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Heritability estimates of traits assist breeders in optimizing cultivar development and can help predict gain from selection in breeding programs. Narrow‐sense heritabilities were measured in agronomic traits and glycoside concentration in four elite stevia (Stevia rebaudiana Bertoni) populations using half‐sib family analysis. Gain from selection at 20% selection intensity was estimated for morphological, yield, and glycoside traits. Experiments were conducted in two field locations in North Carolina. Variation was observed across cultigens for agronomic traits including stem height, branch width, and leaf size, for both subjective and objective measurements. Yield, lodging resistance, disease resistance, and percent survival showed a high genetic variation across cultigens. In addition, glycosides including Reb A, Reb C, and Reb D also showed high variation by cultigen. Heritability estimates for plant morphological traits were highest for stem height, and moderate for yield and branch width. However, other traits such as leaf area and lodging resistance showed low heritability (0.10). Glycosides including Reb A, Reb C, Reb D, stevioside and total steviol glycosides (TSG) showed high heritability. The estimated gain from selection per year for stem height (24 mm/year), leaf area (131 mm2/year) and yield (dry weight) (1.2 Mg/ha per year) suggest significant improvements are possible per breeding cycle. The estimated gain for glycoside concentration and percent of TSG of Reb A (26.44 mg/g, 14.51%), Reb C (20.20 mg/g, 20.17%), Reb D (1.70 mg/g, 1.49%), stevioside (6.73 mg/g, 10.72%), and TSG (20.90 mg/g), similarly, indicate significant gains are possible per breeding cycle. Core Ideas: The stevia cultigens evaluated showed high variability for yield and glycoside traits.Heritability estimates for morphological traits were medium to high for plant height, width, and yield.Heritability estimates for Reb A, Reb C, Reb D, stevioside, and total steviol glycosides showed high heritability.Estimated gain from selection for yield and glycosides suggest significant improvement are possible. [ABSTRACT FROM AUTHOR]

High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm−1) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R2) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (SCHNS, wt.%) using CHNS data and the model predicted sulfur (SFTIR, wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide. [ABSTRACT FROM AUTHOR]

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effectiveness of Driver's model (DM) in developing the conceptual understanding (CU) of first-class intermediate students in geometry and polygons via quasi-experimental design, for a random sample of 62 female-students divided into two groups, the experimental group with 31 students who were taught the module by DM, and the control group with 31 students were taught the module by the traditional teaching method. The study found significant statistical variances regarding scores means between the two groups at the three levels of explanation, interpretation, and application for the experimental group with a high impact reached at0.31 along with a high impact, too, regarding the mean of the total score of CU test for the same group, which reached at 0.43. Thus, the study recommends using DM in mathematics teaching. [ABSTRACT FROM AUTHOR]

Background: Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data. Results: Using a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. Conclusions: This proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies. [ABSTRACT FROM AUTHOR]

A range of indicators, such as the average variance extracted (AVE), is commonly used to validate constructs. In statistics, AVE is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error. These conventional indices are formed by factor loadings resulting from estimated least squares or maximum likelihood regressions. Thus, a new proposition that provides new factor loadings may result in a more informative AVE index. Consequently, this study consists of the improvement of the index by using adaptive regressions. A Monte Carlo simulation study was performed considering different numbers of outliers generated by distributions with symmetry deviations and excess kurtosis and sample sizes defined as n = 50, 100, and 200. The conclusion was that, in formative structural models, the adaptive linear regression (ALR) method showed good efficiency for correctly specified models. The results obtained from the ALR method for models with specification errors showed low efficiency, as expected. [ABSTRACT FROM AUTHOR]

Additive genetic variance, VA, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g. increased close-relative inbreeding) can alter VA, but how they do so depends on the (typically unknown) gene action and allele frequencies across many loci. For example, VA increases proportionally with the inbreeding coefficient when allelic effects are additive, but smaller (or larger) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for dominance effects to deflate VA under inbreeding. Applying a powerful paired pedigree design in Drosophila serrata, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity (not allele frequency). Despite close inbreeding and substantial power to detect small VA, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of VA, providing insight into historical selection and future evolutionary potential. [ABSTRACT FROM AUTHOR]

Background: Dairy cattle production systems are mostly based on purebreds, but recently the use of crossbreeding has received increased interest. For genetic evaluations including crossbreds, several methods based on single-step genomic best linear unbiased prediction (ssGBLUP) have been proposed, including metafounder ssGBLUP (MF-ssGBLUP) and breed-specific ssGBLUP (BS-ssGBLUP). Ideally, models that account for breed effects should perform better than simple models, but knowledge on the performance of these methods is lacking for two-way crossbred cattle. In addition, the differences in the estimates of genetic parameters (such as the genetic variance component and heritability) between these methods have rarely been investigated. Therefore, the aims of this study were to (1) compare the estimates of genetic parameters for average daily gain (ADG) and feed conversion ratio (FCR) between these methods; and (2) evaluate the impact of these methods on the predictive ability for crossbred performance. Methods: Bivariate models using standard ssGBLUP, MF-ssGBLUP and BS-ssGBLUP for the genetic evaluation of ADG and FCR were investigated. To measure the predictive ability of these three methods, we estimated four estimators, bias, dispersion, population accuracy and ratio of population accuracies, using the linear regression (LR) method. Results: The results show that, for both ADG and FCR, the heritabilities were low with the three methods. For FCR, the differences in the estimated genetic parameters were small between the three methods, while for ADG, those estimated with BS-ssGBLUP deviated largely from those estimated with the other two methods. Bias and dispersion were similar across the three methods. Population accuracies for both ADG and FCR were always higher with MF-ssGBLUP than with ssGBLUP, while with BS-ssGBLUP the population accuracy was highest for FCR and lowest for ADG. Conclusions: Our results indicate that in the genetic evaluation for crossbred performance in a two-way crossbred cattle production system, the predictive ability of MF-ssGBLUP and BS-ssGBLUP is greater than that of ssGBLUP, when the estimated variance components are consistent across the three methods. Compared with BS-ssGBLUP, MF-ssGBLUP is more robust in its superiority over ssGBLUP. [ABSTRACT FROM AUTHOR]

Background: Gut metabolites are key actors in host-microbiota crosstalk with effect on health. The study of the gut metabolome is an emerging topic in livestock, which can help understand its effect on key traits such as animal resilience and welfare. Animal resilience has now become a major trait of interest because of the high demand for more sustainable production. Composition of the gut microbiome can reveal mechanisms that underlie animal resilience because of its influence on host immunity. Environmental variance (VE), specifically the residual variance, is one measure of resilience. The aim of this study was to identify gut metabolites that underlie differences in the resilience potential of animals originating from a divergent selection for VE of litter size (LS). We performed an untargeted gut metabolome analysis in two divergent rabbit populations for low (n = 13) and high (n = 13) VE of LS. Partial least square-discriminant analysis was undertaken, and Bayesian statistics were computed to determine dissimilarities in the gut metabolites between these two rabbit populations. Results: We identified 15 metabolites that discriminate rabbits from the divergent populations with a prediction performance of 99.2% and 90.4% for the resilient and non-resilient populations, respectively. These metabolites were suggested to be biomarkers of animal resilience as they were the most reliable. Among these, five that derived from the microbiota metabolism (3-(4-hydroxyphenyl)lactate, 5-aminovalerate, and equol, N6-acetyllysine, and serine), were suggested to be indicators of dissimilarities in the microbiome composition between the rabbit populations. The abundances of acylcarnitines and metabolites derived from the phenylalanine, tyrosine, and tryptophan metabolism were low in the resilient population and these pathways can, therefore impact the inflammatory response and health status of animals. Conclusions: This is the first study to identify gut metabolites that could act as potential resilience biomarkers. The results support differences in resilience between the two studied rabbit populations that were generated by selection for VE of LS. Furthermore, selection for VE of LS modified the gut metabolome, which could be another factor that modulates animal resilience. Further studies are needed to determine the causal role of these metabolites in health and disease. [ABSTRACT FROM AUTHOR]

Twin and adoption studies compare the similarities of people with differing degrees of relatedness to estimate genetic and environmental contributions to trait population variance. The analytic workhorse of these kinds of variancefocused designs is the intraclass correlation, which estimates similarity between pairs of individuals. Group means, by contrast, play no overt role in estimating genetic and environmental influences. Although this focus on variance has made very important contributions to understanding psychological characteristics, we contend that the exclusion of mean effects from behavioral genetic designs may have obscured key environmental influences and impeded full appreciation of the ubiquity and nature of gene–environment interplay in human outcomes. We provide empirical examples already in the literature and a theoretical framework for thinking through the incorporation of mean effects using largely forgotten, non-Mendelian theory regarding how genes influence human outcomes. We conclude that the field needs to develop models capable of fully incorporating mean effects into twin and adoption studies. [ABSTRACT FROM AUTHOR]

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey’s ladder, and Yeo–Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey’s ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen’s independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey’s ladder transformation. [ABSTRACT FROM AUTHOR]

In this article, we establish a new class of nonparametric Shewhart-type control charts based on order statistics with signaling runs-type rules. The proposed charts offer to the practitioner the opportunity to reach, as close as possible, a pre-specified level of performance by determining appropriately their design parameters. Special monitoring schemes, already established in the literature, are ascertained to be members of the proposed class. In addition, several new nonparametric control charts that belong to the family are introduced and studied in some detail. Exact formulae for the variance of the run length distribution and the average run length (ARL) for the proposed monitoring schemes are also derived. A numerical investigation is carried out and demonstrates that the proposed schemes acquire competitive performance in detecting the shift of the underlying distribution. Although the large number of design parameters is quite hard to handle, the numerical results presented throughout the lines of the present manuscript provide practical guidance for the implementation of the proposed charts. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel soft-clipping discrete beta GARCH (ScDBGARCH) model that provides an available method to model bounded time series with under-dispersion, equi-dispersion or over-dispersion. The new model not only allows positive dependence, but also negative dependence. The stochastic properties of the models are established, and these results are, in turn, used in the analysis of the asymptotic properties of the conditional maximum likelihood (CML) estimator of the new model. In addition, we apply the new model to measles infection to show its improved performance. [ABSTRACT FROM AUTHOR]

Income inequality remains one of the most burning issues discussed in the world. The difficulty of the problem arises from its multiple manifestations at regional and local levels and unique patterns within countries. This paper employs a multilevel approach to identify factors that influence income and wage inequalities at regional and municipal scales in Russia. We carried out the study on data from 2017 municipalities of 75 Russian regions from 2015 to 2019. A Hierarchical Linear Model with Cross-Classified Random Effects (HLMHCM) allowed us to establish that most of the total variances in population income and average wages accounted for the regional scale. Our analysis revealed different variances of income per capita and average wage; we disclosed the reasons for these disparities. We also found a mixed relationship between income inequality and social transfers. These variables influence income growth but change the relationship between income and labour productivity. Our study underlined that the impacts of shares of employees in agriculture and manufacturing should be considered together with labour productivity in these industries. [ABSTRACT FROM AUTHOR]

Practitioners often face the situation of comparing any set of k distributions, which may follow neither normality nor equality of variances. We propose a semiparametric model to compare those distributions using an exponential tilt method. This extends the classical analysis of variance models when all distributions are unknown by relaxing its assumptions. The proposed model is optimal when one of the distributions is known. Large-sample estimates of the model parameters are derived, and the hypotheses for the equality of the distributions are tested for one-at-a-time and simultaneous comparison cases. Real data examples from NASA meteorology experiments and social credit card limits are analyzed to illustrate our approach. The proposed approach is shown to be preferable in a simulated power comparison with existing parametric and nonparametric methods. [ABSTRACT FROM AUTHOR]

Estimated heritability of educational attainment (EA) varies widely, from 23% to 80%, with growing evidence suggesting the degree to which genetic variation contributes to individual differences in EA is highly dependent upon situational factors. We aimed to decompose EA into influences attributable to genetic propensity and to environmental context and their interplay, while considering influences of rearing household economic status (HES) and sex. We use the Project Talent Twin and Sibling Study, drawn from the population-representative cohort of high school students assessed in 1960 and followed through 2014, to ages 68−72. Data from 3552 twins and siblings from 1741 families were analyzed using multilevel regression and multiple group structural equation models. Individuals from less-advantaged backgrounds had lower EA and less variation. Genetic variance accounted for 51% of the total variance, but within women and men, 40% and 58% of the total variance respectively. Men had stable genetic variance on EA across all HES strata, whereas high HES women showed the same level of genetic influence as men, and lower HES women had constrained genetic influence on EA. Unexpectedly, middle HES women showed the largest constraints in genetic influence on EA. Shared family environment appears to make an outsized contribution to greater variability for women in this middle stratum and whether they pursue more EA. Implications are that without considering early life opportunity, genetic studies on education may mischaracterize sex differences because education reflects different degrees of genetic and environmental influences for women and men. [ABSTRACT FROM AUTHOR]

Common causal estimands include the average treatment effect, the average treatment effect of the treated, and the average treatment effect on the controls. Using augmented inverse probability weighting methods, parametric models are judiciously leveraged to yield doubly robust estimators, that is, estimators that are consistent when at least one the parametric models is correctly specified. Three sources of uncertainty are associated when we evaluate these estimators and their variances, that is, when we estimate the treatment and outcome regression models as well as the desired treatment effect. In this article, we propose methods to calculate the variance of the normalized, doubly robust average treatment effect of the treated and average treatment effect on the controls estimators and investigate their finite sample properties. We consider both the asymptotic sandwich variance estimation, the standard bootstrap as well as two wild bootstrap methods. For the asymptotic approximations, we incorporate the aforementioned uncertainties via estimating equations. Moreover, unlike the standard bootstrap procedures, the proposed wild bootstrap methods use perturbations of the influence functions of the estimators through independently distributed random variables. We conduct an extensive simulation study where we vary the heterogeneity of the treatment effect as well as the proportion of participants assigned to the active treatment group. We illustrate the methods using an observational study of critical ill patients on the use of right heart catherization. [ABSTRACT FROM AUTHOR]

Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode Caenorhabditis elegans that accumulated mutations in a nearly neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of C. elegans genotypes allows selection on new phenotypic dimensions of locomotion behavior. [ABSTRACT FROM AUTHOR]

Based on the perspective of government and enterprises, we explore the cooperative strategy and cost-sharing problem of cooperative open sharing of data between government and enterprises. In order to accurately analyze the data-opening strategies of government and enterprises, stochastic differential game theory is applied to construct the Nash non-cooperative game, Stackelberg master-slave game and cooperative game models with government and enterprises as game subjects to obtain the optimal open data effort, the optimal trajectory of social data open sharing level and the optimal benefit function of government and enterprises in three scenarios. Combined with numerical simulations to analyze the sensitivity of the relevant parameters affecting the level of social data openness, the results of the study revealed the following: 1 When the government's income distribution ratio is greater than 1/3, the benefits of the government and the enterprises under the Stackelberg master-slave game and the effort to open and share data are greater than in the Nash non-cooperative situation; in the case of a cooperative game, the degree of effort and total revenue of both parties reach the Pareto optimal state. 2 When the government's income distribution ratio is greater than 1/3, the expectation and variance of the open data and shared stock under the cost-sharing situation and the corresponding limit value are all greater than the value in the Nash non-cooperative situation, and in the cooperative game, the expectation and variance of open data and shared stock and its corresponding limit value are the greatest. 3 The government and enterprises coexist with profit and risk under the influence of random interference factors, and high profit means high risk. This research provides a theoretical basis and practical guidance for promoting the open sharing of government and enterprise data. [ABSTRACT FROM AUTHOR]

Background: The genetic correlation between purebred (PB) and crossbred (CB) performances ( r pc ) partially determines the response in CB when selection is on PB performance in the parental lines. An earlier study has derived expressions for an upper and lower bound of r pc , using the variance components of the parental purebred lines, including e.g. the additive genetic variance in the sire line for the trait expressed in one of the dam lines. How to estimate these variance components is not obvious, because animals from one parental line do not have phenotypes for the trait expressed in the other line. Thus, the aim of this study was to propose and compare three methods for approximating the required variance components. The first two methods are based on (co)variances of genomic estimated breeding values (GEBV) in the line of interest, either accounting for shrinkage (VCGEBV-S) or not (VCGEBV). The third method uses restricted maximum likelihood (REML) estimates directly from univariate and bivariate analyses (VCREML) by ignoring that the variance components should refer to the line of interest, rather than to the line in which the trait is expressed. We validated these methods by comparing the resulting predicted bounds of r pc with the r pc estimated from PB and CB data for five traits in a three-way cross in pigs. Results: With both VCGEBV and VCREML, the estimated r pc (plus or minus one standard error) was between the upper and lower bounds in 14 out of 15 cases. However, the range between the bounds was much smaller with VCREML (0.15–0.22) than with VCGEBV (0.44–0.57). With VCGEBV-S, the estimated r pc was between the upper and lower bounds in only six out of 15 cases, with the bounds ranging from 0.21 to 0.44. Conclusions: We conclude that using REML estimates of variance components within and between parental lines to predict the bounds of r pc resulted in better predictions than methods based on GEBV. Thus, we recommend that the studies that estimate r pc with genotype data also report estimated genetic variance components within and between the parental lines. [ABSTRACT FROM AUTHOR]

Distributions of the variance parameter values developed during the validation process. Comparisons of these prior distributions to the run-specific average are one measure used by analysts to assess the reliability of a STRmix deconvolution. This study examined the behavior of three different STRmix variance parameters under standard amplification and interpretation conditions, as well as under a variety of challenging conditions, with the goal of making comparisons to the prior distributions more practical and meaningful. Using information found in STRmix v2.8 Interpretation Reports, we plotted the log10 of each variance parameter against the log10 of the template amount of the highest-level contributor (Tc) for a large set of mixture data amplified under standard conditions. We observed nonlinear trends in these plots, which we regressed to fourth-order polynomials, and used the regression data to establish typical ranges for the variance parameters over the Tc range. We then compared the typical variance parameter ranges to log10(variance parameter) v log10(Tc) plots for mixtures amplified and interpreted under a variety of challenging conditions. We observed several distinct patterns to variance parameter shifts in the challenged data interpretations in comparison to the unchallenged data interpretations, as well as distinct shifts in the unchallenged variance parameters away from their prior gamma distribution modes over specific ranges of Tc. These findings suggest that employing empirically determined working ranges for variance parameters may be an improved means of detecting whether aberrations in the interpretation were meaningful enough to trigger greater scrutiny of the electropherogram and genotype interpretation. [ABSTRACT FROM AUTHOR]

We obtain an explicit formula for the variance of the number of k-peaks in a uniformly random permutation. This is then used to obtain an asymptotic formula for the variance of the length of longest k-alternating subsequence in random permutations. Also a central limit is proved for the latter statistic. [ABSTRACT FROM AUTHOR]

Half diallel among six Egyptian cotton varieties were used for yield and its components and fiber quality. Results illuminated that mean squares of each genotype (G), Parents (P), Crosses (C), (P vs C), general (GCA) and specific (SCA) combining ability were significant or highly significant for most studied traits. Mean performances of most the 15 F1 hybrids were better than their corresponded parents. Parental varieties recorded variable performances for studied traits. Giza 95 (P2) followed by Giza 94 (P1) were superior for most yield traits and also, Giza 94 (P1) followed by Giza 75 (P3) which possessed best values for all fiber quality traits. Diallel cross analysis exhibited that, the cross P4 x P6 gave the highest SCA effects (Ŝij) for B/P, SCY/P, LY/P and FF. Furthermore, the crosses P1 x P3, P2 x P5, P2 x P3, P3xP6 and P2xP6 were desirable for BW, L%, FS, UHM and UI, respectively. Magnitudes of SCA variance were larger than those of GCA variance, for all studied traits except for L% indicating the predominance of non-additive genetic variance in the inheritance of these traits. It could be concluded that yield components and fiber properties, were mainly controlled by dominance variance effect. The estimated heritability values in broad sense (h² b.s.%) were larger than the heritability values in narrow sense (h2 n.s.%) for all studied traits. [ABSTRACT FROM AUTHOR]

In this paper, we determine the effect of the Fermi convolution power on the variance function of a Cauchy-Stieltjes Kernel (CSK) family. We then use the criteria of convergence for a sequence of variance functions to give an approximation of elements of the CSK family generated by the Fermi Poisson distribution. [ABSTRACT FROM AUTHOR]

In this paper, a new estimator for kernel quantile estimation is given to reduce the bias. The asymptotic properties of the proposed estimator was established and it turned out that the bias has been reduced to the fourth power of the bandwidth, while the bias of the estimators considered has the second power of the bandwidth, while the variance remains at the same order. Futhermore, we calculate the optimal bandwidth which minimizes the asymptotic mean squared error. A simulation study and a real data example are carried out to illustrate the performance of the proposed estimator and compared with other existing approaches mentioned. [ABSTRACT FROM AUTHOR]

An exact test procedure based on the generalized variable approach for testing homogeneity of variances of several independent populations belonging to the same location-scale family is proposed. An extensive simulation study within the class of location-scale families reveals the uniformly superior performance of the proposed test over existing popularly used parametric and non-parametric tests, both in terms of the type-I error and power function. In normal populations, [Liu X, Xu X. A new generalized p-value approach for testing the homogeneity of variances. Stat Probab Lett. 2010;80:1486–1491] test designed specifically for normal populations is found equally compatible with the proposed test. The proposed test is found to be robust against moderate deviation from model specification and the location-scale assumption. The proposed test is illustrated with two real life examples. [ABSTRACT FROM AUTHOR]

More and more sensor and automation data are available that enable animal breeders to define novel traits. However, sensor and automation data are often frequently measured differently (e.g., milk yield and different milk components are continuously measured during each milking). These differences are challenging animal breeders to define traits and use the most appropriate analytical models for genetic evaluation and breeding values. Traditionally, the process from raw data to breeding value estimations involves several steps: data curation, trait definition, variance component estimation, genetic evaluation, and validation of the estimated breeding values (EBV). All these steps often take many iterations and several research projects to optimize the final genetic evaluations. To make this entire process—from raw data to validated EBV—more efficient, we combined all these steps in a cloud environment that allows for faster processing and a faster data distribution time. We used real data (including 1,782,373,113 daily milk-yield records of 1,120,550 dairy cows) and a real trait (a resilience trait based on the deviations from expected milk yields) to demonstrate the functioning of this cloud environment. The daily milk-yield records were incorporated into our cloud solution, in which we have set up central binary large object storage. Subsequent steps were all performed in the cloud. The data set was preprocessed in approximately 6 h to obtain the resilience indicator for 352,871 cows in the first 3 lactations. Estimation of genetic parameters (heritabilities and genetic correlations) was performed by splitting the data into 5 subsets in ASReml, and prediction of subsequent EBV was performed on the entire data set using MiXBLUP. Together with the validation of breeding values, this process encompassed 16.5 h. By combining the different steps from preprocessing sensor data to genetic evaluation of new traits in one cloud environment, we generated EBV and validation plots in approximately 1 working day. Moreover, our setup is a flexible design and can be adapted easily to test new, longitudinal sensor-driven traits and compare the performance of these new traits to previous ones. [ABSTRACT FROM AUTHOR]

Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve. [ABSTRACT FROM AUTHOR]

This research examined the item and rater variabilities in students' evaluation of teaching and courses exercise at the University of Cape Coast (UCC) through the lenses of the Many-Facet Rasch Model (MFRM). The study covered students during the 2019/2020 academic year in the selected university, analysing secondary data obtained from the Directorate of Academic Planning and Quality Assurance, UCC (DAPQA-UCC). The data were analysed by conducting partial credit MFRM analyses. It was found that the sources of measurement errors in the student evaluation exercise included halo effect, non-functional item structure, inconsistent students' ratings, rater leniency, and non-functional rating scale. It was concluded that data from students' appraisal of lecturers’ teaching should be used with caution. It was recommended that DAPQA-UCC and the university management should train students on the evaluation of teaching, as well as review the existing evaluation form for appraising courses and teaching by subjecting the instrument to rigorous validation procedures.

How organisms that are part of the same trophic network respond to environmental variability over small spatial scales has been studied in a multitude of systems. Prevailing theory suggests a large role for plasticity in key traits among interacting species that allows matching of life cycles or life‐history traits across environmental gradients, for instance insects tracking host‐plant phenology across variable environments (Posledovich et al. 2018). A key aspect that remains understudied is the extent of intrapopulation variability in plasticity and whether stressful conditions canalize plasticity to an optimal level, or alternatively if variation in plasticity indeed could increase fitness in itself via alternative strategies. In a From the Cover article in this issue of Molecular Ecology, Kahilainen et al. (2022) investigate this issue in a classical insect study system, the metapopulation of the Glanville fritillary butterfly (Melitea cinxia) in the Åland archipelago of Finland. The authors first establish how a key host plant responds to water limitation, then quantify among‐family variation in larval growth and development across control and water‐limited host plants. Finally, they use RNA sequencing to gain mechanistic insights into some of these among‐family differences in larval performance in response to host‐plant variation, finding results suggesting the existence of heritable, intrapopulation variability in ecologically relevant plasticity. This final step represents a critically important and often overlooked component of efforts to predict sensitivity of biological systems to changing environmental conditions, since it provides a key metric of adaptive resilience present in the system. [ABSTRACT FROM AUTHOR]

Background: University students are known to have risky sexual behaviours (RSBs). The severity of the RSB is influenced by many factors, including the family environment, exposure to adverse childhood events (ACEs), and the use of addictive substances. However, there is limited information about the influence of ACEs and the family environment of these students in low-and medium-income countries (LMICs). Therefore, a pilot study was conducted among university students from a LMIC, Uganda. Methods: The present study comprised a cross-sectional online survey among Ugandan students at a public university (N = 316; 75% male; 52.2% aged between 18–22 years). The survey included questions relating to socio-demographic information, family environmental information, the Sexual Risk Survey (SRS), and the Adverse Childhood Experiences-International Questionnaire (ACE-IQ). Results: Over half (53.8%) reported having had sexual intercourse. Males reported over two times higher mean total SRS score compared to females (χ2= 4.06, p = 0.044). Approximately one-sixth of the sample had drunk alcohol or used illicit psychoactive substances in the past six months (16.1%). Among four regression analysis models, sociodemographic variables predicted the highest variance (13%), followed by family environment variables (10%), and both psychoactive substance use history (past six months) and ACEs individually explained approximately 5% variance in total SRS score, with the final model predicting 33% of the variance in RSB. Conclusions: The present study demonstrated a gender disparity with males involved in more RSB than females, as has been reported in most previous RSB studies. Family environment, sociodemographic factors, substance use, and ACEs all appear to contribute to RSB among university students. These findings will benefit other researchers exploring factors associated with RSB among university students and will help develop interventions to reduce RSB to protect students from unwanted pregnancies, sexually transmitted diseases, and HIV/AIDS. [ABSTRACT FROM AUTHOR]

The obesity epidemic, largely driven by the accessibility of ultra‐processed high‐energy foods, is one of the most pressing public health challenges of the 21st century. Consequently, there is increasing concern about the impacts of diet‐induced obesity on behavior and cognition. While research on this matter continues, to date, no study has explicitly investigated the effect of obesogenic diet on variance and covariance (correlation) in behavioral traits. Here, we examined how an obesogenic versus control diet impacts means and (co‐)variances of traits associated with body condition, behavior, and cognition in a laboratory population of ~160 adult zebrafish (Danio rerio). Overall, an obesogenic diet increased variation in several zebrafish traits. Zebrafish on an obesogenic diet were significantly heavier and displayed higher body weight variability; fasting blood glucose levels were similar between control and treatment zebrafish. During behavioral assays, zebrafish on the obesogenic diet displayed more exploratory behavior and were less reactive to video stimuli with conspecifics during a personality test, but these significant differences were sex‐specific. Zebrafish on an obesogenic diet also displayed repeatable responses in aversive learning tests whereas control zebrafish did not, suggesting an obesogenic diet resulted in more consistent, yet impaired, behavioral responses. Where behavioral syndromes existed (inter‐class correlations between personality traits), they did not differ between obesogenic and control zebrafish groups. By integrating a multifaceted, holistic approach that incorporates components of (co‐)variances, future studies will greatly benefit by quantifying neglected dimensions of obesogenic diets on behavioral changes. [ABSTRACT FROM AUTHOR]

Context: Greater production of oocytes and embryos from Gir donors contributes to greater fertility and genetic progress. Aims: This study aimed to obtain genetic parameters for oocyte and embryo production in the Gir breed. Methods: Repeatability and random regression models were applied to data consisting of 17 526 Ovum Pick Up observations from 1641 Gir donors from five different herds. Single and multi-trait analyses were carried out with the application of both models for the traits: number of viable oocytes, number of total oocytes and number of embryos, using the BLUPF90 family programs. Legendre polynomials of second order were used in the random regression model. Key results: Considering the repeatability model, additive genetic variance ranged from 0.06 to 0.13 and permanent environment variance ranged from 0.05 to 0.08 for all evaluated traits. Residual variance ranged from 0.30 to 0.45. Heritability estimates were 0.10 for number of embryos, 0.24 for total oocytes, and 0.25 for viable oocytes. Repeatability estimates were moderate, ranging from 0.20 to 0.40, and genetic correlation estimates were always above 0.80. Phenotypic correlation was high only between viable and total oocytes (0.95), and moderate in the other cases. Random regression model results were consistent with those from the repeatability model. The heritability values remained similar throughout the donors' ages, with moderate values for viable and total oocytes, and low values for number of embryos. Genetic correlations among ages for each trait were moderate to high. Also, the genetic correlations between traits within each age were high, with values always above 0.7. Conclusions: Selection of Gir donors for total oocyte production at any time, between 1 and 16 years of age, might lead to an increase in the number of viable oocytes and embryos obtained, but it's preferable at younger ages to hasten genetic progress. Repeatability models could be the best method, as they require less computational effort when compared to the random regression models and the parameter estimates do not vary substantially throughout different ages of the donor. Implications: The use of repeatability models to estimate genetic parameters of oocytes and embryos resulted in similar results compared to random regression models. In vitro embryo production expanded in the past decade, as a result of the progressive adoption of this technique as a choice for embryo production, mainly in zebu breeds. Studies about embryo production are needed for its better exploration in animal breeding programs. We found that the number of oocytes obtained by follicular aspiration can be used as a trait to select better donors for embryo in vitro production. Our results may contribute to improve Gir dairy cattle reproductive performance. [ABSTRACT FROM AUTHOR]

Context: Residual feed intake is a relevant trait for beef cattle, given the positive impact on reducing feeding costs and greenhouse gas emissions. The lack of large databases is a restriction when estimating accurate genetic parameters for dry matter intake (DMI) and residual feed intake (RFI), and combining different data sets could be an alternative to increase the amount of data and achieve better estimations. Aim: The main objective was to compare Uruguayan data (URY; 780 bulls) and Canadian data (CAN; 1597 bulls), and to assess the adequacy of pooling both data sets (ALL) for the estimation of genetic parameters for DMI and RFI. Methods: Feed intake and growth traits phenotypes in both data sets were measured following the same protocols established by the Beef Improvement Federation. Pedigree connections among data sets existed, but were weak. Performance data were analysed for each data set, and individual partial regression coefficients for each energy sink on DMI were obtained and compared. Univariate and multivariate variance components were estimated by the restricted maximum likelihood (REML) for DMI, RFI and their energy sinks traits (average daily gain, metabolic mid weight and back fat thickness). Key results: There were some differences in phenotypic performance among data (P < 0.01); however, no differences (P > 0.1) were observed for phenotypic values of RFI between sets. Heritability estimates for DMI were 0.42 (URY), 0.41 (CAN) and 0.45 for ALL data, whereas heritability estimates for RFI were 0.34 (URY), 0.20 (CAN) and 0.25 for ALL data. The results obtained indicate selection on reducing RFI could lead to a decrease in DMI, without compromising other performance traits, as genetic correlations between RFI, growth and liveweight were low or close to 0 (−0.12–0.07). Conclusions: As genetic parameters were similar between national data sets (URY, CAN), pooling data (ALL) provided more accurate parameter estimations, as they presented smaller standard deviations, especially in multivariate analysis. Implications: Parameters estimated here may be used in international or national genetic evaluation programs. Residual feed intake is a relevant trait for beef cattle, given the positive impact on reducing feeding costs and greenhouse gas emissions. The study of genetic parameters for this trait is required, with the aim to be included into breeding programs. As the availability of large databases for this trait is limited, we study the performance of two sources of data, evaluating if they are possible to be pooled to estimate more accurate genetic parameters. [ABSTRACT FROM AUTHOR]

Genetic and residual variances of traits are important input parameters for best linear unbiased prediction (BLUP) breeding value estimation. In honeybees, estimates of these variances are often associated with large standard errors, entailing a risk to perform genetic evaluations under wrong premises. The consequences hereof have not been sufficiently studied. In particular, there are no adequate investigations on this topic accounting for multi‐trait selection or genetic peculiarities of the honeybee. We performed simulation studies and explored the consequences of selection for honeybee populations with a broad range of true and assumed genetic parameters. We found that in single‐trait evaluations, the response to selection was barely compromised by assuming erroneous parameters, so that reductions in genetic progress after 20 years never exceeded 21%. Phenotypic selection appeared inferior to BLUP selection, particularly under low heritabilities. Parameter choices for genetic evaluation had great effects on inbreeding development. By wrongly assuming high heritabilities, inbreeding rates were reduced by up to 74%. When parallel selection was performed for two traits, the right choice of genetic parameters appeared considerably more crucial as several incorrect premises yielded inadvertent negative selection for one of the traits. This phenomenon occurred in multiple constellations in which the selection traits expressed a negative genetic correlation. It was not reflected in the estimated breeding values. Our results indicate that breeding efforts heavily rely on detailed knowledge on genetic parameters, particularly when multi‐trait selection is performed. Thus, considerable effort should be invested into precise parameter estimations. [ABSTRACT FROM AUTHOR]

Premise: The evolutionary response of a trait to environmental change depends upon the level of additive genetic variance. It has been long argued that sustained selection will tend to deplete additive genetic variance as favored alleles approach fixation. Non‐additive genetic variance, due to interactions among alleles within and between loci, does not immediately contribute to an evolutionary response. However, shifts in the allele frequencies within and between interacting loci may convert non‐additive variance into additive variance. Here we consider the possibility that an environmental shift may alter allelic interactions in ways that convert dominance into additive genetic variance. Methods: We grew a pedigreed population of Brassica rapa in greenhouse and field conditions. The field conditions mimicked agricultural conditions from which the base population was drawn, while the greenhouse featured benign conditions. We used Bayesian models to estimate the additive, dominance, and maternal components of quantitative genetic variance. We also estimated genetic correlations across environments using parental breeding values. Results: Although the additive genetic variance was elevated in the greenhouse condition, no consistent pattens emerged that would indicate a conversion of dominance variance. The unusually low genetic variance and broad confidence intervals for the variance estimates obtained through this analysis preclude definitive interpretations. Conclusions: Further studies are needed to determine whether between‐environment changes in additive genetic variance can be traced to conversion of dominance variance. [ABSTRACT FROM AUTHOR]

The rapid growth in genomic selection data provides unprecedented opportunities to discover and utilize complex genetic effects for improving phenotypes, but the methodology is lacking. Epistasis effects are interaction effects, and haplotype effects may contain local high-order epistasis effects. Multifactorial methods with SNP, haplotype, and epistasis effects up to the third-order are developed to investigate the contributions of global low-order and local high-order epistasis effects to the phenotypic variance and the accuracy of genomic prediction of quantitative traits. These methods include genomic best linear unbiased prediction (GBLUP) with associated reliability for individuals with and without phenotypic observations, including a computationally efficient GBLUP method for large validation populations, and genomic restricted maximum estimation (GREML) of the variance and associated heritability using a combination of EM-REML and AI-REML iterative algorithms. These methods were developed for two models, Model- I with 10 effect types and Model-II with 13 effect types, including intra- and inter-chromosome pairwise epistasis effects that replace the pairwise epistasis effects of Model-I. GREML heritability estimate and GBLUP effect estimate for each effect of an effect type are derived, except for third-order epistasis effects. The multifactorial models evaluate each effect type based on the phenotypic values adjusted for the remaining effect types and can use more effect types than separate models of SNP, haplotype, and epistasis effects, providing a methodology capability to evaluate the contributions of complex genetic effects to the phenotypic variance and prediction accuracy and to discover and utilize complex genetic effects for improving the phenotypes of quantitative traits. [ABSTRACT FROM AUTHOR]

This study investigated the main factors influencing the genetic variance and the variance of breeding values (EBV). The first is the variance of genetic values in the base population, and the latter is the variance of genetic values in the population under evaluation. These variances are important as improper variances can lead to systematic bias. The inverse of the genetic relationship matrix (K-1) and the phenotypic variance are the main factors influencing the genetic variance and heritability (h2). These factors and h2 are also the main factors influencing the variance of EBVs. Pedigree- and genomic-based relationship matrices (A and G as K) and phenotypes on 599 wheat lines were used. Also, data were simulated, and a hybrid (genomic-pedigree) relationship matrix (H as K) and phenotypes were used. First, matrix K underwent a transformation (K* = wK + a11' + ßI), and the responses in the mean and variation of diag(K-1) and offdiag(K-1) elements, and genetic variance in the form of h2 were recorded. Then, the original K was inverted, and matrix K-1 underwent the same transformations as K, and the responses in the h2 estimate and the variance of EBVs in the forms of correlation and regression coefficients with the EBVs estimated based on the original K-1 were recorded. In response to weighting K by w, the estimated genetic variance changed by 1/w. We found that µ(diag(K)) - µ(offdiag(K)) influences the genetic variance. As such, a did not change the genetic variance, and increasing ß increased the estimated genetic variance. Weighting K-1 by w was equivalent to weighting K by 1/w. Using the weighted K-1 together with its corresponding h2, EBVs remained unchanged, which shows the importance of using variance components that are compatible with the K-1. Increasing ßI added to K-1 increased the estimated genetic variance, and the effect of a11' was minor. We found that larger variation of diag(K-1) and higher concentration of offdiag(K-1) around the mean (0) are responsible for lower h2 estimate and variance of EBVs. [ABSTRACT FROM AUTHOR]

Niche breadth, the range of environments that individuals, populations, and species can tolerate, is a fundamental ecological and evolutionary property, yet few studies have examined how niche breadth is partitioned across biological scales. We use a published dataset of thermal performance for a single population from each of 10 closely related species of western North American monkeyflowers (genus Mimulus) to investigate whether populations achieve broad thermal niches through general purpose genotypes, specialized genotypes with divergent environmental optima, and/or variation among genotypes in the degree of generalization. We found the strongest relative support for the hypothesis that populations with greater genetic variation for thermal optimum had broader thermal niches, and for every unit increase in among‐family variance in thermal optimum, population‐level thermal breadth increased by 0.508°C. While the niche breadth of a single genotype represented up to 86% of population‐level niche breadth, genotype‐level niche breadth had a weaker positive effect on population‐level breadth, with every 1°C increase in genotypic thermal breadth resulting in a 0.062°C increase in population breadth. Genetic variation for thermal breadth was not predictive of population‐level thermal breadth. These findings suggest that populations of Mimulus species have achieved broad thermal niches primarily through genotypes with divergent thermal optima and to a lesser extent via general‐purpose genotypes. Future work examining additional biological hierarchies would provide a more comprehensive understanding of how niche breadth partitioning impacts the vulnerabilities of individuals, populations, and species to environmental change. [ABSTRACT FROM AUTHOR]

Around the world, different parts of most Satureja species are traditionally used to control a wide range of human diseases. The pharmacological and ethnomedical properties of most Satureja species have been evaluated; however, there is no document on the morphological and essential oil (EO) profile of the plants concerning environmental variations. Therefore, this study aimed to assess the variability and correlation of morphological traits and EO compositions of 30 Satureja populations (from 10 species) in various climates of Iran. The plants were collected at the flowering stage in a completely randomized block design (CRBD) in 2018 and were evaluated based on multivariate analysis. The permutational multivariate analysis of variance (PERMANOVA) results showed significant differences in morphological traits as well as EO composition in different populations. The redundancy analysis (RDA) showed EO profiles were significantly affected by maximum and minimum temperature and humidity, while changes in morphological characteristics were more related to a maximum temperature, humidity, average maximum temperature, and minimum temperature. Principal component analysis (PCA) showed that among 29 morphometric traits, only seven significantly changed the EO quality, and also RDA identified the number of the main branch and vegetative leaf width as the main responsible traits representing the changes in EO profile. According to the significant effects of climatic parameters on morphological and phytochemical compounds of Satureja species, selecting effective environmental factors in the habitats to access the more elevated amount of active substances and desired morphological traits can help breeders to grow and cultivate suitable populations. [ABSTRACT FROM AUTHOR]

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast. [ABSTRACT FROM AUTHOR]

The life histories of organisms are expressed as rates of development, reproduction, and survival. However, individuals may experience differential outcomes for the same set of rates. Such individual stochasticity generates variance around familiar mean measures of life history traits, such as life expectancy and the reproductive number R0. By writing life cycles as Markov chains, we calculate variance and other indices of variability for longevity, lifetime reproductive output (LRO), age at offspring production, and age at maturity for 83 animal and 332 plant populations from the Comadre and Compadre matrix databases. We find that the magnitude within and variability between populations in variance indices in LRO, especially, are surprisingly high. We furthermore use principal components analysis to assess how the inclusion of variance indices of different demographic outcomes affects life history constraints. We find that these indices, to a similar or greater degree than the mean, explain the variation in life history strategies among plants and animals. [ABSTRACT FROM AUTHOR]

A common type of textual variant in Hebrew is the alternation of ד and ר. Such variance has traditionally been attributed to scribes and confined to the written layer of transmission. However, given the articulatory proximity of/d/and/r/, orally-induced variance should also be considered. I present several instances in Hebrew and Aramaic, where the context suggests that the two letters were confused in speech. This in turn sheds light on two other matters: first, it offers a new point of view for examining the question of the two allophones of/r/reported in the Tiberian Masoretic tradition, seemingly in support of Aron Dotan's view on the matter; second, it removes one of the difficulties raised against Yaacov Sussman's theory about the orality of the Mishna in the Amoraic period, by reducing a scribal variant צוערי/סועדי)) to a phonetic one. [ABSTRACT FROM AUTHOR]

Aims: The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? Methods: The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results: Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion: The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice.

Aims: This study assesses patient barriers to successful telemedicine care in orthopaedic practices in a large academic practice in the COVID-19 era. Methods: In all, 381 patients scheduled for telemedicine visits with three orthopaedic surgeons in a large academic practice from 1 April 2020 to 12 June 2020 were asked to participate in a telephone survey using a standardized Institutional Review Board-approved script. An unsuccessful telemedicine visit was defined as patient-reported difficulty of use or reported dissatisfaction with teleconferencing. Patient barriers were defined as explicitly reported barriers of unsatisfactory visit using a process-based satisfaction metric. Statistical analyses were conducted using analysis of variances (ANOVAs), ranked ANOVAs, post-hoc pairwise testing, and chi-squared independent analysis with 95% confidence interval. Results: The survey response rate was 39.9% (n = 152). The mean age of patients was 51.1 years (17 to 85), and 55 patients (38%) were male. Of 146 respondents with completion of survey, 27 (18.5%) reported a barrier to completing their telemedicine visit. The majority of patients were satisfied with using telemedicine for their orthopaedic appointment (88.8%), and found the experience to be easy (86.6%). Patient-reported barriers included lack of proper equipment/internet connection (n = 13; 8.6%), scheduling difficulty (n = 2; 1.3%), difficulty following directions (n = 10; 6.6%), and patient-reported discomfort (n = 2; 1.3%). Barriers based on patient characteristics were age > 61 years, non-English primary language, inexperience with video conferencing, and unwillingness to try telemedicine prior to COVID-19. Conclusion: The barriers identified in this study could be used to screen patients who would potentially have an unsuccessful telemedicine visit, allowing practices to provide assistance to patients to reduce the risk of an unsuccessful visit. Cite this article: Bone Jt Open 2021;2(9):745–751.

Iron is essential for many biological processes, but iron levels must be tightly regulated to avoid harmful effects of both iron deficiency and overload. Here, we perform genome-wide association studies on four iron-related biomarkers (serum iron, serum ferritin, transferrin saturation, total iron-binding capacity) in the Trøndelag Health Study (HUNT), the Michigan Genomics Initiative (MGI), and the SardiNIA study, followed by their meta-analysis with publicly available summary statistics, analyzing up to 257,953 individuals. We identify 123 genetic loci associated with iron traits. Among 19 novel protein-altering variants, we observe a rare missense variant (rs367731784) in HUNT, which suggests a role for DNAJC13 in transferrin recycling. We further validate recently published results using genetic risk scores for each biomarker in HUNT (6% variance in serum iron explained) and present linear and non-linear Mendelian randomization analyses of the traits on all-cause mortality. We find evidence of a harmful effect of increased serum iron and transferrin saturation in linear analyses that estimate population-averaged effects. However, there was weak evidence of a protective effect of increasing serum iron at the very low end of its distribution. Our findings contribute to our understanding of the genes affecting iron status and its consequences on human health. A GWAS on the four major iron-related biomarkers is conducted using data from the Trøndelag Health Study (HUNT), Michigan Genomics Initiative (MGI), and SardiNIA study, to identify 123 loci associated with iron homeostasis and effect on all-cause mortality. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike. Author summary: We used a new method called genomic structural equation modeling to analyse data from 165,717 men and 193,073 women from the UKBiobank. Our results revealed that genetic influences on BMI were very stable between ages 40 and 73. In other words, there did not appear to be any age-dependent genetic influences on BMI during this period. The same results were seen in men and women. [ABSTRACT FROM AUTHOR]

Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene–environment and gene–gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well. [ABSTRACT FROM AUTHOR]

Background: Multi-trait genetic parameter estimation is an important topic for target traits with few records and with a low heritability and when the genetic correlation between target and secondary traits is strong. However, estimating correlations between multiple traits is difficult for both Bayesian and non-Bayesian inferences. We extended a Hamiltonian Monte Carlo approach using the No-U-Turn Sampler (NUTS) to a multi-trait animal model and investigated the performance of estimating (co)variance components and breeding values, compared to those for restricted maximum likelihood and Gibbs sampling with a population size of 2314 and 578 in a simulated and real pig dataset, respectively. For real data, we used publicly available data for three traits from the Pig Improvement Company (PIC). For simulation data, we generated two quantitative traits by using the genotypes of the PIC data. For NUTS, two prior distributions were adopted: Lewandowski-Kurowicka-Joe (LKJ) and inverse-Wishart distributions. Results: For the two simulated traits with heritabilities of 0.1 and 0.5, most estimates of the genetic and residual variances for NUTS with the LKJ prior were closer to the true values and had smaller root mean square errors and smaller mean absolute errors, compared to NUTS with inverse-Wishart priors, Gibbs sampling and restricted maximum likelihood. The accuracies of estimated breeding values for lowly heritable traits for NUTS with LKJ and inverse-Wishart priors were 14.8% and 11.1% higher than those for Gibbs sampling and restricted maximum likelihood, respectively, with a population size of 578. For the trivariate animal model with real pig data, the estimates of the genetic correlations for Gibbs sampling and restricted maximum likelihood were strongly affected by population size, compared to NUTS. For both the simulated and pig data, the genetic variances and heritabilities for NUTS with an inverse-Wishart prior were overestimated for low-heritability traits when the population size was 578. Conclusions: The accuracies of variance components and breeding values estimates for a multi-trait animal model using NUTS with the LKJ prior were equal to or higher than those obtained with restricted maximum likelihood or Gibbs sampling. Therefore, when the population size is small, NUTS with an LKJ prior could be an alternative sampling method for multi-trait analysis in animal breeding. [ABSTRACT FROM AUTHOR]

The Machine Learning Model (MLM) has garnered popularity in rehabilitation, ranging from developing algorithms in outcome prediction, prognostication, and training artificial intelligence. High-quality data plays a critical role in algorithm development. Limited studies have explored factors that may influence the MLM algorithm performance in predicting spasticity severity level. The objectives of this study were to train and validate a MLM algorithm for spasticity assessment and determine the algorithm's prediction performance in predicting ambiguous spasticity datasets. Forty-seven persons with central nervous system pathology that fulfilled the inclusion and exclusion criteria were recruited. Four biomechanical properties of spasticity were obtained using off-the-shelf wearable sensors. The data were analyzed individually, and ambiguous datasets were separated. The acceptable inertial data were used to train and validate MLM in predicting spasticity. The trained and validated MLM algorithm was later deployed to predict the ambiguous spasticity datasets. A series of MLM were applied, including Support Vector Machine, Decision Tree, and Random Forest. The MLM's performance accuracy of the validation data was 96%, 52%, and 72%, respectively. The validated MLM accuracy performance level predicting ambiguous datasets reduces to 20%, 23%, and 23%, respectively. This study elucidates data biases and variances of disease background, pathophysiological and anatomical factors that have to be considered in MLM training. [ABSTRACT FROM AUTHOR]