| 2 | Metabolic Profiling in Banana Pseudo-Stem Reveals a Diverse Set of Bioactive Compounds with Potential Nutritional and Industrial Applications显示文摘Banana(Musa spp.)is an ancient and popular fruit plant with highly nutritious fruit.The pseudo-stem of banana represents on average 75%of the total dry mass but its valorization as a nutritional and industrial by-product is limited.Recent advances in metabolomics have paved the way to understand and evaluate the presence of diverse sets of metabolites in different plant parts.This study aimed at exploring the diversity of primary and secondary metabolites in the banana pseudo-stem.Hereby,we identified and quantified 373 metabolites from a diverse range of classes including,alkaloids,flavonoids,lipids,phenolic acids,amino acids and its derivatives,nucleotide and its derivatives,organic acids,lignans and coumarins,tannins,and terpene using the widely-targeted metabolomics approach.Banana pseudo-stem is enriched in metabolites for utilization in the food industry(L-lysine and L-tryptophan,L-glutamic acid,Phenylalanine,Palmitoleic acid,α-Linolenic acid,and Lauric acid,and Adenine)and pharmaceutical industry(Guanosine and Cimidahurinine,Bergapten,Coumarins,Procyanidin A2,Procyanidin B1,Procyanidin B3,Procyanidin B2,and Procyanidin B4,Asiatic acid).The metabolome of banana pseudo-stem with integration across multiomics data may provide the opportunity to exploit the rich metabolome of banana pseudo-stem for industrial and nutritional applications. | Guiming Deng Ou Sheng Fangcheng Bi Chunyu Li Tongxin Dou Tao Dong Qiaosong Yang Huijun Gao Jing Liu Xiaohong Zhong Miao Peng Ganjun Yi Weidi He Chunhua Hu | 2020 | Phyton-International Journal of Experimental Botany2020，89，4： | 0 |
| 3 | A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder显示文摘The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model. | Pengcheng Wei Bo Wang Xiaojun Dai Li Li Fangcheng He | 2023 | Digital Communications and Networks2023，9，1： | 0 |
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