24 research outputs found

    Missing Faith in Batson: Continued Discrimination Against African Americans Through Religion-Based Peremptory Challenges

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    Interferon ő≥ Attenuates Insulin Signaling, Lipid Storage, and Differentiation in Human Adipocytes via Activation of the JAK/STAT Pathway*

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    Recent reports demonstrate T-cell infiltration of adipose tissue in early obesity. We hypothesized that interferon (IFN) ő≥, a major T-cell inflammatory cytokine, would attenuate human adipocyte functions and sought to establish signaling mechanisms. Differentiated human adipocytes were treated with IFNő≥ ¬Ī pharmacological inhibitors prior to insulin stimulation. [3H]Glucose uptake and AKT phosphorylation were assessed as markers of insulin sensitivity. IFNő≥ induced sustained loss of insulin-stimulated glucose uptake in human adipocytes, coincident with reduced Akt phosphorylation and down-regulation of the insulin receptor, insulin receptor substrate-1, and GLUT4. Loss of adipocyte triglyceride storage was observed with IFNő≥ co-incident with reduced expression of peroxisome proliferator-activated receptor ő≥, adiponectin, perilipin, fatty acid synthase, and lipoprotein lipase. Treatment with IFNő≥ also blocked differentiation of pre-adipocytes to the mature phenotype. IFNő≥-induced robust STAT1 phosphorylation and SOCS1 mRNA expression, with modest, transient STAT3 phosphorylation and SOCS3 induction. Preincubation with a non-selective JAK inhibitor restored glucose uptake and Akt phosphorylation while completely reversing IFNő≥ suppression of adipogenic mRNAs and adipocyte differentiation. Specific inhibition of JAK2 or JAK3 failed to block IFNő≥ effects suggesting a predominant role for JAK1-STAT1. We demonstrate that IFNő≥ attenuates insulin sensitivity and suppresses differentiation in human adipocytes, an effect most likely mediated via sustained JAK-STAT1 pathway activation

    Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

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    International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/cc charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1¬Ī0.6\pm0.6% and 84.1¬Ī0.6\pm0.6%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

    Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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    International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

    Highly-parallelized simulation of a pixelated LArTPC on a GPU