Showing papers by "University of Extremadura published in 2018"

Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Abstract: Summary Background Neurological disorders are now the leading source of disability globally, and ageing is increasing the burden of neurodegenerative disorders, including Parkinson's disease. We aimed to determine the global burden of Parkinson's disease between 1990 and 2016 to identify trends and to enable appropriate public health, medical, and scientific responses. Methods Through a systematic analysis of epidemiological studies, we estimated global, regional, and country-specific prevalence and years of life lived with disability for Parkinson's disease from 1990 to 2016. We estimated the proportion of mild, moderate, and severe Parkinson's disease on the basis of studies that used the Hoehn and Yahr scale and assigned disability weights to each level. We jointly modelled prevalence and excess mortality risk in a natural history model to derive estimates of deaths due to Parkinson's disease. Death counts were multiplied by values from the Global Burden of Disease study's standard life expectancy to compute years of life lost. Disability-adjusted life-years (DALYs) were computed as the sum of years lived with disability and years of life lost. We also analysed results based on the Socio-demographic Index, a compound measure of income per capita, education, and fertility. Findings In 2016, 6·1 million (95% uncertainty interval [UI] 5·0–7·3) individuals had Parkinson's disease globally, compared with 2·5 million (2·0–3·0) in 1990. This increase was not solely due to increasing numbers of older people, because age-standardised prevalence rates increased by 21·7% (95% UI 18·1–25·3) over the same period (compared with an increase of 74·3%, 95% UI 69·2–79·6, for crude prevalence rates). Parkinson's disease caused 3·2 million (95% UI 2·6–4·0) DALYs and 211 296 deaths (95% UI 167 771–265 160) in 2016. The male-to-female ratios of age-standardised prevalence rates were similar in 2016 (1·40, 95% UI 1·36–1·43) and 1990 (1·37, 1·34–1·40). From 1990 to 2016, age-standardised prevalence, DALY rates, and death rates increased for all global burden of disease regions except for southern Latin America, eastern Europe, and Oceania. In addition, age-standardised DALY rates generally increased across the Socio-demographic Index. Interpretation Over the past generation, the global burden of Parkinson's disease has more than doubled as a result of increasing numbers of older people, with potential contributions from longer disease duration and environmental factors. Demographic and potentially other factors are poised to increase the future burden of Parkinson's disease substantially. Funding Bill & Melinda Gates Foundation.

Forest resilience to drought varies across biomes

Abstract: Forecasted increase drought frequency and severity may drive worldwide declines in forest productivity. Species-level responses to a drier world are likely to be influenced by their functional traits. Here, we analyse forest resilience to drought using an extensive network of tree-ring width data and satellite imagery. We compiled proxies of forest growth and productivity (TRWi, absolutely dated ring-width indices; NDVI, Normalized Difference Vegetation Index) for 11 tree species and 502 forests in Spain corresponding to Mediterranean, temperate, and continental biomes. Four different components of forest resilience to drought were calculated based on TRWi and NDVI data before, during, and after four major droughts (1986, 1994-1995, 1999, and 2005), and pointed out that TRWi data were more sensitive metrics of forest resilience to drought than NDVI data. Resilience was related to both drought severity and forest composition. Evergreen gymnosperms dominating semi-arid Mediterranean forests showed the lowest resistance to drought, but higher recovery than deciduous angiosperms dominating humid temperate forests. Moreover, semi-arid gymnosperm forests presented a negative temporal trend in the resistance to drought, but this pattern was absent in continental and temperate forests. Although gymnosperms in dry Mediterranean forests showed a faster recovery after drought, their recovery potential could be constrained if droughts become more frequent. Conversely, angiosperms and gymnosperms inhabiting temperate and continental sites might have problems to recover after more intense droughts since they resist drought but are less able to recover afterwards.

Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach

Abstract: Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification.

Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling

Abstract: This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models Specifically, the proposed MSCP-based classification framework consists of the following three steps First, a pretrained CNN model is used to extract multilayer feature maps Then, the feature maps are stacked together, and a covariance matrix is calculated for the stacked features Each entry of the resulting covariance matrix stands for the covariance of two different feature maps, which provides a natural and innovative way to exploit the complementary information provided by feature maps coming from different layers Finally, the extracted covariance matrices are used as features for classification by a support vector machine The experimental results, conducted on three challenging data sets, demonstrate that the proposed MSCP method can not only consistently outperform the corresponding single-layer model but also achieve better classification performance than other pretrained CNN-based scene classification methods

Detection of amyloid fibrils in Parkinson’s disease using plasmonic chirality

Abstract: Amyloid fibrils, which are closely associated with various neurodegenerative diseases, are the final products in many protein aggregation pathways. The identification of fibrils at low concentration is, therefore, pivotal in disease diagnosis and development of therapeutic strategies. We report a methodology for the specific identification of amyloid fibrils using chiroptical effects in plasmonic nanoparticles. The formation of amyloid fibrils based on α-synuclein was probed using gold nanorods, which showed no apparent interaction with monomeric proteins but effective adsorption onto fibril structures via noncovalent interactions. The amyloid structure drives a helical nanorod arrangement, resulting in intense optical activity at the surface plasmon resonance wavelengths. This sensing technique was successfully applied to human brain homogenates of patients affected by Parkinson’s disease, wherein protein fibrils related to the disease were identified through chiral signals from Au nanorods in the visible and near IR, whereas healthy brain samples did not exhibit any meaningful optical activity. The technique was additionally extended to the specific detection of infectious amyloids formed by prion proteins, thereby confirming the wide potential of the technique. The intense chiral response driven by strong dipolar coupling in helical Au nanorod arrangements allowed us to detect amyloid fibrils down to nanomolar concentrations.

Accuracy, intra- and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time-motion analyses in soccer.

Abstract: There is interest in the accuracy and inter-unit reliability of position-tracking systems to monitor players. Research into this technology, although relatively recent, has grown exponentially in t...

A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation

Abstract: In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial–contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for each test pixel in the subspace, its most similar neighboring pixels (within a local spatial window) are clustered using the cosine distance measurement. The test pixel and its neighbors are used to calculate a local CM for FE purposes. Each nondiagonal entry in the matrix characterizes the correlation between different spectral bands. Finally, these matrices are used as spatial–spectral features and fed to a support vector machine for classification purposes. The proposed method offers a new strategy to characterize the spatial–spectral information in the HSI prior to classification. Experimental results have been conducted using three publicly available hyperspectral data sets for classification, indicating that the proposed method can outperform several state-of-the-art techniques, especially when the training samples available are limited.

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.

The Impact of Heavy Grazing on Soil Quality and Pasture Production in Rangelands of SW Spain

Abstract: Soil degradation phenomena, including water erosion and physical and biological processes, have already been reported in rangelands of southwestern Spain. The increasing numbers of livestock since 1986 have been highlighted as one of the key causes. The main goal of this work is to analyse the effects of the excessive number of animals on soil quality and pasture production on privately owned farms dedicated to extensive ranching. Soil properties and surface cover, pasture production, rainfall and land management variables such as livestock density were analysed during a period of 3 years (2008–2011). The study was carried out in 22 livestock enclosures selected from ten farms distributed throughout the Spanish region of Extremadura. The occurrence of bare soil patches and water erosion processes, as well as an increase of mean bulk density in the soil layer from 5 to 10 cm in depth, was observed in the enclosures with animal stocking rates exceeding 1 AU ha−1. Indications that confirm the negative effects of increased bulk density on pasture production and quality were also found. Copyright © 2016 John Wiley & Sons, Ltd.

Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods

Abstract: • The Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI. Project number PN II-RU-TE-2014-4-1093

