Genomic structural variations lead to dysregulation of important coding and non-coding RNA species in dilated cardiomyopathy

The transcriptome needs to be tightly regulated by mechanisms that include transcription factors, enhancers, and repressors as well as non-coding RNAs. Besides this dynamic regulation, a large part of phenotypic variability of eukaryotes is expressed through changes in gene transcription caused by genetic variation. In this study, we evaluate genome-wide structural genomic variants (SVs) and their association with gene expression in the human heart. We detected 3,898 individual SVs affecting all classes of gene transcripts (e.g., mRNA, miRNA, lncRNA) and regulatory genomic regions (e.g., enhancer or TFBS). In a cohort of patients (n = 50) with dilated cardiomyopathy (DCM), 80,635 non-protein-coding elements of the genome are deleted or duplicated by SVs, containing 3,758 long non-coding RNAs and 1,756 protein-coding transcripts. 65.3% of the SV-eQTLs do not harbor a significant SNV-eQTL, and for the regions with both classes of association, we find similar effect sizes. In case of deleted protein-coding exons, we find downregulation of the associated transcripts, duplication events, however, do not show significant changes over all events. In summary, we are first to describe the genomic variability associated with SVs in heart failure due to DCM and dissect their impact on the transcriptome. Overall, SVs explain up to 7.5% of the variation of cardiac gene expression, underlining the importance to study human myocardial gene expression in the context of the individual genome. This has immediate implications for studies on basic mechanisms of cardiac maladaptation, biomarkers, and (gene) therapeutic studies alike

User-centric inference based on history of context data in pervasive environments

Pervasive computing systems need to be strongly proactive. Context-awareness contributes to this, thus minimizing human-machine interaction. Context-aware systems are greatly enhanced by the utilization of recorded history of the users ' situations and interactions. In this paper, an approach is proposed for modelling, storing and exploiting history-ofcontext, in order to predict or estimate context information. The proposed framework is context-type-independent, requires minimal processing and storage resources, and can be used for data compression. It is based on multiple context prediction rule generation models, demonstrates high prediction success ratio, and has been empirically evaluated via extensive experiments. Categories and Subject Descriptor