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Background Oligoarticular juvenile idiopathic arthritis (oJIA) is the most frequent form of chronic arthritis in children; the clinical course is extremely variable. In this study we have characterized by flow cytometry synovial B and T cells subsets in patients with oJIA in order to identify any parameters that could predict a more aggressive course of disease. Methods B and T cells from synovial fluid (SF) of 39 patients with oJIA were characterized by flow cytometry. In 22 patients SF was analysed at the onset of the disease (GroupA), in 17 SF was analysed at articular relapse (Group B). All patients in Group A were followed up for at least for 2 years after SF analysis: 13 patients relapsed during the follow-up period. Results Comparison of SF from Group A and Group B demonstrated an activated phenotype in relapsed patients, with higher Switched Memory B cells (58.53 vs 36.07% of CD19+, p-value 0.004) and lower Naive B cells (8.53 vs 25.9 of CD19+, p-value 0.002) in Group B. Furthermore, patients from Group A who did not relapse showed lower percentages of synovial DNT (2.38 vs 1.50% of CD3+TCRalpha/beta+, p-value 0.025) and γδ T cells (19.1 vs 15.0% of CD3+ cells, p-value 0.004) at the onset, if compared with other Group A patients. Conclusions In oJIA relapse SF present an activated B phenotype. Patients at disease onset with DNTs

Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research: et al., VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis-driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method-to-method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO-CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal., This work has been performed in the framework of the VALUE COST Action ES1102, under FP7 programme J.M.G. and S.H. acknowledge partial funding from MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). B.H. and D.R. acknowledge COMPLEX project (FP7-ENV-2012, No: 308601). M.T. was supported by HOPE project (MINECO, CGL2014-52571-R). Participation of M.D., P.S. and R.H. was funded by the Ministry of Education, Youth, and Sports of the Czech Republic contracts LD12029, LD14043 and LD12059, respectively.

Many species are found today in the form of fragmented populations occupying patches of remnant habitat in human-altered landscapes. The persistence of these population networks requires a balance between extinction and colonization events assumed to be primarily related to patch area and isolation, but the contribution of factors such as the characteristics of patch and matrix habitats, the species' traits (habitat specialization and dispersal capabilities) and variation in climatic conditions have seldom been evaluated simultaneously. The identification of environmental variables associated with patch occupancy and turnover may be especially useful to enhance the persistence of multiple species under current global change. However, for robust inference on occupancy and related parameters, we must account for detection errors, a commonly overlooked problem that leads to biased estimates and misleading conclusions about population dynamics. Here, we provide direct empirical evidence of the effects of different environmental variables on the extinction and colonization rates of a rich butterfly community in the western Mediterranean. The analysis was based on a 17-year data set containing detection/nondetection data on 73 butterfly species for 26 sites in north-eastern Spain. Using multiseason occupancy models, which take into account species' detectability, we were able to obtain robust estimates of local extinction and colonization probabilities for each species and test the potential effects of site covariates such as the area of suitable habitat, topographic variability, landscape permeability around the site and climatic variability in aridity conditions. Results revealed a general pattern across species with local habitat composition and landscape features as stronger predictors of occupancy dynamics compared with topography and local aridity. Increasing area of suitable habitat in a site strongly decreased local extinction risks and, for a number of species, both higher amounts of suitable habitat and more permeable landscapes increased colonization rates. Nevertheless, increased topographic variability decreased the extinction risk of bad dispersers, a group of species with significantly lower colonization rates. Our models predicted higher sensitivity of the butterfly assemblages to deterministic changes in habitat features rather than to stochastic weather patterns, with some relationships being clearly dependent on the species' traits. © 2013 British Ecological Society., The CBMS is funded by the Departament de Territori i Paisatge de la Generalitat de Catalunya. Funds were also provided by the Spanish Ministry of Science (grant ref. CGL2009-08298) and Regional Government of Balearic Islands (FEDER funding). The Diputació de Barcelona and Patronat Metropolitá Parc de Collserola have also given financial support to this project

We analyzed glacier snout fluctuation data in Piedmont and Val d'Aosta (Italy) and studied the impact of climate variability on valley glaciers in the western Italian Alps. The study period covered about 70 years in the 20th century; we focused on the last 50 years where a large number of temperature and precipitation time series are available. Superposed onto a general recession trend, we detected strong oscillations on shorter time scales and we showed that they are significantly correlated with fluctuations in winter precipitation rates and average summer temperatures. On the basis of these results, we constructed a simple lagged-linear empirical stochastic model that explained upto 66% of the variance of the snout fluctuation data. The model produces reliable out-of-sample predictions of the impact of climate variability on the glaciers of the western Alps and it can be used to estimate the average response of Alpine glaciers to different scenarios of climate change, provided the morphology of the individual glaciers does not change completely. Copyright © 2007 Royal Meteorological Society