Bayesian discrete lognormal regression model for genomic prediction.

KEY MESSAGE: Genomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a novel Bayesian discrete lognormal regression model. Genomic selection is a powerful tool in modern breeding programs that uses genomic information to predict the performance of individuals and select those with desirable traits. It has revolutionized animal and plant breeding, as it allows breeders to identify the best candidates without labor-intensive and time-consuming phenotypic evaluations. While several statistical models have been developed, most of them have been for quantitative continuous traits and only a few for count responses. In this paper, we propose a discrete lognormal regression model in the Bayesian context, that with a Gibbs sampler to explore the corresponding posterior distribution and make the predictions. Two datasets of resistance disease is used in the wheat crop and are then evaluated against the traditional Gaussian model and a lognormal model. The results indicate the proposed model is a competitive and natural model for predicting count genomic traits.

A Novel Approach to Obtain Vaccine Effectiveness Continuous Profiles. Example Case: COVID-19 in Elderly Mexicans.

Population-wide vaccination is the most promising long-term COVID-19 disease management strategy. However, the protection offered by the currently available COVID-19 vaccines wanes over time, requiring boosters to be periodically given, which represents an unattainable challenge, especially if it is necessary to apply several doses per year. Therefore, it is essential to design strategies that contribute to maximizing the control of the pandemic with the available vaccines. Achieving this objective requires knowing, as precisely and accurately as possible, the changes in vaccine effectiveness over time in each population group, considering the eventual dependence on age, sex, etc. Thus, the present work proposes a novel approach to calculating realistic effectiveness profiles against symptomatic disease. In addition, this strategy can be adapted to estimate realistic effectiveness profiles against hospitalizations or deaths. All such time-dependent profiles allow the design of improved vaccination schedules, where each dose can be administrated to the population groups so that the fulfillment of the containment objectives is maximized. As a practical example for this analysis, vaccination against COVID-19 in Mexico was considered. However, this methodology can be applied to other countries' data or to characterize future vaccines with time-dependent effectiveness values. Since this strategy uses aggregated observational data collected from massive databases, assumptions about the data validity and the course of the studied epidemic could eventually be necessary.

Geographical network model for COVID-19 spread among dynamic epidemic regions.

Pandemic due to SARS-CoV-2 (COVID-19) has affected to world in several aspects: high number of confirmed cases, high number of deaths, low economic growth, among others. Understanding of spatio-temporal dynamics of the virus is helpful and necessary for decision making, for instance to decide where, whether and how, non-pharmaceutical intervention policies are to be applied. This point has not been properly addressed in literature since typical strategies do not consider marked differences on the epidemic spread across country or large territory. Those strategies assume similarities and apply similar interventions instead. This work is focused on posing a methodology where spatio-temporal epidemic dynamics is captured by means of dividing a territory in time-varying epidemic regions, according to geographical closeness and infection level. In addition, a novel Lagrangian-SEIR-based model is posed for describing the dynamic within and between those regions. The capabilities of this methodology for identifying local outbreaks and reproducing the epidemic curve are discussed for the case of COVID-19 epidemic in Jalisco state (Mexico). The contagions from July 31, 2020 to March 31, 2021 are analyzed, with monthly adjustments, and the estimates obtained at the level of the epidemic regions present satisfactory results since Relative Root Mean Squared Error RRMSE is below 15% in most of regions, and at the level of the whole state outstanding with RRMSE below 5%.

Maximum a posteriori Threshold Genomic Prediction Model for Ordinal Traits.

Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data. For this reason, here we propose a Maximum a posteriori Threshold Genomic Prediction (MAPT) model for ordinal traits that is more efficient than the conventional Bayesian Threshold Genomic Prediction model for ordinal traits. The MAPT performs the predictions of the Threshold Genomic Prediction model by using the maximum a posteriori estimation of the parameters, that is, the values of the parameters that maximize the joint posterior density. We compared the prediction performance of the proposed MAPT to the conventional Bayesian Threshold Genomic Prediction model, the multinomial Ridge regression and support vector machine on 8 real data sets. We found that the proposed MAPT was competitive with regard to the multinomial and support vector machine models in terms of prediction performance, and slightly better than the conventional Bayesian Threshold Genomic Prediction model. With regard to the implementation time, we found that in general the MAPT and the support vector machine were the best, while the slowest was the multinomial Ridge regression model. However, it is important to point out that the successful implementation of the proposed MAPT model depends on the informative priors used to avoid underestimation of variance components.

Heats of combustion of the main carbohydrates contained in plant-source foods.

In a previous review, the experiments of American chemist W.O. Atwater were critically examined, with the findings demonstrating certain weaknesses that could compromise the validity of the values currently used for metabolizable energy. An examination of published works on the heat of combustion of carbohydrates reveals 2 types of weaknesses: the inaccuracy and imprecision of the calorimetric data used, and the averaging procedure employed to estimate such representative values. The present review focuses on the first type of weakness, namely the inaccuracy and imprecision of the calorimetric data used in previous studies. An exhaustive bibliographic search yielded almost 100 heat of combustion values for some of the 6 main carbohydrates contained in plant-source foods (glucose, fructose, sucrose, maltose, starch, and cellulose). These heats of combustion were subjected to rigorous statistical analysis to propose the following for each carbohydrate: (1) an interval (termed a bibliographic interval) that very likely includes the actual heat of combustion value and (2) a "representative value" (calculated to produce the minimum level of inaccuracy). In addition, an estimation of the maximum level of inaccuracy that could be expected when using such a representative value is reported.

Development and validation of a high performance liquid chromatography-diode array detection method for the determination of aging markers in tequila.

A solid-phase extraction procedure followed by analysis by high performance liquid chromatography (HPLC) with UV-vis photodiode array detection (DAD) is proposed to simultaneously determine 11 aging markers in tequila. The method showed good intraday (n=5) and interday (n=3) precision, RSD<1.6% in both cases, for each of the identified compounds. The calibration curves were linear at the tested ranges (R(2)>0.999). Good recoveries (84.2-108.5%) were obtained for 10 of the 11 compounds studied; and the LOD and LOQ ranged from 0.62 to 4.09 microg/mL and 1.9-12.4 microg/mL, respectively. The proposed methodology was applied to a set of 15 authentic tequila samples grouped by aging state (blanco, reposado and añejo). An ANOVA analysis combined with discriminant analysis with stepwise backward variable selection was used to differentiate between the various aging groups based on their oak related compounds content.

Multiple response optimization analysis for pretreatments of Tequila's stillages for VFAs and hydrogen production.

The objective of this work was study the effect of three pretreatments (alkalinization, thermical treatment, and sonication) on Tequila's stillages hydrolysis process in acidogenesis stage, through the following response variables: soluble chemical oxygen demand (CODs), total sugar and volatile fatty acids profile and the hydrogen production at the time. The stillages were subject to these pretreatments (according to a 2(3) factorial design); afterward they were transferred to a batch reactor at 35 degrees C and inoculated with an anaerobic digestor sludge. Multiple response optimization (MRO) analysis was done to find the global optimum for the response variables described above. This optimum is able to maximize simultaneously all these variables. It was found adequate to be useful hydrolyzing the organic matter present in Tequila's stillages. Mathematical models were fitted to observe the estimated effects of pretreatments on each response variable, then the MRO was applied.

Analysis of laser-induced fluorescence spectra of in vitro plant tissue cultures.

We demonstrate the effectiveness of laser-induced fluorescence (LIF) for monitoring the development and stress detection of in vitro tissue cultures in a nondestructive and noninvasive way. The changes in LIF spectra caused by the induction of organogenesis, the increase of the F690/F740 ratio as a result of the stress originated in the organogenic explants due to shoot emergence, and the relationship between fluorescence spectra and shoot development were detected by LIF through closed containers of Saintpaulia ionantha.