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At least 451 records · Page 25

MSiReader v1.0: Evolving Open-Source Mass Spectrometry Imaging Software for Targeted and Untargeted Analyses

A major update to the mass spectrometry imaging (MSI) software MSiReader is presented, offering a multitude of newly added features critical to MSI analyses. MSiReader is a free, open-source, and vendor-neutral software written in the MATLAB platform and is capable of analyzing most common MSI data formats. A standalone version of the software, which does not require a MATLAB license, is also distributed. The newly incorporated data analysis features expand the utility of MSiReader beyond simple visualization of molecular distributions. The MSiQuantification tool allows researchers to calculate absolute concentrations from quantification MSI experiments exclusively through MSiReader software, significantly reducing data analysis time. An image overlay feature allows the incorporation of complementary imaging modalities to be displayed with the MSI data. A polarity filter has also been incorporated into the data loading step, allowing the facile analysis of polarity switching experiments without the need for data parsing prior to loading the data file into MSiReader. A quality assurance feature to generate a mass measurement accuracy (MMA) heatmap for an analyte of interest has also been added to allow for the investigation of MMA across the imaging experiment. Most importantly, as new features have been added performance has not degraded, in fact it has been dramatically improved. These new tools and the improvements to the performance in MSiReader v1.0 enable the MSI community to evaluate their data in greater depth and in less time. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

An integrated data analysis tool for improving measurements on the MST RFP

Many plasma diagnostics contain complementary information. For example, the double-foil soft x-ray system (SXR) and the Thomson Scattering diagnostic (TS) on the Madison Symmetric Torus both measure electron temperature. The complementary information from these diagnostics can be combined using a systematic method based on integrated data analysis techniques, leading to more accurate and sensitive results. An integrated data analysis tool based on Bayesian probability theory was able to estimate electron temperatures that are consistent with both the SXR and TS diagnostics and more precise than either. A Markov Chain Monte Carlo analysis to increase the flexibility of the tool was implemented and benchmarked against a grid search method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SU-C-207-01: Four-Dimensional Inverse Geometry Computed Tomography: Concept and Its Validation

Purpose: In past few years, the inverse geometry computed tomography (IGCT) system has been developed to overcome shortcomings of a conventional computed tomography (CT) system such as scatter problem induced from large detector size and cone-beam artifact. In this study, we intend to present a concept of a four-dimensional (4D) IGCT system that has positive aspects above all with temporal resolution for dynamic studies and reduction of motion artifact. Methods: Contrary to conventional CT system, projection data at a certain angle in IGCT was a group of fractionated narrow cone-beam projection data, projection group (PG), acquired from multi-source array which have extremely short time gap of sequential operation between each of sources. At this, for 4D IGCT imaging, time-related data acquisition parameters were determined by combining multi-source scanning time for collecting one PG with conventional 4D CBCT data acquisition sequence. Over a gantry rotation, acquired PGs from multi-source array were tagged time and angle for 4D image reconstruction. Acquired PGs were sorted into 10 phase and image reconstructions were independently performed at each phase. Image reconstruction algorithm based upon filtered-backprojection was used in this study. Results: The 4D IGCT had uniform image without cone-beam artifact on the contrary to 4D CBCT image. In addition, the 4D IGCT images of each phase had no significant artifact induced from motion compared with 3D CT. Conclusion: The 4D IGCT image seems to give relatively accurate dynamic information of patient anatomy based on the results were more endurable than 3D CT about motion artifact. From this, it will be useful for dynamic study and respiratory-correlated radiation therapy. This work was supported by the Industrial R&D program of MOTIE/KEIT [10048997, Development of the core technology for integrated therapy devices based on real-time MRI guided tumor tracking] and the Mid-career Researcher Program (2014R1A2A1A10050270) through the National Research Foundation of Korea funded by the Ministry of Science, ICT&Future Planning.

60 APPLIED LIFE SCIENCES↗

Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition

The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed based on application knowledge, while other times they must be estimated based on general spatial/temporal variation. In this paper, we develop a novel multilevel approach which allows users to impose region-wise compression error bounds. Our method utilizes the byproduct of a multilevel compressor to detect regions where details are rich and we provide the theoretical underpinning for region-wise error control. With spatially varying precision preservation, our approach can achieve significantly higher compression ratios than single-error bounded compression approaches and control errors in the regions of interest.We conduct the evaluations on two climate use cases – one targeting small-scale, node features and the other focusing on long, areal features. For both use cases, the locations of the features were unknown ahead of the compression. By selecting approximately 16% of the data based on multi-scale spatial variations and compressing those regions with smaller error tolerances than the rest, our approach improves the accuracy of post-analysis by approximately 2 × compared to single-error-bounded compression at the same compression ratio. Using the same error bound for the region of interest, our approach can achieve an increase of more than 50% in overall compression ratio.

Gong, Qian↗

Learning Coagulation Processes With Combinatorial Neural Networks

Abstract Simulating the evolution of a coagulating aerosol or cloud of droplets in a key problem in atmospheric science. We present a proof of concept for modeling coagulation processes using a novel combinatorial neural network (CombNN) architecture. Using two types of data from a high‐detail particle‐resolved aerosol simulation, we show that CombNN models outperform standard neural networks and are competitive in accuracy with traditional state‐of‐the‐art sectional models. These CombNN models could have application in learning coarse‐grained coagulation models for multi‐species aerosols and for learning coagulation models from observed size‐distribution data.

54 ENVIRONMENTAL SCIENCES↗

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

54 ENVIRONMENTAL SCIENCES↗

Acoustic emission signal processing technique to characterize reactor in-pile phenomena

Existing and developing advanced sensor technologies and instrumentation will allow non-intrusive in-pile measurement of temperature, extension, and fission gases when coupled with advanced signal processing algorithms. The transmitted measured sensor signals from inside to the outside of containment structure are corrupted by noise and are attenuated, thereby reducing the signal strength and the signal-to-noise ratio. Identification and extraction of actual signal (representative of an in-pile phenomenon) is a challenging and complicated process. In the paper, empirical mode decomposition technique is utilized to reconstruct actual sensor signal by partially combining intrinsic mode functions. Reconstructed signal will correspond to phenomena and/or failure modes occurring inside the reactor. In addition, it allows accurate non-intrusive monitoring and trending of in-pile phenomena.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires drawing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the “missing link” needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new “ab initio” organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Synchronization of Alternative Models in a Supermodel and the Learning of Critical Behavior

“Supermodeling” climate by allowing different models to assimilate data from one another in run time has been shown to give results superior to those of any one model and superior to any weighted average of model outputs. The only free parameters, connection strengths between corresponding variables in each pair of models, are determined using some form of machine learning. It is demonstrated that supermodeling succeeds because near critical states, interscale interactions are important but unresolved processes cannot be effectively represented diagnostically in any single parameterization scheme. In two examples, a pair of toy quasigeostrophic (QG) channel models of the midlatitudes and a pair of ECHAM5 models of the tropical Pacific atmosphere with a common ocean, supermodels dynamically combine parameterization schemes so as to capture criticality, associated critical structures, and the supporting scale interactions. The QG supermodeling scheme extends a previous configuration in which two such models synchronize with intermodel connections only between medium-scale components of the flow; here the connections are trained against a third “real” model. Intermittent blocking patterns characterize the critical behavior thus obtained, even where such patterns are missing in the constituent models. In the ECHAM-based climate supermodel, the corresponding critical structure is the single ITCZ pattern, a pattern that occurs in neither of the constituent models. In conclusion, for supermodels of both types, power spectra indicate enhanced interscale interactions in frequency or energy ranges of physical interest, in agreement with observed data, and supporting a generalized form of the self-organized criticality hypothesis.

