Interfacial structure of wild-type and mutant forms of puroindoline-b bound to DPPG monolayers

The interaction of wild-type puroindoline-b (Pin-b+) and two mutant forms having single residue substitutions (G46S or W44R) with L-alpha-dipalmitoylphosphatidyl-dl-glycerol (DPPG) as a Langmuir monolayer at the air/water interface was investigated by neutron reflectivity (NR) and Brewster angle microscopy (BAM). NR profiles were fitted using a three-layer model to enable differences in penetration of protein between the lipid headgroup and acyl regions to be determined. The data showed similar surface excesses for each of the three proteins at the interface; however, it was revealed that the depth of penetration of protein into the lipid region differed for each protein with Pin-b+ penetrating further into the acyl region of the lipid compared to the mutant forms of the protein that interacted with the headgroup region only. BAM images revealed that the domain structure of the DPPG monolayers was disrupted when Pin-b+ adsorption had reached equilibrium, suggesting protein penetration had led to compression of the lipid region. In contrast, the domain structure was unaffected by the W44R mutant, suggesting no change in compression of the lipid region and hence little or no penetration of protein into the lipid layer.

A parallel genetic algorithm for the Steiner Problem in Networks

This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.

Synthesis of a systolic array genetic algorithm

The paper presents a design for a hardware genetic algorithm which uses a pipeline of systolic arrays. These arrays have been designed using systolic synthesis techniques which involve expressing the algorithm as a set of uniform recurrence relations. The final design divorces the fitness function evaluation from the hardware and can process chromosomes of different lengths, giving the design a generic quality. The paper demonstrates the design methodology by progressively re-writing a simple genetic algorithm, expressed in C code, into a form from which systolic structures can be deduced. This paper extends previous work by introducing a simplification to a previous systolic design for the genetic algorithm. The simplification results in the removal of 2N 2 + 4N cells and reduces the time complexity by 3N + 1 cycles.

The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology

We advocate the use of systolic design techniques to create custom hardware for Custom Computing Machines. We have developed a hardware genetic algorithm based on systolic arrays to illustrate the feasibility of the approach. The architecture is independent of the lengths of chromosomes used and can be scaled in size to accommodate different population sizes. An FPGA prototype design can process 16 million genes per second.

Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm

Capturing the pattern of structural change is a relevant task in applied demand analysis, as consumer preferences may vary significantly over time. Filtering and smoothing techniques have recently played an increasingly relevant role. A dynamic Almost Ideal Demand System with random walk parameters is estimated in order to detect modifications in consumer habits and preferences, as well as changes in the behavioural response to prices and income. Systemwise estimation, consistent with the underlying constraints from economic theory, is achieved through the EM algorithm. The proposed model is applied to UK aggregate consumption of alcohol and tobacco, using quarterly data from 1963 to 2003. Increased alcohol consumption is explained by a preference shift, addictive behaviour and a lower price elasticity. The dynamic and time-varying specification is consistent with the theoretical requirements imposed at each sample point. (c) 2005 Elsevier B.V. All rights reserved.

CAMCR: Computer assisted mixture model analysis for capture-recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

The structure of echovirus type 12 bound to a two-domain fragment of its cellular attachment protein decay-accelerating factor (CD 55)

Echovirus type 12 (EV12), an enterovirus of the Picornaviridae family, uses the complement regulator, decay-accelerating factor (DAF, CD55) as a cellular receptor. We have calculated a three-dimensional reconstruction of EV12 bound to a fragment of DAF, consisting of short consensus repeat domains 3 and 4, from cryo-negative stain electron microscopy data (EMD #1057). This shows that, as for an earlier reconstruction of the related echovirus type 7 bound to DAF, attachment is not within the viral canyon but occurs close to the two-fold symmetry axes. Despite this general similarity, our reconstruction reveals a receptor interaction that is quite different from that observed for EV7. Fitting of the crystallographic co-ordinates for DAF34 and EV11 into the reconstruction shows a close agreement between the crystal structure of the receptor fragment and the density for the virus-bound receptor, allowing unambiguous positioning of the receptor with respect to the virion (PDB #1UPN). Our finding that the mode of virus-receptor interaction in EV12 is distinct from that seen for EV7 raises interesting questions regarding the evolution and biological significance of the DAF-binding phenotype in these viruses.

Use of a novel Hill-climbing genetic algorithm in protein folding simulations

We have developed a novel Hill-climbing genetic algorithm (GA) for simulation of protein folding. The program (written in C) builds a set of Cartesian points to represent an unfolded polypeptide's backbone. The dihedral angles determining the chain's configuration are stored in an array of chromosome structures that is copied and then mutated. The fitness of the mutated chain's configuration is determined by its radius of gyration. A four-helix bundle was used to optimise simulation conditions, and the program was compared with other, larger, genetic algorithms on a variety of structures. The program ran 50% faster than other GA programs. Overall, tests on 100 non-redundant structures gave comparable results to other genetic algorithms, with the Hill-climbing program running from between 20 and 50% faster. Examples including crambin, cytochrome c, cytochrome B and hemerythrin gave good secondary structure fits with overall alpha carbon atom rms deviations of between 5 and 5.6 Angstrom with an optimised hydrophobic term in the fitness function. (C) 2003 Elsevier Ltd. All rights reserved.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Glutathione and related thiol compounds II: the importance of protein bound glutathione and related protein-bound compounds in gluten proteins

Protein-bound glutathione (PSSG) and protein-bound related thiol compounds, i.e. cysteine (PSSCys), glutamyl-cysteine (PSSGlu-Cys) and cysteinyl-glycine (PSSCys-Gly), were analysed in proteins of Osborne fractions, i.e. gliadin, glutenin and gliadin-, glutenin-subfractions separated by gel filtration chromatography, gel protein and the total gluten proteins separated from wheat varieties with varying breadmaking performances. The results showed that PSSG and some protein-bound related thiol compounds were found in monomeric gliadins, indicating that glutathione and some related thiol compounds are able to form disulphide bonds (SS) with sulphydryl group (SH) of those proteins and the formation of those disulphide bonds may prevent those monomeric proteins from binding to other proteins. It was also observed that a larger amount of PSSG in glutenin proteins was negatively correlated with the molecular weight (M-w) distribution of glutenin polymers, suggesting that PSSG and protein-bound related thiol compounds may play an important role in controlling polymerisation of glutenin. Furthermore, it was found that the level of PSSG in gel protein from flours with poor breadmaking performances was constantly higher and significantly different (p < 0.05) from that of flours with good breadmaking performance. The same trend was observed with gluten samples from breadmaking and biscuitmaking flours. (C) 2003 Elsevier Ltd. All rights reserved.

Variable step LMS algorithm using the accumulated instantaneous error concept

The convergence speed of the standard Least Mean Square adaptive array may be degraded in mobile communication environments. Different conventional variable step size LMS algorithms were proposed to enhance the convergence speed while maintaining low steady state error. In this paper, a new variable step LMS algorithm, using the accumulated instantaneous error concept is proposed. In the proposed algorithm, the accumulated instantaneous error is used to update the step size parameter of standard LMS is varied. Simulation results show that the proposed algorithm is simpler and yields better performance than conventional variable step LMS.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.