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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Tissue Photolithography

Tissue lithography will enable physicians and researchers to obtain macromolecules with high purity (greater than 90 percent) from desired cells in conventionally processed, clinical tissues by simply annotating the desired cells on a computer screen. After identifying the desired cells, a suitable lithography mask will be generated to protect the contents of the desired cells while allowing destruction of all undesired cells by irradiation with ultraviolet light. The DNA from the protected cells can be used in a number of downstream applications including DNA sequencing. The purity (i.e., macromolecules isolated form specific cell types) of such specimens will greatly enhance the value and information of downstream applications. In this method, the specific cells are isolated on a microscope slide using photolithography, which will be faster, more specific, and less expensive than current methods. It relies on the fact that many biological molecules such as DNA are photosensitive and can be destroyed by ultraviolet irradiation. Therefore, it is possible to protect the contents of desired cells, yet destroy undesired cells. This approach leverages the technologies of the microelectronics industry, which can make features smaller than 1 micrometer with photolithography. A variety of ways has been created to achieve identification of the desired cell, and also to designate the other cells for destruction. This can be accomplished through chrome masks, direct laser writing, and also active masking using dynamic arrays. Image recognition is envisioned as one method for identifying cell nuclei and cell membranes. The pathologist can identify the cells of interest using a microscopic computerized image of the slide, and appropriate custom software. In one of the approaches described in this work, the software converts the selection into a digital mask that can be fed into a direct laser writer, e.g. the Heidelberg DWL66. Such a machine uses a metalized glass plate (with chrome metallization) on which there is a thin layer of photoresist. The laser transfers the digital mask onto the photoresist by direct writing, with typical best resolution of 2 micrometers. The plate is then developed to remove the exposed photoresist, which leaves the exposed areas susceptible to chemical chrome etch. The etch removes the unprotected chrome. The rest of the photoresist is then removed, by either ultraviolet organic solvent or over-development. The remaining chrome pattern is quickly oxidized by atmospheric exposure (typically within 30 seconds). The ready chrome mask is now applied to the tissue slide and aligned manually, or using automatic software and pre-designed alignment marks. The slide plate sandwich is then exposed to UV to destroy the DNA of the unwanted cells. The slide and plate are separated and the slide is processed in a standard way to prepare for polymerase chain reaction (PCR) and potential identification of cancer sequences.

Wade, Lawrence A.↗
NASA NTRS2012

Specificity and Transfer in Learning How to Follow Navigation Instructions

We report a series of experiments that use a navigation task in which instructions for navigating in a space displayed as grids on a computer screen are given to subjects who then attempt to follow them by mouse clicking on the grids. The navigation task was broken down into component dimensions (e.g., presentation mode of the instructions, length of the instructions, characteristics of the display, size of the grids, response type). For each task dimension, one condition was used at training and the same or another condition was used at test. Each task dimension was examined in terms of two measures. One measure provided an index of transfer (i.e., better performance at test than at training when test and training involved different conditions), and the other provided an index of specificity (i.e., better performance at test when training and test conditions were the same than when training and test conditions were different). By and large, these two indices were complementary, so there was evidence of either transfer or specificity but not both. For one dimension transfer but no specificity was evident, and for another dimension specificity but no transfer was evident. For the remaining dimensions, however, there was asymmetrical transfer, with transfer evident for some conditions and specificity evident for others. The findings are interpreted within the procedural reinstatement framework. They have practical implications concerning how to optimize training and how much fidelity to the testing situation is necessary when training.

Healy, Alice F.↗
NASA NTRS2012

OVERSMART Reporting Tool for Flow Computations Over Large Grid Systems

Structured grid solvers such as NASA's OVERFLOW compressible Navier-Stokes flow solver can generate large data files that contain convergence histories for flow equation residuals, turbulence model equation residuals, component forces and moments, and component relative motion dynamics variables. Most of today's large-scale problems can extend to hundreds of grids, and over 100 million grid points. However, due to the lack of efficient tools, only a small fraction of information contained in these files is analyzed. OVERSMART (OVERFLOW Solution Monitoring And Reporting Tool) provides a comprehensive report of solution convergence of flow computations over large, complex grid systems. It produces a one-page executive summary of the behavior of flow equation residuals, turbulence model equation residuals, and component forces and moments. Under the automatic option, a matrix of commonly viewed plots such as residual histograms, composite residuals, sub-iteration bar graphs, and component forces and moments is automatically generated. Specific plots required by the user can also be prescribed via a command file or a graphical user interface. Output is directed to the user s computer screen and/or to an html file for archival purposes. The current implementation has been targeted for the OVERFLOW flow solver, which is used to obtain a flow solution on structured overset grids. The OVERSMART framework allows easy extension to other flow solvers.

