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While the microbiome of activated sludge (AS) in wastewater treatment plants (WWTPs) plays a vital role in shaping the resistome, identifying the potential bacterial hosts of antibiotic resistance genes (ARGs) in WWTPs remains challenging. The objective of this study is to explore the feasibility of using a machine learning approach, random forests (RF’s), to identify the strength of associations between ARGs and bacterial taxa in metagenomic datasets from the activated sludge of WWTPs. Our results show that the abundance of select ARGs can be predicted by RF’s using abundant genera (Candidatus Accumulibacter, Dechloromonas, Pesudomonas, and Thauera, etc.), (opportunistic) pathogens and indicators (Bacteroides, Clostridium, and Streptococcus, etc.), and nitrifiers (Nitrosomonas and Nitrospira, etc.) as explanatory variables. The correlations between predicted and observed abundance of ARGs (erm(B), tet(O), tet(Q), etc.) ranged from medium (0.400 0.600) when validated on testing datasets. Compared to those belonging to the other two groups, individual genera in the group of (opportunistic) pathogens and indicator bacteria had more positive functional relationships with select ARGs, suggesting genera in this group (e.g., Bacteroides, Clostridium, and Streptococcus) may be hosts of select ARGs. Furthermore, RF’s with (opportunistic) pathogens and indicators as explanatory variables were used to predict the abundance of select ARGs in a full-scale WWTP successfully. Machine learning approaches such as RF’s can potentially identify bacterial hosts of ARGs and reveal possible functional relationships between the ARGs and microbial community in the AS of WWTPs.Increasing number of emerging pollutants in environments requires an effective approach which can facilitate the prediction of reactivity and provide insights into the reaction mechanisms. Computational chemistry is exactly the tool to fulfill this demand with its good performance in theoretical investigation of chemical reactions at molecular level. In this study, chlorination of sulfonamide antibiotics is used as an illustration to present a systematic strategy demonstrating how computational chemistry can be applied to investigate the reaction behavior of emerging pollutants. Sulfonamides is a class of micropollutants that contain the common structure of 4-aminobenzenesulfonmaide while differ in their heterocycles. Selleck RSL3 Based on the calculated conceptual DFT indices, the reactive sites of sulfonamide are successfully predicted, which locate on their common structure of 4-aminobenzenesulfonmaide. Therefore, all sulfonamides follow the similar reaction pathway. Product identification by LTQ-Orbitrap MS further verifies the in silico prediction. Three critical pathways are discovered, i.e., S-N bond cleavage, Cl-substitution onto aniline-N, and the following rearrangement to lose -SO2- group, among which Cl-substitution is the key step due to its lowest free energy barrier. Heterocycles impact the reaction rate by affecting the electronic density of aniline group. In general, the more electron-donating the heterocycle is, the more readily sulfonamides to be chlorinated.A method based on gas chromatography coupled with electron ionization mass spectrometry employing N,O-bis(trimethylsilyl)trifluoroacetamide with trimethylchlorosilane as derivatization agent was developed to quantify short-chain carboxylic acids (C1-C6) in hospital wastewater treated by wet air oxidation, an advanced oxidation process. Extraction from water and derivatization of volatile and semi-volatile short chain carboxylic acids were optimized and validated and limits of quantification (LOQ = 0.049 mg L-1-4.15 mg L-1), repeatability (RSD = 1.7-12.8%), recovery (31-119%) and trueness (relative bias = -19.0-3.4%) were acceptable. The validated method was successfully applied to monitor the concentration of organic acids formed after wet air oxidation of water samples. Results showed that the method described herein allowed to identify 38% and up to 46% of the final chemical oxygen demand’s composition after wet air oxidation of acetaminophen spiked in deionised water and hospital wastewater samples, respectively. The developed method also allowed to perform qualitative non-targeted analysis in hospital wastewater samples after treatment. Results demonstrated that glycerol, methenamine, and benzoic acid were also present in the samples and their presence was confirmed with reference standards.Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking. Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS), which allows sequential 40-sample injection followed by electrophoretic separation and specific mass detection. To achieve consecutive analysis of polyamine samples, 1 M formic acid was used as the background electrolyte (BGE). The BGE spacer volume had an apparent effect on peak resolution among samples, and 20 nL was selected as the optimal volume. The use of polyamine isotopomers as the internal standard enabled the correction of matrix effects in MS detection. This method is sensitive, selective and quantitative, and its utility was demonstrated by screening polyamines in 359 salivary samples within 360 min, resulting in discrimination of colorectal cancer patients from noncancer controls.The effects of conventional extrusion (CV) and extrusion-cooking (EC) were investigated on 100% yellow lentils (YL). Both the extrusion processes led to pasta with good cooking quality (cooking loss 7.0-7.1 g/100 g, firmness 530-608 N), even in overcooking (cooking loss 7.7-7.9 g/100 g, firmness 418-513 N). Contrary to what is known for gluten-free cereals, CV is effective in producing pasta from native YL with no need for a pre-gelatinization step. However, pasta from EC showed a higher compression energy (2898 versus 2448 N*mm). In this sample, starch presented a higher degree of gelatinization (75.5 versus 57.6 g/100 g) and lower enthalpy (0.97 versus 1.07 J/g). At the same time, EC promoted a more compact structure that required higher temperature for melting (66.49 versus 63.16 °C) and showing pasting properties (79.1 versus 74.7 °C). Thus, by selecting suitable extrusion conditions it is possible to improve the cooking behavior of 100% pulse pasta.