Core microbiota for nutrient digestion remained and ammonia utilization increased after continuous batch culture of rumen microbiota in vitro.

Introduction: This study aimed to investigate the digestive function, urea utilization ability, and bacterial composition changes in rumen microbiota under high urea (5% urea in diet) over 23 days of continuous batch culture in vitro. Methods: The gas production, dry matter digestibility, and bacterial counts were determined for the continuously batch-cultured rumen fluid (CRF). The changes in fermentation parameters, NH3-N utilization efficiency, and microbial taxa were analyzed in CRF and were compared with that of fresh rumen fluid (RF), frozen rumen fluid (FRF, frozen rumen fluid at -80°C for 1 month), and the mixed rumen fluid (MRF, 3/4 RF mixed with 1/4 CRF) with in vitro rumen fermentation. Results: The results showed that the dry matter digestibility remained stable while both the microbial counts and diversity significantly decreased over the 23 days of continuous batch culture. However, the NH3-N utilization efficiency of the CRF group was significantly higher than that of RF, FRF, and MRF groups (p < 0.05), while five core genera including Succinivibrio, Prevotella, Streptococcus, F082, and Megasphaera were retained after 23 days of continuous batch culture. The NH3-N utilization efficiency was effectively improved after continuous batch culture in vitro, and Streptococcus, Succinivibrio, Clostridium_sensu_stricto_1, p.251.o5, Oxalobacter, Bacteroidales_UCG.001, and p.1088.a5_gut_group were identified to explain 75.72% of the variation in NH3-N utilization efficiency with the RandomForest model. Conclusion: Thus, core bacterial composition and function retained under high urea (5% urea in diet) over 23 days of continuous batch culture in vitro, and bacterial biomarkers for ammonia utilization were illustrated in this study. These findings might provide potential applications in improving the efficiency and safety of non-protein nitrogen utilization in ruminants.

The effect of early colonized gut microbiota on the growth performance of suckling lambs.

The early colonized gut microbiota during the newborn period has been reported to play important roles in the health and immunity of animals; however, whether they can affect the growth performance of suckling lambs is still unclear. In this study, a total of 84 newborn lambs were assigned into LF-1 (top 15%), LF-2 (medium 70%), and LF-3 (bottom 15%) groups according to their average body weight gain at 30 days of age. Fecal samples of lambs (LF) as well as feces (MF), vagina (VAG), colostrum (COL), teat skin (TEAT) samples of ewes, and the air sediment (AIR) in the delivery room were collected 72 h after birth, and then the 16S rRNA gene was sequenced on the Illumina MiSeq platform. The results showed that the early colonized gut microbiota had a significant effect on the growth performance of suckling lambs with alpha and beta diversity (p < 0.05), and we observed that the contribution of early colonized bacteria on the growth performance of lambs increased with age (from BW30 at 25.35% to BW45 at 31.10%; from ADG30 at 33.02% to ADG45 at 39.79% by measuring the relative effects of factors that influence growth performance). The early colonized gut microbiota of suckling lambs with high growth performance was similar to that in VAG, MF, and AIR (p < 0.05). With the RandomForest machine learning algorithm, we detected 11, 11, 6, and 4 bacterial taxa at the genus level that were associated with BW30, BW45, ADG30, and ADG45 of suckling lambs, respectively, and the correlation analysis showed that Butyricicoccus, Ruminococcus_gnavus_group, Ruminococcaceae_Other, and Fusobacterium could significantly affect the growth performance (BW30, BW45, ADG30, and ADG45) of suckling lambs (p < 0.05). In conclusion, the early colonized gut microbiota could significantly affect the growth performance of suckling lambs, and targeting the early colonized gut microbiota might be an alternative strategy to improve the growth performance of suckling lambs.

A Regulatory Game Analysis of Smart Aging Platforms Considering Privacy Protection.

Privacy and information protection are important issues in the era of big data. At present, China's elderly care industry is gradually adopting the supply model of smart elderly care to alleviate the contradiction between supply and demand. However, the low level of regulation of smart aging platforms may lead to a low level of privacy protection on the platforms. Therefore, in this paper, based on the evolutionary game and Lyapunov theory, we discuss the willingness of elderly people to participate in regulation, the privacy protection status of platform service providers, and the degree of government regulation, as well as the key factors affecting the equilibrium of the three-party game system, and conduct simulation analysis and game system optimization using MATLAB. The simulation results show that A1(0,0,1) and A5(0,0,0) can be transformed to A8(1,1,0) by adjusting the parameters, i.e., the optimal ESS is participation, high-quality protection, and low investment supervision; the service income of the elderly, the loss of privacy leakage, the investment cost of service providers, and the amount of government rewards and punishments are the key factors affecting the tripartite game system. By analyzing the impact of factors, such as benefits and costs, on privacy protection and the regulation of smart senior care platforms, the level of privacy protection of smart senior care platforms can be improved and the process of the comprehensive regulation of domestic senior care services can be promoted.

A Model of Perceived Co-creation Value in Tourism Service Setting: An Application of Structure Equation Modeling.

This study explores how the perceived co-creation values (PCVs) from tourists' perspectives are applied in the customized tour arrangement service setting. The sequential qualitative and quantitative methods are adopted for this study. The initial qualitative method in terms of the proactive semi-structured interview is conducted to identify and explore the dimension of the PCV construct and to develop its measurement scale. The quantitative method by the structure equation model is employed for the proposed conceptual model fitness assessment and consolidation. Our work contributes to the progression of value co-creation research in a customized tourism context and provides a valid and reliable PCV instrument to tourism practitioners for a better service platform designing. The mediating role of customer satisfaction (CS) between PCV and customer loyalty (CL) offers service providers a deeper understanding of customer psychology and behavior, and thus, the loyal customer cultivation strategy.

Prediction model of PSO-BP neural network on coliform amount in special food.

Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.