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Statistical postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench (EUMETNET postprocessing benchmark), a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark (31 December 2022) and on Zenodo (https://doi.org/10.5281/zenodo.7429236, Demaeyer, 2022b and https://doi.org/10.5281/zenodo.7708362, Bhend et al., 2023). We provide examples showing how to download and use the data, we propose a set of evaluation methods, and we perform a first benchmark of several methods for the correction of 2 m temperature forecasts.

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models.

The European drought of 1921 is assessed in terms of its impacts on society and in terms of its physical characteristics. The development of impacts of the drought are categorized by a systematic survey of newspaper reports from five European newspapers covering the area from England to the Czech Republic and other parts of Europe. This is coupled to a reconstruction of daily temperature and precipitation based on meteorological measurements to quantify the drought severity and extent, and reanalysis data are used to identify its drivers. This analysis shows that the first impacts of the drought started to appear in early spring and lingered on until well into autumn and winter, affecting water supply and agriculture and livestock farming. The dominant impact in western Europe is on agriculture and livestock farming while in central Europe the effects of wildfires were reported on most often. The peak in the number of reports is in late summer. Preceding the first impacts was the dry autumn of 1920 and winter 1920–1921. The area hardest hit by the drought in the following spring and summer was the triangle between Brussels, Paris and Lyon, but a vast stretch of the continent, from Ireland to the Ukraine, was affected. The reported impacts on water supply and water-borne transport in that region were matched by an analysis of the hydrological situation over the Seine catchment. On average, the 1921 summer was not particularly hot, but the heatwave which was observed at the end of July saw temperatures matching those of the heatwaves in modern summers. Similar to modern droughts, an anticyclone was present roughly over the British Isles, maintaining sunny and dry weather in Europe and steering away cyclones to the north. Its persistence makes it exceptional in comparison to modern droughts. The 1921 drought stands out as the most severe and most widespread drought in Europe since the start of the 20th century. The precipitation deficit in all seasons was large, but in none of the seasons in 1920 and 1921 was the precipitation deficit the largest on record. The severity of the 1921 drought relates to the conservative nature of drought which amplifies the lack of precipitation in autumn and winter into the following spring and summer.

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

Isoprene is a highly reactive volatile organic compound emitted by vegetation, known to be a precursor of secondary organic aerosols and to enhance tropospheric ozone formation under polluted conditions. Isoprene emissions respond strongly to changes in meteorological parameters such as temperature and solar radiation. In addition, the increasing CO2 concentration has a dual effect, as it causes both a direct emission inhibition as well as an increase in biomass through fertilization. In this study we used the MEGAN (Model of Emissions of Gases and Aerosols from Nature) emission model coupled with the MOHYCAN (Model of HYdrocarbon emissions by the CANopy) canopy model to calculate the isoprene fluxes emitted by vegetation in the recent past (1979–2014) and in the future (2070–2099) over Europe at a resolution of 0.1° × 0.1°. As a result of the changing climate, modeled isoprene fluxes increased by 1.1 % yr−1 on average in Europe over 1979–2014, with the strongest trends found over eastern Europe and European Russia, whereas accounting for the CO2 inhibition effect led to reduced emission trends (0.76 % yr−1). Comparisons with field campaign measurements at seven European sites suggest that the MEGAN–MOHYCAN model provides a reliable representation of the temporal variability of the isoprene fluxes over timescales between 1 h and several months. For the 1979–2014 period the model was driven by the ECMWF ERA-Interim reanalysis fields, whereas for the comparison of current with projected future emissions, we used meteorology simulated with the ALARO regional climate model. Depending on the representative concentration pathway (RCP) scenarios for greenhouse gas concentration trajectories driving the climate projections, isoprene emissions were found to increase by +7 % (RCP2.6), +33 % (RCP4.5), and +83 % (RCP8.5), compared to the control simulation, and even stronger increases were found when considering the potential impact of CO2 fertilization: +15 % (RCP2.6), +52 % (RCP4.5), and +141 % (RCP8.5). However, the inhibitory CO2 effect goes a long way towards canceling these increases. Based on two distinct parameterizations, representing strong or moderate inhibition, the projected emissions accounting for all effects were estimated to be 0–17 % (strong inhibition) and 11–65 % (moderate inhibition) higher than in the control simulation. The difference obtained using the two CO2 parameterizations underscores the large uncertainty associated to this effect.

Using the regional climate model ALARO-0, the Royal Meteorological Institute of Belgium and Ghent University have performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive the model for the period 1979–2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44°. ALARO-0 is characterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989–2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature, some large biases, which are often also found in the ARPEGE results, persist. For precipitation, on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust, meaning independent of the period used to calculate them. Periods of 20 years are sampled from the 32-year simulation and used to construct the 95 % confidence interval for each score. For most scores, these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant.

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Abstract  We investigate the effects of imbalance on the two-dimensional Berezinskii-Kosterlitz-Thouless superfluid transition for a Fermi gas in a parabolic trap. Thermodynamic parameters of the Fermi gas are determined using the functional integral formalism in combination with a hydrodynamic action functional for the phase fluctuations. [ABSTRACT FROM AUTHOR]

Objectives: City populations are particularly vulnerable to climate change, but it is difficult to reliably estimate the impact on health due to the lack of high-resolution data. We used recently developed regional climate model projections at kilometre resolution combined with demographic projections to estimate the future mortality burden associated with temperatures in the region of Brussels, Belgium., Study Design: The study incorporated a time-series analysis., Methods: Based on quasi-Poisson regression with distributed-lag non-linear models for the historical temperature-mortality relationship, we derive the mortality burden for the near (2020-2044) and mid (2045-2069) future and disaggregated the contributions of demographic and climate changes., Results: The cold-related attributable fraction of deaths is expected to decrease from 6.22% (95% empirical confidence interval: 1.76%; 10.52%) in 1994-2019 to 5.17% (1.08%; 9.09%) in 2045-2069, whereas for heat, this fraction will increase from 1.02% (0.59%; 1.47%) to 1.83% (0.82%; 2.96%), with contributions of both climate and demographic changes. In stratified analyses by age, we found that because of demographic changes, the number of cold-attributable deaths will increase for people aged above 85 years, with 6815 (95% empirical confidence interval: 1424; 12,003) deaths expected in 2045-2069 compared to 5245 (1462; 8867) deaths in 1994-2019. For people aged below 65 years, on the other hand, the number of heat-related deaths will decrease from 456 (265; 658) to 344 (154; 561) deaths., Conclusions: Public health policies that especially target the elderly and the summer-time period are needed to limit the impact of climate change on health., (Copyright © 2024 Sciensano. Published by Elsevier Ltd.. All rights reserved.)

Circulatory-system diseases (CSDs) are responsible for 50-60% of all deaths in Romania. Due to its continental climate, with cold winters and very warm summers, there is a strong temperature dependence of the CSD mortality. Additionally, within its capital Bucharest, the urban heat island (UHI) is expected to enhance (reduce) heat (cold)-related mortality. Using distributed lag non-linear models, we establish the relation between temperature and CSD mortality in Bucharest and its surroundings. A striking finding is the strong temperature-related response to high urban temperatures of women in comparison with men from the total CSDs mortality. In the present climate, estimates of the CSDs attributable fraction (AF) of mortality at high temperatures is about 66% higher in Bucharest than in its rural surroundings for men, while it is about 100% times higher for women. Additionally, the AF in urban areas is also significantly higher for elderly people, and for those with hypertensive and cerebrovascular diseases than in the rural surroundings. On the other hand, in rural areas, men but especially women are currently more vulnerable with respect to low temperatures than in the urban environment. In order to project future thermal-related mortality, we have used five bias-corrected climate projections from regional circulation models under two climate-change scenarios, RCP4.5 and RCP8.5. Analysis of the temperature-mortality associations for future climate reveals the strongest signal under the scenario RCP8.5 for women, elderly people as well as for groups with hypertensive and cerebrovascular diseases. The net AF increase is much larger in urban agglomeration for women (8.2 times higher than in rural surroundings) and elderly people (8.5 times higher than in rural surroundings). However, our estimates of thermal attributable mortality are most likely underestimated due to the poor representation of UHI and future demography., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: There is strong evidence of mortality being associated to extreme temperatures but the extent to which individual or residential factors modulate this temperature vulnerability is less clear., Methods: We conducted a multi-city study with a time-stratified case-crossover design and used conditional logistic regression to examine the association between extreme temperatures and overall natural and cause-specific mortality. City-specific estimates were pooled using a random-effect meta-analysis to describe the global association. Cold and heat effects were assessed by comparing the mortality risks corresponding to the 2.5 th and 97.5 th percentiles of the daily temperature, respectively, with the minimum mortality temperature. For cold, we cumulated the risk over lags of 0 to 28 days before death and 0 to 7 days for heat. We carried out stratified analyses and assessed effect modification by individual characteristics, preexisting chronic health conditions and residential environment (population density, built-up area and air pollutants: PM 2.5 , NO 2 , O 3 and black carbon) to identify more vulnerable population subgroups., Results: Based on 307,859 deaths from natural causes, we found significant cold effect (OR = 1.42, 95%CI: 1.30-1.57) and heat effect (OR = 1.17, 95%CI: 1.12-1.21) for overall natural mortality and for respiratory causes in particular. There were significant effects modifications for some health conditions: people with asthma were at higher risk for cold, and people with psychoses for heat. In addition, people with long or frequent hospital admissions in the year preceding death were at lower risk. Despite large uncertainties, there was suggestion of effect modification by air pollutants: the effect of heat was higher on more polluted days of O 3 and black carbon, and a higher cold effect was observed on more polluted days of PM 2.5 and NO 2 while for O 3 , the effect was lower., Conclusions: These findings allow for targeted planning of public-health measures aiming to prevent the effects of extreme temperatures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

In light of climate change, health risks are expected to be exacerbated by more frequent high temperatures and reduced by less frequent cold extremes. To assess the impact of different climate change scenarios, it is necessary to describe the current effects of temperature on health. A time-stratified case-crossover design fitted with conditional quasi-Poisson regressions and distributed lag non-linear models was applied to estimate specific temperature-mortality associations in nine urban agglomerations in Belgium, and a random-effect meta-analysis was conducted to pool the estimates. Based on 307,859 all-cause natural deaths, the mortality risk associated to low temperature was 1.32 (95% CI: 1.21-1.44) and 1.21 (95% CI: 1.08-1.36) for high temperature relative to the minimum mortality temperature (23.1 °C). Both cold and heat were associated with an increased risk of cardiovascular and respiratory mortality. We observed differences in risk by age category, and women were more vulnerable to heat than men. People living in the most built-up municipalities were at higher risk for heat. Air pollutants did not have a confounding effect. Evidence from this study helps to identify specific populations at risk and is important for current and future public health interventions and prevention strategies.

This study aimed to simulate oak and beech forest growth under various scenarios of climate change and to evaluate how the forest response depends on site properties and particularly on stand characteristics using the individual process-based model HETEROFOR. First, this model was evaluated on a wide range of site conditions. We used data from 36 long-term forest monitoring plots to initialize, calibrate, and evaluate HETEROFOR. This evaluation showed that HETEROFOR predicts individual tree radial growth and height increment reasonably well under different growing conditions when evaluated on independent sites. In our simulations under constant CO 2 concentration ([CO 2 ] cst ) for the 2071-2100 period, climate change induced a moderate net primary production (NPP) gain in continental and mountainous zones and no change in the oceanic zone. The NPP changes were negatively affected by air temperature during the vegetation period and by the annual rainfall decrease. To a lower extent, they were influenced by soil extractable water reserve and stand characteristics. These NPP changes were positively affected by longer vegetation periods and negatively by drought for beech and larger autotrophic respiration costs for oak. For both species, the NPP gain was much larger with rising CO 2 concentration ([CO 2 ] var ) mainly due to the CO 2 fertilisation effect. Even if the species composition and structure had a limited influence on the forest response to climate change, they explained a large part of the NPP variability (44% and 34% for [CO 2 ] cst and [CO 2 ] var , respectively) compared to the climate change scenario (5% and 29%) and the inter-annual climate variability (20% and 16%). This gives the forester the possibility to act on the productivity of broadleaved forests and prepare them for possible adverse effects of climate change by reinforcing their resilience., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Climate change leads to more days with extremely hot temperatures. Previous analyses of heat waves have documented a short-term rise in mortality. The results on the relationship between high temperatures and hospitalisations, especially in vulnerable patients admitted to nursing homes, are inconsistent. The objective of this research was to examine the discrepancy between heat-related mortality and morbidity in nursing homes. A time-stratified case-crossover study about the impact of heat waves on mortality and hospitalisations between 1 January 2013 and 31 December 2017 was conducted in 10 nursing homes over 5 years in Flanders, Belgium. In this study, the events were deaths and hospitalisations. We selected our control days during the same month as the events and matched them by day of the week. Heat waves were the exposure. Conditional logistic regression models were applied. The associations were reported as odds ratios at lag 0, 1, 2, and 3 and their 95% confidence intervals. In the investigated time period, 3048 hospitalisations took place and 1888 residents died. The conditional logistic regression showed that odds ratios of mortality and hospitalisations during heat waves were 1.61 (95% confidence interval 1.10-2.37) and 0.96 (95% confidence interval 0.67-1.36), respectively, at lag 0. Therefore, the increase in mortality during heat waves was statistically significant, but no significant changes in hospitalisations were obtained. Our result suggests that heat waves have an adverse effect on mortality in Flemish nursing homes but have no significant effect on the number of hospitalisations.

Background: Summer temperatures are expected to increase and heat waves will occur more frequently, be longer, and be more intense as a result of global warming. A growing body of evidence indicates that increasing temperature and heatwaves are associated with excess mortality and therefore global heating may become a major public health threat. However, the heat-mortality relationship has been shown to be location-specific and differences could largely be explained by the most frequent temperature. So far, in Belgium there is little known regarding the heat-mortality relationship in the different urban areas., Objectives: The objective of this study is to assess the heat-mortality relationship in the two largest urban areas in Belgium, i.e. Antwerp and Brussels for the warm seasons from 2002 until 2011 taking into account the effect of air pollution., Methods: The threshold in temperature above which mortality increases was determined using segmented regressions for both urban areas. The relationship between daily temperature and mortality above the threshold was investigated using a generalized estimated equation with Poisson distribution to finally determine the percentage of deaths attributable to the effect of heat., Results: Although only 50 km apart, the heat-mortality curves for the two urban areas are different. More specifically, an increase in mortality occurs above a maximum temperature of 25.2 °C in Antwerp and 22.8 °C in Brussels. We estimated that above these thresholds, there is an increase in mortality of 4.9% per 1 °C in Antwerp and of 3.1% in Brussels. During the study period, 1.5% of the deaths in Antwerp and 3.5% of the deaths in Brussels can be attributed to the effect of heat. The thresholds differed considerably from the most frequent temperature, particularly in Antwerp. Adjustment for air pollution attenuated the effect of temperature on mortality and this attenuation was more pronounced when adjusting for ambient ozone., Conclusion: Our results show a significant effect of temperature on mortality above a city-specific threshold, both in Antwerp and in Brussels. These findings are important given the ongoing global warming. Recurrent, intense and longer episodes of high temperature and expected changes in air pollutant levels will have an important impact on health in urban areas., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The aim of this study was to identify predictors of prolonged disease control after discontinuation of tumor necrosis factor inhibitor (TNFi) treatment in patients with rheumatoid arthritis (RA)., Methods: Post-hoc analysis of 439 RA patients (67.3% rheumatoid factor positive) with longstanding RA in remission or with stable low disease activity, randomized to stopping TNFi treatment in the multicenter POET trial. Prolonged acceptable disease control was defined as not restarting TNFi treatment within 12 months after stopping. Baseline demographic and disease-related variables were included in univariate and multivariate logistic regression analysis for identifying predictors of relapse., Results: One year after baseline, 220 patients (50.1%) had not restarted TNFi treatment. Use of an anti-TNF monoclonal antibody (versus a receptor antagonist, OR = 2.41; 95% CI: 1.58-3.67), ≤10 yrs. disease duration (OR = 2.15; 95% CI: 1.42-3.26) and low or moderate multi-biomarker disease activity (MBDA) scores (OR = 2.00; 95% CI: 1.10-3.64) at baseline were independently predictive of successful TNFi discontinuation (area under the receiver operating characteristic curve = 0.66; 95% CI: 0.61-0.71). Results were similar when using no physician-reported flare as the criterion. TNFi-free survival was significantly different for patient groups based on the number of predictors present, ranging from 21.4% of patients with no predictor present to 66.7% of patients with all three predictors present., Conclusion: Patients using an anti-TNF monoclonal antibody, with shorter disease duration and low or moderate baseline MBDA score are most likely to achieve prolonged disease control after TNFi discontinuation., Trial Registration: Netherlands Trial Register NTR3112, 21 October 2011., Competing Interests: Competing interestsThe authors declare that they have no competing interests.

