The effect of power-ultrasound on the pretreatment of acidified aqueous solutions of banana flower-stalk: Structural, chemical and statistical analysis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Association between risk behaviors and adiposity indicators in adolescents from Southern Brazil: A methodological approach

The aim of this study was to analyze the association between risk behaviors and adiposity indicators in adolescents and to discuss some methodological aspects related to this relationship. We evaluated 1,321 adolescents (55.2% female) aged 10-16 years. Relative body fat (%fat) by measurement of triceps and subscapular skinfold thickness and waist circumference (WC) were used as total and central adiposity indicators, respectively. Physical inactivity, time spent in front of the TV, the consumption of soda and/or chocolate, alcohol, and tobacco smoking were analyzed as risk behaviors. Information about the socioeconomic status (categorized into three levels) and nutritional status of the mother (overweight or normal weight) were used as adjustment factors in the analyses of prevalence ratio (PR) of the outcomes and their associated 95% confidence intervals (95% CI). The chi-square test and Poisson regression were used for statistical analyses. Low associations were found between risk behaviors and adiposity indicators. Tobacco smoking was the most positively correlated behavior with adiposity in girls (%fat: PR = 1.61; 95% CI = 1.04-2.47; WC: PR = 1.90; 95% CI = 1.17-3.08) and in adolescents whose mothers were normal weight (%fat: PR = 2.31; 95% CI = 1.33-4.03; WC: PR: 2.31; CI: 1.19-4.46). Additionally, as an important methodological issue, we highlighted the assessment of risk behaviors in adolescents as crucial to producing more robust evidence on the subject. Of the investigated behaviors, we concluded that tobacco smoking is the behavior most associated with adiposity indicators.

A geometric approach to cosmological boundary conditions

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Taylor's power law for ecological communities: an explanation on nonextensive/nonlinear statistical grounds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

A graphical tool for an analytical approach of scattering photons by the Compton effect

The photons scattered by the Compton effect can be used to characterize the physical properties of a given sample due to the influence that the electron density exerts on the number of scattered photons. However, scattering measurements involve experimental and physical factors that must be carefully analyzed to predict uncertainty in the detection of Compton photons. This paper presents a method for the optimization of the geometrical parameters of an experimental arrangement for Compton scattering analysis, based on its relations with the energy and incident flux of the X-ray photons. In addition, the tool enables the statistical analysis of the information displayed and includes the coefficient of variation (CV) measurement for a comparative evaluation of the physical parameters of the model established for the simulation. (C) 2012 Elsevier B.V. All rights reserved.

Respiratory rehabilitation: a physiotherapy approach to the control of asthma symptoms and anxiety

OBJECTIVES: The objectives of this study were to verify the degree of anxiety, respiratory distress, and health-related quality of life in a group of asthmatic patients who have experienced previous panic attacks. Additionally, we evaluated if a respiratory physiotherapy program (breathing retraining) improved both asthma and panic disorder symptoms, resulting in an improvement in the health-related quality of life of asthmatics. METHODS: Asthmatic individuals were assigned to a chest physiotherapy group that included a breathing retraining program held once a week for three months or a paired control group that included a Subtle Touch program. All patients were assessed using the Diagnostic and Statistical Manual of Mental Disorders IV, the Sheehan Anxiety Scale, the Quality of Life Questionnaire, and spirometry parameter measurements. RESULTS: Both groups had high marks for panic disorder and agoraphobia, which limited their quality of life. The Breathing Retraining Group program improved the clinical control of asthma, reduced panic symptoms and agoraphobia, decreased patient scores on the Sheehan Anxiety Scale, and improved their quality of life. Spirometry parameters were unchanged. CONCLUSION: Breathing retraining improves the clinical control of asthma and anxiety symptoms and the health-related quality of life in asthmatic patients.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

COMPLEXITY MEASURE: A QUANTUM INFORMATION APPROACH

In the past decades, all of the efforts at quantifying systems complexity with a general tool has usually relied on using Shannon's classical information framework to address the disorder of the system through the Boltzmann-Gibbs-Shannon entropy, or one of its extensions. However, in recent years, there were some attempts to tackle the quantification of algorithmic complexities in quantum systems based on the Kolmogorov algorithmic complexity, obtaining some discrepant results against the classical approach. Therefore, an approach to the complexity measure is proposed here, using the quantum information formalism, taking advantage of the generality of the classical-based complexities, and being capable of expressing these systems' complexity on other framework than its algorithmic counterparts. To do so, the Shiner-Davison-Landsberg (SDL) complexity framework is considered jointly with linear entropy for the density operators representing the analyzed systems formalism along with the tangle for the entanglement measure. The proposed measure is then applied in a family of maximally entangled mixed state.

