Rotenone induces degeneration of photoreceptors and impairs the dopaminergic system in the rat retina

Rotenone is a widely used pesticide and a potent inhibitor of mitochondrial complex I (NADH-quinone reductase) that elicits the degeneration of dopaminergic neurons and thereby the appearance of a parkinsonian syndrome. Here we have addressed the alterations induced by rotenone at the functional, morphological and molecular levels in the retina, including those involving both dopaminergic and non-dopaminergic retinal neurons. Rotenone-treated rats showed abnormalities in equilibrium, postural instability and involuntary movements. In their outer retina we observed a loss of photoreceptors, and a reduced synaptic connectivity between those remaining and their postsynaptic neurons. A dramatic loss of mitochondria was observed in the inner segments, as well as in the axon terminals of photoreceptors. In the inner retina we observed a decrease in the expression of dopaminergic cell molecular markers, including loss of tyrosine hydroxylase immunoreactivity, associated with a reduction of the dopaminergic plexus and cell bodies. An increase in immunoreactivity of AII amacrine cells for parvalbumin, a Ca2+-scavenging protein, was also detected. These abnormalities were accompanied by a decrease in the amplitude of scotopic and photopic a- and b-waves and an increase in the b-wave implicit time, as well as by a lower amplitude and greater latency in oscillatory potentials. These results indicate that rotenone induces loss of vision by promoting photoreceptor cell death and impairment of the dopaminergic retinal system.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.