Investigation and assimilation of nitrogen from benthic sediments by threee species of coral

We quantified the nitrogen and enzyme hydrolyzable amino acid (EHAA) concentrations of sediments prior to and after corals sloughed, ingested, and egested sediments layered onto their surfaces, for the three coral species Siderastrea siderea, Agaricia agaricites, and Porites astreoides in Jamaica. The percent nitrogen of the sediments egested by all three species was lower than in the sediments available to the corals. Additionally, the sediments sloughed (not ingested) by A. agaricites and P. astreoides were lower in percent nitrogen, while the sediments sloughed by S. siderea had the same percent nitrogen as that of the available sediments. The percent nitrogen of the sediments sloughed and egested by P. astreoides showed significant negative and positive relationships, respectively, to increasing sediment loads, while the percent nitrogen of the sediments sloughed and egested by both S. siderea and A. agaricites showed no relationship to sediment load. EHAA concentrations were not significantly different between the sloughed and available sediments but were significantly lower in the sediments egested by S. siderea and A. agaricites (EHAA concentrations were not measured for P. astreodies sediment fractions). Comparisons of the nitrogen and EHAA concentrations in the sloughed and egested sediments to what was available prior to coral processing show that maximum ingestion was between 0.1 and 0.2 µg N µg−1 coral N cm−2 and between 0.5 and 0.6 µg EHAA·cm−2. Maximum assimilation efficiencies were estimated to be 30–60% of the available nitrogen. The data show that corals ingest and alter the nitrogen concentration of particles that land on their surfaces. The corals’ abilities to process these sediments, and the sediments’ possible contributions to coral nutrition, are discussed based on these results

Inter-Rater Reliability of Historical Data Collected by Non-Medical Research Assistants and Physicians in Patients with Acute Abdominal Pain

OBJECTIVES: In many academic emergency departments (ED), physicians are asked to record clinical data for research that may be time consuming and distracting from patient care. We hypothesized that non-medical research assistants (RAs) could obtain historical information from patients with acute abdominal pain as accurately as physicians.METHODS: Prospective comparative study conducted in an academic ED of 29 RAs to 32 resident physicians (RPs) to assess inter-rater reliability in obtaining historical information in abdominal pain patients. Historical features were independently recorded on standardized data forms by a RA and RP blinded to each others' answers. Discrepancies were resolved by a third person (RA) who asked the patient to state the correct answer on a third questionnaire, constituting the "criterion standard." Inter-rater reliability was assessed using kappa statistics (kappa) and percent crude agreement (CrA).RESULTS: Sixty-five patients were enrolled (mean age 43). Of 43 historical variables assessed, the median agreement was moderate (kappa 0.59 [Interquartile range 0.37-0.69]; CrA 85.9%) and varied across data categories: initial pain location (kappa 0.61 [0.59-0.73]; CrA 87.7%), current pain location (kappa 0.60 [0.47-0.67]; CrA 82.8%), past medical history (kappa 0.60 [0.48-0.74]; CrA 93.8%), associated symptoms (kappa 0.38 [0.37-0.74]; CrA 87.7%), and aggravating/alleviating factors (kappa 0.09 [-0.01-0.21]; CrA 61.5%). When there was disagreement between the RP and the RA, the RA more often agreed with the criterion standard (64% [55-71%]) than the RP (36% [29-45%]).CONCLUSION: Non-medical research assistants who focus on clinical research are often more accurate than physicians, who may be distracted by patient care responsibilities, at obtaining historical information from ED patients with abdominal pain

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples

Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE

The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at arXiv:1802.0870

Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts the raw digitized TPC waveform to the number of ionization electrons passing through a wire plane at a given time. A robust recovery of the number of ionization electrons from both induction and collection anode wire planes will augment the 3D reconstruction, and is particularly important for tomographic reconstruction algorithms. A number of building blocks of the overall procedure are described. The performance of the signal processing is quantitatively evaluated by comparing extracted charge with the true charge through a detailed TPC detector simulation taking into account position-dependent induced current inside a single wire region and across multiple wires. Some areas for further improvement of the performance of the charge extraction procedure are also discussed.Comment: 60 pages, 36 figures. The second part of this work can be found at arXiv:1804.0258

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal