Silibinin reduces in vitro methane production by regulating the rumen microbiome and metabolites

This study used Silibinin as an additive to conduct fermentation experiments, wherein its effects on rumen gas production, fermentation, metabolites, and microbiome were analyzed in vitro. The silibinin inclusion level were 0 g/L (control group), 0.075 g/L, 0.15 g/L, 0.30 g/L, and 0.60 g/L (experimental group). Fermentation parameters, total gas production, carbon dioxide (CO2), methane (CH4), hydrogen (H2), and their percentages were determined. Further analysis of the rumen microbiome’s relative abundance and α/β diversity was performed on the Illumina NovaSeq sequencing platform. Qualitative and quantitative metabolomics analyses were performed to analyze the differential metabolites and metabolic pathways based on non-targeted metabolomics. The result indicated that with an increasing dose of silibinin, there was a linear reduction in total gas production, CO2, CH4, H2 and their respective percentages, and the acetic acid to propionic acid ratio. Concurrent with a linear increase in pH, when silibinin was added at 0.15 g/L and above, the total volatile fatty acid concentration decreased, the acetic acid molar ratio decreased, the propionic acid molar ratio increased, and dry matter digestibility decreased. At the same time, the relative abundance of Prevotella, Isotricha, Ophryoscolex, unclassified_Rotifera, Methanosphaera, Orpinomyces, and Neocallimastix in the rumen decreased after adding 0.60 g/L of silibinin. Simultaneously, the relative abundance of Succiniclasticum, NK4A214_group, Candidatus_Saccharimonas, and unclassified_Lachnospiraceae increased, altering the rumen species composition, community, and structure. Furthermore, it upregulated the ruminal metabolites, such as 2-Phenylacetamide, Phlorizin, Dalspinin, N6-(1,2-Dicarboxyethyl)-AMP, 5,6,7,8-Tetrahydromethanopterin, Flavin mononucleotide adenine dinucleotide reduced form (FMNH), Pyridoxine 5′-phosphate, Silibinin, and Beta-D-Fructose 6-phosphate, affecting phenylalanine metabolism, flavonoid biosynthesis, and folate biosynthesis pathways. In summary, adding silibinin can alter the rumen fermentation parameters and mitigate enteric methane production by regulating rumen microbiota and metabolites, which is important for developing novel rumen methane inhibitors

The Antimicrobial Peptide Mastoparan X Protects Against Enterohemorrhagic Escherichia coli O157:H7 Infection, Inhibits Inflammation, and Enhances the Intestinal Epithelial Barrier

Escherichia coli can cause intestinal diseases in humans and livestock, destroy the intestinal barrier, exacerbate systemic inflammation, and seriously threaten human health and animal husbandry development. The aim of this study was to investigate whether the antimicrobial peptide mastoparan X (MPX) was effective against E. coli infection. BALB/c mice infected with E. coli by intraperitoneal injection, which represents a sepsis model. In this study, MPX exhibited no toxicity in IPEC-J2 cells and notably suppressed the levels of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), myeloperoxidase (MPO), and lactate dehydrogenase (LDH) released by E. coli. In addition, MPX improved the expression of ZO-1, occludin, and claudin and enhanced the wound healing of IPEC-J2 cells. The therapeutic effect of MPX was evaluated in a murine model, revealing that it protected mice from lethal E. coli infection. Furthermore, MPX increased the length of villi and reduced the infiltration of inflammatory cells into the jejunum. SEM and TEM analyses showed that MPX effectively ameliorated the jejunum damage caused by E. coli and increased the number and length of microvilli. In addition, MPX decreased the expression of IL-2, IL-6, TNF-α, p-p38, and p-p65 in the jejunum and colon. Moreover, MPX increased the expression of ZO-1, occludin, and MUC2 in the jejunum and colon, improved the function of the intestinal barrier, and promoted the absorption of nutrients. This study suggests that MPX is an effective therapeutic agent for E. coli infection and other intestinal diseases, laying the foundation for the development of new drugs for bacterial infections

discovery of working activities by email analysis

Email has become one of the most widely used computer applications. As the number of emails we exchange increases at high rate, the number of uses for email increases. Email data patterns may give us other useful information such as personal activities. However, there is no proper visualization tool which can meet this purpose. Our goal is to explore working activity involved in email communication using Treemap algorithm. The results show that the Treemap layout can successfully present the various activities involved in the email flow

