Lactic acid bacteria reduce bacterial diarrhea in rabbits via enhancing immune function and restoring intestinal microbiota homeostasis.

BACKGROUND: Numerous previous reports have demonstrated the efficacy of Lactic acid bacteria (LAB) in promoting growth and preventing disease in animals. In this study, Enterococcus faecium ZJUIDS-R1 and Ligilactobaciiius animalis ZJUIDS-R2 were isolated from the feces of healthy rabbits, and both strains showed good probiotic properties in vitro. Two strains (108CFU/ml/kg/day) were fed to weaned rabbits for 21 days, after which specific bacterial infection was induced to investigate the effects of the strains on bacterial diarrhea in the rabbits. RESULTS: Our data showed that Enterococcus faecium ZJUIDS-R1 and Ligilactobaciiius animalis ZJUIDS-R2 interventions reduced the incidence of diarrhea and systemic inflammatory response, alleviated intestinal damage and increased antibody levels in animals. In addition, Enterococcus faecium ZJUIDS-R1 restored the flora abundance of Ruminococcaceae1. Ligilactobaciiius animalis ZJUIDS-R2 up-regulated the flora abundance of Adlercreutzia and Candidatus Saccharimonas. Both down-regulated the flora abundance of Shuttleworthia and Barnesiella to restore intestinal flora balance, thereby increasing intestinal short-chain fatty acid content. CONCLUSIONS: These findings suggest that Enterococcus faecium ZJUIDS-R1 and Ligilactobaciiius animalis ZJUIDS-R2 were able to improve intestinal immunity, produce organic acids and regulate the balance of intestinal flora to enhance disease resistance and alleviate diarrhea-related diseases in weanling rabbits.

Loop Closure Detection Method Based on Similarity Differences between Image Blocks.

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality.