Departamento de Estadística

""La finalidad de los diseños óptimos es determinar las condiciones experimentales adecuadas de tal forma que se pueda garantizar inferencias estadísticas lo más precisas posibles en términos de mínima varianza. Esta teoría presupone el conocimiento de la función que relaciona las variables explicativas con la variable respuesta, en esta situación, por lo general, se obtienen diseños con tantos puntos de soporte como parámetros tiene el modelo propuesto. Dado que los diseños con $p-$puntos de soporte asumen que la función del modelo es conocida, éstos pueden llegar a ser no tan óptimos en algunas situaciones prácticas, debido a que no permiten probar la bondad de ajuste del modelo asumido [1]. En este artículo se presenta una generalización de la metodología propuesta en [2] para aumentar el número de puntos en el criterio D-optimalidad. Se encuentra una expresión para la varianza de la respuesta predicha en términos de una constante de ponderación delta, la cual permitirá determinar los puntos a adicionar al diseño D-óptimo. Se propone una estrategia para la elección de la constante delta. Finalmente se proporciona un diseño seudo-óptimo con p + s puntos de experimentación.

MSC: 62K05 ""

Background: Molecular mechanisms of plant-pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results: Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion: Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.

Direct seeding of guayule (Parthenium argentatum Gray) in west Texas has been successful. However, studies have involved only USDA and 'Mexican Bulk' lines. New selections developed by plant breeders must be screened for their adaptability to direct seeding. The objective of this study was to evaluate the standard establishment and production parameters (biomass, rubber and resin content, rubber and resin yield) of six direct seeded guayule selections (AZ-R1, AZ-R2, CAL-6, CAL-7, UC-101, and UC-104). An experiment was initiated on a Delnorte gravelly loam on 23 July 1993 at the Texas Agricultural Experiment Station near Fort Stockton, TX. Conditioned and raw seed of the six guayule selections were planted 10 mm deep on raised beds spaced 1 m apart. Laboratory seed germination ranged from 34-92%. Seedling density was greatest with conditioned seed (61 seedlings/m) versus raw seed (37 seedlings/m) when averaged across all lines 30 days after planting. Individually, seedling density in plots established by conditioned seed was greatest with CAL-6 (68 seedlings/m), UC-104 (66 seedlings/m), AZ-R1 (65 seedlings/m), UC-101 (64 seedlings/m), and CAL-7 (61 seedlings/m), and were significantly greater than the seedling density in plots established with raw seed. Both conditioned and raw seed produced adequate guayule stands. Nonetheless, conditioning improved germination, and in the field, conditioned seed was observed to germinate and emerge quicker resulting in more efficient irrigation management. The AZ-R1 seedlings were the most vigorous and were tolerant to flea beetles (Epitrix sp.). Plants were thinned to a 0.36 m spacing in April 1994. Six plants from each selection in the conditioned seed treatments were harvested on 23 February 1995 and 6 March 1996. Each plant was divided into branch (clipped 10 cm above the soil surface) and root sections. Biomass and rubber and resin yields were greatest in the branch versus root fractions. Total rubber yield (branches plus roots) at both harvests was greatest in selections AZ-R2 and UC-104.