Impact of metal on the DNA photocleavage activity and cytotoxicity of ferrocenyl terpyridine 3d metal complexes

Ferrocenyl terpyridine 3d metal complexes and their analogues, viz. [M(Fc-tpy)(2)](ClO(4))(2) (1-4), [Zn(Ph-tpy)(2)](ClO(4))(2) (5) and [Zn(Fc-dpa)(2)]X(2) (X = ClO(4), 6; PF6, 6a), where M = Fe(II) in 1, Co(II) in 2, Cu(II) in 3 and Zn(II) in 4, Fc-tpy is 4'-ferrocenyl-2,2': 6', 2 `'-terpyridine, Ph-tpy is 4'-phenyl-2,2': 6', 2 `'-terpyridine and Fc-dpa is ferrocenyl-N,N-dipicolylmethanamine, are prepared and their DNA binding and photocleavage activity in visible light studied. Complexes 2, 4, 5 and 6a that are structurally characterized by X-ray crystallography show distorted octahedral geometry with the terpyridyl ligands binding to the metal in a meridional fashion, with Fc-dpa in 6a showing a facial binding mode. The Fc-tpy complexes display a charge transfer band in the visible region. The ferrocenyl (Fc) complexes show a quasi-reversible Fc(+)-Fc redox couple within 0.48 to 0.66 V vs. SCE in DMF-0.1 M TBAP. The DNA binding constants of the complexes are similar to 10(4) M(-1). Thermal denaturation and viscometric data suggest DNA surface binding through electrostatic interaction by the positively charged complexes. Barring the Cu(II) complex 3, the complexes do not show any chemical nuclease activity in the presence of glutathione. Complexes 1-4 exhibit significant plasmid DNA photocleavage activity in visible light via a photoredox pathway. Complex 5, without the Fc moiety, does not show any DNA photocleavage activity. The Zn(II) complex 4 shows a significant PDT effect in HeLa cancer cells giving an IC(50) value of 7.5 mu M in visible light, while being less toxic in the dark (IC(50) = 49 mu M).

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

Efficient sequential assignments in proteins with reduced dimensionality 3D HN(CA)NH

We present reduced dimensionality (RD) 3D HN(CA)NH for efficient sequential assignment in proteins. The experiment correlates the N-15 and H-1 chemical shift of a residue ('i') with those of its immediate N-terminal (i - 1) and C-terminal (i + 1) neighbors and provides four-dimensional chemical shift correlations rapidly with high resolution. An assignment strategy is presented which combines the correlations observed in this experiment with amino acid type information obtained from 3D CBCA(CO)NH. By classifying the 20 amino acid types into seven distinct categories based on C-13(beta) chemical shifts, it is observed that a stretch of five sequentially connected residues is sufficient to map uniquely on to the polypeptide for sequence specific resonance assignments. This method is exemplified by application to three different systems: maltose binding protein (42 kDa), intrinsically disordered domain of insulin-like growth factor binding protein-2 and Ubiquitin. Fast data acquisition is demonstrated using longitudinal H-1 relaxation optimization. Overall, 3D HN(CA)NH is a powerful tool for high throughput resonance assignment, in particular for unfolded or intrinsically disordered polypeptides.

Guided Wave based Damage Detection in a Composite T-joint using 3D Scanning Laser Doppler Vibrometer

Composite T-joints are commonly used in modern composite airframe, pressure vessels and piping structures, mainly to increase the bending strength of the joint and prevents buckling of plates and shells, and in multi-cell thin-walled structures. Here we report a detailed study on the propagation of guided ultrasonic wave modes in a composite T-joint and their interactions with delamination in the co-cured co-bonded flange. A well designed guiding path is employed wherein the waves undergo a two step mode conversion process, one is due to the web and joint filler on the back face of the flange and the other is due to the delamination edges close to underneath the accessible surface of the flange. A 3D Laser Doppler Vibrometer is used to obtain the three components of surface displacements/velocities of the accessible face of the flange of the T-joint. The waves are launched by a piezo ceramic wafer bonded on to the back surface of the flange. What is novel in the proposed method is that the location of any change in material/geometric properties can be traced by computing a frequency domain power flow along a scan line. The scan line can be chosen over a grid either during scan or during post-processing of the scan data off-line. The proposed technique eliminates the necessity of baseline data and disassembly of structure for structural interrogation.

Multidimensional data reconstruction for two color fluorescence microscopy

We propose an iterative data reconstruction technique specifically designed for multi-dimensional multi-color fluorescence imaging. Markov random field is employed (for modeling the multi-color image field) in conjunction with the classical maximum likelihood method. It is noted that, ill-posed nature of the inverse problem associated with multi-color fluorescence imaging forces iterative data reconstruction. Reconstruction of three-dimensional (3D) two-color images (obtained from nanobeads and cultured cell samples) show significant reduction in the background noise (improved signal-to-noise ratio) with an impressive overall improvement in the spatial resolution (approximate to 250 nm) of the imaging system. Proposed data reconstruction technique may find immediate application in 3D in vivo and in vitro multi-color fluorescence imaging of biological specimens. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4769058]

Guided-wave-based damage detection in a composite T-joint using 3D scanning laser Doppler vibrometer

Composite T-joints are commonly used in modern composite airframe, pressure vessels and piping structures, mainly to increase the bending strength of the joint and prevents buckling of plates and shells, and in multi-cell thin-walled structures. Here we report a detailed study on the propagation of guided ultrasonic wave modes in a composite T-joint and their interactions with delamination in the co-cured co-bonded flange. A well designed guiding path is employed wherein the waves undergo a two step mode conversion process, one is due to the web and joint filler on the back face of the flange and the other is due to the delamination edges close to underneath the accessible surface of the flange. A 3D Laser Doppler Vibrometer is used to obtain the three components of surface displacements/velocities of the accessible face of the flange of the T-joint. The waves are launched by a piezo ceramic wafer bonded on to the back surface of the flange. What is novel in the proposed method is that the location of any change in material/geometric properties can be traced by computing a frequency domain power flow along a scan line. The scan line can be chosen over a grid either during scan or during post-processing of the scan data off-line. The proposed technique eliminates the necessity of baseline data and disassembly of structure for structural interrogation.

Toward Real-Time Availability of 3D Temperature Maps Created with Temporally Constrained Reconstruction

PurposeTo extend the previously developed temporally constrained reconstruction (TCR) algorithm to allow for real-time availability of three-dimensional (3D) temperature maps capable of monitoring MR-guided high intensity focused ultrasound applications. MethodsA real-time TCR (RT-TCR) algorithm is developed that only uses current and previously acquired undersampled k-space data from a 3D segmented EPI pulse sequence, with the image reconstruction done in a graphics processing unit implementation to overcome computation burden. Simulated and experimental data sets of HIFU heating are used to evaluate the performance of the RT-TCR algorithm. ResultsThe simulation studies demonstrate that the RT-TCR algorithm has subsecond reconstruction time and can accurately measure HIFU-induced temperature rises of 20 degrees C in 15 s for 3D volumes of 16 slices (RMSE = 0.1 degrees C), 24 slices (RMSE = 0.2 degrees C), and 32 slices (RMSE = 0.3 degrees C). Experimental results in ex vivo porcine muscle demonstrate that the RT-TCR approach can reconstruct temperature maps with 192 x 162 x 66 mm 3D volume coverage, 1.5 x 1.5 x 3.0 mm resolution, and 1.2-s scan time with an accuracy of 0.5 degrees C. ConclusionThe RT-TCR algorithm offers an approach to obtaining large coverage 3D temperature maps in real-time for monitoring MR-guided high intensity focused ultrasound treatments. Magn Reson Med 71:1394-1404, 2014. (c) 2013 Wiley Periodicals, Inc.

Diffraction tomography from intensity measurements: an evolutionary stochastic search to invert experimental data

We develop iterative diffraction tomography algorithms, which are similar to the distorted Born algorithms, for inverting scattered intensity data. Within the Born approximation, the unknown scattered field is expressed as a multiplicative perturbation to the incident field. With this, the forward equation becomes stable, which helps us compute nearly oscillation-free solutions that have immediate bearing on the accuracy of the Jacobian computed for use in a deterministic Gauss-Newton (GN) reconstruction. However, since the data are inherently noisy and the sensitivity of measurement to refractive index away from the detectors is poor, we report a derivative-free evolutionary stochastic scheme, providing strictly additive updates in order to bridge the measurement-prediction misfit, to arrive at the refractive index distribution from intensity transport data. The superiority of the stochastic algorithm over the GN scheme for similar settings is demonstrated by the reconstruction of the refractive index profile from simulated and experimentally acquired intensity data. (C) 2014 Optical Society of America

VIRTUAL VIEW SYNTHESIS BY NON-LOCAL MEANS FILTERING USING TEMPORAL DATA

A novel algorithm for Virtual View Synthesis based on Non-Local Means Filtering is presented in this paper. Apart from using the video frames from the nearby cameras and the corresponding per-pixel depth map, this algorithm also makes use of the previously synthesized frame. Simple and efficient, the algorithm can synthesize video at any given virtual viewpoint at a faster rate. In the process, the quality of the synthesized frame is not compromised. Experimental results prove the above mentioned claim. The subjective and objective quality of the synthesized frames are comparable to the existing algorithms.

Evidence for 3D isotropic long range spin-spin interaction near the ferromagnetic transition in bulk and thin film SrRuO3

In the case of metallic ferromagnets there has always been a controversy, i.e. whether the magnetic interaction is itinerant or localized. For example SrRuO3 is known to be an itinerant ferromagnet where the spin-spin interaction is expected to be mean field in nature. However, it is reported to behave like Ising, Heisenberg or mean field by different groups. Despite several theoretical and experimental studies and the importance of strongly correlated systems, the experimental conclusion regarding the type of spin-spin interaction in SrRuO3 is lacking. To resolve this issue, we have investigated the critical behaviour in the vicinity of the paramagnetic-ferromagnetic phase transition using various techniques on polycrystalline as well as (001) oriented SrRuO3 films. Our analysis reveals that the application of a scaling law in the field-cooled magnetization data extracts the value of the critical exponent only when it is measured at H -> 0. To substantiate the actual nature without any ambiguity, the critical behavior is studied across the phase transition using the modified Arrott plot, Kouvel-Fisher plot and M-H isotherms. The critical analysis yields self-consistent beta, gamma and delta values and the spin interaction follows the long-range mean field model. Further the directional dependence of the critical exponent is studied in thin films and it reveals the isotropic nature. It is elucidated that the different experimental protocols followed by different groups are the reason for the ambiguity in determining the critical exponents in SrRuO3.

Evidence for 3D isotropic long range spin-spin interaction near the ferromagnetic transition in bulk and thin film SrRuO3

In the case of metallic ferromagnets there has always been a controversy, i.e. whether the magnetic interaction is itinerant or localized. For example SrRuO3 is known to be an itinerant ferromagnet where the spin-spin interaction is expected to be mean field in nature. However, it is reported to behave like Ising, Heisenberg or mean field by different groups. Despite several theoretical and experimental studies and the importance of strongly correlated systems, the experimental conclusion regarding the type of spin-spin interaction in SrRuO3 is lacking. To resolve this issue, we have investigated the critical behaviour in the vicinity of the paramagnetic-ferromagnetic phase transition using various techniques on polycrystalline as well as (001) oriented SrRuO3 films. Our analysis reveals that the application of a scaling law in the field-cooled magnetization data extracts the value of the critical exponent only when it is measured at H -> 0. To substantiate the actual nature without any ambiguity, the critical behavior is studied across the phase transition using the modified Arrott plot, Kouvel-Fisher plot and M-H isotherms. The critical analysis yields self-consistent beta, gamma and delta values and the spin interaction follows the long-range mean field model. Further the directional dependence of the critical exponent is studied in thin films and it reveals the isotropic nature. It is elucidated that the different experimental protocols followed by different groups are the reason for the ambiguity in determining the critical exponents in SrRuO3.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.

Robust modelling of noisy ARMA signals

In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.

Curvature and wrinkling of premixed flame Kernels comparison between planar laser induced fluorescence (PLIF) and 3D direct numerical simulations (DNS)

A direct comparison between time resolved PLIF measurements of OH and two dimensional slices from a full three dimensional DNS data set of turbulent premixed flame kernels in lean methane/air mixture was presented. The local flame structure and the degree of flame wrinkling were examined in response to differing turbulence intensities and turbulent Reynolds numbers. Simulations were performed using the SEGA DNS code, which is based on the solution of the compressible Navier Stokes, species, and energy equations for a lean hydrocarbon mixture. For the OH PLIF measurements, a cluster of four Nd:YAG laser was fired sequentially at high repetition rates and used to pump a dye laser. The frequency doubled laser beam was formed into a sheet of 40 mm height using a cylindrical telescope. The combination of PLIF and DNS has been demonstrated as a powerful tool for flame analysis. This research will form the basis for the development of sub-grid-scale (SGS) models for LES of lean-premixed combustion systems such as gas turbines. This is an abstract of a paper presented at the 30th International Symposium on Combustion (Chicago, IL 7/25-30/2004).

Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.