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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Calculating fluctuations and self-correlations numerically for causal charge diffusion in relativistic heavy-ion collisions

Here, we study the propagation and diffusion of electric charge fluctuations in the Bjorken hydrodynamic model with both white and Catteneo noise using purely numerical methods. We show that a lattice of noise fluctuations is required to fully calculate the two-point correlators of charge. We introduce a numerical procedure to solve the stochastic differential equations that arise from the charge conservation equation on the lattice event by event. We explicitly identify the self-correlation term in the case of Catteneo noise and provide a physical interpretation. We provide a numerical recipe to remove this contribution from the full two-point correlators. Finally, we calculate the balance functions for charged hadrons. By limiting the speed of signal propagation, we observe the expected narrowing of the balance functions after removing the self-correlations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Stochastic fluctuations in relativistic fluids: Causality, stability, and the information current

We develop a general formalism for introducing stochastic fluctuations around thermodynamic equilibrium which takes into account, for the first time, recent developments in the causality and stability properties of relativistic hydrodynamic theories. The method is valid for any covariantly stable theory of relativistic viscous fluid dynamics derived from a covariant maximum entropy principle. We illustrate the formalism with some applications, showing how it could be used to consistently introduce fluctuations in a model of relativistic heat diffusion and in conformally invariant Israel-Stewart theory in a general hydrodynamic frame. Furthermore, the latter example is used to study the hydrodynamic frame dependence of the symmetric two-point function of fluctuations of the energy-momentum tensor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Electronic signatures of Lorentzian dynamics and charge fluctuations in lithiated graphite structures

Lithium graphite intercalation compounds (Li-GICs) are essential materials for modern day portable electronics and obtaining insights into their atomic structure and thermodynamics is of fundamental interest. Here we explore the electronic and atomic states of Li-GICs at varying degrees of Lithium loading (i.e., “staging”) by means of ab-initio molecular dynamics simulations and simulated X-ray adsorption spectroscopy (XAS). We analyze the atomic correlation functions and shows that the enhancements of the Li-ion entropy with increased staging result from Lorentzian lithium-ion dynamics and charge fluctuations, which activate low-energy phonon modes. The associated electronic signatures are modulations of the unoccupied π*/σ* orbital energy levels and unambiguous fingerprints in Carbon K-edge XAS spectra. Thus, we extend the canonical view of XAS, establishing that these “static” measurements in fact encode the signature of the thermodynamic response and relaxation dynamics of the system. This causal link between atomic structure, spectroscopy, thermodynamics, and information theory can be generally exploited to better understand stability in solid-state electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

adverse childhood experiences↗
DOE OSTI2025

Assessing the cumulative effects of nearshore habitat restoration actions for multiple populations of juvenile salmon in Whidbey Basin, Washington: foundation and approach for synthesis and evaluation

Ecosystem restoration is a common tool for re-establishing ecosystem processes, structures, and functions to improve biodiversity and services in coastal and estuarine ecosystems. In the Salish Sea, salmon habitats have been fragmented, reduced in size, and diminished in quality, and the ecosystem processes that form and sustain these habitats have been degraded and disrupted as well. This loss is especially prevalent in estuaries, where up to 90% of former salmon habitat has been lost or compromised. Salmon species are integral to the identities and cultures of people in the Pacific Northwest, yet salmon abundances remain at historic lows, especially in urbanized areas. Recent investments in restoration are creating rearing habitat and repairing lost ecosystem function. However, restoration efforts in this region have largely proceeded at the site scale, with less attention to big-picture thinking regarding how restoration will effectively recover degraded or lost habitats for target species. As a result, no landscape-scale evaluation program exists, and the cumulative benefits of multiple interventions are unknown. We describe innovative methods for science synthesis related to the evaluation of cumulative effects of ecosystem restoration for Pacific salmon, using years of existing, but disparate data. Building from previous work on cumulative effects evaluation and incorporating a hierarchy of hypotheses approach, we propose using causal inference across numerous hypotheses in a framework to assess the cumulative benefits to Pacific salmon from multiple estuarine restoration projects. We present the framework as a method that can be used to address many complex questions and provide examples from the Salish Sea where the approach is being implemented. The framework draws on science synthesis from numerous fields and uses a hierarchy of hypotheses, causal analysis at multiple scales, and a new hierarchy of synthesis for assessing multiple lines of evidence documenting restoration effects on Pacific salmon. We propose causal inference to synthesize dissimilar data streams, in our case, to identify various manifestations of cumulative effects of restoration and benefits to salmon, and to further inform restoration and recovery planning. A unifying framework would allow for the detection of thresholds at which restoration provides measurable improvement and would greatly advance understanding of the effects of restoration on ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Comparative genome analyses suggest a hemibiotrophic lifestyle and virulence differences for the beech bark disease fungal pathogens Neonectria faginata and Neonectria coccinea

Abstract Neonectria faginata and Neonectria coccinea are the causal agents of the insect-fungus disease complex known as beech bark disease (BBD), known to cause mortality in beech forest stands in North America and Europe. These fungal species have been the focus of extensive ecological and disease management studies, yet less progress has been made toward generating genomic resources for both micro- and macro-evolutionary studies. Here, we report a 42.1 and 42.7 mb highly contiguous genome assemblies of N. faginata and N. coccinea, respectively, obtained using Illumina technology. These species share similar gene number counts (12,941 and 12,991) and percentages of predicted genes with assigned functional categories (64 and 65%). Approximately 32% of the predicted proteomes of both species are homologous to proteins involved in pathogenicity, yet N. coccinea shows a higher number of predicted mitogen-activated protein kinase genes, virulence determinants possibly contributing to differences in disease severity between N. faginata and N. coccinea. A wide range of genes encoding for carbohydrate-active enzymes capable of degradation of complex plant polysaccharides and a small number of predicted secretory effector proteins, secondary metabolite biosynthesis clusters and cytochrome oxidase P450 genes were also found. This arsenal of enzymes and effectors correlates with, and reflects, the hemibiotrophic lifestyle of these two fungal pathogens. Phylogenomic analysis and timetree estimations indicated that the N. faginata and N. coccinea species divergence may have occurred at ∼4.1 million years ago. Differences were also observed in the annotated mitochondrial genomes as they were found to be 81.7 kb (N. faginata) and 43.2 kb (N. coccinea) in size. The mitochondrial DNA expansion observed in N. faginata is attributed to the invasion of introns into diverse intra- and intergenic locations. These first draft genomes of N. faginata and N. coccinea serve as valuable tools to increase our understanding of basic genetics, evolutionary mechanisms and molecular physiology of these two nectriaceous plant pathogenic species.

Salgado-Salazar, Catalina↗
DOE OSTI2023

Precision cosmology with primordial GW backgrounds in presence of astrophysical foregrounds

The era of Gravitational-Wave (GW) astronomy will grant the detection of the astrophysical GW background from unresolved mergers of binary black holes, and the prospect of probing the presence of primordial GW backgrounds. In particular, the low-frequency tail of the GW spectrum for causally-generated primordial signals (like a phase transition) offers an excellent opportunity to measure unambiguously cosmological parameters as the equation of state of the universe, or free-streaming particles at epochs well before recombination. We discuss whether this programme is jeopardised by the uncertainties on the astrophysical GW foregrounds that coexist with a primordial background. We detail the motivated assumptions under which the astrophysical foregrounds can be assumed to be known in shape, and only uncertain in their normalisation. In this case, the sensitivity to a primordial signal can be computed by a simple and numerically agile procedure, where the optimal filter function subtracts the components of the astrophysical foreground that are close in spectral shape to the signal. We show that the degradation of the sensitivity to the signal in presence of astrophysical foregrounds is limited to a factor of a few, and only around the frequencies where the signal is closer to the foregrounds. Our results highlight the importance of modelling the contributions of eccentric or intermediate-mass black hole binaries to the GW background, to consolidate the prospects to perform precision cosmology with primordial GW backgrounds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Evolution of Magnetohydrodynamic Turbulence in the Expanding Solar Wind: Residual Energy and Intermittency

We conduct 3D magnetohydrodynamic simulations of decaying turbulence in the context of the solar wind. To account for the spherical expansion of the solar wind, we implement the expanding box model. The initial turbulence comprises uncorrelated counterpropagating Alfvén waves and exhibits an isotropic power spectrum. Our findings reveal the consistent generation of negative residual energy whenever nonlinear interactions are present, independent of the normalized cross helicity σc and compressibility. The spherical expansion facilitates this process. The resulting residual energy is primarily distributed in the perpendicular direction, with S 2 (b) − S 2 (u) ∝ l ⊥ or equivalently –E r ∝ k$^{–2}_{⟂}$. Here S 2 (b) and S 2 (u) are second-order structure functions of magnetic field and velocity respectively. In most runs, S 2 (b) develops a scaling relation S 2 (b) ∝ $l$$^{1/2}_{⊥}$ (E b ∝ k$^{–3/2}_{⟂}$). In contrast, S 2 (u) is consistently shallower than S 2 (b), which aligns with in situ observations of the solar wind. We observe that the higher-order statistics of the turbulence, which act as a proxy for intermittency, depend on the initial σc and are strongly affected by the expansion effect. Generally, the intermittency is more pronounced when the expansion effect is present. Finally, we find that in our simulations, although the negative residual energy and intermittency grow simultaneously as the turbulence evolves, the causal relation between them seems to be weak, possibly because they are generated on different scales.

Interplanetary turbulence↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
DOE OSTI2021

Simulations of axion minihalos

The axion, motivated as a solution to the strong CP problem, is also a viable dark matter candidate. The axion field takes random values in causally disconnected regions if the symmetry breaking that establishes the particle occurs after inflation, leading to white-noise density fluctuations at low wave numbers and forming dense minihalos with sub-planetary masses subsequently. There have been two recent proposals that appear capable of testing this scenario, namely using pulsar timing arrays and studying cosmological microlensing caustics. Motivated by these proposals, we use N-body simulations to study the formation of substructures from white-noise density fluctuations. The density profiles of our relaxed axion minihalos can be described by the Navarro–Frenk–White profile, and the minihalos’ concentration number agrees well with a simple, physically-motivated model. We develop a semi-analytic formula to fit the mass function from our simulation, which agrees broadly at different redshifts and only differs at factor of two level from classic halo mass functions. This analytic mass function allows us to consider uncertainties in the post-inflation axion scenario, as well as extrapolate our high-redshift simulations results to the present. Our work estimates the present-day abundance of axion substructures, as is necessary for predicting their effect on cosmological microlensing caustics and pulsar timing. Our calculations suggest that if pulsar timing and microlensing probes can reach recent sensitivity forecasts, they may be sensitive to the post-inflation axion dark matter scenario, even when accounting for uncertainties pertaining to axion strings. For pulsar timing, the most significant caveat is whether axion minihalos are disrupted by stars, which our estimates show is mildly important at the most relevant masses. In conclusion, as our gravitational simulations are scale invariant, the results can be extended to models where the dark matter is comprised of other axion-like particles and even clusters of primordial black holes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall β limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Experimental Study of TCP Throughput Profiles and Dynamics Over Dedicated Connections

A throughput profile expressed as a function of the round-trip times of a data transport infrastructure is a critical indicator of its level of optimization, particularly, over dedicated connections. We study the throughput profiles of eleven TCP versions using measurements collected over dedicated hardware-emulated connections with distances spanning round the earth. Among them, BBR has overall higher temporal variations and lower throughput profiles compared to five loss-based TCP versions, and the comparison with other TCP versions is mixed. Using the Poincare map regions of throughput time traces, we show that the richer dynamics of BBR are correlated with its lower throughput profiles. We present basic analytical results that indicate that higher temporal variations of TCP methods lead to lower throughput profiles, thereby establishing the underlying causality. Under external losses, BBR achieves sustained high throughput compared to others as the loss rate is increased. Overall, our results provide insights into the relationship between time dynamics and throughput profiles of TCP versions, in particular, show that limited time traces can be indicative of global properties of throughput profiles.

Rao, Nageswara↗
DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗
DOE OSTI2024

The safety of magnetic resonance imaging contrast agents

Gadolinium-based contrast agents are increasingly used in clinical practice. While these pharmaceuticals are verified causal agents in nephrogenic systemic fibrosis, there is a growing body of literature supporting their role as causal agents in symptoms associated with gadolinium exposure after intravenous use and encephalopathy following intrathecal administration. Gadolinium-based contrast agents are multidentate organic ligands that strongly bind the metal ion to reduce the toxicity of the metal. The notion that cationic gadolinium dissociates from these chelates and causes the disease is prevalent among patients and providers. We hypothesize that non-ligand-bound (soluble) gadolinium will be exceedingly low in patients. Soluble, ionic gadolinium is not likely to be the initial step in mediating any disease. The Kidney Institute of New Mexico was the first to identify gadolinium-rich nanoparticles in skin and kidney tissues from magnetic resonance imaging contrast agents in rodents. In 2023, they found similar nanoparticles in the kidney cells of humans with normal renal function, likely from contrast agents. We suspect these nanoparticles are the mediators of chronic toxicity from magnetic resonance imaging contrast agents. This article explores associations between gadolinium contrast and adverse health outcomes supported by clinical reports and rodent models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Gravity of two photon decay and its quantum coherence

A linear analytical solution is derived for the gravitational shock wave produced by a particle of mass M that decays into a pair of null particles. The resulting space-time is shown to be unperturbed and isotropic, except for a discontinuous perturbation on a spherical null shell. Formulae are derived for the perturbation as a function of polar angle, as measured by an observer at the origin observing clocks on a sphere at distance R. The effect of the shock is interpreted physically as an instantaneous displacement in time and velocity when the shock passes the clocks. The time displacement is shown to be anisotropic, dominated by a quadrupole harmonic aligned with the particle-decay axis, with a magnitude δτ ~ GM/c 3 , independent of R. The velocity displacement is isotropic. The solution is used to derive the gravitational effect of a quantum state with a superposition of a large number of randomly oriented, statistically isotropic particle decays. This approach is shown to provide a well-controlled approximation to estimate the magnitude of gravitational fluctuations in systems composed of null point particles up to the Planck energy in a causal diamond of duration τ = 2R/c, as well as quantum-gravitational fluctuations of black holes and cosmological horizons. Coherent large-angle quantum distortions of macroscopic geometry from fluctuations up to the Planck scale are shown to grow linearly with the duration, with a variance $\langle$δτ 2 $\rangle$ ~ τtP much larger than that produced in models without causal quantum coherence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Non-destructive quantification of anaerobic gut fungi and methanogens in co-culture reveals increased fungal growth rate and changes in metabolic flux relative to mono-culture

Abstract Background Quantification of individual species in microbial co-cultures and consortia is critical to understanding and designing communities with prescribed functions. However, it is difficult to physically separate species or measure species-specific attributes in most multi-species systems. Anaerobic gut fungi (AGF) (Neocallimastigomycetes) are native to the rumen of large herbivores, where they exist as minority members among a wealth of prokaryotes. AGF have significant biotechnological potential owing to their diverse repertoire of potent lignocellulose-degrading carbohydrate-active enzymes (CAZymes), which indirectly bolsters activity of other rumen microbes through metabolic exchange. While decades of literature suggest that polysaccharide degradation and AGF growth are accelerated in co-culture with prokaryotes, particularly methanogens, methods have not been available to measure concentrations of individual species in co-culture. New methods to disentangle the contributions of AGF and rumen prokaryotes are sorely needed to calculate AGF growth rates and metabolic fluxes to prove this hypothesis and understand its causality for predictable co-culture design. Results We present a simple, microplate-based method to measure AGF and methanogen concentrations in co-culture based on fluorescence and absorbance spectroscopies. Using samples of < 2% of the co-culture volume, we demonstrate significant increases in AGF growth rate and xylan and glucose degradation rates in co-culture with methanogens relative to mono-culture. Further, we calculate significant differences in AGF metabolic fluxes in co-culture relative to mono-culture, namely increased flux through the energy-generating hydrogenosome organelle. While calculated fluxes highlight uncertainties in AGF primary metabolism that preclude definitive explanations for this shift, our method will enable steady-state fluxomic experiments to probe AGF metabolism in greater detail. Conclusions The method we present to measure AGF and methanogen concentrations enables direct growth measurements and calculation of metabolic fluxes in co-culture. These metrics are critical to develop a quantitative understanding of interwoven rumen metabolism, as well as the impact of co-culture on polysaccharide degradation and metabolite production. The framework presented here can inspire new methods to probe systems beyond AGF and methanogens. Simple modifications to the method will likely extend its utility to co-cultures with more than two organisms or those grown on solid substrates to facilitate the design and deployment of microbial communities for bioproduction and beyond.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article “Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development” [1]. Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 “envelopes” and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.

13 HYDRO ENERGY↗