ABSTRACT
Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and record merging. To solve the latter, it is mostly assumed that ground truth can be determined, either as master data or from user feedback. However, in many cases, this is not the case because firstly the merging processes cannot be accurate enough, and also the data gathering processes in the different sources are simply imperfect and cannot provide high quality data. Instead of enforcing consistency, we propose to evaluate how concordant or discordant sources are as a measure of trustworthiness (the more discordant are the sources, the less we can trust their data). Thus, we define the discord measurement problem in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different data (for example, cases and deaths), we wish to assess whether the different sources are concordant, or if not, measure how discordant they are. Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
BackgroundThe course of Huntington’s disease (HD) is believed to be modulated by lifestyle and genetic factors. However, we do not understand how the interplay of these affects disease progression. An efficient method of measuring lifestyle factors involves the use of digital monitoring devices, but their long-term use in clinical HD populations has not yet been explored.AimInvestigate the use of digital technologies in a longitudinal observational study to inform our understanding of the contribution of multi-domain lifestyle and genetic factors in the progression of HD.MethodsWe plan to recruit 300-450 people with early to mid-stage HD to a 12-month observational study measuring aspects of physical activity, nutrition and sleep. Participants with existing genome wide association study (GWAS) data will be preferentially recruited. Assessment of dietary, sleep and physical activity habits will be performed at baseline and 12-month follow-up Clinical measures will be obtained from the corresponding annual Enroll-HD assessment (within 8 weeks of the DOMINO-HD assessment). Each participant will wear a Fitbit for the duration of the study. Lifestyle, genetic and clinical data will be linked and propensity score weighting methodology will be applied to examine the causal effect of the multi-domain lifestyle and genetic measures on HD progression.ResultsThe start of recruitment was delayed by 10 months due to Covid-19. As of 1st July 2021, we have recruited 36 participants across 5 clinical sites, with recruitment planned to continue until March 2022.ConclusionSuccessful collection of longitudinal lifestyle data, combined with functional clinical measures and genetic factors will allow, for the first time, the investigation of causal relationships between environmental and genetic modifiers with HD progression. We can then use the information generated to design lifestyle interventions aimed at improving quality of life and prognosis in HD.
ABSTRACT
Curated scientific databases play an important role in the scientific endeavour and support is needed for the significant effort that goes into their creation and maintenance. This demonstration and case study illustrate how curation support has been developed in the Links cross-tier programming language, a functional, strongly typed language with language-integrated query and support for temporal databases. The chosen case study uses weekly released Covid-19 fatality figures from the Scottish government which exhibit updates to previously released data. This data allows the capture and query of update provenance in our prototype. This demonstration will highlight the potential for language-integrated support for curation to simplify and streamline prototyping of web-applications in support of scientific databases. © 2021, Springer Nature Switzerland AG.