Pub Date : 2024-09-28DOI: 10.1186/s13007-024-01256-6
Patrick Langan, Emilie Cavel, Joey Henchy, Villő Bernád, Paul Ruel, Katie O'Dea, Keshawa Yatagampitiya, Hervé Demailly, Laurent Gutierrez, Sónia Negrão
Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.
{"title":"Evaluating waterlogging stress response and recovery in barley (Hordeum vulgare L.): an image-based phenotyping approach.","authors":"Patrick Langan, Emilie Cavel, Joey Henchy, Villő Bernád, Paul Ruel, Katie O'Dea, Keshawa Yatagampitiya, Hervé Demailly, Laurent Gutierrez, Sónia Negrão","doi":"10.1186/s13007-024-01256-6","DOIUrl":"https://doi.org/10.1186/s13007-024-01256-6","url":null,"abstract":"<p><p>Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"146"},"PeriodicalIF":4.7,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11438059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352017","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}
Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods' time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model's computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.
{"title":"Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast.","authors":"Qiang Cao, Dongxue Zhao, Jinpeng Li, JinXuan Li, Guangming Li, Shuai Feng, Tongyu Xu","doi":"10.1186/s13007-024-01275-3","DOIUrl":"https://doi.org/10.1186/s13007-024-01275-3","url":null,"abstract":"<p><p>Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods' time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model's computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"149"},"PeriodicalIF":4.7,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11437801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352020","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}
Pub Date : 2024-09-19DOI: 10.1186/s13007-024-01272-6
Tao Liu, Yuanyuan Zhao, Hui Wang, Wei Wu, Tianle Yang, Weijun Zhang, Shaolong Zhu, Chengming Sun, Zhaosheng Yao
Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha− 1, a drop of 60%. In this study, a method for monitoring weed occurrence in wheat fields was established, and the effects of weeds on wheat growth in different growth periods and weed densities were studied by accurately extracting weeds from wheat fields. The results can provide a reference for weed control and hazard assessment research.
{"title":"Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications","authors":"Tao Liu, Yuanyuan Zhao, Hui Wang, Wei Wu, Tianle Yang, Weijun Zhang, Shaolong Zhu, Chengming Sun, Zhaosheng Yao","doi":"10.1186/s13007-024-01272-6","DOIUrl":"https://doi.org/10.1186/s13007-024-01272-6","url":null,"abstract":"Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha− 1, a drop of 60%. In this study, a method for monitoring weed occurrence in wheat fields was established, and the effects of weeds on wheat growth in different growth periods and weed densities were studied by accurately extracting weeds from wheat fields. The results can provide a reference for weed control and hazard assessment research.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"4 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265895","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}
Pub Date : 2024-09-19DOI: 10.1186/s13007-024-01269-1
Jova Riza Campol, Aung Htay Naing, Hay Mon Aung, Su Bin Cho, Hyunhee Kang, Mi Young Chung, Chang Kil Kim
This study aimed to produce Odontoglossum ringspot virus (ORSV)-free Cymbidium orchid ‘New True’ plants from ORSV-infected mother plants by culturing their meristems and successively repeating subcultures of protocorm-like bodies (PLBs) derived from the meristems. Initially, ORSV was confirmed as the causative agent of viral symptoms in orchid leaves via reverse transcription-polymerase chain reaction (RT-PCR) analysis. Meristems from infected plants were cultured to generate PLBs, which in sequence were repeatedly subcultured up to four times. RT-PCR and quantitative RT-PCR analyses revealed that while ORSV was undetectable in shoots derived from the first subculture, complete elimination of the virus required at least a second subculture. Genetic analysis using inter-simple sequence repeat markers indicated no somaclonal variation between regenerated plants and the mother plant, suggesting that genetic consistency was maintained. Overall, our findings demonstrate that subculturing PLBs for a second time is ideal for producing genetically stable, ORSV-free Cymbidium orchids, thus offering a practical means of generating genetically stable, virus-free plants and enhancing plant health and quality in the orchid industry.
