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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Investigating the Relationship between the Cell Wall Integrity Pathway and Unfolded Protein Response

Plants have made significant contributions to astronaut health in spaceflight missions. To further spaceflight research in optimizing plant viability, this study aims to understand the factors involved in maintaining cell wall integrity, which is vital to plant morphology and structural stability. Spaceflight can negatively impact the cell wall; thus, it is crucial to investigate how to mitigate spaceflight stressors to maintain the integrity of the cell wall. The structural integrity of plants’ cell walls depends on secondary cell wall biogenesis, which enables the repair and architectural support of plants like A. thaliana. This biogenesis is triggered by a signal transduction cascade: first initiated by cell wall stress, the CWI (cell wall integrity) pathway is activated, followed by the UPR (unfolded protein response), then the cell wall’s integrity is maintained through secondary cell wall biogenesis. Through a re-analysis of GeneLab Dataset 321 (GLDS-321), a study from NASA’s Open Science Data Repository that investigates the effects of spaceflight on the UPR, several genes were found to be associated with the cell wall. This proposal postulates a relationship between the UPR and the CWI pathway and their direct effect on secondary cell wall biogenesis by investigating IRX7, a gene associated with secondary cell wall biogenesis. The predicted outcome of overexpressing IRX7 is increased resilience of the cell wall by upregulating both the UPR and the CWI pathway, while silencing IRX7 is predicted to compromise the cell wall integrity by downregulating the UPR and the CWI pathway. This study will give insight into the needed measures to increase cell wall resilience in stressful environments: As spaceflight durations increase and uncertain climate change events progress on Earth, understanding how to optimize cell wall resilience – a fundamental pillar of plant health – can effectively enhance mass crop production and quality and ensure the physical and psychological health of astronauts in long-term space missions.

GL4HS↗
NASA NTRS2024

The Relationship Between Volatile Element Abundances and Iron Carrying Capacity in Lunar Volcanic and Impact Vapors

The highly volatile elements (e.g. H, C, S, Cl) released during lunar volcanism provide important insights into the bulk volatile composition of the Moon. Once erupted, some fraction of the species containing these elements may have migrated to the poles where they could have become sequestered in shadowed cold traps such as those found near the lunar South pole. The volatiles trapped in the lunar South pole are of great interest to the upcoming Artemis missions, which will be operating nearby and could sample some of those materials. Volcanic vapor deposits in the polar regions might also be supplemented by volatiles from other sources both exogenous (i.e. comets) and endogenous (i.e. remobilization of lunar volatiles by impact). Characterizing these endogenous sources is challenging because the highly volatile elements often leave little trace behind in the rocks themselves, which are inherently refractory by nature. Minerals are found on vug, vesicle, or fracture surfaces that have characteristics consistent with phases formed via direct deposition of a vapor, as opposed to crystallization from a liquid or via solid state decomposition. One common vapor-deposited species is metallic Fe, typically observed as subhedral to euhedral crystals found on vug or vesicle walls in mare basalts and on select breccia clast surfaces in regolith soil samples returned from the Moon. A particularly striking example is seen below in Fig. 1, where numerous Fe crystals are deposited onto the wall of a vug in Apollo 17 basalt sample 71036. Euhedral Fe crystals are not very common in Apollo breccia samples, though this may be from the limited number of systematic searches that have been performed. However, when Fe crystals are found, they can occur in extremely high abundance(e.g., sample 15402 which was specifically selected because of its numerous Fe crystals, was found to have a clast with 477 euhedral Fe crystals in a 570 x 570 μm area). What struck us as interesting about this was not only that it takes a large amount of Fe to make these crystal-rich deposits, but also that these crystal-rich deposits are not ubiquitous, and therefore they must be controlled by some aspect of their depositional conditions that varies. Since those conditions are inherently difficult to investigate and are critically interesting, we used thermochemical modeling as a tool to investigate the relationship between vapor-deposited Fe and various intensive and extensive parameters. We assumed that a high abundance of Fe in the vapor is required to form these crystals, thus the mass of Fe in the vapor was used to determine the plausibility of the vapor-deposited, euhedral Fe crystals observed in Apollo samples.

thermodynamics↗
NASA NTRSDate not supplied

Relationships Between Variability in the Stratospheric Polar Vortex, Upper Tropospheric Jets, Tropopauses, and UTLS Ozone

The impacts of stratospheric polar vortex variations on upper troposphere / lower stratosphere (UTLS) circulation and composition are not well understood. We use stratospheric polar vortex geometry / strength diagnostics combined with characterisation of tropopauses and UTLS jets to show links between stratospheric polar vortex variability and regional variations in UTLS jet and tropopause characteristics. Polar vortex diagnostics are from Characterization and Analysis of Vortex Evolution using Algorithms for Region Tracking (CAVE-ART), while UTLS diagnostics are from JEt and Tropopause Products for Analysis and Characterization (JETPAC). We will present results showing UTLS jet and tropopause characteristics for strong and weak stratospheric vortex states (defined using vortex-focused metrics that are less dependent on geometry and position than commonly used diagnostics), as well as regional variations in correlations between CAVE-ART and JETPAC diagnostics. Furthermore, we will focus on preliminary results extending the stratospheric vortex, UTLS jet, and tropopause relationships to assess their impacts on regional ozone variations diagnosed from the M2-SCREAM (MERRA-2 Stratospheric Composition Reanalysis of Aura Microwave Limb Sounder) composition reanalysis.

Gloria Manney↗
NASA NTRS2024

Parameterizing Spectral Surface Reflectance Relationships for the Dark Target Aerosol Algorithm Applied to A Geostationary Imager

Originally developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) in polar, sun-synchronous low-earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a Surface Reflectance Parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard, the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD)retrievals from ABIE. The primary reason appears to be that geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regards to solar angles coupled with fixed view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parametrization of both red-SWIR and blue-red spectral relationships to include additional information.The revised Red-SWIR SRP includes solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue-red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of DT AOD compared to AERONET AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03.The agreement between DT and AERONET AOD is established through regression slope of 1.06 and y-intercept of 0.01with correlation coefficient is0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 + 15%) is notably risen from 54% to 78%.

Mijin Kim↗
NASA NTRS2024

Northern Colorado Water Resources: Using Earth Observations to Assess Relationships between Snowpack and Wildfires for Water Utility Management

In recent years, wildfires in the western U.S. have increased in size, frequency, and severity. Colorado is no exception to this trend, with many recent wildfires raising concerns. The Cameron Peak and East Troublesome Fires, the two largest recorded wildfires in Colorado history, burned over 400,000 acres in 2020. Wildfires pose a significant threat to high-elevation ecosystems, especially in terms of disturbances to watershed resources. Colorado’s annual water supply is dependent upon the melt and runoff of high-elevation snowpack, thus understanding wildfire effects is crucial. This project partnered with Northern Water, a water management company in Colorado, to explore fire impacts on Colorado Front Range watersheds and water supply forecasts. We investigated the use of remote sensing to study the influences of wildfire on snow depth, as Northern Water had previously relied on field-based observations of snowpack for their water supply forecasts. This project utilized airborne LiDAR data from Airborne Snow Observatories, Inc., Landsat 8 OLI derived products, as well as in situ snow survey data from Colorado State University to assess and model changes in snowpack over time. We uncovered key drivers of change in snowpack such as burn severity and snow zone and created a random forest model to determine and investigate their relationships. Despite temporal data availability limitations, analysis also showed that remotely-sensed snow depth measurements were strongly correlated with in situ snow depth measurements, revealing the accuracy and feasibility of using remote sensing technologies to study landscape-scale snowpack characteristics for water utility management purposes.

airborne LiDAR↗
NASA NTRSDate not supplied

Understanding Relationships Between Satellite, Model, and Ground-Based Surface Temperature Characterizations From Overcast to Clear Conditions in Support of Satellite Remote Sensing of Clouds and Radiation

Accurate and consistent global estimates of cloud coverage and their properties are fundamental to long-term Earth radiation budget (ERB) monitoring efforts like the Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud detection algorithms often apply thresholding approaches to identify where clouds occur by comparing satellite-measured radiances with those that are expected under cloud-free conditions. In addition, once a cloud is detected, the derivation of cloud optical and microphysical properties also requires knowledge of the background radiances below the cloud. In the infrared, knowledge of the surface emissivity and the expected skin temperature under both cloudy and cloud-free conditions is needed. These traits are generally well known over the oceans. Over land, however, comparisons between satellite-derived land surface temperature (LST) with that characterized in numerical weather analyses reveal large differences in many parts of the world, often exceeding 5 K, which can lead to significant satellite cloud detection and cloud property retrieval errors. Furthermore, clouds have a dramatic influence on the LST, and therefore characterization of that model parameter also depends on the capability of the model to accurately resolve clouds. Thus, the LST characterized in models is, at times, a poor approximation for what would otherwise be observed, thereby impeding accurate satellite cloud retrievals. As a result, we seek to develop a more robust method for estimating the LST required for satellite cloud characterizations. This effort is accomplished through a combination of surface emission/air temperature relationship studies in all-sky conditions using ground measurement stations, along with deep neural network (DNN) estimates of expected LST under overcast and cloud-free conditions. We demonstrate that substituting DNN-predicted LST for that generated by numerical models can mitigate model-inherent diurnal dependencies and reduce overall bias and uncertainty relative to satellite/ground observations by 0.5–4 K and 0.5–2 K, respectively. It is expected that this work will lead to improved satellite cloud retrievals that enhance ERB monitoring efforts.

B Scarino↗
NASA NTRSDate not supplied

Assessing the Relationships Between Sensorimotor Biomarkers and Post-Landing Functional Task Performance

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study was to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts were recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests included assessments of sensory dependency and adaptability. Visual dependency involved treadmill walking while viewing a moving virtual visual scene. Vestibular perceptual thresholds were measured while seated during lateral translations. Proprioception dependency was measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability was performed (1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms (adaptive Functional Mobility Test, aFMT). The neuroimaging tests characterized individual differences in regional brain volumes (using Structural MRI) and white matter microstructure (using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests utilized saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Thirty ISS crewmembers were recruited, including 12 first-time fliers, 6F, and mission durations lasting 185.5 ± 45.5 days, mean ± std. We utilized a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There was considerable variability among all post-flight performance outcomes. Based on an ordinal scale survey, 72% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. Understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.

biomarkers↗
DOE OSTI2021

Robotic Understanding of Spatial Relationships Using Neural-Logic Learning

Understanding spatial relations of objects is critical in many robotic applications such as grasping, manipulation, and obstacle avoidance. Humans can simply reason object's spatial relations from a glimpse of a scene based on prior knowledge of spatial constraints. The proposed method enables a robot to comprehend spatial relationships among objects from RGB-D data. This paper proposed a neural-logic learning framework to learn and reason spatial relations from raw data by following logic rules on spatial constraints. The neural-logic network consists of three blocks: grounding block, spatial logic block, and inference block. The grounding block extracts high-level features from the raw sensory data. The spatial logic blocks can predicate fundamental spatial relations by training a neural network with spatial constraints. The inference block can infer complex spatial relations based on the predicated fundamental spatial relations. Simulations and robotic experiments evaluated the performance of the proposed method.

Wang, Dali↗
DOE OSTI2020

Developing Decommissioning Cost Estimating Relationships and the Decommissioning Estimate Analysis Library Estimating System - 20423

DOE-EM's Consolidated Business Center (EM-CBC) Office of Cost Estimating has developed a system of Decommissioning Cost Estimating Relationships (CERs) and Models to improve the reliability and efficiency of its estimates. The development builds on the multi-year process of collecting and organizing historical cost data from DOEEMs decommissioning projects and estimate model development to support project and program-level decommissioning estimates. The latest work has extended these models down to project scope element and component building area levels, allowing the actual costs of work for these estimate components to be assembled into estimates that reflect specific projects. The CERs have been incorporated into two estimator-friendly products - as cost libraries in the MII estimating program and as an MS Excel-based model, and are available for DOE federal staff use. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Attention for Causal Relationship Discovery from Biological Neural Dynamics

This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks. Our study primarily focuses on a proof-of-concept investigation based on simulated neural dynamics, for which the ground-truth causality is known through the underlying connectivity matrix. For transformer models trained to forecast neuronal population dynamics, we show that the cross-attention module effectively captures the causal relationship among neurons, with an accuracy equal to or superior to that of the most popular Granger causality discovery method. While we acknowledge that real-world neurobiology data will bring further challenges, including dynamic connectivity and unobserved variability, this research offers an encouraging preliminary glimpse into the utility of the transformer model for causal representation learning in neuroscience.

Lu, Ziyu↗
DOE OSTI2025

PROCESS-STRUCTURE-PROPERTY RELATIONSHIPS IN LASER POWDER BED FUSION PRODUCED 17-4 PH STEEL

Laser powder bed fusion (LPBF) is a metal additive manufacturing method that produces non-traditional microstructures as a result of the rapid solidification and thermal cycling inherent to the process. When using LPBF-produced material in application, these unique microstructures challenge the applicability of well developed mechanical property databases achieved by conventional heat treatments. For wider adoption of this technology, a more holistic understanding is necessary on how process attributes develop material structure, which dictate mechanical properties. This dissertation explores the process– structure–property relationships in LPBF 17-4 PH steel through systematic evaluation of atmospheric processing and heat treatment effects on microstructure and mechanical performance. Specimens were fabricated under controlled build environments, subjected to a range of solutionizing, homogenizing, and aging treatments, and characterized using optical microscopy, electron back scatter diffraction (EBSD), and X-ray diffraction (XRD) to quantify phase evolution. Tensile testing was performed to directly link heat treatment pathway and nitrogen absorption to mechanical performance. This work demonstrates where conventional heat treatment standards are applicable to LPBF 17-4 PH steel and where modifications are required. By directly correlating phase stability, nitrogen effects, and tensile response, this work provides practical guidelines for tailoring post-processing strategies. These findings underscore that successful application of LPBF 17-4 PH steel requires explicit consideration of both build environment and post-processing. By linking processing conditions to microstructure and performance, this work advances understanding of critical variables that govern reliability of additively manufactured precipitation-hardened stainless steels in demanding applications.

Brown, Benjamin [Kansas City National Security Cam↗
DOE OSTI2026

Modeling Marangoni Convection using a Velocity-Time Relationship

The Waste Treatment and Immobilization Plant (WTP) at the U.S. Department of Energy’s (DOE’s) Handford site is designed to treat 56 million gallons of radioactive waste through vitrification. The melting vessels physical and chemical integrity is essential to maintain, as failure has a high safety and financial penalty. Monofrax® K-3 refractory, the material widely used for the melting vessel, undergoes significant corrosion over time, concentrated at the triple-point junction between the gaseous atmosphere, refractory, and glass melt, producing a characteristic neck-like profile that significantly limits refractory service life. Accurate prediction of the corrosion within the melter can be used to prevent this and extend the lifetime of the melters. A computational fluid dynamics (CFD) model was previously developed, and predicted subsurface refractory corrosion, but failed to predict the neck-line profile observed experimentally. To address this limitation, the glass-melt meniscus geometry at the glass-refractory interface was estimated from experimentally measured neck profiles and wettability experiments. A curve was fitted to the neck profile to identify the triple point location, and the meniscus shape was characterized by a contact angle of 1°. A velocity profile accounting for Marangoni convection-driven corrosion was derived from the estimated meniscus geometry. Surface tension measurements from LORPM14R1 glasses of varying composition were coupled with K-3 coupon dissolution rates to establish a surface tension–time relationship. This analytical velocity-time model was integrated into the existing CFD framework to improve prediction of the neck corrosion profile. Work was done in a computational setting with national collaboration, resulting in professional development through presentational and technical writing growth.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Polymer Size–Catalytic Activity Relationships in Solution by Fluorescence Correlation Spectroscopy

