Foreground removal from CMB temperature maps using an MLP neural network

One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.Comment: Accepted for publication in Astrophysics and Space Scienc

Small-molecule pyrimidine inhibitors of the cdc2-like (Clk) and dual specificity tyrosine phosphorylation-regulated (Dyrk) kinases: Development of chemical probe ML315

Substituted pyrimidine inhibitors of the Clk and Dyrk kinases have been developed, exploring structure-activity relationships around four different chemotypes. The most potent compounds have low-nanomolar inhibitory activity against Clk1, Clk2, Clk4, Dyrk1A and Dyrk1B. Kinome scans with 442 kinases using agents representing three of the chemotypes show these inhibitors to be highly selective for the Clk and Dyrk families. Further off-target pharmacological evaluation with ML315, the most selective agent, supports this conclusion

Demand for Water-based Leisure Activity

This paper reports on the demand for water-based leisure activity in Ireland based on data from a nationally representative telephone survey. Participation and trip demand are modelled using an augmented Poisson count model and consumer surplus welfare estimates are derived. The model is also used to investigate the level of social exclusion in water-based leisure activity. The demand for four activities is examined: sea angling, boating, swimming and other beach/sea/island day-trips. Results indicate that Irish rivers, wetlands, estuaries and seas are highly valued, while there is some evidence of social exclusion in water-based leisure activity.