54 ENVIRONMENTAL SCIENCES↗

Radiation Tolerant Electronics and Digital Processing for the Phase-1 Read-out Upgrade of the ATLAS Liquid Argon Calorimeters

The ATLAS Liquid Argon calorimeters are designed and built to study proton-proton collisions produced at the LHC at centre-of-mass energies up to 14 TeV. Liquid argon (LAr) sampling calorimeters are employed for all electromagnetic calorimetry in the pseudorapidity region |η|<3.2, and for hadronic calorimetry in the region from |η|=1.5 to |η|=4.9. Although the nominal LHC experimental programme is still in progress, an upgrade of the read-out electronics is being launched to cope with luminosities of up to 3x10{sup 34} cm{sup -2}s{sup -1}, which are beyond the original design by a factor of 3. An improved spatial granularity of the trigger primitives is therefore proposed in order to improve the identification performance for trigger signatures, like electrons, photons, tau leptons, jets, total and missing energy, at high background rejection rates. For the upgrade Phase-1 in 2018, new LAr Trigger Digitizer Boards (LTDB) are being designed to receive higher granularity signals, digitize them on detector and send them via fast optical links to a new LAr digital processing system (LDPS). The LDPS applies a digital filtering and identifies significant energy depositions in each trigger channel. The refined trigger primitives are then transmitted to the first level trigger system to extract improved trigger signatures. The read-out of the trigger signals will process 34000 so-called Super Cells at every LHC bunch-crossing at a frequency of 40 MHz. The new LTDB on-detector electronics is designed to be radiation tolerant in order to be operated for the remaining live-time of the ATLAS detector up to a total luminosity of 3000 fb{sup -1}. For the analog-to-digital conversion (12-bit ADC at 40 MSPS), the data serialization and the fast optical link (5.44 Gb/s) custom components have been developed. They have been qualified for the expected radiation environment of a total ionization dose of 1.3 kGy and a hadron fluence of 6 x 10{sup 13} h/cm{sup 2} with energies above 20 MeV. For the digital components like the ADC, cross-sections for single event effects have been determined. This talk will present R and D results from tests of the radiation tolerant components, the fast data processing electronics and prototypes of the LTDB and LDPS boards. First experience from a Demonstrator setup will be reported, in which about 1/10 of the full Super Cell readout will be equipped with prototype versions of the LTDB and LDPS boards. The Demonstrator will be operated in parallel to the regular ATLAS trigger read-out during the upcoming LHC run. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Angular Correlation Date Measurements with the GeRMAC system

Advanced modeling and simulation efforts have improved at Idaho National Laboratory in recent years with a solid foundation of experimental results. Current computational methods represent significant modeling capabilities but are limited by the accuracy and availability of nuclear data. The creation of pre- and post-processing software tools to address these limitations is fundamental to the improvement of nuclear science modeling capacities. One aspect of predictive modeling tools deals with gamma-rays emitted from radionuclides, including fissile or fissionable material, fission products, or activation products, produced in reactor experiments or other neutron environments. The resulting radionuclides decay in unique ways, providing complications upon measurement as a result of random and cascade, or true, coincidence summing. These effects are not easily quantified during modeling efforts of gamma-ray source terms., The germanium rotational measurements for angular correlation (GeRMAC) system was built to quantify the relative angles for gamma rays emitted by radionuclides of interest to investigate true coincidence, or cascade, summing as well as the nuclear energy levels of decay schemes of interest. Proof of concept studies utilize a series of laboratory check sources to provide validity, and it will soon be used to perform the same measurements for fission products of interest. The resulting data can be used to implement into a Monte Carlo code, such as Geant4, to provide more precise gamma-ray source terms following irradiations of materials.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Deep learning tackles cloud detection

To better understand Earth systems, atmospheric scientists use cloud data collected by a remote sensing tool called lidar. These data are an important part of global forecasting, but analyzing lidar images by hand is time-consuming. Researchers at Pacific Northwest National Laboratory teamed up to see if a deep learning model could process lidar data better than current automatic detection techniques.

