Distinctive expansion of potential virulence genes in the genome of the oomycete fish pathogen Saprolegnia parasitica.

Oomycetes in the class Saprolegniomycetidae of the Eukaryotic kingdom Stramenopila have evolved as severe pathogens of amphibians, crustaceans, fish and insects, resulting in major losses in aquaculture and damage to aquatic ecosystems. We have sequenced the 63 Mb genome of the fresh water fish pathogen, Saprolegnia parasitica. Approximately 1/3 of the assembled genome exhibits loss of heterozygosity, indicating an efficient mechanism for revealing new variation. Comparison of S. parasitica with plant pathogenic oomycetes suggests that during evolution the host cellular environment has driven distinct patterns of gene expansion and loss in the genomes of plant and animal pathogens. S. parasitica possesses one of the largest repertoires of proteases (270) among eukaryotes that are deployed in waves at different points during infection as determined from RNA-Seq data. In contrast, despite being capable of living saprotrophically, parasitism has led to loss of inorganic nitrogen and sulfur assimilation pathways, strikingly similar to losses in obligate plant pathogenic oomycetes and fungi. The large gene families that are hallmarks of plant pathogenic oomycetes such as Phytophthora appear to be lacking in S. parasitica, including those encoding RXLR effectors, Crinkler's, and Necrosis Inducing-Like Proteins (NLP). S. parasitica also has a very large kinome of 543 kinases, 10% of which is induced upon infection. Moreover, S. parasitica encodes several genes typical of animals or animal-pathogens and lacking from other oomycetes, including disintegrins and galactose-binding lectins, whose expression and evolutionary origins implicate horizontal gene transfer in the evolution of animal pathogenesis in S. parasitica

European Option Pricing by Using the Support Vector Regression Approach

We explore the pricing performance of Support Vector Regression for pricing S&P 500 index call options. Support Vector Regression is a novel nonparametric methodology that has been developed in the context of statistical learning theory, and until now it has not been widely used in financial econometric applications. This new method is compared with the Black and Scholes (1973) option pricing model, using standard implied parameters and parameters derived via the Deterministic Volatility Functions approach. The empirical analysis has shown promising results for the Support Vector Regression models