Vascular Smooth Muscle Cell Plasticity and Autophagy in Dissecting Aortic Aneurysms.

Objective- Recent studies suggested the occurrence of phenotypic switching of vascular smooth muscle cells (VSMCs) during the development of aortic aneurysm (AA). However, lineage-tracing studies are still lacking, and the behavior of VSMCs during the formation of dissecting AA is poorly understood. Approach and Results- We used multicolor lineage tracing of VSMCs to track their fate after injury in murine models of Ang II (angiotensin II)-induced dissecting AA. We also addressed the direct impact of autophagy on the response of VSMCs to AA dissection. Finally, we studied the relevance of these processes to human AAs. Here, we show that a subset of medial VSMCs undergoes clonal expansion and that VSMC outgrowths are observed in the adventitia and borders of the false channel during Ang II-induced development of dissecting AA. The clonally expanded VSMCs undergo phenotypic switching with downregulation of VSMC differentiation markers and upregulation of phagocytic markers, indicative of functional changes. In particular, autophagy and endoplasmic reticulum stress responses are activated in the injured VSMCs. Loss of autophagy in VSMCs through deletion of autophagy protein 5 gene ( Atg5) increases the susceptibility of VSMCs to death, enhances endoplasmic reticulum stress activation, and promotes IRE (inositol-requiring enzyme) 1α-dependent VSMC inflammation. These alterations culminate in increased severity of aortic disease and higher incidence of fatal AA dissection in mice with VSMC-restricted deletion of Atg5. We also report increased expression of autophagy and endoplasmic reticulum stress markers in VSMCs of human dissecting AAs. Conclusions- VSMCs undergo clonal expansion and phenotypic switching in Ang II-induced dissecting AAs in mice. We also identify a critical role for autophagy in regulating VSMC death and endoplasmic reticulum stress-dependent inflammation with important consequences for aortic wall homeostasis and repair

Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990-2010

The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990-2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends detected by observations? Do the models manage to reproduce observed trends? How close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000-2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set-up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the model-simulated trends could be regarded as an indicator for modelling uncertainty.The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5 from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM10) mu g m(-3) (or between 10 % and 30 %) across most of Europe (by 0.5-2 mu g m(-3) in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM2.5, relative PM10 trends are weaker due to large interannual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30 %-40 % over most of Europe, increasing to 50 %-60 % in the northern and eastern parts of the EDT domain.Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble-simulated trends are - 0.24 and -0.22 mu g m(-3) yr(-1) for PM10 and PM2.5, which are somewhat weaker than the observed trends of - 0.35 and -0.40 mu g m(-3) yr(-1) respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are -1.7 % yr(-1) and -2.0 % yr(-1) from the model ensemble and -2.1 % yr(-1) and -2.9 % yr(-1) from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries.The strongest decreasing PM trends and the largest number of sites with significant trends are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modelled versus observed PM trends are limited to the regions where the sites are located.The analysis reveals considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in SO42- concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH4+ and NO3- to PM10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appear to be a dominant factor in bringing down PM10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties (including the implications of not accounting for forest fires and natural mineral dust by some of the models) and the effect of inter-annual meteorological variability on the trend analysis.Peer reviewe

BUILDING BRIDGES FOR INNOVATION IN AGEING : SYNERGIES BETWEEN ACTION GROUPS OF THE EIP ON AHA

The Strategic Implementation Plan of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) proposed six Action Groups. After almost three years of activity, many achievements have been obtained through commitments or collaborative work of the Action Groups. However, they have often worked in silos and, consequently, synergies between Action Groups have been proposed to strengthen the triple win of the EIP on AHA. The paper presents the methodology and current status of the Task Force on EIP on AHA synergies. Synergies are in line with the Action Groups' new Renovated Action Plan (2016-2018) to ensure that their future objectives are coherent and fully connected. The outcomes and impact of synergies are using the Monitoring and Assessment Framework for the EIP on AHA (MAFEIP). Eight proposals for synergies have been approved by the Task Force: Five cross-cutting synergies which can be used for all current and future synergies as they consider overarching domains (appropriate polypharmacy, citizen empowerment, teaching and coaching on AHA, deployment of synergies to EU regions, Responsible Research and Innovation), and three cross-cutting synergies focussing on current Action Group activities (falls, frailty, integrated care and chronic respiratory diseases).Peer reviewe

Computer-generated Visual Summaries of Spatial Databases: Chorems or not Chorems?

