Effect of Electron-Beam Irradiation on Functional Compounds and Biological Activities in Peanut Shells.

Peanut shells, rich in antioxidants, remain underutilized due to limited research. The present study investigated the changes in the functional compound content and skin aging-related enzyme inhibitory activities of peanut shells by electron-beam treatment with different sample states and irradiation doses. In addition, phenolic compounds in the peanut shells were identified and quantified using ultra-performance liquid chromatography with ion mobility mass spectrometry-quadrupole time-of-flight and high-performance liquid chromatography with a photodiode array detector, respectively. Total phenolic compound content in solid treatment gradually increased from 110.31 to 189.03 mg gallic acid equivalent/g as the irradiation dose increased. Additionally, electron-beam irradiation significantly increased 5,7-dihydroxychrome, eriodictyol, and luteolin content in the solid treatment compared to the control. However, liquid treatment was less effective in terms of functional compound content compared to the solid treatment. The enhanced functional compound content in the solid treatment clearly augmented the antioxidant activity of the peanut shells irradiated with an electron-beam. Similarly, electron-beam irradiation substantially increased collagenase and elastase inhibitory activities in the solid treatment. Mutagenicity assay confirmed the stability of toxicity associated with the electron-beam irradiation. In conclusion, electron-beam-irradiated peanut shells could serve as an important by-product with potential applications in functional cosmetic materials.

Device-Algorithm Co-Optimization for an On-Chip Trainable Capacitor-Based Synaptic Device with IGZO TFT and Retention-Centric Tiki-Taka Algorithm.

Analog in-memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on-chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal-oxide semiconductor (Si-CMOS) and capacitor-based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si-CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n-type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on-chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non-idealities such as drifting references and long-term retention loss is proposed, demonstrating the importance of device-algorithm co-optimization.

Effect of Atmospheric-Pressure Plasma on Functional Compounds and Physiological Activities in Peanut Shells.

Peanut (Arachis hypogaea L.) shell, an abundant by-product of peanut production, contains a complex combination of organic compounds, including flavonoids. Changes in the total phenolic content, flavonoid content, antioxidant capacities, and skin aging-related enzyme (tyrosinase, elastase, and collagenase)-inhibitory activities of peanut shell were investigated after treatment in pressure swing reactors under controlled gas conditions using surface dielectric barrier discharge with different plasma (NOx and O3) and temperature (25 and 150 °C) treatments. Plasma treatment under ozone-rich conditions at 150 °C significantly affected the total phenolic (270.70 mg gallic acid equivalent (GAE)/g) and flavonoid (120.02 mg catechin equivalent (CE)/g) contents of peanut shell compared with the control (253.94 and 117.74 mg CE/g, respectively) (p < 0.05). In addition, with the same treatment, an increase in functional compound content clearly enhanced the antioxidant activities of components in peanut shell extracts. However, the NOx-rich treatment was significantly less effective than the O3 treatment (p < 0.05) in terms of the total phenolic content, flavonoid content, and antioxidant activities. Similarly, peanut shells treated in the reactor under O3-rich plasma conditions at 150 ℃ had higher tyrosinase, elastase, and collagenase inhibition rates (55.72%, 85.69%, and 86.43%, respectively) compared to the control (35.81%, 80.78%, and 83.53%, respectively). Our findings revealed that a reactor operated with O3-rich plasma-activated gas at 150 °C was better-suited for producing functional industrial materials from the by-products of peanuts.

Tissue-specific distribution of primary and secondary metabolites of Baemoochae (×Brassicoraphanus) and its changes as a function of developmental stages.

The distribution and changes in the primary and secondary metabolite profiles of Baemoochae, an inter-generic hybrid of Chinese cabbage and radish, during the plant's developmental stages were investigated. Metabolites were analyzed using gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight (UHPLC-ESI-qTOF MS). Free sugar, organic acid, and amino acid composition depended on the tissue type and developmental stage of Baemoochae. For example, glucose and alanine levels were higher in mature leaves than in young leaves; citric acid content in mature roots was lower than that in young roots. Several glucosinolates were identified for the first time in Baemoochae. Glucoraphasatin was predominant in both leaves and roots, regardless of plant maturity. Total glucosinolate content was significantly higher in roots than in leaves and in mature than in young plants. The roots of mature Baemoochae could be used as a rich source of glucosinolates, with several potential health-promoting effects.

