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Abstract Objective Anti‐CD20 therapy is a highly effective treatment for multiple sclerosis (MS). In this study, we investigated MS‐related changes in peripheral blood mononuclear cell (PBMC) subsets compared to healthy controls and longitudinal changes related to the treatment. Methods Multicolor spectral flow cytometry analysis was performed on 78 samples to characterize disease‐ and treatment‐related PBMC clusters. Blood samples from MS patients were collected at baseline and up to 8 months post‐treatment, with three collection points after treatment initiation. Unsupervised clustering tools and manual gating were applied to identify subclusters of interest and quantify changes. Results B cells were depleted from the periphery after anti‐CD20 treatment as expected, and we observed an isolated acute, transitory drop in the proportion of natural killer (NK) and NKT cells among the main populations of PBMC (P = 0.03, P = 0.004). Major affected PBMC subpopulations were cytotoxic immune cells (NK, NKT, and CD8+ T cells), and we observed a higher proportion of cytotoxic cells with reduced brain‐homing ability and a higher regulatory function as a long‐term anti‐CD20‐related effect. Additionally, anti‐CD20 therapy altered distributions of memory CD8+ T cells and reduced exhaustion markers in both CD4+ and CD8+ T cells. Interpretation The findings of this study elucidate phenotypic clusters of NK and CD8+ T cells, which have previously been underexplored in the context of anti‐CD20 therapy. Phenotypic modifications towards a more regulatory and controlled phenotype suggest that these subpopulations may play a critical and previously unrecognized role in mediating the therapeutic efficacy of anti‐CD20 treatments.

Climate change is expected to alter the hydrological cycle resulting in large-scale impacts on water availability. However, future climate change impact assessments are highly uncertain. For the first time, multiple global climate (three) and hydrological models (eight) were used to systematically assess the hydrological response to climate change and project the future state of global water resources. This multi-model ensemble allows us to investigate how the hydrology models contribute to the uncertainty in projected hydrological changes compared to the climate models. Due to their systematic biases, GCM outputs cannot be used directly in hydrological impact studies, so a statistical bias correction has been applied. The results show a large spread in projected changes in water resources within the climate–hydrology modelling chain for some regions. They clearly demonstrate that climate models are not the only source of uncertainty for hydrological change, and that the spread resulting from the choice of the hydrology model is larger than the spread originating from the climate models over many areas. But there are also areas showing a robust change signal, such as at high latitudes and in some midlatitude regions, where the models agree on the sign of projected hydrological changes, indicative of higher confidence in this ensemble mean signal. In many catchments an increase of available water resources is expected but there are some severe decreases in Central and Southern Europe, the Middle East, the Mississippi River basin, southern Africa, southern China and south-eastern Australia.

General circulation models (GCMs) project an increasing frequency and intensity of heavy rainfall events due to global climate change. This rather holds true for regions that are even expected to experience an overall decrease in average annual precipitation. Consequently, this may be attended by an increasing frequency and magnitude of flood events. However, time series of GCMs show a bias in simulating 20th century precipitation and temperature fields and, therefore, cannot directly be used to force hydrological models in order to assess the impact of the projected climate change on certain components of the hydrological cycle. For a posteriori correction, the so-called delta change approach is widely-used which adds the 30-year monthly differences for temperature or ratios for precipitation of the GCM data to each month of a historic climate data set. As the variability of the climate variables in the scenario period is not transferred, this approach is especially questionable if discharge extremes are to be analyzed. In order to preserve the variability given by the GCM, methods of statistical bias correction are applied. This study aims to investigate the impact of two methods of bias correction, the delta change approach and a statistical bias correction, on the large scale modeling of flood discharges, using the example of 25 macroscale catchments in Europe. The discharge simulation is carried out with the global integrated model WaterGAP3 (Water – Global Assessment and Prognosis). Results show that the two bias correction methods lead to distinctively different trends in future flood flows.

Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series of subsurface runoff (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected hydroclimatic regions). Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison of drought identified from global hydrological models and spatial event analyses.

Water abstractions for irrigation purposes are higher than for any other pan-European water use sector and have a large influence on river runoff regimes. This modelling experiment assesses historic and current irrigation water demands for different crops in five arc minute spatial resolution for pan-Europe. Two different modelling frameworks have been applied in this study. First, soft-coupling the dynamic vegetation model LPJmL with the land use model LandSHIFT leads to overestimations of national irrigation water demands, which are rather high in the southern Mediterranean countries. This can be explained by unlimited water supply in the model structure and illegal or not gauged water abstractions in the reported data sets. The second modelling framework is WaterGAP3, which has an integrated conceptual crop specific irrigation module. Irrigation water requirements as modelled with WaterGAP3 feature a more realistic representation of pan-European water withdrawals. However, in colder humid regions, irrigation water demands are often underestimated. Additionally, a national database on crop-specific irrigated area and water withdrawal for all 42 countries within pan-Europe has been set up and integrated in both model frameworks.

Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures on time. The accuracy and the lead time of the predictions for head waters primarily depend on the meteorological forecasts. Ensemble forecasts are a means of framing the uncertainty of the potential future development of the hydro-meteorological situation. This contribution presents a flood management strategy based on probabilistic hydrological forecasts driven by operational meteorological ensemble prediction systems. The meteorological ensemble forecasts are transformed into discharge ensemble forecasts by a rainfall-runoff model. Exceedance probabilities for critical discharge values and probabilistic maps of inundation areas can be computed and presented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system integrates ensemble forecasts with different spatial resolution and different lead times. The hydrological models are controlled in an adaptive way, mainly depending on the lead time of the forecast, the expected magnitude of the flood event and the availability of measured data. The aforementioned flood forecast techniques have been applied to a case study. The Mulde River Basin (South-Eastern Germany, Czech Republic) has often been affected by severe flood events including local flash floods. Hindcasts for the large scale extreme flood in August 2002 have been computed using meteorological predictions from both the COSMO-LEPS ensemble prediction system and the deterministic COSMO-DE local model. The temporal evolution of a) the meteorological forecast uncertainty and b) the probability of exceeding flood alert levels is discussed. Results from the hindcast simulations demonstrate, that the systems would have predicted a high probability of an extreme flood event, if they would already have been operational in 2002. COSMO-LEPS showed a reasonably good performance within a lead time of 2 to 3 days. Some of the deterministic very short-range forecast initializations were able to predict the dynamics of the event, but others underpredicted rainfall. Thus a lagged average ensemble approach is suggested. The findings from the case study support the often proposed added value of ensemble forecasts and their probabilistic evaluation for flood management decisions.

Multi-objective optimization algorithms are widely used for the calibration of conceptual hydrological models. Such algorithms yield a set of Pareto-optimal solutions, reflecting the model structure uncertainty. In this study, a multi-objective optimization strategy is suggested, which aims at reducing the model structure uncertainty by considering parameter interaction within Pareto-optimal solutions. The approach has been used to develop a nested setup of a rainfall-runoff model, which is integrated in a probabilistic meso-/macroscale flood forecasting system. The optimization strategy aided in determining the best combination of a lumped (computationally efficient in operational real time forecasting) and a semi-distributed parameterization of the hydrological model. First results are shown for two subbasins of the Mulde catchment in Germany. The different phenomena of parameter interaction were analysed in this case study to reduce the model structure uncertainties.

Abstract Circumstantial evidence suggests that B cells may instruct T cells to break tolerance. Here, to test this hypothesis, we used a murine model in which a single B cell clone precipitates an autoreactive response resembling systemic lupus erythematosus (SLE). The initiating clone did not need to enter germinal centers to precipitate epitope spreading. Rather, it localized to extrafollicular splenic bridging channels early in the response. Autoantibody produced by the initiating clone was not sufficient to drive the autoreactive response. Subsequent epitope spreading depended on antigen presentation and was compartmentalized by major histocompatibility complex (MHC). B cells carrying two MHC haplotypes could bridge the MHC barrier between B cells that did not share MHC. Thus, B cells directly relay autoreactivity between two separate compartments of MHC-restricted T cells, leading to inclusion of distinct B cell populations in germinal centers. Our findings demonstrate that B cells initiate and propagate the autoimmune response.

Durch Forschungen im Hochland von Angola wurden einige Beweise verschiedenartigen Ursprungs entdeckt, die auf Wirkungen kalter klimatischer Bedingungen in geologisch junger Zeit zurückgehen und wie folgt zusammengefaßt werden können: 1.Die Ausbildung einer früher oberflächenbildenden Steinsohle, wenige Dezimeter unterhalb der heutigen Topographie, scheint sehr weit verbreitet zu sein, sogar über die angegebenen Lokalitäten hinaus. 2. Warventone in der Nähe von Silva Porto entstanden gleichfalls unter ähnlichen klimatischen Einflüssen. Von besonderem Interesse ist jedoch die Diskrepanz zwischen der herkömmlichen Warvenzählung und der Auswertung von Dünnschliffen. Zusätzliche Untersuchungen an anderen Proben werden nötig sein, um das Problem weiter zu verfolgen. 3. Schließlich wurden stark verwulstete, ehemals geschichtete Materialien, Würgeböden, nahe der Oberfläche auf dem „Planalto" südlich von Nova Lisboa gefunden und makro- sowie mikroskopisch untersucht. In der vergleichenden Betrachtung der erarbeiteten verschiedenen Fakten ergibt sich aus der vorläufigen Korrelation mit der bis jetzt verfügbaren Chronologie des Quartärs das Haupt Gamblian (30500—14503 vor der Gegenwart) als Entstehungszeit.