Effects of water re-allocation in the Ebro river basin: A multiregional input-output and geographical analysis

The quality and availability of water are affected by numerous variables, through which the evaluation of water uses from different perspectives, and policy proposals to save water have now become essential. This paper aims to study water use and the water footprint from a river basin perspective, taking into account regions, sectors, and municipalities, while considering the physical frontier along with the administrative sectors. To this end, we have constructed a multi-regional input-output table for the Ebro river basin, disaggregating the primary sector into 18 different crops and 6 livestock groups. We pay special attention to crop production because it is the most water-consuming industry. The construction of the multi-regional input-output model represents an important contribution to the literature, in itself, since, to the best of our knowledge, it is the first to be built for this large basin. We extend this multi-regional input-output model to assess the water footprint by sectors and regions within the basin. We use these data to propose two scenarios: reallocating final demand to reduce the blue water footprint (scenario 1), and increasing value added (scenario 2). These scenarios outline the opportunity costs of saving water in socioeconomic terms in the basin. In another application, we downscale the multi-regional input-output model results at the municipal level and depict them using a geographical information system, identifying the hotspots and the areas that would pay for the socioeconomic opportunity costs of saving water. Our results suggest that saving 1 hm 3 of blue water could cost around €41, 500 of value added if we consider the entire basin. However, this water re-allocation implies losses and gains at the municipal level: some municipalities would reduce value added by more than €30, 000, while others would gain more than €85, 000 of value added. These tools and results can be useful for policy makers when considering re-allocating water. The contribution and the novelty of this paper is the construction of the multiregional input-output model for the Ebro river basin, and its link with geographical systems analysis at the municipal level

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

A Multiregional Input–Output Hydro-Economic Modeling Framework: An Application to the Ebro River Basin

Sustainable water management is challenging because of the wide range of agents who need water and the different kinds of use, in a context of limited water availability. The availability of water for use, at a given point in time and space, depends on numerous physical and climatic variables, as well as upstream uses and downstream commitments. Therefore, any analysis of water use and management must inevitably be made in the context of such variability. This paper develops an integrated, multiregional, hydro-economic modeling framework to analyze the spatial and temporal dependencies between economic agents in the different regions and areas of a river basin. We combine hydro-economic modeling (partial economic equilibrium) and a multiregional input–output model (general equilibrium) to take advantage of both methodologies. Spatial variability is considered in the input–output models, but variability in both time and space is also considered by the hydro-economic model. Hydro-economic models are used to quantify direct impacts, but not indirect impacts in some specific sectors of the economy, while the input–output model reveals the relationships between all sectors and regions, and facilitates the assessment of total impacts (direct plus indirect) of a range of scenarios. While the methodology described in this paper is applicable to any river basin, the case study considered is the Ebro River Basin, in Spain. To show the potential of the modeling framework, two scenarios are simulated to assess the impacts on water use, value added, and jobs across scales. The results of these scenarios show that decreases in water availability have negative impacts on socio-economic variables (value added and employment). The trade-off between water availability and socio-economic variables depends on the temporal and spatial variability of the resource, and affects each location in the basin in a different way, demonstrating the importance of the methodology developed

The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations.

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations

Distribution of selected mutations along the different affected genes and their related functional categories.

<p>A) Number of mutated samples by gene according to the described mutation filtering protocol (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148346#pone.0148346.s002" target="_blank">S2 Fig</a>). Only 33 recurrent genes were included. B) Number of mutated samples by gene category (bottom). The corresponding number of genes included in each functional category (top) is also represented in order to avoid any size bias. C) The functional categories are distributed depending on the observed mutated samples and the known number of belonging genes. Both transcription factor and ubiquitination categories shown and excess of mutated samples. D) The number of mutated samples per category in the haloplex cohort were compared against a reference population of healthy samples (1000 genomes). Here, while metabolism and signalling genes categories appear poorly mutated, ubiquitination appears remarkably more mutated than the reference population.</p

Network of significant gene co-occurrences.

<p>Genes are represented by nodes and their sizes defined from the number of significant co-occurrences they are implied. Edges represent co-occurrences between pairs of genes. Every edge is labelled with the number of samples that carries the mutated pair of genes as follows: higher than expected co-occurrences are coloured in green, while lower than expected (only one) in red. Edge width is proportional to the statistical p-value of chi-square test. Those genes co-occurring only at one single patient are painted in white. Seven co-occurrence subnetworks arise from the significant co-occurrence network, where remarkably a single component connected the half of represented genes. In contrast, 3 pairs are simultaneously mutated only in 2 different individuals, and 3 significant co-occurrence subnetworks, only in 1 patient.</p

Mutated genes with a higher frequency in our cohort than expected in the 1000 genomes repository.

<p>Mutated genes with a higher frequency in our cohort than expected in the 1000 genomes repository.</p

Network-based analysis (SNOW) applied to 46 selected genes (33 recurrent and <i>RARA</i>, <i>PML</i>, and <i>FLT3</i> genes).

<p>The network was complemented with the co-occurrence relationships, in order to summarize the two kind of significant results. Significant network-based analysis genes are coloured in light blue and stroked with a magenta border whether they resulted also significant in co-occurrence analysis. Genes only included by co-occurrence are coloured in magenta. Intermediate genes were painted in white and square shaped. While grey edges represent protein-protein interaction, relationships, broad orange dashed lines describe significant co-occurrences. Moreover, main genes are grouped depending on their biological role (cohesin complex, signalling pathways, spliceosome, RHO-GTPase, retinoic acid regulators and other cellular processes roles).</p

SNV and indel filtering steps for identification of somatic variants.

<p>SNV and indel filtering steps for identification of somatic variants.</p