Measurement of the muon flux from 400 GeV/c protons interacting in a thick molybdenum/tungsten target

The SHiP experiment is proposed to search for very weakly interacting particles beyond the Standard Model which are produced in a 400 GeV/c proton beam dump at the CERN SPS. About 1011 muons per spill will be produced in the dump. To design the experiment such that the muon-induced background is minimized, a precise knowledge of the muon spectrum is required. To validate the muon flux generated by our Pythia and GEANT4 based Monte Carlo simulation (FairShip), we have measured the muon flux emanating from a SHiP-like target at the SPS. This target, consisting of 13 interaction lengths of slabs of molybdenum and tungsten, followed by a 2.4 m iron hadron absorber was placed in the H4 400 GeV/c proton beam line. To identify muons and to measure the momentum spectrum, a spectrometer instrumented with drift tubes and a muon tagger were used. During a 3-week period a dataset for analysis corresponding to (3.27±0.07) × 1011 protons on target was recorded. This amounts to approximatively 1% of a SHiP spill

Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment

In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion films followed by a silicon pixel tracker was installed in front of the Goliath magnet at the H4 proton beam-line. Behind the magnet, scintillating-fibre, drift-tube and RPC detectors were placed. The purpose of this run was to validate the measurement's feasibility, to develop the required analysis tools and fine-tune the detector layout. In this paper, we present the track reconstruction in the pixel tracker and the track matching with the moving emulsion detector. The pixel detector performed as expected and it is shown that, after proper alignment, a vertex matching rate of 87% is achieved

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Characterization of an L-Ascorbate Catabolic Pathway with Unprecedented Enzymatic Transformations

L-Ascorbate (vitamin C) is ubiquitous in both our diet and the environment. Ralstonia eutropha H16 (Cupriavidus necator ATCC 17699) uses L-ascorbate as sole carbon source but lacks the genes encoding the known catabolic pathways. RNAseq identified eight candidate catabolic genes. Sequence similarity networks and genome neighborhood networks guided predictions for function of the encoded proteins; the predictions were confirmed by in vitro assays and in vivo growth phenotypes of gene deletion mutants. L-Ascorbate, a lactone, is oxidized and ring-opened by enzymes in the cytochrome b561 and gluconolactonase families, respectively, to form 2,3-diketo-L-gulonate. A protein predicted to have a WD40-like fold catalyzes an unprecedented benzilic acid rearrangement involving migration of a carboxylate group to form 2-carboxy-L-lyxonolactone; the lactone is hydrolyzed by a member of the amidohydrolase superfamily to yield 2-carboxy-L-lyxonate. A member of the PdxA family of oxidative decarboxylases catalyzes a novel decarboxylation that uses NAD+ catalytically. The product, L-lyxonate, is catabolized to alpha-ketoglutarate by a previously characterized pathway.</p