The speciation history of Anaspides tasmaniae (Crustacea: Malacostraca) and its close relatives (family Anaspididae) was studied by phylogenetic and molecular clock analyses of mitochondrial DNA sequences. The phylogenetic analyses revealed that the Anaspides morphotype conceals at least three cryptic species belonging to different parts of its range. The occurrence of multiple cryptic phylogenetic species within one morphological type shows that substantial genetic evolution has occurred independently of morphological evolution. Molecular clock dating of the speciation events that generated both the cryptic and the morphological species of Anaspididae indicated continuous speciation within this group since the Palaeocene ~55 million years ago. This relatively constant rate of recent morphological and cryptic speciation within the Anaspididae suggests that the speciation rate in this group does not correlate with its low extinction rate or morphological conservatism.

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We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.

The Buckley-Leverett (nonlinear advection) equation is often used to describe twophase flow in porous media. We develop a new probabilistic method to quantify parametric uncertainty in the Buckley-Leverett model. Our approach is based on the concept of a fine-grained cumulative density function (CDF) and provides a full statistical description of the system states. Hence, it enables one to obtain not only average system response but also the probability of rare events, which is critical for risk assessment. We obtain a closed-form, semianalytical solution for the CDF of the state variable (fluid saturation) and test it against the results from Monte Carlo simulations.

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