Espacialização da temperatura do ar anual no estado de Alagoas com diferentes modelos digitais de elevação e resoluções espaciais

The aim of this study was to establish a digital elevation model and its horizontal resolution to interpolate the annual air temperature for the Alagoas State by means of multiple linear regression models. A multiple linear regression model was adjusted to series (11 to 34 years) of annual air temperatures obtained from 28 weather stations in the states of Alagoas, Bahia, Pernambuco and Sergipe, in the Northeast of Brazil, in function of latitude, longitude and altitude. The elevation models SRTM and GTOPO30 were used in the analysis, with original resolutions of 90 and 900 m, respectively. The SRTM was resampled for horizontal resolutions of 125, 250, 500, 750 and 900 m. For spatializing the annual mean air temperature for the state of Alagoas, a multiple linear regression model was used for each elevation and spatial resolution on a grid of the latitude and longitude. In Alagoas, estimates based on SRTM data resulted in a standard error of estimate (0.57 degrees C) and dispersion (r(2) = 0.62) lower than those obtained from GTOPO30 (0.93 degrees C and 0.20). In terms of SRTM resolutions, no significant differences were observed between the standard error (0.55 degrees C; 750 m - 0.58 degrees C; 250m) and dispersion (0.60; 500 m - 0.65; 750 m) estimates. The spatialization of annual air temperature in Alagoas, via multiple regression models applied to SRTM data showed higher concordance than that obtained with the GTOPO30, independent of the spatial resolution.

Extensions of discrete triangular distributions and boundary bias in kernel estimation for discrete functions

Asymmetric discrete triangular distributions are introduced in order to extend the symmetric ones serving for discrete associated kernels in the nonparametric estimation for discrete functions. The extension from one to two orders around the mode provides a large family of discrete distributions having a finite support. Establishing a bridge between Dirac and discrete uniform distributions, some different shapes are also obtained and their properties are investigated. In particular, the mean and variance are pointed out. Applications to discrete kernel estimators are given with a solution to a boundary bias problem. (C) 2010 Elsevier B.V. All rights reserved.

Cenários de mudanças climáticas para a América do Sul : impacto no zoneamento agroclimático da cana-de-açúcar

O Painel Intergovernamental sobre Mudanças Climáticas (IPCC) através do seu Quarto Relatório de Avaliação das Mudanças Climáticas Globais (IPCC-AR4), publicado em 2007, atribui as emissões de gases de efeito estufa como a principal causa do aumento médio das temperaturas e alerta para uma elevação entre 1,8 ºC e 6,4 ºC até 2100, podendo modificar assim a aptidão climática para as culturas agrícolas em diversas regiões do planeta. Diante disso, existe a necessidade de substituição dos combustíveis fósseis por fontes renováveis e limpas de energia, como o etanol. A cana-de-açúcar apresenta-se, portanto, como uma cultura estratégica na produção do etanol. O presente trabalho teve como objetivos: 1) avaliar o desempenho dos Modelos Climáticos Globais (MCGs) do IPCC-AR4 na simulação de dados climáticos de temperatura do ar e precipitação pluviométrica para o período anual e mensal; 2) elaborar o zoneamento agroclimático da cana-de-açúcar para a América do Sul considerando o clima referência e o futuro para as décadas de 2020, 2050 e 2080 em função do cenário de emissão A1B considerado pessimista e que usa um equilíbrio entre todas as fontes de energia. Para a avaliação do desempenho dos MCGs, foram utilizados dados climáticos médios mensais observados de precipitação e temperatura do ar provenientes do Climatic Research Unit (CRU) e dados simulados oriundos dos 22 MCGs do IPCC (cenário 20c3m) compreendidos entre o período de 1961-1990, além do Multimodel (ensemble) – MM que é a média da combinação dos dados de todos os modelos. O desempenho dos MCGs foi avaliado pelos índices estatísticos: desvio padrão, correlação, raiz quadrada da média do quadrado das diferenças centralizadas e o “bias” dos dados simulados com os observados, que foram representados no diagrama de Taylor. Para a etapa da elaboração do zoneamento agroclimático procedeu-se o cálculo dos balanços hídricos (referência e futuros) da cultura, pelo método de Thornthwaite & Mather (1955). Para o cenário referência, utilizaram-se dados das médias mensais da precipitação e temperatura provenientes do CRU, enquanto que para as projeções futuras, dados provenientes das anomalias do Multimodel (ensemble) – MM para as décadas de 2020, 2050 e 2080, que foram ajustados, obtendo-se assim as projeções futuras para cada período analisado. Baseado nos mapas temáticos reclassificados de deficiência hídrica anual, temperatura média anual, excedente hídrico anual e no índice de satisfação das necessidades de água (ISNA), realizou-se uma sobreposição dessas informações obtendo assim, os mapas finais do zoneamento agroclimático da cana-de-açúcar. Posteriormente ao zoneamento, realizou-se a análise das transições (ganhos, perdas e persistências) entre as classes de aptidão climática da cultura. Os resultados mostram que o Multimodel (ensemble) – MM para o período mensal apresenta o melhor desempenho entre os modelos analisados. As áreas inaptas correspondem a maior parte da América do Sul e uma expressiva transição entre as classes de aptidão climática da cultura.

A new frontier in biodiversity inventory: a proposal for estimators of phylogenetic and functional diversity

Copyright © 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society.

Perfil neuropsicológico y sesgos cognitivos de adolescentes con diferentes patrones de consumo de alcohol. Neuropsychological profile and cognitive bias in adolescents with different alcohol consumption patterns.

Identificación y caracterización del problema objeto de estudio: Diversas investigaciones señalan que el uso o consumo de alcohol comienza durante la adolescencia. En este periodo del desarrollo típicamente se han reportado deterioros asociados con el consumo de alcohol sobre diversas funciones cognitivas. Sin embargo, los hallazgos no siempre han sido concluyentes. Hipótesis: a) Existe una mayor cantidad de déficits neuropsicológicos (especialmente en los dominios asociados con la toma de decisiones) en los grupos de adolescentes que presentan patrones de consumo elevados o tipo binge y en los adolescentes bajo tratamiento; b) Existen sesgos cognitivos de tipo implícito en los adolescentes expuestos a publicidades referidas a bebidas alcohólicas; y c) Existe mayor presencia de expectativas negativas en el grupo conformado por niños, así como una mayor presencia de expectativas positivas en el grupo conformado por adolescentes. Planteo de Objetivos: se pretende lograr un perfil neuropsicológico de adolescentes de ambos sexos en función de patrones de consumo de alcohol bien diferenciados. Asimismo, se intentará determinar la presencia de sesgos cognitivos hacia estímulos asociados con el alcohol en función de las expectativas hacia el psicotrópico y el tipo de consumo del mismo que presenta la población bajo estudio. Finalmente, se buscará determinar si existen cambios evolutivos en las expectativas hacia el alcohol que pudieran indicar la presencia de mayor vulnerabilidad al consumo de alcohol. Materiales y métodos a utilizar: Se utilizarán pruebas neuropsicológicas (IGT, WCST, otras) y cognitivas (AEQ o similares). Se apelará al uso de diseños ex post facto prospectivos de tipo simple o factoriales, diseños factoriales, estudios instrumentales y estudios descriptivos de poblaciones mediante encuestas. Resultados esperados: 1) Se espera encontrar una mayor cantidad de déficits neuropsicológicos (especialmente en los dominios asociados con la toma de decisiones) en los grupos de adolescentes con patrones de consumo de alcohol elevados y binge, así como en el grupo de adolescentes bajo tratamiento. 2) Se espera encontrar la presencia de sesgos cognitivos implícitos en aquellos adolescentes expuestos a publicidades referidas a bebidas alcohólicas en contraposición a los adolescentes expuestos a publicidades de bebidas no-alcohólicas. 3) Se espera encontrar una mayor presencia de expectativas negativas en el grupo conformado por niños, y una mayor presencia de expectativas positivas en el grupo conformado por adolescentes, con lo que se verificaría el supuesto que señala que las EA se modifican a lo largo del desarrollo. Asimismo, se espera confirmar que las EA cambian en función de la experiencia de consumo de alcohol. Importancia del proyecto: En el ámbito local no se han realizado estudios sistemáticos de esta naturaleza, la información que se obtenga permitirá evaluar el impacto del uso y abuso de alcohol durante la adolescencia así como diseñar y ejecutar mejores estrategias de prevención y de rehabilitación y, al mismo tiempo, facilitará la toma de decisiones en el campo de la planificación de políticas sanitarias dirigidas a los adolescentes.

