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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
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 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 OSTI2021

Genomics, Exometabolomics, and Metabolic Probing Reveal Conserved Proteolytic Metabolism of Thermoflexus hugenholtzii and Three Candidate Species From China and Japan

Thermoflexus hugenholtzii JAD2 T , the only cultured representative of the Chloroflexota order Thermoflexales, is abundant in Great Boiling Spring (GBS), NV, United States, and close relatives inhabit geothermal systems globally. However, no defined medium exists for T. hugenholtzii JAD2 T and no single carbon source is known to support its growth, leaving key knowledge gaps in its metabolism and nutritional needs. Here, we report comparative genomic analysis of the draft genome of T. hugenholtzii JAD2 T and eight closely related metagenome-assembled genomes (MAGs) from geothermal sites in China, Japan, and the United States, representing “Candidatus Thermoflexus japonica,” “Candidatus Thermoflexus tengchongensis,” and “Candidatus Thermoflexus sinensis.” Genomics was integrated with targeted exometabolomics and 13 C metabolic probing of T. hugenholtzii. The Thermoflexus genomes each code for complete central carbon metabolic pathways and an unusually high abundance and diversity of peptidases, particularly Metallo- and Serine peptidase families, along with ABC transporters for peptides and some amino acids. The T. hugenholtzii JAD2 T exometabolome provided evidence of extracellular proteolytic activity based on the accumulation of free amino acids. However, several neutral and polar amino acids appear not to be utilized, based on their accumulation in the medium and the lack of annotated transporters. Adenine and adenosine were scavenged, and thymine and nicotinic acid were released, suggesting interdependency with other organisms in situ. Metabolic probing of T. hugenholtzii JAD2 T using 13 C-labeled compounds provided evidence of oxidation of glucose, pyruvate, cysteine, and citrate, and functioning glycolytic, tricarboxylic acid (TCA), and oxidative pentose-phosphate pathways (PPPs). However, differential use of position-specific 13 C-labeled compounds showed that glycolysis and the TCA cycle were uncoupled. Thus, despite the high abundance of Thermoflexus in sediments of some geothermal systems, they appear to be highly focused on chemoorganotrophy, particularly protein degradation, and may interact extensively with other microorganisms in situ.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Advances and Challenges in Fluorescence in situ Hybridization for Visualizing Fungal Endobacteria

We report that Several bacteria have long been known to interact intimately with fungi, but molecular approaches have only recently uncovered how cosmopolitan these interactions are in nature. Currently, bacterial–fungal interactions (BFI) are inferred based on patterns of co-occurrence in amplicon sequencing investigations. However, determining the nature of these interactions, whether the bacteria are internally or externally associated, remains a grand challenge in BFI research. Fluorescence in situhybridization (FISH) is a robust method that targets unique sequences of interest which can be employed for visualizing intra-hyphal targets, such as mitochondrial organelles or, as in this study, bacteria. We evaluate the challenges and employable strategies to resolve intra-hyphal BFI to address pertinent criteria in BFI research, such as culturing media, spatial distribution of bacteria, and abundance of bacterial 16S rRNA copies for fluorescent labeling. While these experimental factors influence labeling and detection of endobacteria, we demonstrate how to overcome these challenges thorough permeabilization, appropriate media choice, and targeted amplification using hybridization chain reaction FISH. Such microscopy imaging approaches can now be utilized by the broader research community to complement sequence-based investigations and provide more conclusive evidence on the nature of specific bacterial–fungal relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Detection and Aggregation of Listeria Monocytogenes Using Polyclonal Antibody Gold-Coated Magnetic Nanoshells Surface-Enhanced Raman Spectroscopy Substrates

