NLTE analysis of spectra: OBA stars

Methods of calculation of NLTE model atmosphere are discussed. The NLTE trace element procedure is compared with the full NLTE model atmosphere calculation. Differences between LTE and NLTE atmosphere modeling are evaluated. The ways of model atom construction are discussed. Finally, modelling of expanding atmospheres of hot stars with winds is briefly reviewed.Comment: in Determination of Atmospheric Parameters of B-, A-, F- and G-Type Stars, E. Niemczura et al. eds., Springer, in pres

Pressure-induced hole doping of the Hg-based cuprate superconductors

We investigate the electronic structure and the hole content in the copper-oxygen planes of Hg based high Tc cuprates for one to four CuO2 layers and hydrostatic pressures up to 15 GPa. We find that with the pressure-induced additional number of holes of the order of 0.05e the density of states at the Fermi level changes approximately by a factor of 2. At the same time the saddle point is moved to the Fermi level accompanied by an enhanced k_z dispersion. This finding explains the pressure behavior of Tc and leads to the conclusion that the applicability of the van Hove scenario is restricted. By comparison with experiment, we estimate the coupling constant to be of the order of 1, ruling out the weak coupling limit.Comment: 4 pages, 4 figure

Thermal emission from low-field neutron stars

We present a new grid of LTE model atmospheres for weakly magnetic (B<=10e10G) neutron stars, using opacity and equation of state data from the OPAL project and employing a fully frequency- and angle-dependent radiation transfer. We discuss the differences from earlier models, including a comparison with a detailed NLTE calculation. We suggest heating of the outer layers of the neutron star atmosphere as an explanation for the featureless X-ray spectra of RX J1856.5-3754 and RX J0720.4-3125 recently observed with Chandra and XMM.Comment: 8 pages A&A(5)-Latex, 6 Figures, A&A in press. The model spectra presented here are available as XSPEC tables at http://www.astro.soton.ac.uk/~btg/outgoing/nsspec

CLAffinity:A software tool for identification of optimum ligand affinity for competition-based primary screens

[Image: see text] A simplistic assumption in setting up a competition assay is that a low affinity labeled ligand can be more easily displaced from a target protein than a high affinity ligand, which in turn produces a more sensitive assay. An often-cited paper correctly rallies against this assumption and recommends the use of the highest affinity ligand available for experiments aiming to determine competitive inhibitor affinities. However, we have noted this advice being applied incorrectly to competition-based primary screens where the goal is optimum assay sensitivity, enabling a clear yes/no binding determination for even low affinity interactions. The published advice only applies to secondary, confirmatory assays intended for accurate affinity determination of primary screening hits. We demonstrate that using very high affinity ligands in competition-based primary screening can lead to reduced assay sensitivity and, ultimately, the discarding of potentially valuable active compounds. We build on techniques developed in our PyBindingCurve software for a mechanistic understanding of complex biological interaction systems, developing the “CLAffinity tool” for simulating competition experiments using protein, ligand, and inhibitor concentrations common to drug screening campaigns. CLAffinity reveals optimum labeled ligand affinity ranges based on assay parameters, rather than general rules to optimize assay sensitivity. We provide the open source CLAffinity software toolset to carry out assay simulations and a video summarizing key findings to aid in understanding, along with a simple lookup table allowing identification of optimal dynamic ranges for competition-based primary screens. The application of our freely available software and lookup tables will lead to the consistent creation of more performant competition-based primary screens identifying valuable hit compounds, particularly for difficult targets

Co-evolution of RDF Datasets

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.Comment: 18 pages, 4 figures, Accepted in ICWE, 201

Higher-order brain areas associated with real-time functional MRI neurofeedback training of the somato-motor cortex.

Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings

Magnetization reversal times in the 2D Ising model

We present a theoretical framework which is generally applicable to the study of time scales of activated processes in systems with Brownian type dynamics. This framework is applied to a prototype system: magnetization reversal times in the 2D Ising model. Direct simulation results for the magnetization reversal times, spanning more than five orders of magnitude, are compared with theoretical predictions; the two agree in most cases within 20%.Comment: 9 pages, 8 figure