Origem e dispersão dos Tupiguarani: o que diz a morfologia craniana?

A origem e a dispersão dos povos Tupiguarani têm sido intensamente debatidas entre arqueólogos e linguistas nas últimas cinco décadas. Em resumo, pode-se dizer que a ideia de que esses povos, que ocuparam grande parte do território brasileiro e parte da Bolívia, do Paraguai, do Uruguai e da Argentina, tiveram sua etnogênese na Amazônia e dali partiram para o leste e para o sul, por volta de 2.500 anos antes do presente, é bastante aceita entre os especialistas, embora uma dispersão no sentido oposto, isto é, do sul para o norte, com origem na bacia do Tietê-Paraná, não seja completamente descartada. Entre os arqueólogos que consideram a Amazônia como berço desses povos, alguns acreditam que esse surgimento se deu na Amazônia central. Outros acreditam que a etnogênese Tupiguarani ocorreu no sudoeste da Amazônia, onde hoje se concentra a maior diversidade linguística do tronco Tupi. Neste trabalho, a morfologia de 19 crânios associados à cerâmica Tupiguarani ou etnograficamente classificados como tais foram comparados a várias séries cranianas pré-históricas e etnográficas brasileiras por meio de estatísticas multivariadas. Duas técnicas multivariadas foram empregadas: Análise de Componentes Principais, aplicada sobre os centróides de cada série, e Distâncias de Mahalanobis, aplicadas aos dados individuais. Os resultados obtidos sugerem uma origem amazônica para os povos Tupiguarani, sobretudo pela forte associação encontrada entre crânios Tupi e Guarani do sudeste e do sul brasileiro e dos Tupi do norte do Brasil, com os espécimes provenientes da ilha de Marajó incluídos no estudo.

Dimorfismo sexual alar em Aedes scapularis (Diptera: Culicidae)

A detecção do sexo de mosquitos da família Culicidae é importante em estudos faunísticos e epidemiológicos, pois somente as fêmeas possuem competência vetora para patógenos. O dimorfismo sexual de genitália e de apêndices cefálicos é, em geral, facilmente visível em culicídeos. As asas também podem ser dimórficas e assim poderiam complementar o procedimento de sexagem. No entanto, tal distinção não é facilmente notável à observação direta. Visando descrever formalmente o dimorfismo sexual alar em Aedes scapularis, um culicídeo vetorialmente competente para arbovírus e filárias, asas de machos e fêmeas foram comparadas usando-se métodos de morfometria geométrica e análise estatística multivariada. Nestas análises, populações dos municípios São Paulo e Pariquera-Açu (Estado de São Paulo) foram amostradas. A forma das asas mostrou evidente dimorfismo sexual, o que permitiu um índice de acurácia de 100% em testes-cegos de reclassificação, independentemente da origem geográfica. Já o tamanho alar foi sexualmente dimórfico apenas na população de São Paulo. Aparentemente, a forma alar é evolutivamente mais estável que o tamanho, interpretação que está de acordo com a teoria de Dujardin (2008b), de que a forma alar de insetos seria composta por caracteres genéticos quantitativos e pouco influenciada por fatores não-genéticos, enquanto que o tamanho alar seria predominantemente determinado por plasticidade decorrente de influências ambientais.

Evaluating the Taxonomic Identity in Four Species of the Lobose Testate Amoebae Genus Arcella Ehrenberg, 1832

The taxonomic identity in microbial eukaryotes remains an impediment to discussing ecology, biogeography and phylogeny, mainly due to a lack of standards in organism descriptions and few comparative works. The lobose testate amoebae (Arcellinida) present an ideal study system, as progress is severely hindered due to taxonomic confusion. In the present survey, we have examined the morphology, biometry and ecology of 2400 individuals in the genus Arcella Ehrenberg, 1832, collected from the Tiete River in Sao Paulo, Brazil. We then contrasted these new data with 26 previously described species, varieties and forms, looking for consistencies and trying to establish distinct entities. Using a combination of morphology and multivariate statistics we were able to determine 4 distinct taxa (Arcella hemisphaerica, Arcella discoides, Arcella gibbosa and Arcella brasiliensis), each of them encompassing a number of other non-distinct nominal taxa. We describe in detail each of the 4 taxa with notes on ecology and biogeography, and list the indistinguishable names in an effort to make identification and taxonomy in the testate amoebae a more objective and precise exercise by clarifying the taxonomic identity.

Changes in the genetic structure of Bacteria and microbial activity in an agricultural soil amended with tannery sludge

The application of tannery sludge to soils is a form of recycling; however, few studies have examined the impacts of this practice on soil microbial properties. We studied effects of two applications (2006 and 2007) of tannery sludge (with a low chromium content) on the structure of the bacterial community and on the microbial activity of soils. We fertilized an agricultural area in Rolandia, Parana state, Brazil with different doses of sludge based on total N content, which ranged from 0 to 1200 kg N ha(-1). Sludge remained on the soil surface for three months before being plowed. Soils were sampled seven times during the experiment. Bacterial community structure, assessed by denaturing gradient gel electrophoresis (DGGE), was modified by the application of tannery sludge. Soon after the first application, there was clear separation between the bacterial communities in different treatments, such that each dose of sludge was associated with a specific community. These differences remained until 300 days after application and also after the second sludge application, but 666 days after the beginning of the experiment no differences were found in the bacterial communities of the lowest doses and the control. The principal response curve (PRC) analysis showed that the first sludge application strongly stimulated biological activity even 300 days after application. The second application also stimulated activity, but at a lower magnitude and for a shorter time, given that 260 days after the second application there was no difference in biological activity among treatments. PRC also showed that the properties most influenced by the application of tannery sludge were enzymatic activities related to N cycling (asparaginase and urease). The redundancy analysis (RDA) showed that tannery sludge`s influence on microbial activity is mainly related to increases in inorganic N and soil pH. Results showed that changes in the structure of the bacterial community in the studied soils were directly related to changes of their biological activity. (C) 2010 Elsevier Ltd. All rights reserved.

Characterization of Brazilian lager and brown ale beers based on color, phenolic compounds, and antioxidant activity using chemometrics

BACKGROUND: Epidemiological studies have shown that beer has positive effects on inhibiting atherosclerosis, decreasing the content of serum low-density lipoprotein cholesterol and triglycerides, by acting as in vivo free radical scavenger. In this research, the antioxidant activity of commercial Brazilian beers (n = 29) was determined by the oxygen radical absorbance capacity (ORAC) and 1,1 -diphenyl-2-picrylhydrazyl (DPPH(center dot)) assays and results were analyzed by chemometrics. RESULTS: The brown ale samples (n = 11) presented higher (P < 0.05) flavonoids (124.01 mg L(-1)), total phenolics (362.22 mg L(-1)), non-flavonoid phenolics (238.21 mg L(-1)), lightness (69.48), redness (35.75), yellowness (55.71), color intensity (66.86), hue angle (59.14), color saturation (0.9620), DPPH(center dot) values (30.96% inhibition), and ORAC values (3,659.36 mu mol Trolox equivalents L(-1)), compared to lager samples (n = 18). Brown ale beers presented higher antioxidant properties (P < 0.05) measured by ORAC (1.93 times higher) and DPPH (1.65 times higher) compared to lager beer. ORAC values correlated well with the content of flavonoids (r = 0.47; P = 0.01), total phenolic compounds (r = 0.44; P < 0.01) and DPPH (r = 0.67; P < 0.01). DPPH values also correlated well to the content of flavonoids (r = 0.69; P < 0.01), total phenolic compounds (r = 0.60; P < 0.01), and non-flavonoid compounds (r = 0.46; P = 0.01). CONCLUSION: The results suggest that brown ale beers, and less significantly lager beers, could be sources of bioactive compounds with suitable free radical scavenging properties. (C) 2010 Society of Chemical Industry

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

Use of Bayesian Methods for Multivariate Bioequivalence Measures

In this paper, we introduce a Bayesian analysis for bioequivalence data assuming multivariate pharmacokinetic measures. With the introduction of correlation parameters between the pharmacokinetic measures or between the random effects in the bioequivalence models, we observe a good improvement in the bioequivalence results. These results are of great practical interest since they can yield higher accuracy and reliability for the bioequivalence tests, usually assumed by regulatory offices. An example is introduced to illustrate the proposed methodology by comparing the usual univariate bioequivalence methods with multivariate bioequivalence. We also consider some usual existing discrimination Bayesian methods to choose the best model to be used in bioequivalence studies.

Bayesian analysis of skew-t multivariate null intercept measurement error model

The multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.

Multivariate analysis of the effects of soil parameters and environmental factors on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae

The aim of the present study was to evaluate the effect of soil characteristics (pH, macro- and micro-nutrients), environmental factors (temperature, humidity, period of the year and time of day of collection) and meteorological conditions (rain, sun, cloud and cloud/rain) on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae. The total flavonoid contents of leaf samples harvested from plants cultivated or collected under different conditions were quantified by high-performance liquid chromatography with ultraviolet detection (HPLC-UV/PAD). Chemometric treatment of the data by principal component (PCA) and hierarchic cluster analyses (HCA) showed that the samples did not present a specific classification in relation to the environmental and soil variables studied, and that the environmental variables were not significant in describing the data set. However, the levels of the elements Fe, B and Cu present in the soil showed an inverse correlation with the total flavonoid contents of the leaves of P. incarnata.

An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations

Background: Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers. Results: In this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10(-5) for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers. Conclusions: Our results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.