Title of the Paper: Non thermal effects of the electromagnetic waves
on DNA: Study on E. coli
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Authors: Fatima Jebai, Mohamad Ezzedine, Manal Khalife,
Nissrine Daou and Riad Hamamieh
Abstract: -E. coli strain TB1 are used to determine the effects of microwaves (MW) of frequency 900 MHz and an intensity of field exposure 6 V/m at ambient temperature. Our experiment was preformed by studying the effect of MW on DNA. We transformed unexposed bacteria with exposed pUC18 plasmid. We did not find any variation in the transformation ratio (100 transformants/μg DNA). We did not find any variation in the number of blue colonies (100% blue colonies). Analysis of exposed DNA with quantitative PCR technique was realized to determine the quantity of broken DNA strands after MW exposure. By comparison between exposed and control DNA no difference was observed. Electrophoresis and spectroscopic analysis of exposed DNA did not reveal any hyperchrome effect. In order to confirm our results we sequenced exposed pUC18 plasmid but again no alteration of the DNA on the molecular level was observed.
Keywords: Microwaves, Elecromagnetic, Mobile, DNA, Mutation, HSP
Title of the Paper: A New Methodology for Segmentation of
Functional Magnetic Resonance Imaging Using
Functional Echo State Neural Network
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Authors: Ravichandran C G and Ravindran G
Abstract: In this paper a new intelligent segmentation of functional magnetic resonance imaging
(fMRI) has been implemented using echo state neural network (ESNN). fMRI is a non-invasive
method which can be used to indirectly localize neuronal activations in the human brain. The term
segmentation includes not only the detection and localization, but also the delineation of activation
region in the brain. Perfect segmentation is important especially for the detection and position of
brain tumor. In spite of the existing segmentation methods, we have proposed a novel estimation
method for accurate segmentation irrespective of noise level. The Recurrent ESNN is an estimation
method able to produce an accurate segmentation when compared to the contextual clustering
segmentation method. In order to show the accuracy of segmentation, the existing Contextual
clustering (CC) segmentation method has been considered. Peak Signal to Noise Ratio (PSNR) of the
segmented image of ESNN is 6 and found to be higher than PSNR of CC 57. The segmented images
can be used in Medical Imaging application like 3D Reconstruction.
Keywords: Echo state neural network (ESNN), intelligent segmentation, Functional magnetic
resonance imaging (fMRI), Contextual clustering (CC)
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