Novel sparse component analysis approach to free radical EPR spectra decomposition

1,2,4-Triazolato paddlewheel dibismuth complexes with very short Bi(ii)–Bi(ii) bonds: bismuth(iii) oxidation of 1,2,4-triazolato anions into neutral N-1,2,4-triazolyl radicals

Bismuth(iii) oxidation of 1,2,4-triazolato anions allowed paddlewheel 1,2,4-triazolato dibismuth complexes to be isolated and the reaction involved the neutral N-1,2,4-triazolyl radicals that were evidenced by EPR analysis.

Noniterative convex optimization methods for network component analysis

This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.

Blind separation of electron paramagnetic resonance signals using diversity minimization

This paper presents a method for the blind separation of Electron Paramagnetic Resonance (EPR) spectroscopy signals that can aid in the detection of free radicals in living organisms. Observed EPR signals are often mixtures of source signals that are approximately "sparse", with a small number of narrow segments of the signal much larger than the remaining parts. We develop a method to separate the sources through minimizing a p-norm-like diversity measure under some mild assumptions which are generally valid for EPR signals. Simulations demonstrate that the proposed method performs well on EPR signal separation, with better robustness to noise compared to other techniques.

Multi-component analysis: blind extraction of pure components mass spectra using sparse component analysis

The paper presents sparse component analysis (SCA)-based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on l(1) norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds.

Sparsity enhancement for blind deconvolution of ultrasonic signals in nondestructive testing application

The received signal in ultrasonic pulse-echo inspection can be modeled as a convolution between an impulse response and the reflection sequence, which is the impulse characteristic of the inspected object. Deconvolution aims at approximately inverting this process to improve the time resolution so that the overlap between echoes from closely spaced reflectors becomes small. This paper presents a modified minimum entropy blind deconvolution algorithm for deconvolving ultrasonic signals. Enhancement of the resolution is achieved by using the presented method. In addition, the presented approach will, in many cases, lead to a faster computation. A nonlinear function is the key point to the efficiency of the modified blind deconvolution algorithm, which is used to increase the sparsity of the iteration output and to decrease the influence of the added noise by replacing each iteration output by output of the nonlinear function. Simulations showed the efficiency of the modification as compared with minimum entropy deconvolution when deconvolving synthetic ultrasonic signals. Experimental results using real ultrasonic data evaluated further that the exact solution consistently yields good performance. The thickness of a thin steel sample can be calculated by the modified blind deconvolution filter with a reasonable accuracy.