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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data for "Enhancing 2-Pyrone Synthase Efficiency by High-Throughput Mass-Spectrometric Quantification and In Vitro/In Vivo Catalytic Performance Correlation"

Engineering efficient biocatalysts is essential for metabolic engineering to produce valuable bioproducts from renewable resources. However, due to the complexity of cellular metabolic networks, it is challenging to translate success in vitro into high performance in cells. To meet such a challenge, an accurate and efficient quantification method is necessary to screen a large set of mutants from complex cell culture and a careful correlation between the catalysis parameters in vitro and performance in cells is required. In this study, we employed a mass-spectrometry based high-throughput quantitative method to screen new mutants of 2-pyrone synthase (2PS) for triacetic acid lactone (TAL) biosynthesis through directed evolution in E. coli. From the process, we discovered two mutants with the highest improvement (46 fold) in titer and the fastest kcat (44 fold) over the wild type 2PS, respectively, among those reported in the literature. A careful examination of the correlation between intracellular substrate concentration, Michaelis-Menten parameters and TAL titer for these two mutants reveals that a fast reaction rate under limiting intracellular substrate concentrations is important for in-cell biocatalysis. Such properties can be tuned by protein engineering and synthetic biology to adopt these engineered proteins for the maximum activities in different intracellular environments.

catalysis↗
DOE OSTI2022

Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind’s predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server ( http://papers.genomics.lbl.gov/carbon ) and takes just 30 seconds for the typical genome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High-throughput screening of the ReFRAME, Pandemic Box, and COVID Box drug repurposing libraries against SARS-CoV-2 nsp15 endoribonuclease to identify small-molecule inhibitors of viral activity

SARS-CoV-2 has caused a global pandemic, and has taken over 1.7 million lives as of mid-December, 2020. Although great progress has been made in the development of effective countermeasures, with several pharmaceutical companies approved or poised to deliver vaccines to market, there is still an unmet need of essential antiviral drugs with therapeutic impact for the treatment of moderate-to-severe COVID-19. Towards this goal, a high-throughput assay was used to screen SARS-CoV-2 nsp15 uracil-dependent endonuclease (endoU) function against 13 thousand compounds from drug and lead repurposing compound libraries. While over 80% of initial hit compounds were pan-assay inhibitory compounds, three hits were confirmed as nsp15 endoU inhibitors in the 1–20 μM range in vitro. Furthermore, Exebryl-1, a ß-amyloid anti-aggregation molecule for Alzheimer’s therapy, was shown to have antiviral activity between 10 to 66 μM, in Vero 76, Caco-2, and Calu-3 cells. Although the inhibitory concentrations determined for Exebryl-1 exceed those recommended for therapeutic intervention, our findings show great promise for further optimization of Exebryl-1 as an nsp15 endoU inhibitor and as a SARS-CoV-2 antiviral.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High throughput embryonic zebrafish test with automated dechorionation to evaluate nanomaterial toxicity

Engineered nanomaterials pose occupational health and environmental concerns as they possess unique physical and chemical properties that can contribute to toxicity. High throughput toxicity screening methods are needed to address the increasing number of nanomaterials in production. Here we used a zebrafish photomotor response (PMR) test to evaluate a set of fifteen nanomaterials with military relevance. Automated dechorionation of zebrafish embryos was used to enhance nanomaterials bioavailability. Optimal PMR activity in zebrafish embryos was found at 30–31 hours post-fertilization (hpf). Behavioral and toxicological responses were measured at 30 and 120 hpf; behavioral responses were found for thirteen of the fifteen nanomaterials and acute toxicity (LC50) levels for nine of the fifteen nanomaterials below the maximum test concentration of 500 μg/ml. Physico-chemical characterization of the nanomaterials detected endotoxin and bacterial contamination in two of the tested samples, which may have contributed to observed toxicity and reinforces the need for physical and chemical characterization of nanomaterials use in toxicity testing. The zebrafish PMR test, together with automated dechorionation, provides an initial rapid assessment of the behavioral effects and toxicity of engineered nanomaterials that can be followed up by physico-chemical characterization if toxicity is detected, reducing the amount of time and monetary constraints of physico-chemical testing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Metabolic multireactor: Practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

