Neural networks

An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructive algorithms, Kohonen and K-means unupervised algorithms, RAMnets, first and second order training methods, and Bayesian regularisation methods.

Solid bridge formation between spray-dried sodium carbonate particles

Moisture migration caking of pharmaceutical excipients in the absence of load is a significant quality and stability issue. This study uses Atomic Force Microscopy (AFM) to examine a solid bridge formed between two 20µm spray-dried sodium carbonate particles. The bridge is grown by repeatedly exposing the system to 70% RH and 30% RH cycles at 25?C. A comparison is made with the idealised bridge model developed by Tanaka (1978) which was previously verified using crystalline systems. The resulting system was found to be more complex and grew in two stages. The first stage consisted of linear growth to 5 cycles, followed by a more gradual expansion and the appearance of crystalline structures.

Developments towards practical free-space quantum cryptography

We describe a free space quantum cryptography system which is designed to allow continuous unattended key exchanges for periods of several days, and over ranges of a few kilometres. The system uses a four-laser faint-pulse transmission system running at a pulse rate of 10MHz to generate the required four alternative polarization states. The receiver module similarly automatically selects a measurement basis and performs polarization measurements with four avalanche photodiodes. The controlling software can implement the full key exchange including sifting, error correction, and privacy amplification required to generate a secure key.

Effects of dihydrolipoic acid (DHLA), α-lipoic acid. N-acetyl cysteine and ascorbate on xenobiotic-mediated methaemoglobin formation in human erythrocytes in vitro

α-Lipoic acid, dihydrolipoic acid (DHLA), N-acetyl cysteine and ascorbate were compared with methylene blue for their ability to attenuate and/or reduce methaemoglobin formation induced by sodium nitrite, 4-aminophenol and dapsone hydroxylamine in human erythrocytes. Neither α-lipoic acid, DHLA, N-acetyl cysteine nor ascorbate had any significant effects on methaemoglobin formed by nitrite, either from pre-treatment, simultaneous addition or post 30 min addition of the agents up to the 60 min time point, although N-acetyl cysteine did reduce methaemoglobin formation at 120 min (P<0.05). In all three treatment groups at 30, 60 and 120 min, there were no significant effects mediated by DHLA or N-acetyl cysteine on 4-aminophenol (1 mM)-mediated haemoglobin oxidation. Ascorbate caused marked significant reductions in 4-aminophenol methaemoglobin in all treatment groups at 30-120 min except at 30 min in the simultaneous addition group (P<0.0001). Neither α-lipoic acid, nor N-acetyl cysteine showed any effects on hydroxylamine-mediated methaemoglobin formation at 30 and 60 in all treatment groups. In contrast, DHLA significantly reduced hydroxylamine-mediated methaemoglobin formation at all three time points after pre-incubation and simultaneous addition (P<0.001), while ascorbate was ineffective. Compared with methylene blue, which was effective in reducing methaemoglobin formation by all three toxins (P<0.01), ascorbate was only highly effective against 4-aminophenol mediated methaemoglobin, whilst the DHLA-mediated attenuation of dapsone hydroxylamine-mediated methaemoglobin formation indicates a possible clinical application in high-dose dapsone therapy. © 2003 Elsevier B.V. All rights reserved.

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942.

A predictor of membrane class:discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.

Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.

LIPPRED:a web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.