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Additional file 1: Figure S1. Characterization of MSNs. a Transmission electron micrographs of MSNs of different sizes (scale bar = 200 nm). b Corresponding histogram and Gaussian fit of the measured MSN size distribution. Figure S2. Absorption spectroscopy of MSN-Cy3, FITC-Bifidobacterium, and MSNs-Bifidobacterium. Figure S3. Representative images of colonies formed by MSNs-Bi on the culture medium were used to evaluate the activity of Bifidobacterium in Krebs-Henseleit solution. Figure S4. Characterization of MSN-encapsulated Bifidobacterium and E. coli. MSNs loading rates for Bifidobacterium (a) and E. coli (b) and the viability of Bifidobacterium (c) and E. coli (d) during exposure to SIF. Figure S5. Distribution of MSNs-Bi in the gastrointestinal tract after intranasal administration. DAPI (blue), FITC (green), and Cy3 (Red). (scale bar = 200 μm). Figure S6. Distribution of MSNs-Bi in the lung after intranasal administration. DAPI (blue), FITC (green), and Cy3 (Red). (scale bar = 200 μm). Figure S7. Alpha diversity analysis of the gut microbiome of C57BL/6 mice (WT) and APP/PS1 mice treated with PBS, MSNs, Bifidobacterium, and MSNs-Bi. Bi refers to Bifidobacterium. Boxplots show the index of Chao1, ACE, and Shannon. Figure S8. The area fraction of ThioS-stained Aβ plaques in the stomach, duodenum, jejunum, ileum, cecum, and colon from APP/PS1 mice treated with PBS, MSNs, Bifidobacterium, and MSNs-Bi. Two-way ANOVA, ****P < 0.001. Figure S9. IP-Western blotting images of the brain, spinal cord, blood, stomach, duodenum, jejunum, ileum, cecum, and colon from APP/PS1 mice treated with PBS, MSNs, Bifidobacterium, and MSNs-Bi. Figure S10. Relative fluorescence intensity was used to monitor changes in the level of Cy3 in MSNs.

This paper describes a computer system which makes robot understand Chinese natural language sentences dealing with the situation of robot. For a declarative sentence, the system draws the Internal expression of the sentence and stores it in a database; for a question sentence, the system answers it through the reasoning ability of prolog system; for a imperative sentence , the system makes our robot act according to the natural language command.

This paper describes the overall design of a robot audio system which has been realized by us, it makes robots understand all of three kinds of sentences input by speech and limited in the situations of robots, namely, declarative sentence, imperative sentence and question sentence. Furthermore, we present the realizing strategy of its robot-plan subroutine and speech recognition subroutine.