Early selection in open-pollinated Eucalyptus families based on competition covariates

The objetive of this work was to evaluate the influence of intergenotypic competition in open-pollinated families of Eucalyptus and its effects on early selection efficiency. Two experiments were carried out, in which the timber volume was evaluated at three ages, in a randomized complete block design. Data from the three years of evaluation (experiment 1, at 2, 4, and 7 years; and experiment 2, at 2, 5, and 7 years) were analyzed using mixed models. The following were estimated: variance components, genetic parameters, selection gains, effective number, early selection efficiency, selection gain per unit time, and coincidence of selection with and without the use of competition covariates. Competition effect was nonsignificant for ages under three years, and adjustment using competition covariates was unnecessary. Early selection for families is effective; families that have a late growth spurt are more vulnerable to competition, which markedly impairs ranking at the end of the cycle. Early selection is efficient according to all adopted criteria, and the age of around three years is the most recommended, given the high efficiency and accuracy rate in the indication of trees and families. The addition of competition covariates at the end of the cycle improves early selection efficiency for almost all studied criteria.

Food frequency consumption and lipoproteins serum levels in the population of an urban area, Brazil

OBJECTIVE: To identify the association between food group consumption frequency and serum lipoprotein levels among adults. METHODS: The observations were made during a cross-sectional survey of a representative sample of men and women over 20 years old living in Cotia county, S. Paulo, Brazil. Data on food frequency consumption, serum lipids, and other covariates were available for 1,045 adults. Multivariate analyses adjusted by age, gender, body mass index, waist-to-hip ratio, educational level, family income, physical activity, smoking, and alcohol consumption were performed. RESULTS: Consumption of processed meat, chicken, red meat, eggs and dairy foods were each positively and significantly correlated with LDL-C, whereas the intake of vegetables and fruits showed an inverse correlation. Daily consumption of processed meat, chicken, red meat, eggs, and dairy foods were associated with 16.6 mg/dl, 14.5 mg/dl, 11.1 mg/dl, 5.8 mg/dl, and 4.6 mg/dl increase in blood LDL-C, respectively. Increases of daily consumption of fruit and vegetables were associated with 5.2 mg/dl and 5.5 mg/dl decreases in LDL-C, respectively. Alcohol beverage consumption showed a significant positive correlation with HDL-C. CONCLUSIONS: Dietary habits in the study population seem to contribute substantially to the variation in blood LDL and HDL concentrations. Substantially CHD risk reduction could be achieved with dietary changes.

Reproductive outcomes in an area adjacent to a petrochemical plant in southern Brazil

OBJECTIVE: To evaluate possible adverse reproductive outcomes in an area adjacent to a petrochemical plant in southern Brazil. METHODS: A review of 17,113 birth records of the main hospital of the municipality of Montenegro, southern Brazil, from 1983 to 1998 was carried out. Three groups of cases were selected: (1) newborns with major congenital malformations; (2) newborns with low birth weight (<2,500 g); and (3) stillborns (>500 g). A control was assigned to each case. Controls were the first newborns weighing > or = 2,500 g without malformations and of case-matching sex. Mother's residence during pregnancy was used as an exposure parameter. Statistical analyses were performed using Chi-square test or Fisher test, odds ratio, 0.05 significance level, and 95% confidence interval. RESULTS: For unadjusted analysis, it was found a correlation between low birth weight and geographical proximity of mother's residence to the petrochemical plant (OR = 1.66; 95% CI = 1.01--2.72) or residence on the way of preferential wind direction (OR = 1.62; 95% CI = 1.03--2.56). When other covariates were added in the conditional logistic regression (maternal smoking habits, chronic disease and age), there was no association. CONCLUSIONS: Despite final results were negative, low birth weight could be a good parameter of environmental contamination and should be closely monitored in the studied area.

