Comparison of the H-imino NMR spectra (5°C, HO) of (top) and (bottom) apical stem-loop

<p><b>Copyright information:</b></p><p>Taken from "Thermodynamics and NMR studies on and HBV encapsidation signals"</p><p></p><p>Nucleic Acids Research 2007;35(8):2800-2811.</p><p>Published online 11 Apr 2007</p><p>PMCID:PMC1885660.</p><p>© 2007 The Author(s)</p> Assigned peaks are labeled by residue numbers and unassigned peaks by asterisks. Residue numbers in the upper part of are superscripted with an asterisk for clarity

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

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

Summary of σ parameter for rbf kernel function.

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

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

The number of samples included in a training and independent test set.

<p>The number of samples included in a training and independent test set.</p

NMR-Based Chemosensing via <i>p</i>‑H₂ Hyperpolarization: Application to Natural Extracts

When dealing with trace analysis of complex mixtures, NMR suffers from both low sensitivity and signal overlap. NMR chemosensing, in which the association between an analyte and a receptor is “signaled” by an NMR response, has been proposed as a valuable analytical tool for biofluids and natural extracts. Such chemosensors offer the possibility to simultaneously detect and distinguish different analytes in solution, which makes them particularly suitable for analytical applications on complex mixtures. In this study, we have combined NMR chemosensing with nuclear spin hyperpolarization. This was realized using an iridium complex as a receptor in the presence of parahydrogen: association of the target analytes to the metal center results in approximately 1000-fold enhancement of the NMR response. This amplification allows the detection, identification, and quantification of analytes at low-micromolar concentrations, provided they can weakly associate to the iridium chemosensor. Here, our NMR chemosensing approach was applied to the quantitative determination of several flavor components in methanol extracts of ground coffee

Interconverting Conformations of Slipped-DNA Junctions Formed by Trinucleotide Repeats Affect Repair Outcome

Expansions of (CTG)·(CAG) repeated DNAs are the mutagenic cause of 14 neurological diseases, likely arising through the formation and processing of slipped-strand DNAs. These transient intermediates of repeat length mutations are formed by out-of-register mispairing of repeat units on complementary strands. The three-way slipped-DNA junction, at which the excess repeats slip out from the duplex, is a poorly understood feature common to these mutagenic intermediates. Here, we reveal that slipped junctions can assume a surprising number of interconverting conformations where the strand opposite the slip-out either is fully base paired or has one or two unpaired nucleotides. These unpaired nucleotides can also arise opposite either of the nonslipped junction arms. Junction conformation can affect binding by various structure-specific DNA repair proteins and can also alter correct nick-directed repair levels. Junctions that have the potential to contain unpaired nucleotides are repaired with a significantly higher efficiency than constrained fully paired junctions. Surprisingly, certain junction conformations are aberrantly repaired to expansion mutations: misdirection of repair to the non-nicked strand opposite the slip-out leads to integration of the excess slipped-out repeats rather than their excision. Thus, slipped-junction structure can determine whether repair attempts lead to correction or expansion mutations