Foreground removal from WMAP 7yr polarization maps using an MLP neural network

One of the fundamental problems in extracting the cosmic microwave background signal (CMB) from millimeter/submillimeter observations is the pollution by emission from the Milky Way: synchrotron, free-free, and thermal dust emission. To extract the fundamental cosmological parameters from CMB signal, it is mandatory to minimize this pollution since it will create systematic errors in the CMB power spectra. In previous investigations, it has been demonstrated that the neural network method provide high quality CMB maps from temperature data. Here the analysis is extended to polarization maps. As a concrete example, the WMAP 7-year polarization data, the most reliable determination of the polarization properties of the CMB, has been analysed. The analysis has adopted the frequency maps, noise models, window functions and the foreground models as provided by the WMAP Team, and no auxiliary data is included. Within this framework it is demonstrated that the network can extract the CMB polarization signal with no sign of pollution by the polarized foregrounds. The errors in the derived polarization power spectra are improved compared to the errors derived by the WMAP Team.Comment: Accepted for publication in Astrophysics & Space Scienc

Expression and autoregulation of transforming growth factor beta receptor mRNA in small-cell lung cancer cell lines.

In small-cell lung cancer cell lines resistance to growth inhibition by transforming growth factor (TGF)-beta 1, was previously shown to correlate with lack of TGF-beta receptor I (RI) and II (RII) proteins. To further investigate the role of these receptors, the expression of mRNA for RI, RII and beta-glycan (RIII) was examined. The results showed that loss of RII mRNA correlated with TGF-beta 1 resistance. In contrast, RI-and beta-glycan mRNA was expressed by all cell lines, including those lacking expression of these proteins. According to Southern blot analysis, the loss of type II mRNA was not due to gross structural changes in the gene. The effect of TGF-beta 1 on expression of TGF-beta receptor mRNA (receptor autoregulation) was examined by quantitative Northern blotting in four cell lines with different expression of TGF-beta receptor proteins. In two cell lines expressing all three TGF-beta receptor proteins beta-glycan mRNA was rapidly down-regulated and this effect was sustained throughout the 24 h observation period. RI and RII mRNAs were slightly increased 24 h after treatment. In one cell line sensitive to growth inhibition by TGF-beta, 1 but lacking beta-glycan expression, and one cell line expressing only beta-glycan and thus TGF-beta 1 -resistant, no autoregulation of mRNA of either TGF-beta receptor was demonstrated. The results suggest that TGF-beta 1 regulates the expression of its receptors, in particular beta-glycan, and that this effect is dependent on co-expression of beta-glycan, RI and RII

Elevated fetal steroidogenic activity in autism

Autism affects males more than females, giving rise to the idea that the influence of steroid hormones on early fetal brain development may be one important early biological risk factor. Utilizing the Danish Historic Birth Cohort and Danish Psychiatric Central Register, we identified all amniotic fluid samples of males born between 1993 and 1999 who later received ICD-10 (International Classification of Diseases, 10th Revision) diagnoses of autism, Asperger syndrome or PDD-NOS (pervasive developmental disorder not otherwise specified) (n=128) compared with matched typically developing controls. Concentration levels of Δ4 sex steroids (progesterone, 17α-hydroxy-progesterone, androstenedione and testosterone) and cortisol were measured with liquid chromatography tandem mass spectrometry. All hormones were positively associated with each other and principal component analysis confirmed that one generalized latent steroidogenic factor was driving much of the variation in the data. The autism group showed elevations across all hormones on this latent generalized steroidogenic factor (Cohen's d=0.37, P=0.0009) and this elevation was uniform across ICD-10 diagnostic label. These results provide the first direct evidence of elevated fetal steroidogenic activity in autism. Such elevations may be important as epigenetic fetal programming mechanisms and may interact with other important pathophysiological factors in autism

Phonon-induced quadrupolar ordering of the magnetic superconductor TmNi2_2B2_2C

We present synchrotron x-ray diffraction studies revealing that the lattice of thulium borocarbide is distorted below T_Q = 13.5 K at zero field. T_Q increases and the amplitude of the displacements is drastically enhanced, by a factor of 10 at 60 kOe, when a magnetic field is applied along [100]. The distortion occurs at the same wave vector as the antiferromagnetic ordering induced by the a-axis field. A model is presented that accounts for the properties of the quadrupolar phase and explains the peculiar behavior of the antiferromagnetic ordering previously observed in this compound.Comment: submitted to PR

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page