As atividades circenses e a formação continuada de professores: análise de um processo

Circus activities have formed over the years as an important content to be exploited by the teachers in the school environment, and current projections, the inclusion of circus activities in physical education classes has been presented and defended by several authors. That being so, the objective was to offer a continuing education program thematising circus activities having as research focused on the analysis of this training process, as well as their implications, contributions, opportunities and challenges for teacher pedagogical practice. The research, qualitative, was developed in two phases: a questionnaire for physical education teachers working in public schools in order to highlight the reasons for the absence of most of the teachers in the training program. The second phase included the development of the continuing education program content circus activities in the continuing education of physical education teachers, the two teachers in the school environment, as well as analysis and reflection of teacher participation in the training program, described in daily class and daily meetings ending this step with a final interview. Participated in the study, 13 physical education teachers of the municipal school system of a city of São Paulo, of which only two teachers participated in the development of the training program in schools. The teachers manifestations through the questionnaire and participation in the training program showed that teachers make themselves available to participate in continuing education programs, however, the priorities of each teacher (such as family, leisure or other chores) can demarcate difficulties in establishing common to all teachers moments, preventing the effective participation of teachers within the continuing education programs. On the other hand, the school is set up as a rich space of experiences and exchanges of experience, contributing to the development of continuing education programs

Sleep length, working hours and socio-demographic variables are associated with time attending evening classes among working college students

It is the aim of the present study to assess factors associated with time spent in class among working college students. Eighty-two working students from 21 to 26 years old participated in this study. They were enrolled in an evening course of the University of Sao Paulo, Brazil. Participants answered a questionnaire on living and working conditions. During seven consecutive days, they wore an actigraph, filled out daily activity diaries (including time spent in classes) and the Karolinska Sleepiness Scale every three hours from waking until bedtime. Linear regression analyses were performed in order to assess the variables associated with time spent in classes. The results showed that gender, sleep length, excessive sleepiness, alcoholic beverage consumption (during workdays) and working hours were associated factors with time spent in class. Thus, those who spent less time in class were males, slept longer hours, reported excessive sleepiness on Saturdays, worked longer hours, and reported alcohol consumption. The combined effects of long work hours (>40 h/week) and reduced sleep length may affect lifestyles and academic performance. Future studies should aim to look at adverse health effects induced by reduced sleep duration, even among working students who spent more time attending evening classes.

Selection and recombination drive the evolution of MHC class II DRB diversity in ungulates

Major histocompatibility complex (MHC) antigen-presenting genes are the most variable loci in vertebrate genomes. Host-parasite co-evolution is assumed to maintain the excessive polymorphism in the MHC loci. However, the molecular mechanisms underlying the striking diversity in the MHC remain contentious. The extent to which recombination contributes to the diversity at MHC loci in natural populations is still controversial, and there have been only few comparative studies that make quantitative estimates of recombination rates. In this study, we performed a comparative analysis for 15 different ungulates species to estimate the population recombination rate, and to quantify levels of selection. As expected for all species, we observed signatures of strong positive selection, and identified individual residues experiencing selection that were congruent with those constituting the peptide-binding region of the human DRB gene. However, in addition for each species, we also observed recombination rates that were significantly different from zero on the basis of likelihood-permutation tests, and in other non-quantitative analyses. Patterns of synonymous and non-synonymous sequence diversity were consistent with differing demographic histories between species, but recent simulation studies by other authors suggest inference of selection and recombination is likely to be robust to such deviations from standard models. If high rates of recombination are common in MHC genes of other taxa, re-evaluation of many inference-based phylogenetic analyses of MHC loci, such as estimates of the divergence time of alleles and trans-specific polymorphism, may be required.

Squared Extrapolation Methods (SQUAREM): A New Class of Simple and Efficient Numerical Schemes for Accelerating the Convergence of the EM Algorithm

We derive a new class of iterative schemes for accelerating the convergence of the EM algorithm, by exploiting the connection between fixed point iterations and extrapolation methods. First, we present a general formulation of one-step iterative schemes, which are obtained by cycling with the extrapolation methods. We, then square the one-step schemes to obtain the new class of methods, which we call SQUAREM. Squaring a one-step iterative scheme is simply applying it twice within each cycle of the extrapolation method. Here we focus on the first order or rank-one extrapolation methods for two reasons, (1) simplicity, and (2) computational efficiency. In particular, we study two first order extrapolation methods, the reduced rank extrapolation (RRE1) and minimal polynomial extrapolation (MPE1). The convergence of the new schemes, both one-step and squared, is non-monotonic with respect to the residual norm. The first order one-step and SQUAREM schemes are linearly convergent, like the EM algorithm but they have a faster rate of convergence. We demonstrate, through five different examples, the effectiveness of the first order SQUAREM schemes, SqRRE1 and SqMPE1, in accelerating the EM algorithm. The SQUAREM schemes are also shown to be vastly superior to their one-step counterparts, RRE1 and MPE1, in terms of computational efficiency. The proposed extrapolation schemes can fail due to the numerical problems of stagnation and near breakdown. We have developed a new hybrid iterative scheme that combines the RRE1 and MPE1 schemes in such a manner that it overcomes both stagnation and near breakdown. The squared first order hybrid scheme, SqHyb1, emerges as the iterative scheme of choice based on our numerical experiments. It combines the fast convergence of the SqMPE1, while avoiding near breakdowns, with the stability of SqRRE1, while avoiding stagnations. The SQUAREM methods can be incorporated very easily into an existing EM algorithm. They only require the basic EM step for their implementation and do not require any other auxiliary quantities such as the complete data log likelihood, and its gradient or hessian. They are an attractive option in problems with a very large number of parameters, and in problems where the statistical model is complex, the EM algorithm is slow and each EM step is computationally demanding.

Mixture Cure Survival Models with Dependent Censoring

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival time and the censoring time. To our knowledge, no one has studies the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model which allows for dependent censoring. In particular, we derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time using a class of Archimedean copula models. Within this framework, we consider the parameter estimation, the cure detection, and the two-sample comparison of latency distribution in the presence of dependent censoring when a proportion of patients is deemed cured. Large sample results using the martingale theory are obtained. We applied the proposed methodologies to the SEER prostate cancer data.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

Impact of working memory training on memory performance in old-old adults

Memory impairments constitute an increasing objective and subjective problem with advancing age. The aim of the present study was to investigate the impact of working memory training on memory performance. The authors trained a sample of 80-year-old adults twice weekly over a time period of 3 months. Participants were tested on 4 different memory measures before, immediately after, and 1 year after training completion. The authors found overall increased memory performance in the experimental group compared to an active control group immediately after training completion. This increase was especially pronounced in visual working memory performance and, to a smaller degree, also in visual episodic memory. No group differences were found 1 year after training completion. The results indicate that even in old?old adults, brain plasticity is strong enough to result in transfer effects, that is, performance increases in tasks that were not trained during the intervention.

Student Performance in Traditional vs. Online Format: Evidence from an MBA Level Introductory Economics Class

All previous studies comparing online and face-to-face format for instruction of economics compared courses that were either online or face-to-face format and regressed exam scores on selected student characteristics. This approach is subject to the econometric problems of self-selection omitted unobserved variables. Our study uses two methods to deal with these problems. First we eliminate self-selection bias by using students from a course that uses both instruction formats. Second, we use the exam questions as the unit of observation, and eliminate omitted variable bias by using an indicator variable for each student to capture the effect of differences in unobserved student characteristics on learning outcomes. We report the finding that students had a significantly greater chance of answering a question correctly if it came from a chapter covered online.

The urban middle class in the instability of new democracies

The recent revolts of the middle class in the national capitals of the Philippines and Thailand have raised a new question about democratic consolidation. Why would the urban middle class, which is expected to stabilize democracy, expel the democratically elected leaders through extra-constitutional action? This article seeks to explain such middle class deviation from democratic institutions through an examination of urban primacy and the change in the winning coalition. The authoritarian regime previously in power tended to give considerable favor to the primate city to prevent it revolting against the ruler, because it could have become a menace to his power. But after democratization the new administration shifts policy orientation from an urban to rural bias because it needs to garner support from rural voters to win elections. Such a shift dissatisfies the middle class in the primate city. In this article I take up the Philippines as a case study to examine this theory.