Digital Commons Network^™

Melanoma And Seborrheic Keratosis Differentiation Using Texture Features, Srinivas V. Deshabhoina, Scott E. Umbaugh, William V. Stoecker, Randy Hays Moss, Subhashini K. Srinivasan

Chemistry Faculty Research & Creative Works

Purpose: To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.

Methods: A systematic approach to consistent classification of skin tumors is described. Texture features, based on the second-order histogram, were used to identify the features or a combination of features that could consistently differentiate a malignant skin tumor (melanoma) from a benign one (seborrheic keratosis). Two hundred and seventy-one skin tumor images were separated into training and test sets for accuracy and consistency. Automatic induction was applied to generate classification rules. Data analysis and modeling tools were used to gain further insight into the feature space. …

Regulation And Localization Of Endogenous Human Tristetraprolin, Anna-Marie Fairhurst, John E. Connolly, Katharine A Hintz, Nicolas J Goulding

Dartmouth Scholarship

Tumor necrosis factor (TNF) has been implicated in the development and pathogenicity of infectious diseases and autoimmune disorders, such as septic shock and arthritis. The zinc-finger protein tristetraprolin (TTP) has been identified as a major regulator of TNF biosynthesis. To define its intracellular location and examine its regulation of TNF, a quantitive intracellular staining assay specific for TTP was developed. We establish for the first time that in peripheral blood leukocytes, express

Virtual Assembly With Biologically Inspired Intelligence, Xiaobu Yuan, Simon X. Yang

Computer Science Publications

This paper investigates the introduction of biologically inspired intelligence into virtual assembly. It develops a approach to assist product engineers making assembly-related manufacturing decisions without actually realizing the physical products. This approach extracts the knowledge of mechanical assembly by allowing human operators to perform assembly operations directly in the virtual environment. The incorporation of a biologically inspired neural network into an interactive assembly planner further leads to the improvement of flexible product manufacturing, i.e., automatically producing alternative assembly sequences with robot-level instructions for evaluation and optimization. Complexity analysis and simulation study demonstrate the effectiveness and efficiency of this approach.