Efeito do desbaste seletivo sobre a divergência genética em progênies de Pinus caribaea Morelet var. bahamensis

The objective of this work was to study the effect of selective thinning on! the genetic divergence in progenies of Pinus caribaea var. bahamensis, aiming to identify the most productive and divergent progenies for the use of improvement program. The test of progenies containing 119 progenies and two commercial controls were planted in March 1990, using 11 x 11 square lattice design, sextuple, partially balanced, disposed in lineal plots with six trees in the spacing of 3,0 x 3,0m. 13 years after planting thinning was realized (selection for DBH), with 50% selection intensity based on Multi-effect index, leaving three trees per plot in all the experiment. The evaluations were done at four situations: A (before the thinning); B (thinned trees); C (remaining trees after thinning) and D (one year after thinning). The analyzed traits were: height, diameter at breast height (DBH), volume, form of stem and wood density. The genetic divergence among the progenies was studied with aid of the canonical variables and of clustering of Tocher method using the generalized distance matrix of Mahalanobis (D(2)) as estimate of the genetic similarity. The progenies were grouped in four groups in situation A, fourteen in the situation B, two in the situation C and three in the situation D. The selective thinning of the trees within of the progenies caused a change in the genetic divergence among the progenies, genetically homogenizing the progenies, as demonstrated by the generalized distances of Mahalanobis, clustering of Tocher' and canonical variables methods; The. thinning made possible a high uniformity in respect to the relative contribution, of the traits for the total genetic divergence. The techniques, of clustering were efficient to identify groups of divergent,progenies for the use hybridization and little divergent progenies for the use in backcross program.

Structure and genetic relationships between Brazilian naturalized and exotic purebred goat domestic goat (Capra hircus) breeds based on microsatellites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic variation within and among species of genus Arachis, section Rhizomatosae

The genus Arachis is endemic to South America and comprises 80 species, 69 of which have already been described and eleven not yet published. The genus includes the cultivated peanut ( A. hypogaea) and several forage species, the most important ones being A. glabrata and A. pintoi. Accessions of section Rhizomatosae, including three tetraploid species 2n = 4x = 40 (A. glabrata, A. pseudovillosa and A. nitida nom. nud.) and one diploid species 2n = 2x = 20 (A. burkartii), were evaluated using RAPD markers to assay genetic variability within and among species. The ten random primers used yielded a total of 113 polymorphic bands. The data were scored as the presence or absence of each band in each sample. A distance matrix and dendrogram were obtained using Link's coefficient and the neighbor-joining method. Most accessions analyzed grouped into two major clusters: the first comprised most accessions of A. glabrata and accessions of A. nitida, and the second cluster comprised accessions of A. burkartii. Arachis pseudovillosa and a few accessions of A. glabrata and A. nitida were placed between these major clusters. The diploid and tetraploid species were grouped quite separately, suggesting that the tetraploids did not originate from the diploid species analyzed.

Efeito do desbaste seletivo sobre a divergência genética em progênies de Pinus caribaea Morelet var. bahamensis

The objective of this work was to study the effect of selective thinning on the genetic divergence in progenies of Pinus caribaea var. bahamensis, aiming to identify the most productive and divergent progenies for the use of improvement program. The test of progenies containing 119 progenies and two commercial controls were planted in March 1990, using 11 × 11 square lattice design, sextuple, partially balanced, disposed in lineal plots with six trees in the spacing of 3,0 × 3,0m. 13 years after planting thinning was realized (selection for DBH), with 50% selection intensity based on Multi-effect index, leaving three trees per plot in all the experiment. The evaluations were done at four situations: A (before the thinning); B (thinned trees); C (remaining trees after thinning) and D (one year after thinning). The analyzed traits were: height, diameter at breast height (DBH), volume, form of stem and wood density. The genetic divergence among the progenies was studied with aid of the canonical variables and of clustering of Tocher method, using the generalized distance matrix of Mahalanobis (D2) as estimate of the genetic similarity. The progenies were grouped in four groups in situation A, fourteen in the situation B, two in the situation C and three in the situation D. The selective thinning of the trees within of the progenies caused a change in the genetic divergence among the progenies, genetically homogenizing the progenies, as demonstrated by the generalized distances of Mahalanobis, clustering of Tocher' and canonical variables methods. The thinning made possible a high uniformity in respect to the relative contribution of the traits for the total genetic divergence. The techniques of clustering were efficient to identify groups of divergent progenies for the use hybridization and little divergent progenies for the use in backcross program.

