The Experts below are selected from a list of 201603 Experts worldwide ranked by ideXlab platform
Robert Edwards - One of the best experts on this subject based on the ideXlab platform.
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testing a chemical series inspired by Plant Stress oxylipin signalling agents for herbicide safening activity
Pest Management Science, 2018Co-Authors: Melissa Brazierhicks, Kathryn M Knight, Jonathan D Sellars, Patrick G Steel, Robert EdwardsAbstract:BACKGROUND Herbicide safening in cereals is linked to a rapid xenobiotic response (XR), involving the induction of glutathione transferases (GSTs). The XR is also invoked by oxidized fatty acids (oxylipins) released during Plant Stress, suggesting a link between these signalling agents and safening. To examine this relationship, a series of compounds modelled on the oxylipins 12‐oxophytodienoic acid and phytoprostane 1, varying in lipophilicity and electrophilicity, were synthesized. Compounds were then tested for their ability to invoke the XR in Arabidopsis and protect rice seedlings exposed to the herbicide pretilachlor, as compared with the safener fenclorim. RESULTS Of the 21 compounds tested, three invoked the rapid GST induction associated with fenclorim. All compounds possessed two electrophilic carbon centres and a lipophilic group characteristic of both oxylipins and fenclorim. Minor effects observed in protecting rice seedlings from herbicide damage positively correlated with the XR, but did not provide functional safening. CONCLUSION The design of safeners based on the characteristics of oxylipins proved successful in deriving compounds that invoke a rapid XR in Arabidopsis but not in providing classical safening in a cereal. The results further support a link between safener and oxylipin signalling, but also highlight species‐dependent differences in the responses to these compounds.
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testing a chemical series inspired by Plant Stress oxylipin signalling agents for herbicide safening activity
Pest Management Science, 2018Co-Authors: Melissa Brazierhicks, Kathryn M Knight, Jonathan D Sellars, Patrick G Steel, Robert EdwardsAbstract:BACKGROUND Herbicide safening in cereals is linked to a rapid xenobiotic response (XR), involving the induction of glutathione transferases (GSTs). The XR is also invoked by oxidized fatty acids (oxylipins) released during Plant Stress, suggesting a link between these signalling agents and safening. To examine this relationship, a series of compounds modelled on the oxylipins 12-oxophytodienoic acid and phytoprostane 1, varying in lipophilicity and electrophilicity, were synthesized. Compounds were then tested for their ability to invoke the XR in Arabidopsis and protect rice seedlings exposed to the herbicide pretilachlor, as compared with the safener fenclorim. RESULTS Of the 21 compounds tested, three invoked the rapid GST induction associated with fenclorim. All compounds possessed two electrophilic carbon centres and a lipophilic group characteristic of both oxylipins and fenclorim. Minor effects observed in protecting rice seedlings from herbicide damage positively correlated with the XR, but did not provide functional safening. CONCLUSION The design of safeners based on the characteristics of oxylipins proved successful in deriving compounds that invoke a rapid XR in Arabidopsis but not in providing classical safening in a cereal. The results further support a link between safener and oxylipin signalling, but also highlight species-dependent differences in the responses to these compounds. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Jonathan D Sellars - One of the best experts on this subject based on the ideXlab platform.
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testing a chemical series inspired by Plant Stress oxylipin signalling agents for herbicide safening activity
Pest Management Science, 2018Co-Authors: Melissa Brazierhicks, Kathryn M Knight, Jonathan D Sellars, Patrick G Steel, Robert EdwardsAbstract:BACKGROUND Herbicide safening in cereals is linked to a rapid xenobiotic response (XR), involving the induction of glutathione transferases (GSTs). The XR is also invoked by oxidized fatty acids (oxylipins) released during Plant Stress, suggesting a link between these signalling agents and safening. To examine this relationship, a series of compounds modelled on the oxylipins 12‐oxophytodienoic acid and phytoprostane 1, varying in lipophilicity and electrophilicity, were synthesized. Compounds were then tested for their ability to invoke the XR in Arabidopsis and protect rice seedlings exposed to the herbicide pretilachlor, as compared with the safener fenclorim. RESULTS Of the 21 compounds tested, three invoked the rapid GST induction associated with fenclorim. All compounds possessed two electrophilic carbon centres and a lipophilic group characteristic of both oxylipins and fenclorim. Minor effects observed in protecting rice seedlings from herbicide damage positively correlated with the XR, but did not provide functional safening. CONCLUSION The design of safeners based on the characteristics of oxylipins proved successful in deriving compounds that invoke a rapid XR in Arabidopsis but not in providing classical safening in a cereal. The results further support a link between safener and oxylipin signalling, but also highlight species‐dependent differences in the responses to these compounds.
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testing a chemical series inspired by Plant Stress oxylipin signalling agents for herbicide safening activity
Pest Management Science, 2018Co-Authors: Melissa Brazierhicks, Kathryn M Knight, Jonathan D Sellars, Patrick G Steel, Robert EdwardsAbstract:BACKGROUND Herbicide safening in cereals is linked to a rapid xenobiotic response (XR), involving the induction of glutathione transferases (GSTs). The XR is also invoked by oxidized fatty acids (oxylipins) released during Plant Stress, suggesting a link between these signalling agents and safening. To examine this relationship, a series of compounds modelled on the oxylipins 12-oxophytodienoic acid and phytoprostane 1, varying in lipophilicity and electrophilicity, were synthesized. Compounds were then tested for their ability to invoke the XR in Arabidopsis and protect rice seedlings exposed to the herbicide pretilachlor, as compared with the safener fenclorim. RESULTS Of the 21 compounds tested, three invoked the rapid GST induction associated with fenclorim. All compounds possessed two electrophilic carbon centres and a lipophilic group characteristic of both oxylipins and fenclorim. Minor effects observed in protecting rice seedlings from herbicide damage positively correlated with the XR, but did not provide functional safening. CONCLUSION The design of safeners based on the characteristics of oxylipins proved successful in deriving compounds that invoke a rapid XR in Arabidopsis but not in providing classical safening in a cereal. The results further support a link between safener and oxylipin signalling, but also highlight species-dependent differences in the responses to these compounds. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Melissa Brazierhicks - One of the best experts on this subject based on the ideXlab platform.
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testing a chemical series inspired by Plant Stress oxylipin signalling agents for herbicide safening activity
Pest Management Science, 2018Co-Authors: Melissa Brazierhicks, Kathryn M Knight, Jonathan D Sellars, Patrick G Steel, Robert EdwardsAbstract:BACKGROUND Herbicide safening in cereals is linked to a rapid xenobiotic response (XR), involving the induction of glutathione transferases (GSTs). The XR is also invoked by oxidized fatty acids (oxylipins) released during Plant Stress, suggesting a link between these signalling agents and safening. To examine this relationship, a series of compounds modelled on the oxylipins 12‐oxophytodienoic acid and phytoprostane 1, varying in lipophilicity and electrophilicity, were synthesized. Compounds were then tested for their ability to invoke the XR in Arabidopsis and protect rice seedlings exposed to the herbicide pretilachlor, as compared with the safener fenclorim. RESULTS Of the 21 compounds tested, three invoked the rapid GST induction associated with fenclorim. All compounds possessed two electrophilic carbon centres and a lipophilic group characteristic of both oxylipins and fenclorim. Minor effects observed in protecting rice seedlings from herbicide damage positively correlated with the XR, but did not provide functional safening. CONCLUSION The design of safeners based on the characteristics of oxylipins proved successful in deriving compounds that invoke a rapid XR in Arabidopsis but not in providing classical safening in a cereal. The results further support a link between safener and oxylipin signalling, but also highlight species‐dependent differences in the responses to these compounds.
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testing a chemical series inspired by Plant Stress oxylipin signalling agents for herbicide safening activity
Pest Management Science, 2018Co-Authors: Melissa Brazierhicks, Kathryn M Knight, Jonathan D Sellars, Patrick G Steel, Robert EdwardsAbstract:BACKGROUND Herbicide safening in cereals is linked to a rapid xenobiotic response (XR), involving the induction of glutathione transferases (GSTs). The XR is also invoked by oxidized fatty acids (oxylipins) released during Plant Stress, suggesting a link between these signalling agents and safening. To examine this relationship, a series of compounds modelled on the oxylipins 12-oxophytodienoic acid and phytoprostane 1, varying in lipophilicity and electrophilicity, were synthesized. Compounds were then tested for their ability to invoke the XR in Arabidopsis and protect rice seedlings exposed to the herbicide pretilachlor, as compared with the safener fenclorim. RESULTS Of the 21 compounds tested, three invoked the rapid GST induction associated with fenclorim. All compounds possessed two electrophilic carbon centres and a lipophilic group characteristic of both oxylipins and fenclorim. Minor effects observed in protecting rice seedlings from herbicide damage positively correlated with the XR, but did not provide functional safening. CONCLUSION The design of safeners based on the characteristics of oxylipins proved successful in deriving compounds that invoke a rapid XR in Arabidopsis but not in providing classical safening in a cereal. The results further support a link between safener and oxylipin signalling, but also highlight species-dependent differences in the responses to these compounds. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Arti Singh - One of the best experts on this subject based on the ideXlab platform.
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challenges and opportunities in machine augmented Plant Stress phenotyping
Trends in Plant Science, 2021Co-Authors: Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Sarah Jones, Daren S Mueller, Kulbir Sandhu, Koushik NagasubramanianAbstract:Plant Stress phenotyping is essential to select Stress-resistant varieties and develop better Stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of Stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and Stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of Plant Stress phenotyping at multiple scales across different types of Stresses, program goals, and environments.
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deep learning for Plant Stress phenotyping trends and future perspectives
Trends in Plant Science, 2018Co-Authors: Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian, Arti SinghAbstract:Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the Plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image–based Plant Stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in Plant science.
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an explainable deep machine vision framework for Plant Stress phenotyping
Proceedings of the National Academy of Sciences of the United States of America, 2018Co-Authors: Sambuddha Ghosal, Arti Singh, Baskar Ganapathysubramanian, Asheesh K. Singh, David Blystone, Soumik SarkarAbstract:Current approaches for accurate identification, classification, and quantification of biotic and abiotic Stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework's ability to identify and classify a diverse set of foliar Stresses in soybean [Glycine max (L.) Merr.] with remarkable accuracy. We also present an explanation mechanism, using the top-K high-resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of Stress severity, allowing for identification (type of foliar Stress), classification (low, medium, or high Stress), and quantification (Stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) Stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high-throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar Stresses would have significant implications in scientific research, Plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g., unmanned air vehicles and automated ground scouts) for rapid, large-scale scouting or as a mobile application for real-time detection of Stress by farmers and researchers.
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A real-time phenotyping framework using machine learning for Plant Stress severity rating in soybean
Plant Methods, 2017Co-Authors: Hsiang Sing Naik, Jiaoping Zhang, Alec Lofquist, Teshale Assefa, David Ackerman, Arti Singh, Soumik Sarkar, Asheesh K. Singh, Baskar GanapathysubramanianAbstract:Phenotyping is a critical component of Plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of Plant Stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict Plant Stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with Plant Stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic Stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated Stress trait detection and quantification for Plant research, breeding and Stress scouting applications.
Soumik Sarkar - One of the best experts on this subject based on the ideXlab platform.
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challenges and opportunities in machine augmented Plant Stress phenotyping
Trends in Plant Science, 2021Co-Authors: Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Sarah Jones, Daren S Mueller, Kulbir Sandhu, Koushik NagasubramanianAbstract:Plant Stress phenotyping is essential to select Stress-resistant varieties and develop better Stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of Stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and Stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of Plant Stress phenotyping at multiple scales across different types of Stresses, program goals, and environments.
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deep learning for Plant Stress phenotyping trends and future perspectives
Trends in Plant Science, 2018Co-Authors: Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian, Arti SinghAbstract:Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the Plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image–based Plant Stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in Plant science.
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an explainable deep machine vision framework for Plant Stress phenotyping
Proceedings of the National Academy of Sciences of the United States of America, 2018Co-Authors: Sambuddha Ghosal, Arti Singh, Baskar Ganapathysubramanian, Asheesh K. Singh, David Blystone, Soumik SarkarAbstract:Current approaches for accurate identification, classification, and quantification of biotic and abiotic Stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework's ability to identify and classify a diverse set of foliar Stresses in soybean [Glycine max (L.) Merr.] with remarkable accuracy. We also present an explanation mechanism, using the top-K high-resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of Stress severity, allowing for identification (type of foliar Stress), classification (low, medium, or high Stress), and quantification (Stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) Stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high-throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar Stresses would have significant implications in scientific research, Plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g., unmanned air vehicles and automated ground scouts) for rapid, large-scale scouting or as a mobile application for real-time detection of Stress by farmers and researchers.
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A real-time phenotyping framework using machine learning for Plant Stress severity rating in soybean
Plant Methods, 2017Co-Authors: Hsiang Sing Naik, Jiaoping Zhang, Alec Lofquist, Teshale Assefa, David Ackerman, Arti Singh, Soumik Sarkar, Asheesh K. Singh, Baskar GanapathysubramanianAbstract:Phenotyping is a critical component of Plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of Plant Stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict Plant Stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with Plant Stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic Stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated Stress trait detection and quantification for Plant research, breeding and Stress scouting applications.
