Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

Delicate structural coordination of the Severe Acute Respiratory Syndrome coronavirus Nsp13 upon ATP hydrolysis.

Abstract: To date, an effective therapeutic treatment that confers strong attenuation toward coronaviruses (CoVs) remains elusive. Of all the potential drug targets, the helicase of CoVs is considered to be one of the most important. Here, we first present the structure of the full-length Nsp13 helicase of SARS-CoV (SARS-Nsp13) and investigate the structural coordination of its five domains and how these contribute to its translocation and unwinding activity. A translocation model is proposed for the Upf1-like helicase members according to three different structural conditions in solution characterized through H/D exchange assay, including substrate state (SARS-Nsp13-dsDNA bound with AMPPNP), transition state (bound with ADP-AlF4-) and product state (bound with ADP). We observed that the β19-β20 loop on the 1A domain is involved in unwinding process directly. Furthermore, we have shown that the RNA dependent RNA polymerase (RdRp), SARS-Nsp12, can enhance the helicase activity of SARS-Nsp13 through interacting with it directly. The interacting regions were identified and can be considered common across CoVs, which provides new insights into the Replication and Transcription Complex (RTC) of CoVs.

Microwave-assisted synthesis of carbon nanodots through an eggshell membrane and their fluorescent application.

Abstract: Carbon nanodots (C-Dots) as a new form of carbonaceous nanomaterials have aroused much interest and intensive research due to their inspiring properties. Compared to traditional semiconductor quantum dots, these newly emergent nanodots possess a number of advantageous characteristics, among which low-toxicity is particularly fascinating. More and more research into C-Dots have focused on synthesis methods and biology-related applications. Microwave-assisted approaches have attracted attention because microwave treatment can provide intensive and efficient energy, and as a consequence shorten the reaction time. In this article, we designed a “green”, rapid, eco-friendly and waste-reused approach to synthesize fluorescent and water-soluble C-Dots from eggshell membrane (ESM) ashes according to a microwave-assisted process. ESM selected as the carbon source was a common protein-rich waste in daily life and can be obtained easily and cheaply. The C-Dots from our method showed the maximal fluorescence emission peak at 450 nm and the fluorescence quantum yield was about 14%. We further designed a sensitive probe for glutathione based on the fluorescence turn off and on of the C-Dots–Cu2+ system, which showed a linear range of 0.5–80 μmol L−1 and detection limit of 0.48 μmol L−1. In general, the C-Dots prepared briefly and inexpensively from ESM revealed excellent fluorescent property with promising potential for applications such as sample detection and biotechnology.

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

Abstract: Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that $\boldsymbol {\ell }_{\boldsymbol {1}}$ -, $\boldsymbol {\ell }_{\boldsymbol {2}}$ -, $\boldsymbol {\ell }_{\boldsymbol {\infty }}$ -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.