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.

Resveratrol modulates the phosphoinositide 3-kinase pathway through an estrogen receptor α-dependent mechanism: Relevance in cell proliferation

Abstract: Resveratrol (RES), a natural phytoalexin, has antiproliferative activity in human-derived cancer cells and in rodent models of tumor development. We have previously shown that RES induced apoptotic death in estrogen-responsive MCF-7 human breast cancer cells. Recent data have indicated that the estrogen receptor-α (ERα), through interaction with p85, regulates phosphoinositide 3-kinase (PI3K) activity, revealing a physiologic, nonnuclear function of the ERα potentially relevant in cell proliferation and apoptosis. In our study, using MCF-7, we have analyzed the ability of RES to modulate the ERα-dependent PI3K pathway. Immunoprecipitation and kinase activity assays showed that RES increased the ERα-associated PI3K activity with a maximum stimulatory effect at concentrations close to 10 μM; concentrations >50 μM decreased PI3K activity. Stimulation of PI3K activity by RES was ERα-dependent since it could be blocked by the antiestrogen ICI 182,780. RES did not affect p85 protein expression but induced the proteasome-dependent degradation of the ERα. Nevertheless, the amount of PI3K immunoprecipitated by the ERα remained unchanged in presence of RES, indicating that ERα availability was not limiting PI3K activity. Phosphoprotein kinase B (pPKB/AKT) followed the pattern of PI3K activity, whereas RES did not affect total PKB/AKT expression. PKB/AKT downstream target glycogen synthase kinase 3 (GSK3) also showed a phosphorylation pattern that followed PI3K activity. We propose a mechanism through which RES could inhibit survival and proliferation of estrogen-responsive cells by interfering with an ERα-associated PI3K pathway, following a process that could be independent of the nuclear functions of the ERα. © 2003 Wiley-Liss, Inc.

Spectral–Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing

Abstract: Spectral unmixing aims at estimating the fractional abundances of a set of pure spectral materials (endmembers) in each pixel of a hyperspectral image. The wide availability of large spectral libraries has fostered the role of sparse regression techniques in the task of characterizing mixed pixels in remotely sensed hyperspectral images. A general solution for sparse unmixing methods consists of using the $\ell _{1}$ regularizer to control the sparsity, resulting in a very promising performance but also suffering from sensitivity to large and small sparse coefficients. A recent trend to address this issue is to introduce weighting factors to penalize the nonzero coefficients in the unmixing solution. While most methods for this purpose focus on analyzing the hyperspectral data by considering the pixels as independent entities, it is known that there exists a strong spatial correlation among features in hyperspectral images. This information can be naturally exploited in order to improve the representation of pixels in the scene. In order to take advantage of the spatial information for hyperspectral unmixing, in this paper, we develop a new spectral–spatial weighted sparse unmixing (S2WSU) framework, which uses both spectral and spatial weighting factors, further imposing sparsity on the solution. Our experimental results, conducted using both simulated and real hyperspectral data sets, illustrate the good potential of the proposed S2WSU, which can greatly improve the abundance estimation results when compared with other advanced spectral unmixing methods.

Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections

Abstract: Aim The presence-only data stored in natural history collections is the most important source of information available regarding the distribution of organisms These data and profile techniques can be used to generate species distribution models (SDMs), but pseudo-absences must be generated to use group discriminative techniques In this study, we evaluated whether the SDMs generated with pseudo-absences are reliable and also if there are differences in the results obtained with profile and group discriminative techniques Location Ecuador, South America Methods The SDMs were generated with a training data set for each of the five species of Anthurium and six different methods: two profile techniques (BIOCLIM and Gower’s distance index), three group discriminative techniques [logistic multiple regression (LMR), multivariate adaptative regression splines (MARS) and Maxent] and a mixed modelling approach genetic algorithm for rule-set production (GARP), which employs a combination of profile and group discriminative techniques and generates its own pseudo-absences For LMR, MARS and Maxent, three types of absences were generated: (1) random pseudo-absences in equal number to presences and excluding a buffer area around presences (except for Maxent, which assumes that this background sample includes presences), (2) a large number (10,000) of random pseudo-absences, also excluding a buffer area around each presence and (3) ‘target-group absences’ (TGA), consisting of sites where other species of the group have been collected by the specialist, but not the species being modelled To compare the predictive performance of the SDMs, the area under the curve statistic was calculated using an independent testing data set for each species Results MARS, Maxent and LMR produce better results than the profile techniques The models created with TGA are generally more accurate than those generated with pseudo-absences Main conclusions The advantages and disadvantages of different options for using pseudo-absences and TGA with profile and group discriminative modelling techniques are explained and recommendations are made for the future