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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Automated, reliable, and efficient continental-scale replication of 7.3 petabytes of computational simulation data: A case study

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) computational simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee—a task motivated by a need for increased reliability, capacity, and performance. This task presented significant challenges: the need to move 29 million files twice under time pressure from aging storage hardware; a source file system bottleneck limiting throughput to 1.5 GB/s; frequent site maintenance windows; and the need for complete reliability at scale. We addressed these challenges using a simple replication tool that invoked Globus to transfer large bundles of files while tracking progress in a database, dynamically rerouting transfers to work around maintenance periods and file system limitations. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure. The replication tool is available at https://github.com/esgf2-us/data-replication-tools.

Globus↗
DOE OSTI2014

A Drop in the Bucket or a Pebble in a Pond: Commercial Building Partners’ Replication of EEMs Across Their Portfolios

This study presents findings from questionnaire and interview data investigating replication efforts of Commercial Building Partnership (CBP) partners that worked directly with the Pacific Northwest National Laboratory (PNNL). PNNL partnered with 12 organizations on new and retrofit construction projects as part of the U.S. Department of Energy (DOE) CBP program. PNNL and other national laboratories collaborate with industry leaders that own large portfolios of buildings to develop high performance projects for new construction and renovation. This project accelerates market adoption of commercially available energy saving technologies into the design process for new and upgraded commercial buildings. The labs provide assistance to the partners’ design teams and make a business case for energy investments. From the owner’s perspective, a sound investment results in energy savings based on corporate objectives and design. Through a feedback questionnaire, along with personal interviews, PNNL gathered qualitative and quantitative information relating to replication efforts by each organization. Data through this process were analyzed to provide insight into two primary research areas: 1) CBP partners’ replication efforts of technologies and approaches used in the CBP project to the rest of the organization’s building portfolio (including replication verification), and, 2) the market potential for technology diffusion into the total U.S. commercial building stock, as a direct result of the CBP entire program.

Commercial Building Partnerships↗
DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"

This dataset contains BEPAM model code and input data to the replicate the results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land." The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output_0213-2020_Complete_daycent-agversion-[rental payment level]%_[biomass price].gms. (BEPAM-CRP model-Sep2020.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

biomass price↗
DOE OSTI2018

The ATLAS Data Management System Rucio: Supporting LHC Run-2 and beyond

With this contribution we present some recent developments made to Rucio, the data management system of the High-Energy Physics Experiment ATLAS. Already managing 300 Petabytes of both official and user data, Rucio has seen incremental improvements throughout LHC Run-2, and is currently laying the groundwork for HEP computing in the HL-LHC era. The focus of this contribution are (a) the automations that have been put in place such as data rebalancing or dynamic replication of user data, as well as their supporting infrastructures such as real-time networking metrics or transfer time predictions; (b) the flexible approach towards inclusion of heterogeneous storage systems, including object stores, while unifying the potential access paths using generally available tools and protocols; (c) machine learning approaches to help with transfer throughput estimation; and (d) the adoption of Rucio for two other experiments, AMS and Xenon1t. We conclude by presenting operational numbers and figures to quantify these improvements, and extrapolate the necessary changes and developments for future LHC runs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database or NP-MRD (https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction and retrieval of nuclear magnetic resonance (NMR) data on natural products. The NP-MRD was initially established to support compound de-replication and data dissemination for the natural products community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics and nutrition science fields. Indeed, since its launch in 2021, the NP-MRD has expanded enormously in size, scope and popularity. The current version of NP-MRD now contains nearly 7X more compounds (281,859 vs. 40,908) and 7X more NMR spectra (5.1 million vs. 817,000) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11,000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from a 165 spectra per year to more than 10,000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20,000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, important improvements to the visualization and display of NMR spectra, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Artifical Intelligence↗
DOE OSTI2024

BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data

Abstract BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, training of artificial intelligence models and computational chemistry methods development. This update reports significant growth and enhancements since our last review in 2016. Of note, the database now contains 2.9 million binding measurements spanning 1.3 million compounds and thousands of protein targets. This growth is largely attributable to our unique focus on curating data from US patents, which has yielded a substantial influx of novel binding data. Recent improvements include a remake of the website following responsive web design principles, enhanced search and filtering capabilities, new data download options and webservices and establishment of a long-term data archive replicated across dispersed sites. We also discuss BindingDB’s positioning relative to related resources, its open data sharing policies, insights gleaned from the dataset and plans for future growth and development.

Liu, Tiqing↗
DOE OSTI2018

Correlation of Platinum Cytotoxicity to Drug-DNA Adduct Levels in a Breast Cancer Cell Line Panel

Platinum drugs, including carboplatin and oxaliplatin, are commonly used chemotherapy drugs that kill cancer cells by forming toxic drug-DNA adducts. These drugs have a proven, but modest, efficacy against several aggressive subtypes of breast cancer but also cause several side effects that can lead to the cessation of treatment. There is a clinical need to identify patients who will respond to platinum drugs in order to better inform clinical decision making. Diagnostic microdosing involves dosing patients or patient samples with subtherapeutic doses of radiolabeled platinum followed by measurement of platinum-DNA adducts in blood or tumor tissue and may be used to predict patient response. We exposed here a panel of six breast cancer cell lines to 14 C-labeled carboplatin or oxaliplatin at therapeutic and microdose (1% therapeutic dose) concentrations for a range of exposure lengths and isolated DNA from the cells. The DNA was converted to graphite, and measurement of radiocarbon due to platinum-DNA adduction was quantified via accelerator mass spectrometry (AMS). We observed a linear correlation in adduct levels between the microdose and therapeutic dose, and the level of platinum-DNA adducts corresponded to cell line drug sensitivity for both carboplatin and oxaliplatin. These results showed a clear separation in adduct levels between the sensitive and resistant groups of cell lines that could not be fully explained or predicted by changes in DNA repair rates or mutations in DNA repair genes. Further, we were able to quantitate oxaliplatin-DNA adducts in the blood and tumor tissue of a metastatic breast cancer patient. Together, these data support the use of diagnostic microdosing for predicting patient sensitivity to platinum. Future studies will be aimed at replicating this data in a clinical feasibility trial.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Structural and mechanistic insights into Mcm2-7 double-hexamer assembly and function

Eukaryotic cells license each DNA replication origin during G1 phase by assembling a prereplication complex that contains a Mcm2–7 (minichromosome maintenance proteins 2–7) double hexamer. During S phase, each Mcm2–7 hexamer forms the core of a replicative DNA helicase. However, the mechanisms of origin licensing and helicase activation are poorly understood. The helicase loaders ORC–Cdc6 function to recruit a single Cdt1–Mcm2–7 heptamer to replication origins prior to Cdt1 release and ORC–Cdc6–Mcm2–7 complex formation, but how the second Mcm2–7 hexamer is recruited to promote double-hexamer formation is not well understood. Here, structural evidence for intermediates consisting of an ORC–Cdc6–Mcm2–7 complex and an ORC–Cdc6–Mcm2–7–Mcm2–7 complex are reported, which together provide new insights into DNA licensing. Detailed structural analysis of the loaded Mcm2–7 double-hexamer complex demonstrates that the two hexamers are interlocked and misaligned along the DNA axis and lack ATP hydrolysis activity that is essential for DNA helicase activity. Moreover, we show that the head-to-head juxtaposition of the Mcm2–7 double hexamer generates a new protein interaction surface that creates a multisubunit-binding site for an S-phase protein kinase that is known to activate DNA replication. The data suggest how the double hexamer is assembled and how helicase activity is regulated during DNA licensing, with implications for cell cycle control of DNA replication and genome stability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The Effects of Assumed Source Depth and Shear-Wave Velocity on Moment Tensors Estimated for Small, Contained Chemical Explosions in Granite

ABSTRACT The Source Phenomenology Experiment (SPE-Arizona) included of a series of chemical explosions detonated within a copper mine in Arizona. This study focuses on ground motions from detonations in the copper mine, which are analyzed to assess the uniqueness of the resulting source representation when the source region propagation characteristics have a range of possible models. P-wave velocities are well constrained by refraction data with less constraint of the S-wave velocities. The effects of explosion source depth and VS are assessed with Green’s functions for a range of models in which VP is held constant. Propagation models with a Poisson’s value of 0.25 and a source depth 30–60 m most accurately replicate the data. The explosion was detonated at a centroid depth of 30 m, so trade-offs in depth are demonstrated. The compensated linear vector dipole and explosion components of the Green’s functions convolved with a Mueller–Murphy source function are compared. Both produce significant energy in the 2–12 Hz band, due to surface-wave contributions with no clear depth dependencies above 20 Hz. The range of propagation models is used with the observational data to invert for the frequency-domain moment tensor. Fits to the data from these inversions have cross-correlation values of 0.64, demonstrating effectiveness in replicating the observations with the assumed propagation path effects and resulting source function. Inversions produce horizontal dipoles (Mxx and Myy), roughly half the maximum amplitude of Mzz, consistent with a compensated linear vector dipole source, which is frequency dependent. Denny and Johnson, Mueller–Murphy, Walter and Ford, and the revised Mueller–Murphy source models, parameterized for granite, are compared to the moment tensors. Despite a nonisotropic moment tensor source, the revised Mueller–Murphy isotropic source model best replicates the long-period moments, overshoot, and corner frequency.

Geochemistry & Geophysics↗
DOE OSTI2021

Integrating Cambium Marginal Costs into Electric Sector Decisions: Opportunities to Integrate Cambium Marginal Cost Data into Berkeley Lab Analysis and Technical Assistance

NREL’s Cambium tool generates forward-looking simulations of marginal wholesale electricity costs associated with NREL’s Standard Scenarios. The scenarios include growing shares of variable renewable energy (VRE, i.e. wind and solar), among other power sector assumptions, between 2018 and 2050. The tool’s primary output—hourly costs at more than 130 balancing areas—could serve as public and transparent data source that supports electric-sector decision-making processes across the U.S. Berkeley Lab conducts a large range of analyses that use historical and forward-looking wholesale electricity prices to inform electric-sector decisions. In this report, Berkeley Lab uses its expertise to evaluate the Cambium cost data. We compare Cambium data with historical wholesale prices for the year 2018 and other modeled prices for the year 2030. We then present eight case studies in which Berkeley Lab researchers use Cambium data to replicate previous analyses based on other price datasets. We describe where primary findings and underlying key price dynamics align or differ, and highlight possible novel insights from the Cambium data. Finally, we qualitatively evaluate the suitability of Cambium costs in ten additional Berkeley Lab studies, though a direct comparison with alternative price data was not feasible at this time. The goal is to inform how electric-sector decision-makers and DOE program offices may be able to use this cohesive dataset, and to highlight what improvements to Cambium may make it even more useful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Improved assemblies using a source-agnostic pipeline for MetaGenomic Assembly by Merging (MeGAMerge) of contigs

Assembly of metagenomic samples is a very complex process, with algorithms designed to address sequencing platform-specific issues, (read length, data volume, and/or community complexity), while also faced with genomes that differ greatly in nucleotide compositional biases and in abundance. To address these issues, we have developed a post-assembly process: MetaGenomic Assembly by Merging (MeGAMerge). We compare this process to the performance of several assemblers, using both real, and in-silico generated samples of different community composition and complexity. MeGAMerge consistently outperforms individual assembly methods, producing larger contigs with an increased number of predicted genes, without replication of data. MeGAMerge contigs are supported by read mapping and contig alignment data, when using synthetically-derived and real metagenomic data, as well as by gene prediction analyses and similarity searches. Ultimately, MeGAMerge is a flexible method that generates improved metagenome assemblies, with the ability to accommodate upcoming sequencing platforms, as well as present and future assembly algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Wildfire Smoke PM2.5 per Zipcode

This dataset contains daily aggregated measurements of daily PM2.5 from ambient wildfire smoke in the contiguous United States, spanning from 2006 to 2016. The data is sourced from a study by Childs et al. (2022), titled "Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US" published in Environmental Science & Technology. To compute the standardized weight across different zip codes, we computed the weight calculation on a 10km grids. To compute the weight, we obtain area-weights that add up to 1 in each polygon of zipcodes. This enabled us to calculate the smoke values per zipcode for the aforementioned period. Those interested in replicating our data processing pipeline can access it at https://github.com/NSAPH-Data-Processing/smoke_aggregation.

PM2.5↗
DOE OSTI2021

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗
DOE OSTI2016

Robust phenotyping strategies for evaluation of stem non-structural carbohydrates (NSC) in rice

Rice plants ( Oryza sativa ) accumulate excess photoassimilates in the form of non-structural carbohydrates (NSCs) in their stems prior to heading that can later be mobilized to supplement photosynthate production during grain-filling. Despite longstanding interest in stem NSC for rice improvement, the dynamics of NSC accumulation, remobilization, and re-accumulation that have genetic potential for optimization have not been systematically investigated. Here we conducted three pilot experiments to lay the groundwork for large-scale diversity studies on rice stem NSC. We assessed the relationship of stem NSC components with 21 agronomic traits in large-scale, tropical yield trials using 33 breeder-nominated lines, established an appropriate experimental design for future genetic studies using a Bayesian framework to sample sub-datasets from highly replicated greenhouse data using 36 genetically diverse genotypes, and used 434 phenotypically divergent rice stem samples to develop two partial least-squares (PLS) models using near-infrared (NIR) spectra for accurate, rapid prediction of rice stem starch, sucrose, and total non-structural carbohydrates. Lastly, we find evidence that stem reserves are most critical for short-duration varieties and suggest that pre-heading stem NSC is worthy of further experimentation for breeding early maturing rice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Proteome profile of the endomembrane of developing coleoptiles from switchgrass ( Panicum virgatum )

The cost-effective production of biofuels from lignocellulosic material will likely require manipulation of plant biomass, specifically cell walls. The North American native prairie grass Panicum virgatum (switchgrass) is seen as a potential biofuel crop with an array of genetic resources currently being developed. We have characterized the endomembrane proteome of switchgrass coleoptiles to provide additional information to the switchgrass community. In total, we identified 1750 unique proteins from two biological replicates. These data have been deposited in the ProteomeXchange with the identifier PXD001351 (http://proteomecentral.proteomexchange.org/dataset/PXD001351).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Kobuvirus VP3 protein restricts the IFN-β-triggered signaling pathway by inhibiting STAT2-IRF9 and STAT2-STAT2 complex formation

Emerged porcine kobuvirus (PKV) has adversely affected the global swine industry since 2008, but the etiological biology of PKV is unclear. Screening PKV-encoded structural and non-structural proteins with a type I IFN-responsive luciferase reporter showed that PKV VP3 protein inhibited the IFN-β-triggered signaling pathway, resulting in the decrease of VSV-GFP replication. QPCR data showed that IFN-β downstream cytokine genes were suppressed without cell-type specificity as well. The results from biochemical experiments indicated that PKV VP3 associated with STAT2 and IRF9, and interfered with the formation of the STAT2-IRF9 and STAT2-STAT2 complex, impairing nuclear translocation of STAT2 and IRF9. Taken together, these data reveal a new mechanism for immune evasion of PKV. - Highlights: •PKV VP3 inhibits the IFN-β-triggered signaling pathway. •VP3 associates with STAT2 and IRF9. •VP3 blocks the STAT2-IRF9 nuclear translocation. •VP3 utilizes a novel strategy for innate immune evasion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Uncertainty quantification for high explosive reactant and product equations of state

Equations of state (EOSs) are typically represented as physics-informed models with tunable parameters that are adjusted to replicate calibration data as closely as possible. Uncertainty quantification (UQ) allows for the development of an ensemble of EOS parameters that are consistent with the calibration data instead of a single EOS. In this work, we perform UQ for the reactant and product EOSs for a variety of high explosives (HEs). In doing so, we demonstrate a strategy for dealing with heterogeneous (both experimental and calculated) data. We also use a statistical distance metric to quantify the differences between the various HEs using the UQ results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