No abstract

Wind energy has been explored as a viable alternative to fossil fuels in many small island developing states such as those in the Caribbean for a long time. Central to evaluating the feasibility of any wind energy project is choosing the most appropriate wind speed model. This is a function of the metric used to assess the goodness of fit of the statistical models tested. This paper compares a number of common metrics then proposes an alternative to the application-blind statistical tools commonly used. Wind speeds at two locations are considered: Crown Point, Tobago; and Piarco, Trinidad. Hourly wind speeds over a 15-year period have been analyzed for both sites. The available data is modelled using the Birnbaum-Saunders, Exponential, Gamma, Generalized Extreme Value, Generalized Pareto, Nakagami, Normal, Rayleigh and Weibull probability distributions. The distributions were compared graphically and their parameters were estimated using maximum likelihood estimation. Goodness of fit was assessed using the normalised mean square error testing, Chi-squared testing, Kolmogorov-Smirnov, R-squared, Akaike information criteria and Bayesian information criteria tests and the distributions ranked. The distribution ranking varied widely depending on the test used highlighting the need for a more contextualized goodness of fit metric. With this in mind, the concept of application-specific information criteria (ASIC) for testing goodness of fit is introduced. This allows distributions to be ranked by secondary features which are a function of both the primary data and the application space.

Influenza viruses cause seasonal and pandemic influenza, remaining a threat to human health. The type I interferon (IFN)-mediated innate immune response is one of the central obstacles influenza A virus (IAV) must overcome to successfully replicate within the host. Here, we demonstrate that sphingosine 1-phosphate (S1P) lyase (SPL) enhances the type I IFN response, but this antiviral innate immunity is counteracted by IAV infection. Although SPL is an enzyme that metabolizes S1P, SPL was found to interact with IKKɛ and promote IKKɛ-mediated type I IFN responses. Thus, when SPL was knocked out of host cells, IAV replication increased and the elements of IKKɛ-induced type I IFN response decreased. However, IAV infection destabilized the SPL-mediated type I IFN response by inducing the degradation of SPL. Importantly, nonstructural protein 1 (NS1) of IAV triggered the depletion of SPL. SPL was ubiquitinated upon IAV infection or NS1 expression, whereas NS1-deficient IAV failed to elicit ubiquitination or downregulation of SPL. Transiently overexpressed SPL increased the levels of auto-phosphorylation of IKKɛ, resulting in enhanced expression of type I IFN and IFN-stimulated genes. However, this induction was markedly inhibited by IAV NS1. Collectively, this study reveals a pro-IFN function of SPL as well as a novel strategy employed by IAV to subvert the type I IFN response, providing new insights into the interplay between IAV and host innate immunity.