Effect of combined electrolyzed reduced water and slightly acidic electrolyzed water spraying on the control of Salmonella, eggshell quality, and shelf life of eggs during storage.

Slightly acidic electrolyzed water (SAEW) is a safe and effective disinfectant, but its sterilizing efficiency is compromised by organic matter on the egg surface. Electrolyzed reduced water (ERW) is a harmless cleaner with a decontamination effect on a variety of surfaces and can be used to remove organic matter. This study assesses the effectiveness of a combination of ERW and SAEW in eliminating Salmonella and manure mixture from egg surfaces, as well as its impact on egg quality during storage. The results show that ERW (74.14%) was more effective than deionized water (DW, 64.69%) and SAEW (70.20%) (P < 0.05) in removing manure from egg surfaces. The damage to the cuticle of eggshell treated with ERW for 28 s was similar to that of DW (P > 0.05) and less than that of SAEW (P < 0.05). Spraying ERW for 10 s followed by SAEW for 18 s (ERW + SAEW) completely removed Salmonella from the egg surface, with no bacteria detected in the residual wash solution. Additionally, ERW + SAEW demonstrated superior preservation of egg quality during storage at 25℃ than the control and ERW single treatment (P < 0.05). Moreover, ERW + SAEW resulted in less weight loss compared to SAEW single treatment (P < 0.05). In conclusion, the sequential use of ERW and SAEW appears to be a promising approach for sterilizing eggs. It not only removes organic matter and Salmonella from the egg surface but also improves the preservation quality of the egg at 25 ℃.

GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network.

RNA N6-methyladenine (m6A), which is produced by the methylation of the N6 position of eukaryotic adenine, is a relatively common post-transcriptional modification on the surface of the molecule, which frequently plays a crucial role in biological processes. Biological experimental methods to identify m6A have been studied and implemented in recent years, but they cannot be promoted widely due to drawbacks such as the time and cost of reagents and equipment. Therefore, researchers have proposed computational strategies for identifying m6A sites, but these strategies do not account for the mechanism of methylation occurrence or the structure of RNA molecules. This study, therefore, proposed a novel deep learning model for predicting m6A sites, GR-m6A, which predicts m6A sites by extracting features from the physicochemical properties and spatial structure of molecules via residual networks. In GR-m6A, each RNA base string is represented by SMILES as two matrices comprising topology structural information and node attributes with molecular physicochemical characteristics. The feature encoding matrix was then obtained by fusing the topology matrix and the node matrix in accordance with the graphical convolutional network principle. Correspondingly, the more discriminative features were extracted from the encoding matrix using the residual neural network and predicted using a multilayer perceptron. As evident from the 5-fold cross-validation and independent validation, the GR-m6A model outperformed other existing methods. Thus, we hope that GR-m6A can aid researchers in predicting mammalian m6A loci. The source code and database are available at https://github.com/YingLiangjxau/GR-m6A.

Investigation of physico-chemical properties of hydrochar and composition of bio-oil from the hydrothermal treatment of dairy manure: Effect of type and usage volume of extractant.

Hydrothermal treatment for dairy manure into value-added hydrochar and bio-oil is a potential technology for its resource utilization. During the process of treatment, extractant is applied to the separation of hydrochar and bio-oil. In this study, three polar extractants (ethyl acetate, dichloromethane, diethyl ether) and two nonpolar extractants (n-hexane and petroleum ether) were used, and the physico-chemical properties of hydrochar and the composition of bio-oil were investigated. Compared with nonpolar extractants, polar extractants could extract the bio-oil absorbed on the hydrochar exterior and interior surface, resulting in more mass loss of hydrochar and better extraction performance on the production of bio-oil. The decrease of H/C atomic ratio and the increase of O/C atomic ratio indicated the demethanation tendency to occur during the extraction process, and enhanced the hydrochar stability. The scanning electron microscope and specific surface area analysis revealed that polar extractant had a more positive effect than nonpolar extractant on the occurrence of disperse spherical microparticles and the augment of hydrochar specific surface area. The bio-oil from polar extractant mainly consisted of N-containing compounds, ketones, phenols and acids, while the bio-oil from nonpolar extractant mainly consisted of esters, alkanes and aromatics. These results reveal that the hydrochar extracted by polar solvent exerts a greater potential for the production of carbon-based material.