The impact of night work on subjective reports of well-being: an exploratory study of health care workers from five nations

OBJECTIVE: To carry out a survey data collection from health care workers in Brazil, Croatia, Poland, Ukraine and the USA with two primary goals: (1) to provide information about which aspects of well-being are most likely to need attention when shiftwork management solutions are being developed, and (2) to explore whether nations are likely to differ with respect to the impacts of night work on the well-being of workers involved in health care work. METHODS: The respondents from each nation were sorted into night worker and non-night worker groups. Worker perceptions of being physically tired, mentally tired, and tense at the end of the workday were examined. Subjective reports of perceived felt age were also studied. For each of these four dependent variables, an ANCOVA analysis was carried out. Hours worked per week, stability of weekly work schedule, and chronological age were the covariates for these analyses. RESULTS: The results clearly support the general proposal that nations differ significantly in worker perceptions of well-being. In addition, perceptions of physical and mental tiredness at the end of the workday were higher for night workers. For the perception of being physically tired at the end of a workday, the manner and degree to which the night shift impacts the workers varies by nation. CONCLUSIONS: Additional research is needed to determine if the nation and work schedule differences observed are related to differences in job tasks, work schedule structure, off-the-job variables, and/or other worker demographic variables.

HIV testing among pregnant women in Brazil: rates and predictors

OBJECTIVE: To assess rates of offering and uptake of HIV testing and their predictors among women who attended prenatal care. METHODS: A population-based cross-sectional study was conducted among postpartum women (N=2,234) who attended at least one prenatal care visit in 12 cities. Independent and probabilistic samples were selected in the cities studied. Sociodemographic data, information about prenatal care and access to HIV prevention interventions during the current pregnancy were collected. Bivariate and multivariate analyses were carried out to assess independent effects of the covariates on offering and uptake of HIV testing. Data collection took place between November 1999 and April 2000. RESULTS: Overall, 77.5% of the women reported undergoing HIV testing during the current pregnancy. Offering of HIV testing was positively associated with: previous knowledge about prevention of mother-to-child transmission of HIV; higher number of prenatal care visits; higher level of education and being white. HIV testing acceptance rate was 92.5%. CONCLUSIONS: The study results indicate that dissemination of information about prevention of mother-to-child transmission among women may contribute to increasing HIV testing coverage during pregnancy. Non-white women with lower level of education should be prioritized. Strategies to increase attendance of vulnerable women to prenatal care and to raise awareness among health care workers are of utmost importance.

Housing conditions as a social determinant of low birthweight and preterm low birthweight

OBJECTIVE: To assess the relationship between housing conditions and low birthweight and preterm low birthweight among low-income women. METHODS: A case-control study was conducted with post-partum women living in the city of Rio de Janeiro, Southeast Brazil, in 2003-2005. Two groups of cases, low birthweight (n=96) and preterm low birthweight infants (n=68), were compared against normal weight term controls (n=393). Housing conditions were categorized into three levels: adequate, inadequate, and highly inadequate. Covariates included sociodemographic and anthropometric characteristics, risk behaviors, violence, anxiety, satisfaction during pregnancy, obstetric history and prenatal care. RESULTS: Poor housing conditions was independently associated with low birthweight (inadequate - OR 2.3 [1.1;4.6]; highly inadequate - OR 7.6 [2.1;27.6]) and preterm low birthweight (inadequate - OR 2.2 [1.1;4.3]; highly inadequate - OR 7.6 [2.4;23.9]) and factors associated with outcomes were inadequate prenatal care and previous preterm birth. Low income and low maternal body mass index remained associated with low birthweight. CONCLUSIONS: Poor housing conditions were associated with low birthweight and preterm low birthweight.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Evaluation of Sexual Dimorphism in the Efficacy and Safety of Simvastatin/Atorvastatin Therapy in a Southern Brazilian Cohort

Background: Dyslipidemia is the primary risk factor for cardiovascular disease, and statins have been effective in controlling lipid levels. Sex differences in the pharmacokinetics and pharmacodynamics of statins contribute to interindividual variations in drug efficacy and toxicity. Objective: To evaluate the presence of sexual dimorphism in the efficacy and safety of simvastatin/atorvastatin treatment. Methods: Lipid levels of 495 patients (331 women and 164 men) were measured at baseline and after 6 ± 3 months of simvastatin/atorvastatin treatment to assess the efficacy and safety profiles of both drugs. Results: Women had higher baseline levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) compared with men (p < 0.0001). After treatment, women exhibited a greater decrease in plasma TC and LDL-C levels compared with men. After adjustment for covariates, baseline levels of TC and LDL-C influenced more than 30% of the efficacy of lipid-lowering therapy (p < 0.001), regardless of sex. Myalgia [with or without changes in creatine phosphokinase (CPK) levels] occurred more frequently in women (25.9%; p = 0.002), whereas an increase in CPK and/or abnormal liver function was more frequent in in men (17.9%; p = 0.017). Conclusions: Our results show that baseline TC and LDL-C levels are the main predictors of simvastatin/atorvastatin therapy efficacy, regardless of sex. In addition, they suggest the presence of sexual dimorphism in the safety of simvastatin/atorvastatin. The effect of sex differences on receptors, transporter proteins, and gene expression pathways needs to be better evaluated and characterized to confirm these observations.

Armadillo meat intake was not associated with leprosy in a case control study, Curitiba (Brazil)

Leprosy's progression and its maintained endemic status, despite the availability of effective treatments, are not fully understood and recent studies have highlighted the possibility of involved Mycobacterium leprae ambient reservoirs. Wild armadillos can carry leprosy and, because their meat is eaten by humans, development of the disease among armadillo meat consumers has been investigated. This study evaluated the frequency of armadillo meat intake among leprosy patients as well as age and gender matched controls with other skin diseases from a dermatological unit. Armadillo meat consumption among both groups was adjusted by demographic and socioeconomic covariates based on a conditional multiple logistic regression model. One hundred twenty-one cases and 242 controls were evaluated; they differed in socioeconomic variables such as family income, hometown population and access to treated water. The multivariate analysis did not show an association between the intake of armadillo meat and leprosy (odds ratio = 1.07; CI 95% 0.56-2.04), even when only cases with no known contacts were analyzed. We conclude that leprosy is not associated with the intake of armadillo meat in these patients.

Appraisal of Phlebotomus argentipes habitat suitability using a remotely sensed index in the kala-azar endemic focus of Bihar, India

Visceral leishmaniasis, or kala-azar, is recognised as a serious emerging public health problem in India. In this study, environmental parameters, such as land surface temperature (LST) and renormalised difference vegetation indices (RDVI), were used to delineate the association between environmental variables and Phlebotomus argentipes abundance in a representative endemic region of Bihar, India. The adult P. argentipes were collected between September 2009-February 2010 using the hand-held aspirator technique. The distribution of P. argentipes was analysed with the LST and RDVI of the peak and lean seasons. The association between environmental covariates and P. argentipes density was analysed a multivariate linear regression model. The sandfly density at its maximum in September, whereas the minimum density was recorded in January. The regression model indicated that the season, minimum LST, mean LST and mean RDVI were the best environmental covariates for the P. argentipes distribution. The final model indicated that nearly 74% of the variance of sandfly density could be explained by these environmental covariates. This approach might be useful for mapping and predicting the distribution of P. argentipes, which may help the health agencies that are involved in the kala-azar control programme focus on high-risk areas.

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

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.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

Survival analysis of female dogs with mammary tumors after mastectomy: epidemiological, clinical and morphological aspects

Abstract: Mammary gland tumors are the most common type of tumors in bitches but research on survival time after diagnosis is scarce. The purpose of this study was to investigate the relationship between survival time after mastectomy and a number of clinical and morphological variables. Data was collected retrospectively on bitches with mammary tumors seen at the Small Animal Surgery Clinic Service at the University of Brasília. All subjects had undergone mastectomy. Survival analysis was conducted using Cox's proportional hazard method. Of the 139 subjects analyzed, 68 died and 71 survived until the end of the study (64 months). Mean age was 11.76 years (SD=2.71), 53.84% were small dogs. 76.92% of the tumors were malignant, and 65.73% had both thoracic and inguinal glands affected. Survival time in months was associated with age (hazard rate ratios [HRR] =1.23, p-value =1.4x10-4), animal size (HRR between giant and small animals =2.61, p-value =0.02), nodule size (HRR =1.09, p-value =0.03), histological type (HRR between solid carcinoma and carcinoma in a mixed tumor =2.40, p-value =0.02), time between diagnosis and surgery (TDS, with HRR =1.21, p-value =2.7x10-15), and the interaction TDS*follow-up time (HRR =0.98, p-value =1.6x10-11). The present study is one of the few on the subject matter. Several important covariates were evaluated and age, animal size, nodule size, histological type, TDS and TDS*follow up time were identified as significantly associated to survival time.