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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Transient Covalent Bond in Abiotic Nonequilibrium Systems

Abstract Biochemical systems accomplish many critical functions with by operating out‐of‐equilibrium using the energy of chemical fuels. The formation of a transient covalent bond is a simple but very effective tool in designing analogous reaction networks. This Minireview focuses on the fuel chemistries that have been used to generate transient bonds in recent demonstrations of abiotic nonequilibrium systems (i.e., systems that do not make use of biological components). Fuel reactions are divided into two fundamental classifications depending on whether the fuel contributes structural elements to the activated state, a distinction that dictates how they can be used. Reported systems are further categorized by overall fuel reaction (e.g., hydrolysis of alkylating agents, carbodiimide hydration) and illustrate how similar chemistry can be used to effect a wide range of nonequilibrium behavior, ranging from self‐assembly to the operation of molecular machines.

Kariyawasam, Lasith S.↗
DOE OSTI2020

The Transient Covalent Bond in Abiotic Nonequilibrium Systems

Biochemical systems accomplish many critical functions with by operating out-of-equilibrium using the energy of chemical fuels. The formation of a transient covalent bond is a simple but very effective tool in designing analogous reaction networks. This Minireview focuses on the fuel chemistries that have been used to generate transient bonds in recent demonstrations of abiotic nonequilibrium systems (i.e., systems that do not make use of biological components). Here, fuel reactions are divided into two fundamental classifications depending on whether the fuel contributes structural elements to the activated state, a distinction that dictates how they can be used. Reported systems are further categorized by overall fuel reaction (e.g., hydrolysis of alkylating agents, carbodiimide hydration) and illustrate how similar chemistry can be used to effect a wide range of nonequilibrium behavior, ranging from self-assembly to the operation of molecular machines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Hijacking a rapid and scalable metagenomic method reveals subgenome dynamics and evolution in polyploid plants

Premise: The genomes of polyploid plants archive the evolutionary events leading to their present forms. However, plant polyploid genomes present numerous hurdles to the genome comparison algorithms for classification of polyploid types and exploring genome dynamics. Methods: Here, the problem of intra- and inter-genome comparison for examining polyploid genomes is reframed as a metagenomic problem, enabling the use of the rapid and scalable MinHashing approach. To determine how types of polyploidy are described by this metagenomic approach, plant genomes were examined from across the polyploid spectrum for both k-mer composition and frequency with a range of k-mer sizes. In this approach, no subgenome-specific k-mers are identified; rather, whole-chromosome k-mer subspaces were utilized. Results: Given chromosome-scale genome assemblies with sufficient subgenome-specific repetitive element content, literature-verified subgenomic and genomic evolutionary relationships were revealed, including distinguishing auto- from allopolyploidy and putative progenitor genome assignment. The sequences responsible were the rapidly evolving landscape of transposable elements. An investigation into the MinHashing parameters revealed that the downsampled k-mer space (genomic signatures) produced excellent approximations of sequence similarity. Furthermore, the clustering approach used for comparison of the genomic signatures is scrutinized to ensure applicability of the metagenomics-based method. Discussion: The easily implementable and highly computationally efficient MinHashing-based sequence comparison strategy enables comparative subgenomics and genomics for large and complex polyploid plant genomes. Such comparisons provide evidence for polyploidy-type subgenomic assignments. In cases where subgenome-specific repeat signal may not be adequate given a chromosomes' global k-mer profile, alternative methods that are more specific but more computationally complex outperform this approach.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

MACHO 311.37557.169: A VY Scl star

Abstract Optical surveys, such as the MACHO project, often uncover variable stars whose classification requires follow‐up observations by other instruments. We performed X‐ray spectroscopy and photometry of the unusual variable star MACHO 311.37557.169 with XMM‐Newton in April 2018, supplemented by archival X‐ray and optical spectrographic data. The star has a bolometric X‐ray luminosity of about 1 × 10 32 erg s −1 cm −2 and a heavily absorbed two‐temperature plasma spectrum. The shape of its light curve, its overall brightness, its X‐ray spectrum, and the emission lines in its optical spectrum suggest that it is most likely a VY Scl cataclysmic variable.

Wörpel, Hauke↗
DOE OSTI2025

Accelerating the Structure Exploration of Diverse Bi–Pt Nanoclusters via Physics‐Informed Machine Learning Potential and Particle Swarm Optimization

Bimetallic Bi–Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically machine-learned potential and CALYPSO particle swarm optimization global searches, 34 Bi20-Pt20 nanoclusters are systematically classified. The results reveal that Bi atoms predominantly occupy surface sites, driven by charge transfer effects. Cohesive energy trends alone prove insufficient for structure differentiation, necessitating a data-driven approach employing principal component analysis and K-means clustering. Furthermore, vibrational, electronic, and infrared spectral analyses provide additional insights into structure-property relationships. The findings offer an original framework for the automated classification and analysis of bimetallic nanoclusters, enhancing the understanding of their stability and functional properties.

bimetallic nanoparticles↗
DOE OSTI2022

Newly developed sugarbeet lines with altered postharvest respiration rates differ in transcription factor and glycolytic enzyme expression

Respiration is the principal cause for postharvest sucrose loss in sugarbeet (Beta vulgaris L.) roots. Although reductions in respiration rate could mitigate these losses, developing sugarbeet cultivars and storage procedures that reduce respiration are hindered by a lack of knowledge of the genetic and metabolic factors that control storage respiration rate. Research was conducted to identify genes and gene products that affect storage respiration rate by creating two sugarbeet lines that differ in respiration rate and characterizing gene expression differences between these lines. Sugarbeet lines F1056 and F1057, which differ by up to 42% in respiration rate, were created by divergent selection of a sugarbeet population using root respiration rate after 30 d in storage as the principal selection criterion. RNA sequencing identified 287 differentially expressed genes (DEGs) between these lines on the day of harvest and after 28-d storage. Of these DEGs, nine encoded transcription factors and five encoded enzymes involved in the respiratory pathway. Other DEGs contributed to a variety of biological and molecular functions based on gene ontology classifications. Of respiratory pathway DEGS, genes for NAD+- and NADP+-dependent forms of glyceraldehyde-3-phosphate dehydrogenase were of note due to their high upregulation in the high respiring line and for their established role in glycolysis, a pathway identified as a likely bottleneck in respiratory substrate production. Overall, lines F1056 and F1057 provide new tools for investigating genetic and physiological differences in storage respiration rate, and their DEGs identify candidates for genes affecting sugarbeet root respiration rate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The persistence and conversion of coastal foredune and swale vegetation community distributions 63 years later

Vegetation shifts can directly alter habitat dynamics and indirectly impact habitat stability relative to disturbance response. Barrier island dune habitats exhibit spatiotemporally dynamic topography that is affected by vegetation. However, vegetation distribution data can be rare and vegetation persistence is largely unknown such that species turnover over in communities can occur unnoticed. This is true despite concerns and documented cases of woody encroachment related to climate change in these ecogeomorphic habitats where physical stability to resist storm erosion varies with vegetation distribution and density. In 1956 and 1957, the vegetation of Island Beach State Park, NJ, was mapped as a permanent record for subsequent ecological study. In June 2020, we remapped the vegetation of 5.4 of 17 km north to south, seaward of the thicket community boundary. We maintained the same classification system as the historic record, physically mapping vegetation patches via GPS. We quantified changes in thicket, heather, and grass community distribution between the two time periods. Habitat persistence and conversion varied in the 63 years. Heath communities saw 80%–97% habitat loss in conversion to woody thicket. Conversely, thicket community distribution drastically increased, replacing heath where it was previously prevalent. This represents the first known documented instances of woody encroachment for Morella pensylvanica. When they did not expand, thicket communities receded landward or underwent turnover to an invasive species. Woody species of interest for dune stabilization occupied areas of similar habitat characteristics. Dune vegetation distribution persistence from 1957 to 2020 was relatively consistent and stable with the exception of heath habitat conversion. Barrier island stability is directly related to vegetation stability such that understanding where community shifts might occur over time can aid in managing and modeling efforts surrounding these dynamic ecogeomorphic habitats.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ionic Liquid Engineering in Perovskite Photovoltaics

Over the past decade, perovskite photovoltaics have approached other currently available technologies and proven to be the most prospective type of solar cells. Although the many-sided research in this very active field has generated consistent results with regard to their undisputed consistently increasing power conversion efficiency, it also produced several rather contradictory opinions. Among other important details, debate surrounding their proneness to surface degradation and poor mechanical robustness, as well as the environmental footprint of this materials class, remains a moot point. The application of ionic liquids appears as one of the potential remedies to some of these challenges due to their high conductivity, the opportunities for chemical “tuning” of the structure, and relatively lower environmental footprint. This article provides an overview, classification, and applications of ionic liquids in perovskite solar cells. Here, we summarize the use and role of ionic liquids as versatile additives, solvents, and modifiers in perovskite precursor solution, in charge transport layer, and in interfacial and stability engineering. Finally, challenges and the future prospects for the design and/or selection of ionic liquids with a specific profile that meets the requirements for next-generation highly efficient and stable perovskite solar cells are proposed.

14 SOLAR ENERGY↗
DOE OSTI2020

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high‐throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho‐histone H3 biomarkers into a single‐flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546–558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With “compound” serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule‐based software program Derek Nexus, developed by Lhasa Limited. Compound sub‐classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step‐wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

Wheeldon, Ryan P.↗
DOE OSTI2023

Inadvertent Hatchery Rainbow Trout Oncorhynchus mykiss Discovered in Native Westslope Cutthroat Trout Oncorhynchus clarkii lewisi Habitat: A Cautionary Tale

Abstract Fish hatcheries across North America release billions of fish into the wild every year to augment harvest opportunities in sport, commercial, and tribal fisheries, as well as to recover depressed populations. Despite these benefits, wild fish populations may be negatively affected when hatchery fish compete and/or hybridize with native species. Native Westslope Cutthroat Trout Oncorhynchus clarkii lewisi have decreased by 90% across their historic range; however, the Flathead River in Montana is a rangewide stronghold for these imperiled fish. The primary threat to Westslope Cutthroat Trout is hybridization with invasive Rainbow Trout O. mykiss, which leads to hybrid swarm formation and genomic extinction. We collected Rainbow Trout in Mill Creek, a tributary of the Flathead River, downstream from a hatchery that produces the species, and used otolith 87Sr/86Sr to determine whether individual fish were inadvertently introduced from the hatchery. Differences in 87Sr/86Sr among water samples from Mill Creek and the marine-derived hatchery food provided high-classification accuracy to determine the natal origins of Mill Creek Rainbow Trout. We found that 83 of the 106 Rainbow Trout collected (78%) were of hatchery origin. Otolith microstructure analysis indicated these fish had been in Mill Creek for ~1–32 weeks prior to capture. Our results illustrate the utility of otolith 87Sr/86Sr to identify hatchery fish in natural habitats and point to a source of invasive Rainbow Trout that pose conservation risks to native fishes in the drainage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The Effect of Membrane Casting Irregularities on Initial Fuel Cell Performance

Membrane electrode assemblies (MEA) for polymer electrolyte membrane fuel cells (PEMFC) may possess as-manufactured non-uniformities in any of its constituent components. This article studies casting irregularities located within the PEM to understand their potential impact on MEA initial performance. Membrane material was cast on a lab-scale polymer casting line operating either within or at the boundaries of the process window. The resulting membrane material was either pristine in the former case, or, in the latter case, contained air bubbles, cracks, and other irregularities, ranging in size from about 0.6 to 3 mm. Spatial polarization experiments were conducted using a 121-channel segmented fuel cell system, and thermal imaging was performed subsequently to map hydrogen crossover to spatial performance. While total-cell polarization data was minimally impacted by the irregularities, the spatial diagnostics showed local performance impacts that, in operation over time, could cause degradation in performance or earlier failure of the MEA. Such impacts could lead to these irregularities being classified as defects, i.e., manufacturing variations that should be identified and not included in a fuel cell stack. Classification of irregularities as defects will ultimately assist the industry by contributing to the development of threshold detection limits for in-line quality control diagnostics.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Observational study of the South American low-level jet during the SALLJEX

