Different Sources Of Variation Research Articles

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 1: Design and Early Results of the SNMMI Dosimetry Challenge.

In this work, we present details and initial results from a 177Lu dosimetry challenge that has been designed to collect data from the global nuclear medicine community aiming at identifying, understanding, and quantitatively characterizing the consequences of the various sources of variability in dosimetry. Methods: The challenge covers different approaches to performing dosimetry: planar, hybrid, and pure SPECT. It consists of 5 different and independent tasks to measure the variability of each step in the dosimetry workflow. Each task involves the calculation of absorbed doses to organs and tumors and was meant to be performed in sequential order. The order of the tasks is such that results from a previous one would not affect subsequent ones. Different sources of variability are removed as the participants advance through the challenge by giving them the data required to begin the calculations at different steps of the dosimetry workflow. Data from 2 patients after a therapeutic administration of 177Lu-DOTATATE were used for this study. The data are hosted in Deep Blue Data, a data repository service run by the University of Michigan. Participants submit results in standardized spreadsheets and with a short description summarizing their methods. Results: In total, 178 participants have signed up for the challenge, and 119 submissions have been received. Sixty percent of submissions have used voxelized dose methods, with 47% of those using commercial software. In initial analysis, the volume of organs showed a variability of up to 49.8% whereas for lesions this was up to 176%. Variability in time-integrated activity was up to 192%. Mean absorbed doses varied up to 57.7%. Segmentation is the step that required the longest time to complete, with a median of 43 min. The median total time to perform the full calculation was 89 min. Conclusion: To advance dosimetry and encourage its routine use in radiopharmaceutical therapy applications, it is critical that dosimetry results be reproducible across centers. Our initial results provide insights into the variability associated with performing dose calculations. It is expected that this dataset, including results from future stages, will result in efforts to standardize and harmonize methods and procedures.

The killing fields. A Bayesian analysis of crop eradication and organized crime violence in Mexico

Both qualitative and quantitative approaches have been used to study the violence in Mexico during the “war on drugs” under Felipe Calderón’s government. In reference to the latter, although works now exist that analyze the problem via statistical models, there remain aspects yet to cover. In an effort to fill these gaps, the present article aspires to study the effects of eradicating illicit crops on generating violence. As a hypothesis, we propose that violence by organized crime does not occur randomly but rather in response to a spatial and temporal pattern, which is influenced by the eradication of illicit crops.With the object of testing this hypothesis, we undertook regional modeling and selected the most relevant array of covariates and developing parameters for a hierarchical Bayesian spatiotemporal model, with space and time effects being separable. The model captures different sources of variation through a hierarchy of parameters for considering predictive variables and random effects.The findings show a pattern of violence that correlates with the hectares of eradicated illicit crops, allowing us to see that as the measure of eradicated hectares rises, violent events are increasingly perpetuated by organized crime.

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules: A Multicenter Phantom Study.

BackgroundRadiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications.ObjectivesThe aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies.Materials and MethodsA chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research.ResultsA total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate <0.001). Finally, we obtained a subset of 19 stable, informative, and nonredundant RFs under all 3 sources of variability. These RFs exhibited clinical effectiveness and showed better prediction performance than unstable RFs in the validation dataset (P = 0.017, Delong test).ConclusionsThe stability of RFs was affected to different degrees by test-retest and differences in CT manufacturers and models and CT acquisition and reconstruction parameters, but the influences of these factors showed relatively consistent patterns. We also obtained a subset of 19 stable, informative, and nonredundant RFs that should be preferably used to enhance the generalizability of further radiomics research.

A PID Control Algorithm for a Post-Prandial Hypoglycemic Clamp Study.

Post-prandial hypoglycemia occurs 2-5 hours after food intake, in not only insulin-treated patients with diabetes but also other metabolic disorders. For example, postprandial hypoglycemia is an increasingly recognized late metabolic complication of bariatric surgery (also known as PBH), particularly gastric bypass. Underlying mechanisms remain incompletely understood to date. Besides excessive insulin exposure, impaired counter-regulation may be a further pathophysiological feature. To test this hypothesis, we need standardized postprandial hypoglycemic clamp procedures in affected and unaffected individuals allowing to reach identical predefined postprandial hypoglycemic trajectories. Generally, in these experiments, clinical investigators manually adjust glucose infusion rate (GIR) to clamp blood glucose (BG) to a target hypoglycemic value. Nevertheless, reaching the desired target by manual adjustment may be challenging and possible glycemic undershoots when approaching hypoglycemia can be a safety concern for patients. In this study, we developed a PID algorithm to assist clinical investigators in adjusting GIR to reach the predefined trajectory and hypoglycemic target. The algorithm is developed in a manual mode to permit the clinical investigator to interfere. We test the controller in silico by simulating glucose-insulin dynamics in PBH and healthy nonsurgical individuals. Different scenarios are designed to test the robustness of the algorithm to different sources of variability and to errors, e.g. outliers in the BG measurements, sampling delays or missed measurements. The results prove that the PID algorithm is capable of accurately and safely reaching the target BG level, on both healthy and PBH subjects, with a median deviation from reference of 2.8% and 2.4% respectively.Clinical relevance- This control algorithm enables standardized, accurate and safe postprandial hypoglycemic clamps, as evidenced in silico in PBH patients and controls.

Serum Calprotectin a Potential Biomarker in Juvenile Idiopathic Arthritis: A Meta-Analysis.

Juvenile idiopathic arthritis (JIA) is the most common inflammatory chronic disease affecting children and adolescents. Today, there are no specific biomarkers of inflammation. Therefore, it is important to identify new markers as predictors of disease activity. Recently, some researchers have directed their interest toward a protein, calprotectin (CLP), as a potential biomarker. The primary objective of our systematic review and meta-analysis was to analyze the possible role of CLP in JIA. Method: A literature search was conducted using PubMed, EMBASE, Scopus, Science Direct on 10 August 2021. The selection of studies was made using the PRISMA 2020 guidelines. Cohen’s d with 95% CI and p-value were used as a measure of effect size. The random effects model was used to account for different sources of variation among studies. Heterogeneity was assessed using Q statistics and I2. The publication bias was analyzed and represented by a funnel plot, and funnel plot symmetry was assessed with Egger’s test. Results: Our results at follow-up showed a statistically significant difference between patients with active disease compared to patients with inactive disease: 0.39 (0.16; 0.62), p = 0.001; without statistical heterogeneity. Another important aspect that emerged were the differences between the systemic disease form and any form of inactive disease showing a different concentration of calprotectin: 0.74 (0.40; 1.08), p < 0.001; without statistical heterogeneity. On the other hand, meta-regression analyses performed on gender, age, duration of disease, percentage of patients with ANA+ or RF+, medium value of ESR or CRP were not statistically significant. A statistically significant difference in serum calprotectin concentration between patients with JIA and healthy controls were observed. In fact, it presented lower values in the control group. Conclusions: The use of serum CLP could represent, in the future, a useful tool in JIA in order to stratify disease activity more accurately and may aid a more tailored approach to drug of choice in children with JIA. Further studies are needed to evaluate CLP as a predictor of flare in combination with other potential biomarkers of subclinical disease activity.

Similarity evaluation of 3D surface topography measurements

With the recent advances in three-dimensional (3D) optical scanning technologies, 3D surface topography measurement plays an increasingly important role in many fields, such as product quality inspection in additive manufacturing (AM), gauge capability analysis, and firearm identification in forensic science. In this paper, we establish a thorough and flexible new framework of surface topography data comparison that generates a scaled similarity score for a pair of measurements, and distinguishes matched and non-matched pairs based on the score. If two measurements are portraying the same surface, they are defined as a matched pair. If not, they are defined as a non-matched pair. This similarity evaluation framework can be very useful in comparing optical scanning systems, quantifying different sources of variation in a process, and monitoring the stability and uniformity of a process, thus has a great potential to improve the quality assurance cycle of AM processes in the long run. We illustrate the framework and statistically evaluate the binary classification performance on data measured from additive manufactured parts on a large scale. We also examine how different systems, repeated measurements, and operators affect the similarity score and classification performance. We compare our work with a baseline method using the surface roughness average parameter Ra. The results show that the methodology can distinguish matched and non-matched pairs with high accuracy, and it outperforms the baseline method greatly. The framework serves as a benchmark method and can be generalized to be used in other fields where surface topography plays a critical role.

What, how, and how much do herbivores eat? The Continuous Bite Monitoring method for assessing forage intake of grazing animals.

Determining herbage intake is pivotal for studies on grazing ecology. Direct observation of animals allows describing the interactions of animals with the pastoral environment along the complex grazing process. The objectives of the study were to evaluate the reliability of the continuous bite monitoring (CBM) method in determining herbage intake in grazing sheep compared to the standard double‐weighing technique method during 45‐min feeding bouts; evaluate the degree of agreement between the two techniques; and to test the effect of different potential sources of variation on the reliability of the CBM. The CBM method has been used to describe the intake behavior of grazing herbivores. In this study, we evaluated a new approach to this method, that is, whether it is a good proxy for determining the intake of grazing animals. Three experiments with grazing sheep were carried out in which we tested for different sources of variations, such as the number of observers, level of detail of bite coding grid, forage species, forage allowance, sward surface height heterogeneity, experiment site, and animal weight, to determine the short‐term intake rate (45 min). Observer (Pexp1 = 0.018, Pexp2 = 0.078, and Pexp3 = 0.006), sward surface height (Pexp2 < 0.001), total number of bites observed per grazing session (Pexp2 < 0.001 and Pexp3 < 0.001), and sward depletion (Pexp3 < 0.001) were found to affect the absolute error of intake estimation. The results showed a high correlation and agreement between the two methods in the three experiments, although intake was overestimation by CBM on experiments 2 and 3 (181.38 and 214.24 units, respectively). This outcome indicates the potential of CBM to determining forage intake with the benefit of a greater level of detail on foraging patterns and components of the diet. Furthermore, direct observation is not invasive nor disrupts natural animal behavior.

Many ways to make darker flies: Intra- and interspecific variation in Drosophila body pigmentation components.

Body pigmentation is an evolutionarily diversified and ecologically relevant trait with substantial variation within and between species, and important roles in animal survival and reproduction. Insect pigmentation, in particular, provides some of the most compelling examples of adaptive evolution, including its ecological significance and genetic bases. Pigmentation includes multiple aspects of color and color pattern that may vary more or less independently, and can be under different selective pressures. We decompose Drosophila thorax and abdominal pigmentation, a valuable eco‐evo‐devo model, into distinct measurable traits related to color and color pattern. We investigate intra‐ and interspecific variation for those traits and assess its different sources. For each body part, we measured overall darkness, as well as four other pigmentation properties distinguishing between background color and color of the darker pattern elements that decorate each body part. By focusing on two standard D. melanogaster laboratory populations, we show that pigmentation components vary and covary in distinct manners depending on sex, genetic background, and temperature during development. Studying three natural populations of D. melanogaster along a latitudinal cline and five other Drosophila species, we then show that evolution of lighter or darker bodies can be achieved by changing distinct component traits. Our results paint a much more complex picture of body pigmentation variation than previous studies could uncover, including patterns of sexual dimorphism, thermal plasticity, and interspecific diversity. These findings underscore the value of detailed quantitative phenotyping and analysis of different sources of variation for a better understanding of phenotypic variation and diversification, and the ecological pressures and genetic mechanisms underlying them.

Adolescent Attachment: A Social Relations Perspective on Family Relations.

While attachment at an early age is almost exclusively determined by parental behavior, variation in the quality of attachment in adolescence seems strong to be influenced by the complex interplay between the adolescent's internal working model and its environment. To unravel the different sources of variance in adolescent attachment relationships we applied the Social Relations Model. Two parents and two adolescents from 70 relatively well-functioning families reported on their attachment relationships within a family context. In this study, attachment was conceptualized by two different dimensions of attachment, namely anxiety (for being abandoned) and avoidance (of intimacy). Our findings suggest adolescents experience little attachment anxiety throughout adolescence, but they report significantly more attachment avoidance toward their parents. The variance in the quality of adolescents' attachment relationships was mainly determined by characteristics of the adolescent and the unique adjustments they make to the attachment figure.

Importance of sources of variability, scales and experimental design: A case study about the effects of biochar and slurry application on soil properties in agricultural silty loam soils

AbstractIn designed experiments, different sources of variability and an adequate scale of measurement need to be considered, but not all approaches in common usage are equally valid. In order to elucidate the importance of sources of variability and choice of scale, we conducted an experiment where the effects of biochar and slurry applications on soil properties related to soil fertility were studied for different designs: (a) for a field‐scale sampling design with either a model soil (without natural variability) as an internal control or with composited soils, (b) for a design with a focus on amendment variabilities, and (c) for three individual field‐scale designs with true field replication and a combined analysis representative of the population of loess‐derived soils. Three silty loam sites in Germany were sampled and the soil macroaggregates were crushed. For each design, six treatments (0, 0.15 and 0.30 g slurry‐N kg−1 with and without 30 g biochar kg−1) were applied before incubating the units under constant soil moisture conditions for 78 days. CO2 fluxes were monitored and soils were analysed for macroaggregate yields and associated organic carbon (C). Mixed‐effects models were used to describe the effects. For all soil properties, results for the loess sites differed with respect to significant contributions of fixed effects for at least one site, suggesting the need for a general inclusion of different sites. Analysis using a multilevel model allowed generalizations for loess soils to be made and showed that site:slurry:biochar and site:slurry interactions were not negligible for macroaggregate yields. The use of a model soil as an internal control enabled observation of variabilities other than those related to soils or amendments. Experiments incorporating natural variability in soils or amendments resulted in partially different outcomes, indicating the need to include all important sources of variability.Highlights Effects of biochar and slurry applications were studied for different designs and mixed‐effects models were used to describe the effects. Including an internal control allowed observation of, e.g., methodological and analytical variabilities. The results suggested the need for a general inclusion of different sites. Analysis using a multilevel model allowed generalizations for loess soils. The results indicated the need to include all important sources of variability.

Measuring rater bias in diagnostic tests with ordinal ratings.

Diagnostic tests are frequently reliant upon the interpretation of images by skilled raters. In many clinical settings, however, the variability observed between experts' ratings plays a detrimental role in the degree of confidence in these interpretations, leading to uncertainty in the diagnostic process. For example, in breast cancer testing, radiologists interpret mammographic images, while breast biopsy results are examined by pathologists. Each of these procedures involves elements of subjectivity. We propose here a flexible two-stage Bayesian latent variable model to investigate how the skills of individual raters impact the diagnostic accuracy of image-related testing in large-scale medical testing studies. A strength of the proposed model is that the true disease status of a patient within a reasonable time frame may or may not be known. In these studies, many raters each contribute classifications on a large sample of patients using a defined ordinal grading scale, leading to a complex correlation structure between ratings. Our modeling approach considers the different sources of variability contributed by experts and patients while accounting for correlations present between ratings and patients, in contrast to currently available methods. We propose a novel measure of a rater's ability (magnifier) that, in contrast to conventional measures of sensitivity and specificity, is robust to the underlying prevalence of disease in the population, providing an alternative measure of diagnostic accuracy across patient populations. Extensive simulation studies demonstrate lower bias in estimation of parameters and measures of accuracy, and illustrate outperformance of the proposed model when compared with existing models. Receiver operator characteristic curves are derived to assess the diagnostic accuracy of individual experts and their overall performance. Our proposed modeling approach is applied to a large breast imaging study for known disease status and a uterine cancer dataset for unknown disease status.

Numerical approach to study the effect of shape defect in multi-fastened joints during the assembly process

When designing joined structures, deterministic approaches are commonly considered insufficient to ensure a good compromise between mechanical performances and manufacturing tolerances due to the many sources of variability. Reliability-based design approaches are promising, but they require structural models that take into account the different sources of variability and the complex physical phenomena that govern their effects on mechanical behaviour.This paper proposes a method to analyse the fastening process of parts that include geometric form defects. The structural behaviour of the joint is simulated using a finite element model combining connectors and rigid surfaces to represent fasteners. With this approach, the calculation time can be drastically reduced while frictional contact is maintained between fasteners and parts. Shape defects are generated by translating parts mesh nodes. The method is applied to a case study and its efficiency is evaluated by analysing the evolution of axial bolt preloads and transverse bolt forces during the assembly process. Results demonstrate the ability of the method to simulate different clamping sequences and to capture the interaction between shape defects, bolt-hole clearance and target axial preload.