Objective: In this study we aimed to evaluate the effect of lowering the cut point of the 2010 criteria to identify more patients with RA among early inflammatory arthritis patients., Methods: We included early arthritis patients from the Rotterdam Early Arthritis Cohort with at least one joint with clinical synovitis and symptoms for <1 year, with no other explanation for their symptoms. The demographic and clinical characteristics of each patient were recorded at baseline. Patients were classified as case or non-case at the 1-year follow-up by the definition used in the development of the 2010 criteria (MTX initiation). To assess the diagnostic performance of the 2010 criteria, the sensitivity and specificity at each cut point were determined., Results: We included 557 patients in our analysis. At the 1-year follow-up, 253 patients (45%) were classified as case (MTX use). In the group of patients who scored 0-5 points (n = 328), 98 patients (30%) were classified as case (MTX use). The sensitivity and specificity of the 2010 criteria using the cut point of 6 were 61% and 76%, respectively. With the cut point of 5, the sensitivity would increase to 76% and the specificity would decrease to 68%., Conclusion: By lowering the cut point of the 2010 criteria from 6 to 5 points, we were able to identify 15% more RA patients at the cost of 8% more false-positive patients., (© The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: To investigate if erythrocyte-methotrexate-polyglutamate (MTX-PG) concentrations in patients with rheumatoid arthritis (RA) are associated with disease activity or adverse events., Methods: We used a longitudinal study design with two cohorts. The derivation cohort included 102 and the validation cohort included 285 patients with RA on MTX. We measured erythrocyte-MTX-PG with 1-5 glutamate residues at 3 months, 6 months and 9 months after MTX start with a liquid chromatography (LC)-mass spectrometry (MS)/MS assay. Outcomes were disease activity score in 28 joints (DAS28) and adverse events. Longitudinal associations of MTX-PG concentrations after 3 months, 6 months and 9 months with DAS28 were tested with a linear mixed model adjusted for age, gender, baseline DAS28, MTX dose and comedication., Results: In the derivation cohort, mean DAS28 decreased from 4.26 (SE=0.14) at baseline to 2.72 (SE=0.13) after 9 months. Thirty per cent of patients in the derivation cohort experienced more than three adverse events after 3 months, which decreased to 18% after 9 months. In the validation cohort, DAS28 and adverse events were comparable with the derivation cohort. In the derivation cohort, MTX-PG1 (ß=-0.005), MTX-PG2 (ß=-0.022), MTX-PG3 (β=-0.007) and total MTX-PG (ß=-0.004) were associated (p<0.05) with lower DAS28 over 9 months. In the validation cohort, MTX-PG2 (ß=-0.015), MTX-PG3 (ß=-0.010), MTX-PG4 (ß=-0.008) and total MTX-PG (ß=-0.003) were associated with lower DAS28 over 9 months. None of the MTX-PGs was associated with adverse events., Conclusions: In this first longitudinal study, we showed that an increase in erythrocyte-MTX-PG concentration was associated with a decreased DAS28 over 9 months in two cohorts, and is therefore a potential tool for therapeutic drug monitoring of MTX in RA., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objectives: To compare 1-year clinical efficacy of (1) initial triple disease-modifying antirheumatic drug therapy (iTDT) with initial methotrexate (MTX) monotherapy (iMM) and (2) different glucocorticoid (GC) bridging therapies: oral versus a single intramuscular injection in early rheumatoid arthritis., Methods: In a single-blinded randomised clinical trial patients were randomised into three arms: (A) iTDT (methotrexate+sulfasalazine+hydroxychloroquine) with GCs intramuscularly; (B) iTDT with an oral GC tapering scheme and (C) MTX with oral GCs similar to B. Primary outcomes were (1) area under the curve (AUC) of Health Assessment Questionnaire (HAQ) and Disease Activity Score (DAS) and (2) the proportion of patients with radiographic progression., Results: 281 patients were randomly assigned to arms A (n=91), B (n=93) or C (n=97). The AUC DAS and HAQ were respectively -2.39 (95% CI -4.77 to -0.00) and -1.67 (95% CI -3.35 to 0.02) lower in patients receiving iTDT than in those receiving iMM. After 3 months, treatment failure occurred less often in the iTDT group, resulting in 40% fewer treatment intensifications. The difference in treatment intensifications between the arms required to maintain the predefined treatment goal remained over time. No differences were seen between the two GC bridging therapies. Respectively 21%, 24% and 23% of patients in arms A, B and C had radiographic progression after 1 year. Patients receiving iTDT had more adjustments of their medication owing to adverse events than those receiving iMM., Conclusions: Treatment goals are attained more quickly and maintained with fewer treatment intensifications with iTDT than with iMM. However, no difference in radiographic progression is seen. Both GC bridging therapies are equally effective and, therefore, both can be used., Trial Registration Number: ISRCTN26791028., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

We present a simple model of network growth and solve it by writing the dynamic equations for its macroscopic characteristics such as the degree distribution and degree correlations. This allows us to study carefully the percolation transition using a generating functions theory. The model considers a network with a fixed number of nodes wherein links are introduced using degree-dependent linking probabilities pk. To illustrate the techniques and support our findings using Monte Carlo simulations, we introduce the exemplary linking rule pk∝k-α, with α between -1 and +∞. This parameter may be used to interpolate between different regimes. For negative α, links are most likely attached to high-degree nodes. On the other hand, in case α>0, nodes with low degrees are connected and the model asymptotically approaches a process undergoing explosive percolation.

In a recent Letter, Friedman and Landsberg discussed the underlying mechanism of explosive phase transitions on complex networks [Phys. Rev. Lett. 103, 255701 (2009).]. This Brief Report presents a modest though more insightful extension of their arguments. We discuss the implications of their results on the cluster-size distribution and deduce that, under general conditions, the percolation transition will be explosive if the mean number of nodes per cluster diverges in the thermodynamic limit and prior to the transition threshold. In other words, if upon increase of the network size n the amount of clusters in the network does not grow proportionally to n, the percolation transition is explosive. Simulations and analytical calculations on various models support our findings.

Biased (degree-dependent) percolation was recently shown to provide strategies for turning robust networks fragile and vice versa. Here, we present more detailed results for biased edge percolation on scale-free networks. We assume a network in which the probability for an edge between nodes i and j to be retained is proportional to (k(i)k(j)(-alpha) with k(i) and k(j) the degrees of the nodes. We discuss two methods of network reconstruction, sequential and simultaneous, and investigate their properties by analytical and numerical means. The system is examined away from the percolation transition, where the size of the giant cluster is obtained, and close to the transition, where nonuniversal critical exponents are extracted using the generating-functions method. The theory is found to agree quite well with simulations. By presenting an extension of the Fortuin-Kasteleyn construction, we find that biased percolation is well-described by the q-->1 limit of the q -state Potts model with inhomogeneous couplings.

Weighted scale-free networks with topology-dependent interactions are studied. It is shown that the possible universality classes of critical behavior, which are known to depend on topology, can also be explored by tuning the form of the interactions at fixed topology. For a model of opinion formation, simple mean field and scaling arguments show that a mapping gamma'=(gamma-mu)(1-mu) describes how a shift of the standard exponent gamma of the degree distribution can absorb the effect of degree-dependent pair interactions J(ij) proportional to (k(i)k(j))(-mu), where k(i) stands for the degree of vertex i. This prediction is verified by extensive numerical investigations using the cavity method and Monte Carlo simulations. The critical temperature of the model is obtained through the Bethe-Peierls approximation and with the replica technique. The mapping can be extended to nonequilibrium models such as those describing the spreading of a disease on a network.

Scale-free networks with topology-dependent interactions are studied. It is shown that the universality classes of critical behavior, which conventionally depend only on topology, can also be explored by tuning the interactions. A mapping, gamma'=(gamma-mu)/(1-mu), describes how a shift of the standard exponent gamma of the degree distribution P(q) can absorb the effect of degree-dependent pair interactions J(ij)proportional to(q(i)q(j))(-mu). The replica technique, cavity method, and Monte Carlo simulation support the physical picture suggested by Landau theory for the critical exponents and by the Bethe-Peierls approximation for the critical temperature. The equivalence of topology and interaction holds for equilibrium and nonequilibrium systems, and is illustrated with interdisciplinary applications.

The possibility of wetting phase transitions in Bose-Einstein condensed gases is predicted on the basis of Gross-Pitaevskii theory. The surface of a binary mixture of Bose-Einstein condensates can undergo a first-order wetting phase transition upon varying the interparticle interactions, using, e.g., Feshbach resonances. Interesting ultra-low-temperature effects shape the wetting phase diagram. The prewetting transition is, contrary to general expectations, not of first order but critical, and the prewetting line does not meet the bulk phase coexistence line tangentially. Experimental verification of these extraordinary results is called for, especially now that it has become possible, using optical methods, to realize a planar "hard wall" boundary for the condensates.