Biomimetic oxidative treatment of spruce wood studied by pyrolysis-molecular beam mass spectrometry coupled with multivariate analysis and (13)C-labeled tetramethylammonium hydroxide thermochemolysis: implications for fungal degradation of wood

In this work, pyrolysis-molecular beam mass spectrometry analysis coupled with principal components analysis and (13)C-labeled tetramethylammonium hydroxide thermochemolysis were used to study lignin oxidation, depolymerization, and demethylation of spruce wood treated by biomimetic oxidative systems. Neat Fenton and chelator-mediated Fenton reaction (CMFR) systems as well as cellulosic enzyme treatments were used to mimic the nonenzymatic process involved in wood brown-rot biodegradation. The results suggest that compared with enzymatic processes, Fenton-based treatment more readily opens the structure of the lignocellulosic matrix, freeing cellulose fibrils from the matrix. The results demonstrate that, under the current treatment conditions, Fenton and CMFR treatment cause limited demethoxylation of lignin in the insoluble wood residue. However, analysis of a water-extractable fraction revealed considerable soluble lignin residue structures that had undergone side chain oxidation as well as demethoxylation upon CMFR treatment. This research has implications for our understanding of nonenzymatic degradation of wood and the diffusion of CMFR agents in the wood cell wall during fungal degradation processes.

Variability on red blood cell transfusion practices among Brazilian neonatal intensive care units

BACKGROUND: Guidelines for red blood cell (RBC) transfusions exist; however, transfusion practices vary among centers. This study aimed to analyze transfusion practices and the impact of patients and institutional characteristics on the indications of RBC transfusions in preterm infants. STUDY DESIGN AND METHODS: RBC transfusion practices were investigated in a multicenter prospective cohort of preterm infants with a birth weight of less than 1500 g born at eight public university neonatal intensive care units of the Brazilian Network on Neonatal Research. Variables associated with any RBC transfusions were analyzed by logistic regression analysis. RESULTS: Of 952 very-low-birth-weight infants, 532 (55.9%) received at least one RBC transfusion. The percentages of transfused neonates were 48.9, 54.5, 56.0, 61.2, 56.3, 47.8, 75.4, and 44.7%, respectively, for Centers 1 through 8. The number of transfusions during the first 28 days of life was higher in Center 4 and 7 than in other centers. After 28 days, the number of transfusions decreased, except for Center 7. Multivariate logistic regression analysis showed higher likelihood of transfusion in infants with late onset sepsis (odds ratio [OR], 2.8; 95% confidence interval [CI], 1.8-4.4), intraventricular hemorrhage (OR, 9.4; 95% CI, 3.3-26.8), intubation at birth (OR, 1.7; 95% CI, 1.0-2.8), need for umbilical catheter (OR, 2.4; 95% CI, 1.3-4.4), days on mechanical ventilation (OR, 1.1; 95% CI, 1.0-1.2), oxygen therapy (OR, 1.1; 95% CI, 1.0-1.1), parenteral nutrition (OR, 1.1; 95% CI, 1.0-1.1), and birth center (p < 0.001). CONCLUSIONS: The need of RBC transfusions in very-low-birth-weight preterm infants was associated with clinical conditions and birth center. The distribution of the number of transfusions during hospital stay may be used as a measure of neonatal care quality.

MULTIVARIATE STATISTICAL METHOD IN OPTIMIZATION OF PROTEIN EXTRACTION FROM SOYBEAN FLOUR WITH DAIRY WHEY

The aim objective of this project was to evaluate the protein extraction of soybean flour in dairy whey, by the multivariate statistical method with 2(3) experiments. Influence of three variables were considered: temperature, pH and percentage of sodium chloride against the process specific variable ( percentage of protein extraction). It was observed that, during the protein extraction against time and temperature, the treatments at 80 degrees C for 2h presented great values of total protein (5.99%). The increasing for the percentage of protein extraction was major according to the heating time. Therefore, the maximum point from the function that represents the protein extraction was analysed by factorial experiment 2(3). By the results, it was noted that all the variables were important to extraction. After the statistical analyses, was observed that the parameters as pH, temperature, and percentage of sodium chloride, did not sufficient for the extraction process, since did not possible to obtain the inflection point from mathematical function, however, by the other hand, the mathematical model was significant, as well as, predictive.

Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

Multivariate analysis of sensory characteristics of coffee grains (Coffea arabica L.) in the region of upper Paranaiba

This study aimed to examine the sensory characteristics of the grains of 21 cultivars of Coffea arabica L. and Coffea canephora Pierre from the essays of genetic improvement of EPAMIG, located in Patrocinio Municipality, Minas Gerais State, where they were collected through cloths stripping method and washed. Subsequently to dry (11 to 12% moisture b.u.), we obtained the coffee designated as natural. The evaluated varieties were: Acaia Cerrado MG 1474; Bourbon Vermelho DATERRA; Catigua MG 1; Catigua MG 2; Catual Amarelo IAC 62; Catuai Vermelho IAC 15; H 419-3-1-4-2; H 419-6-2 -5-2; H 419-6-2-5-3; H 419-6-2-7-3 Vermelho; H 493-1-2-10; H 514-7-10-1 Vermelho; H 514-7-10-6; H 515-4-2-2; H 518-3-6-1; Icatu Amarelo IAC 3282; Mundo Novo 379-19; Mundo Novo TAO 376-4; Rubi MG 1192; Sacramento MG 1 and Topazio MG 1190, from 2005/2006 and 2006/2007 seasons. The cultivars according to the first principal component with notes above 80 points, regarded as superior drink according to attributes with the highest scores (flavor, sweetness, balance, acidity, clean drink, and aspect) were: Catigua MG2, Rubi MG 1192, 514-7-10-6 H, H 419-3-1-4-2, H 419-6-2-5-2, 493-1-2-10 H, H 514-7-10-1 Vermelho, Catigua MG1, Sacramento MG1, 419-6-2-5-3 H, H 515-9-2-2 and Catuai Amarelo IAC 62.

A log-extended Weibull regression model

A bathtub-shaped failure rate function is very useful in survival analysis and reliability studies. The well-known lifetime distributions do not have this property. For the first time, we propose a location-scale regression model based on the logarithm of an extended Weibull distribution which has the ability to deal with bathtub-shaped failure rate functions. We use the method of maximum likelihood to estimate the model parameters and some inferential procedures are presented. We reanalyze a real data set under the new model and the log-modified Weibull regression model. We perform a model check based on martingale-type residuals and generated envelopes and the statistics AIC and BIC to select appropriate models. (C) 2009 Elsevier B.V. All rights reserved.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

On estimation and influence diagnostics for zero-inflated negative binomial regression models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

Endophytic Colonization of Potato (Solanum tuberosum L.) by a Novel Competent Bacterial Endophyte, Pseudomonas putida Strain P9, and Its Effect on Associated Bacterial Communities

Pseudomonas putida strain P9 is a novel competent endophyte from potato. P9 causes cultivar-dependent suppression of Phytophthora infestans. Colonization of the rhizoplane and endosphere of potato plants by P9 and its rifampin-resistant derivative P9R was studied. The purposes of this work were to follow the fate of P9 inside growing potato plants and to establish its effect on associated microbial communities. The effects of P9 and P9R inoculation were studied in two separate experiments. The roots of transplants of three different cultivars of potato were dipped in suspensions of P9 or P9R cells, and the plants were planted in soil. The fate of both strains was followed by examining colony growth and by performing PCR-denaturing gradient gel electrophoresis (PCR-DGGE). Colonies of both strains were recovered from rhizoplane and endosphere samples of all three cultivars at two growth stages. A conspicuous band, representing P9 and P9R, was found in all Pseudomonas PCR-DGGE fingerprints for treated plants. The numbers of P9R CFU and the P9R-specific band intensities for the different replicate samples were positively correlated, as determined by linear regression analysis. The effects of plant growth stage, genotype, and the presence of P9R on associated microbial communities were examined by multivariate and unweighted-pair group method with arithmetic mean cluster analyses of PCR-DGGE fingerprints. The presence of strain P9R had an effect on bacterial groups identified as Pseudomonas azotoformans, Pseudomonas veronii, and Pseudomonas syringae. In conclusion, strain P9 is an avid colonizer of potato plants, competing with microbial populations indigenous to the potato phytosphere. Bacterization with a biocontrol agent has an important and previously unexplored effect on plant-associated communities.

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.

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.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Paclitaxel associated with cholesterol-rich nanoemulsions promotes atherosclerosis regression in the rabbit

A cholesterol-rich nanoemulsion (LDE) that resembles LDL binds to the LDL receptors and after injection into the blood stream may concentrate in cells with LDL receptor overexpression, as occurs in neoplasias and other proliferative processes. Thus, LDE can be used as vehicle to target drugs against those cells. The current study was designed to verify in rabbits whether LDE concentrates in the lesioned rabbit artery and whether a paclitaxel derivative, paclitaxel oleate, associated to LDE could reduce the atherosclerotic lesions. Sixteen male New Zealand rabbits were fed a 1% cholesterol diet for 60 days. Starting from day 30 under cholesterol feeding, eight animals were treated with four weekly intravenous injections of LDE-paclitaxel (4 mg/kg) and eight with four weekly intravenous saline solution injections for additional 30 days. On day 60, the animals were sacrificed for analysis. The uptake of LDE labeled with [C-14]-cholesteryl oleate by the aortic arch of cholesterol-fed rabbits was twice as much that observed in animals fed only regular chow. LDE-paclitaxel reduced the lesion areas of cholesterol-fed animals by 60% and intima-media ratio fourfold and inhibited the macrophage migration and the smooth muscle cell proliferation and invasion of the intima. LDE-paclitaxel treatment had no toxicity. In conclusion, LDE-paclitaxel produced pronounced atherosclerosis regression without toxicity and has shown remarkable potential in cardiovascular therapeutics. (c) 2008 Published by Elsevier Ireland Ltd.

Evaluation of natural and synthetic compounds according to their antioxidant activity using a multivariate approach

The antioxidant activity of natural and synthetic compounds was evaluated using five in vitro methods: ferric reducing/antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydradzyl (DPPH), oxygen radical absorption capacity (ORAL), oxidation of an aqueous dispersion of linoleic acid accelerated by azo-initiators (LAOX), and oxidation of a meat homogenate submitted to a thermal treatment (TBARS). All results were expressed as Trolox equivalents. The application of multivariate statistical techniques suggested that the phenolic compounds (caffeic acid, carnosic acid, genistein and resveratrol), beyond their high antioxidant activity measured by the DPPH, FRAP and TBARS methods, showed the highest ability to react with the radicals in the ORAC methodology, compared to the other compounds evaluated in this study (ascorbic acid, erythorbate, tocopherol, BHT, Trolox, tryptophan, citric acid, EDTA, glutathione, lecithin, methionine and tyrosine). This property was significantly correlated with the number of phenolic rings and catecholic structure present in the molecule. Based on a multivariate analysis, it is possible to select compounds from different clusters and explore their antioxidant activity interactions in food products.