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Analytical performance specifications (APS) are usually compared to the intermediate reproducibility uncertainty of measuring a particular measurand using a single in vitro diagnostic medical device (IVD MD). Healthcare systems assembling multiple laboratories that include several IVD MDs and cater to patients suffering from long-term disease conditions mean that samples from a patient are analyzed using a few IVD MDs, sometimes from different manufacturers, but rarely all IVD MDs in the healthcare system. The reproducibility uncertainty for results of a measurand measured within a healthcare system and the components of this measurement uncertainty is useful in strategies to minimize bias and overall measurement uncertainty within the healthcare system. The root mean squares deviation (RMSD) calculated as the sample standard deviation (SD) and relative SD includes both imprecision and bias and is appropriate for expressing such uncertainties. Results from commutable stabilized internal and external control samples, from measuring split natural patient samples or using big-data techniques, are essential in monitoring bias and measurement uncertainties in healthcare systems. Variance component analysis (VCA) can be employed to quantify the relative contributions of the most influential factors causing measurement uncertainty. Such results represent invaluable information for minimizing measurement uncertainty in the interest of the healthcare systemʼs patients. [ABSTRACT FROM AUTHOR]

The study of plant functional traits and variation among and within species can help illuminate functional coordination and trade-offs in key processes that allow plants to grow, reproduce and survive. We studied 20 leaf, above-ground stem, below-ground stem and fine-root traits of 17 Costus species from forests in Costa Rica and Panama to answer the following questions: (i) Do congeneric species show above-ground and below-ground trait coordination and trade-offs consistent with theory of resource acquisition and conservation? (ii) Is there correlated evolution among traits? (iii) Given the diversity of habitats over which Costus occurs, what is the relative contribution of site and species to trait variation? We performed a principal components analysis (PCA) to assess for the existence of a spectrum of trait variation and found that the first two PCs accounted for 21.4 % and 17.8 % of the total trait variation, respectively, with the first axis of variation being consistent with a continuum of resource-acquisitive and resource-conservative traits in water acquisition and use, and the second axis of variation being related to the leaf economics spectrum. Stomatal conductance was negatively related to both above-ground stem and rhizome specific density, and these relationships became stronger after accounting for evolutionary relatedness, indicating correlated evolution. Despite elevation and climatic differences among sites, high trait variation was ascribed to individuals rather than to sites. We conclude that Costus species present trait coordination and trade-offs that allow species to be categorized as having a resource-acquisitive or resource-conservative functional strategy, consistent with a whole-plant functional strategy with evident coordination and trade-offs between above-ground and below-ground function. Our results also show that herbaceous species and species with rhizomes tend to agree with trade-offs found in more species-rich comparisons.

The Social Relations Model (SRM) is a conceptual and mathematical model of interpersonal responses in dyads. The SRM permits estimation of responses of one to many (the actor effect) and the responses of many to the one (the partner effect) at the individual level of analysis. The SRM also permits estimation of the unique responses of actors and partners in specific dyadic arrangements (the relationship effect). During the four decades that the SRM has been used empirically, most attention was focused on estimation of variance and covariance of components. More recently, second stage modeling has occurred in which SRM effect estimates are used as variables in multivariate models. Consequently, it has become important to have good predictions of SRM actor, partner, and relationship effects. A method proposed by Warner, Kenny, and Stoto has been used to predict these effects. Here we propose an alternative matrix-based estimation method that predicts the latent SRM random effects from their conditional expected values given observed data. Analytic work and Monte Carlo simulations indicate that our conditional-expectation predictions of SRM effects are more valid and precise than the traditional predictions. They will improve second-stage Social Relations Modeling and also have practical uses as well (in, e.g., determining employee salary raises). [ABSTRACT FROM AUTHOR]

The study of plant functional traits and variation among and within species can help illuminate functional coordination and trade-offs in key processes that allow plants to grow, reproduce and survive. We studied 20 leaf, above-ground stem, below-ground stem and fine-root traits of 17 Costus species from forests in Costa Rica and Panama to answer the following questions: (i) Do congeneric species show above-ground and below-ground trait coordination and trade-offs consistent with theory of resource acquisition and conservation? (ii) Is there correlated evolution among traits? (iii) Given the diversity of habitats over which Costus occurs, what is the relative contribution of site and species to trait variation? We performed a principal components analysis (PCA) to assess for the existence of a spectrum of trait variation and found that the first two PCs accounted for 21.4 % and 17.8 % of the total trait variation, respectively, with the first axis of variation being consistent with a continuum of resource-acquisitive and resource-conservative traits in water acquisition and use, and the second axis of variation being related to the leaf economics spectrum. Stomatal conductance was negatively related to both above-ground stem and rhizome specific density, and these relationships became stronger after accounting for evolutionary relatedness, indicating correlated evolution. Despite elevation and climatic differences among sites, high trait variation was ascribed to individuals rather than to sites. We conclude that Costus species present trait coordination and trade-offs that allow species to be categorized as having a resource-acquisitive or resource-conservative functional strategy, consistent with a whole-plant functional strategy with evident coordination and trade-offs between above-ground and below-ground function. Our results also show that herbaceous species and species with rhizomes tend to agree with trade-offs found in more species-rich comparisons. [ABSTRACT FROM AUTHOR]

The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called ‘inclusive’ R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

The coefficient of determination R² quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R² of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R² and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR², an R package that quantifies part R² for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR² also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called 'inclusive' R², as the square of the structure coefficients times total R². Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR² implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR² with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors. [ABSTRACT FROM AUTHOR]

Abstract Background There are an exceedingly large number of sequence variants discovered through whole genome sequencing in most populations, including cattle. Deciphering which of these affect complex traits is a major challenge. In this study we hypothesize that variants in some functional classes, such as splice site regions, coding regions, DNA methylated regions and long noncoding RNA will explain more variance in complex traits than others. Two variance component approaches were used to test this hypothesis – the first determines if variants in a functional class capture a greater proportion of the variance, than expected by chance, the second uses the proportion of variance explained when variants in all annotations are fitted simultaneously. Results Our data set consisted of 28.3 million imputed whole genome sequence variants in 16,581 dairy cattle with records for 6 complex trait phenotypes, including production and fertility. We found that sequence variants in splice site regions and synonymous classes captured the greatest proportion of the variance, explaining up to 50% of the variance across all traits. We also found sequence variants in target sites for DNA methylation (genomic regions that are found be highly methylated in bovine placentas), captured a significant proportion of the variance. Per sequence variant, splice site variants explain the highest proportion of variance in this study. The proportion of variance captured by the missense predicted deleterious (from SIFT) and missense tolerated classes was relatively small. Conclusion The results demonstrate using functional annotations to filter whole genome sequence variants into more informative subsets could be useful for prioritization of the variants that are more likely to be associated with complex traits. In addition to variants found in splice sites and protein coding genes regulatory variants and those found in DNA methylated regions, explained considerable variation in milk production and fertility traits. In our analysis synonymous variants captured a significant proportion of the variance, which raises the possible explanation that synonymous mutations might have some effects, or more likely that these variants are miss-annotated, or alternatively the results reflect imperfect imputation of the actual causative variants.

Abstract Background In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, particularly in presence of interaction. The present paper proposes an extension of the variance component analysis to estimate the various components of the variance, including an interaction component in case of unbalanced data. Results We applied an experimental design that uses a serial dilution to generate known relative protein concentrations and estimated these concentrations by two processing algorithms, a classical and a more recent one. The extended method allowed estimating the variances explained by the dilution and the technical process by each algorithm in an experiment with 9 proteins: L-FABP, 14.3.3 sigma, Calgi, Def.A6, Villin, Calmo, I-FABP, Peroxi-5, and S100A14. Whereas, the recent algorithm gave a higher dilution variance and a lower technical variance than the classical one in two proteins with three peptides (L-FABP and Villin), there were no significant difference between the two algorithms on all proteins. Conclusions The extension of the variance component analysis was able to estimate correctly the variance components of protein concentration measurement in case of unbalanced design.

