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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Aftershocks of the 2012 Off-Coast of Sumatra Earthquake Sequence

Aftershocks of the 2012 Off-Coast of Sumatra Earthquake Sequence exhibit a complex and diffuse spatial distribution. The first-order complexity in aftershock distribution is clear and well beyond the influence of typical earthquake location uncertainty. The sequence included rupture of multiple fault segments, spatially separated. We use surface-wave based relative centroid locations to examine whether, at the small scale, the distribution of the aftershocks was influenced by location errors. Surface-wave based relative location has delineated precise oceanic transform fault earthquake locations in multiple regions. However, the relocated aftershocks off the coast of Sumatra seldom align along simple linear trends that are compatible with the corresponding fault strikes as estimated for the GCMT catalog. The relocation of roughly 60 moderate-earthquake epicentroids suggests that the faulting involved in the 2012 earthquake aftershock sequence included strain release along many short fault segments. Statistical analysis and temporal variations of aftershocks show a typical decay of the aftershocks but a relatively low number of aftershocks, as is common for intraplate oceanic earthquakes. Coulomb stress calculations indicate that most of the moderate-magnitude aftershocks are compatible with stress changes predicted by the large-event slip models. Furthermore the patterns in the aftershocks suggest that the formation of the boundary and eventual localization of deformation between the Indian and Australian plate is a complicated process.

2012 Sumatra Earthquake↗
DOE OSTI2023

The little skate genome and the evolutionary emergence of wing-like fins

Skates are cartilaginous fish whose body plan features enlarged wing-like pectoral fins, enabling them to thrive in benthic environments. However, the molecular underpinnings of this unique trait remain unclear. Here we investigate the origin of this phenotypic innovation by developing the little skate Leucoraja erinacea as a genomically enabled model. Analysis of a high-quality chromosome-scale genome sequence for the little skate shows that it preserves many ancestral jawed vertebrate features compared with other sequenced genomes, including numerous ancient microchromosomes. Combining genome comparisons with extensive regulatory datasets in developing fins—including gene expression, chromatin occupancy and three-dimensional conformation—we find skate-specific genomic rearrangements that alter the three-dimensional regulatory landscape of genes that are involved in the planar cell polarity pathway. Functional inhibition of planar cell polarity signalling resulted in a reduction in anterior fin size, confirming that this pathway is a major contributor to batoid fin morphology. We also identified a fin-specific enhancer that interacts with several hoxa genes, consistent with the redeployment of hox gene expression in anterior pectoral fins, and confirmed its potential to activate transcription in the anterior fin using zebrafish reporter assays. Our findings underscore the central role of genome reorganization and regulatory variation in the evolution of phenotypes, shedding light on the molecular origin of an enigmatic trait.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Robust predictions of specialized metabolism genes through machine learning

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.

data integration↗
DOE OSTI2010

THE ACS LCID PROJECT. VI. THE STAR FORMATION HISTORY OF THE TUCANA dSph AND THE RELATIVE AGES OF THE ISOLATED dSph GALAXIES

We present a detailed study of the star formation history (SFH) of the Tucana dwarf spheroidal galaxy. High-quality, deep HST/ACS data, collected in the framework of the LCID project, allowed us to obtain the deepest color-magnitude diagram to date, reaching the old main-sequence turnoff (F814 {approx} 29) with good photometric accuracy. Our analysis, based on three different SFH codes, shows that Tucana is an old and metal-poor stellar system, which experienced a strong initial burst of star formation at a very early epoch ({approx_equal}13 Gyr ago) which lasted a maximum of 1 Gyr (sigma value). We are not able to unambiguously answer the question of whether most star formation in Tucana occurred before or after the end of the reionization era, and we analyze alternative scenarios that may explain the transformation of Tucana from a gas-rich galaxy into a dSph. Current measurements of its radial velocity do not preclude that Tucana may have crossed the inner regions of the Local Group (LG) once, and so gas stripping by ram pressure and tides due to a close interaction cannot be ruled out. A single pericenter passage would generate insufficient tidal heating to turn an originally disky dwarf into a true dSph; however, this possibility would be consistent with the observed residual rotation in Tucana. On the other hand, the high star formation rate measured at early times may have injected enough energy into the interstellar medium to blow out a significant fraction of the initial gas content. Gas that is heated but not blown out would also be more easily stripped via ram pressure. We compare the SFH inferred for Tucana with that of Cetus, the other isolated LG dSph galaxy in the LCID sample. We show that the formation time of the bulk of star formation in Cetus is clearly delayed with respect to that of Tucana. This reinforces the conclusion of Monelli et al. that Cetus formed the vast majority of its stars after the end of the reionization era implying, therefore, that small dwarf galaxies are not necessarily strongly affected by reionization, in agreement with many state-of-the-art cosmological models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

NONLINEAR TIDES IN CLOSE BINARY SYSTEMS

We study the excitation and damping of tides in close binary systems, accounting for the leading-order nonlinear corrections to linear tidal theory. These nonlinear corrections include two distinct physical effects: three-mode nonlinear interactions, i.e., the redistribution of energy among stellar modes of oscillation, and nonlinear excitation of stellar normal modes by the time-varying gravitational potential of the companion. This paper, the first in a series, presents the formalism for studying nonlinear tides and studies the nonlinear stability of the linear tidal flow. Although the formalism we present is applicable to binaries containing stars, planets, and/or compact objects, we focus on non-rotating solar-type stars with stellar or planetary companions. Our primary results include the following: (1) The linear tidal solution almost universally used in studies of binary evolution is unstable over much of the parameter space in which it is employed. More specifically, resonantly excited internal gravity waves in solar-type stars are nonlinearly unstable to parametric resonance for companion masses M' {approx}> 10-100 M{sub Circled-Plus} at orbital periods P Almost-Equal-To 1-10 days. The nearly static 'equilibrium' tidal distortion is, however, stable to parametric resonance except for solar binaries with P {approx}< 2-5 days. (2) For companion masses larger than a few Jupiter masses, the dynamical tide causes short length scale waves to grow so rapidly that they must be treated as traveling waves, rather than standing waves. (3) We show that the global three-wave treatment of parametric instability typically used in the astrophysics literature does not yield the fastest-growing daughter modes or instability threshold in many cases. We find a form of parametric instability in which a single parent wave excites a very large number of daughter waves (N Almost-Equal-To 10{sup 3}[P/10 days] for a solar-type star) and drives them as a single coherent unit with growth rates that are a factor of Almost-Equal-To N faster than the standard three-wave parametric instability. These are local instabilities viewed through the lens of global analysis; the coherent global growth rate follows local rates in the regions where the shear is strongest. In solar-type stars, the dynamical tide is unstable to this collective version of the parametric instability for even sub-Jupiter companion masses with P {approx}< a month. (4) Independent of the parametric instability, the dynamical and equilibrium tides excite a wide range of stellar p-modes and g-modes by nonlinear inhomogeneous forcing; this coupling appears particularly efficient at draining energy out of the dynamical tide and may be more important than either wave breaking or parametric resonance at determining the nonlinear dissipation of the dynamical tide.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

CHARACTERIZATION OF A SPURIOUS ONE-YEAR SIGNAL IN HARPS DATA

The HARPS spectrograph is showing an extreme stability, close to the m s{sup −1} level, over more than 10 years of data. However, the radial velocities of some stars are contaminated by a spurious one-year signal with an amplitude that can be as high as a few m s{sup −1}. This signal is in opposition of phase with the revolution of Earth around the Sun and can be explained by the deformation of spectral lines crossing block stitchings of the CCD when the spectrum of an observed star is alternatively blueshifted and redshifted due to the motion of Earth around the Sun. This annual perturbation can be suppressed by either removing those affected spectral lines from the correlation mask used by the cross-correlation technique to derive precise radial velocities, or by simply fitting a yearly sinusoid to the radial velocity data. This is mandatory if we want to detect long-period low-amplitude signals in the HARPS radial velocities of quiet solar-type stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. Here, the explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2010

DIRECT OBSERVATIONAL EVIDENCE OF FILAMENT MATERIAL WITHIN INTERPLANETARY CORONAL MASS EJECTIONS

Coronal mass ejections (CMEs) are explosive events that escape the Sun's corona carrying solar material and energy into the heliosphere. The classic picture of a CME observed in the corona presents a 'three-part structure', including a bright front at the leading edge indicating dense plasma, a low-density cavity, the possible signature of an embedded magnetic flux rope, and the so-called core, a high-density region observed to be associated with an erupting filament. Although there are experimental analogs to the first two parts of the CME when observed in situ, there are only a handful of in situ observations of cold, filament-type plasma. This has been a source of major uncertainty and qualitative disagreement between remote and in situ observations of these ejecta. We present the first comprehensive and long-term survey of such low charge states observed by the Advanced Composition Explorer Solar Wind Ion Composition Spectrometer, using a novel data analysis process developed to identify ions with low ionic charge states. Using a very stringent set of observational signatures, we find that more than 4% of detected interplanetary CMEs have significant contributions of ions with low charge states. These time periods of low-charge ions often occur concurrent with some of the hottest ions, previously interpreted to be affected by flare heating during the CME initiation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

THE VERTICAL COMPONENT OF THE SUPERGRANULAR MOTION

Supergranules are observed at the solar photosphere as a cellular horizontal flow pattern with flow diverging from cell centers and converging on cell boundaries. Clark and Johnson calculated that mass conservation leads to an expected vertical flow of only 10 m s{sup -1}, which has been difficult to observe. In the present work, Doppler images near the disk center from Michelson Doppler Imager are averaged about locations of cell centers to obtain the necessary signal-to-noise ratio to see the vertical flow. It is found that, for an average over 1100 cell centers, there is a 10 m s{sup -1} upflow at cell center and a 5 m s{sup -1} downflow at the cell boundaries, confirming the previous estimate. The rms vertical flow is 4 m s{sup -1}, smaller than Giovanelli's upper limit of 10 m s{sup -1}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

EVIDENCE FOR EXTENDED STAR FORMATION IN THE OLD, METAL-RICH OPEN CLUSTER, NGC 6791?

NGC 6791 is an old, metal-rich star cluster normally considered to be a disk open cluster. Its red giant branch is broad in color yet, to date, there is no evidence for a metallicity spread among its stars. The turnoff region of the main sequence is also wider than expected from broadband photometric errors. Analysis of the color-magnitude diagram reveals a color gradient between the core of the cluster and its periphery; we evaluate the potential explanations for this trend. While binarity and photometric errors appear unlikely, reddening variations across the face of the cluster cannot be excluded. We argue that a viable alternative explanation for this color trend is an age spread resulting from a protracted formation time for the cluster; the stars of the inner region of NGC 6791 appear to be older by {approx}1 Gyr on average than those of the outer region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The reference genome for the northeastern Pacific bull kelp, Nereocystis luetkeana

Bull kelp, Nereocystis luetkeana, is a northeastern Pacific kelp with broad distribution from Alaska to central California. Its population declines have caused severe concerns in northern California, the Salish Sea in Washington, and recently in some populations in Oregon. Despite bull kelp's accumulated ecological and physiological studies, an assembled and annotated genomic reference was still unavailable. Here, we report the complete and annotated genome of Nereocystis luetkeana, produced by the California Conservation Genomics Project (CCGP), which aims to reveal genomic diversity patterns across California by sequencing the complete genomes of approximately 150 carefully selected species. The genome was assembled into 1562 scaffolds with 449.82 Mb, 80x of coverage and 22 952 gene models. BUSCO assembly showed a completeness score of 72% for the stramenopiles gene set. The mitochondria and chloroplast genome sequences have 37 Kb and 131 Mb, respectively. The orthology analysis between 10 Phaeophycean genomes showed 1065 expanded and 286 unique orthogroups for this species. Pairwise comparisons showed 542 orthogroups present only in N. luetkeana and M. pyrifera, another large-body kelp. The enrichment analysis of these orthogroups showed important functions related to central metabolism and signaling due to ATPases enrichment in these two species. This genome assembly will provide an essential resource for the ecology, evolution, conservation, and breeding of bull kelp.

