Plant Methods最新文献

{"title":"GRABSEEDS: extraction of plant organ traits through image analysis","authors":"Haibao Tang, Wenqian Kong, Pheonah Nabukalu, Johnathan S. Lomas, Michel Moser, Jisen Zhang, Mengwei Jiang, Xingtan Zhang, Andrew H. Paterson, Won Cheol Yim","doi":"10.1186/s13007-024-01268-2","DOIUrl":"https://doi.org/10.1186/s13007-024-01268-2","url":null,"abstract":"Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"11 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid real-time quantitative colorimetric LAMP methodology for field detection of Verticillium dahliae in crude olive-plant samples","authors":"Maria Megariti, Alexandra Panagou, Georgios Patsis, George Papadakis, Alexandros K. Pantazis, Epaminondas J. Paplomatas, Aliki K. Tzima, Emmanouil A. Markakis, Electra Gizeli","doi":"10.1186/s13007-024-01251-x","DOIUrl":"https://doi.org/10.1186/s13007-024-01251-x","url":null,"abstract":"Verticilium dahliae is the most important wilt pathogen of olive trees with a broad host range causing devastating diseases currently without any effective chemical control. Traditional detection methodologies are based on symptoms-observation or lab-detection using time consuming culturing or molecular techniques. Therefore, there is an increasing need for portable tools that can detect rapidly V. dahliae in the field. In this work, we report the development of a novel method for the rapid, reliable and on-site detection of V. dahliae using a newly designed isothermal LAMP assay and crude extracts of olive wood. For the detection of the fungus, LAMP primers were designed targeting the internal transcribed spacer (ITS) region of the rRNA gene. The above assay was combined with a purpose-built prototype portable device which allowed real time quantitative colorimetric detection of V. dahliae in 35 min. The limit of detection of our assay was found to be 0.8 fg/μl reaction and the specificity 100% as indicated by zero cross-reactivity to common pathogens found in olive trees. Moreover, detection of V. dahliae in purified DNA gave a sensitivity of 100% (Ct < 30) and 80% (Ct > 30) while the detection of the fungus in unpurified crude wood extracts showed a sensitivity of 80% when multisampling was implemented. The superiority of the LAMP methodology regarding robustness and sensitivity was demonstrated when only LAMP was able to detect V. dahliae in crude samples from naturally infected trees with very low infection levels, while nested PCR and SYBR qPCR failed to detect the pathogen in an unpurified form. This study describes the development of a new real time LAMP assay, targeting the ITS region of the rRNA gene of V. dahliae in olive trees combined with a 3D-printed portable device for field testing using a tablet. The assay is characterized by high sensitivity and specificity as well as ability to operate using directly crude samples such as woody tissue or petioles. The reported methodology is setting the basis for the development of an on-site detection methodology for V. dahliae in olive trees, but also for other plant pathogens.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"8 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Exploring Agrobacterium-mediated genetic transformation methods and its applications in Lilium","authors":"Xinyue Fan, Hongmei Sun","doi":"10.1186/s13007-024-01262-8","DOIUrl":"https://doi.org/10.1186/s13007-024-01262-8","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Correction: Plant Methods (2024) 20:120&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;https://doi.org/10.1186/s13007-024-01246-8&lt;/b&gt;&lt;/p&gt;&lt;p&gt;In this article ref. 4 was incorrect ‘Li JW, Zhang XC, Wang MR, Bi WL, Faisal M, Da Silva JAT, et al. Development, progress and future prospects in cryobiotechnology of &lt;i&gt;Lilium&lt;/i&gt; spp. Plant Method. 2019;15:125’ and should have been’ Li JW, Zhang XC, Wang MR, Bi WL, Faisal M, Teixeira da Silva JA, et al. Development, progress and future prospects in cryobiotechnology of &lt;i&gt;Lilium&lt;/i&gt; spp. Plant Method. 2019;15:125’.&lt;/p&gt;&lt;p&gt;The original article has been corrected.&lt;/p&gt;&lt;h3&gt;Authors and Affiliations&lt;/h3&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Key Laboratory of Protected Horticulture of Education Ministry, College of Horticulture, Shenyang Agricultural University, Shenyang, 110866, China&lt;/p&gt;&lt;p&gt;Xinyue Fan &amp; Hongmei Sun&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;National and Local Joint Engineering Research Center of Northern Horticultural Facilities Design and Application Technology, Shenyang, 110866, China&lt;/p&gt;&lt;p&gt;Hongmei Sun&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;span&gt;Authors&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;span&gt;Xinyue Fan&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Hongmei Sun&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;h3&gt;Corresponding author&lt;/h3&gt;&lt;p&gt;Correspondence to Hongmei Sun.&lt;/p&gt;&lt;h3&gt;Publisher’s note&lt;/h3&gt;&lt;p&gt;Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.&lt;/p&gt;&lt;p&gt;The online version of the original article can be found at https://doi.org/10.1186/s13007-024-01246-8.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Open Access&lt;/b&gt; This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.&lt;/p&gt;\u0000&lt;p&gt;Reprints and permissions&lt;/p&gt;&lt;img alt=\"Check for updates. Verify currency and authenticity via CrossMark\" height=\"81\" loading=\"lazy\" src=\"data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjgxIiB3aWR0aD0iNTciIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PGcgZmlsbD0ibm9uZSIgZmlsbC1ydWxlPSJldmVub2RkIj48cGF0aC","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"37 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM","authors":"Xuanyu Chen, Wei He, Zhihao Ye, Junyi Gai, Wei Lu, Guangnan Xing","doi":"10.1186/s13007-024-01263-7","DOIUrl":"https://doi.org/10.1186/s13007-024-01263-7","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Correction: Plant methods (2024) 20: 130&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;https://doi.org/10.1186/s13007-024-01257-5&lt;/b&gt;&lt;/p&gt;&lt;p&gt;In this article Guangnan Xing should have been denoted as a corresponding author.&lt;/p&gt;&lt;p&gt;The original article has been corrected.&lt;/p&gt;&lt;h3&gt;Authors and Affiliations&lt;/h3&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China&lt;/p&gt;&lt;p&gt;Xuanyu Chen &amp; Wei Lu&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China&lt;/p&gt;&lt;p&gt;Wei He&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics &amp; Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China&lt;/p&gt;&lt;p&gt;Zhihao Ye, Junyi Gai &amp; Guangnan Xing&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;span&gt;Authors&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;span&gt;Xuanyu Chen&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Wei He&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Zhihao Ye&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Junyi Gai&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Wei Lu&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Guangnan Xing&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;h3&gt;Corresponding authors&lt;/h3&gt;&lt;p&gt;Correspondence to Wei Lu or Guangnan Xing.&lt;/p&gt;&lt;h3&gt;Publisher’s note&lt;/h3&gt;&lt;p&gt;Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.&lt;/p&gt;&lt;p&gt;The online version of the original article can be found at https://doi.org/10.1186/s13007-024-01257-5.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Open Access&lt;/b&gt; This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons li","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"9 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nondestructive detection of saline-alkali stress in wheat (Triticum aestivum L.) seedlings via fusion technology.","authors":"Ying Gu, Guoqing Feng, Peichen Hou, Yanan Zhou, He Zhang, Xiaodong Wang, Bin Luo, Liping Chen","doi":"10.1186/s13007-024-01248-6","DOIUrl":"10.1186/s13007-024-01248-6","url":null,"abstract":"<p><strong>Background: </strong>Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management.</p><p><strong>Results: </strong>This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.</p><p><strong>Conclusions: </strong>Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"136"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142140796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.","authors":"Sikiru Adeniyi Atanda, Nonoy Bandillo","doi":"10.1186/s13007-024-01258-4","DOIUrl":"10.1186/s13007-024-01258-4","url":null,"abstract":"<p><p>The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"133"},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142110846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian approach for estimating the uncertainty on the contribution of nitrogen fixation and calculation of nutrient balances in grain legumes.","authors":"Francisco Palmero, Trevor J Hefley, Josefina Lacasa, Luiz Felipe Almeida, Ricardo J Haro, Fernando O Garcia, Fernando Salvagiotti, Ignacio A Ciampitti","doi":"10.1186/s13007-024-01261-9","DOIUrl":"10.1186/s13007-024-01261-9","url":null,"abstract":"<p><strong>Background: </strong>The proportion of nitrogen (N) derived from the atmosphere (Ndfa) is a fundamental component of the plant N demand in legume species. To estimate the N benefit of grain legumes for the subsequent crop in the rotation, a simplified N balance is frequently used. This balance is calculated as the difference between fixed N and removed N by grains. The Ndfa needed to achieve a neutral N balance (hereafter <math><mi>θ</mi></math> ) is usually estimated through a simple linear regression model between Ndfa and N balance. This quantity is routinely estimated without accounting for the uncertainty in the estimate, which is needed to perform formal statistical inference about <math><mi>θ</mi></math> . In this article, we utilized a global database to describe the development of a novel Bayesian framework to quantify the uncertainty of <math><mi>θ</mi></math> . This study aimed to (i) develop a Bayesian framework to quantify the uncertainty of <math><mi>θ</mi></math> , and (ii) contrast the use of this Bayesian framework with the widely used delta and bootstrapping methods under different data availability scenarios.</p><p><strong>Results: </strong>The delta method, bootstrapping, and Bayesian inference provided nearly equivalent numerical values when the range of values for Ndfa was thoroughly explored during data collection (e.g., 6-91%), and the number of observations was relatively high (e.g., <math><mrow><mo>≥</mo> <mn>100</mn></mrow> </math> ). When the Ndfa tested was narrow and/or sample size was small, the delta method and bootstrapping provided confidence intervals containing biologically non-meaningful values (i.e. < 0% or > 100%). However, under a narrow Ndfa range and small sample size, the developed Bayesian inference framework obtained biologically meaningful values in the uncertainty estimation.</p><p><strong>Conclusion: </strong>In this study, we showed that the developed Bayesian framework was preferable under limited data conditions ─by using informative priors─ and when uncertainty estimation had to be constrained (regularized) to obtain meaningful inference. The presented Bayesian framework lays the foundation not only to conduct formal comparisons or hypothesis testing involving <math><mi>θ</mi></math> , but also to learn about its expected value, variance, and higher moments such as skewness and kurtosis under different agroecological and crop management conditions. This framework can also be transferred to estimate balances for other nutrients and/or field crops to gain knowledge on global crop nutrient balances.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"134"},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining Fourier-transform infrared spectroscopy and multivariate analysis for chemotyping of cell wall composition in Mungbean (Vigna radiata (L.) Wizcek).","authors":"Shouvik Das, Vikrant Bhati, Bhagwat Prasad Dewangan, Apurva Gangal, Gyan Prakash Mishra, Harsh Kumar Dikshit, Prashant Anupama Mohan Pawar","doi":"10.1186/s13007-024-01260-w","DOIUrl":"10.1186/s13007-024-01260-w","url":null,"abstract":"<p><strong>Background: </strong>Dissection of complex plant cell wall structures demands a sensitive and quantitative method. FTIR is used regularly as a screening method to identify specific linkages in cell walls. However, quantification and assigning spectral bands to particular cell wall components is still a major challenge, specifically in crop species. In this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand the plant cell wall composition. This method was validated by analysing different varieties of mungbean which is one of the most important legume crops grown widely in Asia.</p><p><strong>Results: </strong>Using standards and extraction of a specific component of cell wall components, we assigned 1050-1060 cm^-1 and 1390-1420 cm^-1 wavenumbers that can be widely used to quantify cellulose and lignin, respectively, in Arabidopsis, Populus, rice and mungbean. Also, using KBr as a diluent, we established a method that can relatively quantify the cellulose and lignin composition among different tissue types of the above species. We further used this method to quantify cellulose and lignin in field-grown mungbean genotypes. The ATR-FTIR-based study revealed the cellulose content variation ranges from 27.9% to 52.3%, and the lignin content variation ranges from 13.7% to 31.6% in mungbean genotypes.</p><p><strong>Conclusion: </strong>Multivariate analysis of FT-IR data revealed differences in total cell wall (600-2000 cm^-1), cellulose (1000-1100 cm^-1) and lignin (1390-1420 cm^-1) among leaf and stem of four plant species. Overall, our data suggested that ATR-FTIR can be used for the relative quantification of lignin and cellulose in different plant species. This method was successfully applied for rapid screening of cell wall composition in mungbean stem, and similarly, it can be used for screening other crops or tree species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"135"},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging.","authors":"Yiwen Ju, Alexander E Liu, Kenan Oestreich, Tina Wang, Christopher N Topp, Tao Ju","doi":"10.1186/s13007-024-01240-0","DOIUrl":"10.1186/s13007-024-01240-0","url":null,"abstract":"<p><strong>Background: </strong>The use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739-57. https://doi.org/10.1093/plphys/kiab311 .). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.</p><p><strong>Results: </strong>We developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z .) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of [Formula: see text] on a desktop workstation. Our software and test dataset are freely distributed on Github.</p><p><strong>Conclusions: </strong>TopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"132"},"PeriodicalIF":4.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142073473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cucumber pathogenic spores' detection using the GCS-YOLOv8 network with microscopic images in natural scenes.","authors":"Xinyi Zhu, Feifei Chen, Chen Qiao, Yiding Zhang, Lingxian Zhang, Wei Gao, Yong Wang","doi":"10.1186/s13007-024-01243-x","DOIUrl":"10.1186/s13007-024-01243-x","url":null,"abstract":"<p><p>Fungal diseases are the main factors affecting the quality and production of vegetables. Rapid and accurate detection of pathogenic spores is of great practical significance for early prediction and prevention of diseases. However, there are some problems with microscopic images collected in the natural environment, such as complex backgrounds, more disturbing materials, small size of spores, and various forms. Therefore, this study proposed an improved detection method of GCS-YOLOv8 (Global context and CARFAE and Small detector-optimized YOLOv8), effectively improving the detection accuracy of small-target pathogen spores in natural scenes. Firstly, by adding a small target detection layer in the network, the network's sensitivity to small targets is enhanced, and the problem of low detection accuracy of the small target is effectively improved. Secondly, Global Context attention is introduced in Backbone to optimize the CSPDarknet53 to 2-Stage FPN (C2F) module and model global context information. At the same time, the feature up-sampling module Content-Aware Reassembly of Features (CARAFE) was introduced into Neck to enhance the ability of the network to extract spore features in natural scenes further. Finally, we used an Explainable Artificial Intelligence (XAI) approach to interpret the model's predictions. The experimental results showed that the improved GCS-YOLOv8 model could detect the spores of the three fungi with an accuracy of 0.926 and a model size of 22.8 MB, which was significantly superior to the existing model and showed good robustness under different brightness conditions. The test on the microscopic images of the infection structure of cucumber down mildew also proved that the model had good generalization. Therefore, this study realized the accurate detection of pathogen spores in natural scenes and provided feasible technical support for early predicting and preventing fungal diseases.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"131"},"PeriodicalIF":4.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11337645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142018304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}