Effect of heuristics on serendipity in path-based storytelling with linked data

Path-based storytelling with Linked Data on the Web provides users the ability to discover concepts in an entertaining and educational way. Given a query context, many state-of-the-art pathfinding approaches aim at telling a story that coincides with the user's expectations by investigating paths over Linked Data on the Web. By taking into account serendipity in storytelling, we aim at improving and tailoring existing approaches towards better fitting user expectations so that users are able to discover interesting knowledge without feeling unsure or even lost in the story facts. To this end, we propose to optimize the link estimation between - and the selection of facts in a story by increasing the consistency and relevancy of links between facts through additional domain delineation and refinement steps. In order to address multiple aspects of serendipity, we propose and investigate combinations of weights and heuristics in paths forming the essential building blocks for each story. Our experimental findings with stories based on DBpedia indicate the improvements when applying the optimized algorithm

Assessment of Offspring DNA Methylation across the Lifecourse Associated with Prenatal Maternal Smoking Using Bayesian Mixture Modelling

A growing body of research has implicated DNA methylation as a potential mediator of the effects of maternal smoking in pregnancy on offspring ill-health. Data were available from a UK birth cohort of children with DNA methylation measured at birth, age 7 and 17. One issue when analysing genome-wide DNA methylation data is the correlation of methylation levels between CpG sites, though this can be crudely bypassed using a data reduction method. In this manuscript we investigate the effect of sustained maternal smoking in pregnancy on longitudinal DNA methylation in their offspring using a Bayesian hierarchical mixture model. This model avoids the data reduction used in previous analyses. Four of the 28 previously identified, smoking related CpG sites were shown to have offspring methylation related to maternal smoking using this method, replicating findings in well-known smoking related genes MYO1G and GFI1. Further weak associations were found at the AHRR and CYP1A1 loci. In conclusion, we have demonstrated the utility of the Bayesian mixture model method for investigation of longitudinal DNA methylation data and this method should be considered for use in whole genome applications

Association between cigarette smoking status and voting intentions: Cross sectional surveys in England 2015-2020

Background and aims: Cigarette smoking takes place within a cultural and social context. Political views and practices are an important part of that context. To gain a better understanding of smoking, it may be helpful to understand its association with voting patterns as an expression of the political views and practices of the population who smoke. This study aimed to assess the association between cigarette smoking and voting intentions and to examine how far any association can be explained by sociodemographic factors and alcohol use. Methods: Pooled monthly representative repeat cross-sectional household surveys of adults (16+) in England (N = 55,482) between 2015 and 2020 were used to assess the association between cigarette smoking status and voting intentions, and whether this was accounted for by age, occupational grade, gender, region and alcohol use. Voting intention was measured by asking ‘How would you vote if there were a General Election tomorrow?’ Respondents chose from a list of the major English political parties or indicated their intention not to vote. Results: In adjusted multinomial regression, compared with intending to vote Conservative (majority party of government during the period), being undecided (aOR1.22 [1.13-1.33] <0.001), intending to vote Labour (aOR1.27 [1.16-1.36] <0.001), to vote “Other” (aOR1.54 [1.37-1.72] <0.001), or not to vote (aOR1.93 [1.77-2.11] <0.001) was associated with higher odds of current relative to never smoking rates. Intending to vote for the Liberal Democrats was associated with a significant lower odds of current smoking prevalence (aOR0.80 [0.70-0.91] <0.001) compared with intending to vote Conservative. Conclusions: Controlling for a range of other factors, current as compared with never-smokers appear more likely to intend not to vote, to be undecided, to vote for Labour or a non-mainstream party, and less likely to vote for the Liberal Democrats, compared with the Conservative party