Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Identification and staining of distinct populations of secretory organelles in astrocytes.

Increasing evidence indicates that astrocytes, the most abundant glial cell type in the brain, respond to an elevation in cytoplasmic calcium concentration ([Ca(2+)]i) by releasing chemical transmitters (also called gliotransmitters) via regulated exocytosis of heterogeneous classes of organelles. By this process, astrocytes exert modulatory influences on neighboring cells and are thought to participate in the control of synaptic circuits and cerebral blood flow. Studying the properties of exocytosis in astrocytes is a challenge, because the cell biological basis of this process is incompletely defined. Astrocytic exocytosis involves multiple populations of secretory vesicles, including synaptic-like microvesicles (SLMVs), dense-core granules (DCGs), and lysosomes. Here we summarize the available information for identifying individual populations of secretory organelles in astrocytes, including DCGs, SLMVs, and lysosomes, and present experimental procedures for specifically staining such populations.