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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.12176 |
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| _version_ | 1866912126071734272 |
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| author | Furuta, Tatsuhiro Fan, Shuya Takada, Tadao Kondo, Yohei Fujitsuka, Mamoru Maruyama, Atsushi Kawai, Kiyohiko Nakamura, Kazuma |
| author_facet | Furuta, Tatsuhiro Fan, Shuya Takada, Tadao Kondo, Yohei Fujitsuka, Mamoru Maruyama, Atsushi Kawai, Kiyohiko Nakamura, Kazuma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hidden Markov model analysis to fluorescence blinking of fluorescently labeled DNA Furuta, Tatsuhiro Fan, Shuya Takada, Tadao Kondo, Yohei Fujitsuka, Mamoru Maruyama, Atsushi Kawai, Kiyohiko Nakamura, Kazuma Materials Science 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. |
| title | Hidden Markov model analysis to fluorescence blinking of fluorescently labeled DNA |
| topic | Materials Science |
| url | https://arxiv.org/abs/2411.12176 |