RESUMO
In this study, the transformation mechanisms of extracellular polymeric substances (EPS) during ultraviolet/peracetic acid (UV/PAA) disinfection were elucidated based on multiple molecular-level analyses. After UV/PAA disinfection, the contents of soluble EPS (S-EPS), loosely bound EPS (LB-EPS) and tightly bound EPS (TB-EPS) were reduced by 70.47 %, 57.05 % and 47.46 %, respectively. Fluorescence excitation-emission matrix-parallel factor and Fourier transform ion cyclotron resonance mass spectrometry analyses showed that during UV/PAA disinfection, EPS was transformed from the state characterized by high aromaticity, low saturation and low oxidation to the one with reduced aromaticity, increased saturation and higher oxidation. Specifically, sulfur-containing molecules (CHOS, CHONS, etc.) in EPS were converted into highly saturated and oxidized species (such as CHO), with the aromaticity index (AImod) decreasing by up to 53.84 %. Molecular characteristics analyses further indicated that saturation degree, oxidation state of carbon and molecular weight exhibited the most significant changes in S-EPS, LB-EPS and TB-EPS, respectively. Additionally, mechanistic analysis revealed that oxygen addition reaction was the predominant reaction for S-EPS (+O) and TB-EPS (+3O) (accounting for 31.78 % and 36.47 %, respectively), while the dealkylation was the main reaction for LB-EPS (29.73 %). The results were consistent with functional groups sequential responses analyzed by Fourier transform infrared and two-dimensional correlation spectroscopy, and were further verified by density functional theory calculations. Most reactions were thermodynamically feasible, with reaction sites predominantly located at functional groups such as CO, CO, CN and aromatic rings. Moreover, metabolomics analysis suggested that changes in metabolites in raw secondary effluent during UV/PAA disinfection were strongly correlated with EPS transformation. Our study not only provides a strong basis for understanding EPS transformation during UV/PAA disinfection at molecular-level but also offers valuable insights for the application this promising disinfection process.
RESUMO
Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.
RESUMO
Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.