The Association Between Cardiovascular Risk Factors and High Blood Pressure in Adolescents: A School-Based Study

Objective: Although previous studies have analyzed the association between cardiovascular risk factors and blood pressure in adolescents, few studies conducted in developing countries analyzed whether the aggregation of risk factors contributes to an increased risk of high blood pressure in adolescents. The objective of this study was to assess the association between cardiovascular risk factors (including general overweight, abdominal obesity, high consumption of foods rich in fats, and insufficient physical activity levels) and high blood pressure in adolescents.Methods: This study was carried out from 2007 to 2008 with 1021 adolescents (528 girls) from primary schools located in the city of Londrina- Brazil. Blood pressure was assessed using an oscillometric device. General overweight was obtained through body mass index, abdominal obesity was assessed using waist circumference, and the consumption of foods rich in fat and physical activity were assessed using a questionnaire. The sum of these risk factors was determined.Results: Adolescents with three or four aggregated risk factors were more likely to have higher values of systolic and diastolic blood pressure when compared with adolescents who did not have any cardiovascular risk factors (P = 0.001 for both). Logistic regression indicated that groups of adolescents with 2 (OR = 2.46 [1.11-5.42]; P = 0.026), 3 (OR = 4.97 [2.07-11.92]; P = 0.001) or 4 risk factors (OR = 6.79 [2.24-19.9]; P = 0.001) presented an increased likelihood of high blood pressure.Conclusions: The number of cardiovascular risk factors was found to be related to high blood pressure in adolescents. (C) 2014 Wiley Periodicals, Inc.

Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.

The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A note on model uncertainty in linear regression

We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.

Analysis of criteria for metabolic syndrome in a population-based study of Japanese-Brazilians

Objective: Criteria for metabolic syndrome (MS) differ particularly regarding the definition of central obesity and consequently, there could be differences in the assessment of cardiovascular risk. We estimated the prevalence of metabolic syndrome, compared the agreement of the World Health Organization (WHO) criteria with the standard and a modified National Cholesterol Education Program (NCEP) criterion and investigated whether additional factors were associated with the diagnosis of the syndrome in a Japanese descendant population.Methods: In this cross-sectional, population-based survey, 1166 Japanese-Brazilians (533 men, 633 women) aged 57.4 +/- 12.4 years with mean body mass index (BMI) and waist of 25.2 +/- 4.0 kg/m(2) and 84.5 +/- 10.6 cm, respectively, were included. McNemar and kappa statistics were used to assess the concordance between WHO criteria with the standard and a modified NCEP criteria (waist of 90 and 80 cm, for men and women, respectively). in logistic regression analysis, a number of metabolic variables and albumin-to-creatinine ratio were included to test independent associations with metabolic syndrome defined by the modified NCEP criteria.Results: According to WHO, 55.4% (95% Cl 52.5-58.2%) of the subjects had MS and to NCEP 47.4% (95% Cl 44.6-50.0%). WHO criterion detected 48.3% of central obese subjects while NCEP only 14.0%. Kappa statistics showed a good strength of agreement (k = 0.67, p < 0.01) between WHO and NCEP standard definitions of MS. Using the modified NCEP criterion for Asians, more subjects with metabolic syndrome were identified (58%) and agreement with WHO was improved (k = 0.72, p < 0.001). However, similar Framingham risk scores were attributed to the subsets of subjects classified by any of the three criteria. Areas under the receiver operating characteristic curves, obtained for the modified waist values to diagnose metabolic syndrome according to WHO, were > 0.80 and corresponded, respectively, to sensitivity and specificity of 63 and 83% for men and 77 and 72% for women. In final logistic regression model, age, male sex, BMI and homeostasis model assessment-insulin resistance but not with albumin-to-creatinine ratio (ACR) were independently associated with the syndrome.Conclusions: High prevalence of MS, independent of the criterion considered, was found in this Japanese-Brazilian population. The replacement of waist cutoff by those proposed by WHO for Asians lead to this diagnosis in a higher number of subjects with elevated cardiovascular risk. Our data did not support that ACR should be included in the classical definition of MS in Japanese descendants as previously suggested by WHO.

Model updating of the non-linear vibrating structures through Volterra series and proper orthogonal decomposition

Classical procedures for model updating in non-linear mechanical systems based on vibration data can fail because the common linear metrics are not sensitive for non-linear behavior caused by gaps, backlash, bolts, joints, materials, etc. Several strategies were proposed in the literature in order to allow a correct representative model of non-linear structures. The present paper evaluates the performance of two approaches based on different objective functions. The first one is a time domain methodology based on the proper orthogonal decomposition constructed from the output time histories. The second approach uses objective functions with multiples convolutions described by the first and second order discrete-time Volterra kernels. In order to discuss the results, a benchmark of a clamped-clamped beam with an pre-applied static load is simulated and updated using proper orthogonal decomposition and Volterra Series. The comparisons and discussions of the results show the practical applicability and drawbacks of both approaches.

Prevalence of Treponema Species Detected in Endodontic Infections: Systematic Review and Meta-regression Analysis

Introduction: This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic eases. Methods: The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus data-bases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Results: Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval, 35.9-47.0). In the multivariate model of meta-regression analysis, primary endodontic infections (P < .001), acute apical abscess, symptomatic apical periodontitis (P < .001), and concomitant presence of 2 or more species (P = .028) explained the heterogeneity regarding the prevalence rates of Treponema species. Conclusions: Our findings suggest that Treponema species are important pathogens involved in endodontic infections, particularly in cases of primary and acute infections.

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

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

Transparent organic-inorganic nanocomposites membranes based on carboxymethylcellulose and synthetic clay

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

Estimation of the diagnostic accuracy of the glyco-C and US9 gene-based polymerase chain reaction technique for the detection of bovine Herpesvirus type 5 DNA in decomposed brain suspension from a slaughter house using Bayesian analysis, Brazil

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

Prevalence of asthma and risk factors associated: population based study in Sao Paulo, Southeastern Brazil, 2008-2009

OBJECTIVE: To assess the prevalence of asthma and risk factors associated in children and adolescents. METHODS: Population-based cross-sectional study with 1,185 female and male children and adolescents carried out in the city of Sao Paulo, Southeastern Brazil, from 2008 to 2009. Data were collected through home interviews. Respondents were selected from two-stage (census tract, household) cluster random sampling stratified by gender and age. Multiple Poisson regression was used in the adjusted analysis between the outcome and socioeconomic, demographic, lifestyle and health condition variables. RESULTS: Of all respondents, 9.1% (95%CI 7.0; 11.7) reported asthma. After adjustment, the following variables were found independently associated with asthma: age (0 to 4 years vs. 15 to 19) (PR 3.18, 95%CI 1.20;8.42); age (5 to 9 years vs. 15 to 19) (PR 6.37, 95%CI 2.64;15.39); age (10 to 14 years vs. 15 to 19) (PR 4.51,95%CI 1.95;10.40); allergy (yes vs. no) (PR 2.22, 95%CI 1.24;4.00); rhinitis (yes vs. no) (PR 2.13, 95%CI 1.22;3.73); health conditions in the 15 days preceding the interview (yes vs. no) (PR 1.96, 95%CI 1.23;3.11); number of rooms in the household (1 to 3 vs. 4 and more) (PR 1.67, 95%CI 1.05;2.66); and skin color (black and mixed vs. white) (PR 2.00, 95%CI 1.14;3.49). CONCLUSIONS: This study showed the importance of factors associated with asthma including rhinitis and allergy; age between 5 to 9 years old; black and mixed skin color; and household with few rooms. Frequent health problems are seen as a common consequence of asthma.

The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

Urea-Based Synthesis of Zinc Oxide Nanostructures at Low Temperature

The preparation of nanometer-sized structures of zinc oxide (ZnO) from zinc acetate and urea as raw materials was performed using conventional water bath heating and a microwave hydrothermal (MH) method in an aqueous solution. The oxide formation is controlled by decomposition of the added urea in the sealed autoclave. The influence of urea and the synthesis method on the final product formation are discussed. Broadband photoluminescence (PL) behavior in visible-range spectra was observed with a maximum peak centered in the green region which was attributed to different defects and the structural changes involved with ZnO crystals which were produced during the nucleation process.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.