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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions

The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed.

Agriculture↗
DOE OSTI2021

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗
DOE OSTI2016

SU-F-J-72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy

Purpose: To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all-in-one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques. Methods: ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model-View-ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deep modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness. Results: ACE can take patient’s CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases. Conclusion: ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Tessera: Open source software for accelerated data science

Extracting useful, actionable information from data can be a formidable challenge for the safeguards, nonproliferation, and arms control verification communities. Data scientists are often on the “front-lines” of making sense of complex and large datasets. They require flexible tools that make it easy to rapidly reformat large datasets, interactively explore and visualize data, develop statistical algorithms, and validate their approaches—and they need to perform these activities with minimal lines of code. Existing commercial software solutions often lack extensibility and the flexibility required to address the nuances of the demanding and dynamic environments where data scientists work. To address this need, Pacific Northwest National Laboratory developed Tessera, an open source software suite designed to enable data scientists to interactively perform their craft at the terabyte scale. Tessera automatically manages the complicated tasks of distributed storage and computation, empowering data scientists to do what they do best: tackling critical research and mission objectives by deriving insight from data. We illustrate the use of Tessera with an example analysis of computer network data.

Sego, Landon H.↗
DOE OSTI2016

WE-DE-207B-10: Library-Based X-Ray Scatter Correction for Dedicated Cone-Beam Breast CT: Clinical Validation

Purpose: Scatter contamination is detrimental to image quality in dedicated cone-beam breast CT (CBBCT), resulting in cupping artifacts and loss of contrast in reconstructed images. Such effects impede visualization of breast lesions and the quantitative accuracy. Previously, we proposed a library-based software approach to suppress scatter on CBBCT images. In this work, we quantify the efficacy and stability of this approach using datasets from 15 human subjects. Methods: A pre-computed scatter library is generated using Monte Carlo simulations for semi-ellipsoid breast models and homogeneous fibroglandular/adipose tissue mixture encompassing the range reported in literature. Projection datasets from 15 human subjects that cover 95 percentile of breast dimensions and fibroglandular volume fraction were included in the analysis. Our investigations indicate that it is sufficient to consider the breast dimensions alone and variation in fibroglandular fraction does not significantly affect the scatter-to-primary ratio. The breast diameter is measured from a first-pass reconstruction; the appropriate scatter distribution is selected from the library; and, deformed by considering the discrepancy in total projection intensity between the clinical dataset and the simulated semi-ellipsoidal breast. The deformed scatter-distribution is subtracted from the measured projections for scatter correction. Spatial non-uniformity (SNU) and contrast-to-noise ratio (CNR) were used as quantitative metrics to evaluate the results. Results: On the 15 patient cases, our method reduced the overall image spatial non-uniformity (SNU) from 7.14%±2.94% (mean ± standard deviation) to 2.47%±0.68% in coronal view and from 10.14%±4.1% to 3.02% ±1.26% in sagittal view. The average contrast to noise ratio (CNR) improved by a factor of 1.49±0.40 in coronal view and by 2.12±1.54 in sagittal view. Conclusion: We demonstrate the robustness and effectiveness of a library-based scatter correction method using patient datasets with large variability in breast dimensions and composition. The high computational efficiency and simplicity in implementation make this attractive for clinical implementation. Supported partly by NIH R21EB019597, R21CA134128 and R01CA195512.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Basic Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Basic Energy Sciences, November 3-5, 2015, Rockville, Maryland

Computers have revolutionized every aspect of our lives. Yet in science, the most tantalizing applications of computing lie just beyond our reach. The current quest to build an exascale computer with one thousand times the capability of today’s fastest machines (and more than a million times that of a laptop) will take researchers over the next horizon. The field of materials, chemical reactions, and compounds is inherently complex. Imagine millions of new materials with new functionalities waiting to be discovered — while researchers also seek to extend those materials that are known to a dizzying number of new forms. We could translate massive amounts of data from high precision experiments into new understanding through data mining and analysis. We could have at our disposal the ability to predict the properties of these materials, to follow their transformations during reactions on an atom-by-atom basis, and to discover completely new chemical pathways or physical states of matter. Extending these predictions from the nanoscale to the mesoscale, from the ultrafast world of reactions to long-time simulations to predict the lifetime performance of materials, and to the discovery of new materials and processes will have a profound impact on energy technology. In addition, discovery of new materials is vital to move computing beyond Moore’s law. To realize this vision, more than hardware is needed. New algorithms to take advantage of the increase in computing power, new programming paradigms, and new ways of mining massive data sets are needed as well. This report summarizes the opportunities and the requisite computing ecosystem needed to realize the potential before us. In addition to pursuing new and more complete physical models and theoretical frameworks, this review found that the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) would directly affect the Basic Energy Sciences (BES) mission need. Simulation, visualization, and data analysis are crucial for advances in energy science and technology. Revolutionary mathematical, software, and algorithm developments are required in all areas of BES science to take advantage of exascale computing architectures and to meet data analysis, management, and workflow needs. In partnership with ASCR, BES has an emerging and pressing need to develop new and disruptive capabilities in data science. More capable and larger high-performance computing (HPC) and data ecosystems are required to support priority research in BES. Continued success in BES research requires developing the next-generation workforce through education and training and by providing sustained career opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection

Purpose: To evaluate the feasibility of using computed tomography texture analysis (CTTA) parameters for predicting malignant risk grade and mitosis index of gastrointestinal stromal tumors (GISTs), compared with visual inspection. Method and materials: CTTA was performed on portal phase CT images of 145 surgically confirmed GISTs (mean size: 42.9 ± 37.5 mm), using TexRAD software. Mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis of CTTA parameters, on spatial scaling factor (SSF), 2–6 were compared by risk grade, mitosis rate, and the presence or absence of necrosis on visual inspection. CTTA parameters were correlated with risk grade. Diagnostic performance was evaluated with receiver operating characteristic curve analysis. Enhancement pattern, necrosis, heterogeneity, calcification, growth pattern, and mucosal ulceration were subjectively evaluated by two observers. Results: Three to four parameters at different scales were significantly different according to the risk grade, mitosis rate, and the presence or absence of necrosis (p < 0.041). MPP at fine or medium scale (r = − 0.547 to − 393) and kurtosis at coarse scale (r = 0.424–0.454) correlated significantly with risk grade (p < 0.001). HG-GIST was best differentiated from LG-GIST by MPP at SSF 2 (AUC, 0.782), and kurtosis at SSF 4 (AUC, 0.779) (all p < 0.001). CT features predictive of HG-GIST were density lower than or equal to that of the erector spinae muscles on enhanced images (OR 2.1; p = 0.037; AUC, 0.59), necrosis (OR, 6.1; p < 0.001; AUC, 0.70), heterogeneity (OR, 4.3; p < 0.001; AUC, 0.67), and mucosal ulceration (OR, 3.3; p = 0.002; AUC, 0.62). Conclusion: Using TexRAD, MPP and kurtosis are feasible in predicting risk grade and mitosis index of GISTs. CTTA demonstrated meaningful accuracy in preoperative risk stratification of GISTs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗
DOE OSTI2024

VIBES: a workflow for annotating and visualizing viral sequences integrated into bacterial genomes

Abstract Bacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists. Some prophages have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES (Viral Integrations in Bacterial genomES), a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab-separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES’s primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1072 Pseudomonas spp. genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

High-throughput cardiac safety evaluation and multi-parameter arrhythmia profiling of cardiomyocytes using microelectrode arrays

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak for field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty

Dynamic security assessment (DSA) is a critical function to evaluate power grids’ capability to survive the transition caused by a set of disturbances to an acceptable steady-state condition. Its computational burden is heavy. With the challenges brought by renewable energy and new smart grid technologies, DSA under uncertainty has to be considered to study the impact of forecast errors on DSA simulation, which further increases the computational burden. To address this challenge, this paper presents a high performance computational framework to support DSA simulation user uncertainty. The computational framework aims to provide a seamless workflow that links data from high performance computing, statistical analysis, to visualization so that a problem can be easily expressed in a way compatible with different functions. It also enables software compatibility such that application development can be more efficient. Case study results of the ESCA60 system and a western U.S. size system show the advantages and efficiency of the computational framework.

dynamic security assessment, high performance comp↗
DOE OSTI2015

A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

McClay, Wilbert↗
DOE OSTI2016

SU-F-T-80: A Mobile Application for Intra-Operative Electron Radiotherapy Treatment Planning

Purpose: Intraoperative electron radiotherapy (IORT) poses a unique set of challenges for treatment planning. Planning must be performed in a busy operating room environment over a short timeframe often with little advance knowledge of the treatment depth or applicator size. Furthermore, IORT accelerators can have a large number of possible applicators, requiring extensive databooks that must be searched for the appropriate dosimetric parameters. The goal of this work is to develop a software tool to assist in the planning process that is suited to the challenges faced in the IORT environment. Methods: We developed a mobile application using HTML5 and Javascript that can be deployed to tablet devices suitable for use in the operating room. The user selects the desired treatment parameters cone diameter, bevel angle, and energy (a total of 141 datasets) and desired bolus. The application generates an interactive display that allows the user to dynamically select points on the depth-dose curve and to visualize the shape of the corresponding isodose contours. The user can indicate a prescription isodose line or depth. The software performs a monitor unit calculation and generates a PDF report. Results: We present our application, which is now used routinely in our IORT practice. It has been employed successfully in over 23 cases. The interactivity of the isodose distributions was found to be of particular use to physicians who are less-frequent IORT users, as well as for the education of residents and trainees. Conclusion: This software has served as a useful tool in IORT planning, and demonstrates the need for treatment planning tools that are designed for the specialized challenges encountered in IORT. This software is the subject of a license agreement with the IntraOp Medical Corporation. This software is the subject of a license agreement between Massachusetts General Hospital / Partners Healthcare and the IntraOp Medical Corporation. CLW is consulting on software development with the IntraOp Medical Corporation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Open Reproducible Electron Microscopy Data Analysis

Electron microscopy (EM) is a cornerstone technique in the materials and biological sciences capable of imaging structures at nano- to atomic-scale resolution. Advances in technologies mean that one acquires datasets at increasing data rates and sizes. These advancements present enormous opportunities for researchers to understand complex systems. However, processing the resulting large-scale, complex data in a reproducible and shareable way is a real challenge for researchers. The building, managing, and maintaining complex workflows in a reproducible manner requires extensive knowledge in several areas outside the researchers’ core skill sets, such as software engineering, data science, and high-performance computing (HPC). Our work demonstrates an innovative approach to solving these problems, enabling reproducible EM data analysis through container encapsulated pipelines. Using modern container technologies, we encapsulate processing elements and connect them using shared memory. We expose user-friendly, advanced algorithms and tools to allow end users to utilize without expert programming skills. The platform enables reproducible, scalable, shareable pipelines for the analysis and visualization of EM data. Focusing on interoperability, we leverage the DOE and other agencies’ existing investments to provide a powerful software platform for EM data analysis.

Harris, Christopher↗
DOE OSTI2013

polymec

Polymec is a set of tools that can be used by a computational scientist/researcher to construct application software that solves nonlinear differential equations on unstructured grids (including point meshes) on parallel computers. It consists of a set of libraries written in C99 that can be linked into an application to give it access to parallel solvers and visualization capabilities. Interfaces to several DOE-funded open source libraries are provided as a convenience to re-searchers to aid the implementation of application software.

Johnson, Jeffery↗
DOE OSTI2021

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2022

Discovery of unknown posttranslational modifications by top-down mass spectrometry

Post-translational modifications can be important biomarkers of function within biological systems. Top-down mass spectrometry is a valuable tool for the characterization of combinatorial modifications on proteins such as histones that have a range of complex modifications. In this chapter, we present a top-down liquid chromatography-mass spectrometry experimental and data analysis workflow for the identification of novel modifications on proteins. The data analysis workflow presented here is reliant on the combination of proteoform searching using TopPIC and data visualization and fragment ion spectral confirmation using LcMsSpectator, part of the Informed-Proteomics package of software.

Wilson, Jesse W.↗