A Full-Genomic Sequence-Verified Protein-Coding Gene Collection for Francisella tularensis

The rapid development of new technologies for the high throughput (HT) study of proteins has increased the demand for comprehensive plasmid clone resources that support protein expression. These clones must be full-length, sequence-verified and in a flexible format. The generation of these resources requires automated pipelines supported by software management systems. Although the availability of clone resources is growing, current collections are either not complete or not fully sequence-verified. We report an automated pipeline, supported by several software applications that enabled the construction of the first comprehensive sequence-verified plasmid clone resource for more than 96% of protein coding sequences of the genome of F. tularensis, a highly virulent human pathogen and the causative agent of tularemia. This clone resource was applied to a HT protein purification pipeline successfully producing recombinant proteins for 72% of the genes. These methods and resources represent significant technological steps towards exploiting the genomic information of F. tularensis in discovery applications

Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web

A system is proposed that combines textual and visual statistics in a single index vector for content-based search of a WWW image database. Textual statistics are captured in vector form using latent semantic indexing based on text in the containing HTML document. Visual statistics are captured in vector form using color and orientation histograms. By using an integrated approach, it becomes possible to take advantage of possible statistical couplings between the content of the document (latent semantic content) and the contents of images (visual statistics). The combined approach allows improved performance in conducting content-based search. Search performance experiments are reported for a database containing 350,000 images collected from the WWW

Unsupervised learning of skeletons from motion

Abstract. Humans demonstrate a remarkable ability to parse complicated motion sequences into their constituent structures and motions. We investigate this problem, attempting to learn the structure of one or more articulated objects, given a time-series of two-dimensional feature positions. We model the observed sequence in terms of “stick figure ” objects, under the assumption that the relative joint angles between sticks can change over time, but their lengths and connectivities are fixed. We formulate the problem in a single probabilistic model that includes multiple sub-components: associating the features with particular sticks, determining the proper number of sticks, and finding which sticks are physically joined. We test the algorithm on challenging datasets of 2D projections of optical human motion capture and feature trajectories from real videos.