bBecause of its remarkable ability to acquire antibiotic resistance and to survive in nosocomial environments, Acinetobacter baumannii has become a significant nosocomial infectious agent worldwide. Tigecycline is one of the few therapeutic options for treating infections caused by A. baumannii isolates. However, tigecycline resistance has increasingly been reported. Our aim was to assess the prevalence and characteristics of efflux-based tigecycline resistance in clinical isolates of A. baumannii collected from a hospital in China. A total of 74 A. baumannii isolates, including 64 tigecycline-nonsusceptible A. baumannii (TNAB) and 10 tigecycline-susceptible A. baumannii (TSAB) isolates, were analyzed. The majority of them were determined to be positive for adeABC, adeRS, adeIJK, and abeM, while the adeE gene was found in only one TSAB isolate. Compared with the levels in TSAB isolates, the mean expression levels of adeB, adeJ, adeG, and abeM in TNAB isolates were observed to increase 29-, 3-, 0.7-, and 1-fold, respectively. The efflux pump inhibitors (EPIs) phenyl-arginine-␤-naphthylamide (PA␤N) and carbonyl cyanide 3-chlorophenylhydrazone (CCCP) could partially reverse the resistance pattern of tigecycline. Moreover, the tetX1 gene was detected in 12 (18.8%) TNAB isolates. To our knowledge, this is the first report of the tetX1 gene being detected in A. baumannii isolates. ST208 and ST191, which both clustered into clonal complex 92 (CC92), were the predominant sequence types (STs). This study showed that the active efflux pump AdeABC appeared to play important roles in the tigecycline resistance of A. baumannii. The dissemination of TNAB isolates in our hospital is attributable mainly to the spread of CC92.

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Summary Structural health monitoring via quantities that can reflect behaviors of concrete dams, like horizontal and vertical displacements, rotations, stresses and strains, seepage, and so forth, is an important method to evaluate operational states of concrete dams correctly and predict the future structural behaviors accurately. Traditionally, statistical model is widely applied in practical engineering for structural health monitoring. In this paper, an extreme learning machine (ELM)‐based health monitoring model is proposed for displacement prediction of gravity dams. ELM is one type of https://en.wikipedia.org/wiki/Feedforward_neural_networks with a single layer of hidden nodes, where the weights connecting inputs to hidden nodes are randomly assigned. The model can produce good generalization performance and learns faster than networks trained using the back propagation algorithm. The advantages such as easy operating, high prediction accuracy, and fast training speed of the ELM health monitoring model are verified by monitoring data of a real concrete dam. Results are also compared with that of the back propagation neural networks, multiple linear regression, and stepwise regression models for dam health monitoring.

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ZSM-5 that uses TPAOH as a template has an Al-rich exterior and defective Si-rich interior; thus, a simple base leaching selectively removed the Si-rich interior while the Al-rich exterior was protected. This catalyst showed no change in stability comparing with parent ZSM-5 during the MTP reaction that was attributed to the enclosed hollow structure and richly acidic outer shell. A preliminary fluorination, however, both removed defective Si-sites and caused distortion in tetrahedral aluminum that made the outer shell susceptible to alkaline treatment. These distorted tetrahedral Al were mostly leached out by NaOH in 1 min. Furthermore, aluminum in the filtrate was slowly redeposited onto the zeolite, serving as external pore-directing agents to control silicon dissolution from the Si-rich interior. This dealumination-realumination alkaline treatment process led to a higher solid yield and a uniform opened-mesopore structure with mesopores around 13 nm in diameter. This material was characterized by SEM, TEM, N adsorption, and mercury porosimetry. In addition, NH-TPD, OH-IR, Al MAS NMR, andH MAS NMR results demonstrated that the reinserted Al were unlike the framework Al, contributing less to acidity. The dealumination-realumination process, therefore, was also capable of tuning the acidity of the mesoporous ZSM-5. This mesoporous catalyst exhibited a longer lifetime and a higher propylene selectivity than other catalysts with an enclosed mesopore structure.

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