California Conservation Genomics Project—CCGP↗
DOE OSTI2024

Human Liver Epithelial Cells (HuH7) Response to HCoV-229E Infection Epigenomics (ATAC-Seq) (ACS-DP4)

The purpose of this experiment was to evaluate how wild-type Human coronavirus strain 229E (HCoV-299E) infection alters chromatin accessibility in infected cells. Sample data was obtained from mock-infected cells, UV-inactivated virus treated cells, and replication competent HCoV-229E infected immortalized human liver cells (HuH7) at 24 hours post infection. Samples were processed using ATAC-seq methods for reported bar coded libraries. Sample data was acquired using an Illumina Hi-Seq 2500 sequencer system and further processed for ATAC-Seq expression analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SFA-VirOmics

PNNL's Soil Microbiome Science Focus Area (SFA) is focused on understanding the basic biology underpinning how interactions among various soil microbial community members, across trophic levels, lead to the emergence of community functions. Moisture, in particular, drives microbial interactions and influences everything from cell function to substrate fate within soils. The group predicts this results in repeatable, predictable phenotypes. The sum of these phenotypes comprises the “soil metaphenome”. Understanding how the soil metaphenome shifts in response to moisture will provide a basis for modeling and predicting these shifts in reaction network responses. Visit the PNNL Soil Microbiome Science Focus Area Program homepage for more information. PNNL’s Soil Microbiome SFA Virome Dataset Annotation page is an extension to the PNNL Soil Microbiome SFA repository on DataHub allows for exploring and downloading integrated experimental omics dataset annotations, associated experimental metadata, pre- and post-processed viral data files, and other associated materials directly related to experimental viral project data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗
DOE OSTI2022

A Structural Perspective on the RNA Editing of Plant Respiratory Complexes

The last steps of respiration, a core energy-harvesting process, are carried out by a chain of multi-subunit complexes in the inner mitochondrial membrane. Several essential subunits of the respiratory complexes are RNA-edited in plants, frequently leading to changes in the encoded amino acids. While the impact of RNA editing is clear at the sequence and phenotypic levels, the underlying biochemical explanations for these effects have remained obscure. Here, we used the structures of plant respiratory complex I, complex III2 and complex IV to analyze the impact of the amino acid changes of RNA editing in terms of their location and biochemical features. Through specific examples, we demonstrate how the structural information can explain the phenotypes of RNA-editing mutants. This work shows how the structural perspective can bridge the gap between sequence and phenotype and provides a framework for the continued analysis of RNA-editing mutants in plant mitochondria and, by extension, in chloroplasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Are High-redshift Galaxies Hot? Temperature of z > 5 Galaxies and Implications for Their Dust Properties

Recent studies have found a significant evolution and scatter in the relationship between the UV spectral slope ( β {sub UV}) and the infrared excess (IRX; L {sub IR}/ L {sub UV}) at z > 4, suggesting different dust properties of these galaxies. The total far-infrared (FIR) luminosity is key for this analysis, but it is poorly constrained in normal (main-sequence) star-forming z > 5 galaxies, where often only one single FIR point is available. To better inform estimates of the FIR luminosity, we construct a sample of local galaxies and three low-redshift analogues of z > 5 systems. The trends in this sample suggest that normal high-redshift galaxies have a warmer infrared (IR) spectral energy distribution (SED) compared to average z < 4 galaxies that are used as priors in these studies. The blueshifted peak and mid-IR excess emission could be explained by a combination of a larger fraction of metal-poor interstellar medium being optically thin to ultraviolet (UV) light and a stronger UV radiation field due to high star formation densities. Assuming a maximally warm IR SED suggests a 0.6 dex increase in total FIR luminosities, which removes some tension between the dust attenuation models and observations of the IRX− β relation at z > 5. Despite this, some galaxies still fall below the minimum IRX− β relation derived with standard dust cloud models. We propose that radiation pressure in these highly star-forming galaxies causes a spatial offset between dust clouds and young star-forming regions within the lifetime of O/B stars. These offsets change the radiation balance and create viewing-angle effects that can change UV colors at fixed IRX. We provide a modified model that can explain the location of these galaxies on the IRX− β diagram.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. The explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973). tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