Cropping Practices Baseline Data, 2002

Crooping practices produced by Iowa Departmment of Agriculture and Land Stewardship

Bgee: integrating and comparing heterogeneous transcriptome data among species

Gene expression patterns are a key feature in understanding gene function, notably in development. Comparing gene expression patterns between animals is a major step in the study of gene function as well as of animal evolution. It also provides a link between genes and phenotypes. Thus we have developed Bgee, a database designed to compare expression patterns between animals, by implementing ontologies describing anatomies and developmental stages of species, and then designing homology relationships between anatomies and comparison criteria between developmental stages. To define homology relationships between anatomical features we have developed the software Homolonto, which uses a modified ontology alignment approach to propose homology relationships between ontologies. Bgee then uses these aligned ontologies, onto which heterogeneous expression data types are mapped. These already include microarrays and ESTs.

Employment and Wages Covered by Unemployment Insurance, Final County Data, Third Quarter, 2003

Third Quarterly County information for Census of Employment & Wages - County

Employment and Wages Covered by Unemployment Insurance, Final Statewide Data, Third Quarter, 2003

Third Quarterly County information for Census of Employment & Wage, Statewide

Employment and Wages Covered by Unemployment Insurance, Final County Data, Fourth Quarter, 2003

Fourth Quarterly County information for Census of Employment & Wage, County

Employment and Wages Covered by Unemployment Insurance, Final Statewide Data, Fourth Quarter, 2003

Fourth Quarterly County information for Census of Employment & Wage, Statewide

Quarterly Census of Employment and Wages, Final County Data, First Quarter, 2004

First Quarterly County information for Census of Employment & Wage, County

Quarterly Census of Employment and Wages, Final Statewide Data, First Quarter, 2004

First Quarterly County information for Census of Employment & Wage, Statewide

Quarterly Census of Employment and Wages, Preliminary County Data, Second Quarter, 2004

Second Quarterly County information for Census of Employment & Wage, County

Quarterly Census of Employment and Wages, Preliminary Statewide Data, Second Quarter, 2004

Second Quarterly County information for Census of Employment & Wage, Statewide

Probabilistic evidential assessment of gunshot residue particle evidence (Part II) : Bayesian parameter estimation for experimental count data.

Part I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models - in the form of Bayesian networks - address the issues of background presence of GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphical models. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR).