| 1. |
- Haas, Jan, et al.
(författare)
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Atlas of the clinical genetics of human dilated cardiomyopathy
- 2015
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Ingår i: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 36:18, s. 1123-U43
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Tidskriftsartikel (refereegranskat)abstract
- Aim: We were able to show that targeted Next-Generation Sequencing is well suited to be applied in clinical routine diagnostics, substantiating the ongoing paradigm shift from low- to high-throughput genomics in medicine. By means of our atlas of the genetics of human DCM, we aspire to soon be able to apply our findings to the individual patient with cardiomyopathy in daily clinical practice. Numerous genes are known to cause dilated cardiomyopathy (DCM). However, until now technological limitations have hindered elucidation of the contribution of all clinically relevant disease genes to DCM phenotypes in larger cohorts. We now utilized next-generation sequencing to overcome these limitations and screened all DCM disease genes in a large cohort. Methods and results: In this multi-centre, multi-national study, we have enrolled 639 patients with sporadic or familial DCM. To all samples, we applied a standardized protocol for ultra-high coverage next-generation sequencing of 84 genes, leading to 99.1% coverage of the target region with at least 50-fold and a mean read depth of 2415. In this well characterized cohort, we find the highest number of known cardiomyopathy mutations in plakophilin-2, myosin-binding protein C-3, and desmoplakin. When we include yet unknown but predicted disease variants, we find titin, plakophilin-2, myosin-binding protein-C 3, desmoplakin, ryanodine receptor 2, desmocollin-2, desmoglein-2, and SCN5A variants among the most commonly mutated genes. The overlap between DCM, hypertrophic cardiomyopathy (HCM), and channelopathy causing mutations is considerably high. Of note, we find that >38% of patients have compound or combined mutations and 12.8% have three or even more mutations. When comparing patients recruited in the eight participating European countries we find remarkably little differences in mutation frequencies and affected genes. Conclusion: This is to our knowledge, the first study that comprehensively investigated the genetics of DCM in a large-scale cohort and across a broad gene panel of the known DCM genes. Our results underline the high analytical quality and feasibility of Next-Generation Sequencing in clinical genetic diagnostics and provide a sound database of the genetic causes of DCM.
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| 2. |
- Johansson, Jonas, et al.
(författare)
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Comparing Topological Performance Measures and Physical Flow Models for Vulnerability Analysis of Power Systems
- 2012
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Ingår i: 11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012. - 9781622764365 ; 8, s. 6863-6872
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Konferensbidrag (refereegranskat)abstract
- Critical infrastructures must be both robust and resilient in order to ensure the functioning of society. A key activity in ensuring their proper function is finding and addressing system weaknesses by the means of risk and vulnerability analysis. A critical factor of such analysis is the ability to determine the negative consequences of various types of contingencies. Numerous mathematical and simulation models exist which can be used to this end. We suggest a classification of these models, which span from very simple topologically oriented models to very advanced dynamical models. There arc rather few studies comparing the implications of using different modeling approaches in the context of comprehensive vulnerability analysis of critical infrastructures. In this paper such a study is presented for the analysis of electric power systems with the aim of improving understanding of the tradeoffs between simplicity and fidelity in this context. More specifically, the purpose of the paper is to compare different models for vulnerability analysis of electric power systems and explore the consequence measures achieved with these models. To this end the IEEE RTS96 system is used. The results give guidance for appropriate models to use when analyzing large-scale interdependent infrastructure systems, where simulation times quickly become insurmountable when using more advanced models. It is concluded that some performance measures may give reasonable estimates on average of the consequences that arise when the system is perturbed, but may have limited value when estimating the consequences of specific failure scenarios.
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| 3. |
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| 4. |
- LaRocca, Sarah, et al.
(författare)
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Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems.
- 2015
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Ingår i: Risk Analysis: an official publication of the Society for Risk Analysis. - : Wiley. - 1539-6924. ; 35:4, s. 608-623
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Tidskriftsartikel (refereegranskat)abstract
- Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.
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| 5. |
- LaRocca, Sarah, et al.
(författare)
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Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems
- 2013
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to nd and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist which can be used to this end. However, there are relatively few studies comparing the implications of using dierent modeling approaches in the context of comprehensive risk analysis of critical infrastructures. Thus in this paper, we suggest a classication of these models, which span from simple topologically-oriented models to advanced physical ow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeos between simplicity and delity in models used in the context of risk analysis. Specically, the purpose of this paper is to compare performances measures achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplied topological measures can be combined using statistical methods to be used as a surrogate for physical ow models. The results of our work provide guidance as to appropriate models or combination of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.
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