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Martin
Meere

School of
Mathematics, Statistics and Applied Mathematics

Drug-eluting stents are now commonly used in the treatment of coronary artery disease. These devices increase the flow of blood through blocked arteries and provide mechanical support to the artery wall. They also protect the artery from re-blockage due to inflammation by releasing an anti-inflammatory drug into the surrounding tissue from a polymer that coats the stent. However, the permanent presence of a polymer in the body is now thought to increase the likelihood of a dangerous blood clot forming on thestent. Consequently, a new generation of stents are being developed that do not rely on a polymer to release the drug.

In these polymer-free stents, the drug is either sprayed directly onto a bare metal surface or infused in a metallic porous medium. Polymer free stents are a relatively new technology and no mathematical models have yet been developed to describe drug release from them. In this talk, some preliminary ideas for the modelling of polymer free stents are presented. The proposed models are based principally on dissolution theory and the theory of diffusion in porous media.

gene expression data

Emma
Holian, Norma Coffey and John Hinde

School of Mathematics, Statistics and Applied Mathematics

School of Mathematics, Statistics and Applied Mathematics

Time-course microarray analyses involve measuring the expression levels of thousands of genes repeatedly through time. Multivariate clustering methods such as principal components analysis, k-means clustering, finite mixture models etc.\\ have difficulties handling missing values, require uniform sampling for all genes, fail to account for the correlation between measurements made on the same gene or do not facilitate the removal of noise from the measured data thus ignoring any smoothness that may be evident in the expression profiles. This talk proposes the use of curve-based clustering, which can handle the latter issues. We use the linear

mixed effects model representation of penalized spline smoothing to estimate the gene expression curves which provides a framework for simultaneously determining a smooth estimate of the mean expression profile in each cluster, determining estimates of the gene-specific expression profiles within a cluster through the use of additional random effects and clustering expression profiles using mixtures of mixed effects models.\\

Coffey, N., J. Hinde, and E. Holian. ``Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data.'' Computational Statistics & Data Analysis 71 (2014): 14-29.

Tim
Downing

School of Mathematics, Statistics and Applied Mathematics

School of Mathematics, Statistics and Applied Mathematics

Mixing between genetically distinct pathogens within a population leads to novel combinations with altered host virulence and drug resistance. Such unique specimens represent either undiscovered lineages or re-assortments between established groups: comparison with known DNA patterns (haplotypes) provides a framework for determining ancestry and predicting biological traits. Current methods of allele frequency correlation, variant distribution modality and admixture modelling are effective for breeding between sub-species, but are untested for monomorphic populationswhere discriminatory mutations are rare. Haplotype distribution, size and length provided sufficient power to distinguish samples with just 3.4 mean pairwise SNPs/Mb in a sample of 191 Indian subcontinent clinical isolates of Leishmania donovani sampled in 2002-11 during two drug treatment eras. Model-based population clustering identified six genetically homogeneous populations with little evidence of recent interbreeding. These originated in the 1850s and showed a genetic bottleneck-recovery signature from anti-parasite pesticide spraying campaigns ending in the 1960s. Population-free membership assignment, phylogenetic trees and admixture statistics indicated six recent isolates were discovered whose haplotypes were mixes of these populations, despite as few as 60 genome-wide polymorphisms differentiating the main groups. These six hybrids were distinguishable from seven rare lineages

whose haplotype structure did not resemble any previous sample. Predicting resistance to future second-line or combination drug therapies using genetic data is now a tangible goal.

Jorge
Bruno, Aisling McCluskey

School of Mathematics, Statistics and Applied Mathematics

- a is in R for all a in X,
- for all a, b in X, there is R in R such that a, b in R.

We explore some properties that are characteristic of R-relations using a categorical framework.

Rosemary
A. Bailey

University of St. Andrews, Scotland

University of St. Andrews, Scotland

TBA

School of Mathematics, Statistics and Applied Mathematics

National University of Ireland, Galway, University Road, Galway, Ireland.

Phone: +353 (0)91 492332 (direct) , +353 (0)91 524411 x2332 (switchboard)

Fax: +353 (0)91 494542 Email: Mary.Kelly@nuigalway.ie

This page was last updated Wednesday, April 30

National University of Ireland, Galway, University Road, Galway, Ireland.

Phone: +353 (0)91 492332 (direct) , +353 (0)91 524411 x2332 (switchboard)

Fax: +353 (0)91 494542 Email: Mary.Kelly@nuigalway.ie

This page was last updated Wednesday, April 30