Genetic Diversity and Structure of Local Pear Cultivars from Mountainous Areas from Aragon (Northeastern Spain)

The genetic diversity of pear local varieties prospected in mountainous areas from Northeastern Spain (Pyrenees and Iberian Cordillera) is not well known so far. In this study, an overall set of 252 accessions (178 prospected in mountainous areas from Aragon and a diverse set of 74 reference cultivars) was analyzed using 14 SSRs in order to estimate its genetic diversity and to identify the genetic structure and relationships among the pear germplasm studied. A total of 251 distinct alleles were successfully amplified with an average of 17.9 alleles per locus and with a wide genetic diversity (mean expected heterozygosity of 0.82). In total, 228 unique genotypes were identified and 210 genotypes were represented by a single accession indicating a situation of extreme vulnerability of these pear genetic resources held in the CITA collection. An amount of 32.9% of accessions were considered triploids displaying three alleles at least into two loci. Genetic analyses performed by a model-based Bayesian procedure, principal coordinate analysis and analysis of molecular variance supported the presence of a genetic stratification with the existence of four sub-groups among the accessions, with a highly significant differentiation (FST = 0.132; p < 0.001). These results shed light on the characterization and genetic relatedness between these local accessions and currently cultivated pear cultivars and highlight the importance to safeguarding this diversity that might be essential for new breeding programs.

A Bayesian test for seasonality in medical data

In medical research, the results from seasonality analyses provide valuable information that eventually can help to clarify the etiology of poorly understood diseases. We present a Bayesian procedure for the analysis of seasonal variation in medical data. The method is a Bayesian version of a frequentist test that performs very well. Statistical seasonality analyses of medical data often involve a short time series, 12 observations with small amplitude and small sample size. Among the specialized procedures already developed for such analyses, only one is Bayesian; the method we present in this paper appears to be the second such Bayesian procedure. Easy to understand and apply, the method is versatile because it can be used to analyze different types of seasonal variation. We illustrate the procedure’s application with two examples of real data.

Bayesian Inference on Regression Model with an Unknown Change Point

In this work, we describe a Bayesian procedure for detection of change-point when we have an unknown change point in regression model. Bayesian approach with posterior inference for change points was provided to know the particular change point that is optimal while Gibbs sampler was used to estimate the parameters of the change point model. The simulation experiments show that all the posterior means are quite close to their true parameter values. The performance of this method is recommended for multiple change points.

Determination of Groups of Risks at the Diseases COVID-19

Introduction. In the group of risk at people with COVID-19 there are persons with the such chronic diseases: heart-vessel system; respiratory system; endocrine system; oncologic diseases; immune-deficit states; patients with kidney insufficiency. For every disease there is the concrete set of genes the mutations of which multiply the risk of development of illness. Determination of DNA of sick and healthy people resulted in determination of the genes, related to the diseases which arise up at COVID-19. At persons having by had COVID-19 with the certain disease, with the high stake of probability took place points mutations in certain genes. These people can be brought in a teaching sampling «sick», in a class «healthy» persons are brought in with the negative result of PCR. Purpose of the article. On the basis of teaching selections to develop the effective methods of determination of groups of risks of diseases which COVID-19 accompanies. Results. We consider that genes in a left table column are signs for Bayesian procedure. Work of procedure is executed on the basis of count of amount of mutations or their absence in the teaching selections of classes «sick» and «healthy». We correlate the explored person in that class «sick» and «healthy», for which result of procedure higher. Conclusions. Determination of DNA of sick and healthy people resulted in determination of the genes related to the concrete diseases, including with the diseases which arise up at COVID-19. It is shown that the presence of points mutations in the genes of DNA of man results in the certain disease. On the basis of Bayesian procedure of recognition it is possible effectively to determine the groups of risks of diseases which COVID-19 accompanies. Keywords: determination of DNA, the points mutations, Bayesian procedure of recognition.

Towards formal models of psychopathological traits that explain symptom trajectories

Background A dominant methodology in contemporary clinical neuroscience is the use of dimensional self-report questionnaires to measure features such as psychological traits (e.g., trait anxiety) and states (e.g., depressed mood). These dimensions are then mapped to biological measures and computational parameters. Researchers pursuing this approach tend to equate a symptom inventory score (plus noise) with some latent psychological trait. Main text We argue this approach implies weak, tacit, models of traits that provide fixed predictions of individual symptoms, and thus cannot account for symptom trajectories within individuals. This problem persists because (1) researchers are not familiarized with formal models that relate internal traits to within-subject symptom variation and (2) rely on an assumption that trait self-report inventories accurately indicate latent traits. To address these concerns, we offer a computational model of trait depression that demonstrates how parameters instantiating a given trait remain stable while manifest symptom expression varies predictably. We simulate patterns of mood variation from both the computational model and the standard self-report model and describe how to quantify the relative validity of each model using a Bayesian procedure. Conclusions Ultimately, we would urge a tempering of a reliance on self-report inventories and recommend a shift towards developing mechanistic trait models that can explain within-subject symptom dynamics.

Bayesian Perspective in the Selection of Bean Genotypes

Changes in the relative performance of genotypes have made it necessary for more in-depth investigations to be carried out through reliable analyses of adaptability and stability. The present study was conducted to compare the efficiency of different informative priors in the Bayesian method of Eberhart &amp; Russel with frequentist methods. Fifteen black-bean genotypes from the municipalities of Bel&eacute;m do S&atilde;o Francisco and Petrolina (PE, Brazil) were evaluated in 2011 and 2012 in a randomized-block design with three replicates. Eberhart &amp; Russel&rsquo;s methodology was applied using the GENES software and the Bayesian procedure using the R software through the MCMCregress function of the MCMCpack package. The quality of Bayesian analysis differed according to the a priori information entered in the model. The Bayesian approach using frequentist analysis had greater accuracy in the estimate of adaptability and stability, where model 1 which uses the a priori information, was the most suitable to obtain reliable estimates according to the BayesFactor function. The inference, using information from previous studies, showed to be imprecise and equivalent to the linear-model methodology. In addition, it was realized that the input of a priori information is important because it increases the quality of the adjustment of the model.

A Bayesian Approach to Item Development: Working with Alternative Item Types

Describes a Bayesian procedure for weighting and aggregating individual predictors based on drawings and associations. Discusses forensic psychological applications of the procedure, especially in situations where impression management is a factor. Highlights conceptual differences between Bayesian methods and IRT and CTT systems.

A Bayesian Approach to Item Development: Working with Alternative Item Types

Describes a Bayesian procedure for weighting and aggregating individual predictors based on drawings and associations. Discusses forensic psychological applications of the procedure, especially in situations where impression management is a factor. Highlights conceptual differences between Bayesian methods and IRT and CTT systems.