Search NASA⌕ Search

SEARCH · Search NASA

Results for “Reference Model 2”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

The feasibility of a regional CTDI{sub vol} to estimate organ dose from tube current modulated CT exams

Purpose: In AAPM Task Group 204, the size-specific dose estimate (SSDE) was developed by providing size adjustment factors which are applied to the Computed Tomography (CT) standardized dose metric, CTDI{sub vol}. However, that work focused on fixed tube current scans and did not specifically address tube current modulation (TCM) scans, which are currently the majority of clinical scans performed. The purpose of this study was to extend the SSDE concept to account for TCM by investigating the feasibility of using anatomic and organ specific regions of scanner output to improve accuracy of dose estimates. Methods: Thirty-nine adult abdomen/pelvis and 32 chest scans from clinically indicated CT exams acquired on a multidetector CT using TCM were obtained with Institutional Review Board approval for generating voxelized models. Along with image data, raw projection data were obtained to extract TCM functions for use in Monte Carlo simulations. Patient size was calculated using the effective diameter described in TG 204. In addition, the scanner-reported CTDI{sub vol} (CTDI{sub vol,global}) was obtained for each patient, which is based on the average tube current across the entire scan. For the abdomen/pelvis scans, liver, spleen, and kidneys were manually segmented from the patient datasets; for the chest scans, lungs and for female models only, glandular breast tissue were segmented. For each patient organ doses were estimated using Monte Carlo Methods. To investigate the utility of regional measures of scanner output, regional and organ anatomic boundaries were identified from image data and used to calculate regional and organ-specific average tube current values. From these regional and organ-specific averages, CTDI{sub vol} values, referred to as regional and organ-specific CTDI{sub vol}, were calculated for each patient. Using an approach similar to TG 204, all CTDI{sub vol} values were used to normalize simulated organ doses; and the ability of each normalized dose to correlate with patient size was investigated. Results: For all five organs, the correlations with patient size increased when organ doses were normalized by regional and organ-specific CTDI{sub vol} values. For example, when estimating dose to the liver, CTDI{sub vol,global} yielded a R{sup 2} value of 0.26, which improved to 0.77 and 0.86, when using the regional and organ-specific CTDI{sub vol} for abdomen and liver, respectively. For breast dose, the global CTDI{sub vol} yielded a R{sup 2} value of 0.08, which improved to 0.58 and 0.83, when using the regional and organ-specific CTDI{sub vol} for chest and breasts, respectively. The R{sup 2} values also increased once the thoracic models were separated for the analysis into females and males, indicating differences between genders in this region not explained by a simple measure of effective diameter. Conclusions: This work demonstrated the utility of regional and organ-specific CTDI{sub vol} as normalization factors when using TCM. It was demonstrated that CTDI{sub vol,global} is not an effective normalization factor in TCM exams where attenuation (and therefore tube current) varies considerably throughout the scan, such as abdomen/pelvis and even thorax. These exams can be more accurately assessed for dose using regional CTDI{sub vol} descriptors that account for local variations in scanner output present when TCM is employed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Climate and air pollution implications of potential energy infrastructure and policy measures in India

India is a rapidly developing economy with interrelated air quality, sustainable development, and climate change mitigation goals. There are unique challenges to achieving each of these goals as well as potential tradeoffs among them. This study examines the implications of possible future energy, climate, and air pollution control policies and measures in India through 2050. We take a scenario approach using the GCAM global energy-climate-land model combined with the Hector simple climate model and the TM5-FASST air quality source-receptor model to examine energy, climate and air quality outcomes. Reducing use of traditional biomass in buildings can reduce primary carbonaceous particulate emissions well below 2015 levels. However, policies that are more ambitious than current plans would likely be required to reduce SO 2 and NO x emissions well below 2015 levels. Among single policy cases considered, pricing of greenhouse gas (GHG) emissions and expansion of natural gas infrastructure have the largest impacts on overall energy system changes relative to the reference scenario. Ambitious air pollution control and GHG policies lead to the largest reductions in air pollution concentrations and radiative forcing, respectively. However, ambitious air pollution control and GHG policies differ in the extent to which they support or impede other policy objectives. Forcing increases due to reduced aerosols from ambitious air pollution policies can be mitigated, at least in part, by applying air pollution control and GHG policies together.

India↗
DOE OSTI2026

ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized nonlinear conservation laws from sparse and noisy data

Multi-query applications such as parameter estimation, uncertainty quantification and design optimization for parameterized partial differential equation (PDE) systems are expensive. While reduced/latent state dynamics approaches for parameterized PDEs offer a viable alternative, these approaches rely on high-quality data and struggle with highly sparse spatiotemporal noisy measurements typically obtained from experiments. Furthermore, there is no guarantee that these models satisfy governing physical conservation laws. In this article, we propose a reduced state dynamics approach, referred to as ECLEIRS, that embeds exact conservation in the solution and flux representation by utilizing a space-time divergence-free neural network formulation. We compare ECLEIRS with other reduced state dynamics approaches, those that do not enforce any physical constraints and those with physics-informed loss functions, for three shock-propagation problems: 1-D advection, 1-D Burgers and 2-D Euler equations. In conclusion, the numerical experiments conducted in this study demonstrate that ECLEIRS provides the most accurate prediction of dynamics for unseen parameters even in the presence of highly sparse and noisy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

New insights from uncultivated genomes of the global human gut microbiome

The genome sequences of many species of the human gut microbiome remain unknown, largely owing to challenges in cultivating microorganisms under laboratory conditions. Here we address this problem by reconstructing 60,664 draft prokaryotic genomes from 3,810 faecal metagenomes, from geographically and phenotypically diverse humans. These genomes provide reference points for 2,058 newly identified species-level operational taxonomic units (OTUs), which represents a 50% increase over the previously known phylogenetic diversity of sequenced gut bacteria. On average, the newly identified OTUs comprise 33% of richness and 28% of species abundance per individual, and are enriched in humans from rural populations. A meta-analysis of clinical gut-microbiome studies pinpointed numerous disease associations for the newly identified OTUs, which have the potential to improve predictive models. Finally, our analysis revealed that uncultured gut species have undergone genome reduction that has resulted in the loss of certain biosynthetic pathways, which may offer clues for improving cultivation strategies in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Flow-plane decorrelations in heavy-ion collisions with multiple-plane cumulants

The azimuthal correlations between local flow planes at different (pseudo)rapidities (η) may reveal important details of the initial nuclear matter density distributions in heavy-ion collisions. Extensive experimental measurements of a factorization ratio (r 2 ) and its derivative (F 2 ) have shown evidence of the longitudinal flow-plane decorrelation. However, nonflow effects also affect this observable and prevent a quantitative understanding of the phenomenon. In this paper, to distinguish decorrelation and nonflow effects, we propose a new cumulant observable, T 2 , which largely suppresses nonflow. Here, the technique sensitivity to different initial-state scenarios and nonflow effects are tested with a simple Monte Carlo model, and in the end, the method is applied to events simulated by a multiphase transport model (AMPT) for Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV. We also emphasize that a distinct decorrelation signal requires not only the right sign of an observable but also its proper dependence on the η window of the reference flow plane to be consistent with the pertinent decorrelation picture.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A&L Annual Report: Kapton Strength

Exploding Foil Initiator (EFI) systems function by launching a polymer flyer (often referred to as a slapper) at a high explosive (HE) pellet, which is intended to shock-initiate the HE. Flyer characteristics (e.g., shape, planarity, etc.) are extremely important in this respect, governing their ability to initiate the HE. Polymer models currently used in these systems are largely unvalidated, potentially compromising the integrity of magneto-hydrodynamic (MHD) predictions of EFI function. An improved EFI MHD modelling capability is expected to: (1) expedite optimization of EFI-based initiation system design, (2) enable consideration of EFI-related aging and corresponding lifetime predictions, and (3) potentially minimize the number of costly experiments required to certify EFI designs. To address this knowledge gap, several years ago we began a computational project to develop a higher-fidelity model for Kapton, a Polyimide copolymer most commonly employed as the flyer material in EFI devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Siena Galaxy Atlas 2020

Abstract We present the 2020 version of the Siena Galaxy Atlas (SGA-2020), a multiwavelength optical and infrared imaging atlas of 383,620 nearby galaxies. The SGA-2020 uses optical grz imaging over ≈20,000 deg 2 from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9 and infrared imaging in four bands (spanning 3.4–22 μ m) from the 6 year unWISE coadds; it is more than 95% complete for galaxies larger than R (26) ≈ 25″ and r < 18 measured at the 26 mag arcsec −2 isophote in the r band. The atlas delivers precise coordinates, multiwavelength mosaics, azimuthally averaged optical surface-brightness profiles, model images and photometry, and additional ancillary metadata for the full sample. Coupled with existing and forthcoming optical spectroscopy from the DESI, the SGA-2020 will facilitate new detailed studies of the star formation and mass assembly histories of nearby galaxies; enable precise measurements of the local velocity field via the Tully–Fisher and fundamental plane relations; serve as a reference sample of lasting legacy value for time-domain and multimessenger astronomical events; and more.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Theory Aided Design of Active and Durable Nanoscale Cathode Catalysts (Final Report)

One of the greatest technological hurdles to deployment of fuel cells relates to the sluggish activity, low durability and the high cost of the catalysts that are currently employed. For automotive PEM fuel cells to become commercially viable, the Pt-specific power density would need to be reduced to less than 0.2gPt/kW (at cell voltages >0.65 V). This would require the Pt loadings to be less than 0.15 mg Pt /cm 2 MEA within the membrane electrode assembly. This could be achieved by enhancing the catalytic activity at the cathode, thus lowering its overpotential. Various different Pt-alloys have shown 2-4 times enhanced activities over Pt alone but still suffer some of the same durability issues as that of the pure Pt. There is a general loss of active Pt due to dissolution and sintering. While there have been a number of elegant fundamental experimental and theoretical studies on ideal single crystal Pt and Pt alloy surfaces which have helped to elucidate the factors that control the activity, there have been very few fundamental studies focused on the stability, reactivity and durability of well-defined Pt nanoparticles. We carried out ab initio density functional theory together with a novel double reference method that we developed to simulate constant potential electrochemical systems in order to model the electrocatalytic reduction of oxygen over model Pt alloy surfaces and nanoparticles. These simulations were used to probe the factors that control the electrocatalytic activity, guide the potential selection of new materials and test their stability under reaction conditions. Ab initio calculations were used to determine the reaction energies and activation barriers for a comprehensive array of different elementary adsorption, desorption, surface reaction and diffusion steps over Pt and Pt alloys involved in the electroreduction of oxygen as a function of surface coverage, the electrochemical potential and temperature. The calculations were used to simulate the potential dependent adsorption and surface reaction energies along with activation barriers in order to determine the kinetics for different surface structures and structural features (step edge and corner sites) to provide the necessary input for kinetic Monte Carlo simulations to follow the rates of reaction. More coarse-grained simulated annealing methods were used to help establish the lowest energy structures and morphologies for different Pt and Pt-alloy nanoparticles that form. The results from the DFT calculations were used to establish an ab initio-derived kinetic database that was used in both 2D and 3D kinetic Monte Carlo simulations that were used to follow the electrocatalytic performance over different particle sizes, shapes and compositions. The ab initio calculations together with the kinetic Monte Carlo simulations were used to complete the following objectives: 1) Determine the controlling elementary reaction pathways and intrinsic kinetics involved in ORR and their potential dependent behavior; 2) Establish the influence of the extrinsic reaction environment including surface structure, alloy composition and spatial arrangement and the humidity on ORR kinetics; 3) Elucidate the effects of particle size and morphology as well as the atomic structure and composition of nanoparticles of Pt, Pt 3 Co, Pt 3 Ni, Pt 3 Fe and other Pt-alloys on the electrocatalytic activity and reaction selectivity; 5) Construct surface Pourbaix phase diagrams for different Pt and Pt-alloys in order to map out the most stable surface phases for different material compositions and reaction conditions; 6) Elucidate the mechanisms that control dissolution and re-deposition of Pt.

08 HYDROGEN↗
DOE OSTI2022

Diagnosing the pedestal magnetic field and magnetohydrodynamics radial structure with pedestal–scrape of layer electron cyclotron emission radiation inversion in H-mode plasma (invited)

Forward modeling is used to interpret inversion patterns of the pedestal–Scrape of Layer (SOL) Electron Cyclotron Emission (ECE) in DIII-D H-mode experiments. The modeling not only significantly improves the ECE data interpretation quality but also leads to the potential measurements of (1) the magnetic field strength |B| at the separatrix, (2) the pedestal |B| evolution during an inter-Edge Localized Mode (ELM) period, and (3) the pedestal Magnetohydrodynamics (MHD) radial structure. The ECE shine-through effect leads to three types of pedestal–SOL radiation inversions that are discussed in this paper. The first type of inversion is the non-monotonic T e,rad profile with respect to the major radius. Using the ECE frequency at the minimum T e,rad , the inversion can be applied to measure the magnetic field |B| at the separatrix and calibrate the mapping of the ECE channels with respect to the separatrix. The second type of inversion refers to the opposite phase between the radiation fluctuations δT e,rad at the pedestal and SOL. This δT e,rad phase inversion is sensitive to density and temperature fluctuations at the pedestal foot and, thus, can be used to qualitatively measure the MHD radial structure. The third type of inversion appears when the pedestal and SOL T e,rad evolve in an opposite trend, which can be used to infer the pedestal |B| field change during an inter-ELM period. Finally, the bandwidth effect on measuring δT e,rad due to pedestal MHD is also investigated in the radiation modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Equity implications of net-zero emissions: A multi-model analysis of energy expenditures across income classes under economy-wide deep decarbonization policies

With companies, states, and countries targeting net-zero emissions around midcentury, there are questions about how these targets alter household welfare and finances, including distributional effects across income groups. This paper examines the distributional dimensions of technology transitions and net-zero policies with a focus on welfare impacts across household incomes. The analysis uses a model intercomparison with a range of energy-economy models using harmonized policy scenarios reaching economy-wide, net-zero CO 2 emissions across the United States in 2050. Here we employ a novel linking approach that connects output from detailed energy system models with survey microdata on energy expenditures across income classes to provide distributional analysis of net-zero policies. Although there are differences in model structure and input assumptions, we find broad agreement in qualitative trends in policy incidence and energy burdens across income groups. Models generally agree that direct energy expenditures for many households will likely decline over time with reference and net-zero policies. However, there is variation in the extent of changes relative to current levels, energy burdens relative to reference levels, and electricity expenditures. Policy design, primarily how climate policy revenues are used, has first-order impacts on distributional outcomes. Net-zero policy costs, in both absolute and relative terms, are unevenly distributed across households, and relative increases in energy expenditures are higher for lowest-income households. However, we also find that recycled revenues from climate policies have countervailing effects when rebated on a per-capita basis, offsetting higher energy burdens and potentially even leading to net progressive outcomes. Model results also show carbon Laffer curves, where revenues from net-zero policies increase but then decline with higher stringencies, which can diminish the progressive effects of climate policies. We also illustrate how using annual income deciles for distributional analysis instead of expenditure deciles can overstate the progressivity of emissions policies by overweighting revenue impacts on the lowest-income deciles.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

MO-D-BRD-02: In Memoriam of Bengt Bjarngard: SBRT II: Small Field Dosimetry - TG155

Specialized radiation treatment such as SRS/SRT. SBRT, IMRT, VMAT, Tomotherapy, CyberKnife and Gamma Knife use small fields or combination of small fields where dosimetry is challenging and uncertain due to non-equilibrium conditions such as longitudinal and lateral disequilibrium. Additionally the primary photon fluence is greatly affected by the obstruction of the source size by the jaws creating a large dose gradient across the field. Electronic equilibrium is a phenomenon associated with the range of secondary particles which depend on the beam energy, photon spectrum and the composition of the medium. Additionally, the finite size of detectors creates volume averaging and fluence perturbations especially in small fields. The IAEA/AAPM has provided a frame work for non-compliant reference dosimetry in small fields1. The AAPM TG-1552 has adopted this frame work to provide guidelines in relative dosimetry. This course provides the insight of TG-155 that defines small field, provides recommendations for suitable detectors and associated correction factors to convert reading to dose. Recommendations of a good working practice for relative dosimetry measurements (PDD, TMR, output factor, etc.) and dose calculations based on the new formulation is are elaborated. It also discusses beam modeling and dose calculations as a critical step in clinical utilization of small field radiotherapy. Small errors in beam data, approximations in dose algorithms, or misaligned of detectors and field settings can propagate into large errors in planned and delivered dose. The modeling and treatment planning aspects of small field dosimetry are reviewed with emphasis on the most critical parts for ensuring accurate and safe radiation therapy. Discussion on k(fmsr, fclin) for commercially available detectors are also provided.1 P. Alfonso, P. Andreo, R. Capote, M. S. Huq, W. Kilby, P. Kjall, T. R. Mackie, H. Palmans, K. Rosser, J. Seuntjens, W. Ullrich and S. Vatnitsky, “A new formalism for reference dosimetry of small and nonstandard fields,” Med Phys 35, 5179–5186 (2008).2 I. J. Das, P. Francescon, A. Ahnesjo, M. M. Aspradakis, C. W. Cheng, G. X. Ding, G. S. Ibbott, M. Oldham, M. S. Huq, C. S. Reft and S. O.A., “Task Group 155 report: Small fields and non-equilibrium condition photon beam dosimetry,” Med Phys (under review). Learning Objectives: Physics of cavity theory for non-equilibrium condition and definition of small fields Description of absolute and relative dosimetry in the context of TG-155 Choice of detector in small field dosimetry, perturbations and corrections (K(fmsr, fclin)) for accurate dosimetry Understand the advantages and limitations of commercially available detectors for small field dosimetry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

The Implications of Global Change for the Co-Evolution of Argentina's Integrated Energy-Water-Land Systems

This study seeks to understand how Argentina’s energy, water, and land (EWL) systems will co-evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina’s sub-national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder-engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid-century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina’s agricultural production by 2050 (relative to 2020) by using 50,000 km 2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net-zero carbon emissions globally shortly after mid-century projects that Argentina could use 100,000 km 2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina’s river basins. The Colorado and Negro basins could experience moderate-to-severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate-induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi-scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human-earth systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Mechanistic investigation of SARS-CoV-2 main protease to accelerate design of covalent inhibitors

Targeted covalent inhibition represents one possible strategy to block the function of SARS-CoV-2 Main Protease (M PRO ), an enzyme that plays a critical role in the replication of the novel SARS-CoV-2. Toward the design of covalent inhibitors, we built a covalent inhibitor dataset using deep learning models followed by high throughput virtual screening of these candidates against M PRO . Two top-ranking inhibitors were selected for mechanistic investigations—one with an activated ester warhead that has a piperazine core and the other with an acrylamide warhead. Specifically, we performed a detailed analysis of the free energetics of covalent inhibition by hybrid quantum mechanics/molecular mechanics simulations. Cleavage of a fragment of the non-structured protein (NSP) from the SARS-CoV-2 genome was also simulated for reference. Simulations show that both candidates form more stable enzyme-inhibitor (E-I) complexes than the chosen NSP. It was found that both the NSP fragment and the activated ester inhibitor react with CYS145 of M PRO in a concerted manner, whereas the acrylamide inhibitor follows a stepwise mechanism. Most importantly, the reversible reaction and the subsequent hydrolysis reaction from E-I complexes are less probable when compared to the reactions with an NSP fragment, showing promise for these candidates to be the base for efficient M PRO inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Design of electric-field assisted surface plasmon resonance system for the detection of heavy metal ions in water

Surface Plasmon Resonance (SPR) sensors are widely used in diverse applications. For detecting heavy metal ions in water, surface functionalization of the metal surface is typically used to adsorb target molecules, where the ionic concentration is detected via a resonance shift (resonance angle, resonance wavelength or intensity). This paper studies the potential of a possible alternative approach that could eliminate the need of using surface functionalization by the application of an external electric field in the flow channel. The exerted electrical force on the ions pushes them against the surface for enhanced adsorption; hence it is referred to as “Electric-Field assisted SPR system”. High system sensitivity is achieved by monitoring the time dynamics of the signal shift. The ion deposition dynamics are discussed using a derived theoretical model based on ion mobility in water. On the application of an appropriate force, the target ions stack onto the sensor surface depending on the ionic concentration of target solution, ion mass, and flow rate. In the experimental part, a broad detection range of target cadmium ions (Cd{sup 2+}) in water from several parts per million (ppm) down to a few parts per billion (ppb) can be detected.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Transcriptome and DNA methylome divergence of inflorescence development between 2 ecotypes in Panicum hallii

The morphological diversity of the inflorescence determines flower and seed production, which is critical for plant adaptation. Hall's panicgrass (Panicum hallii, P. hallii) is a wild perennial grass that has been developed as a model to study perennial grass biology and adaptive evolution. Highly divergent inflorescences have evolved between the 2 major ecotypes in P. hallii, the upland ecotype (P. hallii var hallii, HAL2 genotype) with compact inflorescence and large seed and the lowland ecotype (P. hallii var filipes, FIL2 genotype) with an open inflorescence and small seed. Here we conducted a comparative analysis of the transcriptome and DNA methylome, an epigenetic mark that influences gene expression regulation, across different stages of inflorescence development using genomic references for each ecotype. Global transcriptome analysis of differentially expressed genes (DEGs) and co-expression modules underlying the inflorescence divergence revealed the potential role of cytokinin signaling in heterochronic changes. Comparing DNA methylome profiles revealed a remarkable level of differential DNA methylation associated with the evolution of P. hallii inflorescence. We found that a large proportion of differentially methylated regions (DMRs) were located in the flanking regulatory regions of genes. Intriguingly, we observed a substantial bias of CHH hypermethylation in the promoters of FIL2 genes. The integration of DEGs, DMRs, and $K_a$/$K_s$ ratio results characterized the evolutionary features of DMR-associated DEGs that contribute to the divergence of the P. hallii inflorescence. This study provides insights into the transcriptome and epigenetic landscape of inflorescence divergence in P. hallii and a genomic resource for perennial grass biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

SU-E-T-532: Validation and Implementation of Model-Based Patient Specific Quality Assurance Using Mobius3D and MobiusFX in a Clinical Setting

Purpose: This work carries out the commissioning and validation of the Mobius3D and MobiusFX software tools, which can replace the time-consuming measurement-based patient specific quality assurance (PSQA). Methods: The beam model supplied by Mobius3D was validated against a 21EX linac's beam measured data. Complex patient (VMAT) plans using Eclipse treatment planning system (TPS) was used to test the consistency between Mobius3D (calculates dose using patient image and field data) and MobiusFx (calculates dose using treatment dynalog files). Dose difference and gamma analysis (3%/3mm) between Mobius3D and MobiusFx were used to assess treatment plan and treatment delivery consistency. An end-to-end test was performed to validate Mobius3D and MobiusFx against ion chamber measurements. Effect of the dosimetric leaf gap (DLG) on Mobius3D dose calculation was additionally investigated. Results: Mobius3D beam model parameters matched within 1%-3% with our beam measured data. A comparison of Mobius3D and MobiusFx dose matrices for VMAT planned prostate cases showed (0.33±0.07)% mean dose difference with gamma values above 95%. The end-to-end test showed dose differences of 1% between Mobius3D and MobiusFx. Dependence of Mobius3D dose calculation upon DLG was explored by introducing a ±0.5 mm change in the default value for DLG. This change resulted in agreement differences above 2% Conclusion: Use of reference beam data would appear to speed up commissioning process for the clinical implementation of Mobius3D. However, careful consideration is required when comparing the information provided by the software, since large dose variations can be seen when the proper parameters are not optimized. The plan and delivered dose were in good agreement; hence MobiusFx has the potential to significantly speed up the PSQA process and at the same time helps to verify treatment parameters that are not possible with measurement-based PSQA.

60 APPLIED LIFE SCIENCES↗