Complete mitochondrial genome sequences are presented from two dipluran hexapods (i.e., a group of “primarily wingless insects”) of the genus Campodea and compared to those of other arthropods. Their gene order is the same as in most other hexapods and crustaceans. Structural changes have occurred in tRNA-C, tRNA-R, tRNA-SI and tRNA-S2 as well as in… [Read More]

Presented here is the complete genome sequence of Thiomicrospira crunogena XCL-2, representative of ubiquitous chemolithoautotrophic sulfur-oxidizing bacteria isolated from deep-sea hydrothermal vents. This gammaproteobacterium has a single chromosome (2,427,734 base pairs), and its genome illustrates many of the adaptations that have enabled it to thrive at vents globally. It has 14 methyl-accepting chemotaxis protein genes,… [Read More]

Haemophihis somnus is an opportunistic bacterial pathogen capable of causing pneumonia, septicemia, and other systemic infections in bovines. An H. somnus isolate from bovine abortion (strain 649) was found to carry a similar to 1.3 kb plasmid (pHS649) that contained partial homology to two previously sequenced Haemophilus/Histophilus plasmids by BLAST analyses. Sequence analysis of pHS649… [Read More]

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The alphaproteobacterium Nitrobacter winogradskyi (ATCC 25391) is a gram-negative facultative chemolithoautotroph capable of extracting energy from the oxidation of nitrite to nitrate. Sequencing and analysis of its genome revealed a single circular chromosome of 3,402,093 bp encoding 3,143 predicted proteins. There were extensive similarities to genes in two alphaproteobacteria, Bradyrhizobium japonicum USDA110 (1,300 genes) and… [Read More]

The thermal phenotype of an organism (heat and cold tolerance, thermal range, and thermal plasticity) is an essential feature of how the organism performs across thermal environments and in response to thermal stress. Porcelain crabs are of interest in addressing questions of thermal phenotype because of their high species diversity and the large variation in… [Read More]

This paper proposes a new active learning approach, confidence- based active learning, for training a wide range of classifiers. This approach is based on identifying and annotating uncertain samples. The uncertainty value of each sample is measured by its conditional error. The approach takes advantage of current classifiers’ probability preserving and ordering properties. It calibrates… [Read More]

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