The decay of space charge in a glassy epoxy resin following voltage removal

The PEA technique is used to measure the distribution of space charge in an epoxy resin after polarisation for one week at an applied field of 7.14kV/mm over a range of temperatures. The decay of the space charge is followed for times up to 114 hours after removal of the voltage and analysed in terms of a number of alternative decay mechanisms. It is shown that the rate-determining stage of the decay mechanism is that of a thermally activated process that has been associated with charge de-trapping. At times greater than 102s the de-trapping process behaves as though the space charge field does not exist and the retention time of the space charge depends only upon the depth of the deepest occupied traps and the temperature

PCA Tomography and its application to nearby galactic nuclei

With the development of modern technologies such as IFUs, it is possible to obtain data cubes in which one produces images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We briefly describe a method of analysis of data cubes (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. We applied the method, for illustration purpose, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.Comment: 4 pages, 1 figure, 1 table, to be published in the Proceedings of the IAU Symposium no. 26

Structural Insights into Differences in Drug-binding Selectivity between Two Forms of Human α1-Acid Glycoprotein Genetic Variants, the A and F1*S Forms

Human α1-acid glycoprotein (hAGP) in serum functions as a carrier of basic drugs. In most individuals, hAGP exists as a mixture of two genetic variants, the F1*S and A variants, which bind drugs with different selectivities. We prepared a mutant of the A variant, C149R, and showed that its drug-binding properties were indistinguishable from those of the wild type. In this study, we determined the crystal structures of this mutant hAGP alone and complexed with disopyramide (DSP), amitriptyline (AMT), and the nonspecific drug chlorpromazine (CPZ). The crystal structures revealed that the drug-binding pocket on the A variant is located within an eight-stranded β-barrel, similar to that found in the F1*S variant and other lipocalin family proteins. However, the binding region of the A variant is narrower than that of the F1*S variant. In the crystal structures of complexes with DSP and AMT, the two aromatic rings of each drug interact with Phe-49 and Phe-112 at the bottom of the binding pocket. Although the structure of CPZ is similar to those of DSP and AMT, its fused aromatic ring system, which is extended in length by the addition of a chlorine atom, appears to dictate an alternative mode of binding, which explains its nonselective binding to the F1*S and A variant hAGPs. Modeling experiments based on the co-crystal structures suggest that, in complexes of DSP, AMT, or CPZ with the F1*S variant, Phe-114 sterically hinders interactions with DSP and AMT, but not CPZ. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc

On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for example, predict the target class of the tested sample. Recently, an implementation of the k-NN, named as the Locally Linear Reconstruction (LLR) [11], has been proposed. The salient feature of the latter is that by invoking a quadratic optimization process, it is capable of systematically setting model parameters, such as the number of neighbors (specified by the parameter, k) and the weights. However, the LLR takes more time than other conventional methods when it has to be applied to classification tasks. To overcome this problem, we propose a strategy of using a PRS to efficiently compute the optimization problem. In this paper, we demonstrate, first of all, that by completely discarding the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the quadratic optimization problem can be computed far more expediently. The values of the corresponding indices are comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding classification accuracies are noticeably less. The proposed method has been tested on artificial and real-life data sets, and the results obtained are very promising, and has potential in PR applications