Anticipatory Stress Responses and Immune Evasion in Fungal Pathogens

This is the final version. Available on open access from Cell Press via the DOI in this recordIn certain niches, microbes encounter environmental challenges that are temporally linked. In such cases, microbial fitness is enhanced by the evolution of anticipatory responses where the initial challenge simultaneously activates pre-emptive protection against the second impending challenge. The accumulation of anticipatory responses in domesticated yeasts, which have been termed 'adaptive prediction', has led to the emergence of 'core stress responses' that provide stress cross-protection. Protective anticipatory responses also seem to be common in fungal pathogens of humans. These responses reflect the selective pressures that these fungi have faced relatively recently in their evolutionary history. Consequently, some pathogens have evolved 'core environmental responses' which exploit host signals to trigger immune evasion strategies that protect them against imminent immune attack.Medical Research Council (MRC)University of AberdeenUniversity of Exete

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

Experiment Simulation Configurations Approximating DUNE TDR

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment consisting of a high-power, broadband neutrino beam, a highly capable near detector located on site at Fermilab, in Batavia, Illinois, and a massive liquid argon time projection chamber (LArTPC) far detector located at the 4850L of Sanford Underground Research Facility in Lead, South Dakota. The long-baseline physics sensitivity calculations presented in the DUNE Physics TDR, and in a related physics paper, rely upon simulation of the neutrino beam line, simulation of neutrino interactions in the near and far detectors, fully automated event reconstruction and neutrino classification, and detailed implementation of systematic uncertainties. The purpose of this posting is to provide a simplified summary of the simulations that went into this analysis to the community, in order to facilitate phenomenological studies of long-baseline oscillation at DUNE. Simulated neutrino flux files and a GLoBES configuration describing the far detector reconstruction and selection performance are included as ancillary files to this posting. A simple analysis using these configurations in GLoBES produces sensitivity that is similar, but not identical, to the official DUNE sensitivity. DUNE welcomes those interested in performing phenomenological work as members of the collaboration, but also recognizes the benefit of making these configurations readily available to the wider community

Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I: Introduction to DUNE

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology

Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume II of this TDR, DUNE Physics, describes the array of identified scientific opportunities and key goals. Crucially, we also report our best current understanding of the capability of DUNE to realize these goals, along with the detailed arguments and investigations on which this understanding is based. This TDR volume documents the scientific basis underlying the conception and design of the LBNF/DUNE experimental configurations. As a result, the description of DUNE's experimental capabilities constitutes the bulk of the document. Key linkages between requirements for successful execution of the physics program and primary specifications of the experimental configurations are drawn and summarized. This document also serves a wider purpose as a statement on the scientific potential of DUNE as a central component within a global program of frontier theoretical and experimental particle physics research. Thus, the presentation also aims to serve as a resource for the particle physics community at large

Anticipatory Stress Responses and Immune Evasion in Fungal Pathogens

In certain niches, microbes encounter environmental challenges that are temporally linked. In such cases, microbial fitness is enhanced by the evolution of anticipatory responses where the initial challenge simultaneously activates pre-emptive protection against the second impending challenge. The accumulation of anticipatory responses in domesticated yeasts, which have been termed 'adaptive prediction', has led to the emergence of 'core stress responses' that provide stress cross-protection. Protective anticipatory responses also seem to be common in fungal pathogens of humans. These responses reflect the selective pressures that these fungi have faced relatively recently in their evolutionary history. Consequently, some pathogens have evolved 'core environmental responses' which exploit host signals to trigger immune evasion strategies that protect them against imminent immune attack

A CO ₂ sensing module modulates β-1,3-glucan exposure in <i>Candida albicans</i>

Our innate immune defenses have evolved to protect us against microbial infection in part via receptor-mediated detection of “pathogen-associated molecular patterns” (PAMPs) expressed by invading microbes, which then triggers their immune clearance. Despite this surveillance, many microbial species are able to colonize healthy, immune-competent individuals, without causing infection. To do so, these microbes must evade immunity. The commensal fungus Candida albicans exploits a variety of strategies to evade immunity, one of which involves reducing the exposure of a proinflammatory PAMP (β-1,3-glucan) at its cell surface. Most of the β-1,3-glucan is located in the inner layer of the C. albicans cell wall, hidden by an outer layer of mannan fibrils. Nevertheless, some β-1,3-glucan can become exposed at the fungal cell surface. However, in response to certain specific host signals, such as lactate or hypoxia, C. albican s activates an anticipatory protective response that decreases β-1,3-glucan exposure, thereby reducing the susceptibility of the fungus to impending innate immune attack. Here, we exploited the natural phenotypic variability of C. albicans clinical isolates to identify strains that do not display the response to β-1,3-glucan masking signals observed for the reference isolate, SC5314. Then, using genome-wide transcriptional profiling, we compared these non-responsive isolates with responsive controls to identify genes potentially involved in β-1,3-glucan masking. Mutational analysis of these genes revealed that a sensing module that was previously associated with CO 2 sensing also modulates β-1,3-glucan exposure in response to hypoxia and lactate in this major fungal pathogen of humans. </jats:p

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

AbstractLiquid 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.</jats:p

Low exposure long-baseline neutrino oscillation sensitivity of the DUNE experiment

The Deep Underground Neutrino Experiment (DUNE) will produce world-leading neutrino oscillation measurements over the lifetime of the experiment. In this work, we explore DUNE’s sensitivity to observe charge-parity violation (CPV) in the neutrino sector, and to resolve the mass ordering, for exposures of up to 100 kiloton-megawatt-calendar years (kt-MW-CY), where calendar years include an assumption of 57% accelerator uptime based on past accelerator performance at Fermilab. The analysis includes detailed uncertainties on the flux prediction, the neutrino interaction model, and detector effects. We demonstrate that DUNE will be able to unambiguously resolve the neutrino mass ordering at a 4 σ ( 5 σ ) level with a 66 (100) kt-MW-CY far detector exposure, and has the ability to make strong statements at significantly shorter exposures depending on the true value of other oscillation parameters, with a median sensitivity of 3 σ for almost all true δ CP values after only 24 kt-MW-CY. We also show that DUNE has the potential to make a robust measurement of CPV at a 3 σ level with a 100 kt-MW-CY exposure for the maximally CP-violating values δ CP = ± π / 2 . Additionally, the dependence of DUNE’s sensitivity on the exposure taken in neutrino-enhanced and antineutrino-enhanced running is discussed. An equal fraction of exposure taken in each beam mode is found to be close to optimal when considered over the entire space of interest