Lifespan Normative Modeling of Internalizing & Psychotic Disorders

Case-control approaches have dominated the fields of clinical neuroimaging and computational psychiatry. These methods, which require disorder-related effects to overlap across individuals, have created an inconsistent set of patient-control group differences. Normative modeling has refocused objectives on individualized predictions that allow stratification of groups and careful separation of different sources of variation.

Investigation of process variation in vertically stacked gate-all-around nanowire transistor and SRAM circuit

An investigation into the intrinsic process variations including random dopant fluctuation (RDF), interface trap fluctuation, work-function variation (WFV) and oxide thickness variation was undertaken in a vertically stacked gate-all-around nanowire transistor. The fluctuation of the electrical characteristics induced by different sources of variation was discussed. The impact of process variations on static random access memory (SRAM) was also studied. On-state current could be affected by RDF in the source and drain region and the standard deviation can reach 17.2% of the median. WFV can cause obvious fluctuations in threshold voltage and consequently in the read static noise margin (RSNM) of SRAM. It has been discovered that the RSNM was deteriorated by 6.8% by WFV, which causes great uncertainty among all variation sources.

Causes of plasticity and consistency of dispersal behaviour in a group-living spider

Within animal populations, behaviour often varies consistently among individuals, yet it remains unclear what generates and maintains this variation in ‘animal personality’. To understand how multiple sources of variation might combine to promote interindividual differences in behaviour, we examined dispersal behaviour in a colonial spider, Cyrtophora citricola. We measured the repeatability of two behaviours in the laboratory that are associated with dispersal: latency to tiptoe (a dispersal behaviour in spiders) and latency to explore a new environment. We then tested five hypothesized sources of consistent variation across two generations of spiders: sex, food resources, transient internal states, heritability and parental effects. Spiders were modestly consistent in both latency to tiptoe and latency to explore. Males behaved similarly to females of the same size; however, males mature at a smaller size than females, and females became much more hesitant to tiptoe and explore as they grew. Food availability had no effect on either behaviour. Repeatability of exploration was higher within an instar than across instars, suggesting that some aspect of spiders' internal state, other than food, maintained relatively high repeatability over short periods. Tiptoe behaviour was significantly heritable for female relatives, but not for male relatives. For exploratory behaviour, siblings showed significant heritability, while parents and offspring did not, suggesting that early natal environments shared by siblings, rather than genes, might account for their similar behaviour. Finally, although food had no effect on the generation of spiders to which the diet was applied, poorly fed fathers had offspring that tiptoed more readily than well-fed fathers, suggesting paternal effects potentially transmitted through epigenetics. Tiptoe and exploratory behaviours were correlated within individuals, yet the overall modest interindividual variation in these two dispersal behaviours was maintained by disparate effects of different sources of variation.

A robust pathological voices recognition system based on DCNN and scattering transform

The Deep Neural Networks (DNNs) have recently shown a high performance applied to speech classification tasks. In this paper, we argue that the improved accuracy generated by the Deep Convolutional Neural Network (DCNN) classifier is the result of their ability to extract discriminative representations. They are efficient to the different sources of variability in speech signals.We propose, in this study, a new algorithm, called ST-DCNN in order to classify normal and pathological voices. We demonstrate the improvement of recognizing voices theory with advances in speech features in order to improve the identification pathological voices. The proposed approach operates in two steps: First, we extract scatter wavelet features. Then, we introduce the DCNN for voices classification.The performance of the proposed system is evaluated based on silent and noisy environments using various Signal-to-Noise Ratio (SNR) levels. The results underscore that our proposed system shows better performance using scattering wavelet and DCNN in a silent environment with 99.62% of recognition rate.

Modelling strawberry quality in a longitudinal study under the marketing concept of branding

BackgroundMarketing strategies, such as branding, redefine how consumers perceive quality and create new requirements related to season length and quality homogeneity, among others. For short-day (SD) strawberry cultivar brands, the commercial season is short due to a dependency on temperature and photoperiod. A plausible strategy to extend the commercialization period is to use different varieties within a single brand; however, this has led to inconsistent quality in other fruit crops. A form of quality assessment to evaluate the impact of a multi-varietal brand on sensory quality is a critical longitudinal study with several sources of variability, such as the inherent variation among assessors and fruit replicates that can affect the reliability of the results. Therefore, this study aimed to develop a methodology to assess the sensorial and physicochemical quality of strawberry brands in two contexts: a short-term season composed of two SD cultivars and a long-term season with one SD and one day-neutral (DN) cultivar. ResultsNew statistical models are proposed in this study. An ANOVA mixed model with assessors and replicates as random terms and a multiple factor analysis highlighted a lack of homogeneity with regard to texture parameters and sourness, while partial least square models identified aroma and sweetness as the best quality indicators. ConclusionsThis work has successfully illustrated a methodology that is capable of handling critical aspects of longitudinal studies by using univariate models that account for different sources of variability and constrained multivariate models to relate parameters with overall liking. A long-term brand is a viable solution to valorise strawberries, as parameter heterogeneity did not affect overall quality.

Potential Therapeutic Effects of Curcumin on Glycemic and Lipid Profile in Uncomplicated Type 2 Diabetes-A Meta-Analysis of Randomized Controlled Trial.

Diabetes mellitus is an important issue for public health, and it is growing in the world. In recent years, there has been a growing research interest on efficacy evidence of the curcumin use in the regulation of glycemia and lipidaemia. The molecular structure of curcumins allows to intercept reactive oxygen species (ROI) that are particularly harmful in chronic inflammation and tumorigenesis models. The aim of our study performed a systematic review and meta-analysis to evaluate the effect of curcumin on glycemic and lipid profile in subjects with uncomplicated type 2 diabetes. The papers included in the meta-analysis were sought in the MEDLINE, EMBASE, Scopus, Clinicaltrials.gov, Web of Science, and Cochrane Library databases as of October 2020. The sizes were pooled across studies in order to obtain an overall effect size. A random effects model was used to account for different sources of variation among studies. Cohen’s d, with 95% confidence interval (CI) was used as a measure of the effect size. Heterogeneity was assessed while using Q statistics. The ANOVA-Q test was used to value the differences among groups. Publication bias was analyzed and represented by a funnel plot. Curcumin treatment does not show a statistically significant reduction between treated and untreated patients. On the other hand, glycosylated hemoglobin, homeostasis model assessment (HOMA), and low-density lipoprotein (LDL) showed a statistically significant reduction in subjects that were treated with curcumin, respectively (p = 0.008, p < 0.001, p = 0.021). When considering HBA1c, the meta-regressions only showed statistical significance for gender (p = 0.034). Our meta-analysis seems to confirm the benefits on glucose metabolism, with results that appear to be more solid than those of lipid metabolism. However, further studies are needed in order to test the efficacy and safety of curcumin in uncomplicated type 2 diabetes.

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge.

We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.

Combining ability and gene action for seed yield and its component traits in green gram (Vigna radiata L.)

An investigation to study of combining ability and gene action was conducted on green gram for seed yield per plant and its component traits. For this study, line x tester design of mating was used taking six genotypes as lines and four genotypes as testers. The variance analysis revealed significant differences for different sources of variation for most of the traits. The higher specific combining ability (SCA) values were compared to general combining ability (GCA) values. The higher degree of dominance indicated preponderance of non additive gene action. The dominance variance was predominant for all the characters except for pods per plant indicating the major role of non-additive gene action and it was highest for harvest index (HI) followed by plant height and days to 50% flowering. The parents, viz., BM 4 and JM 721 among line and AKM 8802 among testers exhibited positive significant effects and higher per se performance for seed yield per plant and other component traits and hence considered as superior parents. The hybrids viz., BM4 x PDM1, JM 10 x AKM 8802, JM 721 x PDM1, ML131 x HUM 1 and TARM 2 x TJM 3 were observed as superior specific combiners for seed yield per plant and its component traits. These hybrids or cross combinations might be utilized for further improvement of seed yield in green gram.