Showing papers by "University of Extremadura published in 2014"

Revisiting the Sunspot Number. A 400-Year Perspective on the Solar Cycle

Abstract: Our knowledge of the long-term evolution of solar activity and of its primary modulation, the 11-year cycle, largely depends on a single direct observational record: the visual sunspot counts that retrace the last 4 centuries, since the invention of the astronomical telescope. Currently, this activity index is available in two main forms: the International Sunspot Number initiated by R. Wolf in 1849 and the Group Number constructed more recently by Hoyt and Schatten (Sol. Phys. 179:189–219, 1998a, 181:491–512, 1998b). Unfortunately, those two series do not match by various aspects, inducing confusions and contradictions when used in crucial contemporary studies of the solar dynamo or of the solar forcing on the Earth climate. Recently, new efforts have been undertaken to diagnose and correct flaws and biases affecting both sunspot series, in the framework of a series of dedicated Sunspot Number Workshops. Here, we present a global overview of our current understanding of the sunspot number calibration.

Collaborative Sparse Regression for Hyperspectral Unmixing

Abstract: Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach.

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Abstract: Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]-[7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing - blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts - discovered by early remote sensing researchers through empirical observations [3]-[7] and refined by later research - are elegant and very different from statistical independence-based BSS approaches established in the SP field. Moreover, the latest research on blind HU is rapidly adopting advanced techniques, such as those in sparse SP and optimization. The present development of blind HU seems to be converging to a point where the lines between remote sensing-originated ideas and advanced SP and optimization concepts are no longer clear, and insights from both sides would be used to establish better methods.

Incorporation of Pharmacogenomics into Routine Clinical Practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline Development Process

Abstract: The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine's Standards for Developing Trustworthy Clinical Practice Guidelines.

Immunomodulatory Potential of Human Adipose Mesenchymal Stem Cells Derived Exosomes on in vitro Stimulated T Cells

Abstract: In the recent years, it has been widely demonstrated that the biological activity of mesenchymal stem cells (MSCs) is mediated through the release of paracrine factors. Many of these factors are released into exosomes, which are small membranous vesicles that participate in cell-cell communication. Exosomes from MSCs are thought to have similar functions to MSCs such as repairing and regeneration of damaged tissue, but little is known about the immunomodulatory effect of these vesicles. Based on previous reports where the immunomodulatory capacity of MSCs has been demonstrated, here we hypothesized that exosomes from MSCs may have an immunomodulatory role on the differentiation, activation and function of different lymphocyte subsets. According to this hypothesis, in vitro experiments were performed to characterize the immunomodulatory effect of MSCs-derived exosomes on in vitro stimulated T cells. The phenotypic characterization of cytotoxic and helper T cells (activation and differentiation markers) together with functional assays (proliferation and IFN-γ production) demonstrated that MSCs-derived exosomes exerted an inhibition effect in the differentiation and activation of T cells as well as a reduced T cell proliferation and IFN-γ release on in vitro expanded cells. In summary, here we demonstrate that MSCs-derived exosomes are a cell-derived product that could be considered as a therapeutic agent for the treatment of inflammation-related diseases.

Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery

Abstract: Anomaly detection is an active topic in hyperspectral imaging, with many practical applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses the covariance matrix and mean vector to represent background signals, assuming that the background information adjusts to a multivariate normal distribution. However, in general, real images present very complex backgrounds. As a result, in many situations, the background information cannot be properly modeled. An important reason is that that background samples often contain also anomalous pixels and noise, which lead to a high false alarm rate. Therefore, the characterization of the background is essential for successful anomaly detection. In this paper, we develop two novel approaches: weighted-RXD (W-RXD) and linear filter-based RXD (LF-RXD) aimed at improving background in RXD-based anomaly detection. By reducing the weight of the anomalous pixels or noise signals and increasing the weight of the background samples, W-RXD can provide better estimations of the background information. In turn, LF-RXD uses the probability of each pixel as background to filter wrong anomalous or noisy instances. Our experimental results, intended to analyze the performance of the newly developed anomaly detectors, indicate that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.

Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles

Abstract: In recent years, sparse representations have been widely studied in the context of remote sensing image analysis. In this paper, we propose to exploit sparse representations of morphological attribute profiles for remotely sensed image classification. Specifically, we use extended multiattribute profiles (EMAPs) to integrate the spatial and spectral information contained in the data. EMAPs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of structural information. Although the EMAPs' feature vectors may have high dimensionality, they lie in class-dependent low-dimensional subpaces or submanifolds. In this paper, we use the sparse representation classification framework to exploit this characteristic of the EMAPs. In short, by gathering representative samples of the low-dimensional class-dependent structures, any given sample may by sparsely represented, and thus classified, with respect to the gathered samples. Our experiments reveal that the proposed approach exploits the inherent low-dimensional structure of the EMAPs to provide state-of-the-art classification results for different multi/hyperspectral data sets.

Studying the influence of surface topography on bacterial adhesion using spatially organized microtopographic surface patterns.

Abstract: The influence of surface topography on bacterial adhesion has been investigated using a range of spatially organized microtopographic surface patterns generated on polydimethylsiloxane (PDMS) and three unrelated bacterial strains. The results presented indicate that bacterial cells actively choose their position to settle, differentiating upper and lower areas in all the surface patterns evaluated. Such selective adhesion depends on the cells’ size and shape relative to the dimensions of the surface topographical features and surface hydrophobicity/hydrophilicity. Moreover, it was found that all the topographies investigated provoke a significant reduction in bacterial adhesion (30–45%) relative to the smooth control samples regardless of surface hydrophobicity/hydrophilicity. This remarkable finding constitutes a general phenomenon, occurring in both Gram-positive and Gram-negative cells with spherical or rod shape, dictated by only surface topography. Collectively, the results presented in this study de...

Using 3D photo-reconstruction methods to estimate gully headcut erosion

Abstract: In this paper, for the first time, three-dimensional photo-reconstruction methods (3D-PR) based on Structure from Motion (SfM) and MultiView-Stereo (MVS) techniques are tested for estimating the volume of gully headcut retreat. The study was carried out using 5 small headcuts in SW Spain: two headcuts located along the channel and 3 lateral-bank headcuts. Firstly, the accuracy of the resulting models was tested using as benchmark a 3D model obtained by means of a Terrestrial Laser Scanner (TLS). Results of this analysis showed centimetre-level accuracies with average distances between the two point clouds for the five headcuts ranging from 0.009 m to 0.025 m. Then, using a Digital Elevation Model of Differences approach (DoDs) the volume of soil loss was estimated for every headcut. Total soil loss ranged from − 0.246 m3 (erosion) to 0.114 m3 (deposition) for a wet period (289 mm) of 54 days in 2013. A different dynamic was observed for the main and lateral-bank headcuts, which showed erosion and deposition, respectively. Additionally, the use of historical photographs was explored with the aim of estimating long or medium-term erosion rates in gully heads. Results of this simulation pointed out to a clear decrease in the accuracy of the model when the photos are not acquired sequentially around the headcut. Finally, some methodological advices about the use of this 3D-PR procedure for monitoring small geomorphological features are presented.

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.

Robocasting of 45S5 bioactive glass scaffolds for bone tissue engineering

Abstract: Three dimensional scaffolds with controlled pore architecture were prepared from 45S5 Bioglass ® powders by robocasting (direct-writing) using carboxymethyl cellulose (CMC) as the single processing additive. A comprehensive sintering study of the resulting structures was performed within the temperature range 500–1050 °C. Robocast scaffolds with interconnected porosities ranging from 60 to 80% were obtained for a fixed scaffold design. All scaffolds exhibited compressive strengths comparable to that of cancellous bone (2–13 MPa), including those sintered at temperatures below the crystallization temperature of 45S5 bioactive glass. These strength values are substantially higher than any previously reported data for 45S5 Bioglass ® scaffolds and imply that robocasting is the first technique which can be considered suitable for producing vitreous 45S5 scaffolds with a sufficient mechanical integrity for any practical application. Moreover, this process will enable the development of 45S5 Bioglass ® scaffolds with customized external geometry, and optimized pore architecture.

Tax incentives… or subsidies for business R&D?

Abstract: We study whether firms’ actual use of RD (2) one size may not fit all in innovation policy when the type or intensity of market failure differs across firm size, and (3) subsidies may be better suited than tax credits to encourage firms, especially young knowledge-based firms, to start doing R&D.

Two-Sample Two-Stage Least Squares (TSTSLS) Estimates of Earnings Mobility: How Consistent are They?

Abstract: Academics and policymakers have shown great interest in cross-national comparisons of intergenerational earnings mobility. However, producing consistent and comparable estimates of earnings mobility is not a trivial task. In most countries researchers are unable to observe earnings information for two generations. They are thus forced to rely upon imputed data instead. This paper builds upon previous work by considering the consistency of the intergenerational correlation (ρ) as well as the elasticity (β), how this changes when using a range of different instrumental (imputer) variables, and highlighting an important but infrequently discussed measurement issue. Our key finding is that, while TSTSLS estimates of β and ρ are both likely to be inconsistent, the magnitude of this problem is much greater for the former than it is for the latter. We conclude by offering advice on estimating earnings mobility using this methodology.

Toward Ultimate Nanoplasmonics Modeling

Abstract: Advances in the field of nanoplasmonics are hindered by the limited capabilities of simulation tools in dealing with realistic systems comprising regions that extend over many light wavelengths. We show that the optical response of unprecedentedly large systems can be accurately calculated by using a combination of surface integral equation (SIE) method of moments (MoM) formulation and an expansion of the electromagnetic fields in a suitable set of spatial wave functions via fast multipole methods. We start with a critical review of volume versus surface integral methods, followed by a short tutorial on the key features that render plasmons useful for sensing (field enhancement and confinement). We then use the SIE-MoM to examine the plasmonic and sensing capabilities of various systems with increasing degrees of complexity, including both individual and interacting gold nanorods and nanostars, as well as large random and periodic arrangements of ∼1000 gold nanorods. We believe that the present results and methodology raise the standard of numerical electromagnetic simulations in the field of nanoplasmonics to a new level, which can be beneficial for the design of advanced nanophotonic devices and optical sensing structures.

Interethnic variability of CYP2D6 alleles and of predicted and measured metabolic phenotypes across world populations

Abstract: Introduction: The frequency of CYP2D6 alleles, related to either a lack of or increased enzymatic activity, which may lead to poor metabolism (PM) or ultrarapid metabolism (UM), can vary across ethnic groups and hence across geographic regions.Areas covered: Worldwide original research papers on CYP2D6 allelic frequencies, metabolic phenotype frequencies measured with a probe drug, and/or genotype frequencies that studied > 50 healthy volunteers, were included in analyses to describe the distributions of alleles, phenotypes predicted from genotypes (predicted poor metabolizers [gPMs], predicted ultrarapid metabolizers [gUMs]) and metabolic phenotypes (mPMs, mUMs) across ethnic groups and geographic regions. The analysis included 44,572 individuals studied in 172 original research papers.Expert opinion: As of today, Africa and Asia are under-represented in this area relative to the total number of their inhabitants, so that further studies in these regions are warranted. The CYP2D6*4 allele frequency was h...

Playground Designs to Increase Physical Activity Levels During School Recess A Systematic Review

Abstract: School recess provides a major opportunity to increase children's physical activity levels. Various studies have described strategies to increase levels of physical activity. The purpose of this systematic review is therefore to examine the interventions proposed as forms of increasing children's physical activity levels during recess. A systematic search of seven databases was made from the July 1 to July 5, 2012, leading to a final set of eight studies (a total of 2,383 subjects-599 "preschoolers" and 1,784 "schoolchildren") meeting the inclusion criteria. These studies were classified according to the intervention used: playground markings, game equipment, playground markings plus physical structures, and playground markings plus game equipment. The results of these studies indicate that the strategies analyzed do have the potential to increase physical activity levels during recess. The cumulative evidence was (a) that interventions based on playground markings, game equipment, or a combination of the two, do not seem to increase the physical activity of preschoolers and schoolchildren during recess and (ii) that interventions based on playground markings plus physical structures do increase the physical activity of schoolchildren during recess in the short to medium term.

Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization

Abstract: Remotely sensed hyperspectral image classification is a very challenging task. This is due to many different aspects, such as the presence of mixed pixels in the data or the limited information available a priori. This has fostered the need to develop techniques able to exploit the rich spatial and spectral information present in the scenes while, at the same time, dealing with mixed pixels and limited training samples. In this paper, we present a new spectral–spatial classifier for hyperspectral data that specifically addresses the issue of mixed pixel characterization. In our presented approach, the spectral information is characterized both locally and globally, which represents an innovation with regard to previous approaches for probabilistic classification of hyperspectral data. Specifically, we use a subspace-based multinomial logistic regression method for learning the posterior probabilities and a pixel-based probabilistic support vector machine classifier as an indicator to locally determine the number of mixed components that participate in each pixel. The information provided by local and global probabilities is then fused and interpreted in order to characterize mixed pixels. Finally, spatial information is characterized by including a Markov random field (MRF) regularizer. Our experimental results, conducted using both synthetic and real hyperspectral images, indicate that the proposed classifier leads to state-of-the-art performance when compared with other approaches, particularly in scenarios in which very limited training samples are available.