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

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize

Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked tocis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, includingvgt1,ZmTRE1and the MITE transposon nearZmNAC111under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functionalcis-variation accessible at scale for genetic studies and targeted engineering of complex traits.

Genetics & Heredity↗
DOE OSTI2021

Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies

Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structural and functional analyses explain Pea KAI2 receptor diversity and reveal stereoselective catalysis during signal perception

KAI2 proteins are plant α/β hydrolase receptors which perceive smoke-derived butenolide signals and endogenous, yet unidentified KAI2-ligands (KLs). The number of functional KAI2 receptors varies among species and KAI2 gene duplication and sub-functionalization likely plays an adaptative role by altering specificity towards different KLs. Legumes represent one of the largest families of flowering plants and contain many agronomic crops. Prior to their diversification, KAI2 underwent duplication resulting in KAI2A and KAI2B. Here we demonstrate that Pisum sativum KAI2A and KAI2B are active receptors and enzymes with divergent ligand stereoselectivity. KAI2B has a higher affinity for and hydrolyses a broader range of substrates including strigolactone-like stereoisomers. We determine the crystal structures of PsKAI2B in apo and butenolide-bound states. The biochemical, structural, and mass spectra analyses of KAI2s reveal a transient intermediate on the catalytic serine and a stable adduct on the catalytic histidine, confirming its role as a bona fide enzyme. Our work uncovers the stereoselectivity of ligand perception and catalysis by diverged KAI2 receptors and proposes adaptive sensitivity to KAR/KL and strigolactones by KAI2B.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Low quantum efficiency of μ-oxo iron bisporphyrin photocatalysts explained with femtosecond M-edge XANES

Bridged μ-oxo iron porphyrins serve as photocatalysts for oxidative organic transformations, but suffer from low photon-to-product efficiency. This low photochemical quantum yield is most commonly attributed to the short lifetime of a disproportionated TPPFe(II)/TPPFe(IV)=O state, but an alternate hypothesis suggests that the majority photoproduct is a catalytically inactive ligand-centered TPPFe(III)+/TPPFe(III)–O – ion pair. We use femtosecond optical and extreme ultraviolet (XUV) spectroscopy to investigate the early photodynamics of the μ-oxo iron bisporphyrin (TPPFe) 2 O and identify the primary loss mechanism. XUV spectroscopy probes 3p → 3d transitions, corresponding to M 2,3 -edge XANES spectra, and is a distinctive probe of the metal oxidation state. Excitation of the mixed π–π*/ligand-to-metal charge transfer (LMCT) band results in the formation of an iron(II)/iron(III) LMCT state in tens of femtoseconds. This state decays on a subpicosecond timescale to the ligand-centred iron(III) ion pair state, and no TPPFe(IV)=O species is observed within the sensitivity of the measurement. Finally, the lack of an iron(II)/iron(IV) XANES spectrum suggests that preferential formation of the inactive iron(III) ion pair state is a main cause of the low quantum yield of this and similar bisporphyrins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

ACES-GNN: can graph neural network learn to explain activity cliffs?

Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Explaining an unusual electromigration behavior—A comprehensive experimental and theoretical analysis using finite element method

In metallic interconnects, it is generally assumed that electromigration (EM) failure location is independent of the applied electrical current and always occurs at the highest-current-density area. Our experiments show otherwise. We designed an Al interconnect that alters its failure location by only varying the applied current density. The failure occurs near the high for a current above 2 × 10 7 A/cm 2 , but at a location with 59% of the maximum for lower current densities. Thermoreflectance thermal imaging is employed to gather time-dependent high-resolution spatial temperature distributions of the Al interconnect during EM. More importantly, we propose a computationally inexpensive 2D finite element method that tracks EM evolution in time and matches well with the observations from different experimental conditions. A detailed analysis covering the major driving forces of EM is carried out to understand the complex physics behind EM. The atomic depletion rate contributed by each force is quantitatively studied. By examining the results from every tested experimental condition, the model reveals that the temperature gradient is the key reason causing atomic depletion near the failure location. Graphical illustrations and qualitative analysis are provided to intuitively show the key findings of our work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Adjustments to the law of the wall above an Amazon forest explained by a spectral link

Modification to the law of the wall represented by a dimensionless correction function ϕ RSL (z/h) is derived using atmospheric turbulence measurements collected at two sites in the Amazon in near-neutral stratification, where z is the distance from the forest floor and h is the mean canopy height. The sites are the Amazon Tall Tower Observatory for z/h∈ [1,2.3] and the Green Ocean Amazon (GoAmazon) site for z/h∈ [1,1.4]. Here, a link between the vertical velocity spectrum E ww (k) (k is the longitudinal wavenumber) and ϕ RSL is then established using a co-spectral budget (CSB) model interpreted by the moving-equilibrium hypothesis. The key finding is that ϕ RSL is determined by the ratio of two turbulent viscosities and is given as ν t,BL /ν t,RSL, where ν t,RSL = (1/A)∫$^{∞}_{0}$ τ(k)E ww (k)dk, ν t,BL = k v (z−d)u * , τ(k) is a scale-dependent decorrelation time scale between velocity components, A = C R /(1−C I ) = 4.5 is predicted from the Rotta constant C R = 1.8, and the isotropization of production constant C I = 3/5 given by rapid distortion theory, k v is the von Kármán constant, u * is the friction velocity at the canopy top, and d is the zero-plane displacement. Because the transfer of energy across scales is conserved in E ww (k) and is determined by the turbulent kinetic energy dissipation rate (ε), the CSB model also predicts that ϕ RSL scales with L BL /L d , where L BL is the length scale of attached eddies to z = d, and L d = u$_{*}^{3}$/ε is a macro-scale dissipation length.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Analytic rate theory of polariton relaxation that explains long polariton lifetime

Hybridization of a molecular exciton with a quantized photon creates a polariton. Despite extensive experimental investigations, the apparent lifetime of the exciton–polariton is not well-understood. For this work, we examined the steady-state population dynamics for a Holstein–Tavis–Cumming Hamiltonian to illuminate the long-term polaritonic dynamics and lifetime of the exciton–polariton in an optical cavity. For a realistic description of polariton relaxation, cavity loss and various exciton decay channels are included in the model. We found that in the presence of weak but finite exciton loss, the apparent lifetime of the lower polariton coincides with the out-of-cavity exciton lifetime and is independent of cavity-matter detuning. This is a simple explanation for the experimentally observed lifetimes for exciton polaritons and theoretically justifies the dark state reservoir hypothesis. Furthermore, if the upper polariton is initially populated, the system reaches the steady state very quickly, leading to single-exponential polariton relaxation. Starting from the lower polariton leads to a longer pre-steady-state time period, leading to double-exponential relaxation. Finally, we considered the effect of site orientational disorders and the exciton frequency disorderers. Under the collective limit, the effects of this disorder can be included in Fermi’s golden rule population dynamics without explicit sampling. For the exciton energy disorders, numerical calculations are needed. Our theoretical framework is applicable to interpret exciton–polariton experiments, especially related to the measured apparent lifetime of polaritons.

Chemical dynamics↗
DOE OSTI2021

Linking species traits and demography to explain complex temperature responses across levels of organization

Significance Microbes regulate nutrient flux and carbon storage within ecosystems, making them essential to the global carbon cycle and ecosystem responses to climate change. Understanding how climate change will alter microbial communities and how this will feed back to influence the pace of climate change requires linking processes across levels of organization, from individual organisms to whole ecosystems. We show that the physical characteristics (traits) of protist species influence how their populations will respond to changes in temperature. These species-level temperature responses also alter the composition, dynamics, and functioning of whole microbial communities. Using traits to understand how complex, multilevel microbial processes change with temperature can help us anticipate possible consequences of rapid global climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Land management explains major trends in forest structure and composition over the last millennium in California’s Klamath Mountains

Significance We provide the first assessment of aboveground live tree biomass in a mixed conifer forest over the late Holocene. The biomass record, coupled with local Native oral history and fire scar records, shows that Native burning practices, along with a natural lightning-based fire regime, promoted long-term stability of the forest structure and composition for at least 1 millennium in a California forest. This record demonstrates that climate alone cannot account for observed forest conditions. Instead, forests were also shaped by a regime of frequent fire, including intentional ignitions by Native people. This work suggests a large-scale intervention could be required to achieve the historical conditions that supported forest resiliency and reflected Indigenous influence.

Knight, Clarke A.↗
DOE OSTI2022

Reimport of carbon from cytosolic and vacuolar sugar pools into the Calvin–Benson cycle explains photosynthesis labeling anomalies

Significance Photosynthesis metabolites are quickly labeled when 13 CO 2 is fed to leaves, but the time course of labeling reveals additional contributing processes involved in the metabolic dynamics of photosynthesis. The existence of three such processes is demonstrated, and a metabolic flux model is developed to explore and characterize them. The model is consistent with a slow return of carbon from cytosolic and vacuolar sugars into the Calvin–Benson cycle through the oxidative pentose phosphate pathway. Our results provide insight into how carbon assimilation is integrated into the metabolic network of photosynthetic cells with implications for global carbon fluxes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Osmotic and phoretic competition explains chemotaxic assembly and sorting

Microscale objects responding to chemical gradients by migrating toward or away from a preferred species is a simple yet constitutive mechanism by which transport occurs in biological organisms. Synthetic chemotaxis provides key physical descriptions of simplified systems that can be used in biological models, or in the creation of advanced responsive material systems. In this article, we provide a quantitative framework for understanding synthetic chemotaxis of microparticles which involves a competition between phoresis and osmosis. We present separate quantitative measurements of phoresis and osmosis acting on individual taxing particles, finding that phoresis follows the long-predicted v ∼ 1 / r 2 scaling while the osmotic contribution depends on the geometry and details of the system, and must be solved on a case-by-case basis. Through this, we are able to develop a more accurate picture of particle transport at the single particle level. Equipped with this approach, we go on to describe how high concentrations of particles in a symmetric chemical gradient grow close-packed hives that reach a steady-state size tunable through light intensity or particle size. Last, we demonstrate that mixed particles experiencing the same chemical gradient will selectively migrate toward or away depending on the nature of the particle surface, thereby locally sorting out a particular species. We anticipate these results will be important in describing both biological and synthetic chemotaxis in phoretic systems and should bring a wealth of studies that take advantage of competing osmotic flows to illicit unexpected dynamic active behavior.

Science & Technology - Other Topics↗
DOE OSTI2020

Mechanistic insights explain the transforming potential of the T507K substitution in the protein-tyrosine phosphatase SHP2

The protein-tyrosine phosphatase SHP2 is an allosteric enzyme critical for cellular events downstream of growth factor receptors. Mutations in the SHP2 gene have been linked to many different types of human diseases, including developmental disorders, leukemia, and solid tumors. Unlike most SHP2-activating mutations, the T507K substitution in SHP2 is unique in that it exhibits oncogenic Ras-like transforming activity. However, the biochemical basis of how the SHP2/T507K variant elicits transformation remains unclear. By combining kinetic and biophysical methods, X-ray crystallography, and molecular modeling, as well as using cell biology approaches, in this work we uncovered that the T507K substitution alters both SHP2 substrate specificity and its allosteric regulatory mechanism. We found that although SHP2/T507K exists in the closed, autoinhibited conformation similar to the WT enzyme, the interactions between its N-SH2 and protein-tyrosine phosphatase domains are weakened such that SHP2/T507K possesses a higher affinity for the scaffolding protein Grb2-associated binding protein 1 (Gab1). We also discovered that the T507K substitution alters the structure of the SHP2 active site, resulting in a change in SHP2 substrate preference for Sprouty1, a known negative regulator of Ras signaling and a potential tumor suppressor. Our results suggest that SHP2/T507K's shift in substrate specificity coupled with its preferential association of SHP2/T507K with Gab1 enable the mutant SHP2 to more efficiently dephosphorylate Sprouty1 at pTyr-53. This dephosphorylation hyperactivates Ras signaling, which is likely responsible for SHP2/T507K's Ras-like transforming activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