Purpose Heritage or historic building information modelling (BIM), often referred to as HBIM, is becoming an established feature in both research and practice. The advancement of data capture technologies such as laser scanning and improved photogrammetry, along with the continued power of BIM authoring tools, has provided the ability to generate more accurate digital representations of heritage buildings which can then be used during renovation and refurbishment projects. Very often these representations of HBIM are developed to support the design process. What appears to be often overlooked is the issue of conservation and how this can be linked to the BIM process to support the conservation management plan for the building once it is given a new lease of life following the refurbishment process. The paper aims to discuss these issues. Design/methodology/approach The paper presents a review of the context of conservation and HBIM, and then subsequently presents two case studies of how HBIM was applied to high-profile renovation and conservation projects in the UK. In presenting the case studies, a range of issues is identified which support findings from the literature noting that HBIM is predominantly a tool for the geometric modelling of historic fabric with less regard for the actual process of renovation and conservation in historic buildings. Findings Lessons learnt from the case studies and from existing literature are distilled to develop a framework for the implementation of HBIM on heritage renovation projects to support the ongoing conservation of the building as an integral part of a BIM-based asset management strategy. Five key areas are identified in the framework including value, significance, recording, data management and asset management. Building on this framework, a conceptual overlay is proposed to the current Level 2 BIM process to support conservation heritage projects. Originality/value This paper addresses the issue of HBIM application to conservation heritage projects. Whilst previous work in the field has identified conservation as a key area, there is very little work focusing on the process of conservation in the HBIM context. This work provides a framework and overlay which could be used by practitioners and researchers to ensure that HBIM is fully exploited and a more standardised method is employed which could be used on conservation heritage renovation projects.
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals.Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for
Background Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived by Relief-based feature importance algorithms that efficiently detect both univariate and interaction effects (MultiSURF) or exclusively interaction effects (MultiSURF*). We compare these interaction-sensitive GSEA approaches to traditional χ2 rankings in simulated genome-wide array data, and in a target and replication cohort of congenital heart disease patients with conotruncal defects (CTDs). Results In the simulation study and for both CTD datasets, both Relief-based approaches to GSEA captured more relevant and significant gene ontology terms compared to the univariate GSEA. Key terms and themes of interest include cell adhesion, migration, and signaling. A leading edge analysis highlighted semaphorins and their receptors, the Slit-Robo pathway, and other genes with roles in the secondary heart field and outflow tract development. Conclusions Our results indicate that interaction-sensitive approaches to enrichment analysis can improve upon traditional univariate GSEA. This approach replicated univariate findings and identified additional and more robust support for the role of the secondary heart field and cardiac neural crest cell migration in the development of CTDs.
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