Search NASA⌕ Search

SEARCH · Search NASA

Results for “LABELLING”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗
DOE OSTI2025

National User Resource for Biological Accelerator Mass Spectrometry (Final Report)

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions. Over the next five years, our goals are to: 1. Improve the efficiency of operation for AMS measurements through installation of new interfaces to our AMS systems, technical modifications to improve gas accepting ion source efficiency and upgrading our data analysis software for improved ease of use and data reporting. 2. Increase the accessibility and visibility of ultra-sensitive 14C measurements for the biomedical research community by training of new investigators and expanding our national user base. 3. Provide high throughput, ultra-sensitive 14C analysis for the NIGMS and NIH user community.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2026

Electrochemical hydrogen isotope fractionation - fundamental insights leading to process scale up

Introduction Deuterium and tritium, the two isotopes of hydrogen besides protium, are utilized in many industries. Deuterium is routinely used as a nuclear reactor moderator and molecular labeling agent, while tritium is used as an autoradiography label in medical imaging, and in an emerging market for self-powered lighting for watches, exit signs, firearm night-sights and future fusion fuel. Tritium is produced naturally at very low steady-state levels due to cosmic rays, natural nuclear decay reactions, as well as commercial nuclear fission reactors, and terrestrial nuclear testing conducted primarily in the mid-twentieth century. Tritium often accumulates in the heavy water used in nuclear fission reactors and sometimes appears in groundwater near nuclear reactors due to slight leakage from water sources in the reactor. Because of the health and environmental impacts from tritium leaking into the water sources, as well as the value of this precious material, it is desirable to remove and recover as much tritium as reasonably possible from contaminated water.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Morphology-Based Building Use-Type Modeling: Learnability-First Schema Discovery

This technical memorandum documents an update to the building use-type classification workflow, used in LandScan Mosaic, that replaces a fixed, semantically defined class schema with a learnability-first schema discovery procedure. Historically, the target label schema was specified a priori (e.g., predicting a chosen set of use-type codes), and model training and evaluation were performed within that fixed label space. In the updated workflow, the pipeline first evaluates which non-residential distinctions are learnable under spatial generalization and then collapses ambiguous classes into data-driven groupings before finalizing the schema used for production training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cyclotron Production of PET Radiometals in Liquid Targets: Aspects and Prospects

: The present review describes the methodological aspects and prospects of the productionof Positron Emission Tomography (PET) radiometals in a liquid target using low-medium energymedical cyclotrons. The main objective of this review is to delineate and discuss the critical factorsinvolved in the liquid target production of radiometals, including type of salt solution, solutioncomposition, beam energy, beam current, the effect of irradiation duration (length of irradiation)and challenges posed by in-target chemistry in relation with irradiation parameters. We also summarizethe optimal parameters for the production of various radiometals in liquid targets. : Additionally, we discuss the future prospects of PET radiometals production in the liquid targetsfor academic research and clinical applications. Significant emphasis has been given to the productionof 68 Ga using liquid targets due to the growing demand for 68 Ga labeled PSMA vectors, [ 68 Ga]-Ga-DOTATATE, [ 68 Ga]Ga-DOTANOC and some upcoming 68 Ga labeled radiopharmaceuticals.Other PET radiometals included in the discussion are 86 Y, 63 Zn and 89 Zr.

Pharmacology & Pharmacy↗
DOE OSTI2025

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗
DOE OSTI2025

RectifHydPlus: Forty Year Hydropower Generation Reanalysis for Conterminous United States, Version 1.1.

This dataset contains monthly hydropower net-generation totals for 590 plants (each >10 MW) across the conterminous United States (CONUS) from 1980 to 2019. RectifHydPlus v1.1 includes one harmonized table of historical monthly generation—backfilled with observed monthly values where available—and two companion tables: (i) an estimates-only version with no backfill and (ii) a hydrological-control version that removes the effects of capacity and operational change. Each table comprises 23,600 records (590 plants × 40 years). The dataset was developed to address temporal gaps and inconsistencies in publicly available hydropower generation data as available through EIA-923 survey reports. Each record includes a quality label denoting the underlying proxy—from best (direct reservoir releases) to weakest (pattern copied from similar years). By combining the agency-reported survey records with observed and simulated hydrologic releases, RectifHydPlus offers complete, quality-labeled monthly estimates suitable for trend analysis and generation of hydropower generation inputs for energy-water modeling.

