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DOE OSTI2022

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m 2 , we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m 2 , we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Novel interaction interfaces mediate the interaction between the NEIL1 DNA glycosylase and mitochondrial transcription factor A

The maintenance of human mitochondrial DNA (mtDNA) is critical for proper cellular function as damage to mtDNA, if left unrepaired, can lead to a diverse array of pathologies. Of the pathways identified to participate in DNA repair within the mitochondria, base excision repair (BER) is the most extensively studied. Protein-protein interactions drive the step-by-step coordination required for the successful completion of this pathway and are important for crosstalk with other mitochondrial factors involved in genome maintenance. Human NEIL1 is one of seven DNA glycosylases that initiates BER in both the nuclear and mitochondrial compartments. In the current work, we scrutinized the interaction between NEIL1 and mitochondrial transcription factor A (TFAM), a protein that is essential for various aspects of mtDNA metabolism. We note, for the first time, that both the N- and C- terminal domains of NEIL1 interact with TFAM revealing a unique NEIL1 protein-binding interface. The interaction between the two proteins, as observed biochemically, appears to be transient and is most apparent at concentrations of low salt. The presence of DNA (or RNA) also positively influences the interaction between the two proteins, and molar mass estimates indicate that duplex DNA is required for complex formation at higher salt concentrations. Hydrogen deuterium exchange mass spectrometry data reveal that both proteins exchange less deuterium upon DNA binding, indicative of an interaction, and the addition of NEIL1 to the TFAM-DNA complex alters the interaction landscape. The transcriptional activity of TFAM appears to be independent of NEIL1 expression under normal cellular conditions, however, in the presence of DNA damage, we observe a significant reduction in the mRNA expression of TFAM-transcribed mitochondrial genes in the absence of NEIL1. Overall, our data indicate that the interaction between NEIL1 and TFAM can be modulated by local environment such as salt concentrations, protein availability, the presence of nucleic acids, as well as the presence of DNA damage.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Data from: Emerging wild virus of native grass bioenergy feedstock is well established in the Midwestern USA and associated with premature stand senescence

This dataset includes values for the prevalence of switchgrass mosaic virus (Genus Marafivirus, Family Tymoviridae) detected with molecular diagnostics (RT-PCR) in individual Panicum virgatum (switchgrass) plants and in Graminella leafhoppers that feed on them. Surveys were conducted in 15 sites in August 2012. Stands surveyed had been established for some time and represent a range of landscape contexts. Measures of stand height and percent senescence were also collected. Land cover composition surrounding each site was calculated from the USDA-NASS Cropland Data Layer and estimates of drought impact were derived from the US Drought Monitor.

09 BIOMASS FUELS↗

NASA NTRSDate not supplied

Scientific Content Curation in an Open Science Era

Today’s open science environment, in combination with the Big Data era, means more scientific data, software, tools, documentation, publications and other resources are available than ever. The promise of the open science era is that scientists will spend less time reinventing the wheel and more time doing actionable research. Yet navigating this vast and complex information landscape can feel overwhelming to scientists trying to get their bearings. In this presentation, we define and discuss the importance of scientific content curation for enhancing discovery and use of scientific data and information. We also share two examples of scientific content curation in action: the Catalog of Archived Suborbital Earth Science Investigations (CASEI) and the Science Discovery Engine (SDE).

Kaylin Bugbee↗

DOE OSTI2022

CompF2: Theoretical Calculations and Simulation Topical Group Report

This report summarizes the work of the Computational Frontier topical group on theoretical calculations and simulation for Snowmass 2021. We discuss the challenges, potential solutions, and needs facing six diverse but related topical areas that span the subject of theoretical calculations and simulation in high energy physics (HEP): cosmic calculations, particle accelerator modeling, detector simulation, event generators, perturbative calculations, and lattice QCD (quantum chromodynamics). The challenges arise from the next generations of HEP experiments, which will include more complex instruments, provide larger data volumes, and perform more precise measurements. Calculations and simulations will need to keep up with these increased requirements. The other aspect of the challenge is the evolution of computing landscape away from general-purpose computing on CPUs and toward special-purpose accelerators and coprocessors such as GPUs and FPGAs. These newer devices can provide substantial improvements for certain categories of algorithms, at the expense of more specialized programming and memory and data access patterns.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

First search for an anomalous excess of charged-current $\nu_e$ interactions without visible pions using the full MicroBooNE dataset

This public note presents an investigation of low-energy electron-neutrino events in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment. This search is motivated by the excess of low energy electromagnetic events observed by the MiniBooNE experiment and, more broadly, by the landscape of neutrino anomalies observed at short baselines. This is the first measurement to use all of the data collected by the MicroBooNE experiment, corresponding to $1.1\times 10^{21}$ protons on target. Two exclusive samples of electron neutrinos without visible pions are used, one with visible protons and one without any visible protons. MicroBooNE data is compared to two empirical models of the MiniBooNE low energy excess, one obtained by enhancing the electron-neutrino content as a function of the neutrino energy, and one representing the excess for the first time as a function of the kinematics of shower energy and angle. This measurement excludes an electron-like interpretation of the MiniBooNE excess based on these models at $\geq 99\%$ confidence level in all kinematic variables.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Factors that influence the activity of biomass-degrading enzymes in the presence of ionic liquids—a review

Ionic liquids (ILs) are seen as a more sustainable alternative to volatile organic solvents that are accelerating innovations in many industries such as energy storage, separations, and bioprocessing. The ability to effectively deconstruct lignocellulosic biomass is a significant hurdle in the biorefining/bioprocessing industry and presents limitations towards the commercial production of bioproducts (such as biofuels, biomaterials, etc. ). Certain ILs have been shown to promote effective lignin removal, cellulose recovery, and sugar yields from various biomass feedstocks such as corn stover, wheat straw, sugarcane bagasse, sorghum, switchgrass, miscanthus, poplar, pine, eucalyptus, and certain mixtures of municipal solid waste. However, these improvements are often counteracted by the limited biocompatibility of ILs, which results in an IL-induced reduction in enzyme activity and stability—an important downstream step in the conversion of biomass to biofuels/bioproducts. As a result, significant efforts have been made to discover and engineer compatible enzyme-IL systems and to improve our understanding on the effect that these ILs have on these systems. This review seeks to examine the impact of ionic liquids on enzymes involved in lignocellulosic biomass deconstruction, with a specific focus on their relevance in the context of pretreatment. Beyond presenting an overview of the ionic liquid pretreatment landscape, we outline the main factors that influence enzyme activity and stability in the presence of ILs This data is consolidated and analyzed to apply this body of knowledge towards new innovations that could lead to improvements in the processing of biomass to biofuels and bioproducts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1998

Diurnal Reflectance Changes in Vegetation Observed with AVIRIS

Among the most important short-term dynamic biological processes are diurnal changes in canopy water relations. Plant regulation of water transport through stomatal openings affects other gaseous transport processes, often dramatically decreasing photosynthetic fixation of carbon dioxide during periods of water stress. Water stress reduces stomatal conductance of water vapor through the leaf surface and alters the diurnal timing of stomatal opening. Under non-water stressed conditions, stomates typically open soon after dawn and transpire water vapor throughout the daylight period. During stress periods, stomates may close for part of the day, generally near mid-day. Under prolonged stress conditions, stomatal closure shifts to earlier times during the day; stomates may close by mid-morning and remain closed until the following morning - or remain closed entirely. Under these conditions the relationship between canopy greenness (e.g., measured with a vegetation index or by spectral mixture analysis) and photosynthetic fixation of carbon is lost and the remotely sensed vegetation metric is a poor predictor of gas exchange. Prediction of stomatal regulation and exchange of water and trace gases is critical for ecosystem and climate models to correctly estimate budgets of these gases and understand or predict other processes like gross and net ecosystem primary production. Plant gas exchange has been extensively studied by physiologists at the leaf and whole plant level and by biometeorologists at somewhat larger scales. While these energy driven processes follow a predictable if somewhat asymmetric diurnal cycle dependent on soil water availability and the constraints imposed by the solar energy budget, they are nonetheless difficult to measure at the tree and stand levels using conventional methods. Ecologists have long been interested in the potential of remote sensing for monitoring physiological changes using multi-temporal images. Much of this research has focused on day-to-day changes in water use, especially for agricultural applications. Ustin et al. showed seasonal changes in canopy water content in chaparral shrub could be estimated using optical methods. Vanderbilt et al. followed asymmetric diurnal changes in the reflectance of a walnut orchard, but could not attribute specific reflectance changes to specific changes in canopy architecture or physiology. Forests and shrub lands in California experience prolonged periods of drought, sometimes extending six months without precipitation. The conifer and evergreen chaparral communities common to the foothill region around the central valley of California retain their foliage throughout the summer and have low transpiration rates despite high net radiation and temperature conditions. In contrast, grasslands and drought resistant deciduous species in the same habitat are seasonally dormant in summer. Because of differences in the mechanisms of drought tolerance, rooting depth and physiology between different plant communities in the region, it is likely that they display differences in diurnal water relations. The presence of diverse plant communities provides an opportunity to investigate possible diurnal landscape patterns in water relations that could be observed by an airborne hyperspectral scanner. This investigation of AVIRIS data collected over forest and shrub land represents the continuation of a prior investigation involving spectral mixture analysis of diurnal effects in the same AVIRIS data set.

