At the present paper, an analytical method based on temperature controlled solid-liquid extraction (TC-SLE) utilizing a synthesized ionic liquid, (N-butylpyridinium hexafluorophosphate, [BPy]PF 6 ), as solid solvent and phenanthroline (PT) as an extractant was developed to determine micro levels of Fe 2+ in tea by PT spectrophotometry. TC-SLE was carried out in two continuous steps: Fe 2+ can be completely extracted by PT-[BPy]PF 6 or back-extracted at 80˝C and the two phases were separated automatically by cooling to room temperature. Fe 2+ , after back-extraction, needs 2 mol/L HNO 3 as stripping agent and the whole process was determined by PT spectrophotometry at room temperature. The extracted species was neutral Fe(PT) m Cl 2 (m = 1) according to slope analysis in the Fe 2+ -[BPy]PF 6 -PT TC-SLE system. The calibration curve was Y = 0.20856X´0.000775 (correlation coefficient = 0.99991). The linear calibration range was 0.10-4.50 µg/mL and the limit of detection for Fe 2+ is 7.0ˆ10´2 µg/mL. In this method, the contents of Fe 2+ in Tieguanyin tea were determined with RSDs (n = 5) 3.05% and recoveries in range of 90.6%-108.6%.
Colistin sulfate is widely used in both human and veterinary medicine. However, its effect on the microbial ecologyis unknown. In this study, we determined the effect of colistin sulfate on the diversity of soil microorganisms by amplified rDNA restriction analysis (ARDRA) and high-throughput sequencing.ARDRAshowed that the diversity of DNA from soil microorganisms was reduced after soil was treated with colistin sulfate, with the most dramatic reductionobserved after 35days of treatment. High-throughput sequencing showed that the Chao1 and abundance-based coverage estimators (ACE) were reduced in the soils treated with colistin sulfate for 35 dayscompared to those treated with colistin sulfate for 7days. Furthermore, Chao1 and ACE tended to be lower when higher concentration of colistin sulfate was used, suggesting that the microbial abundance is reduced by colistin sulfate in a dose-dependent manner. Shannon index showed that the diversity of soil microorganism was reduced upon treatment with colistin sulfate compared to the untreated control group. Following 7days of treatment, Bacillus, Clostridiumand Sphingomonas were sensitive to all the concentration of colistin sulfate used in this study. Following 35days of treatment, the abundance of Choroplast, Haliangium, Pseudomonas, Lactococcus, and Clostridium was significantly decreased. Our results demonstrated that colistin sulfate especially at high concentration (≥5mg/kg) could alter the population structure of microorganisms and consequently the microbial community function in soil.
The automatic detection of subway tunnel lining disease is realised primarily by using industrial cameras and deep learning.However, due to the uniqueness of subway tunnel environments, industrial cameras can be subject to excess interference and most of the methods are not based on disease characteristics, leaving much room for improvement. This paper proposes a method using point cloud data and a mask region-based convolutional neural network. Using two types of diseasesthat is, lining water leakage and dropblocks-as the research objects, the reflection intensity and three-dimensional space information of laser point cloud data were respectively used to generate grayscale and depth maps. Based on the MASK R-CNN learning framework, the proposed method realises the simultaneous detection of two types of diseases, and performs pixel-by-pixel analysis on the feature map through the mask branch to generate a binarised mask. Experiments showed that the disease identification method proposed in this paper could achieve a global accuracy rate of more than 95%, the mAP index value being 0.4. The prediction boxes obtained could cover a more complete disease area. The proposed method combined the advantages of a grayscale map, depth map and mask R-CNN network to achieve simultaneous object detection and instance segmentation for water leakage and drop blocks, and achieved high recognition accuracy and excellent mask results-that is, the area value of the mask could be used as the basis for judging the severity of the disease, providing a certain reference for subsequent maintenance and actual operational application.
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