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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Uniformly Ordered Binary Decision Algorithm for Benchmark Experiment Correlations in Whisper Validation

When performing a validation exercise for determining the upper subcritical limit of a nuclear criticality safety application, an analyst should select and perform a statistical analysis on a population of benchmark experiments that are neutronically similar to the application. The size of this population should be sufficiently large such that the statistical analysis has a high degree of confidence that the bias plus bias uncertainty (calculational margin) has been accurately quantified. A complication arises because many benchmark experiments share common components, leading to correlations in their measured effective multiplication factors. Correlations between benchmark experiments within the population reduces its predictive power. This motivates the need for methods that consider benchmark experiment correlations and ensure adequate statistical significance of results. The Whisper code is a statistical analysis pack- age that incorporates nuclear data sensitivity coefficients from MCNP to assess benchmark experiment similarity and then performs an extreme-value analysis to estimate the bias plus bias uncertainty. The original methodology in Whisper does not consider the effect of benchmark experiment correlations when making this estimation, and this summary proposes the uniformly ordered binary decision algorithm to address this shortcoming. The original methodology in Whisper computes similarity coefficients ck for an application compared to all benchmark experiments in its library and develops weighting factors for a selected population proportional to the ck values. The methodology can be interpreted as statistically emulating a validation exercise for a particular application where the weighting factors may be viewed as the likelihood that an analyst would include a particular benchmark experiment within the population. The effective sample size of the population is the expected or mean number of benchmark experiments in the population. The uniformly ordered binary decision algorithm identifies clusters of correlated benchmark experiments within the population and then computes adjusted weighting factors based on the magnitude of the correlation coefficients within the cluster to compute a reduced effective sample size accounting for the lower information content because of correlations. Benchmark experiments within the cluster are ordered randomly with equal probability and probabilistic decisions are made as to whether a benchmark. experiment within the cluster should treated as redundant with a previous one; if two redundant benchmark experiments are included, then the conservative worst case bias plus bias uncertainty is used and the pair is counted as a single benchmark experiment in the population. Results are provided for HEU solutions in a research version of the Whisper software using benchmark experiment correlations provided by DICE, the Database for the International Criticality Safety Benchmark Evaluation Project (ICSBEP). These show that there can be a significant increase in the bias plus bias uncertainty because the effective sample size is reduced, and therefore the algorithm, needing to meet sample size requirements, expands the benchmark experiment population by accepting less similar benchmark experiments that would have otherwise not been included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Agilent CRADA (Abstract)

The CRADA between Agilent Technologies Inc. and Battelle will focus on five software components as listed below: Prototype 4D Feature Finding functionality with a particular focus on recovering low level features and extending the bottom end dynamic range of IM-MS technology. Compare and contrast developments to current 4D Feature Finding capabilities. Highlight important algorithmic aspects employed. Implement the PNNL saturation correction algorithm. Agilent will give PNNL the needed data file access API and assistance in understanding it implementation and any needed instrumental aspects. Supported high resolution products to include Agilent’s TOF, QTOF and IM-QTOF mass spectrometers. PNNL will then work with Agilent to benchmark performance. Implementation of the PNNL Hadamard de-multiplexing algorithm. Agilent will give provide PNNL the needed date file access API access and as needed assistance in understanding the current Agilent multiplexed IM offering. PNNL will then work with Agilent on benchmark performance. Add ion mobility collision cross sections to existing and new metabolomic libraries for data analysis with Agilent’s informatics program MPP/ID Browser. PNNL will work with Agilent to create a software pipeline that takes data from chemical and metabolic standards and properly formats it for inclusion in MPP accessible libraries, using the collision cross section as a new separation dimension. Improvements of MPP multidimensional matching to identify metabolomic features using multiple characteristics beyond retention time and accurate mass. Most significantly matching will include analyte collision cross section with proposed support for sample fraction or RapidFire cartridge and fragmentation spectra. PNNL will work with Agilent to modify and improve the current MPP analysis pipeline to allow for creating, aligning, and identifying MS features defined by accurate mass, collision cross section and chromatographic retention time. As additional criteria such as fraction or RapidFire cartridge type are supported in the identification process, then they also will become part of the automation workflow. This includes the automation of said system to work with command line program (i.e. not a GUI) sufficient for programmatic execution in a pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Clustering of CODEX clusters

The clustering of galaxy clusters links the spatial nonuniformity of dark matter halos to the growth of the primordial spectrum of perturbations. The amplitude of the clustering signal is widely used to estimate the halo mass of astrophysical objects. The advent of cluster mass calibrations enables using clustering in cosmological studies. Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the ROentgen SATellite All-Sky Survey and then identified as galaxy clusters using the code redMaPPer run on the photometry of the Sloan Digital Sky Survey. We develop methods for precisely accounting for the sample selection effects on the clustering and demonstrate their robustness using numerical simulations. Methods. Using the clean CODEX sample, which was obtained by applying a redshift-dependent richness selection, we computed the two-point autocorrelation function of galaxy clusters in the 0.1 < z < 0.3 and 0.3 < z < 0.5 redshift bins. We compared the bias in the measured correlation function with values obtained in numerical simulations using a similar cluster mass range. Results. By fitting a power law, we measured a correlation length r 0 = 18.7 ± 1.1 and slope γ = 1.98 ± 0.14 for the correlation function in the full redshift range. By fixing the other cosmological parameters to their nine-year Wilkinson Microwave Anisotropy Probe values, we reproduced the observed shape of the correlation function under the following cosmological conditions: Ω m0 = $0.22_{-0.03}^{+0.04}$ and S 8 = σ 8 (Ω m0 /0.3)0.5 = $0.85_{-0.08}^{+0.10}$ with estimated additional systematic errors of σ Ω m0 = 0.02 and σS 8 = 0.20. We illustrate the complementarity of clustering constraints by combining them with CODEX cosmological constraints based on the X-ray luminosity function, deriving Ωm = 0.25 ± 0.01 and σ 8 = $0.81_{-0.02}^{+0.01}$ with an estimated additional systematic error of σ Ω m0 = 0.07 and σ σ8 = 0.04. The mass calibration and statistical quality of the mass tracers are the dominant source of uncertainty.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Gut microbiome associations with outcome following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) in pigs immunized with a PRRS modified live virus vaccine

Porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) are two of the most significant pathogens affecting swine. Co-infections are common and result in respiratory disease and reduced weight gain in growing pigs. Although PRRS modified live virus (MLV) vaccines are widely used to decrease PRRS-associated losses, they are generally considered inadequate for disease control. The gut microbiome provides an alternative strategy to enhance vaccine efficacy and improve PRRS control. The objective of this work was to identify gut microbiome characteristics associated with improved outcome in pigs immunized with a PRRS MLV and co-challenged with PRRSV and PCV2b. Twenty-eight days after vaccination and prior to co-challenge, fecal samples were collected from an experimental population of 50 nursery pigs. At 42 days post-challenge, 20 pigs were retrospectively identified as having high or low growth outcomes during the post-challenge period. Gut microbiomes of the two outcome groups were compared using the Lawrence Livermore Microbial Detection Array (LLMDA) and 16S rDNA sequencing. High growth outcomes were associated with several gut microbiome characteristics, such as increased bacterial diversity, increased Bacteroides pectinophilus, decreased Mycoplasmataceae species diversity, higher Firmicutes:Bacteroidetes ratios, increased relative abundance of the phylum Spirochaetes, reduced relative abundance of the family Lachnospiraceae, and increased Lachnospiraceae species C6A11 and P6B14. Overall, this study identifies gut microbiomes associated with improved outcomes in PRRS vaccinated pigs following a polymicrobial respiratory challenge and provides evidence towards the gut microbiome playing a role in PRRS vaccine efficacy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identifying the secondary electron cutoff in ultraviolet photoemission spectra for work function measurements of non-ideal surfaces

