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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
DOE OSTI2021

leapR: An R Package for Multiomic Pathway Analysis

A generalized goal of many high-throughput data studies is to identify functional mecha-nisms that underlie observed biological phenomena, whether disease outcomes or metabolic out-put. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, tran-scriptomic, proteomic, metabolomic) are faced with a challenge of utilizing the data in a way that maximizes utility. However, methods for integration of multiple forms of molecular data into a biolog-ically coherent frameworks are needed. Furthermore, we have developed a framework to assess biological pathway activity that relates to phenotypic outcome using multi-source data. Availability and implementation: The leapR package with user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Outer midplane neutral density measurements and H-mode fueling studies in NSTX-U

Neutral density profiles are measured on the outboard midplane of the National Spherical Torus Experiment Upgrade (NSTX-U) using a two dimensional camera (ENDD, edge neutral density diagnostic) filtered for deuterium Balmer α (D α ) emission interpreted via simulations using the Monte Carlo neutral transport code DEGAS 2. Deuterium atomic densities n D are calculated by inverting the line-integrated D α brightness and using local measurements of electron density ne and temperature T e to determine atomic rate coefficients. The assumptions used in the derivation of n D from D α emissivity are validated using DEGAS 2 to estimate contributions to emissivity due to electron impact excitation and molecular processes. In this work, experimental measurements and DEGAS 2 simulations are compared over a database of L- and H-mode discharges, showing good agreement in D α emissivity profiles. Residual disagreement between experiment and simulations is suggestive of the possible role of intermittent transport and uncertainties in the molecular data. DEGAS 2 simulations are further used to complement the ENDD diagnostic, extracting neutral (atomic and molecular) densities at locations where the ENDD measurement are not made or where the assumptions used in the ENDD analysis are not valid. Deuterium atomic densities and ionization profiles on the outer midplane are compared for L-mode and H-mode discharges in NSTX-U. One-way coupling of DEGAS 2 to UEDGE multi-fluid simulations constrained by experimental data is used to study edge fueling and neutral penetration in NSTX-U discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity

Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data ( N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The P( 4 S) + NH( 3 Σ – ) and N( 4 S) + PH( 3 Σ – )reactions as sources of interstellar phosphorus nitride

Phosphorus nitride (PN) is believed to be one of the major reservoirs of phosphorus in the interstellar medium (ISM). For this reason, understanding which reactions produce PN in space and predicting their rate coefficients is important for modelling the relative abundances of P-bearing species and clarifying the role of phosphorus in astrochemistry. Here, in this work, we explore the potential energy surfaces of the $\textrm{P}(^4\textrm{S}) + \textrm{NH}(^3\Sigma^-)$ and $\textrm{N}(^4\textrm{S}) + \textrm{PH}(^3\Sigma^-)$ reactions and the formation of $\textrm{H}(^2\textrm{S}) + \textrm{PN}(^1\Sigma^+)$ through high accuracy ab initio calculations and the variable reaction coordinate transition state theory (VRC-TST). We found that both reactions proceed without an activation barrier and with similar rate coefficients that can be described by a modified Arrhenius equation ( $k(T)=\alpha\!\left( T/300 \right)^{\beta} \exp\!{(\!-\!\gamma/T)})$ with $\alpha=0.93\times 10^{-10}\rm cm^3\,s^{-1}$ , $\beta=-0.18$ and $\gamma=0.24\, \rm K$ for the $\textrm{P} + \textrm{NH} \longrightarrow \textrm{H} + \textrm{PN}$ reaction and $\alpha=0.88\times 10^{-10}\rm cm^3\,s^{-1}$ , $\beta=-0.18$ and $\gamma=1.01\, \rm K$ for the $\textrm{N} + \textrm{PH} \longrightarrow \textrm{H} + \textrm{PN}$ one. Both reactions are expected to be relevant for modelling PN abundances even in the cold environments of the ISM. Given the abundance of hydrogen in space, we have also predicted rate coefficients for the destruction of PN via H + PN collisions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2024

Signatures of transposon-mediated genome inflation, host specialization, and photoentrainment in Entomophthora muscae and allied entomophthoralean fungi

Despite over a century of observations, the obligate insect parasites within the order Entomophthorales remain poorly characterized at the genetic level. In this manuscript, we present a genome for a laboratory-tractable Entomophthora muscae isolate that infects fruit flies. Our E. muscae assembly is 1.03 Gb, consists of 7810 contigs and contains 81.3% complete fungal BUSCOs. Using a comparative approach with recent datasets from entomophthoralean fungi, we show that giant genomes are the norm within Entomophthoraceae owing to extensive, but not recent, Ty3 retrotransposon activity. In addition, we find that E. muscae and its closest allies possess genes that are likely homologs to the blue-light sensor white-collar 1, a Neurospora crassa gene that has a well-established role in maintaining circadian rhythms. We uncover evidence that E. muscae diverged from other entomophthoralean fungi by expansion of existing families, rather than loss of particular domains, and possesses a potentially unique suite of secreted catabolic enzymes, consistent with E. muscae’s species-specific, biotrophic lifestyle. Finally, we offer a head-to-head comparison of morphological and molecular data for species within the E. muscae species complex that support the need for taxonomic revision within this group. Altogether, we provide a genetic and molecular foundation that we hope will provide a platform for the continued study of the unique biology of entomophthoralean fungi.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Artificial intelligence “sees” split electrons

Chemical bonds between atoms are stabilized by the exchange-correlation (xc) energy, a quantum-mechanical effect in which “social distancing” by electrons lowers their electrostatic repulsion energy. Kohn-Sham density functional theory (DFT) states that the electron density determines this xc energy, but the density functional must be approximated. Furthermore, this is usually done by satisfying exact constraints of the exact functional (making the approximation predictive), by fitting to data (making it interpolative), or both. Two exact constraints—the ensemble-based piecewise linear variation of the total energy with respect to fractional electron number and fractional electron z-component of spin —require hard-to-control nonlocality. On page 1385 of this issue, Kirkpatrick et al. have taken a big step toward more accurate predictions for chemistry through the machine learning of molecular data plus the fractional charge and spin constraints, expressed as data that a machine can learn.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Disentangling Sources of Gene Tree Discordance in Phylogenomic Data Sets: Testing Ancient Hybridizations in Amaranthaceae s.l

Gene tree discordance in large genomic data sets can be caused by evolutionary processes such as incomplete lineage sorting and hybridization, as well as model violation, and errors in data processing, orthology inference, and gene tree estimation. Species tree methods that identify and accommodate all sources of conflict are not available, but a combination of multiple approaches can help tease apart alternative sources of conflict. Here, using a phylotranscriptomic analysis in combination with reference genomes, we test a hypothesis of ancient hybridization events within the plant family Amaranthaceae s.l. that was previously supported by morphological, ecological, and Sanger-based molecular data. The data set included seven genomes and 88 transcriptomes, 17 generated for this study. We examined gene-tree discordance using coalescent-based species trees and network inference, gene tree discordance analyses, site pattern tests of introgression, topology tests, synteny analyses, and simulations. We found that a combination of processes might have generated the high levels of gene tree discordance in the backbone of Amaranthaceae s.l. Furthermore, we found evidence that three consecutive short internal branches produce anomalous trees contributing to the discordance. Overall, our results suggest that Amaranthaceae s.l. might be a product of an ancient and rapid lineage diversification, and remains, and probably will remain, unresolved. This work highlights the potential problems of identifiability associated with the sources of gene tree discordance including, in particular, phylogenetic network methods. Our results also demonstrate the importance of thoroughly testing for multiple sources of conflict in phylogenomic analyses, especially in the context of ancient, rapid radiations. We provide several recommendations for exploring conflicting signals in such situations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms

Spatially resolved mass spectrometry (MS)-based multi-omics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, in this study, we developed workflows for comprehensive multi-omics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time of flight (timsTOF)-MS. Afterwards, we detected 6,571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6,542 unique proteins across a single poplar root tissue section. These results were comparable to using each assay independently without modifications. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics-levels across biological tissues.

Veličković, Marija [Pacific Northwest National Lab↗
DOE OSTI2022

A comprehensive study of the radiative properties of NO—a first step toward a complete air opacity

Here, in this paper we propose a methodology to calculate the radiative properties of the diatomic molecular constituents of air, and utilize the present approach to model the radiative properties of nitrogen monoxide, NO. We also investigate the important physics involved in calculating accurate radiative quantities for air, such as the Planck and Rosseland mean opacities, and emission and absorption coefficients, as well as the couplings accounted for in rovibrational calculations. Complete active space self-consistent field multi-reference configuration interaction (CAS-MRCI) calculations were performed in order to model the NO X 2 Π, a 4 Π, b 4 Σ - , 1 2 Σ + , 2 2 Σ + , 3 2 Σ + , G 2 Σ - , B' 2 Δ, (C, B) 2 2 Π, (H', L) 3 2 Π, and 1 2 Φ adiabatic states, and calculate the respective molecular data. The γ X 2 Π - A 2 Σ + , ε X 2 Π - D 2 Σ + , β' X 2 Π - B' 2 Δ, '11 000 Å' A 2 Σ + - D 2 Σ + , 'infrared' X 2 Π - X 2 Π and X 2 Π - (C, B) 2 2 Π (δ and β) band systems are investigated in monochromatic spectra calculations, as well as the Ogawa a 4 Π - b 4 Σ - band and several other band systems. Several conclusions are drawn, such as the importance of including the Ogawa band, which has not been included in previous air radiative models or comprehensive line-list calculations, as well as the importance of performing coupled rovibrational line-list calculations in order to accurately calculate the Rosseland means. We also found that the additional band systems modeled here contribute significantly to the total Planck and Rosseland means.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Estimating rates and patterns of diversification with incomplete sampling: a case study in the rosids

Premise Recent advances in generating large‐scale phylogenies enable broad‐scale estimation of species diversification. These now common approaches typically are characterized by (1) incomplete species coverage without explicit sampling methodologies and/or (2) sparse backbone representation, and usually rely on presumed phylogenetic placements to account for species without molecular data. We used empirical examples to examine the effects of incomplete sampling on diversification estimation and provide constructive suggestions to ecologists and evolutionary biologists based on those results. Methods We used a supermatrix for rosids and one well‐sampled subclade (Cucurbitaceae) as empirical case studies. We compared results using these large phylogenies with those based on a previously inferred, smaller supermatrix and on a synthetic tree resource with complete taxonomic coverage. Finally, we simulated random and representative taxon sampling and explored the impact of sampling on three commonly used methods, both parametric (RPANDA and BAMM) and semiparametric (DR). Results We found that the impact of sampling on diversification estimates was idiosyncratic and often strong. Compared to full empirical sampling, representative and random sampling schemes either depressed or inflated speciation rates, depending on methods and sampling schemes. No method was entirely robust to poor sampling, but BAMM was least sensitive to moderate levels of missing taxa. Conclusions We suggest caution against uncritical modeling of missing taxa using taxonomic data for poorly sampled trees and in the use of summary backbone trees and other data sets with high representative bias, and we stress the importance of explicit sampling methodologies in macroevolutionary studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

Carbon cycling↗
DOE OSTI2021

A pectin methyltransferase modulates polysaccharide dynamics and interactions in Arabidopsis primary cell walls: Evidence from solid-state NMR

Plant cell walls contain cellulose embedded in matrix polysaccharides. Understanding carbohydrate structures and interactions is critical to the production of biofuel and biomaterials using these natural resources. Here we present a solid-state NMR study of cellulose and pectin in 13 C-labeled cell walls of Arabidopsis wild-type and mutant plants. Using 1D 13 C and 2D 13 C– 13 C correlation experiments, we detected a highly branched arabinan structure in qua2 and tsd2 samples, two allelic mutants for a pectin methyltransferase. Both mutants show close physical association between cellulose and the backbones of pectic homogalacturonan and rhamnogalacturonan-I. Relaxation and dipolar order parameters revealed enhanced microsecond dynamics due to polymer disorder in the mutants, but restricted motional amplitudes due to tighter pectin-cellulose associations. These molecular data shed light on polymer structure and packing in these two pectin mutants, helping to elucidate how pectin could influence cell wall architecture at the nanoscale, cell wall mechanics, and plant growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Resolving Heterogeneous Dynamics of Excess Protons in Aqueous Solution with Rate Theory

Rate theories have found great utility across the chemical sciences by providing a physically transparent way to analyze dynamical processes. Here we demonstrate the benefits of using transition state theory and Marcus theory to study the rate of proton transfer in HCl solutions. By using long ab initio molecular dynamics simulations we show that good agreement is obtained between these two different formulations of rate theory and how they can be used to study the pathways and life-time of proton transfer in aqueous solution. Since both rate theory formulations utilize identical sets of molecular data, this provides a self-consistent theoretical picture of the rates of aqueous phase proton transfer. Specifically, we isolate and quantify the rates of proton transfer, ion-pair dissociation, and solvent exchange in concentrated HCl solutions. Our analysis predicts a concentration dependence to both proton transfer and ion-pairing. Moreover, our estimate of the life-time for the Zundel species is 0.8 ps and 1.3 ps for 2M and 8M HCl, respectively. In this paper, we demonstrate that concentration effects, can indeed be quantified through the combination of state-of-the-art simulation and theory and provides a picture of the important correlations between the cation (hydronium) and the counter-ion in acid solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Learning local equivariant representations for large-scale atomistic dynamics

Abstract A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Metagenome-assembled-genomes recovered from the Arctic drift expedition MOSAiC

The Multidisciplinary Observatory for Study of the Arctic Climate (MOSAiC) expedition consisted of a year-long drifting survey of the Central Arctic Ocean. The ecosystems component of MOSAiC included the sampling of molecular data, with metagenomes collected from a diverse range of environments. The generation of metagenome-assembled-genomes (MAGs) from metagenomes are a starting point for genome-resolved analyses. This dataset presents a catalogue of MAGs recovered from a set of 73 samples from MOSAiC, including 2407 prokaryotic and 56 eukaryotic MAGs, as well as annotations of a near complete eukaryotic MAG using the Joint Genome Institute (JGI) annotation pipeline. The metagenomic samples are from the surface ocean, chlorophyll maximum, mesopelagic and bathypelagic, within leads and under-ice ocean, as well as melt ponds, ice ridges, and first- and second-year sea ice. This set of MAGs can be used to benchmark microbial biodiversity in the Central Arctic Ocean, compare individual strains across space and time, and to study changes in Arctic microbial communities from the winter to summer, at a genomic level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

N-dependent dynamics of root growth and nitrate and ammonium uptake are altered by the bacterium Herbaspirillum seropedicae in the cereal model Brachypodium distachyon

Nitrogen (N) fixation in cereals by root-associated bacteria is a promising solution for reducing use of chemical N fertilizers in agriculture. However, plant and bacterial responses are unpredictable across environments. We hypothesized that cereal responses to N-fixing bacteria are dynamic, depending on N supply and time. To quantify the dynamics, a gnotobiotic, fabricated ecosystem (EcoFAB) was adapted to analyse N mass balance, to image shoot and root growth, and to measure gene expression of Brachypodium distachyon inoculated with the N-fixing bacterium Herbaspirillum seropedicae. Phenotyping throughput of EcoFAB-N was 25–30 plants h -1 with open software and imaging systems. Herbaspirillum seropedicae inoculation of B. distachyon shifted root and shoot growth, nitrate versus ammonium uptake, and gene expression with time; directions and magnitude depended on N availability. Primary roots were longer and root hairs shorter regardless of N, with stronger changes at low N. At higher N, H. seropedicae provided 11% of the total plant N that came from sources other than the seed or the nutrient solution. The time-resolved phenotypic and molecular data point to distinct modes of action: at 5 mM NH 4 NO 3 the benefit appears through N fixation, while at 0.5 mM NH 4 NO 3 the mechanism appears to be plant physiological, with H. seropedicae promoting uptake of N from the root medium.Future work could fine-tune plant and root-associated microorganisms to growth and nutrient dynamics.

59 BASIC BIOLOGICAL SCIENCES↗