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At least 127 records · Page 7

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DOE OSTI2024

Expression of a mammalian RNA demethylase increases flower number and floral stem branching in Arabidopsis thaliana

Abstract RNA methylation plays a central regulatory role in plant biology and is a relatively new target for plant improvement efforts. In nearly all cases, perturbation of the RNA methylation machinery results in deleterious phenotypes. However, a recent landmark paper reported that transcriptome‐wide use of the human RNA demethylase FTO substantially increased the yield of rice and potatoes. Here, we have performed the first independent replication of those results and demonstrated broader transferability of the trait, finding increased flower and fruit count in the model species Arabidopsis thaliana . We also performed RNA‐seq of our FTO‐transgenic plants, which we analyzed in conjunction with previously published datasets to detect several previously unrecognized patterns in the functional and structural classification of the upregulated and downregulated genes. From these, we present mechanistic hypotheses to explain these surprising results with the goal of spurring more widespread interest in this promising new approach to plant engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

An Algal Nutrient‐Replete, Optimized Medium for Fast Growth and High Triacylglycerol Accumulation

Microalgae are promising sources to sustainably meet the global needs for energy and products. Algae grow under different trophic conditions, where nutritional status regulates biosynthetic pathways, energy production, and growth. The green alga Chromochloris zofingiensis has strong economic potential because it co-produces biofuel precursors and the high-value antioxidant astaxanthin while accumulating biomass when grown mixotrophically. As an emerging reference alga for photosynthesis, metabolism, and bioproduction, C. zofingiensis needs a defined, optimized medium to standardize experiments during fast growth for batch cultivation. Because the interplay of glucose treatment (+Glc) and mineral deficiency influences photosynthesis, growth, and the production of lipids and astaxanthin, we designed a replete nutrient medium tailored to the C. zofingiensis cellular ionome. We combined inductively coupled plasma mass spectrometry (ICP-MS) and +Glc growth curves to determine a medium that is nutrient replete for at least 5 days of +Glc logarithmic growth. We found that there are high nutritional needs for phosphorus and sulfur during mixotrophy. Iron was the only element measured for which the cellular concentration correlated with exogenous concentration and was iteratively adjusted until the cellular ionome was consistent through the logarithmic growth phase. This Chromochloris-Optimized Ratio of Elements (CORE) medium supports fast growth and high biomass and lipid accumulation without causing excess nutrient toxicity. This defined, nutrient-replete standard is important for future C. zofingiensis investigations and can be adapted for other species to support high biomass for batch cultivation. The method used to develop CORE medium shows how ionomics informs replicable media design and may be applied in industrial settings to inform cost-effective biofuel production.

Jeffers, Tim L. [University of California, Berkele↗

DOE OSTI2024

A fast and sensitive size-exclusion chromatography method for plasma extracellular vesicle proteomic analysis

Extracellular vesicles (EVs) carry diverse biomolecules derived from their parental cells, making their components excellent biomarker candidates. However, purifying EVs is a major hurdle in biomarker discovery since current methods require large amounts of samples, are time-consuming and typically have poor reproducibility. Here we describe a simple, fast, and sensitive EV fractionation method using size exclusion chromatography (SEC) on a fast protein liquid chromatography (FPLC) system. Our method uses a Superose 6 Increase 5/150, which has a bed volume of 2.9 mL. The FPLC system and small column size enable reproducible separation of only 50 µL of human plasma in 15 min. To demonstrate the utility of our method, we used longitudinal samples from a group of individuals who underwent intense exercise. A total of 838 proteins were identified, of which, 261 were previously characterized as EV proteins, including classical markers, such as cluster of differentiation (CD)9 and CD81. Quantitative analysis showed low technical variability with correlation coefficients greater than 0.9 between replicates. The analysis captured differences in relevant EV proteins involved in response to physical activity. Our method enables fast and sensitive fractionation of plasma EVs with low variability, which will facilitate biomarker studies in large clinical cohorts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗

DOE OSTI2026

Visual Systems Mapping to Define and Compare Woody Biomass LCAs for Sustainable Systems

The challenge addressed in this research centres on the need to choose between several biomass sources and energy production processes, while supporting rural economies and resilience of forest systems. A key barrier to effective decision-making for strategies using biomass is the lack of standardized and transparent life cycle assessment (LCA) baselines. These baselines are critical for assessing the impacts of biomass strategies but often vary due to regional factors and chosen simplifying assumptions of the LCAs. However, omitting key variables can mean the LCA omits key feedback and balancing loops relevant to fully assessing impacts of the change or test scenario. To address these complexities, this project employs a systems engineering approach: visual systems mapping. This technique is used to define the boundaries and dynamic behaviours of LCA baselines, enhancing transparency. By examining five literature sources and their documented baseline scenarios, the systems mapping case-studies demonstrates an approach to documenting and archiving these baselines. Recommendations are that visual systems mapping should be used to document key assumptions, such as baselines, of LCAs. Further, where possible open data repositories should hold key information about LCA baselines and reproducible workflows (e.g., using open-source tools) should be used to improve transparency and comparability in LCAs. Given the consensus within the broader scientific community on the importance of replicable data practices, this research reinforces the need for standardized frameworks and systems engineering tools in LCAs. This research demonstrates a pathway to more transparent, standardized, and comparable LCAs, that may bolster decisions for biomass systems.

Davis, Maggie [ORNL] (ORCID:0000000181319328)↗

DOE OSTI2024

Celestial gluon amplitudes from the outside in

We show that, given a two-dimensional realization of the celestial OPE in self-dual Yang-Mills, we can find a scalar source around which scattering amplitudes replicate correlation functions computed from the 2D ‘gluon’ operators in a limit where a dynamic massless scalar decouples. We derive conditions on the two-dimensional three-point correlation function so that such a source exists and give two particular examples of this construction, one in which gluons are constructed from vertex operators in the semiclassical limit of Liouville theory and another in which the soft gluons arise from generalized free fields. Finally, we identify a bulk dual to the level of the boundary Kac-Moody algebra and discuss moving beyond the decoupling limit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Aligned yet large dipoles: a SMEFT study

We study a non-universal flavor scenario at the level of the Standard Model Effective Field Theory, according to which the matrix of Wilson coefficients cuW of an up-type electroweak quark dipole operator is aligned with the up-type Yukawa coupling. Such an alignment usually follows from the assumption of Minimal Flavor Violation (MFV), away from which we step by allowing the entries of cuW to be sizable along the first quark generations. A particular example, which we refer to as “inverse hierarchy MFV”, features Wilson coefficients inversely proportional to quark masses, and arises from BSM models respecting MFV and containing heavy fields that replicate the mass hierarchy of SM quarks. We then analyze the phenomenology driven by cuW at colliders and at lower-energy flavor experiments. We show that precision measurements of the process pp → Wh → γγℓν at FCC-hh could set an upper bound on |cuW| ≲ O (10 -2 )(Λ/TeV) 2 , with Λ the cutoff of the effective field theory. This bound is an order of magnitude stronger than the existing LHC bounds. Moreover, we estimate that Wh → b$\overline{b}$ℓν at HL-LHC could also give competitive bounds. In the low-energy regime, we consider bounds arising from rare kaon decays, which turn out to be loose, |c$_{uW}^{11}$ | < O (1)(Λ/TeV) 2 . We finally demonstrate that our flavor and operator assumptions can be derived from a weakly-coupled UV model, which we choose to simultaneously illustrate the UV origin of inverse hierarchy MFV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Extracellular DNA Alters Detection of Subtle Bacterial Responses to Soil Rewetting

Microbial communities are often characterized using DNA-based sequencing, but these approaches also capture extracellular DNA (exDNA) released from dead cells, potentially altering inference about microbial responses to environmental change. This may be especially important during pulse disturbances, such as soil drying–rewetting, which can increase microbial mortality and transient necromass pools. We assessed whether exDNA altered inference about bacterial responses to drying–rewetting (an 80 mm simulated rainfall event following a 28-day drought) in conventionally tilled corn and perennial switchgrass soils. We quantified bacterial abundance (16 S rRNA gene copies), alpha diversity, and community composition in paired soil samples with exDNA included (+ exDNA) and in samples treated with propidium monoazide (PMAxx) to reduce amplification of exDNA (− exDNA). At our level of replication (n = 4), PMAxx treatment did not significantly alter overall temporal response patterns (i.e., no significant main effect of DNA treatment or DNA × time interaction). However, PMAxx treatment increased sensitivity to detect some pairwise temporal changes in bacterial abundance and community composition in corn soils following rewetting. exDNA pools were proportionally highest immediately after rewetting in corn soils, suggesting transient extracellular DNA may contribute to masking during disturbance recovery. In contrast, PMAxx treatment had comparatively small effects in switchgrass soils, which exhibited weaker temporal responses overall. Inclusion of exDNA also changed which taxa appeared most responsive to rewetting. Together, our results suggest that exDNA does not uniformly bias soil microbial inference, but may reduce detectability of subtle disturbance-driven shifts in certain soils. Future studies should advance knowledge of microbial turnover and necromass dynamics, particularly using multiple complementary methods, to help predict when exDNA is most likely to influence ecological inference.

drying-rewetting↗

DOE OSTI2024

Rainfall events stimulate episodic associative nitrogen fixation in switchgrass

Abstract Associative N 2 fixation (ANF) is widespread but poorly characterized, limiting our ability to estimate global inputs from N 2 fixation. In some places, ANF rates are at or below detection most of the time but occasionally and unpredictably spiking to very high rates. Here we test the hypothesis that plant phenology and rainfall events stimulate ANF episodes. We measured ANF in intact soil cores in switchgrass ( Panicum virgatum L.) in Michigan, USA. We used rain exclusion shelters to impose three rainfall treatments with each receiving 60 mm of water over a 20-day period but at different frequencies. We concurrently established a treatment that received ambient rainfall, and all four treatments were replicated four times. To assess the effects of plant phenology, we measured ANF at key phenological stages in the ambient treatment. To assess the effects of rainfall, we measured ANF immediately before and immediately after each wetting event in each treatment involving rainfall manipulation. We found that the previous day’s rainfall could explain 29% of the variation in ANF rates within the ambient treatment alone, and that bulk soil C:N ratio was also positively correlated with ANF, explaining 18% of the variation alone. Wetting events increased ANF and the magnitude of response to wetting increased with the amount of water added and decreased with the amount of inorganic N added in water. ANF episodes thus appear to be driven primarily by wetting events. Wetting events likely increase C availability, promote microbial growth, and make rhizosphere conditions conducive to ANF.

Vizza, Carmella (ORCID:0000000292690357)↗

DOE OSTI2025

Reflection grating fabrication for the Rockets for Extended-source X-ray Spectroscopy

Abstract The Rockets for Extended-source X-ray Spectroscopy (tREXS) grating spectrograph uses modules of reflection gratings to collect spectroscopic data from extended astronomical sources of soft X-rays. Two blazed master gratings were produced on silicon substrates with electron-beam lithography (EBL) and complementary nanofabrication processes that include KOH etching. Substrate-conformal imprint lithography (SCIL) was then used to create 191 replicas of the two grating masters for use in the flight instrument. Diffraction efficiency was measured for several replica gratings, which achieve a peak of$$ \varvec{>} $$ > 70% absolute efficiency near 0.22 keV and an average of$$ \varvec{\approx } $$ ≈ 50% absolute efficiency across the measured band, from 0.18 – 0.8 keV. Here we detail the nanofabrication of the grating masters, including the EBL parameters and tREXS-specific fabrication considerations, and the SCIL replication process used to generate the final instrument gratings. A discussion of grating characterization and areas for future improvement is also presented.

Astronomy & Astrophysics↗

DOE OSTI2026

Ozonolysis of selenium: an alternative oxidation method for radiochemical separations of arsenic and selenium

Many arsenic and selenium radiochemical separation methods use hydrogen peroxide (H 2 O 2 ) to oxidize selenite (Se(IV)) to selenate (Se(VI)), but this reaction is challenging to control and replicate. Here, this study introduces a simple ozonolysis apparatus with an in-line oxygen-ozone generator and impinger system for rapid selenium oxidation in aqueous samples as an alternative to H 2 O 2 . The system quickly converts Se(IV) to Se(VI), enabling successful separations of arsenic-73 and selenium-75 with high recoveries using ion-exchange chromatography (1-X8). Compared to traditional H 2 O2 treatment, ozone oxidation is a faster and more easily controlled method for selenium redox chemistry.

and nuclear chemistry↗

DOE OSTI2025

Microstructure Clones

Background: A material’s microstructure drives its material performance. Contemporary crystal plasticity experiments compare full-field strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which features of the observed deformation are deterministic vs statistical; thus, differences between model and experiment may or may not be significant. Objective: This paper introduces the invention of microstructure clones. Microstructure clones are 2D oligocrystal specimens that have nearly identical microstructures to remedy the aforementioned experimental limitations. Having specimens with nearly identical microstructures will allow for multiple destructive tests of a microstructure (either as repeats or intentionally different experiments), an ability to “see the future” by providing insight into how a specimen will deform, variability quantification, and experimental investigations of response to small microstructural changes. Methods: This work introduces microstructure clones. Repeatability of these clones is demonstrated in tensile bars of pure nickel. Local strain measurements from digital image correlation are compared between clone specimens and compared to results from a crystal plasticity finite element model. Results: Two sets of microstructure clones were tested in this study and displayed very consistent deformation responses within each clone set. Small observed differences in deformation invite investigation into microstructure stochasticity and the effect of small microstructural and loading differences. Conclusions: Microstructure clones represent a significant shift in understanding structure–property relationships. This work reshapes experimental crystal plasticity to allow for experiments that control for specific variables, quantification of microstructural stochasticity (and other sources of stochasticity), and opportunities for replicating experiments.

Crystal Plasticity↗

DOE OSTI2024

A Simplified Method for Predicting Shaker Voltage in IMMATs

Impedance Matched Multi-Axis Tests (IMMATs) can replicate in-service vibration induced stress more accurately than single axis shaker table tests as they can better match a part’s operational boundary conditions and excite it in multiple degrees of freedom simultaneously. Here, the shakers used in IMMATs are less powerful than shaker tables, so shaker force limits can be exceeded during tests if they are not placed adequately for the desired environment. The ability to predict shaker voltage and force before performing a test is, therefore, helpful in selecting shaker locations so that their limits are not exceeded. In this study, electrodynamic shakers were modeled as discrete electromechanical systems, and the shaker parameters were chosen to match experimentally obtained acceleration/voltage frequency response functions (FRFs). These models were coupled to a finite element model of the device under test (DUT) via dynamic substructuring, and the substructured model was demonstrated to accurately predict shaker voltage as well as the error in reproducing the environment at multiple accelerometer locations. A simple method called the FRF Multiplication method, in which the FRF of the substructured system is approximated as the product of two separate FRFs of the shaker and DUT respectively, was proposed and applied to the same system, yielding similar voltage and error predictions to those obtained using substructuring. Simple case studies were presented to explore the applicability of the proposed method, and it was demonstrated to have similar accuracy to the substructuring method in a range of cases. Additionally, we showed that while it was not possible to derive a unique model of the shakers from acceleration/voltage FRFs alone, the models that could be obtained were sufficient to predict test error almost perfectly and shaker voltage with less than 40 percent error.

42 ENGINEERING↗

DOE OSTI2024

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

Machine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest

Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. Here, we focused our analysis on standard meteorological variables; fluxes of CO 2 , sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO 2 and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m -2 y -1 , mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m -2 y -1 ). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions.

