Regional economic dynamics and convergence in the european union

Deepening in the European Union (EU) integration process has enhanced the question of economic disparities at a regional level. Theconvergence process observed until the late seventies was exhausted onwards incoincidence with important changes in the economic activity. The paper showshow these factors would have provoked a regional differenciated response that,despite being important, would have not strengthened the decrease in regionalinequalities. We use an alternative and (in our opinion) richer approach to thetraditional convergence analysis, where the evolution of the whole regionaldistribution is what matters and not that of a representative economy. Moreover,when analysing inequalities among regional economies, the geographical spaceacquire an outstanding role. Hence, we apply spatial association tests and relatethem to the convergence analysis

Global tractography with embedded anatomical priors for quantitative connectivity analysis.

Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter (WM) by exploiting the directional information described with diffusion magnetic resonance. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood. Global methods, on the other hand, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem. The latter have shown improvements compared to previous techniques but these algorithms still suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are usually considered during the reconstruction process, the recovered fiber tracts are not guaranteed to connect cortical regions and, as a matter of fact, most of them stop prematurely in the WM; this violates important properties of neural connections, which are known to originate in the gray matter (GM) and develop in the WM. Hence, this shortcoming poses serious limitations for the use of these techniques for the assessment of the structural connectivity between brain regions and, de facto, it can potentially bias any subsequent analysis. Moreover, the estimated tracts are not quantitative, every fiber contributes with the same weight toward the predicted diffusion signal. In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications which: (i) explicitly enforces anatomical priors of the tracts in the optimization and (ii) considers the effective contribution of each of them, i.e., volume, to the acquired diffusion magnetic resonance imaging (MRI) image. We evaluated our approach on both a realistic diffusion MRI phantom and in vivo data, and also compared its performance to existing tractography algorithms.

Understanding and meeting the needs of the older population: a global challenge.

BACKGROUND: In the past century, there has been a significant rise in life expectancy in almost all regions of the world, contributing to an increasingly older population. The aging of the population, in conjunction with urbanization and industrialization, has resulted in an important epidemiological transition marked by a widespread increase in the prevalence of chronic diseases and their sequelae. Current trends suggest that the transition will have a greater impact on developing countries compared to developed countries. An adequate response to the transition requires a strong emphasis on primary prevention and adequate resource allocation.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

The EU strategy of policy convergence with its neighbours in the area of trade

The objective of this paper is to ascertain whether the EU is seeking policy convergence with its neighbours in the area of trade by means of EU regulations. For each trade- related topic, we carried out a content analysis of the available official documents to identify the model of relations that has been established between the EU and four neighbouring countries (Morocco, Algeria, Ukraine and Georgia). The findings indicate that Europeanization is the EU strategy in most cases. However, adaptation to European regulations is only a long-term aim. When international regulations exist in a specific area, the EU usually demands the internationalization of a country¿s regulations as a first step. When there are no international regulations, the convergence process is established on the basis of bilaterally developed norms. EU strategy also varies depending on the country. Its relations with Algeria are the most particular. We conclude that the EU is promoting policy convergence with its neighbours in the area of trade mainly on the basis of international and bilaterally-developed regulations.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Regional economic dynamics and convergence in the european union

Deepening in the European Union (EU) integration process has enhanced the question of economic disparities at a regional level. Theconvergence process observed until the late seventies was exhausted onwards incoincidence with important changes in the economic activity. The paper showshow these factors would have provoked a regional differenciated response that,despite being important, would have not strengthened the decrease in regionalinequalities. We use an alternative and (in our opinion) richer approach to thetraditional convergence analysis, where the evolution of the whole regionaldistribution is what matters and not that of a representative economy. Moreover,when analysing inequalities among regional economies, the geographical spaceacquire an outstanding role. Hence, we apply spatial association tests and relatethem to the convergence analysis

Phylogenomics of C(4) photosynthesis in sedges (Cyperaceae): multiple appearances and genetic convergence.

C(4) photosynthesis is an adaptive trait conferring an advantage in warm and open habitats. It originated multiple times and is currently reported in 18 plant families. It has been recently shown that phosphoenolpyruvate carboxylase (PEPC), a key enzyme of the C(4) pathway, evolved through numerous independent but convergent genetic changes in grasses (Poaceae). To compare the genetics of multiple C(4) origins on a broader scale, we reconstructed the evolutionary history of the C(4) pathway in sedges (Cyperaceae), the second most species-rich C(4) family. A sedge phylogeny based on two plastome genes (rbcL and ndhF) has previously identified six fully C(4) clades. Here, a relaxed molecular clock was used to calibrate this tree and showed that the first C(4) acquisition occurred in this family between 19.6 and 10.1 Ma. According to analyses of PEPC-encoding genes (ppc), at least five distinct C(4) origins are present in sedges. Two C(4) Eleocharis species, which were unrelated in the plastid phylogeny, acquired their C(4)-specific PEPC genes from a single source, probably through reticulate evolution or a horizontal transfer event. Acquisitions of C(4) PEPC in sedges have been driven by positive selection on at least 16 codons (3.5% of the studied gene segment). These sites underwent parallel genetic changes across the five sedge C(4) origins. Five of these sites underwent identical changes also in grass and eudicot C(4) lineages, indicating that genetic convergence is most important within families but that identical genetic changes occurred even among distantly related taxa. These lines of evidence give new insights into the constraints that govern molecular evolution.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

The functions of DNA methylation by CcrM in Caulobacter crescentus: a global approach.

DNA methylation is involved in a diversity of processes in bacteria, including maintenance of genome integrity and regulation of gene expression. Here, using Caulobacter crescentus as a model, we exploit genome-wide experimental methods to uncover the functions of CcrM, a DNA methyltransferase conserved in most Alphaproteobacteria. Using single molecule sequencing, we provide evidence that most CcrM target motifs (GANTC) switch from a fully methylated to a hemi-methylated state when they are replicated, and back to a fully methylated state at the onset of cell division. We show that DNA methylation by CcrM is not required for the control of the initiation of chromosome replication or for DNA mismatch repair. By contrast, our transcriptome analysis shows that >10% of the genes are misexpressed in cells lacking or constitutively over-expressing CcrM. Strikingly, GANTC methylation is needed for the efficient transcription of dozens of genes that are essential for cell cycle progression, in particular for DNA metabolism and cell division. Many of them are controlled by promoters methylated by CcrM and co-regulated by other global cell cycle regulators, demonstrating an extensive cross talk between DNA methylation and the complex regulatory network that controls the cell cycle of C. crescentus and, presumably, of many other Alphaproteobacteria.