Plant science corpus
The plant science corpus consists of the titles and abstracts of plant science articles in PubMed published prior to 2021 with a small number of 2021 records due to modification of records.
Botany/trend/methods↗
SEARCH · Search NASA
Results for “Plant Science”
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.
Assessing the evolution of research topics in a biological field using plant science as an example
Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.
60 APPLIED LIFE SCIENCES↗
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↗
Plant science history manuscript GitHub repo
This archive contains codes, notebooks, and other information for reproducing the study that links to this file. The original files are in the plant_sci_hist Github repository which will likely be updated when the code base is adopted for other projects. Thus, this archive is provided so a frozen version used for publishing the plant science history manuscript that references this file.
Botany/trend/methods↗
Raw data for publication: Qiande et al. 2025. Frontiers in Plant Science (accepted for publication).
Raw data for publication: Moni Qiande and Fredy Altpeter. 2025. Comparing four heat-inducible promoters in stably transformed sugarcane regarding spatial and temporal control of transgene expression reveals candidates to drive stem preferred transgene expression. Frontiers in Plant Science (accepted for publication).
09 BIOMASS FUELS↗
The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants
The plant community has a strong track-record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g., 10x genomics), has resulted in an explosion of outstanding single cell and nuclei datasets that can be put in an in situ context within tissues (e.g., a cell atlas)1. In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due to accessibility, ease-of-use and access to expertise along with depth of analysis benefits. On the other hand, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis2, as the search for plant-based natural products has played a significant role in improving human health throughout history. However, the employment of proteomics and metabolomics assays for characterizing plant cell processes now remains significantly behind transcriptional approaches, even though both provide a direct functional readout of cell states and phenotypes.
Anderton, Christopher R. [BATTELLE (PACIFIC NW LAB↗
Imaging plant metabolism in situ
Abstract Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Overview and Current Status of Neutron Imaging at Oak Ridge National Laboratory
Neutron imaging is a non-destructive technique used to study the internal structure and composition of a wide range of materials. At Oak Ridge National Laboratory (ORNL), there are two neutron imaging beamlines that provide complementary capabilities that serve a diverse community, from materials science and engineering to biomedical and plant sciences. This report provides an overview of the current ORNL imaging capabilities that support materials research for a broad user community.
Torres, James [ORNL] (ORCID:0000000289407610)↗
Utilizing plant synthetic biology to accelerate plant-microbe interactions research
Plant-microbe interactions are critical to ecosystem resilience and substantially influence crop production. From the perspective of plant science, two important focus areas concerning plant-microbe interactions include: 1) understanding plant molecular mechanisms involved in plant-microbe interfaces and 2) engineering plants for increasing plant disease resistance or enhancing beneficial interactions with microbes to increase their resilience to biotic and abiotic stress conditions. Molecular biology and genetics approaches have been used to investigate the molecular mechanisms underlying plant responses to various beneficial and pathogenic microbes. While these approaches are valuable for elucidating the functions of individual genes and pathways, they fall short of unraveling the complex cross-talk across pathways or systems that plants employ to respond and adapt to environmental stresses. Also, genetic engineering of plants to increase disease resistance or enhance symbiosis with microbes has mainly been attempted or conducted through targeted manipulation of single genes/pathways of plants. Recent advancements in synthetic biology tool development are paving the way for multi-gene characterization and engineering in plants in relation to plant-microbe interactions. Here, we briefly summarize the current understanding of plant molecular pathways involved in plant interactions with beneficial and pathogenic microorganisms. Then, we highlight the progress in applying plant synthetic biology to elucidate the molecular basis of plant responses to microbes, enhance plant disease resistance, engineer synthetic symbiosis, and conduct in situ microbiome engineering. Lastly, we discuss the challenges, opportunities, and future directions for advancing plant-microbe interactions research using the capabilities of plant synthetic biology.
59 BASIC BIOLOGICAL SCIENCES↗
Microbial Design for a Developing Bioeconomy: Frontier Science for the Bioeconomy Workshop Series
The Microbial Design for a Developing Bioeconomy workshop report is one of a four-part Frontier Science for the Bioeconomy series hosted by the DOE Biological and Environmental Research program’s Biological Systems Science Division. The series defines frontiers of plant science, agriculture, synthetic biology, biobased materials, and environmental microbiome science that will unlock the promise of an innovative, resilient U.S. bioeconomy.
59 BASIC BIOLOGICAL SCIENCES↗
Complementing model species with model clades
Abstract Model species continue to underpin groundbreaking plant science research. At the same time, the phylogenetic resolution of the land plant tree of life continues to improve. The intersection of these 2 research paths creates a unique opportunity to further extend the usefulness of model species across larger taxonomic groups. Here we promote the utility of the Arabidopsis thaliana model species, especially the ability to connect its genetic and functional resources, to species across the entire Brassicales order. We focus on the utility of using genomics and phylogenomics to bridge the evolution and diversification of several traits across the Brassicales to the resources in Arabidopsis, thereby extending scope from a model species by establishing a “model clade.” These Brassicales-wide traits are discussed in the context of both the model species Arabidopsis and the family Brassicaceae. We promote the utility of such a “model clade” and make suggestions for building global networks to support future studies in the model order Brassicales.
59 BASIC BIOLOGICAL SCIENCES↗
Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research
In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.
59 BASIC BIOLOGICAL SCIENCES↗
EcoBOT: an AI/ML enabled automated phenotyping capability for model plants
Introduction: Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods: The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results: The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion: The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.
AI image analysis↗
Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters
Arate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.
3D reconstruction↗
Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case
In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.
59 BASIC BIOLOGICAL SCIENCES↗
Group VII ethylene response factors forming distinct regulatory loops mediate submergence responses
Group VII ethylene response factors (ERFVIIs), whose stability is oxygen concentration-dependent, play key roles in regulating hypoxia response genes in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) during submergence. To understand the evolution of flooding tolerance in cereal crops, we evaluated whether Brachypodium distachyon ERFVII genes (BdERFVIIs) are related to submergence tolerance. We found that three BdERFVIIs, BdERF108, BdERF018, and BdERF961, form a feedback regulatory loop to mediate downstream responses. BdERF108 and BdERF018 activated the expression of BdERF961 and PHYTOGLOBIN 1 (PGB1), which promoted nitric oxide turnover and preserved ERFVII protein stability. The activation of PGB1 was subsequently counteracted by increased BdERF961 accumulation through negative feedback regulation. Interestingly, we found that OsERF67, the orthologue of BdERF961 in rice, activated PHYTOGLOBIN (OsHB2) expression and formed distinct regulatory loops during submergence. Overall, the divergent regulatory mechanisms exhibited by orthologs collectively offer perspectives for the development of submergence-tolerant crops.
54 ENVIRONMENTAL SCIENCES↗
Chloroplast phosphate transporter CrPHT4-7 regulates phosphate homeostasis and photosynthesis in Chlamydomonas
Abstract In eukaryotic cells, phosphorus is assimilated and utilized primarily as phosphate (Pi). Pi homeostasis is mediated by transporters that have not yet been adequately characterized in green algae. This study reports on PHOSPHATE TRANSPORTER 4-7 (CrPHT4-7) from Chlamydomonas reinhardtii, a member of the PHT4 transporter family, which exhibits remarkable similarity to AtPHT4;4 from Arabidopsis (Arabidopsis thaliana), a chloroplastic ascorbate transporter. Using fluorescent protein tagging, we show that CrPHT4-7 resides in the chloroplast envelope membrane. Crpht4-7 mutants, generated by the CRISPR/Cas12a-mediated single-strand templated repair, show retarded growth, especially in high light, reduced ATP level, strong ascorbate accumulation, and diminished non-photochemical quenching in high light. On the other hand, total cellular phosphorous content was unaffected, and the phenotype of the Crpht4-7 mutants could not be alleviated by ample Pi supply. CrPHT4-7-overexpressing lines exhibit enhanced biomass accumulation under high light conditions in comparison with the wild-type strain. Expressing CrPHT4-7 in a yeast (Saccharomyces cerevisiae) strain lacking Pi transporters substantially recovered its slow growth phenotype, demonstrating that CrPHT4-7 transports Pi. Even though CrPHT4-7 shows a high degree of similarity to AtPHT4;4, it does not display any substantial ascorbate transport activity in yeast or intact algal cells. Thus, the results demonstrate that CrPHT4-7 functions as a chloroplastic Pi transporter essential for maintaining Pi homeostasis and photosynthesis in C. reinhardtii.
59 BASIC BIOLOGICAL SCIENCES↗
Arabidopsis cytochrome b 5 proteins support fatty acid ω-3 but not ω-6 desaturation
Fatty acids are primary components of lipids, which serve as major energy sources in cells and play essential roles in membrane structure, signaling, and metabolic regulation (Shanklin and Cahoon 1998). The degree of fatty acid unsaturation critically influences lipid physicochemical properties, thereby affecting membrane fluidity and biological function (Nguyen et al. 2019). In Arabidopsis thaliana, fatty acid desaturation occurs via 2 parallel pathways: the “prokaryotic pathway” in plastids, involving glycosylglycerides, such as monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), and phospholipid phosphatidylglycerol (PG); and the “eukaryotic pathway” in the endoplasmic reticulum (ER), involving phosphatidylcholine (PC) (Lou et al. 2014) (Supplementary Figure S1). Seven fatty acid desaturases (FADs) in Arabidopsis differentially desaturate each glycerolipid class in the plastid and ER (Nguyen et al. 2019). FAD2, an ER-resident ω-6 fatty acid desaturase, catalyzes the conversion of oleic acid (18:1) to linoleic acid (18:2), which can be further desaturated to α-linolenic acid (18:3) by FAD3, an ER-resident ω-3 fatty acid desaturase. In plastids, FAD6 catalyzes the desaturation of 18:1/16:1 to produce 18:2/16:2, while FAD7 and FAD8 redundantly convert 18:2/16:2 to 18:3/16:3 (Li-Beisson et al. 2013; Nguyen et al. 2019). Additionally, fatty acids synthesized in the ER can also be reimported into plastids to their site of de novo synthesis (Xu et al. 2010). All FADs require reducing power, in the form of 2 electrons, for catalysis, but the sources of the electrons vary between their subcellular localizations. In the ER, FAD2 and FAD3 receive electrons from a cytochrome b 5 (CB5)-based electron transfer chain comprising cytochrome b 5 reductase (CBR) and CB5. In contrast, ferredoxin serves as the electron donor for plastid-localized FAD6, FAD7, and FAD8 (Ohlrogge and Browse 1995; Andreu et al. 2007). While the relative contributions of the 2 pathways to total cellular desaturation products vary across tissues and species, most polyunsaturated FA biosynthesis in seeds occurs via ER-resident FAD2 and FAD3 (Miquel and Browse 1992; Ohlrogge and Browse 1995).
59 BASIC BIOLOGICAL SCIENCES↗