Staphylococcus aureus (S. aureus) has been recognized as one of the important zoonotic pathogens. However, it was limited about the epidemiology and genetic characteristics of S. aureus isolated from pigs in Hunan province, china. The aim of this study was to determine the characteristics of 163 S. aureus isolated from 590 pigs in Hunan Province, China. All isolates were characterized by agr typing, detection of virulence genes and antibiotic resistance genes, lethal test of mice and antibiotic susceptibility tests. The results showed that 30 strains of the 163 isolates were divided into agrI (18.40%), agrII (36/163, 22.09%), agrIII (20/163, 12.27%,), agrIV (20/163,12.27%) and the remaining 57 isolates were amplified negative by agr primers. In the 163 isolates, the detection rate of the virulence genes hlb, hld, hla, icaA, seb, fnbA, eta, etb, sea, tst and pvl ranged from 2.45% to 100%. The 43 isolates that were lethal to the mice, had β-hemolytic activity, the number of virulence genes of which was 7.8% higher than that of the remaining 120 non-fatal strains. The resistance rates of the 163 isolates to the 15 antibiotics were 0% (0/163) - 100% (163/163). All isolates were susceptible to Vancomycin and only 7 isolates were methicillin - resistant S. aureus (MRSA). The detection rates of the 11 resistance genes was 0% (0/163) - 100% (163/163). This study first to describes the epidemiology and characteristics of S. aureus from pigs in Hunan Province, which will help in tracking the evolution of epidemic strains and preventing pig-human transmission events.

Recent advances in artificial intelligence have opened up new avenues for microstructure characterization, notably in metallic materials. Physical and mechanical properties generally depend on the microstructure of the metallic material. On the other hand, microstructural characterization takes time and calls for specific techniques that don’t always lead to conclusive results quickly. To address this issue, this research focuses on the application of artificial intelligence approaches to microstructural categorization. We demonstrate the advantages of the AI approach using an example of Al-Si alloy, a material that is widely employed in a variety of industries. To specify a suitable convolutional neural network (CNN) approach for the microstructural classification of the Al-Si alloy, CNN models were trained and compared using DenseNet201, Inception v3, InceptionResNetV2, ResNet152V2, VGG16, and Xception architectures. Resulting from the comparison, it was determined that the developed supervised transfer learning model can execute the microstructural classification of Al-Si alloy microstructural images. This paper is an attempt to advance methods of microstructure recognition/classification/characterization by using Deep Learning approaches. The significance of the established model is demonstrated and its accordance with the literature data. Also, necessity is shown of developing material models and optimization through systematic microstructural investigation, production conditions, and material attributes.