Search NASA⌕ Search

SEARCH · Search NASA

Results for “Spatial correlation”

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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multiple rare-earth ion environments in amorphous ( G d 2 O 3 ) 0.230 ( P 2 O 5 ) 0.770 revealed by gadolinium K -edge anomalous x-ray scattering

A Gd K-edge anomalous X-ray scattering (AXS) study is performed on the rare-earth (R) phosphate glass, (Gd 2 O 3 ) 0.230 (P 2 O 5 ) 0.770 , in order to determine Gd…Gd separations in its local structure. The minimum rare-earth separation is of particular interest given that the optical properties of these glasses can quench when rare-earth ions become too close to each other. To this end, a weak Gd…Gd pairwise correlation is located at 4.2(1) Å which is representative of a meta-phosphate R…R separation. More intense first neighbor Gd…Gd pairwise correlations are found at the larger radial distributions, 4.8(1) Å, 5.1(1) Å and 5.4(1) Å. These reflect a mixed ultra-phosphate and meta-phosphate structural character, respectively. A second neighbor Gd…Gd pairwise correlation lies at 6.6(1) Å which is indicative of meta-phosphate structures. Meta- and ultra-phosphate classifications are made by comparing the R…R separations against those of rare-earth phosphate crystal structures, R(PO 3 ) 3 and RP 5 O 14 respectively, or difference pair distribution function (ΔPDF) features determined on similar glasses using difference neutron scattering methods. The local structure of this glass is therefore found to display multiple rare-earth ion environments, presumably because its composition lies between these two stoichiometric formulae. These Gd…Gd separations are well resolved in the ΔPDFs that represent the AXS signal. Indeed, the spatial resolution is so good that it also enables the identification of R…X (X = R, P, O) pairwise correlations up to r ~ 9 Å; their average separations lie at r ~ 7.1(1) Å, 7.6(1) Å 7.9(1) Å, 8.4(1) Å and 8.7(1) Å. This is the first report of a Gd K-edge AXS study on an amorphous material. Its demonstrated ability to characterize the local structure of a glass up to such a long-range of r, heralds exciting prospects for AXS studies on other ternary non-crystalline materials. However, the technical challenge of such an experiment should not be underestimated, as is highlighted in this work where probing AXS signal near the Gd K-edge is found to produce inelastic X-ray scattering that precludes the normal AXS methods of data processing. Furthermore, it is shown that AXS results are not only tractable but they also reveal local structure of rare-earth phosphate glasses that is important from a materials-centered perspective and which could not be obtained by other materials characterization methods.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ultrafast nano-imaging and nano-spectroscopy

Ultrafast pump–probe nano-imaging combines scanning probe-based optical near-field microscopy with ultrafast spectroscopy to enable imaging with deep sub-wavelength spatial resolution, femtosecond temporal resolution and simultaneous spectral resolution. Ultrafast nano-imaging has gained increased attention for its ability to provide far-from-equilibrium excitation and excited-state contrast. With coherent and nonlinear probing, coupled electron, spin and lattice dynamics on elementary timescale and length scale can be resolved. Through nano-movies, ultrafast nano-imaging visualizes correlated quantum dynamics underlying the properties of solid-state materials, semiconductors, molecular electronic, photonic, photovoltaic and other functional materials. With nanometre spatial resolution, this method probes elementary dynamic processes across multiple length scales that are otherwise obscured in conventional ultrafast spectroscopy in which heterogeneities are spatially averaged. Furthermore, this Primer describes the theoretical background and experimental implementation of ultrafast nano-imaging; signal interpretation and modelling; representative examples and a perspective for the future development of the field.

Microscopy↗
DOE OSTI2018

Spatially Resolved Recombination Analysis of CuIn $_\text{x}$ Ga $_\text{1-x}$ Se $_{2}$ Absorbers With Alkali Postdeposition Treatments

In this contribution, we probe spatial variations in charge-carrier recombination in CuIn x Ga 1-x Se 2 (CIGS) absorbers grown on soda-lime glass (SLG) and alkali-free sapphire substrates with NaF and KF postdeposition treatments (PDTs). Temperature-and illumination-dependent device measurements are used to track interface recombination and recombination in the quasi-neutral region. The analysis of these data reveals that the benefit of alkali PDTs depends on the substrate: interface recombination is reduced in devices grown on sapphire substrates, whereas recombination in the quasi-neutral regions is reduced in devices grown on SLG substrates. Cathodoluminescence (CL) spectrum imaging is used to study the spatial distribution of recombination with respect to the grain structure. The grain-boundary CL contrast is similar in films with no PDT, NaF PDT, or KF PDT. A reduced grain-boundary contrast is observed with a NaF + KF PDT; however, suggesting a reduced recombination strength at the grain boundaries (GBs) for combined NaF + KF treatment. CL spectra indicate band tailing, consistent with the fluctuating potential model. Fluctuating potentials are believed to reduce open-circuit voltage, but their spatial distribution has not been studied. Here, CL spectrum imaging data are used to generate maps of the root-mean-square value of the potential energy fluctuations-γ. These maps reveal a bimodal ..gamma.. distribution for all samples: ..gamma.. is generally in the range ~15-50 meV or ~100-180 meV. The higher ..gamma.. range is more significantly affected by the PDTs; after the PDTs, it is strongly correlated with GBs. The lower ..gamma.. range is correlated with higher emission intensity regions, typically grain interiors, and increases in area fraction after the PDTs. These results demonstrate how spatially resolved luminescence and device characterization measurements can be used to monitor changes in recombination in CIGS films and photovoltaic devices. Such measurements can complement empirical device optimization and help improve device performance.

14 SOLAR ENERGY↗
DOE OSTI2013

Laboratory-Based Cryogenic Soft X-ray Tomography with Correlative Cryo-Light and Electron Microscopy

Here we present a novel laboratory-based cryogenic soft X-ray microscope for whole cell tomography of frozen hydrated samples. We demonstrate the capabilities of this compact cryogenic microscope by visualizing internal sub-cellular structures of Saccharomyces cerevisiae cells. The microscope is shown to achieve better than 50 nm spatial resolution with a Siemens star test sample. For whole biological cells, the microscope can image specimens up to 5 micrometers thick. Structures as small as 90 nm can be detected in tomographic reconstructions at roughly 70 nm spatial resolution following a low cumulative radiation dose of only 7.2 MGy. Furthermore, the design of the specimen chamber utilizes a standard sample support that permits multimodal correlative imaging of the exact same unstained yeast cell via cryo-fluorescence light microscopy, cryo-soft x-ray microscopy and cryo-transmission electron microscopy. This completely laboratory-based cryogenic soft x-ray microscope will therefore enable greater access to three-dimensional ultrastructure determination of biological whole cells without chemical fixation or physical sectioning.

Compact light source↗
DOE OSTI2023

Nonempirical Range-Separated Hybrid Functional with Spatially Dependent Screened Exchange

Electronic structure calculations based on density functional theory (DFT) have successfully predicted numerous ground-state properties of a variety of molecules and materials. However, exchange and correlation functionals currently used in the literature, including semilocal and hybrid functionals, are often inaccurate to describe the electronic properties of heterogeneous solids, especially systems composed of building blocks with large dielectric mismatch. Here, in this study, we present a dielectric-dependent range-separated hybrid functional, screened-exchange range-separated hybrid (SE-RSH), for the investigation of heterogeneous materials. We define a spatially dependent fraction of exact exchange inspired by the static Coulomb-hole and screened-exchange (COHSEX) approximation used in many-body perturbation theory, and we show that the proposed functional accurately predicts the electronic structure of several nonmetallic interfaces, three- and two-dimensional, pristine, and defective solids and nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Spatial analysis of concentrations of multiple air pollutants using NASA DISCOVER-AQ aircraft measurements: Implications for exposure assessment

Highlights: • Aircraft measurements showed spatially varying relations of multiple air pollutants. • NO{sub x}, toluene, and xylenes/ethylbenzene were reasonable proxies of traffic and urban air pollution. • Between-pollutant concentration correlations and ratios varied by space. • Multiple pollutant relations provided insights for exposure assessment and multipollutant epidemiology. In recent years, multipollutant approaches have been employed to investigate the association with health outcomes to better represent real-world conditions than more traditional analysis that considers a single pollutant. With regard to the exposure assessment of a mixture of air pollutants, it is critical to understand the spatial variability in multipollutant relations in order to assess their potential health implications. In this study, we investigated the spatial relations of multiple pollutant concentrations (i.e., NO{sub x}, NO{sub y}, black carbon, carbon monoxide, acetaldehyde, formaldehyde, toluene, xylenes/ethylbenzene, ozone, water-soluble organic carbon, and aerosol extinction) observed from the P-3B aircraft in the 2011 NASA field campaign in Baltimore/Washington D.C. areas during July 2011. The between-pollutant Pearson correlations and Z-scores (calculated from log-transformed concentrations) between near-highways and non-highways and between near-urban centers and non-urban centers varied by pollutant pair and space. We found generally lower correlations between NO{sub x} and other pollutants for near-highways (average r = 0.36) than for non-highways (average r = 0.41) and also for non-urban centers (average r = 0.37) than for near-urban centers (average r = 0.41). This indicated that the temporal associations between NO{sub x} and health outcomes might be less affected by other pollutants, which were also related to same health outcomes, for near-highways and non-urban centers. The analysis of between-pollutant Z-scores showed varying spatial relations for popular traffic-related pollutants with the Z-score differences of 0.43 (NO{sub x}-carbon monoxide), 0.29 (NO{sub x}-black carbon), and 0.17 (black carbon-carbon monoxide) between near-highways and non-highways. This result exhibited heterogeneous traffic-related pollutant mixtures with the proximity to highways, potentially leading to the diverse extent of health associations. Furthermore, a mixed effects model presented pollutant-specific associations between the concentrations and the proximity to highways and urban centers, showing larger declines for NO{sub x}, xylenes/ethylbenzene, toluene, and NO{sub y} than those for the pollutants related to secondary pollutant formation. The model also demonstrated the different sensitivity of each pollutant to meteorological parameters, which may modify the spatial and temporal variability in the relations between the pollutants. Our findings provide insights for exposure assessment studies to better understand the cumulative health consequences associated with multiple air pollutants simultaneously.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Organic-matter decomposition along a temperature gradient in a forested headwater stream

Here, we used a natural temperature gradient in Walker Branch, a spring-fed forested stream in eastern Tennessee, USA, to examine the influence of temperature on organic-matter decomposition. These upstream sites are warmer than downstream sites in winter and are cooler than downstream sites in summer. We used a cotton-strip assay to examine breakdown of a substrate of uniform quality (95% cellulose) along the temperature gradient monthly for 2 y and litter bags to examine the interactive effects of leaf-litter quality (labile red maple [Acer rubrum] and tulip poplar [Liriodendron tulipifera], and less labile white oak [Quercus alba]), invertebrates, and temperature on breakdown rates along the downstream temperature gradient for 90 d in winter. Cotton-strip tensile loss and leaf-litter breakdown rates were highly variable. Tensile-loss rates likely were driven by a combination of daily and diel temperature, discharge, streamwater nutrients that varied seasonally and spatially along the temperature gradient. Leaf litter breakdown rates tended to be faster in warmer upstream sites (red maple = 0.0452/d, tulip poplar = 0.0376/d, white oak = 0.0142/d) and slower in cooler downstream sites (red maple = 0.0312/d, tulip poplar = 0.0236/d, white oak = 0.0107/d), and breakdown rates were positively correlated with total invertebrate density. Furthermore, temperature sensitivity of decomposition was similar among the 3 litter types. These results highlight the high degree of spatial and temporal heterogeneity that can exist for ecosystem processes and their drivers. Quantifying this heterogeneity is important when scaling functional metrics to stream and watershed scales and for understanding how organic-matter processing will respond to the warmer streamwater temperatures expected as a result of global climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Spin skyrmion gaps as signatures of strong-coupling insulators in magic-angle twisted bilayer graphene

Abstract The flat electronic bands in magic-angle twisted bilayer graphene (MATBG) host a variety of correlated insulating ground states, many of which are predicted to support charged excitations with topologically non-trivial spin and/or valley skyrmion textures. However, it has remained challenging to experimentally address their ground state order and excitations, both because some of the proposed states do not couple directly to experimental probes, and because they are highly sensitive to spatial inhomogeneities in real samples. Here, using a scanning single-electron transistor, we observe thermodynamic gaps at even integer moiré filling factors at low magnetic fields. We find evidence of a field-tuned crossover from charged spin skyrmions to bare particle-like excitations, suggesting that the underlying ground state belongs to the manifold of strong-coupling insulators. From the spatial dependence of these states and the chemical potential variation within the flat bands, we infer a link between the stability of the correlated ground states and local twist angle and strain. Our work advances the microscopic understanding of the correlated insulators in MATBG and their unconventional excitations.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization

Purpose: Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical properties of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution. Methods: The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization. Results: On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Co-regulation of water and energy in the spatial heterogeneity of drought resistance and resilience

Vegetation resistance and resilience to drought are linked to the stability of terrestrial ecosystems under climate change. However, the factors driving the spatial heterogeneity in drought resistance and resilience remain poorly understood. In the study, we utilized multiple satellite-derived vegetation indices to calculate and analyze changes in drought resistance and resilience across various biomes worldwide. Results indicated that drought resistance showed a significant increase with the increase in water availability, but no significant relationship was observed between drought resistance and energy. In contrast, drought resilience exhibited a significant increase with an increase in energy rather than in water. Furthermore, a negative correlation was observed between drought resistance and resilience across different biomes worldwide, indicating a trade-off between resistance and resilience. However, the strength of the negative correlation varied based on water and energy conditions. These findings provide compelling evidence that water and energy co-regulated the spatial heterogeneity in drought resistance and resilience across the globe. The robust linear relationship between drought resistance and resilience and available water and energy demonstrated in our study is critical to accurately predicting and assessing the impact of climate change on vegetation growth and terrestrial carbon cycling in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Intermolecular structural correlations in model globular and unconcatenated ring polymer liquids

We employ the field theoretic polymer integral equation theory to construct a segment-level theory for the thermodynamics and pair structure of dense liquids of interpenetrating ring polymers and a simple globule model. The latter is defined by a fractal mass distribution on all internal length scales with an exponent equal to the spatial dimension (d F = d s = 3). In an isochoric ensemble the dimensionless compressibility and pressure is predicted to vary exponentially with macromolecular volume fraction. An intermolecular correlation hole exists down to small length scales. This model appears to be useful for a recently studied experimental soft nanoparticle suspension, and also serves as a reference system for our analysis of ring liquids. Motivated by simulations, a two-fractal exponent ring model is adopted for the intramolecular structure factor. At smaller lengths it describes chain-like macromolecules, while on larger scales it corresponds to a space-filling object in the sense that d F = d s = 3. The crossover between these two regimes is of order the entanglement length of the linear chain analog. Based on a constant compressibility ensemble, the effective volume fraction grows at intermediate values of degree of polymerization (N), and crosses over to a very slow logarithmic growth at large N. A weaker intermolecular correlation hole is predicted. The number of nearest neighbor rings increases dramatically at small N, akin to linear chain melts, but then tends to saturate at large N, in accord with simulations. We determine that the tools developed may be relevant for other partially interpenetrating soft objects such as core–shell nanogels or microgels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Syntrophic interactions and mechanisms underpinning anaerobic methane oxidation: targeted metaproteogenomics, single-cell protein detection and quantitative isotope imaging of microbial consortia

Syntrophy and mutualism play a central role in carbon and nutrient cycling by microorganisms. Yet, our ability to effectively study symbionts in culture has been hindered by the inherent interdependence of syntrophic associations, their dynamic behavior, and their frequent existence at thermodynamic limits. Now solutions to these challenges are emerging in the form of new methodologies. Developing strategies that establish links between the identity of microorganisms and their metabolic potential, as well as techniques that can probe metabolic networks on a scale that captures individual molecule exchange and processing, is at the forefront of microbial ecology. Understanding the interactions between microorganisms on this level, at a resolution previously intractable, will lead to our greater understanding of carbon turnover and microbial community resilience to environmental perturbations. In this project, we studied an enigmatic syntrophic association between uncultured methane-oxidizing archaea and sulfate-reducing bacteria. This environmental archaeal-bacterial partnership represents a globally important sink for methane in anoxic environments. The specific goals of this project were organized into 3 major tasks designed to address questions relating to the ecophysiology of these syntrophic organisms under changing environmental conditions (e.g. different electron acceptors and nutrients), primarily through the development of microanalytical imaging methods which enable the visualization of the spatial distribution of the partners within aggregates, consumption and exchange of isotopically labeled substrates, and expression of targeted proteins identified via metaproteomics. The advanced tool set developed here to collect, correlate, and analyze these high resolution image and isotope-based datasets from methane-oxidizing consortia has the potential to be widely applicable for studying and modeling patterns of activity and interactions across a broad range of spatially structured microbial partnerships, including other syntrophic associations, microbial mats, biofilms, and plant-microbe or animal-microbe symbioses in nature.

03 NATURAL GAS↗
DOE OSTI2015

Theoretical analysis on the measurement errors of local 2D DIC: Part I temporal and spatial uncertainty quantification of displacement measurements

This study presents a theoretical uncertainty quantification of displacement measurements by subset-based 2D-digital image correlation. A generalized solution to estimate the random error of displacement measurement is presented. The obtained solution suggests that the random error of displacement measurements is determined by the image noise, the summation of the intensity gradient in a subset, the subpixel part of displacement, and the interpolation scheme. The proposed method is validated with virtual digital image correlation tests.

2D-DIC↗
DOE OSTI2023

Photoconversion in Disordered Semiconductors: Spatial, Spectral, and Temporal Insights through Nonlinear Microscopy (Final Technical Report)

Conversion of solar photons to useable energy requires two processes to occur successfully in a light-harvesting material: 1) the initial photoexcited state must efficiently produce excited states that are free to move, and 2) those states must be transported to a new location where they can do useful work before any potential is lost as heat. The core focus of this project examined the spatial and energetic dynamics of these two primary events of photoconversion in solution-processed semiconducting materials. The electronic properties of these systems are complex and strongly influenced by the cumulative interaction of local morphology and composition, along with defects, interfaces, and trap sites. It is therefore important to interrogate these systems at the local level, where structural and compositional heterogeneity can be precisely correlated to variation in functionality. Utilizing ultrafast laser spectroscopy coupled together with optical microscopy, this project examined how excited states and free charges spatially and energetically evolve on length scales between 10 nanometers and 10 microns and on timescales from 50 femtoseconds to 1 nanosecond. Insights gained from measurements made on these characteristic length and time scales provided new insight into how macroscopic functionality in disordered photovoltaic materials emerges from the interplay of nanoscale and mesoscale interactions.

14 SOLAR ENERGY↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

TIME-RESOLVED EMISSION FROM BRIGHT HOT PIXELS OF AN ACTIVE REGION OBSERVED IN THE EUV BAND WITH SDO/AIA AND MULTI-STRANDED LOOP MODELING

Evidence of small amounts of very hot plasma has been found in active regions and might be an indication of impulsive heating released at spatial scales smaller than the cross-section of a single loop. We investigate the heating and substructure of coronal loops in the core of one such active region by analyzing the light curves in the smallest resolution elements of solar observations in two EUV channels (94 and 335 Å) from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. We model the evolution of a bundle of strands heated by a storm of nanoflares by means of a hydrodynamic 0D loop model (EBTEL). The light curves obtained from a random combination of those of single strands are compared to the observed light curves either in a single pixel or in a row of pixels, simultaneously in the two channels, and using two independent methods: an artificial intelligent system (Probabilistic Neural Network) and a simple cross-correlation technique. We explore the space of the parameters to constrain the distribution of the heat pulses, their duration, their spatial size, and, as a feedback on the data, their signatures on the light curves. From both methods the best agreement is obtained for a relatively large population of events (1000) with a short duration (less than 1 minute) and a relatively shallow distribution (power law with index 1.5) in a limited energy range (1.5 decades). The feedback on the data indicates that bumps in the light curves, especially in the 94 Å channel, are signatures of a heating excess that occurred a few minutes before.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