Multinomial event naive Bayesian modeling for SAGE data classification

Data mining, Classification, SAGE data, 68T10, 92D20,

An Orderly Power Utilization Scheme Based on an Intelligent Multi-Agent Apanage Management System

Orderly power utilization (OPU) is an important measure to alleviate contradiction between supply and demand in a power system peak load period. As a load management system becomes smarter, it is necessary to fully explore the interactive ability among users and make schemes for OPU more applicable. Therefore, an intelligent multi-agent apanage management system that includes a mutual aid mechanism (MAM) is proposed. In the decision-making scheme, users’ participation patterns and the potential of peak shifting and willingness are considered, as well as the interests of both power consumers and power grid are comprehensively considered. For residential users, the charging time for their electric vehicles (EVs) is managed to consume the locally distributed power generation. To fully exploit user response potential, the algorithm for improved clustering by fast search and find of density peaks (I-CFSFDP), i.e., clusters the power load curve, is proposed. To conduct electrical mutual aid among users and adjust the schemes reasonably, a multi-objective optimization model (M2OM) is established based on the cluster load curves. The objectives include the OPU control cost, the user’s electricity cost, and the consumption of distributed photovoltaic (PV). Our results of a case study show that the above method is effective and economical for improving interactive ability among users. Agents can coordinate their apanage power resources optimally. Experiments and examples verify the practicability and effectiveness of the improved algorithm proposed in this study

Uncovering the differences in flavor volatiles of different colored foxtail millets based on gas chromatography-ion migration spectrometry and chemometrics

The differences of volatile organic compounds in commercially available foxtail millets with different colors (black, green, white and yellow) were assayed through gas chromatography-ion migration spectrometry (GC-IMS) to explore their volatile flavor characteristics. Fifty-five volatile components were found in various colored foxtail millets, including 25 kinds of aldehydes (accounting for 39.19–48.69%), 10 ketones (25.36–32.37%), 15 alcohols (20.19–24.11%), 2 ethers (2.29–2.45%), 2 furans (1.49–2.95%) and 1 ester (0.27–0.39%). Aldehydes, alcohols and ketones were the chief volatiles in different colored foxtail millet, followed by furans, esters and ethers. These identified volatile flavor components in various colored foxtail millets obtained by GC-IMS could be well distinguished by principal components and cluster analysis. Meanwhile, a stable prediction model was fitted viapartial least squares-discriminant analysis (PLS-DA), in which 17 kinds of differentially volatile components were screened out based on variable importance in projection (VIP>1). These findings might provide certain information for understanding the flavor traits of colored foxtail millets in future

Antimicrobial peptides sourced from post-butter processing waste yak milk protein hydrolysates

Abstract Yak butter is one of the most important foods for the Tibetan people. Of note, its production yields waste yak milk as a by-product. In this work, waste yak milk protein hydrolysates made via Pepsin hydrolysis were shown to have antimicrobial activity. Furthermore, an innovative method of magnetic liposome adsorption combined with reversed-phase high performance liquid chromatography (RP-HPLC) was developed to screen for and purify the antimicrobial peptides. Two antimicrobial peptides were obtained and their amino acid sequences were determined by N-sequencing, namely Arg-Val-Met-Phe-Lys-Trp-Ala and Lys-Val-Ile-Ser-Met-Ile. The antimicrobial activity spectra of Arg-Val-Met-Phe-Lys-Trp-Ala included Bacillus subtilis, Staphylcoccus aureus, Listeria innocua, Escherichia coli, Enterobacter cloacae and Salmonella paratyphi, while the Lys-Val-Ile-Ser-Met-Ile peptide shows not only bacterial growth inhibition but also of fungi. Haemolytic testing suggested that these two antimicrobial peptides could be considered to have no haemolytic effect at their minimum inhibitory concentrations (MICs)

Effects of Different Yeasts on Texture and Flavor of Sweet Potato Bread

In order to investigate the effects of different yeasts on the quality and flavor of sweet potato bread, the rheological properties, color, texture and flavor of sweet potato bread were compared and analyzed. The results showed that different yeasts had significant effects on the color, texture and flavor of sweet potato bread. The results of dynamic rheology showed that the G' and G" of yeast No.4 fermented dough were significantly higher than other groups(P<0.05). The hardness, chewiness, elasticity, cohesion and resilience of commercial yeast fermented bread were significantly lower than those of old yeast fermented bread(P<0.05). The L* value and W value of No.4 old dough yeast fermented bread were significantly higher than others(P<0.05). The results of electronic nose test showed that the main flavor substance of sweet potato bread fermented by 7 different yeasts was inorganic sulfide. A total of 61 volatile compounds were detected by GC-IMS, which were mainly alcohols, ketones and acids, followed by aldehydes, ethers, esters and pyridines. Principal component analysis showed that there were significant differences in volatile flavor compounds of 7 kinds of yeast fermented breads, two principal component contribution rate of 85%, indicating GC-IMS technology could distinguish bread fermented by different yeasts. To sum up, this paper made a detailed comparative analysis of the nature and flavor differences of bread fermented by different yeasts, and would provide a scientific basis for yeast selection in bread production

