Number of options in a movement sequence affects learners' behavior in a self-controlled practice condition

Self controlling practice implies a process of decision making which suggests that the options in a self controlled practice condition could affect learners The number of task components with no fixed position in a movement sequence may affect the (Nay learners self control their practice A 200 cm coincident timing track with 90 light emitting diodes (LEDs)-the first and the last LEDs being the warning and the target lights respectively was set so that the apparent speed of the light along the track was 1 33 m/sec Participants were required to touch six sensors sequentially the last one coincidently with the lighting of the tar get light (timing task) Group 1 (n=55) had only one constraint and were instructed to touch the sensors in any order except for the last sensor which had to be the one positioned close to the target light Group 2 (n=53) had three constraints the first two and the last sensor to be touched Both groups practiced the task until timing error was less than 30 msec on three consecutive trials There were no statistically significant differences between groups in the number of trials needed to reach the performance criterion but (a) participants in Group 2 created fewer sequences corn pared to Group 1 and (b) were more likely to use the same sequence throughout the learning process The number of options for a movement sequence affected the way learners self-controlled their practice but had no effect on the amount of practice to reach criterion performance.

Maternal decision on obtaining prenatal care: a study in Brazil

Objective: to address the social aspects of pregnancy and the views of pregnant women regarding prenatal assistance in Brazil. Design: this qualitative study was focused on describing the Social Representations of prenatal care held by pregnant women. The discourse of the collective subject (DCS) framework was used to analyse the data collected, within the theoretical background of social representations, as proposed and developed by Serge Moscovici. Participants and setting: 21 pregnant women who were users of the publicly funded Brazilian unified health-care system and resided in the area served by its family health programme in a low- to middle-income neighbourhood on the outskirts of Campo Grande, the capital of the state of Mato Grosso do Sul, in southwestern Brazil. Data were collected by conducting in-depth, face-to-face interviews from January to October 2006. Findings: all participants were married. Formal education of the participants was less than five years in four cases, between five and eight years in six cases, and greater than 11 years in 10 cases. Nine participants had informal jobs and earned up to US$ 200 per month, four paricipants had administrative jobs and earned over US$ 500 per month, and eight participants did not work. No specific racial/ethnic background predominated. Lack of adherence to prenatal care allowed for the identification of two DCS themes: `organisation of prenatal care services` and `lifestyle features`. Key conclusions: the respondents were found to have negative feelings about pregnancy which manifest as many fears, including the fear of harming their children`s health, of being punished during labour, and of being reprimanded by health-care professionals for overlooking their prenatal care, in addition to the insecurity felt towards the infant and self. Implications for practice: the findings reveal that communication between pregnant women and healthcare professionals has been ineffective and that prenatal care has not been effective for the group interviewed-features that are likely to be found among other low- to middle-income groups living elsewhere in Brazil. (C) 2009 Elsevier Ltd. All rights reserved.

Financial distress, financial constraint and investment decision: Evidence from Brazil

This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Contribution of soil organic carbon to the ion exchange capacity of tropical soils

