Rift Valley fever virus (RVFV) is the causative agent of RiftValley fever, a widespread disease of domestic animals and humans in sub-Saharan Africa. Laboratory rats have frequently been used as an animal model for studying the pathogenesis of Rift Valley fever. It is shown here that Lewis rats (LEW/mol) are susceptible to infection with RVFV, whereas Wistar-Furth (WF/mol) rats are resistant to RVFV infection. LEW/mol rats developed acute hepatitis and died after infection with RVFV strain ZH548, whereas WF/mol rats survived the infection. Cross-breeding of resistant WF/mol rats with susceptible LEW/mol rats demonstrated that resistance is segregated as a single dominant gene. Primary hepatocytes but not glial cells from WF/mol rats showed the resistant phenotype in cell culture, indicating that resistance was cell type-specific. Moreover, when cultured hepatocytes were stimulated with interferon (IFN) type I there was no indication of a regulatory role of IFN in the RVFV-resistance gene expression in WF/mol rats. Interestingly, previous reports have shown that LEW rats from a different breeding stock (LEW/mai) are resistant to RVFV infections, whereas WF/mai rats are susceptible. Thus, inbred rat strains seem to differ in virus susceptibility depending on their breeding histories. A better genetic characterization of inbred rat strains and a revision in nomenclature is needed to improve animal experimentation in the future.

Identifying scalability bottlenecks in parallel applications is a vital but also laborious and expensive task. Empirical performance models have proven to be helpful to find such limitations, though they require a set of experiments in order to gain valuable insights. Therefore, the experiment design determines the quality and cost of the models. Extra-P is an empirical modeling tool that uses small-scale experiments to assess the scalability of applications. Its current version requires an exponential number of experiments per model parameter. This makes the creation of empirical performance models very expensive, and in some situations even impractical. In this paper, we propose a novel parameter-value selection heuristic, which functions as a guideline for the experiment design, leveraging sparse performance-modeling, a technique that only needs a polynomial number of experiments per model parameter. Using synthetic analysis and data from three different case studies, we show that our solution reduces the average modeling costs by about 85% while retaining 92% of the model accuracy.

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