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91.
Joon‐Young Jun Sosuke Nakajima Koji Yamazaki Yuji Kawai Hajime Yasui Yasuyuki Konishi 《International Journal of Food Science & Technology》2015,50(4):871-877
Cystoseira hakodatensis is an unutilised brown algae belonging to family Sargassaceae. A crude methanol extract from the algae showed inhibitory effects on the growths of Bacillus cereus and Bacillus licheniformis. To isolate the major antimicrobial agent, a sequential active‐guided isolation procedure was applied: liquid–liquid extraction, column chromatography and bio‐autography. A marked antimicrobial agent (active α) was isolated in hydrophobic fraction and was determined to phenolics without carbohydrates and proteins by phytochemical test. Regarding the antimicrobial potential, the isolated active α showed better inhibitory effects against B. cereus and B. licheniformis at 2 and 4 times of lower concentrations (62.5 and 31.3 μg mL?1) in comparison with epigallocatechin gallate. These results showed that C. hakodatensis is a potential source of antimicrobial agent capable of preventing the growth of the two bacteria. 相似文献
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A conceptualization of intended learning outcomes supporting self‐regulated learners in indicating learning paths
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P. Tangworakitthaworn L. Gilbert G.B. Wills 《Journal of Computer Assisted Learning》2015,31(5):393-404
Intended learning outcomes (ILOs) indicate what learners will be able to achieve after they are taught. Traditionally, ILOs are expressed as plain text or unstructured documents. What if all ILOs of a specific course of study can be conceptualized through a structured diagrammatic technique? It was hypothesized that learners can benefit from this conceptualization in learning, especially in self‐regulated learning. The aim of this study was to investigate whether the ILOs represented in unstructured or structured formats can facilitate learners to identify learning paths. The results revealed that the mean ratings of all learning paths were statistically significantly higher with structured ILOs. 相似文献
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Thin Film Thermoelectric Metal–Organic Framework with High Seebeck Coefficient and Low Thermal Conductivity
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Machine Learning - We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence,... 相似文献