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Sparse Component Analysis (SCA) is demonstrated for blind extraction of three pure component spectra from only two measured mixed spectra in 13 C and 1 H nuclear magnetic resonance (NMR) spectroscopy. This appears to be the first time to report such results and that is the first novelty of the paper. Presented concept is general and directly applicable to experimental scenarios that possibly would require use of more than two mixtures. However, it is important to emphasize that number of required mixtures is always less than number of components present in these mixtures. The second novelty is formulation of blind NMR spectra decomposition exploiting sparseness of the pure components in the wavelet basis defined by either Morlet or Mexican hat wavelet. This enabled accurate estimation of the concentration matrix and number of pure components by means of data clustering algorithm and pure components § Patent pending under the number PCT/HR2008/000037. 2 spectra by means of linear programming with constraints from both 1 H and 13 C NMR experimental data.The third novelty is capability of proposed method to estimate number of pure components in demanding underdetermined blind source separation (uBSS) scenario. This is in contrast to majority of the BSS algorithms that assume this information to be known in advance. Presented results are important for the NMR spectroscopy-associated data analysis in pharmaceutical industry, medicine diagnostics and natural products research.

Enediyne compounds - new promises in anticancer therapyScientists of all kinds have long been intrigued by the nature, action and potential of natural toxins that possess exceptional antibacterial and anticancer activities. These compounds, named enediynes, are among the most effective chemotherapeutic agents known. Often compared with intelligent weapons, due to the unique structure and sophisticated mechanism by which they destroy double-helical DNA, enediyne antibiotics are nowadays the most promising leaders in the anticancer therapy. Apart from their diversity, enediyne compounds share some structural and functional similarities. One fragment of a structure is responsible for the recognition and transport, another part acts as molecular trigger while the third, reactive enediyne unit, undergoes Bergman cycloaromatization and causes DNA breakage. Members of the enediyne family are already in clinical use to treat various cancers, but more general use is limited by their complex structure, which makes them formidable targets for synthetic chemists. There are three main approaches in the design of new enediyne-related compounds: improvement of enediyne "warheads", increasing the selectivity and control of chemical or photo-induced activation. This paper gives an overview of naturally occurring enediynes, their mode of action and efforts undertaken to design artificial enediyne-related DNA cleaving agents.

Underdetermined blind separation of nonnegative dependent sources consists in decomposing set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixturewise mapping of observed data in high-dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness constrained nonnegative matrix factorization (NMF) therein. Thus, original problem is converted into new one with increased number of mixtures, increased number of dependent sources and higher-order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, that is the case for mass spectra of analytes, sparseness constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on original problem.The method is exemplified on numerical and experimental examples related respectively to extraction of ten dependent components from five mixtures and to extraction of ten dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples.

Owing to the diversity of carbohydrate structures and their significance for the function of many biopolymers, structural analysis of various carbohydrate-related compounds is of great importance. Electrospray ionization tandem mass spectrometry (ESI-MS/MS) was used to establish the fragmentation behaviour of a range of sugar-peptide adducts as model compounds of widespread glycoprotein structures. The compounds used in this study were chosen to provide correlation of distinct fragment ions with specific structural differences, namely position and type of carbohydrate-peptide bond and structure of the sugar moiety. All compounds show N- and C-terminal sequence ions along with losses of up to three water molecules. Fructose-related Amadori compounds exhibit M + 78 modified N-terminal peptide fragment ions. Fragmentation of glucose-peptide esters is characterized by the sugar ring fragmentation. Additionally, under the ESI-MS conditions applied, the esters studied undergo intramolecular reaction giving cyclic sugar-peptide structures that can be traced by the presence of N-terminal peptide M + 42 adducts. Detailed analysis of cyclic fructose-related compound comprising structural features of both studied groups revealed a rich fragmentation pattern derived from amino acid residues and water molecules losses from [M - 2H(2)O + H](+) ion. Also, some interesting differences were found with respect to the nature of carbohydrate moieties.