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Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-art performance. In order to understand the current research status of TKGC methods based on deep learning and promote further development in this field, in this paper, for the first time, we summarize the deep learning-based methods in TKGC research. First, we detail the background of TKGC, including task definition, benchmark datasets, and evaluation protocol. Then, we divide the existing deep learning-based TKGC methods into eight fine-grained categories according to their core technology and summarize them. Finally, we conclude the paper and present three future research directions for TKGC. [ABSTRACT FROM AUTHOR]

Background: The association between socioeconomic status and individual behaviors (SES/IB) and anxiety disorders has garnered increasing attention, yet the impact of SES/IB on anxiety disorders remains unclear. Therefore, we employed a Mendelian randomization (MR) design to evaluate the causal relationship between SES/IB and anxiety disorders., Methods: We conducted a two-sample MR study to assess the causal effects of SES and IB (smoking behaviors, drinking behaviors, sleeping behaviors, habitual physical activity, leisure sedentary behaviors, and reproductive behaviors) on anxiety disorders. A series of filtering steps were taken to select eligible genetic instruments robustly associated with each of the traits. The inverse variance weighted was used for preliminary analysis, and multiple methods were used for sensitivity testing., Results: After Bonferroni correction and rigorous quality control, we found that educational attainment (odds ratio [OR]:0.75; 95 % confidence interval [CI]:0.69-0.81; P = 9.21E-12), alcohol consumption per week (OR: 1.62; 95 % CI: 1.33-1.97; P = 1.80E-06), insomnia (OR: 1.68; 95 % CI: 1.43-1.97; P = 1.45E-10),age at first birth (OR: 0.59; 95 % CI: 0.50-0.68; P = 1.31E-11),and number of sexual partners (OR: 2.19; 95 % CI: 1.71-2.80; P = 6.64E-10) were causally associated with anxiety disorders., Limitations: The subjects included in this study were all of European descent, and whether this finding can be generalized to other populations needs to be further demonstrated., Conclusions: The MR Study provides strong evidence that some factors influence anxiety disorders., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Study Objectives: Sleep disorders and psychiatric disorders frequently coexist and interact, yet the shared genetic basis linking these two domains remains poorly understood., Methods: We investigated the genetic correlation and overlap between seven sleep/circadian traits and three psychiatric disorders at the level of genome-wide association studies (GWAS), utilizing LDSC, HDL and GPA. To identify potential polygenic single nucleotide variations (SNVs) within each trait pair, we used PLACO, while gene-level analyses were performed using MAGMA and POPS. Furthermore, the functions and biological mechanisms, enriched phenotypes, tissues, cellular features, and pathways were thoroughly investigated using FUMA, deTS, and enrichment analyses at the biological pathway level., Results: Our study revealed extensive genetic associations and overlap in all 21 trait pairs. We identified 18,494 SNVs and 543 independent genomic risk loci, with 113 confirmed as causative through colocalization analysis. These loci collectively spanned 196 unique chromosomal regions. We pinpointed 43 distinct pleiotropic genes exhibiting significant enrichment in behavioral/physiological phenotypes, nervous system phenotypes, and brain tissue. Aberrations in synaptic structure and function, neurogenesis and development, as well as immune responses, particularly involving the MAPK pathway, emerged as potential underpinnings of the biology of sleep/circadian traits and psychiatric disorders., Conclusions: We identified shared loci and specific sets of genes between sleep/circadian traits and psychiatric disorders, shedding light on the genetic etiology. These discoveries hold promise as potential targets for novel drug interventions, providing valuable insights for the development of therapeutic strategies for these disorders., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)