The anaerobic granular sludge from an Internal Circulation (IC) reactor of a paper mill wastewater treatment plant were seeded in an Anammox upflow anaerobic sludge blanket reactor. After 185 days operation, the reactor was finally started up by increasing the influent ammonium and nitrite concentrations to 224 mg/L and 255 mg/L, respectively, with volumetric nitrogen removal rate increasing to 3.76 kg/(m3·d). The physicochemical characteristics of the cultivated Anammox granules were observed by scanning electron microscope, transmission electron microscope and Fourier Transform infrared spectroscopy (FTIR). Results suggested that during the start-up course, the granular sludge initially disintegrated and then re-aggregated. FTIR spectra results revealed that the Anammox granular sludge contained abundant functional groups, indicating that it may also possess good adsorption properties. The ecological structure of the granular sludge, analyzed by the metagenomic sequencing methods, suggested that the relative abundance of the dominant bacterial community in the seeding sludge, i.e., Proteobacteria, Firmicutes, Bacteroidetes, significantly reduced, while Planctomycetes which contains anaerobic ammonium oxidation bacteria remarkably increased from 1.59% to 23.24% in the Anammox granules.
Ammonia ; chemistry ; Bacteria ; Bioreactors ; Nitrogen ; chemistry ; Sewage ; microbiology ; Waste Disposal, Fluid ; methods ; Waste Water ; chemistry

Objective:To develop an imaging-assisted diagnostic tool for schizophrenia based on multimodal magnetic resonance imaging and artificial intelligence techniques.Methods:Three independent datasets were utilized. For each subject, four brain structural metrics including grey matter volume (GMV), white matter volume (WMV), cortical thickness (CT) and deformation-based morphometry (DBM) indicators were extracted from the structural magnetic resonance imaging (sMRI) data, and three brain functional metrics including amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were extracted from the functional magnetic resonance imaging (fMRI) data. To distinguish patients with schizophrenia and healthy controls, single-metric classification models and multi-metrics-fusion classification models were trained and tested using a within-dataset and a between-dataset cross-validation strategy.Results:The results of within-dataset cross-validation showed that the highest accuracy of the single-metric classifications for schizophrenia diagnosis was 86.18% (FC), while the multi-metric-fusion classifications could reach an accuracy of 90.21%. The results of between-datasets cross-validation showed that the highest accuracy of the single-metric classifications for schizophrenia diagnosis was 69.02% (ReHo), while the multi-metric-fusion classifications could reach an accuracy of 71.25%.Conclusion:The functional metrics generally outperforms the structural metrics for the classification between patients with schizophrenia and heathy controls. Additionally, fusion of multi-modal brain imaging metrics can improve the classification performance. Specifically, the fusion of CT, DBM, WMV, FC and ReHo demonstrates the highest classification accuracy, which is a potential tool for imaging-assisted diagnosis of schizophrenia.