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.

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.

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.

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.