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The project established a Tight Oil Field Laboratory to address technical challenges in developing stacked unconventional reservoirs in the Powder River Basin. Key activities included data compilation, subsurface mapping, drilling, logging, coring, deployment of fiber optics and microseismic, completion and stimulation optimization, and well performance evaluation.
Macrophages are central players in the immune response, manifesting divergent phenotypes to control inflammation and innate immunity through the release of cytokines and other regulatory factor-dependent signaling pathways. In recent years, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features critical for macrophage functions. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of macrophage activation. Metabolites well-known to be associated with immunoactivation (e.g., glucose and arginine) and immunosuppression (e.g., tryptophan and vitamin D3) were amongst the most critical effectors. Intracellular metabolic mechanisms linked to critical suppressive effectors were then assessed, identifying a suppressive role for de novo nucleotide synthesis. Finally, the underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying metabolic connections between activation and metabolic effectors.
The SNL ATDM Data and Visualization work consolidates existing ATDM activities in scalable data management and visualization. Part of the responsibilities of the SNL ATDM Data and Visualization Project is the maintenance and development of visualization resources for ATDM applications on Exascale platforms. The ATDM Scalable Visualization project provides visualization and analysis required to satisfy the needs of the ASC/ATDM applications on next-generation, many-core platforms. This involves many activities including the re-engineering of visualization algorithms, services, and tools that enable ASC customers to carry out data analysis on ASC systems and ACES platforms. Current tools include scalable data analysis software released open source through ParaView, VTK, and Catalyst. We are also both leveraging and contributing to VTK-m, a many-core visualization library, to satisfy our visualization needs on advanced architectures. The scope of the Scalable Visualization under ATDM at SNL is R&D for the programming model and implementation of visualization code for ASC/ATDM projects and ASC/ATDM application support.
We use SDO/HMI and SOLIS/VSM photospheric magnetic field measurements to model the force-free coronal field above a solar active region, assuming magnetic forces dominate. We take measurement uncertainties caused by, e.g., noise and the particular inversion technique, into account. After searching for the optimum modeling parameters for the particular data sets, we compare the resulting nonlinear force-free model fields. We show the degree of agreement of the coronal field reconstructions from the different data sources by comparing the relative free energy content, the vertical distribution of the magnetic pressure, and the vertically integrated current density. Though the longitudinal and transverse magnetic flux measured by the VSM and HMI is clearly different, we find considerable similarities in the modeled fields. This indicates the robustness of the algorithm we use to calculate the nonlinear force-free fields against differences and deficiencies of the photospheric vector maps used as an input. We also depict how much the absolute values of the total force-free, virial, and the free magnetic energy differ and how the orientation of the longitudinal and transverse components of the HMI- and VSM-based model volumes compare to each other.
Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and bit masks; receiving in an origin endpoint of the PAMI a collective instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint; constructing a bit mask for the received collective instruction; selecting, from among the associated algorithms and bit masks, a data communications algorithm in dependence upon the constructed bit mask; and executing the collective instruction, transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and bit masks; receiving in an origin endpoint of the PAMI a collective instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint; constructing a bit mask for the received collective instruction; selecting, from among the associated algorithms and bit masks, a data communications algorithm in dependence upon the constructed bit mask; and executing the collective instruction, transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
This report is an update to the Phase II Corrective Action Investigation Plan for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nevada Test Site, Nye County, Nevada (NNSA/NSO, 2009) activities resulting from data collection and analysis conducted between 2009 and 2019. This report contains two sections: (1) a summary of how the new data have informed the updated approach to complete the characterization activities under the corrective action investigation (CAI), and (2) a table documenting the specific activities identified in the CAIP with a status and expected outcome of each activity (see Table A-1 in Appendix A). As stated in Sections 1.5 and 1.5.1 of the CAIP, the CAIP was conceptualized to be iterative, with continuous review and assessment of the data, data analysis, and modeling to provide a guide for decisions regarding additional work. This report provides the revised plan, based on an assessment of the data, to complete the characterization activities identified in the CAIP.