{"title":"Production of genetically stable and Odontoglossum ringspot virus-free Cymbidium orchid ‘New True’ plants via meristem-derived protocorm-like body (PLB) subcultures","authors":"Jova Riza Campol, Aung Htay Naing, Hay Mon Aung, Su Bin Cho, Hyunhee Kang, Mi Young Chung, Chang Kil Kim","doi":"10.1186/s13007-024-01269-1","DOIUrl":"https://doi.org/10.1186/s13007-024-01269-1","url":null,"abstract":"This study aimed to produce Odontoglossum ringspot virus (ORSV)-free Cymbidium orchid ‘New True’ plants from ORSV-infected mother plants by culturing their meristems and successively repeating subcultures of protocorm-like bodies (PLBs) derived from the meristems. Initially, ORSV was confirmed as the causative agent of viral symptoms in orchid leaves via reverse transcription-polymerase chain reaction (RT-PCR) analysis. Meristems from infected plants were cultured to generate PLBs, which in sequence were repeatedly subcultured up to four times. RT-PCR and quantitative RT-PCR analyses revealed that while ORSV was undetectable in shoots derived from the first subculture, complete elimination of the virus required at least a second subculture. Genetic analysis using inter-simple sequence repeat markers indicated no somaclonal variation between regenerated plants and the mother plant, suggesting that genetic consistency was maintained. Overall, our findings demonstrate that subculturing PLBs for a second time is ideal for producing genetically stable, ORSV-free Cymbidium orchids, thus offering a practical means of generating genetically stable, virus-free plants and enhancing plant health and quality in the orchid industry.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"83 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265946","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}
Pub Date : 2024-09-16DOI: 10.1186/s13007-024-01267-3
Michael K. Y. Ting, Yang Gao, Rouhollah Barahimipour, Rabea Ghandour, Jinghan Liu, Federico Martinez-Seidel, Julia Smirnova, Vincent Leon Gotsmann, Axel Fischer, Michael J. Haydon, Felix Willmund, Reimo Zoschke
Ribosome profiling (or Ribo-seq) is a technique that provides genome-wide information on the translational landscape (translatome). Across different plant studies, variable methodological setups have been described which raises questions about the general comparability of data that were generated from diverging methodologies. Furthermore, a common problem when performing Ribo-seq are abundant rRNA fragments that are wastefully incorporated into the libraries and dramatically reduce sequencing depth. To remove these rRNA contaminants, it is common to perform preliminary trials to identify these fragments because they are thought to vary depending on nuclease treatment, tissue source, and plant species. Here, we compile valuable insights gathered over years of generating Ribo-seq datasets from different species and experimental setups. We highlight which technical steps are important for maintaining cross experiment comparability and describe a highly efficient approach for rRNA removal. Furthermore, we provide evidence that many rRNA fragments are structurally preserved over diverse nuclease regimes, as well as across plant species. Using a recently published cryo-electron microscopy (cryo-EM) structure of the tobacco 80S ribosome, we show that the most abundant rRNA fragments are spatially derived from the solvent-exposed surface of the ribosome. The guidelines presented here shall aid newcomers in establishing ribosome profiling in new plant species and provide insights that will help in customizing the methodology for individual research goals.
{"title":"Optimization of ribosome profiling in plants including structural analysis of rRNA fragments","authors":"Michael K. Y. Ting, Yang Gao, Rouhollah Barahimipour, Rabea Ghandour, Jinghan Liu, Federico Martinez-Seidel, Julia Smirnova, Vincent Leon Gotsmann, Axel Fischer, Michael J. Haydon, Felix Willmund, Reimo Zoschke","doi":"10.1186/s13007-024-01267-3","DOIUrl":"https://doi.org/10.1186/s13007-024-01267-3","url":null,"abstract":"Ribosome profiling (or Ribo-seq) is a technique that provides genome-wide information on the translational landscape (translatome). Across different plant studies, variable methodological setups have been described which raises questions about the general comparability of data that were generated from diverging methodologies. Furthermore, a common problem when performing Ribo-seq are abundant rRNA fragments that are wastefully incorporated into the libraries and dramatically reduce sequencing depth. To remove these rRNA contaminants, it is common to perform preliminary trials to identify these fragments because they are thought to vary depending on nuclease treatment, tissue source, and plant species. Here, we compile valuable insights gathered over years of generating Ribo-seq datasets from different species and experimental setups. We highlight which technical steps are important for maintaining cross experiment comparability and describe a highly efficient approach for rRNA removal. Furthermore, we provide evidence that many rRNA fragments are structurally preserved over diverse nuclease regimes, as well as across plant species. Using a recently published cryo-electron microscopy (cryo-EM) structure of the tobacco 80S ribosome, we show that the most abundant rRNA fragments are spatially derived from the solvent-exposed surface of the ribosome. The guidelines presented here shall aid newcomers in establishing ribosome profiling in new plant species and provide insights that will help in customizing the methodology for individual research goals.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"50 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265892","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}
Pub Date : 2024-09-16DOI: 10.1186/s13007-024-01266-4
Olivia Doolan, Mathew G. Lewsey, Marta Peirats-Llobet, Neil Bricklebank, Nicola Aberdein
Grains make up a large proportion of both human and animal diets. With threats to food production, such as climate change, growing sustainable and successful crops is essential to food security in the future. Germination is one of the most important stages in a plant’s lifecycle and is key to the success of the resulting plant as the grain undergoes morphological changes and the development of specific organs. Micro-computed tomography is a non-destructive imaging technique based on the differing x-ray attenuations of materials which we have applied for the accurate analysis of grain morphology during the germination phase. Micro Computed Tomography conditions and parameters were tested to establish an optimal protocol for the 3-dimensional analysis of barley grains. When comparing optimal scanning conditions, it was established that no filter, 0.4 degrees rotation step, 5 average frames, and 2016 × 1344 camera binning is optimal for imaging germinating grains. It was determined that the optimal protocol for scanning during the germination timeline was to scan individual grains at 0 h after imbibition (HAI) and then the same grain again at set time points (1, 3, 6, 24 HAI) to avoid any negative effects from X-ray radiation or disruption to growing conditions. Here we sought to develop a method for the accurate analysis of grain morphology without the negative effects of possible radiation exposure. Several factors have been considered, such as the scanning conditions, reconstruction, and possible effects of X-ray radiation on the growth rate of the grains. The parameters chosen in this study give effective and reliable results for the 3-dimensional analysis of macro structures within barley grains while causing minimal disruption to grain development.
