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.

Melatonin potentiates chemotherapy‐induced cytotoxicity and apoptosis in rat pancreatic tumor cells

Abstract: Melatonin has antitumor activity via several mechanisms including its antiproliferative and proapoptotic effects in addition to its potent antioxidant action. Thus, melatonin has proven useful in the treatment of tumors in association with chemotherapeutic drugs. This study was performed to evaluate the effect of melatonin on the cytotoxicity and apoptosis induced by three different chemotherapeutic agents, namely 5-fluorouracil (5-FU), cisplatin, and doxorubicin in the rat pancreatic tumor cell line AR42J. We found that both melatonin and the three chemotherapeutic drugs induce a time-dependent decrease in AR42J cell viability, reaching the highest cytotoxic effect after 48 hr of incubation. Furthermore, melatonin significantly augmented the cytotoxicity of the chemotherapeutic agents. Consistently, cotreatment of AR42J cells with each of the chemotherapeutic agents in the presence of melatonin increased the population of apoptotic cells, elevated mitochondrial membrane depolarization, and augmented intracellular reactive oxygen species (ROS) production compared to treatment with each chemotherapeutic agent alone. These results provide evidence that in vitro melatonin enhances chemotherapy-induced cytotoxicity and apoptosis in rat pancreatic tumor AR42J cells and, therefore, melatonin may be potentially applied to pancreatic tumor treatment as a powerful synergistic agent in combination with chemotherapeutic drugs.

CMV and Immunosenescence: from basics to clinics

Abstract: Alone among herpesviruses, persistent Cytomegalovirus (CMV) markedly alters the numbers and proportions of peripheral immune cells in infected-vs-uninfected people. Because the rate of CMV infection increases with age in most countries, it has been suggested that it drives or at least exacerbates "immunosenescence". This contention remains controversial and was the primary subject of the Third International Workshop on CMV & Immunosenescence which was held in Cordoba, Spain, 15-16th March, 2012. Discussions focused on several main themes including the effects of CMV on adaptive immunity and immunosenescence, characterization of CMV-specific T cells, impact of CMV infection and ageing on innate immunity, and finally, most important, the clinical implications of immunosenescence and CMV infection. Here we summarize the major findings of this workshop.

Impact of Initialization on Design of Endmember Extraction Algorithms

Abstract: Many endmember extraction algorithms (EEAs) have been developed to find endmembers that are assumed to be pure signatures in hyperspectral data. However, two issues arising in EEAs have not been addressed: one is the knowledge of the number of endmembers that must be provided a priori, and the other is the initialization of EEAs, where most EEAs initialize their endmember-searching processes by using randomly generated endmembers, which generally result in inconsistent final selected endmembers. Unfortunately, there has been no previous work reported on how to address these two issues, i.e., how to select a set of appropriate initial endmembers and how to determine the number of endmembers p. This paper takes up these two issues and describes two-stage processes to improve EEAs. First, a recently developed concept of virtual dimensionality (VD) is used to determine how many endmembers are needed to be generated for an EEA. Experiments show that the VD is an adequate measure for estimating p. Second, since EEAs are sensitive to initial endmembers, a properly selected set of initial endmembers can make significant improvements on the searching process. In doing so, a new concept of endmember initialization algorithm (EIA) is thus proposed, and four different algorithms are suggested for this purpose. It is surprisingly found that many EIA-generated initial endmembers turn out to be the final desired endmembers. A further objective is to demonstrate that EEAs implemented in conjunction with EIA-generated initial endmembers can significantly reduce the number of endmember replacements as well as the computing time during endmember search

Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression

Abstract: In this letter, we propose a new semisupervised learning (SSL) algorithm for remotely sensed hyperspectral image classification. Our main contribution is the development of a new soft sparse multinomial logistic regression model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. The proposed algorithm represents an innovative contribution with regard to conventional SSL algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with real hyperspectral images, in which comparisons with conventional semisupervised self-learning algorithms with hard labels are carried out. In such comparisons, our method exhibits state-of-the-art performance.