Background and the purpose of the study: Affinity-based TARGET DECONVOLUTION is an emerging method for the identification of interactions between drugs/drug candidates and cellular proteins, and helps to predict potential activities and side effects of a given compound. In the present study, we hypothesized that a part of safranal pharmacological effects, one of the major constituent of Crocus sativus L., relies on its physical interaction with TARGET proteins.Methods: Affinity chromatography solid support was prepared by covalent attachment of safranal to agarose beads.After passing tissue lysate through the column, safranal-bound proteins were isolated and separated on SDS-PAGE or two-dimensional gel electrophoresis. Proteins were identified using MALDI-TOF/TOF mass spectrometry and Mascot software.Results and major conclusion: Data showed that safranal physically binds to beta actin, cytochrome b-c1 complex sub-unit 1, trifunctional enzyme sub-unit beta and ATP synthase sub-unit alpha and beta. These interactions may explain part of safranal’s pharmacological effects. However, phenotypic and/or biological relevance of these interactions remains to be elucidated by future pharmacological studies.

Background: Traditional drug discovery approaches are mainly relied on the observed phenotypic changes following administration of a plant extract, drug candidate or natural product. Recently, TARGET-based approaches are becoming more popular. The present study aimed to identify the cellular TARGETs of crocin, the bioactive dietary carotenoid present in saffron, using an affinity-based method.Methods: Heart, kidney and brain tissues of BALB/c mice were homogenized and extracted for the experiments.TARGET DECONVOLUTION was carried out by first passing cell lysate through an affinity column prepared by covalently attaching crocin to agarose beads. Isolated proteins were separated on a 2D gel, trypsinized in situ and identified by MALDI-TOF/TOF mass spectrometry. MASCOT search engine was used to analyze Mass Data.Results: Part of proteome that physically interacts with crocin was found to consist of beta-actin-like protein 2, cytochrome b-c1 complex subunit 1, ATP synthase subunit beta, tubulin beta-3 chain, tubulin beta-6 chain, 14-3-3 protein beta/alpha, V-type proton ATPase catalytic subunitA, 60 kDa heat shock protein, creatine kinase b-type, peroxiredoxin-2, cytochrome b-c1 complex subunit 2, acetyl-coA acetyltransferase, cytochrome c1, proteasome subunit alpha type-6 and proteasome subunit alpha type-4.Conclusion: The present findings revealed that crocin physically binds to a wide range of cellular proteins such as structural proteins, membrane transporters, and enzymes involved in ATP and redox homeostasis and signal transduction.

In order to increase the signal/noise ratio, one of the important challenges in seismic data processing is suppression of random noises. In this paper f-x DECONVOLUTION or DECONVOLUTION in frequency-space domain is employed for reduction of random noises from seismic section. This is based on data transformation from time-space domain to frequency-space domain. Seismic events are correlatable along x-direction from trace to trace but random noises are not. In fact f-x DECONVOLUTION is able to predict coherent events from trace to trace in space direction. In this paper a computer code for f-x DECONVOLUTION has been written. This program applied on shot records and zero offset section with different levels of random noises. Consequently, the ability of f-x DECONVOLUTION in reduction of random noises has been proved.

DECONVOLUTION is considered as a successful tool in seismic exploration for increasing the temporal resolution of the data. Gabor DECONVOLUTION is proposed to treat the non-stationarity issue of the problem by breaking it into several stationary sub-problems via a Gaussian window, solving them independently, and then, recombining/projecting the sub-solutions into an approximate solution to the original nonstationary problem. The projected Gabor DECONVOLUTION has recently been proposed by the second author as an improvement over Gabor DECONVOLUTION. In the projected Gabor DECONVOLUTION, the sub-problems are projected to a unified problem in time domain, and then, the resulting problem is solved. This modification brings useful advantages over the Gabor DECONVOLUTION including an improved convergence property, more efficiency for sparse DECONVOLUTION, more flexibility for incorporating prior information in the presence of noise, and more reflectivity structure via a least-squares method. In this paper, we propose a method for sparse and non-sparse DECONVOLUTION of non-stationary seismic signals in the presence of Gaussian and spike-like random noises. Numerical tests using simulated and field data are presented to show high performance of the proposed method for generating accurate and stable reflectivity models from nonstationary seismograms....

Due to the spherical divergence and specifically absorption in the earth, amplitude of a propagating seism wave varies as a function of time. This stretches the wavelet in time and reduces the time (or vertical) resolution of the seismic sections. To overcome the problem one has to apply so called spatial migration or DECONVOLUTION on data. Usually the least square Wiener DECONVOLUTION is used to boost up the attenuated frequency components. Unfortunately, the Wiener based DECONVOLUTION methods assume that the source generated seismic wavelet is stationary (i.e. its frequency content remains unchanged within the record). A method of DECONVOLUTION in the Gabor domain is applied in this paper that considers the seismic data as a non-stationary phenomenon.The Gabor transform (Equation 1) is a windowed or short time Fourier transform, where the window used to isolate the frequency content of input record in time, is a Gaussian type. According to the uncertainty principle, the Gabor transform has the least uncertainty among other windowed Fourier transforms.Please clik on PDF icon to viwe the complet abstract.

A genetic based algorithm for DECONVOLUTION of Printed Circuit Board (PCB) thermal images is presented.The DECONVOLUTION of thermal images is modeled as an optimization problem, whose cost function is to be minimized based on mechanics of natural selection and genetics. The proposed algorithm can be configured with all available apriori information to speedup the solution computation. The paper presents the results for DECONVOLUTION using the proposed genetic algorithm and its utility in PCB infrared thermal testing.

We introduce a new concept coined "Semi blind DECONVOLUTION" and present an algorithm to solve the problems that can be categorized as such. In fact, the problem of estimating the input of an unknown nonminimum phase FIR system using only noisy observed output and an initial model of the original input signal is considered and called semi blind DECONVOLUTION in this paper. Here, unlike conventional blind DECONVOLUTION where some assumptions on the statistical properties of the white source signal are needed to be made, an initial estimation of the original input, to be identified based on some prior knowledge, is whitened and used instead of the usual i.i.d input. We, first, justify the basis of our proposed algorithm then, the algorithm is further developed by using an initial model, as the first estimation of the input signal. Furthermore, a constrained optimization is used to estimate the DECONVOLUTION filter to satisfy more than just one criterion. As an application we apply our proposed semi blind DECONVOLUTION algorithm to estimate the glottal flow excitation of vowels. The voiced speech signal is modeled as an ARMA process whose input is the glottal flow with: 1- an AR filter whose coefficients are obtained using the closed phase-LPC method on the actual speech and 2- an MA filter whose input is the glottal excitation and its output is the LPC residual. It is thus clear that both the input signal and the MA filter coefficients are unknown whilst a physiological model exists for the input. Therefore, we are dealing in fact with a semi blind DECONVOLUTION problem when trying to identify simultaneously the glottal flow and the MA part of the ARMA model of the vocal tract. The efficiency of the algorithm is assessed on real voiced speech sounds /a/ and /e/ as practical case examples.

Classical image DECONVOLUTION seeks an estimate of the true image when the blur kernel or the point spread function (PSF) of the blurring system is known a priori. However, blind image DECONVOLUTION addresses the much more complicated, but realistic problem where the PSF is unknown. Bayesian inference approach with appropriate priors on the image and the blur has been used successfully to solve this blind problem, in particular with a Gaussian prior and a joint maximum a posteriori (JMAP) estimation. However, this technique is unstable and suffers from significant ringing artifacts in various applications. To overcome these limitations, we propose a regularized version using $H^1$ regularization terms on both the sharp image and the blur kernel. We present also useful techniques for estimating the smoothing parameters.  We were able to derive an efficient algorithm that produces high quality deblurred results compared to some well-known methods in the literature.