Screening for DNA copy number aberrations in mucinous adenocarcinoma arising from the minor salivary gland: two case reports.
ABSTRACT Mucinous adenocarcinoma (MAC) is a rare malignancy in the minor salivary gland. To our knowledge, genomic alterations in this tumor have not been reported previously. To identify DNA copy number aberrations, we applied comparative genomic hybridization (CGH) to four samples of MAC in minor salivary gland derived from two patients: a primary tumor and two cervical metastatic lymph nodes from one patient, and a primary tumor from the other patient. Copy number increases were commonly detected in 1q21∼q31 and 20q13, and these may play an important role in MAC carcinogenesis. Copy number increases in 1q, 12p, 12q, and 20q were commonly detected in all three samples derived from patient 1, and gain of 7p and loss of chromosome 4 were additionally detected in the two samples derived from metastatic lymph nodes. Amplifications were also detected in the chromosomal regions 8q22∼qter, 12p11∼p12, 12q11∼q21, and 20q13. Amplification of MDM2 (12q15) and of AURKA (20q13) was detected with fluorescence in situ hybridization. The DNA copy number aberrations detected in MAC in minor salivary glands were different from those reported for colorectal MAC. The present findings are novel in identifying genomic alterations of MAC arising from the minor salivary gland.
Conference Proceeding: An evolutive algorithm for blind adaptive beamforming in GPS applications[show abstract] [hide abstract]
ABSTRACT: In this work we propose an alternative approach to the problem of digital beamforming. We employ an evolutive algorithm, named opt-aiNet, to optimize the directed-decision (DD) criterion, a well-known equalization criterion, in order to determine the optimal coefficients of the antenna array. The approach is applied to a GPS application scenario, where the multimodal search capability of the opt-aiNet algorithm is used to simultaneously find the coefficient vectors that capture the signals coming from all the visible satellitesMachine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop; 01/2004