Neutrino Mass from R-parity Violation in Split Supersymmetry

We investigate how the observed neutrino data can be accommodated by R-parity violation in Split Supersymmetry. The atmospheric neutrino mass and mixing are explained by the bilinear parameters $\xi_i$ inducing the neutrino-neutralino mixing as in the usual low-energy supersymmetry. Among various one-loop corrections, only the quark-squark exchanging diagrams involving the order-one trilinear couplings $\lambda'_{i23,i32}$ can generate the solar neutrino mass and mixing if the scalar mass $m_S$ is not larger than $10^9$ GeV. This scheme requires an unpleasant hierarchical structure of the couplings, e.g., $\lambda_{i23,i32}\sim 1$, $\lambda'_{i33} \lesssim 10^{-4}$ and $\xi_i \lesssim 10^{-6}$. On the other hand, the model has a distinct collider signature of the lightest neutralino which can decay only to the final states, $l_i W^{(*)}$ and $\nu Z^{(*)}$, arising from the bilinear mixing. Thus, the measurement of the ratio; $\Gamma(e W^{(*)}) : \Gamma(\mu W^{(*)}) : \Gamma(\tau W^{(*)})$ would provide a clean probe of the small reactor and large atmospheric neutrino mixing angles as far as the neutralino mass is larger than 62 GeV.Comment: 10 pages, 3 figures, version submitted to JHE

Spin configuration of top quark pair production with large extra dimensions at photon-photon colliders

Top quark pair production at photon-photon colliders is studied in low scale quantum gravity scenario. From the dependence of the cross sections on the spin configuration of the top quark and anti-quark, we introduce a new observable, top spin asymmetry. It is shown that there exists a special top spin basis where with the polarized parent electron beams the top spin asymmetry vanishes in the standard model but retains substantial values with the large extra dimension effects. We also present lower bounds of the quantum gravity scale $M_S$ from total cross sections with various combinations of the laser, electron beam, and top quark pair polarizations. The measurements of the top spin state $(t_\uparrow\bar{t}_\downarrow)$ with unpolarized initial beams are shown to be most effective, enhancing by about 5% the $M_S$ bounds with respect to totally unpolarized case.Comment: 18 pages, 4 figures, ReVTe

Loss of viability during freeze-thaw of intact and adherent human embryonic stem cells with conventional slow-cooling protocols is predominantly due to apoptosis rather than cellular necrosis

10.1007/s11373-005-9051-9Journal of Biomedical Science133433-44

Climatic yield potential of Japonica???type rice in the Korean Peninsula under RCP scenarios using the ensemble of multi???GCM and multi???RCM chains

Rice production in the Korean Peninsula (KP) in the near future (2021-2050) is analysed in terms of the climatic yield potential (CYP) index for Japonica-type rice. Data obtained from the dynamically downscaled daily temperature and sunshine duration for the Historical period (1981-2010) and near future under two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios are utilized. To reduce uncertainties that might be induced by using a Coupled General Circulation Model (CGCM)-a Regional Climate Model (RCM) chain in dynamical downscaling, two CGCM-three RCM chains are used to estimate the CYP index. The results show that the mean rice production decreases, mainly due to the increase of the temperature during the grain-filling period (40 days after the heading date). According to multi model ensemble, the optimum heading date in the near future will be approximately 12 days later and the maximum CYP will be even higher than in the Historical. This implies that the rice production is projected to decrease if the heading date is selected based on the optimum heading date of Historical, but to increase if based on that of near future. The mean rice production during the period of ripening is projected to decrease (to about 95% (RCP4.5) and 93% (RCP8.5) of the Historical) in the western and southern regions of the KP, but to increase (to about 104% (RCP4.5) and 106% (RCP8.5) of the Historical) in the northeastern coastal regions of the KP. However, if the optimum heading date is selected in the near future climate, the peak rice production is projected to increase (to about 105% (RCP4.5) and 104% (RCP8.5) of the Historical) in the western, southern and northeastern coastal regions of the KP, but to decrease (to about 98% (RCP4.5) and 96% (RCP8.5) of the Historical) in the southeastern coastal regions of the KP

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace–Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

Item does not contain fulltextPatterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction

A multi-resolution scheme for distortion-minimizing mapping between human subcortical structures based on geodesic construction on Riemannian manifolds

10.1016/j.neuroimage.2011.05.066NeuroImage5741376-1392NEIM