Searching for the squark flavor mixing in CP violations of Bs -> K+ K- and K0bar K0 decays

We study CP violations in the B_s-> K+K- and Bs->K0K0 decays in order to find the contribution of the supersymmetry, which comes from the gluino-squark mediated flavor changing current. We obtain the allowed region of the squark flavor mixing parameters by putting the experimental data, the mass difference Delta M_Bs, the CP violating phase phi_s in Bs to J/psi phi decay and the b to s gamma branching ratio. In addition to these data, we take into account the constraint from the asymmetry of B0->K+pi because the Bs->K+K- decay is related with the B0->K+pi- decay by replacing the spectator s with d. Under these constraints, we predict the magnitudes of the CP violation in the Bs->K+K- and Bs->K0K0 decays. The predicted region of the CP violation C_{K+K-} is strongly cut from the direct CP violation of barB0 to K-pi+, therefore, the deviation from the SM prediction of C_{K+K-} is not found. On the other hand, the CP violation S_{K+K-} is possibly deviated from the SM prediction considerably, in the region of 0.1- 0.5. Since the standard model predictions of C_{K0bar K0} and S_{K0bar K0} are very small, the squark contribution can be detectable in C_{K0bar K0} and S_{K0bar K0}. These magnitudes are expected in the region C_{K0bar K0}=-0.06-0.06 and S_{K0bar K0}=-0.5-0.3. More precise data of these CP violations provide us a crucial test for the gluino-squark mediated flavor changing current.Comment: 20 pages, 10 figures, discussions added, references added. arXiv admin note: substantial text overlap with arXiv:1307.037

Electrophysiological analysis of mammalian cells expressing hERG using automated 384-well-patch-clamp

BACKGROUND: An in vitro electrophysiological assay system, which can assess compound effects and thus show cardiotoxicity including arrhythmia risks of test drugs, is an essential method in the field of drug development and toxicology. METHODS: In this study, high-throughput electrophysiological recordings of human embryonic kidney (HEK 293) cells and Chinese hamster ovary (CHO) cells stably expressing human ether-a-go-go related gene (hERG) were performed utilizing an automated 384-well-patch-clamp system, which records up to 384 cells simultaneously. hERG channel inhibition, which is closely related to a drug-induced QT prolongation and is increasing the risk of sudden cardiac death, was investigated in the high-throughput screening patch-clamp system. RESULTS: In the automated patch-clamp measurements performed here, K(v) currents were investigated with high efficiency. Various hERG channel blockers showed concentration-dependent inhibition, the 50 % inhibitory concentrations (IC(50)) of those blockers were in good agreement with previous reports. CONCLUSIONS: The high-throughput patch-clamp system has a high potential in the field of pharmacology, toxicology, and cardiac physiology, and will contribute to the acceleration of pharmaceutical drug development and drug safety testing

Bleeding classification of enhanced wireless capsule endoscopy images using deep convolutional neural network

This paper investigates the performance of a Deep Convolutional Neural Network (DCNN) algorithm to identify bleeding areas of wireless capsule endoscopy (WCE) images without known prior knowledge of bleeding and normal features of the images. In this study, a pre-processing technique has been proposed to improve the classification accuracy of WCE images into bleeding areas and normal areas by enhancing the WCE images. The proposed technique is applied to WCE images from six cases and divided into one training case and five test cases. To evaluate the effectiveness of the processes, the results were then compared between DCNN, SVM and Fuzzy, and also between DCNN with completely enhanced images and DCNN with normalized images. DCNN has shown to give a better result compared to SVM and Fuzzy logic; and the latter experiment has shown that the WCE images that have undergone the proposed enhancement technique gives better classification result compared to those images that did not go through the technique. The specificity, sensitivity and average are 0.8703, 0.8271 and 0.8907 respectively. In conclusion, DCNN has been proven to be able to successfully detecting bleeding areas from images without having any specific knowledge on imaging diagnosis or pathology