Mitotic inheritance of the DNA methylome is a challenging task for the maintenance of cell identity. Whether DNA methylation pattern in different genomic contexts can all be faithfully maintained is an open question. A replication-coupled DNA methylation maintenance model was proposed decades ago, but some observations suggest that a replication-uncoupled maintenance mechanism exists. However, the capacity and the underlying molecular events of replication-uncoupled maintenance are unclear. By measuring maintenance kinetics at the single-molecule level and assessing mutant cells with perturbation of various mechanisms, we found that the kinetics of replication-coupled maintenance are governed by the UHRF1-Ligase 1 and PCNA-DNMT1 interactions, whereas nucleosome occupancy and the interaction between UHRF1 and methylated H3K9 specifically regulate replication-uncoupled maintenance. Surprisingly, replication-uncoupled maintenance is sufficiently robust to largely restore the methylome when replication-coupled maintenance is severely impaired. However, solo-WCGW sites and other CpG sites displaying aging-and cancer-associated hypomethylation exhibit low maintenance efficiency, suggesting that although quite robust, mitotic inheritance of methylation is imperfect and that this imperfection may contribute to selective hypomethylation during aging and tumorigenesis.

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel‐wise amplitude of low‐frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross‐validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical‐cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross‐validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.

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Loneliness results from lacking satisfied social connections. However, little is known how trait loneliness, which is a stable personal characteristic, is influenced by different types of social support (i.e. emotional and instrumental support) through the brain activity associated with loneliness. To explore these questions, data of resting-state functional magnetic resonance imaging (R-fMRI) of 92 healthy participants were analyzed. We identified loneliness-related brain regions by correlating participants’ loneliness scores with amplitudes of low-frequency fluctuation (ALFF) of R-fMRI data. We then conducted mediation analyses to test whether the negative relation between each type of social support and loneliness was explained via the neural activity in the loneliness-related brain regions. The results showed that loneliness was positively related to the mean ALFF value within right inferior temporal gyrus (ITG). In addition, the negative relation between emotional support and loneliness was explained by a decrease in the spontaneous neural activity within right ITG but this pattern was not observed for instrumental support. These results suggest the importance of social information processing on trait loneliness and highlight the need to differentiate the functions of different types of social support on mental health from a neural perspective.