This research introduces a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The scheme employs a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) to efficiently simulate aerosol activation, the vertical profile of supersaturation, and the activated cloud droplet number concentration (CDNC) near the cloud base. The calculated maximum supersaturation values using the QDGE scheme were compared with multiple parcel model simulations under various aerosol and environmental conditions. The differences are all below 0.18 %, indicating good performance and accuracy of the QDGE scheme. We evaluated the QDGE scheme by specifying observed environmental thermodynamic variables and aerosol information from 31 cloud cases as input and comparing the simulated CDNC with cloud observations. The average of mean relative error ($\overline{MRE}$) of the simulated CDNC for cloud cases in each campaign ranges from 17.30 % in Brazil to 25.90 % in China, indicating that the QDGE scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Additionally, we carried out an error analysis by calculating the maximum information coefficient (MIC) between the MRE and input variables for the individual campaigns and all cloud cases. MIC values were then sorted by aerosol properties, pollution level, environmental humidity, and dynamic condition according to their relative importance to MRE. Based on the error analysis, we found that the magnitude of MRE is more relevant to the specification of input aerosol pollution level in marine regions and aerosol hygroscopicity in continental regions than to other variables in the simulation.
The analysis activities for fast reactors involve using many different pieces of software that are relied upon for their predictive capabilities. For this software to be considered reliable, documented proof that the predictions of the software are accurate is required. In this manuscript, the validation work that covers some of the Argonne software used in fast reactor design activities is discussed and displayed. This validation work includes neutron and gamma flux distributions, reaction rate distributions, and reactivity worth. In an ideal world, a reactor development program would have access to a comprehensive set of experimental facilities to help inform the design aspects of the reactor itself. While thermal-hydraulics experiments, and to a limited degree mechanical experiments, can be carried out today for validation needs, neutronics related experimental facilities are rather impractical because of the lack of experimental facilities. Given the desired time table for construction of new reactors, the reconstitution or creation of new neutronic experimental facilities is untenable and thus those reactor development programs must rely upon any available experimental measurements that are qualitatively similar to the design. While a methodology has been proposed to assess the similarity between the past experimental measurements and the reactor itself, that aspect is beyond the scope of this manuscript. In this manuscript, the focus is entirely placed on the analysis results for a series of experiments carried out at the ZPPR facility in Idaho in the mid-1980s. In this regard, this manuscript only shows the validation of the stated neutronics software for specific loadings of the ZPPR reactor. Because of the fuel form, its proposed enrichment, and the material content of the reactor core, the ZPPR-15 experiments were identified as potential validation data for the reactor. The ZPPR-15 experiments were intended as mockups of a 330 MWe Integral Fast Reactor program which was a follow on program to the Clinch River Breeder Reactor. In the ZPPR-15 series of experiments, measurements of the neutron spectrum, control rod worth, sodium void worth, foil reaction rate distributions, Doppler worth of heated samples, gamma dose, and axial expansion worth were all carried out and published. In many cases, these reactivity coefficients are good candidates to validate the reactivity coefficient calculation scheme used by the analysis software and included in the safety analysis activities of fast reactor development projects today. This manuscript discusses the modeling methodology and accuracy of the calculated experimental results using the LANL software MCNP and the ANL software package ARC (Argonne Reactor Codes). As will be shown, for many of the experimental measurements, the two software packages are found to be good predictive analysis tools for those experiments. In other cases, problems with the analysis methodology or underlying cross section data are exposed which indicates where predictive analysis is not as reliable. Finally, in some of the measurements the conclusion is reached that the experimental measurement cannot be reproduced with the analysis software as it is simply too difficult.
The analysis activities for fast reactors involve using many different pieces of software that are relied upon for their predictive capabilities. For this software to be considered reliable, documented proof that the predictions of the software are accurate is required. In this manuscript, the validation work that covers some of the Argonne software used in fast reactor design activities is discussed and displayed. This validation work includes neutron and gamma flux distributions, reaction rate distributions, and reactivity worth. In an ideal world, a reactor development program would have access to a comprehensive set of experimental facilities to help inform the design aspects of the reactor itself. While thermal-hydraulics experiments, and to a limited degree mechanical experiments, can be carried out today for validation needs, neutronics related experimental facilities are rather impractical because of the lack of experimental facilities. Given the desired time table for construction of new reactors, the reconstitution or creation of new neutronic experimental facilities is untenable and thus those reactor development programs must rely upon any available experimental measurements that are qualitatively similar to the design. While a methodology has been proposed to assess the similarity between the past experimental measurements and the reactor itself, that aspect is beyond the scope of this manuscript. In this manuscript, the focus is entirely placed on the analysis results for a series of experiments carried out at the ZPPR facility in Idaho in the mid-1980s. In this regard, this manuscript only shows the validation of the stated neutronics software for specific loadings of the ZPPR reactor. Because of the fuel form, its proposed enrichment, and the material content of the reactor core, the ZPPR-15 experiments were identified as potential validation data for the reactor. The ZPPR-15 experiments were intended as mockups of a 330 MWe Integral Fast Reactor program which was a follow on program to the Clinch River Breeder Reactor. In the ZPPR-15 series of experiments, measurements of the neutron spectrum, control rod worth, sodium void worth, foil reaction rate distributions, Doppler worth of heated samples, gamma dose, and axial expansion worth were all carried out and published. In many cases, these reactivity coefficients are good candidates to validate the reactivity coefficient calculation scheme used by the analysis software and included in the safety analysis activities of fast reactor development projects today. This manuscript discusses the modeling methodology and accuracy of the calculated experimental results using the LANL software MCNP and the ANL software package ARC (Argonne Reactor Codes). As will be shown, for many of the experimental measurements, the two software packages are found to be good predictive analysis tools for those experiments. In other cases, problems with the analysis methodology or underlying cross section data are exposed which indicates where predictive analysis is not as reliable. Finally, in some of the measurements the conclusion is reached that the experimental measurement cannot be reproduced with the analysis software as it is simply too difficult.
Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effects of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 ± 0.01 kcal/mol using as few as 2000 training samples on the QM7 dataset.
Improving nuclear data for short-lived fission product yields will further our fundamental understanding of fission, which is needed across various scientific fields and applications. One method of attaining the needed product yield data is through cyclic neutron activation, which allows a target to be irradiated in a neutron environment and then transported for counting of the radionuclides produced, typically via g spectroscopy. Recently, such a system has been constructed and commissioned at Pacific Northwest National Laboratory. Targets are shuttled between the head of a D-T neutron generator and a counting station with a transit time of 2 seconds. As part of the characterization of this system, the neutron flux was studied using two activation targets. The neutron flux from the deuterium-tritium fusion generator was determined to be 9.95(33) x10^8 n/cm2-s with a peak energy of 14.9 MeV and a spread of approximately 4 decades between the epithermal and 14 MeV peak group flux.
Unprocessed active distributed temperature sensing (DTS) data from 3 boreholes in the Guelph, ON Canada region. Data from borehole 1 was collected during a fluid injection while data from boreholes 2 and 3 were collected under natural gradient conditions in a lined borehole. The column labels/headers (in the first row) define the time since start of measurement in seconds and the row labels/headers (in the first column) are the object IDs that are defined in the metadata. Each object ID is a sampling location whose exact location is defined in the metadata file. Data in each cell are temperature in Celsius at time and sampling location as defined above.
This package contains the Historical NARSTO Program Assessments and Website Content. NARSTO was a public/private partnership dedicated to improving management of air quality in North America. It was established on February 13, 1995 when representatives of Canada, the United States, and Mexico signed the NARSTO Charter in a ceremony at the White House. (Originally North American Research Strategy for Tropospheric Ozone.) The NARSTO organization, having met the goals of its 1995 charter, was disbanded as of December 31, 2010. Since its founding, NARSTO has completed four major scientific Assessments of critical air quality management issues. NARSTO maintained the Quality Systems Science Center and the NARSTO Data Archive for storing data from NARSTO Affiliated Research Activities and making these data available to the scientific community. NARSTO also facilitated activities, such as the Reactivity Research Working Group, which provide critical reviews of the state of the science in areas of interest to air quality policy makers. NARSTO worked to improve collaboration between the air-quality and health-sciences research communities, to advance understanding of the scientific issues involved in effecting a multi-pollutant/multi-media approach to air quality management, and to increase understanding of the linkages between air quality and climate change.The NARSTO assessments and narsto.org website content are available at this data archive and may be downloaded in one .zip file. Data from NARSTO sponsored field campaigns will remain available through the Permanent Data Archive maintained by the NASA Langley Research Center Atmospheric Science Data Center (https://asdc.larc.nasa.gov/project/NARSTO).
Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.
During radiological clean-up activities, gamma scan survey data collected from across an industrial site revealed spatially varying concentrations in radioactivity related to naturally occurring soil constituents or dissimilar fill materials that may have been placed at the site. A three-step process was developed to assess the data in order to identify radiation anomalies that most likely could represent small, buried discrete radioactive objects (ROs). The three-step process consists of: (1) define the radiological composition of the site isotopically, (2) spatially parse the data into more homogeneous subgroupings based on the site's radiological composition, and (3) perform analysis of the data subgroupings to identify statistical and spatial population outliers (i.e., radiation anomalies) that otherwise may be masked by the overall variation in the data. Using this process, locations containing buried low-specific activity ROs or higher activity ROs whose radiation is attenuated by soil can be identified and investigated. (authors)
Clan CD cysteine peptidases, a structurally related group of peptidases that include mammalian caspases, exhibit a wide range of important functions, along with a variety of specificities and activation mechanisms. However, for the clostripain family (denoted C11), little is currently known. Here, we describe the first crystal structure of a C11 protein from the human gut bacterium, Parabacteroides merdae (PmC11), determined to 1.7-Å resolution. PmC11 is a monomeric cysteine peptidase that comprises an extended caspase-like α/β/α sandwich and an unusual C-terminal domain. It shares core structural elements with clan CD cysteine peptidases but otherwise structurally differs from the other families in the clan. These studies also revealed a well ordered break in the polypeptide chain at Lys 147 , resulting in a large conformational rearrangement close to the active site. Biochemical and kinetic analysis revealed Lys 147 to be an intramolecular processing site at which cleavage is required for full activation of the enzyme, suggesting an autoinhibitory mechanism for self-preservation. PmC11 has an acidic binding pocket and a preference for basic substrates, and accepts substrates with Arg and Lys in P1 and does not require Ca 2+ for activity. Altogether, these data provide insights into the mechanism and activity of PmC11 and a detailed framework for studies on C11 peptidases from other phylogenetic kingdoms.
SUMMARY Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images in the vicinity of a borehole compared to conventional surface seismic data sets, which can lack higher-frequency wavefield components due to longer travel paths and increased attenuation. An increasingly used approach for downhole acquisition is vertical seismic profiling (VSP), which has become more attractive when coupled with distributed acoustic sensing (DAS) using optical fibres installed in wells. The main difficulty for generating high-quality images with full-wavefield imaging tools for DAS VSP data, though, is the need for an accurate velocity model. To build plausible velocity models using active-source DAS VSP data, we adopt a 3-D image-domain elastic transmission tomography technique, originally developed for surface-recorded passive (microseismic) data, by exchanging the source and receiver positions (i.e. reciprocity) to mimic a passive-seismic surface monitoring scenario. The inversion approach exploits various images for each source constructed through time-reverse imaging (TRI) of downgoing P- and S-wave first-arrival waveforms. The TRI process uses the kinetic term of the (extended) PS energy imaging condition that exhibits sufficient sensitivity to velocity model errors. The method automatically updates the P- and S-wave velocity models to optimize image focusing via adjoint-state inversion. We illustrate the efficacy of the adopted elastic inversion technique using an active-source DAS 3-D VSP field data set acquired in the North Slope of Alaska. The numerical experiments demonstrate that the inverted elastic velocity models can be further used in full-wavefield acoustic/elastic imaging algorithms to obtain accurate subsurface images.