Cultivo da microalga marinha Chaetoceros calcitrans (BACILLARIOPHYCEAE) utilizando diferentes tipos de água marinha artificial

The use of microalgae as live food to a wide variety of organisms is one of the most important aspects in aquaculture. Several commercial formulations have been available in the marine aquarium market in order to prepare artificial sea water (ASW). The present study accounted microalgae Chaetoceros calcitrans performance cultured using different ASW in comparison to natural seawater(NSW). it was carried out using red Sea, Coralife and oceanic, three different ASW brands and NSW as control. nonaerated cultures were grew in 400 mL with Conwy culture medium with five replicates each under defined conditions. All cultures began with an algal inoculum of 208.000 cells/mL. a 5-mL aliquot was removed daily from each culture for cells counts. data obtained using polynomial regression test demonstrated that all ASW brands reached higher algal density rates than the one with NSW, though the three ASW brands were significantly heterogeneous. C. calcitrans raised with oceanic and red Sea brands showed similar growth rates and both were higher than Coralife brand. the results suggest that all three ASW brands studied can be used in the culture of this microalgae specie.

Technical and economical evaluation of a harvester, working under different spacing and planting arrangement conditions in eucalypts plantations

Plantation spacing selection has the primary objective of assigning each tree enough space for maximum growth and best quality to be attained with a minimum cost. From the harvest standpoint, an increase in stand density directly implies a decrease of individual tree volume, reducing also harvester productive capacity. The objective of this research is to assess the effects of several initial spacings and arrangements in eucalyptus plantations on production capacity, operational capacity and costs of forest harvester. Real operational data were collected from two eucalypt plantations at different initial spacing of 6.0, 7.5, 9.0, 12 and 18 m(2) per tree. Simulation data were obtained from a forest harvester simulator. Using spacing (E), mean tree volume (MV), diameter at breast height (DBH) and height (H) values, a stepwise regression test procedure was run, and correlations computed in order to measure their participation in operational capacity. Operational costs were computed with an accounting method proposed by FAO. Mean tree volume (MV) explained 88% of forest harvester operational capacity. Spacing (E) affected 8.5% of harvester operational capacity; wider spacings were related to higher individual tree volumes. Harvesting operation costs were lower in wider spaced treatments.

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.

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.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Optimizing the CAMCOG test in the screening for mild cognitive impairment and incipient dementia: saving time with relevant domains

Objective: To identify the CAMCOG sub-items that best contribute for the identification of patients with mild cognitive impairment (MCI) and incipient Alzheimer`s disease (AD) in clinical practice. Methods: Cross-sectional assessment of 272 older adults (98 MCI, 82 AD, and 92 controls) with a standardized neuropsychological battery and the CAMCOG schedule. Backward logistic regression analysis with diagnosis (MCI and controls) as dependent variable and the sub-items of the CAMCOG as independent variable was carried out to determine the CAMCOG sub-items that predicted the diagnosis of MCI. Results: Lower scores on Language, Memory, Praxis, and Calculation CAMCOG sub-items were significantly associated with the diagnosis of MCI. A composite score obtained by the sum of these scores significantly discriminated MCI patients from comparison groups. This reduced version of the CAMCOG showed similar diagnostic accuracy than the original schedule for the identification of patients with MCI as compared to controls (AUC = 0.80 +/- 0.03 for the reduced CAMCOG; AUC = 0.79 +/- 0.03 for the original CAMCOG). Conclusion: This reduced version of the CAMCOG had similar diagnostic properties as the original CAMCOG and was faster and easier to administer, rendering it more suitable for the screening of subtle cognitive deficits in general clinical practice. Copyright (C) 2010 John Wiley & Sons, Ltd.

Improved likelihood inference for the shape parameter in Weibull regression

We obtain adjustments to the profile likelihood function in Weibull regression models with and without censoring. Specifically, we consider two different modified profile likelihoods: (i) the one proposed by Cox and Reid [Cox, D.R. and Reid, N., 1987, Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society B, 49, 1-39.], and (ii) an approximation to the one proposed by Barndorff-Nielsen [Barndorff-Nielsen, O.E., 1983, On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343-365.], the approximation having been obtained using the results by Fraser and Reid [Fraser, D.A.S. and Reid, N., 1995, Ancillaries and third-order significance. Utilitas Mathematica, 47, 33-53.] and by Fraser et al. [Fraser, D.A.S., Reid, N. and Wu, J., 1999, A simple formula for tail probabilities for frequentist and Bayesian inference. Biometrika, 86, 655-661.]. We focus on point estimation and likelihood ratio tests on the shape parameter in the class of Weibull regression models. We derive some distributional properties of the different maximum likelihood estimators and likelihood ratio tests. The numerical evidence presented in the paper favors the approximation to Barndorff-Nielsen`s adjustment.

Improved score tests in symmetric linear regression models

The class of symmetric linear regression models has the normal linear regression model as a special case and includes several models that assume that the errors follow a symmetric distribution with longer-than-normal tails. An important member of this class is the t linear regression model, which is commonly used as an alternative to the usual normal regression model when the data contain extreme or outlying observations. In this article, we develop second-order asymptotic theory for score tests in this class of models. We obtain Bartlett-corrected score statistics for testing hypotheses on the regression and the dispersion parameters. The corrected statistics have chi-squared distributions with errors of order O(n(-3/2)), n being the sample size. The corrections represent an improvement over the corresponding original Rao`s score statistics, which are chi-squared distributed up to errors of order O(n(-1)). Simulation results show that the corrected score tests perform much better than their uncorrected counterparts in samples of small or moderate size.

Size and power properties of some tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the non-null distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the Birnbaum-Saunders regression model. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the shape parameter. Monte Carlo simulation is presented in order to compare the finite-sample performance of these tests. We also present two empirical applications. (C) 2010 Elsevier B.V. All rights reserved.

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented. (C) 2010 Elsevier B.V. All rights reserved.

Improved likelihood inference in beta regression

We consider the issue of performing accurate small-sample likelihood-based inference in beta regression models, which are useful for modelling continuous proportions that are affected by independent variables. We derive small-sample adjustments to the likelihood ratio statistic in this class of models. The adjusted statistics can be easily implemented from standard statistical software. We present Monte Carlo simulations showing that inference based on the adjusted statistics we propose is much more reliable than that based on the usual likelihood ratio statistic. A real data example is presented.

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a chi (2) distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.

Some restriction tests in a new class of regression models for proportions

The main purpose of this work is to study the behaviour of Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-32] adjusted likelihood ratio statistic in testing simple hypothesis in a new class of regression models proposed here. The proposed class of regression models considers Dirichlet distributed observations, and the parameters that index the Dirichlet distributions are related to covariates and unknown regression coefficients. This class is useful for modelling data consisting of multivariate positive observations summing to one and generalizes the beta regression model described in Vasconcellos and Cribari-Neto [Vasconcellos, K.L.P., Cribari-Neto, F., 2005. Improved maximum likelihood estimation in a new class of beta regression models. Brazilian journal of Probability and Statistics 19,13-31]. We show that, for our model, Skovgaard`s adjusted likelihood ratio statistics have a simple compact form that can be easily implemented in standard statistical software. The adjusted statistic is approximately chi-squared distributed with a high degree of accuracy. Some numerical simulations show that the modified test is more reliable in finite samples than the usual likelihood ratio procedure. An empirical application is also presented and discussed. (C) 2009 Elsevier B.V. All rights reserved.

Fluoride release profile of a nanofilled resin-modified glass ionomer cement

The present study aimed to compare the fluoride (F-) release pattern of a nanofilled resin-modified glass ionomer cement (GIC) (Ketac N100 - KN) with available GICs used in dental practice (resin-modified GIC - Vitremer - V; conventional GIC - Ketac Molar - KM) and a nanofilled resin composite (Filtek Supreme - RC). Discs of each material (n=6) were placed into 4 mL of deionized water in sealed polyethylene vials and shaken, for 15 days. F- release (μg F-/cm²) was measured each day using a fluoride-ion specific electrode. Cumulative F- release means were statistically analyzed by linear regression analysis. In order to analyze the differences among materials and the influence of time in the daily F- release, 2-way ANOVA test was performed (α=0.05). The linear fits between the cumulative F- release profiles of RC and KM and time were weak. KN and V presented a strong relationship between cumulative F- release and time. There were significant differences between the daily F- release overtime up to the third day only for GICs materials. The daily F- release means for RC were similar overtime. The results indicate that the F- release profile of the nanofilled resin-modified GIC is comparable to the resin-modified GIC.

Diagnostic and clinical factors associated with pulpal and periapical pain

A retrospective survey was designed to identify diagnostic subgroups and clinical factors associated with odontogenic pain and discomfort in dental urgency patients. A consecutive sample of 1,765 patients seeking treatment for dental pain at the Urgency Service of the Dental School of the Federal University of Goiás, Brazil, was selected. Inclusion criteria were pulpal or periapical pain that occurred before dental treatment (minimum 6 months after the last dental appointment), and the exclusion criteria were teeth with odontogenic developmental anomalies and missing information or incomplete records. Clinical and radiographic examinations were performed to assess clinical presentation of pain complaints including origin, duration, frequency and location of pain, palpation, percussion and vitality tests, radiographic features, endodontic diagnosis and characteristics of teeth. Chi-square test and multiple logistic regression were used to analyze association between pulpal and periapical pain and independent variables. The most frequent endodontic diagnosis of pulpal pain were symptomatic pulpitis (28.3%) and hyperreactive pulpalgia (14.4%), and the most frequent periapical pain was symptomatic apical periodontitis of infectious origin (26.4%). Regression analysis revealed that closed pulp chamber and caries were highly associated with pulpal pain and, conversely, open pulp chamber was associated with periapical pain (p<0.001). Endodontic diagnosis and local factors associated with pulpal and periapical pain suggest that the important clinical factor of pulpal pain was closed pulp chamber and caries, and of periapical pain was open pulp chamber.