A network meta-analysis of risk factors of infection among close contacts of COVID-19.

Objective: We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts. Methods: We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA). Results: 25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 (OR: 12.11, 95 % confidence interval (CI): 6.51-22.55), 7.76 (OR: 7.76, 95 % CI: 4.09-14.73), 4.65 (OR: 4.65, 95 % CI: 2.66-8.51) and 8.23 OR: 8.23, 95 % CI: 4.23-16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %). Conclusion: The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs.

High removal efficiencies of antibiotics and low accumulation of antibiotic resistant genes obtained in microbial fuel cell-constructed wetlands intensified by sponge iron.

Using microbial fuel cells with constructed wetlands (MFC-CWs) for eliminating antibiotics has recently attracted extensive attention. However, antibiotic removal efficiencies in MFC-CWs must be enhanced, and the accumulation of antibiotic resistant genes (ARGs) remains an unmanageable issue. This study tries to enhance the antibiotic removal in synthetic wastewater and reduce ARGs by adding sponge iron (s-Fe0) and calcium peroxide to the anode and cathode of MFC-CWs, respectively, and/or simultaneously. The results demonstrated that adding s-Fe0 and calcium peroxide to MFC-CWs could improve the removal efficiencies of sulfamethoxazole (SMX) and tetracycline (TC) by 0.8-1.3% and 6.0-8.7%. Therein, s-Fe0 also significantly reduced 84.10-94.11% and 49.61-60.63% of total sul and tet genes, respectively. Furthermore, s-Fe0 improved the voltage output, power density, columbic efficiency, and reduced the internal resistance of reactors. The intensification to the electrode layers posed a significant effect on the microbial community composition and functions, which motivated the shift of antibiotic removal, accumulation of ARGs and bioelectricity generation in MFC-CWs. Given the overall performance of MFC-CWs, adding s-Fe0 to the anode region of MFC-CWs was found to be an effective strategy for removing antibiotics and reducing the accumulation of ARGs.

Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging.

The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both frequently used method to construct estimation model based on remote sensing imaging. The aim of this study was to find out which estimation model of apple tree canopy chlorophyll content based on the vegetation indices constructed with visible, red edge and near-infrared bands of the sensor of Sentinel-2 was more accurate and stabler. The results were as follows: The calibration set coefficient of determination (R2) value of 0.729 and validation set R2 value of 0.667 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were higher than those of the model using the BPNN method by 8.2% and 11.0%, respectively. The calibration set root mean square error (RMSE) of 0.159 and validation set RMSE of 0.178 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were lower than those of the model using the BPNN method by 5.9% and 3.8%, respectively.