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Potential use of machine learning methods in assessment of Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with antifungal agents
Fungal Biology ( IF 2.9 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.funbio.2019.11.006
Andrea Tarazona 1 , Eva M Mateo 1 , José V Gómez 1 , David Romera 1 , Fernando Mateo 2
Affiliation  

Abstract The use of Fusarium-controlling fungicides is necessary to limit crop loss. Little is known about the effect of commercial antifungal formulations at sub-lethal doses, and their interaction with abiotic factors, on Fusarium culmorum and F. proliferatum development and on zearalenone and fumonisin biosynthesis, respectively. In the present study different treatments based on sulfur, trifloxystrobin and demethylation inhibitor fungicides (cyproconazole, tebuconazole and prothioconazole) under different environmental conditions, in Maize Extract Medium (MEM), are assayed in vitro. Then, several machine learning methods (neural networks, random forest and extreme gradient boosted trees) have been applied and compared for the first time for modeling growth rate of F. culmorum and F. proliferatum and zearalenone and fumonisin production, respectively. The most effective antifungal treatment was prothioconazole, 250 g/L + tebuconazole, 150 g/L. Effective doses of this formulation for reduction or total fungal growth inhibition ranged as follows ED50 0.49–1.70, ED90 2.57–6.02 and ED100 4.0–8.0 μg/mL, depending on the species, water activity and temperature. Overall, the growth rate and mycotoxin levels in cultures decreased when doses increased. However, some treatments in combination with certain aw and temperature values significantly induced toxin production. The extreme gradient boosted tree was the machine learning model able to predict growth rate and mycotoxin production with minimum error and maximum R2 value.

中文翻译:

机器学习方法在评估用抗真菌剂治疗中的镰刀菌和增殖镰刀菌的生长和霉菌毒素产生中的潜在用途

摘要 必须使用控制镰刀菌的杀菌剂来限制作物损失。关于亚致死剂量的商业抗真菌制剂及其与非生物因子的相互作用、分别对镰刀菌和 F. proliferatum 发育以及玉米赤霉烯酮和伏马菌素生物合成的影响知之甚少。在本研究中,在玉米提取培养基 (MEM) 中,在不同环境条件下,基于硫、嘧菌酯和去甲基化抑制剂杀菌剂(环丙康唑、戊唑醇和丙硫菌唑)的不同处理进行了体外测定。然后,首次应用并比较了几种机器学习方法(神经网络、随机森林和极端梯度增强树),以模拟 F. culmorum 和 F. proliferatum 以及玉米赤霉烯酮和伏马菌素生产的增长率,分别。最有效的抗真菌治疗是丙硫菌唑,250 g/L + 戊唑醇,150 g/L。该制剂用于减少或总真菌生长抑制的有效剂量范围如下:ED50 0.49–1.70、ED90 2.57–6.02 和 ED100 4.0–8.0 μg/mL,具体取决于物种、水分活度和温度。总体而言,当剂量增加时,培养物中的生长速度和真菌毒素水平降低。然而,一些处理结合某些 aw 和温度值显着诱导毒素产生。极端梯度提升树是机器学习模型,能够以最小的误差和最大的 R2 值预测增长率和霉菌毒素的产生。该制剂用于减少或总真菌生长抑制的有效剂量范围如下:ED50 0.49–1.70、ED90 2.57–6.02 和 ED100 4.0–8.0 μg/mL,具体取决于物种、水分活度和温度。总体而言,当剂量增加时,培养物中的生长速度和真菌毒素水平降低。然而,一些处理结合某些 aw 和温度值显着诱导毒素产生。极端梯度提升树是机器学习模型,能够以最小的误差和最大的 R2 值预测增长率和霉菌毒素的产生。该制剂用于减少或总真菌生长抑制的有效剂量范围如下:ED50 0.49–1.70、ED90 2.57–6.02 和 ED100 4.0–8.0 μg/mL,具体取决于物种、水分活度和温度。总体而言,当剂量增加时,培养物中的生长速度和真菌毒素水平降低。然而,一些处理结合某些 aw 和温度值显着诱导毒素产生。极端梯度提升树是机器学习模型,能够以最小的误差和最大的 R2 值预测增长率和霉菌毒素的产生。某些处理与某些 aw 和温度值相结合显着诱导毒素产生。极端梯度提升树是机器学习模型,能够以最小的误差和最大的 R2 值预测增长率和霉菌毒素的产生。某些处理与某些 aw 和温度值相结合显着诱导毒素产生。极端梯度提升树是机器学习模型,能够以最小的误差和最大的 R2 值预测增长率和霉菌毒素的产生。
更新日期:2021-02-01
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