Characterization of DNA topoisomerase I from Mycobacterium tuberculosis: DNA cleavage and religation properties and inhibition of its activity

Type I DNA topoisomerases from bacteria catalyse relaxation of negatively supercoiled DNA in a Mg2+ dependent manner. Although topoisomerases of distinct classes have been subjected for anti-cancer and anti-infective drug development, bacterial type I enzymes are way behind in this regard. Our studies with Mycobacterium smegmatis topoisomerase I (MstopoI) revealed several of its distinct properties compared to the well studied Escherichia coli topoisomerase I (EctopoI) suggesting the possibility of targeting the mycobacterial enzyme for inhibitor development. Here, we describe Mycobacterium tuberculosis topoisomerase I (MttopoI) and compare its properties with MstopoI and EctopoI. The enzyme cleaves DNA at preferred sites in a pattern similar to its ortholog from M. smegmatis. Oligonucleotides containing the specific recognition sequence inhibited the activity of the enzyme in a manner similar to that of MstopoI. Substitution of the acidic residues, D111 and E115 which are involved in Mg2+ co-ordination, to alanines affected the DNA relaxation activity. Unlike the wild type enzyme, D111A was dependent on Mg2+ for DNA cleavage and both the mutants were compromised in religation. The monoclonal antibody (mAb), 2F3G4, developed against MstopoI inhibited the relaxation activity of MttopoI. These studies affirm the characteristics of MttopoI to be similar to MstopoI and set a stage to target it for the development of specific small molecule inhibitors. (C) 2012 Elsevier Inc. All rights reserved.

Fast Likelihood Computation in Speech Recognition using Matrices

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication. In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on 1,138 work vocabulary RM1 task and 6,224 word vocabulary TIMIT task using Sphinx 3.7 system show that, for a typical case the matrix multiplication based approach leads to overall speedup of 46 % on RM1 task and 115 % for TIMIT task. Our low-rank approximation methods provide a way for trading off recognition accuracy for a further increase in computational performance extending overall speedups up to 61 % for RM1 and 119 % for TIMIT for an increase of word error rate (WER) from 3.2 to 3.5 % for RM1 and for no increase in WER for TIMIT. We also express pairwise Euclidean distance computation phase in Dynamic Time Warping (DTW) in terms of matrix multiplication leading to saving of approximately of computational operations. In our experiments using efficient implementation of matrix multiplication, this leads to a speedup of 5.6 in computing the pairwise Euclidean distances and overall speedup up to 3.25 for DTW.

Isolation of a High Affinity Neutralizing Monoclonal Antibody against 2009 Pandemic H1N1 Virus That Binds at the `Sa' Antigenic Site

Influenza virus evades host immunity through antigenic drift and shift, and continues to circulate in the human population causing periodic outbreaks including the recent 2009 pandemic. A large segment of the population was potentially susceptible to this novel strain of virus. Historically, monoclonal antibodies (MAbs) have been fundamental tools for diagnosis and epitope mapping of influenza viruses and their importance as an alternate treatment option is also being realized. The current study describes isolation of a high affinity (K-D = 2.1 +/- 0.4 pM) murine MAb, MA2077 that binds specifically to the hemagglutinin (HA) surface glycoprotein of the pandemic virus. The antibody neutralized the 2009 pandemic H1N1 virus in an in vitro microneutralization assay (IC50 = 0.08 mu g/ml). MA2077 also showed hemagglutination inhibition activity (HI titre of 0.50 mu g/ml) against the pandemic virus. In a competition ELISA, MA2077 competed with the binding site of the human MAb, 2D1 (isolated from a survivor of the 1918 Spanish flu pandemic) on pandemic H1N1 HA. Epitope mapping studies using yeast cell-surface display of a stable HA1 fragment, wherein `Sa' and `Sb' sites were independently mutated, localized the binding site of MA2077 within the `Sa' antigenic site. These studies will facilitate our understanding of antigen antibody interaction in the context of neutralization of the pandemic influenza virus.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Molecular Design of Synthetic Benzimidazoles for the Switchover of the Duplex to G-quadruplex DNA Recognition

Benzimidazole derivatives are well known for their antibacterial, antiviral, anticonvulsant, antihistaminic, anthelmintic and antidepressant activities. Benzimidazole's unique base-selective DNA recognition property has been studied widely. However, most of the early benzimidazole systems have been targeted towards the binding of duplex DNA. Here we have shown the evolution and progress of the design and synthesis of new benzimidazole systems towards selective recognition of the double-stranded DNA first. Then in order to achieve selective recognition of the G-quadruplex DNA and utilize their potential as future anti-cancer drug candidates, we have demonstrated their selective cytotoxicity towards the cancer cells and potent telomerase inhibition ability.

