How effective are rewards programs in promoting payment card usage? Empirical evidence

Card issuers have mainly relied on rewards programs as their main strategic driver to increase electronic payments. However, there is scarce evidence on the effectiveness of rewards programs. This paper offers novel evidence on two key issues: i) it measures the impact of rewards programs on the use of payment cards; and ii) it quantifies their economic impact in terms of the cash substitution. The results show that rewards may significantly modify preferences for card payments, their economic impact vary significantly across types of rewards and merchant activities and rewards seem to be more effective on average for debit cardholders. JEL Classification: G20, D12, E41cardholders, merchants, payment cards, preferences, rewards

Selection, tinkering and emergence in complex networks: crossing the land of tinkering

Complex biological networks have very different origins than technologic ones. The latter involve extensive design and, as engineered structures, include a high level of optimization. The former involve (in principle) contingency and structural constraints, with new structures being incorporated through tinkering with previously evolved modules or units. However, the observation of the topological features of different biological nets suggests that nature can have a limited repertoire of ”attractors” that essentially optimize communication under some basic constraints of cost and architecture or that allow the biological nets to reach a high degree of homeostasis. Conversely, the topological features exhibited by some technology graphs indicate that tinkering and internal constraints play a key role, in spite of the ”designed” nature of these structures. Previous scenarios suggested to explain the overall trends of evolution are re-analyzed in light of topological patterns.Peer ReviewedPostprint (author's final draft

Determination of Soluble Sugars in Arabidopsis thaliana Leaves by Anion Exchange Chromatography

Determination of soluble sugars is basic for the study of carbon metabolism in plants. Soluble sugar quantitation can be achieved by enzymatic methods implying different coupled reactions. Here we describe a simple method that allows rapid determination of the most abundant soluble sugars (glucose, fructose and sucrose) in Arabidopsis leaves by anion exchange chromatography. We have applied this method to study the levels of soluble sugars during the photoperiodic transition to flowering (Ortiz-Marchena et al., 2014).España, MINECO projects CSD2007-00057, BIO2008-02292, and BIO2011-28847-C02-00España, Junta de Andalucía P06-CVI-01450 and P08-AGR-0358

Purification of Starch Granules from Arabidopsis Leaves and Determination of Granule-Bound Starch Synthase Activity

Starch constitutes the most important carbon reserve in plants and is composed of branched amylopectin and linear amylose. The latter is synthesized exclusively by the Granule-Bound Starch Synthase (GBSS, EC 2.4.1.21). Here we report a readily reproducible, specific and highly sensitive protocol, which includes the isolation of intact starch granules from Arabidopsis thaliana leaves and the subsequent determination of GBSS activity. We have applied this method to study GBSS activity in diurnal cycles in vegetative growth and during the photoperiodic transition to flowering in Arabidopsis (Tenorio et al., 2003; Ortiz-Marchena et al., 2014).España,MINECO CSD2007-00057, BIO2008-02292, and BIO2011-28847-C02-00España, Junta de Andalucía P06-CVI-01450 and P08-AGR-0358

Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis

Background: Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA). Results: We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA. First, we give a proper theoretical background to bi-clustering using Lattice Theory and provide a set of analysis tools revolving around K-Formal Concept Analysis (K-FCA), a lattice-theoretic unsupervised learning technique for real-valued matrices. By using different kinds of cost structures to quantify expression we obtain different sequences of hierarchical bi-clusterings for gene under- and over-expression using thresholds. Consequently, we provide a method with interleaved analysis steps and visualization devices so that the sequences of lattices for a particular experiment summarize the researcher’s vision of the data. This also allows us to define measures of persistence and robustness of biclusters to assess them. Second, the resulting biclusters are used to index external omics databases—for instance, Gene Ontology (GO)—thus offering a new way of accessing publicly available resources. This provides different flavors of gene set enrichment against which to assess the biclusters, by obtaining their p-values according to the terminology of those resources. We illustrate the exploration procedure on a real data example confirming results previously published. Conclusions: The GED analysis problem gets transformed into the exploration of a sequence of lattices enabling the visualization of the hierarchical structure of the biclusters with a certain degree of granularity. The ability of FCA-based bi-clustering methods to index external databases such as GO allows us to obtain a quality measure of the biclusters, to observe the evolution of a gene throughout the different biclusters it appears in, to look for relevant biclusters—by observing their genes and what their persistence is—to infer, for instance, hypotheses on their function

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

An evolutionarily conserved DOF-CONSTANS module controls plant photoperiodic signaling

© 2015 American Society of Plant Biologists. All Rights Reserved. The response to daylength is a crucial process that evolved very early in plant evolution, entitling the early green eukaryote to predict seasonal variability and attune its physiological responses to the environment. The photoperiod responses evolved into the complex signaling pathways that govern the angiosperm floral transition today. The Chlamydomonas reinhardtii DNA-Binding with One Finger (CrDOF) gene controls transcription in a photoperiod-dependent manner, and its misexpression influences algal growth and viability. In short days, CrDOF enhances CrCO expression, a homolog of plant CONSTANS (CO), by direct binding to its promoter, while it reduces the expression of cell division genes in long days independently of CrCO. In Arabidopsis (Arabidopsis thaliana), transgenic plants overexpressing CrDOF show floral delay and reduced expression of the photoperiodic genes CO and FLOWERING LOCUS T. The conservation of the DOF-CO module during plant evolution could be an important clue to understanding diversification by the inheritance of conserved gene toolkits in key developmental programs.Peer Reviewe

El sistema de garantía juvenil y la estrategia española de activación para el empleo 2014-2016

Ministerio de Economía y Competitividad DER 2012-3675

ChlamyNET: a Chlamydomonas gene co-expression network reveals global properties of the transcriptome and the early setup of key co-expression patterns in the green lineage

Background Chlamydomonas reinhardtii is the model organism that serves as a reference for studies in algal genomics and physiology. It is of special interest in the study of the evolution of regulatory pathways from algae to higher plants. Additionally, it has recently gained attention as a potential source for bio-fuel and bio-hydrogen production. The genome of Chlamydomonas is available, facilitating the analysis of its transcriptome by RNA-seq data. This has produced a massive amount of data that remains fragmented making necessary the application of integrative approaches based on molecular systems biology. Results We constructed a gene co-expression network based on RNA-seq data and developed a web-based tool, ChlamyNET, for the exploration of the Chlamydomonas transcriptome. ChlamyNET exhibits a scale-free and small world topology. Applying clustering techniques, we identified nine gene clusters that capture the structure of the transcriptome under the analyzed conditions. One of the most central clusters was shown to be involved in carbon/nitrogen metabolism and signalling, whereas one of the most peripheral clusters was involved in DNA replication and cell cycle regulation. The transcription factors and regulators in the Chlamydomonas genome have been identified in ChlamyNET. The biological processes potentially regulated by them as well as their putative transcription factor binding sites were determined. The putative light regulated transcription factors and regulators in the Chlamydomonas genome were analyzed in order to provide a case study on the use of ChlamyNET. Finally, we used an independent data set to cross-validate the predictive power of ChlamyNET. Conclusions The topological properties of ChlamyNET suggest that the Chlamydomonas transcriptome posseses important characteristics related to error tolerance, vulnerability and information propagation. The central part of ChlamyNET constitutes the core of the transcriptome where most authoritative hub genes are located interconnecting key biological processes such as light response with carbon and nitrogen metabolism. Our study reveals that key elements in the regulation of carbon and nitrogen metabolism, light response and cell cycle identified in higher plants were already established in Chlamydomonas. These conserved elements are not only limited to transcription factors, regulators and their targets, but also include the cis-regulatory elements recognized by them.España, Ministerio de Economía y Competitividad CSD2007-00057España, Ministerio de Economía y Competitividad BIO2011-28847-C02-00España, Ministerio de Economía y Competitividad BIO2014-52425-