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In order to save energy and protect environment, how to utilize ventilation air methane (VAM) is a subject that needed urgently to be studied. The methane concentration in VAM varies greatly, making it difficult for traditional burners to burn directly. Therefore, the feasibility of using VAM as a combustion gas for gas turbines is considered. When natural gas is used as the fuel for a micro gas turbine, the changes in air inlet parameters will directly affect its performance. This article uses experimental and numerical simulation methods to analyze the feasibility of using VAM as combustion air for micro gas turbines. The results show that factors such as intake pressure, temperature, and composition of gas turbines have an impact on combustion and pollutant emissions; the comparison between VAM and air as combustion gases shows that the rate of temperature increase in the combustion chamber increases, and the concentration of NO at the outlet of the combustion chamber increases by 0.97 % and the concentration of CO increases by 3.76 %. Based on the comprehensive analysis of gas turbine performance, it is concluded that the application of mine exhaust air to micro gas turbines has certain feasibility.

This article uses experimental methods to measure the operational performance and pollutant emissions of micro gas turbines in different intake environments, and analyzes the feasibility of replacing mine ventilation fans with micro gas turbines. The results show that when the compressor efficiency is increased from 70 % to 90 %, the total efficiency of the micro combustion engine is increased from 28% to 34 %, with an increase of up to 21.4 %. When the demand power is 25 kW, the fluctuation range of NOx emission concentration is within 5.4 %. When the intake opening is below 50 %, the effect is obvious. When the intake opening is reduced from 50 % to 25 %, the performance of the micro gas turbine decreases by 0.7 %; The influence of intake humidity on various operating parameters of micro gas turbines is generally within 3 %, which can meet the requirements of high humidity environment operation; The intake temperature has the greatest impact, with an increase in intake temperature leading to a decrease in power generation efficiency. After comprehensive analysis, we believe that the application of gas turbines in mines is feasible.

Objective To investigate the effect of type Ⅰ interferons (IFN-α and IFN-β) and type Ⅱ interferon (IFN-γ) on the expression of adenosine deaminase acting on RNA 1 (ADAR1, P110 and P150) and hepatitis B virus (HBV) replication in hepatocellular carcinoma (HCC) cells. Methods Western blotting and qRT-PCR were used to detect the expression of ADAR1 in human HCC cell lines HepG2 and Huh7 following treatment with different concentrations of IFN-α, IFN-β or IFN-γ. Southern blotting was employed to detect the changes in HBV DNA level in response to IFN-α treatment in Huh7 and HepG2 cells transfected with HBV replication plasmids. Results In Huh7 and HepG2 cells, treatments with IFN-α and IFN-β significantly increased the protein expression of ADAR1 P150 in a time- and dose-dependent manner (P < 0.01) and also obviously augmented the protein level of ADAR1 P110; IFN-γ did not significantly affected the expression of either ADAR1 P110 or ADAR1 P150. IFN-α treatment dose-dependently inhibited HBV DNA replication in Huh7 cells (P < 0.05). Conclusion Type Ⅰ interferons time- and dose-dependently increase the expression of ADAR1 in Huh7 and HepG2 cells, suggesting a possible role of ADAR1 in mediating the anti-HBV effect of IFN-α.

Though specific growth rate (SGR) has potential prognostic value for oropharyngeal squamous cell carcinoma (OPSCC), there is sparse literature defining these rates. Our aims were to establish the SGRs of primary tumors (PTs) and lymph nodes (LNs) in OPSCC and to correlate SGR with oncologic outcome. A pilot study was designed with a retrospective analysis examining 54 patients from the University of California, Davis with OPSCC (diagnosed 2012-2019). Radiation oncology software and pretreatment serial CT scans were used to measure PT and LN volumes to calculate SGR and doubling time (DT). The mean PT-SGR was 1.2 ± 2.2%/day and the mean LN-SGR was 1.6 ± 1.9%/day. There was no statistically significant difference between slow-growing and fast-growing cohorts in terms of age, gender, smoking status, tumor subsite, HPV status (as determined with p16 staining), initial volume, or overall stage. SGR had no impact on 2-year overall survival, disease-free survival, or disease-specific survival. We found the average daily growth rates for OPSCC to be 1.2%/day and 1.6%/day. Our findings suggest PT- and LN-SGR are independent factors, not heavily influenced by known biomarkers and patient characteristics, without a statistical impact on prognosis. This information has value in patient counseling regarding tumor growth and in providing patients worried about fast-growing tumors the appropriate reassurance.

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose-volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.