Serum insulin-like activity in genetic and experimental diabetes mellitus

A modification of the epididymal fat pad assay for insulin-like activity has been applied to serum from normal subjects, untreated diabetic patients, and dogs with experimentally induced diabetes. Diabetic patients exhibited abnormally elevated serum ILA levels before and after glucose loading, and also demonstrated a delay in their ILA response to glucose. Dogs with experimental diabetes did not show an elevation in their ILA values. Genetically determined diabetes is obviously different from simple insulin deficiency diabetes. The data confirm and extend previous observations that a significant degree of hyperinsulinemia exists in the garden variety of mildly diabetic patients. Unusually high levels of insulin-like activity were found in 7 of 27 apparently normal subjects. Their ILA exceeded at most times the mean of those found in the diabetic patients. Two of these subjects later discovered a family history of diabetes. Some of the implications of these observations are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32090/1/0000140.pd

Synthesising graphics card programs from DSLs

Over the last five years, graphics cards have become a tempting target for scientific computing, thanks to unrivaled peak performance, often producing a runtime speed-up of x10 to x25 over comparable CPU solutions. However, this increase can be difficult to achieve, and doing so often requires a fundamental rethink. This is especially problematic in scientific computing, where experts do not want to learn yet another architecture. In this paper we develop a method for automatically parallelising recursive functions of the sort found in scientific papers. Using a static analysis of the function dependencies we identify sets - partitions - of independent elements, which we use to synthesise an effcient GPU implementation using polyhedral code generation techniques. We then augment our language with DSL extensions to support a wider variety of applications, and demonstrate the effectiveness of this with three case studies, showing significant performance improvement over equivalent CPU methods, and similar effciency to hand-Tuned GPU implementations. © 2012 ACM

BigFoot: Bayesian Alignment and Phylogenetic Footprinting with MCMC BMC

Background: We have previously combined statistical alignment and phylogenetic footprinting to detect conserved functional elements without assuming a fixed alignment. Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction. Our method utilized standard dynamic programming hidden markov model algorithms to analyze up to four sequences. Results: We present a novel approach, implemented in the software package BigFoot, for performing phylogenetic footprinting on greater numbers of sequences. We have developed a Markov chain Monte Carlo (MCMC) approach which samples both sequence alignments and locations of slowly evolving regions. We implement our method as an extension of the existing StatAlign software package and test it on well-annotated regions controlling the expression of the even-skipped gene in Drosophila and the α-globin gene in vertebrates. The results exhibit how adding additional sequences to the analysis has the potential to improve the accuracy of functional predictions, and demonstrate how BigFoot outperforms existing alignment-based phylogenetic footprinting techniques. Conclusion: BigFoot extends a combined alignment and phylogenetic footprinting approach to analyze larger amounts of sequence data using MCMC. Our approach is robust to alignment error and uncertainty and can be applied to a variety of biological datasets. The source code and documentation are publicly available for download from http://www.stats.ox.ac.uk/~satija/BigFoot/ </p