A new glycoside, recurvataside (1) and six known compounds, quinovic acid (2), quinovic acid 28-O-β-D-glucopyranosyl ester (3), 3-O-β-D-glucopyranosylquinovic acid (4), 3-O-β-Dglucopyranosylquinovic acid 28-O-β-D-glucopyranosyl ester (5), pomolic acid (6), and ursolic acid (7) were isolated from aerial parts of Mussaenda recurvata. The structure of compound 1 was identified from its spectroscopic data and by comparison with the literature.Recurvataside represents the first occurrence of δ-oleanolic acid saponin bearing two Dglucose units at C-3 and C-28 in nature. This is the first time δ-oleanane-type saponin reported in the genus Mussaenda. Compounds 1-7 were evaluated the cytotoxicity against two cancer cell lines MCF-7 and HepG2. Among them, only compound 7 exhibited moderate activity against MCF-7 and HepG2 cell lines with IC 50 value of 16.97 ± 1.55 and 20.28 ± 1.00 μM, respectively. Compounds 1-7 were also tested for their inhibitory NO production in LPS-stimulated RAW264.7 cells. Compounds 3, 5, and 7 showed significant reduction of nitrite accumulation in LPS-stimulated RAW 264.7 cells with the IC 50 values of 8. 81 ± 0.48, 13.42 ± 0.84, and 18.37 ± 0.67 μM, respectively
The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.
Chemical investigation of the ethyl acetate extract of Ficus consociata leaves, collected at Bien Hoa city, Dong Nai province, led to the isolation and structural elucidation of seven compounds, including luteolin (1), cirsiliol (2), isoquercetin (3), quercetin 3-O-α-L-arabinopyranoside (4), nikotoflorin (5), hesperidin (6) and (2E,4E,1'S,2'R,4'S,6'R)dihydrophaseic acid (7). Their chemical structures were elucidated by a combination of electronic circular dichroism (ECD) experiments and spectroscopic data (HR-MS, 1D, 2D NMR) analysis, and comparison with those reported in the literature. Although, these compounds were already known in other species, but this is the first report on chemical constituents of F. consociata.
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