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A close relationship between math performance and math anxiety has been demonstrated, but how different presentations of mathematics are associated with math anxiety has not been investigated. This study recruited 826 elementary school students in grades 5 and 6. All students were given math trait and state anxiety questionnaires; a nonverbal matrix reasoning task; and verbalized, symbolic, and situational fraction problem‐solving tasks. After data cleaning, 475 boys and 323 girls (798 in total) were included in the analysis (mean age = 11.79, SD = 0.82). Partial correlation analysis showed that students' math traits and state anxiety were more closely related to symbolic fraction problem‐solving than to verbalized and situational fraction problem‐solving. Mixed linear model analysis showed that math state anxiety for symbolic problem‐solving was significantly greater than that for verbalized and situational problem‐solving. Based on these findings, we concluded that the presentation of symbolic math is more likely to induce math anxiety than verbalized or situational math. This finding has potential practical applications in math anxiety interventions and education. [ABSTRACT FROM AUTHOR]

Complex engineering and technological processes typically generate data with a non‐trivial hierarchical structure. To this end, in this article we propose a full procedure for optimizing such processes through optimal experimental designs and modeling. In order to study a hierarchical structure, several types of experimental factors may arise, making the building of the experimental design challenging. Starting from the analysis of a preliminary dataset and a pilot design including nested, branching, and shared experimental factors, as well as a new type of experimental factor called composite‐form‐factor, we build a hierarchical D‐optimal experimental design using genetic algorithms. We apply our proposal to a real case‐study in the rail sector aimed at optimizing the payload distribution of freight trains. In this case‐study we also achieve the best train configuration by minimizing the in‐train forces. The results are very satisfactory and confirm that our full procedure represents a valid method to be successfully applied for solving similar technological problems. [ABSTRACT FROM AUTHOR]

In the present study, 148 commercial barley cultivars were assessed by 14 AFLP primer combinations and 32 SSRs primer pairs. Population structure, linkage disequilibrium, and genomic regions associated with physiological traits under drought stress were investigated. The phenotypic results showed a high level of diversity between studied cultivars. The studied barley cultivars were divided into two subgroups. Linkage disequilibrium analysis revealed that r2 values among all possible marker pairs have an average value of 0.0178. The mixed linear model procedure showed that totally, 207 loci had a significant association with investigated traits. 120 QTLs out of 207 were detected for traits under normal conditions, and 90 QTLs were detected for traits under drought stress conditions. Identified QTLs after validation and transferring to SCAR markers in the case of AFLPs can be used to develop MAS strategies for barley breeding programs. Some common markers were identified for a particular trait or some traits across normal and drought stress conditions. These markers show low interaction with environmental conditions (stable markers); therefore, selection by them for a trait under normal conditions will improve the trait value under stress conditions, too. [ABSTRACT FROM AUTHOR]

Objectives: The established between-subjects associations between affect and somatic symptoms have often been interpreted as indicating a causal effect of affect on somatic symptoms, but it is doubtful whether this is valid. In this study, we evaluate the association between positive affect (PA), negative affect (NA), and somatic symptoms at both the between- and within-subject level.Design and Methods: Diary data were collected in the context of an online study called 'HowNutsAreTheDutch'. Participants filled out an online questionnaire, three times a day for 30 consecutive days. A mixed linear model was used to test the contemporaneous and lagged associations between affect and somatic symptoms.Results: Five hundred and eighty-six participants (481 females, median age 39.6 years [range 18.1-71.4]) were included with a total number of 28,264 completed questionnaires. At the between-subjects level, a positive association between NA and somatic symptoms was found (B = .60, p < .001), whereas the negative association between PA and somatic symptoms was much smaller (B = -.14, p = .062). At the within-subject level, PA (B = -.33, p < .001) was more strongly associated with somatic symptoms than NA (B = .13, p < .001). The lagged analyses showed a negative association between previous-day PA and somatic symptoms (B = -.05, p = .001).Conclusions: The results suggest that NA is more important for differences in symptom levels between subjects, whereas PA is more important for variations in symptom levels within subjects. Moreover, our results suggest that an increase in PA is followed by a decrease in somatic symptoms after 24 hr, which suggests a causal effect. Statement of contribution What is already known on this subject? Affect and somatic processes are closely linked. Cross-sectional studies show, for example, that people with higher levels of negative affect tend to report more somatic symptoms. Findings between individuals, though, might camouflage processes at within-individual level, and it might not always be possible to translate findings at the population level to the individual. However, diary studies are upcoming and show more about processes on individual level. What does this study add? Highlights the difference between processes at the within-individual and the between-individual level. Shows the importance of positive affect at individual level in relation to somatic symptoms. Shows the benefits of the use of new techniques in diary studies. [ABSTRACT FROM AUTHOR]

