一种机器学习方法来预测整个城市化梯度的植物界抗体丰富度
Rui-Ao Ma1, Yi-Hui Ding1, Shifa Zhong1
1School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
Environment international
|July 4, 2025
概括
抗生素耐药性基因 (ARG) 在城市植物叶子中比在土壤中更为丰富,随着城市化而增加. 这凸显了城市绿地中耐药性传播带来的潜在公共卫生风险.
科学领域:
- 环境微生物学环境微生物学
- 城市生态学 城市生态学
- 公共卫生 公共卫生
背景情况:
- 城市化与绿色空间中抗生素耐药性基因 (ARG) 的增加有关.
- 城市土壤中的ARG的分布和驱动因素与植物圈的分布仍然不清楚.
研究的目的:
- 为了研究土壤和植物界沿着城市化梯度的ARG丰富性.
- 确定影响ARG分布和传输潜力的因素.
- 使用机器学习来预测ARG的丰富性.
主要方法:
- 从沿着城市化梯度的湿地公园收集的结合土壤和植物圈样本.
- 对ARG丰度与城市化指数的相关性分析.
- 机器学习模型 (随机森林) 用于预测.
主要成果:
- 生物圈ARG的丰富性与城市化更好地相关,并沿着梯度增加.
- 生物圈显示了更多的ARG移动基因元素对,表明传输潜力更高.
- 靠近建筑区和微生物多样性是植物界ARG的关键驱动因素.
结论:
- 城市绿地植物圈是ARG的重要储存库,其丰富程度与城市化有关.
- 城市发电厂中ARG传染潜力较高,对公共健康构成风险.
- 机器学习准确地预测了植物界ARG的丰富性,有助于风险评估.
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