Abstract Absolute values of work functions can be determined in ultraviolet photoemission spectroscopy (UPS) by measuring the minimum kinetic energy of secondary electrons generated by a known photon energy. However, some samples can produce spectra that are difficult to interpret due to additional intensity below the true secondary electron cutoff. Disordered absorbates on elemental metals add small intensity below the onset for the transition metal surfaces studied, which can be attributed to energy losses after photoelectrons are generated. In contrast, spectra from WO 3−x films can produce multiple onsets with comparable intensity which do not fit this model. False onsets (in the context of work function measurements) can be minimized by optimizing experimental detection parameters including limiting analyzer acceptance angles and pass energy. True work functions can be identified by examining the onsets as the sample bias is varied.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Sandwich Hybridization Assay for In Situ Real-Time Cyanobacterial Detection and Monitoring: A Review

As cyanobacterial harmful algal bloom (cHAB) events increase in scale, severity, frequency, and duration around the world, rapid and accurate monitoring and characterization tools have become critically essential for regulatory and management decision-making. The composition of cHAB-forming cyanobacteria community can change significantly over time and space and be altered by sample preservation and transportation, making in situ monitoring necessary to obtain real-time and localized information. Sandwich hybridization assay (SHA) utilizes capture oligonucleotide probes for sensitive detection of target-specific nucleic acid sequences. As an amplification-free molecular biology technology, SHA can be adapted for in-situ, real-time or near real-time detection and qualitatively or semi-quantitatively monitoring of cHAB-forming cyanobacteria, owing to its characteristics such as being rapid, portable, inexpensive, and amenable to automation, high sensitivity, specificity and robustness, and multiplexing (i.e., detecting multiple targets simultaneously). Despite its successful application in the monitoring of marine and freshwater phytoplankton, there is still room for improvement. The ability to identify a cHAB community rapidly would decrease delays in cyanotoxin analyses, reduce costs, and increase sample throughput, allowing for timely actions to improve environmental and human health and the understanding of short- and long-term bloom dynamics. Real-time detection and quantitation of HAB-forming cyanobacteria is essential for improving environmental and public health and reducing associated costs. We review and propose to apply SHA for in situ cHABs monitoring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Searching for Active Low-mass Stars in the CMa Star-forming Region: Multi-band Photometry with T80S

An exotic environment surrounds the young stellar groups associated with the Canis Major (CMa) OB1/R1 region, which probably was formed under feedback from at least three supernova events having occurred a few million years ago. We use astrometric data from the Gaia-DR2 to confirm the membership of the stars in CMa R1, based on proper motion and parallax, which revealed 514 new members and candidates. The mean age of 5 Myr estimated from the color–magnitude diagram characterizes the sources as likely pre-main-sequence candidates. In total, a sample of 694 stars detected with the T80-South telescope was analyzed according to different color–color diagrams, which were compared with theoretical colors from evolutionary models, aiming to reveal the objects that exhibit color excess due to accretion processes. Accretion and magnetic activity were also explored on the basis of empirical flux–flux relation, such as F {sub 660} and F {sub 861} that are related to Hα and Ca ii triplet emission, respectively. A low fraction (∼3 percent) of the sample have Hα excess and other colors expected for stars exhibiting chromospheric activity. The number of Class I and Class II objects, identified by the infrared (Wide-field Infrared Survey Explorer) colors, indicates a disk fraction of ∼6 percent, which is lower than that expected for stellar clusters with a similar age. Such a large sample of objects associated with CMa R1 without evidences of circumstellar accretion can be interpreted as a lack of disk-bearing stars, unusual for young star-forming regions. However, this may be explained as the result of supernova events.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2022

The Epidermal Microbiome Within an Aggregation of Leopard Sharks ( Triakis semifasciata ) Has Taxonomic Flexibility with Gene Functional Stability Across Three Time-points

The epidermis of Chondrichthyan fishes consists of dermal denticles with production of minimal but protein-rich mucus that collectively, influence the attachment and biofilm development of microbes, facilitating a unique epidermal microbiome. Here, we use metagenomics to provide the taxonomic and functional characterization of the epidermal microbiome of the Triakis semifasciata (leopard shark) at three time-points collected across 4 years to identify links between microbial groups and host metabolism. Our aims include (1) describing the variation of microbiome taxa over time and identifying recurrent microbiome members (present across all time-points); (2) investigating the relationship between the recurrent and flexible taxa (those which are not found consistently across time-points); (3) describing the functional compositions of the microbiome which may suggest links with the host metabolism; and (4) identifying whether metabolic processes are shared across microbial genera or are unique to specific taxa. Microbial members of the microbiome showed high similarity between all individuals (Bray–Curtis similarity index = 82.7, where 0 = no overlap, 100 = total overlap) with the relative abundance of those members varying across sampling time-points, suggesting flexibility of taxa in the microbiome. One hundred and eighty-eight genera were identified as recurrent, including Pseudomonas, Erythrobacter, Alcanivorax, Marinobacter, and Sphingopxis being consistently abundant across time-points, while Limnobacter and Xyella exhibited switching patterns with high relative abundance in 2013, Sphingobium and Sphingomona in 2015, and Altermonas, Leeuwenhoekiella, Gramella, and Maribacter in 2017. Of the 188 genera identified as recurrent, the top 19 relatively abundant genera formed three recurrent groups. The microbiome also displayed high functional similarity between individuals (Bray–Curtis similarity index = 97.6) with gene function composition remaining consistent across all time-points. These results show that while the presence of microbial genera exhibits consistency across time-points, their abundances do fluctuate. Microbial functions however remain stable across time-points; thus, we suggest the leopard shark microbiomes exhibit functional redundancy. We show coexistence of microbes hosted in elasmobranch microbiomes that encode genes involved in utilizing nitrogen, but not fixing nitrogen, degrading urea, and resistant to heavy metal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Petrographic and spectral study of hydrothermal mineralization in drill core from Hawaii: A potential analog to alteration in the martian subsurface

Abstract Continuous rock core was collected for 1764 m (5786’) on the Pohakuloa Army Training base near the center of the big island of Hawaii. The core traverses basaltic lava flows from the volcano's shield-building phase, and perched aquifers and higher temperature groundwaters were encountered. The collected samples record water-rock interactions of basaltic materials in a setting that may be a model for groundwater interactions on Mars. We collected visible and infrared point spectra of materials in the lowest portion of the core, where alteration was noted to become more prominent. We identified three types of phyllosilicate spectral signatures and three types of zeolites. The phyllosilicates show similarity to those identified on Mars using data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). Based on the field survey, 25 depths were selected for sampling and petrographic analysis of thin sections. The spectral data and thin section work have a strong agreement in the types of materials identified by the two different techniques. Both the spectral and petrographic data indicate low to moderate temperature geothermal alteration occurred in the lower half of the core. The field spectra are a useful reconnaissance tool for selecting mineralogic diversity for subsequent higher resolution and more time-consuming laboratory analysis.

Geochemistry & Geophysics↗
DOE OSTI2023

Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.

