A pathway-focused complementary DNA microarray and gene ontology analysis were used to investigate gene expression profiles in the amygdala, hippocampus, nucleus accumbens, prefrontal cortex (PFC) and ventral tegmental area of C3H/HeJ and C57BL/6J mice receiving nicotine in drinking water (100 μg/ml in 2% saccharin for 2 weeks). A balanced experimental design and rigorous statistical analysis have led to the identification of 3.5-22.1% and 4.1-14.3% of the 638 sequence-verified genes as significantly modulated in the aforementioned brain regions of the C3H/HeJ and C57BL/6J strains, respectively. Comparisons of differential expression among brain tissues showed that only a small number of genes were altered in multiple brain regions, suggesting presence of a brain region-specific transcriptional response to nicotine. Subsequent principal component analysis and Expression Analysis Systematic Explorer analysis showed significant enrichment of biological processes both in C3H/HeJ and C57BL/6J mice, i.e. cell cycle/proliferation, organogenesis and transmission of nerve impulse. Finally, we verified the observed changes in expression using real-time reverse transcriptase polymerase chain reaction for six representative genes in the PFC region, providing an independent replication of our microarray results. Together, this report represents the first comprehensive gene expression profiling investigation of the changes caused by nicotine in brain tissues of the two mouse strains known to exhibit differential behavioral and physiological responses to nicotine. © 2007 Blackwell Publishing Ltd

Bacterial biofilm segmentation poses significant challenges due to lack of apparent structure, poor imaging resolution, limited contrast between conterminous cells and high density of cells that overlap. Although there exist bacterial segmentation algorithms in the existing art, they fail to delineate cells in dense biofilms, especially in 3D imaging scenarios in which the cells are growing and subdividing in a complex manner. A graph-based data clustering method, LCuts, is presented with the application on bacterial cell segmentation. By constructing a weighted graph with node features in locations and principal orientations, the proposed method can automatically classify and detect differently oriented aggregations of linear structures (represent by bacteria in the application). The method assists in the assessment of several facets, such as bacterium tracking, cluster growth, and mapping of migration patterns of bacterial biofilms. Quantitative and qualitative measures for 2D data demonstrate the superiority of proposed method over the state of the art. Preliminary 3D results exhibit reliable classification of the cells with 97% accuracy.

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