Visión panorámica de las precipitaciones pluviales combinadas por los efectos de los ciclones tropicales Ingrid-Manuel

MOISES MICHEL ROSENGAUS MOSHINSKY Felipe Arreguin (2016, [Artículo])

Se presenta un análisis panorámico de las precipitaciones pluviales que generaron los ciclones tropicales Ingrid y Manuel durante los días en que actuaron de forma simultánea sobre México, con énfasis en las características que no corresponden a la simple superposición de los campos de lluvias que cada uno de ellos hubiese generado de manera individual. Se muestra que la circulación combinada de la humedad alrededor de ambos vórtices explica conceptualmente las extensiones anómalas de la zona lluviosa que se manifestaron hacia el noreste de México, en el caso de Ingrid, y hacia el sureste de México, en el caso de Manuel. Se muestra que el evento combinado resultó mucho mayor que el referente previo en la zona del Pacífico (huracán Pauline, en 1997). Para tres cuencas específicas cercanas a Acapulco, Guerrero, se calcularon los periodos de retorno de las lluvias ocurridas durante este evento, identificando que, aunque las acumulaciones diarias resultaron extremas, las secuencias ocurridas de 2 a 5 días consecutivos corresponden a un periodo de retorno mucho mayor.

Huracanes Hidrometeorología Acapulco CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA

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

Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])

Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES