Ultralight and robustly compressible silica aerogel enhanced by AC/C sponge with high oil/water separation

Journal of Porous Materials - In recent years, oil spills and industrial organic pollutants have caused irreparable damage to the environment and biological ecosystems. Therefore, the treatment of...     

Simultaneous fractionation of multiple classes of polyphenols from honeybush tea using solid-phase extraction

The structural diversity of polyphenols and the inherent limitations of current extraction techniques pose a challenge to extract polyphenols using a simple and green method. Hence, in this study, a method was developed to simultaneously fractionate multiple classes of polyphenols by only varying ethanol-water solutions. Honeybush tea, which is rich in polyphenols, was selected as a model for this study. Solvent extraction followed by solid-phase extraction (SPE) was developed to obtain a polyphenol-rich fraction from six honeybush samples. Based on a gradient elution programme (10%, 30%, 50%, 70% and 90% (v/v) ethanol-water solution) of SPE, the Strata X cartridge showed a better recovery of most targeted polyphenols under 0.9 mL of the drying volume and 1 mL min⁻¹ of the dispensing speed. The elution programme for fractionating most polyphenols was as follows: single elution with 50% ethanol, followed by twice elution with 70% ethanol. The antioxidant capacity was used to analyse the differences among the polyphenol-rich fractions from six honeybush samples. Principal component analysis (PCA) revealed that unfermented C. genistoides (GG) has the greatest antioxidant capacity among the honeybush species studied. Additionally, mangiferin, isomangiferin and vicenin-2 were the main contributors to the antioxidant capacity in six honeybush fractions according to the correlation study.     

Phenolic profiling reveals the metabolite basis of flesh colour and fresh-cut browning in apple fruit

Flesh colour, which is affected by cultivars and browning, can largely impact consumer acceptance in fresh-cut apples. The study profiled phenolic metabolites from apple flesh of twenty-three cultivars by widely targeted metabolomics. Comparison among white- and yellow-fleshed cultivars showed fifteen phenolics, mainly quercetin 3-O-glucoside, hyperoside, hesperetin 5-O-glucoside and cymaroside, in white-fleshed apples were significantly higher than those in yellow-fleshed apples. It may indicate a metabolite basis of yellow and white flesh colour, and better potential nutrition in white-fleshed apples. In addition, ten phenolic metabolites including five cyanidin glycosides showed significant differences between the highest and the lowest browning groups, indicating them may be crucial in browning of fresh-cut apple. This work elucidates the differences of phenolic profiles among apple cultivars with different flesh colour and provides useful data to evaluate the suitability of apple for fresh-cut processing.     

Adaptive response and tolerance to weak acids in Saccharomyces cerevisiae boulardii: a metabolomics approach

Weak acids inhibit the growth of probiotics, such as Saccharomyces boulardii. We explored the tolerance of S. boulardii to different weak acids. S. boulardii had better fermentation ability under lactic acid conditions compared with acetic and butyric acid conditions; however, the budding of S. boulardii was significantly stronger than that of Saccharomyces cerevisiae under acetic acid conditions. Although the surface structure of S. boulardii was destroyed, it produced more daughter cells. S. boulardii metabolites were also significantly different from S. cerevisiae under acidic stress. The growth of S. boulardii under weak acid conditions differed significantly from that of S. cerevisiae. S. boulardii-mediated fingerprints under weak acid conditions were identified as latent biomarkers, related to fructose and mannose metabolism, tricarboxylic acid cycle, and the glycolysis pathway. Identified biomarkers will aid in the genetic engineering of S. boulardii and other Saccharomyces strains for improved acid resistance and biomass yield.     

FireNet-MLstm for classifying liver lesions by using deep features in CT images

Multimedia Tools and Applications - Nowadays working in the medical imaging domain remains a big challenge, since the collecting such datasets is so complex, deep learning techniques have...