An atomic hydrogen beam to test ASACUSA's apparatus for antihydrogen spectroscopy

The ASACUSA collaboration aims to measure the ground state hyperfine splitting (GS-HFS) of antihydrogen, the antimatter pendant to atomic hydrogen. Comparisons of the corresponding transitions in those two systems will provide sensitive tests of the CPT symmetry, the combination of the three discrete symmetries charge conjugation, parity, and time reversal. For offline tests of the GS-HFS spectroscopy apparatus we constructed a source of cold polarised atomic hydrogen. In these proceedings we report the successful observation of the hyperfine structure transitions of atomic hydrogen with our apparatus in the earth's magnetic field.Comment: 8 pages, 4 figures, proceedings for conference EXA 2014 (Exotic Atoms - Vienna

COMPARISON OF A HIGH-RESOLUTION REGIONAL SIMULATION AND THE ERA40 REANALYSIS OVER THE ALPINE REGION

Within the EU project ALP-IMP a high-resolution regional simulation driven by the ERA40 reanalysis has been performed for the Greater Alpine Region (GAR) for the period 1958 to the present. A comparison of the high-resolution simulation and the ERA40 reanalysis regridded to 1 deg resolution with four different monthly mean temperature datasets for the GAR shows for both very high correlations of around 0.9, and in general slightly higher correlations for the regional simulation. Correlations of the regional simulation and the reanalysis with observations increase with spacial scale. The separation of the GAR into six subregions identifies the Po plain as a region where the high-resolution simulation as well as ERA40 have problems in reproducing the instrumental measurements

COMPARISON OF A HIGH-RESOLUTION REGIONAL SIMULATION AND THE ERA40 REANALYSIS OVER THE ALPINE REGION

Within the EU project ALP-IMP a high-resolution regional simulation driven by the ERA40 reanalysis has been performed for the Greater Alpine Region (GAR) for the period 1958 to the present. A comparison of the high-resolution simulation and the ERA40 reanalysis regridded to 1 deg resolution with four different monthly mean temperature datasets for the GAR shows for both very high correlations of around 0.9, and in general slightly higher correlations for the regional simulation. Correlations of the regional simulation and the reanalysis with observations increase with spacial scale. The separation of the GAR into six subregions identifies the Po plain as a region where the high-resolution simulation as well as ERA40 have problems in reproducing the instrumental measurements

Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing

In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup

Revisiting G3BP1 as a RasGAP binding protein: sensitization of tumor cells to chemotherapy by the RasGAP 317-326 sequence does not involve G3BP1.

RasGAP is a multifunctional protein that controls Ras activity and that is found in chromosomal passenger complexes. It also negatively or positively regulates apoptosis depending on the extent of its cleavage by caspase-3. RasGAP has been reported to bind to G3BP1 (RasGAP SH3-domain-binding protein 1), a protein regulating mRNA stability and stress granule formation. The region of RasGAP (amino acids 317-326) thought to bind to G3BP1 corresponds exactly to the sequence within fragment N2, a caspase-3-generated fragment of RasGAP, that mediates sensitization of tumor cells to genotoxins. While assessing the contribution of G3BP1 in the anti-cancer function of a cell-permeable peptide containing the 317-326 sequence of RasGAP (TAT-RasGAP₃₁₇₋₃₂₆), we found that, in conditions where G3BP1 and RasGAP bind to known partners, no interaction between G3BP1 and RasGAP could be detected. TAT-RasGAP₃₁₇₋₃₂₆ did not modulate binding of G3BP1 to USP10, stress granule formation or c-myc mRNA levels. Finally, TAT-RasGAP₃₁₇₋₃₂₆ was able to sensitize G3BP1 knock-out cells to cisplatin-induced apoptosis. Collectively these results indicate that G3BP1 and its putative RasGAP binding region have no functional influence on each other. Importantly, our data provide arguments against G3BP1 being a genuine RasGAP-binding partner. Hence, G3BP1-mediated signaling may not involve RasGAP

Cross-validation of bias-corrected climate simulations is misleading

We demonstrate both analytically and with a modelling example that cross-validation of free-running bias-corrected climate change simulations against observations is misleading. The underlying reasoning is as follows: a cross-validation can have in principle two outcomes. A negative (in the sense of not rejecting a null hypothesis), if the residual bias in the validation period after bias correction vanishes; and a positive, if the residual bias in the validation period after bias correction is large. It can be shown analytically that the residual bias depends solely on the difference between the simulated and observed change between calibration and validation periods. This change, however, depends mainly on the realizations of internal variability in the observations and climate model. As a consequence, the outcome of a cross-validation is also dominated by internal variability, and does not allow for any conclusion about the sensibility of a bias correction. In particular, a sensible bias correction may be rejected (false positive) and a non-sensible bias correction may be accepted (false negative). We therefore propose to avoid cross-validation when evaluating bias correction of free-running bias-corrected climate change simulations against observations. Instead, one should evaluate non-calibrated temporal, spatial and process-based aspects.</p