Differential cellular recognition pattern to M. tuberculosis targets defined by IFN-gamma and IL-17 production in blood from TB plus patients from Honduras as compared to health care workers: TB and immune responses in patients from Honduras

Background: A better understanding of the quality of cellular immune responses directed against molecularly defined targets will guide the development of TB diagnostics and identification of molecularly defined, clinically relevant M.tb vaccine candidates. Methods: Recombinant proteins (n = 8) and peptide pools (n = 14) from M. tuberculosis (M.tb) targets were used to compare cellular immune responses defined by IFN-gamma and IL-17 production using a Whole Blood Assay (WBA) in a cohort of 148 individuals, i.e. patients with TB + (n = 38), TB- individuals with other pulmonary diseases (n = 81) and individuals exposed to TB without evidence of clinical TB (health care workers, n = 29). Results: M.tb antigens Rv2958c (glycosyltransferase), Rv2962c (mycolyltransferase), Rv1886c (Ag85B), Rv3804c (Ag85A), and the PPE family member Rv3347c were frequently recognized, defined by IFN-gamma production, in blood from healthy individuals exposed to M.tb (health care workers). A different recognition pattern was found for IL-17 production in blood from M.tb exposed individuals responding to TB10.4 (Rv0288), Ag85B (Rv1886c) and the PPE family members Rv0978c and Rv1917c. Conclusions: The pattern of immune target recognition is different in regard to IFN-gamma and IL-17 production to defined molecular M.tb targets in PBMCs from individuals frequently exposed to M.tb. The data represent the first mapping of cellular immune responses against M.tb targets in TB patients from Honduras.

Power-law transformation for enhanced recognition of born-digital word images

In this paper, we discuss the issues related to word recognition in born-digital word images. We introduce a novel method of power-law transformation on the word image for binarization. We show the improvement in image binarization and the consequent increase in the recognition performance of OCR engine on the word image. The optimal value of gamma for a word image is automatically chosen by our algorithm with fixed stroke width threshold. We have exhaustively experimented our algorithm by varying the gamma and stroke width threshold value. By varying the gamma value, we found that our algorithm performed better than the results reported in the literature. On the ICDAR Robust Reading Systems Challenge-1: Word Recognition Task on born digital dataset, as compared to the recognition rate of 61.5% achieved by TH-OCR after suitable pre-processing by Yang et. al. and 63.4% by ABBYY Fine Reader (used as baseline by the competition organizers without any preprocessing), we achieved 82.9% using Omnipage OCR applied on the images after being processed by our algorithm.

Recognition of Kannada characters extracted from scene images

In this paper, we describe a method for feature extraction and classification of characters manually isolated from scene or natural images. Characters in a scene image may be affected by low resolution, uneven illumination or occlusion. We propose a novel method to perform binarization on gray scale images by minimizing energy functional. Discrete Cosine Transform and Angular Radial Transform are used to extract the features from characters after normalization for scale and translation. We have evaluated our method on the complete test set of Chars74k dataset for English and Kannada scripts consisting of handwritten and synthesized characters, as well as characters extracted from camera captured images. We utilize only synthesized and handwritten characters from this dataset as training set. Nearest neighbor classification is used in our experiments.

Language models for online handwritten Tamil word recognition

N-gram language models and lexicon-based word-recognition are popular methods in the literature to improve recognition accuracies of online and offline handwritten data. However, there are very few works that deal with application of these techniques on online Tamil handwritten data. In this paper, we explore methods of developing symbol-level language models and a lexicon from a large Tamil text corpus and their application to improving symbol and word recognition accuracies. On a test database of around 2000 words, we find that bigram language models improve symbol (3%) and word recognition (8%) accuracies and while lexicon methods offer much greater improvements (30%) in terms of word recognition, there is a large dependency on choosing the right lexicon. For comparison to lexicon and language model based methods, we have also explored re-evaluation techniques which involve the use of expert classifiers to improve symbol and word recognition accuracies.

Benchmarking recognition results on camera captured word image data sets

We have benchmarked the maximum obtainable recognition accuracy on five publicly available standard word image data sets using semi-automated segmentation and a commercial OCR. These images have been cropped from camera captured scene images, born digital images (BDI) and street view images. Using the Matlab based tool developed by us, we have annotated at the pixel level more than 3600 word images from the five data sets. The word images binarized by the tool, as well as by our own midline analysis and propagation of segmentation (MAPS) algorithm are recognized using the trial version of Nuance Omnipage OCR and these two results are compared with the best reported in the literature. The benchmark word recognition rates obtained on ICDAR 2003, Sign evaluation, Street view, Born-digital and ICDAR 2011 data sets are 83.9%, 89.3%, 79.6%, 88.5% and 86.7%, respectively. The results obtained from MAPS binarized word images without the use of any lexicon are 64.5% and 71.7% for ICDAR 2003 and 2011 respectively, and these values are higher than the best reported values in the literature of 61.1% and 41.2%, respectively. MAPS results of 82.8% for BDI 2011 dataset matches the performance of the state of the art method based on power law transform.

A cyclic peptide mimic of an RNA recognition motif of human La protein is a potent inhibitor of hepatitis C virus

Due to limited available therapeutic options, developing new lead compounds against hepatitis C virus is an urgent need. Human La protein stimulates hepatitis C virus translation through interaction with the hepatitis C viral RNA. A cyclic peptide mimicking the beta-turn of the human La protein that interacts with the viral RNA was synthesized. It inhibits hepatitis C viral RNA translation significantly better than the corresponding linear peptide at longer post-treatment times. The cyclic peptide also inhibited replication as measured by replicon RNA levels using real time RT-PCR. The cyclic peptide emerges as a promising lead compound against hepatitis C.

Antibody-mediated catalysis: Induction and therapeutic relevance

Abzymes are immunoglobulins endowed with enzymatic activities. The catalytic activity of an abzyme resides in the variable domain of the antibody, which is constituted by the close spatial arrangement of amino acid residues involved in catalysis. The origin of abzymes is conferred by the innate diversity of the immunoglobulin gene repertoire. Under deregulated immune conditions, as in autoimmune diseases, the generation of abzymes to self-antigens could be deleterious. Technical advancement in the ability to generate monoclonal antibodies has been exploited in the generation of abzymes with defined specificities and activities. Therapeutic applications of abzymes are being investigated with the generation of monoclonal abzymes against several pathogenesis-associated antigens. Here, we review the different contexts in which abzymes are generated, and we discuss the relevance of monoclonal abzymes for the treatment of human diseases.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Mechanistic Insights into the Neutralization of Cytotoxic Abrin by the Monoclonal Antibody D6F10

Abrin, an A/B toxin obtained from the Abrus precatorius plant is extremely toxic and a potential bio-warfare agent. Till date there is no antidote or vaccine available against this toxin. The only known neutralizing monoclonal antibody against abrin, namely D6F10, has been shown to rescue the toxicity of abrin in cells as well as in mice. The present study focuses on mapping the epitopic region to understand the mechanism of neutralization of abrin by the antibody D6F10. Truncation and mutational analysis of abrin A chain revealed that the amino acids 74-123 of abrin A chain contain the core epitope and the residues Thr112, Gly114 and Arg118 are crucial for binding of the antibody. In silico analysis of the position of the mapped epitope indicated that it is present close to the active site cleft of abrin A chain. Thus, binding of the antibody near the active site blocks the enzymatic activity of abrin A chain, thereby rescuing inhibition of protein synthesis by the toxin in vitro. At 1: 10 molar concentration of abrin: antibody, the antibody D6F10 rescued cells from abrin-mediated inhibition of protein synthesis but did not prevent cell attachment of abrin. Further, internalization of the antibody bound to abrin was observed in cells by confocal microscopy. This is a novel finding which suggests that the antibody might function intracellularly and possibly explains the rescue of abrin's toxicity by the antibody in whole cells and animals. To our knowledge, this study is the first report on a neutralizing epitope for abrin and provides mechanistic insights into the poorly understood mode of action of anti-A chain antibodies against several toxins including ricin.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.