QSAR models of reaction rate constants of alkenes with ozone and hydroxyl radical

The reaction rate constants of ozone with 95 alkenes (-logkO3) and the hydroxyl radical (•OH) with 98 alkenes (-logkOH) in the atmosphere were predicted by quantitative structure-activity relationship (QSAR) models. Density functional theory (DFT) calculations were carried out on respective ground-state alkenes and transition-state structures of degradation processes in the atmosphere. Stepwise multiple linear regression (MLR) and general regression neural network (GRNN) techniques were used to develop the models. The GRNN model of -logkO3 based on three descriptors and the optimal spread σ of 0.09 has the mean root mean square (rms) error of 0.344; the GRNN model of -logkOH having four descriptors and the optimal spread σ of 0.14 produces the mean rms error of 0.097. Compared with literature models, the GRNN models in this article show better statistical characteristics. The importance of transition state descriptors in predicting kO3 and kOH of atmospheric degradation processes has been demonstrated

EPPDC: An Efficient Privacy-Preserving Scheme for Data Collection in Smart Grid

Different from the traditional grid, smart grid builds a real-time connection network between the user and the grid company by smart terminals, which can achieve bidirectional data transmission and information control. In smart grid, the smart meters send various information to the power generators and substations. Frequent data collection meets real-time management, but it tends to raise privacy concerns from the users about privacy information leakage. Based on the blind signature and the key distribution scheme, an efficient and privacy-preserving data collection (EPPDC) scheme is proposed for smart grid to cope with the above problems. In EPPDC scheme, the users' data information is transmitted to the local aggregator by building gateway with privacy preserving. In addition, the security analysis indicates that EPPDC scheme not only can resist replay attack, but also has source authentication and data integrity, confidentiality, unforgeability, nonrepudiation, and evolution of shared keys. Furthermore, performance analysis shows that EPPDC scheme has less computation cost than existing scheme

QSAR Models of Reaction Rate Constants of Alkenes with Ozone and Hydroxyl Radical

The reaction rate constants of ozone with 95 alkenes (-logkO3) and the hydroxyl radical (•OH) with 98 alkenes (-logkOH) in the atmosphere were predicted by quantitative structure-activity relationship (QSAR) models. Density functional theory (DFT) calculations were carried out on respective ground-state alkenes and transition-state structures of degradation processes in the atmosphere. Stepwise multiple linear regression (MLR) and general regression neural network (GRNN) techniques were used to develop the models. The GRNN model of -logkO3 based on three descriptors and the optimal spread σ of 0.09 has the mean root mean square (rms) error of 0.344; the GRNN model of -logkOH having four descriptors and the optimal spread σ of 0.14 produces the mean rms error of 0.097. Compared with literature models, the GRNN models in this article show better statistical characteristics. The importance of transition state descriptors in predicting kO3 and kOH of atmospheric degradation processes has been demonstrated

Study on the features of coal rock failure potential signal based on multiscale multifractal analysis method

The surface potential signals induced by the deformation and failure of coal and rock contain key information on damage evolution. It has been widely studied in the field of coal and rock dynamic disaster monitoring. However, most of these studies focus on the fluctuation features of potential time series signals in a single time dimension. There is a lack of in-depth research on the nonlinear and multiscale feature changes of the time series signals. To solve this problem, a monitoring system for the potential of coal and rock failure is built, and the potential time series signals of raw coal and gabbro samples are synchronously tested. Through the multiscale multifractal analysis (MMA) method, the nonlinear features of potential signals at multiple scales are studied in depth. The singularity index, singularity dimension, local Hurst index and other parameters of the potential time series signals are obtained. The Hurst surface is quantified by the L2 norm. The experimental results show that the overall potential signals of raw coal and gabbro show multiscale multifractal features, and the potential multifractal maps before and after crack initiation show some differences. Compared with gabbro, the positive and negative trends of the singularity index difference Δα of the potential signals of the coal samples at different positions in the pre-loading and post-loading phases show different features. It indicates a stronger non-linear evolution of the coal samples. The L2 norm of the local Hurst index at multiple scales better reflects the long-range correlation between different channel potential signals of the sample. It can quantify the nonlinear evolution features of the sample time series signals, thereby achieving the prediction of coal rock instability and failure