Testing for the Equality of Variance for the Four-parameter Logistic (4PL) Model for the Detection of Vibrio cholerae DNA with Polystyrene-coacrylic Acid Composite Nanospheres

When it comes to finding the best fit of nonlinear curves to acceptable models, linear regression with least squares is the most effective technique. Because residuals (the difference between observed and predicted data) must follow a normal distribution and the data must be free of outliers and uniform variance, statistical tests are used to identify the most appropriate model for a given situation (homoscedasticity). If all of these characteristics are satisfied, the system is said to be robust. In parametric nonlinear regression, one of the numerous assumptions is that the within-group variances of the groups are all the same, which is one of several assumptions (exhibit homoscedasticity). If the variances vary from one another (show heteroscedasticity), then the model is not statistically competent to describe the data as a whole. Data on the detection of Vibrio cholerae DNA with polystyrene-coacrylic acid composite nanospheres as modelled using the nonlinear four-parameter logistic (4PL) regression was preliminary check for homogeneity of variance using the Bartlett’s and Levene’s tests. It was found that the critical value of 2 was 28.869, according to Bartlett's test findings. Excel's CHIDIST function yielded a probability of 0.389 (not significant), suggesting that the variances of the residuals did not change significantly. The p-value for Levenes's test was 0.917, indicating that there were no distinct changes between the residual variances meaning that the use of the 4-PL model in fitting the data was adequate statistically.

Soybean seedling performance in diferente seed treatments

The objective of this work was to evaluate the performance of soybean seedlings in different seed treatments. The experiment was conducted in the municipality of Mineiros, GO. The soil was classified as Quartzarenic Entisol. The experimental design was randomized blocks in factorial 5x4, corresponding to seed treatments (Water, Cruiser, Fipronil Alta, Fortenza and Standak Top) in four soybean cultivars (Bonus, Ultra, Extra and BKS7830), in four replications. Before planting, pre-planting desiccation was performed. The fertilization used was 450 kg ha-1 of fertilizer 05-25-15 applied in the furrow and in a single dose next to the sowing. During the conduction of the experiment the control of pests, diseases and weeds were carried out as necessary, respecting the best practices and integrated management. The data obtained were subjected to the assumptions of the statistical model, verifying the normality and homogeneity of the residual variances, as well as the additivity of the model. Uni and multivariate tools were applied. The analysis were performed at the interface Rbio and R. The interaction of soybean cultivars and types of seed treatment showed variations in all analysis evaluated in soybean seedlings. The best performances were verified among the cultivars BKS7830 that expressed the largest shoot fresh matter when submitted to Cruiser seed treatment, while the highest root length was expressed in the cultivar Ultra in the Fortenza seed treatment.

Measurement Invariance of Test Anxiety Across Four School Subjects

School-subject-specific test anxieties have been widely examined, but there is a lack of analyses of measurement invariance of test anxiety across subjects. In order to preclude a mixture of test anxiety construct differences across school subjects with actual test anxiety differences and to ensure meaningful comparisons of test anxiety across school subjects, we examined such measurement invariance. Two test anxiety factors (worry and emotionality) were inspected across four school subjects (mathematics, physics, German, and English) in a sample of secondary school students ( N = 1,280). Strict measurement invariance was ascertained (i.e., comparable factor loadings, intercepts, and residual variances of the items of worry and emotionality factors across school subjects). The correlations of subject-specific test anxiety factors with subject-specific academic self-concepts and grades showed a convergent/divergent correlation pattern, thereby supporting criterion-related validity. The results of this study provide insights into the comparability of test anxiety assessments across school subjects.

Correlations and canonical variables applied to the distinction of soybean cultivars in a tropical environment

The objective of this study was to evaluate the performance of soybean cultivars through their correlations and canonical variables in a tropical environment. The study was conducted in the municipality of Mineiros, GO, Brazil. The soil was classified as Quartzarenic Neosol (Entisol). The experimental design used was in randomized blocks consisting of 10 soybean cultivars (Bônus, Desafio, Flecha, Foco, ICS7019, M5917, M7110, Power, ST721 and ST797) in four replications. Before planting, pre-planting desiccation was performed. The fertilizer used was 450 kg ha-1 of fertilizer 05-25-15 applied in the furrow and in a single dose next to the seeding. During the conduct of the experiment, pest control was carried out respecting good practices and integrated management. At the end of the cycle of each cultivar, 10 plants were collected at random and then the agronomic attributes were taken. The data obtained were submitted to the assumptions of the statistical model, verifying the normality and homogeneity of the residual variances, as well as the additivity of the model. Univariate and multivariate models were used. The analyzes were performed on the Rbio and R interface, in addition to the Software Genes. According to the summary of analysis of variance, it was observed that all cultivars differed for all characteristics. It was concluded that the soybean cultivars Flecha and M5917 presented the highest yields among the others in a tropical environment; the cultivars differed, showing a strong correlation between the number of grains per plant and yield, with the other variables analyzed; the univariate and multivariate tools were efficient and complementary in data analysis.

