Visual-Context Boosting for Eye Detection

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.

Context and conformation dictate function of a transcription antitermination switch

In bacteriophage, transcription elongation is regulated by the N protein, which binds a nascent mRNA hairpin ( termed boxB) and enables RNA polymerase to read through distal terminators. We have examined the structure, energetics and in vivo function of a number of N boxB complexes derived from in vitro protein selection. Trp18 fully stacks on the RNA loop in the wild-type structure, and can become partially or completely unstacked when the sequence context is changed three or four residues away, resulting in a recognition interface in which the best binding residues depend on the sequence context. Notably, in vivo antitermination activity correlates with the presence of a stacked aromatic residue at position 18, but not with N boxB binding affinity. Our work demonstrates that RNA polymerase responds to subtle conformational changes in cis-acting regulatory complexes and that approximation of components is not sufficient to generate a fully functional transcription switch.