Background and Aims Bioactive gibberellins are commonly applied to increase the size of berries in seedless tablegrapes. The genetic determinants for the response of berry size to gibberellic acid ( GA3) were investigated in a progeny (n = 137) of Ruby Seedless × Sultanina. Methods and Results Seed and berry size-related traits were measured at harvest (18° Brix). Heritability was high (∼79%) for all traits under study, especially seed dry mass (>90%). All these traits responded to GA3 and showed interaction between genotype and treatment (g × GA3). Based on quantitative trait loci ( QTL) analysis and the grapevine reference genome, we identified about 200 annotated genes located in the corresponding confidence intervals in linkage groups 2 and 18. Nineteen of these genes were selected for further characterisation because of their possible participation in g × GA3. Conclusions The response to GA3 has an important genetic basis which is given by QTLs localised on linkage groups 2 and 18. Under GA3 treatment, the seed loses its genetic control on berry size given by its major QTLs, indicating a possible interaction between this genetic determinant and GA3. Despite the masking effect, VvAGL11 is associated with the intensity of the response to GA3. Significance of the Study These results indicate the complex role of g × GA3 at the genetic level in the control of berry size. [ABSTRACT FROM AUTHOR]

ABSTRACT The aim of this article is to develop methodology for detecting influential observations in crossover models with random individual effects. Various case-weighted perturbations are performed. We obtain the influence of the perturbations on each parameter estimator and on their dispersion matrices. The obtained results exhibit the possibility to obtain closed-form expressions of the influence using the residuals in mixed linear models. Some graphical tools are also presented. [ABSTRACT FROM AUTHOR]

Deforestation usually results in significant losses of soil organic carbon (SOC). The rate and factors determining the recovery of this C pool with afforestation are still poorly understood. This paper provides a review of the influence of afforestation on SOC stocks based on a meta-analysis of 33 recent publications (totaling 120 sites and 189 observations), with the aim of determining the factors responsible for the restoration of SOC following afforestation. Based on a mixed linear model, the meta-analysis indicates that the main factors that contribute to restoring SOC stocks after afforestation are: previous land use, tree species planted, soil clay content, preplanting disturbance and, to a lesser extent, climatic zone. Specifically, this meta-analysis (1) indicates that the positive impact of afforestation on SOC stocks is more pronounced in cropland soils than in pastures or natural grasslands; (2) suggests that broadleaf tree species have a greater capacity to accumulate SOC than coniferous species; (3) underscores that afforestation using pine species does not result in a net loss of the whole soil-profile carbon stocks compared with initial values (agricultural soil) when the surface organic layer is included in the accounting; (4) demonstrates that clay-rich soils (> 33%) have a greater capacity to accumulate SOC than soils with a lower clay content (< 33%); (5) indicates that minimizing preplanting disturbances may increase the rate at which SOC stocks are replenished; and (6) suggests that afforestation carried out in the boreal climate zone results in small SOC losses compared with other climate zones, probably because trees grow more slowly under these conditions, although this does not rule out gains over time after the conversion. This study also highlights the importance of the methodological approach used when developing the sampling design, especially the inclusion of the organic layer in the accounting. [ABSTRACT FROM AUTHOR]

In the present study, a strategy was proposed for constructing plant core subsets by clusters based on the combination of continuous data for genotypic values and discrete data for molecular marker information. A mixed linear model approach was used to predict genotypic values for eliminating the environment effect. The “mixed genetic distance” was designed to solve the difficult problem of combining continuous and discrete data to construct a core subset by cluster. Four commonly used genetic distances for continuous data (Euclidean distance, standardized Euclidean distance, city block distance, and Mahalanobis distance) were used to assess the validity of the continuous data part of the mixed genetic distance; three commonly used genetic distances for discrete data (cosine distance, correlation distance, and Jaccard distance) were used to assess the validity of the discrete data part of the mixed genetic distance. A rice germplasm group with eight quantitative traits and information for 60 molecular markers was used to evaluate the validity of the new strategy. The results suggest that the validity of both parts of the mixed genetic distance are equal to or higher than the common genetic distance. The core subset constructed on the basis of a combination of data for genotypic values and molecular marker information was more representative than that constructed on the basis of data from genotypic values or molecular marker information alone. Moreover, the strategy of using combined data was able to treat dominant marker information and could combine any other continuous data and discrete data together to perform cluster to construct a plant core subset. (Managing editor: Ya-Qin Han) [ABSTRACT FROM AUTHOR]

Time series of population abundance estimates often are the only data avail- able for evaluating the prospects for persistence of a species of concern. With such data, it is possible to perform a population viability analysis (PVA) with diffusion approximation methods using estimates of the mean population trend and the variance of the trend, the so-called process variation. Sampling error in the data, however, may bias estimators of process variation derived by simple methods. We develop a restricted maximum likelihood (REML)-based method for estimating trend, process variation, and sampling error from a single time series based on a discrete-time model of density-independent growth coupled with a model of the sampling process. Transformation of the data yields a conventional linear mixed model, in which the variance components are functions of the process variation and sampling error. Simulation results show essentially unbiased estimators of trend, process variation, and sampling error over a range of process variation/sampling error combinations. Example data analyses are provided for the Whooping Crane (Grus americana), grizzly bear (Ursus arctos horribilis), California Condor (Gymnogyps californianus), and Puerto Rican Parrot (Amazona vittata). This REML-based method is useful for PVA methods that depend on accurate estimation of process variation from time-series data. [ABSTRACT FROM AUTHOR]

Hamilton (A standard error for the estimated state vector of a state-space model. J. Economet. 33 (1986), 387–97) and Ansley and Kohn (Prediction mean squared error for state space models with estimated parameters. Biometrika 73 (1986), 467–73) have both proposed corrections to the naive approximation (obtained via substitution of the maximum likelihood estimates for the unknown parameters) of the Bayesian prediction mean squared error (MSE) for state space models, when the model's parameters are estimated from the data. Our work extends theirs in that we propose enhancements by identifying missing terms of the same order as that in their corrections. Because the approximations to the MSE are often subject to a frequentist interpretation, we compare our proposed enhancements with their original versions and with the naive approximation through a simulation study. For simplicity, we use the random walk plus noise model to develop the theory and to get our empirical results in the main body of the text. We also illustrate the differences between the various approximations with the Purse Snatching in Chicago series. Our empirical results show that (i) as expected, the underestimation in the naive approximation decreases as the sample size increases; (ii) the improved Ansley–Kohn approximation is the best compromise considering theoretical exactness, bias, precision and computational requirements, though the original Ansley–Kohn method performs quite well; finally, (iii) both the original and the improved Hamilton methods marginally improve the naive approximation. These conclusions also hold true with the Purse Snatching series. [ABSTRACT FROM AUTHOR]