Positive selection determines T cell receptor V beta 14 gene usage by CD8+ T cells.

We report here a mAb, 14-2, reactive with TCRs that include V beta 14. The frequency of V beta 14+ T cells varies with CD4 and CD8 subset and is controlled by the H-2 genes. Thus CD8+ T cells from H-2b mice include approximately 2.3% V beta 14+ T cells while CD8+ T cells from mice expressing K kappa include greater than 8% V beta 14+ T cells. In all strains examined, 7-8% of CD4+ T cells express V beta 14. The frequent usage of V beta 14 in CD8+ T cells of K kappa-expressing mice is a result of preferential positive selection of V beta 14+ CD8+ T cells as demonstrated by analysis of radiation chimeras. These studies demonstrate that H-2-dependent positive selection occurs in unmanipulated mice. Furthermore, the results imply that positive selection, and possibly H-2 restriction, can be strongly influenced by a V beta domain, with some independence from the beta-junctional sequence and alpha chain

Some Issues in a Gauge Model of Unparticles

We address in a recent gauge model of unparticles the issues that are important for consistency of a gauge theory, i.e., unitarity and Ward identity of physical amplitudes. We find that non-integrable singularities arise in physical quantities like cross section and decay rate from gauge interactions of unparticles. We also show that Ward identity is violated due to the lack of a dispersion relation for charged unparticles although the Ward-Takahashi identity for general Green functions is incorporated in the model. A previous observation that the unparticle's (with scaling dimension d) contribution to the gauge boson self-energy is a factor (2-d) of the particle's has been extended to the Green function of triple gauge bosons. This (2-d) rule may be generally true for any point Green functions of gauge bosons. This implies that the model would be trivial even as one that mimics certain dynamical effects on gauge bosons in which unparticles serve as an interpolating field.Comment: v1:16 pages, 3 figures. v2: some clarifications made and presentation improved, calculation and conclusion not modified; refs added and updated. Version to appear in EPJ

From the chiral magnetic wave to the charge dependence of elliptic flow

The quark-gluon plasma formed in heavy ion collisions contains charged chiral fermions evolving in an external magnetic field. At finite density of electric charge or baryon number (resulting either from nuclear stopping or from fluctuations), the triangle anomaly induces in the plasma the Chiral Magnetic Wave (CMW). The CMW first induces a separation of the right and left chiral charges along the magnetic field; the resulting dipolar axial charge density in turn induces the oppositely directed vector charge currents leading to an electric quadrupole moment of the quark-gluon plasma. Boosted by the strong collective flow, the electric quadrupole moment translates into the charge dependence of the elliptic flow coefficients, so that $v_2(\pi^+) < v_2(\pi^-)$ (at positive net charge). Using the latest quantitative simulations of the produced magnetic field and solving the CMW equation, we make further quantitative estimates of the produced $v_2$ splitting and its centrality dependence. We compare the results with the available experimental data.Comment: Contains 12 pages, 6 figures, written as a proceeding for the talk of Y. Burnier at the conference "P and CP-odd Effects in Hot and Dense Matter 2012" held in BN

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Multi-wavelength variability properties of Fermi blazar S5 0716+714

S5 0716+714 is a typical BL Lacertae object. In this paper we present the analysis and results of long term simultaneous observations in the radio, near-infrared, optical, X-ray and $\gamma$-ray bands, together with our own photometric observations for this source. The light curves show that the variability amplitudes in $\gamma$-ray and optical bands are larger than those in the hard X-ray and radio bands and that the spectral energy distribution (SED) peaks move to shorter wavelengths when the source becomes brighter, which are similar to other blazars, i.e., more variable at wavelengths shorter than the SED peak frequencies. Analysis shows that the characteristic variability timescales in the 14.5 GHz, the optical, the X-ray, and the $\gamma$-ray bands are comparable to each other. The variations of the hard X-ray and 14.5 GHz emissions are correlated with zero-lag, so are the V band and $\gamma$-ray variations, which are consistent with the leptonic models. Coincidences of $\gamma$-ray and optical flares with a dramatic change of the optical polarization are detected. Hadronic models do not have the same nature explanation for these observations as the leptonic models. A strong optical flare correlating a $\gamma$-ray flare whose peak flux is lower than the average flux is detected. Leptonic model can explain this variability phenomenon through simultaneous SED modeling. Different leptonic models are distinguished by average SED modeling. The synchrotron plus synchrotron self-Compton (SSC) model is ruled out due to the extreme input parameters. Scattering of external seed photons, such as the hot dust or broad line region emission, and the SSC process are probably both needed to explain the $\gamma$-ray emission of S5 0716+714.Comment: 43 pages, 13 figures, 3 tables, to be appeared in Ap

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM