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At least 397 records · Page 22

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DOE OSTI2020

Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

58 GEOSCIENCES↗

DOE OSTI2023

Molecular dynamics study of grain boundary and radiation effects on tritium population and diffusion in zirconium

Tritium population thermodynamics and transport kinetics critically define the tritium storage performance of zirconium tritides that can be used for a variety of nuclear applications including tritium-producing burnable absorber rods. Both thermodynamic and kinetic properties can be sensitive to grain sizes of materials and can be significantly altered by irradiated defects during operation under the reactor environments. A thorough experimental characterization of how these properties evolve under different reactor conditions and different initial grain structures is extremely challenging. Here molecular dynamics simulations are used to investigate tritium population and diffusion in zirconium with and without different planar symmetric and asymmetric tilt grain boundaries and irradiated defects. Here, we found that in addition to trapping tritium, the most significant effect of planar grain boundaries is to increase tritium diffusivity on the boundary plane. Furthermore, fine grain structures are found to mitigate the change of tritium diffusivity due to irradiated point defects as these point defects are likely to migrate to and sink at grain boundaries.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Excited states in semi-magic 82 Ge populated in the βn-decay of 83 Ga

For this work, the βn and β-decay of the neutron-rich 83 Ga isotopes produced in proton-induced fission of 238 U were studied at the Holifield Radioactive Ion Beam Facility (HRIBF) at Oak Ridge National Laboratory using on-line mass separation and the hybrid-3Hen neutron-γ detector array. Excited states in semi-magic 82 Ge populated in βn decay of 83 Ga were identified using neutron-γ coincidences. The measured relative population of excited states in 82 Ge is compared with a βn emission model, where the neutron emission model combines the strength distribution obtained from the shell-model calculations with the statistical Hauser-Feshbach model.

3HEN↗

DOE OSTI2024

Wildfires increasingly threaten oil and gas wells in the western United States with disproportionate impacts on marginalized populations

The western United States is home to most of the nation’s oil and gas production and, increasingly, wildfires. Here, we examined historical threats of wildfires for oil and gas wells, the extent to which wildfires are projected to threaten wells as climate change progresses, and exposure of human populations to these wells. From 1984 to 2019, we found that, cumulatively, 102,882 wells were located in wildfire burn areas, and 348,853 people were exposed (resided within ≤ 1 km). During this period, we observed a 5-fold increase in the number of wells in wildfire burn areas and a doubling of the population within 1 km of these wells. These trends are projected to increase by late century, likely threatening human health. Approximately 2.9 million people reside within 1 km of wells in areas with high wildfire risk, and Black, Hispanic, and Native American people have disproportionately high exposure to wildfire-threatened wells.

02 PETROLEUM↗

DOE OSTI2021

Structure of high-lying levels populated in the 96 Y → 96 Zr β decay

The nature of the high-lying final levels of the 96 Y gs β decay, one of the three most important contributors to the high-energy reactor antineutrino spectrum, has been investigated in high-resolution γ-ray spectroscopy following the β decay as well as in a campaign of inelastic photon scattering experiments. The comprehensive approach establishes 1 - levels associated with the Pygmy Dipole Resonance as high-lying final levels in the β decay. Branching ratios extracted from β decay complement photon scattering and allow the absolute E1 excitation strength to be determined for levels populated in both reactions. The combined data represents a comprehensive approach to the wavefunction of the 1 - levels below the Qβ value, which are investigated in the Quasiparticle Phonon Model. The calculations reveal that the components populated in β decay contribute only with small amplitudes to the complex wavefunction of these 1 - levels. A comparison of the β decay results to data from total absorption γ -ray spectroscopy demonstrates a good agreement between both measurements.

Pygmy dipole resonance↗

DOE OSTI2021

Competition within low-density bacterial populations as an unexpected factor regulating carbon decomposition in bulk soil

Bacterial decomposition of organic matter in soils is generally believed to be mainly controlled by the access bacteria have to organic substrate. The influence of bacterial traits on this control has, however, received little attention. Using the substrate-dependent Monod growth model, we develop a bioreactive transport model to screen the interactive impacts of spatial dispersion and bacterial traits on mineralization. Bacterial traits primarily involved in the bacterial response to the substrate concentration, such as the maximum specific uptake rate and efficiency, the adaptation time of the uptake rate and the initial population density, are considered. We compare the model results with two sets of previously performed cm-scale soil-core experiments in which the mineralization of the pesticide 2,4-D was measured under well-controlled initial distributions and transport conditions. Bacterial dispersion away from the initial substrate location induced a significant increase in 2,4-D mineralization. Additionally, it reveals an increase of specific uptake rates at lower bacterial densities, more than compensating the decrease of specific uptake rates caused by substrate dilution. This regulation of bacterial activities by density, caused by the local depletion of substrate by competing bacteria, becomes dominant for bacteria with an efficient uptake of substrate at low substrate concentrations (a common feature of oligotrophs). Such oligotrophs, commonly found in soils, compete with each other for substrate even at remarkably low population densities. The ratio-dependent Contois growth model, which includes a density regulation in the expression of the uptake efficiency, is more accurate and convenient to calibrate than the substrate-dependent Monod model, at least under these conditions. In view of their strong interactions, bioreactive and transport processes cannot be handled independently but should be integrated, in particular when reactive processes of interest are carried out by oligotrophs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Single-Cell Metabolic Profiling: Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Plant Cell Populations

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. Small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 tesla Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Finally, ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells, and providing information on metabolic heterogeneity in the cell population.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). Additionally, the new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on LinHn clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Simulation of Continental Shallow Cumulus Populations Using an Observation-Constrained Cloud-System Resolving Model

Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign are simulated by using an observation-constrained cloud-system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four-dimensional ensemble-variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow-to-deep transitions of convective clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Impacts of climate change, population growth, and power sector decarbonization on urban building energy use

Climate, technologies, and socio-economic changes will influence future building energy use in cities. However, current low-resolution regional and state-level analyses are insufficient to reliably assist city-level decision-making. Here we estimate mid-century hourly building energy consumption in 277 U.S. urban areas using a bottom-up approach. The projected future climate change results in heterogeneous changes in energy use intensity (EUI) among urban areas, particularly under higher warming scenarios, with on average 10.1–37.7% increases in the frequency of peak building electricity EUI but over 110% increases in some cities. For each 1 °C of warming, the mean city-scale space-conditioning EUI experiences an average increase/decrease of ~14%/ ~ 10% for space cooling/heating. Heterogeneous city-scale building source energy use changes are primarily driven by population and power sector changes, on average ranging from –9% to 40% with consistent south–north gradients under different scenarios. Across the scenarios considered here, the changes in city-scale building source energy use, when averaged over all urban areas, are as follows: –2.5% to –2.0% due to climate change, 7.3% to 52.2% due to population growth, and –17.1% to –8.9% due to power sector decarbonization. Our findings underscore the necessity of considering intercity heterogeneity when developing sustainable and resilient urban energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Population-level control of two manganese oxidases expands the niche for bacterial manganese biomineralization

Abstract The enzymatic oxidation of aqueous divalent manganese (Mn) is a widespread microbial trait that produces reactive Mn(III, IV) oxide minerals. These biominerals drive carbon, nutrient, and trace metal cycles, thus playing important environmental and ecological roles. However, the regulatory mechanisms and physiological functions of Mn biomineralization are unknown. This challenge arises from the common occurrence of multiple Mn oxidases within the same organism and the use of Mn oxides as indicators of combined gene activity. Through the detection of gene activation in individual cells, we discover that expression ofmnxGandmcoA, two Mn oxidase-encoding genes inPseudomonas putidaGB-1, is confined to subsets of cells within the population, with each gene showing distinct spatiotemporal patterns that reflect local microenvironments. These coordinated intra-population dynamics control Mn biomineralization and illuminate the strategies used by microbial communities to dictate the extent, location, and timing of biogeochemical transformations.

Biotechnology & Applied Microbiology↗

DOE OSTI2022

J-PLUS: Uncovering a large population of extreme [OIII] emitters in the local Universe

Over the past decades, several studies have discovered a population of galaxies that undergo very strong star formation events. They are called extreme emission line galaxies (EELGs). We exploit the capabilities of the Javalambre Photometric Local Universe Survey (J-PLUS), a wide-field multifilter survey, with which 2000 square degrees of the northern sky are already observed. We use it to identify EELGs at low redshift by their [OIII]5007 emission line. We intend to provide a more complete, deep, and less biased sample of local EELGs. We selected objects with an excess of flux in the J-PLUS medium-band J0515 filter, which covers the [OIII] line at z < 0.06. We removed contaminants (stars and higher-redshift systems) using J-PLUS and WISE infrared photometry, with SDSS spectra as a benchmark. We performed spectral energy distribution fitting to estimate the physical properties of the galaxies: line fluxes, equivalent widths (EWs), masses, stellar population ages, and so on. We identify 466 EELGs at z < 0.06 with [OIII] EW over 300 Å and an r-band magnitude below 20, of which 411 were previously unknown. Most show compact morphologies, low stellar masses (log(M • /M ⊙ ) ~ 8.13 –0.58 +0.61 ), low dust extinction (E( B–V ) ~ 0.1 –0.1 +0.2 ), and very young bursts of star formation (3.0 –2.0 +2.7 Myr). Our method is up to ~20 times more efficient in detecting EELGs per Mpc 3 than broadband surveys, and it is as complete as magnitude-limited spectroscopic surveys (but reaches fainter objects). The sample is not directly biased against strong Hα emitters, in contrast with works using broadband surveys. We demonstrate that J-PLUS can identify a large sample of previously unknown EELGs showing unique properties following a clear selection process. A fraction of the EELGs are probably similar to the first galaxies in the Universe, but they are at a much lower redshift, which makes them ideal targets for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Decoding the formation of hammerhead ion populations observed by Parker Solar Probe

Context. In situ observations by the Parker Solar Probe (PSP) have revealed new properties of the proton velocity distributions (VDs), including hammerhead features that suggest a non-isotropic broadening of the beams. Aims. The present work proposes a very plausible explanation for the formation of hammerhead proton populations through the action of a proton firehose-like instability triggered by the proton beam. Methods. We investigated a self-generated firehose-like instability driven by the relative drift of ion populations using a simplified moment-based quasi-linear (QL) theory. While simpler and faster than advanced numerical simulations, this toy model provided rapid insights and concisely highlighted the role of plasma micro-instabilities in relaxing the observed anisotropies of particle VDs in the solar wind and space plasmas. Results. The QL theory proposed here shows that the resulting transverse waves are right-hand polarized and have two consequences on the protons: (i) They reduce the relative drift between the beam and the core, but above all, (ii) they induce a strong perpendicular temperature anisotropy specific to the observed hammerhead ion beam. Moreover, the long-run QL results suggest that these hammerhead distributions are rather transitory states that are still subject to relaxation mechanisms, in which instabilities such as the one discussed here are very likely involved. This foundational work motivates future detailed studies using advanced methods.

Shaaban, Shaaban M. (ORCID:000000030465598X)↗

DOE OSTI2026

Heat metrics and thresholds reshape population exposure and inequality signals

Extreme heat is intensifying worldwide, yet estimates of heat hazard and exposure inequality depend on both the heat metric and how extreme days are defined. Using summer 2022 across the Mediterranean, we quantify population heat exposure with four metrics—land surface temperature (LST), air temperature (Ta), heat index (HI), and wet-bulb globe temperature (WBGT)—under absolute (fixed-value) and relative (anomaly-based) thresholds. Under absolute thresholds, total heat exposure differs by more than two orders of magnitude across metrics (31.3 billion person-days for Ta vs 0.3 billion for HI). Geographic hotspots also diverge: WBGT concentrates in humid coastal North Africa (e.g. the Nile Delta), whereas Ta and LST are more widespread. Under relative thresholds, exposure totals converge and cross-metric hotspot agreement increases (e.g. Ta–WBGT top-tercile overlap increases from 10.7% to 29.0%), shifting hotspots toward densely populated southern Europe. Crucially, the exposure–deprivation relationship also reverses across threshold frameworks: absolute thresholds concentrate exposure in more deprived North Africa and the Middle East, whereas relative thresholds shift the burden toward less-deprived European cities. This sensitivity is decision-relevant: city rankings based on WBGT exposure duration are almost completely reordered when switching threshold frameworks. Threshold choice therefore systematically reshapes hotspot patterns and inequality signals. Reporting both absolute and relative exposures can reveal hidden hotspots and support more targeted heat-risk monitoring and intervention planning.

Mediterranean↗

DOE OSTI2024

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning

Abstract Motivation Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM’s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Diabetes and hypertension are related to amyloid-beta burden in the population-based Rotterdam Study

Abstract Higher vascular disease burden increases the likelihood of developing dementia, including Alzheimer’s disease. Better understanding the association between vascular risk factors and Alzheimer’s disease pathology at the predementia stage is critical for developing effective strategies to delay cognitive decline. In this work, we estimated the impact of six vascular risk factors on the presence and severity of in vivo measured brain amyloid-beta (Aβ) plaques in participants from the population-based Rotterdam Study. Vascular risk factors (hypertension, hypercholesterolaemia, diabetes, obesity, physical inactivity and smoking) were assessed 13 (2004–2008) and 7 years (2009–2014) prior to 18F-florbetaben PET (2018–2021) in 635 dementia-free participants. Vascular risk factors were associated with binary amyloid PET status or continuous PET readouts (standard uptake value ratios, SUVrs) using logistic and linear regression models, respectively, adjusted for age, sex, education, APOE4 risk allele count and time between vascular risk and PET assessment. Participants’ mean age at time of amyloid PET was 69 years (range: 60–90), 325 (51.2%) were women and 190 (29.9%) carried at least one APOE4 risk allele. The adjusted prevalence estimates of an amyloid-positive PET status markedly increased with age [12.8% (95% CI 11.6; 14) in 60–69 years versus 35% (36; 40.8) in 80–89 years age groups] and APOE4 allele count [9.7% (8.8; 10.6) in non-carriers versus 38.4% (36; 40.8) to 60.4% (54; 66.8) in carriers of one or two risk allele(s)]. Diabetes 7 years prior to PET assessment was associated with a higher risk of a positive amyloid status [odds ratio (95% CI) = 3.68 (1.76; 7.61), P < 0.001] and higher standard uptake value ratios, indicating more severe Aβ pathology [standardized beta = 0.40 (0.17; 0.64), P = 0.001]. Hypertension was associated with higher SUVr values in APOE4 carriers (mean SUVr difference of 0.09), but not in non-carriers (mean SUVr difference 0.02; P = 0.005). In contrast, hypercholesterolaemia was related to lower SUVr values in APOE4 carriers (mean SUVr difference −0.06), but not in non-carriers (mean SUVr difference 0.02). Obesity, physical inactivity and smoking were not related to amyloid PET measures. The current findings suggest a contribution of diabetes, hypertension and hypercholesterolaemia to the pathophysiology of Alzheimer’s disease in a general population of older non-demented adults. As these conditions respond well to lifestyle modification and drug treatment, further research should focus on the preventative effect of early risk management on the development of Alzheimer’s disease neuropathology.

van Arendonk, Joyce (ORCID:0000000172941633)↗

DOE OSTI2024

Dosage optimization for reducing tumor burden using a phenotype-structured population model with a drug-resistance continuum

Abstract Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.

Life Sciences & Biomedicine - Other Topics↗

DOE OSTI2020

Comparison of stellar populations in simulated and real post-starburst galaxies in MaNGA

ABSTRACT Recent integral field spectroscopic (IFS) surveys have revealed radial gradients in the optical spectral indices of post-starburst (PSB) galaxies, which can be used to constrain their formation histories. We study the spectral indices of post-processed mock IFS datacubes of binary merger simulations, carefully matched to the properties of the MaNGA IFS survey, with a variety of black hole (BH) feedback models, progenitor galaxies, orbits, and mass ratios. Based on our simulation sample, we find that only major mergers on prograde–prograde or retrograde–prograde orbits in combination with a mechanical BH feedback model can form galaxies with weak enough ongoing star formation, and therefore absent H α emission, to be selected by traditional PSB selection methods. We find strong fluctuations in nebular emission line strengths, even within the PSB phase, suggesting that H α selected PSBs are only a subsample of the underlying population. The global PSB population can be more robustly identified using stellar continuum-based approaches. The difficulty in reproducing the very young PSBs in simulations potentially indicates that new sub-resolution star formation recipes are required to properly model the process of star formation quenching. In our simulations, we find that the starburst peaks at the same time at all radii, but is stronger and more prolonged in the inner regions. This results in a strong time evolution in the radial gradients of the spectral indices that can be used to estimate the age of the starburst without reliance on detailed star formation histories from spectral synthesis models.

Zheng, Yirui↗