Genetic associations between carcass traits measured by real-time ultrasound and scrotal circumference and growth traits in Nelore cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modeling of average growth curve in Santa Ines sheep using random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameters of total milk yield and factors describing the shape of lactation curve in dairy buffaloes

The objective of this study was to apply factor analysis to describe lactation curves in dairy buffaloes in order to estimate the phenotypic and genetic association between common latent factors and cumulative milk yield. A total of 31 257 monthly test-day milk yield records from buffaloes belonging to herds located in the state of São Paulo were used to estimate two common latent factors, which were then analysed in a multi-trait animal model for estimating genetic parameters. Estimates of (co)variance components for the two common latent factors and cumulated 270-d milk yield were obtained by Bayesian inference using a multiple trait animal model. Contemporary group, number of milkings per day (two levels) and age of buffalo cow at calving (linear and quadratic) as covariate were included in the model as fixed effects. The additive genetic, permanent environmental and residual effects were included as random effects. The first common latent factor (F1) was associated with persistency of lactation and the second common latent factor (F2) with the level of production in early lactation. Heritability estimates for Fl and F2 were 0.12 and 0.07, respectively. Genetic correlation estimates between El and F2 with cumulative milk yield were positive and moderate (0.63 and 0.52). Multivariate statistics employing factor analysis allowed the extraction of two variables (latent factors) that described the shape of the lactation curve. It is expected that the response to selection to increase lactation persistency is higher than the response obtained from selecting animals to increase lactation peak. Selection for higher total milk yield would result in a favourable correlated response to increase the level of production in early lactation and the lactation persistency.

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Evaluation of development traits of beef buffalo in Brazil using multivariate analysis

The buffalo population in Brazil increased about 12.9% between 1998 and 2003, to 2.8 million head, evidencing the importance of this species for the country. The objective this work was evaluation of animal growth using multivariate analysis. The data were from 2,944 water buffalo from 10 herds raised in pasture conditions in Brazil. Principal components and genetic distances were estimated using proc PRINCOMP and proc CANDISC in SAS (SAS Inst. Inc. Cary, NC, USA). Variables analyzed were birth weight (BW), age at weaning (AW), weaning weight (WT), weight adjusted to 205 d (W205), total gain between BW and WT (TG), daily gain between BW and WT (DG), weight adjusted to 365 d (W365), total gain between WT and W365 (TG3), daily gain between WT and W365 (TGD3), weight adjusted to 550 d (W550) and weight adjusted to 730 d (W730). Means and standard deviations for each variable were 39.4 +/- 3.2 kg, 225.6 +/- 38.8 d, 209.4 +/- 39.4 kg, 195.4 +/- 30.2 kg, 157.4 +/- 32.0 kg, 0.77 +/- 0.16 kg/d, 282.0 +/- 43.5 kg, 73.9 +/- 33.9 kg, 0.53 +/- 0.21 kg/d, 406.8 +/- 67.9 kg, and 468.2 +/- 70.6 kg, respectively. The eigenvalues to four first principal components were 5.29, 2.54, 1.66, 1.01, and justify 48%, 23%, 15% and 9%, respectively, with a total cumulative 95%. We created an index using the first principal component which is Y. 0.0552 BW + 0.0438 AW + 0.3142 WT + 0.3549 W205 + 0.3426 TG + 0.3426 DG + 0.4070 W365- 0.1531 TG3 - 0.2059 TGD3 - 0.3833 W550 - 0.3966 W730. This index accounted for 48% the variation in the correlation matrix. This principal component emphasizes early growth of the animal. Estimates the pair-wise squared distances between herds, D2(i vertical bar j)= ((x) over bar (i)-(x) over bar (j))' cov(-1)((x) over bar (i)-(x) over bar (j)), using with basis the average of weight of animals, showed the largest distance between herds eight (Murrah: DF) and seven (Murrah: Amazon) and the closest distance between herds one (Mediterranean - RS) and five (Jafarabadi - SP).

Association of Haplotypes in the CXCR2 Gene with Periodontitis in a Brazilian Population

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

Historical biogeography and conservation of the golden-striped salamander (Chioglossa lusitanica) in northwestern Iberia: integrating ecological, phenotypic and phylogeographic data

The golden-striped salamander (Chioglossa lusitanica) is an endemic species inhabiting stream-side habitats in mountainous areas in the northwestern Iberian Peninsula. This salamandrid is listed in the IUCN Red Data Book as a threatened species. The combination of bioclimatic modeling of the species distribution and multivariate analysis of genetic and phenotypic data strengthens previous hypotheses concerning the historical biogeography of C. lusitanica: the Pleistocene subdivision of the species' range and a process of postglacial recolonization. Discrepancies between bioclimatic modeling predictions and the present-day distribution suggest that the species may still be expanding its range northwards. We propose the identification of two distinct units for the conservation of the species and suggest that this information should be taken into account in defining key areas for conservation in the Iberian Peninsula.

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic divergence among Psidium accessions based on biochemical and agronomic variables

Sessenta e nove acessos de Psidium, coletados em seis estados brasileiros, foram analisados para dois métodos não hierárquicos de agrupamento e por componentes principais (CP), visando orientar programas de melhoramento. Foram analisadas as variáveis ácido ascórbico, β-caroteno, licopeno, fenóis totais, flavonóides totais, atividade antioxidante, acidez titulável, sólidos solúveis, açúcares solúveis totais, teor de umidade, diâmetro lateral e transversal do fruto, peso da polpa e das sementes/fruto, número e produção de frutos/planta. Foram observados agrupamentos específicos para os acessos de araçazeiros no método de Tocher e do k-means e na dispersão tridimensional dos quatro CPs. Os acessos de araçazeiros foram separados dos de goiabeira. Não foi observado nenhum agrupamento específico por estado de coleta, indicando a inexistência de barreiras na propagação dos acessos de goiabeira. As análises sugerem a prospecção de maior número de amostras de germoplasma num menor número de regiões, bem como acessos divergentes com alto teor de compostos nutricionais.

Effects of genetic polymorphisms CYP1A1, GSTM1, GSTT1 and GSTP1 on urinary 1-hydroxypyrene levels in sugarcane workers

Sugarcane workers in Brazil are exposed to various genotoxic compounds, including polycyclic aromatic hydrocarbons (PAHs), derived from an incomplete combustion process of burnt sugarcane fields. The effects of the occupational exposure to sugarcane fields burning were measured in urine samples of sugarcane workers from the northwest of the State of São Paulo when exposed (harvesting) and when non-exposed (non-harvesting). The urinary levels of 1-hydroxypyrene (1-OHP) and the influence of the genetic polymorphisms CYP1A1, GSTM1, GSTT1 and GSTP1 were evaluated. Our results showed that the 1-OHP levels were significantly higher (P < 0.0000) in the exposed sugarcane workers (0.318 mu mol mol(-1) creatinine) than in the non-exposed workers (0.035 mu mol mol(-1) creatinine). In an unvaried analysis, no influence regarding the polymorphisms was observed. However, multivariate regression analysis showed that the CYP1A1*4 polymorphism in the exposed group, and age and the GSTP1 polymorphism in the non-exposed group significantly influenced urinary 1-OHP excretion levels (P < 0.10). The same group of sugarcane workers was significantly more exposed to PAHs during the harvesting period than during the non-harvesting period. (c) 2006 Elsevier B.V. All rights reserved.

Unbalanced Three-Phase Induction Motors Starting and Arc Welding Machines Short-Circuit Modeling

Three-Phase Induction Motors (TIM) and Arc Welding Machines (AWM) are loads of special behavior widely used in industrial and commercial installations, and therefore may contribute significantly to the deterioration of the quality of energy supplied by utilities. This paper proposes a modeling in constant power of the unbalanced TIM starting using Genetic Algorithm (GA) and AWM short-circuit based on their statics characteristics curves. The proposed models are compared with the conventional models in the literature. The results showed the good performance of the proposed models, allowing a more precise analysis of the real requests of these loads.

Estimates of genetic parameters, and cluster and principal components analyses of breeding values related to egg production traits in a White Leghorn population

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

Bayesian and multivariate methods applied to favorability quantification in recôncavo Basin and Ribeira Belt, Brazil

A methodology to define favorable areas in petroleum and mineral exploration is applied, which consists in weighting the exploratory variables, in order to characterize their importance as exploration guides. The exploration data are spatially integrated in the selected area to establish the association between variables and deposits, and the relationships among distribution, topology, and indicator pattern of all variables. Two methods of statistical analysis were compared. The first one is the Weights of Evidence Modeling, a conditional probability approach (Agterberg, 1989a), and the second one is the Principal Components Analysis (Pan, 1993). In the conditional method, the favorability estimation is based on the probability of deposit and variable joint occurrence, with the weights being defined as natural logarithms of likelihood ratios. In the multivariate analysis, the cells which contain deposits are selected as control cells and the weights are determined by eigendecomposition, being represented by the coefficients of the eigenvector related to the system's largest eigenvalue. The two techniques of weighting and complementary procedures were tested on two case studies: 1. Recôncavo Basin, Northeast Brazil (for Petroleum) and 2. Itaiacoca Formation of Ribeira Belt, Southeast Brazil (for Pb-Zn Mississippi Valley Type deposits). The applied methodology proved to be easy to use and of great assistance to predict the favorability in large areas, particularly in the initial phase of exploration programs. © 1998 International Association for Mathematical Geology.