DNA methylation variability arises due to concurrent genetic and environmental influences. Each of them is a mixture of regular and noisy sources, whose relative contribution has not been satisfactorily understood yet. We conduct a systematic assessment of the age-dependent methylation by the signal-to-noise ratio and identify a wealth of “deterministic” CpG probes (about 90%), whose methylation variability likely originates due to genetic and general environmental factors. The remaining 10% of “stochastic” CpG probes are arguably governed by the biological noise or incidental environmental factors. Investigating the mathematical functional relationship between methylation levels and variability, we find that in about 90% of the age-associated differentially methylated positions, the variability changes as the square of the methylation level, whereas in the most of the remaining cases the dependence is linear. Furthermore, we demonstrate that the methylation level itself in more than 15% cases varies nonlinearly with age (according to the power law), in contrast to the previously assumed linear changes. Our findings present ample evidence of the ubiquity of strong DNA methylation regulation, resulting in the individual age-dependent and nonlinear methylation trajectories, whose divergence explains the cross-sectional variability. It may also serve a basis for constructing novel nonlinear epigenetic clocks.

Gliomas, the most frequent type of primary tumor of the central nervous system in adults, results in significant morbidity and mortality. Despite the development of novel, complex, multidisciplinary, and targeted therapies, glioma therapy has not progressed much over the last decades. Therefore, there is an urgent need to develop novel patient-adjusted immunotherapies that actively stimulate antitumor T cells, generate long-term memory, and result in significant clinical benefits. This work aimed to investigate the efficacy and molecular mechanism of dendritic cell (DC) vaccines loaded with glioma cells undergoing immunogenic cell death (ICD) induced by photosens-based photodynamic therapy (PS-PDT) and to identify reliable prognostic gene signatures for predicting the overall survival of patients. Analysis of the transcriptional program of the ICD-based DC vaccine led to the identification of robust induction of Th17 signature when used as a vaccine. These DCs demonstrate retinoic acid receptor-related orphan receptor-γt dependent efficacy in an orthotopic mouse model. Moreover, comparative analysis of the transcriptome program of the ICD-based DC vaccine with transcriptome data from the TCGA-LGG dataset identified a four-gene signature (CFH, GALNT3, SMC4, VAV3) associated with overall survival of glioma patients. This model was validated on overall survival of CGGA-LGG, TCGA-GBM, and CGGA-GBM datasets to determine whether it has a similar prognostic value. To that end, the sensitivity and specificity of the prognostic model for predicting overall survival were evaluated by calculating the area under the curve of the time-dependent receiver operating characteristic curve. The values of area under the curve for TCGA-LGG, CGGA-LGG, TCGA-GBM, and CGGA-GBM for predicting five-year survival rates were, respectively, 0.75, 0.73, 0.9, and 0.69. These data open attractive prospects for improving glioma therapy by employing ICD and PS-PDT-based DC vaccines to induce Th17 immunity and to use this prognostic model to predict the overall survival of glioma patients.

Quantum systems, when interacting with their environments, may exhibit nonequilibrium states that are tempting to be interpreted as quantum analogs of chaotic attractors. However, different from the Hamiltonian case, the toolbox for quantifying dissipative quantum chaos remains limited. In particular, quantum generalizations of Lyapunov exponents, the main quantifiers of classical chaos, are established only within the framework of continuous measurements. We propose an alternative generalization based on the unraveling of quantum master equation into an ensemble of “quantum trajectories,” by using the so-called Monte Carlo wave-function method. We illustrate the idea with a periodically modulated open quantum dimer and demonstrate that the transition to quantum chaos matches the period-doubling route to chaos in the corresponding mean-field system.

Open quantum systems can exhibit complex states, for which classification and quantification are still not well resolved. The Kerr-nonlinear cavity, periodically modulated in time by coherent pumping of the intracavity photonic mode, is one of the examples. Unraveling the corresponding Markovian master equation into an ensemble of quantum trajectories and employing the recently proposed calculation of quantum Lyapunov exponents [I. I. Yusipov et al., Chaos 29, 063130 (2019)], we identify “chaotic” and “regular” regimes there. In particular, we show that chaotic regimes manifest an intermediate power-law asymptotics in the distribution of photon waiting times. This distribution can be retrieved by monitoring photon emission with a single-photon detector so that chaotic and regular states can be discriminated without disturbing the intracavity dynamics.

Background/Objectives: Gliomas are complex and heterogeneous brain tumors characterized by an unfavorable clinical course and a fatal prognosis, which can be improved by an early determination of tumor kind. Here, we developed explainable machine learning (ML) models for classifying three major glioma subtypes (astrocytoma, oligodendroglioma, and glioblastoma) and predicting survival rates based on RNA-seq data. Methods: We analyzed publicly available datasets and applied feature selection techniques to identify key biomarkers. Using various ML models, we performed classification and survival analysis to develop robust predictive models. The best-performing models were then interpreted using Shapley additive explanations (SHAP). Results: Thirteen key genes (TERT, NOX4, MMP9, TRIM67, ZDHHC18, HDAC1, TUBB6, ADM, NOG, CHEK2, KCNJ11, KCNIP2, and VEGFA) proved to be closely associated with glioma subtypes as well as survival. Support Vector Machine (SVM) turned out to be the optimal classification model with the balanced accuracy of 0.816 and the area under the receiver operating characteristic curve (AUC) of 0.896 for the test datasets. The Case-Control Cox regression model (CoxCC) proved best for predicting survival with the Harrell’s C-index of 0.809 and 0.8 for the test datasets. Using SHAP we revealed the gene expression influence on the outputs of both models, thus enhancing the transparency of the prediction generation process. Conclusions: The results indicated that the developed models could serve as a valuable practical tool for clinicians, assisting them in diagnosing and determining optimal treatment strategies for patients with glioma.