University of Extremadura

About: University of Extremadura is a education organization based out in Badajoz, Spain. It is known for research contribution in the topics: Population & Hyperspectral imaging. The organization has 7856 authors who have published 18299 publications receiving 396126 citations. The organization is also known as: Universidad de Extremadura.

Selenium modulates oxidative stress-induced cell apoptosis in human myeloid HL-60 cells through regulation of calcium release and caspase-3 and -9 activities.

Abstract: Selenium is an essential chemopreventive antioxidant element to oxidative stress, although high concentrations of selenium induce toxic and oxidative effects on the human body. However, the mechanisms behind these effects remain elusive. We investigated toxic effects of different selenium concentrations in human promyelocytic leukemia HL-60 cells by evaluating Ca2+ mobilization, cell viability and caspase-3 and -9 activities at different sample times. We found the toxic concentration and toxic time of H2O2 as 100 μm and 10 h on cell viability in the cells using four different concentrations of H2O2 (1 μm–1 mm) and six different incubation times (30 min, 1, 2, 5, 10, 24 h). Then, we found the therapeutic concentration of selenium to be 200 nm by cells incubated in eight different concentrations of selenium (10 nm–1 mm) for 1 h. We measured Ca2+ release, cell viability and caspase-3 and -9 activities in cells incubated with high and low selenium concentrations at 30 min and 1, 2, 5, 10 and 24 h. Selenium (200 nm) elicited mild endoplasmic reticulum stress and mediated cell survival by modulating Ca2+ release, the caspases and cell apoptosis, whereas selenium concentrations as high as 1 mm induced severe endoplasmic reticulum stress and caused cell death by activating modulating Ca2+ release, the caspases and cell apoptosis. In conclusion, these results explained the molecular mechanisms of the chemoprotective effect of different concentrations of selenium on oxidative stress-induced apoptosis.

Social Capital and Subjective Wellbeing in Europe: A New Approach on Social Capital

Abstract: This paper aims to analyze the relationship between the various dimensions of social capital and subjective wellbeing. Data used in this study come from the fourth wave of the European Social Survey and different measures of wellbeing are used to take account of both the cognitive and affective processes of individual wellbeing (i.e. life satisfaction, happiness, and subjective wellbeing). A factor analysis is performed to summarize information coming from a large set of variables into different components corresponding to each dimension of social capital (i.e. networks, norms, and trust). Among the results, we find that the impact of social capital on subjective wellbeing differ depending on the component of social capital which is under analysis. In particular, social networks, social trust and institutional trust are the components that show a higher correlation with subjective wellbeing. Furthermore, in addition to the positive effects of the individual variables, our results suggest that social capital at the aggregate level positively correlates with individual wellbeing, thus pointing to an external or environmental effect of social capital.

MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression

Abstract: Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures assumed to be known a priori and presented in a large collection, termed spectral library or dictionary, usually acquired in laboratory. Sparse unmixing has attracted much attention as it sidesteps two common limitations of classic spectral unmixing approaches, namely, the lack of pure pixels in hyperspectral scenes and the need to estimate the number of endmembers in a given scene, which are very difficult tasks. However, the high mutual coherence of spectral libraries, jointly with their ever-growing dimensionality, strongly limits the operational applicability of sparse unmixing. In this paper, we introduce a two-step algorithm aimed at mitigating the aforementioned limitations. The algorithm exploits the usual low dimensionality of the hyperspectral data sets. The first step, which is similar to the multiple signal classification array signal processing algorithm, identifies a subset of the library elements, which contains the endmember signatures. Because this subset has cardinality much smaller than the initial number of library elements, the sparse regression we are led to is much more well conditioned than the initial one using the complete library. The second step applies collaborative sparse regression, which is a form of structured sparse regression, exploiting the fact that only a few spectral signatures in the library are active. The effectiveness of the proposed approach, termed MUSIC-CSR, is extensively validated using both simulated and real hyperspectral data sets.