Charlie P. Bailey, Carolyn A. Sonter, Jeremy L. Jones, Sabu Pandey, Simon Haberle, Karen C. B. S. Santos, Maria L. Absy, Romina Rader
Premise
Pollen collected by honey bees from different plant species often differs in color, and this has been used as a basis for plant identification. The objective of this study was to develop a new, low-cost protocol to sort pollen pellets by color using high-energy violet light and visible light to determine whether pollen pellet color is associated with variations in plant species identity.
Methods and Results
We identified 35 distinct colors and found that 52% of pollen subsamples (n = 200) were dominated by a single taxon. Among these near-pure pellets, only one color consistently represented a single pollen taxon (Asteraceae: Cichorioideae). Across the spectrum of colors spanning yellows, oranges, and browns, similarly colored pollen pellets contained pollen from multiple plant families ranging from two to 13 families per color.
Conclusions
Sorting pollen pellets illuminated under high-energy violet light lit from four directions within a custom-made light box aided in distinguishing pellet composition, especially in pellets within the same color.
{"title":"Does sorting by color using visible and high-energy violet light improve classification of taxa in honey bee pollen pellets?","authors":"Charlie P. Bailey, Carolyn A. Sonter, Jeremy L. Jones, Sabu Pandey, Simon Haberle, Karen C. B. S. Santos, Maria L. Absy, Romina Rader","doi":"10.1002/aps3.11514","DOIUrl":"10.1002/aps3.11514","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Pollen collected by honey bees from different plant species often differs in color, and this has been used as a basis for plant identification. The objective of this study was to develop a new, low-cost protocol to sort pollen pellets by color using high-energy violet light and visible light to determine whether pollen pellet color is associated with variations in plant species identity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>We identified 35 distinct colors and found that 52% of pollen subsamples (<i>n</i> = 200) were dominated by a single taxon. Among these near-pure pellets, only one color consistently represented a single pollen taxon (Asteraceae: Cichorioideae). Across the spectrum of colors spanning yellows, oranges, and browns, similarly colored pollen pellets contained pollen from multiple plant families ranging from two to 13 families per color.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Sorting pollen pellets illuminated under high-energy violet light lit from four directions within a custom-made light box aided in distinguishing pellet composition, especially in pellets within the same color.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e5/98/APS3-11-e11514.PMC10083439.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian S. Bowman, Ryan Traband, Xuesong Wang, Sara P. Knowles, Sassoum Lo, Zhenyu Jia, Nicholi Vorsa, Ira A. Herniter
Premise
The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.
Methods and Results
MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold–based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner.
Conclusions
MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.
{"title":"Multiple Leaf Sample Extraction System (MuLES): A tool to improve automated morphometric leaf studies","authors":"Christian S. Bowman, Ryan Traband, Xuesong Wang, Sara P. Knowles, Sassoum Lo, Zhenyu Jia, Nicholi Vorsa, Ira A. Herniter","doi":"10.1002/aps3.11513","DOIUrl":"10.1002/aps3.11513","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold–based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8d/19/APS3-11-e11513.PMC10083438.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Denise L. Lindsay, Xin Guan, Nathan E. Harms, James T. Cronin, Laura A. Meyerson, Richard F. Lance
Premise
To genetically discriminate subspecies of the common reed (Phragmites australis), we developed real-time quantitative (qPCR) assays for identifying P. australis subsp. americanus, P. australis subsp. australis, and P. australis subsp. berlandieri.
Methods and Results
Utilizing study-generated chloroplast DNA sequences, we developed three novel qPCR assays. Assays were verified on individuals of each subspecies and against two non-target species, Arundo donax and Phalaris arundinacea. One assay amplifies only P. australis subsp. americanus, one amplifies P. australis subsp. australis and/or P. australis subsp. berlandieri, and one amplifies P. australis subsp. americanus and/or P. australis subsp. australis. This protocol enhances currently available rapid identification methods by providing genetic discrimination of all three subspecies.
Conclusions
The newly developed assays were validated using P. australis samples from across the United States. Application of these assays outside of this geographic range should be preceded by additional testing.
{"title":"DNA assays for genetic discrimination of three Phragmites australis subspecies in the United States","authors":"Denise L. Lindsay, Xin Guan, Nathan E. Harms, James T. Cronin, Laura A. Meyerson, Richard F. Lance","doi":"10.1002/aps3.11512","DOIUrl":"https://doi.org/10.1002/aps3.11512","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>To genetically discriminate subspecies of the common reed (<i>Phragmites australis</i>), we developed real-time quantitative (qPCR) assays for identifying <i>P. australis</i> subsp. <i>americanus</i>, <i>P. australis</i> subsp. <i>australis</i>, and <i>P. australis</i> subsp. <i>berlandieri</i>.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>Utilizing study-generated chloroplast DNA sequences, we developed three novel qPCR assays. Assays were verified on individuals of each subspecies and against two non-target species, <i>Arundo donax</i> and <i>Phalaris arundinacea</i>. One assay amplifies only <i>P. australis</i> subsp. <i>americanus</i>, one amplifies <i>P. australis</i> subsp. <i>australis</i> and/or <i>P. australis</i> subsp. <i>berlandieri</i>, and one amplifies <i>P. australis</i> subsp. <i>americanus</i> and/or <i>P. australis</i> subsp. <i>australis</i>. This protocol enhances currently available rapid identification methods by providing genetic discrimination of all three subspecies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The newly developed assays were validated using <i>P. australis</i> samples from across the United States. Application of these assays outside of this geographic range should be preceded by additional testing.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50140455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peggy Martinez, Marcelo Serpe, Rachael Barron, Sven Buerki
Premise
Determining the tolerance of plant populations to climate change requires the development of biotechnological protocols producing genetically identical individuals used for genotype-by-environment experiments. Such protocols are missing for slow-growth, woody plants; to address this gap, this study uses Artemisia tridentata, a western North American keystone shrub, as model.
Methods and Results
The production of individual lines is a two-step process: in vitro propagation under aseptic conditions followed by ex vitro acclimation and hardening. Due to aseptic growth conditions, in vitro plantlets exhibit maladapted phenotypes, and this protocol focuses on presenting an approach promoting morphogenesis for slow-growth, woody species. Survival was used as the main criterion determining successful acclimation and hardening. Phenotypic changes were confirmed by inspecting leaf anatomy, and shoot water potential was used to ensure that plantlets were not water stressed.
Conclusions
Although our protocol has lower survival rates (11–41%) compared to protocols developed for herbaceous, fast-growing species, it provides a benchmark for slow-growth, woody species occurring in dry ecosystems.
{"title":"Acclimation and hardening of a slow-growing woody species emblematic to western North America from in vitro plantlets","authors":"Peggy Martinez, Marcelo Serpe, Rachael Barron, Sven Buerki","doi":"10.1002/aps3.11515","DOIUrl":"10.1002/aps3.11515","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Determining the tolerance of plant populations to climate change requires the development of biotechnological protocols producing genetically identical individuals used for genotype-by-environment experiments. Such protocols are missing for slow-growth, woody plants; to address this gap, this study uses <i>Artemisia tridentata</i>, a western North American keystone shrub, as model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>The production of individual lines is a two-step process: in vitro propagation under aseptic conditions followed by ex vitro acclimation and hardening. Due to aseptic growth conditions, in vitro plantlets exhibit maladapted phenotypes, and this protocol focuses on presenting an approach promoting morphogenesis for slow-growth, woody species. Survival was used as the main criterion determining successful acclimation and hardening. Phenotypic changes were confirmed by inspecting leaf anatomy, and shoot water potential was used to ensure that plantlets were not water stressed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Although our protocol has lower survival rates (11–41%) compared to protocols developed for herbaceous, fast-growing species, it provides a benchmark for slow-growth, woody species occurring in dry ecosystems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a6/24/APS3-11-e11515.PMC10083460.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The editors gratefully acknowledge our reviewers, who have generously given their time and expertise to review manuscripts submitted to Applications in Plant Sciences. The list includes those who reviewed manuscripts from December 31, 2021, to December 31, 2022. Thank you for helping APPS maintain a prompt and fair peer-review process.
C. Gary Olds, Jessie W. Berta-Thompson, Justin J. Loucks, Richard A. Levy, Andrew W. Wilson
Premise
Fungaria are an underutilized resource for understanding fungal biodiversity. The effort and cost of producing DNA barcode sequence data for large numbers of fungal specimens can be prohibitive. This study applies a modified metabarcoding approach that provides a labor-efficient and cost-effective solution for sequencing the fungal DNA barcodes of hundreds of specimens at once.
Methods
We applied a two-step PCR approach using nested, barcoded primers to sequence the fungal nrITS2 region of 766 macrofungal specimens using the Illumina platform. The specimens represent a broad taxonomic sampling of the Dikarya. Of these, 382 Lactarius specimens were analyzed to identify molecular operational taxonomic units (MOTUs) using a phylogenetic approach. The raw sequences were trimmed, filtered, assessed, and analyzed using the DADA2 amplicon de-noising toolkit and Biopython. The sequences were compared to the NCBI and UNITE databases and Sanger nrITS sequences from the same specimens.
Results
The taxonomic identities derived from the nrITS2 sequence data were >90% accurate across all specimens sampled. A phylogenetic analysis of the Lactarius sequences identified 20 MOTUs.
Discussion
The results demonstrate the capacity of these methods to produce nrITS2 sequences from large numbers of fungarium specimens. This provides an opportunity to more effectively use fungarium collections to advance fungal diversity identification and documentation.
{"title":"Applying a modified metabarcoding approach for the sequencing of macrofungal specimens from fungarium collections","authors":"C. Gary Olds, Jessie W. Berta-Thompson, Justin J. Loucks, Richard A. Levy, Andrew W. Wilson","doi":"10.1002/aps3.11508","DOIUrl":"10.1002/aps3.11508","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Fungaria are an underutilized resource for understanding fungal biodiversity. The effort and cost of producing DNA barcode sequence data for large numbers of fungal specimens can be prohibitive. This study applies a modified metabarcoding approach that provides a labor-efficient and cost-effective solution for sequencing the fungal DNA barcodes of hundreds of specimens at once.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We applied a two-step PCR approach using nested, barcoded primers to sequence the fungal nrITS2 region of 766 macrofungal specimens using the Illumina platform. The specimens represent a broad taxonomic sampling of the Dikarya. Of these, 382 <i>Lactarius</i> specimens were analyzed to identify molecular operational taxonomic units (MOTUs) using a phylogenetic approach. The raw sequences were trimmed, filtered, assessed, and analyzed using the DADA2 amplicon de-noising toolkit and Biopython. The sequences were compared to the NCBI and UNITE databases and Sanger nrITS sequences from the same specimens.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The taxonomic identities derived from the nrITS2 sequence data were >90% accurate across all specimens sampled. A phylogenetic analysis of the <i>Lactarius</i> sequences identified 20 MOTUs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The results demonstrate the capacity of these methods to produce nrITS2 sequences from large numbers of fungarium specimens. This provides an opportunity to more effectively use fungarium collections to advance fungal diversity identification and documentation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10768797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexia Stettinius, Hal Holmes, Qian Zhang, Isabelle Mehochko, Misa Winters, Ruby Hutchison, Adam Maxwell, Jason Holliday, Eli Vlaisavljevich
Premise
Sample preparation in genomics is a critical step that is often overlooked in molecular workflows and impacts the success of downstream genetic applications. This study explores the use of a recently developed focused ultrasound extraction (FUSE) technique to enable the rapid release of DNA from plant tissues for genetic analysis.
Methods
FUSE generates a dense acoustic cavitation bubble cloud that pulverizes targeted tissue into acellular debris. This technique was applied to leaf samples of American chestnut (Castanea dentata), tulip poplar (Liriodendron tulipifera), red maple (Acer rubrum), and chestnut oak (Quercus montana).
Results
We observed that FUSE can extract high quantities of DNA in 9–15 min, compared to the 30 min required for control DNA extraction methods. FUSE extracted DNA quantities of 24.33 ± 6.51 ng/mg and 35.32 ± 9.21 ng/mg from American chestnut and red maple, respectively, while control methods yielded 6.22 ± 0.87 ng/mg and 11.51 ± 1.95 ng/mg, respectively. The quality of the DNA released by FUSE allowed for successful amplification and next-generation sequencing.
Discussion
These results indicate that FUSE can improve DNA extraction efficiency for leaf tissues. Continued development of this technology aims to adapt to field-deployable systems to increase the cataloging of genetic biodiversity, particularly in low-resource biodiversity hotspots.
{"title":"DNA release from plant tissue using focused ultrasound extraction (FUSE)","authors":"Alexia Stettinius, Hal Holmes, Qian Zhang, Isabelle Mehochko, Misa Winters, Ruby Hutchison, Adam Maxwell, Jason Holliday, Eli Vlaisavljevich","doi":"10.1002/aps3.11510","DOIUrl":"10.1002/aps3.11510","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Sample preparation in genomics is a critical step that is often overlooked in molecular workflows and impacts the success of downstream genetic applications. This study explores the use of a recently developed focused ultrasound extraction (FUSE) technique to enable the rapid release of DNA from plant tissues for genetic analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>FUSE generates a dense acoustic cavitation bubble cloud that pulverizes targeted tissue into acellular debris. This technique was applied to leaf samples of American chestnut (<i>Castanea dentata</i>), tulip poplar (<i>Liriodendron tulipifera</i>), red maple (<i>Acer rubrum</i>), and chestnut oak (<i>Quercus montana</i>).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We observed that FUSE can extract high quantities of DNA in 9–15 min, compared to the 30 min required for control DNA extraction methods. FUSE extracted DNA quantities of 24.33 ± 6.51 ng/mg and 35.32 ± 9.21 ng/mg from American chestnut and red maple, respectively, while control methods yielded 6.22 ± 0.87 ng/mg and 11.51 ± 1.95 ng/mg, respectively. The quality of the DNA released by FUSE allowed for successful amplification and next-generation sequencing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>These results indicate that FUSE can improve DNA extraction efficiency for leaf tissues. Continued development of this technology aims to adapt to field-deployable systems to increase the cataloging of genetic biodiversity, particularly in low-resource biodiversity hotspots.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/df/34/APS3-11-e11510.PMC9934592.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10768800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Orla L. Sherwood, Rebecca Carroll, Stephen Burke, Paul F. McCabe, Joanna Kacprzyk
Premise
We developed a novel, cost-effective protocol that facilitates testing anoxia tolerance in plants without access to specialized equipment.
Methods and Results
Arabidopsis thaliana and barley (Hordeum vulgare) seedlings were treated in airtight 2-L Kilner jars. An anoxic atmosphere was generated using Oxoid AnaeroGen 2.5-L sachets placed on in-house, custom-built wire stands. The performed experiments confirmed a higher sensitivity to low oxygen stress previously observed in anac017 A. thaliana mutants and the positive effect of exogenous sucrose on anoxia tolerance reported by previous studies in A. thaliana. Barley seedlings displayed typical responses to anoxia treatment, including shoot growth cessation and the induction of marker genes for anaerobic metabolism and ethylene biosynthesis in root tissue.
Conclusions
The results validate the novel method as an inexpensive, simple alternative for testing anoxia tolerance in plants, where access to an anaerobic workstation is not possible. The novel protocol requires minimum investment and is easily adaptable.
{"title":"A simple and cost-effective method for studying anoxia tolerance in plants","authors":"Orla L. Sherwood, Rebecca Carroll, Stephen Burke, Paul F. McCabe, Joanna Kacprzyk","doi":"10.1002/aps3.11509","DOIUrl":"10.1002/aps3.11509","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>We developed a novel, cost-effective protocol that facilitates testing anoxia tolerance in plants without access to specialized equipment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p><i>Arabidopsis thaliana</i> and barley (<i>Hordeum vulgare</i>) seedlings were treated in airtight 2-L Kilner jars. An anoxic atmosphere was generated using Oxoid AnaeroGen 2.5-L sachets placed on in-house, custom-built wire stands. The performed experiments confirmed a higher sensitivity to low oxygen stress previously observed in <i>anac017 A. thaliana</i> mutants and the positive effect of exogenous sucrose on anoxia tolerance reported by previous studies in <i>A. thaliana</i>. Barley seedlings displayed typical responses to anoxia treatment, including shoot growth cessation and the induction of marker genes for anaerobic metabolism and ethylene biosynthesis in root tissue.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The results validate the novel method as an inexpensive, simple alternative for testing anoxia tolerance in plants, where access to an anaerobic workstation is not possible. The novel protocol requires minimum investment and is easily adaptable.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10756730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Logan D. Pierz, Dilyn R. Heslinga, C. Robin Buell, Miranda J. Haus
Premise
Plant disease severity assessments are used to quantify plant–pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis.
Methods
Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot.
Results
Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output.
Discussion
Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
{"title":"An image-based technique for automated root disease severity assessment using PlantCV","authors":"Logan D. Pierz, Dilyn R. Heslinga, C. Robin Buell, Miranda J. Haus","doi":"10.1002/aps3.11507","DOIUrl":"10.1002/aps3.11507","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Plant disease severity assessments are used to quantify plant–pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (<i>R</i><sup>2</sup> = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10768802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rachel A. Perez-Udell, Andrew T. Udell, Shu-Mei Chang
Premise
Petal color is an ecologically important trait, and uncovering color variation over a geographic range, particularly in species with large distributions and/or short bloom times, requires extensive fieldwork. We have developed an alternative method that segments images from citizen science repositories using Python and k-means clustering in the hue-saturation-value (HSV) color space.
Methods
Our method uses k-means clustering to aggregate like-color pixels in sample images to generate the HSV color space encapsulating the color range of petals. Using the HSV values, our method isolates photographs containing clusters in that range and bins them into a classification scheme based on user-defined categories.
Results
We demonstrate the application of this method using two species: one with a continuous range of variation of pink-purple petals in Geranium maculatum, and one with a binary classification of white versus blue in Linanthus parryae. We demonstrate results that are repeatable and accurate.
Discussion
This method provides a flexible, robust, and easily adjustable approach for the classification of color images from citizen science repositories. By using color to classify images, this pipeline sidesteps many of the issues encountered using more traditional computer vision applications. This approach provides a tool for making use of large citizen scientist data sets.
{"title":"An automated pipeline for supervised classification of petal color from citizen science photographs","authors":"Rachel A. Perez-Udell, Andrew T. Udell, Shu-Mei Chang","doi":"10.1002/aps3.11505","DOIUrl":"10.1002/aps3.11505","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Petal color is an ecologically important trait, and uncovering color variation over a geographic range, particularly in species with large distributions and/or short bloom times, requires extensive fieldwork. We have developed an alternative method that segments images from citizen science repositories using Python and <i>k</i>-means clustering in the hue-saturation-value (HSV) color space.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Our method uses <i>k</i>-means clustering to aggregate like-color pixels in sample images to generate the HSV color space encapsulating the color range of petals. Using the HSV values, our method isolates photographs containing clusters in that range and bins them into a classification scheme based on user-defined categories.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We demonstrate the application of this method using two species: one with a continuous range of variation of pink-purple petals in <i>Geranium maculatum</i>, and one with a binary classification of white versus blue in <i>Linanthus parryae</i>. We demonstrate results that are repeatable and accurate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>This method provides a flexible, robust, and easily adjustable approach for the classification of color images from citizen science repositories. By using color to classify images, this pipeline sidesteps many of the issues encountered using more traditional computer vision applications. This approach provides a tool for making use of large citizen scientist data sets.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5c/78/APS3-11-e11505.PMC9934523.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10756729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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