Metabolism impacts upon Candida immunogenicity and pathogenicity at multiple levels

Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved. Open Access funded by Wellcome TrustNon peer reviewedPublisher PD

Smart Materials as Intelligent Insulation

In order to provide a robust infrastructure for the transmission and distribution of electrical power, understanding and monitoring equipment ageing and failure is of paramount importance. Commonly, failure is associated with degradation of the dielectric material; therefore the introduction of a smart moiety into the material is a potentially attractive means of continual condition monitoring. It is important that any introduction of smart groups into the dielectric does not have any detrimental effect on the desirable electrical and mechanical properties of the bulk material. Initial work focussed on the introduction of fluorophores into a model dielectric system. Fluorescence is known to be a visible effect even at very low concentrations of active fluorophores and therefore was thought well suited to such an application. It was necessary both to optimise the active fluorophore itself and to determine the most appropriate manner in which to introduce the fluorophores into the insulating system. This presentation will describe the effect of introducing fluorophores into polymeric systems on the dielectric properties of the material and the findings thus far [1]. Alternative smart material systems will also be discussed along with the benefits and limitations of smart materials as electric field sensors

Boundary States and Black Hole Entropy

Black hole entropy is derived from a sum over boundary states. The boundary states are labeled by energy and momentum surface densities, and parametrized by the boundary metric. The sum over state labels is expressed as a functional integral with measure determined by the density of states. The sum over metrics is expressed as a functional integral with measure determined by the universal expression for the inverse temperature gradient at the horizon. The analysis applies to any stationary, nonextreme black hole in any theory of gravitational and matter fields.Comment: 4 pages, Revte

Characterization of a spheromak plasma gun: The effect of refractory electrode coatings

In order to investigate the proposition that high-Z impurities are responsible for the anomalously short lifetime of the Caltech spheromak, the center electrode of the spheromak plasma gun has been coated with a variety of metals (bare steel, copper, nickel, chromium, rhodium, and tungsten). Visible light (230–890 nm) emitted directly from the plasma in the gun breech was monitored for each of the coated electrodes. Plasma density and temperature and spheromak lifetime were compared for each electrode. Results indicate little difference in gun performance or macroscopic plasma parameters. The chromium and tungsten electrodes performed marginally better in that a previously reported helicity injection effect [Phys. Rev. Lett. 64, 2144 (1990)] is only observed in discharges using these electrode coatings. There are subtle differences in the detailed line emission spectra from the different electrodes, but the spectra are remarkably similar. The fact that (1) contrary to expectations, attempts to reduce high-Z impurities had only marginal effect on the spheromak lifetime coupled with (2) an estimate of Zeff<2 based on a 0-D model suggests that it is not impurities but some other mechanism that limits the lifetime of small, cold spheromaks. We will discuss the general characteristics of the spheromak gun as well as effects due to the coatings

Black Hole Entropy from Conformal Field Theory in Any Dimension

Restricted to a black hole horizon, the ``gauge'' algebra of surface deformations in general relativity contains a Virasoro subalgebra with a calculable central charge. The fields in any quantum theory of gravity must transform accordingly, i.e., they must admit a conformal field theory description. Applying Cardy's formula for the asymptotic density of states, I use this result to derive the Bekenstein-Hawking entropy. This method is universal---it holds for any black hole, and requires no details of quantum gravity---but it is also explicitly statistical mechanical, based on counting microscopic states.Comment: 9 pages, LaTeX, no figures. Slightly shortened and polished for journal; no significant changes in substanc

Towards the 3D-Imaging of Sources

Geometric details of a nuclear reaction zone, at the time of particle emission, can be restored from low relative-velocity particle-correlations, following imaging. Some of the source details get erased and are a potential cause of problems in the imaging, in the form of instabilities. These can be coped with by following the method of discretized optimization for the restored sources. So far it has been possible to produce 1-dimensional emission source images, corresponding to the reactions averaged over all possible spatial directions. Currently, efforts are in progress to restore angular details.Comment: Talk given at the Int. Workshop on Hot and Dense Matter in Relativistic Heavy Ion Collisions, March 24-27, 2004, Budapest; 10 pages, 6 figure

As-built design specification for MISMAP

The MISMAP program, which is part of the CLASFYT package, is described. The program is designed to compare classification values with ground truth values for a segment and produce a comparison map and summary table

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page