Diagnostic analysis of experimental artefacts in DOSY NMR data by covariance matrix of the residuals

Multivariate curve resolution (MCR) has been applied to separate pure spectra and pure decay profiles of DOSY NMR data. Given good initial guesses of the pure decay profiles, and combined with the nonlinear least square regression (NLR), MCR can result in good separation of the pure components. Nevertheless, due to the presence of artefacts in experimental data, validation of a MCR model is still necessary. In this paper, the covariance matrix of the residuals (CMR), obtained by postmultiplying the residual matrix with its transpose, is proposed to evaluate the quality of the results of an experimental data set. Plots of the rows of this matrix give a general impression of the covariance in the frequency domain of the residual matrix. Different patterns in the plot indicate possible causes of experimental imperfections. This new criterion can be used as diagnosis in order to improve experimental settings as well as suggest appropriate preprocessing of DOSY NMR data.     

Predicting nucleic acid torsion angle values using artificial neural networks

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles , and from torsion angles , , and for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for , , and are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.     

Effects of including spatial information in clustering multivariate image data

Multivariate image data provide detailed information in variable and image space. Most traditional clustering methods are based on variable information only and ignore spatial information. A method based on both variable and spatial information could improve the results substantially.

In this review, we study the benefits and the pitfalls of including spatial information in chemometric clustering techniques. Spatial information is taken into account in initialization of clustering parameters, during cluster iterations by adjusting the similarity measure or at a post-processing step. We illustrate the effect of taking spatial information into account by a univariate synthetic data set and two real-world multivariate data sets. We show that methods that include neighboring pixel information in the clustering procedure improve the performance accuracy of the clustering in most cases. Homogeneous regions in the image are better recognized and the amount of noise is reduced by these methods.     

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl-experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood-brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease.     

Real-life applications of the MULVADO software package for processing DOSY NMR data

MULVADO is a newly developed software package for DOSY NMR data processing, based on multivariate curve resolution (MCR), one of the principal multivariate methods for processing DOSY data. This paper will evaluate this software package by using real-life data of materials used in the printing industry: two data sets from the same ink sample but of different quality. Also a sample of an organic photoconductor and a toner sample are analysed. Compared with the routine DOSY output from monoexponential fitting, one of the single channel algorithms in the commercial Bruker software, MULVADO provides several advantages. The key advantage of MCR is that it overcomes the fluctuation problem (non-consistent diffusion coefficient of the same component). The combination of non-linear regression (NLR) and MCR can yield more accurate resolution of a complex mixture. In addition, the data pre-processing techniques in MULVADO minimise the negative effects of experimental artefacts on the results of the data. In this paper, the challenges for analysing polymer samples and other more complex samples will also be discussed.     

Applications of pattern recognition

This poster illustrates the lecture on Pattern Recognition and gives recently published and unpublished examples, mainly from the laboratory from the first author. The applications concern:

- the determination of metabolic pathways of branched chain fatty acids (by clustering),

- the development of a genetic classification of meteorites (by clustering),

- the classification of cholinergic agents according to their interaction with different receptors (by clustering),

- the structure of a data set consisting of gaschromatographic profiles in samples collected in pollution monitoring stations (by factor analysis and pattern recognition),

- factors determining GLC behaviour of solutes (by factor analysis and multiple regression),

- the classification of olive oils according to geographic origin (by principal components and pattern recognition),

- the diagnosis of thyroid status (by pattern recognition).

     

Near-infrared imaging spectroscopy (NIRIS) and image rank analysis for remote identification of plastics in mixed waste

An infrared camera with focal plane InSb array detector has been applied to the characterization of macroscopic samples of household waste over distances up to two meters. Per waste sample (singelized), a sequence of images was taken at six optical wavelength ranges in the near infrared region (1100 nm - 2500 nm). The obtained three-dimensional data stack served as individual fingerprint per sample. An abstract factor rotation of this stack of six images into a spectroscopical meaningful intermediate six-element vector by Multivariate Image Rank Analysis (MIRA) finally provided a decision limit for the discrimination of plastics and nonplastics. A correct classification of better than 80% has been reached. The experimental NIRIS set-up has been automated so far to allow an on-line identification of a real world waste sample within a few seconds.     

Molecular challenges in modern chemometrics

Since the very beginning of the discipline, chemometrics has mainly focussed on analytical chemical problems such as calibration. With the growing importance of databases and applications in medicinal and computational chemistry, the domains of analytical chemistry and chemometrics have been enlarged significantly in recent years. Especially the relation between molecular structure and function has become of considerable interest. Despite the huge quantities of data that are available nowadays, it is often difficult to recognise and extract relevant chemical information for the problem at hand. One of the main obstacles is the definition of an appropriate representation of a molecule. Although a variety of different representations are used, none are generally applicable.

This paper focuses on the challenges that arise in the chemometrical analysis of molecular structures, the relation between structure and function and the relation between molecular representation and chemometrical modelling. Exciting opportunities for further research are illustrated using an example concerning the prediction of co-crystallisation behaviour for small organic molecules with cephalosporin antibiotics.     

Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

Background  

Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5–4 Hz, 4–8 Hz, 8–10 Hz, 10–13 Hz, 13–30 Hz and 30–45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks.