The fact that dietary restriction (DR) and long-term rapamycin treatment (RALL) can ameliorate the aging process has been reported by many researchers. As the interface between external and genetic factors, epigenetic modification such as DNA methylation may have latent effects on the aging rate at the molecular level. To understand the mechanism behind the impacts of dietary restriction and rapamycin on aging, DNA methylation and gene expression changes were measured in the hippocampi of different-aged mice. Examining the single-base resolution of DNA methylation, we discovered that both dietary restriction and rapamycin treatment can maintain DNA methylation in a younger state compared to normal-aged mice. Through functional enrichment analysis of genes in which DNA methylation or gene expression can be affected by DR/RALL, we found that DR/RALL may retard aging through a relationship in which DNA methylation and gene expression work together not only in the same gene but also in the same biological process. This study is instructive for understanding the maintenance of DNA methylation by DR/RALL in the aging process, as well as the role of DR and RALL in the amelioration of aging.

Entity matching (EM) aims to determine whether two tuples from heterogeneous data sources refer to the same real-world entity and serves as a fundamental task in data integration, knowledge graph construction, and intelligent information retrieval. However, existing EM methods still suffer from three major limitations: insufficient semantic representation for heterogeneous tuples, difficulty in constructing reliable supervision signals in zero-shot settings, and inadequate collaboration among multiple semantic views, which together result in limited robustness and unstable performance. To address these limitations, we propose a novel self-supervised entity matching framework based on multi-semantic collaboration (MSSC-EM). MSSC-EM consists of three tightly coupled modules: RAG-based Information Augmentation (RIA), Automatic Data Augmentation (ADA), and Collaborative Learning of Multi-Semantic Features (CL-MSF). In MSSC-EM, first, RIA employs Retrieval-Augmented Generation (RAG) to mine implicit contextual semantics from an external knowledge corpus and enrich the original tuple representations. Second, ADA constructs pseudo-positive and negative labels through an Incremental Knowledge Graph Construction (IKGC) strategy and enhances their reliability via confidence-aware Monte Carlo Dropout (MC-Dropout) filtering, thereby providing high-quality supervision signals without manual annotation. Finally, CL-MSF collaboratively learns digital, structural, and relational semantic features to improve the robustness and discriminative capability of tuple representations. Extensive experimental results on eight real-world EM benchmarks demonstrate that, under the unsupervised evaluation setting, MSSC-EM achieves the best or tied-best performance among the compared unsupervised methods. Under the supervised evaluation setting, MSSC-EM further outperforms or matches the compared self-supervised methods on most datasets and achieves competitive results against the compared supervised baselines.