Highly weathered soils represent about 3 billion ha of the tropical region. Oxisols represent about 60% of the Brazilian territory (more than 5 million km 2), in areas of great agricultural importance. Soil organic carbon (SOC) can be responsible for more than 80% of the cation exchange capacity (CEC) of highly weathered soils, such as Oxisols and Ultisols. The objective of this study was to estimate the contribution of the SOC to the CEC of Brazilian soils from different orders. Surface samples (0.0 to 0.2 m) of 30 uncultivated soils (13 Oxisols, 6 Ultisols, 5 Alfisols, 3 Entisols, I Histosol, 1 Inceptisol. and I Molisol), under native forests and from reforestation sites from Sao Paulo State, Brazil, were collected in order to obtain a large variation of (electro)chemical, physical, and mineralogical soil attributes. Total content of SOC was quantified by titulometric and colorimetric methods. Effective cation exchange capacity (ECEC) was obtained by two methods: the indirect method-summation-estimated the ECECi from the sum of basic cations (Ca+ Mg+ K+ Na) and exchangeable Al; and the direct ECECd obtained by the compulsive exchange method, using unbuffered BaCl2 solution. The contribution of SOC to the soil CEC was estimated by the Bennema statistical method. The amount of SOC var ied from 6.6 g kg(-1) to 213.4 g kg(-1). while clay contents varied from 40 g kg(-1) to 716 g kg(-1). Soil organic carbon contents were strongly associated to the clay contents, suggesting that clay content was the primary variable in controling the variability of SOC contents in the samples. Cation exchange capacity varied from 7.0 mmol(c) kg(-1) to 137.8 mmol(c) kg(-1) and had a positive Correlation with SOC. The mean contribution (per grain) of the SOC (1.64 mmol(c)) for the soil CEC was more than 44 times higher than the contribution of the clay fraction (0.04 mmol(c),). A regression model that considered the SOC content as the only significant variable explained 60% of the variation in the soil total CEC. The importance of SOC was related to soil pedogenetic process, since its contribution to the soil CEC was more evident in Oxisols with predominance of Fe and Al (oxihydr)oxides in the mineral fraction or in Ultisols, that presented illuviated clay. The influence of SOC in the sign and in the magnitude of the net charge of soils reinforce the importance of agricultural management systems that preserve high levels of SOC, in order to improve their sustainability.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.

Incorporating demand-side aspects into regional policy: variations in the importance of private investment decision factors across regions

Most regional programs focus on the supply side of regions, emphasizing the attraction conditions offered, such as infrastructure, labor skills, tax incentives, etc. This study analyzes one aspect of the demand side, that is, how investment decisions of private firms are made by asking the question: ""Do corporations decide the same way on investments in different parts of the territory?"" The paper analyzes the investments of 373 large Brazilian firms during 1996-2004. Based on the investment decisions of these firms, the role of sales, cash-flow, external financing, and working capital is investigated through regression analysis. The regional influence is captured by explanatory variables representing regional and firm characteristics, and by interaction dummies between the region and the main investment determinants. The results indicate significant differences across regions in the importance of investment determinants. This information is important for regional development policy, because different mechanisms should be used in different regions to foster private investments.

Age-dependent increase in blood pressure in two different Native American communities in Brazil

Objective Cardiovascular risk factors were surveyed in two Indian populations (Guarani, n=60; Tupinikin, n=496) and in a non-Indian group (n=114) living in the same reserve in southeast Brazilian coast. The relationship between an age-dependent blood pressure (BP) increase with salt consumption was also investigated. Methods Overnight (12 h) urine was collected to evaluate Na excretion. Fasting glucose and lipids, anthropometry, BP, ECG and carotid-femoral pulse wave velocity (PWV) were measured in a clinic visit. Participation (318 men/352 women, age 20-94 years; mean=37.6 +/- 14.9 years) comprised 80% of the eligible population. Results The prevalence of hypertension, diabetes and high cholesterol was similar in Tupinikins and in non-Indians and higher than in Guaranis. The prevalence of smoking and obesity was higher in the latter group. Hypertension and diabetes were detected in only one individual of the Guarani group. Mean BP adjusted to age and BMI was significantly lower (P<0.01) in Guaranis (82.8 +/- 1.6 mmHg) than in Tupinikins (92.3 +/- 0.5 mmHg) and non-Indians (91.6 +/- 1.1 mmHg). Urinary Na excretion (mEq/12h), however, was similar in the three groups (Guarani=94 +/- 40; Tupinikin=105 +/- 56; non-Indian=109 +/- 55; P>0.05). PWV (m/s) was lower (P<0.01) in Guarani (7.5 +/- 1.4) than in Tupinikins (8.8 +/- 2.2) and non-Indians (8.4 +/- 2.0). Multiple regression analysis showed that age and waist-to-hip ratio (WHR) were independent predictors of SBP and DBP (r(2)=0.44) in Tupinikins, whereas the WHR was the unique independent predictor of BP variability in Guaranis (r(2)=0.22). Conclusion Lower BP levels in Guaranis cannot be explained by low salt intake observed in other primitive populations. J Hypertens 27:1753-1760 (C) 2009 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Validation of body condition score as a predictor of subcutaneous fat in Nelore (Bos indicus) cows

