Plant “electrome” can be pushed toward a self-organized critical state by external cues: Evidences from a study with soybean seedlings subject to different environmental conditions

Abstract

In the present study, we have investigated how the low-voltage electrical signals of soybean seedlings change their temporal dynamic under different environmental conditions (cold, low light, and low osmotic potential). We have used electrophytografic technique (EPG) with sub-dermal electrodes inserted in 15-days-old seedlings located between root and shoot, accounting for a significant part of the individual seedlings. Herein, to work on a specific framework to settle this type of the study, we are adopting the term "electrome" as a reference to the totality of electrical activity measured. Taking into account the non-linear dynamic of the plants electrophysiology, we have hypothesized that the stimuli, as applied in a constant way, could push the system to a critical state, exhibiting spikes without a characteristic size, indicating self-organized criticality (SOC). The results from the power spectral density analysis (PSD), showed that the interval of the large majority of the β exponents were between 1.5 and 3, indicating that the time series, regardless environmental conditions, showed long-range temporal correlation (long memory for β≠0 and β≠2). The analyses from the histograms of the runs showed different patterns of distributions concerning the experimental conditions. However, the runs exhibiting typical spikes, mostly under low light and osmotic stress, showed power law distribution with exponent μ ≅ 2, which is an indicative for SOC. Overall, our results have confirmed that the temporal dynamic of the electrical signaling shows a complex non-linear behavior with long-range persistence. Moreover, the hypothesis that plant electrome can exhibit a self-organized critical state evoked by environmental cues, dissipating energy by bursts of electrical spikes without a characteristic size, was reinforced. Finally, new perspectives for research and additional hypothesis were presented.

Extracellular ionic fluxes suggest the basis for cellular life at the 1/f ridge of extended criticality

“…However, further exploration demands extreme (stable) experimental conditions (especially temperature) and even more sophisticated measuring devices (an 8½-digit voltmeter), which were beyond our reach. Only then, can an important subject concerning external, environmental cues at the organism level (Souza et al 2017) and many others that are mentioned here be properly addressed. Moreover, all future experiments should be performed with caution (Yakushenko et al 2014) to avoid any artefacts that might originate from the chemistry of the nanoparticles that are used in the solute-semiconductor interface.…”

Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic

“…All datasets used herein to test the different methods of classification are part of the study published by Souza et al (2017). The data consist of time series of low-voltage variation ( V Δ in μV ) measured in soybean plants subjected to different environmental stimuli: cold, low light and osmotic stress.…”

“…For instance, Sukhov et al (2014) and Magdalena et al (2017) have demonstrated the role of electrical signals in the regulation of photosynthetic responses to different stimuli. Very recently, Souza et al (2017) proposed the concept of "plant electrome" based on the general proposition of "electrome" by De Loof (De Loof, 2016), describing the totality of the ionic currents in different scales of plant organization. By measuring low-voltage electrical signal using eletrophytography (EPG) (Debono, 2013;Souza et al, 2017), Souza et al (2017) showed that different environmental stimuli could change some characteristics of the temporal dynamic of the electrical signaling, including the level of complexity.…”

“…Moreover, the environmental stimuli changed the type of color noise of the electrical signals. However, it was not clear if the different environmental cues (cold, low light, and osmotic stress) caused distinct effects on the plant signals (Souza et al, 2017).…”

“…Shaik and Ramakrishna (2014) used machine learning techniques to identify multiple stress conditions genes for broad resistance in rice, and Behmann et al (2015) presented a review of different machine learning techniques applied for biotic stress identification in precision crop protection. Chatterjee et al (2014) In this paper, we propose to use the concept of plant electrome (Souza et al, 2017) to automatically identify whether different environmental cues cause specific changes in the electrical signals of soybean plants. In order to verify such hypothesis, we considered using machine learning algorithms and arithmetic intervalar, a branch of mathematical tools that allows one to extend standard numbers to an interval representation.…”