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

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparative Assessment of Data-driven Process Models in Health Information Technology

Process mining for conformance analysis consists of comparing a reference process model against a data-driven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman’s correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Identification and interpretation of tectonic features from ERTS-A imagery

The author has identified the following significant results. A set of criteria characterizing known mineralized areas are: (1) occurrence at structural bends, discontinuities, complex deformations along fault zones and intersections; (2) complex terrain textures produced by fracture intersections; (3) color and tone anomalies produced by igneous intrusives, alteration effects, and oxidation. Significant fracture systems which appear to be most commonly related to mineralization in central and northeastern Nevada trend northeast-southwest, north-south, and north-northwest. In the area from Goldfield and Beatty, Nevada, to south of Las Vegas, ERTS-1 and Skylab imagery have been examined together to study an apparent correlation of mineralized areas with suspected hydrothermal alteration effects. It was observed that areas of mineralization are of complex structure, usually cut by many fractures, and sometimes have a similar mottled or variegated appearance. Although the Beatty-Rhyolite mining district is now largely inactive, several analogous areas not known to be mineralized have been identified. Identification of specific mineral prospects within general targets requires extensive field work and detailed geophysical exploration.

Abdel-Gawad, M.↗
NASA NTRS2018

High‐Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands

TROPOspheric Monitoring Instrument (TROPOMI), on-board the Sentinel-5 Precursersatellite, is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible,near-infrared, and shortwave infrared. From these spectra several important air quality and climate-relatedatmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatialresolution from a satellite platform. We present the first retrievals of TROPOMI NO2over the Canadian OilSands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument,and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for derivingemissions from individual mining facilities. Further, the first TROPOMI NO2validation is presented,consisting of aircraft and surface in situ NO2observations, and ground-based remote-sensingmeasurements between March and May 2018. Our comparisons show that the TROPOMI NO2verticalcolumn densities are highly correlated with the aircraft and surface in situ NO2observations, and theground-based remote-sensing measurements with a low bias (15–30 %); this bias can be reduced byimproved air mass factors. Plain Language Summary: Nitrogen dioxide (NO2) is a pollutant that is linked to respiratoryhealth issues and has negative environmental impacts such as soil and water acidification. Near thesurface the most significant sources of NO2are fossil fuel combustion and biomass burning. With a recentlylaunched satellite instrument (TROPOspheric Monitoring Instrument [TROPOMI]), NO2can be measuredwith an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents thefirst TROPOMI NO2measurements near the Canadian Oil Sands and shows that these measurements havean outstanding ability to detect NO2on a very high horizontal resolution that is unprecedented for satelliteNO2observations. Further, these satellite measurements are in excellent agreement with aircraft andground-based measurements.

TROPOspheric Monitoring Instrument (TROPOMI)↗
DOE OSTI2016

Effective Long-term Monitoring Strategies by Integrating Reactive Transport Models with In situ Geochemical Measurements - 16212

Long-term monitoring of contaminant transport in groundwater is expected to account for a large fraction of future life-cycle cleanup costs at the DOE sites. The Attenuation-Based Remedies in the Subsurface Applied Field Research Initiative (ABRS AFRI) is developing an innovative approach for cost-effective in situ long-term monitoring. The approach is based on strategically adding the measurements of controlling variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control the plume mobility and its spatial and temporal distribution. In situ measurements of these variables - supplemented with a reduced number of standard periodic groundwater samples - are expected to lead to more cost-effective monitoring, and can also serve as an early warning system for detecting unexpected plume migration. The objective of this study is to support the development of such a long-term monitoring approach through the application of advanced computational methods, including (1) statistical data mining and analysis, and (2) three-dimensional flow and reactive chemical transport modeling. We have performed the statistical data analysis of historical and current monitoring data at the Savannah River Site (SRS) F-Area to quantify the correlations between the controlling variables and radioactive contaminant concentrations. In parallel, we have used 3D modeling of flow and contaminant transport to provide the prediction of the contaminant plume evolution. The results of both data analysis and modeling confirmed that the correlations between controlling variables and contaminant concentrations are significant. Modeling results also suggest that the correlation parameters will change in the future, which is important to assess the long-term efficacy of the proposed monitoring approach. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Update on NREL Outdoor Exposure Campaign of Solar Mirrors

Beginning in 2018, the National Renewable Energy Laboratory restarted exposure campaigns on new and archived samples as part of a multiyear project, some with outdoor exposure for more than a decade. By resuming exposure and collecting and analyzing data on thousands of samples going back decades, several goals can be advanced that can be difficult to determine within the timeline of most projects: 1) correlating an accelerated exposure campaign to outdoor aging, specifically with xenon arc lamp exposure chambers; 2) drawing conclusions between specific corrosion mechanisms and weather patterns; and 3) finding novel relationships between mirror composition and performance. In addition to building and mining a database, we will experiment with new characterization techniques, primarily focused on macroscopic and microscopic imaging. In introducing these techniques more broadly, it may be possible to reveal a more direct line between optical performance and exposure campaigns by better understanding the degradation mechanisms occurring.

characterization techniques↗
DOE OSTI2025

Generation of a fluorescent short-lifetime oxygen nanosensor by coupling of an exciplex forming dye pair

Typical oxygen-sensitive dyes have lifetimes in the μs-ms regime for sensitive oxygen detection based on molecular quenching. Due to the long triplet-state lifetimes and poor quenching efficiency of these probes, it has been difficult to develop short lifetime probes. We have developed oxygen sensors using a short-lifetime dye (∼30–60 ns) that forms an anthracene-aniline exciplex, which is quenched by molecular oxygen through an excited-state charge transfer mechanism. To achieve measurements in aqueous systems, we have tethered the dyes together via a C3 alkyl linker to facilitate greater exciplex formation at low dye concentration (20 μg mL −1 ) and encapsulated the resulting compound into polymeric nanoparticles to form an aqueous-dispersible short-lifetime oxygen nanosensor. The sensors emit at 420 nm (unfolded dye) and 500 nm (exciplex), and the ratiometric Stern-Volmer Constant is 1.52 ± 0.02 L mol −1 , showing good sensitivity from 0 % to 40 % dissolved oxygen without the tradeoff of a long lifetime needed for direct molecular quenching. In this paper, we characterize this sensor for both lifetime-based measurements using time-correlated single-photon counting and ratiometric intensity-based measurements using the unfolded dye emission as an internal reference peak.

Biological and medical sciences↗
NASA NTRS1979

Processing of multispectral thermal IR data for geologic applications

Multispectral thermal IR data were acquired with a 24-channel scanner flown in an aircraft over the E. Tintic Utah mining district. These digital image data required extensive computer processing in order to put the information into a format useful for a geologic photointerpreter. Simple enhancement procedures were not sufficient to reveal the total information content because the data were highly correlated in all channels. The data were shown to be dominated by temperature variations across the scene, while the much more subtle spectral variations between the different rock types were of interest. The image processing techniques employed to analyze these data are described.

Kahle, A. B.↗
DOE OSTI2021

Trace explosive residue detection of HMX and RDX in post-detonation dust from an open-air environment

