Wearable device-measured moderate to vigorous physical activity and risk of degenerative aortic valve stenosis.

BACKGROUND AND AIMS: Physical activity has proven effective in preventing atherosclerotic cardiovascular disease, but its role in preventing degenerative valvular heart disease (VHD) remains uncertain. This study aimed to explore the dose-response association between moderate to vigorous physical activity (MVPA) volume and the risk of degenerative VHD among middle-aged adults. METHODS: A full week of accelerometer-derived MVPA data from 87 248 UK Biobank participants (median age 63.3, female: 56.9%) between 2013 and 2015 were used for primary analysis. Questionnaire-derived MVPA data from 361 681 UK Biobank participants (median age 57.7, female: 52.7%) between 2006 and 2010 were used for secondary analysis. The primary outcome was the diagnosis of incident degenerative VHD, including aortic valve stenosis (AS), aortic valve regurgitation (AR), and mitral valve regurgitation (MR). The secondary outcome was VHD-related intervention or mortality. RESULTS: In the accelerometer-derived MVPA cohort, 555 incident AS, 201 incident AR, and 655 incident MR occurred during a median follow-up of 8.11 years. Increased MVPA volume showed a steady decline in AS risk and subsequent AS-related intervention or mortality risk, levelling off beyond approximately 300 min/week. In contrast, its association with AR or MR incidence was less apparent. The adjusted rates of AS incidence (95% confidence interval) across MVPA quartiles (Q1-Q4) were 11.60 (10.20, 13.20), 7.82 (6.63, 9.23), 5.74 (4.67, 7.08), and 5.91 (4.73, 7.39) per 10 000 person-years. The corresponding adjusted rates of AS-related intervention or mortality were 4.37 (3.52, 5.43), 2.81 (2.13, 3.71), 1.93 (1.36, 2.75), and 2.14 (1.50, 3.06) per 10 000 person-years, respectively. Aortic valve stenosis risk reduction was also observed with questionnaire-based MVPA data [adjusted absolute difference Q4 vs. Q1: AS incidence, -1.41 (-.67, -2.14) per 10 000 person-years; AS-related intervention or mortality, -.38 (-.04, -.88) per 10 000 person-years]. The beneficial association remained consistent in high-risk populations for AS, including patients with hypertension, obesity, dyslipidaemia, and chronic kidney disease. CONCLUSIONS: Higher MVPA volume was associated with a lower risk of developing AS and subsequent AS-related intervention or mortality. Future research needs to validate these findings in diverse populations with longer durations and repeated periods of activity monitoring.

Transcriptome analysis of Sesuvium portulacastrum L. uncovers key genes and pathways involved in root formation in response to low-temperature stress.

Sesuvium portulacastrum L., a perennial facultative halophyte, is extensively distributed across tropical and subtropical coastal regions. Its limited cold tolerance significantly impacts both the productivity and the geographical distribution of this species in higher-latitude areas. In this study, we employed RNA-Seq technology to delineate the transcriptomic alterations in Sesuvium plants exposed to low temperatures, thus advancing our comprehension of the molecular underpinnings of this physiological adaptation and root formation. Our findings demonstrated differential expression of 10,805, 16,389, and 10,503 genes in the low versus moderate temperature (LT vs. MT), moderate versus high temperature (MT vs. HT), and low versus high temperature (LT vs. HT) comparative analyses, respectively. Notably, the gene categories "structural molecule activity", "ribosome biogenesis", and "ribosome" were particularly enriched among the LT vs. HT-specific differentially expressed genes (DEGs). When synthesizing the insights from these three comparative studies, the principal pathways associated with the cold response mechanism were identified as "carbon fixation in photosynthetic organisms", "starch and sucrose metabolism", "plant hormone signal transduction", "glycolysis/gluconeogenesis", and "photosynthesis". In addition, we elucidated the involvement of auxin signaling pathways, adventitious root formation (ARF), lateral root formation (LRF), and novel genes associated with shoot system development in root formation. Subsequently, we constructed a network diagram to investigate the interplay between hormone levels and pivotal genes, thereby clarifying the regulatory pathways of plant root formation under low-temperature stress and isolating key genes instrumental in root development. This study has provided critical insights into the molecular mechanisms that facilitate the adaptation to cold stress and root formation in S. portulacastrum.

NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups.

Real-world network data consisting of social interactions can be incomplete due to deliberately erased or unsuccessful data collection, which cause the misleading of social interaction analysis for many various time-aware applications. Naturally, the link prediction task has drawn much research interest to predict the missing edges in the incomplete social network. However, existing studies of link prediction cannot effectively capture the entangling topological and temporal dynamics already residing in the social network, thus cannot effectively reasoning the missing interactions in dynamic networks. In this paper, we propose the NEAWalk, a novel model to infer the missing social interaction based on topological-temporal features of patterns in the social group. NEAWalk samples the query-relevant walks containing both the historical and evolving information by focusing on the temporal constraint and designs a dual-view anonymization procedure for extracting both topological and temporal features from the collected walks to conduct the inference. Two-track experiments on several well-known network datasets demonstrate that the NEAWalk stably achieves superior performance against several state-of-the-art baseline methods.

UniSKGRep: A unified representation learning framework of social network and knowledge graph.

The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two embedding spaces, respectively. Then the Inter-graph model is learned to associate the two separate spaces via bridging the semantics of overlapping node pairs. In addition, the overlapping node enhancement module is designed to effectively align two spaces with the consideration of a relatively small number of overlapping nodes. The two spaces are gradually unified by continuously iterating the joint training procedure. Extensive experiments on two real-world SKG datasets have proved the effectiveness of UniSKGRep in yielding general and substantial performance improvement compared with the strong baselines in various downstream tasks.