Turner, Sean [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Artificial Intelligence

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MassIVE MSV000094084 - Data files for manuscript - Selected ion monitoring for orbitrap-based metabolomics: Please open README-data-for-SIM-paper-20240210.doc for details.

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage. Please open README-data-for-SIM-paper-20240210.doc for details. [doi:10.25345/C5JH3DD3K] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Rabinowitz, Joshua↗
DOE OSTI2021

MassIVE MSV000087434 - Raw data files for NetID manuscript

This dataset is associated with this manuscript: Metabolite discovery through global annotation of untargeted metabolomics data. Li Chen, Wenyun Lu, Lin Wang, Xi Xing, Xin Teng, Xianfeng Zeng, Antonio D Muscarella, Yihui Shen, Alexis Cowan, Melanie R McReynolds, Brandon Kennedy, Ashley M Lato, Shawn R Campagna, Mona Singh, Joshua Rabinowitz. The dataset has six main folders: (1) Yeast-MS1 (2) Yeast-labeling (3) Liver-MS1 (4) Liver-targeted-MS2 (separate folders for mzxml files and matched csv files used as the inclusion list to set up PRM targeted-MS2 runs) (5) Mouse-labeling (6) Glucosyl_taurine-liver-vs-synthesis. Please refer to README-raw-data-NetID-20210512.doc for more details. [doi:10.25345/C5WV53] [dataset license: CC0 1.0 Universal (CC0 1.0)]

full scan MS1↗
DOE OSTI2025

Datasets for Custom-trained Machine-learning Interatomic Potentials: Nitric Acid Aqueous Solution

This dataset was generated using an iterative active learning strategy with the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials (MLIPs) for aqueous nitric acid. Each active-learning cycle consisted of three stages: (1) training, (2) exploration, and (3) labeling. The initial training set comprised approximately 800 randomly selected configurations from a previous study by Lewis et al. (https://doi.org/10.1021/jp205510q), which investigated nitric acid solutions at 2, 3, 4, and 5 mol/L. For all configurations, single-point calculations of atomic forces and total energies were performed at the quantum density functional theory BLYP-D2 and PBE-D3 levels of theory using the CP2K Quickstep module. Valence electrons were treated explicitly, while core electrons on all atoms were represented by norm-conserving Goedecker–Teter–Hutter (GTH) pseudopotentials. Long-range dispersion interactions were accounted for using Grimme dispersion corrections. Wave functions were expanded in a mixed Gaussian-and-plane-wave scheme using TZV2P-MOLOPT basis sets for all elements and an 800 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent field convergence was accelerated using orbital transformation and Direct Inversion in the Iterative Subspace, with a convergence threshold of 10^{-6}. All single-point calculations were carried out in periodic orthorhombic cells whose dimensions match those of the molecular configurations sampled from earlier trajectories. The CELL_REF keyword in CP2K was used to define a fixed reference cell, ensuring consistency in the reference data used for MLIP training, particularly when cell fluctuations are present in NpT simulations. The resulting high-fidelity energies and forces constitute the ground-truth labels used to train the MLIPs contained in this dataset.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2023

Evaluation of 134 Ce/ 134 La as a PET Imaging Theranostic Pair for 225 Ac α-Radiotherapeutics

225 Ac-targeted α-radiotherapy is a promising approach to treating malignancies, including prostate cancer. However, α-emitting isotopes are difficult to image because of low administered activities and a low fraction of suitable γ-emissions. The in vivo generator 134 Ce/ 134 La has been proposed as a potential PET imaging surrogate for the therapeutic nuclides 225 Ac and 227 Th. In this report, we detail efficient radiolabeling methods using the 225 Ac-chelators DOTA and MACROPA. These methods were applied to radiolabeling of prostate cancer imaging agents, including PSMA-617 and MACROPA-PEG 4 -YS5, for evaluation of their in vivo pharmacokinetic characteristics and comparison to the corresponding 225 Ac analogs. Methods: Radiolabeling was performed by mixing DOTA/MACROPA chelates with 134 Ce/ 134 La in NH 4 OAc, pH 8.0, at room temperature, and radiochemical yields were monitored by radio–thin-layer chromatography. In vivo biodistributions of 134 Ce-DOTA/MACROPA.NH 2 complexes were assayed through dynamic small-animal PET/CT imaging and ex vivo biodistribution studies over 1 h in healthy C57BL/6 mice, compared with free 134 CeCl 3 . In vivo, preclinical imaging of 134 Ce-PSMA-617 and 134 Ce-MACROPA-PEG 4 -YS5 was performed on 22Rv1 tumor–bearing male nu/nu-mice. Ex vivo biodistribution was performed for 134 Ce/ 225 Ac-MACROPA-PEG 4 -YS5 conjugates. Results: 134 Ce-MACROPA.NH 2 demonstrated near-quantitative labeling with 1:1 ligand-to-metal ratios at room temperature, whereas a 10:1 ligand-to-metal ratio and elevated temperatures were required for DOTA. Rapid urinary excretion and low liver and bone uptake were seen for 134 Ce/ 225 Ac-DOTA/MACROPA. NH 2 conjugates in comparison to free 134 CeCl 3 confirmed high in vivo stability. An interesting observation during the radiolabeling of tumor-targeting vectors PSMA-617 and MACROPA-PEG 4 -YS5—that the daughter 134 La was expelled from the chelate after the decay of parent 134 Ce—was confirmed through radio–thin-layer chromatography and reverse-phase high-performance liquid chromatography. Both conjugates, 134 Ce-PSMA-617 and 134 Ce-MACROPA-PEG 4 -YS5, displayed tumor uptake in 22Rv1 tumor–bearing mice. The ex vivo biodistribution of 134 Ce-MACROPA.NH 2 , 134 Ce-DOTA and 134 Ce-MACROPA-PEG 4 -YS5 corroborated well with the respective 225 Ac-conjugates. Conclusion: These results demonstrate the PET imaging potential for 134 Ce/ 134 La-labeled small-molecule and antibody agents. The similar 225 Ac and 134 Ce/ 134 La-chemical and pharmacokinetic characteristics suggest that the 134 Ce/ 134 La pair may act as a PET imaging surrogate for 225 Ac-based radioligand therapies.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Luo, Yufeng (ORCID:0000000246230683)↗
DOE OSTI2015

Uncovering the dual role of RHAMM as an HA receptor and a regulator of CD44 expression in RHAMM-expressing mesenchymal progenitor cells

The interaction of hyaluronan (HA) with mesenchymal progenitor cells impacts trafficking and fate after tissue colonization during wound repair and these events contribute to diseases such as cancer. How this interaction occurs is poorly understood. Using 10T½ cells as a mesenchymal progenitor model and fluorescent (F-HA) or gold-labeled HA (G-HA) polymers, we studied the role of two HA receptors, RHAMM and CD44, in HA binding and uptake in non-adherent and adherent mesenchymal progenitor (10T½) cells to mimic aspects of cell trafficking and tissue colonization. We show that fluorescent labeled HA (F-HA) binding/uptake was high in non-adherent cells but dropped over time as cells became increasingly adherent. Non-adherent cells displayed both CD44 and RHAMM but only function-blocking anti-RHAMM and not anti-CD44 antibodies significantly reduced F-HA binding/uptake. Adherent cells, which also expressed CD44 and RHAMM, primarily utilized CD44 to bind to F-HA since anti-CD44 but not antiRHAMM antibodies blocked F-HA uptake. RHAMM overexpression in adherent 10T½ cells led to increased F-HA uptake but this increased binding remained CD44 dependent. Further studies showed that RHAMM-transfection increased CD44 mRNA and protein expression while blocking RHAMM function reduced expression. Collectively, these results suggest that cellular microenvironments in which these receptors function as HA binding proteins differ significantly, and that RHAMM plays at least two roles in FHA binding by acting as an HA receptor in non-attached cells and by regulating CD44 expression and display in attached cells. Our findings demonstrate adhesion-dependent mechanisms governing HA binding/ uptake that may impact development of new mesenchymal cell-based therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Proteomic Stable Isotope Probing Reveals Biosynthesis Dynamics of Slow Growing Methane Based Microbial Communities

Marine methane seep habitats represent an important control on the global flux of methane between the subsurface and water column reservoirs. Meta-omics studies have begun to outline community-wide metabolic potential, but expression patterns of proteins that enact sulfate-mediated anaerobic methane oxidation in seeps are poorly characterized. Proteomic stable isotope probing (proteomic SIP) offers an additional layer of information for characterizing phylogenetically specific, functionally relevant activity in mixed microbial communities. Here we applied proteomic SIP to 15NH4+ and CH4 amended seep sediment microcosms in an attempt to track the protein synthesis of slow-growing, low-energy microbial systems. Across all samples, 3495 proteins were identified, 21% of which were 15N-labeled. We observed active synthesis (15N enrichment) of all proteins believed to be involved in sulfate reduction and reverse methanogenesis including methylenetetrahydromethanopterin reductase (Mer). The abundance and phylogenetic range of methyl-coenzyme M reductase (Mcr) orthologs produced during incubation experiments suggests that seeps provide sufficient niches for multiple organisms performing analogous metabolisms. Twenty-eight previously unreported post-translational modifications of McrA were measured, indicating dynamic enzymatic machinery and offering a dimension of functional diversity beyond gene-dictated sequence. RNA polymerase associated with putative sulfur-oxidizing Epsilonproteobacteria and aerobic Gammaproteobacteria were more abundant among pre-incubation proteins, suggesting diminished metabolic activity in long-term anoxic, sulfidic experimental incubations. Twenty-six proteins of unknown function were detected in all proteomic experiments and actively expressed in labeled experiments, suggesting that they play important roles in methane seep ecosystems. The addition of stable isotope probing to environmental proteomics experiments provides a mechanism to begin to assess the degree to which diagnostic meatbolic proteins are long-lived or acively synthesized in complex, slow-growing microbial communities. Our work here demonstrates that sediment-hosted microbial assemblages in marine methane seeps are dynamic, heterogeneous systems with broad functional diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

SIPSim: A Modeling Toolkit to Predict Accuracy and Aid Design of DNA-SIP Experiments

DNA Stable isotope probing (DNA-SIP) is a powerful method that links identity to function within microbial communities. The combination of DNA-SIP with multiplexed high throughput DNA sequencing enables simultaneous mapping of in situ assimilation dynamics for thousands of microbial taxonomic units. Hence, high throughput sequencing enabled SIP has enormous potential to reveal patterns of carbon and nitrogen exchange within microbial food webs. There are several different methods for analyzing DNA-SIP data and despite the power of SIP experiments, it remains difficult to comprehensively evaluate method accuracy across a wide range of experimental parameters. We have developed a toolset (SIPSim) that simulates DNA-SIP data, and we use this toolset to systematically evaluate different methods for analyzing DNA-SIP data. Specifically, we employ SIPSim to evaluate the effects that key experimental parameters (e.g., level of isotopic enrichment, number of labeled taxa, relative abundance of labeled taxa, community richness, community evenness, and beta-diversity) have on the specificity, sensitivity, and balanced accuracy (defined as the product of specificity and sensitivity) of DNA-SIP analyses. Furthermore, SIPSim can predict analytical accuracy and power as a function of experimental design and community characteristics, and thus should be of great use in the design and interpretation of DNA-SIP experiments.

59 BASIC BIOLOGICAL SCIENCES↗