The South American Low-Level Jet (SALLJ) east of the Andes is a narrow maximum northerly wind present just above the boundary layer. It is an important component of the tropical-extratropical exchange of heat and moisture in South America and favors deep moist convection in Southeastern South America (SESA). In the present study, the SALLJ characteristics during the 2002-2003 summer season are investigated using upper-air measurements collected during the SALLJ Experiment (SALLJEX). At most stations, the highest wind speed was observed below 3000 m between 06 and 12 UTC, mainly in association with a maximization of the northerly component of the wind while the zonal component reached its minimum. Here, an increase in the maximum wind speed from 10°S to Santa Cruz de la Sierra’s latitude and from there a weakening southward was found throughout the SALLJEX network. Additionally, SALLJEX observations allowed to locate SALLJ core purely from observations in a region just east of the Andes in agreement with previous studies. A new localized kinematic classification was developed and applied in this study to analyze the relationship between the SALLJ intensity and the thermodynamic properties of the lower layers of the atmosphere. Weak, moderate, and strong SALLJ categories were defined with a local threshold of the northerly wind speed maximum at each station. The strongest SALLJs were frequently observed at nighttime, while weaker SALLJs were likely to occur at any time of the day. A stabilization of the nocturnal boundary layer was observed between weak and moderate cases whereas a destabilization between moderate and strong cases were found near the time of the wind speed maximum (06 UTC) at Santiago del Estero and Resistencia stations. A deepening of the convective boundary layer and an increase of its mean temperature between weak and strong category during the previous afternoon to nocturnal SALLJs, reveal a possible contribution to the mechanisms that generate the maximum wind speed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating Cryo–TEM Reconstruction Accuracy of Self–Assembled Polymer Nanostructures

Cryogenic transmission electron microscopy (cryo–TEM) combined with single particle analysis (SPA) is an emerging imaging approach for soft materials. However, the accuracy of SPA–reconstructed nanostructures, particularly those formed by synthetic polymers, remains uncertain due to potential packing heterogeneity of the nanostructures. In this study, the combination of molecular dynamics (MD) simulations and image simulations is utilized to validate the accuracy of cryo–TEM 3D reconstructions of self–assembled polypeptoid fibril nanostructures. Using CryoSPARC software, image simulations, 2D classifications, ab initio reconstructions, and homogenous refinements are performed. By comparing the results with atomic models, the recovery of molecular details is assessed, heterogeneous structures are identified, and the influence of extraction location on the reconstructions is evaluated. In conclusion, these findings confirm the fidelity of single particle analysis in accurately resolving complex structural characteristics and heterogeneous structures, exhibiting its potential as a valuable tool for detailed structural analysis of synthetic polymers and soft materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Long-Term Photovoltaic System Performance in Cold, Snowy Climates

As countries around the world transition towards renewable energy, there is increasing interest in using photovoltaic (PV) technologies to help decarbonize northern and alpine communities due to their scalability and affordability. However, a barrier to large-scale adoption of PV in cold climates is long-term performance uncertainty under snowfall, freeze-thaw cycles, low temperatures, and high winds. In this work, we provide a comprehensive review of published silicon degradation rates in cold Koppen-Geiger climate classifications of Dfb (humid continental), Dfc (subarctic), and ET (tundra). We first analyze the system degradation rates of three subarctic ground-mounted photovoltaic sites in North America using the RdTools year-on-year method: an Al-BSF double-axis tracking site in Fairbanks, Alaska (65degrees N); a PERC and silicon heterojunction bifacial vertical and south-tilted site in Fairbanks, Alaska; and a PERC south-facing fixed-tilt site in Fort Simpson, Northwest Territories (62degrees N). Degradation rates of these newly analyzed sites vary between -0.4%/year and -1.5%/year. Combining these data with previously reported cold climate degradation rates, we show that the distribution of cold climate degradation peaks at -0.1%/year to -0.2%/year but has a large tail with rates above -0.5%/year. The average reported cold climate degradation rate is -0.45%/year, whereas the median value is -0.33%/year. These results suggest that despite frequent freeze-thaw cycles and potential exposure to high wind and snow loads, PV systems in cold climates tend to degrade slower than PV systems in warmer climates. The limited sample size of reported degradation rates in cold climates (27) motivates the need for further data acquisition and monitoring efforts as new technologies are deployed.

14 SOLAR ENERGY↗
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 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.↗