Synthesis, Structure, Function and Biomedical Studies of Nucleic Acid Derivatized with Selenium

Nucleic acids are macromolecules in cells for storing and transferring genetic information. Moreover, nucleic acids, especially RNAs, can fold into well-defined 3D structures and catalyze biochemical reactions. As ubiquitous biological molecules in all living systems, nucleic acids are important drug targets, and they can also be used in diagnostics and therapeutics. Structural information of nucleic acids provides the foundation for DNA and RNA function studies. X-ray crystallography has been a useful tool for structural studies of bio-macromolecules at atomic level. There are two major problems in macromolecular crystal structure determination: phasing and crystallization. Although selenium derivatization is routinely used for solving novel protein structures through the MAD phasing technique, the phase problem is still a critical issue in nucleic acid crystallography. The covalent selenium-derivatization of nucleic acids has been proven to be a useful strategy for solving the phase problem in nucleic acid X-ray crystallography. Besides the facilitation of nucleic acid crystallography, there is also a wide range of other applications for selenium-derivatized nucleic acids (SeNA). The investigation presented in this dissertation mainly focuses on the following research subjects (1) Synthesis and characterization of selenium-derivatized nucleic acids for X-ray crystallography, especially phosphoroselenoate RNAs. They are generated and used for crystallization. (2) Application of selenium-derivatized RNA for RNA interference. Phosphoroselenoate RNAs are tested for RNAi activities. (3) Synthesis and characterization of the uridine 5’-triphosphate modified with selenium at position 4. (4) Facile synthesis and antitumor activities of selenium modified deoxyribonucleosides. MeSe-thymidine nucleosides have shown antitumor activity in cell assays

A Hotspot Discovery Method Based on Improved FIHC Clustering Algorithm

It was difficult to find the microblog hotspot because the characteristics of microblog were short, rapid, change and so on. A microblog hotspot detection method based on MFIHC and TOPSIS was proposed in order to solve the problem. Firstly, the calculation of HowNet similarity was used in the score function of FIHC, the semantic links between frequent words were considered, and the initial clusters based on frequent words were produced more accurately. Then the initial cluster of the text repletion of mircoblog was reduced, and the idea of Single-Pass clustering was used to the reduced topic cluster in order to get the Hotspot. At last, an improved TOPSIS model was used to sort the hot topics in order to get the rank of the hot topics. Compared with the other text clustering algorithms and hotspot detection methods, the method has good effect, and can be a more comprehensive response to the current hot topics