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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MONet/1000 Soils metagenome pathway modelling narrative w/ auto batch import

This narrative performs metabolic modeling and flux balance analysis (FBA) using metagenome-assembled genomes (MAGs) from the 1000 Soils samples, as described by Song et al. (2026, accepted). The set of MAGs (in FASTA format) is converted into a set of assembly objects compatible with functional annotation via RASTtk, yielding a set of genome objects that undergo metabolic modeling via OMEGGA. These genome objects are then used to conduct FBA, generating tables of metabolite uptake rates across the MAGs under investigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Comprehensive Urine Proteome Database Generated From Patients With Various Renal Conditions and Prostate Cancer

Urine proteins can serve as viable biomarkers for diagnosing and monitoring various diseases. A comprehensive urine proteome database, generated from a variety of urine samples with different disease conditions, can serve as a reference resource for facilitating discovery of potential urine protein biomarkers. Herein, we present a urine proteome database generated from multiple datasets using 2D LC-MS/MS proteome profiling of urine samples from healthy individuals (HI), renal transplant patients with acute rejection (AR) and stable graft (STA), patients with non-specific proteinuria (NS), and patients with prostate cancer (PC). A total of ~28,000 unique peptides spanning ~2,200 unique proteins were identified with a false discovery rate of <0.5% at the protein level. Over one third of the annotated proteins were plasma membrane proteins and another one third were extracellular proteins according to gene ontology analysis. Ingenuity Pathway Analysis of these proteins revealed 349 potential biomarkers in the literature-curated database. Forty-three percentage of all known cluster of differentiation (CD) proteins were identified in the various human urine samples. Interestingly, following comparisons with five recently published urine proteome profiling studies, which applied similar approaches, there are still ~400 proteins which are unique to this current study. These may represent potential disease-associated proteins. Among them, several proteins such as serpin B3, renin receptor, and periostin have been reported as pathological markers for renal failure and prostate cancer, respectively. Taken together, our data should provide valuable information for future discovery and validation studies of urine protein biomarkers for various diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

An approach for broad molecular imaging of the root-soil interface via indirect matrix-assisted laser desorption/ionization mass spectrometry

Understanding of rhizospheric processes is limited by the need for imaging complex molecular transformations at relevant spatial scales within the root soil continuum. In this work, we demonstrate a method to enable this analysis by first extracting organic compounds from the rhizosphere onto a PVDF membrane while maintaining their 2D distribution. We then image the distribution of chemical compounds using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). This approach permitted us to visualize and identify compounds on the root surface and presumed root exudates in the rhizosphere. Within a 1.8 cm x 0.6 cm sampling area of a switchgrass rhizosphere, we could observe at least four chemically distinct zones. Using high performance Fourier transform ion cyclotron MS, we were able to accurately annotate numerous molecules co-localized to each of these zones.

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

Perimatrix of middle ear cholesteatoma: A granulation tissue with a specific transcriptomic signature

Objectives/Hypothesis: To establish comprehensive transcriptomic profiles of cholesteatoma perimatrix tissue and granulation tissue from chronic otitis media (COM) that did not develop cholesteatoma, which can indicate molecular pathways involved in the cholesteatoma perimatrix pathology and invasiveness. Study Design: Retrospective Case Series. Methods: Transcriptome data were obtained from cholesteatoma perimatrix tissue and COM granulation tissue by an Illumina iScan microarray. Differentially expressed genes (DEGs) were subsequently analyzed using both bioinformatical functional annotation and network analysis. Expression of candidate genes (MMP9 and LCN2) was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on a larger group of samples. Results: Analysis of the transcriptome led to the identification of 169 differentially expressed genes between investigated tissues. Bioinformatic analysis suggested that most significant biological processes involving DEGs were previously described in cholesteatoma pathology. Network analysis identified ERBB2, TFAP2A, and TP63 as major hubs of the DEGs molecular network. Furthermore, it was observed that the cellular component most significantly enriched in DEGs was extracellular space containing 47 DEGs. Using qRT-PCR, it was confirmed that mRNA levels of the major extracellular hub (MMP9) are increased, whereas its interacting molecule (LCN2) mRNA levels were decreased in cholesteatoma perimatrix tissue compared to COM granulation tissue. Conclusions: The current study approach offers an overall look at molecular mechanisms that describe the cholesteatoma entity by focusing exclusively on the perimatrix processes in comparison to COM granulation tissue. The observed differences in gene expression between cholesteatoma perimatrix and COM granulation tissue could suggest novel markers potentially influenced by the perimatrix–matrix molecular interplay, which is not present in COM without cholesteatoma. Level of Evidence: NA. Laryngoscope, 130:E220–E227, 2020. © 2019 The American Laryngological, Rhinological and Otological Society, Inc.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2014

