Reidar Arneberg

Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles.

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.

Configuration interaction calculations of satellite structure in photoelectron spectra of H2O

Abstract Configuration interaction wavefunctions were computed for the satellite peaks in the core and the valence photoelectron spectra of H 2 O. Relative intensities were computed in the sudden approximation including electron correlation in the neutral ground state. The intensity profile of the O 1s ESCA spectrum is understood in terms of single excitations from the 3a 1 and the 1b 1 orbitals to low lying virtual MOs. Strong correlation effects are observed for the levels in the inner valence region where the satellites derive their intensity both from the 2a 1 and 3a 1 MOs.

Pre-analytical influence on the low molecular weight cerebrospinal fluid proteome

Cerebrospinal fluid (CSF) is a perfect source to search for new biomarkers to improve early diagnosis of neurological diseases. Standardization of pre‐analytical handling of the sample is, however, important to obtain acceptable analytical quality. In the present study, MALDI‐TOF MS was used to examine the influence of pre‐analytical sample procedures on the low molecular weight (MW) CSF proteome. Different storage conditions like temperature and duration or the addition of as little as 0.2 µL blood/mL neat CSF caused significant changes in the mass spectra. The performance of different types of MW cut‐off spin cartridges from different suppliers used to enrich the low MW CSF proteome showed great variance in cut‐off accuracy, stability and reproducibility. The described analytical method achieved a polypeptide discriminating limit of approximately 800 pM, two to three orders of magnitude lower than reported for plasma. Based on this study, we recommend that CSF is centrifuged immediately after sampling, prior to storage at –80ºC without addition of protease inhibitors. Guanidinium hydrochloride is preferred to break protein‐protein interactions. A spin cartridge with cut‐off limit above the intended analytical mass range is recommended. Our study contributes to the important task of developing standardized pre‐analytical protocols for the proteomic study of CSF.

Theoretical and experimental studies of the valence photoelectron spectrum of C2H2

A high resolution ESCA spectrum of C2H2 was recorded using monochromatized AlKα excitation and was analyzed by means of configuration interaction and multiple configuration SCF wave functions. The role of different schemes for electron‐configurational selection in the initial and final states on transition moments and energies was investigated. The spectrum shows a prominent satellite structure in the inner valence region, which is analyzed and discussed in terms of electron correlation, dissociative photoionization, interference, and vibronic coupling effects.

Radiative Electron Rearrangement and Hole-Mixing Effects in Molecular X-Ray Emission

Radiative electron rearrangement (RER) and hole-mixing effects in molecular ultra-soft X-ray spectra (USX) are theoretically analyzed at several levels of approximation and compared with X-ray photoelectron (XPS) transitions which result in the same residual ionic states. Numerical applications are carried out for the CO molecule with configuration interaction wavefunctions built on orbitals separately optimized for initial and final states. The USX spectra are more complex than the XPS spectrum in the energy ranges corresponding to RER and break-down of the MO-model due to interference from excited components of the initial state wavefunctions and due to non-orthogonality. This breaks the branching ratio rule that predicts equal satellite to parent ionic intensities for the XPS and the different core-sited USX spectra and does accordingly also limit the applicability of local selection rules for the hole-mixing in the USX spectrum.

A Multivariate Approach To Reveal Biomarker Signatures for Disease Classification: Application to Mass Spectral Profiles of Cerebrospinal Fluid from Patients with Multiple Sclerosis

Mass spectral profiles from cerebrospinal fluid (CSF) are used as input to a novel multivariate approach to select features responsible for the separation of patients with multiple sclerosis (MS) from control groups. Our targeted statistical approach makes it possible to systematically remove features in the spectral fingerprints masking the components expressing the disease pattern. The low molecular weight CSF proteome from 54 patients with MS and a range of other neurological diseases (OND), as well as neurological healthy controls (NHC), is analyzed in replicates using mass spectral profiling. Statistically validated partial least-squares discriminant analysis (PLS-DA) models are created as a first step to separate the groups. Using the group membership as a target, the most discriminatory projection in the multivariate space spanned by the spectral profiles is revealed. From the resulting target-projected component, the spectral regions most significantly contributing to group separation are identified using the nonparametric discriminating variable (DIVA) test together with the so-called selectivity ratio (SR) plot. Our approach is general and can be applied for other diseases and instrumental techniques as well.

Multiple excitations in the core photoelectron spectrum of acetylene

Abstract A high-resolution core photoelectron spectrum of free acetylene is presented. Configuration interaction and complete active space SCF calculations were carried out for assignment and prediction of energies and intensities in the spectrum. The shake-up structure is found to be strongly dominated by intervalence π g to π g transitions for which multiple 2-, 3- and 4-fold excitations are important.

Multielectron transitions in the K-shell electron energy loss spectrum of N2

Abstract Ab initio investigation on the role of multielectron transitions in the discrete and near continuum parts of the K-shell electron energy loss spectrum of N 2 are reported. An explanation for observed fine structure in the continuum is suggested.

Determination of optimum number of components in partial least squares regression from distributions of the root-mean-squared error obtained by Monte Carlo resampling: Determination of optimum number of components in PLS regression

Monte Carlo resampling is utilized to determine the number of components in partial least squares (PLS) regression. The data are randomly and repeatedly divided into calibration and validation samples. For each repetition, the root‐mean‐squared error (RMSE) is determined for the validation samples for a = 1, 2, … , A PLS components to provide a distribution of RMSE values for each number of PLS components. These distributions are used to determine the median RMSE for each number of PLS components. The component (Amin) having the lowest median RMSE is located. The fraction p of the RMSE values of Amin exceeding the median RMSE for the preceding component is determined. This fraction p represents a probability measure that can be used to decide if the RMSE for the Amin PLS component is significantly lower than the RMSE for the preceding component for a preselected threshold (pupper). If so, it defines the optimum number of PLS components. If not, the process is repeated for the previous components until significance is achieved. The pupper = 0.5 implies that the median is used for selecting the optimum number of components. The RMSE is approximately normally distributed on the smallest components. This can be utilized to relate p to a fraction of a standard deviation. For instance, p = 0.308 corresponds to half a standard deviation if RMSE is normally distributed.

Investigation of serum protein profiles in scrapie infected sheep by means of SELDI-TOF-MS and multivariate data analysis.

BackgroundClassical scrapie in sheep is a fatal neurodegenerative disease associated with the conversion PrPC to PrPSc. Much is known about genetic susceptibility, uptake and dissemination of PrPSc in the body, but many aspects of prion diseases are still unknown. Different proteomic techniques have been used during the last decade to investigate differences in protein profiles between affected animals and healthy controls. We have investigated the protein profiles in serum of sheep with scrapie and healthy controls by SELDI-TOF-MS and LC-MS/MS. Latent Variable methods such as Principal Component Analysis, Partial Least Squares-Discriminant Analysis and Target Projection methods were used to describe the MS data.ResultsThe serum proteomic profiles showed variable differences between the groups both throughout the incubation period and at the clinical end stage of scrapie. At the end stage, the target projection model separated the two groups with a sensitivity of 97.8%, and serum amyloid A was identified as one of the protein peaks that differed significantly between the groups.ConclusionsAt the clinical end stage of classical scrapie, ten SELDI peaks significantly discriminated the scrapie group from the healthy controls. During the non-clinical incubation period, individual SELDI peaks were differently expressed between the groups at different time points. Investigations of differences in -omic profiles can contribute to new insights into the underlying disease processes and pathways, and advance our understanding of prion diseases, but comparison and validation across laboratories is difficult and challenging.