Aird, Jeanie A.↗
DOE OSTI2024

Applications of LIF to Document Natural Variability of Chlorophyll Content and Cu Uptake in Moss

Chlorophyll has long been used as a natural indicator of plant health and photosynthetic efficiency. Laser-induced fluorescence (LIF) is an emerging technique for understanding broad spectrum organic processes and has more recently been used to monitor chlorophyll response in plants. Previous work has focused on developing a LIF technique for imaging moss mats to identify metal contamination with the current focus shifting toward application to moss fronds and aiding sample collection for chemical analysis. Two laser systems (CoCoBi a Nd:YGa pulsed laser system and Chl-SL with two blue continuous semiconductor diodes) were used to collect images of moss fronds exposed to increasing levels of Cu (1, 10, and 100 nmol/cm 2 ) using a CMOS camera. The best methods for the preprocessing of images were conducted before the analysis of fluorescence signatures were compared to a control. The Chl-SL system performed better than the CoCoBi, with dynamic time warping (DTW) proving the most effective for image analysis. Manual thresholding to remove lower decimal code values improved the data distributions and proved whether using one or two fronds in an image was more advantageous. A higher DTW difference from the control correlated to lower chlorophyll a/b ratios and a higher metal content, indicating that LIF, with the aid of image processing, can be an effective technique for identifying Cu contamination shortly after an event.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics

Data package for Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon This data is published under a CC0 license. The authors encourage data reuse and request attribution by referencing the below citations for the data packages and associated manuscript. Please cite as: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics. [Data Set] PNNL DataHub. doi: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. MSV000097435: GLBRC soil yearlong incubation 13C-SIP-Lipidomics [Data Set] MassIVE. doi:10.25345/C57659T3K Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon. In Prep This data package consists of compound-specific 13C SIP-lipidomics data from a yearlong tracer incubation experiment designed to investigate microbial lipid persistence in switchgrass bioenergy crop soils. In order to explore how lipid structure may modulate the persistence of C in soil lipids, we leveraged soils from two sites (Michigan - sandy texture, Wisconsin - silty texture) operated by the U.S. Department of Energy-funded Great Lakes Bioenergy Research Center (GLBRC). These sites had comparable climates, identical management practices, but contrasting soil textures, allowing us to assess the variability of lipid accrual or degradation in soils as well as provide insight regarding the degree to which edaphic properties may regulate the retention of soil lipids. Untargeted lipidomics analyses were performed to identify 13C-labeled lipids in the soil microbiome after long-term incubation. Soils were supplemented with 100 micrograms glucose per gram dry soil (99 atom % 13C or natural abundance for paired control) and incubated; samples were collected two months and one year after glucose addition. Lipid extracts (MPLEx) were analyzed by LC-MS/MS and identified using LIQUID. Calculation of isotopic enrichment of lipids was performed by targeted approach using TarMet to quantify lipid isotopologues and IsoCorrectoR to correct for natural abundance isotopes. Contents: Data package contents reported here are the first version and contain downstream analysis files for the raw LC-MS mass spectrometry files (.mzXML) deposited at the MassIVE database repository under accession MSV000097435 (80 experimental runs; 5.85 GB) | MassIVE DOI: 10.25345/C57659T3K. Support files include the additional data download 'Read Me' file containing data descriptor information. Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. Data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location. Available Data Downloads (0.3 GB): "GLBRC soil yearlong incubation 13C-SIP-Lipidomics_readme.txt" - 'Read Me' data package content file (txt) "GLBRC_DataPackage_analysis files" - Data processing files (Rmd) and saved intermediate data processing outputs (rds, csv, xlsx) "GLBRC_13C_lipidomics_dataset.xlsx" - processed data in tabular format (xlsx) Linked Software: LIQUID LC-MS Analysis Software | 10.5281/zenodo.6459462 Lipid Mini-On Software Tools | 10.5281/zenodo.1492803 pmartR Omics Statistical Software | 10.5281/zenodo.6108667 xcms (v4.3.3) TarMet (v1.1.1) IsoCorrectoR (1.24.0) Funding Acknowledgments: This research was supported by an Early Career Research Program award funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (OBER) Genomic Science program under FWP 68292, FWP 07880 and EMSL Exploratory Research Project 51095. A portion of this work was performed in the William R. Wiley Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by OBER and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated by Battelle for the DOE under Contract DE-AC05-76RLO1830.

