Five new flavonoids, cryptoconones A-E (1-5), along with six known compounds (6-11), were isolated from the stems of Cryptocarya concinna. The structures of these compounds were elucidated on the basis of spectroscopic data interpretation, and the absolute configurations were determined via circular dichroism spectra and X-ray crystal analysis. The cytotoxic and antimicrobial activities of these compounds were also evaluated. Compounds 9 and 10 exhibited moderate cytotoxic activities against HCT116, HT-29, SW480, and MDA-MB-231 cell lines with IC50 values ranging from 6.25 to 9.35 µM. Compounds 8 and 11 exhibited antimicrobial activity against Fusarium moniliforme and Botrytis cinerea, respectively, with the same minimum inhibitory concentration of 5 µg/mL.

The brown planthopper (Nilaparvata lugens, BPH) is a destructive insect pest posing a serious threat to rice production. The fungal entomopathogen Metarhizium anisopliae is a promising alternative that can be used for BPH biocontrol. Recent studies have highlighted the significant involvement of gut microbiota in the insect–fungus interactions. In the presented study, we investigated the effects of topical fungal infection on the gut microbial community structure in BPH. Our results revealed that topical infection with M. anisopliae increased the bacterial load and altered the bacterial community structure in the gut of BPH. The relative abundances of the dominant gut bacteria at the order, family and genus level were significantly different between fungus-infected and uninfected groups. At the genus level, the uninfected BPH harbored high proportions of Pantoea and Enterobacter in the gut, whereas the fungus-infected BPH gut was absolutely dominated by Acinetobacter. Moreover, topical fungal infection significantly inhibited the expressions of immune-related genes encoding anti-microbial protein and dual oxidase that were involved in the maintenance of gut microbiota homeostasis, indicating that gut bacteria imbalance might be attributed in part to the suppression of gut immunity caused by fungal pathogen. Our results highlighted the importance of the gut microbial community during interactions between fungal pathogens and insect hosts.

Seven novel oligostilbenoids, isolated from Hopea chinensis, may biosynthetically derived from the common intermediate vaticanol A.

Load forecasting, as the baseline for decision-making, plays a key role in the management and control of the grid. Nevertheless, the rapid evolution of the smart grid has brought a dramatic increase in the volume of user-side data, traditional load forecasting approaches have to face the challenge of ensuring the accuracy of dynamic forecasting under the circumstance of the widespread application of big data. Meanwhile, the advance of the Industrial Internet of Things (IIoT) enables smart meters to acquire more plentiful data, which improves the accuracy of short-term load forecasting with appropriate utilization, but the gradual increase in the magnitude of data brought by IIoT technique has also left the computing equipment under great pressure. To address these challenges, in this paper, a Long Short-Term Memory(LSTM) network based short-term load forecasting approach deployed in the distributed cloud-edge environment, named Alice, is devised to deliver more precise results for the smart forecasting of power load. It adopts the LSTM network to perform the forecasting tasks and extends the whole system to the cloud-edge platform to implement parallel neural computing. Moreover, the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm is invited to enhance the efficiency of LSTM. Eventually, experimental evaluations on real dataset elaborate the superiority of the proposed Alice approach when compared to other state-of-the-art approaches.