The fungal strains were obtained by single colony isolation technique from the soil of the world’s unique proved independent selenium deposit. The anti-tumor activity was assayed by MTT method using MDA-MB-231cells, Hela cells and SMMC-7721 cells, respectively, together with morphological observation of these cells under inversed light microscope. The 73 terrestrial fungal strains were isolated from the soil samples collected from 6 different areas in Enshi area of HuBei Province, China. Among them, 32 strains produced fermentation products obviously inhibiting the Hela cells, 13 strains inhibiting the MDA-MB-231 cells and 9 strains inhibiting the SMMC-7721 cells with IC50 value less than 90 µg/ml. These antitumor-metabolite-producing strains have provided fungal strains for further studies on the bioactive metabolites.
Taking 171 counties of Beijing-Tiajin-Hebei Region as research units, based on spatial analysis model of GIS and geographic weighted regression model, the spatial-temporal characteristics of GDP per capita and its cause in 1993- 2013 were revealed. Results were as follows. GDP per capita in the Beijing-Tianjin-Hebei Region at county level showed rapid growth trend with expanding difference; GDP per capita at county level showed a significant positive correlation, that is to say, the pattern of high-high concentration and low-low concentration was enhanced. Beijing-Tianjin-Tangshan Region was always the hot economic development zone in Beijing-Tianjin-Hebei Region, the GDP per capita of most counties in Hebei Province was at low level, and cold economic development belt of “Laiyuan County-Gaoyang County-Wuyi County-Zaoqiang Qiu County” was gradually formed. GDP per capita at county level showed spatial pattern of “northeast-southwest”, and the overall trend was enhanced. Wen’an County was the core of GDP per capita gravity, and the centre of economic gravity moved southwest firstly and then northeast, indicating that the economic development function in the northeast of Beijing-Tianjin-Hebei Region further strengthen. Compared with OLS model, the fitting effect of GWR model was improved obviously. The development of GDP per capita in 2013 was mainly promoted by the gross industrial output value per capita, the proportion of value-added of the tertiary industry, the contracted investment actually utilized per capita and urbanization level.
To investigate the spatial interaction effect and nodal attractions of cities, a set of inter-city social network location-based check-in data with a time span of one year among 348 municipal cities in China is examined with a PSO (Particle Swarm Optimization) method and the gravity model. Twelve variables related with economic development, industrial structure, population scale and structure, tourism competitiveness and educational level are introduced to further investigate their influences on nodal attractions of cities. The results indicate a distance decay effect which is relatively weaker than in other systems, suggesting that human mobility at the regional level is less sensitive to the change in geographic distance. A close examination of the nodal attractions suggests variables related to the cities’ tourism competitiveness, maturity of development and population scale significantly influence the value of nodal attractions. This article will serve as a stepping-stone for a better future understanding of human travel pattern, check-in behaviors and the real meaning of nodal attractions in some complicated networks.
This paper made a comprehensive discussion about the spatial differentiation, evolution trend, correlation and driving factors of the regional agricultural mechanization level by using TOPSIS method, trend surface analysis, ESDA and GWR model. The results show that Hebei county’s agricultural mechanization level presents obvious spatial directivity and topographical distribution differences in 2013. The agricultural mechanization level develops better in central southeast plain, followed by the northwest plateau, and hilly region relatively worse. There exists a significant spatial autocorrelation characteristic and regional convergence phenomenon. The southern area of Hebei major in HH type, and northern area major in LL type. GWR shows great superiority in explaining the spatial non-stationary of elements, and reveals both positive and negative correlations between farmland scale and plant structure (expect for terrain), which is different from OLS result that all the factors are positive. In the future the government can consider such measures as enhancing the cultivated land scale and proportion of planting structure in the plateau area, considering other factors in the plain area to promote county’s agricultural mechanization level.
Taking Tunchang County in Hainan Province as a case study, dual self-organizing model accounting for geographical space as well as attribute space, was proposed. The geographic space information included the longitude and the latitude of the administrative villages. These indices such as the potential, the urgency and the feasibility were combined to construct the attribute space. The results demonstrated that the potential, the urgency and the feasibility of land reclamation were quite different among the villages. The model scores for the villages were significantly higher in the southern region than that in the northern region, and they were higher in the eastern region than that in the western region. The most desired land reclamation projects would be carried out in Poxin Town, Nankun Town, Xichang Town, Tuncheng Town. The 161 villages were divided into 6 project regions through dual self-organizing model. Based on the comprehensive score, the 6 project regions were classified into three types: priority remediation area (near-term), key remediation area (medium-term) and moderate remediation area (long-term). The area percent of three types were 25.14%, 41.83% and 33.03%, respectively. The developing orientations and suggestions for the land reclamation projects were given according to the characteristics of different influence factors. The results provide the scientific foundation in planning and implementing the project of land reclamation in Tunchang County, and is helpful in improving the level of land consolidation planning as well as promoting the land reclamation progress and the sustainable development.
Taking 171 counties of Beijing-Tianjin-Hebei region as research units, adopting GIS spatial analysis methods, it is revealed that spatial difference of agricultural labor productivity in 1994, 2000, 2006 and 2012. With geographically weighted regression model, the causes for the spatial difference of labor productivity in 2000 and 2012 are revealed. The results indicate that the agricultural labor productivity at county level shows unbalanced development with remarkable special differentiation. The counties in Beijing-Tianjin-Tangshan region possess higher agricultural labor productivity, however, there is a slow increase in labor productivity for the counties in Beijing, obvious decrease in number of agglomeration unit. The agricultural labor productivity of the counties in Shijiazhuang surrounding area sees high-level agglomeration; Agricultural labor productivity of the counties in Zhangjiakou, Chengde, Baoding and Xingtai is situated at a relatively low level. During the research period, agricultural labor productivity has a rapid increase, with no obvious polarization trend. In four research years, agricultural labor productivity at county level shows positive correlation but with weakened agglomerating level, so agricultural labor productivity at county level shows a decentralized sign. Simulation result of geographically weighted regression model is significantly better than ordinary least squares. Parameter estimation results for regression coefficients of controlled variables of 171 countries are different. Driving factors of labor productivity of agricultural work are featured as localization other than unbalanced linkage, and effects of agricultural labor productivity in previous stage are most obvious. Therefore, current status of agricultural labor productivity and driving factor should be combined to optimize agricultural labor productivity in Beijing-Tianjin-Hebei region.