45 Partitioning SNP Heritability in Related Individuals

Partitioning SNP heritability by many functional annotations has been a successful tool for understanding the genetic architecture of complex traits in human genetic studies. Similar analyses are being extended to animal research, as (imputed) whole-genome sequence data of many individuals and various functional annotations have become available in livestock animals. Though many approaches have been developed for heritability partition (e.g., LDSC and HE-reg), they are mostly based on approximations tailored to human populations and few can produce statistically efficient estimates for animal genomic studies where individuals are often related. To tackle this issue, we present a stochastic MINQUE (Minimum Norm Quadratic Unbiased Estimation) approach for partitioning SNP heritability, which we refer to as MPH. We provide a theoretical analysis comparing LDSC and HE-reg with REML and MPH and demonstrate what LDSC and HE-reg (and similar methods) take advantage of in their approximations: sparse relationships between individuals and relatively weak linkage disequilibrium. We also show that our method is mathematically equivalent to the MC-REML approach implemented in BOLT. MPH has three key features. First, it is comparable to genomic REML in terms of accuracy, while being at least one order of magnitude faster than GCTA and BOLT and using only ~1/4 of memory as much as GCTA, when applied to sequence data and many variance components (or functional annotation categories). Second, it can do weighted analyses if residual variances are unequal (such as DYD). Third, it works for many overlapping functional annotations. Using simulations based on a human pedigree and a dairy cattle pedigree, we illustrate the benefits of our method for partitioning SNP heritability in pedigree-based studies. We also demonstrate that it is feasible to efficiently partition SNP heritability for animal genomes with strong, long-span LD. MPH is freely available at https://jiang18.github.io/mph.

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Genetic parameters and genetic trends of conformation and management traits in Dairy Gir cattle

ABSTRACT The objective of this study was to estimate genetic parameters and genetic trends of different conformation and management traits regularly measured within the context of the National Dairy Gir Breeding Program (PNMGL). The estimation of genetic and residual variances for each trait was performed using average information restricted maximum likelihood (AI-REML) procedure in AIREMLF90 program software. The population was divided into three subpopulations constituted by measured females (with phenotype records), all females, and males. Linear regressions were applied for each trait, considering two periods of birth (1st period: 1938-1996; 2nd period: 1997-2012). The estimated heritability of conformation and management traits varied from 0.01 to 0.53, denoting a perspective of genetic improvement through selection and corrective matings for purebred Dairy Gir populations. The average genetic changes in conformation and management traits were, in general, variable and inexpressive, showing that the selection of Dairy Gir may have had been directed essentially to increase milk yield. The analysis of the two periods of birth indicated that some linear traits present progress (although inexpressive) in the 2nd period (more recent period).

A new pipeline for the normalization and pooling of metabolomics data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through PC-PR2 analysis; (iii) application of linear mixed models to remove unwanted variability, including samples originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Evaluating Cluster-Level Factor Models with Lavaan and Mplus

Background: Researchers frequently use the responses of individuals in clusters to measure cluster-level constructs. Examples are the use of student evaluations to measure teaching quality, or the use of employee ratings of organizational climate. In earlier research, Stapleton and Johnson (2019) provided advice for measuring cluster-level constructs based on a simulation study with inadvertently confounded design factors. We extended their simulation study using both Mplus and lavaan to reveal how their conclusions were dependent on their study conditions. Methods: We generated data sets from the so-called configural model and the simultaneous shared-and-configural model, both with and without nonzero residual variances at the cluster level. We fitted models to these data sets using different maximum likelihood estimation algorithms. Results: Stapleton and Johnson’s results were highly contingent on their confounded design factors. Convergence rates could be very different across algorithms, depending on whether between-level residual variances were zero in the population or in the fitted model. We discovered a worrying convergence issue with the default settings in Mplus, resulting in seemingly converged solutions that are actually not. Rejection rates of the normal-theory test statistic were as expected, while rejection rates of the scaled test statistic were seriously inflated in several conditions. Conclusions: The defaults in Mplus carry specific risks that are easily checked but not well advertised. Our results also shine a different light on earlier advice on the use of measurement models for shared factors.

130 Utilization of Nutrack to Assess Variance Components and Heritability of Activity Traits

Overall activity and behavior are integral components of sows remaining productive in the herd. This investigation studied overall activity of group housed replacement gilts and the heritability of various activity traits. Beginning around 20 wk of age, video recorded data of approximately 75 gilts/group for a total of 2,378 gilts over 32 groups was collected for 7 consecutive d using the NUtrack System, which tracks distance travelled (m), avg speed (m/s), angle rotated (degrees), and time standing (s), sitting (s), eating (s), and laying (s). The recorded phenotypes were standardized to the distribution observed within a pen for each group. The final values used for analysis were the average daily standardized values. Data were analyzed using mixed models (RStudio V 1.2.5033) including effects of sire, dam, dam’s sire and dam, dam’s grandsire and granddam, farrowing group, barn, pen, and on-test date. Sire had an effect on every activity trait P < 0.001), and dam had an effect on average speed (P < 0.001). The dam’s sire had an effect on all activity traits (P < 0.001) and the dam’s grandsire had an effect on average speed (P < 0.001). Heritabilities and variance components of activity traits were estimated in ASReml 4 using an animal model with a two-generation pedigree. Genetic variances are 0.17 +/- 0.029, 0.19 +/- 0.034, and 0.11 +/- 0.024, residual variances are 0.37 +/- 0.023, 0.41 +/- 0.027, and 0.41 +/- 0.022, phenotypic variances are 0.54 +/- 0.018, 0.60 +/- 0.020, and 0.52 +/- 0.016, and heritabilities are 0.32 +/- 0.048, 0.32 +/- 0.049, and 0.21 +/- 0.044 for average speed, distance, and lie respectively. NUtrack offers potential to aid in selection decisions. Given the results presented herein, continued investigation into these activity traits and their association with sow longevity is warranted.