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

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Planar shock-induced bubble collapse and jetting in water captured via X-ray phase contrast imaging

Shock wave–bubble interactions in water manifest rich dynamics driven by a combination of strong pressure and density mismatches. They have a wide variety of applications, including the injection of pharmaceuticals, and through scaling, enable the exploration of various aspects of high-energy-density systems such as inertial confinement fusion. In this work, the interaction between a micrometric nitrogen bubble and a planar shock wave, characterized by a Mach number of M = 1.24 and a peak pressure of p max = 0.57 , is experimentally recorded using ultra-high-speed x-ray phase contrast imaging. Highly resolved radiographs provide access to all phase discontinuities along the beam path, offering quantities such as the time-varying bubble size, the speed of a jet produced during the bubble collapse, and the time evolution of the shock wave front, which are critical benchmark data for numerical scheme validation. This study addresses the lack of well-characterized, repeatable, and high spatiotemporal resolution experiments at negative Atwood numbers by providing shock–bubble visualization and corresponding numerical simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Harnessing citizen science to contextualize adaptation mechanism discovery

Species occupying broad geographic regions have evolved multiple mechanisms to regulate phenological characteristics, enabling adaptations to diverse native habitats. By developing computer vision AI to process citizen science observations across native habitats over North America, we uncovered a consistent latitudinal trend of earlier flowering at higher latitudes in warm-season perennial grasses. To explore the underlying mechanisms of adaptation, we conducted common garden experiments with one species (switchgrass) and discovered the opposite latitudinal flowering-time trend. Integration of differential plasticity of GI-Hd1-FTL1 haplotypes of flowering time regulatory genes, haplotype range, and local environmental profiles found that observations from native habitats capture only part of the genotype-environment-phenotype spectrum established in common garden experiments, therefore reconciling the discrepancy. Two mechanisms emerged as key forces shaping current haplotype ranges and influencing future shifts. Our study highlights the power of combining citizen science observations with designed experiments to uncover mechanisms of adaptation across spatiotemporal scales.

FTL1↗
NASA NTRS2005

Three-dimensional characterization of tethered microspheres by total internal reflection fluorescence microscopy

Tethered particle microscopy is a powerful tool to study the dynamics of DNA molecules and DNA-protein complexes in single-molecule experiments. We demonstrate that stroboscopic total internal reflection microscopy can be used to characterize the three-dimensional spatiotemporal motion of DNA-tethered particles. By calculating characteristic measures such as symmetry and time constants of the motion, well-formed tethers can be distinguished from defective ones for which the motion is dominated by aberrant surface effects. This improves the reliability of measurements on tether dynamics. For instance, in observations of protein-mediated DNA looping, loop formation is distinguished from adsorption and other nonspecific events.

Non-NASA Center↗
DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Cook, Bruce D.↗
DOE OSTI2024

Feasibility of an experiment on clumping induced by the Crow instability along a shocked cylinder

The growth of three-dimensional perturbations subject to the Crow instability along a vortex dipole resulting from the passage of a shock wave through a heavy gaseous cylinder is examined numerically. A linear stability analysis is performed based on geometric parameters extracted from two-dimensional simulations to determine the range of unstable wavenumbers, which is found to extend from 0.0 to 1.3 when normalized by the core separation distance. The analysis is then verified by comparison to three-dimensional simulations, which clearly show the development of the instability and the pinch-off of the vortex dipole into isolated vortex rings, which manifest as clumps of the original cylinder material. A scaling law is developed to determine the relevant spatiotemporal scales of the instability development, which is then used to assess the feasibility of a high-energy-density experiment visualizing clump formation. Specifically, a shocked cylinder with an initial diameter of 100 μm consisting of a perturbation of approximate wavelength and amplitude of 600 and 10 μm, respectively, is expected to form clumps resulting from the Crow instability approximately 40 ns after it is shocked, with dynamics which can be readily visualized on the Omega EP laser facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
NASA NTRS2013

Tool for Automated Retrieval of Generic Event Tracks (TARGET)

Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.

Clune, Thomas↗
DOE OSTI2024

Tethys: A Spatiotemporal Downscaling Model for Global Water Demand

Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Probing Surface Plasmon Dynamics in Periodic Nanostructures through Ultrafast Electron Microscopy

Surface plasmon polaritons (SPPs) can be manipulated to localize and guide light in subwavelength distances, enabling them to find applications in a wide range of areas, from sensing to quantum computing. Among several methods of SPP excitation, periodic arrays of nano- and microstructures are of particular interest, as they enable engineering SPP properties through structural parameters. Here, in this study, using the photon-induced near-field electron microscopy (PINEM) technique, we investigated the mode formation, coupling, interference, and decay of SPPs in square and hexagonal arrays of circular nanoholes under both visible and near-infrared excitation. Polarization-resolved analysis revealed the key factors governing SPP localization and interference patterns, showing that the periodicity and symmetry of the array primarily determine the SPP interference patterns and their orientation, while pump polarization mainly modulates their intensity. Time-resolved PINEM measurements demonstrated the spatial dependence of the SPP temporal characteristics. In addition, cathodoluminescence (CL) spectroscopy was employed to examine the intrinsic plasmonic characteristics of the structure. Finite difference time domain (FDTD) simulations showed strong agreement with both PINEM and CL measurements on the spatial and spectral behavior of SPPs. Understanding the spatiotemporal dynamics of SPPs on nanostructures beyond the diffraction limit is crucial for optimizing plasmonic structures for advanced photonic and quantum technologies.

Plasmonics↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
DOE OSTI2026

Spatiotemporal Thermal Coupling in VO 2 Device Arrays

Correlated oxides such as VO 2 exhibit an electrically driven insulator–metal transition (IMT) that underlies their promise for neuromorphic and memory devices. Yet the IMT is not a uniform bulk process but a spatiotemporal phenomenon in which local heating nucleates filaments, contracts or dissolves them with the electric field, and couples to the environment. In this work, we directly image the VO 2 IMT dynamics by mid-wave infrared, thermography synchronized with electrical transport, resolving device temperature with micrometer spatial and microsecond temporal resolution. At the single-device level, we capture the full cycle of filament nucleation, contraction, and relaxation during current/voltage-driven resistive switching. At the array level, we show that heat propagates across etched gaps with an effective length scale of ∼131 µm, enabling cooperative behaviors among electrically isolated devices. Short-range distanced devices exhibit mutual filament attraction and sequential dissolution, while long-range distanced devices differentiate into distinct roles: drivers that initiate switching, cooperative responders that undergo assisted self-oscillations, and passive reporters that record the thermal field. Furthermore, these results reframe thermal crosstalk, long regarded as parasitic, as an intrinsic coupling channel and design principle for organizing collective switching behaviors, with direct implications for emergent circuit functionality in neuromorphic and unconventional computing architectures.

coupling↗
NASA NTRS2000

Spatiotemporal processing of linear acceleration: primary afferent and central vestibular neuron responses

Spatiotemporal convergence and two-dimensional (2-D) neural tuning have been proposed as a major neural mechanism in the signal processing of linear acceleration. To examine this hypothesis, we studied the firing properties of primary otolith afferents and central otolith neurons that respond exclusively to horizontal linear accelerations of the head (0.16-10 Hz) in alert rhesus monkeys. Unlike primary afferents, the majority of central otolith neurons exhibited 2-D spatial tuning to linear acceleration. As a result, central otolith dynamics vary as a function of movement direction. During movement along the maximum sensitivity direction, the dynamics of all central otolith neurons differed significantly from those observed for the primary afferent population. Specifically at low frequencies (</=0.5 Hz), the firing rate of the majority of central otolith neurons peaked in phase with linear velocity, in contrast to primary afferents that peaked in phase with linear acceleration. At least three different groups of central response dynamics were described according to the properties observed for motion along the maximum sensitivity direction. "High-pass" neurons exhibited increasing gains and phase values as a function of frequency. "Flat" neurons were characterized by relatively flat gains and constant phase lags (approximately 20-55 degrees ). A few neurons ("low-pass") were characterized by decreasing gain and phase as a function of frequency. The response dynamics of central otolith neurons suggest that the approximately 90 degrees phase lags observed at low frequencies are not the result of a neural integration but rather the effect of nonminimum phase behavior, which could arise at least partly through spatiotemporal convergence. Neither afferent nor central otolith neurons discriminated between gravitational and inertial components of linear acceleration. Thus response sensitivity was indistinguishable during 0.5-Hz pitch oscillations and fore-aft movements. The fact that otolith-only central neurons with "high-pass" filter properties exhibit semicircular canal-like dynamics during head tilts might have important consequences for the conclusions of previous studies of sensory convergence and sensorimotor transformations in central vestibular neurons.

NASA Discipline Neuroscience↗
DOE OSTI2026

String-Breaking Dynamics in Quantum Adiabatic and Diabatic Processes

Confinement prohibits isolation of color charges, e.g., quarks, in nature via a process called string breaking : the separation of two charges results in an increase in the energy of a color flux, visualized as a string, connecting those charges. Eventually, creating additional charges is energetically favored, hence breaking the string. Such a phenomenon can be probed in simpler models, including quantum spin chains, enabling enhanced understanding of string-breaking dynamics. A challenging task is to understand how string breaking occurs as time elapses, in an out-of-equilibrium setting. This work establishes the phenomenology of dynamical string breaking induced by a gradual increase of string tension over time. It, thus, goes beyond instantaneous quench processes and enables tracking the real-time evolution of strings in a more controlled setting. We focus on domain-wall confinement in a family of quantum Ising chains. Our results indicate that, for sufficiently short strings and slow evolution, string breaking can be described by the transition dynamics of a two-state quantum system akin to a Landau-Zener process. For longer strings, a more intricate spatiotemporal pattern emerges: the string breaks by forming a superposition of bubbles (domains of flipped spins of varying sizes), which involve highly excited states. We finally demonstrate that string breaking driven only by quantum fluctuations can be realized in the presence of sufficiently long-ranged interactions. This work holds immediate relevance for studying string breaking in quantum-simulation experiments.

Ising model↗
NASA NTRS2013

NASA Tech Briefs, April 2013

Topics covered include: Fully Integrated, Miniature, High-Frequency Flow Probe Utilizing MEMS Leadless SOI Technology; Nanoscale Surface Plasmonics Sensor With Nanofluidic Control; Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes; Neural Network Back-Propagation Algorithm for Sensing Hypergols; Bulk Moisture and Salinity Sensor; Change-Based Satellite Monitoring Using Broad Coverage and Targetable Sensing; Circularly Polarized Microwave Antenna Element with Very Low Off-Axis Cross-Polarization; Ultra-Low Heat-Leak, High-Temperature Superconducting Current Leads for Space Applications; Flash Cracking Reactor for Waste Plastic Processing; An Automated Safe-to-Mate (ASTM) Tester; Wireless Chalcogenide Nanoionic-Based Radio-Frequency Switch; Compute Element and Interface Box for the Hazard Detection System; DOT Transmit Module; Composite Aerogel Multifoil Protective Shielding; Li-Ion Electrolytes with Improved Safety and Tolerance to High-Voltage Systems; Polymer-Reinforced, Non-Brittle, Lightweight Cryogenic Insulation; Controlled, Site-Specific Functionalization of Carbon Nanotubes with Diazonium Salts; Regenerable Sorbent for CO2 Removal; Sprayable Aerogel Bead Compositions With High Shear Flow Resistance and High Thermal Insulation Value; Lexan Linear Shaped Charge Holder with Magnets and Backing Plate; Robotic Ankle for Omnidirectional Rock Anchors; Wind, Wave, and Tidal Energy Without Power Conditioning; An Active Heater Control Concept to Meet IXO Type Mirror Module Thermal-Structural Distortion Requirement; Waterless Clothes-Cleaning Machine; Integrated Electrical Wire Insulation Repair System; LVGEMS Time-of-Flight Mass Spectrometry on Satellites; Surface Inspection Tool for Optical Detection of Surface Defects; Per-Pixel, Dual-Counter Scheme for Optical Communications; Certification-Based Process Analysis; Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization; Smart-Divert Powered Descent Guidance to Avoid the Backshell Landing Dispersion Ellipse; Estimating Foreign-Object-Debris Density from Photogrammetry Data; Adaptive Sampling of Spatiotemporal Phenomena with Optimization Criteria; Building a 2.5D Digital Elevation Model From 2D Imagery; Eyes on the Earth 3D; Target Trailing With Safe Navigation for Maritime Autonomous Surface Vehicles; Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS; ISTP CDF Skeleton Editor; Uplink Summary Generator (ULSGEN) Version 1.0; Robotics On-Board Trainer (ROBoT); Software Engineering Tools for Scientific Models; Automatic Data Filter Customization Using a Genetic Algorithm; Tracker Toolkit; Towards Efficient Scientific Data Management Using Cloud Storage; On a Formal Tool for Reasoning About Flight Software Cost Analysis; A Nanostructured Composites Thermal Switch Controls Internal and External Short Circuit in Lithium Ion Batteries; Spacecraft Crew Cabin Condensation Control; and Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex.

Source record↗
DOE OSTI2025

ESMs Latent Space Exploration for Uncertainty Quantification and Spatiotemporal Downscaling

This final report for DOE Award DE-SC0023044 presents advances in two key areas of climate modeling: (1) representative climate model selection and (2) Earth System Model (ESM) downscaling using hybrid AI methods. The first section introduces a reordered, three-stage workflow to select representative GCM runs that more effectively balance historical skill with ensemble spread, validated across Texas, Bihar, and New York. The second section introduces two novel super-resolution frameworks, ViSIR and ViFOR, that integrate Vision Transformers with sinusoidal and Fourier-based implicit neural representations. These models achieve state-of-the-art reconstruction accuracy for ESM variables including surface temperature and heat fluxes. The report includes detailed methodology, benchmarks, and results, demonstrating significant gains in uncertainty quantification, spatial fidelity, and scalability for climate-impact studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
NASA NTRS2019

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