Lattice damage produced in GaN by swift heavy ions

Wurtzite GaN epilayers bombarded at 300 K with 200 MeV Au-197(16+) ions are studied by a combination of transmission electron microscopy (TEM) and Rutherford backscattering/channeling spectrometry (RBS/C). Results reveal the formation of near-continuous tracks propagating throughout the entire similar to1.5-mum-thick GaN film. These tracks, similar to100 Angstrom in diameter, exhibit a large degree of structural disordering but do not appear to be amorphous. Throughout the bombarded epilayer, high-resolution TEM reveals planar defects which are parallel to the basal plane of the GaN film. The gross level of lattice disorder, as measured by RBS/C, gradually increases with increasing ion fluence up to similar to10(13) cm(-2). For larger fluences, delamination of the nitride film from the sapphire substrate occurs. Based on these results, physical mechanisms of the formation of lattice disorder in GaN in such a high electronic stopping power regime are discussed. (C) 2004 American Institute of Physics.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

The pathophysiology of 'brain shrinkage' in alcoholics - Structural and molecular changes and clinical implications

This article represents a symposium of the 2004 ISBRA Congress held in Mannheim. The presentations were: Review of the neuropathological and neurochemical changes seen in alcohol-related ' brain shrinkage ' by Clive Harper; In Vivo Detection of Macrostructural and Microstructural Markers of Brain Integrity in Human Alcoholism and a Rodent Model of Alcoholism by Adolf Pfefferbaum, Elfar Adalsteinsson and Edith Sullivan; Gene and Protein Changes in the Brains of Alcoholics with ' Brain Shrinkage ' by Joanne Lewohl and Peter Dodd; Cross sectional and longitudinal MR spectroscopy studies of chronic adult alcoholics by Michael Taylor; Brain Atrophy Associated with Impairment on a Simulated Gambling Task in Long-Term Abstinent Alcoholics by George Fein and Bennett Landman.

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.

A method to lower the detection limit for optical beat signals from a frequency-dithered stabilized laser

A narrow absorption feature in an atomic or molecular gas (such as iodine or methane) is used as the frequency reference in many stabilized lasers. As part of the stabilization scheme an optical frequency dither is applied to the laser. In optical heterodyne experiments, this dither is transferred to the RF beat signal, reducing the spectral power density and hence the signal to noise ratio over that in the absence of dither. We removed the dither by mixing the raw beat signal with a dithered local oscillator signal. When the dither waveform is matched to that of the reference laser the output signal from the mixer is rendered dither free. Application of this method to a Winters iodine-stabilized helium-neon laser reduced the bandwidth of the beat signal from 6 MHz to 390 kHz, thereby lowering the detection threshold from 5 pW of laser power to 3 pW. In addition, a simple signal detection model is developed which predicts similar threshold reductions.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Specific PCR based detection of Phytophthora medicaginis using the intergenic spacer region of the ribosomal DNA

A technique based on the polymerase chain reaction (PCR) for the specific detection of Phytophthora medicaginis was developed using nucleotide sequence information of the ribosomal DNA (rDNA) regions. The complete IGS 2 region between the 5 S gene of one rDNA repeat and the small subunit of the adjacent repeat was sequenced for P. medicaginis and related species. The entire nucleotide sequence length of the IGS 2 of P. medicaginis was 3566 bp. A pair of oligonucleotide primers (PPED04 and PPED05), which allowed amplification of a specific fragment (364 bp) within the IGS 2 of P. medicaginis using the PCR, was designed. Specific amplification of this fragment from P. medicaginis was highly sensitive, detecting template DNA as low as 4 ng and in a host-pathogen DNA ratio of 1000000:1. Specific PCR amplification using PPED04 and PPED05 was successful in detecting P. medicaginis in lucerne stems infected under glasshouse conditions and field infected lucerne roots. The procedures developed in this work have application to improved identification and detection of a wide range of Phytophthora spp. in plants and soil.

Structural analysis and molecular model of a self-incompatibility RNase from wild tomato

Self-incompatibility RNases (S-RNases) are an allelic series of style glycoproteins associated with rejection of self-pollen in solanaceous plants. The nucleotide sequences of S-RNase alleles from several genera have been determined, but the structure of the gene products has only been described for those from Nicotiana alata. We report on the N-glycan structures and the disulfide bonding of the S-3-RNase from wild tomato (Lycopersicon peruvianum) and use this and other information to construct a model of this molecule. The S-3-RNase has a single N-glycosylation site (Asn-28) to which one of three N-glycans is attached. S-3-RNase has seven Cys residues; six are involved in disulfide linkages (Cys-16-Cys-21, Cys-46-Cys-91, and Cys-166-Cys-177), and one has a free thiol group (Cys-150). The disulfide-bonding pattern is consistent with that observed in RNase Rh, a related RNase for which radiographic-crystallographic information is available. A molecular model of the S-3-RNase shows that four of the most variable regions of the S-RNases are clustered on one surface of the molecule. This is discussed in the context of recent experiments that set out to determine the regions of the S-RNase important for recognition during the self-incompatibility response.

