Three new triterpene glycosides, intercedensides A (1), B (2), and C (3), were isolated from the sea cucumber Mensamria intercedens Lampert, which is found in the South China Sea, and their structures have been elucidated by spectroscopic analysis (NMR and ESIMS) and chemical transformations. Intercedensides A (1) and C (3) have a conjugated double bond (22E,24-diene) in the side chain of the aglycon. Intercedenside B (2) has two beta-D-xylose and two sulfate groups in the carbohydrate chain. All three glycosides showed significant cytotoxicity against 10 human tumor cell lines with ED(50) in the range 0.6-4.0 microg/mL. Intercedenside A (1) exhibited significant in vivo antineoplastic activity against mouse Lewis lung cancer and mouse S180 sarcoma. On the basis of these initially promising results, intercedensides A-C merit further study as potential anticancer agents.

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Proanthocyanidins were purified from avocado (Persea americana) fruit, and their structures were analyzed by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) and high-performance liquid chromatography-electrospray ionization-QTRAP mass spectrometry (HPLC-ESI-QTRAP MS) techniques. The results obtained from mass spectrometry (MS) analysis demonstrated that the proanthocyanidins were homo- and heteropolymers of procyanidins, prodelphinidins, propelargonidins, and procyanidin gallate. From the enzyme analysis, the results showed that they could inhibit the monophenolase and diphenolase activities of tyrosinase. The inhibition mechanism of the proanthocyanidins on the enzyme was further studied, and the results indicated that they were reversible and competitive inhibitors. Finally, the results acquired from molecular docking, fluorescence quenching, and copper ion interacting tests revealed that adjacent hydroxyl groups on the B ring of proanthocyanidins could chelate the dicopper catalytic center of the enzyme. In addtion, proanthocyanidins were proven to be an efficient quencher of substrates. This study would lay a scientific foundation for their use in agriculture, food, and nutrition industries.

Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km 2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method.

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A strain LF70 endophytic fungus was isolated from the leaves of Huperzia serrata. The fungus was identified as Cladosporium cladosporioides LF70 according to its morphological characteristics and nuclear ribosomal DNA ITS sequence analysis. The strain could produce Huperzine A (HupA) identified through thin layer chromatography (TLC) and high-performance liquid chromatography (HPLC) with authentic HupA. The amount of HupA produced by this endophytic fungus was quantified to be 56.84 lg/L by HPLC, which was higher than that of other reported endophytic fungi, Acremonium sp., Blastomyces sp., and Botrytis sp. Acetylcholinesterase inhibition activity of HupA produced by strain LF70 was also similar to authentic HupA in vitro. Isolation of such a fungus may provide a promising alternative approach to producing HupA, which is used in treating Alzheimer's disease and preventing further memory degeneration.