Search NASA⌕ Search

SEARCH · Search NASA

Results for “imports”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dynamic Importance Sampling

Dynamic Importance (DynIm) Sampling is a new approach for importance sampling in high-dimensional space. DynIm has two key characteristics. (1) Importance sampling: The notion of importance of candidate samples is used to guide the sampling, where importance is defined based on the (dis)similarity from previously selected samples (via Euclidean distance metric). (2) Dynamic sampling: The sampling can be performed dynamically, as new candidates are generated and new samples must be selected. DynIm was developed as part of a machine learning based coupling of scales in large multiscale simulations and can be used for a variety of applications.

Bhatia, Harsh↗
DOE OSTI2021

Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems

For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critical components that are out of service during the restoration. For this purpose, there is a need for component importance analysis. While it has been investigated extensively for individual systems, component importance considering interdependence among transmission, distribution and communication (T&D&C) systems has not been systematically analyzed and widely adopted. In this study, we propose a component importance assessment method in the context of interdependence between T&D&C networks. Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics. The proposed methodology is further validated with integrated synthetic Illinois regional transmission, distribution, and communication (T&D&C) systems, the results reveal the unique characteristics of component/node importance, which are strongly affected by the network topologies and cross-domain node mapping.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
DOE OSTI2020

Coexpressed subunits of dual genetic origin define a conserved supercomplex mediating essential protein import into chloroplasts

Significance Chloroplasts are of vital importance in photosynthetic eukaryotic organisms. Like mitochondria, they contain their own genomes. Nevertheless, most chloroplast proteins are encoded by nuclear genes, translated in the cytosol, and must cross the chloroplast envelope membranes to reach their proper destinations inside the organelle. Despite its fundamental role, our knowledge of the machinery catalyzing chloroplast protein import is incomplete and controversial. Here, we address the evolutionary conservation, composition, and function of the chloroplast protein import machinery using the green alga Chlamydomonas reinhardtii as a model system. Our findings help clarify the current debate regarding the composition of the chloroplast protein import machinery, provide evidence for cross-compartmental coordination of its biogenesis, and open promising avenues for its structural characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Household energy resilience in extreme weather events: An investigation of energy service importance, HVAC usage behaviors, and willingness to pay

Household energy resilience, particularly maintaining essential energy services within an acceptable and affordable level, is critical during extreme weather events (e.g., winter storms, heat waves) with power shortages or outages. However, the research on the importance and acceptable compromise levels of energy services, HVAC usage behavior changes, and willingness to pay (WTP) for energy services during disasters is limited. Therefore, this study investigates the importance and compromise of household energy services, HVAC usage behaviors, and WTP, as three key dimensions of household energy resilience, in both normal days and disaster scenarios through surveys (n=485). First, 12 common residential end-uses are grouped into high, mid, and low-priority services using hierarchical clustering, in which high-priority services (perceived as important and un-compromisable) include food cooking and storage (refrigerator and freezer) and thermal comfort (space heating and cooling), mid-priority services involve water heating and lighting, while low-priority services are mostly related to entertainment and housework (e.g., TV, laundry). Also, HVAC use patterns demonstrate that occupants become more energy conservative during disasters, e.g., reducing durations of running HVAC and adjusting temperature setpoints to save energy. In terms of energy cost, approximately 60% of respondents are willing to pay more to maintain energy services during disasters, although only 13% are prepared to pay over double the normal energy prices. The regression models further reveal that household energy resilience varies across different sociodemographic groups (e.g., age, income, family size) and is impacted by disaster experience and beliefs in clean energy and climate. In conclusion, these research findings provide important perspectives to understand household energy resilience and inform measures for disaster adaptation of households in extreme weather events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Global Simulations Suggest Biomass Burning Aerosol Emissions From Grassland Fires Could Be Important Ice Nucleating Particles

Ice nucleating particles (INP) capable of nucleating ice crystals via immersion freezing at temperatures above approximately −35°C may strongly influence cloud glaciation, with implications for global precipitation and climate feedback. In addition to mineral dust, soil dust, and marine organics, laboratory and field measurements suggest biomass burning aerosols (BBA) can act as immersion-mode INP between around −30°C and −15°C. However, the contribution of BBA to the global INP budget remains poorly understood due to poor knowledge of which fuels yield INPs, uncertainties in global coverage of those fuels, and unknown size distributions of the INPs in the BBA. Nonetheless, with some understanding of these uncertainties from sensitivity studies, the relative importance of ice nucleation activity of BBA compared to other INP sources can be quantified. In this work, we investigate the potential global importance of BBA as INP using a global aerosol-climate model, specifically the UK Met Office Unified Model (UM). We evaluate the model using field campaign data sets. We examine potential uncertainties in fuel types and particle sizes on BBA-based INP concentrations. Averaged over June–September between 15°S and 50°S, BBA is a more important INP than dust and marine INP about 30% of the time at altitudes with temperatures between −30°C and −20°C. Our simulations therefore suggest BBA INPs may be at least as important as mineral dust and marine INP over the atmospheric regions and seasons where grassland fires are frequent.

Gohil, Kanishk [Carnegie Mellon University, Pittsb↗
DOE OSTI2024

An essential and highly selective protein import pathway encoded by nucleus-forming phage

Targeting proteins to specific subcellular destinations is essential in prokaryotes, eukaryotes, and the viruses that infect them. Chimalliviridae phages encapsulate their genomes in a nucleus-like replication compartment composed of the protein chimallin (ChmA) that excludes ribosomes and decouples transcription from translation. These phages selectively partition proteins between the phage nucleus and the bacterial cytoplasm. Currently, the genes and signals that govern selective protein import into the phage nucleus are unknown. Here, we identify two components of this protein import pathway: a species-specific surface-exposed region of a phage intranuclear protein required for nuclear entry and a conserved protein, PicA (Protein importer of chimalliviruses A), that facilitates cargo protein trafficking across the phage nuclear shell. We also identify a defective cargo protein that is targeted to PicA on the nuclear periphery but fails to enter the nucleus, providing insight into the mechanism of nuclear protein trafficking. Using CRISPRi-ART protein expression knockdown of PicA, we show that PicA is essential early in the chimallivirus replication cycle. Together, our results allow us to propose a multistep model for the Protein Import Chimallivirus pathway, where proteins are targeted to PicA by amino acids on their surface and then licensed by PicA for nuclear entry. The divergence in the selectivity of this pathway between closely related chimalliviruses implicates its role as a key player in the evolutionary arms race between competing phages and their hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools and data for modeling this risk is highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data’s high uncertainty. This report outlines initial work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant models. This research paves the way for the development of methods to reduce fire data uncertainty used in modeling. The Nuclear Regulatory Commission has mandated that nuclear power plants perform fire risk modeling. However, there are several issues with the current risk modeling implementation that affect the results. Approved modeling methods can be overly conservative and often do not match plant experience. Also, the data used in the modeling can have high uncertainties and is influenced by expert judgement. To evaluate input data uncertainty, researchers performed an initial review of several fire experiments done at Sandia National Laboratories. Uncertainties for fire data can come from many sources, such as experiment design constraints, environmental conditions, or other plant-specific aspects. There are many different significant and insignificant parameters driving the uncertainty. Additionally, the uncertainty of the different input data used in the fire modeling could have a significant or insignificant effect on the entire plant risk. A four-step methodology was developed to perform Integrated Probabilistic Risk Assessment Importance Ranking. A demonstration case using these steps was set up and three of the four steps were completed in fiscal year (FY) 2019 and the fourth step done FY 2020. These steps are: 1. The qualitative analysis of potential sources was conducted with the following items identified for the demonstration. • Maximum heat release rate • Time to maximum heat release rate • Duration of max heat release rate • Time to decay • Thermal conductivity of concrete • Specific heat of concrete • Density of concrete • Cable jacket thickness 2. A quantitative characterization of dominant sources of uncertainty was performed. A list of distributions and determined values of the dominant sources is shown in Appendix A. 3. A quantitative screening of the potential sources of uncertainty using Morris Elementary Effects Analysis was completed. An experimental model using the physics-based fire modeling tool Fire Dynamics Simulator was developed and coupled with the Risk Analysis Virtual Environment. The Morris analysis identified at least two parameters that can be eliminated as significant contributors (specific heat of concrete and cable jacket thickness). 4. Global importance measure (Global IM) analysis to generate a comprehensive ranking based on their influence on the plant risk. In this research, a moment-independent Global IM is used since it can address (a) uncertainty in the input parameters of the fire model, (b) uncertainty in the risk outputs, and (c) non-linearity and interactions among input parameters in the fire model, more accurately than the correlation-based and variance-based global methods. The observations from the research showed that, depending on the initial and boundary conditions of the fire scenarios, fire-induced damage could have a very small probability and could be dominated by the tail of the uncertainty distribution; hence, the accuracy of the correlation-based and variance-based methods is questionable. The moment-independent Global IM analysis in this research provides a better understanding of how experimental uncertainty data affects industry’s plant models and where improvements in that data will have the largest benefit for improving fire modeling accuracy in causing core damage. Among the five unscreened parameters obtained from the Morris EE analysis, the Global IM analysis results for the case study indicated that max heat release rate and fire location are the most important parameters. The report also outlines benefits of using a unified computational platform that integrates the underlying simulations (e.g., a fire progression model), quantitative screening (using the Morris EE method), and the Global IM analysis. A unified platform can (i) facilitate the ranking of input parameters considering multiple key fire scenarios simultaneously, rather than considering one scenario at a time, (ii) contribute to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, (iii) facilitate the sampling-based uncertainty quantification for Fire PRA, and (iv) help generating both “industry-wide” and "plant-specific" ranking of uncertainty sources in Fire PRA. Future research should be done to include additional parameters such as detection/suppression or cable fire spread. Adding a PRA software such as SAPHIRE to the RAVEN platform would help with plant model integration and improve treatment of fire-induced dependency. The I-PRA risk importance ranking methodology offered in this report can provide valuable information for efficiently (a) enhancing the realism of Fire PRA for existing plants and (b) supporting the development of Dynamic Fire PRA for advanced reactors and new plants.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effects of red imported fire ant ( Solenopsis invicta ) control on carrion use by vertebrate scavengers

Carrion is an important resource, and competition for carrion by microbial, invertebrate, and vertebrate scavenging communities is great. In particular, carrion is important to facultative and obligate vertebrate scavengers globally, and these species provide an irreplaceable ecological service by efficiently removing carcasses, which potentially harbor pathogens, from the environment. Although vertebrates efficiently acquire carcasses, they compete intensively with invertebrate and microbial communities, especially during warmer months when invertebrate and microbial activity peaks. The presence of invasive invertebrates may further exacerbate competition among scavenging groups; however, little is known about how invasive invertebrates can influence invertebrate scavenger dynamics and ultimately competition with vertebrate scavengers. The red imported fire ant (Solenopsis invicta; RIFA) is an invasive species of particular interest in North America, as it is a dominant predator and decomposer throughout much their range. We hypothesized that in areas where abundant, RIFA can outcompete vertebrates for carrion resources, altering nutrient cycling and food web dynamics. We tested this hypothesis by experimentally excluding RIFA from rabbit carcasses and found strong seasonal effects in our data; however, surprisingly, we found no effect of RIFA control on vertebrate scavenging efficiency, rates of carcass detection, carcass persistence, or the richness of local scavengers. Rather, we observed a highly efficient vertebrate scavenging community that assimilated 87.4% of carcasses during our study. Furthermore, this high rate of vertebrate scavenging efficiency was maintained in both control (93.0%) and RIFA treatment (81.8%) sites, suggesting presence of abundant RIFA populations did not limit vertebrate acquisition of carrion. Future research should focus on the interactions of RIFA with the remaining invertebrate scavenging community to further investigate whether RIFA impact the assimilation of carrion resources within invertebrate communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling

Global Sensitivity Analysis (GSA) aims to understand the relative importance of uncertain input variables to model response. Conventional GSA involves calculating sensitivity (Sobol’) indices for a model with known model parameter distributions. However, model parameters are affected by aleatory and epistemic uncertainty, with the latter often caused by lack of data. In this paper, we propose a new framework to quantify uncertainty in probability model-form and model parameters resulting from small datasets and integrate these uncertainties into Sobol’ index estimates. First, the process establishes, through Bayesian multimodel inference, a set of candidate probability models and their associated probabilities. Imprecise Sobol’ indices are calculated from these probability models using an importance sampling reweighting approach. This results in probabilistic Sobol’ indices, whose distribution characterizes uncertainty in the sensitivity resulting from small dataset size. The imprecise Sobol’ indices thus provide a measure of confidence in the sensitivity estimate and, moreover, can be used to inform data collection efforts targeted to minimize the impact of uncertainties. Through an example studying the parameters of a Timoshenko beam, we show that these probabilistic Sobol’ indices converge to the true/deterministic Sobol’ indices as the dataset size increases and hence, distribution-form uncertainty reduces. The approach is then applied to assess the sensitivity of the out-of-plane properties of an E-glass fiber composite material to its constituent properties. This second example illustrates the approach for an important class of materials with wide-ranging applications when data may be lacking for some input parameters.

42 ENGINEERING↗
DOE OSTI2023

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function– based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Cysteine Mutants of the Major Facilitator Superfamily-Type Transporter CcoA Provide Insight into Copper Import

Copper (Cu) is a redox-active micronutrient that is both essential and toxic. Its cellular homeostasis is critical for supporting cuproprotein maturation while avoiding excessive oxidative stress. The Cu importer CcoA is the prototype of the widespread CalT subfamily of the MFS-type transporters. Hence, understanding its molecular mechanism of function is significant. Here, we show that CcoA undergoes a thiol:disulfide oxidoreduction cycle, which is important for its Cu import activity.

MFS-type transporters↗
DOE OSTI2023

The importance of explicitly representing the streambed in watershed models

Abstract The streambed is the critical interface between the aquatic and terrestrial systems and hosts important biogeochemical hot spots within river corridors. Although the streambed characteristics are significantly different from those of its surrounding soil, the streambed itself has not been explicitly represented in watershed models. Here, we explicitly incorporated a streambed layer into an integrated hydrologic model through model parameterization and discretization. We examined the hydrological effects of streambed characteristics, including hydraulic conductivity ( K ), layer thickness, and resolution, on the exchange fluxes across the streambed as well as the streamflow at the watershed outlet. The numerical experiments were performed in the American River Watershed, a headwater, mountainous watershed within the Yakima River Basin in central Washington. Despite having a negligible effect on the watershed streamflow, an explicit representation of the streambed with distinctive properties dramatically changed the magnitude and variability of the exchange flux. In general, a larger streambed K along with a thicker streambed layer induced larger exchange fluxes. The exchange flux was most sensitive to the streambed resolution. A finer streambed resolution increased exchange fluxes per unit area while reducing the overall exchange volumes across the entire streambed. The amount of baseflow decreased by 6% as the streambed resolution increased from 250 to 50 m. This finding is important because these hydrological changes may, in turn, affect the exchange of nutrients and contaminants between surface water and groundwater and the associated biogeochemical processes. Our work demonstrated the importance of representing streambeds in fully distributed, process‐based watershed models to better capture the exchange flow dynamics in river corridors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Importance of Depth and Artificial Structure as Predictors of Female Red Snapper Reproductive Parameters

