24 research outputs found

    Effects of prenatal stress on the expression of NR1.

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    <p>A: NR1 levels in the hippocampus. B: NR1 levels in the frontal cortex. C: NR1 levels in the striatum. Values represent means ± SEM. n=10 per group. <b><sup><i>*</i></sup></b><i>p</i><0.05 vs CON, <sup>**</sup><i>p</i><0.01 vs CON, <sup>+</sup><i>p</i><0.05 vs CON, <sup>++</sup><i>p</i><0.01 vs PS+NS.</p

    Timeline showing a summary of the experimental design.

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    <p>G: gestational age; P: postnatal age (days). PS: prenatal restraint stress; OFT: open field test.</p

    Effects of prenatal stress on the sucrose preference test.

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    <p>A: The percentage of sucrose consumed of female offspring at sucrose concentrations of 1 - 32%. B: The percentage of sucrose consumed of male offspring at sucrose concentrations of 1 - 32%. C: The percentage of sucrose consumed of both sexes at sucrose concentrations of 1%. D: The percentage of sucrose consumed of both sexes at sucrose concentrations of 32%. Values represent means ± SEM. n=10 per group. <b><sup><i>*</i></sup></b><i>p</i><0.05 vs CON, <sup>+</sup><i>p</i><0.05 vs PS+NS.</p

    Effects of prenatal stress on the expression of NR2A.

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    <p>A: NR2A levels in the hippocampus. B: NR2A levels in the frontal cortex. C: NR2A levels in the striatum. Values represent means ± SEM. n=10 per group. <sup>**</sup>p<0.01 vs CON, <sup>++</sup><i>p</i><0.01 vs PS+NS.</p

    Effects of prenatal stress on the body weights of offspring.

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    <p>A: Weekly body weights of female offspring from PND 0 to PND 28. B: Weekly body weights of male offspring from PND 0 to PND 28. C: Body weights of both sexes on PND 28. Values represent means ± SEM. n=10 per group. <b><sup><i>#</i></sup></b><i>p</i><0.05 vs female.</p

    Effects of prenatal stress on the open field test.

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    <p>A: The number of grid crossings of both sexes. B: The rearing counts of both sexes. Values represent means ± SEM. n=10 per group. <b><sup><i>*</i></sup></b><i>p</i><0.05 vs CON, <sup>**</sup><i>p</i><0.01 vs CON, <sup>+</sup><i>p</i><0.05 vs CON, <sup>++</sup><i>p</i><0.01 vs PS+NS.</p

    Performances of different feature combinations on NS800 (5-fold cross validation, %).

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    <p>Performances of different feature combinations on NS800 (5-fold cross validation, %).</p

    Performances of NMRDSP, DSP and Frag1D for three classes of different sequence identities.

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    <p>Performances of NMRDSP, DSP and Frag1D for three classes of different sequence identities.</p

    An example of normalization and alphabetization of Cystine C NMR CS data.

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    <p>After normalization, the values of NMR CS distribute from zero to one (horizontal ordinate). After alphabetization, each sub-region is expressed a character (top). The performances of pre-processing are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083532#pone-0083532-t001" target="_blank">Table 1</a>.</p

    NMRDSP: An Accurate Prediction of Protein Shape Strings from NMR Chemical Shifts and Sequence Data

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    <div><p>Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at <a href="http://cal.tongji.edu.cn/NMRDSP/index.jsp" target="_blank">http://cal.tongji.edu.cn/NMRDSP/index.jsp</a>.</p></div
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