Single-nucleotide polymorphisms (SNPs) have been the focus of much attention in human genetics because they are extremely abundant and well-suited for automated large-scale genotyping. Human SNPs, however, are less informative than other types of genetic markers (such as simple-sequence length polymorphisms or microsatellites) and thus more loci are required for mapping traits. SNPs offer similar advantages for experimental genetic organisms such as the mouse, but they entail no loss of informativeness because bi-allelic markers are fully informative in analysing crosses between inbred strains. Here we report a large-scale analysis of SNPs in the mouse genome. We characterized the rate of nucleotide polymorphism in eight mouse strains and identified a collection of 2,848 SNPs located in 1,755 sequence-tagged sites (STSs) using high-density oligonucleotide arrays. Three-quarters of these SNPs have been mapped on the mouse genome, providing a first-generation SNP map of the mouse. We have also developed a multiplex genotyping procedure by which a genome scan can be performed with only six genotyping reactions per animal.

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A new pyrolysis model was developed to predict the individual product (noncondensable volatiles, condensable volatiles, and char) yield for Ecolomondo's industrial waste tire pyrolysis process. This novel predictive kinetics-based model couples product selectivity data obtained from thermogravimetric analysis experiments to a global single-step decomposition reaction term to reproduce the nonlinear relationship between product selectivity and temperature. A transient energy balance based on a lumped capacitance method was also used to calculate the tire shred temperature using the rotary drum wall temperature as an input. The kinetics model was compared to experimental oil production data from the industrial process as well as existing models in the literature. It is shown that the model can successfully predict the oil production of the industrial process and the model accuracy is greater for smooth operating conditions. On the other hand, other pyrolysis models from the literature failed to accurately predict the oil production.

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