James E. Ries

Large-scale generation and analysis of expressed sequence tags from porcine ovary.

Abstract One method to identify the factors that control ovarian function is to characterize the genes that are expressed in ovary. In the present study, cDNA libraries from fetal, neonatal, and prepubertal porcine ovaries, pubertal ovaries on different days of the estrous cycle (Days 0 [follicle], 5, and 12 [follicle and corpus luteum]), and follicles isolated from weaned sows (diameter, 2, 4, 6, and 8 mm) were constructed and sequenced. A total of 22 176 cDNAs were sequenced, of which 15 613 were of sufficient quality for clustering. Clustering of cDNAs resulted in 8507 contigs, 6294 (74%) of which were comprised of a single sequence. Sixty-eight percent of the contigs had consensus sequences that were homologous to existing Tentative Consensus (TC) sequences or mature transcripts (ET) in The Institute for Genomic Research Porcine Gene Index. The consensus sequences were classified according to the Gene Ontology Index. Most cDNA-encoded proteins were components of the nucleus, ribosome, or mitochondrion. The proteins primarily functioned in binding, catalysis, and transport. Nearly 75% of the proteins were involved in metabolism and cell growth and/or maintenance. Analysis of the cDNA frequency across different libraries demonstrated differential gene expression within different-size follicles, between follicles and corpora lutea, and across developmental time-points. The expression of selected genes (analyzed by ribonuclease protection assay and Northern blotting) was consistent with the frequency of their respective cDNA in the individual libraries. This porcine ovary unigene set will be useful for identifying factors and mechanisms controlling ovarian follicular development in a variety of species.

An examination of natural language as a query formation tool for retrieving information on e‐health from Pub Med

The Internet has become the first tool of choice for performing information searches. The concepts of e-health and related topics have become areas of interest for professional researchers and lay users alike, and increasingly, users turn to the Internet for information about health and health care. The purpose of this study is to examine complexity and variability of natural language as observed in using search terms that express the concept of e-health. We survey effects of this complexity in terms of precision of information retrieved, the shift in the terminology for an emerging concept and with regards to the degree of overlap between query results. We have designed a series of natural language queries and used them to perform a series of searches on Pub Med. Subsequent analysis examined the degree to which relevant information can be retrieved using terms regarding e-health. The degree to which relevant returns overlap between searches has also been examined. Our results show significant variation in relevance and overlap between different query terms and a high degree of precision among search results that have high incidence of overlap.