Showing papers by "University of Extremadura published in 2012"

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

Abstract: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and highly mixed pixels. Then, contextual information is included using a multilevel logistic Markov-Gibbs Markov random field prior. Finally, a maximum a posteriori segmentation is efficiently computed by the min-cut-based integer optimization algorithm. The proposed segmentation approach is experimentally evaluated using both simulated and real hyperspectral data sets, exhibiting state-of-the-art performance when compared with recently introduced hyperspectral image classification methods. The integration of subspace projection methods with the MLR algorithm, combined with the use of spatial-contextual information, represents an innovative contribution in the literature. This approach is shown to provide accurate characterization of hyperspectral imagery in both the spectral and the spatial domain.

Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

Abstract: Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery.

AUX/LAX Genes Encode a Family of Auxin Influx Transporters That Perform Distinct Functions during Arabidopsis Development

Abstract: Auxin transport, which is mediated by specialized influx and efflux carriers, plays a major role in many aspects of plant growth and development. AUXIN1 (AUX1) has been demonstrated to encode a high-affinity auxin influx carrier. In Arabidopsis thaliana, AUX1 belongs to a small multigene family comprising four highly conserved genes (i.e., AUX1 and LIKE AUX1 [LAX] genes LAX1, LAX2, and LAX3). We report that all four members of this AUX/LAX family display auxin uptake functions. Despite the conservation of their biochemical function, AUX1, LAX1, and LAX3 have been described to regulate distinct auxin-dependent developmental processes. Here, we report that LAX2 regulates vascular patterning in cotyledons. We also describe how regulatory and coding sequences of AUX/LAX genes have undergone subfunctionalization based on their distinct patterns of spatial expression and the inability of LAX sequences to rescue aux1 mutant phenotypes, respectively. Despite their high sequence similarity at the protein level, transgenic studies reveal that LAX proteins are not correctly targeted in the AUX1 expression domain. Domain swapping studies suggest that the N-terminal half of AUX1 is essential for correct LAX localization. We conclude that Arabidopsis AUX/LAX genes encode a family of auxin influx transporters that perform distinct developmental functions and have evolved distinct regulatory mechanisms.

Auxin regulates aquaporin function to facilitate lateral root emergence

Abstract: Aquaporins are membrane channels that facilitate water movement across cell membranes. In plants, aquaporins contribute to water relations. Here, we establish a new link between aquaporin-dependent tissue hydraulics and auxin-regulated root development in Arabidopsis thaliana. We report that most aquaporin genes are repressed during lateral root formation and by exogenous auxin treatment. Auxin reduces root hydraulic conductivity both at the cell and whole-organ levels. The highly expressed aquaporin PIP2;1 is progressively excluded from the site of the auxin response maximum in lateral root primordia (LRP) whilst being maintained at their base and underlying vascular tissues. Modelling predicts that the positive and negative perturbations of PIP2;1 expression alter water flow into LRP, thereby slowing lateral root emergence (LRE). Consistent with this mechanism, pip2;1 mutants and PIP2;1-overexpressing lines exhibit delayed LRE. We conclude that auxin promotes LRE by regulating the spatial and temporal distribution of aquaporin-dependent root tissue water transport.

A further step forward in measuring journals' scientific prestige: The SJR2 indicator

Abstract: A new size-independent indicator of scientific journal prestige, the SJR2 indicator, is proposed. This indicator takes into account not only the prestige of the citing scientific journal but also its closeness to the cited journal using the cosine of the angle between the vectors of the two journals' cocitation profiles. To eliminate the size effect, the accumulated prestige is divided by the fraction of the journal's citable documents, thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores. Its method of computation is described, and the results of its implementation on the Scopus 2008 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), and SNIP, the comparison being made both overall and within specific scientific areas. All three, the SJR2 indicator, the SNIP indicator and the JIF distributions, were found to fit well to a logarithmic law. Although the three metrics were strongly correlated, there were major changes in rank. In addition, the SJR2 was distributed more equalized than the JIF by Subject Area and almost as equalized as the SNIP, and better than both at the lower level of Specific Subject Areas. The incorporation of the cosine increased the values of the flows of prestige between thematically close journals.

On Birkhoff orthogonality and isosceles orthogonality in normed linear spaces

Abstract: We survey mainly recent results on the two most important orthogonality types in normed linear spaces, namely on Birkhoff orthogonality and on isosceles (or James) orthogonality. We lay special emphasis on their fundamental properties, on their differences and connections, and on geometric results and problems inspired by the respective theoretical framework. At the beginning we also present other interesting types of orthogonality. This survey can also be taken as an update of existing related representations.

Associations of Yeasts with Spotted-Wing Drosophila (Drosophila suzukii; Diptera: Drosophilidae) in Cherries and Raspberries

Abstract: A rich history of investigation documents various Drosophila-yeast mutualisms, suggesting that Drosophila suzukii similarly has an association with a specific yeast species or community. To discover candidate yeast species, yeasts were isolated from larval frass, adult midguts, and fruit hosts of D. suzukii. Terminal restriction fragment length polymorphism (TRFLP) technology and decimal dilution plating were used to identify and determine the relative abundance of yeast species present in fruit juice samples that were either infested with D. suzukii or not infested. Yeasts were less abundant in uninfested than infested samples. A total of 126 independent yeast isolates were cultivated from frass, midguts, and fruit hosts of D. suzukii, representing 28 species of yeasts, with Hanseniaspora uvarum predominating. This suggests an association between D. suzukii and H. uvarum that could be utilized for pest management of the highly pestiferous D. suzukii.

Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing

Abstract: Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to estimating the abundance of pure spectral constituents (endmembers) in each (possibly mixed) observation collected by the imaging instrument. In recent years, several endmember extraction algorithms (EEAs) have been proposed for automated endmember extraction from hyperspectral data sets. Traditionally, EEAs extract/select only one single standard endmember spectrum for each of the presented endmember classes or scene components. The use of fixed endmember spectra, however, is a simplification since in many cases the conditions of the scene components are spatially and temporally variable. As a result, variation in endmember spectral signatures is not always accounted for and, hence, spectral unmixing can lead to poor accuracy of the estimated endmember fractions. Here, we address this issue by developing a simple strategy to adapt available EEAs to select multiple endmembers (or bundles) per scene component. We run the EEAs in randomly selected subsets of the original hyperspectral image, and group the extracted samples of pure materials in a bundle using a clustering technique. The output is a spectral library of pure materials, extracted automatically from the input scene. The proposed technique is applied to several common EEAs and combined with an endmember variability reduction technique for unmixing purposes. Experiments with both simulated and real hyperspectral data sets indicate that the proposed strategy can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.

Decreased expression of DNAM-1 on NK cells from acute myeloid leukemia patients

Abstract: This study tested the hypothesis that the expression of CD112 and CD155 (DNAM-1 ligands) on leukemic blasts induces a decreased expression of the activating receptor DNAM-1 on natural killer (NK) cells from acute myeloid leukemia (AML) patients. DNAM-1 is a co-receptor involved in the activation of NK cell cytotoxicity after its interaction with its ligands CD112 and CD155 on target cells. Here we study the expression of DNAM-1 on NK cells and DNAM-1 ligands on blasts from AML patients stratified by age. The results demonstrate that NK cells from AML patients younger than 65 years have a reduced expression of DNAM-1 compared with age-matched controls. The analysis of DNAM-1 ligands showed a high expression of CD112 and CD155 on leukemic blasts. An inverse correlation between CD112 expression on leukemic blasts and DNAM-1 expression on NK cells was found. Furthermore, downregulation of DNAM-1 was induced on healthy donors' NK cells after in vitro culture with leukemic blasts expressing DNAM-1 ligands. In conclusion, these results support the hypothesis that receptor-ligand crosslinking downregulates DNAM-1 expression on NK cells from patients <65 years of age. Considering the relevance of DNAM-1 in NK recognition and killing of leukemic cells, the reduced expression of this receptor on NK cells from AML patients can represent an additional mechanism of tumor escape.

Lifetime obesity in patients with eating disorders: increasing prevalence, clinical and personality correlates.

Abstract: Objectives: The aims of our study were to examine the lifetime prevalence of obesity rate in eating disorders (ED) subtypes and to examine whether there have been temporal changes among the last 10 years and to explore clinical differences between ED with and without lifetime obesity. Methods: Participants were 1383 ED female patients (DSM-IV criteria) consecutively admitted, between 2001 and 2010, to Bellvitge University Hospital. They were assessed by means of the Eating Disorders Inventory-2, the Symptom Checklist-90—Revised, the Bulimic Investigatory Test Edinburgh and the Temperament and Character Inventory—Revised. Results: The prevalence of lifetime obesity in ED cases was 28.8% (ranging from 5% in anorexia nervosa to 87% in binge-eating disorders). Over the last 10 years, there has been a threefold increase in lifetime obesity in ED patients (p < .001). People with an ED and obesity had higher levels of childhood and family obesity (p < .001), a later age of onset and longer ED duration; and had higher levels of eating, general and personality symptomatology. Conclusions: Over the last 10 years, the prevalence of obesity associated with disorders characterized by the presence of binge episodes, namely bulimic disorders, is increasing, and this is linked with greater clinical severity and a poorer prognosis. Copyright © 2012 John Wiley & Sons, Ltd and Eating Disorders Association.

Spatial-Spectral Preprocessing Prior to Endmember Identification and Unmixing of Remotely Sensed Hyperspectral Data

Abstract: Spectral unmixing amounts at estimating the abundance of pure spectral signatures (called endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to insufficient spatial resolution and other phenomena. A challenging problem is how to automatically identify endmembers, as the presence of mixed pixels generally prevents the localization of pure spectral signatures in transition areas between different land-cover classes. A possible strategy to address this problem is to guide the endmember identification process to spatially homogeneous areas, expected to contain the purest signatures available in the scene. For this purpose, several spatial preprocessing methods have been used prior to endmember identification. However, the preprocessing methods developed thus far only exploit the spatial information and relegate the use of spectral information to the subsequent endmember identification stage. In this paper, we develop a new spatial-spectral preprocessing (SSPP) module which can be used prior to endmember identification and spectral unmixing. The method first derives a spatial homogeneity index for each pixel in the hyperspectral image. This index is relatively insensitive to the noise present in the data. At the same time, it performs unsupervised clustering to identify a set of clusters in spectral space. Finally, it fuses spatial and spectral information by selecting a subset of spatially homogeneous and spectrally pure pixels from each cluster. These pixels constitute the new set of candidates for endmember identification. An innovative contribution of this paper is the combination of spatial and spectral information at the preprocessing stage. Another contribution is the combination, for the first time in the literature, of preprocessing techniques with endmember identification algorithms that do not assume the presence of pure signatures in the scene. An experimental comparison of the proposed method in combination with different endmember identification techniques is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that endmember identification techniques (with and without the pure signature assumption) can greatly benefit from the proposed preprocessing approach, which considers both spatial and spectral information.

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.

Value co-creation and university teaching quality Consequences for the European Higher Education Area (EHEA)

Abstract: Purpose - The purpose of this paper is to investigate value co-creation in assessing higher education (HE) teaching quality by acknowledging the influence of all interacting parties: teachers, students and general university service. The paper questions the appropriateness of student satisfaction surveys for assessing lecturer performance. Design/methodology/approach - By introducing co-creation and interaction between several stakeholders the paper deals with a complex problem which is best addressed through multiple approaches. The paper uses a literature review of HE quality together with empirical case study research of one university based on data from documents, student surveys and interviews with lecturers. The data are interpreted in the light of the recent theory of service (S-D) logic and many-to-many marketing. Findings - The paper highlights the complexity of HE service and recommends that EHEA assumes a co-creation perspective. Resources are provided by lecturers, students and university service which require an interactive approach through which the parties integrate these resources. The information asymmetry between lecturers and students invalidates student satisfaction surveys as an instrument to assess teaching quality. The complexity of HE teaching cannot be boiled down to a single number that forms the ground for comparison between lecturers. Originality/value - The paper offers a more valid perspective on HE quality by applying the concepts of value co-creation and resource integration. It shows that the current one-sided student evaluation of teachers is inadequate.

Analyzing Lateral Root Development: How to Move Forward

Abstract: Roots are important to plants for a wide variety of processes, including nutrient and water uptake, anchoring and mechanical support, storage functions, and as the major interface between the plant and various biotic and abiotic factors in the soil environment. Therefore, understanding the development and architecture of roots holds potential for the manipulation of root traits to improve the productivity and sustainability of agricultural systems and to better understand and manage natural ecosystems. While lateral root development is a traceable process along the primary root and different stages can be found along this longitudinal axis of time and development, root system architecture is complex and difficult to quantify. Here, we comment on assays to describe lateral root phenotypes and propose ways to move forward regarding the description of root system architecture, also considering crops and the environment.

A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification

Abstract: Over the last years, many feature extraction techniques have been integrated in processing chains intended for hyperspectral image classification. In the context of supervised classification, it has been shown that the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate extraction of features prior to classification, thus mitigating the curse of dimensionality introduced by the Hughes effect. Recently, a new strategy for feature extraction prior to classification based on spectral unmixing concepts has been introduced. This strategy has shown success when the spatial resolution of the hyperspectral image is not enough to separate different spectral constituents at a sub-pixel level. Another advantage over statistical transformations such as principal component analysis (PCA) or the minimum noise fraction (MNF) is that unmixing-based features are physically meaningful since they can be interpreted as the abundance of spectral constituents. In turn, previously developed unmixing-based feature extraction chains do not include spatial information. In this paper, two new contributions are proposed. First, we develop a new unmixing-based feature extraction technique which integrates the spatial and the spectral information using a combination of unsupervised clustering and partial spectral unmixing. Second, we conduct a quantitative and comparative assessment of unmixing-based versus traditional (supervised and unsupervised) feature extraction techniques in the context of hyperspectral image classification. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and type of feature extraction techniques needed for different classification scenarios.

Development of adsorbents for CO2 capture from waste materials: a review

Abstract: In recent years, a number of scientific papers have been published on the use of residues from industrial and agricultural operations to develop adsorbents for CO2 capture at low, medium, and high temperatures. This is mainly because these waste materials are low-cost and abundant, and may contribute to a reduction of the total costs in carbon capture technologies. In addition, environmental concerns may also be addressed by developing strategies that can use these waste materials instead of burning or sending them to landfills. This review analyses the recent progress made in the development of CO2 adsorbents made from waste precursors. The different preparation approaches developed for the synthesis of adsorbents, including type of raw waste-material, experimental methods, and further modifications, are reviewed. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

Enskog kinetic theory for monodisperse gas–solid flows

Abstract: The Enskog kinetic theory is used as a starting point to model a suspension of solid particles in a viscous gas. Unlike previous efforts for similar suspensions, the gas-phase contribution to the instantaneous particle acceleration appearing in the Enskog equation is modelled using a Langevin equation, which can be applied to a wide parameter space (e.g. high Reynolds number). Attention here is limited to low Reynolds number flow, however, in order to assess the influence of the gas phase on the constitutive relations, which was assumed to be negligible in a previous analytical treatment. The Chapman–Enskog method is used to derive the constitutive relations needed for the conservation of mass, momentum and granular energy. The results indicate that the Langevin model for instantaneous gas–solid force matches the form of the previous analytical treatment, indicating the promise of this method for regions of the parameter space outside of those attainable by analytical methods (e.g. higher Reynolds number). The results also indicate that the effect of the gas phase on the constitutive relations for the solid-phase shear viscosity and Dufour coefficient is non-negligible, particularly in relatively dilute systems. Moreover, unlike their granular (no gas phase) counterparts, the shear viscosity in gas–solid systems is found to be zero in the dilute limit and the Dufour coefficient is found to be non-zero in the elastic limit.