Rempfert, Kaitlin R [Pacific Northwest National La↗
DOE OSTI2026

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.

Materials science↗
DOE OSTI2023

Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model’s historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However, for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ETF Grout Expansion: Evaluation of MgO Additions and Struvite Precipitation Protocol for Potential Expansion

The ETF ammonia tolerant grout waste form was developed by the Vitreous State Laboratory (VSL) to stabilize ammonium and thereby prevent emission of ammonia vapor during solidification of an ammonium-rich, concentrated sulfate aqueous waste stream generated at the Hanford Effluent Treatment Facility (ETF). VSL did not observe expansion in samples prepared during the waste form development work. However, Savannah River National Laboratory (SRNL) personnel did encounter expansion, in some cases up to 20+ volume percent expansion in ETF grout samples while preforming work scope to evaluate compositional ranges for ETF brine and of the precipitation and solidification reagent. The work requests can be found in Washington River Protection Services (WRPS) Statement of Work (SOW), Requisitions #: 339922 Revisions 0 and 1, March 11, 2021, and November 2021. Additional evaluation was requested by WRPS SOW Requisitions #: 356730, Revision 1, February 1, 2022, to determine whether differences in the slags or differences in sample preparation protocols used by VSL and SRNL were the cause of the expansion observed in the SRNL samples. G. Chen, WRPS, arranged a materials exchange between VSL and SRNL and coordinated the VSL protocol for grouting and curing 1L of Base Case ETF simulant. Materials were exchanged between VSL and SRNL and both laboratories performed the VSL struvite precipitation-solidification protocol and cured samples for at least 28 days. VSL and SRNL encountered expansion, at varying degrees, in all samples prepared under the material exchange and grouting protocol. Additional material exchange testing between VSL and SRNL and alternative materials evaluation to further investigate the causes of grout expansion and differentiate the effects of various dry materials, grouting procedures, and curing containers was requested by WRPS SOW Requisitions #: 356730, Revision 2, January 10, 2023. Pacific Northwest National Laboratory (PNNL) assembled and distributed materials to both laboratories for testing. Observations from this study indicated variations in MgO additions, water-to-dry mix (dm) ratios and curing containers did not correlate to ETF ammonia grout expansion. In comparison to previous work, SRNL observed the addition rate of NaOH to be a potential factor for the final pH in the struvite precipitation step. In the current testing NaOH was added in bulk (5-10 mL at one time) during the precipitation step. The resulting pH was 7.57-9.15 which is slightly higher than pH observed at a slower addition rate. Additional testing is needed to identify the presence and/or struvite stability in high-pH struvite precipitant ETF ammonia grout samples. Therefore, based on current data, it is unknown if NaOH addition rate is the fundamental cause of grout expansion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Fungal elemental profiling unleashed through rapid laser-induced breakdown spectroscopy (LIBS)

Elemental profiling of fungal species as a phenotyping tool is an understudied topic and is typically performed to examine plant tissue or non-biological materials. Traditional analytical techniques such as inductively coupled plasma–optical emission spectroscopy (ICP-OES) and inductively coupled plasma–mass spectrometry (ICP-MS) have been used to identify elemental profiles of fungi; however, these techniques can be cumbersome due to the difficulty of preparing samples. Additionally, the instruments used for these techniques can be expensive to procure and operate. Laser-induced breakdown spectroscopy (LIBS) is an alternative elemental analytical technique—one that is sensitive across the periodic table, easy to use on various sample types, and is cost-effective in both procurement and operation. LIBS has not been used on axenic filamentous fungal isolates grown in substrate media. In this work, as a proof of concept, we used LIBS on two genetically distinct fungal species grown on a nutrient-rich and nutrient-poor substrate media to determine whether robust elemental profiles can be detected and whether differences between the fungal isolates can be identified. This data set contains the raw LIBS spectral data for the summarized results described inRush, et. al. 2024

elemental profiling↗