Ginsenosides Inhibit the Proliferation of Lung Cancer Cells and Suppress the Rate of Metastasis by Modulating EGFR/VEGF Signaling Pathways.

Ginsenosides Rg3 and Rg5 obtained from Panax (ginseng) have shown significant anticancer activity via the PI3K-Akt signaling pathway. This study evaluated the anticancer and antimetastatic effects of a combination of Rg3 and Rg5 on lung cancer cells. A combination of Rg3 and Rg5 was treated for lung cancer cell line A549 and human lung tumor xenograft mouse model, and anti-metastatic effects on Matrigel plug implantation in mice. The combination of Rg3 and Rg5 showed potent antiproliferative effects on A549 cells with IC50 values of 44.6 and 36.0 µM for Rg3 and Rg5 respectively. The combination of Rg3 and Rg5 (30 µM each) showed 48% cell viability as compared to Rg3 (72% viability) and Rg5 (64% viability) at 30 µM concentrations. The combination of Rg3 and Rg5 induced apoptosis in A549 cells characterized by activation of caspase-9 and caspase-3 and cleavage of PARP, as well as suppression of the autophagic marker LC3A/B. The antitumoral potentials of the combination of Rg3 and Rg5 were ascertained in a lung tumor xenograft mouse model with high efficacy as compared to individual ginsenosides. The metastasislimiting properties of the combination of Rg3 and Rg5 were assessed in Matrigel plug implantation in mice which showed the potent efficacy of the combination as compared to individual ginsenoside. Mechanistically, the combination of Rg3 and Rg5 inhibited the expression of PI3K/Akt/mTOR and EGFR/VEGF signaling pathways in lung cancer cells. Results suggest that the combination of Rg3 and Rg5 suppressed the tumor cell proliferation in lung cancer cells and limited the rate of metastasis which further suggest that the combination has a significant effect as compared to the administration of single ginsenoside.

Greenspace configuration impact on the urban heat island in the Olympic Area of Beijing.

As urban green spaces have significant cooling effects on the urban heat island (UHI), a precise understanding of these effects is necessary to devise precise greenspace strategies for abating the UHI. This paper explores the impacts of different greenspace (trees, grass, and water) patterns on the UHI in Beijing's Olympic Area, using different grid cell sizes and spatial statistical models. Greenspace pattern metrics include percent cover, mean patch size (MPS), mean patch shape index (MSI), edge density (ED), and largest percent index (LPI). The results show that different greenspace metrics have varying effects on surface temperature. The spatial error model (SEM) turns out to be a good choice for estimating the relationship between Land Surface Temperature (LST) and the greenspace metrics. The regression coefficients of these metrics vary with grid cell size. Tree and grass edge densities have opposite effects, which suggest that trees should be planted in smaller clusters, whereas grass should be planted in larger and continuous patches in order to reach maximum LST cooling. The optimal grid cell size is in the [120-240 m] range. These findings can help urban planners mitigate the UHI in a city with limited green space availability.

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations.

As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September-November) and winter (December-February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.

Modeling the impact of 2D/3D urban indicators on the urban heat island over different seasons: A boosted regression tree approach.

Understanding how complex urban factors affect the Urban Heat Island (UHI) is crucial for assessing the impacts of urban planning and environmental management on the thermal environment. This paper investigates the relationships between two-dimensional (2D) and three-dimensional (3D) factors and land surface temperatures (LST) within the Olympic Area of Beijing in different seasons, using the boosted regression tree (BRT) model. The BRT model captures the specific contributions of each urban factor to LST in each season and across a continuum of magnitudes for this factor. The results show that these relationships are complex and highly nonlinear. The four most common dominant factors are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), a gravity index for parks (GPI), and average building height (BH). The most important factor in spring is NDBI, with a 45.5% contribution rate. In the other seasons, NDVI is the dominant factor, with contributions of 40% in summer, 21% in autumn, and 19% in winter. NDVI has an overall negative impact on LST in spring and summer, with a quadratic nonlinear decreasing curve, but a positive one in autumn and winter. The 2D land-use variables are most strongly related to LST in summer and spring, but 3D building-related variables have stronger impacts in colder weather. The Sky View Factor (SVF), a 3D measure of urban morphology, has also strong impacts in summer and winter. Both a building-based and a DSM-based SVFs are computed. The latter accounts for buildings, bridges, and trees. In contrast to a building-based SVF, the DSM-based SVF reduces LST when it varies between 0 and 0.75, reflecting the effects of high-density tree canopies that increase shades and evapotranspiration while blocking sky view. The marginal effect curves produced by the BRT are often characterized by thresholds. For instance, the maximal NDVI effect in summer takes place when NDVI = 0.7, suggesting that a very intense green coverage is not necessary to achieve maximal thermal results. Implications for urban planning and environmental management are outlined, including the increased use of evergreen trees that provide thermal benefits in both summer and winter.

Spatiotemporal Characteristics of Air Quality across Weifang from 2014-2018.

Air pollution has become a severe threat and challenge in China. Focusing on air quality in a heavily polluted city (Weifang Cty), this study aims to investigate spatial and temporal distribution characteristics of air pollution and identify the influence of weather factors on primary pollutants in Weifang over a long period from 2014-2018. The results indicate the annual Air quality Index (AQI) in Weifang has decreased since 2014 but is still far from the standard for excellent air quality. The primary pollutants are O3 (Ozone), PM10 (Particles with aerodynamic diameter ≤10 µm), and PM2.5 (Particles with aerodynamic diameter ≤10 µm); the annual concentrations of PM10 and PM2.5 show a significant reduction but that of O3 is basically unchanged. Seasonally, PM10 and PM2.5 show a U-shaped pattern, while O3 exhibits inverted U-shaped variations, and different pollutants also present different characteristics daily. Spatially, O3 exhibits a high level in the central region and a low level in the rural areas, while PM10 and PM2.5 are high in the northwest and low in the southeast. Additionally, the concentration of pollutants is greatly affected by meteorological factors, with PM2.5 being negatively correlated with temperature and wind speed, while O3 is positively correlated with the temperature. This research investigated the spatiotemporal characteristics of the air pollution and provided important policy advice based on the findings, which can be used to mitigate air pollution.

Spatial regression models of park and land-use impacts on the urban heat island in central Beijing.

Understanding the relationship between urban land structure and land surface temperatures (LST) is important for mitigating the urban heat island (UHI). This paper explores this relationship within central Beijing, an area located within the 2nd Ring Road. The urban variables include the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Build-up Index (NDBI), the area of building footprints, the area of main roads, the area of water bodies and a gravity index for parks that account for both park size and distance. The data are captured over 8 grids of square cells (30 m, 60 m, 90 m, 120 m, 150 m, 180 m, 210 m, 240 m). The research involves: (1) estimating land surface temperatures using Landsat 8 satellite imagery, (2) building the database of urban variables, and (3) conducting regression analyses. The results show that (1) all the variables impact surface temperatures, (2) spatial regressions are necessary to capture neighboring effects, and (3) higher-order polynomial functions are more suitable for capturing the effects of NDVI and NDBI.