Investigation of the Causative Reactant of the Apoplast Electropotentials of Plants

Gensler, W.; Yan, Tai Lai

doi:10.1149/1.2095475

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…As anticipated, non-harmful stimuli did not induce significant changes in either the plant’s water status or electrome dynamics, potentially indicating the habitual nature of such stimuli and their association with non-damaging effects. 38 , 46–48 Nonetheless, it was crucial to confirm this observation, considering the electrome’s high sensitivity to even subtle stimuli and variations attributable to individual plant characteristics. 30 , 49 …”

Section: Discussionmentioning

confidence: 99%

“…To monitor the plant electrome, the electrophytography (EPG) technique was employed. 34 , 37 , 38 Electrical signals were captured within a Faraday cage using the MP36 electronic data acquisition system (Biopac Systems, Goleta, CA, USA), comprising four channels with high input impedance (10 GΩ). The MP36 was configured following electrocardiography (ECG-AHA; 0.5–100 Hz) standards, utilizing a sampling rate of 62.5 Hz.…”

Section: Methodsmentioning

confidence: 99%

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Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

de Toledo,

Reissig,

Senko

et al. 2024

Plant Signaling & Behavior

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Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant’s electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus. This study aimed to characterize the electrome of common bean ( Phaseolus vulgaris L.) cv. BRS-Expedito, subjected to different water availabilities, seeking patterns linked to these stimuli. For this purpose, bean plants in the vegetative stage were subjected to the following treatments: (I) distilled water; (II) half-strength Hoagland’s nutrient solution; (III) −2 MPa PEG solution; and (IV) −2 MPa NaCl solution. Electrical signals were recorded within a Faraday’s cage using the MP36 electronic system for data acquisition. Concurrently, plant water status was assessed by monitoring leaf turgor variation. Leaf temperature was additionally measured. Various analyses were conducted on the electrical time series data, including arithmetic average of voltage variation, skewness, kurtosis, Probability Density Function (PDF), autocorrelation, Power Spectral Density (PSD), Approximate Entropy (ApEn), Fast Fourier Transform (FFT), and Multiscale Approximate Entropy (ApEn(s)). Statistical analyses were performed on leaf temperature, voltage variation, skewness, kurtosis, PDF µ exponent, autocorrelation, PSD β exponent, and approximate entropy data. Machine Learning analyses were applied to identify classifiable patterns in the electrical time series. Characterization of the electrome of BRS-Expedito beans revealed stimulus-dependent profiles, even when alterations in water availability stimuli were similar in terms of quality and intensity. Additionally, it was observed that the bean electrome exhibits high levels of complexity, which are altered by different stimuli, with more intense and aversive stimuli leading to drastic reductions in complexity levels. Notably, one of the significant findings was the 100% accuracy of Small Vector Machine in detecting salt stress using electrome data. Furthermore, the study highlighted alterations in the plant electrome under low water potential before observable leaf turgor changes. This work demonstrates the potential use of the electrome as a physiological indicator of the water status in bean plants.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

de Toledo,

Reissig,

Senko

et al. 2024

Plant Signaling & Behavior

View full text Add to dashboard Cite

show abstract

“…Electrochemical phenomena in plants and trees have attracted researchers since the eighteenth century (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). The cells of many biological organs generate electric potentials that result in the flow of electrical currents (17).…”

Section: Introductionmentioning

confidence: 99%

Citrus Greening (Huanglongbing): Fast Electrochemical Detection and Phytomonitoring of the Trees Diseases

Volkov

Brown²

2014

ECS Trans.

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Citrus greening is probably the most important disease of citrus in the world. Here we show that citrus greening infection induces a very strong decrease in the amplitude of the electrical potential difference in a citrus tree tissue or in a fruit. The non-disruptive method, consisting of a data acquisition system interfaced with computer and identical nonpolarizable reversible electrodes connected to trees, was used for the measuring of electrical potentials and the detection of citrus greening in a tree’s tissue. The amplitude and time dependence of a registered electrical potential difference in a leaf, stem or fruit is sensitive to a tree’s health. Infected trees have a very low DC signal in comparison with healthy citrus trees. This electrophysiological method permits fast detection of citrus greening in citrus trees and can be used for a detection of other diseases in plants and trees. Monitoring the electrical signaling in higher plants represents a promising method to investigate fast electrical communication during environmental changes.

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At the Roots of Plant Neurobiology

Shepherd

2012

Plant Electrophysiology

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Investigation of the Causative Reactant of the Apoplast Electropotentials of Plants

Cited by 10 publications

References 20 publications

Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

Citrus Greening (Huanglongbing): Fast Electrochemical Detection and Phytomonitoring of the Trees Diseases

At the Roots of Plant Neurobiology

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