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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

A Science Strategy for Space Physics

This report by the Committee on Solar and Space Physics and the Committee on Solar-Terrestrial Research recommends the major directions for scientific research in space physics for the coming decade. As a field of science, space physics has passed through the stage of simply looking to see what is out beyond Earth's atmosphere. It has become a 'hard' science, focusing on understanding the fundamental interactions between charged particles, electromagnetic fields, and gases in the natural laboratory consisting of the galaxy, the Sun, the heliosphere, and planetary magnetospheres, ionospheres, and upper atmospheres. The motivation for space physics research goes far beyond basic physics and intellectual curiosity, however, because long-term variations in the brightness of the Sun virtually affect the habitability of the Earth, while sudden rearrangements of magnetic fields above the solar surface can have profound effects on the delicate balance of the forces that shape our environment in space and on the human technology that is sensitive to that balance. The several subfields of space physics share the following objectives: to understand the fundamental laws or processes of nature as they apply to space plasmas and rarefied gases both on the microscale and in the larger complex systems that constitute the domain of space physics; to understand the links between changes in the Sun and the resulting effects at the Earth, with the eventual goal of predicting the significant effects on the terrestrial environment; and to continue the exploration and description of the plasmas and rarefied gases in the solar system.

Source record↗
DOE OSTI2019

Graph Generation with a Focusing Lexicon

Reference datasets are an essential part of developing effective data exploration tools. Many reference datasets are synthetic in nature, seeding real-world properties into generation routines. Others are laboriously curated from original source materials. This is a time-consuming process that requires significant domain expertise all along the process. This paper presents a third path: deriving a network from real-world source materials using automated tools. By properly grounding the tools in a small collection of curated documents, a larger corpus be automatically processed with greater confidence. The end system leverages human knowledge (in the initial document curation), but with much less time commitment enabled by automation. Our ultimate goal is to produce reference datasets, derived from real-world data that capture aspects salient to a specific activity in the context of all of the activities in the data-set. Our reference datasets take the form of graphs.

Chandra Shekar, Mayanka↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2000

Spontaneous fluctuations in cerebral blood flow: insights from extended-duration recordings in humans

To determine the dependence of cerebral blood flow (CBF) on arterial pressure over prolonged time periods, we measured beat-to-beat changes in mean CBF velocity in the middle cerebral artery (transcranial Doppler) and mean arterial pressure (Finapres) continuously for 2 h in six healthy subjects (5 men and 1 woman, 18-40 yr old) during supine rest. Fluctuations in velocity and pressure were quantified by the range [(peak - trough)/mean] and coefficients of variation (SD/mean) in the time domain and by spectral analysis in the frequency domain. Mean velocity and pressure over the 2-h recordings were 60 +/- 7 cm/s and 83 +/- 8 mmHg, associated with ranges of 77 +/- 8 and 89 +/- 10% and coefficients of variation of 9.3 +/- 2.2 and 7.9 +/- 2.3%, respectively. Spectral power of the velocity and pressure was predominantly distributed in the frequency range of 0.00014-0.1 Hz and increased inversely with frequency, indicating characteristics of an inverse power law (1/f(alpha)). However, linear regression on a log-log scale revealed that the slope of spectral power of pressure and velocity was steeper in the high-frequency (0.02-0.5 Hz) than in the low-frequency range (0.002-0.02 Hz), suggesting different regulatory mechanisms in these two frequency ranges. Furthermore, the spectral slope of pressure was significantly steeper than that of velocity in the low-frequency range, consistent with the low transfer function gain and low coherence estimated at these frequencies. We conclude that 1) long-term fluctuations in CBF velocity are prominent and similar to those observed in arterial pressure, 2) spectral power of CBF velocity reveals characteristics of 1/f(alpha), and 3) cerebral attenuation of oscillations in CBF velocity in response to changes in pressure may be more effective at low than that at high frequencies, emphasizing the frequency dependence of cerebral autoregulation.

NASA Discipline Cardiopulmonary↗
DOE OSTI2021

Arabidopsis thaliana EARLY RESPONSIVE TO DEHYDRATION 7 Localizes to Lipid Droplets via Its Senescence Domain

