A chimeric T cell receptor with super‐signaling properties

A key question yet to be resolved concerns the structure and function relationship of the TCR complex. How does antigen recognition by the TCR‐αβ chains result in the activation of distinct signal transduction pathways by the CD3‐γδϵ/ζ complex? To investigate which part of the TCR‐β chain is involved in TCR signaling, we exchanged different domains of the constant regions of the TCR‐β chain with the corresponding TCR‐γ chain domains. We show here that hybridoma cells expressing a chimeric TCR‐β chain (βIII) containing intracellular and transmembrane TCR‐γ amino acids, together with a wild‐type TCR‐α (αwt) chain, were 10 times more sensitive to antigenic stimulation compared to cells expressing TCR‐αwt/βwt chains. This super‐signaling phenotype of the βIII chain was observed in two different TCRs. One specific for an alloantigen (I‐Abm12) and one for an autoantigen (I‐Ab/MOG35-55). We found that this chimeric αwt/βIII TCR had normal association with CD3‐γδϵ and ζ chains. To investigate the effect of the chimeric βIII chain in transgenic T cells, we made MOG35-55‐specific TCR transgenic mice expressing either the αwt/βwt or chimeric αwt/βIII TCR. Similar to what was observed in hybridoma cells, transgenic αwt/βIII T cells showed a super‐signaling phenotype upon antigenic stimulation. Further studies may help us understand the effect of increased TCR signaling on autoimmunity and may lead to the identification of signaling molecules that can be targeted to stop the progression of autoimmune disorders such as multiple sclerosi

Measuring the Weak Phase gamma in Color Allowed B->DKpi Decays

We present a method to measure the weak phase gamma in the three-body decay of charged B mesons to the final states D K pi0. These decays are mediated by interfering amplitudes which are color-allowed and hence relatively large. As a result, large CP violation effects that could be observed with high statistical significance are possible. In addition, the three-body decay helps resolve discrete ambiguities that are usually present in measurements of the weak phase. The experimental implications of conducting these measurements with three-body decays are discussed, and the sensitivity of the method is evaluated using a simulation.Comment: 18 pages, LaTex, 15 eps and ps figure

Dynamical simulation of DCC formation in Bjorken rods

Using a semi-classical treatment of the linear sigma model, we simulate the dynamical evolution of an initially hot cylindrical rod endowed with a longitudinal Bjorken scaling expansion (a ``Bjorken rod''). The field equation is propagated until full decoupling has occurred and the asymptotic many-body state of free pions is then obtained by a suitable Fourier decomposition of the field and a subsequent stochastic determination of the number of quanta in each elementary mode. The resulting transverse pion spectrum exhibits visible enhancements below 200 MeV due to the parametric amplification caused by the oscillatory relaxation of the chiral order parameter. Ensembles of such final states are subjected to various event-by-event analyses. The factorial moments of the multiplicity distribution suggest that the soft pions are non-statistical. Furthermore, their emission patterns exhibit azimuthal correlations that have a bearing on the domain size in the source. Finally, the distribution of the neutral pion fraction shows a significant broadening for the soft pions which grows steadily as the number of azimuthal segments is increased. All of these features are indicative of disoriented chiral condensates and it may be interesting to apply similar analyses to actual data from high-energy nuclear collision experiments.Comment: 38 pages total, incl 26 ps figures ([email protected]

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented