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Pseudomonas aeruginosa is a multidrug-resistant Gram-negative pathogen and one of the leading causes of ventilator-associated pneumonia and infections in patients with chronic obstructive pulmonary disease and cystic fibrosis. Murepavadin is a peptidomimetic that specifically targets outer-membrane lipopolysaccharide transport protein LptD of P. aeruginosa. In this study, we find that murepavadin enhances the bactericidal efficacy of ciprofloxacin. We further demonstrate that murepavadin increases intracellular accumulation of ciprofloxacin by suppressing drug efflux. In addition, the murepavadin–ciprofloxacin combination exhibits a synergistic bactericidal effect in an acute murine pneumonia model. In conclusion, our results identify an effective drug combination for the treatment of P. aeruginosa infections. [ABSTRACT FROM AUTHOR]

A point-of-interest (POI) represents a specific point location that may be useful or interesting for people, and therefore, each and every building footprint in a topographic map can be recognized as a POI. Automatic extraction of building footprints using remote sensing images has become a challenging and important research topic, which is in demand for urban planning and development. Extensive studies have explored a variety of semantic segmentation methods using deep learning algorithms to achieve better performance in building footprint extraction; however, the existing algorithms were shown to have some limitations, which lead to poor segmentation results. Building roofs were recognized as building footprints in the previous studies. This is prone to error especially for high-rise buildings due to different sensor view angles. In this article, we propose a multitask Res-U-Net model with an attention mechanism for the extraction of the building roofs and the whole building shapes from remote sensing images and then use an offset vector method to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. We also apply the online food delivery (OFD) data to parse the POI name of every building footprint. Several strategies are also developed in combination with the proposed model, including data augmentation and postprocessing. We conduct numerical experiments using real data of remote sensing images and OFD historical order data. Results demonstrate that our proposed model achieves a total F1-score of 77.05% and an intersection over union (IoU) of 63.55% in terms of the building roof segmentation, and an overall F1-score of 79.02% and an IoU of 66.05% for the whole building shape segmentation, which both achieve the best performance among all baseline models. [ABSTRACT FROM AUTHOR]

Barium titanate/polyvinylidene fluoride (BaTiO 3 /PVDF) piezoelectric membrane was successfully prepared and generated in-situ vibrations to reduce membrane fouling by applying alternating current (AC) signal for oily bilge water ultrafiltration. The effect of in-situ vibration on membrane fouling was investigated through changing in the excitation alternating voltage and its frequency, pH, crossflow rate. The results indicated that the piezoelectric membrane by applying AC signal remarkably alleviated the membrane fouling for bilge water ultrafiltration. The membrane fouling decreased with increasing the AC signal voltage. The final steady-state permeate flux from the piezoelectric membrane for bilge water ultrafiltration increased with the AC signal voltage, raising it by up to 63.4% at AC signal voltage of 20 V compared to that of the membrane without applying AC voltage. The high permeate flux was obtained at the resonant frequency of 220 kHz. During the 50-h ultrafiltration of bilge water with the piezoelectric membrane excited at 220 kHz and 15 V, the permeate flux from the membrane was stable. The oil concentration in outflow from the piezoelectric membrane was below 14 ppm, which met the discharged level required by IMO convention. The total organic carbon removal rate in bilge water was over 94%.