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Related Experiment Video

Updated: Jun 4, 2025

Author Spotlight: Advancing Stomatal Research with Automated Aperture Measurement

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Published on: February 9, 2024

Plant Microbe Interaction-Predicting the Pathogen Internalization Through Stomata Using Computational Neural Network

Linze Li^1,2, Shakeel Ahmed^1,2, Mukhtar Iderawumi Abdulraheem^1,2

¹College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.

Foods (Basel, Switzerland)

|December 17, 2024

Summary

This summary is machine-generated.

This study uses neural networks to predict how plant stomata internalize pathogens like Salmonella enterica. Higher humidity significantly increases pathogen internalization likelihood and reduces time, aiding foodborne illness prevention.

Keywords:

computational modeling foliar water uptake foodborne illness neural networking plant–pathogen interaction

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Assessing Stomatal Response to Live Bacterial Cells using Whole Leaf Imaging

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Published on: October 2, 2010

Area of Science:

Plant pathology and food safety
Computational biology and bioinformatics
Environmental microbiology

Background:

Foodborne diseases pose significant public health challenges.
Plant-pathogen interactions, particularly pathogen entry via stomata, are influenced by environmental factors like humidity and temperature.
Understanding these interactions is key to preventing foodborne hazards.

Purpose of the Study:

To develop a computational model using neural networks to predict pathogen internalization via plant stomata.
To quantitatively assess the influence of environmental factors (humidity, temperature) on pathogen internalization.
To provide a novel approach for understanding plant-microbe interactions in the context of food safety.

Main Methods:

Utilized computational modeling with neural networks to simulate and predict pathogen internalization.
Assessed internalization likelihood and duration for bacterial pathogens, specifically Salmonella enterica (S. enterica).
Analyzed the impact of varying humidity levels (50% and 100%) on internalization parameters.

Main Results:

Pathogen internalization likelihood ranged from 0.6200 to 0.8820.
Internalization time varied between 4000 s and 5080 s.
A 100% humidity level resulted in approximately 1042.73 s shorter internalization time and a 26.2% increase in internalization likelihood compared to 50% humidity.

Conclusions:

The developed neural network model effectively predicts pathogen internalization via stomata.
Environmental conditions, especially humidity, significantly impact the rate and likelihood of pathogen entry into plants.
This research offers a technologically advanced strategy for understanding and mitigating foodborne illness risks.