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Jiang X, Yang T, He F, Zhang F, Jiang X, Wang C, Gao T, Long R, Li M, Yang Q, Wang Y, Zhang T, Kang J. A genome-wide association study reveals novel loci and candidate genes associated with plant height variation in Medicago sativa. BMC PLANT BIOLOGY 2024; 24:544. [PMID: 38872112 DOI: 10.1186/s12870-024-05151-z] [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: 10/08/2023] [Accepted: 05/15/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Plant height (PH) is an important agronomic trait influenced by a complex genetic network. However, the genetic basis for the variation in PH in Medicago sativa remains largely unknown. In this study, a comprehensive genome-wide association analysis was performed to identify genomic regions associated with PH using a diverse panel of 220 accessions of M. sativa worldwide. RESULTS Our study identified eight novel single nucleotide polymorphisms (SNPs) significantly associated with PH evaluated in five environments, explaining 8.59-12.27% of the phenotypic variance. Among these SNPs, the favorable genotype of chr6__31716285 had a low frequency of 16.4%. Msa0882400, located proximal to this SNP, was annotated as phosphate transporter 3;1, and its role in regulating alfalfa PH was supported by transcriptome and candidate gene association analysis. In addition, 21 candidate genes were annotated within the associated regions that are involved in various biological processes related to plant growth and development. CONCLUSIONS Our findings provide new molecular markers for marker-assisted selection in M. sativa breeding programs. Furthermore, this study enhances our understanding of the underlying genetic and molecular mechanisms governing PH variations in M. sativa.
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Affiliation(s)
- Xueqian Jiang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tianhui Yang
- Institute of Animal Science, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan, Ningxia, China
- Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, Inner Mongolia, China
| | - Fei He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fan Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xu Jiang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chuan Wang
- Institute of Animal Science, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan, Ningxia, China
| | - Ting Gao
- Institute of Animal Science, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan, Ningxia, China
| | - Ruicai Long
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingna Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qingchuan Yang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yue Wang
- Beijing NO.19 High School, Beijing, China
| | - Tiejun Zhang
- School of Grassland Science, Beijing Forestry University, Beijing, China.
| | - Junmei Kang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
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Nagle MF, Yuan J, Kaur D, Ma C, Peremyslova E, Jiang Y, Niño de Rivera A, Jawdy S, Chen JG, Feng K, Yates TB, Tuskan GA, Muchero W, Fuxin L, Strauss SH. GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa. G3 (BETHESDA, MD.) 2024; 14:jkae026. [PMID: 38325329 PMCID: PMC10989874 DOI: 10.1093/g3journal/jkae026] [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: 11/14/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/09/2024]
Abstract
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
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Affiliation(s)
- Michael F Nagle
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Jialin Yuan
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Damanpreet Kaur
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Ekaterina Peremyslova
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Yuan Jiang
- Statistics Department, Oregon State University, 239 Weniger Hall, Corvallis, OR 97331, USA
| | - Alexa Niño de Rivera
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Sara Jawdy
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Kai Feng
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Timothy B Yates
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Li Fuxin
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Steven H Strauss
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
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Vello E, Letourneau M, Aguirre J, Bureau TE. Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms. FRONTIERS IN PLANT SCIENCE 2024; 14:1303429. [PMID: 38273948 PMCID: PMC10808381 DOI: 10.3389/fpls.2023.1303429] [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: 09/29/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024]
Abstract
Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.
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Affiliation(s)
- Emilio Vello
- Department of Biology, McGill University, Montreal, QC, Canada
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Mostafa S, Mondal D, Panjvani K, Kochian L, Stavness I. Explainable deep learning in plant phenotyping. Front Artif Intell 2023; 6:1203546. [PMID: 37795496 PMCID: PMC10546035 DOI: 10.3389/frai.2023.1203546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/25/2023] [Indexed: 10/06/2023] Open
Abstract
The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.
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Affiliation(s)
- Sakib Mostafa
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Debajyoti Mondal
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Leon Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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Nagle MF, Yuan J, Kaur D, Ma C, Peremyslova E, Jiang Y, Zahl B, Niño de Rivera A, Muchero W, Fuxin L, Strauss SH. GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa. HORTICULTURE RESEARCH 2023; 10:uhad125. [PMID: 37560019 PMCID: PMC10407606 DOI: 10.1093/hr/uhad125] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/05/2023] [Indexed: 08/11/2023]
Abstract
Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of Populus trichocarpa. GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, reactive oxygen species signaling, and other processes with known roles in root development. Numerous genes with uncharacterized functions and/or cryptic roles were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of AR.
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Affiliation(s)
- Michael F Nagle
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Jialin Yuan
- Department of Electrical Engineering and Computer Science, Oregon State University, 110 SW Park Terrace, Corvallis, OR, 97331, United States
| | - Damanpreet Kaur
- Department of Electrical Engineering and Computer Science, Oregon State University, 110 SW Park Terrace, Corvallis, OR, 97331, United States
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Ekaterina Peremyslova
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Yuan Jiang
- Statistics Department, Oregon State University, 103 SW Memorial Place, Corvallis, OR, 97331, United States
| | - Bahiya Zahl
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Alexa Niño de Rivera
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States
- Bredesen Center for Interdisciplinary Research, University of Tennessee, 821 Volunteer Blvd., Knoxville, TN, 37996, United States
| | - Li Fuxin
- Department of Electrical Engineering and Computer Science, Oregon State University, 110 SW Park Terrace, Corvallis, OR, 97331, United States
| | - Steven H Strauss
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
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Solimani F, Cardellicchio A, Nitti M, Lako A, Dimauro G, Renò V. A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping. INFORMATION 2023. [DOI: 10.3390/info14040214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
Abstract
Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.
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Affiliation(s)
- Firozeh Solimani
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Angelo Cardellicchio
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Massimiliano Nitti
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Alfred Lako
- Faculty of Civil Engineering, Polytechnic University of Tirana, Bulevardi Dëshmorët e Kombit Nr. 4, 1000 Tiranë, Albania
| | - Giovanni Dimauro
- Department of Computer Science, University of Bari, Via E. Orabona, 4, 70125 Bari, Italy
| | - Vito Renò
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
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Wang Y, Sun Z, Wang Q, Xie J, Yu L. Transcriptomics and metabolomics revealed that phosphate improves the cold tolerance of alfalfa. FRONTIERS IN PLANT SCIENCE 2023; 14:1100601. [PMID: 36968379 PMCID: PMC10034057 DOI: 10.3389/fpls.2023.1100601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Alfalfa (Medicago sativa L.) is a highly nutritious leguminous forage that plays an essential role in animal husbandry. In the middle and high latitudes of the northern hemisphere, there are problems with its low rates of overwintering and production. The application of phosphate (P) is an important measure to improve the cold resistance and production of alfalfa, but little is known about the mechanism of P in improving the cold resistance of alfalfa. METHODS This study integrated the transcriptome and metabolome to explain the mechanism of alfalfa in response to low-temperature stress under two applications of P (50 and 200 mg kg-1) and a control of none applied. RESULTS The application of P fertilizer improved the root structure and increased the content of soluble sugar and soluble protein in the root crown. In addition, there were 49 differentially expressed genes (DEGs) with 23 upregulated and 24 metabolites with 12 upregulated when 50 mg kg-1 of P was applied. In contrast, there were 224 DEGs with 173 upregulated and 12 metabolites with 6 upregulated in the plants treated with 200 mg kg-1 of P compared with the Control Check (CK). These genes and metabolites were significantly enriched in the biosynthesis of other secondary metabolites and the metabolic pathways of carbohydrates and amino acids. The integration of the transcriptome and metabolome indicated that P affected the biosynthesis of N-acetyl-L-phenylalanine, L-serine, lactose, and isocitrate during the period of increasing cold. It could also affect the expression of related genes that regulate cold tolerance in alfalfa. DISCUSSION Our findings could contribute to a deeper understanding of the mechanism that alfalfa uses to tolerate cold and lay a theoretical foundation for breeding alfalfa that is highly efficient at utilizing phosphorus.
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Affiliation(s)
- Yuntao Wang
- Grassland Research Institute, Chinese Academy of Agricultural Science, Hohhot, Inner Mongolia, China
| | - Zhen Sun
- College of Grassland, Qingdao Agricultural University, Qingdao, Shandong, China
| | - Qiqi Wang
- Grassland Research Institute, Chinese Academy of Agricultural Science, Hohhot, Inner Mongolia, China
| | - Jihong Xie
- Grassland Research Institute, Chinese Academy of Agricultural Science, Hohhot, Inner Mongolia, China
| | - Linqing Yu
- College of Life Science, Inner Mongolia University, Hohhot, Inner Mongolia, China
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