We describe a novel sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 microm diameter microbeads. After constructing a microbead library of DNA templates by in vitro cloning, we assembled a planar array of a million template-containing microbeads in a flow cell at a density greater than 3x10(6) microbeads/cm2. Sequences of the free ends of the cloned templates on each microbead were then simultaneously analyzed using a fluorescence-based signature sequencing method that does not require DNA fragment separation. Signature sequences of 16-20 bases were obtained by repeated cycles of enzymatic cleavage with a type IIs restriction endonuclease, adaptor ligation, and sequence interrogation by encoded hybridization probes. The approach was validated by sequencing over 269,000 signatures from two cDNA libraries constructed from a fully sequenced strain of Saccharomyces cerevisiae, and by measuring gene expression levels in the human cell line THP-1. The approach provides an unprecedented depth of analysis permitting application of powerful statistical techniques for discovery of functional relationships among genes, whether known or unknown beforehand, or whether expressed at high or very low levels.

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This paper presents a method to evaluate image quality using the continuous wavelet transform. The method utilizes a bank of filters tuned to different scales and orientations to extract the image details. The filters are designed according to the criterion suggested by Antoine and Murenzi.1 The wavelet transform of a given image and the reconstructed images at various quality levels are represented in the form of energy density plots. These density plots highlight image features such as edges, object boundaries and texture. Thus, they represent the details contained in the image. A quality metric is proposed based on the absolute difference between the energy densities corresponding to the original and reconstructed images. The proposed metric is used to measure the relative quality of the image. In addition, the metric is also used to study the performance of a specific ATR (automatic target recognition) algorithm as a function of image quality.

It is often challenging to ascribe an objective measure of confidence for identifications based on surveillance imagery from a crime scene. The present work seeks to address this deficiency in the case of garment comparison evidence by developing a quantitative method for establishing a conservative lower bound on the likelihood ratio (LR) for identifications involving patterned garments. The method is based on statistical analysis of pattern offset measurements taken from a sample of garments of the same type (manufacturer, style, and size) as the seized evidence. The developed analysis framework was demonstrated on different types of garments over a range of modeled surveillance imaging scenarios with variable image quality; the lower bounds on the LRs ranged from approximately 10-1 to over 400-1. The statistical model was tested and validated through a large-scale empirical study involving both simulated and human observer-performed garment comparisons.