Background. More and more studies focus on the relationship between the gastrointestinal microbiome and type 2 diabetes, but few of them have actually explored the relationship between enterotypes and type 2 diabetes. Materials and Methods. We enrolled 134 patients with type 2 diabetes and 37 nondiabetic controls. The anthropometric and clinical indices of each subject were measured. Fecal samples of each subject were also collected and were processed for 16S rDNA sequencing. Multiple logistic regression analysis was used to determine the associations of enterotypes with type 2 diabetes. Multiple linear regression analysis was used to explore the relationship between lipopolysaccharide levels and insulin sensitivity after adjusting for age, BMI, TG, HDL-C, DAO, and TNF-α. The correlation analysis between factors and microbiota was identified using Spearman correlation analysis. The correlation analysis between factors was identified using partial correlation analysis. Results. Gut microbiota in type 2 diabetes group exhibited lower bacterial diversity compared with nondiabetic controls. The fecal communities from all subjects clustered into two enterotypes distinguished by the levels of Bacteroides and Prevotella. Logistic regression analysis showed that the Bacteroides enterotype was an independent risk factor for type 2 diabetes by decreasing insulin sensitivity. The levels of lipopolysaccharide and tumor necrosis factor-alpha were higher in the Bacteroides enterotype compared to the Prevotella enterotype. Partial correlation analysis showed that lipopolysaccharide was closely associated with diamine oxidase, tumor necrosis factor-alpha, and Gutt insulin sensitivity index after adjusting for multiple covariates. Furthermore, the level of lipopolysaccharide was found to be an independent risk factor for insulin sensitivity. Conclusions. We identified two enterotypes, Bacteroides and Prevotella, among all subjects. Our results showed that the Bacteroides enterotype was an independent risk factor for type 2 diabetes, which was due to increased levels of lipopolysaccharide causing decreased insulin sensitivity.

Abstract:The main challenge of precise point positioning (PPP) applications is the long convergence time of typically a half hour, or even more, to achieve centimeter accuracy. Even when the multi-constellation is involved and ambiguity resolution is implemented, it still requires about ten minutes. It is becoming a hot spot to incorporate the low Earth orbit (LEO) satellite constellation for enhancing the Global Navigation Satellite System (GNSS), named here as LEO-enhanced GNSS (LeGNSS). In this system, the LEO satellites cannot only receive GNSS signals, but also serve as GNSS satellites by transmitting similar navigation signals to the ground users, but with higher signal strength and much faster geometric change due to their low altitude. As a result, the convergence time of PPP is expected to be shortened to a few minutes, or even seconds. Simulation software is developed to simulate GNSS and LEO observations for ground stations taking into account tropospheric delay, satellite clock errors, observation noises, as well as other error sources. Then the number of visible satellites, the geometry dilution of precision (GDOP), and the convergence time of the kinematic mode of PPP are evaluated on a global scale compared to those of GNSS systems. The simulation results show that LeGNSS can decrease the PPP convergence to 5 min. If there are more LEO satellites included in the LeGNSS, it is expected that the initialization of PPP can be further shortened.

No abstract