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Saaidi R, Tayalati Y, Elfatimy A. Optimizing positron emission tomography for accurate plant imaging using Monte Carlo simulations to correct positron range effects. Sci Rep 2025; 15:13847. [PMID: 40263448 PMCID: PMC12015257 DOI: 10.1038/s41598-025-95670-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 03/24/2025] [Indexed: 04/24/2025] Open
Abstract
Positron Emission Tomography (PET) is a valuable tool for plant imaging, but its accuracy can be compromised by positron range effects. This study improves PET accuracy using the GATE Monte Carlo simulation tool to estimate and correct these effects. The GATE model was validated for the Siemens Biograph Vision system using the NEMA NU 2-2018 protocol, showing alignment with experimental data. Deviations were within 9% for sensitivity and 3% for peak Noise Equivalent Count Rate (NECR). Different isotopes (18F, 11C, 15O, and 30P) and plant phantom properties were analyzed for their impact on reconstructed images. A sixfold enhancement was observed for 15O and a threefold improvement for 11C when a magnetic field was applied to the plant phantom. Our findings suggest that integrating PET with magnetic resonance imaging can help address Positron range effects in plant imaging. This study provides valuable insights into PET imaging and offers refined methodologies for clinical and plant-centric research. Our research validates the use of GATE Monte Carlo simulation for Biograph Vision and advances our understanding of Positron range phenomena and potential mitigation strategies for precise PET Plant imaging.
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Affiliation(s)
- Rahal Saaidi
- School of Applied and Engineering Physics, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco.
| | - Yahya Tayalati
- School of Applied and Engineering Physics, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco
- Faculty of Sciences, Mohammed V University in Rabat, Avenue des Nations Unies, Rabat, Morocco
| | - Abdelouahed Elfatimy
- School of Applied and Engineering Physics, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco
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2
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Wu L, Shao H, Li J, Chen C, Hu N, Yang B, Weng H, Xiang L, Ye D. Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0180. [PMID: 38779576 PMCID: PMC11109595 DOI: 10.34133/plantphenomics.0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/29/2024] [Indexed: 05/25/2024]
Abstract
The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.
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Affiliation(s)
- Libin Wu
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Han Shao
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Center for Artificial Intelligence in Agriculture, School of Future Technology,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jiayi Li
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Chen Chen
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Nana Hu
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Center for Artificial Intelligence in Agriculture, School of Future Technology,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Biyun Yang
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Lirong Xiang
- Department of Biological and Agricultural Engineering,
North Carolina State University, Raleigh, NC 27606, USA
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
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3
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Tausta SL, Fontaine K, Hillmer AT, Strobel SA. Fluoride transport in Arabidopsis thaliana plants is impaired in Fluoride EXporter (FEX) mutants. PLANT MOLECULAR BIOLOGY 2024; 114:17. [PMID: 38342783 PMCID: PMC10859346 DOI: 10.1007/s11103-023-01413-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/20/2023] [Indexed: 02/13/2024]
Abstract
Fluoride is an environmental toxin prevalent in water, soil, and air. A fluoride transporter called Fluoride EXporter (FEX) has been discovered across all domains of life, including bacteria, single cell eukaryotes, and all plants, that is required for fluoride tolerance. How FEX functions to protect multicellular plants is unknown. In order to distinguish between different models, the dynamic movement of fluoride in wildtype (WT) and fex mutant plants was monitored using [18F]fluoride with positron emission tomography. Significant differences were observed in the washout behavior following initial fluoride uptake between plants with and without a functioning FEX. [18F]Fluoride traveled quickly up the floral stem and into terminal tissues in WT plants. In contrast, the fluoride did not move out of the lower regions of the stem in mutant plants resulting in clearance rates near zero. The roots were not the primary locus of FEX action, nor did FEX direct fluoride to a specific tissue. Fluoride efflux by WT plants was saturated at high fluoride concentrations resulting in a pattern like the fex mutant. The kinetics of fluoride movement suggested that FEX mediates a fluoride transport mechanism throughout the plant where each individual cell benefits from FEX expression.
