Effects of tris (2-chloroethyl) phosphate exposure on gut microbiome using the simulator of the human intestinal microbial ecosystem (SHIME).

Tris (2-chloroethyl) phosphate (TCEP) has been widely used, and its health risk has received increasing attention. However, the rare research has been conducted on the effects of TCEP exposure on changes in the structure of the human gut microbiome and metabolic functions. In this experiment, Simulator of the human intestinal microbial ecosystem (SHIME) was applied to explore the influences of TCEP on the human gut bacteria community and structure. The results obtained from high-throughput sequencing of 16S rRNA gene have clearly revealed differences among control and exposure groups. High-dose TCEP exposure increased the Shannon and Simpson indexes in the results of α-diversity of the gut microbiome. At phylum level, Firmicutes occupied a higher proportion of gut microbiota, while the proportion of Bacteroidetes decreased. In the genus-level analysis, the relative abundance of Bacteroides descended with the TCEP exposure dose increased in the ascending colon, while the abundances of Roseburia, Lachnospira, Coprococcus and Lachnoclostridium were obviously correlated with exposure dose in each colon. The results of short chain fatty acids (SCFAs) showed a remarkable effect on the distribution after TCEP exposure. In the ascending colon, the control group had the highest acetate concentration (1.666 ± 0.085 mg⋅mL-1), while acetate concentrations in lose-dose medium-dose and high-doseTCEP exposure groups were 1.119 ± 0.084 mg⋅mL-1, 0.437 ± 0.053 mg⋅mL-1 and 0.548 ± 0.106 mg⋅mL-1, respectively. TCEP exposure resulted in a decrease in acetate and propionate concentrations, while increasing butyrate concentrations in each colon. Dorea, Fusicatenibacter, Kineothrix, Lachnospira, and Roseburia showed an increasing tendency in abundance under TCEP exposure, while they had a negatively correlation with acetate and propionate concentrations and positively related with butyrate concentrations. Overall, this study confirms that TCEP exposure alters both the composition and metabolic function of intestinal microbial communities, to arouse public concern about its negative health effects.

The disruption on gut microbiome of Decabromodiphenyl ethane exposure in the simulator of the human intestinal microbial ecosystem (SHIME).

The health risks of Decabromodiphenyl ethane (DBDPE) with its cardiovascular toxicity, liver toxicity and cytotoxicity had been generally acknowledged. However, the influence on gut microbiome and short-chain fatty acids (SCFAs) metabolism caused by DBDPE exposure remained unknown. In this study, three exposure groups (5, 50, 500 mg/L) and control group were used to investigate the effect of DBDPE by using simulator of the human intestinal microbial ecosystem (SHIME). 16S rRNA gene high-throughput sequencing illustrated that high dose DBDPE exposure increased the α-diversity of gut microbiota, while reduced the abundance of Firmicutes and Proteobacteria. In addition, the low dose (5 mg/L) DBDPE inhibited the increasing of SCFAs, but the medium and high dose (50 and 500 mg/L) DBDPE promoted the advancement, especially in ascending colon. Notably, DBDPE exposure lead a similar changing of acetic acid and butyric acid contents in different sections of the colon. This study confirmed the alternation of composition and metabolic function in gut microbial community due to DBDPE exposure, indicating an intestinal damage and appealing for more attention concentrated on the health effects of DBDPE exposure.

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS.

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system's logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.