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The plethora of pathways leading to health problems of later life has made the causal assessment of widowhood on the health of older adults difficult. This research aimed to assess the short‐term impact of widowhood on the objective health of older adults and to the moderating role of community‐based care (CBC) in improving the health outcomes of older adults. In this study, the activities of daily living (ADLs) and number of hospitalisations of older adults were measured, and PSM‐DID models were conducted. Widowhood had a significant negative impact on the objective health of older adults. Widowed adults had higher ADL scores (B = 0.569, 95 percent CI: 0.295 to 0.844) and more hospitalisations (B = 2.551, 95 percent CI: 1.189 to 3.914) than nonwidowed adults. Meanwhile, CBC in urban areas can significantly reduce ADL scores (B = −0.154, SE = 0.082) and the number of hospitalisations (B = −1.402, SE = 0.348) in older adults, whereas CBC in rural areas can only significantly reduce ADL scores (B = −0.197, SE = 0.087). Taken together, there is an urgent need to focus on the health of widowed and older adults and to provide CBC in both urban and rural areas equally. [ABSTRACT FROM AUTHOR]

With the development of new sequencing technology, the entire N6-methyl-adenosine (m(6)A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset. [ABSTRACT FROM AUTHOR]