Biologia de Anastrepha fraterculus (Diptera: Tephritidae) em frutos de Campomanesia xanthocarpa.

O objetivo do presente trabalho foi avaliar, em condições controladas, os parâmetros biológicos de A. fraterculus em frutos de guabiroba Campomanesia xanthocarpa

Use of response surface methodology to optimization of extraction of enzymes from pineapple pulp.

The enzymatic reactions are very important for food. They are responsíble for the formation of highly desirable cornpound, but can cause disorders and undesirable consequences. The physiological behaviour of vegetables and fruits is already known, so that most of its deterioration changes in flavour, colour and nutritional value are caused by enzymes of the Oxidoreductases group, mainly peroxidases (PER) and polyphenol oxidase (PPO); The degradation of pectic polysaccharides involves two important enzymatic processed whose action is due to pectin methytesterase (PME) and po/ygalacturonase (PG) enzymes which affect the consistency and texture of fruit during ripening and postharvest handling. Pineapp/e is a tropical fruit very appreciated by consumers, but its physical-chemical and biochemical composition varies according to the time and place of production, and maturity stage. In order to optimize the extraction process of enzymes PER, PPO, PME and PG from pineapple pulp (ev. Peróla) the methodology of response surface was used (STATISTICA 7.0) to study the effect of pH (from 4.0 to 8.0) and NaCI concentration (from 0.0 to 2.0 M) of lhe buffer solulion used in lhe extraction of lhe enzymes. The results showed that the best conditions for enzymes extraction were 1.0 M NaCl, pH 6.0 for PER (4752.9 UI g), 1.0 M NaCI and pH 3.5 for PME (5.8 UI g), 2.0 M NaCI and pH 7.4 for PG (0,000034 Ulg.h·l) and 2.0 M NaCI and pH 4.0 for PPO (81.6 UI g).Disponível em: Acesso em: 16 fev. 2011. Edição de Abstracts of the 7th International Pineapple Symposium, Johor Baru, Malaysia, 2010

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand