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Biological nitrogen removal technology has bottlenecks such as long start-up time and low efficiency in the process of high ammonia nitrogen wastewater treatment due to the specificity of wastewater, while the screened single high-efficiency nitrogen removal strain suffers from poor stability in the actual wastewater treatment process. In this study, based on the screening of a large number of highly efficient heterotrophic nitrification- aerobic denitrification strains,the core strains 139# and 142# were identified through partial factorial experiments, and the strain combinations were optimized by combining metabolic correlation and stratified sampling methods to artificially construct an efficient nitrogen removal community. The optimum combination of strains was obtained by re-screening with sludge at pH 5.5, and the ability of the constructed community for nitrogen removal was verified by using a reactor to simulate weak acidic wastewater treatment. The results showed that the screened high-efficiency bacterial community P44-P83-A88-P94-A142 achieved 96.97% removal of NH4+-N at 48 h under the condition of initial NH4+-N mass concentration of 200 mg/L and initial pH of 5.5. Within the reactor to simulate the actual wastewater environment, the addition of the bacteria at a weakly acidic pH of 5.5 increased the removal rate of NH4+-N from 44.89% to 75.17% and the effluent pH from 6.9 to 8.3. This study can provide a theoretical basis for the application of artificially constructed bacterial community in the treatment of weakly acidic wastewater for nitrogen removal.

We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis.