Proof of concept for rational design of hepatitis C virus E2 core nanoparticle vaccines
Explore the source record for details and available documents.
He, Linling↗
SEARCH · Search NASA
Results for “proof of concept”
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.
Additively Manufactured Compliant Hybrid Gas Thrust Bearing for Supercritical Carbon Dioxide Turbomachinery: Design and Proof of Concept Testing
The following paper presents a new type of gas lubricated thrust bearing that utilizes additive manufacturing or also known as direct metal laser melting (DMLM) to fabricate the bearing. The motivation for the new bearing concept is derived from the need for highly efficient supercritical carbon dioxide (sCO2) turbomachinery in the mega-watt power range. The paper provides a review of existing gas thrust-bearing technology, outlines the need for the new DMLM concept, and discusses proof-of-concept testing results. The new concept combines hydrostatic pressurization with individual tilting pads that are flexibly mounted using hermetic squeeze film dampers (HSFD) in the bearing-pad support. This paper describes the thrust-bearing concept and discusses the final design approach. Proof-of-concept testing in air for a 6.8 in. (173 mm) outer diameter thrust gas bearing was performed; with thrust loading, up to 1500 lbs (6.67 kN) and a thrust runner speed of 10krpm (91 m/s tip speed). The experiments were performed with a bent shaft resulting in thrust runner axial vibration magnitudes of 2.9 mils (74 μm) p-p and dynamic thrust loads of 270 lbs (1.2 kN) p-p. In addition, force deflection characteristics and stiffness coefficients of the bearing system are presented for an inlet hydrostatic pressure of 380 psi (2.62 MPa). Results at 10 krpm show that the pad support architecture was able to sustain high levels of dynamic misalignment equaling 6 times the nominal film clearance while demonstrating a unit load-carrying capacity of 55 psi (0.34 Mpa). Gas-film force deflection tests portrayed nonlinear behavior like a hardening spring, while the bearing pad support stiffness was measured to be linear and independent of gas film thickness.
Engineering↗
Proof-of-concept Talbot–Lau x-ray interferometry with a high-intensity, high-repetition-rate, laser-driven K-alpha source
Talbot–Lau x-ray interferometry is a grating-based phase-contrast technique, which enables measurement of refractive index changes in matter with micrometric spatial resolution. The technique has been established using a variety of hard x-ray sources, including synchrotron, free-electron lasers, and x-ray tubes, and could be used in the optical range for low-density plasmas. The tremendous development of table-top high-power lasers makes the use of high-intensity, laser-driven K-alpha sources appealing for Talbot–Lau interferometer applications in both high-energy-density plasma experiments and biological imaging. To this end, we present the first, to the best of our knowledge, feasibility study of Talbot–Lau phase-contrast imaging using a high-repetition-rate laser of moderate energy (100 mJ at a repetition rate of 10 Hz) to irradiate a copper backlighter foil. The results from up to 900 laser pulses were integrated to form interferometric images. A constant fringe contrast of 20% is demonstrated over 100 accumulations, while the signal-to-noise ratio continued to increase with the number of shots. Phase retrieval is demonstrated without prior ex-situ phase stepping. Instead, correlation matrices are used to compensate for the displacement between reference acquisition and the probing of a PMMA target rod. The steps for improved measurements with more energetic laser systems are discussed. The final results are in good agreement with the theoretically predicted outcomes, demonstrating the applicability of this diagnostic to a range of laser facilities for use across several disciplines.
Bouffetier, V. (ORCID:0000000160791260)↗
The use of seismic spatial gradients in a single layer neural network for seismic source discrimination: proof of concept
This report describes a proof-of-concept method of seismic source discrimination using seismic gradiometry and a common machine learning technique. The tests described here are purely numerical, using synthetic seismic data and well understood mathematical techniques. The primary innovation described here is the application of a richer seismic data set derived from seismic gradiometry. Seismic gradiometry is a method to estimate the time variable spatial gradient of the wavefield to compute various wavefield attributes such as slowness, dynamic strain, and rotational motions. With the addition of these wavefield attributes, we are afforded up to twenty "compo- nents" of time series data measured at a single point on, or in, the Earth. This is in direct contrast to conventional three-component seismic data collected at several locations using a seismic network. Using the gradiometrically-derived wavefield components directly in a single-layer neural network, I show that it is possible to discriminate between three common seismic source types (earthquakes, explosions, and opening fractures) for various noise conditions and gradiometry configurations.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Produced Water-Economic, Socio, Environmental Simulation Model (PW-ESEim) Model: Proof-of-Concept for Southeastern New Mexico
A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.
54 ENVIRONMENTAL SCIENCES↗
FY 2021 Filter Test for TRU Waste Drum POC Proof-of-Concept Unit
Continuing last year’s (fiscal year 2020) work, a Proof-of-Concept (POC) instrument was developed to assess the functionality of filters on transuranic waste containers (commonly called 55 Gallon ring-top drums) without requiring removal of the drum lid. The purpose of this work is to determine the air flow and pressure characteristics associated with filter clogging, filter pressure drop, headspace volume, leakage around the lid seal and influence of the additional filter on a bag-out bag.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Antimicrobial Peptides Can Facilitate Whole Blood Safety from Bacteria: A Proof of Concept
With continuous improvements to blood donor deferrals and the availability of sensitive tests for donation screening for infectious agents, bacterial contamination of whole blood (WB) and blood components stored for transfusion is a rare event. Nonetheless, it still occurs and remains a transfusion-associated risk in terms of septic transfusion reactions (STRs) and transfusion-transmitted bacterial infections with morbidity and mortality outcomes. One of the risk mitigation strategies for bacterial contamination is to implement treatment with currently available proactive pathogen reduction technologies (PRTs) for these transfusion products. Here, as a proof of concept, we tested two recently developed unique cationic antimicrobial peptides (AMPs; D-CONGA and D-CONGA-Q7) for WB safety from bacterial contamination. In this study, WB was inoculated with Escherichia coli and Staphylococcus epidermidis and treated with the two peptides to evaluate their bactericidal efficacy. The results demonstrated that D-CONGA and D-CONGA-Q7 exhibit potent inhibitory activity against the bacteria with a minimal inhibitory concentration (MIC) range of 4–8 and 1–8 μM, respectively, depending on the bacterial species tested. Time-kill kinetics further confirmed that the peptides exhibit bactericidal efficacy at 8 μM by achieving a 5-log 10 reduction (99.999%) of the bacterial load in WB with a time-dependent killing profile. Furthermore, even at 20 μM, the AMPs did not negatively impact hemolysis or hemostatic properties. We have further demonstrated using a cationic exchange resin that the cationic AMPs can be separated and removed from WB after the peptide treatments. During 35-day WB storage at 2–8 °C, 4 μM D-CONGA-Q7 one-time treatment prevented S. epidermidis growth and preserved WB quality and integrity. Overall, the results described here provide the first proof of concept that certain AMPs, such as D-CONGA and D-CONGA-Q7, can facilitate WB safety from bacteria during storage.
60 APPLIED LIFE SCIENCES↗
Historical review and proof-of-concept future method demonstration of adaptive mesh refinement in nuclear engineering for increased fidelity and computational efficiency
As the nuclear industry's use of computational tool increases, the need for increased fidelity and computational efficiency is well known. While most approaches to increased fidelity rely on applying a fine mesh over the problem domain, a more efficient method is to apply an adaptive mesh refinement (AMR) algorithm to the mesh definition. In the field of nuclear engineering, AMR has previously been used in conjunction with deterministic methods, including: S{sub N} transport methods, Lattice Boltzmann Methods, and COMSOL. The future of AMR in nuclear engineering is to couple it to a Monte Carlo code with the goal of reducing calculation time. A proof-of-concept example yielded positive results for using the gradient of the flux as a refinement criteria. The refinement criteria was varied from 0.01 to 0.10, which yielded a recommended range of 0.01 to 0.04, and the number of refinement iterations was varied from 0 to 7, with diminishing returns seen after 5 iterations. After the success of the proof-of-concept exercise, work began on creating a full program coupling MCNP6.2 and the AMR algorithm in the deal.II library. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind
Here, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.
17 WIND ENERGY↗
Targeted Particle Fractionation Technologies: Proof of Concept
Most methods of particle-size analysis involve particle fractionation which, is often done by filtration or the use of screens. This paper provides proof of concept testing for new in-pipe particle fractionation technologies that can be used at laboratory to industrial scales. These technologies are extensions of recent advancements in bump arrays as well as testing of new inline angled-to-the-flow slats, and advanced boycott-based Avalanche separation technologies. Experiments were conducted for three targeted particle fractionation technologies to evaluate the in-pipe removal of oversized sands in Newtonian slurries prior to the introduction of bump arrays. The mesofluidic system uses an array of staggered posts as configured in a bump array for Newtonian slurry conditions. The methods and results of these experiments are described in this paper. This information could be applied in most industrial and laboratory systems to rapidly remove oversized particles from a flowing slurry.
slurry, solids removal, particle fractionation, bu↗
Developing the Proof of Concept for the SERI QC Flag Translation
SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the second of six in the Data Quality and Uncertainty Integration project, develops the proof of concept, including programming in C and a collection of spreadsheet tools, for estimating the operational uncertainty from three-component solar irradiance measurements using information available from SERI QC. The report establishes a field reference for determining the operational uncertainty for each irradiance component based on the SERI QC coupling. Results demonstrate that the operational uncertainty estimates based on global irradiance best represent the collective results for all sky conditions.
