Showing papers by "University of Extremadura published in 2016"

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

Abstract: A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets. These experiments indicate that our algorithm achieves very promising anomaly detection performance.

Big Data for Remote Sensing: Challenges and Opportunities

Abstract: Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.

Widespread Phytophthora infestations in European nurseries put forest, semi-natural and horticultural ecosystems at high risk of Phytophthora diseases

Abstract: An analysis of incidence of Phytophthora spp. in 732 European nurseries producing forest transplants, larger specimen trees, landscape plants and ornamentals, plus 2525 areas in which trees and shrubs were planted, is presented based on work conducted by 37 research groups in 23 European countries between 1972 and 2013. Forty-nine Phytophthora taxa were recorded in 670 nurseries (91.5%); within these nurseries, 1614 of 1992 nursery stands (81.0%) were infested, although most affected plants appeared healthy. In forest and landscape plantings, 56 Phytophthora taxa were recovered from 1667 of 2525 tested sites (66.0%). Affected plants frequently showed symptoms such as crown thinning, chlorosis and dieback caused by extensive fine root losses and/or collar rot. Many well-known highly damaging host-Phytophthora combinations were frequently detected but 297 and 407 new Phytophthora-host associations were also observed in nurseries and plantings, respectively. On average, 1.3 Phytophthora species/taxa per infested nursery stand and planting site were isolated. At least 47 of the 68 Phytophthora species/taxa detected in nurseries and plantings were exotic species several of which are considered well established in both nurseries and plantings in Europe. Seven known Phytophthora species/taxa were found for the first time in Europe, while 10 taxa had not been previously recorded from nurseries or plantings; in addition, 5 taxa were first detections on woody plant species. Seven Phytophthora taxa were previously unknown to science. The reasons for these failures of plant biosecurity in Europe, implications for forest and semi-natural ecosystems and possible ways to improve biosecurity are discussed.

New Trends in Aryl Hydrocarbon Receptor Biology

Abstract: Traditionally considered as a critical intermediate in the toxic and carcinogenic response to dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin, TCDD), the Aryl hydrocarbon/Dioxin receptor (AhR) has proven to be also an important regulator of cell physiology and organ homeostasis AhR has become an interesting and actual area of research mainly boosted by a significant number of recent studies analyzing its contribution to the proper functioning of the immune, hepatic, cardiovascular, vascular and reproductive systems At the cellular level, AhR establishes functional interactions with signaling pathways governing cell proliferation and cell cycle, cell morphology, cell adhesion and cell migration Two exciting new aspects in AhR biology deal with its implication in the control of cell differentiation and its more than likely involvement in cell pluripotency and stemness In fact, it is possible that AhR could help modulate the balance between differentiation and pluripotency in normal and transformed tumor cells At the molecular level, AhR regulates an increasingly large array of physiologically relevant genes either by traditional transcription-dependent mechanisms or by unforeseen processes involving genomic insulators, chromatin dynamics and the transcription of mobile genetic elements AhR is also closely related to epigenetics, not only from the point of view of target gene expression but also with respect to its own regulation by promoter methylation It is reasonable to consider that deregulation of these many functions could have a causative role, or at least contribute to, human disease Consequently, several laboratories have proposed that AhR could be a valuable tool as diagnostic marker and/or therapeutic target in human pathologies An additional point of interest is the possibility of regulating AhR activity by endogenous non-toxic low weight molecules agonist or antagonist molecules that could be present or included in the diet In this review, we will address these molecular and functional features of AhR biology within physiological and pathological contexts

Performance and Perception in the Flipped Learning Model: An Initial Approach to Evaluate the Effectiveness of a New Teaching Methodology in a General Science Classroom

Abstract: “Flipped classroom” teaching methodology is a type of blended learning in which the traditional class setting is inverted. Lecture is shifted outside of class, while the classroom time is employed to solve problems or doing practical works through the discussion/peer collaboration of students and instructors. This relatively new instructional methodology claims that flipping your classroom engages more effectively students with the learning process, achieving better teaching results. Thus, this research aimed to evaluate the effects of the flipped classroom on the students’ performance and perception of this new methodology. This study was conducted in a general science course, sophomore of the Primary Education bachelor degree in the Training Teaching School of the University of Extremadura (Spain) during the course 2014/2015. In order to assess the suitability of the proposed methodology, the class was divided in two groups. For the first group, a traditional methodology was followed, and it was used as control. On the other hand, the “flipped classroom” methodology was used in the second group, where the students were given diverse materials, such as video lessons and reading materials, before the class to be revised at home by them. Online questionnaires were as well provided to assess the progress of the students before the class. Finally, the results were compared in terms of students’ achievements and a post-task survey was also conducted to know the students’ perceptions. A statistically significant difference was found on all assessments with the flipped class students performing higher on average. In addition, most students had a favorable perception about the flipped classroom noting the ability to pause, rewind and review lectures, as well as increased individualized learning and increased teacher availability.

Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing

Abstract: Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. It amounts to identifying a set of pure spectral signatures, which are called endmembers, and their corresponding fractional, draftrulesabun-dances in each pixel of the hyperspectral image. Over the last years, different algorithms have been developed for each of the three main steps of the spectral unmixing chain: 1) estimation of the number of endmembers in a scene; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene. However, few algorithms can perform all the stages involved in the hyperspectral unmixing process. Such algorithms are highly desirable to avoid the propagation of errors within the chain. In this paper, we develop a new algorithm, which is termed robust collaborative nonnegative matrix factorization (R-CoNMF), that can perform the three steps of the hyperspectral unmixing chain. In comparison with other conventional methods, R-CoNMF starts with an overestimated number of endmembers and removes the redundant endmembers by means of collaborative regularization. Our experimental results indicate that the proposed method provides better or competitive performance when compared with other widely used methods.

Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

Abstract: In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which intrinsically have a tensor-based data structure. In this paper, a new multiclass support tensor machine (STM) is specifically developed for hyperspectral image classification. Our newly proposed STM processes the hyperspectral image as a data cube and then identifies the information classes in tensor space. The multiclass STM is developed from a set of binary STM classifiers using the one-against-one parallel strategy. As a part of our tensor-based processing chain, a multilinear principal component analysis (MPCA) is used for preprocessing, in order to reduce the tensorial data redundancy and, at the same time, preserve the tensorial structure information in sparse and high-order subspaces. As a result, the contributions of this work are twofold: a new multiclass STM model for hyperspectral image classification is developed, and a tensorial image interpretation framework is constructed, which provides a system consisting of tensor-based feature representation, feature extraction, and classification. Experiments with four hyperspectral data sets, covering agricultural and urban areas, are conducted to validate the effectiveness of the proposed framework. Our experimental results show that the proposed STM and MPCA-STM can achieve better results than traditional SVM-based classifiers.

A Revised Collection of Sunspot Group Numbers

Abstract: We describe a revised collection of the number of sunspot groups from 1610 to the present. This new collection is based on the work of Hoyt and Schatten (Solar Phys. 179, 189, 1998). The main changes are the elimination of a considerable number of observations during the Maunder Minimum (hereafter, MM) and the inclusion of several long series of observations. Numerous minor changes are also described. Moreover, we have calculated the active-day percentage during the MM from this new collection as a reliable index of the solar activity. Thus, the level of solar activity obtained in this work is greater than the level obtained using the original Hoyt and Schatten data, although it remains compatible with a grand minimum of solar activity. The new collection is available in digital format.

Melatonin sensitizes human cervical cancer HeLa cells to cisplatin-induced cytotoxicity and apoptosis: effects on oxidative stress and DNA fragmentation

Abstract: Melatonin has antitumor activity via several mechanisms including its antiproliferative and pro-apoptotic effects as well as its potent antioxidant actions, although recent evidence has indicated that melatonin may perform pro-oxidant actions in tumor cells. Therefore, melatonin may be useful in the treatment of tumors in association with chemotherapy drugs. This study was intended to evaluate the in vitro effect of melatonin on the cytotoxic and pro-apoptotic actions of various chemotherapeutic agents in cervical cancer HeLa cells. Herein, we found that both melatonin and three of the chemotherapeutic drugs tested, namely cisplatin (CIS), 5-fluorouracil (5-FU), and doxorubicin, induced a decrease in HeLa cell viability. Furthermore, melatonin significantly increased the cytotoxic effect of such chemotherapeutic agents. Consistently, costimulation of HeLa cells with any chemotherapeutic agent in the presence of melatonin further increased caspase-3 activation, particularly in CIS- and 5-FU-challenged cells. Likewise, concomitant treatments with melatonin and CIS significantly enhanced the ratio of cells entering mitochondrial apoptosis due to reactive oxygen species (ROS) overproduction, substantially augmented the population of apoptotic cells, and markedly enlarged DNA fragmentation compared to the treatments with CIS alone. Nonetheless, melatonin only displayed moderate chemosensitizing effects in 5-FU-stimulated HeLa cells, as suggested by slight increments in the percentage of cells stimulated for ROS production and in the proportion of early apoptotic cells compared to the treatments with 5-FU alone. In summary, our findings provided evidence that in vitro melatonin strongly enhances CIS-induced cytotoxicity and apoptosis in HeLa cells and, hence, the indoleamine could be potentially applied to cervical cancer treatment as a powerful synergistic agent.

Molecular mechanisms of STIM/Orai communication.

Abstract: Ca2+ entry into the cell via store-operated Ca2+ release-activated Ca2+ (CRAC) channels triggers diverse signaling cascades that affect cellular processes like cell growth, gene regulation, secreti...

Lateral root emergence in Arabidopsis is dependent on transcription factor LBD29 regulation of auxin influx carrier LAX3.

Abstract: Lateral root primordia (LRP) originate from pericycle stem cells located deep within parental root tissues. LRP emerge through overlying root tissues by inducing auxin-dependent cell separation and hydraulic changes in adjacent cells. The auxin-inducible auxin influx carrier LAX3 plays a key role concentrating this signal in cells overlying LRP. Delimiting LAX3 expression to two adjacent cell files overlying new LRP is crucial to ensure that auxin-regulated cell separation occurs solely along their shared walls. Multiscale modeling has predicted that this highly focused pattern of expression requires auxin to sequentially induce auxin efflux and influx carriers PIN3 and LAX3, respectively. Consistent with model predictions, we report that auxin-inducible LAX3 expression is regulated indirectly by AUXIN RESPONSE FACTOR 7 (ARF7). Yeast one-hybrid screens revealed that the LAX3 promoter is bound by the transcription factor LBD29, which is a direct target for regulation by ARF7. Disrupting auxin-inducible LBD29 expression or expressing an LBD29-SRDX transcriptional repressor phenocopied the lax3 mutant, resulting in delayed lateral root emergence. We conclude that sequential LBD29 and LAX3 induction by auxin is required to coordinate cell separation and organ emergence.

Addressing voice recording replications for Parkinson's disease detection

Abstract: A general subject-based Bayesian approach has been proposed.Special treatment is provided for the probit model.Latent variables are used to provide a predictive model that can handle replications.A Gibbs sampling-based method is derived to compute the model parameters.The approach is used to discriminate healthy people from people suffering PD. A clinical expert system has been developed for detection of Parkinson's Disease (PD). The system extracts features from voice recordings and considers an advanced statistical approach for pattern recognition. The significance of the work lies on the development and use of a novel subject-based Bayesian approach to account for the dependent nature of the data in a replicated measure-based design. The ideas under this approach are conceptually simple and easy-to-implement by using Gibbs sampling. Available information could be included in the model through the prior distribution. In order to assess the performance of the proposed system, a voice recording replication-based experiment has been specifically conducted to discriminate healthy people from people suffering PD. The experiment involved 80 subjects, half of them affected by PD. The proposed system is able to discriminate acceptably well healthy people from people with PD in spite that the experiment has a reduced number of subjects.

Frontline employees’ collaboration in industrial service innovation: routes of co-creation’s effects on new service performance

Abstract: From a Service-Dominant Logic (S-DL) perspective, employees constitute operant resources that firms can draw to enhance the outcomes of innovation efforts. While research acknowledges that frontline employees (FLEs) constitute, through service encounters, a key interface for the transfer of valuable external knowledge into the firm, the range of potential benefits derived from FLE-driven innovation deserves more investigation. Using a sample of knowledge intensive business services firms (KIBS), this study examines how the collaboration with FLEs along the new service development (NSD) process, namely FLE co-creation, impacts on service innovation performance following two routes of different effects. Partial least squares structural equation modeling (PLS-SEM) results indicate that FLE co-creation benefits the NS success among FLEs and firm’s customers, the constituents of the resources route. FLE co-creation also has a positive effect on the NSD speed, which in turn enhances the NS quality. NSD speed and NS quality integrate the operational route, which proves to be the most effective path to impact the NS market performance. Accordingly, KIBS managers must value their FLEs as essential partners to achieve successful innovation from an internal and external perspective, and develop the appropriate mechanisms to guarantee their effective involvement along the NSD process.

A Revised Collection of Sunspot Group Numbers

Abstract: We describe a revised collection of the number of sunspot groups from 1610 to the present. This new collection is based on the work of Hoyt and Schatten (Solar Phys. 179, 189, 1998). The main changes are the elimination of a considerable number of observations during the Maunder Minimum (hereafter, MM) and the inclusion of several long series of observations. Numerous minor changes are also described. Moreover, we have calculated the active-day percentage during the MM from this new collection as a reliable index of the solar activity. Thus, the level of solar activity obtained in this work is greater than the level obtained using the original Hoyt and Schatten data, although it remains compatible with a grand minimum of solar activity. The new collection is available in digital format.

Students’ Perceptions and Emotions Toward Learning in a Flipped General Science Classroom

Abstract: Recently, the inverted instruction methodologies are gaining attentions in higher educations by claiming that flipping the classroom engages more effectively students with the learning process. Besides, students’ perceptions and emotions involved in their learning process must be assessed in order to gauge the usability of this relatively new instruction methodology, since it is vital in the educational formation. For this reason, this study intends to evaluate the students’ perceptions and emotions when a flipped classroom setting is used as instruction methodology. This research was conducted in a general science course, sophomore of the Primary Education bachelor degree in the Training Teaching School of the University of Extremadura (Spain). The results show that the students have the overall positive perceptions to a flipped classroom setting. Particularly, over 80 % of them considered that the course was a valuable learning experience. They also found this course more interactive and were willing to have more courses following a flipped model. According to the students’ emotions toward a flipped classroom course, the highest scores were given to the positive emotions, being fun and enthusiasm along with keyword frequency test. Then, the lowest scores were corresponded to negative emotions, being boredom and fear. Therefore, the students attending to a flipped course demonstrated to have more positive and less negative emotions. The results obtained in this study allow drawing a promising tendency about the students’ perceptions and emotions toward the flipped classroom methodology and will contribute to fully frame this relatively new instruction methodology.

Cloud Removal Based on Sparse Representation via Multitemporal Dictionary Learning

Abstract: Cloud covers, which generally appear in optical remote sensing images, limit the use of collected images in many applications. It is known that removing these cloud effects is a necessary preprocessing step in remote sensing image analysis. In general, auxiliary images need to be used as the reference images to determine the true ground cover underneath cloud-contaminated areas. In this paper, a new cloud removal approach, which is called multitemporal dictionary learning (MDL), is proposed. Dictionaries of the cloudy areas (target data) and the cloud-free areas (reference data) are learned separately in the spectral domain. The removal process is conducted by combining coefficients from the reference image and the dictionary learned from the target image. This method could well recover the data contaminated by thin and thick clouds or cloud shadows. Our experimental results show that the MDL method is effective in removing clouds from both quantitative and qualitative viewpoints.

Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures

Abstract: Cloud computing offers the possibility to store and process massive amounts of remotely sensed hyperspectral data in a distributed way. Dimensionality reduction is an important task in hyperspectral imaging, as hyperspectral data often contains redundancy that can be removed prior to analysis of the data in repositories. In this regard, the development of dimensionality reduction techniques in cloud computing environments can provide both efficient storage and preprocessing of the data. In this paper, we develop a parallel and distributed implementation of a widely used technique for hyperspectral dimensionality reduction: principal component analysis (PCA), based on cloud computing architectures. Our implementation utilizes Hadoop’s distributed file system (HDFS) to realize distributed storage, uses Apache Spark as the computing engine, and is developed based on the map-reduce parallel model, taking full advantage of the high throughput access and high performance distributed computing capabilities of cloud computing environments. We first optimized the traditional PCA algorithm to be well suited for parallel and distributed computing, and then we implemented it on a real cloud computing architecture. Our experimental results, conducted using several hyperspectral datasets, reveal very high performance for the proposed distributed parallel method.

Grasslands in 'Old World' and 'New World' Mediterranean-climate zones: Past trends, current status and future research priorities

Abstract: Despite their ecological, economic and social importance, grasslands in areas with Mediterranean climates continue to receive limited scientific, political and media attention. The main objectives of this review are to compare and contrast dryland grasslands in the ‘Old World’ regions of the Mediterranean basin (southern Europe, western Asia and North Africa) with those of ‘New World’ regions with Mediterranean climates (Australia and Chile) and to identify common research priorities. The common characteristics and differences in climate, soils, native vegetation, importance of the livestock sector and the socio-economic background for the different Mediterranean environments are examined. Past trends and the current status of temporary and permanent Mediterranean grasslands are also described. Some common issues between these regions are as follows: (i) adaptation to climate change; (ii) increasing persistence and drought survival of both annual and perennial species; (iii) the important role of forage legumes; (iv) maintaining grassland plant diversity; and (v) improved ecosystem services, such as carbon sequestration, control of soil erosion and wildfires, and preservation of both wild and domestic biodiversity. The favourable climate in these regions, which allows year-round grazing and the growth of legumes, should be exploited to improve the sustainability of grassland-based, extensive farming systems and the quality of their animal products, while at the same time improving ecosystem services. The decreasing support for grassland research and development programmes requires increased international scientific and technical cooperation among the few institutions operating in the different Mediterranean-climate areas of the World to provide innovative and sustainable solutions to farmers.

Comparison of Impedance-Source Networks for Two and Multilevel Buck–Boost Inverter Applications

Abstract: Impedance-source networks are an increasingly popular solution in power converter applications, especially in single-stage buck–boost power conversion to avoid additional front-end dc–dc power converters. In the survey papers published, no analytical comparisons of different topologies have been described, which makes it difficult to choose the best option. Thus, the aim of this paper is to present a comprehensive analytical comparison of the impedance-source-based buck–boost inverters in terms of passive component count and semiconductor stress. Based on the waveform of the input current, i.e., with or without a transformer, and with or without inductor coupling, the impedance-source converters are classified. The main criterion in our comprehensive comparison is the energy stored in the passive elements, which is considered both under constant and predefined high frequency current ripple in the inductors and the voltage ripple across the capacitors. Two-level and multilevel solutions are described. The conclusions provide a “one-stop” information source and a selection guide of impedance-source-based buck–boost inverters for different applications.