We present a system for carrying out small batch reactor oxygen consumption experiments on water and sediment samples for environmental questions. In general, it provides several advantages that can help researchers achieve impactful experiments at relatively low costs and high data quality. In particular, it allows for multiple reactors to be operated and their oxygen concentrations to be measured simultaneously, providing high throughput and high time-resolution data, which can be advantageous. Most existing literature on similar small batch-reactor metabolic studies is limited to either only a few samples, or only a few time points per sample, which can restrict the ability for researchers to learn from their experiments. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. As such we do not delve deeply into the specifics of the fluorescent dye sensing mechanism. Instead, we focus on practical considerations. We describe the construction and operation of the calibration and experimental systems, and answer many of the questions likely to come up when other researchers choose to build and operate a similar system themselves (questions we ourselves had when we first built the system). In this way, we hope to provide an approachable and easy to use research article that can help other researchers construct and operate a similar system that can be tailored to ask their own research questions, with a minimum of confusion and missteps along the way.

3D printing↗
DOE OSTI2023

High throughput analysis of B cell dynamics and neutralizing antibody development during immunization with a novel clade C HIV-1 envelope

A protective HIV-1 vaccine has been hampered by a limited understanding of how B cells acquire neutralizing activity. Our previous vaccines expressing two different HIV-1 envelopes elicited robust antigen specific serum IgG titers in 20 rhesus macaques; yet serum from only two animals neutralized the autologous virus. Here, we used high throughput immunoglobulin receptor and single cell RNA sequencing to characterize the overall expansion, recall, and maturation of antigen specific B cells longitudinally over 90 weeks. Diversification and expansion of many B cell clonotypes occurred broadly in the absence of serum neutralization. However, in one animal that developed neutralization, two neutralizing B cell clonotypes arose from the same immunoglobulin germline and were tracked longitudinally. Early antibody variants with high identity to germline neutralized the autologous virus while later variants acquired somatic hypermutation and increased neutralization potency. The early engagement of precursors capable of neutralization with little to no SHM followed by prolonged affinity maturation allowed the two neutralizing lineages to successfully persist despite many other antigen specific B cells. The findings provide new insight into B cells responding to HIV-1 envelope during heterologous prime and boost immunization in rhesus macaques and the development of selected autologous neutralizing antibody lineages.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Evaluating E. coli genome‐scale metabolic model accuracy with high‐throughput mutant fitness data

Abstract The Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

National Dataset of EV Charging Stations With Estimates of Load and Vehicle Throughput

Current data from the AFDC provide locations and many details about EV charging stations, but not estimates of their peak loads or the number of vehicles they can accommodate. This dataset will augment the AFDC charging station locations with estimates of transmission load and vehicle throughput based on engineering specifications of the chargers, charging patterns based on vehicle types, battery capacities, and user behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optode performance data associated with: Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

This data package is associated with the publication “Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments”, submitted to PlosONE (Kaufman et al. 2023; 10.1101/2023.03.28.534656).We carried out many testing and calibration experiments on a system of small oxygen consumption batch reactors designed for use with water and sediment samples for environmental questions. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. Our primary focus was on practical considerations, such as temperature effects, lighting angle effects, sterilization, and other similar situations that a user may find useful. Most of the tests required comparing “base” calibration curves to “treatment” calibration curves to determine the extent to which the treatment impacted the reported measurements. This data package contains the performance and calibration data collected for that purpose.This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) diffusion test result files; (5) limit of detection test result files; (6) temperature impact files; (7) a main data file that contains test results for all other tests; and (8) an R script that uses Kolmogorov-Smirnov tests to determine whether treatment calibrations are significantly different from their respective base calibrations. All files are .csv, .txt, .Rmd, or .pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improving radiograph analysis throughput through transfer learning and object detection

SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Identification of a Class of WNK Isoform-Specific Inhibitors Through High-Throughput Screening

Introduction: WNK [with no lysine (K)] kinases are serine/threonine kinases associated with familial hyperkalemic hypertension (FHHt). WNKs are therapeutic targets for blood pressure regulation, stroke and several cancers including triple negative breast cancer and glioblastoma. Here, we searched for and characterized novel WNK kinase inhibitors. Methods: We used a ~210,000-compound library in a high-throughput screen, re-acquisition and assay, commercial specificity screens and crystallography to identify WNK-isoform-selective inhibitors. Results: We identified five classes of compounds that inhibit the kinase activity of WNK1: quinoline compounds, halo-sulfones, cyclopropane-containing thiazoles, piperazine-containing compounds, and nitrophenol-derived compounds. The compounds are strongly pan-WNK selective, inhibiting all four WNK isoforms. A class of quinoline compounds was identified that further shows selectivity among the WNK isoforms, being more potent toward WNK3 than WNK1. The crystal structure of the quinoline-derived SW120619 bound to the kinase domain of WNK3 reveals active site binding, and comparison to the WNK1 structure reveals the potential origin of isoform specificity. Discussion: The newly discovered classes of compounds may be starting points for generating pharmacological tools and potential drugs treating hypertension and cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Smart Microbial Cell Technology: A high-throughput platform to optimize biocatalysts

Los Alamos National Laboratory developed an ultra-high-throughput screening platform to engineer custom biocatalysts that enhance the rate of chemical reactions critical in pharmaceuticals, renewable energy, and environmental cleanup. Current methods to find biocatalysts are slow. This platform scans genetic variations to optimize a single enzyme or microbial cell to generate a product efficiently. It selects rare mutations needed for biocatalyst optimization orders of magnitude faster than current screening methods. A custom sensor reporter gene circuit causes cells to fluoresce when they are making the target product. When coupled to flow cytometry, a million biocatalyst variants can be screened in hours.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reducing roughness and improving efficiency of MAPbI3 perovskite solar cells made by high-throughput photonic curing

For perovskite solar cells (PSCs) to be commercially viable, the slow and energy-insufficient thermal annealing step must be eliminated. Among the photo-irradiation methods proposed to replace thermal annealing, photonic curing is the fastest conversion method. Photonic curing delivers short (20 μs to 100 ms) but intense light pulses from a broadband (200-1500 nm) xenon flash lamp, making it the only method to convert perovskite under 20 ms. This processing time can be extrapolated to a roll-to-roll web speed of 40 m/min based on laboratory processing conditions. However, most reported PSCs made by photonic curing under 1 second have inferior performance (~10% PCE). Although SEM images show dense and pinhole-free perovskite films, AFM images indicate secondary wavy features of 500 nm-wide ridge and 80 nm-deep trenches on photonically cured perovskite films, the existence of which correlates with poor device performance. We suggest that this morphology feature is produced by volatile solvent evaporation during the fast photonic curing process. Two approaches have been made to remedy this issue: (1) adding CH2I2 as the third solvent in the conventional DMF-DMSO system and (2) applying a controlled air-blowing step before photonic curing to remove excess solvent further. Combining these two approaches produces photonically- cured perovskite films with a comparable film roughness and device performance. Alkyl halide additives have been reported to enhance PSC performance by modulated solvent-solute interactions and C-X (X = Cl, Br, and I) cleavage. Photonic curing can cleave CH2I2, producing disassociated iodide ions to replenish iodine loss induced by photonic curing, which is confirmed by EDX. As a co-solvent, the high boiling point of CH2I2 can also make the solvent less volatile, reducing surface roughness in photonically cured perovskite films. Additionally, photonically-cured perovskite films have longer PL lifetimes and a higher recombination resistance compared to thermally-annealed counterparts. As a result, we demonstrate that photonic curing is a suitable method to replace thermal annealing in high-throughput PSC fabrication.

14 SOLAR ENERGY↗