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132 resultados, página 3 de 10

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

A pre-pandemic study about recreational uses in the Mexico Park located at Mexico City (year 2017)

Ramiro Flores-Xolocotzi Sergio Ceballos (2022, [Artículo, Artículo])

A recreational study was carried out through surveys in the Parque México in Mexico City. For this, the relationship between visit patterns with socioeconomic information, uses and perceptions of visitors with 18 years old or older was analyzed. This research uses descriptive statistics and a non-linear canonical correlation analysis to analyze relationships between variables. An ordered probit regression was also performed to determine the variables that explain the frequency of recreational use. It was obtained that the Park mainly receives visitors with at least bachelor's degree (78.6%) and high incomes (more than 50% have a monthly family income higher than $10,000.00 pesos and 27.6% receive more than $30,000.00 per month). Considering the results, the conclusions are that although the highest percentage of the studied population comes from neighborhoods outside the Roma-Condesa Corridor: then the visitors who live in the Corridor and who have higher incomes, have weight in the description of the model. The results allow to conclude too, that higher income increases the frequency of use. It is also observed that the park is used during the Monday to Friday by more than 50% of the population of visitors and with a high percentage of use in the mornings.

urban forestry urban planning leisure green areas correlación canónica no lineal parque urbano probit recreación CIENCIAS SOCIALES CIENCIAS SOCIALES