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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 685 records · Page 38

Mapping Hsp104 interactions using cross‐linking mass spectrometry

Molecular machines from the AAA+ (ATPases Associated with diverse cellular Activity) superfamily of protein disaggregases play important roles in protein folding, disaggregation and DNA processing. Recent cryo-EM structures of AAA+ molecular machines have uncovered nuanced changes in their conformation that underlie their specialized functions. Structural knowledge of these molecular machines in complex with substrates begins to explain their mechanism of activity. Here, we explore how cross-linking mass spectrometry (XL-MS) can be used to interpret changes in conformation induced by ATP in Hsp104 and how a substrate may interact with Hsp104. We applied a panel of cross-linking reagents to produce cross-linking maps of Hsp104 and interpret our data on previously determined X-ray and cryo-EM structures of Hsp104 from a thermophilic yeast, Calcarisporiella thermophila. We developed an analysis pipeline to differentiate between intra-subunit and inter-subunit contacts within the hexameric homo-oligomer. We identify cross-links that break the asymmetry that is present in Hsp104 in an ATP-hydrolysis competent conformation but is absent in an ATP-hydrolysis-defective mutant. Finally, we identify contacts between Hsp104 and a selected protein (proprotein convertase subtilisin/kexin type 9 PCSK9) to reveal contacts on the central channel of Hsp104 across the length of this protein indicating that we might have trapped interactions consistent with its translocation. Our simple and robust XL-MS-based experiments and methods help interpret how these molecular machines change conformation and bind to other proteins even in the context of homo-oligomeric assemblies enabling coupling state-of-the-art modeling approaches with XL-MS.

60 APPLIED LIFE SCIENCES↗

Studies of reaction mechanisms during stabilization of electrospun polyacrylonitrile carbon nanofibers

Various reaction mechanisms such as cyclization, oxidation, dehydrogenation, and crosslinking are studied during stabilization of electrospun polyacrylonitrile nanofibers using different in situ techniques such as differential scanning calorimetry (DSC), shrinkage measurement, and dynamic mechanical analysis (DMA). DSC results show that oxidation preferentially occurs in cyclized structure. It is also found that the cyclization reaction has the highest activation energy followed by oxidation/dehydrogenation and crosslinking reactions. In situ shrinkage measurement and DMA data are used to study the extent of cyclization and cross-linking reactions, respectively, in air. Comparing the in situ shrinkage measurement with DSC data, it is found that cyclization reaction in air progresses in two different mechanisms such as radical cyclization, which depends only on the temperature and ionic cyclization, which is limited by the rate of oxygen diffusion. In conclusion, it is found that complete cyclization time occurs at about 189 min for isothermal heat treatment at 260 °C with 5 °C/min ramp, while cross-linking reaction becomes dominant at 132 min.

36 MATERIALS SCIENCE↗

Global temperature response to the major volcanic eruptions in multiple reanalysis data sets

Abstract. The global temperature responses to the eruptions of Mount Agung in 1963, El Chichón in 1982, and Mount Pinatubo in 1991 are investigated using nine currently available reanalysis data sets (JRA-55, MERRA, ERA-Interim, NCEP-CFSR, JRA-25, ERA-40, NCEP-1, NCEP-2, and 20CR). Multiple linear regression is applied to the zonal and monthly mean time series of temperature for two periods, 1979–2009 (for eight reanalysis data sets) and 1958–2001 (for four reanalysis data sets), by considering explanatory factors of seasonal harmonics, linear trends, Quasi-Biennial Oscillation, solar cycle, and El Niño Southern Oscillation. The residuals are used to define the volcanic signals for the three eruptions separately, and common and different responses among the older and newer reanalysis data sets are highlighted for each eruption. In response to the Mount Pinatubo eruption, most reanalysis data sets show strong warming signals (up to 2–3 K for 1-year average) in the tropical lower stratosphere and weak cooling signals (down to -1 K) in the subtropical upper troposphere. For the El Chichón eruption, warming signals in the tropical lower stratosphere are somewhat smaller than those for the Mount Pinatubo eruption. The response to the Mount Agung eruption is asymmetric about the equator with strong warming in the Southern Hemisphere midlatitude upper troposphere to lower stratosphere. Comparison of the results from several different reanalysis data sets confirms the atmospheric temperature response to these major eruptions qualitatively, but also shows quantitative differences even among the most recent reanalysis data sets. The consistencies and differences among different reanalysis data sets provide a measure of the confidence and uncertainty in our current understanding of the volcanic response. The results of this intercomparison study may be useful for validation of climate model responses to volcanic forcing and for assessing proposed geoengineering by stratospheric aerosol injection, as well as to link studies using only a single reanalysis data set to other studies using a different reanalysis data set.

54 ENVIRONMENTAL SCIENCES↗

Great Basin NV Play Fairway Analysis - Carson Sink

All datasets and products specific to the Carson Sink Basin. Includes a packed ArcMap (.mpk), individually zipped shapefiles, and a file geodatabase for the Carson Sink area; a GeoSoft Oasis montaj project containing GM-SYS 2D gravity profiles along the trace of our seismic reflection lines; a 3D model in EarthVision; spreadsheet of links to published maps; and spreadsheets of well data.

15 GEOTHERMAL ENERGY↗

Overview of the front-end electronics of CMS HGCal - including readout and powering

The end-cap calorimeters of CMS will be upgraded to a single High Granularity Calorimeter (HG-Cal) for the HL-LHC, including both silicon sensors and scintillator tiles with on-tile SiPMs as active elements. The readout of the active elements is performed by an ASIC (HGCROC in 130 nm CMOS technology) that measures the amplitude and arrival time of the signals. The amplitude is measured over a large dynamic range to allow calibration with single particles and the measurement of TeV showers. The time of arrival of high-energy showers will be measured with a precision of around 30 ps. A second pair of “concentrator” ASICs - ECON-T and ECON-D - takes the data from the HGCROC channels and packages them for transmission via optical links to the off-detector electronics. The ECON-T transmits trigger data at 40 MHz, to form part of the level-1 trigger. The ECON-D transmits concentrated data packets at up to 1 MHz, upon reception of a level-1 trigger signal. In addition to these ASICs, HGCal will use modified versions of common HL-LHC electronics developments, for the power chain and the optical control and readout. The dense nature of the HGCal provides additional challenges for the electronic boards and cabling. In this proceedings the overall HGCal front electronics scheme, including the latest performance of the HGCROC and ECON ASICs is presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

EGS Collab Experiment 2: Shear Stimulation ERT Monitoring Data

This repository contains the electrical resistivity tomography (ERT) monitoring data that was collected before, during, and after shear stimulation attempts were conducted at EGS Collab. These tests were carried out on the SURF 4100 level during Experiment 2 in March, 2022. Flow and stimulation data corresponding to this dataset are available in a different GDR dataset, which is linked below. Also included here are the E4D input files that were used to process the ERT data. E4D is an open source ERT monitoring and inversion code which is linked for use below.

15 GEOTHERMAL ENERGY↗

Planning of distributed data production for High Energy and Nuclear Physics

Modern experiments in High Energy and Nuclear Physics heavily rely on distributed computations using multiple computational facilities across the world. One of the essential types of the computations is a distributed data production where petabytes of raw files from a single source has to be processed once (per production campaign) using thousands of CPUs at distant locations and the output has to be transferred back to that source. The data distribution over a large system does not necessary match the distribution of storage, network and CPU capacity. Therefore, bottlenecks may appear and lead to increased latency and degraded performance. In this paper we propose a new scheduling approach for distributed data production which is based on the network flow maximization model. In our approach a central planner defines how much input and output data should be transferred over each network link in order to maximize the computational throughput. Such plans are created periodically for a fixed planning time interval using up-to-date information on network, storage and CPU resources. The centrally created plans are executed in a distributed manner by dedicated services running at participating sites. In conclusion, our simulations based on the log records from the data production framework of the experiment STAR (Solenoid Tracker at RHIC) have shown that the proposed model systematically provides a better performance compared to the simulated traditional techniques.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Global transpiration data from sap flow measurements: the SAPFLUXNET database

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

58 GEOSCIENCES↗

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗

Boxfish

Boxfish is a tool for visualizing performance data. It allows users to quickly project performance data gathered from parallel applications onto intuitive domains. It also includes a projection mechanism that automatically finds relations in performance data so the the data can easily be mapped onto non-obviously related domains. For this release, Boxfish allows performance data to be projected onto the nodes and links of a computer network, as well as onto grids or patch-structured applications. This allows a user of the visualization tool to quickly see correlations between performance data and network structure, application data, or communication patterns.

Source record↗

Competing Use Analysis for Offshore Renewables and Subsea Cables

Demand for abundant and diverse resources in the oceans is growing, necessitating marine spatial planning. To inform development of Marine Hydrokinetic (MHK) and Offshore Wind (OSW) resources, the Department of Energy (DOE) has asked NREL to identify the competing uses areas between promising MHK/OSW sites and submarine power and telecommunications cables. The first step in this work is to identify and quantify the overlap between the MHK/OSW resource availability and existing cable routes. The analysis is done in terms of resource area because the task of quantifying actual impacts on available resource is a non-trivial undertaking that involves subjective decisions of identifying resource opportunities. Quantifying overlap in-terms of resource area?on the other hand?is significantly more straight forward, and useful to marine spatial planners. This dataset of polygons describes recommended setback areas from submarine cables for marine renewable energy Development. Two archive files are provided: 1. NREL Cables Setback-Polygons.zip contains the following GeoJSON files: 1.a. cables_100ft.geojson: The Code of Federal Regulations (CFR 585.301) specifies that the legal right of way for submarine cables is 100 ft (~30 meters) to either side of the cable (i.e., 200 ft wide). 1.b. cables_2z.geojson: The International Cable Protection Committee (ICPC) of the North American Submarine Cable Association (NASCA) recommends setback distances for new facilities as the maximum of 500 m or twice the bottom depth (2z), per ICPC Recommendation 13 No. 2 (Communications Security, Reliability and Interoperability Council IV 2014). For depths 250 m, 2 * depth is to be used. 1.c. cables_3z.geojson: The ICPC of NASCA recommends setback distances for new cables as the maximum of 500 m or three times the bottom depth (3z), per ICPC Recommendation 2 No. 10 (Communications Security, Reliability and Interoperability Council IV 2014). So for depths 167 m, 3 * depth is to be used. 2. NREL Submarine Cable Analysis Code Data Presentations.zip contains all files (code, data, README, etc) from release 0.1 of this Github repository for possibly regenerating the product as submarine cable configurations change: https://github.com/ecoquants/nrel-cables. For more details, please see the link to the full report for which this data product is a supplemental output: Submarine Cable Analysis for U.S. Marine Renewable Energy Development by Ben Best and Levi Kilcher National Renewable Energy Laboratory

16 TIDAL AND WAVE POWER↗

Boxfish

Boxfish is a tool for visualizing performance data. It allows users to quickly project performance data gathered from parallel applications onto intuitive domains. It also includes a projection mechanism that automatically finds relations in performance data so the the data can easily be mapped onto non-obviously related domains. For this release, Boxfish allows performance data to be projected onto the nodes and links of a computer network, as well as onto grids or patch-structured applications. This allows a user of the visualization tool to quickly see correlations between performance data and network structure, application data, or communication patterns.

Source record↗

Detecting Anomalies from End-to-End Internet Performance Measurements (PingER) Using Cluster Based Local Outlier Factor

PingER (Ping End-to-End Reporting) is a worldwide end-to-end Internet performance measurement framework. It was developed by the SLAC National Accelerator Laboratory, Stanford, USA and running from the last 20 years. It has more than 700 monitoring agents and remote sites which monitor the performance of Internet links around 170 countries of the world. At present, the size of the compressed PingER data set is about 60 GB comprising of 100,000 flat files. The data is publicly available for valuable Internet performance analyses. However, the data sets suffer from missing values and anomalies due to congestion, bottleneck links, queuing overflow, network software misconfiguration, hardware failure, cable cuts, and social upheavals. Therefore, the objective of this paper is to detect such performance drops or spikes labeled as anomalies or outliers for the PingER data set. In the proposed approach, the raw text files of the data set are transformed into a PingER dimensional model. The missing values are imputed using the k-NN algorithm. The data is partitioned into similar instances using the k-means clustering algorithm. Afterward, clustering is integrated with the Local Outlier Factor (LOF) using the Cluster Based Local Outlier Factor (CBLOF) algorithm to detect the anomalies or outliers from the PingER data. Lastly, anomalies are further analyzed to identify the time frame and location of the hosts generating the major percentage of the anomalies in the PingER data set ranging from 1998 to 2016.

97 MATHEMATICS AND COMPUTING↗

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Transcriptomics (PB-DP3)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Sample data was acquired using a Illumina HiSeq sequencer system and processed for RNA sequencing (RNA-Seq) expression analysis. Transcriptomic differential expression analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism. Processed RNA-Seq datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed RNA-seq results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Metabolomics (PB-DP5)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Culture samples were collected at 0, 0.5, 1, 2, 4, 6, and 8 hours for extracellular sucrose analysis. Circadian metabolomics data was acquired using a Agilent single quadrupole gas chromatography-mass spectrometer and processed using Agilent Mass Hunter for targeted sucrose quantification. Metabolomic analysis of PCC 7942 light-dark cycle cultures transitioned to constant light revealed distinct temporal patterns in sucrose production. Processed metabolomic datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed GC-MS results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗

Robust and High-Waste Loaded Glass Formulations for Hanford Low-Activity Waste - 19050

The Hanford Low-Activity Waste (LAW) vitrification facility will receive LAW batches with abruptly changing compositions that span relatively broad ranges. In particular, the concentration ratios of sodium-to-sulfate, -chloride, -chromate, -phosphate, -potassium, and -aluminate create challenges in glass formulation. Glass compositions that simultaneously satisfy several processability, product quality, and composition constraints with sufficient confidence must be formulated for each LAW batch in near-realtime to enable desired throughput. A method for developing glass compositions while maximizing the waste loading and the process robustness was developed. This method compiled the glass composition-property data collected over nearly two decades, primarily by Pacific Northwest National Laboratory, The Catholic University of America, and Savannah River National Laboratory, in support of both the Hanford Tank Waste Treatment and Immobilization Plant project and the U.S. Department of Energy Office of River Protection Enhanced Waste Glass project. These data were used to develop models that link the melter feed composition to the resulting glass properties. Mathematical constructs were developed to optimize glass compositions for each waste batch in real-time that will simultaneously satisfy processability, product quality, and composition constraints with sufficient statistical confidence. Property prediction and various process uncertainties were reflected in the formulation. The formulation approach directly lends itself to adjustment of glass formulation to achieve program goals. This presentation will summarize the formulation approach and supporting data. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Architecture of a Full-length Retroviral Integrase Monomer and Dimer, Revealed by Small Angle X-ray Scattering and Chemical Cross-linking

We determined the size and shape of full-length avian sarcoma virus (ASV) integrase (IN) monomers and dimers in solution using small angle x-ray scattering. The low resolution data obtained establish constraints for the relative arrangements of the three component domains in both forms. Domain organization within the small angle x-ray envelopes was determined by combining available atomic resolution data for individual domains with results from cross-linking coupled with mass spectrometry. The full-length dimer architecture so revealed is unequivocally different from that proposed from x-ray crystallographic analyses of two-domain fragments, in which interactions between the catalytic core domains play a prominent role. Core-core interactions are detected only in cross-linked IN tetramers and are required for concerted integration. The solution dimer is stabilized by C-terminal domain (CTD-CTD) interactions and by interactions of the N-terminal domain in one subunit with the core and CTD in the second subunit. These results suggest a pathway for formation of functional IN-DNA complexes that has not previously been considered and possible strategies for preventing such assembly.

59 BASIC BIOLOGICAL SCIENCES↗