Abstract
Diabetic retinopathy (DR) is the leading cause of blindness in adults. In diabetes, there is activation of microglial cells and a concomitant release of inflammatory mediators. However, it remains unclear how diabetes triggers an inflammatory response in the retina. Activation of P2 purinergic receptors by adenosine triphosphate (ATP) may contribute to the inflammatory response in the retina, insofar as it has been shown to be associated with microglial activation and cytokine release. In this work, we evaluated how high glucose, used as a model of hyperglycemia, considered the main factor in the development of DR, affects the extracellular levels of ATP in retinal cell cultures. We found that basal extracellular ATP levels were not affected by high glucose or mannitol, but the extracellular elevation of ATP, after a depolarizing stimulus, was significantly higher in retinal cells cultured in high glucose compared with control or mannitol-treated cells. The increase in the extracellular ATP was prevented by application of botulinum neurotoxin A or by removal of extracellular calcium. In addition, degradation of exogenously added ATP was significantly lower in high-glucose-treated cells. It was also observed that, in retinal cells cultured under high-glucose conditions, the changes in the intracellular calcium concentrations were greater than those in control or mannitol-treated cells. In conclusion, in this work we have shown that high glucose alters the purinergic signaling system in the retina, by increasing the exocytotic release of ATP and decreasing its extracellular degradation. The resulting high levels of extracellular ATP may lead to inflammation involved in the pathogenesis of DR. (C) 2008 Wiley-Liss, Inc.

Abstract
The study of cell nuclei is fundamental for plant cell Biology research. To obtain information at; cellular level, researchers image cells' nuclei which were modified with fluorescence proteins, through laser scanning confocal microscopy. These images are normally noisy and suffer from high background fluorescence, making grey-scale segmentation approaches inadequate for a usable detection. To obtain a successful detection even at low contrast we investigate the use of a particular convergence filter, the Symmetric Sliding Band filter (SSBF), for cell detection. This filter is based on gradient convergence and not intensity. As such it can detect low contrast cell nuclei which otherwise would be lost in the background noise. Due to the characteristics of cell nuclei morphology, a symmetry constrain is integrated in the filter which corrects some inadequate detections and results in a filter response that is more discriminative. We evaluate the use of this filter for cell nuclei detection on the Arabidopsis thaliana root tip, where the nuclei were stained using yellow fluorescence protein. The resulting cell nuclei detection precision is 89%.

Abstract
Current knowledge about the early phases of ontology construction is insufficient to support methods and techniques for a collaborative construction of a conceptualization. Following an approach inspired in cognitive semantics, the application and extension of the Conceptual Blending Theory (CBT) to the realm of collaborative semantic tools is proposed. A formal framework is presented together with some examples in the collaborative networks context. A collaborative semantic architecture is presented. This architecture supports the two components of the proposed method: collective conceptualization and consensus reaching.

Abstract
This paper presents a method for automating the selection of the rejection rate of one-class classifiers aiming at optimizing the classifier performance. These classifiers are used in a new classification approach to deal with class imbalance in Thin-Layer Chromatography (TLC) patterns, which is due to the huge difference between the number of normal and pathological cases, as a consequence of the rarity of Lysosomal Storage Disorders (LSD) diseases. The classification is performed in two decision stages, both implemented using optimized one-class classifiers: the first stage aims at recognizing most of the normal samples; the outliers of this first decision level are presented to the second stage, which is a multiclassifier prepared to deal with both pathological and normal patterns. The results that were obtained proved that the proposed methodology is able to overcome some of the difficulties associated with the choice of the rejection rate of one-class classifiers, and generally contribute to the minimization of the imbalance problem in TLC pattern classification.

Abstract
The problem of extracting meaningful patterns from time changing data streams is of increasing importance for the machine learning and data mining communities. We present an algorithm which is able to learn regression trees from fast and unbounded data streams in the presence of concept drifts. To our best knowledge there is no other algorithm for incremental learning regression trees equipped with change detection abilities. The FIRT-DD algorithm has mechanisms for drift detection and model adaptation, which enable to maintain accurate and updated regression models at any time. The drift detection mechanism is based on sequential statistical tests that track the evolution of the local error, at each node of the tree, and inform the learning process for the detected changes. As a response to a local drift, the algorithm is able to adapt the model only locally, avoiding the necessity of a global model adaptation. The adaptation strategy consists of building a new tree whenever a change is suspected in the region and replacing the old ones when the new trees become more accurate. This enables smooth and granular adaptation of the global model. The results from the empirical evaluation performed over several different types of drift show that the algorithm has good capability of consistent detection and proper adaptation to concept drifts.

Abstract
This paper presents our method to support the collaborative conceptualisation process focusing our strategy for building consensus in the context of collaborative networks. This new strategy comes from the application of the results and recommendations obtained in an experimental evaluation performed in the scope of a large European project in the area of industrial engineering. The usage of our strategy and the collaborative platform supporting semantic consensus building in the scope of the European research project H-Know is described.