Calcium is essential for the structural integrity of the cysteine-rich, ligand-binding repeat of the low-density lipoprotein receptor

Seven cysteine-rich repeats form the ligand-binding region of the low-density lipoprotein (LDL) receptor. Each of these repeats is assumed to bind a calcium ion, which is needed for association of the receptor with its ligands, LDL and beta-VLDL. The effects of metal ions on the folding of the reduced N-terminal cysteine-rich repeat have been examined by using reverse-phase high-performance liquid chromatography to follow the formation of fully oxidized isomers with different disulfide connectivities. in the absence of calcium many of the 15 possible isomers formed on oxidation, whereas in its presence the predominant product at equilibrium had the native disulfide bond connectivities. Other metals were far less effective at directing disulfide bond formation: Mn2+ partly mimicked the action of Ca2+, but Ba2+, Sr2+, and Mg2+ had little effect. This metal-ion specificity was also observed in two-dimensional H-1 NMR spectral studies: only Ca2+ induced the native three-dimensional fold. The two paramagnetic ions, Gd3+ and Mn2+, and Cd2+ did not promote adoption of a well-defined structure, and the two paramagnetic ions did not displace calcium ions. The location of calcium ion binding sites in the repeat was also explored by NMR spectroscopy. The absence of chemical shift changes for the side chain proton resonances of Asp26, Asp36, and Glu37 from pH 3.9 to 6.8 in the presence of calcium ions and their proximal location in the NMR structures implicated these side chains as calcium ligands. Deuterium exchange NMR experiments also revealed a network of hydrogen bonds that stabilizes the putative calcium-binding loop.

Functional polymorphism of the human arylamine N-acetyltransferase type 1 gene caused by (CT)-T-190 and G(560)A mutations

Human N-acetyltransferase type 1 (NAT1) catalyses the N- or O-acetylation of various arylamine and heterocyclic amine substrates and is able to bioactivate several known carcinogens. Despite wide inter-individual variability in activity, historically, NAT1 was considered to be monomorphic in nature. However, recent reports of allelic variation at the NAT1 locus suggest that it may be a polymorphically expressed enzyme. In the present study, peripheral blood mononuclear cell NAT1 activity in 85 individuals was found to be bimodally distributed with approximately 8% of the population being slow acetylators. Subsequent sequencing of the individuals having slow acetylator status showed all to have either a (CT)-T-190 or G(560)A base substitution located in the protein encoding region of the NAT1 gene. The (CT)-T-190 base substitution changed a highly conserved Arg(64), which others have shown to be essential for fully functional NAT1 protein. The (CT)-T-190 mutation has not been reported previously and we have named it NAT1*17. The G(560)A mutation is associated with the base substitutions previously observed in the NAT1*10 allele and this variant (NAT1*14) encodes for a protein with reduced acetylation capacity. A novel method using linear PCR and dideoxy terminators was developed for the detection of NAT1*14 and NAT1*17. Neither of these variants was found in the rapid acetylator population. We conclude that both the (CT)-T-190 (NAT1*17) and G(560)A (NAT1*14) NAT1 structural variants are involved in a distinct NAT1 polymorphism. Because NAT1 can bioactivate several carcinogens, this polymorphism may have implications for cancer risk in individual subjects. (C) 1998 Chapman & Hall Ltd.

Structural and functional characterisation of human sulfotransferases

The human aryl sulfotransferases HAST4 and HAST4v vary by only two amino acids but exhibit markedly different affinity towards the sulfonate acceptor p-nitrophenol and the sulfonate donor 3'-phosphoadenosine-5'-phosphosulfate (PAPS). To determine the importance of each of these amino acid differences, chimeric constructs were made of HAST4 and HAST4v. By attaching the last 120 amino acids of HAST-4v to HAST4 (changing Thr235 to Asn235) we have been able to produce a protein that has a K-m for PAPS similar to HAST4v. The reverse construct, HAST4v/4 produces a protein with a K-m for PAPS similar to HAST4. These data suggests that the COOH-terminal of sulfotransferases is involved in co-factor binding. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.