Vanderbilt, V. C.↗

NASA NTRS2017

NASA Earth Observations Informing Energy Management Decision Making

The Energy Sector is experiencing increasing impacts from severe weather and shifting climatic trends, as well as facing a changing political climate, adding uncertainty for stakeholders as they make short- and long-term planning investments. Climate changes such as prolonged extreme heat and drought (leading to wildfire spread, for example), sea level rise, and extreme storms are changing the ways that utilities operate. Energy infrastructure located in coastal or flood-prone areas faces inundation risks, such as damage to energy facilities. The use of renewable energy resources is increasing, requiring more information about their intermittency and spatial patterns. In light of these challenges, public and private stakeholders have collaborated to identify potential data sources, tools, and programmatic ideas. For example, utilities across the country are using cutting-edge technology and data to plan for and adapt to these changes. In the Federal Government, NASA has invested in preliminary work to identify needs and opportunities for satellite data in energy sector application, and the Department of Energy has similarly brought together stakeholders to understand the landscape of climate vulnerability and resilience for utilities and others. However, have these efforts improved community-scale resilience and adaptation efforts? Further, some communities are more vulnerable to climate change and infrastructure impacts than others. This session has two goals. First, panelists seek to share existing and ongoing efforts related to energy management. Second, the session seeks to engage with attendees via group knowledge exchange to connect national energy management efforts to local practice for increased community resilience.

Eckman, Richard↗

NASA NTRS2017

Control and Non-Payload Communications (CNPC) Prototype Radio Validation Flight Test Report

This report provides an overview and results from the unmanned aircraft (UA) Control and Non-Payload Communications (CNPC) Generation 5 prototype radio validation flight test campaign. The radios used in the test campaign were developed under cooperative agreement NNC11AA01A between the NASA Glenn Research Center and Rockwell Collins, Inc., of Cedar Rapids, Iowa. Measurement results are presented for flight tests over hilly terrain, open water, and urban landscape, utilizing radio sets installed into a NASA aircraft and ground stations. Signal strength and frame loss measurement data are analyzed relative to time and aircraft position, specifically addressing the impact of line-of-sight terrain obstructions on CNPC data flow. Both the radio and flight test system are described.

Shalkhauser, Kurt A.↗

NASA NTRSDate not supplied

Large-Scale Simulation of a Distributed Sensing Network Supporting Regional Urban Air Mobility Operations

Urban Air Mobility (UAM) is set to transform transportation in densely populated regions like the San Francisco Bay Area. This paper introduces an innovative simulation approach to explore large-scale UAM scenarios, emphasizing the use of distributed sensing to enhance operational efficiency and safety. The Revolutionary Vertical Lift Technology (RVLT) model is employed as the framework for simulating complex interactions among multiple vehicles within urban landscapes. Strategically deployed ground sensor nodes enable distributed sensing, enhancing situational awareness and operational effectiveness. By integrating empirical data and geographical realism, the simulations provide a systematic analysis of the feasibility, efficiency, and safety considerations associated with UAM deployment in urban environments. Factors such as air traffic density and infrastructural requirements are thoroughly examined, offering actionable insights for policymakers and industry stakeholders. This paper aims to refine the structure and scenarios for large-scale simulations based on distributed sensing, thereby contributing to the advancement of UAM operations.

