No abstract

Angiosarcomas are a rare subtype of sarcomas originating from vascular endothelial cells. Though frequently found in the head and neck area, there are minimal reports of radiation-induced angiosarcomas in this area. They have a poor prognosis due to a high rate of lymph node metastasis and, hence, require to be addressed promptly in order to improve survival. We present a rare case of radiationinduced angiosarcoma in a patient previously irradiated for squamous cell carcinoma of the neck. Due to variable and complex patient presentations of the disease, this case will help raise awareness of an uncommon complication of a common treatment offered to patients.

Motivation:Cell-cycle regulated gene prediction using microarray time-course measurements of the mRNA expression levels of genes has been used by several researchers. The popularly employed approach is Fourier transform (FT) method in conjunction with the set of known cell-cycle regulated genes. In the absence of training data, fourier transform method is sensitive to noise, additive monotonic component arising from cell population growth and deviation from strict sinusoidal form of expression. Known cell cycle regulated genes may not be available for certain organisms or using them for training may bias the prediction.Results: In this paper we propose an Improved Fourier Transform (IFT) method which takes care of several factors such as monotonic additive component of the cell-cycle expression, irregular or partial-cycle sampling of gene expression. The proposed algorithm does not need any known cell-cycle regulated genes for prediction. Apart from alleviating need for training set, it also removes bias towards genes similar to the training set. We have evaluated the developed method on two publicly available datasets: yeast cell-cycle data and HeLa cell-cycle data. The proposed algorithm has performed competitively on both datasets with that of the supervised fourier transform method used. It outperformed other unsupervised methods such as Partial Least Squares (PLS) and Single Pulse Modeling (SPM). This method is easy to comprehend and implement, and runs faster.Software: http://giscompute.gis.nus.edu.sg/cdcAnal