Introduction: Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers.Objectives: The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored.Materials and methods: The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (10⁸, 10⁷, 10⁶, 10⁵, 10⁴, 10³ and 10² CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored.Results and discussion: The best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments.Conclusion: The results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.

Moniezia expansa (Cyclophyllidea: Anoplocephalidae) is a large species of tapeworm that occurs in sheep and cattle and inhabits the small intestine, causing diarrhea and weight declines, leading to stockbreeding losses. Interestingly, the body fat percentage of M. expansa, which lacks the ability to synthesize fatty acids, is as high as 78% (dry weight) and all of the proglottids of M. expansa exhibit a dynamic developmental process from top to bottom. The aim of this paper is to identify the molecular basis of this high body fat percentage, the dynamic expression of developmental genes and their expression regulation patterns. From 12 different proglottids (four sections: scolex and neck, immature, mature and gravid with three replicates), 13,874 transcripts and 680 differentially expressed genes (DEGs) were obtained. The gene expression patterns of the scolex and neck and immature proglottids were very similar, while those of the mature and gravid proglottids differed greatly. In addition, 13 lipid transport-related proteins were found in the DEGs, and the expression levels showed an increasing trend in the four proglottid types. Furthermore, it was shown that 33 homeobox genes, 9 of which were DEGs, had the highest expression in the scolex and neck section. The functional enrichment results of the DEGs were predominantly indicative of development-related processes, and there were also some signal transduction and metabolism results. The most striking result was the finding of Wnt signaling pathways, which appeared multiple times. Furthermore, the weighted gene co-expression networks were divided into 12 modules, of which the brown module was enriched with many development-related genes. We hypothesize that M. expansa uses lipid transport-associated proteins to transport lipids from the host gut to obtain energy to facilitate its high fecundity. In addition, homeobox genes and Wnt signaling pathways play a core role in development and regeneration. The results promote research on the cell differentiation involved in the continuous growth and extension of body structures.

Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.

Listeria monocytogenes (L. monocytogenes) is a Gram-positive intracellular pathogen capable of causing severe infections. The Listeria pathogenicity island 4 (LIPI-4) encodes a phosphotransferase system (PTS) with its EIIB component playing a critical role in carbohydrate phosphorylation and virulence. However, the precise function of EIIB in virulence regulation across diverse pathogenic strains remains unclear. Here, we generated an EIIB deletion mutant (LM873ΔEIIB) and its complemented strain (CLM873ΔEIIB) from the low-virulence strain LM873, and performed comparative analyses with the high-virulence strain LM928 and its corresponding mutants. Deletion of EIIB differentially modulated biofilm formation: suppressing it in LM928 while enhancing it in LM873, accompanied by corresponding transcriptional changes in biofilm-associated and virulence genes. Both mutants exhibited impaired hemolytic activity, whereas motility attenuation was specific to LM928ΔEIIB. At the cellular level, LM873ΔEIIB enhanced adhesion to and invasion of Caco-2 but impaired intracellular proliferation in JEG-3; In contrast, LM928ΔEIIB promoted Caco-2 invasion while attenuating JEG-3 adhesion, invasion, and intracellular replication, as well as reducing invasion and proliferation in RAW264.7 macrophage. Animal experiments demonstrated that EIIB deletion attenuated LM928 colonization in the liver and spleen, but had no significant impact on LM873. Collectively, our findings establish EIIB as a strain-dependent virulence regulator in L. monocytogenes, particularly modulating biofilm formation and host–pathogen interactions.