β2-adrenergic agonists modulate TNF-α induced astrocytic inflammatory gene expression and brain inflammatory cell populations

Background: The NF-kappa B signaling pathway orchestrates many of the intricate aspects of neuroinflammation. Astrocytic beta(2)-adrenergic receptors have emerged as potential regulators in central nervous system inflammation and are potential targets for pharmacological modulation. The aim of this study was to elucidate the crosstalk between astrocytic beta(2)-adrenergic receptors and the TNF-alpha induced inflammatory gene program. Methods: Proinflammatory conditions were generated by the administration of TNF-alpha. Genes that are susceptible to astrocytic crosstalk between beta(2)-adrenergic receptors (stimulated by clenbuterol) and TNF-alpha were identified by qPCR-macroarray-based gene expression analysis in a human 1321 N1 astrocytoma cell line. Transcriptional patterns of the identified genes in vitro were validated by RT-PCR on the 1321 N1 cell line as well as on primary rat astrocytes. In vivo expression patterns were examined by intracerebroventricular administration of clenbuterol and/or TNF-alpha in rats. To examine the impact on the inflammatory cell content of the brain we performed extensive FACS analysis of rat brain immune cells after intracerebroventricular clenbuterol and/or TNF-alpha administration. Results: Parallel transcriptional patterns in vivo and in vitro confirmed the relevance of astrocytic beta(2)-adrenergic receptors as modulators of brain inflammatory responses. Importantly, we observed pronounced effects of beta(2)-adrenergic receptor agonists and TNF-alpha on IL-6, CXCL2, CXCL3, VCAM1, and ICAM1 expression, suggesting a role in inflammatory brain cell homeostasis. Extensive FACS-analysis of inflammatory cell content in the brain demonstrated that clenbuterol/TNF-alpha co-administration skewed the T cell population towards a double negative phenotype and induced a shift in the myeloid brain cell population towards a neutrophilic predominance. Conclusions: Our results show that astrocytic beta(2)-adrenergic receptors are potent regulators of astrocytic TNF-alpha-activated genes in vitro and in vivo, and ultimately modulate the molecular network involved in the homeostasis of inflammatory cells in the central nervous system. Astrocytic beta(2)-adrenergic receptors and their downstream signaling pathway may serve as potential targets to modulate neuroinflammatory responses

Assimilating SAR-derived water level data into a hydraulic model: a case study

Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data

Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter : proof of concept

With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multimission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filterbased data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services

The use of neural network algorithms for the classification of undifferentiated arthritis by synovial histopathology

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