Photocount statistics of ultra-weak photon emission from germinating mung bean

Rafieiolhosseini, Neda; Poplová, Michaela; Sasanpour, Pezhman; Rafii-Tabar, Hashem; Alhossaini, Mahsa Rafiee; Cifra, Michal

doi:10.1016/j.jphotobiol.2016.06.001

Cited by 14 publications

(14 citation statements)

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“…The difference between the Fano factor in model and experimental data from mungs ( Fig 6c and 6d ) is caused by the fact that the experimental data are composed from the chemiluminescence signal and non-Poisson detector noise while the model data are not. If the SNR is low, the non-Poisson detector noise depreciates the final signal and its distribution, as we recently demonstrated [ 26 ]. Although the Fano factor of experimental mungs data after detrending and the PP method is slightly higher than 1 (specifically, it is 1.17), the chi-square two-sample test confirms the hypothesis of a Poisson distribution (summarized in the section Quality of Poisson pre-processing).…”

Section: Resultsmentioning

confidence: 83%

“…The null hypothesis is not rejected for the stationary luminescence experimental data and model data from mung seeds before and after application of the PP method (p-values typically higher than 0.8). The null hypothesis is rejected for detector noise (which is known to be super-Poissonian [ 26 ]) before and after application of the PP method.…”

Section: Resultsmentioning

confidence: 99%

“…Experimental time series and model data are used for the evaluation of the suggested PP method. Three types of experimental data are investigated in total: i) nonstationary luminol-chemiluminescence signals of human neutrophils induced by Phorbol 12 myristate 13-acetate (PMA, Sigma-Aldrich, USA) [ 25 ], ii) stationary signals of endogenous biological chemiluminescence from mung seeds [ 26 ], and iii) noise (dark count) from a photomultiplier tube (PMT) detector module. The experimental data were obtained using a selected low-noise PMT module H7360-01 (Hamamatsu Photonics K.K.)…”

Section: Methodsmentioning

confidence: 99%

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Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

2017

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Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

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Section: Resultsmentioning

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

2017

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“…This light phenomenon differs from a rather bright bioluminescence which is dependent a specific enzymatic complexes which are present only in very specific species such as fireflies and selected jellyfish. What differentiates the general biological autoluminescence from ordinary bioluminescence is, apart the weaker intensity, its ubiquity across biological species ranging from microorganisms [2][3][4][5] through tissue cultures [6][7][8], plants [9][10][11][12][13] up to animals [14] including human [15][16][17]. There are also various synonyma used in the literature describing this light phenomenon such as ultra-weak photon emission [18], ultra-weak bioluminescence [19], endogenous biological chemiluminescence [20], biophotons [21][22][23], etc.…”

Section: Introductionmentioning

confidence: 99%

Short-time fractal analysis of biological autoluminescence

Dlask

Kukal

Poplová

et al. 2019

Preprint

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Biological systems manifest continuous weak autoluminescence, which is present even in the absence of external stimuli. Since this autoluminescence arises from internal metabolic and physiological processes, several works suggested that it could carry information in the time series of the detected photon counts. However, there is little experimental work which would show any difference of this signal from random Poisson noise and some works were prone to artifacts due to lacking or improper reference signals. Here we apply rigorous statistical methods and advanced reference signals to test the hypothesis whether time series of autoluminescence from germinating mung beans display any intrinsic correlations. Utilizing the fractional Brownian bridge that employs short samples of time-series in the method kernel, we suggest that the detected autoluminescence signal from mung beans is not totally random, but it seems to involve a process with a negative memory. Our results contribute to the development of the rigorous methodology of signal analysis of photonic biosignals.

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“…However, the dynamics of EBC during seed germination have not been yet explored in A. thaliana . So far, past reports explored EBC during germination in agriculturally relevant plants such as wheat 15 , 16 , rice 17 and mung beans 18 , 19 . To fill in the gap, we report in this paper on A. thaliana EBC during seed germination and also analyze an effect of externally induced oxidation.…”

Section: Introductionmentioning

confidence: 99%

Endogenous Chemiluminescence from Germinating Arabidopsis Thaliana Seeds

Saeidfirozeh¹,

Shafiekhani²,

Cifra³

et al. 2018

Sci Rep

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It is well known that all biological systems which undergo oxidative metabolism or oxidative stress generate a small amount of light. Since the origin of excited states producing this light is generally accepted to come from chemical reactions, the term endogenous biological chemiluminescence is appropriate. Apart from biomedicine, this phenomenon has potential applications also in plant biology and agriculture like monitoring the germination rate of seeds. While chemiluminescence capability to monitor germination has been measured on multiple agriculturally relevant plants, the standard model plant Arabidopsis thaliana has not been analyzed for this process so far. To fill in this gap, we demonstrate here on A. thaliana that the intensity of endogenous chemiluminescence increases during the germination stage. We showed that the chemiluminescence intensity increases since the second day of germination, but reaches a plateau on the third day, in contrast to other plants germinating from larger seeds studied so far. We also showed that intensity increases after topical application of hydrogen peroxide in a dose-dependent manner. Further, we demonstrated that the entropy of the chemiluminescence time series is similar to random Poisson signals. Our results support a notion that metabolism and oxidative reactions are underlying processes which generate endogenous biological chemiluminescence. Our findings contribute to novel methods for non-invasive and label-free sensing of oxidative processes in plant biology and agriculture.

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Photocount statistics of ultra-weak photon emission from germinating mung bean

Cited by 14 publications

References 20 publications

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

Short-time fractal analysis of biological autoluminescence

Endogenous Chemiluminescence from Germinating Arabidopsis Thaliana Seeds

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