As a fungus with both medicinal and edible value, Wolfiporia cocos (F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components’ content fluctuates in wild and cultivated W. cocos, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of W. cocos, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated W. cocos in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of W. cocos. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated W. cocos in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of W. cocos. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for W. cocos under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of W. cocos, but also provide a basis for quality assessment of medicinal fungi.

The cyanobacterial communities in the surface and bottom waters of Sanya Bay were investigated on April 24 and 25, 2010. Flow cytometry showed that the total cyanobacterial abundance in the surface and bottom layers ranged from 0.7×10 4 to 2.38×10 4 cells mL −1 and from 1×10 4 to 1.8×10 4 cells mL −1 , respectively. Cyanobacterial diversity was analyzed using a molecular fingerprinting technique called denaturing gradient gel electrophoresis (DGGE), followed by DNA sequencing. The results were then interpreted through multivariate statistical analysis. Differences in the compositions of cyanobacterial communities were observed in the surface and bottom waters at the same station, with some bands obtained from both the surface and bottom layers, whereas some bands were present only in one layer. The predominant cyanobacterial species of the excised DGGE bands were related to Synechococcus or Synechococcus-like species (56.2%). Other phylogenetic groups identified included Chroococcidiopsis (6.3%), Cyanobium (6.3%) and some unclassified cyanobacteria (31.2%). A redundancy analysis (RDA) was conducted to reveal the relationships between the cyanobacterial community composition and environmental factors. Analysis results showed that the spatial variations in the cyanobacterial community composition in surface waters was significantly related to chlorophyll a (Chla), the biochemical oxygen demand (BOD), nitrate and phosphate (P<0.05). Meanwhile, the spatial variations in the bottom waters was significantly affected by nitrate, nitrite, and phosphate (P<0.05). Environmental parameters could explain 99.3% and 58.3% of the variations in the surface and bottom layers, respectively. cyanobacterial community composition, PCR-DGGE, Synechococcus, redundancy analysis Citation:Ling J, Zhang Y Y, Dong J D, et al. Spatial variability of cyanobacterial community composition in Sanya Bay as determined by DGGE fingerprinting and multivariate analysis.