Effects of pork sausage on intestinal microecology and metabolism in mice.

BACKGROUND: Processed meat, as an important part of the human diet, has been recognized as a carcinogen by the International Agency for Research on Cancer (IARC). Although numerous epidemiological reports supported the IARC's view, the relevant evidence of a direct association between processed meat and carcinogenicity has been insufficient and the mechanism has been unclear. This study aims to investigate the effects of pork sausage (as a representative example of processed meat) intake on gut microbial communities and metabolites of mice. Microbial communities and metabolites from all groups were analyzed using 16S rRNA gene sequencing and Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometer (UPLC-Q-TOF/MS), respectively. RESULTS: The levels of Bacteroidetes, Bacteroides, Alloprevotella, Lactobacillus, Prevotella_9, Lachnospiraceae_NK4A136_group, Alistipes, Blautia, Proteobacteria, Firmicutes, Allobaculum, Helicobacter, Desulfovibrio, Clostridium_sensu_stricto_1, Ruminococcaceae_UCG-014, Lachnospiraceae_UCG-006 and Streptococcus (P < 0.05) were obviously altered in the mice fed a pork sausage diet. Twenty-seven metabolites from intestinal content samples and fourteen matabolites from whole blood samples were identified as potential biomarkers from multivariate analysis, including Phosphatidic acid (PA), Sphingomyelin (SM), Lysophosphatidylcholine (LysoPC), Diglyceride (DG), D-maltose, N-acylamides and so forth. The significant changes in these biomarkers demonstrate metabonomic variations in pork sausage treated rats, especially carbohydrate metabolism, lipid metabolism, and amino acid metabolism. CONCLUSION: The present study provided evidence that a processed meat diet can increase the risk of colorectal cancer and other diseases significantly by altering the microbial community structure and disrupting the body's metabolic pathways. © 2023 Society of Chemical Industry.

Image-based thickener mud layer height prediction with attention mechanism-based CNN.

Mud layer height of thickener is the key quality index of thickening process which is difficult to achieve real-time detection with existing methods in reality. While the need of developing a soft sensor model which can be used for real-time detection of mud layer height, we proposed an end-to-end mud layer height prediction method with attention mechanism-based convolutional neural network (CNN). The dynamic features are firstly extracted from the image samples based on CNN, and then two types of attention mechanism are embedded sequentially to contribute to more precise prediction results. Compared with the traditional spatial attention mechanism, the regional spatial attention mechanism we proposed selectively divides the spatial feature map into regions, while regions containing important features are assigned larger weights. Adding the channel and regional spatial attention mechanism in CNN not only effectively improve both the precision and calculation speed, but also affect the dimension of the output feature map, so as to avoid the loss of channel or spatial attention information of the feature map. To verify the validity of the proposed method, different attention mechanisms are embedded in the CNN, and the corresponding experiments are carried out on the dataset of the thickener mud layer. The experimental results demonstrate the feasibility and effectiveness of the mud layer height prediction method.