Transcript Profiling of Different Arabidopsis thaliana Ecotypes in Response to Tobacco etch potyvirus Infection

The use of high-throughput transcript profiling techniques has opened the possibility of identifying, in a single experiment, multiple host mRNAs whose levels of accumulation are altered in response to virus infection. Several studies have used this approach to analyze the response of Arabidopsis thaliana to the infection by different RNA and DNA viruses. However, the possible differences in response of genetically heterogeneous ecotypes of the plant to the same virus have never been addressed before. Here we have used a strain of Tobacco etch potyvirus (TEV) experimentally adapted to A. thaliana ecotype Ler-0 and a set of seven plant ecotypes to tackle this question. Each ecotype was inoculated with the same amount of the virus and the outcome of infection characterized phenotypically (i.e., virus infectivity, accumulation, and symptoms development). Using commercial microarrays containing probes for more than 43,000 A. thaliana transcripts, we explored the effect of viral infection on the plant transcriptome. In general, we found that ecotypes differ in the way they perceive and respond to the virus. Some ecotypes developed strong symptoms and accumulated large amounts of viral genomes, while others only developed mild symptoms and accumulated less virus. At the transcriptomic level, ecotypes could be classified into two groups according to the particular genes whose expression was altered upon infection. Moreover, a functional enrichment analyses showed that the two groups differed in the nature of the altered biological processes. For the group constituted by ecotypes developing milder symptoms and allowing for lower virus accumulation, genes involved in abiotic stresses and in the construction of new tissues tend to be up-regulated. For those ecotypes in which infection was more severe and productive, defense genes tend to be up-regulated, deviating the necessary resources from building new tissues

Experimental evolution of an emerging plant virus in host genotypes that differ in their susceptibility to infection

This study evaluates the extent to which genetic differences among host individuals from the same species condition the evolution of a plant RNA virus. We performed a threefold replicated evolution experiment in which Tobacco etch potyvirus isolate At17b (TEV-At17b), adapted to Arabidopsis thaliana ecotype Ler-0, was serially passaged in five genetically heterogeneous ecotypes of A. thaliana. After 15 passages we found that evolved viruses improved their fitness, showed higher infectivity and stronger virulence in their local host ecotypes. The genome of evolved lineages was sequenced and putative adaptive mutations identified. Host-driven convergent mutations have been identified. Evidences supported selection for increased translational efficiency. Next, we sought for the specificity of virus adaptation by infecting all five ecotypes with all 15 evolved virus populations. We found that some ecotypes were more permissive to infection than others, and that some evolved virus isolates were more specialist/generalist than others. The bipartite network linking ecotypes with evolved viruses was significantly nested but not modular, suggesting that hard-to-infect ecotypes were infected by generalist viruses whereas easy-to-infect ecotypes were infected by all viruses, as predicted by a gene-for-gene model of infection.We acknowledge grant BFU2012–30805 from the Spanish Ministerio de Economía y Competitividad to SFE. JMC was supported by a JAE-doc contract from CSIC. JH was supported by a pre-doctoral fellowship from Ministerio de Economía y CompetitividadPeer reviewe

Tracking fraudulent and low-quality display impressions

Display advertising is traded in a complex market with multiple sides and intermediaries, where advertisers are exposed to several forms of potentially fraudulent behavior. Intermediaries often claim to implement measures to detect fraud but provide limited information about those measures. Advertisers are required to trust that self-regulation efforts effectively filter out low-quality ad impressions. In this article, we propose an approach for tracking key display impression metrics by embedding a light JavaScript code in the ad to collect the necessary information to help detect fraudulent activities. We explain these metrics using the campaign cost per thousand (CPT) and the number of impressions per publisher. We test the approach through six display ad campaigns. Our results provide a counterargument against the industry claim that it is effectively filtering out display fraud and show the utility of our approach for advertisers.This work is partly supported by the European Union through SMOOTH (786741) and PIMCITY (871370); the European Social Fund through Ramón y Cajal (RYC-2015-17732); and the Spanish Ministry of Economy and Competitiveness through ECO2015-67763-R and PGC2018-096083-B-I00 projects

La propuesta de Master en Ingeniería Informática de las Universidades Públicas de la Comunidad de Madrid

Versión electrónica de la ponencia presentada en Simposio Internacional de Informática Educativa (SIIE 2004), celebrado en Cáceres en 2004En noviembre de 2002, las universidades públicas madrileñas iniciaron una propuesta de Plan de Estudios de Master en Ingeniería Informática resultado de la convocatoria de Master, regulada por orden 6534/2002, de 26 de noviembre. En el presente documento se resume el resultado del equipo de trabajo constituido por miembros representantes de las seis universidades públicas de la Comunidad de Madrid con los que han colaborado miembros de universidades extranjeras invitadas en calidad de participantes

Volumetric efficiency modelling of internal combustion engines based on a novel adaptive learning algorithm of artificial neural networks

[EN] Air mass flow determination is one of the main variables on the control of internal combustion engines. Effectiveness of intake air systems is evaluated through the volumetric efficiency coefficient. Intake air systems characterization by means of physical models needs either significant amount of input data or notable calculation times. Because of these drawbacks, empirical approaches are often used by means of black-box models based on Artificial Neural Networks. As alternative to the standard gradient descendent method an adaptive learning algorithm is developed based on the increase of hidden layer weight update speed. The results presented in this paper show that the proposed adaptive learning method performs with higher learning speed, reduced computational resources and lower network complexities. A parametric study of several Multiple Layer Perceptron (MLP) networks is carried out with the variation of the number of epochs, number of hidden neurons, momentum coefficient and learning algorithm. The training and validation data are obtained from steady state tests carried out in an automotive turbocharged diesel engine. (C) 2017 Elsevier Ltd. All rights reserved.Authors want to acknowledge the "Apoyo para la investigacion y Desarrollo (PAID)", grant for doctoral studies (FPI S1 2015 2512), of Universitat Politecnica de Valencia.Luján, JM.; Climent, H.; García-Cuevas González, LM.; Moratal-Martínez, AA. (2017). Volumetric efficiency modelling of internal combustion engines based on a novel adaptive learning algorithm of artificial neural networks. Applied Thermal Engineering. 123:625-634. https://doi.org/10.1016/j.applthermaleng.2017.05.087S62563412

Pollutant emissions and diesel oxidation catalyst performance at low ambient temperatures in transient load conditions

[EN] In this paper an experimental analysis of the ambient temperature effect on diesel engine pollutant emissions is carried out. The study is focused on hydrocarbons and carbon monoxide of both engine out pollutants formation analysis and diesel oxidation catalyzer (DOC) performance. The experiments were carried out at transient engine load conditions of Worldwide harmonized Light vehicles Test Cycle (WLTC) at two levels of ambient temperature: 20ºC and -7ºC. The study presented in this work shows significant different results depending on the pollutant analysed. Regarding hydrocarbons, a significant dependence of pollutant formation on ambient temperature is observed, being the emissions at -7 ºC between two and three times the emissions at 20 ºC. The DOC performance between temperatures shows similar conversion efficiency. In the case of carbon monoxide formation, temperature dependence plays a less important role than the engine load conditions. The reduction of air fuel ratio at transient conditions drives to unsteady CO profiles emissions along the WLTC that reduce the pollutant conversion with a greater negative impact at -7 ºC.The authors of this paper wish to thank Juan Antonio Lopez Cascant for his invaluable work during the laboratory setup and the experimental campaign. Authors also want to acknowledge the "Apoyo para la investigacion y Desarrollo (PAID)", grant for doctoral studies (FPI S1 2015 2512), of Universitat Politecnica de Valencia.Luján, JM.; Climent, H.; García-Cuevas González, LM.; Moratal-Martínez, AA. (2018). Pollutant emissions and diesel oxidation catalyst performance at low ambient temperatures in transient load conditions. Applied Thermal Engineering. 129:1527-1537. https://doi.org/10.1016/j.applthermaleng.2017.10.138S1527153712

Selection for Robustness in Mutagenized RNA Viruses

Mutational robustness is defined as the constancy of a phenotype in the face of deleterious mutations. Whether robustness can be directly favored by natural selection remains controversial. Theory and in silico experiments predict that, at high mutation rates, slow-replicating genotypes can potentially outcompete faster counterparts if they benefit from a higher robustness. Here, we experimentally validate this hypothesis, dubbed the “survival of the flattest,” using two populations of the vesicular stomatitis RNA virus. Characterization of fitness distributions and genetic variability indicated that one population showed a higher replication rate, whereas the other was more robust to mutation. The faster replicator outgrew its robust counterpart in standard competition assays, but the outcome was reversed in the presence of chemical mutagens. These results show that selection can directly favor mutational robustness and reveal a novel viral resistance mechanism against treatment by lethal mutagenesis

Automated Generation of Computationally Hard Feature Models Using Evolutionary Algorithms

A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.CICYT TIN2009-07366CICYT TIN2012-32273Junta de Andalucía TIC-5906Junta de Andalucía P12-TIC-186

On the Specific Adsorption of 7-methylguanine on Au(111) Surfaces for the Electroanalytical Sensing of Methylation Levels

The electrochemical determination of 7-methylguanine (7-mG) may result of interest because its presence can serve as probe of cytosine methylation of which is known as the most relevant epigenetic modification of DNA. This work explores the electrochemical response of 7-mG on different gold surfaces, both poly and single crystalline surfaces (Au (110), Au (111) and Au (100)). The results show that the adsorption-desorption process of the 7-mG is sensitive to the surface structure of the gold electrodes. Particularly, 7-mG adsorption-desorption profile on a Au (111) electrode exhibits some specific contributions which are found sensitive to the 7-mG concentration and, thereby could allow its quantification. These results may shed light on the future development of an electrochemical sensor for the diagnosis of the methylation degree in DNA.Authors would like to acknowledge funding obtained through the Spanish Ministry of Science and Innovation (MICINN) CTQ2013-48280-C3-3-R project

ETHOM: An Evolutionary Algorithm for Optimized Feature Models Generation - TECHNICAL REPORT ISA-2012-TR-01 (v. 1.1)

A feature model defines the valid combinations of features in a domain. The automated extraction of information from feature models is a thriv ing topic involving numerous analysis operations, techniques and tools. The progress of this discipline is leading to an increasing concern to test and compare the performance of analysis solutions using tough input mod els that show the behaviour of the tools in extreme situations (e.g. those producing longest execution times or highest memory consumption). Cur rently, these feature models are generated randomly ignoring the internal aspects of the tools under tests. As a result, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this technical report, we model the problem of finding computationally– hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm. Given a tool and an analysis operation, our algorithm generates input models of a predefined size maximizing aspects as the execution time or the memory consumption of the tool when per forming the operation over the model. This allows users and developers to know the behaviour of tools in pessimistic cases providing a better idea of their real power. Experiments using our evolutionary algorithm on a num ber of analysis operations and tools have successfully identified input mod els causing much longer executions times and higher memory consumption than random models of identical or even larger size. Our solution is generic and applicable to a variety of optimization problems on feature models, not only those involving analysis operations. In view of the positive results, we expect this work to be the seed for a new wave of research contributions exploiting the benefit of evolutionary programming in the field of feature modelling