239 research outputs found

    Quantitative Analysis of Peptide–Matrix Interactions in Lyophilized Solids Using Photolytic Labeling

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    Peptide–matrix interactions in lyophilized solids were explored using photolytic labeling with reversed phase high performance liquid chromatography (rp-HPLC) and mass spectrometric (MS) analysis. A model peptide (Ac-QELHKLQ–NHCH<sub>3</sub>) derived from salmon calcitonin was first labeled with a heterobifunctional cross-linker NHS-diazirine (succinimidyl 4,4′-azipentanoate; SDA) at Lys5 in solution, with ∼100% conversion. The SDA labeled peptide was then formulated with the following excipients at a 1:400 molar ratio and lyophilized: sucrose, trehalose, mannitol, histidine, arginine, urea, and NaCl. The lyophilized samples and corresponding solution controls were exposed to UV at 365 nm to induce photolytic labeling, and the products were identified by MS and quantified with rp-HPLC or MS. Peptide–excipient adducts were detected in the lyophilized solids except the NaCl formulation. With the exception of the histidine formulation, peptide–excipient adducts were not detected in solution and the fractional conversion to peptide–water adducts in solution was significantly greater than in lyophilized solids, as expected. In lyophilized solids, the fractional conversion to peptide–water adducts was poorly correlated with bulk moisture content, suggesting that the local water content near the labeled lysine residue differs from the measured bulk average. In lyophilized solids, the fractional conversion to peptide–excipient adducts was assessed using MS extracted ion chromatograms (EIC); subject to the assumption of equal ionization efficiencies, the fractional conversion to excipient adducts varied with excipient type. The results demonstrate that the local environment near the lysine residue of the peptide in the lyophilized solids can be quantitatively probed with a photolytic labeling method

    Additional file 1: of Highly Active and Stable Fe-N-C Oxygen Reduction Electrocatalysts Derived from Electrospinning and In Situ Pyrolysis

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    Figure S1. EDX specter of FN-800 and the insert was the element ratio of C, N and Fe, respectively. Figure S2. Pore size distributions for FN-800. Figure S3. N2 absorption and desorptionof FN-800 without acid treat. Figure S4. XPS survey scan and N1 s high resolution spectra of FN-800 which uncover during carbonization process. Figure S5. Polarization curves at various speeds and a scan rate of 5 mV/s: (a) N-800; (b) F-800; K-L plots (J− 1 vs. ω-1/2) at different potentials of N-800 (c) and F-800 (d). Figure S6. LSV of the Fe-N-doped carbon nanofibers catalysts with different carbonize temperature in the range of 600–1000 °C. Table S1. Comparison of the ORR performance between FN-800 and other reported catalysts in 0.1 M KOH electrolyte. (PDF 843 kb

    Grid partition of ApproxMaxMI and ChiMIC for linear function.

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    <p>1000 data points simulated for functional relationships of the form <i>y</i> = <i>x</i>+<i>η</i>. where <i>η</i> is noise drawn uniformly from (−0.25, 0.25). A: Grid partition for noiseless linear function. B: Grid partition based on ApproxMaxMI for noisy linear function. C: Grid partition based on ChiMIC for noisy linear function.</p

    Metatranscriptomic Study of Common and Host-Specific Patterns of Gene Expression between Pines and Their Symbiotic Ectomycorrhizal Fungi in the Genus <i>Suillus</i>

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    <div><p>Ectomycorrhizal fungi (EMF) represent one of the major guilds of symbiotic fungi associated with roots of forest trees, where they function to improve plant nutrition and fitness in exchange for plant carbon. Many groups of EMF exhibit preference or specificity for different plant host genera; a good example is the genus <i>Suillus</i>, which grows in association with the conifer family Pinaceae. We investigated genetics of EMF host-specificity by cross-inoculating basidiospores of five species of <i>Suillus</i> onto ten species of <i>Pinus</i>, and screened them for their ability to form ectomycorrhizae. Several <i>Suillus</i> spp. including <i>S</i>. <i>granulatus</i>, <i>S</i>. <i>spraguei</i>, and <i>S</i>. <i>americanus</i> readily formed ectomycorrhizae (compatible reaction) with white pine hosts (subgenus <i>Strobus</i>), but were incompatible with other pine hosts (subgenus <i>Pinus)</i>. Metatranscriptomic analysis of inoculated roots reveals that plant and fungus each express unique gene sets during incompatible vs. compatible pairings. The <i>Suillus-Pinus</i> metatranscriptomes utilize highly conserved gene regulatory pathways, including fungal G-protein signaling, secretory pathways, leucine-rich repeat and pathogen resistance proteins that are similar to those associated with host-pathogen interactions in other plant-fungal systems. Metatranscriptomic study of the combined <i>Suillus-Pinus</i> transcriptome has provided new insight into mechanisms of adaptation and coevolution of forest trees with their microbial community, and revealed that genetic regulation of ectomycorrhizal symbiosis utilizes universal gene regulatory pathways used by other types of fungal-plant interactions including pathogenic fungal-host interactions.</p></div

    A New Algorithm to Optimize Maximal Information Coefficient

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    <div><p>The maximal information coefficient (MIC) captures dependences between paired variables, including both functional and non-functional relationships. In this paper, we develop a new method, ChiMIC, to calculate the MIC values. The ChiMIC algorithm uses the chi-square test to terminate grid optimization and then removes the restriction of maximal grid size limitation of original ApproxMaxMI algorithm. Computational experiments show that ChiMIC algorithm can maintain same MIC values for noiseless functional relationships, but gives much smaller MIC values for independent variables. For noise functional relationship, the ChiMIC algorithm can reach the optimal partition much faster. Furthermore, the MCN values based on MIC calculated by ChiMIC can capture the complexity of functional relationships in a better way, and the statistical powers of MIC calculated by ChiMIC are higher than those calculated by ApproxMaxMI. Moreover, the computational costs of ChiMIC are much less than those of ApproxMaxMI. We apply the MIC values tofeature selection and obtain better classification accuracy using features selected by the MIC values from ChiMIC.</p></div

    Grid partition of ApproxMaxMI and ChiMIC for linear function.

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    <p>1000 data points simulated for functional relationships of the form <i>y</i> = <i>x</i>+<i>η</i>. where <i>η</i> is noise drawn uniformly from (−0.25, 0.25). A: Grid partition for noiseless linear function. B: Grid partition based on ApproxMaxMI for noisy linear function. C: Grid partition based on ChiMIC for noisy linear function.</p

    Retained features and independent test accuracy based on MIC and ChiMIC.

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    <p>Retained features and independent test accuracy based on MIC and ChiMIC.</p

    Expression of unique <i>Suillus</i> small-secreted proteins (SSPs) during compatible EMF interactions with <i>Pinus</i>.

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    <p>Heatmap shows normalized gene expression of SSPs for individual <i>Suillus</i> spp. (Sa, Sg, Ss, or Sd) paired with different <i>Pinus</i> spp. Each gene was significantly overexpressed in one of the pair combinations as determined by comparisons with FDR<0.05 using Benjamini-Hochberg test. Gene expression in uninoculated <i>P</i>. <i>taeda</i> roots (“C”) is also shown. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006348#pgen.1006348.s016" target="_blank">S1 Dataset</a> for complete gene annotations and read counts.</p
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