O papel dos fatores ambientais e espaciais sobre a fauna de Ephemeroptera (insecta) em riachos de mata atlântica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise molecular do subcomplexo Brasiliensis (Hemiptera, Triatominae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Compositional nutrient diagnosis of corn using the Mahalanobis distance as nutrient imbalance index

Parent, L. E., Natale, W. and Ziadi, N. 2009. Compositional nutrient diagnosis of corn using the Mahalanobis distance as nutrient imbalance index. Can. J. Soil Sci. 89: 383-390. Compositional nutrient diagnosis (CND) provides a plant nutrient imbalance index (CND - r(2)) with assumed chi(2) distribution. The Mahalanobis distance D(2), which detects outliers in compositional data sets, also has a chi(2) distribution. The objective of this paper was to compare D(2) and CND - r(2) nutrient imbalance indexes in corn (Zea mays L.). We measured grain yield as well as N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn concentrations in the ear leaf at silk stage for 210 calibration sites in the St. Lawrence Lowlands [2300-2700 corn thermal units (CTU)] as well as 30 phosphorus (2300-2700 CTU; 10 sites) and 10 nitrogen (1900-2100 CTU; one site) replicated fertilizer treatments for validation. We derived CND norms as mean, standard deviation, and the inverse covariance matrix of centred log ratios (clr) for high yielding specimens (>= 9.0 Mg grain ha(-1) at 150 g H(2)O kg(-1) moisture content) in the 2300-2700 CTU zone. Using chi(2) = 17 (P < 0.05) with nine degrees of freedom (i.e., nine nutrients) as a rejection criterion for outliers and a yield threshold of 8.6 Mg ha(-1) after Cate-Nelson partitioning between low- and high-yielders in the P validation data set, D(2) misclassified two specimens compared with nine for CND -r(2). The D(2) classification was not significantly different from a chi(2) classification (P > 0.05), but the CND - r(2) classification differed significantly from chi(2) or D(2) (P < 0.001). A threshold value for nutrient imbalance could thus be derived probabilistically for conducting D(2) diagnosis, while the CND - r(2) nutrient imbalance threshold must be calibrated using fertilizer trials. In the proposed CND - D(2) procedure, D(2) is first computed to classify the specimen as possible outlier. Thereafter, nutrient indices are ranked in their order of limitation. The D(2) norms appeared less effective in the 1900-2100 CTU zone.

Electron migration in DNA matrix: an electron transfer reaction

Este artigo apresenta uma área de pesquisa atual, ativa e interessante. Descreve a investigação da química de transferência de elétrons (TE) de um modo geral e resultados de TE em DNA em particular. Dois intercalantes de DNA foram utilizados: Ethidium Bromide como doador (D) e Methyl-viologen como receptor (A), o primeiro intercala-se entre as bases do DNA e o último na sua superfície. Utilizando o modelo de Perrin e medidas de Supressão de Fluorescência obteve-se a distância de migração do elétron; aqui a distância foi considerada o espaçamento linear entre as moléculas de doador e receptor ao longo da molécula de DNA. O valor determinado foi de 22,6 ± 1,1 angstrons e o número de pares de bases entre doador e receptor de 6,6. Na literatura os valores encontrados foram de 26 angstrons e de quase 8 pares de bases. Considera-se que a transferência de elétrons em DNA seja mediada através das interações através do espaço entre os elétrons do tipo p contido nos pares de bases.

THE USE OF PHOTOACOUSTIC - SPECTROSCOPY TO DETERMINE THE CRITICAL DISTANCE FOR ELECTRON-TRANSFER

Photoacoustic spectroscopy was used to determine the critical distance for electron transfer from porphyrin to quinone molecules randomly dispersed in a polymeric matrix. An enhancement of the porphyrin signal was observed as the quinone concentration was increased. The data was analyzed according to the Perrin model [1] and it was found that the electron transfer occurred if the prophyrin-quinone distance was less than 33 Angstrom. To confirm the validity of the method, the fluorescence quenching was also measured for the samples. In this case, the same critical distance was obtained.

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.