谷物在人类和动物的饮食中都占有很大比例。面对气候变化等对粮食生产的威胁，种植可持续的成功作物对未来的粮食安全至关重要。发芽是植物生命周期中最重要的阶段之一，也是植株成活的关键，因为谷物会经历形态变化和特定器官的发育。显微计算机断层扫描是一种非破坏性成像技术，它基于材料的不同 X 射线衰减，我们已将其用于准确分析发芽阶段的谷粒形态。我们对微型计算机断层扫描的条件和参数进行了测试，以确定对大麦谷粒进行三维分析的最佳方案。在比较最佳扫描条件时，确定无滤镜、0.4 度旋转步进、5 个平均帧和 2016 × 1344 相机分档是对发芽谷粒成像的最佳条件。经确定，在发芽时间轴上进行扫描的最佳方案是在浸种后 0 小时（HAI）扫描单个谷粒，然后在设定的时间点（1、3、6、24 HAI）再次扫描同一谷粒，以避免 X 射线辐射的任何负面影响或对生长条件的干扰。在此，我们试图开发一种准确分析谷粒形态的方法，同时避免可能的辐射带来的负面影响。我们考虑了多个因素，如扫描条件、重建以及 X 射线辐射对晶粒生长速度的可能影响。本研究选择的参数可为大麦粒内宏观结构的三维分析提供有效、可靠的结果，同时将对谷物生长的影响降至最低。
{"title":"Micro computed tomography analysis of barley during the first 24 hours of germination","authors":"Olivia Doolan, Mathew G. Lewsey, Marta Peirats-Llobet, Neil Bricklebank, Nicola Aberdein","doi":"10.1186/s13007-024-01266-4","DOIUrl":"https://doi.org/10.1186/s13007-024-01266-4","url":null,"abstract":"Grains make up a large proportion of both human and animal diets. With threats to food production, such as climate change, growing sustainable and successful crops is essential to food security in the future. Germination is one of the most important stages in a plant’s lifecycle and is key to the success of the resulting plant as the grain undergoes morphological changes and the development of specific organs. Micro-computed tomography is a non-destructive imaging technique based on the differing x-ray attenuations of materials which we have applied for the accurate analysis of grain morphology during the germination phase. Micro Computed Tomography conditions and parameters were tested to establish an optimal protocol for the 3-dimensional analysis of barley grains. When comparing optimal scanning conditions, it was established that no filter, 0.4 degrees rotation step, 5 average frames, and 2016 × 1344 camera binning is optimal for imaging germinating grains. It was determined that the optimal protocol for scanning during the germination timeline was to scan individual grains at 0 h after imbibition (HAI) and then the same grain again at set time points (1, 3, 6, 24 HAI) to avoid any negative effects from X-ray radiation or disruption to growing conditions. Here we sought to develop a method for the accurate analysis of grain morphology without the negative effects of possible radiation exposure. Several factors have been considered, such as the scanning conditions, reconstruction, and possible effects of X-ray radiation on the growth rate of the grains. The parameters chosen in this study give effective and reliable results for the 3-dimensional analysis of macro structures within barley grains while causing minimal disruption to grain development.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"19 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265893","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}
Pub Date : 2024-09-12DOI: 10.1186/s13007-024-01265-5
Orly Lavie, Kobi Buxdorf, Leor Eshed Williams
Cannabis sativa L. is a versatile medicinal plant known for its therapeutic properties, derived from its diverse array of secondary metabolites synthesized primarily in female flower organs. Breeding cannabis is challenging due to its dioecious nature, strict regulatory requirements, and the need for photoperiod control to trigger flowering, coupled with highly dispersible pollen that can easily contaminate nearby female flowers. This study aimed to develop a protocol for in vitro flowering in cannabis, investigate factors affecting in vitro flower production, and generate viable in vitro seeds, potentially offering a method for producing sterile cannabinoids or advancing breeding techniques. We show that the life cycle of cannabis can be fully completed in tissue culture; plantlets readily produce inflorescences and viable seeds in vitro. Our findings highlight the superior performance of DKW medium with 2% sucrose in a filtered vessel and emphasize the need for low light intensity during flower induction to optimize production. The improved performance in filtered vessels suggests that plants conduct photosynthesis in vitro, highlighting the need for future investigations into the effects of forced ventilation to refine this system. All tested lines readily developed inflorescences upon induction, with a 100% occurrence rate, including male flowering. We revealed the non-dehiscent trait of in vitro anthers, which is advantageous as it allows for multiple crosses to be conducted in vitro without concerns about cross-contamination. The current work developed and optimized an effective protocol for in vitro flowering and seed production in cannabis, potentially providing a platform for sterile cannabinoid production and an efficient tool for breeding programs. This system allows for the full and consistent control of plant growth conditions year-round, potentially offering the reliable production of sterile molecules suitable for pharmacological use. As a breeding strategy, this method overcomes the complex challenges of breeding cannabis, such as the need for large facilities, by enabling the production of hundreds of lines in a small facility. By offering precise control over factors such as plant growth regulators, light intensity, photoperiod, and temperature, this system also serves as a valuable tool for studying flowering aspects in cannabis.
