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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Comparative genomics provides insights into the cold adaptation of endophytic fungi associated with Deschampsia antarctica

Endophytic fungi from Deschampsia antarctica , the southernmost flowering plant, provide insights into the cold adaptation mechanisms of plant-associated fungi in extreme environments. This study presents the genome sequences and comparative analysis of eight fungal isolates from D. antarctica leaves. These Antarctic fungal isolates were analyzed alongside 121 plant-associated fungal genomes to uncover signatures of adaptation and endophytic specialization. Antarctic endophytes show striking patterns, including reduced genome size (∼26.3 Mb on average), streamlined gene content (∼8844 genes), and notably small secretomes (∼288 proteins). Despite this reduced gene repertoire, they maintain a robust set of genes encoding carbohydrate-active enzymes (CAZymes) but lack those for lignin and bacterial cell wall degradation, indicating a symbiotic lifestyle that avoids host damage and predation. One isolate, Alternaria sp. UNIPAMPA017 stood out, with 26% of its genome occupied by transposable elements. Lifestyle, rather than phylogeny, was the main driver of CAZyme and secretome profiles, underscoring ecological convergence. Compared to endophytes from Arabidopsis and Populus, D. antarctica endophytes harbor fewer pectin-degrading enzymes, reflecting their adaptation to the cell wall structure of their monocot host. Together, these fungi reveal a pattern of genomic reduction and functional fine-tuning, hallmarks of life adapted to persist in cold, nutrient-scarce niches.

Ascomycota↗
DOE OSTI2025

The Marchantia polymorpha pangenome reveals ancient mechanisms of plant adaptation to the environment

Plant adaptation to terrestrial life started 450 million years ago and has played a major role in the evolution of life on Earth. The genetic mechanisms allowing this adaptation to a diversity of terrestrial constraints have been mostly studied by focusing on flowering plants. Here, we gathered a collection of 133 accessions of the model bryophyte Marchantia polymorpha and studied its intraspecific diversity using selection signature analyses, a genome-environment association study and a pangenome. We identified adaptive features, such as peroxidases or nucleotide-binding and leucine-rich repeats (NLRs), also observed in flowering plants, likely inherited from the first land plants. The M. polymorpha pangenome also harbors lineage-specific accessory genes absent from seed plants. We conclude that different land plant lineages still share many elements from the genetic toolkit evolved by their most recent common ancestor to adapt to the terrestrial habitat, refined by lineage-specific polymorphisms and gene family evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights recover quadratic error minimization. This perspective reveals adaptive weighting as a means of selecting sampling distributions that optimize a primal objective, directly linking discretization choices to error metrics. This principled approach yields three key benefits: it enables systematic design of adaptive schemes, reduces discretization error by lowering estimator variance, and enhances learning dynamics by improving gradient signal-to-noise ratio. Extending the framework to operator learning, we demonstrate substantial performance gains across diverse optimizers and architectures. Our results provide a theoretical perspective for residual-based adaptivity and establish a foundation for principled discretization and training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Prioritizing urban heat adaptation infrastructure based on multiple outcomes: Comfort, health, and energy

Globally, cities face increasing extreme heat, impacting comfort, health, and energy consumption. Infrastructure-based heat adaptation strategies can improve these outcomes, but each strategy has a unique mix of benefits and drawbacks. Here, we apply an urbanized meteorological model (WRF) with the newly integrated multilayer BEP-Tree street tree model to dynamically downscale Earth System Model projections and a 3-D microclimate model (TUF-Pedestrian) to simulate the street-scale radiation environment impacting pedestrians. We evaluate the performance of five heat adaptation strategies (street trees, cool roofs, green roofs, rooftop photovoltaics (PV), and reflective pavements) during extreme heat events in three cities with contrasting background climates (Toronto, Phoenix, and Miami), under contemporary and end-of-century projected climates, based on three metrics: outdoor heat stress, air conditioning (AC) energy use, and ventilation of vehicular air pollution. No single adaptation strategy improves all three outcomes. While street trees inhibit ventilation, they reduce outdoor heat stress four times more effectively than the next best strategy via shade provision, fully offsetting heat stress increases under a high-emissions end-of-century climate scenario in all cities studied. Cool roofs and green roofs moderately reduce heat stress and energy use. Alternatively, rooftop PV with energy storage can generate sufficient power for space cooling but have marginal effects on heat stress. Reflective pavements are the least effective across metrics. Where the ventilation of street-level emissions is of less concern, our results clearly support the combination of street trees and rooftop PV as a highly complementary and effective means of adaptive mitigation across different climates and neighborhood densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Drosophila melanogaster pigmentation demonstrates adaptive phenotypic parallelism over multiple spatiotemporal scales

Abstract Populations are capable of responding to environmental change over ecological timescales via adaptive tracking. However, the translation from patterns of allele frequency change to rapid adaptation of complex traits remains unresolved. We used abdominal pigmentation in Drosophila melanogaster as a model phenotype to address the nature, genetic architecture, and repeatability of rapid adaptation in the field. We show that D. melanogaster pigmentation evolves as a highly parallel and deterministic response to shared environmental variation across latitude and season in natural North American populations. We then experimentally evolved replicate, genetically diverse fly populations in field mesocosms to remove any confounding effects of demography and/or cryptic structure that may drive patterns in wild populations; we show that pigmentation rapidly responds, in parallel, in fewer than 15 generations. Thus, pigmentation evolves concordantly in response to spatial and temporal climatic axes. We next examined whether phenotypic differentiation was associated with allele frequency change at loci with established links to genetic variance in pigmentation in natural populations. We found that across all spatial and temporal scales, phenotypic patterns were associated with variation at pigmentation-related loci, and the sets of genes we identified at each scale were largely nonoverlapping. Therefore, our findings suggest that parallel phenotypic evolution is associated with distinct components of the polygenic architecture shifting across each environmental axis to produce redundant adaptive patterns.

Evolutionary Biology↗
DOE OSTI2025

Footprints of Worldwide Adaptation in Structured Populations of Drosophila melanogaster Through the Expanded DEST 2.0 Genomic Resource

Abstract Large-scale genomic resources can place genetic variation into an ecologically informed context. To advance our understanding of the population genetics of the fruit fly Drosophila melanogaster, we present an expanded release of the community-generated population genomics resource Drosophila Evolution over Space and Time (DEST 2.0; https://dest.bio/). This release includes 530 high-quality pooled libraries from flies collected across six continents over more than a decade (2009 to 2021), most at multiple time points per year; 211 of these libraries are sequenced and shared here for the first time. We used this enhanced resource to elucidate several aspects of the species' demographic history and identify novel signs of adaptation across spatial and temporal dimensions. For example, we showed that the spatial genetic structure of populations is stable over time, but that drift due to seasonal contractions of population size causes populations to diverge over time. We identified signals of adaptation that vary between continents in genomic regions associated with xenobiotic resistance, consistent with independent adaptation to common pesticides. Moreover, by analyzing samples collected during spring and fall across Europe, we provide new evidence for seasonal adaptation related to loci associated with pathogen response. Furthermore, we have also released an updated version of the DEST genome browser. This is a useful tool for studying spatiotemporal patterns of genetic variation in this classic model system.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Genomic insights into local adaptation and migration success in reintroduced Coho Salmon of the Wenatchee River basin

ABSTRACT Objective Reintroduction of salmonids into regions where they have been extirpated is a common conservation strategy that is often implemented through natural recolonization, translocation of natural populations, or hatchery-based programs. Locally adapting to specific environmental conditions is critical for long-term population viability, particularly for species like Coho Salmon Oncorhynchus kisutch, which face diverse selective pressures during their migration. This study focused on the mid-Columbia River Coho Salmon reintroduction program managed by Yakama Nation Fisheries, which has successfully reintroduced Coho Salmon into the Wenatchee and Methow River basins, Washington. Notably, these populations have adapted to the longer migration route than those in the founding stock, with selection favoring individuals with an earlier arrival time and that can navigate a 15-km, high-gradient canyon to reach optimal spawning grounds. The objectives of this study were to investigate whether specific genomic regions are under selection for traits associated with return location and timing in Coho Salmon. Methods Low-coverage whole-genome resequencing data were used to screen for genomic regions associated with the phenotypes of interest. Results A weak polygenic signal in female Coho Salmon was found to be associated with return group, with a subset of candidate adaptive regions occurring across eight chromosomes. Conclusions These findings provide insights into the genomic mechanisms underlying local adaptation in reintroduced salmon populations and inform broodstock selection strategies aimed at promoting natural production and long-term population sustainability.

Horn, Rebekah L.↗
DOE OSTI2024

Constant-Depth Preparation of Matrix Product States with Adaptive Quantum Circuits

Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and feedforward operations, have recently emerged as a promising avenue for efficient state preparation, particularly on near-term quantum devices limited to shallow-depth circuits. Matrix product states (MPS) comprise a significant class of many-body entangled states, efficiently describing the ground states of one-dimensional gapped local Hamiltonians and finding applications in a number of recent quantum algorithms. Recently, it has been shown that the Affleck-Kennedy-Lieb-Tasaki state—a paradigmatic example of an MPS—can be exactly prepared with an adaptive quantum circuit of constant depth, an impossible feat with local unitary gates alone due to its nonzero correlation length [Smith , PRX Quantum 4, 020315 (2023)]. In this work, we broaden the scope of this approach and demonstrate that a diverse class of MPS can be exactly prepared using constant-depth adaptive quantum circuits, outperforming theoretically optimal preparation with unitary circuits. We show that this class includes short- and long-ranged entangled MPS, symmetry-protected topological (SPT) and symmetry-broken states, MPS with finite Abelian, non-Abelian, and continuous symmetries, resource states for MBQC, and families of states with tunable correlation length. Moreover, we illustrate the utility of our framework for designing constant-depth sampling protocols, such as for random MPS or for generating MPS in a particular SPT phase. We present sufficient conditions for particular MPS to be preparable in constant time, with global on-site symmetry playing a pivotal role. Altogether, this work demonstrates the immense promise of adaptive quantum circuits for efficiently preparing many-body entangled states and provides explicit algorithms that outperform known protocols to prepare an essential class of states. Published by the American Physical Society 2024

Smith, Kevin C. (ORCID:0000000223971518)↗
DOE OSTI2026

Predicting Adaptively Chosen Observables in Quantum Systems

Recent advances have demonstrated that 𝒪⁡(log 𝑀) measurements suffice to predict 𝑀 properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are chosen independently of the data. This assumption can be violated in practice, where scientists adaptively select properties after looking at previous predictions. This work investigates the adaptive setting for three classes of observables: local, Pauli, and bounded-Frobenius-norm observables. We prove that Ω⁡(√𝑀) samples of an arbitrarily large unknown quantum state are necessary to predict expectation values of 𝑀 adaptively chosen local and Pauli observables, where the system size scales exponentially and polynomially in 𝑀, respectively. We also present computationally efficient algorithms that achieve this information-theoretic lower bound. In contrast, for bounded-Frobenius-norm observables, we devise an algorithm requiring only 𝒪⁡(log 𝑀) samples, independent of system size. These results highlight the potential pitfalls of adaptivity in analyzing data from quantum experiments and provide algorithmic tools to safeguard against erroneous predictions in quantum experiments.

Machine learning↗
DOE OSTI2026

Digital Twin-Enabled Adaptive Control for Hydroelectric Systems: Turbine Governor and Voltage Regulation

This paper presents a comprehensive digital twin (DT) framework for hydroelectric systems that enables adaptive control of turbine governors and excitation systems without requiring detailed manufacturer specifications. The proposed framework integrates neural network-based system identification with stabilizing adaptive control laws for the installed turbine controller and middle-branch adaptive tuning for the installed voltage regulator. Using real operational data from Unit C-8 at Rocky Reach Dam (1,349 MW capacity), highfidelity neural network models are developed to capture turbine and generator dynamics without requiring detailed manufacturer specifications. The DT enables safe controller synthesis and validation in simulation before deployment. For turbine control, the proposed method achieves a 79.9% mean square error (MSE) reduction compared with that of an optimal controller. For voltage regulation, the adaptive excitation controller achieves approximately 42.6% MSE reduction while preserving installed protection logic. The results demonstrate that DT technology provides a practical pathway for modernizing hydropower control systems with minimal operational disruption.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2024

Adaptive Quantum Generative Training using an Unbounded Loss Function

We propose a generative quantum learning algorithm using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum Rényi divergence of order two, an unbounded function that mitigates barren plateaus which inhibit training variational circuits. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We perform numerical experiments of up to 12 qubits comparing our method learning algorithms that use linear objective functions and show that Rényi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal Rényi divergence loss function.

quantum algorithms, quantum machine learning, quan↗
DOE OSTI2025

Spike-Free Adaptive Sliding Mode Control: Application to Permanent Magnet Synchronous Motors

A new methodology for adaptive sliding mode control (ASMC) has been widely used to improve the control performance in various systems. This method exhibits several advantages, including low sliding mode control (SMC) chattering, no knowledge of the system disturbance bound, and no overestimation of the control gain. Despite its advantages, this method can be hampered by the spike phenomenon, slow control gain convergence, and difficulty in achieving optimal performance under varying disturbances. Consequently, this article proposes a spike-free ASMC method with a disturbance observer (DOB) to address these problems. Previous ASMC methods have been analyzed via simulations to verify the aforementioned problems. Here, this analysis highlights the need for disturbance compensation and improvements in the SMC gain adaptation law. Therefore, a DOB is designed to mitigate the spike phenomenon by compensating for disturbances. Subsequently, an SMC gain adaptation law based on disturbance error estimation is designed to eliminate the spike phenomenon completely. The proposed adaptation law makes the SMC gain to converge to a slightly higher value than the disturbance estimation error. Consequently, the proposed method not only eliminates the spike phenomenon, but also ensures optimal performance under varying disturbances. The performance of the proposed method is experimen tally validated through a comparative study.

42 ENGINEERING↗
DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Host-specific adaptation in Fusarium oxysporum correlates with distinct accessory chromosome content in human and plant pathogenic strains

ABSTRACT Fusarium oxysporumis a cross-kingdom pathogen. While some strains cause disseminated fusariosis and blinding corneal infections in humans, others are responsible for devastating vascular wilt diseases in plants. To better understand the distinct adaptations ofF. oxysporumto animal or plant hosts, we conducted a comparative phenotypic and genetic analysis of two strains: MRL8996 (isolated from a keratitis patient) and Fol4287 (isolated from a wilted tomato [Solanum lycopersicum]). Infection of mouse corneas and tomato plants revealed that, while both strains cause symptoms in both hosts, MRL8996 caused more severe corneal disease in mice, whereas Fol4287 induced more pronounced wilting symptoms in tomato plants.In vitroassays using abiotic stress treatments revealed that the human pathogen MRL8996 was better adapted to elevated temperatures, whereas the plant pathogen Fol4287 was more tolerant to osmotic and cell wall stresses. Both strains displayed broad resistance to antifungal treatment, with MRL8996 exhibiting the paradoxical effect of increased tolerance to higher concentrations of the antifungal caspofungin. We identified a set of accessory chromosomes (ACs) that encode genes with different functions and have distinct transposon profiles between MRL8996 and Fol4287. Interestingly, ACs from both genomes also encode proteins with shared functions, such as chromatin remodeling and post-translational protein modifications. Our phenotypic assays and comparative genomics analyses lay the foundation for future studies correlating genotypes with phenotype and for developing targeted antifungals for agricultural and clinical uses. IMPORTANCE Fusarium oxysporumis a cross-kingdom fungal pathogen that infects both plants and animals. In addition to causing many devastating wilt diseases, this group of organisms was recently recognized by the World Health Organization as a high-priority threat to human health. Climate change has increased the risk ofFusariuminfections, asFusariumstrains are highly adaptable to changing environments. Deciphering fungal adaptation mechanisms is crucial to developing appropriate control strategies. We performed a comparative analysis ofFusariumstrains using an animal (mouse) and plant (tomato) host andin vitroconditions that mimic abiotic stress. We also performed comparative genomics analyses to highlight the genetic differences between human and plant pathogens and correlate their phenotypic and genotypic variations. We uncovered important functional hubs shared by plant and human pathogens, such as chromatin modification, transcriptional regulation, and signal transduction, which could be used to identify novel antifungal targets.

Microbiology↗
DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification

SInkhorn Dynamic Domain Adaptation (SIDDA) supplements the experiments presented in 2501.14048, SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks. SIDDA introduces a semi-supervised, automatic domain adaptation method that leverages Sinkhorn divergences to dynamically adjust the regularization in the optimal transport plan and the weighting between classification and domain adaptation loss terms during training.

Pandya, Sneh [Fermi National Accelerator Laborator↗
DOE OSTI2025

Adaptive modification of antiviral defense systems in microbial community under Cr-induced stress

Background The prokaryotic antiviral defense systems are crucial for mediating prokaryote-virus interactions that influence microbiome functioning and evolutionary dynamics. Despite the prevalence and significance of prokaryotic antiviral defense systems, their responses to abiotic stress and ecological consequences remain poorly understood in soil ecosystems. We established microcosm systems with varying concentrations of hexavalent chromium (Cr(VI)) to investigate the adaptive modifications of prokaryotic antiviral defense systems under abiotic stress. Results Utilizing hybrid metagenomic assembly with long-read and short-read sequencing, we discovered that anti- viral defense systems were more diverse and prevalent in heavily polluted soils, which was corroborated by meta-analyses of public datasets from various heavy metal-contaminated sites. As the Cr(VI) concentration increased, prokaryotes with defense systems favoring prokaryote-virus mutualism gradually supplanted those with defense systems incurring high adaptive costs. Additionally, as Cr(VI) concentrations increased, enriched antiviral defense systems exhibited synchronization with microbial heavy metal resistance genes. Furthermore, the proportion of antiviral defense systems carried by mobile genetic elements (MGEs), including plasmids and viruses, increased by approximately 43% and 39%, respectively, with rising Cr concentrations. This trend is conducive to strengthening the dissemination and sharing of defense resources within microbial communities. Conclusions Overall, our study reveals the adaptive modification of prokaryotic antiviral defense systems in soil ecosystems under abiotic stress, as well as their positive contributions to establishing prokaryote-virus mutualism and the evolution of microbial heavy metal resistance. These findings advance our understanding of microbial adaptation in stressful environments and may inspire novel approaches for microbiome manipulation and bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

CMIP7 data request: impacts and adaptation priorities and opportunities

The Coupled Model Intercomparison Project Phase 7 (CMIP7) undertook an extensive process to gather community input and refine data requests related to impacts and adaptation applications of Earth System Model (ESM) outputs. The Impacts and Adaptation (I&A) Data Request Team worked with CMIP7 leadership to distribute an open solicitation across many communities that use climate model outputs requesting inputs for new and existing variables, the most applicable temporal characteristics, and groupings of variables that together allow for specific application opportunities. This input was then collated and translated into CMIP7 standard templates for inclusion in the broader data request, leading to 13 I&A data request opportunities, 60 variable groups and 539 unique variables sought by vulnerability, impacts, adaptation, and climate services user communities. Here, we describe these opportunities and variable groups, as well as new insights into how ESM groups can prioritize outputs that set off a chain of further analyses, ultimately informing decisions impacting society and natural systems. These include an emphasis on high-resolution outputs to allow further modeling of climate impacts at regional and local scales, improved representation of extreme weather events, enhanced accuracy of downscaling and bias-adjustment techniques, and support for more detailed assessments for decision-making in adaptation and mitigation strategies. There is also broad interest in more extensive provisioning of two-dimensional variables at the Earth's surface, prioritizing experiments that enhance our understanding of both the recent past and future scenarios, and providing outputs that allow further downscaling and bias adjustment. We emphasize that variable groups are the fundamental level at which to engage with the I&A data request, matching the scale of input and the way output provision enables specific I&A applications. Given resource constraints, we applaud CMIP7 efforts to foster strong engagement and communication between ESM groups and the I&A team to build consensus around prudent compromises in priority variables, temporal resolutions, simulation experiments, time subsets, and ensemble members.

Ruane, Alex C. [NASA Goddard Inst. for Space Studi↗
DOE OSTI2025

Detecting flash drought events to inform adaptive water management

Abstract: Flash droughts are characterized by a rapid onset of drought conditions, a common feature among various indicators of these events. The lack of a standardized definition and indicator makes flash droughts particularly challenging to predict and manage. We test six state-of-the-art flash drought indicators using 41 years of daily data across all Hydrological Unit Code 4 (HUC4) regions in the contiguous United States. Our results show substantial disagreements among indicators, suggesting that the detection of a flash drought event strongly depends on the choice of indicator. From the perspective of informing management and operations, an open challenge remains: which flash drought indicator should be used to trigger operational adaptations? To this end, we propose a methodological framework that informs adaptive actions by regionally assessing the level of agreement between different indicators. The regional differences in hydroclimatic conditions are expected to be reflected in the level of agreement between indicators. To better inform adaptive actions, the framework will also consider the impacted sectoral water uses and their varying response times. The insights gained from this study improve the understanding of how these different flash drought indicators capture the different aspects of regional differences and impacted sectors. Furthermore, this information can support more targeted adaptive actions to mitigate adverse impacts through improved preparedness and response strategies.

climate resilience↗