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Qi Xuan

Publications and source records attributed to Qi Xuan.

4 recordsLinked to original sources

Growth model for complex networks with hierarchical and modular structures.

A hierarchical and modular network model is suggested by adding a growth rule along with the preferential attachment (PA) rule into Motter's modeling procedure. The proposed model has an increasing number of vertices but a fixed number of modules and hierarchical levels. The vertices form lowest-level modules which in turn constitute higher-level modules hierarchically. The creation of connections between two vertices in a single module or in two different modules of the same level obeys the PA rule. The structural characteristics of this model are investigated analytically and numerically. The results show that the degree distribution, the module size distribution, and the clustering function of the model possess a power-law property which is similar to that in many real-world networks. The model is then used to predict the growth trends of real-world networks with modular and hierarchical structures. By comparing this model with those real-world networks, an interesting conclusion is found: that many real-world networks are in their early stages of development, and when the growth time is large enough, the modules and levels of the networks will be ultimately merged.

Journal Article↗

[Study of genes related to gastric cancer and its premalignant lesions with fluorescent differential display].

BACKGROUND & OBJECTIVE: It is generally recognized that development of gastric cancer arises gradually from premalignant lesions (chronic atrophic gastritis, intestinal metaplasia and dysplasia). Differential display of mRNA is a valuable tool for the identification of differentially expressed genes in human carcinogenesis and development. The search for differentially expressed genes in gastric cancer and its premalignant lesions may help to define molecular alterations in the gastric mucosa tissue that may precede the development of gastric cancer. METHODS: The differentially expressed cDNA bands were isolated and identified by fluorescent differential display in 2 gastric cancer, 2 premalignant lesions, and 2 normal gastric mucosa tissues and then reamplified by PCR. After being cloned, all cDNA fragments were sequenced. Through BLAST software, the sequencing results were compared with GenBank database for homology analysis. Expression of SPTAN1 in 7 gastric cancer tissues, 7 premalignant lesions, and 7 normal gastric mucosa tissues were identified by RT-PCR. RESULTS: Four differentially expressed cDNA fragments were found. Three of them were over-expressed in the gastric cancer tissue. One of them was over-expressed in premalignant lesions and normal gastric mucosa tissue. One cDNA fragment over-expressed in gastric cancer was homologous to SPTAN1 and its over-expression in gastric cancer was confirmed by RT-PCR. The other three cDNA fragments showed significant homology to known gene sequences in GenBank but their functions are unknown. CONCLUSION: Three of the four differentially expressed genes over-expressed in gastric cancer. SPTAN1gene was significantly higher in gastric cancer tissue than in normal gastric mucosa tissue and dysplasia tissue.

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