Carbon cycle↗

DOE OSTI2025

Optimized Illuminance Operation-A Light-Driven Dilution Strategy to Improve Microalgae Biomass Productivity

Periodic dilution is a necessary operation to avoid light limitation due to self-shading as the culture grows dense. However, environmental conditions are constantly changing in outdoor cultivation systems, making it difficult to determine the optimal dilution rate. To address this challenge, this study evaluated a dilution approach based on light penetration to optimize illuminance (OptiLum) in the culture. Biomass concentration was controlled via sensor-feedback directed dilution to ensure that light reaching the bottom of the culture was maintained above the compensation intensity. Under replicated outdoor pond conditions, by keeping the entire culture within a net-positive photosynthetic zone, the OptiLum operation improved the biomass productivities of two top-performing strains, Picochlorum celeri and Tetraselmis striata, by 95 % and 86 %, respectively, compared to conventional semi-continuous batch cultivation. The dilution rate varied daily and was dynamically adjusted based on the light status within the culture, which is concurrently influenced by weather, culture density, and growth rate. The techno-economic analysis showed that the OptiLum operation could reduce biomass production cost by as much as 24 % and 33 % for P. celeri and T. striata, respectively, assuming a low-cost dewatering approach with initial gravity settling of biomass can be realized for both strains. However, a more costly two-stage dewatering strategy, comprising only membranes and centrifuges, may be necessary for non-settling strains, such as P. celeri, which would alternatively increase production costs by 23 % for the OptiLum case. The results demonstrated that the proposed OptiLum operation is a promising approach to improve biomass productivity and lower production cost via weather-responsive and self-adjusting dilution.

09 BIOMASS FUELS↗

DOE OSTI2025

Wave energy in season: a comparative approach to feasibility of seasonal deployments for remote coastal communities

Remote coastal communities, which could be early adopters of wave energy projects, have concerns over costs, conflicts, and potential risks of development. Designers and developers are challenged to address these community concerns as they continue to develop wave energy technologies. One potential means of reducing costs, conflicts, and risks, especially for demonstration and pilot-scale projects, could be planning a deployment that operates for only a portion of the year—a seasonal deployment. Here, in this paper, we examine the impacts of a seasonal deployment in terms of cost, electricity production, operations and maintenance, environmental impacts, and community benefits. We take a holistic, comparative approach to feasibility that can be replicated for other comparative studies. We estimate electricity production using a point absorber WEC modeled near Sitka, AK, USA and optimized for the given sea conditions. We determine that, for remote community sized projects, seasonal deployments could result in small cost savings (less than 10 %), but larger decreases in annual energy production (around 30 % for our case study area). Seasonal deployments could be preferable in places with seasonal energy needs, if failures and device access become a major hindrance to wave energy technology development, or as a cautionary approach to introducing new technology to the oceans. We also determine that a highly seasonal wave resource is not necessarily a requirement for seasonal deployments to be considered. Seasonal deployments are an alternative to year-round deployments that can be considered in places where marine spatial conflict is a seasonal concern.

WEC optimizations↗

DOE OSTI2025

CFD modeling of natural circulation in LiCl-KCl molten salt closed loop

Characterizing flow within a molten salt closed-loop system is crucial for assessing system requirements, evaluating performance, and identifying potential flaws. Direct flow measurement using instrumentation is challenging due to extreme environmental conditions and the limitations associated with measuring molten salt flow under natural convection. Here, this study aims to provide comprehensive insights into the thermal-hydraulic behavior of a closed loop, with a particular focus on temperature distribution and velocity prediction. The Computational Fluid Dynamics (CFD) model demonstrated the capability to effectively simulate and predict both temperature distributions and flow velocities within the molten salt loop. The CFD model's predictive capability was validated by its ability to replicate temperature measurements under varying boundary conditions. The analysis revealed that the CFD model tends to underpredict temperatures in the cold leg and overpredict them in the hot leg, highlighting the need for continuous model refinement and acknowledging the limitations of using a steady-state approach. Furthermore, the potential of using external temperature measurements to estimate internal molten salt temperatures and predict flow velocity was explored, revealing that this approach could introduce up to a 5.5% error in flow velocity calculations. Line probes mapping temperature distributions across the tube's cross-section and molten salt provided valuable insights into temperature gradients, emphasizing the need for a thermal conductivity equation for molten salt with lower uncertainty to achieve more accurate temperature predictions of the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