{"title":"Optimizing cannabis cultivation: an efficient in vitro system for flowering induction","authors":"Orly Lavie, Kobi Buxdorf, Leor Eshed Williams","doi":"10.1186/s13007-024-01265-5","DOIUrl":"https://doi.org/10.1186/s13007-024-01265-5","url":null,"abstract":"Cannabis sativa L. is a versatile medicinal plant known for its therapeutic properties, derived from its diverse array of secondary metabolites synthesized primarily in female flower organs. Breeding cannabis is challenging due to its dioecious nature, strict regulatory requirements, and the need for photoperiod control to trigger flowering, coupled with highly dispersible pollen that can easily contaminate nearby female flowers. This study aimed to develop a protocol for in vitro flowering in cannabis, investigate factors affecting in vitro flower production, and generate viable in vitro seeds, potentially offering a method for producing sterile cannabinoids or advancing breeding techniques. We show that the life cycle of cannabis can be fully completed in tissue culture; plantlets readily produce inflorescences and viable seeds in vitro. Our findings highlight the superior performance of DKW medium with 2% sucrose in a filtered vessel and emphasize the need for low light intensity during flower induction to optimize production. The improved performance in filtered vessels suggests that plants conduct photosynthesis in vitro, highlighting the need for future investigations into the effects of forced ventilation to refine this system. All tested lines readily developed inflorescences upon induction, with a 100% occurrence rate, including male flowering. We revealed the non-dehiscent trait of in vitro anthers, which is advantageous as it allows for multiple crosses to be conducted in vitro without concerns about cross-contamination. The current work developed and optimized an effective protocol for in vitro flowering and seed production in cannabis, potentially providing a platform for sterile cannabinoid production and an efficient tool for breeding programs. This system allows for the full and consistent control of plant growth conditions year-round, potentially offering the reliable production of sterile molecules suitable for pharmacological use. As a breeding strategy, this method overcomes the complex challenges of breeding cannabis, such as the need for large facilities, by enabling the production of hundreds of lines in a small facility. By offering precise control over factors such as plant growth regulators, light intensity, photoperiod, and temperature, this system also serves as a valuable tool for studying flowering aspects in cannabis.","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"58 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208858","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}
Pub Date : 2024-09-12DOI: 10.1186/s13007-024-01268-2
Haibao Tang, Wenqian Kong, Pheonah Nabukalu, Johnathan S. Lomas, Michel Moser, Jisen Zhang, Mengwei Jiang, Xingtan Zhang, Andrew H. Paterson, Won Cheol Yim
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 .
{"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}
Pub Date : 2024-09-09DOI: 10.1186/s13007-024-01251-x
Maria Megariti, Alexandra Panagou, Georgios Patsis, George Papadakis, Alexandros K. Pantazis, Epaminondas J. Paplomatas, Aliki K. Tzima, Emmanouil A. Markakis, Electra Gizeli
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.
{"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}
Pub Date : 2024-09-09DOI: 10.1186/s13007-024-01262-8
Xinyue Fan, Hongmei Sun
<p><b>Correction: Plant Methods (2024) 20:120</b></p><p><b>https://doi.org/10.1186/s13007-024-01246-8</b></p><p>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 <i>Lilium</i> 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 <i>Lilium</i> spp. Plant Method. 2019;15:125’.</p><p>The original article has been corrected.</p><h3>Authors and Affiliations</h3><ol><li><p>Key Laboratory of Protected Horticulture of Education Ministry, College of Horticulture, Shenyang Agricultural University, Shenyang, 110866, China</p><p>Xinyue Fan & Hongmei Sun</p></li><li><p>National and Local Joint Engineering Research Center of Northern Horticultural Facilities Design and Application Technology, Shenyang, 110866, China</p><p>Hongmei Sun</p></li></ol><span>Authors</span><ol><li><span>Xinyue Fan</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Hongmei Sun</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Hongmei Sun.</p><h3>Publisher’s note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p>The online version of the original article can be found at https://doi.org/10.1186/s13007-024-01246-8.</p><p><b>Open Access</b> 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/.</p> <p>Reprints and permissions</p><img alt="Check for updates. Verify currency and authenticity via CrossMark" height="81" loading="lazy" src="data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjgxIiB3aWR0aD0iNTciIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PGcgZmlsbD0ibm9uZSIgZmlsbC1ydWxlPSJldmVub2RkIj48cGF0aC
更正：Plant Methods (2024) 20:120https://doi.org/10.1186/s13007-024-01246-8In 本文参考文献。4 中的 "Li JW, Zhang XC, Wang MR, Bi WL, Faisal M, Da Silva JAT, et al. Development, progress and future prospects in cryobiotechnology of Lilium 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.2019;15:125'.原文已更正。作者及工作单位 沈阳农业大学园艺学院保护园艺教育部重点实验室，沈阳，110866 范新跃 &；孙红梅北方园艺设施设计与应用技术国家地方联合工程研究中心，中国沈阳，110866孙红梅作者：Xinyue Fan查看作者发表的论文您也可以在PubMed Google Scholar中搜索该作者孙红梅查看作者发表的论文您也可以在PubMed Google Scholar中搜索该作者通讯作者：孙红梅。出版者注释施普林格-自然对出版地图和机构隶属关系中的管辖权主张保持中立。原文的在线版本可在 https://doi.org/10.1186/s13007-024-01246-8.Open Access 找到。本文采用知识共享署名-非商业性-禁止衍生 4.0 国际许可协议进行许可，该协议允许以任何媒介或格式进行任何非商业性使用、共享、分发和复制，只要您适当注明原作者和来源，提供知识共享许可协议的链接，并说明您是否修改了许可材料。根据本许可协议，您无权分享源自本文或本文部分内容的改编材料。本文中的图片或其他第三方材料均包含在文章的知识共享许可协议中，除非在材料的信用栏中另有说明。如果材料未包含在文章的知识共享许可协议中，且您打算使用的材料不符合法律规定或超出了许可使用范围，则您需要直接获得版权所有者的许可。要查看该许可的副本，请访问 http://creativecommons.org/licenses/by-nc-nd/4.0/.Reprints and permissionsCite this articleFan, X., Sun, H. Correction：农杆菌介导的遗传转化方法及其在百合中的应用探索。Plant Methods 20, 138 (2024). https://doi.org/10.1186/s13007-024-01262-8Download citationPublished: 09 September 2024DOI: https://doi.org/10.1186/s13007-024-01262-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative.
{"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":"<p><b>Correction: Plant Methods (2024) 20:120</b></p><p><b>https://doi.org/10.1186/s13007-024-01246-8</b></p><p>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 <i>Lilium</i> 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 <i>Lilium</i> spp. Plant Method. 2019;15:125’.</p><p>The original article has been corrected.</p><h3>Authors and Affiliations</h3><ol><li><p>Key Laboratory of Protected Horticulture of Education Ministry, College of Horticulture, Shenyang Agricultural University, Shenyang, 110866, China</p><p>Xinyue Fan & Hongmei Sun</p></li><li><p>National and Local Joint Engineering Research Center of Northern Horticultural Facilities Design and Application Technology, Shenyang, 110866, China</p><p>Hongmei Sun</p></li></ol><span>Authors</span><ol><li><span>Xinyue Fan</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Hongmei Sun</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Hongmei Sun.</p><h3>Publisher’s note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p>The online version of the original article can be found at https://doi.org/10.1186/s13007-024-01246-8.</p><p><b>Open Access</b> 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/.</p>\u0000<p>Reprints and permissions</p><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}
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