54 ENVIRONMENTAL SCIENCES↗

Converging towards atomic and nuclear pressures (Final Report)

his report details the development of transformational tools and techniques, allowing the accurate characterization of matter at the most extreme conditions yet studied in the high energy density (HED) domain. Before this work, most experiments quantitatively mapping the nature of matter in the HED realm were performed using planar geometry, which allows for the isolation and measurement of variables in a single thermodynamic state. Such measurements include equation of state variables, optical conductivity, heat transport and more. However, due to a variety of plasma processes, such experiments are limited to below ~10 TPa (100 Mbar) pressures. To achieve higher pressures, convergent experiments are needed. Historically, convergent experiments have not been used for benchmark data because they are integral measurements. That is each part of a convergent target undergoes a time dependent wide range of states, making it difficult to accurately isolate any particular quantity for a given thermodynamic state. This effort developed innovative convergent HED platforms and techniques enabling the exploration of matter from atomic to nuclear scale pressures. This effort also performed pioneering experiments that yielded the first rigorous benchmark data at these extreme conditions. This funding award began with the overarching goal to understand the behavior of matter at extreme (atomic-to-nuclear scale) pressures. The motivation for these goals lie in the fact that every time scientists explore matter beyond the threshold of an atomic unit, there is a fundamental shift in science. The atomic unit for energy, mass, charge, length, and time have each been explored, and each time such a threshold was crossed, a new sequence of discoveries was made resulting in significant awards such as the Nobel Prize. The only unexplored atomic unit is pressure, and this effort set the course for exploring matter at and beyond such pressures. That most of the recently discovered extrasolar planets and stars, as well as matter in the late implosion stages of inertial fusion targets, have deep internal pressures at and beyond atomic pressures amplifies the importance of this effort. Much of the initial work in developing techniques to create these atomic-to-nuclear pressures already existed within the HED community, predominantly at large scale laser facilities such as the National Ignition Facility (NIF) and the Omega60 laser the University of Rochester and although these experiments were routinely performed the ability to extract information about the underlying physical states and processes remained elusive due to the complexity of the experiments, extreme scales in both time and space, and the integrated nature of all the measurements. This work built a rigorous framework so such measurements can routinely be made. This was done in part through the introduction of Bayesian inference techniques into the field of HED science. These techniques allow for the self-consistent extraction of the relevant variables and their explicit and implicit correlations, so as to make use of integrated experimental data to constrain physical models and provide accurate uncertainty bounds for the data. The techniques developed here are fully transparent and proved immediately useful providing quantitative rigorous benchmarks for physical states and processes at some of the most extreme conditions yet explored on Earth. This funding is directly or partly responsible for 7 publications in major peer-reviewed scientific journals, a doctoral thesis, 3 invited talks at major conferences, and the training of a post-doc, 2 graduate students, and 1 undergraduate student. Beyond the effort supported by this award launched the introduction of Bayesian inference into the HED physics community and helped push the usage if modern data-science techniques within the physics community at large.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Gamma-Rays from Kilonovae and the Cosmic Gamma-Ray Background

The recent detection of the gravitational-wave event GW170817, produced by the coalescence of two neutron stars, and of its optical–infrared counterpart, powered by the radioactive decay of r-process elements, has opened a new window onto gamma-ray astronomy: the direct detection of photons coming from such decays. In this work, we calculate the contribution of kilonovae to the diffuse gamma-ray background in the MeV range, using recent results on the spectra of the gamma-rays emitted in individual events, and we compare it with that from other sources. We find that the contribution from kilonovae is not dominant in such an energy range, but within current uncertainties, and its addition to other sources might help to fit the observational data.

59 BASIC BIOLOGICAL SCIENCES↗

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and forest management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving the plant. X ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. Our group at the Environmental Molecular Sciences Laboratory (EMSL) has developed an XCT-based tool to image and quantitatively analyze plant root structures in their native soil environment. XCT data collected on a Prairie dropseed (Sporobolus heterolepis) specimen was used to visualize its root structure. A combination of open-source software RooTrak and DDV were employed to segment the root from the soil, and calculate its isosurface, respectively. Our own computer script named 3DRoot-SV was developed and used to calculate root volume and surface area from a triangular mesh. The process utilizing a unique combination of tools, from imaging to quantitative root analysis, including the 3DRoot-SV computer script, is described.

58 GEOSCIENCES↗