Measuring the catalytic activity of specific sizes of polymers with active catalysts in solution is typically challenging, due to limited instrument detection sensitivity and/or dynamic range. Here, a fluorescence correlation spectroscopy (FCS) method is developed to determine the catalytic activity of living polymers of a specific apparent size in solution. Deviation from a single-component FCS data fitting, as assessed by χ2, is also introduced and developed as a “speciation index”—a method to evaluate and track changes in the relative amount of distinct polymer sizes with reaction progress. These methods are enabled by incorporating a selectively reactive fluorescent monomer into growing polydicyclopentadiene or polynorbornene during ring-opening metathesis polymerization (ROMP). Compared to polynorbornene, data showed that catalysts in aggregates of polyDCPD retained higher activity for longer—outcomes not directly inferable from simple diffusional-access predictions. Here, the ability to assign catalytic activity to polymers of specific sizes, and then to determine how this activity evolves with reaction progress, support long-term goals in the development and measurement of nano-objects that possess size-dependent catalytic activity.

Active catalyst↗
DOE OSTI2024

Structure-property relationship between lignin structures and properties of 3D-printed lignin composites

Lignin is a low-cost and renewable bioresource with a huge annual production promising to prepare sustainable materials. However, the poor interfacial adhesion between many lignin-polymer pairs deteriorates the mechanical performance of the composites, which seriously limits the application of lignin in 3D printing via fused depositional modeling. This work examined lignin-polyamide 12 (PA 12) intermolecular interactions (e.g., hydrogen bonding) to address the interface challenge. To realize this goal, the phenolic hydroxyl content was increased for a kraft softwood lignin using a LiBr/HBr demethylation procedure, increasing phenoxy content by 61.7%. Increased hydrogen bonding interactions between modified lignin (Pine-Lig-OH) and PA 12 demonstrated a significantly improved molten dynamic modulus by rheological analysis. Regarding mechanical properties, by adding 20 wt% of Pine-Lig-OH, the tensile strength and Young's modulus reached 46.6 MPa and 1.62 GPa, 30.2% and 33.9% higher than PA 12, respectively. Further morphological analysis proved the interfacial interactions are enhanced by showing the difference in the phase gaps. The dynamic mechanical analysis (DMA) supported the conclusion that Pine-Lig-OH could interact with polymer chains, alternating segmental movements due to the strong interaction. Here, this study presents a method to enhance lignin composite properties by promoting interactions with the polymer matrix through modified functional groups, guiding future lignin composite research.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dose-Response Relationship in Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma: A Pooled Analysis of an Asian Liver Radiation Therapy Group Study

Despite the worldwide implementation of stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC), there is a lack of consensus guideline on prescription dose. Herein, this multinational study aimed to investigate the effects of the prescribed radiation dose on oncologic outcomes of SBRT for HCC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Additive manufacturing of nickel-based superalloys: A state-of-the-art review on process-structure-defect-property relationship

Fusion-based additive manufacturing (AM) has significantly grown to fabricate Nickel-based superalloys with design freedom across multiple length scales. Several phenomena such as feedstock/energy source/melt pool interactions, solidification and phase transformations occur during fusion-based AM processes of Nickel-based superalloys, which determine the ultimate microstructure and mechanical performance of the built parts. In this review, here we elaborate a comprehensive discussion on AM Nickel-based superalloys and influential factors including feedstock characteristics (powder morphology, chemistry, contamination, flowability, recycling) and AM processing (parameters, and powder spreading/wall/balling/spattering effects) on their microstructure (micro-segregation, phases formations and grain structures), defect generation (sub-surface/internal defects, microcracks, surface roughness, and residual stress). Furthermore, the mechanical properties of AM Nickel-based superalloys such as tensile, creep and fatigue at room/elevated temperatures are analyzed in accordance with the initial, and post processing effects. Additionally, the commonly utilized modeling approaches in literature to predict the microstructure and mechanical behavior of these alloys are highlighted. Finally, the current challenges and mitigation approaches for future research are identified considering the gaps in the AM Nickel-based superalloys.

36 MATERIALS SCIENCE↗