14 SOLAR ENERGY↗
Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis
Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.
60 APPLIED LIFE SCIENCES↗
Sustainable co-production of plant lipids and cellulosic sugars from transgenic energycane at an industrially relevant scale: A proof of concept for alternative feedstocks
Development of sustainable and scalable technologies to convert lignocellulosic biomass to biofuels is critical to achieving carbon neutrality. The potential of transgenic bioenergy crops as a renewable source of sugars and lipids has been demonstrated at bench-scale. However, scaling up these processes is important for holistic analysis. Here proof-of-concept for chemical-free hydrothermal pretreatment of transgenic energycane-oilcane line L13 at an industrially relevant scale to recover vegetative lipids along with cellulosic sugars is presented. Here, pilot-scale processing of 97 kg of transgenic energycane-oilcane L13 stems and high solids pretreatment of bagasse enhanced the recovery of cellulosic glucose and xylose by 5-fold as compared to untreated bagasse and helped in the enrichment of vegetative lipids in the biomass residues which allowed its recovery at the end of the bioprocess. Palmitic and oleic acids were the predominant fatty acids (FAs) extracted from stems and leaves. The processing did not affect lipid composition. The efficiency of lipid recovery from untreated biomass was 75.9% which improved to 88.7% upon pretreatment. The vegetative tissues of transgenic energycane-oilcane L13 contained 0.42 metric tons/hectare of lipids. Processing vegetative tissues yielded 0.38 metric tons/hectare of lipids. This approaches an oil yield similar to soybean (global average 0.44 metric tons/hectare) and is almost twice as high as the oil yield from sugarcane engineered to hyperaccumulate lipids (0.20 metric tons/hectare). The study suggests that further optimization by state-of-the-art metabolic engineering and biomass processing can establish transgenic bioenergy crops for commercial drop-in fuel production.
59 BASIC BIOLOGICAL SCIENCES↗
Data for Sustainable Co-Production of Plant Lipids and Cellulosic Sugars from Transgenic Energycane at an Industrially Relevant Scale: A Proof of Concept for Alternative Feedstocks
Development of sustainable and scalable technologies to convert lignocellulosic biomass to biofuels is critical to achieving carbon neutrality. The potential of transgenic bioenergy crops as a renewable source of sugars and lipids has been demonstrated at bench-scale. However, scaling up these processes is important for holistic analysis. Here proof-of-concept for chemical-free hydrothermal pretreatment of transgenic energycane-oilcane line L13 at an industrially relevant scale to recover vegetative lipids along with cellulosic sugars is presented. Pilot-scale processing of 97 kg of transgenic energycane-oilcane L13 stems and high solids pretreatment of bagasse enhanced the recovery of cellulosic glucose and xylose by 5-fold as compared to untreated bagasse and helped in the enrichment of vegetative lipids in the biomass residues which allowed its recovery at the end of the bioprocess. Palmitic and oleic acids were the predominant fatty acids (FAs) extracted from stems and leaves. The processing did not affect lipid composition. The efficiency of lipid recovery from untreated biomass was 75.9% which improved to 88.7% upon pretreatment. The vegetative tissues of transgenic energycane-oilcane L13 contained 0.42 metric tons/hectare of lipids. Processing vegetative tissues yielded 0.38 metric tons/hectare of lipids. This approaches an oil yield similar to soybean (global average 0.44 metric tons/hectare) and is almost twice as high as the oil yield from sugarcane engineered to hyperaccumulate lipids (0.20 metric tons/hectare). The study suggests that further optimization by state-of-the-art metabolic engineering and biomass processing can establish transgenic bioenergy crops for commercial drop-in fuel production.
Biomass Analytics↗
Design and proof of concept for targeted phage-based COVID-19 vaccination strategies with a streamlined cold-free supply chain
Significance The COVID-19 pandemic has had an unprecedented impact. Although several vaccines have received emergency use authorization, demand has created enormous logistical challenges—including supply, access, and distribution—that justify research for alternative strategies. Phage are viruses that only infect bacteria and can be safely administered to humans. Here, as a proof-of-concept study, we demonstrate that aerosol vaccination with lung-targeted phage particles displaying short SARS-CoV-2 S protein epitopes and subcutaneous vaccination with targeted AAVP particles carrying the entire S protein gene both elicit systemic and specific immune responses in immunocompetent mice. Given their unique attributes, including sturdiness, simple-to-engineer platform, cost-effectiveness for rapid large-scale production, and stability at room temperature, these phage-based approaches may become attractive tools for COVID-19 vaccine development.
59 BASIC BIOLOGICAL SCIENCES↗
Predicting oncology drug-induced cardiotoxicity with donor-specific iPSC-CMs—a proof-of-concept study with doxorubicin
Abstract Many oncology drugs have been found to induce cardiotoxicity in a subset of patients, which significantly limits their clinical use and impedes the benefit of lifesaving anticancer treatments. Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) carry donor-specific genetic information and have been proposed for exploring the interindividual difference in oncology drug-induced cardiotoxicity. Herein, we evaluated the inter- and intraindividual variability of iPSC-CM-related assays and presented a proof of concept to prospectively predict doxorubicin (DOX)-induced cardiotoxicity (DIC) using donor-specific iPSC-CMs. Our findings demonstrated that donor-specific iPSC-CMs exhibited greater line-to-line variability than the intraindividual variability in impedance cytotoxicity and transcriptome assays. The variable and dose-dependent cytotoxic responses of iPSC-CMs resembled those observed in clinical practice and largely replicated the reported mechanisms. By categorizing iPSC-CMs into resistant and sensitive cell lines based on their time- and concentration-related phenotypic responses to DOX, we found that the sensitivity of donor-specific iPSC-CMs to DOX may predict in vivo DIC risk. Furthermore, we identified a differentially expressed gene, DND microRNA-mediated repression inhibitor 1 (DND1), between the DOX-resistant and DOX-sensitive iPSC-CMs. Our results support the utilization of donor-specific iPSC-CMs in assessing interindividual differences in DIC. Further studies will encompass a large panel of donor-specific iPSC-CMs to identify potential novel molecular and genetic biomarkers for predicting DOX and other oncology drug-induced cardiotoxicity.
Toxicology↗
Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study
Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).
42 ENGINEERING↗
Updates to Relevance Vector Machine: Multiclass Classification, Variable Selection, and Proof-of-Concept Application to Safeguards Fresh Fuel Verification using List-Mode Neutron Collar Data
To expand the capabilities of safeguards authorities to verify the integrity of fresh fuel assemblies, Oak Ridge National Laboratory has retrofit the existing electronics of the JCC-71 uranium neutron coincidence collar, which contains 18 3 He neutron detectors and an external 241 AmLi(α, n) neutron interrogation source arranged to surround a fresh nuclear fuel assembly. The new electronics system allows analysts to record list-mode neutron multiplicity data in addition to the singles and doubles rates that are currently measured. Based on previous proof-of-concept research, analysis of these new data will identify off-normal fuel configurations in an assembly and characterize or localize the specific partial fuel defects. The purpose of this report it to document the analysis algorithm development and then to demonstrate its capability for the safeguards verification of fresh fuel assemblies using list mode neutron collar data. To analyze the complex list-mode data collected with the upgraded uranium neutron collar, multivariate classification algorithms are being developed using a novel classification method, the relevance vector machine. This approach may be applied to multiclass problems to estimate the probability that test data belongs to one of many possible classes of data. In addition, our method identifies the most useful variables/channels for making predictions, which illuminates the basis for the model’s predictions, and this interpretability is largely unique among data analytics methods. Variable selection occurs during model training and parameter tuning and does not need any external hyperparameter tuning routines. Finally, we apply the modified relevance vector machine to a simulated dataset of list-mode neutron collar data generated with the radiation transport code MCNP. The method can correctly identify off-normal fuel configurations, categorize the data according to four fuel defect scenarios, and rank the channels in the data according to prediction utility. For nuclear safeguards applications, it is concluded that this method has the potential to increase the sensitivity and reliability to detect missing fuel rods from a standard 17 x 17 Pressurized Water Reactor (PWR) fresh fuel assembly. Within this analysis, “off-normal” (i.e., missing fuel rods) were correctly classified in 17 simulated test scenarios with one quarter (25%) of the fresh fuel rods missing using a training data set of 58 simulated measurements.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