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IntroductionPneumothorax is a rare but important complication of COVID-19.1 Although barotrauma may account for some cases, many affected patients have not received positive-pressure ventilatory (PPV) support1. The pathophysiology of COVID-pneumothorax is challenging to investigate because imaging data exist in diverse silos and only 0.97% of patients admitted for COVID-19 experience this complication.1 To provide mechanistic insight, we used artificial intelligence at scale to identify cases for detailed analysis from 4 large imaging datasets across 26 centres in 7 countries.MethodsA convolutional neural network was trained to detect pneumothorax on chest x-rays (CXRs) using the open-source CheXpert dataset, which includes 17,313 pneumothoraces. Testing was performed on labelled subsamples of the COVID-19 datasets. After running the model on all COVID-positive CXRs, predicted pneumothoraces were reviewed and the incidence of COVID-pneumothorax was estimated. Available CTs for patients with pneumothorax were assessed by radiologists. Radiology reports were used to curate additional CTs for two datasets.Results and DiscussionQuantitative results are summarised in figure 1. Adjusting for model sensitivity, the estimated incidence of COVID-pneumothorax was 0.97%, consistent with previous research.1 45 pneumothorax patients with CTs were identified; however, 13 unrelated to COVID-19, and 9 iatrogenic cases (except barotrauma) were excluded. Almost all remaining patients displayed diffuse, moderate-to-severe pneumonitis.Most pneumothoraces in patients on PPV were likely related to an interplay of barotrauma and COVID-19, with an acute lung injury pattern on CT. A high proportion demonstrated emphysema and three patients developed cystic abnormalities. One case followed a cavitating pulmonary infarction secondary to pulmonary embolism.Patients who had not received PPV, or had but were stepped down, developed pneumothoraces later in the disease. CT showed patterns consistent with the absorption stage of COVID-19, where consolidation is reduced but ground glass opacification persists with development of irregular bronchial dilatation. Such pneumothoraces perhaps represent increased parenchymal resistance.Abstract P164 Figure 1ConclusionThere are multiple mechanisms of COVID-pneumothorax. Barotrauma in patients with acute lung injury is most common, whilst pneumothorax in the absence of PPV most commonly occurs in the sub-acute, absorption stage of the disease.ReferenceMarciniak SJ, et al. COVID-19 Pneumothorax in the United Kingdom. ERJ 2021.Please refer to page A215 for declarations of interest related to this abstract.

An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neutrino experiments that rely on massive LArTPCs create a need for new ideas and reconstruction approaches. Wire-Cell, proposed in recent years, is a novel tomographic event reconstruction method for LArTPCs. The Wire-Cell 3D imaging approach capitalizes on charge, sparsity, time, and geometry information to reconstruct a topology-agnostic 3D image of the ionization electrons prior to pattern recognition. A second novel method, the many-to-many charge-light matching, then pairs the TPC charge activity to the detected scintillation light signal, thus enabling a powerful rejection of cosmic-ray muons in the MicroBooNE detector. A robust processing of the scintillation light signal and an appropriate clustering of the reconstructed 3D image are fundamental to this technique. In this paper, we describe the principles and algorithms of these techniques and their successful application in the MicroBooNE experiment. A quantitative evaluation of the performance of these techniques is presented. Using these techniques, a 95% efficient pre-selection of neutrino charged-current events is achieved with a 30-fold reduction of non-beam-coincident cosmic-ray muons, and about 80\\% of the selected neutrino charged-current events are reconstructed with at least 70% completeness and 80% purity.

Large Liquid Argon Time Projection Chambers (LArTPCs) are being increasingly adopted in neutrino oscillation experiments because of their superb imaging capabilities through the combination of both tracking and calorimetry in a fully active volume. Active LArTPC neutrino detectors at or near the Earth's surface, such as the MicroBooNE experiment, present a unique analysis challenge because of the large flux of cosmic-ray muons and the slow drift of ionization electrons. We present a novel Wire-Cell-based high-performance generic neutrino-detection technique implemented in MicroBooNE. The cosmic-ray background is reduced by a factor of 1.4\\(\\times10^{5}\\) resulting in a 9.7\\% cosmic contamination in the selected neutrino candidate events, for visible energies greater than 200~MeV, while the neutrino signal efficiency is retained at 88.4\\% for \\(\\nu_{\\mu}\\) charged-current interactions in the fiducial volume in the same energy region. This significantly improved performance compared to existing reconstruction algorithms, marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth's surface.

For a large liquid argon time projection chamber (LArTPC) operating on or near the Earth's surface to detect neutrino interactions, the rejection of cosmogenic background is a critical and challenging task because of the large cosmic ray flux and the long drift time of the TPC. We introduce a superior cosmic background rejection procedure based on the Wire-Cell three-dimensional (3D) event reconstruction for LArTPCs. From an initial 1:20,000 neutrino to cosmic-ray background ratio, we demonstrate these tools on data from the MicroBooNE experiment and create a high performance generic neutrino event selection with a cosmic contamination of 14.9\\% (9.7\\%) for a visible energy region greater than O(200)~MeV. The neutrino interaction selection efficiency is 80.4\\% and 87.6\\% for inclusive \\(\\nu_\\mu\\) charged-current and \\(\\nu_e\\) charged-current interactions, respectively. This significantly improved performance compared to existing reconstruction algorithms, marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth's surface.

MicroBooNE is a near-surface liquid argon (LAr) time projection chamber (TPC) located at Fermilab. We measure the characterisation of muons originating from cosmic interactions in the atmosphere using both the charge collection and light readout detectors. The data is compared with the CORSIKA cosmic-ray simulation. Good agreement is found between the observation, simulation and previous results. Furthermore, the angular resolution of the reconstructed muons inside the TPC is studied in simulation.

We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of \\(e^-\\), \\(\\gamma\\), \\(\\mu^-\\), \\(\\pi^\\pm\\), and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep learning based \\(\\nu_e\\) search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.

The MicroBooNE continuous readout stream is a parallel readout of the MicroBooNE liquid argon time projection chamber (LArTPC) which enables detection of non-beam events such as those from a supernova neutrino burst. The low energies of the supernova neutrinos and the intense cosmic-ray background flux due to the near-surface detector location makes triggering on these events very challenging. Instead, MicroBooNE relies on a delayed trigger generated by SNEWS (the Supernova Early Warning System) for detecting supernova neutrinos. The continuous readout of the LArTPC generates large data volumes, and requires the use of real-time compression algorithms (zero suppression and Huffman compression) implemented in an FPGA (field-programmable gate array) in the readout electronics. We present the results of the optimization of the data reduction algorithms, and their operational performance. To demonstrate the capability of the continuous stream to detect low-energy electrons, a sample of Michel electrons from stopping cosmic-ray muons is reconstructed and compared to a similar sample from the lossless triggered readout stream.

We describe algorithms developed to isolate and accurately reconstruct two-track events that are contained within the MicroBooNE detector. This method is optimized to reconstruct two tracks of lengths longer than 5 cm. This code has applications to searches for neutrino oscillations and measurements of cross sections using quasi-elastic-like charged current events. The algorithms we discuss will be applicable to all detectors running in Fermilab's Short Baseline Neutrino program (SBN), and to any future liquid argon time projection chamber (LArTPC) experiment with beam energies ~1 GeV. The algorithms are publicly available on a GITHUB repository. This reconstruction offers a complementary and independent alternative to the Pandora reconstruction package currently in use in LArTPC experiments, and provides similar reconstruction performance for two-track events.

Large liquid argon time projection chambers (LArTPCs), especially those operating near the surface, are susceptible to space charge effects. In the context of LArTPCs, the space charge effect is the build-up of slow-moving positive ions in the detector primarily due to ionization from cosmic rays, leading to a distortion of the electric field within the detector. This effect leads to a displacement in the reconstructed position of signal ionization electrons in LArTPC detectors (\"spatial distortions\"), as well as to variations in the amount of electron-ion recombination experienced by ionization throughout the volume of the TPC. We present techniques that can be used to measure and correct for space charge effects in large LArTPCs by making use of cosmic muons, including the use of track pairs to unambiguously pin down spatial distortions in three dimensions. The performance of these calibration techniques are studied using both Monte Carlo simulation and MicroBooNE data, utilizing a UV laser system as a means to estimate the systematic bias associated with the calibration methodology.

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2\\% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2\\% after detector calibration.