head , a longer maxilla , and a wider mouth gape .Salmokottelati is further distinguished from Salmoabanticus by the presence of red spots on the body in specimens larger than about 210 mm SL (vs. absent), the shape of the black spots on the flank , the shape of the ring around black spots , and the size of the black spots . It has fewer lateral line scales than Salmoabanticus . Besides the differences mentioned above, it also differs from Salmoabanticus in having a longer head . Males of Salmokottelati differ from males of Salmoabanticus in having a greater predorsal distance , a longer maxilla , a greater eye diameter , and a deeper head at nape .PageBreakSalmokottelati is further distinguished from Salmocoruhensis by fewer scale rows between the anal-fin origin and the lateral line , fewer scale rows between the adipose fin insertion and the lateral line , a longer  and deeper head in males .Salmokottelati is further distinguished from Salmorizeensis by the number and location of black spots on the body. In Salmokottelati, black spots are numerous (60 and more) in males larger than about 160 mm SL and in females between about 160–190 mm SL, present on median, and half of upper and lower of part flank, and the number of both red and black spots increases with increasing size and age in males. In Salmorizeensis, black spots are few (less than 40), present on back and upper part of flank, and their number does not increase with increasing size and age. The new species also differs from Salmorizeensis by the general body colour , the adipose fin almost reaching the base of the caudal fin in males larger than 200 mm SL , and fewer lateral-line scales .Salmokottelati is further distinguished from Salmocaspius from the upper Kura drainage by having fewer lateral line scales , fewer scale rows between the lateral line and the anal-fin origin  and fewer scale rows between the insertion of the adipose fin and the lateral line . In Salmokottelati, the general body colour is greenish to silvery in life (vs. brownish). Morover, males of Salmokottelati have a deeper head than that of Salmocaspius .Salmokottelati is further distinguished from Salmookumusi by the absence of four dark bands on the flank in males and females ; the parr marks vertically oblong ; the black spots circular (vs. irregularly shaped), bigger black spots on dorsal fin . Salmokottelati has a longer head than Salmookumusi . Males of Salmokottelati differs from males of Salmookumusi by having a longer maxilla  and a deeper head .Salmokottelati is further distinguished from Salmoeuphrataeus by the general body colour and the number and position of the black spots on the body: in Salmokottelati, black spots are commonly numerous in males and increasing the number with size (vs. few and not increasing the number) and located on median, and half of upper and lower parts of the flank . The new species also differs from Salmoeuphrataeus in having fewer lateral-line scales , fewer scale rows between the dorsal-fin origin and the lateral line , and fewer scale rows between the anal-fin origin and the lateral line .PageBreakSalmokottelati differs from resident Salmolabrax by a large black spot behind the head , black spots on the body smaller than the pupil (vs. larger), more numerous red spots on the body . Salmokottelati has fewer lateral line scales , fewer scale rows between the dorsal-fin origin and the lateral line , fewer scale rows between the lateral line and the anal-fin origin , fewer scale rows between the adipose fin insertion and the lateral-line , and more numerous gill rakers . Males of Salmokottelati are further distinguished from males of Salmolabrax by having a longer head , a greater predorsal length in males , a deeper head , a longer maxilla , a wider mouth gape , and a longer mouth gape .Salmokottelati can be further distinguished from Salmocf.coruhensis from the Gönen Stream (southern Marmara Sea) by fewer lateral-line scales , fewer scale rows between the lateral line and the anal-fin origin , fewer scales between the adipose fin insertion and the lateral line , a larger adipose fin , and a deeper caudal peduncle . Males of Salmokottelati differs from males of Salmocf.coruhensis by a longer head , a greater predorsal distance , a longer mouth gape , a narrower mouth gape , a longer maxilla , and a deeper head .Salmokottelati is further distinguished from Salmocf.rizeensis from the Rezova Stream (Trace Region) by fewer lateral-line scales , fewer scale rows between the dorsal-fin origin and the lateral line , fewer scale rows between the lateral line and anal-fin origin , fewer scales between the adipose fin insertion and the lateral line , a larger adipose fin , and a deeper caudal peduncle . Males of Salmokottelati differs from males of Salmocf.rizeensis by a longer  and deeper head .Salmoplatycephalus: 34, 75–550 mm SL; Turkey: Kayseri Prov.: Pınarbaşı Stream in Pınarbaşı district, Seyhan River drainage.Salmochilo: 33, 65–235 mm SL; Turkey: Sıvas Prov.: Akdere Stream at Gürün county, Ceyhan River drainage.Salmolabecula: 19, 85–400 mm SL, male; Turkey: Niğde Prov.: Ecemiş Stream at Çamardı county, Seyhan River drainage. 10, 140–241 mm SL; Turkey: Isparta prov.: Kartoz Köprüçay.PageBreakSalmoopimus: 13, 118–180 mm SL; Turkey: Antalya Prov.: Alara Stream at Gündoğmuş. 25, 115, 147–186 mm SL; mm SL; Turkey: Kahramanmaraş prov.: Göçüksu Stream at Kömürköy, Ceyhan River drainage. 4, 175–210 mm SL; Turkey: Kahramanmaraş Prov.: Tekir Stream at Tekir, Ceyhan River drainage. 9, 90–300 mm SL; Turkey: Kahramanmaraş Prov.: Fırnız Stream at Fırnız, Ceyhan River drainage.Salmotigridis: 13, 136–227 mm SL; Turkey: Van Prov.: Çatak Stream, Tigris River drainage. 7, 15–18 mm SL, Turkey: Van Prov.: Müküs Stream, Tigris River drainage.Salmorizeensis: 16, 88–237 mm SL; Turkey: Erzurum Prov.: Ovit(2) [Kan] Stream at Ovit mountain, Çoruh River drainage. 7, 88–237 mm SL; Turkey: Artvin Prov.: Dörtkilise Stream at Tekkale Village, Çoruh River drainage. 12, 75–167 mm SL; Turkey: Artvin Prov.: Çifteköprü Stream at Cankurtaran mountain, Çoruh River drainage. 11, 113–221 mm SL; Turkey: Erzurum Prov.: Yağlı Stream at Yağlı village, Çoruh River drainage. 16, 145–224 mm SL; Turkey: Giresun Prov.: Akbulak stream at Akbulak village, Yeşilırmak River drainage. 10, 122–221 mm SL; Turkey: Kütahya Prov.: Sefaköy Stream at Domaniç, Sakarya River drainage. 10, 111–119 mm SL; Turkey: Kütahya Prov.: Çatalalıç Stream at Domaniç, Sakarya River drainage. 13, 111–220 mm SL; Turkey: Rize Prov.: Çağlayan Stream at Gürcüdüzu figau. 18, 95–226 mm SL; Turkey: Rize Prov.: Şehitlik Stream at Şehitlik village. 12, 90–118 mm SL; Turkey: Rize Prov.: Çayeli Stream at Kaptanpaşa village. 10, 90–238 mm SL; Turkey: Rize Prov.: Ovit Stream at Ovit mountain, İyidere drainage. 14, 120–200 mm SL; Turkey: Rize Prov.: Fırtına Stream on Elevit Plateau. 10, 114–245 mm SL; Turkey: Trabzon Prov.: Değirmen Stream at Çoşandere village. 12, 112–230 mm SL; Turkey: Trabzon Prov.: Solaklı Stream at Demirkapı village.Salmocoruhensis: 13, 90–380 mm SL; Turkey: Erzurum Prov.: Uzundere district; Pehlivanlı Stream at Pehlivanlı village [tributary of Tortum], Çoruh River drainage. 13, 115–330 mm SL; Turkey: Artvin Prov.: Dörtkilise Stream at Tekkale village, Çoruh River drainage. 5, 130–229 mm SL; Turkey: Artvin Prov.: Barhal Stream at Sarıgöl village, Çoruh River drainage. 16, 190–465 mm SL; Turkey: Bayburt Prov.: Ölçer Stream at Ölçer village, Çoruh River drainage. 17, 80–550 mm SL Turkey: Erzurun Prov.: Çayırbaşı (Kırık) Stream at Kırık village, Çoruh River drainage. 6, 160–290 mm SL; Turkey: Erzurum Prov.: Madenköprübaşı district; Büyük Stream at Büyükköy village, Çoruh River drainage. 17, 70–210 mm SL; Turkey: Gümüşhane Prov.: Harşut Stream at Yağmurdere village. 6, 95–117 mm SL; Turkey: Rize Prov.: Sarayköy Stream at Sarayköy village. 6, 100–250 mm SL; Turkey: Rize Prov.: İyidere Stream in İyidere district. 7, 150–450 mm SL; Türkey: Rize Prov.: Fırtına Stream at Çamlıhemşin. 5, 10–280 mm SL; Türkiye: Rize Prov.: Limanköy Stream at Limanköy village. 25, 90–520 mm SL; Türkiye: Rize Prov.: Fırtına Stream at Çat village. 11, 95–228 mm SL; Turkey: Rize Prov.: Kendirli Stream at Kalkandere District on road to Kendirli village, İyidere drainage. 13, 120–450 mm SL; Turkey: Rize Prov.: İyidere Stream (İkizder) at Güneyce. 6, 130–420 mm SL; Turkey: Rize Prov., Veliköy Stream at Veliköy village. 9, 160–450 mm SL; Turkey: Sıvas Prov.: Gemin country, Yeşilırmak River drainage on road of Sıvas.PageBreakSalmoabanticus: 20, 113–300 mm SL; Turkey: Bolu Prov.: outlet of Lake Abant.Salmocaspius: 10, 126–222 mm. SL; Turkey: Ardahan Prov.: Çataldere Stream at Ardahan, Kura River drainage. 30, 110–250 mm. SL; Turkey: Ardahan Prov.: Tora Stream at Ardıcdere village, Kura River drainage. 8, 135–240 mm SL; Turkey: Ardahan Prov.: Aşıkzülal Stream at Aşıkzülal village, Kura River drainage.Salmolabrax: 6, 107–147 mm SL; Ukraine: Ula-Uzen River. 6, 102–160 mm SL; Russia: Krasnodar Prov.: Khosta River.Salmookumusi: 11, 75– 213 mm SL; Turkey: Malatya Prov.: Sürgü Stream, Euphrates River drainage. 33, 68–28 mm SL; Turkey: Sıvas Prov.: Gökpınar Stream , Euphrates River drainage. 18, 88–230 mm SL; Turkey: Erzurum prov.: Şenyurt Stream (tributary of Karasu Stream), Euphrates River drainage. 10, 160–250 mm SL; Turkey: Erzurum Prov.: Ağırcık Stream at Ağırcık Village (tributary of Karasu Stream), Euphrates River drainage. 12, 95–300 mm SL; Turkey: Erzurum Prov.: Sırlı Stream at Sırlı Village (tributary of Karasu Stream), Euphrates River drainage.Salmocf.coruhensis: 28, 95–228 mm SL; Turkey: Çanakkale Province: Çelebi Stream, drainage of Gönen Stream. 12, 108–160 mm SL; Turkey: Çanakkale Province: Kilise Stream; drainage of Gönen River.Salmocf.rizeensis: 50, 90–220 mm SL; Turkey: Kırklareli Province: Rezova Stream.


Thus, our simple models reconcile the seemingly contradictory observations on residual viremia and, with relatively few parameters, recapitulates HIV viral dynamics observed in patients on suppressive therapy.Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. However, some residual virus remains, below the level of detection, in HIV-infected patients on ART. The source of this viremia is an area of debate: does it derive primarily from activation of infected cells in the latent reservoir, or from ongoing viral replication? Observations seem to be contradictory: there is evidence of short term evolution, implying that there must be ongoing viral replication, and viral strains should thus evolve. However, phylogenetic analyses, and rare emergent drug resistance, suggest no long-term viral evolution, implying that virus derived from activated latent cells must dominate. We use simple deterministic and stochastic models to gain insight into residual viremia dynamics in HIV-infected patients. Our modeling relies on two underlying assumptions for patients on suppressive ART: that latent cell activation drives viral dynamics and that the reproductive ratio of treated infection is less than 1. Nonetheless, the contribution of viral replication to residual viremia in patients on ART may be non-negligible. However, even if the portion of viremia attributable to viral replication is significant, our model predicts (1) that latent reservoir re-seeding remains negligible, and (2) some short-term viral evolution is permitted, but long-term evolution can still be limited: stochastic analysis of our model shows that  + individuals, antiretroviral therapy (ART) effectively controls HIV viral loads to below levels detectable by routine tests. However, more sensitive tests can detect some residual viremia. The source of this virus is a matter of debate: does it derive from ongoing viral replication, or from viral production following activation of latently infected cells? Experimental observations support both sides of the argument: in patients on therapy, HIV shows no long-term evolution, and emergence of drug-resistant mutants is rare, implying no ongoing viral replication, but there remains short-term evolution, implying the opposite. To reconcile these observations, we propose a mathematical model of latently and productively infected cells and virus. Using our models we predict that, in patients on suppressive ART, the contribution of viral replication to residual virus, while small, yields short term-evolution. But even if the contribution is large, for example if adherence to therapy is poor, long-term evolution can still be limited, and de novo emergence of drug resistance is rare. Thus, our simple models reconcile the seemingly contradictory observations on residual viremia.In HIV Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads to below detectable levels in most patients. However, infection remains: cessation of treatment is usually followed by HIV rebound to high levels . Ultra-s+ T cell population but it is very long-lived; patients on treatment show a decaying reservoir with a half-life estimated to be between 6 and 44 months on average, so the time to complete eradication may be up to 70 years  > η2. We interpret this constraint as the net decay rate of productively infected cells in the presence of new infections but in the absence of new latent cell activations, δ [1 − (1 − f)R]  estimated that on average, after therapy is interrupted, active viral replication is initiated once every 6 days. This does not imply that there is an average of one new latent cell activation every six days, as there also needs to be ensuing rounds of viral replication following the activation of a latently infected cell that cause viral rebound, rather than a chain of infection that ultimately dies out. Therefore, the actual value of aL remains unclear. We use aL ≈ 174 latent cell activations per day but our qualitative results are not sensitive to this choice, see Sec. E in Beyond the net measured latent reservoir half-life, )R] c.f.  must be + T-cell , so onlyε ≠ 0.99 we recalculate the associated reproductive ratio, R = (1 − ε)R* and then re-compute the associated on-therapy quasi-steady state viral load, V0 = apL0/c(δ(1 − (1 − f)R) − η2) from When investigating viral dynamics under drug efficacies I, into two parts: Ia, infected cells generated by latent cell activation, and Ir, infected