Antineurodegenerative Labdane Diterpenoid Glycosides from the Twigs of Pinus koraiensis

2020 ◽  
Vol 83 (6) ◽  
pp. 1794-1803 ◽  
Author(s):  
Kyoung Jin Park ◽  
Zahra Khan ◽  
Lalita Subedi ◽  
Sun Yeou Kim ◽  
Kang Ro Lee
2018 ◽  
pp. 119-123
Author(s):  
V.G. SHVEDOV ◽  
E.V. STELMAH ◽  
I.L. REVUTSKAYA ◽  
I.S. GUMENNY

2020 ◽  
Vol 100 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Guoyong Yan ◽  
Xiongde Dong ◽  
Binbin Huang ◽  
Honglin Wang ◽  
Ziming Hong ◽  
...  

We conducted a field experiment with four levels of simulated nitrogen (N) deposition (0, 2.5, 5, and 7.5 g N m−2 yr−1, respectively) to investigate the response of litter decomposition of Pinus koraiensis (PK), Tilia amurensis (TA), and their mixture to N deposition during winter and growing seasons. Results showed that N addition significantly increased the mass loss of PK litter and significantly decreased the mass loss of TA litter throughout the 2 yr decomposition processes, which indicated that the different responses in the decomposition of different litters to N addition can be species specific, potentially attributed to different litter chemistry. The faster decomposition of PK litter with N addition occurred mainly in the winter, whereas the slower decomposition of TA litter with N addition occurred during the growing season. Moreover, N addition had a positive effect on the release of phosphorus, magnesium, and manganese for PK litter and had a negative effect on the release of carbon, iron, and lignin for TA litter. Decomposition and nutrient release from mixed litter with N addition showed a non-additive effect. The mass loss from litter in the first winter and over the entire study correlated positively with the initial concentration of cellulose, lignin, and certain nutrients in the litter, demonstrating the potential influence of different tissue chemistries.


Trees ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 1389-1396 ◽  
Author(s):  
Yumei Zhou ◽  
Marcus Schaub ◽  
Lianxuan Shi ◽  
Zhongling Guo ◽  
Anan Fan ◽  
...  

Author(s):  
Meng Ji ◽  
Guangze Jin ◽  
Zhili Liu

AbstractInvestigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth. However, few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species. We divided 67 Pinus koraiensis Sieb. et Zucc. of various sizes (0.3–100 cm diameter at breast height, DBH) into four ontogenetic stages, i.e., young trees, middle-aged trees, mature trees and over-mature trees, and measured the leaf mass per area (LMA), leaf dry matter content (LDMC), and mass-based leaf nitrogen content (N) and phosphorus content (P) of each leaf age group for each sampled tree. One-way analysis of variance (ANOVA) was used to describe the variation in leaf traits by ontogenetic stage and leaf age. The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships. We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age. Most trait-trait relationships were consistent with the leaf economic spectrum (LES) at a global scale. Among them, leaf N content and LDMC showed a significant negative correlation, leaf N and P contents showed a significant positive correlation, and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage. LMA and LDMC showed a significant positive correlation, and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age. Additionally, there were no significant relationships between leaf N content and LMA in most groups, which is contrary to the expectation of the LES. Overall, in the early ontogenetic stages and leaf ages, the leaf traits tend to be related to a "low investment-quick returns" resource strategy. In contrast, in the late ontogenetic stages and leaf ages, they tend to be related to a "high investment-slow returns" resource strategy. Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny.


Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Kirill A. Korznikov ◽  
Dmitry E. Kislov ◽  
Jan Altman ◽  
Jiří Doležal ◽  
Anna S. Vozmishcheva ◽  
...  

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species.


Trees ◽  
2012 ◽  
Vol 27 (2) ◽  
pp. 389-399 ◽  
Author(s):  
Caifeng Yan ◽  
Shijie Han ◽  
Yumei Zhou ◽  
Xingbo Zheng ◽  
Dandan Yu ◽  
...  

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 5574-5585
Author(s):  
Intan Fajar Suri ◽  
Jong Ho Kim ◽  
Byantara Darsan Purusatama ◽  
Go Un Yang ◽  
Denni Prasetia ◽  
...  

Color changes were tested and compared for heat-treated Paulownia tomentosa and Pinus koraiensis wood treated with hot oil or hot air for further utilization of these species. Hot oil and hot air treatments were conducted at 180, 200, and 220 °C for 1, 2, and 3 h. Heat-treated wood color changes were determined using the CIE-Lab color system. Weight changes of the wood before and after heat treatment were also determined. The weight of the oil heat-treated wood increased considerably but it decreased in air heat-treated wood. The oil heat-treated samples showed a greater decrease in lightness (L*) than air heat-treated samples. A significant change in L* was observed in Paulownia tomentosa. The red/green chromaticity (a*) of both wood samples increased at 180 and 200 °C and slightly decreased at 220 °C. The yellow/blue chromaticity (b*) in both wood samples increased at 180 °C, but it rapidly decreased with increasing treatment durations at 200 and 220 °C. The overall color change (ΔE*) in both heat treatments increased with increasing temperature, being higher in Paulownia tomentosa than in Pinus koraiensis. In conclusion, oil heat treatment reduced treatment duration and was a more effective method than air heat treatment in improving wood color.


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