Penthorum chinense Pursh is rich in flavonoids, which have strong antioxidant and anticomplement activities. In order to optimize their extraction conditions, various extraction parameters were chosen to identify their effects on flavonoids extraction. Single factor and Box-Behnken experimental designs consisting of 24 experimental runs and five replicates at zero point were applied to obtain the optimal extraction yield. The optimization conditions for flavonoids extraction were determined as follows: ethanol concentration 60.89%, extraction time 68.15 min, temperature 52.89 °C and liquid/solid ratio 19.70 : 1. The corresponding flavonoids content was 7.19%. The regression equation was found to fit well with the actual situation. Furthermore, the antioxidant activity (the free radical scavenging ability and ferric reducing/antioxidant power) and anticomplement ability of the flavonoids from P. chinense were determined. Results showed that the flavonoids of P. chinense displayed significant antioxidant and anticomplement activities. Its antioxidant activity can compete with ascorbic acid (Vc), whereas its anticomplement activity (IC50 = 111.6 μg ml(-1)) surpassed the effect of heparin (IC50 = 399.7 μg ml(-1)) which was used as the positive control, suggesting that P. chinense flavonoids and their related products could potentially be used as a promising natural agent in the treatment of humoral effector inflammation.

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Abstract:Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: "no change", "totally lost", "shrinking", "expanding", and "merged", in contrast to the commonly used "increase" and "decrease". This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of 'expansion' in 1984-1990 (accounted for 43.42%) and 1990-2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization.