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
In this technical brief, a new subspace state space system identification algorithm for multi-input multi-output bilinear systems driven by white noise inputs is introduced. The new algorithm is based on a uniformly convergent Picard sequence of linear deterministic-stochastic state space subsystems which are easily identifiable by any linear deterministic-stochastic subspace algorithm such as MOESP, N4SID, CVA, or CCA. The key to the proposed algorithm is the fact that the bilinear term is a second-order white noise process. Using a standard linear Kalman filter model, the bilinear term can be estimated and combined with the system inputs at each iteration, thus leading to a linear system with extended inputs of dimension m(n + 1), where n is the system order and m is the dimension of the inputs. It is also shown that the model parameters obtained with the new algorithm converge to those of the true bilinear model. Moreover, the proposed algorithm has the same consistency conditions as the linear subspace identification algorithms when i -> infinity, where i is the number of block rows in the past/future block Hankel data matrices. Typical bilinear subspace identification algorithms available in the literature cannot handle large values of i, thus leading to biased parameter estimates. Unlike existing bilinear subspace identification algorithms whose row dimensions in the data matrices grow exponentially, and hence suffer from the "curse of dimensionality," in the proposed algorithm the dimensions of the data matrices are comparable to those of a linear subspace identification algorithm. A case study is presented with data from a heat exchanger experiment.

Abstract
In this paper the Wiener-Hammerstein system proposed as a benchmark for the SYSID 2009 benchmark session is identified as a bilinear discrete system. The bilinear approximation relies on both facts that the Wiener-Hammerstein system can be described by a Volterra series which can be approximated by bilinear systems. The identification is performed with an iterative bilinear subspace identification algorithm previously proposed by the authors. In order to increase accuracy, polynomial static nonlinearities are added to the bilinear model input. These Hammerstein type bilinear models are then identified using the same iterative subspace identification algorithm. © 2009 IFAC.

Abstract
In this paper we discuss the development and clinical evaluation of a wireless platform for health signs sensing. The sensors measure physical activity, ECG, blood oxygen saturation, temperature and respiratory rate. An important aspect of the approach is that the sensors are integrated into one waist-worn device. A mobile phone collects data from this device and uses data fusion in the scope of a decision support system to trigger additional measurements, classify health conditions or schedule future observations. In these decisions, the user's current physical activity plays an important role as the validity of many health signs measurements is strongly related to physical activity. Due to the integration of the sensors and the use of data fusion it is possible to accurately identify health risks and to react promptly. During clinical trials, for which proper ethical approval was obtained, the system was used by healthy elderly volunteers in Limerick (Ireland) and Ancona (Italy). Results of these trials are also discussed in this paper.

Abstract
Complete optical characterization of biological tissue is desirable to develop clinical methods using optical technologies. Particularly, to develop optical clearing methods in biological tissues, it is necessary to know the composition of the tissue, the percentage of each constituent and corresponding refractive indexes. To obtain such information for rat muscle, we used a simple method to characterize tissue constituents for both content percentage and refractive index. The study consisted on measuring mass with a precision weighting scale and the refractive index with an Abbe refractometer during tissue dehydration. With the collected data, we used a theoretical model to calculate the refractive index and percentage for both interstitial fluid and solid part of the rat muscle. The results obtained are in good agreement with data published by other authors, and were considered of vital information for the optical clearing studies that we planned to perform.

Abstract
During the 2004, 2005 and 2006 growing seasons, physiological and anatomical leaf characteristics and productivity were studied in field-grown grapevines (Vitis vinifera L.) cv. 'Touriga Franca' under ambient (C, 365 +/- 10 ppm) or elevated carbon dioxide vertical bar CO2 vertical bar, (E, 500 +/- 16 ppm) under Open-top chambers (OTC-C and OTC-E, respectively). The elevated vertical bar CO2 vertical bar concentration increased net photosynthetic rate (A), intrinsic water use efficiency (A/g(s)), leaf thickness, Mg concentration, C/N, K/N and Mg/N ratios and decreased stomatal density and N concentration. Nevertheless, stomatal conductance (g(s)), transpiration rate (E), photochemical efficiency (F-v/F-m), leaf water potential, SPAD-values and Red/Far-red ratio transmitted by leaves were not significantly affected by vertical bar CO2 vertical bar. Meanwhile, there is no evidence for downward acclimation of photosynthesis and stomatal conductance. Yield, cluster weight and vigour showed an increase in elevated vertical bar CO2 vertical bar treatment but yield to pruning mass ratio was unaffected. Despite elevated vertical bar CO2 vertical bar stimulates grapevine photosynthesis and yield, more long-term studies, particularly at sub-optimal nutrient and water availability, are needed in order to reveal the grapevine responses to climate change in the Mediterranean area.