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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Morphophysiological Plant Phenotyping for the Development of Plant Breeding Under Drought and Heat Conditions: A Practical Approach

ABSTRACT Currently, the breeding programs focus their efforts on identifying and developing tolerant genotypes to adverse conditions, such as drought and high temperatures. In this context, the physiological approach, which involves phenotyping several traits, is useful for breeding programs. Leaf photosynthetic traits have become one of the main objectives to be evaluated for breeders due to their relationship with improving grain yield and biomass production. Gas exchange ( Ge ) and chlorophyll “a” fluorescence ( Chf ) are the main tools to characterize the photosynthetic activity in real time at the leaf level. Consequently, several association studies using proximal and nonproximal sensing (e.g., RGB, thermography) have been developed. However, for the correct application of this breeding approach, it is essential to have a basic knowledge of both the physiological principles involved in the readings and the limitations of phenotyping due to the characteristics of the devices available on the market. This revision also covers other traits, such as the morphological and anatomical characteristics of leaves and roots, and the use of isotopes complementing Ge and Chf measurements.

Estrada, Félix [Instituto de Investigaciones Agrop↗
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 OSTI2021

Genetic dissection of natural variation in oilseed traits of camelina by whole‐genome resequencing and QTL mapping

Abstract Camelina [ Camelina sativa (L.) Crantz] is an oilseed crop in the Brassicaceae family that is currently being developed as a source of bioenergy and healthy fatty acids. To facilitate modern breeding efforts through marker‐assisted selection and biotechnology, we evaluated genetic variation among a worldwide collection of 222 camelina accessions. We performed whole‐genome resequencing to obtain single nucleotide polymorphism (SNP) markers and to analyze genomic diversity. We also conducted phenotypic field evaluations in two consecutive seasons for variations in key agronomic traits related to oilseed production such as seed size, oil content (OC), fatty acid composition, and flowering time. We determined the population structure of the camelina accessions using 161,301 SNPs. Further, we identified quantitative trait loci (QTL) and candidate genes controlling the above field‐evaluated traits by genome‐wide association studies (GWAS) complemented with linkage mapping using a recombinant inbred line (RIL) population. Characterization of the natural variation at the genome and phenotypic levels provides valuable resources to camelina genetic studies and crop improvement. The QTL and candidate genes should assist in breeding of advanced camelina varieties that can be integrated into the cropping systems for the production of high yield of oils of desired fatty acid composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

Abstract Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genome-wide identification and functional prediction of silicon (Si) transporters in poplar (Populus trichocarpa)

Abstract Silicon (Si) enhances plant tolerance to various biotic and abiotic stressors such as salinity, drought, and heat. In addition, Si can be biomineralized within plants to form organic carbon-containing phytoliths that can have ecosystem-level consequences by contributing to long-term carbon sequestration. Si is taken up and transported in plants via different transporter proteins such as influx transporters (e.g., Lsi1, Lsi6) and efflux transporters (e.g., Lsi2). Additionally, the imported Si can be deposited in plant leaves via silicification process using the Siliplant 1 (e.g., Slp1) protein. Functional homologs of these proteins have been reported in different food crops. Here, we performed a genome-wide analysis to identify different Si transporters and Slp1 homologs in the bioenergy crop poplar ( Populus trichocarpa Torr. and A. Gray ex W. Hook). We identified one channel-type Si influx transporter (PtLsi1; Potri.017G083300), one Si efflux transporter (PtLsi2; Potri.012G144000) and two proteins like Slp1 (PtSlp1a; Potri.004G168600 and PtSlp1b; Potri.009G129900 ) in the P. trichocarpa genome. We found a unique sequence (KPKPPVFKPPPVPI) in PtSlp1a which is repeated six times. Repeated presence of this sequence in PtSlp1a indicates that this protein might be important for silicification processes in P. trichocarpa. The mutation profiles of different Si transporters in a P. trichocarpa genome-wide association study population identified significant and impactful mutations in Potri.004G168600 and Potri.009G129900 . Using a publically accessible database ( http://bar.utoronto.ca/eplant_poplar/ ), digital expression analysis of the putative Si transporters in P. trichocarpa found low to moderate expression in the anticipated tissues, such as roots and leaves. Subcellular localization analysis found that PtLsi1/PtLsi2 are localized in the plasma membrane, whereas PtSlp1a/PtSlp1b are found in the extracellular spaces. Protein–Protein interaction analysis of PtLsi1/PtLsi2 identified Delta-1-pyrroline-5-carboxylate synthase (P5CS) as one of the main interacting partners of PtLsi2, which plays a key role in proline biosynthesis. Proline is a well-known participant in biotic and abiotic stress tolerance in plants. These findings will reinforce future efforts to modify Si accumulation for enhancing plant stress tolerance and carbon sequestration in poplar.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants

Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13 C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Reduced-dimension Bayesian optimization for model calibration of transient vapor compression cycles

Development and calibration of first-principles dynamic models of vapor compression cycles (VCCs) is of critical importance for applications that include control design and fault detection and diagnostics. Nevertheless, the inherent complexity of models that are represented by large systems of differential–algebraic equations leads to significant challenges for model calibration processes that utilize classical gradient-based methods. Bayesian optimization (BO) is a sample-efficient and gradient-free approach using a probabilistic surrogate model and optimal search over a feasible parameter space. Despite the benefits of BO in reducing computational costs, challenges remain in dealing with a high-dimensional calibration task resulting from a large set of parameters that have significant impacts on system behavior and need to be calibrated simultaneously. This paper presents a reduced-dimension BO framework for calibrating transient VCCs models where the calibration space is projected to a low-dimensional subspace for accelerating convergence of the solution algorithm and consequently reducing the number of transient simulations. The proposed approach was demonstrated via two case studies associated with different VCC applications where 10 parameters were calibrated in each case using laboratory measurements. The reduced-dimension BO framework only required 1 / 8 th of the iterations associated with a standard BO method that deals with high-dimensional calibration parameters for converged solutions and yielded comparable accuracy. Furthermore, both calibrated models revealed significant accuracy improvements compared to uncalibrated models.

Ma, Jiacheng↗
DOE OSTI2020

Detect and correct bias in multi-site neuroimaging datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Overall, our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.

42 ENGINEERING↗
DOE OSTI2020

Genetics of gene expression in the aging human brain reveal TDP-43 proteinopathy pathophysiology

Genetic variants that control distant gene expression are highly relevant for disease mechanisms but remain poorly charaterized. Here, leveraging the modular structure of gene coexpression in the aging human brain, we performed genome-wide association studies of gene coexpression modules and identified TMEM106B and RBFOX1 as module quantitative trait loci (modQTLs). The TMEM106B modQTL is a known risk locus of neurodegenerative TDP-43 proteinopathy and has a large-scale dominant transcriptomic effect including lysosomal and splicing dysregulation, recapitulating TDP-43 pathophysiology. Further, a locus in another TDP-43 proteinopathy risk gene, GRN, shows similar transcriptomic effects. TMEM106B and APOE/amyloid-ß converge on altered myelination/lysosomal gene expression, and jointly promote limbic-predominant age-related TDP-43 encephalopathy neuropathological change. Thus, genetic architecture of distant gene expression in the aging human brain elucidates TDP-43 proteinopathy pathophysiology.

Yang, Hyun-Sik↗
DOE OSTI2021

Hyperstoichiometric Uranium Dioxides: Rapid Synthesis and Irradiation-Induced Structural Changes

Uranium dioxide (UO 2 ), the primary fuel for commercial nuclear reactors, incorporates excess oxygen forming a series of hyperstoichiometric oxides. Thin layers of these oxides, such as UO 2.12 , form readily on the fuel surface and influence its properties, performance, and potentially geologic disposal. This work reports a rapid and straightforward combustion process in uranyl nitrate–glycine–water solutions to prepare UO 2.12 nanomaterials and thin films. We also report on the investigation of the structural changes induced in the material by irradiation. Despite the simple processing aspects, the combustion synthesis of UO 2.12 has a sophisticated chemical mechanism involving several exothermic steps. Raman spectroscopy and single-crystal X-ray diffraction (XRD) measurements reveal the formation of a complex compound containing the uranyl moiety, glycine, H 2 O, and NO 3 – groups in reactive solutions and dried combustion precursors. Combustion diagnostic methods, gas-phase mass spectroscopy, differential scanning calorimetry (DSC), and extracted activation energies from DSC measurements show that the rate-limiting step of the process is the reaction of ammonia with nitrogen oxides formed from the decomposition of glycine and uranyl nitrate, respectively. However, the exothermic decomposition of the complex compound determines the maximum temperature of the process. In situ transmission electron microscopy (TEM) imaging and electron diffraction measurements show that the decomposition of the complex compound directly produces UO 2 . The incorporation of oxygen at the cooling stage of the combustion process is responsible for the formation of UO 2.12 . Spin coating of the solutions and brief annealing at 670 K allow the deposition of uniform films of UO 2.12 with thicknesses up to 300 nm on an aluminum substrate. Irradiation of films with Ar 2+ ions (1.7 MeV energy, a fluence of up to 1 × 10 17 ions/cm 2 ) shows unusual defect-simulated grain growth and enhanced chemical mixing of UO 2.12 with the substrate due to the high uranium ion diffusion in films. As a result, the method described in this work allows the preparation of actinide oxide targets for fundamental nuclear science research and studies associated with stockpile stewardship.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Internal Structure of the Central Garlock Fault Zone From Ridgecrest Aftershocks Recorded by Dense Linear Seismic Arrays

Abstract We provide high‐resolution seismic imaging of the central Garlock fault using data recorded by two dense seismic arrays that cross the Ridgecrest rupture zone (B4) and the Garlock fault (A5). Analyses of fault zone head waves and P ‐wave delay times at array A5 show that the Garlock fault is a sharp bimaterial interface with P waves traveling ∼5% faster in the northern crustal block. The across‐fault velocity contrast agrees with regional tomography models and generates clear P ‐wave reflections in waveforms recorded by array B4. Kirchhoff migration of the reflected waves indicates a near‐vertical fault between 2 and 6 km depth. The P ‐wave delay times imply a ∼300‐m‐wide transition zone near the Garlock fault surface trace beneath array A5, offset to the side with faster velocities. The results provide important constraints for derivations of earthquake properties, simulations of ruptures and ground motion, and future imaging studies associated with the Garlock fault.

58 GEOSCIENCES↗
DOE OSTI2023

Simultaneous bright- and dark-field X-ray microscopy at X-ray free electron lasers

Abstract The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally speaking, such tools cannot probe the structural dynamics in a way that is representative of bulk behavior. Synchrotron X-ray diffraction based imaging has long mapped the deeply embedded structural elements, and with enhanced resolution, dark field X-ray microscopy (DFXM) can now map those features with the requisite nm-resolution. However, these techniques still suffer from the required integration times due to limitations from the source and optics. This work extends DFXM to X-ray free electron lasers, showing how the $$10^{12}$$ 10 12 photons per pulse available at these sources offer structural characterization down to 100 fs resolution (orders of magnitude faster than current synchrotron images). We introduce the XFEL DFXM setup with simultaneous bright field microscopy to probe density changes within the same volume. This work presents a comprehensive guide to the multi-modal ultrafast high-resolution X-ray microscope that we constructed and tested at two XFELs, and shows initial data demonstrating two timing strategies to study associated reversible or irreversible lattice dynamics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification of conserved skeletal enhancers associated with craniosynostosis risk genes

