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ABSTRACT This work investigates the impact of marine omega‐3 and physical activity and their interaction on cardiometabolic health as expressed by the serum lipoprotein profile. Using an experimental design that allows for the possibility of interaction, we performed a 6‐week intervention on 44 middle‐aged women living in Western Norway. The women were randomly divided into four groups: one control group with no intervention, a second group performing sessions of moderate intensity three times per week, a third group taking daily supplements of omega‐3 marine fatty acids, and a fourth group combining the interventions for Groups 2 and 3. The difference in the lipoprotein profiles after the intervention from baseline were assessed for statistical significance by comparing groups 2, 3 and 4 with Group 1 using two‐tailed t‐test corrected for multiple testing and selectivity ratios calculated from the discriminatory component in validated partial least squares discriminant models. The results from the univariate and multivariate analyses were qualitatively equivalent: Only the women combining moderate physical activity and omega‐3 supplementation, revealed statistically significant differences in their lipoprotein profile compared to the nonintervention control group. The pattern of change in the lipoprotein profile is associated with improved cardiometabolic health. Use of the design matrix to predict this pattern revealed that the interaction between omega‐3 supplementation and physical activity played a major role in inducing this change. The recognition of the influence of this interaction may be a step towards resolving the long‐lasting debate of the role played by omega‐3 for preventing cardiovascular unhealth. [ABSTRACT FROM AUTHOR]

With linear dependency between the explanatory variables, partial least squares (PLS) regression is commonly used for regression analysis. If the response variable correlates to a high degree with the explanatory variables, a model with excellent predictive ability can usually be obtained. Ranking of variable importance is commonly used to interpret the model and sometimes this interpretation guides further experimentation. For instance, when analyzing natural product extracts for bioactivity, an underlying assumption is that the highest ranked compounds represent the best candidates for isolation and further testing. A problem with this approach is that in most cases, the number of compounds is larger than the number of samples (and usually much larger) and that the concentrations of the compounds correlate. Furthermore, compounds may interact as synergists or as antagonists. If the modeling process does not account for this possibility, the interpretation can be thoroughly wrong because unmodeled variables that strongly influence the response will give rise to confounding of a first‐order PLS model and send the experimenter on a wrong track. We show the consequences of this by a practical example from natural product research. Furthermore, we show that by including the possibility of interactions between explanatory variables, visualization using a selectivity ratio plot may provide model interpretation that can be used to make inferences. [ABSTRACT FROM AUTHOR]

Using whole chromatographic profiles and measurements of total bioactivity as input, a quantitative pattern--activity relationship (QPAR) approach is proposed as a general method for providing two pieces of crucial information about complex bioactive mixtures available: (i) a model for predicting total bioactivity from the chromatographic fingerprint and (ii) the features in the chromatographic profile responsible for the bioactivity. While the first piece of information is already available through existing approaches, the second one results from our ability to remove dominant features in the chromatographic fingerprints which mask the components specifically related to pharmacological activity. Our targeted approach makes information about bioactivity available at the molecular level and provides possibilities for assessment of herbal medicine (HM) possible beyond just authentication and total bioactivity. As an example, the antioxidant property of the HM Radix Puerariae lobatae is measured through its reducing power toward a ferric ion complex. A partial least-squares (PLS) model is created to predict the antioxidant activity from the chromatographic fingerprint. Using the antioxidant activity as a target, the most discriminatory projection in the multivariate space spanned by the chromatographic profiles is revealed. From this target-projected component, the chromatographic regions most strongly connected to antioxidant activity are identified using the so-called selectivity ratio (SR) plot. The results are validated by prediction of samples not included in the modeling step.

The discriminating variable (DIVA) test and the selectivity ratio (SR) plot are developed as quantitative tools for revealing the variables in spectral or chromatographic profiles discriminating best between two groups of samples. The SR plot is visually similar to a spectrum or a chromatogram, but with the most intense regions corresponding to the most discriminating variables. Thus, the variables with highest SR represent the variables most important for interpretation of differences between groups. Regions with variables that are positively or negatively correlated to each other are displayed as corresponding negative and positive regions in the SR plot. The nonparametric DIVA test is designed for connecting SR to discriminatory ability of a variable quantified as probability for correct classification. A mean probability for a certain SR range is calculated as the mean correct classification rate (MCCR) for all variables in the same SR interval. The MCCR is thus similar to a mean sensitivity in each SR interval. In addition to the ranking of all variables according to their discriminatory ability provided by the SR plot, the DIVA test connects a probability measure to each SR interval. Thus, the DIVA test makes it possible to objectively define thresholds corresponding to mean probability levels in the SR plot and provides a quantitative means to select discriminating variables. In order to validate the approach, samples of untreated cerebrospinal fluid (CSF) and samples spiked with a multicomponent peptide standard were analyzed by matrix-assisted laser desorption ionization (MALDI) mass spectrometry. The differences in the multivariate spectral profiles of the two groups were revealed using partial least-squares discriminant analysis (PLS-DA) followed by target projection (TP). The most discriminating mass-to-charge (m/z) regions were revealed by calculating the ratio of explained to unexplained variance for each m/z number on the target-projected component and displaying this measure in SR plots with quantitative boundaries determined from the DIVA test. The results are compared to some established methods for variable selection.

Mass spectral profiles are influenced by several factors that have no relation to compositional differences between samples: baseline effects, shifts in mass-to-charge ratio (m/z) (synchronization/alignment problem), structured noise (heteroscedasticity), and, differences in signal intensifies (normalization problem). Different procedures for pretreatment of whole mass spectral profiles described by almost 50 000 m/z values are investigated in order to find optimal approaches with respect to revealing the information content in the data. In order to quantitatively assess the impact of different procedures for pretreatment of mass spectral profiles, we use factorial designs with the ratio between intergroup and intragroup (replicate) variance as response. We have examined the influence of smoothing, binning, alignment/synchronization, noise pattern, and normalization on data interpretation. Our analysis shows that the spectral profiles have to be corrected for heteroscedastic noise prior to normalization. An nth root transform, where n is a small, positive integer, is used to create a homoscedastic noise structure without destroying the linear correlation structures describing individual components when using whole mass spectral profiles. The choice of n is decided by a simple graphic procedure using replicate information. Log transform is shown to change the heteroscedastic noise structure from being dominant in high-intensity regions, to produce the largest noise in the low-intensity regions. In addition, log transform has a negative effect on the collinearity in the profiles. Factorial designs reveal strong interactions between several of the pretreatment steps, e.g., noise structure and normalization. This underlines the limited usability of looking at the different pretreatment steps in isolation. Binning turns out to be able to substitute smoothing of spectra by, for example, moving average or Savitsky-Golay, while, at the same time, reducing the data point description of the profiles by 1 order of magnitude. Thus, if the sampling density is high, binning seems to be an attractive option for data reduction without the risk of losing information accompanying the integration of profiles into peaks. In the absence of smoothing, binning should be executed prior to alignment. If binning is not performed, the order of pretreatment should be smoothing, alignment, nth root transform, and normalization.

Biofluids, like urine, form very complex matrixes containing a large number of potential biomarkers, that is, changes of endogenous metabolites in response to xenobiotic exposure. This paper describes a fast and sensitive method of screening biomarkers in rat urine. Biomarkers for phospholipidosis, induced by an antidepressant drug, were studied. Urine samples from rats exposed to citalopram were analyzed using solid-phase extraction (SPE) and liquid chromatography mass spectrometry (LC/MS) analysis detecting negative ions. A fast iterative method, called Gentle, was used for the automatic curve resolution, and metabolic fingerprints were obtained. After peak alignment principal component analysis (PCA) was performed for pattern recognition, PCA loadings were studied as a means of discovering potential biomarkers. In this study a number of potential biomarkers of phospholipidosis in rats are discussed. They are reported by their retention time and base peak, as their identification is not within the scope of the study. In addition to the fact that it was possible to differentiate control samples from dosed samples, the data were very easy to interpret, and signals from xenobiotic-related substances were easily removed without affecting the endogenous compounds. The proposed method is a complement or an alternative to NMR for metabolomic applications.

Analytical profiles are commonly normalized to the most intense peak or to constant sum prior to library searches or multivariate analysis. This work examines normalization procedures from a theoretical point of view and their effects on simulated and real data. It is found that while normalization of data with homoscedastic noise to constant sum only introduces the well-known bias toward negative peak correlations, normalization in the presence of heteroscedastic noise has a profound impact on the correlation structure. It follows that heteroscedastic noise in analytical signals should be transformed to homoscedasticity prior to the use of any normalization procedure. For cases where the standard deviation of the noise is proportional to the peak intensity, the log-transform followed by row centering is shown to be an effective cure against the spurious correlations induced by constant-sum normalization. For the case of heteroscedastic noise where the relative standard deviation of the noise is decreasing with increasing peak intensity, a modified Box-Cox power transform is developed for removing heteroscedasticity in data prior to normalization to constant sum. The power transform has several attractive features: (i) it can be tailored to cure different patterns of heteroscedastic noise, (ii) it enhances the information in small peaks compared to large ones, and, (iii) it conserves strong linear correlations between signals.

A resolution parameter to be used for multidetection chromatography is derived. Unlike traditional resolution parameters, the parameter developed in this work can be obtained prior to curve resolution. A rank map providing the number of analytes in every local region of the profile is first estimated. The overall resolution is defined as the ratio between single-analyte regions and regions with one or more analytes. A resolution parameter for each analyte separately is obtained by the same kind of approach. The performance of the resolution parameter for suggesting successful/failed resolution, using alternating regression (AR), is assessed on simulated GC-IR and LC-DAD profiles. The influence of noise, number and relative concentration of analytes, and spectral and chromatographic overlap on the resolution parameter is determined by means of orthogonal design and partial least-squares regression.

The heuristic evolving latent projections (HELP) method resolves four two-component mixtures of drug isomers analyzed by high performance liquid chromatography with diode array detection. HELP is ableto differentiate even the very close spectra of the isomers, resolving them into individual spectra and concentration profiles, including the minor components present in 2% and 10% concentrations. The two reference methods usedfail to resolve the 2% minor component. HELP's relative effectiveness is attributable to its local latent-variable analysis of the zero-component chromatographic region, its visual-numeric approach and its datascope features.

The basic concepts and theoretical foundation of heuristic evolving latent projections (HELP) are introduced. HELP is a new way of resolving complex data from hyphenated analytical methods into individual chromatographic and spectral data. It uses a combined visual and numerical approach for selecting, exposing and analyzing regions in two-way multielement data profiles. Latent-projective graphs and local rank analysis show selective regions in chromatographic and spectral profiles. The ability to select the proper data enables their unique resolution.

Metabonomics is an omics approach to identify and monitor metabolic characteristics, changes, and phenotypes with respect to various synergetic factors such as environment, life style, diet, and potential pathophysiological processes. [...]

A high resolution x-ray emission spectrum of water vapor has been recorded, using a 7 keV electron beam and a 10 m grazing incidence spectrometer. The spectrum is interpreted in terms of dipole transition moments between CI wave functions built from separately optimized molecular orbitals. The principal features of the spectrum are reproduced considering transitions in the singly and doubly ionized species, giving rise to main lines and Wentzel–Druyvesteyn satellites, respectively. [ABSTRACT FROM AUTHOR]

A study of self-association of alcohols in carbon tetrachloride is investigated. Multivariate curve resolution in the near-infrared region is used for this purpose.