UAM↗

NASA NTRSDate not supplied

Large-Scale Simulation of a Distributed Sensing Network Supporting Regional Urban Air Mobility Operations

Aircraft Mobility↗

DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗

DOE OSTI2025

Dynamic Modeling of a Kaplan Hydroturbine Using Optimal Parametric Tuning and Real Plant Operational Data

To address grid variability caused by renewable energy integration and to maintain grid reliability and resilience, hydropower must quickly adjust its power generation over short time periods. This changing energy generation landscape requires advance technology integration and adaptive parameter optimization for hydropower systems via digital twin effort. However, this is difficult owing to the lack of characterization and modeling for the nonlinear nature of hydroturbines. To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. To assess the performance of the proposed optimal parametric tuning technique, operational data from a real-world Kaplan hydroturbine unit are collected and used to model the relationship between the gate opening and the generated power production. The findings show that the proposed technique can effectively and adaptively learn the unknown dynamics of the Kaplan hydroturbine while optimally tune the unknown coefficients to match the generated power output from the real hydroturbine unit with an inaccuracy of less than 5%. The method can be used to provides optimal tuning of parameters critical for controller design, operational optimization and daily maintenance for hydroturbines in general.

13 HYDRO ENERGY↗

DOE OSTI2024

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and don’t always predict therapeutic response. Here we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measured the proteome and phosphoproteome of 210 patients and combined them with genomics and transcriptomic measurements to identify four proteogenomic subtypes that complemented existing genetic subtypes. We then built a predictor to classify samples into subtypes based on 147 molecular features and mapped them to a ‘landscape’. Each region of this landscape corresponded to specific drug response patterns. We then built a drug response prediction model to identify drugs that target distinct subtypes. We can ultimately use these models to predict drug treatment response and prioritize treatments. Finally, we extended our models and mapped a series of cell lines representing various stages of quizartinib resistance into our subtype landscape, predicting and experimentally validating a switch in sensitivity to venetoclax to panobinostat, two drugs with very different mechanisms than quizartinib. Our results show how multi-omics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Transmission risk of Oropouche fever across the Americas

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance. Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas. Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks. Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Chromatin structure predicts survival in glioma patients

The pathological changes in epigenetics and gene regulation that accompany the progression of low-grade to high-grade gliomas are under-studied. The authors use a large set of paired atac-seq and RNA-seq data from surgically resected glioma specimens to infer gene regulatory relationships in glioma. Thirty-eight glioma patient samples underwent atac-seq sequencing and 16 samples underwent additional RNA-seq analysis. Using an atac-seq/RNA-seq correlation matrix, atac-seq peaks were paired with genes based on high correlation values (|r 2 | > 0.6). Samples clustered by IDH1 status but not by grade. Surprisingly there was a trend for IDH1 mutant samples to have more peaks. The majority of peaks are positively correlated with survival and positively correlated with gene expression. Constructing a model of the top six atac-seq peaks created a highly accurate survival prediction model (r 2 = 0.68). Four of these peaks were still significant after controlling for age, grade, pathology, IDH1 status and gender. Grade II, III, and IV (primary) samples have similar transcription factors and gene modules. However, grade IV (recurrent) samples have strikingly few peaks. Patient-derived glioma cultures showed decreased peak counts following radiation indicating that this may be radiation-induced. This study supports the notion that IDH1 mutant and IDH1 wildtype gliomas have different epigenetic landscapes and that accessible chromatin sites mapped by atac-seq peaks tend to be positively correlated with expression. The data in this study leads to a new model of treatment response wherein glioma cells respond to radiation therapy by closing open regions of DNA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