The first phytochemical investigation on stems of Mitrephora thorelii led to the isolation of three new lignanamides, thoreliamides A -C (1 -3), and a new sesquiterpenoid, thorelinin (4), together with ten known compounds. The structures of the new compounds were established on the basis of extensive spectroscopic analyses. Thoreliamide C is the first trimer derived from cinnamic acid amide units.Introduction. -The genus Mitrephora (Annonaceae), including ca. 40 species, is distributed widely throughout tropical areas in Asia. Some plants of this genus have been used as a tonic traditional medicine in Thailand [1]. So far, five species, i.e., M., and M. zippeliana [6] have been investigated. Alkaloids from M. maingayi possessing an unprecedented skeleton and diterpenoids from M. glabra bearing a novel skeleton have been successively reported. The potent and broad anticancer activity of the diterpenoids attracted us to further investigate another species of this genus, Mitrephora thorelii Pierre, distributed in southwest China.Our first phytochemical study reported here led to the isolation and characterization of three new lignanamides, thoreliamides A -C (1 -3, resp.), and a new sesquiterpene, thorelinin (4), along with ten known compounds, liriodenine, oxoputerine, N-trans-sinapoyltyramine, N-trans-feruloyltyramine, N-trans-caffeoyltyramine [7], N-trans-feruloyldopamine, N-trans-feruloyl-3-methyldopamine [8], N-p-coumaroyltyramine [9], cannabisin G, and cannabisin F [10] from stems of M. thorelii. Thoreliamide C (3) is the first cinnamic acid amide trimer reported from the plant kingdom. The structure elucidation of 1 -4 was accomplished on the basis of spectroscopic data, especially 2D-NMR.

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U2-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels.