The linguistic sign: Metonymy and virtuality

Grounded in a rich philosophical and semiotic tradition, the most influential models of the linguistic sign have been Saussure’s intimate connection between the signifier and the signi-fied and Ogden and Richards’ semiotic triangle. Within the triangle, claim the cognitive lin-guists Radden and Kövecses, the sign functions in a metonymic fashion. The triangular semi-otic model is expanded here to a trapezium and calibrated with, on the one hand, Peirce’s conception of virtuality, and on the other hand, with some of the tenets of Langacker’s Cogni-tive Grammar. In conclusion, the question “How does the linguistic sign mean?” is answered thus: it means by virtue of the linguistic form activating (virtually) the entire trapezium-like configuration of forms, concepts, experienced projections, and relationships between all of the above. Activation of the real world remains dubious or indirect. The process is both meto-nymic and virtual, in the sense specified

Uncertainty Propagation in Hypersonic Aerothermoelastic Analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83589/1/AIAA-2010-2964-623.pd

Statistical method on nonrandom clustering with application to somatic mutations in cancer

<p>Abstract</p> <p>Background</p> <p>Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.</p> <p>Results</p> <p>We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and <it>β</it>-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.</p> <p>Conclusions</p> <p>Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.</p

Uncertainty Propagation in Hypersonic Aerothermoelastic Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140527/1/1.c032233.pd