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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

DEEPER: An Intergrated Platform for Deeper Roots

Crops with deeper roots would have multiple benefits, including better drought tolerance, reduced requirement for nitrogen fertilizer, and better sequestration of atmospheric CO 2 . DEEPER is an integrated platform of phenomic, genomic, and in silico technologies to generate maize lines with deeper roots. DEEPER is: LEADER (Leaf Elemental Accumulation from Deep Roots) is a breakthrough technology to nondestructively measure rooting depth by using the plant itself as a sensor. LEADER uses handheld X-ray Fluorescence spectrometry to quantify foliar accumulation of elements that are differentially distributed in the soil profile. LEADER is nondestructive and is orders of magnitude cheaper, faster, and more precise than any competing assay of rooting depth in the field. LEADER is able to distinguish deep-rooted from shallow-rooted maize lines in the field without the need for costly and noisy soil coring. RootRobot/DIRT3D, to automatically phenotype root architecture in any field, combining RootRobot, a mechatronics platform to excavate, clean, section, and image mature root crowns, with DIRT3D, software to quantify architectural traits in 3D. Anatomics, a high-throughput platform to phenotype root anatomy, combining LAT 2.0, a technology for 3D imaging of root anatomy and composition, with RootScan3D, software to automatically extract 3D anatomical and cell wall composition metrics from LAT 2.0 output. Using this platform we discovered two novel root traits, parenchyma cell wall thickness and multiseriate cortical sclerenchyma, that improve rooting depth and drought tolerance in maize and wheat. OpenSimRoot/Deep, software to simulate root interaction with hard subsoils. Using this platform we discovered novel concepts regarding how to increase crop rooting depth by modulating how individual root axes respond to hard soil. DeepGenes, a toolkit of genes, parent lines, and genomic selection strategies to enable breeding hybrids with deeper roots. We discovered 3 novel root genes that increase rooting depth in maize and wheat. DEEPER discovered novel root phenotypes for deeper rooting, and delivered validated ideotypes for deeper-rooted maize; novel technologies to rapidly assess root depth, root architecture and anatomy in field-grown plants; novel software tools for root modeling and 3D image analysis of root architecture and anatomy; and validated genes and genomic selection models to deploy traits for deeper rooting in maize breeding. Each DEEPER technology is transformative in its own right, and exceeds existing technologies. They are mutually synergistic, deployable for field-grown plants, and are ready for application. The phenotyping and modeling technologies are readily applicable to many crops, and genetic leads in maize may have utility in other grasses. Taken as a whole they represent a transformative platform to develop deeper-rooted crops, with greater drought tolerance, reduced fertilizer requirement, and greater carbon sequestration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance

The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson’s correlation coefficient ( r ) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Whole-organism 3D quantitative characterization of zebrafish melanin by silver deposition micro-CT

We previously described X-ray histotomography, a high-resolution, non-destructive form of X-ray microtomography (micro-CT) imaging customized for three-dimensional (3D), digital histology, allowing quantitative, volumetric tissue and organismal phenotyping (Ding et al., 2019). Here, we have combined micro-CT with a novel application of ionic silver staining to characterize melanin distribution in whole zebrafish larvae. The resulting images enabled whole-body, computational analyses of regional melanin content and morphology. Normalized micro-CT reconstructions of silver-stained fish consistently reproduced pigment patterns seen by light microscopy, and further allowed direct quantitative comparisons of melanin content across wild-type and mutant samples, including subtle phenotypes not previously noticed. Silver staining of melanin for micro-CT provides proof-of-principle for whole-body, 3D computational phenomic analysis of a specific cell type at cellular resolution, with potential applications in other model organisms and melanocytic neoplasms. Advances such as this in whole-organism, high-resolution phenotyping provide superior context for studying the phenotypic effects of genetic, disease, and environmental variables.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Spaceflight Environmental-Telemetry Data for Biological Science

There is a critical need for better access and visualization of spaceflight environmental telemetry and mission hardware data from sensors including relative humidity, carbon dioxide, oxygen, radiation, airflow, temperature, acceleration, and acoustics. Under the stewardship of the Ames Life Sciences Data Archive (ALSDA) and GeneLab, an effort is underway to consolidate, normalize and provide accessibility of archived mission environmental data and hardware information, with the purpose of providing important context to biological data. This effort is necessary to provide scientific context of its impact upon biological and biomedical data from spaceflight missions and experiments (genomic, metagenomic, gene expression, proteomic, metabolomic, physiological, phenomics, behavioral; tabular, imaging, video). Environmental spaceflight data is derived from dozens of sources, with various formats, and in the past year a pipeline is in development to collect, curate and present this data efficiently. In the upcoming year, a new Data Visualization Portal will utilize the standardized pipeline data to provide easy user access to compare parameters and environmental conditions between missions, locations, subjects, and durations. Environmental and hardware data enables broad accessibility and analytics, without the need for advanced data informatic expertise. Familiarity with the capabilities and limitations of a variety of existing hardware/tools is a strength that could be applied to creation of improved hardware for future ecosystems on the Moon and Mars. The intention is to make biological and environmental telemetry data maximally open-access and FAIR (findable, accessible, interoperable, reusable) for data mining-informatic approaches to support knowledge discovery necessary for low Earth orbit, cis-Lunar, Mars transit, and Mars surface missions.

Danielle K. Lopez↗
NASA NTRSDate not supplied

Trace Elemental Abundances in Calcium-Aluminum-Rich Inclusions in CV Chondrites

Introduction: Calcium-aluminum-rich inclusions (CAIs), are the first formed solids that define the age of the Solar System [1,2]. CAIs are thought to have condensed from nebular gas [3,4] within the first <1 Ma of Solar System formation [5,6]. CAIs have experienced numerous early Solar System processes including condensation, evaporation, melting, recrystallization, and aqueous alteration [e.g., 7]. The chemical, mineralogical, and textural diversity among CAIs results from a range of chemical and physical processes recorded during nebular and parent body epoch. This study aims to explore the mineralogical, textural, and chemical compositions of CAIs including the trace elemental abundances in CAI phases to determine the early Solar System processes recorded in them. Samples and Analytical Methods: We analyzed one CAI each from CV3 chondrites Northwest Africa (NWA) 5508 designated as ‘Saguaro’, and Northwest Africa (NWA) 12772 designated as ‘Hoopoe’. Back-scatter electron (BSE) images were collected using a Phenom XL scanning electron microscope (SEM) at the Lunar and Planetary Institute (LPI) and the JEOL JXA-8530F electron probe microanalyzer (EPMA) at Johnson Space Center (JSC)-NASA. Additionally, energy dispersive X-ray spectrometry (EDS) elemental maps of select areas for these samples were collected using a 15.0kV beam energy and a 40µA emission current. Using the EPMA, wavelength-dispersive X-ray spectroscopy (WDS) quantitative data were collected. In-situ trace element measurements for both CAIs were determined at JSC-NASA using a Photon Machines 193nm laser ablation system and a Thermo-Scientific Element-XR inductively coupled plasma mass spectrometer (ICP-MS). Analyses consisted of 30s ablations at 10Hz, spot sizes of 20-25µm, and a fluence of 6.0 J/cm2 for anorthite and melilite, and a 3.5 J/cm2 fluence for all other phases. NIST612 was used to correct for instrument drift, while BHVO-2g was used as a primary calibration standard. BCR-2g and in-house mineral standards were regularly measured as unknowns to ensure accuracy. Results: Saguaro is a coarse-grained CAI, ~11 x 6 mm in dimensions. Saguaro contains spinel, Al-rich pyroxene, anorthite, Mg-rich melilite, and minor perovskite in its interior and is therefore classified as a Type B CAI. Individual melilite grains shows normal compositional zoning with an Ak content ranging from ~24 to 54 with no apparent trend from the core to the edge of the CAI. The spinel appears euhedral and occurs both as clusters and as spinel palisades [8]. Two rim sequences surround most of the sample: the inner rim being a Wark-Lovering (WL) rim (~10-35 µm) containing pyroxene, spinel, and melilite (or anorthite), and the outer rim is a finer-grained, thicker (~100 µm), accretionary rim (Fig. 1). The mineral phases in Saguaro record an overall flat REE pattern with an average negative Eu anomaly in pyroxene, and an average positive Eu anomaly in anorthite and melilite respectively. Anorthite, melilite, and pyroxene have a minor depletion in Tm (Fig. 2). The Hoopoe CAI is a compact, coarse-grained ~6 × 4 mm in size. The major mineralogy includes hibonite, spinel, melilite, anorthite, and perovskite. Therefore, it is classified as a compact transitional type A and B. (?)zoning was observed in some hibonites. Individual melilite grains show both reverse and normal zoning, where the Ak content ranges from ~6- to 28. Melilite shows two distinct textures. One texture consisted of smooth melilite that appeared homogenous, while the second appeared to consist of many fine fractures. The spinel also often appears clustered. The WL-rim sequence surrounding Hoopoe is ~25 µm thick and composed of spinel, perovskite, hibonite, and melilite/anorthite. It is then partially surrounded by an outer accretionary rim (~75µm). Like before, refractory metal nuggets appeared concentrated near the WL rims. Other metal assemblages rich in Fe and Ni were also observed. All major mineral phases in Hoopoe display relatively flat REE patterns, except for varying Eu and Tm between phases (Fig. 2). There is a prominent negative Eu anomaly in perovskite and an average positive Eu anomaly in spinel, anorthite, and hibonite respectively (Fig. 2, 3). The mixed phases along the rim of the CAI also display a negative Eu anomaly, and all phases the CAI were depleted in Pb. Discussion: The CV3 CAIs analyzed in this study were classified based on their mineralogy and textures into Type A versus Type B CAIs [10]. Hibonite appears to be pseudomorphically replacing the spinel, (i.e., is hibonite in composition, but appears in the shape of spinel). Spinel palisades. The presence of spinel palisades present in Saguaro are consistent with the melting and recrystallization experienced by this CAI. Trace elemental analyses. Saguaro and Hoopoe display similar trace element patterns to each other, with both appearing generally flat, with anomalies in Eu, and Tm. Melilite and anorthite display positive Eu anomalies in both CAIs, in addition to the hibonite in Hoopoe (Fig. 2). The phases that are depleted in Eu are pyroxene and perovskite in both Saguaro and Hoopoe, respectively (Fig. 3). Given that Eu is volatile in reducing environments [11], this could possibly indicate reducing conditions at the time anorthite and melilite crystallized, with the gas they formed from containing Eu. As these CAIs continued to form, this gas as a result would become depleted in Eu. This also could be supported by the propensity of anorthite and melilite to take up Eu from its surroundings and incorporate it into their structure [12]. In addition, analyzing the assemblage of the phases in the Saguaro, melilite and anorthite (Eu enriched) often surround the pyroxene (Eu depleted) as they are crystallized. This intergrowth of phases and the proximity of the phases would support that the Eu is being incorporated into some phases, preventing it from incorporating into other. Trace elemental analyses of the CAI rims will be evaluated in more detail, as they are complicated by the transient signal being composed of a mixture of mineral phases. Broadly, however, the patterns in the rims of both CAIs are comparable to each other, and for Hoopoe, to the mixed phase patterns in the core (Fig. 3). Other studies have found that CAI rims can be depleted in Ce and Yb [13], however we did not observe these anomalies in the two CAIs discussed here. Given their similarity, the trace elemental analyses of the mixed interior (i.e. core) and rim phases could be interpreted as forming from similar, if not the same, reservoirs. The REE abundance between the rim and core of Hoopoe are also similar, indicating they may have formed from a gas of the same or similar composition. Acknowledgments: We thank the ASU Center for Meteorite Studies for loaning the samples used in this work and Tabb Prissel for his assistance with the analysis. Mouti Al-Hashimi thanks Sam Crossley and Cyrena Goodrich for their help with the LPI SEM training. This work was supported by the LPI Summer Intern Program in Planetary Science and the LPI Cooperative Agreement. References: [1] Connelly J.N. (2012) Science, 338, 651-655. [2] MacPherson G. J. (2014) Treatise on Geochem., 2, 139-179. [3] Grossman L. (1972) GCA, 36, 597-619. [4] Ebel, D.S. (2006) Meteorites and the Early Solar System II (D. S. Lauretta & H. Y. McSween, Eds.) 253-277. [5] MacPherson G. J. (2012) Earth Planet. Sci. Lett., 331-332, 43-54. [6] MacPherson G.J. (2017) GCA, 201, 65-82. [7] Krot A.N. (1995) Meteoritics & Planet. Sci., 30, 748-775. [8] Wark and Lovering (1982) GCA, 46, 2595-2607. [9] Palme H. and Jones A. (2003) Treatise on Geochemistry (H. D. Holland and K. K. Turekian Eds.), 1, 41-61. [10] Grossman L. (1980) Ann. Rev. Earth Planet. Sci., 8, 559-608. [11] Floss C. et al. (1996) GCA, 60, 1975-1997. [12] Mason B. and Martin P. M. (1974) Earth Planet. Sci. Lett., 22, 141-144. [13] Wark B. and Boynton W. V. (2001) Meteoritics & Planet. Sci., 36, 1135-1166.

X Mouti↗
NASA NTRSDate not supplied

Mineralogical Analysis of Calcium-Aluminum-Rich Inclusions Provides Insight Into Post-Formation Processes

Introduction: Calcium-aluminum inclusions (CAIs) are cm- to mm-sized intergrowths of refractory phases found in chondritic meteorites [1]. Their mineral compositions closely match the compositions of the solids thought to condense from an extremely hot (>1500K) gas with a bulk solar composition [2-4], suggesting that CAIs were the earliest solids within the Solar System [2-4]. These inclusions provide invaluable insights into the conditions and dynamics of the early Solar System. CAIs must be transported from their formation region near the protosun to the chondrite parent-body accretion region. The nature of their journey could affect how, when, and where the secondary processes recorded in these CAIs occurred [4]. Did these secondary processes occur in the solar nebula or during accretion with the parent body, or both? How were the CAIs affected by varying thermochemical processes? To better answer these questions, we have undertaken an in-depth, textural study of the secondary alteration of select CAI samples. Methods: We chose three CAIs that experienced a range of post-formation processing to gain better understanding of secondary alteration based on previous preliminary examination [5]. Representative CAIs were chosen from NWA 5508 (CV3), NWA 12772 (CV3), and Coolidge (CL4) carbonaceous chondrites. We used scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) techniques for detailed chemical, mineralogical, and textural analysis of the CAIs. Backscattered electron (BSE) images and X-ray elemental maps were taken of Coolidge and NWA 5508 using the Lunar and Planetary Institute (LPI) Phenom SEM. The JEOL 7900F SEM at NASA Johnson Space Center (JSC) was used to obtain energy dispersive spectroscopy (EDS) chemical maps of all samples. High resolution EBSD analysis identified the mineral phases and textures, providing key information on their nature. Based on the SEM and EBSD data, minerals of interest were selected for quantitative chemical electron probe micro-analysis (EPMA) using the JEOL JXA-8530F at NASA JSC. Results and Discussion: The EDS maps show that the NWA 5508 and NWA 12772 CAIs designated “Saguaro” and “Hoopoe” respectively [5] are enriched in calcium while the Coolidge CAI designated “Cottonwood” is aluminum and magnesium rich. Saguaro. A ~1.5cm diameter igneous Type B CAI with a rounded shape. The dominant phases are melilite, spinel, and Al-Ti pyroxene with minor anorthite. Spinel is subhedral and occurs in clusters. Some of these clusters have a circular geometry which encloses other minerals, known as a palisade structure [6] (Fig. 1). Some palisades form near perfect circles while others are more irregular in shape. Melilite in Saguaro ranges in size from coarse (>250m) to fine-grained (5-7m) and forms intergrown laths. A third of the melilite grains exhibit simple twinning about their <001> axis. The spinel palisades and twinned melilite in Saguaro suggest an igneous history, and the lack of secondary minerals suggests minimal aqueous alteration. At some point in time after the initial condensation of the minerals and formation of the inclusion, the sample was remelted and quickly solidified. The formation of the palisades is still heavily debated. One hypothesis is that the palisades are the rims of smaller CAIs that accreted early on, essentially acting as xenoliths within the larger CAIs [7]. Another hypothesis suggests an igneous origin for palisade structures [6-8] wherein the melt traps gas bubbles, and the spinel nucleates on the surface of this bubble. Based on the WDS spot analyses of 41 melilite grains using EPMA, the data suggest that the composition inside and outside the palisades is nearly identical. This finding indicates that these palisades are likely not exogenous but rather formed from melt-vapor reactions. Our results are consistent with studies by Simon and Grossman,1997 [6] and Zhang et al. (2019) [8]. Hoopoe. A ~0.5cm compact Type A CAI with an irregular shape. The dominant mineral phases are melilite, spinel, and hibonite with minor amounts of anorthite, augite, and perovskite. The melilite ranges in size from ~500 to 50µm. The larger melilite grains have simple twinning along the <001> axis like melilite in Saguaro. Melilite in Hoopoe exhibits crystal-plastic strain with misorientation dominantly about the <010> and <110> axes. Spinel shows subhedral to euhedral morphology and appears in clusters. Hibonite grains are similar to spinel in habit and size but show more plastic strain. The two minerals are often found together with one appearing to replace the other. Perovskite appears in fine grained recrystallized regions alongside fine augite and spinel. The abundant strain and deformation features in the melilite and hibonite suggest that Hoopoe experienced shock. This shock could have occurred in the nebula [9] or from an impact of another body on the parent body asteroid. The appearance of fine-grained (<10m) areas of augite, perovskite, and spinel in the dominantly coarse-grained inclusion suggest recrystallization, possibly due to the sudden increase in pressure and temperature. Cottonwood. This ~0.5cm CAI exhibits distinct mineralogy and textures suggesting a high degree of alteration. It is irregular in shape. The dominant mineral phases are spinel and anorthite with minor amounts of augite and rutile. The two main texture types can be seen in Fig. 2. The first type consists of coarse euhedral to subhedral spinel and anorthite. The second includes fine grained spinel, anorthite, rutile, and iron sulfides. Within these fine-grained regions, the anorthite grains are clustered into domains exhibiting the same crystallographic orientation. Rutile exclusively occurs with fine anorthite indicating a potential relationship between the two. Cottonwood has a clear and unbroken Wark-Lovering [10] rim (Fig. 2) on one side that consists of a sequence of spinel followed by anorthite and an outer layer of augite. The abundance of the fine-grained regions containing iron oxides and iron sulfides, secondary phases such as rutile, and oriented anorthite grains is evidence for recrystallization associated with a high degree of thermal metamorphism. The EPMA analyses on both coarse- and fine-grained spinel show that the fine spinel grains are more enriched in Cr (1-2 wt. %) than their coarse counterparts (0.1-0.5 wt. %). The data suggest thermal metamorphism drove chemical exchange of the previously refractory inclusion, introducing chromium and iron as well as sulfur, which is moderately volatile. Conclusions: The three CAIs analyzed record distinct secondary nebular and parent body processes. Saguaro melted in the nebula as seen by the twinned melilite and spinel palisades. Hoopoe experienced intense shock which deformed its melilite and recrystallized perovskite, augite, and spinel in fine-grained regions. Cottonwood shows evidence of recrystallization and chemical changes consistent with thermal metamorphism occurring after accretion into the parent-body.

V E Burnette↗
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 OSTI2026

PlantCV v4: Image analysis software for high‐throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

Schuhl, Haley [Donald Danforth Plant Science Cente↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2021

RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Abstract Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Root system architecture in cereals: progress, challenges and perspective

We report roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this ‘hidden half’ provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data from Managing Flowering Time in Miscanthus and Sugarcane to Facilitate Intra- and Intergeneric Crosses

Miscanthus is a close relative of saccharum and a potentially valuable genetic resource for improving sugarcane. Differences in flowering time within and between miscanthus and saccharum hinders intra- and interspecific hybridizations. A series of greenhouse experiments were conducted over three years to determine how to synchronize flowering time of saccharum and miscanthus genotypes. We found that day length was an important factor influencing when miscanthus and saccharum flowered. Sugarcane could be induced to flower in a central Illinois greenhouse using supplemental lighting to reduce the rate at which days shortened during the autumn and winter to 1 min d-1, which allowed us to synchronize the flowering of some sugarcane genotypes with Miscanthus genotypes primarily from low latitudes. In a complementary growth chamber experiment, we evaluated 33 miscanthus genotypes, including 28 M. sinensis , 2 M. floridulus , and 3 M. ×giganteus collected from 20.9° S to 44.9° N for response to three day lengths (10 h, 12.5 h, and 15 h). High latitude-adapted M. sinensis flowered mainly under 15 h days, but unexpectedly, short days resulted in short, stocky plants that did not flower; in some cases, flag leaves developed under short days but heading did not occur. In contrast, for M. sinensis and M. floridulus from low latitudes, shorter day lengths typically resulted in earlier flowering, and for some low latitude genotypes, 15 h days resulted in no flowering. However, the highest ratio of reproductive shoots to total number of culms was typically observed for 12.5 h or 15 h days. Latitude of origin was significantly associated with culm length, and the shorter the days, the stronger the relationship. Nearly all entries achieved maximal culm length under the 15 h treatment, but the nearer to the equator an accession originated, the less of a difference in culm length between the short-day treatments and the 15 h day treatment. Under short days, short culms for high-latitude accessions was achieved by different physiological mechanisms for M. sinensis genetic groups from the mainland in comparison to those from Japan; for mainland accessions, the mechanism was reduced internode length, whereas for Japanese accessions the phyllochron under short days was greater than under long days. Thus, for M. sinensis , short days typically hastened floral induction, consistent with the expectations for a facultative short-day plant. However, for high latitude accessions of M. sinensis , days less than 12.5 h also signaled that plants should prepare for winter by producing many short culms with limited elongation and development; moreover, this response was also epistatic to flowering. Thus, to flower M. sinensis that originates from high latitudes synchronously with sugarcane, the former needs day lengths >12.5 h (perhaps as high as 15 h), whereas that the latter needs day lengths <12.5 h.

Feedstock Production↗
DOE OSTI2022

Systems Analysis of the Physiological and Molecular Mechanisms of Sorghum Nitrogen Use Efficiency, Water Use Efficiency and Interactions with the Soil Microbiome (Final Report for DE-SC0014395)

The specific project objectives were to: 1) Conduct deep census surveys of root microbiomes concurrent with phenotypic characterizations of a diverse panel of sorghum genotypes across multiple years to define the microbes associated with the most productive lines under drought and low nitrogen conditions. 2) Associate systems-level genotypic, microbial, and environmental factors with improved sorghum performance using robust statistical approaches. 3) Develop culture collections of sorghum root/leaf associated microbes that recapitulate root-enriched sequences defined in the census. 4) Perform controlled environment experiments for in-depth characterization and hypothesis testing of G sorghum x G microbe x E interactions . Validate physiological mechanisms, map genetic loci for stress tolerance, and determine the persistence of optimal microbial strains under greenhouse and field conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

TGCM: (T)rait, (G)ene, and (C)rop Growth (M)odel Directed Targeted Gene Characterization in Sorghum (Final Technical Report)

Understanding which genes control important crop traits could help scientists develop better bioenergy and food crops more efficiently. However, plant genomes contain tens of thousands of genes, and testing each one individually is expensive and time-consuming. This project developed computational tools to predict which genes are most likely to matter, allowing researchers to focus their efforts where they will have the greatest impact. This project developed and validated integrated approaches combining machine learning, quantitative genetics, and crop growth modeling to improve the efficiency of functional gene characterization in sorghum (Sorghum bicolor), a critical bioenergy and food security crop. The research addressed a fundamental challenge in plant biology: the majority of genes in plant genomes lack experimentally validated functions, making it difficult to prioritize which genes to study using resource-intensive reverse genetics approaches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Metabolomics (PB-DP5)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Culture samples were collected at 0, 0.5, 1, 2, 4, 6, and 8 hours for extracellular sucrose analysis. Circadian metabolomics data was acquired using a Agilent single quadrupole gas chromatography-mass spectrometer and processed using Agilent Mass Hunter for targeted sucrose quantification. Metabolomic analysis of PCC 7942 light-dark cycle cultures transitioned to constant light revealed distinct temporal patterns in sucrose production. Processed metabolomic datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed GC-MS results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