Genetics alone cannot explain most cases of rheumatoid arthritis (RA). Thus, investigating environmental factors such as the gut microbiota may provide new insights into the initiation and progression of RA. In this study, we performed 16S rRNA sequencing to characterise the gut microbiota of DBA1 mice that did or did not develop arthritis after induction with collagen. We found that divergence in the distribution of microbiota after induction was pronounced and significant. Mice susceptible to collagen-induced arthritis (CIA) showed enriched operational taxonomic units (OTUs) affiliated with the genus Lactobacillus as the dominant genus prior to arthritis onset. With disease development, the abundance of OTUs affiliated with the families Bacteroidaceae, Lachnospiraceae, and S24-7 increased significantly in CIA-susceptible mice. Notably, germ-free mice conventionalized with the microbiota from CIA-susceptible mice showed a higher frequency of arthritis induction than those conventionalized with the microbiota from CIA-resistant mice. Consistently, the concentration of the cytokine interleukin-17 in serum and the proportions of CD8+T cells and Th17 lymphocytes in the spleen were significantly higher in the former group, whereas the abundances of dendritic cells, B cells, and Treg cells in the spleen were significantly lower. Our results suggest that the gut microbiome influences arthritis susceptibility.

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The detailed kinetics of the Fischer-Tropsch synthesis over an industrial Fe-Mn catalyst was studied in a continuous integral fixed-bed reactor under the conditions relevant to industrial operations [temperature, 540-600 K; pressure, 1.0-3.0 MPa; H 2 /CO feed ratio, 1.0-3.0; space velocity, (1.6-4.2) × 10 -3 Nm 3 kg of catalyst -1 s -1 ]. Reaction rate equations were derived on the basis of the Langmuir-Hinshelwood-Hougen-Watson type models for the Fischer-Tropsch reactions and the water-gas-shift reaction. Kinetic model candidates were evaluated by the global optimization of kinetic parameters, which were realized by first minimization of multiresponse objective functions with a genetic algorithm approach and second optimization with the conventional Levenberg-Marquardt method. It was found that an alkylidene mechanism based model could produce a good fit of the experimental data. This model shows that the desorption of the products and the insertion of methylene into the metal-alkylidene bond are the ratedetermining steps. The activation energy for olefins formation is 97.37 kJ mol -1 and smaller than that for the paraffin formation (111.48 kJ mol -1 ). In this model, the readsorption and secondary reactions of olefins are taken into account, and deviations of hydrocarbon distribution from the conventional ASF distribution can therefore be quantitatively described. However, the deeper information for the olefin-to-paraffin ratio has not intrinsically been described in the present stage, leaving for the further improvements in models to consider the transportationenhanced readsorption and secondary reaction of olefins more practically in the reactor modeling stage.