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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integration of Omics into a New Comprehensive Rate Law for Competitive Terminal Electron-Accepting Processes in Reactive Transport Models: Application to N, Fe, S, and Contaminant Transformations in Stream and Wetland Sediments

Surface waters represent important sources of alternate energy and drinking water in the United States, and characterizing the biogeochemical processes that affect surface water quality is relevant to the DOE-BER mission. Sediment biogeochemical processes regulate the release of carbon (C), nutrients, and contaminants to surface waters and thus influence water quality. Sediment biogeochemical processes are dynamic and affected by the deposition and remobilization of solid material and changes in environmental conditions driven by water discharge variations. Wetlands are important natural filters of surface waters which may either trap, metabolize, or mobilize nutrients and contaminants. Despite their importance, biogeochemical processes regulating nutrient and contaminant release and C transformation in stream and wetland sediments cannot be predicted accurately by current mathematical models. These reactive transport models largely rely on detectable changes in geochemical conditions to activate metabolic processes, do not accurately account for the competition between microbial processes, and poorly constrain effects of hydrological perturbations on biogeochemical processes. In this BER-SBR exploratory project, metagenomic and geochemical signatures were combined to identify microbially-mediated redox processes in anaerobic stream and wetland sediments from the Savannah River Site (SRS, ANL SFA) and East Fork Poplar Creek (EFPC, ORNL SFA) that play important roles in C, uranium (U), and mercury (Hg) transformations. In addition, sediment incubations were conducted to examine the competition between anaerobic respiration processes Finally, new rate laws were developed for reactive transport models that rely on complementary metagenomic and geochemical signatures to identify the underlying anaerobic microbial processes in stream and wetland sediments, describe the competition between the dominant metabolic processes involved in nutrient release and U and Hg mobilization, and more accurately quantify carbon transformation and the response of microbial processes to changes in redox conditions associated with hydrological forcing. These rate laws were optimized in batch reactors with SRS wetland sediments, where iron and sulfate reduction dominate. Anaerobic carbon remineralization processes followed the expected thermodynamic sequence of microbial respiration with depth in both sediments, except that geochemical signals indicated that sulfate reduction was inactive in EFPC sediments and moderate in SRS wetland sediments. Estimates indicated that microbial iron reduction contributed to at least half of the production of reduced iron in these sediments. Incubations demonstrated that nitrate reduction, denitrification, and dissimilatory nitrate reduction to ammonium were active in the natural EFPC sediment and activated upon nitrate amendment in these nitrate-rich sediments. In turn, these processes were outcompeted by the addition of either iron oxides or sulfate as alternative terminal electron acceptors. Although geochemical products of sulfate reduction were not detected in the incubations, the abundance of sulfate reduction genes increased with depth in the sediment and was equally more pronounced in treatments amended with either iron oxides or sulfate. Simultaneously, anaerobic sulfide oxidizing bacteria coupling sulfide oxidation to DNRA (and not conventional denitrification) were apparently enriched over time, regardless of the treatments. These findings indicate that sulfate reduction is important in freshwater stream sediments and probably catalyzed by a cryptic sulfur cycle involving nitrogen species, in which the sulfur products from sulfate reduction are immediately removed by side reactions and not detectable by geochemical measurements alone. Similar experiments in SRS sediments, however, demonstrated little interaction between nitrogen and sulfur cycling microorganisms. Sulfate reduction was impacted by the addition of more thermodynamically favorable electron acceptors, suggesting either that iron-reducing microorganisms outcompeted sulfate-reducing microorganisms for organic substrate, depleting the stock of electron donor available for sulfate reduction, or that the cryptic sulfur cycle was shunted by the precipitation of FeS generated as a result of the abiotic reduction of iron oxides by dissolved sulfide. A diagnostic modeling exercise was conducted to further investigate the competition between terminal electron accepting processes. As conventional kinetic models typically do not account for cryptic cycles and used inaccurate formulations to describe competition between microbial communities, new metabolic rate laws were developed that explicitly express the electron acceptor-specific enzyme of each energetically favorable metabolic process based on gene abundance detected in the incubations. The model was tested with the sediment slurry incubation data to determine whether substrate competition could explain the decrease in sulfate reduction observed in the presence of iron oxide competitor. The model was able to reproduce geochemical concentrations really well in each treatment once the model was calibrated with the unamended control, suggesting that microbial competition was indeed driven by thermodynamic considerations. Overall, carbon remineralization processes and rates will be reproduced much more realistically with the new metabolic rate laws.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting Plant-Soil Feedback in the Field: Meta-Analysis Reveals That Competition and Environmental Stress Differentially Influence PSF

Past research on plant-soil feedbacks (PSF), largely undertaken in highly controlled greenhouse conditions, has established that plant species differentially alter abiotic and biotic soil conditions that in turn affect growth of other conspecific and heterospecific individuals in that soil. Yet, whether feedbacks under controlled greenhouse conditions reflect feedbacks in natural environments where plants are exposed to a range of abiotic and biotic pressures is still unresolved. To address how environmental context affects PSF, we conducted a meta-analysis of previously published studies that examined plant growth responses to multiple forms of competition, stress, and disturbance across various PSF methodology. We asked the following questions: (1) Can competition, stress, and disturbance alter the direction and/or strength of PSF? (2) Do particular types of competition, stress, or disturbance affect the direction and/or strength of PSF more than others? and (3) Do methods of conducting PSF research (i.e., greenhouse vs. field experiments and whether the source of soil inoculum conditioning is from the field vs. greenhouse) affect plant growth responses to PSF or competition, stress, and disturbance, or their interactions? We discovered four patterns that may be predictive of what future PSF studies conducted under more realistic conditions might reveal. First, relatively little is known about how PSF responds to environmental stress and disturbance compared to plant-plant competition. Second, specific types of competition enhanced negative effects of soil microbes on plant growth, and specific environmental stressors enhanced positive effects of soil microbes on plant growth. Third, whether PSF experiments are conducted in the field or greenhouse can change plant growth responses. And, fourth, how the soil conditioning phase is conducted can change plant growth responses. With more detail than previously shown, these results confirm that environmental context writ large can change plant growth responses in PSF experiments. These data should aid theory and predictions for conservation and restoration applications by showing the relative importance of competition, stress, and disturbance in PSF studies over time. Lastly, these data demonstrate how variation in experimental methods can alter interpretation and conclusions of PSF studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Geothermal Collegiate Competition: Evolution and Impact

In 2010, the U.S. Department of Energy (DOE) announced the launch of its inaugural National Geothermal Student Competition. The competition was open to all colleges, universities, and other post-secondary institutions in the United States, and was labeled as the first-ever to address geothermal education. The GeoVision, published in 2019 by the Geothermal Technologies Office (GTO), stated that improving geothermal energy education and outreach is critical in reducing risks and costs of geothermal technology deployment. The key actions included improving public education and outreach about geothermal energy and providing resources intended to attract and inform a skilled geothermal workforce. Student competitions in particular have the ability to increase awareness of renewable energy fields by engaging multi-disciplinary students in compelling design challenges to prepare them for careers in renewable energy. GTO, in partnership with an administration team at the National Renewable Energy Laboratory (NREL), currently supports the Geothermal Collegiate Competition (GCC), with the main objective of advancing and cultivating collegiate student knowledge and career interest in geothermal energy. The administrating team works toward growing the number of students and collegiate institutions engaged, as well as diversifying the scope of the student design challenges. The main intentions of this publication are documenting the competition’s evolution, setting a baseline for future GCC impact analysis and serving as a guide to other students’ competition design committees looking for ideas to capture and increase the interest of students in collegiate competitions.

Geothermal Collegiate Competition↗
DOE OSTI2020

The ASHRAE Great Energy Predictor III competition: Overview and results

In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. Furthermore, the competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Competitiveness Metrics for Electricity System Technologies

The relative economic competitiveness of power generation technologies is a topic of much interest to diverse electric industry participants. However, assessing competitiveness can be challenging as it requires considering both total costs and total system value of each technology, which are complicated by the (1) numerous and diverse grid services needed to operate a reliable power system; (2) variations in the economic value of the grid services with system state and location, and over multiple timescales, due to the challenges of transporting and storing electricity; and (3) the unique characteristics of different electric system assets. Ideally, metrics designed or used to convey technology competitiveness must consider these complexities, but existing metrics often fall short. For example, the levelized cost of energy does not consider the system economic value of the various technologies nor does it consider services beyond electricity production. Various other metrics have been designed with the purpose of more-accurately communicating the economic viability of electric system technologies. In this report, we summarize the primary sources and components of costs and value and review the known competitiveness metrics by presenting their definitions, applications, advantages, and disadvantages. We also introduce a new set of competitiveness metrics, which we refer to as System Profitability metrics, that more-directly applies the economic principles of return-on-investment to electric system technologies. We use conceptual examples to show how the System Profitability metrics better reflect economic viability and relative technology competitiveness compared with existing metrics. We also describe how competitiveness metrics can be quantified using optimization-based models and demonstrate this capability using a U.S. electric sector capacity expansion model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Liu, Meng↗
DOE OSTI2022

Quantifying Value and Representing Competitiveness of Electricity System Technologies in Economic Models

Evaluating competition between electricity technologies is challenging because it depends on both their costs and their values. While technology costs can typically be estimated from projections of the cost components - capital, fuel, and O&M - estimating a technology's value is more complex due to its dependence on its contributions to multiple different grid services, each with prices that can vary substantially over space and time. In this work, using an electricity model of the contiguous United States, we develop relationships between relative value and share of total generation for major electricity generation technologies which, when paired with projections of technology costs, can be used to estimate technology competitiveness. We identify significant differences in the relationship between relative value and generation share for variable renewable energy (VRE) and non-VRE sources, but we demonstrate that all technologies require consideration of their dynamic values (in addition to cost) when evaluating competitiveness. In addition, we demonstrate that relative value of a technology is substantially impacted by not only its own generation share but also other aspects of the system state, in particular the mix of other technologies present in the system. Finally, we use the developed relative value relationships in combination with projections of future technology costs in a coarse resolution model that competes technologies based on a comprehensive competitiveness metric: profitability-adjusted LCOE (PLCOE). We show that this simple representation of technology competition approximately recovers the generation mix from a detailed model, which is not possible using LCOE alone. Such an approach can be used to improve the representation of technology competition in coarse-resolution models such as integrated assessment models, for which simplified metrics are often needed.

electricity model↗
DOE OSTI2020

Rice Business Plan Competition (Final Report)

During the grant period, Rice University held a total of three regional Clean Energy Business Plan Competitions for graduate level students with a particular focus on attracting student startups in the clean energy space (clean tech, renewable energy, transportation, batteries, sustainability, etc.) Over the three years of the grant, the Rice Business Plan Competition awarded a total of three ${$}$50,000 DOE-sponsored prizes to the winning startups (${$}$150,000 total). The Rice Energy Business Plan Competition’s DOE Clean Tech competition leveraged the resources and infrastructure of the existing annual Rice Business Plan Competition (RBPC), which is the world’s largest and richest intercollegiate graduate-level business plan competition in the world

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Radiation Detection Data Competition Report

In FY2018 through FY2020, NA-22, the Defense Nuclear Nonproliferation Research and Development Program, funded a Data Science project to develop and implement statistical methodology to effectively host data competitions with the goal of leveraging the opportunity provided by crowdsourcing. By accessing and engaging expertise from a broader research community, there is an opportunity to attract innovative solutions from a variety of different research disciplines to advance the ability to solve important non-proliferation problems. This report summarizes the key results of this project after hosting two data competitions focused on urban radiation detection. The first competition was focused on attracting participants from the U.S. national laboratories, while the second, hosted by TopCoder, was open to the broader international community and awarded prize money to the top 10 competitors. At the start of the project, there was strong interest from NA-22 to explore and develop the capability to host data competitions as a means of leveraging the broader community to solve important nuclear nonproliferation problems. Having a standard data set on which to compare different approaches based on clearly defined criteria was desirable to be able to evaluate the state of solutions for important problems. Initially, it was not clear that it would even be possible logistically and bureaucratically to host a competition with an international field of competitors and to award the prize money needed to attract solutions from top competitors. Happily, a path to host the competitions was ultimately found that allowed this powerful accelerator of improvements to be leveraged.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

In Search of Strategic Advantage: Understanding the Landscape of Technology Competition

In an era of seemingly ever-increasing global tensions, technology competition is often mentioned as a pathway for U.S. and allied success. The opening arguments are often very simple. “This is the most important struggle of the 21st century.” “We cannot afford to lose this competition.” “The United States must not fall behind in this race.” “We must be faster, more agile, more committed, more thoughtful than our competitors.” Competition around a particular technology is described as a once in a generational struggle with immense stakes. “This is a Sputnik moment” is a common analogy. The solutions proposed are fairly straightforward – more of everything. We should spend more money. We should build more widgets or more factories. We should innovate more. We should focus more. We should attract more talent. We should file more patents. We should produce more PhDs. If we do more of everything, we will have more technology than our opponents, they will see our technological advantage, they will not challenge us, and therefore we will win. If we fail to do these things, we will lose. To paraphrase Homer Simpson, in national security policy discussions technology competition has become the cause of, and solution to, all of life’s problems. And yet, beyond doing more across the board technology competition is not at all simple. First, it is a concept increasingly muddled together with other big issues such as innovation policy, national defense strategy, great power competition, allied cooperation, public-private partnerships, and a host of other issues. Certain policies, systems, coalitions, and so on may be excellent for tackling one challenge, but far less optional for others. Second, it is inherently dynamic, an action-reaction cycle between multiple players. A brilliant opening move can be squandered or successfully countered in subsequent moves. Third, there are limits to what you can do - limited time, limited financial resources, limited human capital, and limited knowledge of what lies ahead. One is forced to choose.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Grid Optimization Competition Challenge 3 Problem Formulation

This report contains the problem formulation for the Grid Optimization (GO) Competition Challenge 3. The Grid Optimization Competition is run by a team of researchers from a number of organizations, including the sponsor Advanced Research Projects Agency - Energy (ARPA-E), lead organization Pacific Northwest National Laboratory (PNNL), and technical contributors from Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), Georgia Institute of Technology (GT), University of Wisconsin (UW), and others. The GO Competition poses challenge problems in the field of power grid management, invites entrants to develop solvers for these problems, invokes the solvers on a set of problem instances using common hardware, ranks the solvers according to their performance, and awards prizes according to the rankings. The overall goal of the GO Competition is to spur innovative research on high impact and computationally challenging problems in power grid management from initial development through commercial deployment. Complete information about the GO Competition can be found online at [2]. The webpage covers previous Challenges, rules, timeline, registration information, data formats, scoring methods, computational platform information, information on supported solvers and languages, sponsor information, frequently asked questions, administrator contact information, publicly available problem instances, computer code for reading and evaluating problem and solution data, a sandbox for testing solvers, and a solver submission interface, results, and publications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 3

Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public. The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023). For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below.

ACOPF↗
DOE OSTI2023

Interspecific oral rabies vaccine bait competition in the Southeast United States

Here, the United States Department of Agriculture’s National Rabies Management Program (NRMP) has coordinated the use of oral rabies vaccination (ORV) to control the spread of raccoon rabies virus variant west of the Appalachian Mountains since 1997. Working with state and local partners, the NRMP deploys ORV baits containing a rabies vaccine, primarily targeting raccoon populations (Procyon lotor). Bait competition between raccoons and non-target species may limit the effectiveness of ORV programs, but the extent of bait competition remains poorly quantified, particularly in the southeastern United States. We placed placebo ORV baits in bottomland hardwood (n = 637 baits) and upland pine (n = 681 baits) habitats in South Carolina, USA during August-December 2019 and used remote cameras to examine bait competition between raccoons and non-target species. The estimated proportion of bait consumed by raccoons was 18.8 ± 2.1% in bottomland hardwood and 11.6 ± 2.1% in upland pine habitats. Vertebrate competition appeared to have a minimal effect on raccoon uptake as estimated consumption did not exceed 5% for any species or 8% of bait uptake events cumulatively. We estimated that raccoons were the primary consumer of baits in bottomland hardwood, whereas invertebrates were the primary consumer in upland pine (26.7 ± 1.3% of baits). Our results indicate a need to closely consider the effects of invertebrates on bait consumption to minimize their potential impact on ORV bait uptake by target species. Uptake probabilities by raccoons were relatively low but not primarily driven by competition with vertebrates. As such, strategies to increase the specificity of raccoon uptake may be needed to enhance the effectiveness of ORV baiting programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Leaf Trait Plasticity Alters Competitive Ability and Functioning of Simulated Tropical Trees in Response to Elevated Carbon Dioxide

The response of tropical ecosystems to elevated carbon dioxide (CO 2 ) remains a critical uncertainty in projections of future climate. Here, we investigate how leaf trait plasticity in response to elevated CO 2 alters projections of tropical forest competitive dynamics and functioning. We use vegetation demographic model simulations to quantify how plasticity in leaf mass per area and leaf carbon to nitrogen ratio alter the responses of carbon uptake, evapotranspiration, and competitive ability to a doubling of CO 2 in a tropical forest. Observationally constrained leaf trait plasticity in response to CO 2 fertilization reduces the degree to which tropical tree carbon uptake is affected by a doubling of CO 2 (up to -14.7% as compared to a case with no plasticity; 95% confidence interval [CI95%] -14.4 to -15.0). It also diminishes evapotranspiration (up to -7.0%, CI95% -6.4 to -7.7), and lowers competitive ability in comparison to a tree with no plasticity. Consideration of leaf trait plasticity to elevated CO 2 lowers tropical ecosystem carbon uptake and evapotranspirative cooling in the absence of changes in plant-type abundance. However, “plastic” responses to high CO 2 which maintain higher levels of plant productivity, many of which fall outside of the observed range of response, are potentially more competitively advantageous, thus, including changes in plant type abundance may mitigate these decreases in ecosystem functioning. Models that explicitly represent competition between plants with alternative leaf trait plasticity in response to elevated CO 2 are needed to capture these influences on tropical forest functioning and large-scale climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Nonlinear bubble competition of the multimode ablative Rayleigh–Taylor instability and applications to inertial confinement fusion

The self-similar nonlinear evolution of the multimode ablative Rayleigh–Taylor instability (RTI) and the ablation-generated vorticity effect are studied for a range of initial conditions. We show that, unlike classical RTI, the nonlinear multimode bubble-front evolution remains in the bubble competition regime due to ablation-generated vorticity, which accelerates the bubbles, thereby preventing a transition into the bubble-merger regime. We develop an analytical bubble competition model to describe the linear and nonlinear stages of ablative RTI. Here, we show that vorticity inside the multimode bubbles is most significant at small scales with large initial perturbation. Since these small scales persist in the bubble competition regime, the self-similar growth coefficient ab can be enhanced by up to 30% relative to ablative bubble competition without vorticity effects. We use the ablative bubble competition model to explain the hydrodynamic stability boundary observed in OMEGA low-adiabat implosion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Competition response of cloud supersaturation explains diminished Twomey effect for smoky aerosol in the tropical Atlantic

The Twomey effect brightens clouds by increasing aerosol concentrations, which activates more droplets and decreases cloud supersaturation in response to more competition for water vapor. To quantify this competition response, we used marine low cloud observations in clean and smoky conditions at Ascension Island in the tropical South Atlantic during the Layered Aerosol Smoke Interactions with Cloud (LASIC) campaign. These observations show similar increases in droplet number for increased accumulation-mode particles from surface-based and satellite cloud retrievals, demonstrating the importance of below-cloud aerosol measurements for retrieving aerosol–cloud interactions (ACI) in clean and smoky aerosol conditions. Four methods for estimating cloud supersaturation from aerosol–cloud measurements were compared, with cloud scene-based and parcel-based methods showing sufficient variability for a strong dependence on both aerosol accumulation number concentration and cloud-base updraft velocities. Decomposing aerosol-related changes in cloud albedo and optical depth shows the calculated competition response accounts for dampening the activation response by 12 to 35%, explaining the diminished Twomey effect at high aerosol concentrations observed for smoky conditions at LASIC and previously around the world. This result was consistent for independent supersaturation retrievals by cloud scene-based droplet number and cloud condensation nuclei and parcel-based multimode size-resolving Lagrangian methods. Translating aerosol effects to local radiative forcing with clean conditions as a proxy for preindustrial and smoky conditions for present-day showed that the competition response reduces cooling from the Twomey radiative forcing by 12 to 35%, providing an essential process-specific constraint for improving the representation of aerosol competition in climate model simulation of indirect aerosol forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Temporal Gating of Synaptic Competition in the Amygdala by Cannabinoid Receptor Activation

Abstract The acquisition of fear memories involves plasticity of the thalamic and cortical pathways to the lateral amygdala (LA). In turn, the maintenance of synaptic plasticity requires the interplay between input-specific synaptic tags and the allocation of plasticity-related proteins. Based on this interplay, weakly activated synapses can express long-lasting forms of synaptic plasticity by cooperating with strongly activated synapses. Increasing the number of activated synapses can shift cooperation to competition. Synaptic cooperation and competition can determine whether two events, separated in time, are associated or whether a particular event is selected for storage. The rules that determine whether synapses cooperate or compete are unknown. We found that synaptic cooperation and competition, in the LA, are determined by the temporal sequence of cortical and thalamic stimulation and that the strength of the synaptic tag is modulated by the endocannabinoid signaling. This modulation is particularly effective in thalamic synapses, supporting a critical role of endocannabinoids in restricting thalamic plasticity. Also, we found that the availability of synaptic proteins is activity-dependent, shifting competition to cooperation. Our data present the first evidence that presynaptic modulation of synaptic activation, by the cannabinoid signaling, functions as a temporal gating mechanism limiting synaptic cooperation and competition.

Madeira, Natália↗
DOE OSTI2022

Spatial scales of competition and a growth–motility trade-off interact to determine bacterial coexistence

The coexistence of competing species is a long-lasting puzzle in evolutionary ecology research. Despite abundant experimental evidence showing that the opportunity for coexistence decreases as niche overlap increases between species, bacterial species and strains competing for the same resources are commonly found across diverse spatially heterogeneous habitats. We thus hypothesized that the spatial scale of competition may play a key role in determining bacterial coexistence, and interact with other mechanisms that promote coexistence, including a growth–motility trade-off. To test this hypothesis, we let two Pseudomonas putida strains compete at local and regional scales by inoculating them either in a mixed droplet or in separate droplets in the same Petri dish, respectively. We also created conditions that allow the bacterial strains to disperse across abiotic or fungal hyphae networks. We found that competition at the local scale led to competitive exclusion while regional competition promoted coexistence. When competing in the presence of dispersal networks, the growth–motility trade-off promoted coexistence only when the strains were inoculated in separate droplets. Our results provide a mechanism by which existing laboratory data suggesting competitive exclusion at a local scale is reconciled with the widespread coexistence of competing bacterial strains in complex natural environments with dispersal.

59 BASIC BIOLOGICAL SCIENCES↗