Multivariate Kernel Smoothing and Its Applications

Abstract: Preface List of Figures List of Tables List of Algorithms Introduction Exploratory data analysis with density estimation Exploratory data analysis with density derivatives estimation Clustering/Unsupervised learning Classification/Supervised learning Suggestions on how to read this monograph Density estimation Histogram density estimation Kernel density estimation Probability contours as multivariate quantiles Contour colour scales Gains from unconstrained bandwidth matrices Advice for practical bandwidth selection Squared error analysis Asymptotic squared error formulas Optimal bandwidths Convergence of density estimators Further mathematical analysis of density estimators Asymptotic expansion of the MISE Asymptotically optimal bandwidth Vector versus vector half parametrisations Bandwidth selectors for density estimation Normal scale bandwidths Maximal smoothing bandwidths Normal mixture bandwidths Unbiased cross validation bandwidths Biased cross validation bandwidths Plug in bandwidths Smoothed cross validation bandwidths Empirical comparison of bandwidth selectors Theoretical comparison of bandwidth selectors Further mathematical analysis of bandwidth selectors Relative convergence rates of bandwidth selectors Optimal pilot bandwidth selectors Convergence rates with data-based bandwidths Modified density estimation Variable bandwidth density estimators Balloon density estimators Sample point density estimators Bandwidth selectors for variable kernel estimation Transformation density estimators Boundary kernel density estimators Beta boundary kernels Linear boundary kernels Kernel choice Higher order kernels Further mathematical analysis of modified density estimators Asymptotic error for sample point variable bandwidth estimators Asymptotic error for linear boundary estimators Density derivative estimation Kernel density derivative estimators Density gradient estimators Density Hessian estimators General density derivative estimators Gains from unconstrained bandwidth matrices Advice for practical bandwidth selection Empirical comparison of bandwidths of different derivative orders Squared error analysis Bandwidth selection for density derivative estimators Normal scale bandwidths Normal mixture bandwidths Unbiased cross validation bandwidths Plug in bandwidths Smoothed cross validation bandwidths Convergence rates of bandwidth selectors Case study: the normal density Exact MISE Curvature matrix Asymptotic MISE Normal scale bandwidth Asymptotic MSE for curvature estimation Further mathematical analysis Taylor expansions for vector-valued functions Relationship between multivariate normal moments Applications related to density and density derivative estimation Level set estimation Modal region and bump estimation Density support estimation Density-based clustering Stable/unstable manifolds Mean shift clustering Choice of the normalising matrix in the mean shift Density ridge estimation Feature significance Supplementary topics in data analysis Density difference estimation and significance testing Classification Density estimation for data measured with error Classical density deconvolution estimation Weighted density deconvolution estimation Manifold estimation Nearest neighbour estimation Further mathematical analysis Squared error analysis for deconvolution kernel density estimators Optimal selection of the number of nearest neighbours Computational algorithms R implementation Approximate binned estimation Approximate density estimation Approximate density derivative and functional estimation Recursive normal density derivatives Recursive normal functionals Numerical optimisation over matrix spaces

Agroforestry systems of high nature and cultural value in Europe: provision of commercial goods and other ecosystem services

Abstract: Land use systems that integrate woody vegetation with livestock and/or crops and are recognised for their biodiversity and cultural importance can be termed high nature and cultural value (HNCV) agroforestry In this review, based on the literature and stakeholder knowledge, we describe the structure, components and management practices of ten contrasting HNCV agroforestry systems distributed across five European bioclimatic regions We also compile and categorize the ecosystem services provided by these agroforestry systems, following the Common International Classification of Ecosystem Services HNCV agroforestry in Europe generally enhances biodiversity and regulating ecosystem services relative to conventional agriculture and forestry These systems can reduce fire risk, compared to conventional forestry, and can increase carbon sequestration, moderate the microclimate, and reduce soil erosion and nutrient leaching compared to conventional agriculture However, some of the evidence is location specific and a better geographical coverage is needed to generalize patterns at broader scales Although some traditional practices and products have been abandoned, many of the studied systems continue to provide multiple woody and non-woody plant products and high-quality food from livestock and game Some of the cultural value of these systems can also be captured through tourism and local events However there remains a continual challenge for farmers, landowners and society to fully translate the positive social and environmental impacts of HNCV agroforestry into market prices for the products and services

A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution

Abstract: Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applications requiring fine spatial resolution. Even though different machine learning paradigms have been successfully applied in SR, more research is required to improve the SR process without the need of external high-resolution (HR) training examples. This paper proposes a new convolutional generator model to super-resolve low-resolution (LR) remote sensing data from an unsupervised perspective. That is, the proposed generative network is able to initially learn relationships between the LR and HR domains throughout several convolutional, downsampling, batch normalization, and activation layers. Then, the data are symmetrically projected to the target resolution while guaranteeing a reconstruction constraint over the LR input image. An experimental comparison is conducted using 12 different unsupervised SR methods over different test images. Our experiments reveal the potential of the proposed approach to improve the resolution of remote sensing imagery.

Neuropathic Pain: Delving into the Oxidative Origin and the Possible Implication of Transient Receptor Potential Channels.

Abstract: Currently, neuropathic pain is an underestimated socioeconomic health problem affecting millions of people worldwide, which incidence may increase in the next years due to chronification of several diseases, such as cancer and diabetes. Growing evidence links neuropathic pain present in several disorders (i.e. spinal cord injury, cancer, diabetes and alcoholism) to central sensitization, as a global result of mitochondrial dysfunction induced by oxidative and nitrosative stress. Additionally, inflammatory signals and the overload in intracellular calcium ion could be also implicated in this complex network that has not yet been elucidated. Recently, calcium channels namely transient receptor potential (TRP) superfamily, including members of the subfamilies A (TRAP1), M (TRPM2 and 7) and V (TRPV1 and 4), have demonstrated to play an important role in the nociception mediated by sensory neurons, including the dorsal root ganglion. Therefore, as neuropathic pain could be a consequence of the imbalance between reactive oxygen species and endogen antioxidants, antioxidant supplementation may be a very useful treatment option. This kind of therapy would exert its beneficial action through antioxidant and immunoregulatory functions, optimizing mitochondrial function and even increasing the biogenesis of this vital organelle; on balance, antioxidant supplementation would improve the patient´s quality of life. This review seeks to deepen on current knowledge about neuropathic pain, summarizing clinical conditions and probable causes, the relationship existing between oxidative stress, mitochondrial dysfunction and TRP channels activation, and scientific evidence related to antioxidant supplementation.

