gems: An R Package for Simulating from Disease Progression Models

Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package gems allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by gems, explain the functions of gems and their arguments, and show a sample application.

Spinal muscular atrophy: SMN2 pre-mRNA splicing corrected by a U7 snRNA derivative carrying a splicing enhancer sequence

Spinal muscular atrophy (SMA) is a lethal hereditary disease caused by homozygous deletion/inactivation of the survival of motoneuron 1 (SMN1) gene. The nearby SMN2 gene, despite its identical coding capacity, is only an incomplete substitute, because a single nucleotide difference impairs the inclusion of its seventh exon in the messenger RNA (mRNA). This splicing defect can be corrected (transiently) by specially designed oligonucleotides. Here we have developed a more permanent correction strategy based on bifunctional U7 small nuclear RNAs (snRNAs). These carry both an antisense sequence that allows specific binding to exon 7 and a splicing enhancer sequence that will improve the recognition of the targeted exon. When expression cassettes for these RNAs are stably introduced into cells, the U7 snRNAs become incorporated into small nuclear ribonucleoprotein (snRNP) particles that will induce a durable splicing correction. We have optimized this strategy to the point that virtually all SMN2 pre-mRNA becomes correctly spliced. In fibroblasts from an SMA patient, this approach induces a prolonged restoration of SMN protein and ensures its correct localization to discrete nuclear foci (gems).

A physical approach on flood risk vulnerability of buildings

The design of efficient hydrological risk mitigation strategies and their subsequent implementation relies on a careful vulnerability analysis of the elements exposed. Recently, extensive research efforts were undertaken to develop and refine empirical relationships linking the structural vulnerability of buildings to the impact forces of the hazard processes. These empirical vulnerability functions allow estimating the expected direct losses as a result of the hazard scenario based on spatially explicit representation of the process patterns and the elements at risk classified into defined typological categories. However, due to the underlying empiricism of such vulnerability functions, the physics of the damage-generating mechanisms for a well-defined element at risk with its peculiar geometry and structural characteristics remain unveiled, and, as such, the applicability of the empirical approach for planning hazard-proof residential buildings is limited. Therefore, we propose a conceptual assessment scheme to close this gap. This assessment scheme encompasses distinct analytical steps: modelling (a) the process intensity, (b) the impact on the element at risk exposed and (c) the physical response of the building envelope. Furthermore, these results provide the input data for the subsequent damage evaluation and economic damage valuation. This dynamic assessment supports all relevant planning activities with respect to a minimisation of losses, and can be implemented in the operational risk assessment procedure.