História e Filosofia da Biologia como ferramenta no ensino de evolução na formação inicial de professores de Biologia

By establishing a relationship among several sections of Biology, the theory of the evolution enables a more systemized and less fragmented education of this science. However, students seem to have difficulties of understanding the concept of biological evolution, by virtue of misunderstanding conceptual and historical data present in textbooks, among other causes. One of these mentioned distortions is the shock between Lamarck and Darwin’s ideas. We aimed, in this work, to investigate the understanding of Biology’s students in pre-service teachers education concerning the Lamarck and Darwin’s theories at different moments of the Evolution discipline. For this, some steps had been followed: 1) Verification of the previous conceptions; 2) Elaboration of a pedagogical material, containing diverse historical texts of primary and secondary sources; 3) Debates in two didactic modules, using the pedagogical material elaborated; 4) Analysis of the conceptions constructed by the students, from the didactic intervention. The results obtained show the preconceptions of the students interviewed, concerning the Lamarck and Darwin’s theories, resemble the explanations presented in textbooks and the insertion of historical texts in the Evolution lessons can be an interesting strategy to construction of knowledge of this theme

Sentidos e significados do conceito de divisão provenientes de Atividade Orientadora de Ensino

Pós-graduação em Docência para a Educação Básica - FC

Analysis of simple hardware efficiency for temperature measurement in agriculture and environment

This work focuses basically on the design and analysis of simple and low cost hardware systems efficiency for temperature measurement in agricultural area. The main objective is to prove quantitatively, through statistical data analysis, to what extent a simple hardware designed with inexpensive components can be used safely in the indoor temperature measurement in farm buildings, such as greenhouses, warehouse or silos. To verify the of simple hardware efficiency, its data were compared with data from measurements with a high performance LabVIEW platform. This work proved that a simple hardware based on a microcontroller and the LM35 sensor can perform well. It presented a good accuracy but a relatively low precision that can be improved when performed some consecutive signal sampling and then used its average value. Although there are many papers that explain these components, this work has the distinction of presenting a data analysis in numerical form and using high performance systems to ensure critical data comparison.

A internacionalização do ensino superior: estudo de caso sobre a mobilidade internacional de estudantes de graduação da Universidade Estadual Paulista – UNESP, campus de Franca, no período de 2003 a 2014

Pós-graduação em Planejamento e Análise de Políticas Públicas - FCHS

Planejamento e gestão na responsabilidade social: desafios para o Serviço Social

Pós-graduação em Serviço Social - FCHS

E agora José? Ideologias de gênero e autores de violência sexual contra crianças e adolescentes

Pós-graduação em Serviço Social - FCHS

Methane emission from soil under long-term no-till cropping systems

Methane (CH4) emission from agricultural soils increases dramatically as a result of deleterious effect of soil disturbance and nitrogen fertilization on methanotrophic organisms; however, few studies have attempted to evaluate the potential of long-term conservation management systems to mitigate CH4 emissions in tropical and subtropical soils. This study aimed to evaluate the long-term effect (>19 years) of no-till grass- and legume-based cropping systems on annual soil CH4 fluxes in a formerly degraded Acrisol in Southern Brazil. Air sampling was carried out using static chambers and CH4 analysis by gas chromatography. Analysis of historical data set of the experiment evidenced a remarkable effect of high C- and N-input cropping systems on the improvement of biological, chemical, and physical characteristics of this no-tilled soil. Soil CH4 fluxes, which represent a net balance between consumption (-) and production (+) of CH4 in soil, varied from -40 +/- 2 to +62 +/- 78 mu g C m(-2) h(-1). Mean weighted contents of ammonium (NH4+-N) and dissolved organic carbon (DOC) in soil had a positive relationship with accumulated soil CH4 fluxes in the post-management period (r(2) = 0.95, p = 0.05), suggesting an additive effect of these nutrients in suppressing CH4 oxidation and stimulating methanogenesis, respectively, in legume-based cropping systems with high biomass input. Annual CH4 fluxes ranged from -50 +/- 610 to +994 +/- 105 g C ha(-1), which were inversely related to annual biomass-C input (r(2) = 0.99, p = 0.003), with the exception of the cropping system containing pigeon pea, a summer legume that had the highest biologically fixed N input (>300 kg ha(-1) yr(-1)). Our results evidenced a small effect of conservation management systems on decreasing CH4 emissions from soil, despite their significant effect restoring soil quality. We hypothesized that soil CH4 uptake strength has been off-set by an injurious effect of biologically fixed N in legume-based cropping systems on soil methanotrophic microbiota, and by the methanogenesis increase as a result of the O-2 depletion in niches of high biological activity in the surface layer of the no-tillage soil. (C) 2012 Elsevier B.V. All rights reserved.

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.

A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels: An Application to Escherichia coli Bacterium Data

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

Gaussian deconvolution: a useful method for a form-free modeling of scattering data from mono- and multilayered planar systems

A new method for analysis of scattering data from lamellar bilayer systems is presented. The method employs a form-free description of the cross-section structure of the bilayer and the fit is performed directly to the scattering data, introducing also a structure factor when required. The cross-section structure (electron density profile in the case of X-ray scattering) is described by a set of Gaussian functions and the technique is termed Gaussian deconvolution. The coefficients of the Gaussians are optimized using a constrained least-squares routine that induces smoothness of the electron density profile. The optimization is coupled with the point-of-inflection method for determining the optimal weight of the smoothness. With the new approach, it is possible to optimize simultaneously the form factor, structure factor and several other parameters in the model. The applicability of this method is demonstrated by using it in a study of a multilamellar system composed of lecithin bilayers, where the form factor and structure factor are obtained simultaneously, and the obtained results provided new insight into this very well known system.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

Abstract Background Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes. Results Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones. Conclusion We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.

Functional clustering of time series gene expression data by Granger causality

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions: This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

Quadro geral dos direitos da personalidade

Este artigo pretende oferecer uma visão global dos direitos da personalidade, desde a possibilidade de sua aplicação às pessoas jurídicas, passando pela superposição do estudo de seu objeto por outros ramos do Direito, assim como por dados históricos, classificação, análise jurisprudencial, doutrinária e legislativa dos pontos centrais que envolvem tais direitos.