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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Analysis of genomic signatures associated with Variovorax endosphere colonization

This repository contains the analysis code and supporting datasets associated with the study “Genomic signatures in Variovorax enabling colonization of the Populus endosphere.” Beals DG, Carper DL, Hochanadel LH, Jawdy SS, Klingeman DM, Piatkowski BT, Weston DJ, Doktycz MJ, Pelletier DA. 2026. Genomic signatures in Variovorax enabling colonization of the Populus endosphere. mSystems 11:e01605-25. https://doi.org/10.1128/msystems.01605-25 The scripts are organized sequentially (01–07) and document the workflows used for: Sequence-read alignment and feature counting Orthogroup and KEGG Ortholog annotation Count normalization Statistical analysis and aggregation Generation of manuscript figures and tables Repository contents The uncompressed files are the finalized, formatted datasets used to generate the figures and tables reported in the study, including the supplemental CSV files referenced in the manuscript. The accompanying ZIP archive contains the complete codebase and example data_input/ and data_output/ directories illustrating the organization and execution of the analytical workflow. Individual scripts identify the corresponding manuscript analyses and figure panels. Raw sequencing data Raw sequencing reads are available through the NCBI Sequence Read Archive under BioProject accession PRJNA1322484.

Beals, Delaney [ORNL] (ORCID:0000000306274574)↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Predicting promoters in phage genomes using PhagePromoter

Abstract Summary The growing interest in phages as antibacterial agents has led to an increase in the number of sequenced phage genomes, increasing the need for intuitive bioinformatics tools for performing genome annotation. The identification of phage promoters is indeed the most difficult step of this process. Due to the lack of online tools for phage promoter prediction, we developed PhagePromoter, a tool for locating promoters in phage genomes, using machine learning methods. This is the first online tool for predicting promoters that uses phage promoter data and the first to identify both host and phage promoters with different motifs. Availability and implementation This tool was integrated in the Galaxy framework and it is available online at: https://bit.ly/2Dfebfv. Supplementary information Supplementary data are available at Bioinformatics online.

Sampaio, Marta↗
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 OSTI2020

FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genomes and metagenome assemblies

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

Application of LANDSAT data to delimitation of avalanche hazards in Montane Colorado

The author has identified the following significant results. Interpretation of small scale LANDSAT imagery provides a means for determining the general location and distribution of avalanche paths. The accuracy and completeness of small scale mapping is less than is obtained from the interpretation of large scale color infrared photos. Interpretation of enlargement prints (18X) of LANDSAT imagery is superior to small scale imagery, because more detailed information can be extracted and annotated.

Knepper, D. H.↗
NASA NTRS2003

Simple Math is Enough: Two Examples of Inferring Functional Associations from Genomic Data

Non-random features in the genomic data are usually biologically meaningful. The key is to choose the feature well. Having a p-value based score prioritizes the findings. If two proteins share a unusually large number of common interaction partners, they tend to be involved in the same biological process. We used this finding to predict the functions of 81 un-annotated proteins in yeast.

Liang, Shoudan↗
NASA NTRS1989

Laser ranging data analysis

Results of the near real-time analysis of Lageos laser ranging data are reported. The data was analyzed in terms of range bias, time bias, and internal precision; and estimates for the Earth orientation parameters X sub p, Y sub p, and UT1 are obtained. The results of these analyses are reported in a variety of formats. Sample copies of the weekly summaries from the second half of 1988 are included along with an annotated distribution list of the weekly summaries. Annual summaries for 1987 and 1988 are also included.

Source record↗
NASA NTRS1972

The application of color display techniques for the analysis of Nimbus infrared radiation data

A color enhancement system designed for the Applications Technology Satellite (ATS) spin scan experiment has been adapted for the analysis of Nimbus infrared radiation measurements. For a given scene recorded on magnetic tape by the Nimbus scanning radiometers, a virtually unlimited number of color images can be produced at the ATS Operations Control Center from a color selector paper tape input. Linear image interpolation has produced radiation analyses in which each brightness-color interval has a smooth boundary without any mosaic effects. An annotated latitude-longitude gridding program makes it possible to precisely locate geophysical parameters, which permits accurate interpretation of pertinent meteorological, geological, hydrological, and oceanographic features.

Allison, L. J.↗
NASA NTRS2005

Expedition Memory: Towards Agent-based Web Services for Creating and Using Mars Exploration Data.

