The high mortality rate of immunocompromised patients with fungal infections and the limited availability of highly efficacious and safe agents demand the development of new antifungal therapeutics. To rapidly discover such agents, we developed a high-throughput synergy screening (HTSS) strategy for novel microbial natural products. Specifically, a microbial natural product library was screened for hits that synergize the effect of a low dosage of ketoconazole (KTC) that alone shows little detectable fungicidal activity. Through screening of Ϸ20,000 microbial extracts, 12 hits were identified with broadspectrum antifungal activity. Seven of them showed little cytotoxicity against human hepatoma cells. Fractionation of the active extracts revealed beauvericin (BEA) as the most potent component, because it dramatically synergized KTC activity against diverse fungal pathogens by a checkerboard assay. Significantly, in our immunocompromised mouse model, combinations of BEA (0.5 mg/kg) and KTC (0.5 mg/kg) prolonged survival of the host infected with Candida parapsilosis and reduced fungal colony counts in animal organs including kidneys, lungs, and brains. Such an effect was not achieved even with the high dose of 50 mg/kg KTC. These data support synergism between BEA and KTC and thereby a prospective strategy for antifungal therapy.antifungal ͉ beauvericin ͉ ketoconazole
Using monthly meteorological observation data at 633 sites in China during 1961-2012, the drought severity change has been investigated in terms of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) with potential evapotranspiration estimated by the Penman-Monteith equation (SPEI_pm). Significant wetting appeared to have occurred in northwestern corner of China (Xinjiang Province), especially in winter. The middle to northeastern Tibetan Plateau also experienced wetting in the last 52 years in general. Significantly, drying occurred in Central China (mostly in the middle Yellow River basin) and southwestern China (Yunnan-Guizhou Plateau) in spring and in autumn. There is no evidence of an increase in drought severity over China taking the whole country into account. On the contrary, the hyper-arid and arid zones got significantly wetter in the last 52 years as indicated by both SPI and SPEI.
The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.
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