Statistical Innovations Improve Prevalence Estimates of Nutrient Risk Populations: Applications in Sao Paulo, Brazil

The objective of this study was to estimate the prevalence of inadequate micronutrient intake and excess sodium intake among adults age 19 years and older in the city of Sao Paulo, Brazil. Twenty-four hour dietary recall and sociodemographic data were collected from each participant (n=1,663) in a cross-sectional study, Inquiry of Health of Sao Paulo, of a representative sample of the adult population of the city of Sao Paulo in 2003 (ISA-2003). The variability in intake was measured through two replications of the 24-hour recall in a subsample of this population in 2007 (ISA-2007). Usual intake was estimated by the PC-SIDE program (version 1.0, 2003, Department of Statistics, Iowa State University), which uses an approach developed by Iowa State University. The prevalence of nutrient inadequacy was calculated using the Estimated Average Requirement cut-point method for vitamins A and C, thiamin, riboflavin, niacin, copper, phosphorus, and selenium. For vitamin D, pantothenic acid, manganese, and sodium, the proportion of individuals with usual intake equal to or more than the Adequate Intake value was calculated. The percentage of individuals with intake equal to more than the Tolerable Upper Intake Level was calculated for sodium. The highest prevalence of inadequacy for males and females, respectively, occurred for vitamin A (67% and 58%), vitamin C (52% and 62%), thiamin (41% and 50%), and riboflavin (29% and 19%). The adjustment for the within-person variation presented lower prevalence of inadequacy due to removal of within-person variability. All adult residents of Sao Paulo had excess sodium intake, and the rates of nutrient inadequacy were high for certain key micronutrients. J Acad Nutr Diet. 2012;112:1614-1618.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

Prediction of tolerance in children with IgE mediated cow's milk allergy by microarray profiling and chemometric approach

The sera of a retrospective cohort (n = 41) composed of children with well characterized cow's milk allergy collected from multiple visits were analyzed using a protein microarray system measuring four classes of immunoglobulins. The frequency of the visits, age and gender distribution reflected real situation faced by the clinicians at a pediatric reference center for food allergy in 530 Paulo, Brazil. The profiling array results have shown that total IgG and IgA share similar specificity whilst IgM and in particular IgE are distantly related. The correlation of specificity of IgE and IgA is variable amongst the patients and this relationship cannot be used to predict atopy or the onset of tolerance to milk. The array profiling technique has corroborated the clinical selection criteria for this cohort albeit it clearly suggested that 4 out of the 41 patients might have allergies other than milk origin. There was also a good correlation between the array data and ImmunoCAP results, casein in particular. By using qualitative and quantitative multivariate analysis routines it was possible to produce validated statistical models to predict with reasonable accuracy the onset of tolerance to milk proteins. If expanded to larger study groups, the array profiling in combination with the multivariate techniques show potential to improve the prognostic of milk allergic patients. (C) 2012 Elsevier B.V. All rights reserved.

Statistical Information: A Bayesian Perspective

We explore the meaning of information about quantities of interest. Our approach is divided in two scenarios: the analysis of observations and the planning of an experiment. First, we review the Sufficiency, Conditionality and Likelihood principles and how they relate to trivial experiments. Next, we review Blackwell Sufficiency and show that sampling without replacement is Blackwell Sufficient for sampling with replacement. Finally, we unify the two scenarios presenting an extension of the relationship between Blackwell Equivalence and the Likelihood Principle.

A quantitative approach to evolution of music and philosophy

The development of new statistical and computational methods is increasingly making it possible to bridge the gap between hard sciences and humanities. In this study, we propose an approach based on a quantitative evaluation of attributes of objects in fields of humanities, from which concepts such as dialectics and opposition are formally defined mathematically. As case studies, we analyzed the temporal evolution of classical music and philosophy by obtaining data for 8 features characterizing the corresponding fields for 7 well-known composers and philosophers, which were treated with multivariate statistics and pattern recognition methods. A bootstrap method was applied to avoid statistical bias caused by the small sample data set, with which hundreds of artificial composers and philosophers were generated, influenced by the 7 names originally chosen. Upon defining indices for opposition, skewness and counter-dialectics, we confirmed the intuitive analysis of historians in that classical music evolved according to a master apprentice tradition, while in philosophy changes were driven by opposition. Though these case studies were meant only to show the possibility of treating phenomena in humanities quantitatively, including a quantitative measure of concepts such as dialectics and opposition, the results are encouraging for further application of the approach presented here to many other areas, since it is entirely generic.