摘要

    据报道，抗生素抗性基因（ARG）威胁着全世界泳客的公共健康。尽管ARG监测和海滩指南是必要的，但ARG采样和分析仍需要大量努力。因此，在这项研究中，我们使用常规的长期短期记忆（LSTM），LSTM-卷积神经网络（CNN）混合模型和输入注意（IA）-LSTM来预测主要在降雨后沿海出现的ARGs发生。 。为了建立模型，以30分钟为间隔收集了10类环境数据以及在广安里海滩获得的4种主要ARG（即aac（6'-Ib-cr），blaTEM，sul1和tetX）的浓度数据在韩国，使用的是2018年至2019年之间的数据。当单独预测ARG的发生时，常规LSTM和IA-LSTM在训练和测试期间显示出较差的R2值。相反，与传统的LSTM和IA-LSTM相比，LSTM-CNN的精度提高了2到6倍。但是，当同时预测所有ARG的发生时，与LSTM-CNN相比，IA-LSTM模型总体上表现出优越的性能。此外，使用IA-LSTM模型研究了环境变量对预测的影响，并确定了影响每个ARG的输入变量的范围。因此，本研究证明了根据各种环境变量预测海滩上主要ARG发生和分布的可能性，并且该结果有望有助于管理休闲海滩上ARG的发生。

    Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this study, we predicted ARGs occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (LSTM), LSTM-convolutional neural network (CNN) hybrid model, and input attention (IA)-LSTM. To develop the models, 10 categories of environmental data collected at 30-min intervals and concentration data of 4 types of major ARGs (i.e., aac(6′-Ib-cr), blaTEM, sul1, and tetX) obtained at the Gwangalli Beach in South Korea, between 2018 and 2019 were used. When individually predicting ARGs occurrence, the conventional LSTM and IA-LSTM exhibited poor R2 values during training and testing. In contrast, the LSTM-CNN exhibited a 2–6-times improvement in accuracy over those of the conventional LSTM and IA-LSTM. However, when predicting all ARGs occurrence simultaneously, the IA-LSTM model exhibited a superior performance overall compared to that of LSTM-CNN. Additionally, the influence of environmental variables on prediction was investigated using the IA-LSTM model, and the ranges of input variables that affect each ARG were identified. Consequently, this study demonstrated the possibility of predicting the occurrence and distribution of major ARGs at the beach based on various environmental variables, and the results are expected to contribute to management of ARG occurrence at a recreational beach.

    https://www.sciencedirect.com/science/article/abs/pii/S0043135421001998