An FPGA network architecture for accelerating 3DES - CBC

This paper presents a DES/3DES core that will support cipher block chaining (CBC) and also has a built in keygen that together take up about 10% of the resources in a Xilinx Virtex II 1000-4. The core will achieve up to 200Mbit/s of encryption or decryption. Also presented is a network architecture that will allow these CBC capable 3DES cores to perform their processing in parallel.

A pore-lining glutamic acid in the olfactory cyclic nucleotide-gated channel controls external spermine block

Spermine is a potent, voltage-dependent blocker of the olfactory cyclic nucleotide-gated channel from both the intracellular and extracellular sides. However, its sites of action are unknown. This study investigated the external spermine binding site in the rat CNC alpha3 subunit. Neutralization of a glutamic acid residue (E342Q) in the P-loop region eliminated voltage-dependence of block by externally applied spermine. The charge-conservative E342D mutation had little effect on spermine block. Thus, E342 forms the binding site for externally applied spermine. However, spermine remained a potent voltage-independent blocker of the E342Q mutant channel, suggesting that the mutation either created a novel binding site outside the membrane electrical field or that it dramatically changed the properties of the existing pore site. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.