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This investigation focused on assessing and enhancing ultrasound-assisted extraction (UAE) using natural deep eutectic solvents (NADES) to extract phenolics and terpenoids from sweet basil leaves. The initial stage involved evaluating the extraction performance of twelve NADES and ethanol. A NADES comprising lactic acid and glucose with a 2 : 1 molar ratio and 20% water content (WC) obtained the highest total phenolic content (TPC) and total terpenoid content (TTC). Single-factor experiments systematically examined the impact of liquid-to-solid ratio (LSR), water content in NADES (WC), ultrasound power, temperature, and exposure time on the extraction yield. Optimization using Box–Behnken Design (BBD) models for the lactic acid and glucose-based UAE revealed the optimal conditions to be 80 ml/g LSR, 30% water, 300 W, 50°C temperature, and a 15-minute exposure time. Under these optimized parameters, the extraction achieved the highest TPC and TTC at 69.88 mg GAE/g and 110.71 mg UA/g, respectively. This study presents an environmentally friendly and sustainable extraction protocol for the extraction of phenolic compounds and terpenoids from sweet basil leaves. [ABSTRACT FROM AUTHOR]

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set. Measurement(s) diseases and abnormal findings from chest X-ray scans Technology Type(s) AI is used to detect diseases and abnormal findings Sample Characteristic - Location Vietnam [ABSTRACT FROM AUTHOR]

Abstract  Expression of a mycorrhizal fungal-specific phosphate (P) transporter gene (HcPT1) was studied in mycelium of the ectomycorrhizal fungus Hebeloma cylindrosporum, by in situ reverse transcriptase polymerase chain reaction using amplification of complementary DNA sequences. The expression of HcPT1 was visualised under two different P treatments. Mycelium was transferred to liquid medium with or without P and incubated for 5 days. Under P starvation, mycelium growth and vitality was reduced and the expression of HcPT1 up regulated. Enzyme-labelled fluorescent substrate was used to detect gene expression in situ with epi-fluorescence microscopy and to visualise it at the level of the individual hyphae both in starved and non-starved hyphae. Up-regulation of HcPT1 was observed as a more intense fluorescent signal and from the larger proportion of hyphae that showed expression. [ABSTRACT FROM AUTHOR]