Abstract Craniosynostosis, defined by premature fusion of one or multiple cranial sutures, is a common congenital defect affecting more than 1/2000 infants and results in restricted brain expansion. Single gene mutations account for 15%–20% of cases, largely as part of a syndrome, but the majority are nonsyndromic with complex underlying genetics. We hypothesized that the two noncoding genomic regions identified by a GWAS for craniosynostosis contain distal regulatory elements for the risk genes BMPER and BMP2. To identify such regulatory elements, we surveyed conserved noncoding sequences from both risk loci for enhancer activity in transgenic Danio rerio. We identified enhancers from both regions that direct expression to skeletal tissues, consistent with the endogenous expression of bmper and bmp2. For each locus, we also found a skeletal enhancer that also contains a sequence variant associated with craniosynostosis risk. We examined the activity of each enhancer during craniofacial development and found that the BMPER-associated enhancer is active in the restricted region of cartilage closely associated with frontal bone initiation. The same enhancer is active in mouse skeletal tissues, demonstrating evolutionarily conserved activity. Using enhanced yeast one-hybrid assays, we identified transcription factors that bind each enhancer and observed differential binding between alleles, implicating multiple signaling pathways. Our findings help unveil the genetic mechanism of the two craniosynostosis risk loci. More broadly, our combined in vivo approach is applicable to many complex genetic diseases to build a link between association studies and specific genetic mechanisms.

He(何璇), Xuan Anita (ORCID:0000000313221972)↗
DOE OSTI2024

Dual-energy electron storage ring

A dual-energy electron storage ring is a novel concept initially proposed to cool hadron beams at high energies. The design consists of two closed rings operating at significantly different energies: the low-energy ring and the high-energy ring. These two rings are connected by an energy recovery linac (ERL) that provides the necessary energy difference. The ERL features superconducting radio-frequency (SRF) cavities that first accelerate the beam from the low energy E L to the high energy E H and then decelerate the beam from E H to E L in the next pass. The different SRF cavities in the ERL section can be adjusted based on the applications. In this paper, we present a possible layout of a dual-energy electron storage ring. The preliminary optics of the ring is designed to optimize chromaticity correction, dynamic aperture, momentum aperture, beam lifetime, radiation damping, and intrabeam scattering effects. The primary focus of this paper is on the stability conditions and beam dynamics studies associated with this storage ring. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Two-proton decay from α-cluster states in 10 C and 11 N

Two-proton decay from excited states in 10 C and 11 N has been characterized from an analysis of the 2p + 2α and 3p + 2α exit channels. Data from four previously published invariant-mass studies associated with inelastic excitation, multinucleon knockout, and neutron pickup reactions have been considered. A rotational band in 10 C was identified built on the second 0 + state which has strong α-cluster molecular structure. The members of this band were tentatively identified up to J π = 4 + and all identified states have a prompt 2p decay branch. The spins of the 0 + and 2 + members were deduced from comparisons of the momentum correlations in their 2p branches to those from known 2p emitters. The 2 + and the tentative 4 + members are both above the α threshold and exhibit large reduced decay widths for α emission. This band exhibits a similar moment of inertia to its analog in the mirror nucleus 10 Be. Evidence is also presented for a molecular band in 11 N built on the second 3/2 – state which 2p decays. Furthermore, tentative members of this band have been assigned up to J π = 9/2 – and their excitation energies also match their likely analogs in the mirror nucleus 11 Be.

6 ≤ A ≤ 19↗
DOE OSTI2023

Genetic control of photoprotection and photosystem II operating efficiency in plants

Summary Photoprotection against excess light via nonphotochemical quenching (NPQ) is indispensable for plant survival. However, slow NPQ relaxation under low light conditions can decrease yield of field‐grown crops up to 40%. Using semi‐high‐throughput assay, we quantified the kinetics of NPQ and photosystem II operating efficiency (ΦPSII) in a replicated field trial of more than 700 maize ( Zea mays ) genotypes across 2 yr. Parametrized kinetics data were used to conduct genome‐wide association studies. For six candidate genes involved in NPQ and ΦPSII kinetics in maize the loss of function alleles of orthologous genes in Arabidopsis ( Arabidopsis thaliana ) were characterized: two thioredoxin genes, and genes encoding a transporter in the chloroplast envelope, an initiator of chloroplast movement, a putative regulator of cell elongation and stomatal patterning, and a protein involved in plant energy homeostasis. Since maize and Arabidopsis are distantly related, we propose that genes involved in photoprotection and PSII function are conserved across vascular plants. The genes and naturally occurring functional alleles identified here considerably expand the toolbox to achieving a sustainable increase in crop productivity.

59 BASIC BIOLOGICAL SCIENCES↗