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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗
DOE OSTI2025

Spatial top-down proteomics for the functional characterization of human kidney

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗
DOE OSTI2022

METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Geochemistry & Geophysics↗
DOE OSTI2022

CSEM Fluid Monitoring Methodology Using Real Data Examples

Conference presentation at International Meeting for Applied Geoscience & Energy (IMAGE), Houston, Texas, August 28 – September 1, 2023. Using field data from hydrocarbon and CO 2 applications, we illustrate the importance of a workflow and adaption to the target on hand. Verifying the geophysical acquisition and processing steps with 3D modeling and checking them against a 3D anisotropic log-derived model maintains confidence in the workflow and minimizes the influence on the data. This allows us to predict data validity and to certify the data with respect to the borehole logs.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Data from Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts, and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a buffer modification workflow (BMW) in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3–acetate buffer and in solvent with the buffer modified with 15NH3–formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Overall, it identified 26% of ∼27 000 liver LC-MS features as putative metabolites, of which ∼2600 showed HMDB or KEGG database formula match. This workflow is well suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

Conversion↗
DOE OSTI2025

Data for "Discovery, Characterization, and Application of Chromosomal Integration Sites in the Hyperthermophilic Archaeon Sulfolobus islandicus"

Sulfolobus islandicus , an emerging archaeal model organism, offers unique advantages for metabolic engineering and synthetic biology applications owing to its ability to thrive in extreme environments. Although several genetic tools have been established for this organism, the lack of well-characterized chromosomal integration sites has limited its potential as a cellular factory. Here, we systematically identified and characterized 13 artificial CRISPR RNAs targeting eight integration sites in S. islandicus using the CRISPR-COPIES pipeline and a multi-omics-informed computational workflow. We leveraged the endogenous CRISPR-Cas system to integrate the reporter gene lacS and validated heterologous expression through a β-galactosidase assay, revealing significant positional effects. As a proof of concept, we utilized these sites to genetically manipulate lipid ether composition by overexpressing glycerol dibiphytanyl glycerol tetraether (GDGT) ring synthase B (GrsB). This study expands the genetic toolbox for S. islandicus and advances its potential as a robust platform for archaeal synthetic biology and industrial biotechnology.

AI/ML↗
DOE OSTI2026

Frontier Job-Centric Telemetry Dataset

Comprehensive analysis of high-performance computing (HPC) systems requires linking workload execution to system behavior. This kind of analysis is vital for diagnosing performance issues, managing capacity, detecting anomalous workloads, and understanding how applications interact with system hardware. This job-centric telemetry dataset unifies scheduler job records with node-level measurements, enabling direct association between workloads and their corresponding power, thermal, and performance characteristics. It contains sanitized, scheduler related metadata for 152,400 individual jobs that ran on the Frontier supercomputer and ended on selected days throughout 2024 and 2025, a subpopulation of ~6.8% of the total number of allocated jobs with non-zero run time on the system over that same period. Each is linked with files that contain telemetry time series records of the power utilization and temperature behavior of its allocated nodes and their processors during the run time of the job. Where available, a portion of the job files also contain network performance time series. Jobs are sampled from select days that reflect normal levels of user activity and possess job size distributions with large numbers of leadership class jobs (>20% of Frontier nodes). Jobs in this dataset attempt to best represent successful user workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Tailored approach to study Legionella infection using a lattice light sheet microscope (LLSM)

Legionella is a genus of ubiquitous environmental pathogens found in freshwater systems, moist soil, and composted materials. More than four decades of Legionella research has provided important insights into Legionella pathogenesis. Although standard commercial microscopes have led to significant advances in understanding Legionella pathogenesis, great potential exists in the deployment of more advanced imaging techniques to provide additional insights. The lattice light sheet microscope (LLSM) is a recently developed microscope for 4D live cell imaging with high resolution and minimum photo-damage. We built a LLSM with an improved version for the optical layout with two path-stretching mirror sets and a novel reconfigurable galvanometer scanner ( RGS ) module to improve the reproducibility and reliability of the alignment and maintenance of the LLSM. We commissioned this LLSM to study Legionella pneumophila infection with a tailored workflow designed over instrumentation, experiments, and data processing methods. Our results indicate that Legionella pneumophila infection is correlated with a series of morphological signatures such as smoothness, migration pattern and polarity both statistically and dynamically. Our work demonstrates the benefits of using LLSM for studying long-term questions in bacterial infection. Our free-for-use modifications and workflow designs on the use of LLSM system contributes to the adoption and promotion of the state-of-the-art LLSM technology for both academic and commercial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Bayesian Optical Heterodyne Tomography

We introduce a complete workflow for Bayesian quantum state tomography of generic continuous-variable states. We also summarize experimental results applying this workflow to the tomographic reconstruction of thermal and coherent states of light.

