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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 199 records · Page 11

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DOE OSTI2013

Structures of the Porphyromonas gingivalis OxyR regulatory domain explain differences in expression of the OxyR regulon in Escherichia coli and P. gingivalis

Differences in OxyR regulated expression of oxidative stress genes between Escherichia coli and Porphyromonas gingivalis are explained by very minor differences in structure and amino-acid sequence of the respective oxidized and reduced OxyR regulatory domains. These differences affect OxyR quaternary structures and are predicted from model building of full length OxyR–DNA complexes to confer distinct modes of DNA binding on this transcriptional regulator. OxyR transcriptionally regulates Escherichia coli oxidative stress response genes through a reversibly reducible cysteine disulfide biosensor of cellular redox status. Structural changes induced by redox changes in these cysteines are conformationally transmitted to the dimer subunit interfaces, which alters dimer and tetramer interactions with DNA. In contrast to E. coli OxyR regulatory-domain structures, crystal structures of Porphyromonas gingivalis OxyR regulatory domains show minimal differences in dimer configuration on changes in cysteine disulfide redox status. This locked configuration of the P. gingivalis OxyR regulatory-domain dimer closely resembles the oxidized (activating) form of the E. coli OxyR regulatory-domain dimer. It correlates with the observed constitutive activation of some oxidative stress genes in P. gingivalis and is attributable to a single amino-acid insertion in P. gingivalis OxyR relative to E. coli OxyR. Modelling of full-length P. gingivalis, E. coli and Neisseria meningitidis OxyR–DNA complexes predicts different modes of DNA binding for the reduced and oxidized forms of each.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Intermolecular correlations are necessary to explain diffuse scattering from protein crystals

Conformational changes drive protein function, including catalysis, allostery, and signaling. X-ray diffuse scattering from protein crystals has frequently been cited as a probe of these correlated motions, with significant potential to advance our understanding of biological dynamics. However, recent work challenged this prevailing view, suggesting instead that diffuse scattering primarily originates from rigid body motions and could therefore be applied to improve structure determination. To investigate the nature of the disorder giving rise to diffuse scattering, and thus the potential applications of this signal, a diverse repertoire of disorder models was assessed for its ability to reproduce the diffuse signal reconstructed from three protein crystals. This comparison revealed that multiple models of intramolecular conformational dynamics, including ensemble models inferred from the Bragg data, could not explain the signal. Models of rigid body or short-range liquid-like motions, in which dynamics are confined to the biological unit, showed modest agreement with the diffuse maps, but were unable to reproduce experimental features indicative of long-range correlations. Extending a model of liquid-like motions to include disorder across neighboring proteins in the crystal significantly improved agreement with all three systems and highlighted the contribution of intermolecular correlations to the observed signal. These findings anticipate a need to account for intermolecular disorder in order to advance the interpretation of diffuse scattering to either extract biological motions or aid structural inference.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Explaining Neural Spike Activity for Simulated Bio-plausible Network through Deep Sequence Learning

With significant improvements in large-scale simulations of brain models, there is a growing need to develop tools for rapid analysis and interpreting the simulation results. In this work, we explore the potential of sequential deep learning models to understand and explain the network dynamics among the neurons extracted from a large-scale neural simulation in STACS (Simulation Tool for Asynchronous Cortical Stream). Our method employs a representative neuroscience model that abstracts the cortical dynamics with a reservoir of randomly connected spiking neurons with a low stable spike firing rate throughout the simulation duration. We subsequently analyze the spike dynamics of the simulated spiking neural network through an autoencoder model and an attention-based mechanism.

Kulkarni, Shruti↗

DOE OSTI2019

Plant phylogenetic history explains in-stream decomposition at a global scale

1. Evolutionary history and adaptation to climate shape plant traits. Some include leaf traits that influence litter quality. As a result, evolutionary history should affect litter decomposition, a crucial ecosystem process. In addition, litter decomposition is directly influenced by climate. Similarly, we expect plant phylogeny, adaptation and climate to jointly influence litter decomposition. These effects and their interactions have yet to be untangled at a global scale. 2. Here we introduce an analysis of variation in litter decomposition rates in rivers and streams across 285 published studies for 239 species (from ferns to angiosperms) distributed at 494 locations world-wide. We estimated the relative contributions of climatic conditions and phylogenetic heritage on litter decomposition rates, partitioning phylogenetic from climatic effects at the site and species levels using phylogenetic eigenvector analysis and phylogenetic linear mixed models. In addition, we modelled transitions in decomposition rates under a suite of multiple adaptive-regime Ornstein–Uhlenbeck models to test the hypothesis that natural selection has shaped clade-level litter decomposition rates. 3. Leaf litter decomposition rate exhibited a significant phylogenetic signal. Modelling decomposition rate as a function of location, climatic niche and phylogeny consistently recovered phylogeny alone as one of the top models in species-level analyses. Since many previous studies have focused on the same species across many locations, we also conducted analyses at the species × site level. Both phylogenetic and climatic factors were favoured in models of this analysis, but the single most important predictor for angiosperms and for all taxa analysed together was phylogeny alone. 4. Synthesis. For plant species distributed globally at nearly 500 locations we found that plant phylogenetic history is a critically important predictor of litter decomposition rate in rivers and streams, explaining more of the variance in decomposition than site or climatic regime. Thus, our study demonstrates the influence of evolutionary history on suites of plant traits that shape a key ecosystem process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics

Abstract Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population‐specific and pairwise F ST ) within five metapopulations (79 populations, 4583 individuals) of steelhead trout ( Oncorhynchus mykiss ) in the Columbia River Basin, USA . Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate‐related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and F ST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin‐wide to the metapopulation scale). Sensitivity analysis (leave‐one‐population‐out) revealed consistent relationships between climate variables and F ST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes ( N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

Hand, Brian K.↗

DOE OSTI2022

Granal thylakoid structure and function: explaining an enduring mystery of higher plants

Summary In higher plants, photosystems II and I are found in grana stacks and unstacked stroma lamellae, respectively. To connect them, electron carriers negotiate tortuous multi‐media paths and are subject to macromolecular blocking. Why does evolution select an apparently unnecessary, inefficient bipartition? Here we systematically explain this perplexing phenomenon. We propose that grana stacks, acting like bellows in accordions, increase the degree of ultrastructural control on photosynthesis through thylakoid swelling/shrinking induced by osmotic water fluxes. This control coordinates with variations in stomatal conductance and the turgor of guard cells, which act like an accordion's air button. Thylakoid ultrastructural dynamics regulate macromolecular blocking/collision probability, direct diffusional pathlengths, division of function of Cytochrome b 6 f complex between linear and cyclic electron transport, luminal pH via osmotic water fluxes, and the separation of pH dynamics between granal and lamellar lumens in response to environmental variations. With the two functionally asymmetrical photosystems located distantly from each other, the ultrastructural control, nonphotochemical quenching, and carbon‐reaction feedbacks maximally cooperate to balance electron transport with gas exchange, provide homeostasis in fluctuating light environments, and protect photosystems in drought. Grana stacks represent a dry/high irradiance adaptation of photosynthetic machinery to improve fitness in challenging land environments. Our theory unifies many well‐known but seemingly unconnected phenomena of thylakoid structure and function in higher plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Can surface cracks and unipolar arcs explain breakdown and gradient limits?

The authors argue that the physics of unipolar arcs and surface cracks can help understand rf breakdown and vacuum arc data. They outline a model of the basic mechanisms involved in breakdown and explore how the physics of unipolar arcs and cracks can simplify the picture of breakdown and gradient limits in accelerators, tokamaks as well as laser ablation, micrometeorites, and other applications. Cracks are commonly seen in SEM images of arc damage and they are produced as the liquid metal cools. They can produce the required field enhancements to explain field emission data and can produce mechanical failure of the surface that would trigger breakdown events. Unipolar arcs can produce currents sufficient to short out rf structures, and can cause the sort of damage seen in SEM images. They should be unstable, and possibly self-quenching, as seen in optical fluctuations and surface damage. The authors describe some details and consider the predictions of this simple model.

36 MATERIALS SCIENCE↗

DOE OSTI2016

MO-DE-BRA-06: 3D Image Acquisition and Reconstruction Explained with Online Animations

Purpose: Understanding the principles of 3D imaging and image reconstruction is fundamental to the field of medical imaging. Clinicians, technologists, physicists, patients, students, and inquisitive minds all stand to benefit from greater comprehension of the supporting technologies. To help explain the basic principles of 3D imaging, we developed multi-frame animations that convey the concepts of tomographic imaging. The series of free (gif) animations are accessible online, and provide a multimedia introduction to the main concepts of image reconstruction. Methods: Text and animations were created to convey the principles of analytic tomography in CT, PET, and SPECT. Specific topics covered included: principles of sinograms/image data storage, forward projection, principles of PET acquisitions, and filtered backprojection. A total of 8 animations were created and presented for CT, PET, and digital phantom formats. In addition, a free executable is also provided to allow users to create their own tomographic animations – providing an opportunity for interaction and personalization to help foster user interest. Results: Tutorial text and animations have been posted online, freely available to view or download. The animations are in first position in a google search of “image reconstruction animations”. The website currently receives approximately 200 hits/month, from all over the world, and the usage is growing. Positive feedback has been collected from users. Conclusion: We identified a need for improved teaching tools to help visualize the (temporally variant) concepts of image reconstruction, and have shown that animations can be a useful tool for this aspect of education. Furthermore, posting animations freely on the web has shown to be a good way to maximize their impact in the community. In future endeavors, we hope to expand this animated content, to cover principles of iterative reconstruction, as well as other phenomena relating to imaging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Natural Aerosols Explain Seasonal and Spatial Patterns of Southern Ocean Cloud Albedo

Small particles called aerosols act as nucleation sites for cloud drop formation, affecting clouds and cloud properties – ultimately influencing the cloud dynamics, lifetime, water path and areal extent that determine the reflectivity (albedo) of clouds. The concentration Nd of droplets in clouds that influences planetary albedo is sensitive to the availability of aerosol particles on which the droplets form. Natural aerosol concentrations not only affect cloud properties themselves, but also modulate the sensitivity of clouds to changes in anthropogenic aerosols. Here, it is shown that modeled natural aerosols, principally marine biogenic primary and secondary aerosol sources, explain more than half of the spatiotemporal variability in satellite-observed Nd. Enhanced Nd over regions of high biological activity is found to be driven primarily by high concentrations of sulfate aerosol at lower Southern Ocean latitudes (35-45°S) and by organic matter in sea spray aerosol at higher latitudes (45-55°S). Biogenic sources are estimated to increase the summertime mean reflected solar radiation in excess of 10 W m-2 over parts of the Southern Ocean, which is comparable to the annual mean increases expected from anthropogenic aerosols over heavily polluted regions of the Northern Hemisphere.

cloud droplet concentration↗

DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Loss of a satellite could explain Saturn’s obliquity and young rings

The origin of Saturn’s ~26.7° obliquity and ~100-million-year-old rings is unknown. The observed rapid outward migration of Saturn’s largest satellite, Titan, could have raised Saturn’s obliquity through a spin-orbit precession resonance with Neptune. We use Cassini data to refine estimates of Saturn’s moment of inertia, finding that it is just outside the range required for the resonance. We propose that Saturn previously had an additional satellite, which we name Chrysalis, that caused Saturn’s obliquity to increase through the Neptune resonance. Destabilization of Chrysalis’s orbit ~100 million years ago can then explain the proximity of the system to the resonance and the formation of the rings through a grazing encounter with Saturn.

Science & Technology - Other Topics↗

DOE OSTI2018

Explaining low ℓ anomalies in the CMB power spectrum with resonant superstring excitations during inflation

We explore the possibility that both the suppression of the ℓ = 2 multipole moment of the power spectrum of cosmic microwave background temperature fluctuations and the possible dip for ℓ = 10 –30 can be explained as well as a possible new dip for ℓ ≈ 60 as the result of the resonant creation of sequential excitations of a fermionic (or bosonic) closed superstring that couples to the inflaton field. We consider a D = 26 closed bosonic string with one toroidal compact dimension as an illustration of how string excitations might imprint themselves on the CMB. We analyze the existence of successive momentum states, winding states or oscillations on the string as the source of the three possible dips in the power spectrum. Although the evidence of these dips are of marginal statistical significance, this might constitute the first observational evidence of successive superstring excitations in Nature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

AWE-ML: Averaged Weights for Explainable Machine Learning v. 1.0

This software uses averaged weighted estimators to classify instances and then explicate the individual decision process in classification. The software has state of the art performance in accuracy, while maintaining full explainability. It does this by calculating hierarchically averaged probabilities for a large class of feature combinations and updating them ‘on-the-fly’. On the fly updating also allows the model to be accurate, relative to the total dataset, maintaining accuracy in real-time, without a need to retrain the classifier.

Agarwal, Sapan↗

DOE OSTI2021

Explainable Neural Architecture Search (XNAS)

Code for the paper Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Zachariah Carmichael, Tim Moon, and Sam Ade Jacobs. Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multiobjective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

Carmichael, ZachariahJ↗

DOE OSTI2024

Vistransformers Explained

The Vistransformers Explained library is a collection of python notebooks that demonstrate the internal mechanics and uses of visual-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The value added by this code is in-depth explanations of the mathematics behind the sub-modules of the ViT models, including original figures. Additionally, the library contains the code necessary to implement and train the ViT models. The library does not include example training data for the models; instead, it would rely on users generating their own datasets. The code is based on the PyTorch python library. It does not include any files other than python scripts, modules, or notebooks.

Callis, Skylar↗

DOE OSTI2022

Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning

Abstract We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.

Pegion, Kathy↗

DOE OSTI2021

Explaining Globally Inhomogeneous Future Changes in Monsoons Using Simple Moist Energy Diagnostics

Here, this study examines the annual cycle of monsoon precipitation simulated by models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), then uses moist energy diagnostics to explain globally inhomogeneous projected future changes. Rainy season characteristics are quantified using a consistent method across the globe. Model bias is shown to include rainy season onsets tens of days later than observed in some monsoon regions (India, Australia, and North America) and overly large summer precipitation in others (North America, South America, and southern Africa). Projected next-century changes include rainy season lengthening in the two largest Northern Hemisphere monsoon regions (South Asia and central Sahel) and shortening in the two largest Southern Hemisphere regions (South America and southern Africa). Changes in the North American and Australian monsoons are less coherent across models. To understand these changes, relative moist static energy (MSE) is defined as the difference between local and tropical-mean surface air MSE. Future changes in relative MSE in each region correlate well with onset and demise date changes. Furthermore, Southern Hemisphere regions projected to undergo rainy season shortening are spanned by an increasing equator-to-pole MSE gradient, suggesting their rainfall will be increasingly inhibited by fluxes of dry extratropical air; Northern Hemisphere regions with projected lengthening of rainy seasons undergo little change in equator-to-pole MSE gradient. Thus, although model biases raise questions as to the reliability of some projections, these results suggest that globally inhomogeneous future changes in monsoon timing may be understood through simple measures of surface air MSE.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Human Readiness Levels Explained

The Human Readiness Level scale complements and supplements the existing technology readiness level scale to support comprehensive and systematic evaluation of human system aspects throughout a system’s life cycle. The objective is to ensure humans can use a fielded technology or system as intended to support mission operations safely and effectively. This article defines the nine human readiness levels in the scale, explains their meaning, and illustrates their application using a helmet-mounted display example.

60 APPLIED LIFE SCIENCES↗