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Galan-Arriero I.,Sensorimotor Function Group | Avila-Martin G.,Sensorimotor Function Group | Ferrer-Donato A.,Sensorimotor Function Group | Gomez-Soriano J.,Sensorimotor Function Group | And 4 more authors.
Pain | Year: 2014

The p38α mitogenous activated protein kinase (MAPK) cell signaling pathway is a key mechanism of microglia activation and has been studied as a target for neuropathic pain. The effect of UR13870, a p38α MAPK inhibitor, on microglia expression in the anterior cingulate cortex (ACC) and spinal dorsal horn was addressed after T9 contusion spinal cord injury (SCI) in the rat, in addition to behavioral testing of pain-related aversion and anxiety. Administration of intravenous UR13870 (1 mg/kg i.v.) and pregabalin (30 mg/kg i.v.) reduced place escape avoidance paradigm (PEAP) but did not affect open-field anxiety behavior 42 days after SCI. PEAP behavior was also reduced in animals administered daily with oral UR13870 (10 mg/kg p.o.) and preserved spinal tissue 28 days after SCI. Although UR13870 (10 mg/kg p.o.) failed to reduce OX-42 and glial fibrillar acid protein immunoreactivity within the spinal dorsal horn, a reduction toward the control level was observed close to the SCI site. In the anterior cingulate cortex (ACC), a significant increase in OX-42 immunoreactivity was identified after SCI. UR13870 (10 mg/kg p.o.) treatment significantly reduced OX-42, metabotropic glutamate type 5 receptor (mGluR5), and NMDA (N-methyl-d-aspartate) 2B subunit receptor (NR2B) expression in the ACC after SCI. To conclude, oral treatment with a p38α MAPK inhibitor reduces the affective behavioral component of pain after SCI in association with a reduction of microglia and specific glutamate receptors within the ACC. Nevertheless the role of neuroinflammatory processes within the vicinity of the SCI site in the development of affective neuropathic pain cannot be excluded. © 2014 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved. Source

Pajares G.,Complutense University of Madrid | Guijarro M.,Centro Superior Of Estudios Felipe Ii Ingenieria Tecnica En Informatica Of Sistemas | Ribeiro A.,CSIC - Industrial Automation Institute
Neural Networks | Year: 2010

In this paper we propose a new method for combining simple classifiers through the analogue Hopfield Neural Network (HNN) optimization paradigm for classifying natural textures in images. The base classifiers are the Fuzzy clustering (FC) and the parametric Bayesian estimator (BP). An initial unsupervised training phase determines the number of clusters and estimates the parameters for both FC and BP. Then a decision phase is carried out, where we build as many Hopfield Neural Networks as the available number of clusters. The number of nodes at each network is the number of pixels in the image which is to be classified. Each node at each network is initially loaded with a state value, which is the membership degree (provided by FC) that the node (pixel) belongs to the cluster associated to the network. Each state is later iteratively updated during the HNN optimization process taking into account the previous states and two types of external influences exerted by other nodes in its neighborhood. The external influences are mapped as consistencies. One is embedded in an energy term which considers the states of the node to be updated and the states of its neighbors. The other is mapped as the inter-connection weights between the nodes. From BP, we obtain the probabilities that the nodes (pixels) belong to a cluster (network). We define these weights as a relation between states and probabilities between the nodes in the neighborhood of the node which is being updated. This is the classifier combination, making the main finding of this paper. The proposed combined strategy based on the HNN outperforms the simple classifiers and also classical combination strategies. © 2009 Elsevier Ltd. All rights reserved. Source

Burgos-Artizzu X.P.,CSIC - Industrial Automation Institute | Ribeiro A.,CSIC - Industrial Automation Institute | Tellaeche A.,Spanish University for Distance Education (UNED) | Pajares G.,Complutense University of Madrid
Image and Vision Computing | Year: 2010

This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil present in an image showing a region of interest of the crop field. The visual detection of weed, crop and soil is an arduous task due to physical similarities between weeds and crop and to the natural and therefore complex environments (with non-controlled illumination) encountered. The image processing was divided in three different stages at which each different agricultural element is extracted: (1) segmentation of vegetation against non-vegetation (soil), (2) crop row elimination (crop) and (3) weed extraction (weed). For each stage, different and interchangeable methods are proposed, each one using a series of input parameters which value can be changed for further refining the processing. A genetic algorithm was then used to find the best value of parameters and method combination for different sets of images. The whole system was tested on several images from different years and fields, resulting in an average correlation coefficient with real data (bio-mass) of 84%, with up to 96% correlation using the best methods on winter cereal images and of up to 84% on maize images. Moreover, the method's low computational complexity leads to the possibility, as future work, of adapting them to real-time processing. © 2009 Elsevier B.V. All rights reserved. Source

Tellaeche A.,Complutense University of Madrid | Pajares G.,Complutense University of Madrid | Burgos-Artizzu X.P.,CSIC - Industrial Automation Institute | Ribeiro A.,CSIC - Industrial Automation Institute
Applied Soft Computing Journal | Year: 2011

This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies. © 2010 Elsevier B.V. All rights reserved. Source

Sanchez D.G.,CSIC - Industrial Automation Institute | Diaz D.G.,CSIC - Industrial Automation Institute | Hiesgen R.,Esslingen University of Applied Sciences | Wehl I.,Esslingen University of Applied Sciences | Friedrich K.A.,German Aerospace Center
Journal of Electroanalytical Chemistry | Year: 2010

Oscillatory fluctuations of a single polymer electrolyte fuel cell appear upon operation with a dry cathode air supply and a fully humidified anode stream. Periodic transitions between a low and a high current operation point of the oscillating state are observed. The transition time of 20-25 s for the change from the low to the high operation is fast and does not depend on the operating parameters. Contrasting with this behavior, the downward transition depends strongly on the operating conditions. Impedance measurements indicate a high ionic resistance with low water content for the low current operation and a low ionic resistance of the membrane with high water content for the high current operation. An insight into the transitions is obtained by current density distributions at distinct times indicating a propagating active area with defined boundaries. The observations are in agreement with assuming a liquid water reservoir at the anode with a downward transition period depending on the operation conditions. The high current operation possesses a high electro-osmotic drag and a high permeation rate (corresponding to liquid-vapor permeation) leading to a large water flux to the cathode. Subsequently, the liquid reservoir at the anode is consumed leading to an anode drying. The system establishes a new quasi-stable operation point associated with a low current, low electro-osmotic drag coefficient, and a low water permeation (corresponding to vapor-vapor permeation). When liquid water is formed at the anode interface after some time the fast transition to the high current operation occurs. This interpretation is supported by conductive atomic force microscopy current images of the membrane showing a strong dependence of the ionic conductivity on the activation procedures with or without liquid water and also showing oscillatory behavior after the membrane is activated. Specifically, activation with liquid water yields a high conductivity with currents larger by three orders of magnitude. © 2009 Elsevier B.V. All rights reserved. Source

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