Single particle tracking of mRNA molecules and lipid granules in living cells shows that the time averaged mean squared displacement delta2[over ] of individual particles remains a random variable while indicating that the particle motion is subdiffusive. We investigate this type of ergodicity breaking within the continuous time random walk model and show that delta2[over ] differs from the corresponding ensemble average. In particular we derive the distribution for the fluctuations of the random variable delta2[over ]. Similarly we quantify the response to a constant external field, revealing a generalization of the Einstein relation. Consequences for the interpretation of single molecule tracking data are discussed.

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We investigate the distribution of the number of photons emitted by a single molecule undergoing a spectral diffusion process and interacting with a continuous wave field. Using a generating function formalism an exact analytical formula for Mandel's Q parameter is obtained. The solution, which is valid for weak and strong excitation fields, exhibits transitions between (i) quantum sub-Poissonian and classical super-Poissonian behaviors, and (ii) fast to slow modulation limits.

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We investigate the distribution of the number of photons emitted by a single molecule undergoing a spectral diffusion process and interacting with a continuous wave laser field. The spectral diffusion is modeled based on a stochastic approach, in the spirit of the Anderson-Kubo line shape theory. Using a generating function formalism we solve the generalized optical Bloch equations and obtain an exact analytical formula for the line shape and Mandel's Q parameter. The line shape exhibits well-known behaviors, including motional narrowing when the stochastic modulation is fast and power broadening. The Mandel parameter, describing the line shape fluctuations, exhibits a transition from a quantum sub-Poissonian behavior in the fast modulation limit to a classical super-Poissonian behavior found in the slow modulation limit. Our result is applicable for weak and strong laser fields, namely, for arbitrary Rabi frequency. We show how to choose the Rabi frequency in such a way so that the quantum sub-Poissonian nature of the emission process becomes strongest. A lower bound on Q is found and simple limiting behaviors are investigated. A nontrivial behavior is obtained in the intermediate modulation limit, when the time scales for spectral diffusion and the lifetime of the excited state become similar. A comparison is made between our results and previous ones derived, based on the semiclassical generalized Wiener-Khintchine formula.

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The oral microbiota has been observed to be influenced by cigarette smoking and linked to several human diseases. However, research on the effect of cigarette smoking on the oral microbiota has not been systematically conducted in the Chinese population. We profiled the oral microbiota of 316 healthy subjects in the Chinese population by 16S rRNA gene sequencing. The alpha diversity of oral microbiota was different between never smokers and smokers (P = 0.002). Several bacterial taxa were first reported to be associated with cigarette smoking by LEfSe analysis, including Moryella (q = 1.56E-04), Bulleidia (q = 1.65E-06), and Moraxella (q = 3.52E-02) at the genus level and Rothia dentocariosa (q = 1.55E-02), Prevotella melaninogenica (q = 8.48E-08), Prevotella pallens (q = 4.13E-03), Bulleidia moorei (q = 1.79E-06), Rothia aeria (q = 3.83E-06), Actinobacillus parahaemolyticus (q = 2.28E-04), and Haemophilus parainfluenzae (q = 4.82E-02) at the species level. Two nitrite-producing bacteria that can increase the acidity of the oral cavity, Actinomyces and Veillonella, were also enriched in smokers with FDR-adjusted q-values of 3.62E-06 and 1.10E-06, respectively. Notably, we observed that two acid production-related pathways, amino acid-related enzymes (q = 6.19E-05) and amino sugar and nucleotide sugar metabolism (q = 2.63E-06), were increased in smokers by PICRUSt analysis. Finally, the co-occurrence analysis demonstrated that smoker-enriched bacteria were significantly positively associated with each other and were negatively correlated with the bacteria decreased in smokers. Our results suggested that cigarette smoking may affect oral health by creating a different environment by altering bacterial abundance, connections among oral microbiota, and the microbiota and their metabolic function.

One model structural deficiency is that some dynamic characteristics (such as seasonal dynamics) in catchment conditions are not explicitly represented by hydrological models. This study integrates data mining techniques to develop a clustering preprocessing framework for the subannual calibration of hydrological models to simulate seasonal dynamic behaviors. The proposed framework aims to solve the problems caused by missing processes and deficiencies of hydrological models, providing guidance for future model development. A set of climatic‐land surface indices is provided and preprocessed using the maximal information coefficient and the principal component analysis. Two clustering operations are performed based on the preprocessed climatic index and land‐surface index systems. Hydrological data are clustered into subannual periods for calibration. The parameters are independently optimized for each subperiod using a modified parallel calibration scheme and are then combined to generate a continuous simulation. The framework is applied in calibrating the TOPMODEL. The results show that the performance of the model with a clustering preprocessing framework in the middle‐ and low‐flow conditions is significantly improved without reducing the simulation accuracy for high flows. The transposability of the model parameters from the calibration to validation period has been improved significantly as well. The anomalous parameter values may be attributed in part to the convergence problem when using an optimization algorithm. Though well applied in the TOPMODEL, the framework has the potential to be used in other hydrological models.