Assessment of carotenoids in pumpkins after different home cooking conditions.

The aim of this study was to assess the total carotenoid, a- and B-carotene, and 9 and 13-Z- B-carotene isomer contents in C. moschata after different cooking processes

Processamento e caracterização de snack extrudado a partir de farinhas de quirera de arroz e de bandinha de feijão.

Este trabalho teve por objetivo desenvolver nova formulação de snack por extrusão termoplástica a partir de mistura de farinhas de quirera de arroz e de bandinha de feijão, bem como avaliar o potencial nutricional, tecnológico e sensorial do novo produto. A farinha de bandinha de feijão carioca foi incorporada à farinha de quirera de arroz na proporção de 30%. O snack foi produzido em extrusora monorrosca, escala piloto. Os parâmetros de extrusão foram fixos, utilizando-se três zonas de extrusão com temperaturas de 40, 60 e 85 °C; velocidade do parafuso de 177 rpm; taxa de alimentação de 292 g.min?1, e matriz circular de 3,85 mm de diâmetro. A amostra de snack foi submetida a caracterizações fisicoquímica, tecnológica e sensorial. Observou-se efeito significativo da farinha de bandinha de feijão no aumento dos teores proteico e de fibras no snack obtido, quando comparada à farinha de quirera de arroz. Em relação às características tecnológicas do produto, obteve-se 0,17 g.cm?3 para densidade aparente, 7,75 para o índice de expansão e 435, g.f para a dureza instrumental. A formulação estudada foi aceita sensorialmente, com índice de aceitação para impressão global de 76%. Conclui-se que é possível produzir snacks por extrusão a partir da incorporação de 30% de farinha de bandinha de feijão à farinha de quirera de arroz, resultando em produto aceito sensorialmente e com adequado valor nutricional

Composition of the froth secreted by different spittlebug species.

The froth secreted by the spittlebugs protects the immature forms of these pests of pasture and agriculture from environmental damages. Frothes from Deois flavopicta, Mahanarva fimbriolata, Aeneolamia selecta selecta and Deois sp. (schach group) were compared for the purpose of establishing differences that could refleted upon control of these insects. The preparations were analyzed with topochemical methods and polarization microscopy. All of them were found to contain structural proteins and proteoglycans. However, the frothes of M. fimbriolata and Deois sp. appear to differ from the others in terms of anisotropic properties after staining with alcian blue, especially at pH 1.0. Differences in the acid glycosaminoglycans of these frothes, endowing them withdifferent responses to chemical and biological assays for control of these insects is thus suggested

Engorda de pirarucus (Arapaima gigas) em associação com búfalos e suínos.

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The discriminant power of RNA features for pre-miRNA recognition

Computational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). These feature sets used by current tools for pre-miRNA recognition differ in construction and dimension. Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. Current tools achieve similar predictive performance even though the feature sets used - and their computational cost - differ widely. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. More diverse feature sets produce classifiers with significantly higher classification performance compared to feature sets composed only of sequence-structure patterns. However, small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Based on these results, we applied a feature selection method to reduce the computational cost of computing the feature set, while maintaining discriminant power. We obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. Our feature set achieves a sensitivity and specificity within 0.1% of the maximal values obtained with any feature set while it is 34x faster to compute. Even compared to another feature set, which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34x faster to compute.Comment: Submitted to BMC Bioinformatics in October 25, 2013. The material to reproduce the main results from this paper can be downloaded from http://bioinformatics.rutgers.edu/Static/Software/discriminant.tar.g