Abstract The Red Snapper Lutjanus campechanus is a structure‐associated species occurring across a wide depth range in the northern Gulf of Mexico. We used the random forest machine learning algorithm to understand which habitat and individual fish characteristics could predict reproductive parameters of female Red Snapper. We evaluated fish captured from 2016 to 2018 on three artificial structure types with various structure heights at depths of 100 m or less. Overall, we found that depth and month were important predictors for most reproductive parameters, but the type of structure (artificial reefs, oil platforms, and rigs‐to‐reefs structures) was not important. Maturity was correctly classified in 88.9% of the cases when using the random forest ensemble model, with important predictors including FL, depth, structure height, and month of collection. Spawning seasonality (measured as gonadosomatic index [GSI]) was correctly classified in 59.5% of the cases when using histology reproductive phase, FL, month, and depth variables. Reproductively active or inactive females were correctly classified in 89.3% of the cases using GSI, month, FL, and depth, while females in the developing versus spawning capable phases were correctly classified in 82.2% of the cases using GSI, FL, month, and depth. Histological indicators that show potential spawning within a 36‐h period were correctly classified 61.5% of the time, with the best predictors being depth, FL, GSI, and month. Stepwise regression indicated that month was the only factor that significantly predicted contrasts in relative batch fecundity, with significantly greater values in August compared to all other months. Our findings suggest that female Red Snapper reproductive effort is not consistently or well predicted by artificial structure type or height but that a combination of fish FL, month, and depth can predict reproductive characteristics of female Red Snapper.

Brown‐Peterson, Nancy J.↗
DOE OSTI2021

Dendritic cells focus CTL responses toward highly conserved and topologically important HIV-1 epitopes

During early HIV-1 infection, immunodominant T cell responses to highly variable epitopes lead to the establishment of immune escape virus variants. Here we assessed a type 1-polarized monocyte-derived dendritic cell (MDC1)-based approach to selectively elicit cytotoxic T lymphocyte (CTL) responses against highly conserved and topologically important HIV-1 epitopes in HIV-1-infected individuals from the Thailand RV254/SEARCH 010 cohort who initiated antiretroviral therapy (ART) during early infection (Fiebig stages I-IV). Autologous MDC1 were used as antigen presenting cells to induce in vitro CTL responses against HIV-1 Gag, Pol, Env, and Nef as determined by flow cytometry and ELISpot assay. Ultra-conserved or topologically important antigens were respectively identified using the Epigraph tool and a structure-based network analysis approach and compared to overlapping peptides spanning the Gag proteome. MDC1 presenting either the overlapping Gag, Epigraph, or Network 14-21mer peptide pools consistently activated and expanded HIV-1-specific T cells to epitopes identified at the 9-13mer peptide level. Interestingly, some CTL responses occurred outside known or expected HLA associations, providing evidence of new HLA-associated CTL epitopes. Comparative analyses demonstrated more sequence conservation among Epigraph antigens but a higher magnitude of CTL responses to Network and Gag peptide groups. Importantly, CTL responses against topologically constrained Gag epitopes contained in both the Network and Gag peptide pools were selectively enhanced in the Network pool-initiated cultures. Our study supports the use of MDC1 as a therapeutic strategy to induce and focus CTL responses toward putative fitness-constrained regions of HIV-1 to prevent immune escape and control HIV-1 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems in the Northern and Eastern United States

Spring phenology of temperate ecosystems is highly sensitive to climate change, generating various impacts on many important terrestrial surface biophysical processes. Although various prognostic models relying on environmental variables of temperature and photoperiod have been developed for spring phenology, comprehensive ecosystem-scale evaluations over large landscapes and long-time periods remain lacking. Further, environmental variables other than temperature and photoperiod might also importantly constrain spring phenology modelling but remain under-investigation. To address these issues, we leveraged around 20-years datasets of environmental variables (from Daymet and GLDAS products) and the spring phenology metric (i.e., the greenup date) respectively derived from MODIS and PhenoCams across 108 sites in the Northern and Eastern United States. We firstly cross-compared MODIS-derived greenup date with official PhenoCams product with high accuracy (R 2 = 0.70). Then, we evaluated the three prognostic models (i.e., Growing Degree Date (GDD), Sequential (SEQ) and optimality-based (OPT)) with MODIS-derived spring phenology, assessed the model residuals and their associations with soil moisture, rainfall, and solar radiation, and revised the two photoperiod-relevant models (SEQ, OPT) by replacing the daylength variable with solar radiation, which was found to contribute the most to model residuals. We found that 1) all models demonstrated good capability in characterizing spring phenology, with OPT performing the best (RMSE = 8.04 ± 5.05 days), followed by SEQ (RMSE = 10.57 ± 7.77 days) and GDD (RMSE = 10.84 ± 8.42 days), 2) all models displayed high model residuals showing tight correlation with solar radiation (r = 0.45–0.75), and 3) the revised models that included solar radiation significantly performed better with an RMSE reduction by 22.08%. Such results are likely because solar radiation better constrains early growing season plant photosynthesis than photoperiod, supporting the hypothesis of spring phenology as an adaptive strategy to maximize photosynthetic carbon gain (approximated by solar radiation) while minimizing frost damage risk (captured by temperature). Collectively, our study here reveals the underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems, and suggests ways to improve spring phenology modelling and other phenology-related ecological processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mechanism of karyopherin-β2 binding and nuclear import of ALS variants FUS(P525L) and FUS(R495X)

Mutations in the RNA-binding protein FUS cause familial amyotropic lateral sclerosis (ALS). Several mutations that affect the proline-tyrosine nuclear localization signal (PY-NLS) of FUS cause severe juvenile ALS. FUS also undergoes liquid–liquid phase separation (LLPS) to accumulate in stress granules when cells are stressed. In unstressed cells, wild type FUS resides predominantly in the nucleus as it is imported by the importin Karyopherin-β2 (Kapβ2), which binds with high affinity to the C-terminal PY-NLS of FUS. Here, we analyze the interactions between two ALS-related variants FUS(P525L) and FUS(R495X) with importins, especially Kapβ2, since they are still partially localized to the nucleus despite their defective/missing PY-NLSs. The crystal structure of the Kapβ2·FUS(P525L) PY-NLS complex shows the mutant peptide making fewer contacts at the mutation site, explaining decreased affinity for Kapβ2. Biochemical analysis revealed that the truncated FUS(R495X) protein, although missing the PY-NLS, can still bind Kapβ2 and suppresses LLPS. FUS(R495X) uses its C-terminal tandem arginine-glycine-glycine regions, RGG2 and RGG3, to bind the PY-NLS binding site of Kapβ2 for nuclear localization in cells when arginine methylation is inhibited. These findings suggest the importance of the C-terminal RGG regions in nuclear import and LLPS regulation of ALS variants of FUS that carry defective PY-NLSs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Importance of multimodal characterization and influence of residual Li 2 S impurity in amorphous Li 3 PS 4 inorganic electrolytes

Amorphous Li 3 PS 4 (LPS) solid-state electrolytes are promising for energy-dense lithium metal batteries. LPS glass, synthesized from a 3:1 mol ratio of Li 2 S and P 2 S 5 , has high ionic conductivity and can be synthesized by ball milling or solution processing. Ball milling has been attractive because it provides the easiest route to access amorphous LPS with a conductivity of 3.5 × 10 –4 S cm –1 (20 °C). However, achieving the complete reaction of precursors via ball milling can be difficult, and most literature reports use X-ray diffraction (XRD) or Raman spectroscopy to confirm sample purity, both of which have limitations. Furthermore, the effect of residual precursors on ionic conductivity and lithium metal cycling is unknown. In this work, we illustrate the importance of multimodal characterization to determine LPS phase and chemical purity. To determine the residual Li 2 S content in LPS, we show that (1) XRD and 31 P solid state nuclear magnetic resonance (ssNMR) are insufficient and (2) Raman loses sensitivity at concentrations below 12 mol% Li 2 S. Most importantly, we show that 7 Li ssNMR is highly sensitive. Using 7 Li ssNMR, we investigate the effect of ball milling parameters and develop a robust and highly reproducible procedure for pure LPS synthesis. We find that as the residual Li 2 S precursor content increases, LPS conductivity decreases and lithium metal batteries exhibit higher overpotentials and poor cycle life. Furthermore, our work reveals the importance of multimodal characterization techniques for amorphous solid-state electrolyte characterization and will enable better synthetic strategies for highly conductive electrolytes for efficient energy-dense solid-state lithium metal batteries.

25 ENERGY STORAGE↗