Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model.

Autor: Yu X; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin, 300392, China.; College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China., Lv Y; College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China., Wang Q; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin, 300392, China.; College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China., Wang W; College of Chemical Engineering and Material Science, Tianjin University of Science & Technology, Tianjin, 300457, China., Wang Z; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin, 300392, China.; College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China., Wu N; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin, 300392, China. nwu@tjau.edu.cn.; College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China. nwu@tjau.edu.cn., Liu X; College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China., Wang X; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin, 300392, China.; College of Agronomy and Resource and Environment, Tianjin Agricultural University, Tianjin, 300392, China., Xu X; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin, 300392, China.; College of Agronomy and Resource and Environment, Tianjin Agricultural University, Tianjin, 300392, China.
Jazyk: angličtina
Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 May; Vol. 31 (23), pp. 33610-33622. Date of Electronic Publication: 2024 Apr 30.
DOI: 10.1007/s11356-024-33087-2
Abstrakt: Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic composting process parameters on manure-sourced ARGs is important to control their spreading risk. In this study, the effects of process parameters on ARGs during aerobic composting of pig manure were explored through data mining based on 191 valid data collected from literature. Machine learning (ML) models (XGBoost and Random Forest) were utilized to predict the rate of ARGs changes during pig manure composting. The model evaluation index of the XGBoost model (R 2 = 0.651) was higher than that of the Random Forest (R 2 = 0.490), indicating that XGBoost had better prediction performance. Feature importance was further calculated for the XGBoost model, and the XGBoost black box model was interpreted by Shapley additive explanations analysis. Results indicated that the influencing factors on the ARGs variation in pig manure were sequentially divided into thermophilic period, total composting period, composting real time, and thermophilic stage average temperature. The findings gave an insight into the application of ML models to predict and decipher the ARG changes during manure composting and provided suggestions for better composting manipulation and optimization of process parameters.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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