A review of antimalarial plants used in traditional medicine in communities in Portuguese-Speaking countries: Brazil, Mozambique, Cape Verde, Guinea-Bissau, São Tomé and Príncipe and Angola

The isolation of bioactive compounds from medicinal plants, based on traditional use or ethnomedical data, is a highly promising potential approach for identifying new and effective antimalarial drug candidates. The purpose of this review was to create a compilation of the phytochemical studies on medicinal plants used to treat malaria in traditional medicine from the Community of Portuguese-Speaking Countries (CPSC): Angola, Brazil, Cape Verde, Guinea-Bissau, Mozambique and São Tomé and Príncipe. In addition, this review aimed to show that there are several medicinal plants popularly used in these countries for which few scientific studies are available. The primary approach compared the antimalarial activity of native species used in each country with its extracts, fractions and isolated substances. In this context, data shown here could be a tool to help researchers from these regions establish a scientific and technical network on the subject for the CPSC where malaria is a public health problem.

Base temperature and simulation model for nodes appearance in cape gooseberry (Physalis peruviana L.)

Data was analyzed on development of the solanaceen fruit crop Cape gooseberry to evaluate how well a classical thermal time model could describe node appearance in different environments. The data used in the analysis were obtained from experiments conducted in Colombia in open fields and greenhouse condition at two locations with different climate. An empirical, non linear segmented model was used to estimate the base temperature and to parameterize the model for simulation of node appearance vs. time. The base temperature (Tb) used to calculate the thermal time (TT, ºCd) for node appearance was estimated to be 6.29 ºC. The slope of the first linear segment was 0.023 nodes per TT and 0.008 for the second linear segment. The time at which the slope of node apperance changed was 1039.5 ºCd after transplanting, determined from a statistical analysis of model for the first segment. When these coefficients were used to predict node appearance at all locations, the model successfully fit the observed data (RSME=2.1), especially for the first segment where node appearance was more homogeneous than the second segment. More nodes were produced by plants grown under greenhouse conditions and minimum and maximum rates of node appearance rates were also higher.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.