DNA recognition by the first tail-to-tail linked distamycin-like oligopeptide dimers

Sequence-specific bidentate binding to double-stranded (ds)-DNA by 'tail-to-tail' linked dimeric, distamycin analogues is described; compared to their monomeric analogues, these dimers exhibit greater affinity and longer binding site size and open up a novel avenue in the design of minor groove binders that overcome the phasing problem.

Impact of Template Layers on Dielectric and Electrical Properties of Pulsed-Laser Ablated Pb(Mg1/3 Nb2/3)O3 - PbTiO3 Thin Films

A study was done on pulsed laser deposited relaxor ferroelectric thin films of 0.7Pb(Mg1/3Nb2/3)O3-0.3PbTiO3 (PMN-PT) deposited on platinized silicon substrates with template layers to observe the influence of the template layers on physical and electrical properties. Initial results, showed that perovskite phase (80% by volume) was obtained through proper selection of the processing conditions on Pt/Ti/SiO2/Si substrates. The films were grown at 300°C and then annealed in a rapid thermal annealing furnace in the temperature range of 750-850°C to induce crystallization. Comparison of the films annealed at different temperatures revealed a change in crystallinity, perovskite phase formation and grain size. These results were further used to improve the quality of the perovskite PMN-PT phase by inserting thin layers of TiO2 on the Pt substrate. These resulted in an increase in perovskite phase in the films even at lower annealing temperatures. Dielectric studies on the PMN-PT films show very high values of dielectric constant (1300) at room temperature, which further improved with the insertion of the template seed layer. The relaxor properties of the PMN-PT were correlated with Vogel-Fulcher theory to determine the actual nature of the relaxation process.

Role of La0.5Sr0.5COO3 template layers on dielectric and electrical properties of pulsed-laser ablated Pb(Nb2/3Mg1/3)O-3-PbTiO3 thin films

Relaxor ferroelectric thin films of 0.7Pb(Mg1/3Nb2/3)O-3-0.3PbTiO(3) (PMN-PT) deposited on platinized silicon substrates with and without template layers were studied. Perovskite phase (80% by volume) was obtained through proper selection of the processing conditions on bare Pt/Ti/SiO2/Si substrates. The films were initially grown at 300 degreesC using pulsed-laser ablation and subsequently annealed in a rapid thermal annealing furnace in the temperature range of 750-850 degreesC to induce crystallization. Comparison of microstructure of the films annealed at different temperatures showed change in perovskite phase formation and grain size etc. Results from compositional analysis of the films revealed that the films initially possessed high content of lead percentage, which subsequently decreased after annealing at temperature 750-850 degreesC. Films with highest perovskite content were found to form at 820-840 degreesC on Pt substrates where the Pb content was near stoichiometric. Further improvement in the formation of perovskite PMN-PT phase was obtained by using buffer layers of La0.5Sr0.5CoO3 (LSCO) on the Pt substrate. This resulted 100% perovskite phase formation in the films deposited at 650 degreesC. Dielectric studies on the PMN-PT films with LSCO template layers showed high values of relative dielectric constant (3800) with a loss factor (tan delta) of 0.035 at a frequency of 1 kHz at room temperature. (C) 2002 Elsevier Science B.V. All rights reserved.

Conformational basis for substrate recognition and regulation of catalytic activity in Staphylococcus aureus nucleoside di-phosphate kinase

Nucleoside diphosphate kinases (NDK) are characterized by high catalytic turnover rates and diverse substrate specificity. These features make this enzyme an effective activator of a pro-drug an application that has been actively pursued for a variety of therapeutic strategies. The catalytic mechanism of this enzyme is governed by a conserved histidine that coordinates a magnesium ion at the active site. Despite substantial structural and biochemical information on NDK, the mechanistic feature of the phospho-transfer that leads to auto-phosphorylation remains unclear. While the role of the histidine residue is well documented, the other active site residues, in particular the conserved serine remains poorly characterized. Studies on some homologues suggest no role for the serine residue at the active site, while others suggest a crucial role for this serine in the regulation and quaternary association of this enzyme in some species. Here we report the biochemical features of the Staphylococcus aureus NDK and the mutant enzymes. We also describe the crystal structures of the apo-NDK, as a transition state mimic with vanadate and in complex with different nucleotide substrates. These structures formed the basis for molecular dynamics simulations to understand the broad substrate specificity of this enzyme and the role of active site residues in the phospho-transfer mechanism and oligomerization. Put together, these data suggest that concerted changes in the conformation of specific residues facilitate the stabilization of nucleotide complexes thereby enabling the steps involved in the ping-pong reaction mechanism without large changes to the overall structure of this enzyme. (C) 2011 Elsevier B.V. All rights reserved.

Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Comparison of AM-FM Based Features For Robust Speech Recognition

Effective feature extraction for robust speech recognition is a widely addressed topic and currently there is much effort to invoke non-stationary signal models instead of quasi-stationary signal models leading to standard features such as LPC or MFCC. Joint amplitude modulation and frequency modulation (AM-FM) is a classical non-parametric approach to non-stationary signal modeling and recently new feature sets for automatic speech recognition (ASR) have been derived based on a multi-band AM-FM representation of the signal. We consider several of these representations and compare their performances for robust speech recognition in noise, using the AURORA-2 database. We show that FEPSTRUM representation proposed is more effective than others. We also propose an improvement to FEPSTRUM based on the Teager energy operator (TEO) and show that it can selectively outperform even FEPSTRUM

A Profilometric Approach to 3D Face Reconstruction and Its Application to Face Recognition

3D Face Recognition is an active area of research for past several years. For a 3D face recognition system one would like to have an accurate as well as low cost setup for constructing 3D face model. In this paper, we use Profilometry approach to obtain a 3D face model.This method gives a low cost solution to the problem of acquiring 3D data and the 3D face models generated by this method are sufficiently accurate. We also develop an algorithm that can use the 3D face model generated by the above method for the recognition purpose.

Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.