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Affiliation(s)
- S Lori Tausta
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06510, USA
- Institute of Biomolecular Design and Discovery, Yale University West Campus, West Haven, CT, 06516, USA
| | - Kathryn Fontaine
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 330 Cedar Street, New Haven, CT, 06519, USA
- Yale PET Center, Yale University, 801 Howard Avenue, New Haven, CT, 06510, USA
| | - Ansel T Hillmer
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 330 Cedar Street, New Haven, CT, 06519, USA
- Yale PET Center, Yale University, 801 Howard Avenue, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, 17 Hillhouse Avenue, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale School of Medicine, 300 George Street, New Haven, CT, 06510, USA
| | - Scott A Strobel
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06510, USA.
- Institute of Biomolecular Design and Discovery, Yale University West Campus, West Haven, CT, 06516, USA.
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4
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Anshori MF, Dirpan A, Sitaresmi T, Rossi R, Farid M, Hairmansis A, Sapta Purwoko B, Suwarno WB, Nugraha Y. An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review. Heliyon 2023; 9:e21650. [PMID: 38027954 PMCID: PMC10660044 DOI: 10.1016/j.heliyon.2023.e21650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
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Affiliation(s)
| | - Andi Dirpan
- Department of Agricultural Technology, Hasanuddin University, Makassar, 90245, Indonesia
- Center of Excellence in Science and Technology on Food Product Diversification, 90245, Makassar, Indonesia
| | - Trias Sitaresmi
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Riccardo Rossi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence (UNIFI), Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Muh Farid
- Department of Agronomy, Hasanuddin University, Makassar, 90245, Indonesia
| | - Aris Hairmansis
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Bambang Sapta Purwoko
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Willy Bayuardi Suwarno
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Yudhistira Nugraha
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
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5
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Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
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Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
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6
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Herzog M, Pellegrini E, Pedersen O. A meta-analysis of plant tissue O 2 dynamics. FUNCTIONAL PLANT BIOLOGY : FPB 2023; 50:519-531. [PMID: 37160400 DOI: 10.1071/fp22294] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/13/2023] [Indexed: 05/11/2023]
Abstract
Adequate tissue O2 supply is crucial for plant function. We aimed to identify the environmental conditions and plant characteristics that affect plant tissue O2 status. We extracted data and performed meta-analysis on >1500 published tissue O2 measurements from 112 species. Tissue O2 status ranged from anoxic conditions in roots to >53kPa in submerged, photosynthesising shoots. Using information-theoretic model selection, we identified 'submergence', 'light', 'tissue type' as well as 'light×submergence' interaction as significant drivers of tissue O2 status. Median O2 status were especially low (Solanum tuberosum ) tubers and root nodules. Mean shoot and root O2 were ~25% higher in light than in dark when shoots had atmospheric contact. However, light showed a significant interaction with submergence on plant O2 , with a submergence-induced 44% increase in light, compared with a 42% decline in dark, relative to plants with atmospheric contact. During submergence, ambient water column O2 and shoot tissue O2 correlated stronger in darkness than in light conditions. Although use of miniaturised Clark-type O2 electrodes has enhanced understanding of plant O2 dynamics, application of non-invasive methods in plants is still lacking behind its widespread use in mammalian tissues.
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Affiliation(s)
- Max Herzog
- The Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Universitetsparken 4, 3rd Floor, Copenhagen 2100, Denmark
| | - Elisa Pellegrini
- The Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Universitetsparken 4, 3rd Floor, Copenhagen 2100, Denmark; and Department of Food, Agricultural, Environmental and Animal Sciences, University of Udine, via delle Scienze 206, Udine, Italy
| | - Ole Pedersen
- The Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Universitetsparken 4, 3rd Floor, Copenhagen 2100, Denmark; and School of Agriculture and Environment, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
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7
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Ye D, Wu L, Li X, Atoba TO, Wu W, Weng H. A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:1698. [PMID: 37111921 PMCID: PMC10146287 DOI: 10.3390/plants12081698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/08/2023] [Accepted: 04/16/2023] [Indexed: 06/19/2023]
Abstract
Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.
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Affiliation(s)
- Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Libin Wu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xiaobin Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Tolulope Opeyemi Atoba
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Wenhao Wu
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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8
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Jeckel AM, Beran F, Züst T, Younkin G, Petschenka G, Pokharel P, Dreisbach D, Ganal-Vonarburg SC, Robert CAM. Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches. Front Physiol 2022; 13:1001032. [PMID: 36237530 PMCID: PMC9552321 DOI: 10.3389/fphys.2022.1001032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions.
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Affiliation(s)
- Adriana Moriguchi Jeckel
- Laboratory of Chemical Ecology, Institute of Plant Sciences, University of Bern, Bern, Switzerland
| | - Franziska Beran
- Department of Insect Symbiosis, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Tobias Züst
- Department of Systematic and Evolutionary Botany, University of Zürich, Zürich, Switzerland
| | - Gordon Younkin
- Boyce Thompson Institute, Ithaca, NY, United States
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Georg Petschenka
- Department of Applied Entomology, Institute of Phytomedicine, University of Hohenheim, Stuttgart, Germany
| | - Prayan Pokharel
- Department of Applied Entomology, Institute of Phytomedicine, University of Hohenheim, Stuttgart, Germany
| | - Domenic Dreisbach
- Institute for Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany
| | - Stephanie Christine Ganal-Vonarburg
- Department of Visceral Surgery and Medicine, Bern University Hospital, University of Bern, Bern, Switzerland
- Department for BioMedical Research, Visceral Surgery and Medicine, University of Bern, Bern, Switzerland
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9
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D'Ascenzo N, Xie Q, Antonecchia E, Ciardiello M, Pagnani G, Pisante M. Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals. FRONTIERS IN PLANT SCIENCE 2022; 13:882382. [PMID: 35941942 PMCID: PMC9356293 DOI: 10.3389/fpls.2022.882382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.
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Affiliation(s)
- Nicola D'Ascenzo
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, Istituto di Ricovero e Cura a Carattere Scientifico, Pozzilli, Italy
| | - Qingguo Xie
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, Istituto di Ricovero e Cura a Carattere Scientifico, Pozzilli, Italy
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Emanuele Antonecchia
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, Istituto di Ricovero e Cura a Carattere Scientifico, Pozzilli, Italy
| | - Mariachiara Ciardiello
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, Istituto di Ricovero e Cura a Carattere Scientifico, Pozzilli, Italy
| | - Giancarlo Pagnani
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Michele Pisante
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
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10
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Adler SS, Seidel J, Choyke PL. Advances in Preclinical PET. Semin Nucl Med 2022; 52:382-402. [PMID: 35307164 PMCID: PMC9038721 DOI: 10.1053/j.semnuclmed.2022.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/18/2022]
Abstract
The classical intent of PET imaging is to obtain the most accurate estimate of the amount of positron-emitting radiotracer in the smallest possible volume element located anywhere in the imaging subject at any time using the least amount of radioactivity. Reaching this goal, however, is confounded by an enormous array of interlinked technical issues that limit imaging system performance. As a result, advances in PET, human or animal, are the result of cumulative innovations across each of the component elements of PET, from data acquisition to image analysis. In the report that follows, we trace several of these advances across the imaging process with a focus on small animal PET.
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Affiliation(s)
- Stephen S Adler
- Frederick National Laboratory for Cancer Research, Frederick, MD; Molecular Imaging Branch, National Cancer Institute, Bethesda MD
| | - Jurgen Seidel
- Contractor to Frederick National Laboratory for Cancer Research, Leidos biodical Research, Inc., Frederick, MD; Molecular Imaging Branch, National Cancer Institute, Bethesda MD
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda MD.
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11
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Antonecchia E, Bäcker M, Cafolla D, Ciardiello M, Kühl C, Pagnani G, Wang J, Wang S, Zhou F, D'Ascenzo N, Gialanella L, Pisante M, Rose G, Xie Q. Design Study of a Novel Positron Emission Tomography System for Plant Imaging. FRONTIERS IN PLANT SCIENCE 2022; 12:736221. [PMID: 35116047 PMCID: PMC8805640 DOI: 10.3389/fpls.2021.736221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Positron Emission Tomography is a non-disruptive and high-sensitive digital imaging technique which allows to measure in-vivo and non invasively the changes of metabolic and transport mechanisms in plants. When it comes to the early assessment of stress-induced alterations of plant functions, plant PET has the potential of a major breakthrough. The development of dedicated plant PET systems faces a series of technological and experimental difficulties, which make conventional clinical and preclinical PET systems not fully suitable to agronomy. First, the functional and metabolic mechanisms of plants depend on environmental conditions, which can be controlled during the experiment if the scanner is transported into the growing chamber. Second, plants need to be imaged vertically, thus requiring a proper Field Of View. Third, the transverse Field of View needs to adapt to the different plant shapes, according to the species and the experimental protocols. In this paper, we perform a simulation study, proposing a novel design of dedicated plant PET scanners specifically conceived to address these agronomic issues. We estimate their expected sensitivity, count rate performance and spatial resolution, and we identify these specific features, which need to be investigated when realizing a plant PET scanner. Finally, we propose a novel approach to the measurement and verification of the performance of plant PET systems, including the design of dedicated plant phantoms, in order to provide a standard evaluation procedure for this emerging digital imaging agronomic technology.
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Affiliation(s)
- Emanuele Antonecchia
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- Istituto Neurologico Mediterraneo, NEUROMED I.R.C.C.S, Pozzilli, Italy
| | - Markus Bäcker
- Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany
| | - Daniele Cafolla
- Istituto Neurologico Mediterraneo, NEUROMED I.R.C.C.S, Pozzilli, Italy
| | | | - Charlotte Kühl
- Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany
| | - Giancarlo Pagnani
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Jiale Wang
- School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute of University of Science and Technology of China, Quzhou, China
| | - Shuai Wang
- School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute of University of Science and Technology of China, Quzhou, China
| | - Feng Zhou
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Nicola D'Ascenzo
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- Istituto Neurologico Mediterraneo, NEUROMED I.R.C.C.S, Pozzilli, Italy
| | - Lucio Gialanella
- Department of Mathematics and Physics, University of Campania L. Vanvitelli, Caserta, Italy
| | - Michele Pisante
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Georg Rose
- Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany
| | - Qingguo Xie
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- Istituto Neurologico Mediterraneo, NEUROMED I.R.C.C.S, Pozzilli, Italy
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
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Komarov S, Tai YC. Positron Emission Tomography (PET) for Molecular Plant Imaging. Methods Mol Biol 2022; 2539:97-118. [PMID: 35895200 DOI: 10.1007/978-1-0716-2537-8_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Positron emission tomography (PET) is an imaging technology that measures 3D spatial distribution and kinetics of radio-tagged biomolecules in a living subject quantitatively and nondestructively. Commonly used positron-emitting radionuclides include 11C, 13N, and 15O, which are essential elements for plant growth. Combining radiotracer techniques with PET, this in vivo molecular imaging capability offers plant biologists a powerful tool for molecular phenotyping research. While PET is widely used clinically for cancer diagnosis and pre-clinically for drug development, it is an unfamiliar imaging tool for plant biologists. This chapter introduces the basic principles of PET, factors that affect the quantitative accuracy of PET when imaging plants, and techniques for administering radiotracers to plants for a variety of molecular plant imaging applications.
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Affiliation(s)
- Sergey Komarov
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Yuan-Chuan Tai
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
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