Tatsuhiro Furuta | Searcharxiv

arXiv2024

Hidden Markov model analysis to fluorescence blinking of fluorescently labeled DNA

We examine quantitatively the transition process from emitting to not-emitting states of fluorescent molecules with a machine learning technique. In a fluorescently labeled DNA, the fluorescence occurs continuously under irradiation, but it often transfers to the not-emitting state corresponding to a charge-separated state. The trajectory of the fluorescence consists of repetitions of light-emitting (ON) and not-emitting (OFF) states, called blinking, and it contains a very large amount of noise due to the several reasons, so in principle, it is difficult to distinguish the ON and OFF states quantitatively. The fluorescence trajectory is a typical stochastic process, and therefore requires advanced time-series data analysis. In the present study, we analyze the fluorescence trajectories using a hidden Markov model, and calculate the probability density of the ON and OFF duration. From the analysis, we found that the ON-duration probability density can be well described by an exponential function, and the OFF-duration probability density can be well described by a log-normal function, which are verified in terms of Kolmogorov-Smirnov test. The time-bin dependence in the fluorescence trajectory on the probability density is carefully analyzed. We also discuss the ON and OFF processes from failure-rate analysis used in life testing of semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2022

Hidden Markov model analysis for fluorescent time series of quantum dots

We present a hidden Markov model analysis for fluorescent time series of quantum dots. A fundamental quantity to measure optical performance of the quantum dots is a distribution function for the light-emission duration. So far, to estimate it, a threshold value for the fluorescent intensity was introduced, and the light-emission state was evaluated as a state above the threshold. With this definition, the light-emission duration was estimated, and its distribution function was derived as a blinking plot. Due to the noise in the fluorescent data, however, this treatment generates a large number of artificially short-lived emission states, thus leading to an erroneous blinking plot. In the present paper, we propose a hidden Markov model to eliminate these artifacts. The hidden Markov model introduces a hidden variable specifying the light-emission and quenching states behind the observed fluorescence. We found that it is possible to avoid the above artifacts by identifying the state from the hidden-variable time series. We found that, from the analysis of experimental and theoretical benchmark data, the accuracy of our hidden Markov model is beyond human cognitive ability.

cond-mat.mes-hall↗