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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
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 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 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↗
DOE OSTI2024

Altering translation allows E. coli to overcome chemically stabilized G-quadruplexes

To investigate the effects of stabilizing G-quadruplexes on Escherichia coli, cells were grown in the presence of the G-quadruplex stabilizing compound, NMM, and RNA-seq was performed. This was done for the control cell strain (MG1655 dtolC) as well as a strain with a decrease in a translation elongation factor (MG1655 dtolC tufA::kan). TPM values were used for differential gene expression analysis between each growth condition. All samples were grown and sequenced in triplicate.

Keck, James L.↗
DOE OSTI2012

Hardware Architectures for Data-Intensive Computing Problems: A Case Study for String Matching

DNA analysis is an emerging application of high performance bioinformatic. Modern sequencing machinery are able to provide, in few hours, large input streams of data, which needs to be matched against exponentially growing databases of known fragments. The ability to recognize these patterns effectively and fastly may allow extending the scale and the reach of the investigations performed by biology scientists. Aho-Corasick is an exact, multiple pattern matching algorithm often at the base of this application. High performance systems are a promising platform to accelerate this algorithm, which is computationally intensive but also inherently parallel. Nowadays, high performance systems also include heterogeneous processing elements, such as Graphic Processing Units (GPUs), to further accelerate parallel algorithms. Unfortunately, the Aho-Corasick algorithm exhibits large performance variability, depending on the size of the input streams, on the number of patterns to search and on the number of matches, and poses significant challenges on current high performance software and hardware implementations. An adequate mapping of the algorithm on the target architecture, coping with the limit of the underlining hardware, is required to reach the desired high throughputs. In this paper, we discuss the implementation of the Aho-Corasick algorithm for GPU-accelerated high performance systems. We present an optimized implementation of Aho-Corasick for GPUs and discuss its tradeoffs on the Tesla T10 and he new Tesla T20 (codename Fermi) GPUs. We then integrate the optimized GPU code, respectively, in a MPI-based and in a pthreads-based load balancer to enable execution of the algorithm on clusters and large sharedmemory multiprocessors (SMPs) accelerated with multiple GPUs.

Tumeo, Antonino↗
DOE OSTI2018

Population structure of Miscanthus sacchariflorus reveals two major polyploidization events, tetraploid-mediated unidirectional introgression from diploid M. sinensis, and diversity centred around the Yellow Sea

Abstract Background and Aims Miscanthus, a C4 perennial grass native to East Asia, is a promising biomass crop. Miscanthus sacchariflorus has a broad geographic range, is used to produce paper in China and is one of the parents (along with Miscanthus sinensis) of the important biomass species Miscanthus × giganteus. The largest study of M. sacchariflorus population genetics to date is reported here. Methods Collections included 764 individuals across East Asia. Samples were genotyped with 34 605 single nucleotide polymorphisms (SNPs) derived from restriction site-associated DNA sequencing (RAD-seq) and ten plastid microsatellites, and were subjected to ploidy analysis by flow cytometry. Key Results Six major genetic groups within M. sacchariflorus were identified using SNP data: three diploid groups, comprising Yangtze (M. sacchariflorus ssp. lutarioriparius), N China and Korea/NE China/Russia; and three tetraploid groups, comprising N China/Korea/Russia, S Japan and N Japan. Miscanthus sacchariflorus ssp. lutarioriparius was derived from the N China group, with a substantial bottleneck. Japanese and mainland tetraploids originated from independent polyploidization events. Hybrids between diploid M. sacchariflorus and M. sinensis were identified in Korea, but without introgression into either parent species. In contrast, tetraploid M. sacchariflorus in southern Japan and Korea exhibited substantial hybridization and introgression with local diploid M. sinensis. Conclusions Genetic data indicated that the land now under the Yellow Sea was a centre of diversity for M. sacchariflorus during the last glacial maximum, followed by a series of migrations as the climate became warmer and wetter. Overall, M. sacchariflorus has greater genetic diversity than M. sinensis, suggesting that breeding and selection within M. sacchariflorus will be important for the development of improved M. × giganteus. Ornamental M. sacchariflorus genotypes in Europe and North America represent a very narrow portion of the species’ genetic diversity, and thus do not well represent the species as a whole.

Clark, Lindsay V.↗
DOE OSTI2022

Lake Arrowhead Microbial Genomics Conference

The Lake Arrowhead Microbial Genomics Conference (formerly the E. coli and Small Genomes Conference, and the Microbial Genomes Conference) was held September 11-15, 2022, at the UCLA Conference Center at Lake Arrowhead, California. This conference is part of a yearly meeting initiated in 1991 to bring together genome sequencers, bioinformatics specialists, biologists, and geneticists, to forge interactions that would result in meaningful functional genomics. The goal of the meeting was to translate the influx of new genome sequencing information into useful biological studies. The Lake Arrowhead 2022 meeting had a major focus on microbial communities, the human microbiome, pathogens, phage therapy, bioinformatic methods of analysis, bioenergenics, and environmental microbiology. The field of genomics has reached the point where deriving the sequence of an organism’s entire genome is now seen as a beginning rather than an endpoint. Moreover, understanding the sequences of whole communities, and particularly those that make up different microbiomes, is now a central point of the field. The 2022 meeting covered microorganisms for which extensive analyses exist, and those for which new biological and technical strategies are being developed. The focus on biodiversity, the human microbiome, pathogenic organisms and methods of countering them, and bioenergetics added special significance to this meeting. This meeting was designed to have a mix of 36 invited presentations and poster sessions with more than 80 posters, and had 147 participants. This meeting has become established as a major annual microbial genomics conference during the last 29 years and has assured status and quality. It offers a major opportunity for young investigators and students to attend, present posters, and give talks. In fact, 50% of the invited speakers were early career stage scientists. An important impact of the conference was the forging of new collaborations between scientists and particularly young scientists with the array of multidisciplinary researchers at the meeting.

Miller, Jeffrey H.↗
DOE OSTI2024

Investigation of the notch sensitivity of tailorable long fiber discontinuous prepreg composite laminates

Tailorable discontinuous fiber composite laminates provide relative formability beyond that of continuous fiber laminates, while achieving improved mechanical performance over comparable stochastic systems. Here, in this work, the notch sensitivity of engineered prepreg platelet molded composite (PPMC) laminates is investigated using the open-hole tension (OHT) test and compared to available data for stochastic PPMCs and continuous fiber laminates made with the same material. The press-formed thermoplastic composites (AS4/PEKK) were molded with a quasi-isotropic stacking sequence. The discontinuous PPMC laminate was found to be notch insensitive with OHT strengths ranging from 145.4 MPa (CV $=$ 7%) for d/w $=$ 0.5 to 229.3 MPa (CV $=$ 9%) for d/w $=$ 0.25. The highly ordered meso-structure of the engineered PPMC laminate yields comparatively excellent mechanical properties for relatively thin laminates in contrast to stochastic systems. Both net- and gross-section failures were observed for d/w $=$ 0.25, which suggests that the engineered PPMC laminates studied here maintain a degree of inherent, internal stress concentrations that compete with those caused by geometric features such as a circular hole. Computational simulations of the OHT tests with explicitly represented platelets were found to be in good agreement with experimental measurements. The progressive failure analysis was used to conduct a numerical investigation of the stacking sequence and platelet meso-morphology.

36 MATERIALS SCIENCE↗