SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.
Nutrient inputs influence the sustainability of bioenergy crop production through contemporary (shortly after addition) and legacy effects (persisting over years) on microbial nitrogen (N) and carbon cycling, which contribute to greenhouse gas emissions. However, the relative importance of contemporary and legacy effects and how that could vary by crop functional types is poorly understood. Considering its rhizomatous roots and perennial growth, we hypothesized that Miscanthus × giganteu s ( M × g ) would be more sensitive to legacy N fertilization and the historical context of its environment than an annual crop like maize. To test this hypothesis, we examined the effects of legacy and contemporary N inputs on nitrous oxide (N2O) and carbon dioxide (CO2) emissions, as well as key N cycling genes in soils where M × g and maize were grown. A 150-day soil incubation experiment was conducted using soils from a long-term M × g and maize fertility experiment with three historic N fertilization rates (0, 112, and 336 kg N ha−1 year−1) and a contemporary amendment (60 mg N kg−1) with negative control (0 mg N kg−1). We observed significant increases in cumulative N2O emissions in M × g soils relative to maize soils, particularly at higher legacy fertilization rates, while contemporary N had no significant effect. Bacterial amoA gene abundance, which plays a significant role in nitrification in nutrient-rich soils, also increased with higher legacy fertilization rates in M × g soils but was unaffected by the contemporary N. In maize soils, legacy and contemporary N did not significantly affect N2O emissions, but cumulative CO2 emissions and amoA gene abundance significantly increased. The abundances of norB genes were not significantly influenced by either legacy fertilization or contemporary N amendments in either soil. Our findings demonstrate the greater importance of fertilization history over contemporary N in mediating soil N2O emissions, particularly for perennial bioenergy crops.
This is the AmeriFlux version of the carbon flux data for the site US-UiC University of Illinois Maize-Soy. Site Description - Agricultural field planted with maize in a three year rotation with soy (maize-maize-soy). The first soy rotation year was 2010. This field is typically planted in May and harvested in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Explore the source record for details and available documents.
There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.
Setaria viridis is a rapid-life-cycle model panicoid grass. Here, to identify genes that may contribute to inflorescence architecture and thus have the potential to influence grain yield in related crops such as maize, we conducted an N-nitroso-N-methylurea (NMU) mutagenesis of S. viridis and screened for visible inflorescence mutant phenotypes. Of the approximately 2,700 M 2 families screened, we identified four recessive sparse panicle mutants (spp1-spp4) characterized by reduced and uneven branching of the inflorescence. To identify the gene underlying the sparse panicle1 (spp1) phenotype, we performed bulked segregant analysis and deep sequencing to fine map it to an approximately 1 Mb interval. Within this interval, we identified disruptive mutations in two genes. Complementation tests between spp1 and spp3 revealed they were allelic, and deep sequencing of spp3 identified an independent disruptive mutation in SvAUX1 (AUXIN1), one of the two genes in the ~1 Mb interval and the only gene disruption shared between spp1 and spp3. SvAUX1 was found to affect both inflorescence development and root gravitropism in S. viridis. A search for orthologous mutant alleles in maize confirmed a very similar role of ZmAUX1 in maize, which highlights the utility of S. viridis in accelerating functional genomic studies in maize.
The structure of the phenolic polymer in maize grain fibers, with 5.5% Klason lignin content, has been studied. For this, the milled wood lignin (MWL) and dioxane lignin (DL) preparations were isolated and analyzed. The data indicated that the lignin in maize fibers was syringyl rich, mostly involved in β-aryl ether, resinol, and phenylcoumaran substructures. 2D NMR and derivatization followed by reductive cleavage (DFRC) also revealed the occurrence of associated ferulates together with trace amounts of p-coumarates acylating the γ-OH of lignin side chains, predominantly on S-lignin units. More interesting was the occurrence of diferuloylputrescine, a ferulic acid amide, which was identified by 2D NMR and comparison with a synthesized standard, that was apparently incorporated into this lignin. A phenylcoumaran structure involving a diferuloylputrescine coupled through 8–5' linkages to another diferuloylputrescine (or to a ferulate or a guaiacyl lignin unit) was found, providing compelling evidence for its participation in radical coupling reactions. The occurrence of diferuloylputrescine in cell walls of maize kernels and other cereal grains appears to have been missed in previous works, perhaps due to the alkaline hydrolysis commonly used for composition studies.
Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today's growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites - Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET.
Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
Calcium/calmodulin-dependent protein kinase (CCaMK) has been shown to play an important role in abscisic acid (ABA)-induced antioxidant defense and enhance the tolerance of plants to drought stress. However, its downstream molecular events are poorly understood. Here, we identify a NAC transcription factor, ZmNAC84, in maize, which physically interacts with ZmCCaMK in vitro and in vivo. ZmNAC84 display a partially overlapping expression pattern with ZmCCaMK after ABA treatment and H 2 O 2 is required for ABA-induced ZmNAC84 expression. Functional analysis reveals that ZmNAC84 is essential for ABA-induced antioxidant defense in a ZmCCaMK-dependent manner. Furthermore, ZmCCaMK directly phosphorylates S113 of ZmNAC84 in vitro, and S113 is essential for the ABA-induced stimulation of antioxidant defense by ZmCCaMK. Moreover, overexpression of ZmNAC84 in tobacco can improve drought tolerance, and alleviate drought-induced oxidative damage of transgenic plants. These results define a mechanism for ZmCCaMK function in ABA-induced antioxidant defense, where ABA-produced H 2 O 2 first induces expression of ZmCCaMK and ZmNAC84 and activates ZmCCaMK, and subsequently the activated ZmCCaMK phosphorylates ZmNAC84 at S113, thereby inducing antioxidant defense by activating downstream genes.
Not provided.
Not Available
Not Available
Not Available
Herein, we focus on improving the long-term chemical and thermomechan-ical stability of perovskite solar cells (PSCs), two major challenges currently limiting their commercial deployment. Our strategy incorporates a long-chain starch polymer into the perovskite precursor. The starch polymer confers multiple beneficial effects by forming hydrogen bonds with the methylammonium iodide precursor, templating perovskite growth that results in a compact and homogeneous film deposited in a simple one-step coating (antisolvent-free). The inclusion of starch in the methylammonium lead iodide films strongly improves their thermomechanical and environmental stability while maintaining a high photovoltaic performance. The fracture energy (G c ) of the film is increased to above 5 J/m 2 by creating a nanocomposite that provides intrinsic reinforcement at grain boundaries. Additionally, improved optoelectronic properties achieved with the starch polymer enable good photostability of the active layer and enhanced resistance to thermal cycling.
Not provided.