The Difference, System and ‘Double-D’ GMM Panel Estimators in the Presence of Structural Breaks

The effects of structural breaks in dynamic panels are more complicated than in time series models as the bias can be either negative or positive. This paper focuses on the effects of mean shifts in otherwise stationary processes within an instrumental variable panel estimation framework. We show the sources of the bias and a Monte Carlo analysis calibrated on United States bank lending data demonstrates the size of the bias for a range of auto-regressive parameters. We also propose additional moment conditions that can be used to reduce the biases caused by shifts in the mean of the data.

Overcoming the heterologous bias: an in vivo functional analysis of multidrug efflux transporter, CgCdr1p in matched pair clinical isolates of Candida glabrata.

We have taken advantage of the natural milieu of matched pair of azole sensitive (AS) and azole resistant (AR) clinical isolates of Candida glabrata for expressing its major ABC multidrug transporter, CgCdr1p for structure and functional analysis. This was accomplished by tagging a green fluorescent protein (GFP) downstream of ORF of CgCDR1 and integrating the resultant fusion protein at its native chromosomal locus in AS and AR backgrounds. The characterization confirmed that in comparison to AS isolate, CgCdr1p-GFP was over-expressed in AR isolates due to its hyperactive native promoter and the GFP tag did not affect its functionality in either construct. We observed that in addition to Rhodamine 6 G (R6G) and Fluconazole (FLC), a recently identified fluorescent substrate of multidrug transporters Nile Red (NR) could also be expelled by CgCdr1p. Competition assays with these substrates revealed the presence of overlapping multiple drug binding sites in CgCdr1p. Point mutations employing site directed mutagenesis confirmed that the role played by unique amino acid residues critical to ATP catalysis and localization of ABC drug transporter proteins are well conserved in C. glabrata as in other yeasts. This study demonstrates a first in vivo novel system where over-expression of GFP tagged MDR transporter protein can be driven by its own hyperactive promoter of AR isolates. Taken together, this in vivo system can be exploited for the structure and functional analysis of CgCdr1p and similar proteins wherein the artefactual concerns encountered in using heterologous systems are totally excluded.

On the performance of small-area estimators: Fixed vs. random area parameters

Most methods for small-area estimation are based on composite estimators derived from design- or model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated. Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate.Model-based estimators are justified by the assumption of random (interchangeable) area effects; in practice, however, areas are not interchangeable. In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical population, and another that draws samples from an empirical population of a labor force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.

An empirical evaluation of small area estimators

This paper investigates the comparative performance of five small areaestimators. We use Monte Carlo simulation in the context of boththeoretical and empirical populations. In addition to the direct andindirect estimators, we consider the optimal composite estimator withpopulation weights, and two composite estimators with estimatedweights: one that assumes homogeneity of within area variance andsquare bias, and another one that uses area specific estimates ofvariance and square bias. It is found that among the feasibleestimators, the best choice is the one that uses area specificestimates of variance and square bias.

Bias related to body mass index in pediatric echocardiographic z scores.

In pediatric echocardiography, cardiac dimensions are often normalized for weight, height, or body surface area (BSA). The combined influence of height and weight on cardiac size is complex and likely varies with age. We hypothesized that increasing weight for height, as represented by body mass index (BMI) adjusted for age, is poorly accounted for in Z scores normalized for weight, height, or BSA. We aimed to evaluate whether a bias related to BMI was introduced when proximal aorta diameter Z scores are derived from bivariate models (only one normalizing variable), and whether such a bias was reduced when multivariable models are used. We analyzed 1,422 echocardiograms read as normal in children ≤18 years. We computed Z scores of the proximal aorta using allometric, polynomial, and multivariable models with four body size variables. We then assessed the level of residual association of Z scores and BMI adjusted for age and sex. In children ≥6 years, we found a significant residual linear association with BMI-for-age and Z scores for most regression models. Only a multivariable model including weight and height as independent predictors produced a Z score free of linear association with BMI. We concluded that a bias related to BMI was present in Z scores of proximal aorta diameter when normalization was done using bivariate models, regardless of the regression model or the normalizing variable. The use of multivariable models with weight and height as independent predictors should be explored to reduce this potential pitfall when pediatric echocardiography reference values are evaluated.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Statistical properties of population differentiation estimators under stepwise mutation in a finite island model.

Microsatellite loci mutate at an extremely high rate and are generally thought to evolve through a stepwise mutation model. Several differentiation statistics taking into account the particular mutation scheme of the microsatellite have been proposed. The most commonly used is R(ST) which is independent of the mutation rate under a generalized stepwise mutation model. F(ST) and R(ST) are commonly reported in the literature, but often differ widely. Here we compare their statistical performances using individual-based simulations of a finite island model. The simulations were run under different levels of gene flow, mutation rates, population number and sizes. In addition to the per locus statistical properties, we compare two ways of combining R(ST) over loci. Our simulations show that even under a strict stepwise mutation model, no statistic is best overall. All estimators suffer to different extents from large bias and variance. While R(ST) better reflects population differentiation in populations characterized by very low gene-exchange, F(ST) gives better estimates in cases of high levels of gene flow. The number of loci sampled (12, 24, or 96) has only a minor effect on the relative performance of the estimators under study. For all estimators there is a striking effect of the number of samples, with the differentiation estimates showing very odd distributions for two samples.

Geração eletroquímica do hidreto de selênio em sistema de injeção em fluxo com detecção por espectrometria de absorção atômica com chama Ar-Glp

This paper presents a system for electrochemical hydride generation using flow-injection and atomic absorption spectrometry to determine selenium in biological materials. The electrolytic cell was constructed by assembling two reservoirs, one for the sample and the other for the electrolytic solution separated by a Nafion membrane. Each compartment had a Pt electrode. The sample and electrolyte flow-rates, acidic media, and applied current were adjusted to attain the best analytical performance and ensure the membrane lifetime. The atomisation system used a T quartz tube in an air-LPG flame. The composition of the flame, the observation height, and the argon flow rate used to carry the hydrides were critically investigated. The system allowed to perform thirty determinations per hour with a detection limit of 10 mug L-1 of Se. Relative standard deviations were in general lower than 1.5% for a solution containing 20.0 and 34.0 mug L-1 of Se in a typical sample digest. Accuracy was assessed analysing the certified materials: rice flour (NIST-1568) from National Institute of Standard and Technology and dried fish (MA-A-2), whole animal blood (A-2/1974) from the International Atomic Energy Agency.

Bias and standard error for social reciprocity measurements

The directional consistency and skew-symmetry statistics have been proposed as global measurements of social reciprocity. Although both measures can be useful for quantifying social reciprocity, researchers need to know whether these estimators are biased in order to assess descriptive results properly. That is, if estimators are biased, researchers should compare actual values with expected values under the specified null hypothesis. Furthermore, standard errors are needed to enable suitable assessment of discrepancies between actual and expected values. This paper aims to derive some exact and approximate expressions in order to obtain bias and standard error values for both estimators for round-robin designs, although the results can also be extended to other reciprocal designs.

An Autoregressive Spectral Density Estimator at Frequency Zero for Nonstationarity Tests.

Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero at some process. Kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression are commonly used. However, it is known that with substantially correlated errors, the OLS estimate of the AR(1) parameter is severely biased. in this paper, we first show that this least squares bias induces a significant increase in the bias and mean-squared error of kernel-based estimators.