Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

Binding of Gemini Bisbenzimidazole Drugs with Human Telomeric G-Quadruplex Dimers: Effect of the Spacer in the Design of Potent Telomerase Inhibitors

The study of anticancer agents that act via stabilization of telomeric G-quadruplex DNA (G4DNA) is important because such agents often inhibit telomerase activity. Several types of G4DNA binding ligands are known. In these studies, the target structures often involve a single G4 DNA unit formed by short DNA telomeric sequences. However, the 3'-terminal single-stranded human telomeric DNA can form higher-order structures by clustering consecutive quadruplex units (dimers or nmers). Herein, we present new synthetic gemini (twin) bisbenzimidazole ligands, in which the oligo-oxyethylene spacers join the two bisbenzimidazole units for the recognition of both monomeric and dimeric G4DNA, derived from d(T2AG3)4 and d(T2AG3) 8 human telomeric DNA, respectively. The spacer between the two bisbenzimidazoles in the geminis plays a critical role in the G4DNA stability. We report here (i) synthesis of new effective gemini anticancer agents that are selectively more toxic towards the cancer cells than the corresponding normal cells; (ii) formation and characterization of G4DNA dimers in solution as well as computational construction of the dimeric G4DNA structures. The gemini ligands direct the folding of the single-stranded DNA into an unusually stable parallel-stranded G4DNA when it was formed in presence of the ligands in KCl solution and the gemini ligands show spacer length dependent potent telomerase inhibition properties.

Infrared Spectroscopic Studies to Understand the Effect of Drugs at Molecular Level

In the recent past, there have been enormous efforts to understand effect of drugs on human body. Prior to understand the effect of drugs on human body most of the experiments are carried out on cells or model organisms. Here we present our study on the effect of chemotherapeutic drugs on cancer cells and the acetaminophen (APAP) induced hepatotoxicity in mouse model. Histone deacetylase inhibitors (HDIs) have attracted attention as potential drug molecules for the treatment of cancer. These are the chemotherapeutic drugs which have indirect mechanistic action against cancer cells via acting against histone deacetylases (HDAC). It has been known that different HDAC enzymes are over-expressed in various types of cancers for example; HDAC1 is over expressed in prostate, gastric and breast carcinomas. Therefore, in order to optimise chemotherapy, it is important to determine the efficacy of various classes of HDAC inhibitor drugs against variety of over-expressed HDAC enzymes. In the present study, FTIR microspectroscopy has been employed to predict the acetylation and propionylation brought in by HDIs. The liver plays an important role in cellular metabolism and is highly susceptible to drug toxicity. APAP which is an analgesic and antipyretic drug is extensively used for therapeutic purposes and has become the most common cause of acute liver failure (ALF). In the current study, we have focused to understand APAP induced hepatotoxicity using FTIR microspectroscopy. In the IR spectrum the bands corresponding to glycogen, ester group and were found to be suitable markers to predict liver injury at early time point (0.5hr) due to APAP both in tissue and serum in comparison to standard biochemical assays. Our studies show the potential of FTIR spectroscopy as a rapid, sensitive and non invasive detection technique for future clinical diagnosis.

Enhancing the magnetic characteristics of BiFeO3 nanoparticles by Ca, Ba co-doping

Multiferroic nanoparticles (NPs) of pristine and Ca, Ba co-doped BiFeO3 were synthesized by a facile sal gel route. Co-doping was done by fixing the total dopant concentration at 5 mol% and then the relative concentrations of Ca and Ba was varied. Structural, optical and magnetic properties of the NPs were investigated using different techniques. UV-Vis absorption spectra of BiFeO3 NPs showed a substantial blue shift of similar to 100 nm (530 nm -> 430 nm) on Ca. Ba co-doping which corresponds to increase in band gap by 0.5 eV. Fe-57 Mossbauer spectroscopy confirmed that iron is present only in 3(+) valence state in all co-doped samples. The coercive field increased by 18 times for Bi0.95Ca0.01Ba0.04FeO3 samples, which is the maximum enhancement, observed amongst all the 5 mol% doped samples. At the equimolar (2.5 mol % each) concentration of co-dopants, the coercive field shows a significant enhancement of about 9 times (220 Oe -> 2014 Oe) with concomitant increase in saturation magnetization by 7 times. Thus, equimolar co-doping causes simultaneous enhancement of the twin aspects of magnetic properties thereby making them better suited for device applications. (C) 2012 Elsevier B.V. All rights reserved.

