Dendrimer building toolkit: Model building and characterization of various dendrimer architectures

We have developed a graphical user interface based dendrimer builder toolkit (DBT) which can be used to generate the dendrimer configuration of desired generation for various dendrimer architectures. The validation of structures generated by this tool was carried out by studying the structural properties of two well known classes of dendrimers: ethylenediamine cored poly(amidoamine) (PAMAM) dendrimer, diaminobutyl cored poly(propylene imine) (PPI) dendrimer. Using full atomistic molecular dynamics (MD) simulation we have calculated the radius of gyration, shape tensor and monomer density distribution for PAMAM and PPI dendrimer at neutral and high pH. A good agreement between the available simulation and experimental (small angle X-ray and neutron scattering; SAXS, SANS) results and calculated radius of gyration was observed. With this validation we have used DBT to build another new class of nitrogen cored poly(propyl ether imine) dendrimer and study it's structural features using all atomistic MD simulation. DBT is a versatile tool and can be easily used to generate other dendrimer structures with different chemistry and topology. The use of general amber force field to describe the intra-molecular interactions allows us to integrate this tool easily with the widely used molecular dynamics software AMBER. This makes our tool a very useful utility which can help to facilitate the study of dendrimer interaction with nucleic acids, protein and lipid bilayer for various biological applications. © 2012 Wiley Periodicals, Inc.

Dendrimer building toolkit: Model building and characterization of various dendrimer architectures

We have developed a graphical user interface based dendrimer builder toolkit (DBT) which can be used to generate the dendrimer configuration of desired generation for various dendrimer architectures. The validation of structures generated by this tool was carried out by studying the structural properties of two well known classes of dendrimers: ethylenediamine cored poly(amidoamine) (PAMAM) dendrimer, diaminobutyl cored poly(propylene imine) (PPI) dendrimer. Using full atomistic molecular dynamics (MD) simulation we have calculated the radius of gyration, shape tensor and monomer density distribution for PAMAM and PPI dendrimer at neutral and high pH. A good agreement between the available simulation and experimental (small angle X-ray and neutron scattering; SAXS, SANS) results and calculated radius of gyration was observed. With this validation we have used DBT to build another new class of nitrogen cored poly(propyl ether imine) dendrimer and study it's structural features using all atomistic MD simulation. DBT is a versatile tool and can be easily used to generate other dendrimer structures with different chemistry and topology. The use of general amber force field to describe the intra-molecular interactions allows us to integrate this tool easily with the widely used molecular dynamics software AMBER. This makes our tool a very useful utility which can help to facilitate the study of dendrimer interaction with nucleic acids, protein and lipid bilayer for various biological applications. (c) 2012 Wiley Periodicals, Inc.

MAST: multi-script annotation toolkit for scenic text

This paper describes a semi-automatic tool for annotation of multi-script text from natural scene images. To our knowledge, this is the maiden tool that deals with multi-script text or arbitrary orientation. The procedure involves manual seed selection followed by a region growing process to segment each word present in the image. The threshold for region growing can be varied by the user so as to ensure pixel-accurate character segmentation. The text present in the image is tagged word-by-word. A virtual keyboard interface has also been designed for entering the ground truth in ten Indic scripts, besides English. The keyboard interface can easily be generated for any script, thereby expanding the scope of the toolkit. Optionally, each segmented word can further be labeled into its constituent characters/symbols. Polygonal masks are used to split or merge the segmented words into valid characters/symbols. The ground truth is represented by a pixel-level segmented image and a '.txt' file that contains information about the number of words in the image, word bounding boxes, script and ground truth Unicode. The toolkit, developed using MATLAB, can be used to generate ground truth and annotation for any generic document image. Thus, it is useful for researchers in the document image processing community for evaluating the performance of document analysis and recognition techniques. The multi-script annotation toolokit (MAST) is available for free download.

Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

RepEx: Repeat extractor for biological sequences

Genomic sequences are far from being random but are made up of systematically ordered and information rich patterns. These repeated sequence patterns have been vastly utilized for their fundamental importance in understanding the genome function and organization. To this end, a comprehensive toolkit, RepEx, has been developed which extracts repeat (inverted, everted and mirror) patterns from the given genome sequence(s) without any constraints. The toolkit can also be used to fetch the inverted repeats present in the protein sequence (s). Further, it is capable of extracting exact and degenerate repeats with a user defined spacer intervals. It is remarkably more precise and sensitive when compared to the existing tools. An example with comprehensive case studies and a performance evaluation of the proposed toolkit has been presented to authenticate its efficiency and accuracy. (C) 2013 Elsevier Inc. All rights reserved.

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.