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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

Abstract Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)—an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Characterizing 4-string contact interaction using machine learning

Abstract The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry ofn-string contact interaction is feasible.

Physics↗
DOE OSTI2021

Carbon–Nutrient Economy of the Rhizosphere: Improving Biogeochemical Prediction and Scaling Feedbacks from Ecosystem to Regional Scales

Our project has advanced the science of plant-soil-microbial dynamics across these areas: i) nutrient cycling and plant uptake; ii) root exudation and priming; and, iii) mycorrhizal dynamics. Our project has accomplished 5 main developments: 1) Incorporation of phosphorus cycling into the Fixation & Uptake of Nutrients (FUN 3.0) model. 2) Coupling of FUN 3.0 into the E3SM Land Model (ELM). 3) Data collection across a large mycorrhizal gradient in the US, as well data in the tropics, to parameterize, test, and validate the model. 4)Scaling up mycorrhizal association measurements across landscapes using airborne hyperspectral remote sensing data. 5) Evaluation of global carbon and nutrient cycle impacts in the Community Land Model (CLM5.0) from a suite of new global mycorrhizal association maps. Over 25 publications resulted from this project, with more continuing past the project funded lifetime. Paper highlights from most of these publications have already been submitted to the DOE paper submission online system. These publications include journals such as Science and PNAS, as well as top disciplinary journals from the Nature journals, Global Change Biology, New Phytologist, and Ecology Letters, for example. Our project also contributed to improving the process representation, capabilities, and accuracy of the DOE ELM. Overall, this project significantly advanced the science of belowground plant-soil-microbial interactions as well as technical capabilities from remote sensing to modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Towards durable resistance to Septoria stem canker and leaf spot: A molecular understanding of resistance (Final Technical Report)

To identify, validate, and functionally characterize alleles that confer resistance to Septoria canker and leaf spot in Populus. The proposed research will elucidate a major mechanism of resistance to Sphaerulina musiva, the major limiting factor to plantations in eastern North America. Genome-wide association mapping, CRISPR/Cas9, and protein-protein assays will be used, enabling marker-aided breeding, reducing costs, and accelerating development of resistant varieties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Code Demonstration: Approximation of nearly-periodic symplectic maps via structure-preserving neural networks

In this notebook, we use the symplectic gyroceptron architecture from [Duruisseaux et al., 2022] to learn a surrogate map for the nearly-periodic symplectic flow map associated to a nearly-periodic Hamiltonian system composed of two nonlinearly coupled oscillators, where one of them oscillates significantly faster than the other: $\Big\lbrace$ $ {\dot{q}_1 = p_1 \hspace{5mm} \dot{p}_1 = -q_1 - \epsilon \partial_{q1}U(q_1 , q_2 )} \atop {\dot{q}_2 = \epsilon p_2 \hspace{4mm} \dot{p}_2 = -\epsilon q_2 - \epsilon \partial_{q2}U(q_1 , q_2 )}$. These equations of motion are the Hamilton's equations associated to the Hamiltonian $H_{\epsilon}(q_1,q_2,p_1,p_2)=\frac{1}{2}(q^2_1+p^2_1)+\frac{1}{2}\epsilon(q^2_2+p^2_2)+\epsilon$$U(q_1,q_2)$.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physical Correlations and Predictions Emerging from Modern Core-collapse Supernova Theory

Abstract In this paper, we derive correlations between core-collapse supernova observables and progenitor core structures that emerge from our suite of 20 state-of-the-art 3D core-collapse supernova simulations carried to late times. This is the largest such collection of 3D supernova models ever generated and allows one to witness and derive testable patterns that might otherwise be obscured when studying one or a few models in isolation. From this panoramic perspective, we have discovered correlations between explosion energy, neutron star gravitational birth masses, 56 Ni and α -rich freezeout yields, and pulsar kicks and theoretically important correlations with the compactness parameter of progenitor structure. We find a correlation between explosion energy and progenitor mantle binding energy, suggesting that such explosions are self-regulating. We also find a testable correlation between explosion energy and measures of explosion asymmetry, such as the ejecta energy and mass dipoles. While the correlations between two observables are roughly independent of the progenitor zero-age main-sequence (ZAMS) mass, the many correlations we derive with compactness cannot unambiguously be tied to a particular progenitor ZAMS mass. This relationship depends on the compactness/ZAMS mass mapping associated with the massive star progenitor models employed. Therefore, our derived correlations between compactness and observables may be more robust than with ZAMS mass but can nevertheless be used in the future once massive star modeling has converged.

Astronomy & Astrophysics↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. 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. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Genome-wide association analysis of the strength of the MAMP-elicited defense response and resistance to target leaf spot in sorghum

Abstract Plants have the capacity to respond to conserved molecular features known as microbe-associated molecular patterns (MAMPs). The goal of this work was to assess variation in the MAMP response in sorghum, to map loci associated with this variation, and to investigate possible connections with variation in quantitative disease resistance. Using an assay that measures the production of reactive oxygen species, we assessed variation in the MAMP response in a sorghum association mapping population known as the sorghum conversion population (SCP). We identified consistent variation for the response to chitin and flg22—an epitope of flagellin. We identified two SNP loci associated with variation in the flg22 response and one with the chitin response. We also assessed resistance to Target Leaf Spot (TLS) disease caused by the necrotrophic fungus Bipolaris cookei in the SCP. We identified one strong association on chromosome 5 near a previously characterized disease resistance gene. A moderately significant correlation was observed between stronger flg22 response and lower TLS resistance. Possible reasons for this are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Accelerating shared file checkpoint with local burst buffers

A data management system and method for accelerating shared file checkpointing. Written application data is aggregated in an application data file created in a local burst buffer memory at a compute node, and an associated data mapping built index to maintain information related to the offsets into a shared file at which segments of the application data is to be stored in a parallel file system, and where in the buffer those segments are located. The node asynchronously transfers a data file containing the application data and the associated data mapping index to a file server for shared file storage. The data management system and method further accelerates shared file checkpointing in which a shared file, together with a map file that specifies how the shared file is to be distributed, is asynchronously transferred to local burst buffer memories at the nodes to accelerate reading of the shared file.

Gooding, Thomas↗
DOE OSTI2024

Galaxy Clusters Discovered via the Thermal Sunyaev-Zel’dovich Effect in the 500-square-degree SPTpol Survey

We present a catalog of 689 galaxy cluster candidates detected at significance ξ > 4 via their thermal Sunyaev-Zel’dovich (SZ) effect signature in 95 and 150 GHz data from the 500-square-degree SPTpol survey. We use optical and infrared data from the Dark Energy Camera and the Wide-field Infrared Survey Explorer (WISE) and Spitzer satellites, to confirm 544 of these candidates as clusters with ~ 94% purity. The sample has an approximately redshift-independent mass threshold at redshift z > 0.25. The confirmed sample spans 1.5 × 10 14 < M 500 c < 9 × 10 14 M ⊙ /h 70 and 0.03 < z ≲ 1.6 in mass and redshift, respectively, with a median mass of 2.5×10 14 M ⊙ /h 70 and median redshift z = 0.7; 21% of the confirmed clusters are at z > 1. We use external radio data from the Sydney University Molonglo Sky Survey (SUMSS) to estimate contamination to the SZ signal from synchrotron sources. The contamination reduces the recovered ξ by a median value of 0.032, or ~ 0.8% of the ξ = 4 threshold value, and ~ 7% of candidates have a predicted contamination greater than Δξ = 1. With the exception of a small number of systems (< 1%), an analysis of clusters detected in single-frequency 95 and 150 GHz data shows no significant contamination of the SZ signal by emission from dusty or synchrotron sources. This cluster sample, representing the deepest SZ-selected cluster sample to date, will be a key component in upcoming astrophysical and cosmological analyses of clusters. In addition to the cluster catalog, we also release the millimeter-wave maps and associated data products used to produce this sample. These maps have depths of 5.3 (11.7) µK CMB -arcmin at 150 (95) GHz and an effective angular resolution of 1. '2 (1. '7). The SPTpol products are available at https:// pole.uchicago.edu/public/data/sptpol_500d_clusters/index.html, and the NASA LAMBDA website. An interactive sky server with the SPTpol maps and Dark Energy Survey data release 2 images is also available at NCSA https://skyviewer.ncsa.illinois.edu.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Complexes of tubulin oligomers and tau form a viscoelastic intervening network cross-bridging microtubules into bundles

Abstract The axon-initial-segment (AIS) of mature neurons contains microtubule (MT) fascicles (linear bundles) implicated as retrograde diffusion barriers in the retention of MT-associated protein (MAP) tau inside axons. Tau dysfunction and leakage outside of the axon is associated with neurodegeneration. We report on the structure of steady-state MT bundles in varying concentrations of Mg 2+ or Ca 2+ divalent cations in mixtures containing αβ-tubulin, full-length tau, and GTP at 37 °C in a physiological buffer. A concentration-time kinetic phase diagram generated by synchrotron SAXS reveals a wide-spacing MT bundle phase (B ws ), a transient intermediate MT bundle phase (B int ), and a tubulin ring phase. SAXS with TEM of plastic-embedded samples provides evidence of a viscoelastic intervening network (IN) of complexes of tubulin oligomers and tau stabilizing MT bundles. In this model, αβ-tubulin oligomers in the IN are crosslinked by tau’s MT binding repeats, which also link αβ-tubulin oligomers to αβ-tubulin within the MT lattice. The model challenges whether the cross-bridging of MTs is attributed entirely to MAPs. Tubulin-tau complexes in the IN or bound to isolated MTs are potential sites for enzymatic modification of tau, promoting nucleation and growth of tau fibrils in tauopathies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bayesian Approach to Estimation of Water Table Elevations Using Historical Rasters as Prior Information 2019 - 20430

In cases of complex but only partially known geology and a lack of spatial control in observation well locations, water table elevation estimation is very challenging. In some cases, auxiliary information, such as observations of the movement of tracers, operation of injection and extraction wells, and calibration of groundwater models against the historical elevation data, can be combined with expert judgement to estimate flow directions in areas of sparse data and to aid in the production of more reliable contour maps (and associated rasters) than could be produced by relying on sparse well elevation data alone. Given a historical sequence of these raster maps, the question arises how to automate, to the extent possible, the process of producing new raster maps to reflect data from previous times, the current data and the operation of expert judgement. One solution is to adopt a Bayesian point of view and to regard the historical well elevation data, auxiliary information and historical raster maps as prior information. The well elevations for water table wells, as well as those for injection/extraction wells and the data associated with other relevant variables, can be viewed as predictors for the raster surface. From this prior information, we can, conditional on the values of these predictors for a new time period, compute an expected value map and a standard deviation map for the new raster. These then can be taken to specify a prior predictive distribution for the pixels in the new raster map. Then we condition the pixels, corresponding to water level observation wells within the raster, on the observed values in those wells (which in general will differ from the regression estimate) for the new time period. Given the smoothness of the water table surface, we then smooth the surface of deviations from the mean surface, based on the variograms of the historical rasters, and add this smoothed surface to the regression mean surface. The error structure of the produced raster map is defined by the regression error structure and the error due to smoothing based on the estimated variograms. This methodology has been developed and is being further refined for groundwater monitoring and remediation at LANL. It is a very flexible method that can also be applied with a variety of other predictors applied to model the water level wells in the area of interest over the historical record. The smoothness of the spatial process and its possible evolution over time can then be estimated from the residuals from this regression. This can be augmented by expert hydrogeological opinion based on site topography and hydrogeology. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Host genotype controls ecological change in the leaf fungal microbiome

Leaf fungal microbiomes can be fundamental drivers of host plant success, as they contain pathogens that devastate crop plants and taxa that enhance nutrient uptake, discourage herbivory, and antagonize pathogens. We measured leaf fungal diversity with amplicon sequencing across an entire growing season in a diversity panel of switchgrass ( Panicum virgatum ). We also sampled a replicated subset of genotypes across 3 additional sites to compare the importance of time, space, ecology, and genetics. We found a strong successional pattern in the microbiome shaped both by host genetics and environmental factors. Further, we used genome-wide association (GWA) mapping and RNA sequencing to show that 3 cysteine-rich receptor-like kinases (crRLKs) were linked to a genetic locus associated with microbiome structure. We confirmed GWAS results in an independent set of genotypes for both the internal transcribed spacer (ITS) and large subunit (LSU) ribosomal DNA markers. Fungal pathogens were central to microbial covariance networks, and genotypes susceptible to pathogens differed in their expression of the 3 crRLKs, suggesting that host immune genes are a principal means of controlling the entire leaf microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tree Canopies Reflect Mycorrhizal Composition

Mycorrhizae alter global patterns of CO 2 fertilization, carbon storage, and elemental cycling, yet knowledge of their global distributions is currently limited by the availability of forest inventory data. Here, we show that maps of tree-mycorrhizal associations (hereafter “mycorrhizal maps”) can be improved by the novel technology of imaging spectroscopy because mycorrhizal signatures propagate up from plant roots to impact forest canopy chemistry. We analyzed measurements from 143 airborne imaging spectroscopy surveys over 112,975 individual trees collected across 13 years. Results show remarkable accuracy in capturing ground truth observations of mycorrhizal associations from canopy signals across disparate landscapes (R 2 = 0.92, p < 0.01). Additionally, upcoming imaging spectroscopy satellite missions can reveal new insights into landscape-scale variations in water, nitrogen, phosphorus, carotenoid/anthocyanin, and cellulose/lignin composition. Applied globally, this approach could improve the spatial precision of mycorrhizal distributions by a factor of roughly 104 and facilitate the incorporation of dynamic shifts in forest composition into Earth system models.

58 GEOSCIENCES↗
DOE OSTI2021

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

3D reconstruction↗