Prevalence and genomic characterization of Salmonella isolates from commercial chicken eggs retailed in traditional markets in Ghana

Salmonella enterica are important foodborne bacterial pathogens globally associated with poultry. Exposure to Salmonella-contaminated eggs and egg-related products is a major risk for human salmonellosis. Presently, there is a huge data gap regarding the prevalence and circulating serovars of Salmonella in chicken eggs sold in Ghana. In this study, 2,304 eggs (pools of six per sample unit) collected from informal markets in Accra, Kumasi and Tamale, representing the three ecological belts across Ghana, were tested for Salmonella. Antimicrobial susceptibility testing and Whole Genome Sequencing (WGS) of the isolates were performed using standard microdilution protocols and the Illumina NextSeq platform, respectively. The total prevalence of Salmonella was 5.5% with a higher rate of contamination in eggshell (4.9%) over egg content (1.8%). The serovars identified were S. Ajiobo (n = 1), S. Chester (n = 6), S. Hader (n = 7), S. enteritidis (n = 2); and S. I 4:b:- (n = 8). WGS analysis revealed varied sequence types (STs) that were serovar specific. The S. I 4:b:- isolates had a novel ST (ST8938), suggesting a local origin. The two S. enteritidis isolates belonged to ST11 and were identified with an invasive lineage of a global epidemic clade. All isolates were susceptible to ampicillin, azithromycin, cefotaxime, ceftazidime, gentamicin, meropenem, and tigecycline. The phenotypic resistance profiles to seven antimicrobials: chloramphenicol (13%), ciprofloxacin (94%), and nalidixic acid (94%), colistin (13%), trimethoprim (50%) sulfamethoxazole (50%) and tetracycline (50%) corresponded with the presence of antimicrobial resistance (AMR) determinants including quinolones (gyrA (D87N), qnrB81), aminoglycosides (aadA1), (aph(3“)-Ib aph(6)-Id), tetracyclines (tet(A)), phenicols (catA1), trimethoprim (dfrA14 and dfrA1). The S. enteritidis and S. Chester isolates were multidrug resistant (MDR). Several virulence factors were identified, notably cytolethal distending toxin (cdtB gene), rck, pef and spv that may promote host invasion and disease progression in humans. The findings from this study indicate the presence of multidrug resistant and virulent strains of Salmonella serovars in Ghanaian chicken eggs, with the potential to cause human infections. This is a critical baseline information that could be used for Salmonella risk assessment in the egg food chain to mitigate potential future outbreaks

Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

International audienceThe Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents

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

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

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 $\times$ 6 $\times$ 6 m$^3$ liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

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

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