Characterization of NDM-1-producing carbapenemase in Acinetobacter spp.and E.coli isolates from diseased pigs

In recent years,the mobile metallo-β-lactamase(MBL)genes have been found to correspond to one of the most important resistance characters identified in Gramnegative bacteria,severely affecting clinical chemotherapy and threatening public health.The prevalence of mobile MBL genes and their flanking regions in Gram-negative bacteria from diseased pigs in China was investigated.A total of 334 lung samples from diseased pigs were screened for Gram-negative bacteria classified as non-susceptible to meropenem(MIC≥4mg·L^(–1)).Six isolates,including three Escherichia coli,two Acinetobacter baumanii and one A.calcoaeticus,exhibitedMBL production and carried the blaNDM-1 gene.S1-PFGE and Southern blot analysis showed that the blaNDM-1 gene was located on the chromosome of one A.baumanii isolate and on plasmids of various sizes in the other five isolates.MIC testing using broth microdilution revealed that all blaNDM-1-carrying isolates and some of their transconjugants exhibited resistance to almost allβ-lactams tested.Whole genome sequencing revealed that the flanking region of the blaNDM-1 gene from all porcine isolates had high levels of similarity with the corresponding regions in human isolates.One porcine E.coli isolate carrying blaNDM-1 was typed as ST48,a common sequence type in human E.coli isolates.These results suggest the possibility of human-tofood animal transfer of blaNDM-1-producing E.coli,highlighting the need for surveillance of carbapenemase producers among bacteria from food animals.In addition,the prudent use of antimicrobial agents to decrease the opportunities for co-selection of carbapenemase genes in food animals is also urgently needed.

Boosting the optimization of membrane electrode assembly in proton exchange membrane fuel cells guided by explainable artificial intelligence

The utilization of environmentally friendly hydrogen energy requires proton exchange membrane fuel cell de-vices that offer high power output while remaining affordable.However,the current optimization of their key component,i.e.,the membrane electrode assembly,is still based on intuition-guided,inefficient trial-and-error cycles due to its complexity.Hence,we introduce an innovative,explainable artificial intelligence(AI)tool trained as a reliable assistant for a variable analysis and optimum-value prediction.Among the 8 algorithms considered,the surrogate model built with an artificial neural network achieves high replaceability in the experimentally validated multiphysics simulation(R^(2)=0.99845)and a much lower computational cost.For interpretation,partial dependence plots and the Shapley value method are applied to black-box models to intelligently simulate the impact of each parameter on performance.These methods show that a tradeoff existed in the catalyst layer thickness.The AI-guided optimization suggestions regarding catalyst loading and the ion-omer content are fully supported by the experimental results,and the final product achieves 3.2 times the Pt utilization of commercial products with a time cost orders of magnitude smaller.