A multiple spatio‐temporal features fusion approach for short‐term passenger flow forecasting in urban rail transit

Abstract Timely and precise short‐term passenger flow forecasting (STPFF) is a key to intelligent urban rail transit (URT) operation. The uncertainty of passenger travel behaviors leads to passenger flow data showing high nonlinearities and complex spatio‐temporal correlation. Therefore, the ability to capture such correlations determines the prediction performance of the model. However, current methods either consider only the physical topology of URT network or lack the ability to model global spatial dynamics, making it difficult to obtain satisfactory predictions. Therefore, a Multiple Spatio‐Temporal Features Fusion (MSTFF) network is proposed for STPFF in URT networks. Specifically, the connectivity and passenger flow similarity relationships in URT networks are encoded as physical adjacency graph and virtual functional similarity graph, respectively, and then these two complementary graphs are input into two designed Residual Graph Convolutional Gated Recurrent Unit (RGCGRU) modules to extract their spatio‐temporal features. Second, an Attention‐based Gated Recurrent Unit (AGRU) module is designed to capture global dynamic evolutionary trends in both spatial and temporal dimensions. Finally, a fully connected neural network is used to fuse these features. Experiments on two real passenger flow datasets of URT show that MSTFF performs well on various prediction tasks (15, 30, 45, and 60 min)

Characterization of non-volatile and volatile flavor profiles of Coregonus peled meat cooked by different methods

This study investigated the effects of different cooking methods on non-volatile flavor (free amino acids, 5′-nucleotides, and organic acids, etc.) of Coregonus peled meat. The volatile flavor characteristics were also analyzed by electric nose and gas chromatography-ion migration spectrometry (GC-IMS). The results indicated that the content of flavor substances in C. peled meat varied significantly. The electronic tongue results indicated that the richness and umami aftertaste of roasting were significantly greater. The content of sweet free amino acids, 5′-nucleotides, and organic acids was also higher in roasting group. Electronic nose principal component analysis can distinguish C. peled meat cooked (the first two components accounted for 98.50% and 0.97%, respectively). A total of 36 volatile flavor compounds were identified among different groups, including 16 aldehydes, 7 olefine aldehydes, 6 alcohols, 4 ketones, and 3 furans. In general, roasting was recommended and gave more flavor substances in C. peled meat

Circ‐SPECC1 modulates TGFβ2 and autophagy under oxidative stress by sponging miR‐33a to promote hepatocellular carcinoma tumorigenesis

Abstract Circular RNAs (circRNAs) play vital roles in the pathogenesis and development of multiple cancers, including hepatocellular carcinoma (HCC). Nevertheless, the regulatory mechanisms of circ‐SPECC1 in HCC remain poorly understood. In our study, we found that circ‐SPECC1 was apparently downregulated in H2O2‐treated HCC cells. Additionally, knockdown of circ‐SPECC1 inhibited cell proliferation and promoted cell apoptosis of HCC cells under H2O2 treatment. Moreover, circ‐SPECC1 inhibited miR‐33a expression by direct interaction, and miR‐33a inhibitor partially reversed the effect of circ‐SPECC1 knockdown on proliferation and apoptosis of H2O2‐treated HCC cells. Furthermore, TGFβ2 was demonstrated to be a target gene of miR‐33a and TGFβ2 overexpression rescued the phenotypes of HCC cells attenuated by miR‐33a mimics. Meanwhile, autophagy inhibition by 3‐methyladenine (3‐MA) abrogated the effect of miR‐33a mimics on proliferation and apoptosis of H2O2‐treated HCC cells. Finally, knockdown of circ‐SPECC1 hindered tumor growth in vivo. In conclusion, our study demonstrated that circ‐SPECC1 regulated TGFβ2 and autophagy to promote HCC tumorigenesis under oxidative stress via miR‐33a. These findings might provide potential treatment strategies for patients with HCC

Untargeted Metabolomics on Skin Mucus Extract of Channa argus against Staphylococcus aureus: Antimicrobial Activity and Mechanism

Microbial contamination is one of the most common food safety issues that lead to food spoilage and foodborne illness, which readily affects the health of the masses as well as gives rise to huge economic losses. In this study, Channa argus was used as a source of antimicrobial agent that was then analyzed by untargeted metabolomics for its antibacterial mechanism against Staphylococcus aureus. The results indicated that the skin mucus extract of C. argus had great inhibitory action on the growth of S. aureus, and the morphology of S. aureus cells treated with the skin mucus extract exhibited severe morphological damage under scanning electron microscopy. In addition, metabolomics analysis revealed that skin mucus extract stress inhibited the primary metabolic pathways of S. aureus by inducing the tricarboxylic acid cycle and amino acid biosynthesis, which further affected the normal physiological functions of biofilms. In conclusion, the antimicrobial effect of the skin mucus extract is achieved by disrupting cell membrane functions to induce an intracellular metabolic imbalance. Hence, these results conduce to amass novel insights into the antimicrobial mechanism of the skin mucus extract of C. argus against S. aureus