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No description available.

No description available.

No description available.

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Agda is a dependently-typed programming language and theorem prover, supporting proof construction in a functional programming style. Due to its incredibly flexible concrete syntax and support for Unicode identifiers, Agda can be used to construct elegant and expressive proofs in a format that is understandable even to those unfamiliar with the tool. However, the semantics of Agda is lacking resource guarantees of the kind that Haskell programmers are used to with lazy evaluation, where multiple uses of function arguments and let-bound variables still result in the corresponding expressions to be evaluated at most once. With the current compiler backends of Agda, a mathematically-natural way to structure programs therefore frequently results in inefficient compiled programs, where the run-time complexity can be exponentional in cases where corresponding Haskell code executes in linear time. This makes a highly-optimised compiler backend a particularly essential tool for practical development with Agda. The main contributions of this thesis are a series of compiler optimisations that inlines simple projections, removes some expressions with trivial evaluations that can be statically inferred, and reduces the need for repeated evaluations of the same expressions by increasing sharing. We developed transformations that focus on the inherent “loss” of sharing that is frequently the result of compiling Agda programs. Where an Agda developer may imagine that value sharing should exist in the generated Haskell code, it often does not. We present several optimising transformations that re-introduce some of this “lost” sharing without affecting the type-theoretic semantics, then apply these optimisations to several typical Agda applications to examine the memory allocation and execution time effects. In measuring the effects of these optimisations on Agda code we show that overall improvements in runtime on the order of 10-20% are possible. We hope that the development and discussion of these optimisations is useful to the Agda developer community, and may be helpful for future contributors interested in implementing new optimisations for Agda. / Thesis / Master of Science (MSc)

Protein arginine methyltransferase 1 (PRMT1), -4 (also known as coactivator-associated arginine methyltransferase 1; CARM1), and -5 catalyze the methylation of arginine residues on target proteins. In turn, these marked proteins mediate a variety of biological functions. By regulating molecules that are critical to the remodelling of skeletal muscle phenotype, PRMTs may influence skeletal muscle plasticity. Our study tests the hypothesis that the intracellular signals required for muscle adaptation to exercise will be associated with the induction of PRMT expression and activity. C57BL/6 mice were assigned to one of three experimental groups: sedentary (SED), acute bout of exercise (0PE), or acute exercise followed by 3 hours of recovery (3PE). The mice in the exercise groups performed a single bout of treadmill running at 15 m/min for 90 minutes. We observed that PRMT gene expression and global enzyme activity are muscle- specific, generally being higher in slow, oxidative muscle, as compared to faster, more glycolytic tissue. Despite the activation of canonical exercise-induced signalling involving AMPK and PGC-1α, PRMT expression and activity at the whole muscle level were unchanged. However, subcellular analysis revealed the exercise-evoked myonuclear translocation of PRMT1 prior to the nuclear translocation of PGC-1α, which colocalizes the proteins within the organelle after exercise. Acute physical activity also augmented the targeted methyltransferase activities of CARM1, PRMT1, and -5 in the myonuclear compartment, suggesting that PRMT-mediated histone arginine methylation is an integral part of the early signals that drive skeletal muscle plasticity. In summary, our data supports the emergence of PRMTs as important players in the regulation of skeletal muscle plasticity. / Thesis / Master of Science (MSc) / Skeletal muscle is a plastic tissue that can adapt to various physiological demands. Previous work suggests that protein arginine methyltransferases (PRMTs) are important in the regulation of skeletal muscle remodeling. However, their role in exercise-induced skeletal muscle plasticity is unknown. Therefore, the purpose of this study was to investigate the association between the intracellular signals required for muscle adaption and various metrics of PRMT biology. Our data demonstrate that PRMTs exhibit muscle-specific expression and function in mice. The movement of PRMT1 into myonuclei increased following exercise, while the specific methylation status of PRMT targets were also elevated. Overall, our data suggests that muscle-specific PRMT expression may be important for the determination and/or maintenance of different fiber type characteristics. Moreover, distinct PRMT cellular localization and methyltransferase activity may be key signals that contribute to skeletal muscle phenotypic plasticity.

In natural sciences, frequentist paradigm has led statistical practice; however, Bayesian approach has been gaining strength in the last decades. Our study assessed the scalloped hammerhead shark population on the western North Atlantic Ocean using Bayesian methods. This approach allowed incorporate diverse types of errors in the surplus production model and compare the influences of different statistical estimators on the values of the key parameters (r, growth rate; K carrying capacity; depletion, FMSY , fishing levels that would sustain maximum yield; and NMSY, abundance at maximum sustainable yield). Furthermore, we considered multi-levelpriors due to the variety of results on the population growth rate of this species. Our research showed that estimator selection influences the results of the surplus production model and therefore, the value of the target management points. Based on key parameter estimates with uncertainty and Deviance Information Criterion, we suggest that state-space Bayesian models be used for assessing scalloped hammerhead shark or other fish stocks with poor data available. This study found the population was overfished and suffering overfishing. Therefore, based on our research and that there was very low evidence of recovery according with the last data available, we suggest prohibition of fishing for this species because: (1) it is highly depleted (14% of its initial population), (2) the fishery status is very unstable over time, (3) it has a low reproductive rate contributing to a higher risk of overexploitation, and (4) the easiness of misidentification among different hammerhead sharks (smooth, great, scalloped and cryptic species). / Master of Science