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Kreile, Madara,Rots, Dmitrijs,Piekuse, Linda,Cebura, Elizabete,Grutupa, Marika,Kovalova, Zhanna,Lace, Baiba Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.22
Background: Acute lymphoblastic leukemia (ALL) is a complex disease caused by interactions between hazardous exogenous or/and endogenous agents and many mild effect inherited susceptibility mutations. Some of them are known, but their functional roles still requireinvestigation. Age is a recognized risk factor; children with disease onset after the age of ten have worse prognosis, presumably also triggered by inherited factors. Materials and Methods: The MDR1 gene polymorphisms rs1045642, rs2032582 and MTHFR gene polymorphisms rs1801131 and rs1801133 were genotyped in 68 ALL patients in remission and 102 age and gender matched controls; parental DNA samples were also available for 42 probands. Results: No case control association was found between analyzed polymorphisms and a risk of childhood ALL development. Linkage disequilibrium was not observed in a family-based association study either. Only marginal association was observed between genetic marker rs2032582A and later disease onset (p=0.04). Conclusions: Our data suggest that late age of ALL onset could be triggered by mild effect common alleles.
Cabural, Aubrey M.,Catarig Dexter T.,Evangelista Marjory P.,Go Josie Lace Y.,Martillano Mary Hope T.,Banacia Alberto S. 대한전자공학회 2010 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
In this paper, we have presented a system which performs automatic face detection and tracking using Motion Detection and Principal Component Analysis. The system was implemented with the use of a network camera VB-C50i and MATLAB 7.0. We divided the study into three (3) major stages. The first stage was the motion detection. After motion had been detected, face detection happened and then finally the detected face was tracked down. The first stage is motion detection. This was performed by setting a threshold for the maximum difference and used this threshold as an indicator for motion. The second stage for this system is face detection. When the system detected the image, the camera automatically captured the image. The captured image was then zoomed in and the possible face region was taken as input data. This possible face region underwent principal component analysis for face detection. After the principal component analysis detected the face, the image was cropped and zoomed in. The final stage is the tracking down of the face as analyzed in the first two stages. Once the system was able to track down the location of the face, it automatically displayed the detected face area. The whole system functioned in such away that it continued to loop every time a motion was detected. The whole system shows the efficiency and accuracy of automatic face detection and tracking with the use of principal component analysis.