Chapman, Joseph↗
DOE OSTI2017

Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers

Here we present Singularity Hub, a framework to build and deploy Singularity containers for mobility of compute, and the singularity-python software with novel metrics for assessing reproducibility of such containers. Singularity containers make it possible for scientists and developers to package reproducible software, and Singularity Hub adds automation to this workflow by building, capturing metadata for, visualizing, and serving containers programmatically. Our novel metrics, based on custom filters of content hashes of container contents, allow for comparison of an entire container, including operating system, custom software, and metadata. First we will review Singularity Hub’s primary use cases and how the infrastructure has been designed to support modern, common workflows. Next, we conduct three analyses to demonstrate build consistency, reproducibility metric and performance and interpretability, and potential for discovery. This is the first effort to demonstrate a rigorous assessment of measurable similarity between containers and operating systems. We provide these capabilities within Singularity Hub, as well as the source software singularity-python that provides the underlying functionality. Singularity Hub is available at https://singularity-hub.org, and we are excited to provide it as an openly available platform for building, and deploying scientific containers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Modular automated bottom-up proteomic sample preparation for high-throughput applications

Manual proteomic sample preparation methods limit sample throughput and often lead to poor data quality when thousands of samples must be analyzed. Automated liquid handler systems are increasingly used to overcome these issues for many of the sample preparation steps. Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells. The full modular protocol consists of three optimized protocols to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers, and allow flexibility for future protocol development. By using this workflow 50 micrograms of protein from 96 samples can be prepared for tryptic digestion in under an hour. We validate these protocols by analyzing 47 Pseudomonas putida and Rhodosporidium toruloides samples and show that this modular workflow provides robust, reproducible proteomic samples for high-throughput applications. The expected results from these protocols are 94 peptide samples from Gram-negative bacterial and fungal cells prepared for bottom-up quantitative proteomic analysis without the need for desalting column cleanup and with protein relative quantity variance (CV%) below 15%.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Utah FORGE 3-2535: Joint Imaging of Fracture Growth and Estimation of Fracture Properties During EGS Development - 2024 Annual Workshop Presentation

This is a presentation on the Joint Electromagnetic/Seismic/InSAR Imaging of Spatial-Temporal Fracture Growth and Estimation of Physical Fracture Properties During EGS Resource Development by Lawrence Berkeley National Laboratory, presented by David Alumbaugh. This is a video presentation on developing a set of technologies and workflow to image induced fracture generation and growth for an Enhanced Geothermal System (EGS). Using a combination of passive seismic and active source borehole EM and InSAR technology it is anticipated imaging and fracture generation and growth monitoring will be actualized. The workflow will provide a template for imaging induced fracture systems for future EGS systems. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

TEAMER: Numerical Modeling and Optimization of the iProTech Pitching Inertial Pump (PIP) Wave Energy Converter (WEC)

This project focused on developing an automated workflow to evaluate and optimize the iProTech Pitching Inertial Pump (PIP) wave energy converter (WEC) using open-source Python packages and the MATLAB/Simulink tool, WEC-Sim. The process involved parameterizing key design variables, running time-domain simulations, and performing sensitivity analyses to determine their impact on power output. The workflow, designed for the PIP device, is generalized and can be extended to optimize other WECs that can be simulated in WEC-Sim. This work establishes a foundation for future time-domain-based WEC design optimizations. Included in this submission are all figures from the final report and the model inputs required to generate them. This includes Python scripts with inputs that produce the meshes, boundary element method (BEM) models, hydrodynamic coefficients, and the WEC-Sim models used for time-domain analyses. Although data for every single run is not included to save space, all of it can be reproduced using the provided models. Detailed instructions for setting up the environment and running the codes are also included.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Model Data for the Mesh Convergence Study Demonstrating Benefits of Mixed-polyhedral Mesh in Integrated Hydrology Simulations

This archived model data is related to a study introducing a unique method that employs a stream-aligned mixed-polyhedral mesh to effectively and accurately represent river valleys, stream corridors, and narrow engineered channels in integrated hydrology simulations. The study finds that utilizing stream-aligned mixed-polyhedral meshes in integrated hydrology simulations achieves accuracy on par with a finely refined TIN-based mesh while markedly diminishing computational costs. This archive contains scripts and data files needed to generate the ATS model input, including mesh and ATS input files, for all mesh scenarios using the Watershed Workflow package. Additionally, this archive also provides key outputs from the model simulations that are used in the analysis and post-processing scripts to reproduce figures in the manuscript. The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model are in XML format and can be edited in any commonly used text editors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model data for Flood Frequency Analysis using Stochastic Storm Transposition and an Integrated Surface-Subsurface Hydrological Model

This archived provides scripts and input files used for the implementation of a novel approach to conduct process-based Flood Frequency Analysis using a Stochastic Storm Transposition (SST) and an Integrated Surface-Subsurface Hydrological Model (ISSHM). As a proof-of-concept, this study uses the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct flood frequency analysis by simulating the flood response to 5,000 annual synthetic storm events in a ~2000 km2 Southeast Texas watershed.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include TXT, CSV, DAT, SBATCH, SHP, TIF, NetCDF, and HDF5 files, which can be read through Python scripts. The input files for the ATS model and RainyDay model have .XML and .SST extensions, respectively, and can be edited in any commonly used text editors.This archive contains:* Scripts and data files essential for generating the ATS model input. It uses the Watershed Workflow package to produce both mesh and ATS input files. * Jupyter notebooks designated for the ATS model evaluation, covering both long-term simulations and 40 rainfall-runoff events.* Input files required to simulate SST storm events using RainyDay.

54 ENVIRONMENTAL SCIENCES↗