Phenomenological Implications of the Topflavor Model

We explore phenomenologies of the topflavour model for the LEP experiment at $m_{_Z}$ scale and the atomic parity violation (APV) experiment in the $C_s$ atoms at low energies. Implications of the model on the $Z$ peak data are studied in terms of the precision variables $\epsilon_i$'s. We find that the LEP data give more stringent constraints on the model parameters than the APV data.Comment: 23 pages (including 5 .eps figs), ReVTeX, the 1st revised version, to appear in Phys. Lett.

Prevalent de novo somatic mutations in superantigen genes of mouse mammary tumor viruses in the genome of C57BL/6J mice and its potential implication in the immune system

<p>Abstract</p> <p>Background</p> <p>Superantigens (SAgs) of mouse mammary tumor viruses (MMTVs) play a crucial role in T cell selection in the thymus in a T cell receptor (TCR) Vβ-specific manner and SAgs presented by B cells activate T cells in the periphery. The peripheral T cell repertoire is dynamically shaped by the steady induction of T cell tolerance against self antigens throughout the lifespan. We hypothesize that <it>de novo </it>somatic mutation of endogenous MMTV SAgs contributes to the modulation of the peripheral T cell repertoire.</p> <p>Results</p> <p>SAg coding sequences were cloned from the genomic DNAs and/or cDNAs of various tissues of female C57BL/6J mice. A total of 68 unique SAg sequences (54 translated sequences) were identified from the genomic DNAs of liver, lungs, and bone marrow, which are presumed to harbor only three endogenous MMTV loci (<it>Mtv-8</it>, <it>Mtv-9</it>, and <it>Mtv-17</it>). Similarly, 69 unique SAg sequences (58 translated sequences) were cloned from the cDNAs of 18 different tissues. Examination of putative TCR Vβ specificity suggested that some of the SAg isoforms identified in this study have Vβ specificities different from the reference SAgs of <it>Mtv-8</it>, <it>Mtv-9</it>, or <it>Mtv-17</it>.</p> <p>Conclusion</p> <p>The pool of diverse SAg isoforms, generated by <it>de novo </it>somatic mutation, may play a role in the shaping of the peripheral T cell repertoire including the autoimmune T cell population.</p

Approximate Flavour Symmetries and See-Saw Mechanism

We study the approximate flavour symmetries imposed on the lepton sector assuming see-saw mechanism as the neutrino mass structure. We apply the symmetry to various neutrino phenomenologies and obtain constraints on neutrino masses and mixings.Comment: 10 pages, RevTex, 2 PS figures (uuencoded in seperate file). To appear in Mod. Phys. Lett.

Form Factors for Exclusive Semileptonic BB--Decays

We developed the new parton model approach for exclusive semileptonic decays of $B$-meson to $D,~D^*$ by extending the inclusive parton model, and by combining with the results of the HQET, motivated by Drell-Yan process. Without the nearest pole dominance ans\"atze, we {\bf derived} the dependences of hadronic form factors on $q^2$. We also calculated numerically the slope of the Isgur-Wise function, which is consistent with the experimental results.Comment: 20 pages, RevTex, 2 ps figure files(uuencoded in seperate file

Towards Neural Decoding of Imagined Speech based on Spoken Speech

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features.Comment: 4 pages, 2 figure