Abstract Prediction of transcription factor binding sites (TFBS) is an example of application of Bioinformatics where DNA molecules are represented as sequences of A, C, G and T symbols. The most used model in this problem is Position Weight Matrix (PWM). Notwithstanding the advantage of being simple, PWMs cannot capture dependency between nucleotide positions, which may affect prediction performance. Acyclic Probabilistic Finite Automata (APFA) is an alternative model able to accommodate position dependencies. However, APFA is a more complex model, which means more parameters have to be learned. In this paper, we propose an innovative method to identify when position dependencies influence preference for PWMs or APFAs. This implied using position dependency features extracted from 1106 sets of TFBS to infer a decision tree able to predict which is the best model - PWM or APFA - for a given set of TFBSs. According to our results, as few as three pinpointed features are able to choose the best model, providing a balance of performance (average precision) and model simplicity.

A rã-touro americana (Rana catesbeiana) recentemente denominada Lithobates catesbeianus é cria da com propósito comercial em várias regiões do Brasil. Situações estressantes tais como problemas de manejo, criação inadequada e alterações ambientais com consequente redução da imunidade são comuns em produções intensivas. A avaliação destas situações de estresse permite-nos detectar estes probemas e diminuir as injurias causadas pelo confinamento. O principal objetivo deste estudo foi utilizar os marcadores biológicos de cortisol, glicemia e dados hematológicos para avaliar a resposta de girinos de rã-touro submetidos aos mecanismos estressores de captura e hipóxia. Os animais foram distribuídosem três tratamentos: estresse por captura individual com puçá; estresse por captura em massa com puçá e estresse por captura por escoamento. Os resultados obtidos demostraram não haver diferenças estatisticamente significativas entre os parametros avaliados quando comparou-se os grupos com e sem exposição ao ar (normoxia e hipoxia). Com base nestes resultados pode-se concluir que os estímulos estressores avaliados não foram adequados para alterar os valores plasmáticos dos marcadores biológicos testados. Para o cortisol, isto ocorreu provavelmente em virtude da ação sinérgica deste hormônio e a tiroxina, que induz a metamorfose nestes animais.

The American bullfrog (Rana catesbeiana), recently named Lithobates catesbeianus is currently farmed for commercial purposes throughout various regions of Brazil. Stressful situations such as problems of management, inadequate facilities and environmental changes with consequent reduction of immunity are common in intensive production. The assessments of these situations of stress allow us detect these problems decreasing the injuries causes by confinement. The main objective of this study was to use the biological markers of plasma cortisol and glucose level and hematological parameters to evaluate the response of bullfrog tadpoles submitted to stressed mechanisms of capture and hypoxia. The animals were subjected to three treatments: stress due to individual capture with a hand net; stress due to batch capture with a hand net; and stress due to capture by emptying. The results obtained demonstrated that there were no statistically significant differences in the parameters tested when comparing the treatments with and without exposure to air (normoxia and hypoxia). Based on these results we can conclude that the stressful stimuli tested were not adequate to alter the biomarker tested. For the cortisol, probably this should have occurred due to the synergistic action between this hormone and thyroxin, which induces metamorphosis in these animals.

Much forensic inference based upon DNA evidence is made assuming Hardy-Weinberg Equilibrium (HWE) for the genetic loci being used. Several statistical tests to detect and measure deviation from HWE have been devised, and their limitations become more obvious when testing for deviation within multiallelic DNA loci. The most popular methods-Chi-square and Likelihood-ratio tests-are based on asymptotic results and cannot guarantee a good performance in the presence of low frequency genotypes. Since the parameter space dimension increases at a quadratic rate on the number of alleles, some authors suggest applying sequential methods, where the multiallelic case is reformulated as a sequence of "biallelic" tests. However, in this approach it is not obvious how to assess the general evidence of the original hypothesis; nor is it clear how to establish the significance level for its acceptance/rejection. In this work, we introduce a straightforward method for the multiallelic HWE test, which overcomes the aforementioned issues of sequential methods. The core theory for the proposed method is given by the Full Bayesian Significance Test (FBST), an intuitive Bayesian approach which does not assign positive probabilities to zero measure sets when testing sharp hypotheses. We compare FBST performance to Chi-square, Likelihood-ratio and Markov chain tests, in three numerical experiments. The results suggest that FBST is a robust and high performance method for the HWE test, even in the presence of several alleles and small sample sizes.

No mercado financeiro, existem modelos de otimização de portfólios já bem estabelecidos, denominados modelos de média-variância generalizados, ou modelos de Markowitz generalizados. Este modelo considera que um investidor típico, enquanto espera altos retornos, espera também que estes retornos sejam tão certos quanto possível. Neste artigo introduzimos um novo sistema otimizador de mídia baseado no modelo de média-variância, uma abordagem inovadora na área de planejamento de mídia. Após apresentar o modelo em sua máxima generalidade, discutimos possíveis vantagens do paradigma de média-variância, como sua flexibilidade na modelagem do problema de otimização, sua habilidade de lidar com vários índices de performance - satisfazendo a maioria dos requisitos de planejamento - e, o mais importante, a propriedade de diversificar os portfólios de mídia de uma forma natural, sem a necessidade de estipular restrições ad hoc para forçar a diversificação.

In the financial markets, there is a well established portfolio optimization model called generalized mean-variance model (or generalized Markowitz model). This model considers that a typical investor, while expecting returns to be high, also expects returns to be as certain as possible. In this paper we introduce a new media optimization system based on the mean-variance model, a novel approach in media planning. After presenting the model in its full generality, we discuss possible advantages of the mean-variance paradigm, such as its flexibility in modeling the optimization problem, its ability of dealing with many media performance indices - satisfying most of the media plan needs - and, most important, the property of diversifying the media portfolios in a natural way, without the need to set up ad hoc constraints to enforce diversification.