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.

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.

Adjust of regression models to estimate the rectal temperature of broilers for the first 14 days of life

The broiler rectal temperature (t rectal) is one of the most important physiological responses to classify the animal thermal comfort. Therefore, the aim of this study was to adjust regression models in order to predict the rectal temperature (t rectal) of broiler chickens under different thermal conditions based on age (A) and a meteorological variable (air temperature - t air) or a thermal comfort index (temperature and humidity index -THI or black globe humidity index - BGHI) or a physical quantity enthalpy (H). In addition, through the inversion of these models and the expected t rectal intervals for each age, the comfort limits of t air, THI, BGHI and H for the chicks in the heating phase were determined, aiding in the validation of the equations and the preliminary limits for H. The experimental data used to adjust the mathematical models were collected in two commercial poultry farms, with Cobb chicks, from 1 to 14 days of age. It was possible to predict the t rectal of conditions from the expected t rectal and determine the lower and superior comfort thresholds of broilers satisfactorily by applying the four models adjusted; as well as to invert the models for prediction of the environmental H for the chicks first 14 days of life.

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.

Estimate of DMFT index using teeth most affected by dental caries in twelve-year-old children

The objective of the study was to develop regression models to describe the epidemiological profile of dental caries in 12-year-old children in an area of low prevalence of caries. Two distinct random probabilistic samples of schoolchildren (n=1,763) attending public and private schools in Piracicaba, Southeastern Brazil, were studied. Regression models were estimated as a function of the most affected teeth using data collected in 2005 and were validated using a 2001 database. The mean (SD) DMFT index was 1.7 (2.08) in 2001 and the regression equations estimated a DMFT index of 1.67 (1.98), which corresponds to 98.2% of the DMFT index in 2001. The study provided detailed data on the caries profile in 12-year-old children by using an updated analytical approach. Regression models can be an accurate and feasible method that can provide valuable information for the planning and evaluation of oral health services.

Risk of dependence associated with health, social support, and lifestyle

OBJECTIVE To analyze the prevalence of individuals at risk of dependence and its associated factors.METHODS The study was based on data from the Catalan Health Survey, Spain conducted in 2010 and 2011. Logistic regression models from a random sample of 3,842 individuals aged ≥ 15 years were used to classify individuals according to the state of their personal autonomy. Predictive models were proposed to identify indicators that helped distinguish dependent individuals from those at risk of dependence. Variables on health status, social support, and lifestyles were considered.RESULTS We found that 18.6% of the population presented a risk of dependence, especially after age 65. Compared with this group, individuals who reported dependence (11.0%) had difficulties performing activities of daily living and had to receive support to perform them. Habits such as smoking, excessive alcohol consumption, and being sedentary were associated with a higher probability of dependence, particularly for women.CONCLUSIONS Difficulties in carrying out activities of daily living precede the onset of dependence. Preserving personal autonomy and function without receiving support appear to be a preventive factor. Adopting an active and healthy lifestyle helps reduce the risk of dependence.

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.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Multivariate study and regression analysis of gluten-free granola

This study developed a gluten-free granola and evaluated it during storage with the application of multivariate and regression analysis of the sensory and instrumental parameters. The physicochemical, sensory, and nutritional characteristics of a product containing quinoa, amaranth and linseed were evaluated. The crude protein and lipid contents ranged from 97.49 and 122.72 g kg-1 of food, respectively. The polyunsaturated/saturated, and n-6:n-3 fatty acid ratios ranged from 2.82 and 2.59:1, respectively. Granola had the best alpha-linolenic acid content, nutritional indices in the lipid fraction, and mineral content. There were good hygienic and sanitary conditions during storage; probably due to the low water activity of the formulation, which contributed to inhibit microbial growth. The sensory attributes ranged from 'like very much' to 'like slightly', and the regression models were highly fitted and correlated during the storage period. A reduction in the sensory attribute levels and in the product physical stabilisation was verified by principal component analysis. The use of the affective test acceptance and instrumental analysis combined with statistical methods allowed us to obtain promising results about the characteristics of gluten-free granola.

The chronic kidney disease outcomes and practice patterns study Brazil (CKDopps-Brazil): Design, data and methodology

Introduction: The chronic kidney disease outcomes and practice patterns study (CKDopps) is an international observational, prospective, cohort study involving patients with chronic kidney disease (CKD) stages 3-5 [estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2, with a major focus upon care during the advanced CKD period (eGFR < 30 ml/min/1.73 m2)]. During a 1-year enrollment period, each one of the 22 selected clinics will enroll up to 60 advanced CKD patients (eGFR < 30 ml/min/1.73 m2 and not dialysis-dependent) and 20 earlier stage CKD patients (eGFR between 30-59 ml/min/1.73 m2). Exclusion criteria: age < 18 years old, patients on chronic dialysis or prior kidney transplant. The study timeline include up to one year for enrollment of patients at each clinic starting in the end of 2013, followed by up to 2-3 years of patient follow-up with collection of detailed longitudinal patient-level data, annual clinic practice-level surveys, and patient surveys. Analyses will apply regression models to evaluate the contribution of patient-level and clinic practice-level factors to study outcomes, and utilize instrumental variable-type techniques when appropriate. Conclusion: Launching in 2013, CKDopps Brazil will study advanced CKD care in a random selection of nephrology clinics across Brazil to gain understanding of variation in care across the country, and as part of a multinational study to identify optimal treatment practices to slow kidney disease progression and improve outcomes during the transition period to end-stage kidney disease.

Seleção genômica ampla e novos métodos de melhoramento do milho

Os objetivos deste trabalho foram verificar a acurácia do método da Seleção Genômica Ampla (GWS) no melhoramento de milho nas condições de estresse nutricional e propor novos métodos de melhoramento baseados em GWS. Foram estimados os dois componentes da eficiência no uso de nitrogênio e de fósforo (eficiência de absorção e de utilização) em 41 combinações híbridas, em dois experimentos, sob baixa e alta disponibilidades de N e P. Para a genotipagem da população de estimação, foram utilizados 80 marcadores microssatélites. As estimativas dos parâmetros genéticos foram obtidas via REML/BLUP, e a predição dos valores genéticos genômicos, via regressão aleatória (Random Regression - RR) aplicada à seleção genômica ampla (RR-BLUP/GWS). Para os caracteres em que a GWS apresentou altos valores de acurácia, essa foi comparada com os métodos de Seleção Recorrente Intra e Interpopulacional. Com o uso da GWS houve aumento significativo na acurácia seletiva e nos ganhos genéticos por unidade de tempo.

Earnings management and economic crises in the brazilian capital market

The 2008 economic crisis challenged accounting, either demanding recognition and measurement criteria well adjusted to this scenario or even questioning its ability to inform appropriately entities' financial situation before the crisis occurred. So, our purpose was to verify if during economic crises listed companies in the Brazilian capital market tended to adopt earnings management (EM) practices. Our sample consisted in 3,772 firm-years observations, in 13 years - 1997 to 2009. We developed regression models considering discretionary accruals as EM proxy (dependent variable), crisis as a macroeconomic factor (dummy variable of interest), ROA, market-to-book, size, leverage, foreign direct investment (FDI) and sector as control variables. Different for previous EM studies two approaches were used in data panel regression models and multiple crises were observed simultaneously. Statistics tests revealed a significant relation between economic crisis and EM practices concerning listed companies in Brazil in both approaches used.

DISPOSITION EFFECT AMONG BRAZILIAN EQUITY FUND MANAGERS

The disposition effect predicts that investors tend to sell winning stocks too soon and ride losing stocks too long. Despite the wide range of research evidence about this issue, the reasons that lead investors to act this way are still subject to much controversy between rational and behavioral explanations. In this article, the main goal was to test two competing behavioral motivations to justify the disposition effect: prospect theory and mean reversion bias. To achieve it, an analysis of monthly transactions for a sample of 51 Brazilian equity funds from 2002 to 2008 was conducted and regression models with qualitative dependent variables were estimated in order to set the probability of a manager to realize a capital gain or loss as a function of the stock return. The results brought evidence that prospect theory seems to guide the decision-making process of the managers, but the hypothesis that the disposition effect is due to mean reversion bias could not be confirmed.