cells generated by viral replication. The t = 0 representing an arbitrary time after the initiation of treatment and after transient viral load dynamics have passed.In order to determine the extent of new cell infections in a patient under therapy, we split the productively infected cell compartment, f < 1 and 0 < R < 1, one can show that both eigenvalues λ± are negative  =  [λ+ and corresponding eigenvector λ+|, with relative magnitudes corresponding to their respective eigenvector λ1| and |λ−| happen on faster time scales, since |λ1| > |λ+| and |λ−| > |λ+|, and therefore represent transient, short-term dynamics only. Our initial conditions should lie along the eigenvector L0, we set the initial Ia(0) so that Ir(0) so that Ia(t) and Ir(t) areThe eigenvalues of the system tive see . The lar0, 0, 0) . After sMore significantly, the fraction of infected cells that come from residual viral replication, in patients on long-term treatment, isR = Rc where the contributions to the total number of newly infected cells, I, from latent cell activation, Ia, and viral replication, Ir, are equal. We set R to obtainR > Rc, viral replication makes the dominant contribution to the number of new productively infected cells. If R < Rc, the contribution of viral replication is small, and latent cell activation dominates. Recall that the reproductive ratio R is defined here as R = (1 − ε)pβT/(c + βT)δ and is independent of latent cell infection. Since the infected cell death rate δ is 1 day−1  in R = 0; since 0 < R < 1, higher order terms will become very small, and even the Taylor series coefficient of R2, af(1 − f)δ2/(δ − η1)3, is very small since f ∼ O(10−6) − O(10−4). Because the viral load is proportional to the number of infected cells, the fraction of circulating virus associated with viral replication is approximately given by f and reproductive ratio R to simply estimate the fraction of circulating virus associated with ongoing viral replication. Since f ∼ O(10−6) − O(10−4), and δ ≫ η1, we can approximate Ir/I ≈ 2.3% with our baseline parameters (R = 0.023).However, the contribution to residual viremia from viral replication may be non-negligible. We can re-write the ratio R and associated residual plasma viral load. We assume thatR* = 2.3 and baseline drug efficacy ε = 0.99, to fix a. We recover similar results with a fixed using different baseline drug efficacies, ε = 0.6–0.999 . Our model predicts that in the case of ε = 0.999 (figure not shown), in the range of raltegravir monotherapy [otherapy , the conε = 0.6, below our range of primary consideration, which gives R = 0.92. In that case approximately 92% of circulating virus would be associated with ongoing viral replication. However, we claim that a drug efficacy of ε = 0.6 is not realistic under current regimens. The drug efficacy ε = 0.6 is associated with a viral load of 38 copies/mL, a value below the limit of detection of conventional clinical assays. Under recent regimens, the median measured viral load was 3.1 copies/mL [ε = 0.6 is only realistic if the virus has developed some resistance to therapy or if patient adherence is low, in which cases we would expect more viral replication.opies/mL . TherefoOngoing viral replication in patients on effective therapy, however slight, carries with it the probability that the virus may mutate. We now consider the lineage created by newly activated, latently infected cells in our simple model.R < 1, the lineage of infected cells generated from the activation of a single latently infected cell goes extinct as t → ∞. R = 0.23. Each trajectory’s dynamics is given by the branching process analogue of the differential equations model assuming a constant number of target cells T,I(0) = 1, since dynamics are initiated by the activation of a single latently infected cell, simulated using the stochastic simulation algorithm [aL ≈ 7 day−1 is the Poisson rate parameter for the activation times of latently infected cells in the blood; R < 1, the lineages created by the activation of latently infected cells ultimately go extinct, after some, but not many, rounds of viral replication, as shown in R. R = 0.92. Note the difference in timescales between R = 0.92, i.e., ε = 0.6, there are more rounds of viral replication, and the lineages created by the activation of a single latently infected cell last longer than in the R = 0.23 case. These illustrative stochastic simulations make clear why, given identical latent cell activation times, the contribution of ongoing viral replication depends on the drug effectiveness and hence, on R. The total viral load is the sum of the viral load curves shown in R, there are more rounds of viral replication before extinction, the lineages contribute to viral load for a