Chorems can be defined as representations of elementary structure of a geographic space or as schematized representations of territories, and as such they can represent a good candidate for generating visual summaries of spatial databases. Indeed for spatial decision-makers, it is more important to identify and map problems than facts. Until now, chorems were made manually by geographers who needed an exhaustive knowledge of the territory under study, a clear-cut set of rules to decide what the salient phenomena are, and who had no problems to cartography them. Here we present a methodology based on spatial data mining, that both diminish the requirements in terms of starting knowledge, and provide a more rigorous approach to select the important features

An evaluation of European nitrogen and sulfur wet deposition and their trends estimated by six chemistry transport models for the period 1990-2010

The wet deposition of nitrogen and sulfur in Europe for the period 1990–2010 was estimated by six atmospheric chemistry transport models (CHIMERE, CMAQ, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI) within the framework of the EURODELTA-Trends model intercomparison. The simulated wet deposition and its trends for two 11-year periods (1990–2000 and 2000–2010) were evaluated using data from observations from the EMEP European monitoring network. For annual wet deposition of oxidised nitrogen (WNOx), model bias was within 30 % of the average of the observations for most models. There was a tendency for most models to underestimate annual wet deposition of reduced nitrogen (WNHx), although the model bias was within 40 % of the average of the observations. Model bias for WNHx was inversely correlated with model bias for atmospheric concentrations of NH3+NH+4 , suggesting that an underestimation of wet deposition partially contributed to an overestimation of atmospheric concentrations. Model bias was also within about 40 % of the average of the observations for the annual wet deposition of sulfur (WSOx) for most models. Decreasing trends in WNOx were observed at most sites for both 11-year periods, with larger trends, on average, for the second period. The models also estimated predominantly decreasing trends at the monitoring sites and all but one of the models estimated larger trends, on average, for the second period. Decreasing trends were also observed at most sites for WNHx, although larger trends, on average, were observed for the first period. This pattern was not reproduced by the models, which estimated smaller decreasing trends, on average, than those observed or even small increasing trends. The largest observed trends were for WSOx, with decreasing trends at more than 80 % of the sites. On average, the observed trends were larger for the first period. All models were able to reproduce this pattern, although some models underestimated the trends (by up to a factor of 4) and others overestimated them (by up to 40 %), on average. These biases in modelled trends were directly related to the tendency of the models to under- or overestimate annual wet deposition and were smaller for the relative trends (expressed as % yr−1 relative to the deposition at the start of the period). The fact that model biases were fairly constant throughout the time series makes it possible to improve the predictions of wet deposition for future scenarios by adjusting the model estimates using a bias correction calculated from past observations. An analysis of the contributions of various factors to the modelled trends suggests that the predominantly decreasing trends in wet deposition are mostly due to reductions in emissions of the precursors NOx, NH3 and SOx. However, changes in meteorology (e.g. precipitation) and other (non-linear) interactions partially offset the decreasing trends due to emission reductions during the first period but not the second. This suggests that the emission reduction measures had a relatively larger effect on wet deposition during the second period, at least for the sites with observations.publishedVersio

Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010

The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends detected by observations? Do the models manage to reproduce observed trends? How close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set-up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the model-simulated trends could be regarded as an indicator for modelling uncertainty. The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5 from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM10) µg m−3 (or between 10 % and 30 %) across most of Europe (by 0.5–2 µg m−3 in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM2.5, relative PM10 trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30 %–40 % over most of Europe, increasing to 50 %–60 % in the northern and eastern parts of the EDT domain. Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble-simulated trends are −0.24 and −0.22 µg m−3 yr−1 for PM10 and PM2.5, which are somewhat weaker than the observed trends of −0.35 and −0.40 µg m−3 yr−1 respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are −1.7 % yr−1 and −2.0 % yr−1 from the model ensemble and −2.1 % yr−1 and −2.9 % yr−1 from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries. The strongest decreasing PM trends and the largest number of sites with significant trends are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modelled versus observed PM trends are limited to the regions where the sites are located. The analysis reveals considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in concentrations playing an overall dominant role. Also, we see..

Trends of inorganic and organic aerosols and precursor gases in Europe : insights from the EURODELTA multi-model experiment over the 1990-2010 period

International audienceIn the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990-2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas-Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanaly-sis. For this study, long-term observations of particle sulfate (SO 2− 4), total nitrate (TNO 3), total ammonium (TNH x) as well as sulfur dioxide (SO 2) and nitrogen dioxide (NO 2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years Published by Copernicus Publications on behalf of the European Geosciences Union. 4924 G. Ciarelli et al.: Trends of inorganic and organic aerosols and precursor gases in Europe (referred to as PT) but also for two 11-year subperiods: 1990-2000 (referred to as P1) and 2000-2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO 2 concentrations during the first decade, i.e., 1990-2000, with a 64 %-76 % mean relative reduction in SO 2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO 2 concentrations of about 34 %-54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO 2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO 2− 4 concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %-54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods , even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both mod-eled and observational data indicated smaller reductions in SO 2− 4 concentrations compared with their gas-phase precursor (i.e., SO 2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO 3 concentrations indicated a 28 %-39 % and 29 % mean relative reduction in TNO 3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO 3 and particle nitrate (NO − 3) concentrations revealed that the relative reduction in HNO 3 was larger than that for NO − 3 during the P1 period, which was mainly attributed to an increased availability of "free ammonia". By contrast, trends in modeled HNO 3 and NO − 3 concentrations were more comparable during the P2 period. Also, trends of TNH x concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emissions and monoterpene emissions during the 1990-2010 period over Fennoscan-dia and eastern European regions (i.e., around 14 %-27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally , a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic underestimation of the modeled SOA fractions of a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere