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.

A DNA Nanostructure Platform for Directed Assembly of Synthetic Vaccines

Abstract: Safe and effective vaccines offer the best intervention for disease control. One strategy to maximize vaccine immunogenicity without compromising safety is to rationally design molecular complexes that mimic the natural structure of immunogenic microbes but without the disease-causing components. Here we use highly programmable DNA nanostructures as platforms to assemble a model antigen and CpG adjuvants together into nanoscale complexes with precise control of the valency and spatial arrangement of each element. Our results from immunized mice show that compared to a mixture of antigen and CpG molecules, the assembled antigen-adjuvant-DNA complexes induce strong and long-lasting antibody responses against the antigen without stimulating a reaction to the DNA nanostructure itself. This result demonstrates the potential of DNA nanostructures to serve as general platforms for the rational design and construction of a variety of vaccines.

Probabilistic Linguistic MULTIMOORA: A Multicriteria Decision Making Method Based on the Probabilistic Linguistic Expectation Function and the Improved Borda Rule

Abstract: The probabilistic linguistic term set (PLTS) is a powerful technique in representing linguistic evaluations of individuals or groups in the process of decision making. The aim of this paper is to propose a strongly robust method to solve multiexperts multicriteria decision making problems with linguistic evaluations. To enrich the computation and to improve the measures of PLTS, we first define an expectation function of it. In addition, we advance three kinds of probabilistic linguistic distance measures reflecting on the difference of linguistic terms and probabilities at the same time to make up for the defects of the existing distance measures, and then propose the similarity and correlation measures. Integrating the subjective opinions with the correlation coefficients between criteria, we put forward a combined weight determining method. The robustness of the ranking method, MULTIMOORA, is enhanced by the improved Borda rule. Based on these research findings, a probabilistic linguistic MULTIMOORA method is proposed. Finally, the developed method is applied to an empirical example concerning the selection of shared karaoke television brands. The effectiveness of the proposed method is verified by some comparative analyses.

Greenhouse Gas Control

Abstract: Global warming, caused by increasing emissions of CO2 and other greenhouse gases as a result of human activities, is one of the major threats now confronting the environment. How to control the emissions of greenhouse gases is an important problem which should be immediately solved. This chapter will introduce a solving method from a comprehensive viewpoint simultaneously considering the development of the economy and the protection of the environment. A system dynamic model based on multi-objective programming will be exhibited to simulate the evolution of the system in order to provide decision makers with some useful advices.

Landscape of tumor-infiltrating T cell repertoire of human cancers

Abstract: We developed a computational method to infer the complementarity-determining region 3 (CDR3) sequences of tumor-infiltrating T cells in 9,142 RNA-seq samples across 29 cancer types. We identified over 600,000 CDR3 sequences, including 15% that were full length. CDR3 sequence length distribution and amino acid conservation, as well as variable gene usage, for infiltrating T cells in many tumors, except in brain and kidney cancers, resembled those for peripheral blood cells from healthy donors. We observed a strong association between T cell diversity and tumor mutation load, and we predicted SPAG5 and TSSK6 as putative immunogenic cancer/testis antigens in multiple cancers. Finally, we identified three potential immunogenic somatic mutations on the basis of their co-occurrence with CDR3 sequences. One of them, a PRAMEF4 mutation encoding p.Phe300Val, was predicted to result in peptide binding strongly to both MHC class I and class II molecules, with matched HLA types in its carriers. Our analyses have the potential to simultaneously identify immunogenic neoantigens and tumor-reactive T cell clonotypes.

SARS-CoV-2 Mpro inhibitors with antiviral activity in a transgenic mouse model

Abstract: The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continually poses serious threats to global public health. The main protease (Mpro) of SARS-CoV-2 plays a central role in viral replication. We designed and synthesized 32 new bicycloproline-containing Mpro inhibitors derived from either boceprevir or telaprevir, both of which are approved antivirals. All compounds inhibited SARS-CoV-2 Mpro activity in vitro, with 50% inhibitory concentration values ranging from 7.6 to 748.5 nM. The cocrystal structure of Mpro in complex with MI-23, one of the most potent compounds, revealed its interaction mode. Two compounds (MI-09 and MI-30) showed excellent antiviral activity in cell-based assays. In a transgenic mouse model of SARS-CoV-2 infection, oral or intraperitoneal treatment with MI-09 or MI-30 significantly reduced lung viral loads and lung lesions. Both also displayed good pharmacokinetic properties and safety in rats.