NASA GeneLab Concept of Operations

NASA's GeneLab aims to greatly increase the number of scientists that are using data from space biology investigations on board ISS, emphasizing a systems biology approach to the science. When completed, GeneLab will provide the integrated software and hardware infrastructure, analytical tools and reference datasets for an assortment of model organisms. GeneLab will also provide an environment for scientists to collaborate thereby increasing the possibility for data to be reused for future experimentation. To maximize the value of data from life science experiments performed in space and to make the most advantageous use of the remaining ISS research window, GeneLab will apply an open access approach to conducting spaceflight experiments by generating, and sharing the datasets derived from these biological studies in space.Onboard the ISS, a wide variety of model organisms will be studied and returned to Earth for analysis. Laboratories on the ground will analyze these samples and provide genomic, transcriptomic, metabolomic and proteomic data. Upon receipt, NASA will conduct data quality control tasks and format raw data returned from the omics centers into standardized, annotated information sets that can be readily searched and linked to spaceflight metadata. Once prepared, the biological datasets, as well as any analysis completed, will be made public through the GeneLab Space Bioinformatics System webb as edportal. These efforts will support a collaborative research environment for spaceflight studies that will closely resemble environments created by the Department of Energy (DOE), National Center for Biotechnology Information (NCBI), and other institutions in additional areas of study, such as cancer and environmental biology. The results will allow for comparative analyses that will help scientists around the world take a major leap forward in understanding the effect of microgravity, radiation, and other aspects of the space environment on model organisms. These efforts will speed the process of scientific sharing, iteration, and discovery.

Space Life Science↗
DOE OSTI2026

Depth-resolved sagebrush root metabolomics, rhizosphere microbial communities, and geochemistry at the East River Watershed

This data set consists of results from soil nutrient profile, untargeted metabolomics, mass spec imaging, and amplicon sequencing. Data for soil nutrient profile includes common cations (Ca, Mg, Na, and K etc.) extracted from 3 digesting steps – ammonia acetate (for exchangeable cations), nitric acid (for acid dissolved fraction), and hydrofluoric acid/perchloric acid (HF/HClO4) for whole soil digestion. It also includes concentration of organic carbon, inorganic nitrogen (ammonia and nitrate) and phosphorus (Bray-1 P and nitric acid extract), and total nitrogen and phosphorus. Data for untargeted metabolomics includes metabolomic profile for root exudate/tissues and soil extracts from depths at surface soil to saprolite, that were measured using gas chromatography – mass spectrometry (GC-MS), and liquid chromatography – tandem mass spectrometry (LC-MS/MS). Data for mass spec imaging includes spatial distribution of metabolites that were detected and annotated with Fourier transformation ion cyclotron resonance mass spectrometer (FTICR-MS). Data for amplicon sequencing includes the base paired 16S and ITS ribosomal RNA sequences from Miseq Illumina sequencing. All samples were collected from 2 sampling campaign October 2022 and June 2023. Collectively, these datasets enable a mechanistic evaluation of how nutrient acquisition, especially nitrogen and phosphorus, differs between shallow roots operating in soil and deep roots functioning within the fractured bedrock zone. All files are provided as comma-separated values (CSV) fies (.csv) and (GZIP) file (.gz). The compressed .gz FASTQ files can be read directly in R using the dada2 package as part of the amplicon sequence analysis workflow. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research was performed on a project award 60563 (https://dx.doi.org/10.46936/expl.proj.2022.60563/60008727) from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830.

