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Viral waterborne diseases are widespread in cities due largely to the occurrence of enteric viruses in urban rivers, which pose a significant concern to human health. Yet, the application of rapid detection technology for enteric viruses in environmental water remains undeveloped globally. Here, multiple linear regression (MLR) modeling and artificial neural network (ANN) modeling, which used frequently measured physicochemical parameters in river water, were constructed to predict the concentration of enteric viruses including human enteroviruses (EnVs), rotaviruses (HRVs), astroviruses (AstVs), noroviruses GⅡ (HuNoVs GⅡ), and adenoviruses (HAdVs) in rivers. After training, testing, and validating, ANN models showed better performance than any MLR model for predicting the viral concentration in Jinhe River. All determined R-values for ANN models exceeded 0.89, suggesting a strong correlation between the predicted and measured outputs for target enteric viruses. Furthermore, ANN models provided a better congruence between the observed and predicted concentrations of each virus than MLR models did. Together, these findings strongly suggest that ANN modeling can provide more accurate and timely predictions of viral concentrations based on frequent (or routine) measurements of physicochemical parameters in river water, which would improve assessments of waterborne disease prevalence in cities., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Human enteric virus occurrence in bathing beaches poses a potential health risk to swimmers. They may come from several sources, but the understanding of the seasonal contribution of contamination sources to virus occurrence is still lacking. Here, the surveillance of human enteric viruses at the First Bathing Beach in Qingdao was performed January-December 2018. The occurrence of Enteric viruses, assayed with quantitative polymerase chain reaction (qPCR), was analyzed at temporal and spatial levels to determine the viral contamination sources. The results showed that only Astroviruses (AstVs) and Adenoviruses (HAdVs) were found in the swimming area. Their occurrence correlated significantly with the sewage-polluted area, but HAdVs were only found in autumn and AstVs in spring. Meanwhile, enteric viruses in the swimming area showed significantly higher levels than the surrounding area, particularly AstVs in summer with the swimmer crowd. All these data imply that sewage discharge and swimmers co-contribute to the viral occurrence in a seasonal pattern, with the former being more focused in warm seasons (spring and autumn) and the latter in hot seasons (summer). These results indicate that sewage discharge and crowd swimmers, as unsafe swimming conditions, should be avoided to improve public health at the bathing beaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Antibiotic residues in the environment pose a great risk to global public health. They increase antibiotic resistance by enhancing plasmid conjugation among bacteria or mutations within bacterial genomes. However, little is known about whether the putative environmental levels of antibiotics are sufficient to influence plasmid-mediated transformability. In this study, we explored the effect of eight kinds of representative antibiotics and several other compounds on the plasmid transformability of competent Escherichia coli. Only levofloxacin (LEV) at the putative environmental levels was found to facilitate the frequency of PBR322-or RP4-plasmid-mediated transformation by up to 5.3-fold. Additionally, PBR322 transformation frequency could be further enhanced by copper ion or ammonia nitrogen but inhibited by humic acid. However, when competent E. coli was exposed to the minimal inhibitory concentrations (MIC) of the antibiotics, an enhanced plasmid-assimilation ability was observed and plasmid transformation frequency was increased by up to 98.6-fold for all the tested antibiotics. Furthermore, E. coli exhibited a preference for the uptake of plasmids harbouring the resistance genes to the antibiotics it had been exposed to. Among these antibiotics, cephalexin, tetracycline, and kanamycin induced the highest uptake of RP4. The putative environmental levels of LEV enhanced plasmid transformability regardless of the presence of corresponding antibiotic resistance gene (ARG) on the genetic elements, suggesting environmental LEV residues may facilitate dissemination of antibiotic resistance by any plasmid-mediated transformability, thereby posing a great risk to health., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Antibiotic resistance genes (ARGs) have gained global attention due to their public health threat. Extracelluar ARGs (eARGs) can result in the dissemination of antibiotic resistance via free-living ARGs in natural environments, where they promote ARB transmission in drinking water distribution systems. However, eARG pollution in tap water has not been well researched. In this study, concentrations of eARGs and intracellular ARGs (iARGs) in tap water, sampled at Tianjin, China, were investigated for one year. Fourteen eARG types were found at the highest concentration of 1.3 × 10 5 gene copies (GC)/L. TetC was detected in 66.7% of samples, followed by sul1, sul2, and qnrA with the same detection frequency of 41.7%. Fifteen iARGs (including tetA, tetB, tetM, tetQ, tetX, sul1, sul2, sul3, ermB, blaTEM, and qnrA) were continuously detected in all collected tap water samples with sul1 and sul2 the most abundant. Additionally, both eARG and iARG concentrations in tap water presented a seasonal pattern with most abundant prevalence in summer. The concentration of observed intracellular sulfonamide resistance genes showed a significantly positive correlation with total nitrogen concentrations. This study suggested that eARG and iARG pollution of drinking water systems pose a potential risk to human public health., (Copyright © 2018. Published by Elsevier B.V.)