139 research outputs found

    Rozložení tepelných toků na stěnu tokamaku způsobených okrajovými nestabilitami

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    Edge localized modes (ELMs) are a concern for future magnetic fusion devices, such as ITER, due to the large transient heat loads they generate on the plasma facing components. A very promising method of ELM suppression is an application of resonant magnetic perturbations (RMP); however, such application leads to localized places of higher heat fluxes called footprints. Both ELMs and RMP could limit the operational lifetime of the device. In this thesis, we analyze the temporal and spatial distribution of footprints using the tangle distance method in the aim to prevent a transient overheating. We also analyze quasi-double-null configuration of the ITER plasma which can be expected to be the most susceptible to overheating of the upper wall. Based on the modelling, the potentially dangerous configurations of the RMP have been shown. Using the ELM filament model included in the LOCUST GPU code, we study temporal and spatial distribution of the heat fluxes caused by ELMs in the axially symmetric and the asymmetric magnetic field. The results are compared with published experimental observations. Powered by TCPDF (www.tcpdf.org

    14-3-3 preferences determined with different methods on 16,000 peptide sequences.

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    <p>14-3-3 preferences determined with different methods on 16,000 peptide sequences.</p

    Prediction results of peptide motifs binding to 14-3-3 isoforms by different regression techniques.

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    <p>Prediction results of peptide motifs binding to 14-3-3 isoforms by different regression techniques.</p

    Identification of 14-3-3 Proteins Phosphopeptide-Binding Specificity Using an Affinity-Based Computational Approach

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    <div><p>The 14-3-3 proteins are a highly conserved family of homodimeric and heterodimeric molecules, expressed in all eukaryotic cells. In human cells, this family consists of seven distinct but highly homologous 14-3-3 isoforms. 14-3-3<i>σ</i> is the only isoform directly linked to cancer in epithelial cells, which is regulated by major tumor suppressor genes. For each 14-3-3 isoform, we have 1,000 peptide motifs with experimental binding affinity values. In this paper, we present a novel method for identifying peptide motifs binding to 14-3-3<i>σ</i> isoform. First, we propose a sampling criteria to build a predictor for each new peptide sequence. Then, we select nine physicochemical properties of amino acids to describe each peptide motif. We also use auto-cross covariance to extract correlative properties of amino acids in any two positions. Finally, we consider elastic net to predict affinity values of peptide motifs, based on ridge regression and least absolute shrinkage and selection operator (LASSO). Our method tests on the 1,000 known peptide motifs binding to seven 14-3-3 isoforms. On the 14-3-3<i>σ</i> isoform, our method has overall pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) values of 0.84 and 252.31 for <i>N</i>–terminal sublibrary, and 0.77 and 269.13 for <i>C</i>–terminal sublibrary. We predict affinity values of 16,000 peptide sequences and relative binding ability across six permutated positions similar with experimental values. We identify phosphopeptides that preferentially bind to 14-3-3<i>σ</i> over other isoforms. Several positions on peptide motifs are in the same amino acid category with experimental substrate specificity of phosphopeptides binding to 14-3-3<i>σ</i>. Our method is fast and reliable and is a general computational method that can be used in peptide-protein binding identification in proteomics research.</p></div

    List of four preferable binders of 14-3-3<i>σ</i> from 1,000 peptide sequences.

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    <p>List of four preferable binders of 14-3-3<i>σ</i> from 1,000 peptide sequences.</p

    Position-specific scoring matrix on top 500 motifs identified from 16,000 peptide sequences against individual 14-3-3 isoforms.

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    <p>Position-specific scoring matrix on top 500 motifs identified from 16,000 peptide sequences against individual 14-3-3 isoforms.</p

    Nine physicochemical properties for 20 amino acid types.

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    <p>Nine physicochemical properties for 20 amino acid types.</p

    Five categories of 20 amino acids.

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    <p>Five categories of 20 amino acids.</p

    Details on predicting peptide motifs binding to 14-3-3 isoforms.

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    <p>Details on predicting peptide motifs binding to 14-3-3 isoforms.</p

    14-3-3 preferences determined with different methods on 1,000 peptide motifs.

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    <p>14-3-3 preferences determined with different methods on 1,000 peptide motifs.</p
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