EARTH SCIENCE > AGRICULTURE > SOILS > CARBON↗
DOE OSTI2024

Separation of life stages within anaerobic fungi (Neocallimastigomycota) highlights differences in global transcription and metabolism

Anaerobic gut fungi of the phylum Neocallimastigomycota are microbes proficient in valorizing low-cost but difficult-to-breakdown lignocellulosic plant biomass. Characterization of different fungal life stages and how they contribute to biomass breakdown are critical for biotechnological applications, yet we lack foundational knowledge about the transcriptional, metabolic, and enzyme secretion behavior of different life stages of anaerobic gut fungi: zoospores, germlings, immature thalli, and mature zoosporangia. A Miracloth-based technique was developed to enrich cell pellets with zoospores - the free-swimming, flagellated, young life stage of anaerobic gut fungi. By contrast, fungal mats contained relatively more vegetative, encysted, mature sporangia that form films. Global gene expression profiles were compared from two sample types (zoospore-enriched cell pellets vs. mature mats) harvested from the anaerobic gut fungal strain Neocallimastix californiae G1. Despite cultures being grown on glucose, the fungal zoospore-enriched samples were transcriptionally primed to encounter plant matter substrate, as evidenced by upregulation of catabolic carbohydrate-active enzymes and putative carbohydrate transporters. Furthermore, we report significant differential gene expression for gene annotation groups, including putative secondary metabolites and transcription factors. Understanding global gene expression differences between the fungal zoospore-enriched cells and mature fungi aid in characterizing fungal development, unmasking gene function, and guiding cultivation conditions and engineering targets to promote enzyme secretion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.

Mukherjee, Shubhabrata↗
DOE OSTI2019

Genomic Analysis of Metastatic Solid Tumors in Veterans: Findings From the VHA National Precision Oncology Program

The Veterans Health Administration (VHA) is the largest cancer care provider in the United States, with the added challenge of serving more than twice the percentage of patients with cancer in rural areas than the national average. The VHA established the National Precision Oncology Program in 2016 to implement and standardize the practice of precision oncology across the diverse VHA system. Tumor or peripheral blood specimens from veterans with advanced solid tumors who were eligible for treatment were submitted for next-generation sequencing (NGS) at two commercial laboratories. Annotated results were generated by the laboratories and independently using IBM Watson for Genomics. Levels-of-evidence treatment recommendations were based on OncoKB criteria. From July 2016 to June 2018, 3,698 samples from 72 VHA facilities were submitted for NGS testing, of which 3,182 samples (86%) were successfully sequenced. Most samples came from men with lung, prostate, and colorectal cancers. Thirty-four percent of samples were from patients who lived in a rural area. TP53, ATM, and KRAS were among the most commonly mutated genes. Approximately 70% of samples had at least one actionable mutation, with clinical trials identified as the recommended option in more than 50%. Mutations in genes associated with a neuroendocrine prostate cancer phenotype were expressed at increased frequency among veterans than in the general population. The most frequent therapies prescribed in response to NGS testing were immune checkpoint inhibitors, EGFR kinase inhibitors, and PARP inhibitors. Clinical implementation of precision oncology is feasible across the VHA health care system, including rural sites. As a result, veterans have unique occupational exposures that might inform the nature of the mutational signatures identified here. Importantly, these results underscore the importance of increasing clinical trial availability to veterans.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Portable Airborne Laser System Measures Forest-Canopy Height

(PALS) is a combination of laser ranging, video imaging, positioning, and data-processing subsystems designed for measuring the heights of forest canopies along linear transects from tens to thousands of kilometers long. Unlike prior laser ranging systems designed to serve the same purpose, the PALS is not restricted to use aboard a single aircraft of a specific type: the PALS fits into two large suitcases that can be carried to any convenient location, and the PALS can be installed in almost any local aircraft for hire, thereby making it possible to sample remote forests at relatively low cost. The initial cost and the cost of repairing the PALS are also lower because the PALS hardware consists mostly of commercial off-the-shelf (COTS) units that can easily be replaced in the field. The COTS units include a laser ranging transceiver, a charge-coupled-device camera that images the laser-illuminated targets, a differential Global Positioning System (dGPS) receiver capable of operation within the Wide Area Augmentation System, a video titler, a video cassette recorder (VCR), and a laptop computer equipped with two serial ports. The VCR and computer are powered by batteries; the other units are powered at 12 VDC from the 28-VDC aircraft power system via a low-pass filter and a voltage converter. The dGPS receiver feeds location and time data, at an update rate of 0.5 Hz, to the video titler and the computer. The laser ranging transceiver, operating at a sampling rate of 2 kHz, feeds its serial range and amplitude data stream to the computer. The analog video signal from the CCD camera is fed into the video titler wherein the signal is annotated with position and time information. The titler then forwards the annotated signal to the VCR for recording on 8-mm tapes. The dGPS and laser range and amplitude serial data streams are processed by software that displays the laser trace and the dGPS information as they are fed into the computer, subsamples the laser range and amplitude data, interleaves the subsampled data with the dGPS information, and records the resulting interleaved data stream.

Nelson, Ross↗
DOE OSTI2022

GROWdb US River Systems - Samples

GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with a manuscript investigating dissolved organic matter and microbial community linkages across seven globally distributed rivers

This data package is associated with the publication “Meta-metabolome ecology reveals that geochemistry and microbial functional potential are linked to organic matter development across seven rivers” submitted to Science of the Total Environment. This data package includes the data necessary to replicate the analyses presented within the manuscript to investigate dissolved organic matter (DOM) development across broad spatial distances and within divergent biomes. Specifically, we included the Fourier transform ion cyclotron mass spectrometry (FTICR-MS) data, geochemistry data, annotated metagenomic data, and results from ecological null modeling analyses in this data package. Additionally, we included the scripts necessary to generate the figures from the manuscript. Complete metagenomic data associated with this data package can be found at the National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. This dataset consists of (1) four folders; (2) a file-level metadata (flmd) file; (3) a data dictionary (dd) file; (4) a factor sheet describing samples; and (5) a readme. The FTICR Data folder contains (1) the processed Fourier transform ion cyclotron mass spectrometry (FTICR-MS) data; (2) a transformation-weighted characteristics dendrogram generated from the FTICR-MS data; and (3) the script used to generate all FTICR-MS related figures. The Geochemical Data folder contains (1) the single geochemistry data file and (2) the R script responsible for generating associated figures. The Metagenomic Data folder contains (1) annotation information across different levels; (2) carbohydrate active enzyme (CAZyme) information from the dbCAN database (Yin et al., 2012); (3) phylogenetic tree data (FASTAs, alignments, and tree file); and (4) the scripts necessary to analyze all of these data and generate figures. The Null Modeling Data folder contains (1) data generated during null modeling for each river and all rivers combined and (2) the R scripts necessary to process the data. All files are .csv, .pdf, .tsv, .tre, .faa, .afa, .tree, or .R.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Progress and Challenges in Ocean Metaproteomics and Proposed Best Practices for Data Sharing

Ocean metaproteomics is an emerging field enabling discoveries about marine microbial communities and their impact on global biogeochemical processes. Recent ocean metaproteomic studies have provided insight into microbial nutrient transport, colimitation of carbon fixation, the metabolism of microbial biofilms, and dynamics of carbon flux in marine ecosystems. Future methodological developments could provide new capabilities such as characterizing long-term ecosystem changes, biogeochemical reaction rates, and in situ stoichiometries. Yet challenges remain for ocean metaproteomics due to the great biological diversity that produces highly complex mass spectra, as well as the difficulty in obtaining and working with environmental samples. This review summarizes the progress and challenges facing ocean metaproteomic scientists and proposes best practices for data sharing of ocean metaproteomic data sets, including the data types and metadata needed to enable intercomparisons of protein distributions and annotations that could foster global ocean metaproteomic capabilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

DOE JGI Metagenome Workflow

The DOE Joint Genome Institute (JGI) Metagenome Workflow performs metagenome data processing, including assembly; structural, functional, and taxonomic annotation; and binning of metagenomic data sets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (I.-M. A. Chen, K. Chu, K. Palaniappan, A. Ratner, et al., Nucleic Acids Res, 49:D751–D763, 2021, https://doi.org/10.1093/nar/gkaa939) comparative analysis system and provided for download via the JGI data portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here, we describe the different tools, databases, and parameters used at different steps of the workflow to help with the interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a workflow description language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in the Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, et al., Nucleic Acids Res, 49:D723–D733, 2021, https://doi.org/10.1093/nar/gkaa983).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-contrast machine learning improves schistosomiasis diagnostic performance

Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening ofSchistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection ofS. haematobiumthat combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containingS. haematobiumeggs, during two different field studies in Côte d’Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF and DF models alone to combinations of BF and DF models, using annotations from trained microscopists as the gold standard. We found that models trained on DF images, and almost all BF and DF combinations, performed significantly better than models trained on BF images only. When models were trained on images from the first field study (n = 349 patients, 748 images of each contrast), patient-level classification performance on patient images from the second study (n = 375 patients, 752 images of each contrast) met the WHO Diagnostic Target Product Profile (TPP) sensitivity and specificity for the monitoring and evaluation use case (sensitivity for all models and combinations was >75% when evaluated at a confidence score threshold that resulted in specificity >96.5%). When we used images from both field studies for the training set, performance of the models was improved. Overall, this work shows that the use of DF and BF increases the performance of ML models on images from devices with low-cost optics, while retaining the portability, power, and time-to-results of the WHO’s diagnostic TPP. DF requires no additional sample preparation and does not increase the complexity of the imaging system. It thus offers a practical means to improve performance of automated diagnostics forS. haematobiumas well as other microscopy-based diagnostics.

Infectious Diseases↗
DOE OSTI2019

Wide diversity of methane and short-chain alkane metabolisms in uncultured archaea

Methanogenesis is an archaic metabolism of key ecological relevance, with direct impact on the evolution of Earth's climate. Recent findings indicate that the diversity of methane metabolisms and their derivations have probably been vastly underestimated. In this work, by probing thousands of publicly available metagenomes for homologues of methyl-coenzyme M reductase complex (MCR), we have obtained ten metagenome-assembled genomes (MAGs) belonging to potential methanogenic, anaerobic methanotrophic and short-chain alkane-oxidizing archaea. Five of these MAGs represent under-sampled (Verstraetearchaeota, Methanonatronarchaeia, ANME-1 and GoM-Arc1) or previously genomically undescribed (ANME-2c) archaeal lineages. The remaining five MAGs correspond to lineages that are only distantly related to previously known methanogens and span the entire archaeal phylogeny. Comprehensive comparative annotation substantially expands the metabolic diversity and energy conservation systems of MCR-bearing archaea. It also suggests the potential existence of a yet uncharacterized type of methanogenesis linked to short-chain alkane/fatty acid oxidation in a previously undescribed class of archaea ('Candidatus Methanoliparia'). We redefine a common core of marker genes specific to methanogenic, anaerobic methanotrophic and short-chain alkane-oxidizing archaea, and propose a possible scenario for the evolutionary and functional transitions that led to the emergence of such metabolic diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Petrographic and Advanced Geologic Characterization Report on Lively Grove #1 (API# 1218924947)

Lively Grove #1 (LG1 API number 1218924947) well was drilled to a depth of 5,758 feet, from the Glen Dean Limestone to the top of the Precambrian unit. In total, 49 thin sections were taken from Rotary Sidewall Core (RSWC), and one thin section was taken from Whole Core (2,918 feet, New Albany Shale) from 1,700 to 5,872 feet for this report. This report details specifically thin section point-counting analysis that includes mineralogical and pore space analysis, including grain size analysis annotated thin section photomicrographs, scanning electron microscopy (SEM) with Energy Dispersive x-ray Spectroscopy (EDS), X-ray Diffraction (XRD), statistics of grain size analysis on St. Peter Sandstone thin sections, and Argon-Argon (Ar-Ar) dating on Precambrian samples from LG1. Characterized units include the Salem Limestone, New Albany Shale, Trenton Group, Joachim Dolomite, St. Peter Sandstone, Everton Formation, Eminence Formation, Davis Member, Eau Claire Formation, and Precambrian Basement.

01 COAL, LIGNITE, AND PEAT↗