Efficient arabinoxylan assay for wheat: Exploring variability and molecular marker associations in Wholemeal and refined flour.

In this study, we present a modified high throughput phloroglucinol colorimetric assay for the quantification of arabinoxylans (AX) in wheat named PentoQuant. The method was downscaled from a 10 ml glass tube to 2 ml microcentrifuge tube format, resulting in a fivefold increase in throughput while concurrently reducing the overall cost and manual labor required for the analysis. Comparison with established colorimetric assays and gas chromatography validates the modified protocol, demonstrating its superior repeatability, rapidity, and simplicity. The effectiveness of the protocol was tested on 606 unique whole meal (WM) and refined flour (RF) bread wheat samples which revealed the presence of more than a twofold variation in both the soluble (WE-AX) and total (TOT-AX) AX fractions in WM (TOT-AX = 31.9-76.1 mg/g; WE-AX = 4.4-12.6 mg/g) and RF (TOT-AX = 7.7-22.4 mg/g; WE-AX = 3.9-11.4 mg/g). Results obtained from the AX quantification were used to test the effectiveness of four molecular markers associated with AX variation and targeting two major genomic regions on the 1BL and 6BS chromosomes. These markers appeared to be particularly relevant for the WE-AX fraction, providing insights to enable marker-assisted breeding.

Bayesian multitrait kernel methods improve multienvironment genome-based prediction.

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2-17.45% (datasets 1-3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials.

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson's correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson's correlation calculated by fitting a bivariate model was higher than the division of the Pearson's correlation by the squared root of the heritability across traits, by 2.53-11.46%. Across the datasets, the corrected Pearson's correlation was higher than the uncorrected by 5.80-14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

Cerclaje transabdominal / transabdominal Cerclaje

Se estudió una paciente de 39 años con historia de abortos recurrentes en el primer trimestre de gestación a las 12,6, 8 y 20 semanas resueltos con legrado; en el quinto embarazo se produce parto céfalo vaginal previo cerclaje tipo shirodka realizado a las 14 semanas. Sexto embarazo cerclaje tipo Mackdonald a las 13 semanas, presenta ruptura prematura de membranas a las 22 semanas y termina en parto inmaduro. Séptimo y último embarazo previo cerclaje abdominal a las 16 semanas, termina con cesárea a las 38 semanas de gestación. La morbimortalidad materna y la mortalidad fetal alta hacen de la incompetencia ítsmico cervical una alteración patológica de gran importancia clínica que merece nuestro estudio.