Lipid droplets (LDs) are neutral-lipid-containing organelles found in all kingdoms of life and are coated with proteins that carry out a vast array of functions. Compared to mammals and yeast, relatively few LD proteins have been identified in plants, particularly those associated with LDs in vegetative (non-seed) cell types. Thus, to better understand the cellular roles of LDs in plants, a more comprehensive inventory and characterization of LD proteins is required. Here, we performed a proteomics analysis of LDs isolated from drought-stressed Arabidopsis leaves and identified EARLY RESPONSIVE TO DEHYDRATION 7 (ERD7) as a putative LD protein. mCherry-tagged ERD7 localized to both LDs and the cytosol when ectopically expressed in plant cells, and the protein’s C-terminal senescence domain (SD) was both necessary and sufficient for LD targeting. Phylogenetic analysis revealed that ERD7 belongs to a six-member family in Arabidopsis that, along with homologs in other plant species, is separated into two distinct subfamilies. Notably, the SDs of proteins from each subfamily conferred targeting to either LDs or mitochondria. Further, the SD from the ERD7 homolog in humans, spartin, localized to LDs in plant cells, similar to its localization in mammals; although, in mammalian cells, spartin also conditionally localizes to other subcellular compartments, including mitochondria. Disruption of ERD7 gene expression in Arabidopsis revealed no obvious changes in LD numbers or morphology under normal growth conditions, although this does not preclude a role for ERD7 in stress-induced LD dynamics. Consistent with this possibility, a yeast two-hybrid screen using ERD7 as bait identified numerous proteins involved in stress responses, including some that have been identified in other LD proteomes. Collectively, these observations provide new insight to ERD7 and the SD-containing family of proteins in plants and suggest that ERD7 may be involved in functional aspects of plant stress response that also include localization to the LD surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Characterization factors and other air quality impact metrics: Case study for PM 2.5 -emitting area sources from biofuel feedstock supply

In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM 2.5 ) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM 2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM 2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.

09 BIOMASS FUELS↗
DOE OSTI2024

Structural insights reveal interplay between LAG-3 homodimerization, ligand binding, and function

Lymphocyte activation gene-3 (LAG-3) is an inhibitory receptor expressed on activated T cells and an emerging immunotherapy target. Domain 1 (D1) of LAG-3, which has been purported to directly interact with major histocompatibility complex class II (MHCII) and fibrinogen-like protein 1 (FGL1), has been the major focus for the development of therapeutic antibodies that inhibit LAG-3 receptor-ligand interactions and restore T cell function. Here, we present a high-resolution structure of glycosylated mouse LAG-3 ectodomain, identifying that cis-homodimerization, mediated through a network of hydrophobic residues within domain 2 (D2), is critically required for LAG-3 function. Additionally, we found a previously unidentified key protein-glycan interaction in the dimer interface that affects the spatial orientation of the neighboring D1 domain. Mutation of LAG-3 D2 residues reduced dimer formation, dramatically abolished LAG-3 binding to both MHCII and FGL1 ligands, and consequentially inhibited the role of LAG-3 in suppressing T cell responses. Intriguingly, we showed that antibodies directed against D1, D2, and D3 domains are all capable of blocking LAG-3 dimer formation and MHCII and FGL-1 ligand binding, suggesting a potential allosteric model of LAG-3 function tightly regulated by dimerization. Furthermore, our work reveals unique epitopes, in addition to D1, that can be targeted for immunotherapy of cancer and other human diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Recombinant production of a functional SARS-CoV-2 spike receptor binding domain in the green algae Chlamydomonas reinhardtii

Recombinant production of viral proteins can be used to produce vaccine antigens or reagents to identify antibodies in patient serum. Minimally, these proteins must be correctly folded and have appropriate post-translation modifications. Here we report the production of the SARS-CoV-2 spike protein Receptor Binding Domain (RBD) in the green algae Chlamydomonas . RBD fused to a fluorescent reporter protein accumulates as an intact protein when targeted for ER-Golgi retention or secreted from the cell, while a chloroplast localized version is truncated. The ER-retained RBD fusion protein was able to bind the human ACE2 receptor, the host target of SARS-CoV-2, and was specifically out-competed by mammalian cell-produced recombinant RBD, suggesting that the algae produced proteins are sufficiently post-translationally modified to act as authentic SARS-CoV-2 antigens. Because algae can be grown at large scale very inexpensively, this recombinant protein may be a low cost alternative to other expression platforms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Analysis of Issues for Project Scheduling by Multiple, Dispersed Schedulers (distributed Scheduling) and Requirements for Manual Protocols and Computer-based Support

Although computerized operations have significant gains realized in many areas, one area, scheduling, has enjoyed few benefits from automation. The traditional methods of industrial engineering and operations research have not proven robust enough to handle the complexities associated with the scheduling of realistic problems. To address this need, NASA has developed the computer-aided scheduling system (COMPASS), a sophisticated, interactive scheduling tool that is in wide-spread use within NASA and the contractor community. Therefore, COMPASS provides no explicit support for the large class of problems in which several people, perhaps at various locations, build separate schedules that share a common pool of resources. This research examines the issue of distributing scheduling, as applied to application domains characterized by the partial ordering of tasks, limited resources, and time restrictions. The focus of this research is on identifying issues related to distributed scheduling, locating applicable problem domains within NASA, and suggesting areas for ongoing research. The issues that this research identifies are goals, rescheduling requirements, database support, the need for communication and coordination among individual schedulers, the potential for expert system support for scheduling, and the possibility of integrating artificially intelligent schedulers into a network of human schedulers.

Richards, Stephen F.↗
NASA NTRS1993

GenSAA: A tool for advancing satellite monitoring with graphical expert systems

During numerous contacts with a satellite each day, spacecraft analysts must closely monitor real time data for combinations of telemetry parameter values, trends, and other indications that may signify a problem or failure. As satellites become more complex and the number of data items increases, this task is becoming increasingly difficult for humans to perform at acceptable performance levels. At the NASA Goddard Space Flight Center, fault-isolation expert systems have been developed to support data monitoring and fault detection tasks in satellite control centers. Based on the lessons learned during these initial efforts in expert system automation, a new domain-specific expert system development tool named the Generic Spacecraft Analyst Assistant (GenSAA) is being developed to facilitate the rapid development and reuse of real-time expert systems to serve as fault-isolation assistants for spacecraft analysts. Although initially domain-specific in nature, this powerful tool will support the development of highly graphical expert systems for data monitoring purposes throughout the space and commercial industry.

Hughes, Peter M.↗
NASA NTRS1994

Three-dimensional structure of Schistosoma japonicum glutathione S-transferase fused with a six-amino acid conserved neutralizing epitope of gp41 from HIV

The 3-dimensional crystal structure of glutathione S-transferase (GST) of Schistosoma japonicum (Sj) fused with a conserved neutralizing epitope on gp41 (glycoprotein, 41 kDa) of human immunodeficiency virus type 1 (HIV-1) was determined at 2.5 A resolution. The structure of the 3-3 isozyme rat GST of the mu gene class was used as a molecular replacement model. The structure consists of a 4-stranded beta-sheet and 3 alpha-helices in domain 1 and 5 alpha-helices in domain 2. The space group of the Sj GST crystal is P4(sub 3)2(sub 1)2 with unit cell dimensions of a = b = 94.7 A, and c = 58.1 A. The crystal has 1 GST monomer per asymmetric unit, and 2 monomers that form an active dimer are related by crystallographic 2-fold symmetry. In the binding site, the ordered structure of reduced glutathione is observed. The gp41 peptide (Glu-Leu-Asp-Lys-Trp-Ala) fused to the C-terminus of Sj GST forms a loop stabilized by symmetry-related GSTs. The Sj GST structure is compared with previously determined GST structures of mammalian gene classes mu, alpha, and pi. Conserved amino acid residues among the 4 GSTs that are important for hydrophobic and hydrophilic interactions for dimer association and glutathione binding are discussed.

Lim, Kap↗
NASA NTRS2010

Efficient Web Services Policy Combination

Large-scale Web security systems usually involve cooperation between domains with non-identical policies. The network management and Web communication software used by the different organizations presents a stumbling block. Many of the tools used by the various divisions do not have the ability to communicate network management data with each other. At best, this means that manual human intervention into the communication protocols used at various network routers and endpoints is required. Developing practical, sound, and automated ways to compose policies to bridge these differences is a long-standing problem. One of the key subtleties is the need to deal with inconsistencies and defaults where one organization proposes a rule on a particular feature, and another has a different rule or expresses no rule. A general approach is to assign priorities to rules and observe the rules with the highest priorities when there are conflicts. The present methods have inherent inefficiency, which heavily restrict their practical applications. A new, efficient algorithm combines policies utilized for Web services. The method is based on an algorithm that allows an automatic and scalable composition of security policies between multiple organizations. It is based on defeasible policy composition, a promising approach for finding conflicts and resolving priorities between rules. In the general case, policy negotiation is an intractable problem. A promising method, suggested in the literature, is when policies are represented in defeasible logic, and composition is based on rules for non-monotonic inference. In this system, policy writers construct metapolicies describing both the policy that they wish to enforce and annotations describing their composition preferences. These annotations can indicate whether certain policy assertions are required by the policy writer or, if not, under what circumstances the policy writer is willing to compromise and allow other assertions to take precedence. Meta-policies are specified in defeasible logic, a computationally efficient non-monotonic logic developed to model human reasoning. One drawback of this method is that at one point the algorithm starts an exhaustive search of all subsets of the set of conclusions of a defeasible theory. Although the propositional defeasible logic has linear complexity, the set of conclusions here may be large, especially in real-life practical cases. This phenomenon leads to an inefficient exponential explosion of complexity. The current process of getting a Web security policy from combination of two meta-policies consists of two steps. The first is generating a new meta-policy that is a composition of the input meta-policies, and the second is mapping the meta-policy onto a security policy. The new algorithm avoids the exhaustive search in the current algorithm, and provides a security policy that matches all requirements of the involved metapolicies.

Vatan, Farrokh↗
NASA NTRS2012

Zero-Fidelity Simulation: Engaging Team Coordination without Physical, Functional, or Psychological Re-Creation

Team coordination is essential across domains, enabling efficiency and safety. As technology improves, our temptation is to simulate with ever-higher fidelity, by making simulators re-create reality through their physical interfaces, functionality, and by making participants believe they are undertaking the simulated task. However, high-fidelity simulations often miss salient human-human work practices. We introduce the concept of zero-fidelity simulation (ZFS), a move away from literal high-fidelity mimesis of the concrete environment. ZFS alternatively models cooperation and communication as the basis of simulation. The ZFS Team Coordination Game (TeC) is developed from observation of fire emergency response work practice. We identify ways in which team members are mutually dependent on one another for information, and use these as the basis for the ZFS game design. The design creates a need for cooperation by restricting individual activity and requiring communication. The present research analyzes the design of interdependence in the validated ZFS TeC game. We successfully simulate interdependence between roles in emergency response without simulating the concrete environment.

Toups, Zachary O.↗
NASA NTRS2021

Air Pollution Scenario over Pakistan: Characterization and Ranking of Extremely Polluted Cities using Long-Term Concentrations of Aerosols and Trace Gases

Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan’s entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/cu. m, over all Pakistan from 2003 to 2020. This value exceeds Pakistan’s National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/cu. m annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 <35 μg/cu. m). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan’s air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.

Muhammad Bilal↗
DOE OSTI2021

Tonal Emergence: An agent-based model of tonal coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Stratigraphic Identification with Airborne Electromagnetic Methods at the Hanford Site, Washington

Stratigraphic units can influence the fate and transport of subsurface contaminants within groundwater. Units having coarse-grained sediments act as preferential flow pathways, and therefore can accelerate the transport of contaminants to reach human and ecological receptors. At legacy waste sites, detailed knowledge of subsurface stratigraphy can be used for effective monitoring and remediation planning to help minimize risk to human health and the environment. Airborne electromagnetic (AEM) methods can non-invasively provide information on kilometer-scale or larger subsurface stratigraphic features and fill informational gaps in directly sampled data from sparsely located boreholes. In this paper, we present inversion results of a 412 line-km frequency-domain AEM survey to delineate subsurface stratigraphic features at the Hanford Site, located in southeastern Washington State. The inversion was performed using a massively parallel 3D electromagnetic modeling and inversion code, where the modeling is based on solving frequency-domain Maxwell’s equations using an unstructured-mesh finite-element method and the inversion employs a Gauss-Newton optimization scheme. The results are compared to an underlying geologic framework model (GFM), built by interpolating contact depths of stratigraphic units interpreted from site borehole datasets. In areas with good borehole coverage, the inversion results show a good match with the GFM to a depth of about 60 m. Outside of these areas, the inversion results exhibit inconsistencies from the assumptions made to create the GFM, demonstrating that the AEM survey results can be used to improve the understanding of the geological conceptual model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Molecular analysis and essentiality of Aro1 shikimate biosynthesis multi-enzyme in Candida albicans

In the human fungal pathogen Candida albicans, ARO1 encodes an essential multi-enzyme that catalyses consecutive steps in the shikimate pathway for biosynthesis of chorismate, a precursor to folate and the aromatic amino acids. We obtained the first molecular image of C. albicans Aro1 that reveals the architecture of all five enzymatic domains and their arrangement in the context of the full-length protein. Aro1 forms a flexible dimer allowing relative autonomy of enzymatic function of the individual domains. Our activity and in cellulo data suggest that only four of Aro1’s enzymatic domains are functional and essential for viability of C. albicans, whereas the 3-dehydroquinate dehydratase (DHQase) domain is inactive because of active site substitutions. We further demonstrate that in C. albicans, the type II DHQase Dqd1 can compensate for the inactive DHQase domain of Aro1, suggesting an unrecognized essential role for this enzyme in shikimate biosynthesis. In contrast, in Candida glabrata and Candida parapsilosis, which do not encode a Dqd1 homolog, Aro1 DHQase domains are enzymatically active, highlighting diversity across Candida species.

59 BASIC BIOLOGICAL SCIENCES↗