Magnetic nanoshells with tailored surface chemistry can enhance bacterial detection and separation technologies. This work demonstrated a simple technique to detect, capture, and aggregate bacteria with the aid of end-functionalized polyclonal antibody gold-coated magnetic nanoshells (pAb-Lis-AuMNs) as surface-enhanced Raman spectroscopy (SERS) probes. Listeria monocytogenes were used as the pathogenic bacteria and the pAb-LisAuMNs, 300 nm diameter, were used as probes allowing facile magnetic separation and aggregation. An optimized covalent bioconjugation procedure between the magnetic nanoshells and the polyclonal antibody was performed at pH six via a carbodiimide crosslinking reaction. Spectroscopic and morphological characterization techniques confirmed the fabrication of stable pAb-Lis-AuMNs. The resulting pAb-Lis-AuMNs acted as a SERS probe for L. monocytogenes based on the targeted capture via surface binding interactions and magnetically induced aggregation. Label-free SERS measurements were recorded for the minimum detectable amount of L. monocytogenes based on the SERS intensity at the 1388 cm -1 Raman shift. L. monocytogenes concentrations exhibited detection limits in the range of 10 4 –10 7 CFU ml -1 , before and after aggregation. By fitting these concentrations, the limit of detection of this method was ~10 3 CFU ml -1 . Using a low-intensity magnetic field of 35 G, pAb-LisAuMNs aggregated L. monocytogenes as demonstrated with microscopy techniques, including SEM and optical microscopy. Overall, this work presents a label-free SERS probe method comprised of a surface-modified polyclonal antibody sub-micron magnetic nanoshell structures with high sensitivity and magnetic induced separation that could lead to the fabrication of multiple single-step sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Identification of the Tyrosine- and Phenylalanine-Derived Soluble Metabolomes of Sorghum

The synthesis of small organic molecules, known as specialized or secondary metabolites, is one mechanism by which plants resist and tolerate biotic and abiotic stress. Many specialized metabolites are derived from the aromatic amino acids phenylalanine (Phe) and tyrosine (Tyr). In addition, the improved characterization of compounds derived from these amino acids could inform strategies for developing crops with greater resilience and improved traits for the biorefinery. Sorghum and other grasses possess phenylalanine ammonia-lyase (PAL) enzymes that generate cinnamic acid from Phe and bifunctional phenylalanine/tyrosine ammonia-lyase (PTAL) enzymes that generate cinnamic acid and p -coumaric acid from Phe and Tyr, respectively. Cinnamic acid can, in turn, be converted into p -coumaric acid by cinnamate 4-hydroxylase. Thus, Phe and Tyr are both precursors of common downstream products. Not all derivatives of Phe and Tyr are shared, however, and each can act as a precursor for unique metabolites. In this study, 13 C isotopic-labeled precursors and the recently developed Precursor of Origin Determination in Untargeted Metabolomics (PODIUM) mass spectrometry (MS) analytical pipeline were used to identify over 600 MS features derived from Phe and Tyr in sorghum. These features comprised 20% of the MS signal collected by reverse-phase chromatography and detected through negative-ionization. Ninety percent of the labeled mass features were derived from both Phe and Tyr, although the proportional contribution of each precursor varied. In addition, the relative incorporation of Phe and Tyr varied between metabolites and tissues, suggesting the existence of multiple pools of p -coumaric acid that are fed by the two amino acids. Furthermore, Phe incorporation was greater for many known hydroxycinnamate esters and flavonoid glycosides. In contrast, mass features derived exclusively from Tyr were the most abundant in every tissue. The Phe- and Tyr-derived metabolite library was also utilized to retrospectively annotate soluble MS features in two brown midrib mutants ( bmr6 and bmr12 ) identifying several MS features that change significantly in each mutant.

09 BIOMASS FUELS↗
DOE OSTI2023

Systematic comparison of local approaches for isotopically nonstationary metabolic flux analysis

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determine cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA) that relies on the patterns of isotope labeling of metabolites in the network. For metabolic systems, typical for plants, where all potentially labeled atoms effectively have only one source atom pool, only isotopically nonstationary MFA can provide information about intracellular fluxes. There are several global approaches that implement MFA for an entire metabolic network and estimate, at once, a steady-state flux distribution for all reactions with identifiable fluxes in the network. In contrast, local approaches deal with estimation of fluxes for a subset of reactions, with smaller data demand for flux estimation. Here we present a systematic comparative review and benchmarking of the existing local approaches for isotopically nonstationary MFA. The comparison is conducted with respect to the required data and underlying computational problems solved on a synthetic network example. Furthermore, we benchmark the performance of these approaches in estimating fluxes for a subset of reactions using data obtained from the simulation of nitrogen fluxes in the Arabidopsis thaliana core metabolism. The findings pinpoint practical aspects that need to be considered when applying local approaches for flux estimation in large-scale plant metabolic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Classification Analysis of Southwest Pacific Tropical Cyclone Intensity Changes Prior to Landfall

This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending ≥24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (V max ) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the V max at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Characterization of Gramicidin A in Triblock and Diblock Polymersomes and Hybrid Vesicles via Continuous Wave Electron Paramagnetic Resonance Spectroscopy

Studying membrane proteins in a native environment is crucial to understanding their structural and/or functional studies. Often, widely accepted mimetic systems have limitations that prevent the study of some membrane proteins. Micelles, bicelles, and liposomes are common biomimetic systems but have problems with membrane compatibility, limited lipid composition, and heterogeneity. To overcome these limitations, polymersomes and hybrid vesicles have become popular alternatives. Polymersomes form from amphiphilic triblock or diblock copolymers and are considered more robust than liposomes. Hybrid vesicles are a combination of lipids and block copolymers that form vesicles composed of a mixture of the two. These hybrid vesicles are appealing because they have the native lipid environment of bilayers but also the stability and customizability of polymersomes. Gramicidin A was incorporated into these polymersomes and characterized using continuous wave electron paramagnetic resonance (CW-EPR) and transmission electron microscopy (TEM). EPR spectroscopy is a powerful biophysical technique used to study the structure and dynamic properties of membrane proteins in their native environment. Spectroscopic studies of gramicidin A have been limited to liposomes; in this study, the membrane peptide is studied in both polymersomes and hybrid vesicles using CW-EPR spectroscopy. Lineshape analysis of spin-labeled gramicidin A revealed linewidth broadening, suggesting that the thicker polymersome membranes restrict the motion of the spin label more when compared to liposome membranes. Statement of Significance: Understanding membrane proteins’ structures and functions is critical in the study of many diseases. In order to study them in a native environment, membrane mimetics must be developed that can be suitable for obtaining superior biophysical data quality to characterize structural dynamics while maintaining their native functions and structures. Many currently widely accepted methods have limitations, such as a loss of native structure and function, heterogeneous vesicle formation, restricted lipid types for the vesicle formation for many proteins, and experimental artifacts, which leaves rooms for the development of new biomembrane mimetics. The triblock and diblock polymersomes and hybrid versicles utilized in this study may overcome these limitations and provide the stability and customizability of polymersomes, keeping the biocompatibility and functionality of liposomes for EPR studies of membrane proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Development of Multi-Scale X-ray Fluorescence Tomography for Examination of Nanocomposite-Treated Biological Samples

Research in cancer nanotechnology is entering its third decade, and the need to study interactions between nanomaterials and cells remains urgent. Heterogeneity of nanoparticle uptake by different cells and subcellular compartments represent the greatest obstacles to a full understanding of the entire spectrum of nanomaterials’ effects. In this work, we used flow cytometry to evaluate changes in cell cycle associated with non-targeted nanocomposite uptake by individual cells and cell populations. Analogous single cell and cell population changes in nanocomposite uptake were explored by X-ray fluorescence microscopy (XFM). Very few nanoparticles are visible by optical imaging without labeling, but labeling increases nanoparticle complexity and the risk of modified cellular uptake. XFM can be used to evaluate heterogeneity of nanocomposite uptake by directly imaging the metal atoms present in the metal-oxide nanocomposites under investigation. While XFM mapping has been performed iteratively in 2D with the same sample at different resolutions, this study is the first example of serial tomographic imaging at two different resolutions. A cluster of cells exposed to non-targeted nanocomposites was imaged with a micron-sized beam in 3D. Next, the sample was sectioned for immunohistochemistry as well as a high resolution “zoomed in” X-ray fluorescence (XRF) tomography with 80 nm beam spot size. Multiscale XRF tomography will revolutionize our ability to explore cell-to-cell differences in nanomaterial uptake.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data.

Hopwood, Michael W. (ORCID:0000000161901767)↗