Evaluation of flow mixing in an ARID-HV algal raceway using statistics of temporal and spatial distribution of fluid particles
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Xu, Ben (ORCID:0000000265046216)↗
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Q -score as a reliability measure for protein, nucleic acid and small-molecule atomic coordinate models derived from 3DEM maps
Atomic coordinate models are important for the interpretation of 3D maps produced with cryoEM and cryoET (3D electron microscopy; 3DEM). In addition to visual inspection of such maps and models, quantitative metrics can inform about the reliability of the atomic coordinates, in particular how well the model is supported by the experimentally determined 3DEM map. A recently introduced metric, Q-score, was shown to correlate well with the reported resolution of the map for well fitted models. Here, we present new statistical analyses of Q-score based on its application to ∼10 000 maps and models archived in the EMDB (Electron Microscopy Data Bank) and PDB (Protein Data Bank). Further, we introduce two new metrics based on Q-score to represent each map and model relative to all entries in the EMDB and those with similar resolution. We explore through illustrative examples of proteins, nucleic acids and small molecules how Q-scores can indicate whether the atomic coordinates are well fitted to 3DEM maps and also whether some parts of a map may be poorly resolved due to factors such as molecular flexibility, radiation damage and/or conformational heterogeneity. These examples and statistical analyses provide a basis for how Q-scores can be interpreted effectively in order to evaluate 3DEM maps and atomic coordinate models prior to publication and archiving.
B factors↗
Quantitative Monte Carlo-based holmium-166 SPECT reconstruction
Purpose: Quantitative imaging of the radionuclide distribution is of increasing interest for microsphere radioembolization (RE) of liver malignancies, to aid treatment planning and dosimetry. For this purpose, holmium-166 ({sup 166}Ho) microspheres have been developed, which can be visualized with a gamma camera. The objective of this work is to develop and evaluate a new reconstruction method for quantitative {sup 166}Ho SPECT, including Monte Carlo-based modeling of photon contributions from the full energy spectrum.Methods: A fast Monte Carlo (MC) simulator was developed for simulation of {sup 166}Ho projection images and incorporated in a statistical reconstruction algorithm (SPECT-fMC). Photon scatter and attenuation for all photons sampled from the full {sup 166}Ho energy spectrum were modeled during reconstruction by Monte Carlo simulations. The energy- and distance-dependent collimator-detector response was modeled using precalculated convolution kernels. Phantom experiments were performed to quantitatively evaluate image contrast, image noise, count errors, and activity recovery coefficients (ARCs) of SPECT-fMC in comparison with those of an energy window-based method for correction of down-scattered high-energy photons (SPECT-DSW) and a previously presented hybrid method that combines MC simulation of photopeak scatter with energy window-based estimation of down-scattered high-energy contributions (SPECT-ppMC+DSW). Additionally, the impact of SPECT-fMC on whole-body recovered activities (A{sup est}) and estimated radiation absorbed doses was evaluated using clinical SPECT data of six {sup 166}Ho RE patients.Results: At the same noise level, SPECT-fMC images showed substantially higher contrast than SPECT-DSW and SPECT-ppMC+DSW in spheres ≥17 mm in diameter. The count error was reduced from 29% (SPECT-DSW) and 25% (SPECT-ppMC+DSW) to 12% (SPECT-fMC). ARCs in five spherical volumes of 1.96–106.21 ml were improved from 32%–63% (SPECT-DSW) and 50%–80% (SPECT-ppMC+DSW) to 76%–103% (SPECT-fMC). Furthermore, SPECT-fMC recovered whole-body activities were most accurate (A{sup est}= 1.06 × A − 5.90 MBq, R{sup 2}= 0.97) and SPECT-fMC tumor absorbed doses were significantly higher than with SPECT-DSW (p = 0.031) and SPECT-ppMC+DSW (p = 0.031).Conclusions: The quantitative accuracy of {sup 166}Ho SPECT is improved by Monte Carlo-based modeling of the image degrading factors. Consequently, the proposed reconstruction method enables accurate estimation of the radiation absorbed dose in clinical practice.
61 RADIATION PROTECTION AND DOSIMETRY↗
Deactivation and decommissioning web log analysis using big data technology - 15710
The D and D Knowledge Management Information Tool (D and D KM- IT) is a web-based knowledge management information tool built for the D and D user community. Big data is a massive volume of structured and unstructured datasets which is so large that it's difficult to process using traditional database techniques. Web logs are the repository of files which are generated automatically for any operation on the web site. Web log files generated from the D and D KM-IT will be processed using the Apache Hadoop Framework to extract meaningful data. Hadoop is an open-source software framework for storing and processing big data in a distributed environment on large clusters of commodity hardware. Hadoop framework consists of two main layers: Hadoop distributed file system (HDFS) and the Execution engine (MapReduce). Automatic parallelization and distribution; clean and simple programming abstraction. Implementation: Web log files from the D and D KM-IT server are fetched using Apache Flume and loaded into the Hadoop distributed file system. Using the Apache Hcatalog tool, the web log data is parsed and converted into a structured table format. A Pig program is developed to query the tables to retrieve and store the significant information onto the HDFS. The extracted data is fed to business visualization tools such as Microsoft Excel for analysis and reporting. Results The reports generated through the web log data processing will be analyzed to improve the usability and performance of the D and D KM-IT. Conclusion/Future Work: User keyword information can be explored to add new trending topics to the D and D KM-IT application. Statistics generated can be compared to analysis reports from existing analytical tools.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Effect of low tube voltage and low iodine concentration abdominal CT on image quality and radiation dose in children: preliminary study
Purpose: To evaluate the image quality of a double-low protocol (low tube voltage and low iodine concentration) for abdominal CT in children. Materials and methods: The double-low protocol was compared to the conventional protocol in pediatric patients weighing less than 40 kg from May 2016 to December 2016. Double-low protocol (Group A, n = 18): tube voltage, 70 kVp; and iodine concentration,: 250 mgI/mL versus Conventional protocol (Group B, n = 13): tube voltage, 80–100 kVp; and iodine concentration, 350 mgI/mL. Mean attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between the two groups. Image contrast, noise, beam-hardening artifacts, and overall image quality were subjectively scored. Reader performance for correctly differentiating two groups by visual assessment was evaluated. Radiation dose and total iodine load were recorded. Results: The mean attenuations of the portal vein and liver and the mean image noise in Group A were higher than in Group B (p = 0.04, 0.03, 0.004, respectively). The mean SNR and CNR of the main portal vein and liver were lower in Group A without any statistically significant difference. There were no statistically significant differences between the two groups in qualitative analysis (image contrast, image noise, and overall image quality) with substantial agreement between the reviewers (weighted kappa values; 0.59–0.76). Significantly diminished radiation dose and iodine load were observed in Group A compared with Group B (25.0%, 36.8% reduction; p = 0.007, 0.006, respectively). Conclusion: The double-low protocol was feasible for pediatric abdominal CT and reduced both radiation dose and iodine load, while maintaining image quality.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
MO-DE-207A-09: Low-Dose CT Image Reconstruction Via Learning From Different Patient Normal-Dose Images
Purpose: To investigate a novel low-dose CT (LdCT) image reconstruction strategy for lung CT imaging in radiation therapy. Methods: The proposed approach consists of four steps: (1) use the traditional filtered back-projection (FBP) method to reconstruct the LdCT image; (2) calculate structure similarity (SSIM) index between the FBP-reconstructed LdCT image and a set of normal-dose CT (NdCT) images, and select the NdCT image with the highest SSIM as the learning source; (3) segment the NdCT source image into lung and outside tissue regions via simple thresholding, and adopt multiple linear regression to learn high-order Markov random field (MRF) pattern for each tissue region in the NdCT source image; (4) segment the FBP-reconstructed LdCT image into lung and outside regions as well, and apply the learnt MRF prior in each tissue region for statistical iterative reconstruction of the LdCT image following the penalized weighted least squares (PWLS) framework. Quantitative evaluation of the reconstructed images was based on the signal-to-noise ratio (SNR), local binary pattern (LBP) and histogram of oriented gradients (HOG) metrics. Results: It was observed that lung and outside tissue regions have different MRF patterns predicted from the NdCT. Visual inspection showed that our method obviously outperformed the traditional FBP method. Comparing with the region-smoothing PWLS method, our method has, in average, 13% increase in SNR, 15% decrease in LBP difference, and 12% decrease in HOG difference from reference standard for all regions of interest, which indicated the superior performance of the proposed method in terms of image resolution and texture preservation. Conclusion: We proposed a novel LdCT image reconstruction method by learning similar image characteristics from a set of NdCT images, and the to-be-learnt NdCT image does not need to be scans from the same subject. This approach is particularly important for enhancing image quality in radiation therapy.
60 APPLIED LIFE SCIENCES↗
Visualizing red blood cell sickling and the effects of inhibition of sphingosine kinase 1 using soft X-ray tomography
ABSTRACT Sickle cell disease is a destructive genetic disorder characterized by the formation of fibrils of deoxygenated hemoglobin, leading to the red blood cell (RBC) morphology changes that underlie the clinical manifestations of this disease. Using cryogenic soft X-ray tomography (SXT), we characterized the morphology of sickled RBCs in terms of volume and the number of protrusions per cell. We were able to identify statistically a relationship between the number of protrusions and the volume of the cell, which is known to correlate to the severity of sickling. This structural polymorphism allows for the classification of the stages of the sickling process. Recent studies have shown that elevated sphingosine kinase 1 (Sphk1)-mediated sphingosine 1-phosphate production contributes to sickling. Here, we further demonstrate that compound 5C, an inhibitor of Sphk1, has anti-sickling properties. Additionally, the variation in cellular morphology upon treatment suggests that this drug acts to delay the sickling process. SXT is an effective tool that can be used to identify the morphology of the sickling process and assess the effectiveness of potential therapeutics.
59 BASIC BIOLOGICAL SCIENCES↗
Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)
Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.
58 GEOSCIENCES↗
RE-EXAMINING SUNSPOT TILT ANGLE TO INCLUDE ANTI-HALE STATISTICS
Sunspot groups and bipolar magnetic regions (BMRs) serve as an observational diagnostic of the solar cycle. We use Debrecen Photohelographic Data (DPD) from 1974-2014 that determined sunspot tilt angles from daily white light observations, and data provided by Li and Ulrich that determined sunspot magnetic tilt angle using Mount Wilson magnetograms from 1974-2012. The magnetograms allowed for BMR tilt angles that were anti-Hale in configuration, so tilt values ranged from 0 to 360° rather than the more common ±90°. We explore the visual representation of magnetic tilt angles on a traditional butterfly diagram by plotting the mean area-weighted latitude of umbral activity in each bipolar sunspot group, including tilt information. The large scatter of tilt angles over the course of a single cycle and hemisphere prevents Joy's law from being visually identified in the tilt-butterfly diagram without further binning. The average latitude of anti-Hale regions does not differ from the average latitude of all regions in both hemispheres. The distribution of anti-Hale sunspot tilt angles are broadly distributed between 0 and 360° with a weak preference for east-west alignment 180° from their expected Joy's law angle. The anti-Hale sunspots display a log-normal size distribution similar to that of all sunspots, indicating no preferred size for anti-Hale sunspots. We report that 8.4% ± 0.8% of all bipolar sunspot regions are misclassified as Hale in traditional catalogs. This percentage is slightly higher for groups within 5° of the equator due to the misalignment of the magnetic and heliographic equators.
79 ASTRONOMY AND ASTROPHYSICS↗
Value of Nonrigid Registration of Pre-Procedure MR with Post-Procedure CT After Radiofrequency Ablation for Hepatocellular Carcinoma
PurposeTo evaluate the value of pre-radiofrequency ablation (RFA) MR and post-RFA CT registration for the assessment of the therapeutic response of hepatocellular carcinoma (HCC).Materials and MethodsA total of 178 patients with single HCC who received RFA as an initial treatment and had available pre-RFA MR and post-RFA CT images were included in this retrospective study. Two independent readers (one experienced radiologist, one inexperienced radiologist) scored the ablative margin (AM) of treated tumors on a four-point scale (1, residual tumor; 2, incomplete AM; 3, borderline AM; 4, sufficient AM), in two separate sessions: (1) visual comparison between pre-and post-RFA images; (2) with addition of nonrigid registration for pre- and post-RFA images. Local tumor progression (LTP) rates between low-risk (response score, 3–4) and high-risk groups (1–2) were analyzed using the Kaplan–Meier method at each interpretation session.ResultsThe patients’ reassignments after using the registered images were statistically significant for inexperienced reader (p < 0.001). In the inexperienced reader, LTP rates of low- and high-risk groups were significantly different with addition of registered images (session 2) (p < 0.001), but not significantly different in session 1 (p = 0.101). However, in the experienced reader, LTP rates of low- and high-risk groups were significantly different in both interpretation sessions (p < 0.001). Using the registered images, the cumulative incidence of LTP at 2 years was 3.0–6.6%, for the low-risk group, and 18.6–27.8% for the high-risk group.ConclusionRegistration between pre-RFA MR and post-RFA CT images may allow better assessment of the therapeutic response of HCC after RFA, especially for inexperienced radiologists, helping in the risk stratification for LTP.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Experimentally validated high-fidelity simulations of a liquid jet in supersonic crossflow
Here, utilizing recent advancements in computational schemes for compressible, multiphase flows, this work features a parametric study of a pure liquid jet in supersonic crossflow that involves simulating the atomization process for four values of momentum-flux ratio. These simulations are validated against experimental results measured with high-speed X-ray imaging, which confirm the accuracy of the numerical approach. Also, the effect of numerical resolution on some flow behavior is investigated, revealing convergence of the jet shape and surface instability wavelength. Analysis of the resulting sprays includes statistical descriptions of the liquid distribution, liquid structures created through breakup, interfacial instabilities, and dominant flow features. As the flowrate increases, the spray penetrates further, it becomes more disperse, and less liquid impacts the wall, but the droplet size distribution changes little. The wavelength of instabilities on the windward side of the jet diverges from measured trends in subsonic crossflows. In a visualization of the time-averaged flow, counter-rotating vortices are observed along the jet core and in the wake, affecting the process of primary atomization and early droplet trajectories.
42 ENGINEERING↗
FREDA: A Web Application for the Processing, Analysis, and Visualization of Fourier‐Transform Mass Spectrometry Data
The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).
47 OTHER INSTRUMENTATION↗
Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks
Abstract A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.
54 ENVIRONMENTAL SCIENCES↗
Methyl-CpG island-associated genome signature tags
Disclosed is a method for analyzing the organismic complexity of a sample through analysis of the nucleic acid in the sample. In the disclosed method, through a series of steps, including digestion with a type II restriction enzyme, ligation of capture adapters and linkers and digestion with a type IIS restriction enzyme, genome signature tags are produced. The sequences of a statistically significant number of the signature tags are determined and the sequences are used to identify and quantify the organisms in the sample. Various embodiments of the invention described herein include methods for using single point genome signature tags to analyze the related families present in a sample, methods for analyzing sequences associated with hyper- and hypo-methylated CpG islands, methods for visualizing organismic complexity change in a sampling location over time and methods for generating the genome signature tag profile of a sample of fragmented DNA.
59 BASIC BIOLOGICAL SCIENCES↗
Band Excitation in Scanning Probe Microscopy: Recognition and Functional Imaging
Field confinement at the junction between a biased scanning probe microscope s (SPM) tip and solid surface enables local probing of various bias-induced transformations such as polarization switching, ionic motion, or electrochemical reactions to name a few. The nanoscale size of the biased region is smaller or comparable to features like grain boundaries and dislocations, potentially allows for the study of kinetics and thermodynamics at the level of a single defect. In contrast to classical statistically averaged approaches, this allows one to link structure to functionality and deterministically decipher associated mesoscopic and atomistic mechanisms. Furthermore, this type of information can serve as a fingerprint of local material functionality, allowing for local recognition imaging. Here, current progress in multidimensional SPM techniques based on band-excitation time and voltage spectroscopies is illustrated, including discussions on data acquisition, dimensionality reduction, and visualization along with future challenges and opportunities for the field.
Jesse, Stephen↗
Suite of Comprehensive Rapid Analysis Tools for Estimation (SOCRATES) - 19076
Long-term monitoring and remedy implementation require ongoing assessment and analytics, much of which relies on intensive and time-consuming data integration and engineering, data manipulation, and 'big-data' analytics. Many of these analyses, such as groundwater flow direction, contaminant rate attenuation, and non-parametric statistics, are spatial-temporal in nature and involve coalescing disparate data sets from a variety of sources with measurement frequencies from milliseconds to years, as well as total data volumes often exceeding the petabyte scale. Web-based analytical tools are advantageous for ongoing real-time or near-real-time ingestion of data from various sources for general data access and visualization, and also allow for complex, data rich calculations by leveraging expansive networks and virtually unlimited compute power. A web-based framework offers a natural architecture for such endeavors, as access to compute nodes through auto-scaling and dynamic frameworks enables speedy data manipulation and unlimited access to any data set that is made available via an http request. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Z-checker: A framework for assessing lossy compression of scientific data
Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this article, we present a survey of existing lossy compressors. Then, we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties (such as entropy, distribution, power spectrum, principal component analysis, and autocorrelation) of any data set to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality (compression ratio and bit rate) and provide various global distortion analysis comparing the original data with the decompressed data (peak signal-to-noise ratio, normalized mean squared error, rate–distortion, rate-compression error, spectral, distribution, and derivatives) and statistical analysis of the compression error (maximum, minimum, and average error; autocorrelation; and distribution of errors). Z-checker can perform the analysis with either coarse granularity (throughout the whole data set) or fine granularity (by user-defined blocks), such that the users and developers can select the best fit, adaptive compressors for different parts of the data set. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the data sets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific data sets.
97 MATHEMATICS AND COMPUTING↗