DIAGNOSTIC TECHNIQUES OF LOCAL INFLUENCE IN SPATIAL ANALYSIS OF SOYBEAN YIELD

Modeling of spatial dependence structure, concerning geoestatistics approach, is an indispensable tool for fixing parameters that define this structure, applied on interpolation of values in places that are not sampled, by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations on sampled data. Thus, this trial aimed at using diagnostics techniques of local influence in spatial linear Gaussians models, applied at geoestatistics in order to evaluate sensitivity of maximum likelihood estimators and restrict maximum likelihood to small perturbations in these data. So, studies with simulated and experimental data were performed. Those results, obtained from the study of real data, allowed us to conclude that the presence of atypical values among the sampled data can have a strong influence on thematic maps, changing, therefore, the spatial dependence. The application of diagnostics techniques of local influence should be part of any geoestatistic analysis, ensuring that the information from thematic maps has better quality and can be used with greater security by farmers.

Identification of Hammondia heydorni oocysts by a heminested-PCR (hnPCR-AP10) based on the H-heydorni RAPD fragment AP10

Toxoplasma gondii, Hammondia hammondi, Neospora caninum, Neospora hughesi and Hammondia heydorni are members of the Toxoplasmatinae sub-family. They are closely related coccidians with similarly sized oocysts. Molecular diagnostic techniques, especially those based on polymerase chain reaction (PCR), can be successfully applied for the differentiation of Hammondia-like oocysts. In this paper, we describe a rapid and simple method for the identification of H. heydorni oocysts among other members of the Toxoplasmatinae sub-family, using a heminested-PCR (hnPCR-AP10) based on a H. heydorni RAPD fragment available in molecular database. DNA of oocysts of H. heydorni yielded a specific fragment of 289-290 bp in the heminested-PCR assay. No product was yielded when the primers were used for the amplification of DNA extracted from T. gondii, N. caninum, N. hughesi and H. hammondi, thus allowing the differentiation of H. heydorni among other members of the Toxoplasmatinae sub-family. The hnPCR-AP10 was capable of detecting H. heydorni genetic sequences from suspensions with at least 10 oocysts. In conclusion, the hnPCR-AP10 proved to be a reliable method to be used in the identification of H. heydorni oocysts from feces of dogs. (C) 2010 Elsevier B.V. All rights reserved.

Local influence in estimating equations

Local influence diagnostics based on estimating equations as the role of a gradient vector derived from any fit function are developed for repeated measures regression analysis. Our proposal generalizes tools used in other studies (Cook, 1986: Cadigan and Farrell, 2002), considering herein local influence diagnostics for a statistical model where estimation involves an estimating equation in which all observations are not necessarily independent of each other. Moreover, the measures of local influence are illustrated with some simulated data sets to assess influential observations. Applications using real data are presented. (C) 2010 Elsevier B.V. All rights reserved.