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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Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadowsupression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde NethersoleEastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam).Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance.

Purpose Current computed tomography (CT)‐based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four‐dimensional CT (4DCT)‐based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART). Methods A total of 15 free‐breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask‐free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end‐expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel‐wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase‐specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel‐wise, and regional‐wise expansion/contraction tracking, were performed to indirectly validate the proposed method. Results The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration. Conclusions In contrast to conventional CTVI metrics, our method enables to extract additional phase‐resolved respiration‐correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase‐by‐phase cross‐modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR).Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features.Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation.Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19).Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.