Kao, David L.↗
NASA NTRS2017

Modelling and Holographic Visualization of Space Radiation-Induced DNA Damage

Space radiation is composed by a mixture of ions of different energies. Among these, heavy inos are of particular importance because their health effects are poorly understood. In. the recent years, a software named RITRACKS (Relativistic Ion Tracks) was developed to simulate the detailed radiation track structure, several DNA models and DNA damage. As the DNA structure is complex due to packing, it is difficult to the damage using a regular computer screen.

Plante, Ianik↗
NASA NTRSDate not supplied

Designing Molten Salt Eutectics: A Combined Thermodynamic Modeling and Machine Learning Approach

Designing stable electrolytes with target properties is an important challenge in realizing next generation energy storage devices. Molten salt eutectics-based electrolytes are known for their stability with minimal parasitic reactions when compared to traditional organic electrolytes and are an attractive option for different battery chemistries. The operating temperature of the molten salt batteries depends on the melting temperature of the eutectic and hence there is a necessity to discover novel low melting temperature molten salt eutectic mixtures for energy storage applications. In this work we develop a high throughput computational screening approach for molten salt mixtures using thermodynamic modeling and machine learning (ML). COSMO-SAC model and ML approaches were independently developed based on the existing experimental data and these models were further used to predict the eutectic melting temperature and composition of several new binary, ternary, and quaternary mixtures. We show that combining ML and thermodynamic modeling strategies is effective in exploring the vast design space of molten salt mixtures.

Thermodynamics↗
NASA NTRSDate not supplied

Designing Molten Salt Eutectics

Designing stable electrolytes with target properties is an important challenge in realizing next generation energy storage devices. Molten salt eutectics-based electrolytes are known for their stability with minimal parasitic reactions when compared to traditional organic electrolytes and are an attractive option for different battery chemistries. The operating temperature of the molten salt batteries depends on the melting temperature of the eutectic and hence there is a necessity to discover novel low melting temperature molten salt eutectic mixtures for energy storage applications. In this work we develop a high throughput computational screening approach for molten salt mixtures using thermodynamic modeling and machine learning (ML). COSMO-SAC model and ML approaches were independently developed based on the existing experimental data and these models were further used to predict the eutectic melting temperature and composition of several new binary, ternary, and quaternary mixtures. We show that combining ML and thermodynamic modeling strategies is effective in exploring the vast design space of molten salt mixtures.

Ashwin Ravichandran↗
NASA NTRS2021

Remote Concurrent Engineering: A-Team Studies in the Virtual World

NASA Jet Propulsion Laboratory’s (JPL’s)Architecture Team (A-Team) has nearly a decade of experiencein maturing early formulation mission and technology conceptsby combining innovative collaborative engineering methodswith cutting-edge subject matter expertise and advancedanalysis tools in an in-person environment. When COVID-19forced JPL’s workforce to work remotely in March 2020, ATeamhad to quickly pivot from an in-person collaborativeenvironment to a remote working environment.Through introspection, careful planning, and considerablepractice, A-Team was able to develop new operating proceduresto effectively continue early formulation studies in a virtualenvironment. A-Team has held over 57 remote studies in the 10months since the start of mandatory telework at JPL in March2020. In the remote setting, A-Team conducts studies in half-daysessions with clients and subject matter experts (SMEs) viavideoconferencing, shared computer screens, and digitalcollaborative tools.The key lesson is that increased staffing and planning is neededto prepare and successfully run remote A-Team studies. RemoteA-Team studies require careful selection of the appropriatetools for security, accessibility, and usability within theNASA/JPL environment. Knowledge capture methods andtemplates need to be thought out and agreed upon in advance asthere is less room for improvising in a remote format. Variouscommunication channels have to be monitored to allow for teamcoordination while maintaining fruitful participant engagementduring a session. In addition, technical backup for all roleswithin the A-Team have to be identified to allow the study tocontinue even if a team member’s connectivity is temporarilyinterrupted. Finally, careful thought has to be put into methodsand processes to create a collaborative environment in a virtualspace such that a group of experts who are only connected viathe internet can experience the creative spark and flow of a greatcollaborative and innovative study.

Zusack, Steven↗
NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

In this presentation, we will discuss our work in adapting the NASA open source XR software, the Mixed Reality Exploration Toolkit (MRET), to an earth science domain. MRET is a NASA open source XR software for rapidly building extended reality (XR) environments for NASA domain problems, e.g., pulling in CAD models of thermal vac chamber and Roman Space Telescope to do fit checks. Primarily used for hardware integration & test, we have been adapting and extending MRET for science problems. Traditionally, scientists view and analyze the result of calculated or measured observables with static 1-D, 2-D or 3-D plots. It can be difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. Additionally, numerical models, such as the NASA GEOS climate model, are almost exclusively formulated and analyzed on Eulerian grids with points fixed in space and time. However, atmospheric phenomena such as convective clouds, hurricanes and wildfire smoke plumes move with the 3-D flow field, and it is often difficult and unnatural to understand these phenomena in an Eulerian reference frame as opposed to the Lagrangian reference frame in which nature operates. As part of an Earth Science Technology Office (ESTO) proposal, we have been adapting MRET to be a scientific exploration and analysis XR tool with integrated Lagrangian Dynamics (LD) for the Goddard Earth Observing System (GEOS) numerical weather prediction model. We believe this will help scientists identify, track, and understand the evolution of Earth Science phenomena. This presentation will discuss current results in our work in developing this XR tool for Earth Science.

Thomas G Grubb↗
NASA NTRSDate not supplied

Development of an Extended Reality (XR) Tool for Earth Science Visualization

In this presentation, we will discuss our work in adapting the NASA open source XR software, the Mixed Reality Exploration Toolkit (MRET), to an earth science domain. MRET is a NASA open source XR software for rapidly building extended reality (XR) environments for NASA domain problems, e.g., pulling in CAD models of thermal vac chamber and Roman Space Telescope to do fit checks. Primarily used for hardware integration & test, we have been adapting and extending MRET for science problems. Traditionally, scientists view and analyze the result of calculated or measured observables with static 1-D, 2-D or 3-D plots. It can be difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. Additionally, numerical models, such as the NASA GEOS climate model, are almost exclusively formulated and analyzed on Eulerian grids with points fixed in space and time. However, atmospheric phenomena such as convective clouds, hurricanes and wildfire smoke plumes move with the 3-D flow field, and it is often difficult and unnatural to understand these phenomena in an Eulerian reference frame as opposed to the Lagrangian reference frame in which nature operates. As part of an Earth Science Technology Office (ESTO) proposal, we have been adapting MRET to be a scientific exploration and analysis XR tool with integrated Lagrangian Dynamics (LD) for the Goddard Earth Observing System (GEOS) numerical weather prediction model. We believe this will help scientists identify, track, and understand the evolution of Earth Science phenomena. This presentation will discuss current results in our work in developing this XR tool for Earth Science.

Thomas Grubb↗
DOE OSTI2022

Three Modeling Tools Accelerate the Development of Marine Energy Technologies

Enough power flows through U.S. rivers and oceans to meet up to 60% of the country's electricity needs. But before today's early-stage marine energy technologies can harness some of that power, they need to overcome a few challenges, like a volatile ocean and high development costs. And to do that, technology developers need reliable tools to understand and overcome the challenges they face. That's why researchers at the National Renewable Energy Laboratory (NREL) have developed several open-source, validated, and customizable numerical tools for technology developers. With these tools, the marine energy community can assess their devices' potential power output, predict how their technologies might handle extreme loads (like extreme waves), and learn how changing one component might affect their device's performance. The lab's models generate highly accurate, reliable, robust data that can help marine energy devices leap from the computer screen to the ocean and, eventually, the power grid.

current energy↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening

Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history. We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments. In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations. The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2010

Algorithms for High-Speed Noninvasive Eye-Tracking System

Two image-data-processing algorithms are essential to the successful operation of a system of electronic hardware and software that noninvasively tracks the direction of a person s gaze in real time. The system was described in High-Speed Noninvasive Eye-Tracking System (NPO-30700) NASA Tech Briefs, Vol. 31, No. 8 (August 2007), page 51. To recapitulate from the cited article: Like prior commercial noninvasive eyetracking systems, this system is based on (1) illumination of an eye by a low-power infrared light-emitting diode (LED); (2) acquisition of video images of the pupil, iris, and cornea in the reflected infrared light; (3) digitization of the images; and (4) processing the digital image data to determine the direction of gaze from the centroids of the pupil and cornea in the images. Most of the prior commercial noninvasive eyetracking systems rely on standard video cameras, which operate at frame rates of about 30 Hz. Such systems are limited to slow, full-frame operation. The video camera in the present system includes a charge-coupled-device (CCD) image detector plus electronic circuitry capable of implementing an advanced control scheme that effects readout from a small region of interest (ROI), or subwindow, of the full image. Inasmuch as the image features of interest (the cornea and pupil) typically occupy a small part of the camera frame, this ROI capability can be exploited to determine the direction of gaze at a high frame rate by reading out from the ROI that contains the cornea and pupil (but not from the rest of the image) repeatedly. One of the present algorithms exploits the ROI capability. The algorithm takes horizontal row slices and takes advantage of the symmetry of the pupil and cornea circles and of the gray-scale contrasts of the pupil and cornea with respect to other parts of the eye. The algorithm determines which horizontal image slices contain the pupil and cornea, and, on each valid slice, the end coordinates of the pupil and cornea. Information from multiple slices is then combined to robustly locate the centroids of the pupil and cornea images. The other of the two present algorithms is a modified version of an older algorithm for estimating the direction of gaze from the centroids of the pupil and cornea. The modification lies in the use of the coordinates of the centroids, rather than differences between the coordinates of the centroids, in a gaze-mapping equation. The equation locates a gaze point, defined as the intersection of the gaze axis with a surface of interest, which is typically a computer display screen (see figure). The expected advantage of the modification is to make the gaze computation less dependent on some simplifying assumptions that are sometimes not accurate

Talukder, Ashit↗
DOE OSTI2024

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Combining computational modeling and experimental library screening to affinity-mature VEEV-neutralizing antibody F5

Engineered monoclonal antibodies have proven to be highly effective therapeutics in recent viral outbreaks. However, despite technical advancements, an ability to rapidly adapt or increase antibody affinity and by extension, therapeutic efficacy, has yet to be fully realized. We endeavored to stand-up such a pipeline using molecular modeling combined with experimental library screening to increase the affinity of F5, a monoclonal antibody with potent neutralizing activity against Venezuelan Equine Encephalitis Virus (VEEV), to recombinant VEEV (IAB) E1E2 antigen. We modeled the F5/E1E2 binding interface and generated predictions for mutations to improve binding using a Rosetta-based approach and dTERMen, an informatics approach. The modeling was complicated by the fact that a high-resolution structure of F5 is not available and the H3 loop of F5 exceeds the length for which current modeling approaches can determine a unique structure. A subset of the predicted mutations from both methods were incorporated into a phage display library of scFvs. This library and a library generated by error-prone PCR were screened for binding affinity to the recombinant antigen. Results from the screens identified favorable mutations which were incorporated into 12 human-IgG1 variants. The best variant, containing eight mutations, improved KD from 0.63 nM (parental) to 0.01 nM. While this did not improve neutralization or therapeutic potency of F5 against IAB, it did increase cross-reactivity to other closely related VEEV epizootic and enzootic strains, demonstrating the potential of this method to rapidly adapt existing therapeutics to emerging viral strains.

affinity-maturation↗