EPR study of the production of OH radicals in aqueous solutions of uranium irradiated by ultraviolet light

The aim of the study was to establish whether hydroxyl radicals (•OH) were produced in UV-irradiated aqueous solutions of uranyl salts. The production of •OH was studied in uranyl acetate and nitrate solutions by an EPR spin trap method over a wide pH range, with variation of the uranium concentrations. The production of •OH in uranyl solutions irradiated with UV was unequivocally demonstrated for the first time using the EPR spin-trapping method. The production of •OH can be connected to speciation of uranium species in aqueous solutions, showing a complex dependence on the solution pH. When compared with the results of radiative de-excitation of excited uranyl (*UO22+) by the quenching of its fluorescence, the present results indicate that the generation of hydroxyl radicals plays a major role in the fluorescence decay of *UO22+. The role of the presence of carbonates and counter ions pertinent to environmental conditions in biological systems on the production of hydroxyl radicals was also assessed in an attempt to reveal the mechanism of *UO22+ de-excitation. Various mechanisms, including •OH production, are inferred but the main point is that the generation of •OH in uranium containing solutions must be considered when assessing uranium toxicity

On a Gradient-Based Algorithm for Sparse Signal Reconstruction in the Signal/Measurements Domain

Sparse signals can be recovered from a reduced set of samples by using compressive sensing algorithms. In common compressive sensing methods the signal is recovered in the sparsity domain. A method for the reconstruction of sparse signals which reconstructs the missing/unavailable samples/measurements is recently proposed. This method can be efficiently used in signal processing applications where a complete set of signal samples exists. The missing samples are considered as the minimization variables, while the available samples are fixed. Reconstruction of the unavailable signal samples/measurements is preformed using a gradient-based algorithm in the time domain, with an adaptive step. Performance of this algorithm with respect to the step-size and convergence are analyzed and a criterion for the step-size adaptation is proposed in this paper. The step adaptation is based on the gradient direction angles. Illustrative examples and statistical study are presented. Computational efficiency of this algorithm is compared with other two commonly used gradient algorithms that reconstruct signal in the sparsity domain. Uniqueness of the recovered signal is checked using a recently introduced theorem. The algorithm application to the reconstruction of highly corrupted images is presented as well

Sparse analyzer tool for biomedical signals

IF/00325/2015 PCIF/SSI/0102/2017 UIDB/04111/2020The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.publishersversionpublishe

Chocolate – A Bittersweet Antioxidant

We studied the positive health effect of different chocolates (antioxidative effects on stable free radicals, reactive oxygen species (ROS) and for prevention of lipid peroxidation). The results show that all chocolates successfully remove •OH radicals, but only chocolates with high cocoa content are also effective for of •O2 -. The capabilities for chocolate samples to reduce organic radicals are shown to be positive for hydrophobic DPPH which was not the case for hydrophilic Tempone. Only the chocolate samples with high cocoa content were shown to prevent lipid peroxidation induced by Fenton reaction. Obtained results showed that chocolates have diverse antioxidative effects which are not only dependant on the content of cocoa. The other chocolate constituents like: sugar, polyphenols, cocoa butter, emulsifier and other substances should also be considered for determining the positive health effect of chocolates

On the Uniqueness of the Sparse Signals Reconstruction Based on the Missing Samples Variation Analysis

An approach to sparse signals reconstruction considering its missing measurements/samples as variables is recently proposed. Number and positions of missing samples determine the uniqueness of the solution. It has been assumed that analyzed signals are sparse in the discrete Fourier transform (DFT) domain. A theorem for simple uniqueness check is proposed. Two forms of the theorem are presented, for an arbitrary sparse signal and for an already reconstructed signal. The results are demonstrated on illustrative and statistical examples

Decomposition and analysis of signals sparse in the dual polynomial Fourier transform

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Local Smoothness of Graph Signals

Analysis of vertex-varying spectral content of signals on graphs challenges the assumption of vertex invariance and requires the introduction of vertex-frequency representations as a new tool for graph signal analysis. Local smoothness, an important parameter of vertex-varying graph signals, is introduced and defined in this paper. Basic properties of this parameter are given. By using the local smoothness, an ideal vertex-frequency distribution is introduced. The local smoothness estimation is performed based on several forms of the vertex-frequency distributions, including the graph spectrogram, the graph Rihaczek distribution, and a vertex-frequency distribution with reduced interferences. The presented theory is illustrated through numerical examples

On the reconstruction of nonsparse time-frequency signals with sparsity constraint from a reduced set of samples

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