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It has been shown extensively, that chemometric investigations of 1H NMR spectra of rat urine taken from animals dosed with model toxins produce characteristic patterns of metabolic responses and that this permits the identification of biomarkers of toxic response and regeneration. To date, metabonomic methods have been mainly optimised for urine which contains mainly low molecular weight moieties, and thus a conventional 1-dimensional 1H NMR pulse sequence is an efficient means of obtaining information-rich data. In the case of biofluids such as blood plasma or serum, which contain a wide range of macromolecules the resonances of which can overlap with peaks from small molecule metabolites, the information giving rise to sample classification can be concealed in a conventional NMR spectrum andthis presents a different analytical challenge in terms of chemometric analysis of spectral profiles. Here, the use of other types of NMR data have been investigated and it is shown that by using spectra where the peak intensities are edited according to their molecular diffusion coefficients, it is possible to improve differentiation of control animals and those treated with the model hepatotoxin, alpha-naphthylisothiocyanate (ANIT). By using diffusion-edited spectroscopy, plasma lipid moieties are less attenuated than those from small endogenous metabolites and thus the toxin-induced changes to the lipoprotein profiles are more easily detectable.  相似文献   
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It is well known that, for stepsize sufficiently small, compactattractors of ordinary differential equations persist underdiscretization. The present paper describes the structure ofthe discrete-time dynamical system obtained via discretizationon A(Mh)\Mh where Mh is the approximate attractor and A(Mh)is its domain of attraction. The existence of a smooth embeddinginto a continuous-time parallelizable flow is proved. The constructioncan be used to define sections for discretizations and can beinterpreted as a justification of the method of modified equations.  相似文献   
3.
1H nuclear magnetic resonance (NMR)-based metabonomics is a well-established technique used to analyse and interpret complex multiparametric metabolic data, and has a wide number of applications in the development of pharmaceuticals. However, interpretation of biological data can be confounded by extraneous variation in the data such as fluctuations in either experimental conditions or in physiological status. Here we have shown the novel application of a data filtering method, orthogonal signal correction (OSC), to biofluid NMR data to minimise the influence of inter- and intra-spectrometer variation during data acquisition, and also to minimise innate physiological variation. The removal of orthogonal variation exposed features of interest in the NMR data and facilitated interpretation of the derived multivariate models. Furthermore, analysis of the orthogonal variation provided an explanation of the systematic analytical/biological changes responsible for confounding the original NMR data.  相似文献   
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