Spatial Variation of Surface Soil Available Phosphorous and Its Relation with Environmental Factors in the Chaohu Lake Watershed

The study presented in this paper attempts to evaluate the spatial pattern of soil available phosphorus, as well as the relation between soil available phosphorus and environment factors including elevation, slope, precipitation, percentage of cultivated land, percentage of forest land, percentage of construction land and NDVI using statistical methods and GIS spatial analysis techniques. The results showed that the Spline Tension method performed the best in the prediction of soil available phosphorus in the Chaohu Lake watershed. The spatial variation of surface soil available phosphorus was high in Chaohu Lake watershed and the upstream regions around Chaohu Lake, including the west of Chaohu lake (e.g., southwest of Feixi county, east of Shucheng county and north of Lujiang county) and to the north of Chaohu Lake (e.g., south of Hefei city, south of Feidong county, southwest of Juchao district), had the highest soil available phosphorus content. The mean and standard deviation of soil available phosphorus content gradually decreased as the elevation or slope increased. The cultivated land comprised 60.11% of the watershed and of that land 65.63% belonged to the medium to very high SAP level classes, and it played a major role in SAP availability within the watershed and a potential source of phosphorus to Chaohu Lake resulting in eutrophication. Among the land use types, paddy fields have some of the highest maximum values and variation of coefficients. Subwatershed scale soil available phosphorus was significantly affected by elevation, slope, precipitation, percentage of cultivated land and percentage of forest land and was decided by not only these environmental factors but also some other factors such as artificial phosphorus fertilizer application

Activation of Interleukin-1β Release by the Classical Swine Fever Virus Is Dependent on the NLRP3 Inflammasome, Which Affects Virus Growth in Monocytes

Classical swine fever virus (CSFV) is a classic Flavivirus that causes the acute, febrile, and highly contagious disease known as classical swine fever (CSF). Inflammasomes are molecular platforms that trigger the maturation of proinflammatory cytokines to engage innate immune defenses that are induced upon cellular infection or stress. However, the relationship between the inflammasome and CSFV infection has not been thoroughly characterized. To understand the function of the inflammasome response to CSFV infection, we infected porcine peripheral blood monocytes (PBMCs) with CSFV. Our results indicated that CSFV infection induced both the generation of pro-interleukin-1β (pro-IL-1β) and its processing in monocytes, leading to the maturation and secretion of IL-1β through the activation of caspase 1. Moreover, CSFV infection in PBMCs induced the production and cleavage of gasdermin D (GSDMD), which is an inducer of pyroptosis. Additional studies showed that CSFV-induced IL-1β secretion was mediated by NLRP3 and that CSFV infection could sufficiently activate the assembly of the NLRP3 inflammasome in monocytes. These results revealed that CSFV infection inhibited the expression of NLRP3, and knockdown of NLRP3 enhanced the replication of CSFV. In conclusion, these findings demonstrate that the NLRP3 inflammasome plays an important role in the innate immune response to CSFV infection

Regionalizing Aquatic Ecosystems Based on the River Subbasin Taxonomy Concept and Spatial Clustering Techniques

Aquatic ecoregions were increasingly used as spatial units for aquatic ecosystem management at the watershed scale. In this paper, the principle of including land area, comprehensiveness and dominance, conjugation and hierarchy were selected as regionalizing principles. Elevation and drainage density were selected as the regionalizing indicators for the delineation of level I aquatic ecoregions, and percent of construction land area, percent of cultivated land area, soil type and slope for the level II. Under the support of GIS technology, the spatial distribution maps of the two indicators for level I and the four indicators for level II aquatic ecoregion delineation were generated from the raster data based on the 1,107 subwatersheds. River subbasin taxonomy concept, two-step spatial clustering analysis approach and manual-assisted method were used to regionalize aquatic ecosystems in the Taihu Lake watershed. Then the Taihu Lake watershed was divided into two level I aquatic ecoregions, including Ecoregion I1 and Ecoregion I2, and five level II aquatic subecoregions, including Subecoregion II11, Subecoregion II12, Subecoregion II21, Subecoregion II22 and Subecoregion II23. Moreover, the characteristics of the two level I aquatic ecoregions and five level II aquatic subecoregions in the Taihu Lake watershed were summarized, showing that there were significant differences in topography, socio-economic development, water quality and aquatic ecology, etc. The results of quantitative comparison of aquatic life also indicated that the dominant species of fish, benthic density, biomass, dominant species, Shannon-Wiener diversity index, Margalef species richness index, Pielou evenness index and ecological dominance showed great spatial variability between the two level I aquatic ecoregions and five level II aquatic subecoregions. It reflected the spatial heterogeneities and the uneven natures of aquatic ecosystems in the Taihu Lake watershed

Spatial multi-objective land use optimization: extensions to the nondominated sorting genetic algorithm-II

A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research