RESUMEN
Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) is a multisubunit enzyme complex that participates in the generation of superoxide or hydrogen peroxide (H2O2) and plays a key role in several biological functions. Among seven known NOX isoforms, NOX2 was the first identified in phagocytes but is also expressed in several other cell types including endothelial cells, platelets, microglia, neurons, and muscle cells. NOX2 has been assigned multiple roles in regulating many aspects of innate and adaptive immunity, and human and mouse models of NOX2 genetic deletion highlighted this key role. On the other side, NOX2 hyperactivation is involved in the pathogenesis of several diseases with different etiologies but all are characterized by an increase in oxidative stress and inflammatory process. From this point of view, the modulation of NOX2 represents an important therapeutic strategy aimed at reducing the damage associated with its hyperactivation. Although pharmacological strategies to selectively modulate NOX2 are implemented thanks to new biotechnologies, this field of research remains to be explored. Therefore, in this review, we analyzed the role of NOX2 at the crossroads between immunity and pathologies mediated by its hyperactivation. We described (1) the mechanisms of activation and regulation, (2) human, mouse, and cellular models studied to understand the role of NOX2 as an enzyme of innate immunity, (3) some of the pathologies associated with its hyperactivation, and (4) the inhibitory strategies, with reference to the most recent discoveries.
RESUMEN
Recent work in signal processing in general and image processing in particular deals with sparse representation related problems. Two such problems are of paramount importance: an overriding need for designing a well-suited overcomplete dictionary containing a redundant set of atoms-i.e., basis signals-and how to find a sparse representation of a given signal with respect to the chosen dictionary. Dictionary learning techniques, among which we find the popular K-singular value decomposition algorithm, tackle these problems by adapting a dictionary to a set of training data. A common drawback of such techniques is the need for parameter-tuning. In order to overcome this limitation, we propose a fully-automated Bayesian method that considers the uncertainty of the estimates and produces a sparse representation of the data without prior information on the number of non-zeros in each representation vector. We follow a Bayesian approach that uses a three-tiered hierarchical prior to enforce sparsity on the representations and develop an efficient variational inference framework that reduces computational complexity. Furthermore, we describe a greedy approach that speeds up the whole process. Finally, we present experimental results that show superior performance on two different applications with real images: denoising and inpainting.
RESUMEN
Compressed sensing (CS) is a fast and efficient way to obtain compact signal representations. Oftentimes, one wishes to extract some information from the available compressed signal. Since CS signal recovery is typically expensive from a computational point of view, it is inconvenient to first recover the signal and then extract the information. A much more effective approach consists in estimating the information directly from the signal's linear measurements. In this paper, we propose a novel framework for compressive estimation of autoregressive (AR) process parameters based on ad hoc sensing matrix construction. More in detail, we introduce a compressive least square estimator for AR(p) parameters and a specific AR(1) compressive Bayesian estimator. We exploit the proposed techniques to address two important practical problems. The first is compressive covariance estimation for Toeplitz structured covariance matrices where we tackle the problem with a novel parametric approach based on the estimated AR parameters. The second is a block-based compressive imaging system, where we introduce an algorithm that adaptively calculates the number of measurements to be acquired for each block from a set of initial measurements based on its degree of compressibility. We show that the proposed techniques outperform the state-of-the-art methods for these two problems.
