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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Polarization-resolved second-harmonic generation microscopy as a method to visualize protein-crystal domains

Polarization-resolved second-harmonic generation microscopy is applied to the identification of multiple crystalline domains within protein-crystal samples. Polarization-resolved second-harmonic generation (PR-SHG) microscopy is described and applied to identify the presence of multiple crystallographic domains within protein-crystal conglomerates, which was confirmed by synchrotron X-ray diffraction. Principal component analysis (PCA) of PR-SHG images resulted in principal component 2 (PC2) images with areas of contrasting negative and positive values for conglomerated crystals and PC2 images exhibiting uniformly positive or uniformly negative values for single crystals. Qualitative assessment of PC2 images allowed the identification of domains of different internal ordering within protein-crystal samples as well as differentiation between multi-domain conglomerated crystals and single crystals. PR-SHG assessments of crystalline domains were in good agreement with spatially resolved synchrotron X-ray diffraction measurements. These results have implications for improving the productive throughput of protein structure determination through early identification of multi-domain crystals.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Visualization of texture components using MTEX

Knowledge of the appearance of texture components and fibres in pole figures, in inverse pole figures and in Euler space is fundamental for texture analysis. For cubic crystal systems, such as steels, an extensive literature exists and, for example, the book by Matthies, Vinel & Helming [Standard Distributions in Texture Analysis: Maps for the Case of Cubic Orthorhomic Symmetry, (1987), Akademie-Verlag Berlin] provides an atlas to identify texture components. For lower crystal symmetries, however, equivalent comprehensive overviews that can serve as guidance for the interpretation of experimental textures do not exist. Here, this paper closes this gap by providing a set of scripts for the MTEX package [Bachmann, Hielscher & Schaeben (2010). Solid State Phenom.160, 63–68] that allow the texture practitioner to compile such an atlas for a given material system, thus aiding orientation distribution function analysis also for non-cubic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Synchrotron X-ray footprinting as a method to visualize water in proteins

The vast majority of biomolecular processes are controlled or facilitated by water interactions. In enzymes, regulatory proteins, membrane-bound receptors and ion-channels, water bound to functionally important residues creates hydrogen-bonding networks that underlie the mechanism of action of the macromolecule. High-resolution X-ray structures are often difficult to obtain with many of these classes of proteins because sample conditions, such as the necessity of detergents, often impede crystallization. Other biophysical techniques such as neutron scattering, nuclear magnetic resonance and Fourier transform infrared spectroscopy are useful for studying internal water, though each has its own advantages and drawbacks, and often a hybrid approach is required to address important biological problems associated with protein–water interactions. One major area requiring more investigation is the study of bound water molecules which reside in cavities and channels and which are often involved in both the structural and functional aspects of receptor, transporter and ion channel proteins. Recently, significant progress has been made in synchrotron-based radiolytic labeling and mass spectroscopy techniques for both the identification of bound waters and for characterizing the role of water in protein conformational changes at a high degree of spatial and temporal resolution. Finally, here the latest developments and future capabilities of this method for investigating water–protein interactions and its synergy with other synchrotron-based methods are discussed.

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

Michaelis-like complex of SARS-CoV-2 main protease visualized by room-temperature X-ray crystallography

SARS-CoV-2 emerged at the end of 2019 to cause an unprecedented pandemic of the deadly respiratory disease COVID-19 that continues to date. The viral main protease (M pro ) is essential for SARS-CoV-2 replication and is therefore an important drug target. Understanding the catalytic mechanism of M pro , a cysteine protease with a catalytic site comprising the noncanonical Cys145–His41 dyad, can help in guiding drug design. Here, a 2.0 Å resolution room-temperature X-ray crystal structure is reported of a Michaelis-like complex of M pro harboring a single inactivating mutation C145A bound to the octapeptide Ac-SAVLQSGF-CONH 2 corresponding to the nsp4/nsp5 autocleavage site. The peptide substrate is unambiguously defined in subsites S5 to S3′ by strong electron density. Superposition of the Michaelis-like complex with the neutron structure of substrate-free M pro demonstrates that the catalytic site is inherently pre-organized for catalysis prior to substrate binding. Induced fit to the substrate is driven by P1 Gln binding in the predetermined subsite S1 and rearrangement of subsite S2 to accommodate P2 Leu. The Michaelis-like complex structure is ideal for in silico modeling of the SARS-CoV-2 M pro catalytic mechanism.

3CL protease↗
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
DOE OSTI2017

Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing

Scientific user facilities—particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more—operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.

Bethel, E. Wes↗
DOE OSTI2016

The SENSEI Generic In Situ Interface, In: ISAV '16 Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization

The SENSEI generic in situ interface is an API that promotes code portability and reusability. From the simulation view, a developer can instrument their code with the SENSEI API and then make make use of any number of in situ infrastructures. From the method view, a developer can write an in situ method using the SENSEI API, then expect it to run in any number of in situ infrastructures, or be invoked directly from a simulation code, with little or no modification. This paper presents the design principles underlying the SENSEI generic interface, along with some simplified coding examples.

Computer Science↗
DOE OSTI2016

Asynchronous In Situ Connected-Components Analysis for Complex Fluid flows, In: ISAV '16 Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization

The simulation of multiscale physics is an important challenge for scientific computing. For this class of problem, large three-dimensional simulations are performed to advance scientific inquiry. On massively parallel computing systems, the volume of data generated by such approaches can become a productivity bottleneck if the raw data generated from the simulation is analyzed in a post-processing step. To address this, we present a physics-based framework for in situ data reduction that is theoretically grounded in multiscale averaging theory. We show how task parallelism can be exploited to concurrently perform a variety of analysis tasks with data-dependent costs, including the generation of iso-surfaces, morphological analyses, and connected components analysis. All analyses are performed in parallel using distributed memory and use the same domain decomposition as the simulation. A task management framework is constructed to leverage available parallelism within a node for analysis. The capabilities of the framework are to launch asynchronous analysis threads, manage dependencies between different tasks, promote data locality and minimize the impact of data transfers. The framework is applied to analyze GPU-based simulations of two-fluid-phase flow in porous media, generating a set of averaged measures that represents the overall system behavior. We demonstrate how the approach can be applied to perform physically-consistent analysis over fluid sub-regions determined from connected components analysis. Simulations performed on Oak Ridge National Lab's Titan supercomputer are profiled to demonstrate the performance of the associated multi-threaded in situ analysis approach for typical production simulation of two-fluid-phase flow.

Computer Science↗