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An increase in the carbapenem-hydrolyzing capacity of class D β-lactamase has been observed in strains of multiple species, posing a significant challenge to the control of antibiotic resistance. In this study, we aimed to investigate the genetic diversity and phylogenetic characteristics of new blaOXA-48-like variants derived from Shewanella xiamenensis. Three ertapenem-non-susceptible S. xiamenensis strains were identified, one isolated from the blood sample of an inpatient, the other two isolated from the aquatic environment. Phenotypic characterization confirmed that the strains were carbapenemase producers and exhibited antimicrobial resistance patterns to ertapenem, with some showing lower susceptibility to imipenem, chloramphenicol, ciprofloxacin, and tetracycline. No significant resistance to cephalosporins was observed. Sequence analysis revealed that one strain harbored blaOXA-181 and the other two strains harbored blaOXA-48-like genes, with open reading frame (ORF) similarities with blaOXA-48 ranging from 98.49% to 99.62%. The two novel blaOXA-48-like genes, named blaOXA-1038 and blaOXA-1039, respectively, were cloned and expressed in E. coli. The three OXA-48-like enzymes demonstrated significant hydrolysis activity against meropenem, and the classical β-lactamase inhibitor had no significant inhibitory effect. In conclusion, this study demonstrated the diversity of the blaOXA gene and highlighted the emergence of novel OXA carbapenemases in S. xiamenensis. Further attention to S. xiamenensis and OXA carbapenemases is recommended for the effective prevention and control of antibiotic-resistant bacteria. [ABSTRACT FROM AUTHOR]

Due to the huge demand for textile production in China, fabric defect detection is particularly attractive. At present, an increasing number of supervised deep-learning methods are being applied in surface defect detection. However, the annotation of datasets in industrial settings often depends on professional inspectors. Moreover, the methods based on supervised learning require a lot of annotation, which consumes a great deal of time and costs. In this paper, an approach based on self-feature comparison (SFC) was employed that accurately located and segmented fabric texture images to find anomalies with unsupervised learning. The SFC architecture contained the self-feature reconstruction module and the self-feature distillation. Accurate fiber anomaly location and segmentation were generated based on these two modules. Compared with the traditional methods that operate in image space, the comparison of feature space can better locate the anomalies of fiber texture surfaces. Evaluations were performed on the three publicly available databases. The results indicated that our method performed well compared with other methods, and had excellent defect detection ability in the collected textile images. In addition, the visual results showed that our results can be used as a pixel-level candidate label. [ABSTRACT FROM AUTHOR]

Compositionally graded cylinders of Ti–Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of α–β microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress–strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17–1.78 μm, and beta content in the range of 20–83 vol.%. The estimated values of the Young's moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97–130 GPa and 828–1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables. [ABSTRACT FROM AUTHOR]