Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Sorghum”

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.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Production of Designer Xylose-Acetic Acid Enriched Hydrolysate from Bioenergy Sorghum, Oilcane, and Energycane Bagasses

Xylan accounts for up to 40% of the structural carbohydrates in lignocellulosic feedstocks. Along with xylan, acetic acid in sources of hemicellulose can be recovered and marketed as a commodity chemical. Through vibrant bioprocessing innovations, converting xylose and acetic acid into high-value bioproducts via microbial cultures improves the feasibility of lignocellulosic biorefineries. Enzymatic hydrolysis using xylanase supplemented with acetylxylan esterase (AXE) was applied to prepare xylose-acetic acid enriched hydrolysates from bioenergy sorghum, oilcane, or energycane using sequential hydrothermal-mechanical pretreatment. Various biomass solids contents (15 to 25%, w/v) and xylanase loadings (140 to 280 FXU/g biomass) were tested to maximize xylose and acetic acid titers. The xylose and acetic acid yields were significantly improved by supplementing with AXE. The optimal yields of xylose and acetic acid were 92.29% and 62.26% obtained from hydrolyzing energycane and oilcane at 25% and 15% w/v biomass solids using 280 FXU xylanase/g biomass and AXE, respectively.

09 BIOMASS FUELS↗
DOE OSTI2021

Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade? An investigation of natural variation in Sorghum bicolor

Abstract Previous studies have found that maximum quantum yield of CO2 assimilation (Φ CO2,max,app) declines in lower canopies of maize and miscanthus, a maladaptive response to self-shading. These observations were limited to single genotypes, leaving it unclear whether the maladaptive shade response is a general property of this C4 grass tribe, the Andropogoneae. We explored the generality of this maladaptation by testing the hypothesis that erect leaf forms (erectophiles), which allow more light into the lower canopy, suffer less of a decline in photosynthetic efficiency than drooping leaf (planophile) forms. On average, Φ CO2,max,app declined 27% in lower canopy leaves across 35 accessions, but the decline was over twice as great in planophiles than in erectophiles. The loss of photosynthetic efficiency involved a decoupling between electron transport and assimilation. This was not associated with increased bundle sheath leakage, based on 13C measurements. In both planophiles and erectophiles, shaded leaves had greater leaf absorptivity and lower activities of key C4 enzymes than sun leaves. The erectophile form is considered more productive because it allows a more effective distribution of light through the canopy to support photosynthesis. We show that in sorghum, it provides a second benefit, maintenance of higher Φ CO2,max,app to support efficient use of that light resource.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Genome Assembly and Transcriptome of Colletotrichum sublineola CsGL1, a New Resource to Study Anthracnose Disease in Sorghum

Colletotrichum species are globally distributed and well known as members of a destructive phytopathogenic genus, causing the anthracnose disease in a wide variety of crops and fruits. Colletotrichum sublineola is the causal agent of the anthracnose disease in sorghum, causing losses of up to 50% in yield. Here, we used PacBio sequencing combined with RNA-seq to generate a chromosome-level assembly and annotation of the Colletotrichum sublineola strain CsGL1. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Biochemistry & Molecular Biology↗
DOE OSTI2025

Viral‐mediated delivery of morphogenic regulators enables leaf transformation in Sorghum bicolor (L.)

Recent advancements in monocot transformation, using leaf tissue as explant material, have expanded the number of grass species capable of transgenesis. However, the complexity of vectors and reliance on inducible excision of essential morphogenic regulators have so far limited widespread application. Plant RNA viruses, such as Foxtail Mosaic Virus (FoMV), present a unique opportunity to express morphogenic regulator genes, such as Babyboom (Bbm), Wuschel2 (Wus2), Wuschel-like homeobox protein 2a (Wox2a) and the GROWTH-REGULATING FACTOR 4 (GRF4) GRF-INTERACTING FACTOR 1 (GIF1) fusion protein transiently in leaf explant tissues. Furthermore, altruistic delivery of conventional and viral vectors could provide opportunities to simplify vectors used for leaf transformation—facilitating vector optimization and reducing reliance on morphogenic regulator gene integration. In this study, both viral and conventional T-DNA vectors were tested for their ability to promote the formation of embryonic calli, a critical step in leaf transformation protocols, using Sorghum bicolor leaf explants. Although conventional leaf transformation vectors yielded viable embryonic calli (43.2 ± 2.9%: GRF4-GIF1, 50.2 ± 3%: Bbm/Wus2), altruistic conventional vectors employing the GRF4-GIF1 morphogenic regulator resulted in improved efficiencies (61.3 ± 4.7%). Altruistic delivery was further enhanced with the use of viral vectors employing both GRF4-GIF1 and Bbm/Wus2 regulators, resulting in 75.1 ± 2.3% and 79.2 ± 2.5% embryonic calli formation, respectively. Embryonic calli generated from both conventional and viral vectors produced shoots expressing fluorescent reporters, which were confirmed using molecular analysis. This work provides an important proof-of-concept for the use of both altruistic vectors and viral-expressed morphogenic regulators for improving plant transformation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genome-sequenced bacterial collection from sorghum aerial root mucilage

ABSTRACT A collection of 47 bacteria isolated from the mucilage of aerial roots of energy sorghum is available at the Great Lakes Bioenergy Research Center, Michigan State University, Michigan, USA. We enriched bacteria with putative plant-beneficial phenotypes and included information on phenotypic diversity, taxonomy, and whole genome sequences.

Mechan-Llontop, Marco E.↗
DOE OSTI2023

Genome-sequenced bacterial collection from sorghum epicuticular wax

ABSTRACT A collection of 44 isolates isolated from the epicuticular wax of stems of energy sorghum is available at the Great Lakes Bioenergy Researcher Center, Michigan State University, MI, USA. We enriched bacteria with putative plant-beneficial phenotypes and include information on their phenotypic diversity, taxonomy, and whole-genome sequences.

Mechan-Llontop, Marco E.↗
DOE OSTI2020

Whole-Genome Sequence of Brevibacillus borstelensis SDM, Isolated from a Sorghum-Adapted Microbial Community

The isolation of novel microbes from environmental samples continues to be a key strategy for the discovery of new metabolic capacities for the degradation and transformation of lignocellulose. We report the draft genome sequence of a new strain of Brevibacillus borstelensis isolated from a sorghum-adapted microbial community derived from a compost sample.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

UAV-based multispectral time-series imagery of biomass sorghum - 2019

UAV-based high-resolution multispectral time-series orthophotos utilized to understand the relation between growth dynamics, imagery temporal resolution, and end-of-season biomass productivity of biomass sorghum as bioenergy crop. Sensor utilized is a RedEdge Micasense flown at 40 meters above ground level at the Energy Farm- UIUC in 2019.

Bioenergy crops↗
DOE OSTI2022

Sorghum bicolor Core Metabolism Model

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C4 biomass crop Sorghum bicolor (iTJC1414) along with a minimal model for photosynthetic CO2 assimilation, sucrose and TAG biosynthesis in C3 plants. Extending iTJC1414 to a four-cell diel model we simulate C4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

Feedstock Production↗
DOE OSTI2020

Data from High Solids Loading Biorefinery for the Production of Cellulosic Sugars from Bioenergy Sorghum

A novel process applying high solids loading in chemical-free pretreatment and enzymatic hydrolysis was developed to produce sugars from bioenergy sorghum. Hydrothermal pretreatment with 50% solids loading was performed in a pilot scale continuous reactor followed by disc refining. Sugars were extracted from the enzymatic hydrolysis at 10% to 50% solids content using fed-batch operations. Three surfactants (Tween 80, PEG 4000, and PEG 6000) were evaluated to increase sugar yields. Hydrolysis using 2% PEG 4000 had the highest sugar yields. Glucose concentrations of 105, 130, and 147 g/L were obtained from the reaction at 30%, 40%, and 50% solids content, respectively. The maximum sugar concentration of the hydrolysate, including glucose and xylose, obtained was 232 g/L. Additionally, the glucose recovery (73.14%) was increased compared to that of the batch reaction (52.74%) by using two-stage enzymatic hydrolysis combined with fed-batch operation at 50% w/v solids content.

Conversion↗
DOE OSTI2023

Data for Tolerance of Engineered Rhodosporidium toruloides to Sorghum Hydrolysates During Batch and Fed-Batch Lipid Production

Oleaginous yeasts are a promising candidate for the sustainable conversion of lignocellulosic feedstocks into fuels and chemicals, but their growth on these substrates can be inhibited as a result of upstream pretreatment and enzymatic hydrolysis conditions. Previous studies indicate a high citrate buffer concentration during hydrolysis inhibits downstream cell growth and ethanol fermentation in Saccharomyces cerevisiae . In this study, an engineered Rhodosporidium toruloides strain with enhanced lipid accumulation was grown on sorghum hydrolysate with high and low citrate buffer concentrations. Both hydrolysis conditions resulted in similar sugar recovery rates and concentrations. No significant differences in cell growth, sugar utilization rates, or lipid production rates were observed between the two citrate buffer conditions during batch fermentation of R. toruloides . Under fed-batch growth on low-citrate hydrolysate a lipid titer of 16.7 g/L was obtained. Citrate buffer was not found to inhibit growth or lipid production in this engineered R. toruloides strain, nor did reducing the citrate buffer concentration negatively affect sugar yields in the hydrolysate. As this process is scaled-up, $131 per ton of hydrothermally pretreated biomass can be saved by use of the lower citrate buffer concentration during enzymatic hydrolysis.

Conversion↗
DOE OSTI2023

Data for Production of Designer Xylose-Acetic Acid Enriched Hydrolysate from Bioenergy Sorghum, Oilcane, and Energycane Bagasses

Xylan accounts for up to 40% of the structural carbohydrates in lignocellulosic feedstocks. Along with xylan, acetic acid in sources of hemicellulose can be recovered and marketed as a commodity chemical. Through vibrant bioprocessing innovations, converting xylose and acetic acid into high-value bioproducts via microbial cultures improves the feasibility of lignocellulosic biorefineries. Enzymatic hydrolysis using xylanase supplemented with acetylxylan esterase (AXE) was applied to prepare xylose-acetic acid enriched hydrolysates from bioenergy sorghum, oilcane, or energycane using sequential hydrothermal-mechanical pretreatment. Various biomass solids contents (15 to 25%, w/v) and xylanase loadings (140 to 280 FXU/g biomass) were tested to maximize xylose and acetic acid titers. The xylose and acetic acid yields were significantly improved by supplementing with AXE. The optimal yields of xylose and acetic acid were 92.29% and 62.26% obtained from hydrolyzing energycane and oilcane at 25% and 15% w/v biomass solids using 280 FXU xylanase/g biomass and AXE, respectively.

Biomass Analytics↗
DOE OSTI2020

High-throughput screening of Protein-DNA interactions in Sorghum

Drought is the primary abiotic factors negatively affecting agriculture, causing tremendous reductions in yield world-wide. Drought responsive adaptation strategies require complex molecular responses to including hormone signaling and production of osmo-protectants, as well as developmental responses including stomatal closure, changes to leaf morphology, and altered root system architecture. A common strategy to better understand these adaptation mechanisms is to compare plants with differing drought tolerances and has been successful in multiple crop plants including wheat, rice, and maize. To better understand drought adaptation strategies, we compared the cytosolic- and organelle-enriched protein profiles of leaves from two Sorghum bicolor genotypes with differing pre-flowering drought tolerances after 8-weeks of growth under water limitation. In agreement with previous literature findings, we observed significant drought-induced changes in the abundance of multiple heat shock proteins and dehydrins in both genotypes. We also identified unique proteins that might play a role in genotype dependent drought adaptation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

RhizoGrid Indexed Sorghum Rhizosphere Multi-Omics

PerCon SFA project data dentification of spatially resolved biomarkers of drought in Sorghum bicolor rhizosphere molecular-microbe interactions using a novel root cartography "RhizoGrid" system for sampling plants under drought and control conditions across 10 equally sized root zone environments (4 quadrants each). Each quadrant was sampled and processed for 16S amplicon, metabolomics, and X-ray computed tomography (XCT). Data download includes experimental metadata and results files for 16S rRNA sequence analysis of microbial community assembly (processed data files), liquid chromatography mass spectrometry (LC-MS) metabolomics analysis of microbial community root exudates (processed data files), X-ray computed tomography (XCT) spatial gradient analysis (raw and processed data files) of microbial community composition, and related computational modeling outputs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum

We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.

Zhang, Zeyu↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