longer amount of time st round, fk−1, and on the number of infected offspring from the cells present at the (k − 1)st round, giving the recurrence relation fk = h(fk−1), f−1 = 0 [h(x) is the probability generating function for the offspring distribution, i.e., the distribution of the number of infected cells produced from a single infected cell in one generation; in this case, h(x) = 1/(1 − R(1 − x)) (see fk = (1 − Rk+1)/(1 − Rk+2).Here we briefly outline the derivation of the probability distribution of the number of rounds of viral replication before extinction, with the full derivation given in the f−1 = 0 . h(x) is x)) see . Solvingk rounds of viral replication, given a single latent cell activation, is gk = fk − fk−1  probability per round of replication [The stochastic analogue of our simple model of viral replication reveals that there may be a few rounds of viral replication, before lineage extinction. This model prediction is consistent with the observation of recent viral evolution in a genotypic study of episomal HIV cDNA collected prior to viral rebound . Howeverlication , 53, whilication , 18.Lo represent the pre-existing latent population, and Ln represent new latently infected cells, i.e., the re-seeded population, after time t = 0. The ODE We can also use an extension of the λ± remain as before, as in η2 = −λ+. Now λ1 = −η1 and λ+ = −η2 are of the same order, and time scale separation is no longer clear. Further, the separate populations Lo and Ln decay at different rates, so we cannot set an initial condition along the slowest manifold, as was previously done. We therefore take the initial conditions Lo(0) = L0, Ln(0) = 0, and I0 as in eq. (C)  see .f quite large, O(10−1). f = 10−4 and drug efficacy ε = 0.9, corresponding to a reproductive ratio R = 0.23. After 10 years simulation time, the contribution of new latent cells to the total number of latent cells Ln/L is ≈ .00057%. If the total latent reservoir size is 105 cells, the number of new latently infected cells in the reservoir, on average, is .57, i.e., less than 1.In the ε = 0.6, which implies a reproductive ratio R = 0.92 and gives the initial residual plasma viral load of 38 copies/mL, the contribution remains negligible. 10-year dynamics are not shown but are almost identical to Ln/L is ≈ .022%. If the total latent reservoir size is 105 cells, the number of new latently infected cells in the reservoir, on average, is approximately 22 cells only.Even if we assume poor drug efficacy, say We have presented a simple HIV viral dynamics model, extended from the standard model , that re+ counts or viral load on therapy [ε > 0.99, our model predicts that the contribution of ongoing viral replication is so low, < 2–3%, that decreasing that contribution via therapy intensification (increasing ε) should have no discernible impact.Intensification of drug therapy has no appreciable impact on CD4 therapy , 16: forA genotypic study of episomal HIV cDNA collected prior to viral rebound showed evidence of recent evolution : Our mod−5 per round of replication per drug [During suppressive ART, plasma virus shows little or no development of drug resistance mutations , 18 and per drug , 53—recaper drug —is prediOur primary assumption is that latent cell activation drives viral dynamics on therapy. This assumption is supported by the observation that clonal sequences of plasma virus indicate a close relationship with virus archived in the latent reservoir , 20, andR. In particular, since the fraction of new infections that result in latency, f, is very small [R, R but no clear estimate exists for patients on suppressive therapy. Our model predicts that estimation of the reproductive ratio of a patient on therapy, rather than individual parameters that make up the ratio  would allow us to effectively characterize ongoing replication in patients on therapy, analyzing for example the probability of emergent drug resistance across different individuals.An important aspect of our analysis is that our results rely primarily on the with (i.e. change in to within) in-host basic reproductive ratio of HIV in patients on effective therapy, ry small , the fraR, the viral reproductive ratio in patients when on therapy, further reducing the probability of drug resistance emerging from ongoing viral replication.The implication of our modeling on the low probability of emergent drug resistance re-enforces results from Ribeiro et al. (2000) . There tR near 1, and therefore a high proportion (approximately R) of residual viremia to be associated with ongoing viral replication. We used ε = 0.6 as an illustrative example of this case, with R = 0.92 and therefore 92% of residual viremia due to ongoing viral replication Click here for additional data file.