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Background Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many webbased resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison. Results We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach. Conclusion In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation. [ABSTRACT FROM AUTHOR]

Background: During gut colonization, the enteric pathogen Campylobacter jejuni must surmount the toxic effects of reactive oxygen species produced by its own metabolism, the host immune system, and intestinal microflora. Elucidation of C. jejuni oxidative stress defense mechanisms is critical for understanding Campylobacter pathophysiology. Results: The mechanisms of oxidative stress defense in C. jejuni were characterized by transcriptional profiling and phenotypic analysis of wild-type and mutant strains. To define the regulon of the peroxide-sensing regulator, PerR, we constructed an isogenic ΔperR mutant and compared its transcriptome profile with that of the wild-type strain. Transcriptome profiling identified 104 genes that belonged to the PerR regulon. PerR appears to regulate gene expression in a manner that both depends on and is independent of the presence of iron and/or H2O2. Mutation of perR significantly reduced motility. A phenotypic analysis using the chick colonization model showed that the ΔperR mutant exhibited attenuated colonization behavior. An analysis of changes in the transcriptome induced by exposure to H2O2, cumene hydroperoxide, or menadione revealed differential expression of genes belonging to a variety of biological pathways, including classical oxidative stress defense systems, heat shock response, DNA repair and metabolism, fatty acid biosynthesis, and multidrug efflux pumps. Mutagenic and phenotypic studies of the superoxide dismutase SodB, the alkyl-hydroxyperoxidase AhpC, and the catalase KatA, revealed a role for these proteins in oxidative stress defense and chick gut colonization. Conclusion: This study reveals an interplay between PerR, Fur, iron metabolism and oxidative stress defense, and highlights the role of these elements in C. jejuni colonization of the chick cecum and/or subsequent survival. [ABSTRACT FROM AUTHOR]