Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Determinants of vaccination after the Colombian health system reform

OBJECTIVE: To assess the effects of individual, household and healthcare system factors on poor children's use of vaccination after the reform of the Colombian health system. METHODS: A household survey was carried out in a random sample of insured poor population in Bogota, in 1999. The conceptual and analytical framework was based on the Andersen's Behavioral Model of Health Services Utilization. It considers two units of analysis for studying vaccination use and its determinants: the insured poor population, including the children and their families characteristics; and the health care system. Statistical analysis were carried out by chi-square test with 95% confidence intervals, multivariate regression models and Cronbach's alpha coefficient. RESULTS: The logistic regression analysis showed that vaccination use was related not only to population characteristics such as family size (OR=4.3), living area (OR=1.7), child's age (OR=0.7) and head-of-household's years of schooling (OR=0.5), but also strongly related to health care system features, such as having a regular health provider (OR=6.0) and information on providers' schedules and requirements for obtaining care services (OR=2.1). CONCLUSIONS: The low vaccination use and the relevant relationships to health care delivery systems characteristics show that there are barriers in the healthcare system, which should be assessed and eliminated. Non-availability of regular healthcare and deficient information to the population are factors that can limit service utilization.

Optimization of random amplified polymorphic DNA techniques for use in genetic studies of Cuban triatominae

Random amplified polymorphic DNA (RAPD) technique is a simple and reliable method to detect DNA polymorphism. Several factors can affect the amplification profiles, thereby causing false bands and non-reproducibility of assay. In this study, we analyzed the effect of changing the concentration of primer, magnesium chloride, template DNA and Taq DNA polymerase with the objective of determining their optimum concentration for the standardization of RAPD technique for genetic studies of Cuban Triatominae. Reproducible amplification patterns were obtained using 5 pmoL of primer, 2.5 mM of MgCl2, 25 ng of template DNA and 2 U of Taq DNA polymerase in 25 µL of the reaction. A panel of five random primers was used to evaluate the genetic variability of T. flavida. Three of these (OPA-1, OPA-2 and OPA-4) generated reproducible and distinguishable fingerprinting patterns of Triatominae. Numerical analysis of 52 RAPD amplified bands generated for all five primers was carried out with unweighted pair group method analysis (UPGMA). Jaccard's Similarity Coefficient data were used to construct a dendrogram. Two groups could be distinguished by RAPD data and these groups coincided with geographic origin, i.e. the populations captured in areas from east and west of Guanahacabibes, Pinar del Río. T. flavida present low interpopulation variability that could result in greater susceptibility to pesticides in control programs. The RAPD protocol and the selected primers are useful for molecular characterization of Cuban Triatominae.

Identification and differentiation of Candida species from pediatric patients by random amplified polymorphic DNA

Thirty-four Candida isolates were analyzed by random amplified polymorphic DNA using the primer OPG-10:24 Candida albicans; 4 Candida tropicalis; 2 Candida parapsilosis; 2 Candida dubliniensis; 1 Candida glabrata and 1 Candida krusei. The UPGMA-Pearson correlation coefficient was used to calculate the genetic distance between the different Candida groupings. Samples were classified as identical (correlation of 100%); highly related samples (90%); moderately related samples (80%) and unrelated samples (< 70%). The results showed that the RAPD proposed was capable of classifying the isolates coherently (such that same species were in the same dendrogram), except for two isolates of Candida parapsilosis and the positive control (Netherlands, 1973), probably because they are now recognized as three different species. Concerning the only fluconazole-resistant Candida tropicalis isolate with a genotype that was different to the others, the data were insufficient to affirm that the only difference was the sensitivity to fluconazole. We concluded that the Random Amplified Polymorphic DNA proposed might be used to confirm Candida species identified by microbiological methods.

Food consumption of adolescents. A simplified questionnaire for evaluating cardiovascular risk

OBJECTIVE: To develop a simplified questionnaire for self-evaluation by adolescents of foods associated with the risk of coronary diseases. METHODS: Frequency questionnaires about 80 foods were answered by representative samples of 256 adolescents aged 12 to 19 from Rio de Janeiro as part of the Nutrition and Health Research project. The dependent variable was the serum cholesterol predicting equation as influenced by diet, and the independent variables were the foods. The variables were normalized and, using Pearson's correlation coefficient, those with r>0.10 were selected for the regression model. The model was analyzed for sex, age, random sample, and total calories. Those food products that explained 85% of the cholesterol variation equation were present in the caloric model, and contained trans fatty acids were selected for the questionnaire. RESULTS: Sixty-five food products had a statistically significant correlation (P<0.001) with the dependent variable. The simplified questionnaire included 9 food products present in all tested models: steak or broiled meat, hamburger, full-fat cheese, French fries or potato chips, whole milk, pies or cakes, cookies, sausages, butter or margarine. The limit of the added food points for self-evaluation was 100, and over 120 points was considered excessive. CONCLUSION: The scores given to the food products and the criteria for the evaluation of the consumption limits enabled the adolescents to get to know and to balance their intake.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Mathematical models to describe the volumetric shrinkage rate of red beans during drying

The present study aimed to determine the volumetric shrinkage rate of bean (Phaseolus vulgaris L.) seeds during air-drying under different conditions of air, temperature and relative humidity, and to adjust several mathematical models to the empiric values observed, and select the one that best represents the phenomenon. Six mathematical models were adjusted to the experimental values to represent the phenomenon. It was determined the degree of adjustment of each model from the value of the coefficient of determination, the behavior of the distribution of the residuals, and the magnitude of the average relative and estimated errors. The rate of volumetric shrinkage that occurred in bean seeds during drying is between 25 and 37%. It basically depends on the final moisture content, regardless of the air conditions during drying. The Modified Bala & Woods' model best represented the process.

Effective diffusivity and convective mass transfer coefficient during the drying of bananas

In this article, a methodology is used for the simultaneous determination of the effective diffusivity and the convective mass transfer coefficient in porous solids, which can be considered as an infinite cylinder during drying. Two models are used for optimization and drying simulation: model 1 (constant volume and diffusivity, with equilibrium boundary condition), and model 2 (constant volume and diffusivity with convective boundary condition). Optimization algorithms based on the inverse method were coupled to the analytical solutions, and these solutions can be adjusted to experimental data of the drying kinetics. An application of optimization methodology was made to describe the drying kinetics of whole bananas, using experimental data available in the literature. The statistical indicators enable to affirm that the solution of diffusion equation with convective boundary condition generates results superior than those with the equilibrium boundary condition.

The use of ALOS/PALSAR data for estimating sugarcane productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

Cardiovascular risk in middle-aged breast cancer survivors: a comparison between two risk models

PURPOSE: It was to assess the risk of cardiovascular disease (CVD) in breast cancer survivors (BCS).METHODS: This cross-sectional study analyzed 67 BCS, aged 45 -65 years, who underwent complete oncological treatment, but had not received hormone therapy, tamoxifen or aromatase inhibitors during the previous 6 months. Lipid profile and CVD risk were evaluated, the latter using the Framingham and Systematic COronary Risk Evaluation (SCORE) models. The agreement between cardiovascular risk models was analyzed by calculating a kappa coefficient and its 95% confidence interval (CI).RESULTS: Mean subject age was 53.2±6.0 years, with rates of obesity, hypertension, and dyslipidemia of 25, 34 and 90%, respectively. The most frequent lipid abnormalities were high total cholesterol (70%), high LDL-C (51%) and high non-HDL-C (48%) concentrations. Based on the Framingham score, 22% of the participants had a high risk for coronary artery disease. According to the SCORE model, 100 and 93% of the participants were at low risk for fatal CVD in populations at low and high risk, respectively, for CVD. The agreement between the Framingham and SCORE risk models was poor (kappa: 0.1; 95%CI 0.01 -0.2) for populations at high risk for CVD.CONCLUSIONS: These findings indicate the need to include lipid profile and CVD risk assessment in the follow-up of BCS, focusing on adequate control of serum lipid concentrations.

Drying kinetics of jatropha seeds

Given the necessity of developing jatropha cultivation equipment, this work adjusted different mathematical models to experimental data obtained from the drying of jatropha seeds submitted to different drying conditions and selected the best model to describe the drying process. The experiment was carried out at the Federal Institute of Goiás - Rio Verde Campus. Seeds with initial moisture content of approximately 0.50 (kg water/kg dry matter) were dried in a forced air-ventilated oven, at temperatures of 45, 60, 75, 90 and 105°C to moisture content of 0.10 ± 0.005 (kg water/kg dry matter). The experimental data were adjusted to 11 mathematical models to represent the drying process of agricultural products. The models were compared using the coefficient of determination, chi-square test, relative mean error, estimated mean error and residual distribution. It was found that the increase in the air temperature caused a reduction in the drying time of seeds. The models Midilli and Two Terms were suitable to represent the drying process of Jatropha seeds and between them the use of the Midili model is recommended due to its greater simplicity.

Comparison of methods for classification of the coefficient of variation in papaya

ABSTRACT The objective of this work was to study the distribution of values of the coefficient of variation (CV) in the experiments of papaya crop (Carica papaya L.) by proposing ranges to guide researchers in their evaluation for different characters in the field. The data used in this study were obtained by bibliographical review in Brazilian journals, dissertations and thesis. This study considered the following characters: diameter of the stalk, insertion height of the first fruit, plant height, number of fruits per plant, fruit biomass, fruit length, equatorial diameter of the fruit, pulp thickness, fruit firmness, soluble solids and internal cavity diameter, from which, value ranges were obtained for the CV values for each character, based on the methodology proposed by Garcia, Costa and by the standard classification of Pimentel-Gomes. The results obtained in this study indicated that ranges of CV values were different among various characters, presenting a large variation, which justifies the necessity of using specific evaluation range for each character. In addition, the use of classification ranges obtained from methodology of Costa is recommended.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.