Li Linze, Ahmed Shakeel, Abdulraheem Mukhtar Iderawumi, Hussain Fida, Zhang Hao, Wu Junfeng, Raghavan Vijaya, Xu Lulu, Kuan Geng, Hu Jiandong
College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, China.
Foods. 2024 Nov 28;13(23):3848. doi: 10.3390/foods13233848.
Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant-pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as (), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant-microbe interactions. These methods will aid researchers in understanding plant-pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context.
食源性疾病给研究人员带来了巨大挑战，因为叶片水分吸收对病原体通过气孔的内化有很大影响。了解植物与病原体的相互作用，尤其是在湿度和温度波动的情况下，对于制定预防病原体侵入和减少食源性危害的方法至关重要。本研究引入了一种利用神经网络的计算模型来预测病原体通过气孔的内化，这与之前强调生物防治技术的研究形成对比。计算模型评估了诸如（）等细菌病原体内化的可能性和持续时间，考虑了包括湿度和温度在内的各种环境因素。在50%和100%湿度水平下评估，估计的内化可能性范围为0.6200至0.8820，而内化时间从4000秒到5080秒不等。内化时间的差异在100%湿度下大约短1042.73秒，这与内化可能性增加26.2%相关，从0.6200上升到0.8820。已经开发了一个神经网络模型来定量预测这些值，从而增强对植物 - 微生物相互作用的理解。这些方法将帮助研究人员理解植物 - 病原体相互作用，特别是在湿度和温度变化的环境中，并且对于在技术先进的背景下制定预防病原体侵入和应对食源性疾病的策略至关重要。
