Antitubercular therapy decreases nitric oxide production in HIV/TB coinfected patients

BACKGROUND: Nitric oxide (NO) production is increased among patients with human immunodeficiency virus (HIV) infection and also among those with tuberculosis (TB). In this study we sought to determine if there was increased NO production among patients with HIV/TB coinfection and the effect of four weeks chemotherapy on this level. METHODS: 19 patients with HIV/TB coinfection were studied. They were treated with standard four drug antitubercular therapy and sampled at baseline and four weeks. 20 patients with HIV infection, but no opportunistic infections, were disease controls and 20 individuals were healthy controls. Nitrite and citrulline, surrogate markers for NO, were measured spectrophotometrically. RESULTS: The mean age of HIV/TB patients was 28.4 ± 6.8 years and CD4 count was 116 ± 36.6/mm. Mean nitrite level among HIV/TB coinfected was 207.6 ± 48.8 nmol/ml. This was significantly higher than 99.7 ± 26.5 nmol/ml, the value for HIV infected without opportunistic infections and 46.4 ± 16.2 nmol/ml, the value for healthy controls (p value < 0.01). The level of HIV/TB coinfected NO in patients declined to 144.5 ± 34.4 nmol/ml at four weeks of therapy (p value < 0.05). Mean citrulline among HIV/TB coinfected was 1446.8 ± 468.8 nmol/ml. This was significantly higher than 880.8 ± 434.8 nmol/ml, the value for HIV infected without opportunistic infections and 486.6 ± 212.5 nmol/ml, the value for healthy controls (p value < 0.01). Levels of citrolline in HIV/TB infected declined to 1116.2 ± 388.6 nmol/ml at four weeks of therapy (p value < 0.05). CONCLUSIONS: NO production is elevated among patients with HIV infection, especially so among HIV/TB coinfected patients, but declines significantly following 4 weeks of antitubercular therapy

The DUNE Far Detector Interim Design Report, Volume 3: Dual-Phase Module

The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 3 describes the dual-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure

The DUNE Far Detector Interim Design Report, Volume 2: Single-Phase Module

The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 2 describes the single-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure

The DUNE Far Detector Interim Design Report Volume 1: Physics, Technology and Strategies

The DUNE IDR describes the proposed physics program and technical designs of the DUNE Far Detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 1 contains an executive summary that describes the general aims of this document. The remainder of this first volume provides a more detailed description of the DUNE physics program that drives the choice of detector technologies. It also includes concise outlines of two overarching systems that have not yet evolved to consortium structures: computing and calibration. Volumes 2 and 3 of this IDR describe, for the single-phase and dual-phase technologies, respectively, each detector module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure

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

Liquid 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 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 data and simulation

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

Liquid 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