Cytotoxic depsidones and xanthones from Garcinia esculenta Y. H. Li.

Six new compounds, including two depsidones garciculendepsidones A and B (1 and 2), one prenylated xanthone garciculenxanthone (3) and three dimeric xanthones bigarciculenxanthones A-C (4-6), were isolated from the twigs and leaves of Garcinia esculenta Y. H. Li. Their structures were elucidated based on comprehensive analyses of spectral data, including HRESIMS, 1D and 2D NMR, and ECD calculation. All the isolates were tested for their cytotoxicity against five human cancer cell lines (myeloid leukemia HL-60, lung cancer A-549 cells, hepatocellular carcinoma SMMC-7721, breast cancer MDA-MB-231 and colon cancer SW480), among them, compounds 3-5 displayed cytotoxic potential, especially garciculenxanthone (3) had the lowest IC50 value of 8.2 µm for lung cancer A-549 cells.

[Remote sensing inversion of surface soil organic matter at jointing stage of winter wheat based on unmanned aerial vehicle multispectral]. / åŸºäºŽæ— äººæœºçš„å†¬å°éº¦æ‹”èŠ‚æœŸè¡¨å±‚åœŸå£¤æœ‰æœºè´¨å«é‡é¥æ„Ÿåæ¼”.

The rapid monitoring of soil organic matter (SOM) content in large-scale salinized wheat fields can provide data for promoting research in saline soils and carbon cycle. Based on field sampling and remote sensing images of unmanned aerial vehicle, we established remote sensing prediction models of regional SOM using three methods, i.e., multiple linear regression (MLR), partial least squares (PLSR), and support vector machine regression (SVR) for bare land and wheat field, respectively. The models were validated and compared to identify the optimal inversion model of SOM. Moreover, the SOM in the area was inverted using the optimal model, with the inversion results being compared with the data by interpolation. The results showed that the spectrum after the filtering of 5×5 median was best related to surface SOM. Among the three models, the SVR model had the highest prediction accuracy, followed by the PLSR, while the MLR lowest. The SVR model was the best one for estimating wheat field, with coefficient of determination (R2) and root mean square error (RMSE) of 0.89 and 0.20, respectively, and the validated R2 and RMSE were 0.82 and 0.24, respectively. The bare land SOM was also best fitted by the SVR model, with R2 and RMSE were 0.63, 0.26, respectively, and the verified R2 and RMSE were 0.61, 0.25, respectively, but without statistical significance. The inversion of the optimal model revealed that SOM content in this region ranged from 17.51 to 22.53 g·kg-1, with an average of 19.51 g·kg-1, which was generally consistent with the field measurement. Compared with the inversion results, the interpolation data were limited in accuracy. Overall, our study suggested that the unmanned aerial vehicle-based multi-spectral analysis could be applied to quick and accurate estimation of SOM content in saline soil at the jointing stage of winter wheat.

Impact of groundwater depth and soil salinity on riparian plant diversity and distribution in an arid area of China.

Riparian plant diversity in arid regions is sensitive to changes in groundwater. Although it is well known that groundwater has a significant influence on plant diversity, there have been few studies on how groundwater and soil salinity impact plant community in desert riparian ecosystems. Therefore, we surveyed 77 quadrats (100 m × 100 m) to examine the relationship between groundwater depth, groundwater salinity, soil salinity and plant community in the upper reaches of the Tarim River. Data were analyzed with two-way indicator species analysis (TWINSPAN), detrended canonical correspondence analysis (DCCA) and principal component analysis (PCA). The results indicated that Populus euphratica, Tamarix ramosissima, and Phragmites australis were the dominant plants among trees, shrubs and herbs, respectively. Five plant community types were classified. There were significant differences in species diversity, soil moisture, soil salinity, groundwater depth and groundwater salinity across the community types. The composition and distribution of plant community are significantly influenced by groundwater depth, groundwater salinity, soil moisture, distances from the river to the quadrats, soil pH, electrical conductivity, total salt, CO32-, Cl-, SO42-, Ca2+, Mg2+, Na+ and K+. Shallow groundwater depth, low groundwater salinity, and high soil moisture and soil salinity were associated with higher plant diversity.