• Journal of Microbiology
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• v.44 no.6
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• pp.632-640
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• 2006

In this study, the biodegradative activities of monocyclic aromatic compounds were determined from the multi-drug resistant (MDR) Acinetobacter baumannii, which were studied in the form of clinical isolates from a hospital in Korea. These bacteria were capable of biodegrading monocyclic aromatic compounds, such as benzoate and p-hydroxybenzoate. In order to determine which pathways are available for biodegradation in these stains, we conducted proteome analyses of benzoate, and p-hydroxybenzoate-cultured A. baumannii DU202, using 2-DE/MS analysis. As genome DB of A. baumannii was not yet available, MS/MS analysis or de novo sequencing methods were employed in the identification of induced proteins. Benzoate branch enzymes [catechol 1,2-dioxygenase (CatA) and benzoate dioxygenase $\alpha$ subunit (BenA)] of the $\beta$-ketoadipate pathway were identified under benzoate culture condition and p-hydroxybenzoate branch enzymes [protocatechuate 3,4-dioxygenas $\alpha$ subunit (PcaG) and 3-carboxy-cis,cis-muconate cycloisomerase (PcaR)] of the $\beta$-ketoadipate pathway were identified under p-hydroxybenzoate culture condition, respectively, thereby suggesting that strain DU202 utilized the $\beta$-ketoadipate pathway for the biodegradation of monocyclic aromatic compounds. The sequence analysis of two purified dioxygenases (CatA and PcaGH) indicated that CatA is closely associated with the CatA of Acinetobacter radiresistance, but PcaGH is only moderately associated with the PcaGH of Acinetobacter sp. ADPI. Interestingly, the fused form of PcaD and PcaC, carboxymuconolactone decarboxylase (PcaCD), was detected on benzoate-cultured A. baumannii DU202. These results indicate that A. baumannii DU202 exploits a different $\beta$-ketoadipate pathway from other Acinetobacter species.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.