Identification of Novel Single Nucleotide Polymorphisms (SNPs) in Deer (Odocoileus spp.) Using the BovineSNP50 BeadChip

Single nucleotide polymorphisms (SNPs) are growing in popularity as a genetic marker for investigating evolutionary processes. A panel of SNPs is often developed by comparing large quantities of DNA sequence data across multiple individuals to identify polymorphic sites. For non-model species, this is particularly difficult, as performing the necessary large-scale genomic sequencing often exceeds the resources available for the project. In this study, we trial the Bovine SNP50 BeadChip developed in cattle (Bos taurus) for identifying polymorphic SNPs in cervids Odocoileus hemionus (mule deer and black-tailed deer) and O. virginianus (white-tailed deer) in the Pacific Northwest. We found that 38.7% of loci could be genotyped, of which 5% (n = 1068) were polymorphic. Of these 1068 polymorphic SNPs, a mixture of putatively neutral loci (n = 878) and loci under selection (n = 190) were identified with the FST-outlier method. A range of population genetic analyses were implemented using these SNPs and a panel of 10 microsatellite loci. The three types of deer could readily be distinguished with both the SNP and microsatellite datasets. This study demonstrates that commercially developed SNP chips are a viable means of SNP discovery for non-model organisms, even when used between very distantly related species (the Bovidae and Cervidae families diverged some 25.1−30.1 million years before present)

Misuse Detection for Information Retrieval Systems

We present a novel approach to detect misuse within an information retrieval system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. Our approach is based on building and maintaining a profile of the behavior of the system user through tracking, or monitoring of user activity within the information retrieval system. Any new activity of the user is compared to the user profile to detect a potential misuse for the authorized user. We propose four different methods to detect misuse in information retrieval systems. Our experimental results on 2 GB collection favorably demonstrate the validity of our approach

IIT at TREC-2003 Task Classification & Document Structure for Known-Item Search

This year’s TREC 2003 web task incorporated two retrieval tasks into a single set of experiments for Known-Item retrieval. We hypothesized that not all retrieval tasks should use the same retrieval approach when a single search entry point is used. We applied task classifiers on top of traditional web retrieval approaches. Our traditional retrieval is based on fusion of result sets generated by query runs over independent parts of the document structure. Our task classifiers combine query term analysis with known information resources and URL depth. This approach to task classification shows promise: our classified runs improved overall MRR effectiveness over our traditional retrieval results by ~10%; provided an MRR of.665; ranked 87 % of relevant results in the top 10; correctly ranked the #1result 56 % of the time. 67% of the queries performed above the average, and 49 % above the median. Keywords: Known-item search, document structure retrieval, query task classificatio