26 research outputs found

    PS14 Lessons from the rise and fall of Chinese peer-to-peer lending

    No full text
    This paper reviews the development and assesses the future of Peer-to-Peer (P2P) lending in China. Chinese P2P lending has expanded by a factor of 60 over the four years from 2013 and 2017. Consequently, it is now much greater, both in absolute terms and relative to the size of the economy, than in any other country. The industry though has been plagued by problematic often fraudulent business models in what was, until 2015, effectively a regulatory vacuum. A strict new regulatory regime is currently being introduced. However, its introduction, especially the requirements on capital requirements and registration, are substantially reducing the volume of P2P lending. We consider the future of P2P lending concluding that it is facing substantial uncertainties

    The prediction of protein structural class using averaged chemical shifts

    No full text
    <div><p>Knowledge of protein structural class can provide important information about its folding patterns. Many approaches have been developed for the prediction of protein structural classes. However, the information used by these approaches is primarily based on amino acid sequences. In this study, a novel method is presented to predict protein structural classes by use of chemical shift (CS) information derived from nuclear magnetic resonance spectra. Firstly, 399 non-homologue (about 15% identity) proteins were constructed to investigate the distribution of averaged CS values of six nuclei (<sup>13</sup>CO, <sup>13</sup>Cα, <sup>13</sup>Cβ, <sup>1</sup>HN, <sup>1</sup>Hα and <sup>15</sup>N) in three protein structural classes. Subsequently, support vector machine was proposed to predict three protein structural classes by using averaged CS information of six nuclei. Overall accuracy of jackknife cross-validation achieves 87.0%. Finally, the feature selection technique is applied to exclude redundant information and find out an optimized feature set. Results show that the overall accuracy increased to 88.0% by using the averaged CSs of <sup>13</sup>CO, <sup>1</sup>Hα and <sup>15</sup>N. The proposed approach outperformed other state-of-the-art methods in terms of predictive accuracy in particular for low-similarity protein data. We expect that our proposed approach will be an excellent alternative to traditional methods for protein structural class prediction.</p> </div

    iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

    No full text
    <div><p>Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the <em>Saccharomyces cerevisiae</em> genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called <b>iNuc-PhysChem</b>, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by <b>iNuc-PhysChem</b> was over 96% in identifying nucleosomal or linker sequences. As a web-server, <b>iNuc-PhysChem</b> is freely accessible to the public at <a href="http://lin.uestc.edu.cn/server/iNuc-PhysChem">http://lin.uestc.edu.cn/server/iNuc-PhysChem</a>. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor's code can be easily downloaded from the web-server. It is anticipated that <b>iNuc-PhysChem</b> may become a useful high throughput tool for both basic research and drug design.</p> </div

    Comparison of success rates based on different physicochemical properties.

    No full text
    <p>The orange column shows , the rate of correct prediction for the nucleosome-forming dataset (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047843#pone.0047843.e044" target="_blank">Eq.10</a>); the blue column shows , the rate of correct predictions for the nucleosome-inhibiting dataset; the purple column shows , the overall success rate (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047843#pone.0047843.e049" target="_blank">Eq.11</a>).</p

    A plot to show the IFS procedure.

    No full text
    <p>When the top 884 of the 1,788 features were used to perform prediction, the overall success rate reached its peak of 0.967.</p

    A schematic illustration to show the basic architecture of nucleosome.

    No full text
    <p>Nucleosomes form the fundamental repeating units of eukaryotic chromatin (upper panel), each of them consists of approximately 147 base pairs of DNA wrapped in 1.67 left-handed superhelical turns around a histone octamer consisting of 2 copies each of the core histones H2A, H2B, H3, and H4 (lower panel).</p

    A screenshot to show the top page of the iNuc-PhysChem web-server.

    No full text
    <p>Its website address is at <a href="http://lin.uestc.edu.cn/server/iNuc-PhysChem" target="_blank">http://lin.uestc.edu.cn/server/iNuc-PhysChem</a>.</p

    Graphic profiles to show the difference between nucleosomal (red) and linker (blue) sequences.

    No full text
    <p>It contains 12 panels drawn according to their standard feature vectors (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047843#pone.0047843.e021" target="_blank">Eq.5</a>), with each to reflect one of the 12 physicochemical features as marked at the bottom of each panel.</p

    Phylogenetic affiliations and numbers of bacterial 16S rRNA sequences retrieved from clone libraries generated from different fractions.

    No full text
    <p>Numbers in the parentheses of line “<i>In silico</i> T-RF (bp)” indicate clone numbers representing the corresponding T-RF.</p>&<p>Clone sequences retrieved from “light” and “heavy” library was divided into OTU level in each row of the table. The OTUs containing one or no clone in both libraries were not shown in this table.</p><p>“<sup>12</sup>C-hexadecane (D126-1.565)” indicates that the clone library was constructed from the DNA fraction with a BD of 1.565 g. mL<sup>−1</sup> of the unlabeled microcosm on day 126.</p><p>“<sup>13</sup>C-hexadecane (D218-1.582)” indicates that the clone library was constructed from the DNA fraction with a BD of 1.582 g. ml-1 of the <sup>13</sup>C-labeled microcosm on day 218.</p>a<p>Tree clones (account for T-RFs 285, 287 and 288 bp) retrieved from <sup>12</sup>C-hexadecane (D126-1.565) representing for T-RFs 285, 287 and 288 bp are not shown in the table.</p>b<p>One clone (account for T-RF 76 bp) retrieved from <sup>13</sup>C-hexadecane (D218-1.582) accounting for T-RFs 76 is not shown in the tablec: The coverage was calculated based on Good formula <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066784#pone.0066784-Good1" target="_blank">[45]</a>, the 16S rRNA gene sequences were clustered into OTUs with 97% sequence identity.</p

    Relative abundance of archaeal T-RFs across different CsTFA buoyant densities.

    No full text
    <p>Relative abundance of archaeal T-RFs across different CsTFA buoyant densities.</p
    corecore