Explorers ranging over kilometers of rugged, sometimes "feature-less" terrain for over a year could be overwhelmed by tracking and sharing what they have done and learned. An automated system based on the existing Mobile Agents design [ I ] and Mars Exploration Rover experience [2], could serve as an "expedition memory" that would be indexed by voice as wel1 as a web interface, linking people, places, activities, records (voice notes, photographs, samples). and a descriptive scientific ontology. This database would be accessible during EVAs by astronauts, annotated by the remote science team, linked to EVA plans, and allow cross indexing between sites and expeditions. We consider the basic problem, our philosophical approach, technical methods, and uses of the expedition memory for facilitating long-term collaboration between Mars crews and Earth support teams. We emphasize that a "memory" does not mean a database per se, but an interactive service that combines different resources, and ultimately could be like a helpful librarian.

Clancey, William J.↗
DOE OSTI2026

Hyaloscypha finlandica Metabolome Repository

This repository provides the curated data tables, manuscript figure and table exports, dependency records, and workflow scripts supporting an integrated comparative genomics and untargeted LC-MS/MS metabolomics analysis of Hyaloscypha finlandica strain PMI 746, a root-associated dark septate endophyte of poplar. The repository includes genome-mining summaries from antiSMASH, FunBGCeX, BGC-Prophet, and BiG-SCAPE; processed metabolomics inputs; metabolite annotation evidence; statistical outputs; and publication-facing figures and tables. Raw LC-MS/MS spectra, full genome/protein downloads, and large generated tool outputs are referenced through public archive/accession records and are not stored in Git.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗
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 OSTI2019

Role of epaQ , a Previously Uncharacterized Enterococcus faecalis Gene, in Biofilm Development and Antimicrobial Resistance

Enterococcus faecalis is a commensal of the human gastrointestinal tract; it is also an opportunistic pathogen and one of the leading causes of hospital-acquired infections. E. faecalis produces biofilms that are highly resistant to antibiotics, and it has been previously reported that certain genes of the epa operon contribute to biofilm-associated antibiotic resistance. Despite several studies examining the epa operon, many gene products of this operon remain annotated as hypothetical proteins. Here, we further explore the epa operon; we identified epaQ , currently annotated as encoding a hypothetical membrane protein, as being important for biofilm formation in the presence of the antibiotic daptomycin. Mutants with disruptions of epaQ were more susceptible to daptomycin relative to the wild type, suggesting its importance in biofilm-associated antibiotic resistance. Furthermore, the Δ epaQ mutant exhibited an altered biofilm architectural arrangement and formed small aggregates in liquid cultures. Our cumulative data show that epa mutations result in altered polysaccharide content, increased cell surface hydrophobicity, and decreased membrane potential. Surprisingly, several epa mutations significantly increased resistance to the antibiotic ceftriaxone, indicating that the way in which the epa operon impacts antibiotic resistance is antibiotic dependent. Finally, these results further define the key role of epa in antibiotic resistance in biofilms and in biofilm architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

COMPILE: a GWAS computational pipeline for gene discovery in complex genomes

Abstract Background Genome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity. Results COMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer ( Ostrinia nubilalis ). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity. Conclusions The relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.

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

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

Abstract Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3′ UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

Felício, Daniela↗
DOE OSTI2022

FUSARIUM-ID v.3.0: An Updated, Downloadable Resource for Fusarium Species Identification

Species within Fusarium are of global agricultural, medical, and food/feed safety concern and have been extensively characterized. However, accurate identification of species is challenging and usually requires DNA sequence data. FUSARIUM-ID ( http://isolate.fusariumdb.org/blast.php ) is a publicly available database designed to support the identification of Fusarium species using sequences of multiple phylogenetically informative loci, especially the highly informative ~680-bp 5' portion of the translation elongation factor 1-alpha (TEF1) gene that has been adopted as the primary barcoding locus in the genus. However, FUSARIUM-ID v.1.0 and 2.0 had several limitations, including inconsistent metadata annotation for the archived sequences and poor representation of some species complexes and marker loci. Here, we present FUSARIUM-ID v.3.0, which provides the following improvements: (i) additional and updated annotation of metadata for isolates associated with each sequence, (ii) expanded taxon representation in the TEF1 sequence database, (iii) availability of the sequence database as a downloadable file to enable local BLAST queries, and (iv) a tutorial file for users to perform local BLAST searches using either freely available software, such as SequenceServer, BLAST+ executable in the command line, and Galaxy, or the proprietary Geneious software. FUSARIUM-ID will be updated on a regular basis by archiving sequences of TEF1 and other loci from newly identified species and greater in-depth sampling of currently recognized species.

Plant Sciences↗