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

Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

Contains fulltext : 93904.pdf (publisher's version ) (Open Access

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Contains fulltext : 91843.pdf (publisher's version ) (Open Access)12 p

Thermodynamics and NMR studies on Duck,

Hepatitis B virus (HBV) replication is initiated by binding of its reverse transcriptase (P) to the apical stem-loop (AL) and primer loop (PL) of epsilon, a highly conserved RNA element at the 50-end of the RNA pregenome. Mutation studies on duck/heron and human in vitro systems have shown similarities but also differences between their P–epsilon interaction. Here, NMR and UV thermodynamic data on AL (and PL) from these three species are presented. The stabilities of the duck and heron ALs were found to be similar, and much lower than that of human. NMR data show that this low stability stems from an 11-nt internal bulge destabilizing the stem of heron AL. In duck, although structured at low temperature, this region also forms a weak point as its imino resonances broaden to disappearance between 30 and 358C well below the overall AL melting temperature. Surprisingly, the duck- and heron ALs were both found to be capped by a stable well-structured UGUU tetraloop. All avian ALs are expected to adhere to this because of their conserved sequence. Duck PL is stable and structured and, in view of sequence similarities, the same is expected for heron- and human PL

Summary of σ parameter for rbf kernel function.

<p>Summary of σ parameter for rbf kernel function.</p

Representations of the a) kernel mapping of data matrix X into kernel space; b) pseudo samples principle in K-PLS-DA.

<p>k indicates the range of pseudo sample values (uniformly distributed); *Note that there are “p” pseudo sample matrixes and “p” kernel pseudo samples matrixes. **The ŷ-values can be projected into latent variable space. ^#Note that for kernel pseudo samples the loading and <b>b</b> vector of K-PLS-DA model are used. ***These ŷ-values can be represented as “regression coefficients” shown later in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038163#pone-0038163-g004" target="_blank">Figure 4</a> or loading plot shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038163#pone-0038163-g005" target="_blank">Figure 5</a>.</p

The maximum absolute value of “regression coefficients” of original variables.

<p>The maximum absolute value of “regression coefficients” of original variables.</p

Loading plot of pseudo samples trajectories for selected variables.

<p>Numbers in the brackets correspond to variable numbers in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038163#pone-0038163-g004" target="_blank">Figure 4</a>.</p

Schematic example of: (a) “regression coefficients” of original variables trajectories plotted versus their range; (b) the maximum absolute value of “regression coefficients” of original variables trajectories shown in a.

<p>Schematic example of: (a) “regression coefficients” of original variables trajectories plotted versus their range; (b) the maximum absolute value of “regression coefficients” of original variables trajectories shown in a.</p

Conceptual flowchart of kernel-based data fusion.

<p>X₁ and X₂ are two blocks of data. *Note that all optimized parameters, i.e. number of variables, sigma for the rbf kernel, coefficients µ and nr. of LV’s are kept during the model reconstruction using all available samples. The particular steps are described in sections data analysis.</p