Neural Mechanisms of Human Perceptual Learning: Electrophysiological Evidence for a Two-Stage Process

Artículo de publicación ISIBackground: Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. Methodology/Principal Findings: We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.This research was supported by CONICYT doctoral grant to C.M.H. and by an ECOS-Sud/CONICYT grant C08S02 and FONDECYT 1090612 grant to D.C. and F.A

Isolation of colour components from Rubia tinctorum L.: Chromatographic determination, spectrophotometric investigation, dyeing properties and antimicrobial activity

In this paper, a sensitive quantification high performance liquid chromatographic method for analysis of alizarin in madder root (Rubia tinctorum L.) obtained from South of Anatolia, Turkey is reported. The alizarin is separated on Zorbax SB C18 column with a water-acetonitrile gradient as eluent and measured with UV detection at 250 nm. With this method the aglycone alizarin can be analyzed. Regression equation that obtained from the calibration curve, revealed a linear relationship (r2 = 0.9981) between the mass of alizarin injected and the peak area. After, the colour components responsible for dyeing were determinated and its chemical constituents were established based on chemical and spectroscopic investigations. Afterwards, the wool fabrics have been dyed with combined mordanting and mordantless techniques. Fastness to light, washing and rubbing of the dyed fabrics were measured and discussed. Additionally, extracts (ethanolic and aqueous) of R. tinctorum L. root and dyed materials were investigated for their antimicrobial activities against eight pathogens (Aeromanas hydrophila. Bacillus megaterium, Corynebacterium xenosis, Pseudomonas aeruginosa, Micrococcus luteus, Enterococcus faecalis, Stapylococcus aureus and Escherichia coli). The extracts and dyed materials were not effective against the growth of Escherichia coli. The fabric dyed, however, showed less antimicrobial activity, as uptake of this dye in textile material is below minimum inhibitory concentration (MIC)

Artificial neural networks approach for the prediction of thermal balance of SI engine using ethanol-gasoline blends

This study deals with artificial neural network (ANN) modeling of a spark ignition engine to predict engine thermal balance. To acquire data for training and testing of ANN, a four-cylinder, four-stroke test engine was fuelled with ethanol-gasoline blended fuels with various percentages of ethanol and operated at different engine speeds and loads. The performance of the ANN was validated by comparing the prediction data set with the experimental results. Results showed that the ANN provided the best accuracy in modeling the thermal balance with correlation coefficient equal to 0.997, 0.998, 0.996 and 0.992 for useful work, heat lost through exhaust, heat lost to the cooling water and unaccounted losses respectively. The experimental results showed as the percentage of ethanol in the ethanol-gasoline blends is increased, the percentage of useful work is increased, while the heat lost to cooling water and exhaust are decreased compared to neat gasoline fuel operation