ER–mitochondria signaling in Parkinson’s disease

Abstract: Mitochondria form close physical contacts with a specialized domain of the endoplasmic reticulum (ER), known as the mitochondria-associated membrane (MAM). This association constitutes a key signaling hub to regulate several fundamental cellular processes. Alterations in ER-mitochondria signaling have pleiotropic effects on a variety of intracellular events resulting in mitochondrial damage, Ca2+ dyshomeostasis, ER stress and defects in lipid metabolism and autophagy. Intriguingly, many of these cellular processes are perturbed in neurodegenerative diseases. Furthermore, increasing evidence highlights that ER-mitochondria signaling contributes to these diseases, including Parkinson's disease (PD). PD is the second most common neurodegenerative disorder, for which effective mechanism-based treatments remain elusive. Several PD-related proteins localize at mitochondria or MAM and have been shown to participate in ER-mitochondria signaling regulation. Likewise, PD-related mutations have been shown to damage this signaling. Could ER-mitochondria associations be the link between pathogenic mechanisms involved in PD, providing a common mechanism? Would this provide a pharmacological target for treating this devastating disease? In this review, we aim to summarize the current knowledge of ER-mitochondria signaling and the recent evidence concerning damage to this signaling in PD.

Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation

Abstract: Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image. In recent years, nonnegative matrix factorization (NMF) has been highly attractive for this purpose due to the nonnegativity constraint that is often imposed in the abundance estimation step. However, most of the existing NMF-based methods only consider the information in a single layer while neglecting the hierarchical features with hidden information. To alleviate such limitation, in this paper, we propose a new sparsity-constrained deep NMF with total variation (SDNMF-TV) technique for hyperspectral unmixing. First, by adopting the concept of deep learning, the NMF algorithm is extended to deep NMF model. The proposed model consists of pretraining stage and fine-tuning stage , where the former pretrains all factors layer by layer and the latter is used to reduce the total reconstruction error. Second, in order to exploit adequately the spectral and spatial information included in the original hyperspectral image, we enforce two constraints on the abundance matrix. Specifically, the $L_{1/2}$ constraint is adopted, since the distribution of each endmember is sparse in the 2-D space. The TV regularizer is further introduced to promote piecewise smoothness in abundance maps. For the optimization of the proposed model, multiplicative update rules are derived using the gradient descent method. The effectiveness and superiority of the SDNMF-TV algorithm are demonstrated by comparing with other unmixing methods on both synthetic and real data sets.

Balneotherapy, Immune System, and Stress Response: A Hormetic Strategy?

Abstract: Balneotherapy is a clinically effective complementary approach in the treatment of low-grade inflammation- and stress-related pathologies. The biological mechanisms by which immersion in mineral-medicinal water and the application of mud alleviate symptoms of several pathologies are still not completely understood, but it is known that neuroendocrine and immunological responses—including both humoral and cell-mediated immunity—to balneotherapy are involved in these mechanisms of effectiveness; leading to anti-inflammatory, analgesic, antioxidant, chondroprotective, and anabolic effects together with neuroendocrine-immune regulation in different conditions. Hormesis can play a critical role in all these biological effects and mechanisms of effectiveness. The hormetic effects of balneotherapy can be related to non-specific factors such as heat—which induces the heat shock response, and therefore the synthesis and release of heat shock proteins—and also to specific biochemical components such as hydrogen sulfide (H2S) in sulfurous water and radon in radioactive water. Results from several investigations suggest that the beneficial effects of balneotherapy and hydrotherapy are consistent with the concept of hormesis, and thus support a role for hormesis in hydrothermal treatments.

Long-term impact of rainfed agricultural land abandonment on soil erosion in the Western Mediterranean basin:

Abstract: Land abandonment is widespread in the Mediterranean mountains. The impact of agricultural abandonment results in a shift in ecosystem evolution due to changes in soil erosion, but little is known a...

Development and analysis of the Soil Water Infiltration Global database

Abstract: . In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.