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The spatial pattern of forest fire locations is of interest for fire occurrence prediction. A spatial statistical analysis of lightning-caused fires in the province of Heilongjiang, China, between 1973 and 1997, was carried out to investigate the spatial pattern of fires, the way they depart from randomness, and the scales at which spatial correlation occurs. Fire locations were found to be spatially clustered. The results also showed that there are some “hot-point” areas in DaXing’an Ling.

Spike protein is one of the major structural proteins of severe acute respiratory syndrome-coronavirus. It is essential for the interaction of the virons with host cell receptors and subsequent fusion of the viral envelop with host cell membrane to allow infection. Some spike proteins of coronavirus, such as MHV, HCoV-OC43, AIBV and BcoV, are proteolytically cleaved into two subunits, S1 and S2. In contrast, TGV, FIPV and HCoV-229E are not. Many studies have shown that the cleavage of spike protein seriously affects its function. In order to investigate the maturation and proteolytic processing of the S protein of SARS CoV, we generated S1 and S2 subunit specific antibodies (Abs) as well as N, E and 3CL protein-specific Abs. Our results showed that the antibodies could efficiently and specifically bind to their corresponding proteins from E.coli expressed or lysate of SARS-CoV infected Vero-E6 cells by Western blot analysis. Furthermore, the anti-S1 and S2 Abs were proved to be capable of binding to SARS CoV under electron microscope observation. When S2 Ab was used to perform immune precipitation with lysate of SARS-CoV infected cells, a cleaved S2 fragment was detected with S2-specific mAb by Western blot analysis. The data demonstrated that the cleavage of S protein was observed in the lysate, indicating that proteolytic processing of S protein is present in host cells.

The Poisson's and Zero Inflated Poisson (ZIP) models that meet the data structure of forest fire occurrence were used to explore the relationships between the forest fire occurrence and climate factors in Daxing' an Mountains in 1980-2005. Compared with the ordinary least squares (OLS) model which often produced poor fitting results (R2 = 0.215), the Poisson's and ZIP models operated better, and had better prediction ability on the forest fire occurrence. The AIC and Vuong tests further indicated that ZIP model produced better fitting results, and thus, had better prediction ability than Poisson model.