The aim of the present study was to determine the relationship among body weight (BW), body condition score (BCS) and rump fat thickness (RFAT) measured by ultrasonography, and validate the relationship between BCS and RFAT over the time. Two hundred sixty and six Nelore cows had their BW, BCS and RFAT evaluated at five different moments during the production cycle: M1) weaning: M2) parturition, M3) 42 days post-partum; M4) 82 days postpartum and M5) 112 days post-partum. A BCS value was attributed for each cow following a I to 5 points scale. Ultrasonographic images for RFAT measurement were obtained using a 3.5 MHz linear transducer. Images were immediately analyzed as soon as they were formed and frozen. Body condition scores and ultrasound measurements were collected on the same day by a single trained technician. The relationship between BCS and RFAT values was investigated by regression models. The analysis of similarity among the five obtained models was performed using the proc MIXED from SAS and the correlations among variables were analyzed with proc CORR from SAS. The BCS was able to predict RFAT in Nelore cows in all different moments evaluated. Also, it was shown that BCS presented high correlation (r=0.82 to 0.93) and relationship (R(2) = 0.73 to 0.92) with RFAT. However, both BCS and RFAT showed low correlation (r=0.37 to 0.50) and relationship (R(2) = 0.13 to 0.25) with BW. The BCS classification by visual method using a 1 to 5 point scale, was able to predict the RFAT in Nelore cows over the time. (C) 2008 Elsevier B.V. All rights reserved.

Invasive grasses and native Asteraceae in the Brazilian Cerrado

Anthropogenic disturbances frequently modify natural disturbance regimes and foster the invasion and spread of nonindigenous species. However, there is some dispute about whether disturbance events or invasive plants themselves are the major factors promoting the local extinction of native plant species. Here, we used a set of savanna remnants comprising a gradient of invasive grass cover to evaluate whether the species richness of Asteraceae, a major component of the Brazilian Cerrado, is affected by invasive grass cover, or alternatively, whether variation in richness can be directly ascribed to disturbance-related variables. Furthermore, we evaluate whether habitat-specialist Asteraceae differ from habitat generalist species in their responses to grass invasion. Abundance and species richness showed unimodal variation along the invasive grass gradient for both total Asteraceae and habitat-generalists. The cerrado-specialist species, however, showed no clear variation from low-to-intermediate levels of grass cover, but declined monotonically from intermediate-to-higher levels. Through a structural equation model, we found that only invasive grass cover had significant effects on both abundance and species density of Asteraceae. The effect of invasive grass cover was especially high on the cerrado-specialist species, whose proportion declined consistently with increasing invasive dominance. Our results support the prediction that invasive grasses reduce the floristic uniqueness of pristine vegetation physiognomies.

Biochemical and biophysical properties of a highly active recombinant arginase from Leishmania (Leishmania) amazonensis and subcellular localization of native enzyme

Arginase (L-arginine amidinohydrolase, E.C. 3.5.3.1) is a metalloenzyme that catalyses the hydrolysis Of L-arginine to L-ornithine and urea. In Leishmania spp., the biological role of the enzyme may be involved in modulating NO production upon macrophage infection. Previously, we cloned and characterized the arginase gene from Leishmania (Leishmania) amazonensis. In the present work, we successfully expressed the recombinant enzyme in E. coli and performed biochemical and biophysical characterization of both the native and recombinant enzymes. We obtained K-M and V-max. values of 23.9(+/- 0.96) mM and 192.3 mu mol/min mg protein (+/- 14.3), respectively, for the native enzyme. For the recombinant counterpart, K-M was 21.5(+/- 0.90) mM and V-max was 144.9(+/- 8.9) mu mol/min mg. Antibody against the recombinant protein confirmed a glycosomal cellular localization of the enzyme in promastigotes. Data from light scattering and small angle X-ray scattering showed that a trimeric state is the active form of the protein. We determined empirically that a manganese wash at room temperature is the best condition to purify active enzyme. The interaction of the recombinant protein with the immobilized nickel also allowed us to confirm the structural disposition of histidine at positions 3 and 324. The determined structural parameters provide substantial data to facilitate the search for selective inhibitors of parasitic sources of arginase, which could subsequently point to a candidate for leishmaniasis therapy. (c) 2008 Elsevier B.V. All rights reserved.