Identifying relative idiosyncratic and shared contributions to judgments is a fundamental challenge to the study of human behavior, yet there is no established method for estimating these contributions. Using edge cases of stimuli varying in intrarater reliability and interrater agreement—faces (high on both), objects (high on the former, low on the latter), and complex patterns (low on both)—we showed that variance component analyses (VCAs) accurately captured the psychometric properties of the data (Study 1). Simulations showed that the VCA generalizes to any arbitrary continuous rating and that both sample and stimulus set size affect estimate precision (Study 2). Generally, a minimum of 60 raters and 30 stimuli provided reasonable estimates within our simulations. Furthermore, VCA estimates stabilized given more than two repeated measures, consistent with the finding that both intrarater reliability and interrater agreement increased nonlinearly with repeated measures (Study 3). The VCA provides a rigorous examination of where variance lies in data, can be implemented using mixed models with crossed random effects, and is general enough to be useful in any judgment domain in which agreement and disagreement are important to quantify and in which multiple raters independently rate multiple stimuli. [ABSTRACT FROM AUTHOR]

We partition the variances of profits of the companies associated with Korean business groups into business group-specific effects, industry effects, and affiliate company-specific effects, which roughly match corporate-parent effects, industry effects and business unit effects, respectively, in the extant literature. We find substantial corporate-parent effects (here, business group effects) along with industry effects and business unit effects (here, affiliate company-specific effects). This finding may indicate that business groups play an important role in developing countries by circumventing market inefficiencies. Our results also suggest that these effects tend to be smaller in large business groups and to decrease over time. [ABSTRACT FROM AUTHOR]

Objective: People value solitude in varying degrees. Theories and studies suggest that people's appreciation of solitude varies considerably across persons (e.g., an introverted person may value solitude more than an extraverted person), and solitude experiences (i.e., on average, people may value some functions of solitude, e.g., privacy, more than other functions, e.g., self-discovery). What are the unique contributions of these two sources?, Method: We surveyed a quota-based sample of 501 US residents about their perceived importance of a diverse set of 22 solitude functions., Results: Variance component analysis reveals that both sources contributed to the variability of perceived importance of solitude (person: 22%; solitude function: 15%). Crucially, individual idiosyncratic preferences (person-by-solitude function interaction) had a substantial impact (46%). Further analyses explored the role of personality traits, showing that different functions of solitude hold varying importance for different people. For example, neurotic individuals prioritize emotion regulation, introverted individuals value relaxation, and conscientious individuals find solitude important for productivity., Conclusions: People value solitude for idiosyncratic reasons. Scientific inquiries on solitude must consider the fit between a person's characteristics and the specific functions a solitary experience affords. This research suggests that crafting or enhancing positive solitude experiences requires a personalized approach., (© 2024 The Authors. Journal of Personality published by Wiley Periodicals LLC.)

Introduction Sit-stand workstations are a popular intervention to reduce sedentary behavior (SB) in office settings. However, the extent and distribution of SB in office workers long-term accustomed to using sit-stand workstations as a natural part of their work environment are largely unknown. In the present study, we aimed to describe patterns of SB in office workers with long-term access to sit-stand workstations and to determine the extent to which these patterns vary between days and workers. Methods SB was objectively monitored using thigh-worn accelerometers for a full week in 24 office workers who had been equipped with a sit-stand workstation for at least 10 months. A comprehensive set of variables describing SB was calculated for each workday and worker, and distributions of these variables between days and workers were examined. Results On average, workers spent 68% work time sitting [standard deviation (SD) between workers and between days (within worker): 10.4 and 18.2%]; workers changed from sitting to standing/walking 3.2 times per hour (SDs 0.6 and 1.2 h−1); with bouts of sitting being 14.9 min long (SDs 4.2 and 8.5 min). About one-third of the workers spent >75% of their workday sitting. Between-workers variability was significantly different from zero only for percent work time sitting, while between-days (within-worker) variability was substantial for all SB variables. Conclusions Office workers accustomed to using sit-stand workstations showed homogeneous patterns of SB when averaged across several days, except for percent work time seated. However, SB differed substantially between days for any individual worker. The finding that many workers were extensively sedentary suggests that just access to sit-stand workstations may not be a sufficient remedy against SB; additional personalized interventions reinforcing use may be needed. To this end, differences in SB between days should be acknowledged as a potentially valuable source of variation. [ABSTRACT FROM AUTHOR]

Background: There are an exceedingly large number of sequence variants discovered through whole genome sequencing in most populations, including cattle. Deciphering which of these affect complex traits is a major challenge. In this study we hypothesize that variants in some functional classes, such as splice site regions, coding regions, DNA methylated regions and long noncoding RNA will explain more variance in complex traits than others. Two variance component approaches were used to test this hypothesis – the first determines if variants in a functional class capture a greater proportion of the variance, than expected by chance, the second uses the proportion of variance explained when variants in all annotations are fitted simultaneously. Results: Our data set consisted of 28.3 million imputed whole genome sequence variants in 16,581 dairy cattle with records for 6 complex trait phenotypes, including production and fertility. We found that sequence variants in splice site regions and synonymous classes captured the greatest proportion of the variance, explaining up to 50% of the variance across all traits. We also found sequence variants in target sites for DNA methylation (genomic regions that are found be highly methylated in bovine placentas), captured a significant proportion of the variance. Per sequence variant, splice site variants explain the highest proportion of variance in this study. The proportion of variance captured by the missense predicted deleterious (from SIFT) and missense tolerated classes was relatively small. Conclusion: The results demonstrate using functional annotations to filter whole genome sequence variants into more informative subsets could be useful for prioritization of the variants that are more likely to be associated with complex traits. In addition to variants found in splice sites and protein coding genes regulatory variants and those found in DNA methylated regions, explained considerable variation in milk production and fertility traits. In our analysis synonymous variants captured a significant proportion of the variance, which raises the possible explanation that synonymous mutations might have some effects, or more likely that these variants are miss-annotated, or alternatively the results reflect imperfect imputation of the actual causative variants. [ABSTRACT FROM AUTHOR]

Abstract: Linear mixed‐effects models are frequently used for estimating quantitative genetic parameters, including the heritability, as well as the repeatability, of traits. Heritability acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change, whereas repeatability informs us about the individual consistency of phenotypic traits. As quantities of biological interest, it is important that the denominator, the phenotypic variance in both cases, reflects the amount of phenotypic variance in the relevant ecological setting. The current practice of quantifying heritabilities and repeatabilities from mixed‐effects models frequently deprives their denominator of variance explained by fixed effects (often leading to upward bias of heritabilities and repeatabilities), and it has been suggested to omit fixed effects when estimating heritabilities in particular. We advocate an alternative option of fitting models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of the phenotypic variance. The approach is easily implemented and allows optimizing the estimation of phenotypic variance, for example by the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We address the estimation and interpretation of heritabilities in situations in which potential covariates are themselves heritable traits of the organism. Furthermore, we discuss complications that arise in generalized and nonlinear mixed models with fixed effects. In these cases, the variance parameters on the data scale depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations. [ABSTRACT FROM AUTHOR]

Objectives: An interesting consequence of consanguinity is that the inbred singleton becomes informative for genetic variance. We determine the contribution of an inbred singleton to variance component analysis of heritability and linkage. Methods: Statistical theory for the power of variance component analysis of quantitative traits is used to determine the expected contribution of an inbred singleton to likelihood-ratio tests of heritability and linkage. Results: In variance component models, an inbred singleton contributes relatively little to a test of heritability but can contribute substantively to a test of linkage. For small-to-moderate quantitative trait locus (QTL) effects and a level of inbreeding comparable to matings between first cousins (the preferred form of union in many human populations), an inbred singleton can carry nearly 25% of the information of a non-inbred sib pair. In more highly inbred contexts available with experimental animal populations, nonhuman primate colonies, and some human subpopulations, the contribution of an inbred singleton relative to a sib pair can exceed 50%. Conclusions: Inbred individuals, even in isolation from other members of a sample, can contribute to variance component estimation and tests of heritability and linkage. Under certain conditions, the informativeness of the inbred singleton can approach that of a non-inbred sib pair. [ABSTRACT FROM AUTHOR]

Introduction: frailty is an increased vulnerability to adverse health outcomes, across multiple physiological systems, with both environmental and genetic drivers. The two most commonly used measures are Rockwood's frailty index (FI) and Fried's frailty phenotype (FP). Material and methods: the present study included 3626 individuals from the TwinsUK Adult Twin Registry. We used the classical twin model to determine whether FI and FP share the same latent aetiological factors. We also investigated the relationship between frailty and chronic widespread musculoskeletal pain (CWP), another holistic age-related condition with significant clinical impact. Results: FP and FI shared underlying genetic and environmental aetiology. CWP was associated with both frailty measures, and health deficits appeared to mediate the relationship between phenotypic frailty and pain. Latent genetic factors underpinning CWP were shared with frailty. While frailty was increased in the twins reporting pain, co-twin regression analysis indicated that the relationship between CWP and frailty is reduced after accounting for shared genetic and environmental factors. Conclusions: both measures of frailty tap the same root causes, thus this work helps unify frailty research. We confirmed a strong association between CWP and frailty, and showed a large and significant shared genetic aetiology of both phenomena. Our findings argue against pain being a significant causative factor in the development of frailty, favouring common causation. This study highlights the need to manage CWP in frail individuals and undertake a Comprehensive Geriatric Assessment in individuals presenting with CWP. Finally, the search for genetic factors underpinning CWP and frailty could be aided by integrating measures of pain and frailty. [ABSTRACT FROM AUTHOR]

Drug product assay is one of several tests required for new drug products to ensure the quality of the product at release and throughout the life cycle of the product. Drug product assay testing is typically performed by preparing a composite sample of multiple dosage units to obtain an assay value representative of the batch. In some cases replicate composite samples may be prepared and the reportable assay value is the average value of all the replicates. In previously published work by Harrington et al. (2014) [5], a sample preparation composite and replicate strategy for assay was developed to provide a systematic approach which accounts for variability due to the analytical method and dosage form with a standard error of the potency assay criteria based on compendia and regulatory requirements. In this work, this sample preparation composite and replicate strategy for assay is applied to several case studies to demonstrate the utility of this approach and its application at various stages of pharmaceutical drug product development. [ABSTRACT FROM AUTHOR]

Intra-class correlations ( ICC) and repeatabilities ( R) are fundamental statistics for quantifying the reproducibility of measurements and for understanding the structure of biological variation. Linear mixed effects models offer a versatile framework for estimating ICC and R. However, while point estimation and significance testing by likelihood ratio tests is straightforward, the quantification of uncertainty is not as easily achieved., A further complication arises when the analysis is conducted on data with non-Gaussian distributions because the separation of the mean and the variance is less clear-cut for non-Gaussian than for Gaussian models. Nonetheless, there are solutions to approximate repeatability for the most widely used families of generalized linear mixed models (GLMMs)., Here, we introduce the R package rptR for the estimation of ICC and R for Gaussian, binomial and Poisson-distributed data. Uncertainty in estimators is quantified by parametric bootstrapping and significance testing is implemented by likelihood ratio tests and through permutation of residuals. The package allows control for fixed effects and thus the estimation of adjusted repeatabilities (that remove fixed effect variance from the estimate) and enhanced agreement repeatabilities (that add fixed effect variance to the denominator). Furthermore, repeatability can be estimated from random-slope models. The package features convenient summary and plotting functions., Besides repeatabilities, the package also allows the quantification of coefficients of determination R2 as well as of raw variance components. We present an example analysis to demonstrate the core features and discuss some of the limitations of rptR. [ABSTRACT FROM AUTHOR]

A reliable and practical method for characterizing refuse-derived fuels (RDF) with respect to greenhouse gas-relevance (or biomass content) is required by industries and waste management companies. As RDF usually consist of a variety of materials with different physical properties, sampling and sample preparation may represent crucial steps with regard to reliable analysis results. This is particularly valid for analytical methods, which rely on only small test specimens (centigrams), such as the adapted Balance Method (aBM). The aBM was recently developed by the authors and is based on elemental analyses (CHNSO). The investigations focus on elaborating an appropriate sample preparation for the aBM. To this end, two RDF model mixtures are generated out of paper, cardboard and different plastics, and comminuted down to a grain of size of < 0.2 mm using two differing mills as finishing step. The results of the aBM (applied for 52 samples) show that the performance of the method in terms of trueness and variation is competitive relative to standardized methods. Deviations between the determined value and the theoretical biogenic mass fraction are below 4.5%rel (at a probability of 95%). Furthermore, the standard deviation for both mixtures is below ± 3.0%rel. A nested variance component analysis indicates that the last milling step and the step of drawing the test specimens for analysis contribute most to the observed variability. A consecutive application of two types of mills as a finishing step prior to analysis is proposed in order to facilitate a sufficient grinding of plastics as well as of cellulose fibers. [ABSTRACT FROM AUTHOR]

Abnormal lipid levels are important risk factors for cardiovascular diseases. We conducted genome-wide variance component linkage analyses to search for loci influencing total cholesterol (TC), LDL, HDL and triglyceride in families residing in American Samoa and Samoa as well as in a combined sample from the two polities. We adjusted the traits for a number of environmental covariates, such as smoking, alcohol consumption, physical activity, and material lifestyle. We found suggestive univariate linkage with log of the odds (LOD) scores > 3 for LDL on 6p21-p12 (LOD 3.13) in Samoa and on 12q21-q23 (LOD 3.07) in American Samoa. Furthermore, in American Samoa on 12q21, we detected genome-wide linkage (LODeq 3.38) to the bivariate trait TC-LDL. Telomeric of this region, on 12q24, we found suggestive bivariate linkage to TC-HDL (LODeq 3.22) in the combined study sample. In addition, we detected suggestive univariate linkage (LOD 1.9–2.93) on chromosomes 4p-q, 6p, 7q, 9q, 11q, 12q 13q, 15q, 16p, 18q, 19p, 19q and Xq23 and suggestive bivariate linkage (LODeq 2.05–2.62) on chromosomes 6p, 7q, 12p, 12q, and 19p-q. In conclusion, chromosome 6p and 12q may host promising susceptibility loci influencing lipid levels; however, the low degree of overlap between the three study samples strongly encourages further studies of the lipid-related traits.

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (NAOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy. [ABSTRACT FROM AUTHOR]

One of the most important breeding objectives for Japanese pear ( Pyrus pyrifolia Nakai) is to release early-ripening cultivars. To accelerate marker-assisted selection (MAS) for fruit ripening day (FRD), the molecular markers PPACS2 and BGA35, both associated with FRD, were evaluated in six populations by using variance components. The variance explained by PPACS2 ( σ ACS2 2 ) differed greatly among populations, ranging from 5.7 to 101.9. The percentage of total phenotypic variance accounted for by σ ACS2 2 ranged from 8.9% to 40.4%, 22.1% on average. The variance explained by BGA35 ( σ BGA35 2 ) was 44.4–46.3, and the percentage of the total phenotypic variance accounted for by σ BGA35 2 ranged from 18.3% to 24.9%, 21.6% on average. These values are high enough for these markers to be implemented in practical Japanese pear breeding. The 263-bp allele of PPACS2 and the 136-bp allele of BGA35 had early-ripening effects in six populations. No interaction was found between PPACS2 and BGA35, suggesting that the effects of both loci were additive. The 263-bp allele of PPACS2 was previously shown to increase PPACS2 expression and ethylene production, which might have a negative effect on fruit storage and fruit drop, so further investigation of this allele is required. The information obtained in this study will accelerate MAS for FRD using PPACS2 and BGA35 in Japanese pear breeding programs. [ABSTRACT FROM AUTHOR]

It was recently shown that monotone gene action, i.e. order-preservation between allele content and corresponding genotypic values in the mapping from genotypes to phenotypes, is a prerequisite for achieving a predictable parent-offspring relationship across the whole allele frequency spectrum. Here we test the consequential prediction that the design principles underlying gene regulatory networks are likely to generate highly monotone genotype-phenotype maps. To this end we present two measures of the monotonicity of a genotype-phenotype map, one based on allele substitution effects, and the other based on isotonic regression. We apply these measures to genotype-phenotype maps emerging from simulations of 1881 different 3-gene regulatory networks. We confirm that in general, genotype-phenotype maps are indeed highly monotonic across network types. However, regulatory motifs involving incoherent feedforward or positive feedback, as well as pleiotropy in the mapping between genotypes and gene regulatory parameters, are clearly predisposed for generating non-monotonicity. We present analytical results confirming these deep connections between molecular regulatory architecture and monotonicity properties of the genotype-phenotype map. These connections seem to be beyond reach by the classical distinction between additive and non-additive gene action.

The color assessment ability of a multispectral vision system is investigated by a comparison study with color measurements from a traditional colorimeter. The experiment involves fresh and processed meat samples. Meat is a complex material; heterogeneous with varying scattering and reflectance properties, so several factors can influence the instrumental assessment of meat color. In order to assess whether two methods are equivalent, the variation due to these factors must be taken into account. A statistical analysis was conducted and showed that on a calibration sheet the two instruments are equally capable of measuring color. Moreover the vision system provides a more color rich assessment of fresh meat samples with a glossier surface, than the colorimeter. Careful studies of the different sources of variation enable an assessment of the order of magnitude of the variability between methods accounting for other sources of variation leading to the conclusion that color assessment using a multispectral vision system is superior to traditional colorimeter assessments. [ABSTRACT FROM AUTHOR]

A neglected aspect of the genetic analysis of gene expression is the impact of normalization on biological inference. Here we contrast nine different methods for normalization of an Illumina bead-array gene expression profiling dataset consisting of peripheral blood samples from 189 individual participants in the Center for Health Discovery and Well-Being (CHDWB) study in Atlanta, quantifying differences in the inference of global variance components and covariance of gene expression, as well as the detection of eSNPs. The normalization strategies, all relative to raw log2 measures, include simple mean centering, two modes of transcript-level linear adjustment for technical factors, and for differential immune cell counts, variance normalization by inter-quartile range and by quantile, fitting the first 16 Principal Components, and supervised normalization using the SNM procedure with adjustment for cell counts. Robustness of genetic associations as a consequence of Pearson and Spearman rank correlation is also reported for each method, and it shown that the normalization strategy has a far greater impact than correlation method. We describe similarities among methods, discuss the impact on biological interpretation, and make recommendations regarding appropriate strategies.

Genomic selection is an upgrading form of marker-assisted selection for quantitative traits , and it differs from the traditional marker-assisted selection in that markers in the entire genome are used to predict genetic values and the QTL detection step is skipped . Genomic selection holds the promise to be more efficient than the traditional marker-assisted selection for traits controlled by polygenes. Genomic selection for pure breed improvement is based on marker information and thus leads to cost-saving due to early selection before pheno types are measured. When applied to hybrid breeding, genomic selection is anticipated to be even more efficient because genotypes of hybrids are predetermined by their inbred parents. Hybrid breeding has been an important tool to increase crop productivity . Here we proposed and applied an advanced method to predict hybrid performance , in which a subset of all potential hybrids is used as a training sample to predict trait values of all potential hybrids. The method is called genomic best linear unbiased prediction. The technology applied to hybrids is called genomic hybrid breeding. We used 278 randomly selected hybrids derived from 210 recombinant inbred lines of rice as a training sample and predicted all 21,945 potential hybrids. The average yield of top 100 selection shows a 16% increase compared with the average yield of all potential hybrids. The new strategy of marker-guided prediction of hybrid yields serves as a proof of concept for a new technology that may potentially revolutionize hybrid breeding. [ABSTRACT FROM AUTHOR]

Data-based methods and simple graphical analyses can be used broadly to improve quality for manufacturing processes. We provide a case study dealing with the manufacture of stator bars used in electric power generation equipment. We describe the examination of existing data, capability and stability assessment, addressing measurement error, using the preceding evaluations to reduce deviations from target and variability, and subsequent statistical monitoring. Some concluding remarks deal briefly with various further topics, including the importance of a proactive approach. Takeaways and added comments, spread throughout the article, provide further elaboration on or beyond the case study. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]