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

BACKGROUND: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. OBJECTIVE: This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. METHODS: We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. RESULTS: LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. CONCLUSIONS: We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Correction to: Differential expression of major genes involved in the biosynthesis of aliphatic glucosinolates in intergeneric Baemoochae (Brassicaceae) and its parents during development.

Due to an unfortunate turn of events, an incorrect note was provided in the original publication as it should have read.

Differential expression of major genes involved in the biosynthesis of aliphatic glucosinolates in intergeneric Baemoochae (Brassicaceae) and its parents during development.

KEY MESSAGE: Thus study found the temporal and spatial relationship between production of aliphatic glucosinolate compounds and the expression profile of glucosinolate-related genes during growth and development in radish, Chinese cabbage, and their intergeneric hybrid baemoochae plants. Glucosinolates (GSLs) are one of major bioactive compounds in Brassicaceae plants. GSLs play a role in defense against microbes as well as chemo-preventative activity against cancer, which draw attentions from plant scientists. We investigated the temporal relationship between production of aliphatic Glucosinolate (GSLs) compounds and the expression profile of GSL related genes during growth and development in radish, Chinese cabbage, and their intergeneric hybrid, baemoochae. Over the complete life cycle, Glucoraphasatin (GRH) and glucoraphanin (GRE) predominated in radish, whereas gluconapin (GNP), glucobrassicanapin (GBN), and glucoraphanin (GRA) abounded in Chinese cabbage. Baemoochae contained intermediate levels of all GSLs studied, indicating inheritance from both radish and Chinese cabbage. Expression patterns of BCAT4, CYP79F1, CYP83A1, UGT74B1, GRS1, FMOgs-ox1, and AOP2 genes showed a correlation to their corresponding encoded proteins in radish, Chinese cabbage, and baemoochae. Interestingly, there is a sharp change in gene expression pattern involved in side chain modification, particularly GRS1, FMOgs-ox1, and AOP2, among these plants during the vegetative and reproductive stage. For instance, the GRS1 was strongly expressed during leaf development, while both of FMOgs-ox1 and AOP2 was manifested high in floral tissues. Furthermore, expression of GRS1 gene which is responsible for GRH production was predominantly expressed in leaf tissues of radish and baemoochae, whereas it was only slightly detected in Chinese cabbage root tissue, explaining why radish has an abundance of GRH compared to other Brassica plants. Altogether, our comprehensive and comparative data proved that aliphatic GSLs biosynthesis is dynamically and precisely regulated in a tissue- and development-dependent manner in Brassicaceae family members.

Association between Nutrition Label Reading and Nutrient Intake in Korean Adults: Korea National Health and Nutritional Examination Survey, 2007-2009 (KNHANES IV).

BACKGROUND: Nutrition labels provide various information on the nutrient contents of food. However, despite the recent increase in the interest in dietary intake and expansion of related policies, studies on the association between nutrition label reading and dietary intake are lacking in Korea. METHODS: This study analyzed the 2007-2009 KNHANES (Korean National Health and Nutrition Examination Survey) data. To examine macronutrients and micronutrients intake according to nutrition label reading, analysis of covariance was used. Multiple logistic regression analysis was also used to examine the association between adherence to dietary reference intake and nutrition label reading. RESULTS: Nutrition label reading was significantly high among women, youth, and those with high education and high household income. Nutrition label reading was associated with higher intake of calcium and vitamin C in men and the lower intake of calorie, carbohydrates and higher energy ratio of protein in women. Additionally, male nutrition label readers were associated with adherence to dietary reference intake of fiber (odds ratio [OR], 2.00; 95% confidence interval [CI], 1.23 to 3.26) and calcium (OR, 1.26; 95% CI, 1.03 to 1.54). In women, there were no significant differences in the adherence to the dietary reference intake in fat, fiber, sodium, potassium, and calcium according to the nutrition label reading. CONCLUSION: In men, nutrition label reading was associated with healthier intake of several micronutrients, although this was not observed in women. Consideration for clearly reporting vulnerable micronutrients in nutrition labels is necessary.