An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Using fraction images derived from modis data for coffee crop mapping

Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for the supervised automatic classification using the SVM - Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively.

Oculo-facio-cardio-dental syndrome in three succeeding generations: genotypic data and phenotypic features

Oculo-facio-cardio-dental (OFCD) syndrome is a rare X-linked disorder mainly manifesting in females. Patients show ocular, facial, cardiac, and dental abnormalities. OFCD syndrome is caused by heterozygous mutations in the BCOR gene, located in Xp11.4, encoding the BCL6 co-repressor. We report a Croatian family with four female members (grandmother, mother and monozygotic female twins) diagnosed with OFCD syndrome who carry the novel BCOR mutation c.4438C>T (p.R1480*). They present high intrafamilial phenotypic variability with special regard to cardiac defect and cataract that showed more severe disease expression in successive generations. Clinical and radiographic examination of the mother of the twins revealed a talon cusp involving the permanent maxillary right central incisor. This is the first known report of a talon cusp in OFCD syndrome with a novel mutation in the BCOR gene.