A novel halophilic strain that could carry out heterotrophic nitrification and aerobic denitrification was isolated and named as Halomonas campisalis ha3. It removed inorganic nitrogen compounds (e.g. NO3 (-), NO2 (-) and NH4 (+)) simultaneously, and grew well in the medium containing up to 20 % (w/v) NaCl. PCR revealed four genes in the genome of ha3 related to aerobic denitrification: napA, nirS, norB and nosZ. The optimal conditions for aerobic denitrification were pH 9.0, at 37 °C, with 4 % (w/v) NaCl and sodium succinate as carbon source. The nitrogen removal rate was 87.5 mg NO3 (-)-N l(-1 )h(-1). Therefore, this strain is a potential aerobic denitrifier for the treatment of saline wastewater.
A Gram-reaction-positive, facultatively anaerobic, motile, endospore-forming, rod-shaped strain, designated SgZ-7 T , was isolated from a windrow compost pile and was characterized by means of a polyphasic approach. Growth occurred with 0-3 % (w/v) NaCl (optimum 1 %), at pH 6.0-10.0 (optimum pH 7.2) and at 40-60 6C (optimum 50 6C). The main respiratory quinone was MK-7. The predominant polar lipids were phosphatidylethanolamine, phosphatidylglycerol and diphosphatidylglycerol. The major fatty acids were iso-C 15 : 0 and anteiso-C 15 : 0 . The DNA G+C content was 46.6 mol%. The phylogenetic analysis based on 16S rRNA gene sequence comparisons revealed that strain T should be assigned to the genus Bacillus and was related most closely to Bacillus drentensis LMG 21831 T (sequence similarity 97.2 %). The result of the DNA-DNA hybridization experiment revealed a low relatedness (27.2 %) between the isolate and B. drentensis LMG 21831 T
A novel thermotolerant bacterium, designated SgZ-8 T , was isolated from a compost sample. Cells were non-motile, endospore-forming, Gram-staining positive, oxidase-negative and catalasepositive. The isolate was able to grow at 20-65 6C (optimum 50 6C) and pH 6.0-9.0 (optimum 6.5-7.0), and tolerate up to 9.0 % NaCl (w/v) under aerobic conditions. Anaerobic growth occurred with anthraquinone-2,6-disulphonate (AQDS), fumarate and NO 3 -as electron acceptors. Phylogenetic analysis based on the16S rRNA and gyrB genes grouped strain SgZ-8 T into the genus Bacillus, with the highest similarity to Bacillus badius JCM 12228 T (96.2 % for 16S rRNA gene sequence and 83.5 % for gyrB gene sequence) among all recognized species in the genus Bacillus. The G+C content of the genomic DNA was 49.3 mol%. The major isoprenoid quinone was menaquinone 7 (MK-7) and the polar lipids consisted of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and an unidentified phospholipid. The major cellular fatty acid was iso-C 16 : 0 . On the basis of its phenotypic and phylogenetic properties, chemotaxonomic analysis and the results of physiological and biochemical tests, strain SgZ-8 T (5CCTCC AB 2012108 T 5KACC 16706 T ) was designated the type strain of a novel species of the genus Bacillus, for which the name Bacillus thermotolerans sp. nov. is proposed.
Background: Bone suppression of chest X-ray holds the potential to improve the accuracy of target localization in image-guided radiation therapy (IGRT). However, the training dataset for bone suppression is limited because of the scarcity of bone-free radiographs. This study aims to develop a deep learning-based bone suppression method using CT-derived features to reduce the reliance on the bone-free dataset.Methods: In this study, 59 high-resolution lung CT scans were processed to generate the lung digital radiographs (DRs), bone DRs, and bone-free DRs, for the training and internal validation of the proposed cascade convolutional neural network (CCNN). A three-stage image processing framework (CT segmentation, DR simulation, and feature expansion) was developed to expand simulated lung DRs with different weightings of bone intensity. The CCNN consists of a bone detection network and a bone suppression network. In external validation, the trained CCNN was evaluated using 30 chest radiographs.The synthesized bone-suppressed radiographs were compared with the bone-suppressed reference in terms of peak signal-to-noise ratio (PSNR), mean absolute error (MAE), structural similarity index measure (SSIM), and Spearman's correlation coefficient. Furthermore, the effectiveness of the proposed feature expansion method and CCNN model were assessed via the ablation experiment and replacement experiment, respectively. Results: Evaluation on real chest radiographs showed that the bone-suppressed chest radiographs closely matched with the bone-suppressed reference, achieving an accuracy of MAE =0.0087±0.0030, SSIM =0.8458±0.0317, correlation of 0.9554±0.0170, and PNSR of 20.86±1.60. After removing the feature expansion from the CCNN model, the performance decreased in terms of MAE (0.0294±0.0093, −237.9%), SSIM (0.7747±0.0.0416, −8.4%), correlation (0.8772±0.0271, −8.2%), and PSNR (15.53±1.42, −25.5%) metrics.Conclusions: We successfully demonstrated a novel deep learning-based bone suppression method using CT-derived features to reduce the reliance on the bone-free dataset. Implementation of the feature expansion procedures resulted in a remarkable reinforcement of the model performance. For the application of target localization in IGRT, the clinical testing of the proposed method in the context of radiation therapy is a necessary procedure to move from theory into practice.
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