Explosives are often used in industry, geology, mining, and other applications, but it is not always clear what remains after a detonation or the fate and transport of any residual material. The goal of this study was to determine to what extent intact molecules of high explosive (HE) compounds are detectable and quantifiable from post-detonation dust and particulates in a field experiment with varied topography. We focused on HMX (1,3,5,7-Tetranitro-1,3,5,7-tetrazocane), which is less studied in field detonation literature, as the primary explosive material and RDX (1,3,5-Trinitroperhydro-1,3,5-triazine) as the secondary material. The experiment was conducted at Site 300, Lawrence Livermore National Laboratory’s Experimental Test Site, in California, USA. Two 20.4 kg and one 40.8 kg above ground explosions (primarily comprised of LX-14, an HMX-based polymer-bonded high explosive) were detonated on an open-air firing area on separate days. The complex terrain of the firing area (e.g., buildings, berm, low-height obstacles) was advantageous to study HE deposition in relation to plume dynamics. Three types of samples were collected up to 100 m away from each shot: surface swipes of aluminum plates, surface swipes of fixed objects, and filters from air samples. We used atmospheric flow tube-mass spectrometry (AFT-MS) to quantify picogram levels of molecular residue of HE material in the post-detonation dust. An aliquot of sample extract in methanol (e.g., 1 µL of 0.5 mL) was placed onto a resistive material and then thermally desorbed into the AFT-MS. We successfully detected and quantified both HMX and RDX in many of the samples. Based on mass (pg) detected and solution dilution, we back-calculated the mass collected on the swipe or filter (ng per sample). The aerial distribution of molecular residue was consistent with the path of the plume, which was strongly determined by wind speed and direction at the time of each shot. The quantity of material detected appeared to correlate more with distance from the shot and the wind conditions than with shot size. This study demonstrates that the picogram detection levels of AFT-MS are well-suited for quantification of analytes (e.g., HMX and RDX) in environmental samples.

atmospheric flow tube-mass spectrometry (AFT-MS), ↗
NASA NTRS2009

Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral Images

Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with Compact Reconnaissance Imaging Spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel "superpixel" segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.

Image Segmentation↗
DOE OSTI2016

Analyzing How We Do Analysis and Consume Data, Results from the SciDAC-Data Project

One of the main goals of the Dept. of Energy funded SciDAC-Data project is to analyze the more than 410,000 high energy physics datasets that have been collected, generated and defined over the past two decades by experiments using the Fermilab storage facilities. These datasets have been used as the input to over 5.6 million recorded analysis projects, for which detailed analytics have been gathered. The analytics and meta information for these datasets and analysis projects are being combined with knowledge of their part of the HEP analysis chains for major experiments to understand how modern computing and data delivery is being used. We present the first results of this project, which examine in detail how the CDF, D0, NOvA, MINERvA and MicroBooNE experiments have organized, classified and consumed petascale datasets to produce their physics results. The results include analysis of the correlations in dataset/file overlap, data usage patterns, data popularity, dataset dependency and temporary dataset consumption. The results provide critical insight into how workflows and data delivery schemes can be combined with different caching strategies to more efficiently perform the work required to mine these large HEP data volumes and to understand the physics analysis requirements for the next generation of HEP computing facilities. In particular we present a detailed analysis of the NOvA data organization and consumption model corresponding to their first and second oscillation results (2014-2016) and the first look at the analysis of the Tevatron Run II experiments. We present statistical distributions for the characterization of these data and data driven models describing their consumption

Ding, P.↗
DOE OSTI2022

Zinc speciation and desorption kinetics in a mining waste impacted tropical soil amended with phosphate

Mining is an important component of the Brazilian economy. However, it may also contribute to environmental problems such as the pollution of soils with zinc and other potentially toxic metals. Our objective was to evaluate changes in the chemical speciation and mobility of Zn in a soil amended with phosphate. Soil samples were collected from a deactivated mining area in the state of Minas Gerais, Brazil, and amended with NH 4 H 2 PO 4 saturated with deionized water to 70 % of maximum water retention and incubated at 25 ± 2 °C in open containers for 60 days. The soil was chemically and mineralogically characterized, and sequential extraction, desorption kinetics, and speciation were carried out using synchrotron bulk-sample and micro–X-ray Absorption Near-Edge Structure (XANES/μ-XANES) spectroscopy at the Zn K-edge, and X-ray fluorescence microprobe analysis (μ-XRF). The combination of μ-XRF and μ-XANES techniques made it possible to identify Zn hotspots in the main species formed after phosphate remediation. The best fit combination for bulk XANES and μ-XANES was observed in Zn-montmorillonite, Zn-kerolite, Zn-ferrihydrite, and gahnite. In the course of phosphate treatment, gahnite, Zn layered double hydroxides (Zn-LDH), Zn3(PO4), and ZnO were identified by bulk XANES, while Zn-ferrihydrite, Zn-montmorillonite, and scholzite were identified by μ-XANES. Zinc in the phosphate-amended soil had the strongest partial correlations (r' > 0.05) with Ni, Co, Fe, Cr, Mn, Si, P, Cd, Pb, and Cd, while the unamended soil showed the strongest correlation with Cu, Pb, Fe, and Si. The application of NH 4 H 2 PO 4 altered Zn speciation and favored an increase in Zn desorption. Here, the most available Zn contents after phosphate amendment were correlated with the release of exchangeable Zn fractions, associated with carbonate and organic matter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Unraveling Microbial Communities Associated with Methylmercury Production in Paddy Soils

Here, we report rice consumption is now recognized as an important pathway of human exposure to the neurotoxin methylmercury (MeHg), particularly in countries where rice is a staple food. Although the discovery of a two-gene cluster hgcAB has linked Hg methylation to several phylogenetically diverse groups of anaerobic microorganisms converting inorganic mercury (Hg) to MeHg, the prevalence and diversity of Hg methylators in microbial communities of rice paddy soils remain unclear. We characterized the abundance and distribution of hgcAB genes using third-generation PacBio long-read sequencing and Illumina short-read metagenomic sequencing, in combination with quantitative PCR analyses in several mine-impacted paddy soils from southwest China. Both Illumina and PacBio sequencing analyses revealed that Hg methylating communities were dominated by iron-reducing bacteria (i.e., Geobacter ) and methanogens, with a relatively low abundance of hgcA + sulfate-reducing bacteria in the soil. A positive correlation was observed between the MeHg content in soil and the relative abundance of Geobacter carrying the hgcA gene. Phylogenetic analysis also uncovered some hgcAB sequences closely related to three novel Hg methylators, Geobacter anodireducens , Desulfuromonas sp. DDH964, and Desulfovibrio sp. J2, among which G. anodireducens was validated for its ability to methylate Hg. Lastly, these findings shed new light on microbial community composition and major clades likely driving Hg methylation in rice paddy soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tethys Water Demand Data

U.S. water demand varies sharply by sector and region as land use, population, weather patterns, and economic activity co-evolve. High-resolution water demand data is required to capture these dynamics, support integrated energy-water-land modeling, and local-to-regional water scarcity assessments. This dataset contains gridded (1/8 degree), monthly, multi-sector water demand dataset for the contiguous United States (CONUS) covering 1980-2100 across eight future scenarios of human-Earth system change. The dataset covers irrigation, thermoelectric, municipal (public-supply and domestic), livestock, manufacturing, and mining demands, separately for withdrawals and consumption, and includes per-cell renewable vs. non-renewable water source attributions. The dataset is validated against the latest USGS 2010-2020 water-use data for the three largest water demand sectors (Domestic, Electricity, and Irrigation), with correlations ranging from 0.73-0.95 at the HUC6 scale. The two datasets largely agree on an aggregate basis with per-sector bias falling within +/-7%, but they disagree on the spatial allocation of water with individual HUC6 basins having normalized RMSE from 68-171% and median absolute percent difference from 37-86%. This dataset advances prior global products by combining state-resolved sectoral demands from GCAM-USA, future power-plant siting from the CERF model, and scenario-consistent high-resolution climate and population forcing data across the eight scenarios.

GCAM-USA↗
NASA NTRS1974

Evaluation of ERTS-1 data applications to geologic mapping, structural analysis and mineral resource inventory of South America with special emphasis on the Andes Mountain region

The author has identified the following significant results. The La Paz Mosaic and its attendant overlays serve as a model for geologic studies elsewhere in the world. The P.I. and two geologists are mapping the conterminous states at scales of 1:5000,000 and 1:1,000,000. The 1:5 million band 5 mosaic was completed in two days of analysis. The 1:1 million band sheets are being completed at the rate of one per day. Comparison of the preliminary results of the three investigators shows a high correlation of linear and curvilinear features. Comparison with magnetic and gravity data indicates that many features being mapped are deep seated structures that have been active through long periods of geologic time, perhaps dating back to the Precambrian period. A detailed analysis of the El Salvador mining district has been completed. The interpretation is extremely detailed showing a complex pattern of linear features and bedrock outcrop patterns. This is the first product from ERTS-1 to be provided by Chile and shows a high degree of expertise in image interpretation. The Chileans are enthusiastic about their results and are anxious to map the entire country using ERTS.

Carter, W. D.↗
DOE OSTI2020

Phonon spectrum of underdoped HgBa 2 CuO 4+δ investigated by neutron scattering

The cuprates exhibit a prominent charge-density-wave (CDW) instability with a wave vector along [100], i.e., the Cu-O bond direction. Whereas CDW order is most prominent at moderate doping and low temperature, there exists increasing evidence for dynamic charge correlations throughout a large portion of the temperature-doping phase diagram. In particular, the signatures of incipient charge order have been observed as phonon softening and/or broadening near the CDW wave vector approximately halfway through the Brillouin zone. Most of this work is focused on moderately doped cuprates, for which the CDW order is robust, or on optimally doped samples, for which the superconducting transition temperature (T c ) attains its maximum. Here we present a time-of-flight neutron scattering study of phonons in simple-tetragonal HgBa 2 CuO 4+δ (T c = 55 K) at a low doping level where prior work showed the CDW order to be weak. We employ and showcase a new software-based technique that mines a large number of measured Brillouin zones for useful data in order to improve accuracy and counting statistics. Density-functional theory has not provided an accurate description of phonons in HgBa2CuO4+δ, yet we find the right set of parameters to qualitatively reproduce the data. The notable exception is a dispersion minimum in the longitudinal Cu-O bond-stretching branch along [100]. Here, this discrepancy suggests that, while CDW order is weak, there exist significant dynamic charge correlations in the optic phonon range at low doping, near the edge of the superconducting dome.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Predicting taxonomic and functional structure of microbial communities in acid mine drainage

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Data from: An updated gene atlas for maize reveals organ-specific and stress-induced genes

Maize(Zea mays L.), a model species for genetic studies, is one of the two most important crop species worldwide.The genome sequence of the reference genotype, B73, representative of the stiff stalk heterotic group was recently updated (AGPv4) using long‐read sequencing and optical mapping technology. To facilitate the use of AGPv4 and to enable functional genomic studies and association of genotype with phenotype, we determined expression abundances for replicated mRNA‐sequencing datasets from 79 tissues and five abiotic/biotic stress treatments revealing 36,207 expressed genes. Characterization of the B73 transcriptome across six organs revealed 4,154 organ‐specific and 7,704 differentially expressed genes following stress treatment. Gene co‐expression network analyses revealed 12 modules associated with distinct biological processes containing 13,590 genes providing a resource for further association of gene function based on co‐expression patterns. Presence/absence variants (PAVs) previously identified using whole genome resequencing data from 61 additional inbred lines were enriched in organ‐specific and stress‐induced differentially expressed genes, suggesting PAVs may function in phenological variation and adaptation to environment. Relative to core genes conserved across the 62 profiled inbreds, PAVs have lower expression abundances which are correlated with their frequency of dispersion across inbreds and on average, have significantly fewer co‐expression network connections suggesting that a subset of PAVs may be on an evolutionary path to pseudogenization. To facilitate use by the community, we developed the Maize Genomics Resource website(maize.plantbiology.msu.edu) for viewing and data‐mining these resources and deployed two new views on the Maize electronic Fluorescent Pictograph Browser (bar.utoronto. ca/efp_maize)

59 BASIC BIOLOGICAL SCIENCES↗