Ventilation behavior in trained and untrained men during incremental test: evidence of one metabolic transition point

This study aimed to describe and compare the ventilation behavior during an incremental test utilizing three mathematical models and to compare the feature of ventilation curve fitted by the best mathematical model between aerobically trained (TR) and untrained ( UT) men. Thirty five subjects underwent a treadmill test with 1 km.h(-1) increases every minute until exhaustion. Ventilation averages of 20 seconds were plotted against time and fitted by: bi-segmental regression model (2SRM); three-segmental regression model (3SRM); and growth exponential model (GEM). Residual sum of squares (RSS) and mean square error (MSE) were calculated for each model. The correlations between peak VO2 (VO2PEAK), peak speed (Speed(PEAK)), ventilatory threshold identified by the best model (VT2SRM) and the first derivative calculated for workloads below (moderate intensity) and above (heavy intensity) VT2SRM were calculated. The RSS and MSE for GEM were significantly higher (p < 0.01) than for 2SRM and 3SRM in pooled data and in UT, but no significant difference was observed among the mathematical models in TR. In the pooled data, the first derivative of moderate intensities showed significant negative correlations with VT2SRM (r = -0.58; p < 0.01) and Speed(PEAK) (r = -0.46; p < 0.05) while the first derivative of heavy intensities showed significant negative correlation with VT2SRM (r = -0.43; p < 0.05). In UT group the first derivative of moderate intensities showed significant negative correlations with VT2SRM (r = -0.65; p < 0.05) and Speed(PEAK) (r = -0.61; p < 0.05), while the first derivative of heavy intensities showed significant negative correlation with VT2SRM (r= -0.73; p < 0.01), Speed(PEAK) (r = -0.73; p < 0.01) and VO2PEAK (r = -0.61; p < 0.05) in TR group. The ventilation behavior during incremental treadmill test tends to show only one threshold. UT subjects showed a slower ventilation increase during moderate intensities while TR subjects showed a slower ventilation increase during heavy intensities.

Metabolic syndrome and dietary components are associated with coronary artery disease risk score in free-living adults: a cross-sectional study

Background: Coronary artery disease (CAD) is among the main causes of death in developed countries, and diet and lifestyle can influence CAD incidence. Objective: To evaluate the association of coronary artery disease risk score with dietary, anthropometric and biochemical components in adults clinically selected for a lifestyle modification program. Methods: 362 adults (96 men, 266 women, 53.9 +/- 9.4 years) fulfilled the inclusion criteria by presenting all the required data. The Framingham score was calculated and the IV Brazilian Guideline on Dyslipidemia and Prevention of Atherosclerosis was adopted for classification of the CAD risks. Anthropometric assessments included waist circumference (WC), body fat and calculated BMI (kg/m(2)) and muscle-mass index (MMI kg/m(2)). Dietary intake was estimated through 24 h dietary recall. Fasting blood was used for biochemical analysis. Metabolic Syndrome (MS) was diagnosed using NCEP-ATPIII (2001) criteria. Logistic regression was used to determine the odds of CAD risks according to the altered components of MS, dietary, anthropometric, and biochemical components. Results: For a sample with a BMI 28.5 +/- 5.0 kg/m(2) the association with lower risk (<10% CAD) were lower age (<60 years old), and plasma values of uric acid. The presence of MS within low, intermediary, and high CAD risk categories was 30.8%, 55.5%, and 69.8%, respectively. The independent risk factors associated with CAD risk score was MS and uric acid, and the protective factors were recommended intake of saturated fat and fiber and muscle mass index. Conclusion: Recommended intake of saturated fat and dietary fiber, together with proper muscle mass, are inversely associated with CAD risk score. On the other hand, the presence of MS and high plasma uric acid are associated with CAD risk score.

Assessing influence in survival data with a cure fraction and covariates

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.

WOOD DENSITY VARIATION AND TREE RING DEMARCATION IN Gmelina arborea TREES USING X-RAY DENSITOMETRY

Due to its relationship with other properties, wood density is the main wood quality parameter. Modern, accurate methods - such as X-ray densitometry - are applied to determine the spatial distribution of density in wood sections and to evaluate wood quality. The objectives of this study were to determinate the influence of growing conditions on wood density variation and tree ring demarcation of gmelina trees from fast growing plantations in Costa Rica. The wood density was determined by X-ray densitometry method. Wood samples were cut from gmelina trees and were exposed to low X-rays. The radiographic films were developed and scanned using a 256 gray scale with 1000 dpi resolution and the wood density was determined by CRAD and CERD software. The results showed tree-ring boundaries were distinctly delimited in trees growing in site with rainfall lower than 25 10 mm/year. It was demonstrated that tree age, climatic conditions and management of plantation affects wood density and its variability. The specific effect of variables on wood density was quantified by for multiple regression method. It was determined that tree year explained 25.8% of the total variation of density and 19.9% were caused by climatic condition where the tree growing. Wood density was less affected by the intensity of forest management with 5.9% of total variation.