Synthesis and structural characterization of some trisulfide analoges of thiouracil-based antithyroid drugs

Thiourea-based antithyroid drugs are effectively used for the treatment of hyperthyroidism. In this paper, we describe the synthesis of new trisulfides (11-12) from the commonly used thiourea-based antithyroid drugs such as 6-n-propyl-2-thiouracil (PTU) and 6-methyl-2-thiouracil (MTU) in the reaction with I-2/KI system. Structural analysis by single crystal X-ray diffraction studies revealed the stabilization of trisulfides by a lactam-lactim tautomerism facilitating effective intramolecular as well as intermolecular non-covalent interactions. Although the structures of both trisulfides were found to be quite similar, a notable difference in the intermolecular interactions was observed between compounds 11 and 12 leading to different structural patterns. Structural stabilization of these trisulfides by tautomerism followed by intramolecular as well as intermolecular H-bonds makes these molecules as perfect examples in molecular recognition with self-complementary donor and acceptor units within a single molecule. (C) 2012 Elsevier B.V. All rights reserved.

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Fast image reconstruction for fluorescence microscopy

Real-time image reconstruction is essential for improving the temporal resolution of fluorescence microscopy. A number of unavoidable processes such as, optical aberration, noise and scattering degrade image quality, thereby making image reconstruction an ill-posed problem. Maximum likelihood is an attractive technique for data reconstruction especially when the problem is ill-posed. Iterative nature of the maximum likelihood technique eludes real-time imaging. Here we propose and demonstrate a compute unified device architecture (CUDA) based fast computing engine for real-time 3D fluorescence imaging. A maximum performance boost of 210x is reported. Easy availability of powerful computing engines is a boon and may accelerate to realize real-time 3D fluorescence imaging. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.4754604]

Prior image-constrained l(1)-norm-based reconstruction method for effective usage of structural information in diffuse optical tomography

A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in multimodal diffuse optical tomographic imaging is introduced. This approach is based on a prior image-constrained-l(1) minimization scheme and has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the proposed framework is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information. (C) 2012 Optical Society of America

Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using l(1)-norm-based linear image reconstruction method

Traditional image reconstruction methods in rapid dynamic diffuse optical tomography employ l(2)-norm-based regularization, which is known to remove the high-frequency components in the reconstructed images and make them appear smooth. The contrast recovery in these type of methods is typically dependent on the iterative nature of method employed, where the nonlinear iterative technique is known to perform better in comparison to linear techniques (noniterative) with a caveat that nonlinear techniques are computationally complex. Assuming that there is a linear dependency of solution between successive frames resulted in a linear inverse problem. This new framework with the combination of l(1)-norm based regularization can provide better robustness to noise and provide better contrast recovery compared to conventional l(2)-based techniques. Moreover, it is shown that the proposed l(1)-based technique is computationally efficient compared to its counterpart (l(2)-based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame, and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames.

Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs

Resistance to therapy limits the effectiveness of drug treatment in many diseases. Drug resistance can be considered as a successful outcome of the bacterial struggle to survive in the hostile environment of a drug-exposed cell. An important mechanism by which bacteria acquire drug resistance is through mutations in the drug target. Drug resistant strains (multi-drug resistant and extensively drug resistant) of Mycobacterium tuberculosis are being identified at alarming rates, increasing the global burden of tuberculosis. An understanding of the nature of mutations in different drug targets and how they achieve resistance is therefore important. An objective of this study is to first decipher sequence as well as structural bases for the observed resistance in known drug resistant mutants and then to predict positions in each target that are more prone to acquiring drug resistant mutations. A curated database containing hundreds of mutations in the 38 drug targets of nine major clinical drugs, associated with resistance is studied here. Mutations have been classified into those that occur in the binding site itself, those that occur in residues interacting with the binding site and those that occur in outer zones. Structural models of the wild type and mutant forms of the target proteins have been analysed to seek explanations for reduction in drug binding. Stability analysis of an entire array of 19 mutations at each of the residues for each target has been computed using structural models. Conservation indices of individual residues, binding sites and whole proteins are computed based on sequence conservation analysis of the target proteins. The analyses lead to insights about which positions in the polypeptide chain have a higher propensity to acquire drug resistant mutations. Thus critical insights can be obtained about the effect of mutations on drug binding, in terms of which amino acid positions and therefore which interactions should not be heavily relied upon, which in turn can be translated into guidelines for modifying the existing drugs as well as for designing new drugs. The methodology can serve as a general framework to study drug resistant mutants in other micro-organisms as well.

Enhancing the photocytotoxic potential of curcumin on terpyridyl lanthanide(III) complex formation

Lanthanide(III) complexes Ln(R-tpy)(cur)(NO3)(2)] (Ln = La(III) in 1, 2; Gd(III) in 5, 6) and Ln(R-tpy)(scur)(NO3)(2)] (Ln = La(III) in 3, 4; Gd(III) in 7, 8), where R-tpy is 4'-phenyl-2,2':6',2 `'-terpyridine (ph-tpy in 1, 3, 5, 7), 4'-(1-pyrenyl)-2,2':6',2 `'-terpyridine (py-tpy in 2, 4, 6, 8), Hcur is curcumin (in 1, 2, 5, 6) and Hscur is diglucosylcurcumin (in 3, 4, 7, 8), were prepared and their DNA photocleavage activity and photocytotoxicity studied. Complexes La(ph-tpy)(cur)(NO3)(2)] (1) and Gd(ph-tpy)(cur)(NO3)(2)] (5) were structurally characterized. The complexes in aqueous-DMF showed an absorption band near 430 nm and an emission band near 515 nm when excited at 420 nm. The complexes are moderate binders to calf-thymus DNA. They cleave plasmid supercoiled DNA to its nicked circular form in UV-A (365 nm) and visible light (454 nm) via O-1(2) and (OH)-O-center dot pathways. The complexes are remarkably photocytotoxic in HeLa cells in visible light (lambda = 400-700 nm) and are non-toxic in the dark. FACScan analysis of the HeLa cells treated with 2 and 4 showed cell death via an apoptotic pathway. Nuclear localization of 1-4 is evidenced from confocal imaging on HeLa cells. The hydrolytic instability of curcumin gets significantly reduced upon binding to the lanthanide ions while retaining its photocytotoxic potential.

Data-resolution based optimal choice of minimum required measurements for image-guided diffuse optical tomography

Image-guided diffuse optical tomography has the advantage of reducing the total number of optical parameters being reconstructed to the number of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem from underdetermined in nature to overdetermined. In such cases, the minimum required measurements might be far less compared to those of the traditional diffuse optical imaging. An approach to choose these optimally based on a data-resolution matrix is proposed, and it is shown that such a choice does not compromise the reconstruction performance. (C) 2013 Optical Society of America

MULTI-OBJECTIVE GENETIC ALGORITHM FOR EFFICIENT POINT MATCHING IN MULTI-SENSOR SATELLITE IMAGE

This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Active-contour-based automated image quantitation techniques for Western Blot analysis

Western Blot analysis is an analytical technique used in Molecular Biology, Biochemistry, Immunogenetics and other Molecular Biology studies to separate proteins by electrophoresis. The procedure results in images containing nearly rectangular-shaped blots. In this paper, we address the problem of quantitation of the blots using automated image processing techniques. We formulate a special active contour (or snake) called Oblong, which locks on to rectangular shaped objects. Oblongs depend on five free parameters, which is also the minimum number of parameters required for a unique characterization. Unlike many snake formulations, Oblongs do not require explicit gradient computations and therefore the optimization is carried out fast. The performance of Oblongs is assessed on synthesized data and Western Blot Analysis images.