Measuring regular and irregular shapes mathematically is found to be a difficult task, since there Dactolisib ic50 is no single measure available to differentiate various shapes.

It is known that for mammograms, shape features are superior to Haralick and wavelet based features. Various geometrical shape and margin features have been introduced based on maximum and minimum radius of mass to classify the morphology of masses. These geometric features are found to be good in discriminating regular shapes from irregular shapes. In this paper, each mass is described by shape feature vector consists of 17 shape and margin properties. The masses are classified into 4 categories such as round, oval, lobular and irregular. Classifying masses into 4 categories is a very difficult task compared to classifying masses as benign, malignant or normal vs. abnormal. Only shape and margin characteristics can be used to discriminate these 4 categories effectively. Experiments have been conducted S63845 on mammogram images from the Digital Database for Screening Mammography (DDSM) and classified using C5.0 decision tree classifier. Total of 224 DDSM mammogram masses are considered for experiment. The C5.0 decision tree algorithm

is used to generate simple rules, which can be easily implemented and used in fuzzy inference system as if… then.., else statements. The rules are used to construct the generalized fuzzy membership function for classifying the masses as round, oval, lobular or irregular. Proposed approach is twice effective than existing Beamlet based features for classifying the mass as round, oval,

lobular or irregular. (c) 2013 Elsevier Ltd. All rights reserved.”
“Tumor cells use a wide variety of post-translational mechanisms to modify the functional repertoire of their transcriptome. One emerging but still understudied mechanism involves the export of cytoplasmic proteins that then partner with cell-surface receptors and modify both the surface-display kinetics and signaling properties of these receptors. Recent investigations demonstrate moonlighting roles for the proteins epimorphin, FGF1, FGF2, PLK1 and Ku80, to name a few, during oncogenesis and inflammation. Here, we review the molecular mechanisms of unconventional cytoplasmic-protein CP-456773 concentration export by focusing on the mitotic-spindle/hyaluronan-binding protein RHAMM, which is hyper-expressed in many human tumors. Intracellular RHAMM associates with BRCA1 and BARD1; this association attenuates the mitotic-spindle-promoting activity of RHAMM that might contribute to tumor progression by promoting genomic instability. Extracellular RHAMM-CD44 partnering sustains CD44 surface display and enhances CD44-mediated signaling through ERK1 and ERK2 (ERK1/2); it might also contribute to tumor progression by enhancing and/or activating the latent tumor-promoting properties of CD44.