A Bayesian Multiple-Trait and Multiple-Environment Model Using the Matrix Normal Distribution

Genomic selection (GS) is playing a major role in plant breeding for the selection of candidate individuals (animal or plants) early in time. However, for improving GS better statistical models are required. For this reason, in this chapter book we provide an improved version of the Bayesian multiple-trait and multiple-environment (BMTME) model of Montesinos-López et al. that takes into account the correlation between traits (genetic and residual) and between environments since allows general covariance’s matrices. This improved version of the BMTME model was derived using the matrix normal distribution that allows a more easy derivation of all full conditional distributions required, allows a more efficient model in terms of time of implementation. We tested the proposed model using simulated and real data sets. According to our results we have elements to conclude that this model improved considerably in terms of time of implementation and it is better than a Bayesian multiple-trait, multiple-environment model that not take into account general covariance structure for covariance’s of the traits and environments

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson’s correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data

Threshold Models for Genome-Enabled Prediction of Ordinal Categorical Traits in Plant Breeding

Categorical scores for disease susceptibility or resistance often are recorded in plant breeding. The aim of this study was to introduce genomic models for analyzing ordinal characters and to assess the predictive ability of genomic predictions for ordered categorical phenotypes using a threshold model counterpart of the Genomic Best Linear Unbiased Predictor (i.e., TGBLUP). The threshold model was used to relate a hypothetical underlying scale to the outward categorical response. We present an empirical application where a total of nine models, five without interaction and four with genomic x environment interaction (G·E) and genomic additive x additive x environment interaction (GxGxE), were used. We assessed the proposed models using data consisting of 278 maize lines genotyped with 46,347 single-nucleotide polymorphisms and evaluated for disease resistance [with ordinal scores from 1 (no disease) to 5 (complete infection)] in three environments (Colombia, Zimbabwe, and Mexico). Models with GxE captured a sizeable proportion of the total variability, which indicates the importance of introducing interaction to improve prediction accuracy. Relative to models based on main effects only, the models that included GxE achieved 9–14% gains in prediction accuracy; adding additive x additive interactions did not increase prediction accuracy consistently across locations

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

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Genomic Bayesian Prediction Model for Count Data with Genotype x Environment Interaction

Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (nT ) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (nT ). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment G x E interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data

Análisis de validez de constructo y confiabilidad de dos instrumentos para evaluar las actividades de orientación profesiográfica

An analysis of the construct validity and reliability of a questionnaire to evaluate a professions day, as well as a rubric to evaluate the career exhibitions were carried out. The analysis of construct validity was carried out by an exploratory factor analysis and reliability through Cronbach's alpha. The questionnaire showed that only 53% of its items had significant factorial loads (CF>0.65), which were represented in three dimensions (Degree of satisfaction with the characteristics of the career, Degree of satisfaction with the faculty and Recommendation of FIME and the career) of the five we initially proposed. For the rubric, the results corresponded to what we theoretically proposed. The reliability of both instruments was adequate (Cronbach's Alpha: 0.907 and 0.908). We can conclude that the analysis allowed us to refine and improve the instruments; however, the limitation of the present study relates to the sample size, leading to results that cannot be considered as conclusive ones. Implications of the study for career guidance are provided.Se realizó un análisis de la validez de constructo y confiabilidad de un cuestionario para evaluar una jornada profesiográfica y una rúbrica para evaluar la exposición de las carreras. El análisis de validez de constructo se efectuó mediante un análisis factorial exploratorio y la confiabilidad mediante el Alfa de Cronbach. Para el cuestionario, el análisis mostró que solo el 53% de sus ítems presentaron cargas factoriales significativas (CF>0.65), los cuales estuvieron representados en tres dimensiones (Grado de satisfacción con las características de la carrera, Grado de satisfacción con la facultad y Recomendación de la FIME3 y de la carrera) de las cinco que se propusieron de manera inicial. Para la rúbrica, los resultados se correspondieron con lo propuesto teóricamente. La confiabilidad de ambos instrumentos fue adecuada (Alfa de Cronbach de 0.907 y 0.908). Se puede concluir que el análisis efectuado favoreció la depuración de los instrumentos; sin embargo, la limitación del presente estudio fue el tamaño de muestra por lo que los resultados no pueden ser considerados concluyentes. Se aportan implicaciones del estudio para la orientación profesional

The use of deep learning to improve player engagement in a video game through a dynamic difficulty adjustment based on skills classification

The balance between game difficulty and player skill in the evolving landscape of the video game industry is a significant factor in player engagement. This study introduces a deep learning (DL) approach to enhance gameplay by dynamically adjusting game difficulty based on a player’s skill level. Our methodology aims to prevent player disengagement, which can occur if the game difficulty significantly exceeds or falls short of the player’s skill level. Our evaluation indicates that such dynamic adjustment leads to improved gameplay and increased player involvement, with 90% of the players reporting high game enjoyment and immersion levels

An IoT system for remote health monitoring in elderly adults through a wearable device and mobile application

With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.PRODEP | Ref. PRODEP UCOL-EXB-176 (Code: DSA/103.5/15/10874

A novel method for genomic-enabled prediction of cultivars in new environments

IntroductionGenomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs.Objectives of the researchIn this research we proposed a method to improve the prediction accuracy of tested lines in new (untested) environments.Method-1The new method trained the model with a modified response variable (a difference of response variables) that decreases the lack of a non-stationary distribution between the training and testing and improved the prediction accuracy.Comparing new and conventional methodWe compared the prediction accuracy of the conventional genomic best linear unbiased prediction (GBLUP) model (M1) including (or not) genotype × environment interaction (GE) (M1_GE; M1_NO_GE) versus the proposed method (M2) on several data sets.Results and discussionThe gain in prediction accuracy of M2, versus M1_GE, M1_NO_GE in terms of Pearson´s correlation was of at least 4.3%, while in terms of percentage of top-yielding lines captured when was selected the 10% (Best10) and 20% (Best20) of lines was at least of 19.5%, while in terms of Normalized Root Mean Squared Error (NRMSE) was of at least of 42.29%