The root system actively reacts to mechanical stimuli in its environment,transmitting mechanical signals to optimize the utilization of environmental resources.While the mechanical impedance created by the growth medi...The root system actively reacts to mechanical stimuli in its environment,transmitting mechanical signals to optimize the utilization of environmental resources.While the mechanical impedance created by the growth medium serves as the primary source of stimulation for the roots,extensive research has focused on the roots'response to static mechanical stimulation.However,the impact of dynamic mechanical stimulation on root phenotype remains underexplored.In this study,we utilized a low acyl gellan gum/polyacrylamide(GG/PAM)double network elastic hydrogel as the growth medium for rapeseed.We constructed a mechanical device to investigate the effects of reciprocating extrusion stimulation on the growth of the rapeseed root system.After three weeks of mechanical stimulation,the root system exhibited a significant increase in lateral roots.This branching enhanced the roots'anchoring and penetration into the hydrogel,thereby improving the root system's adaptability to its environment.Our findings offer valuable data and insights into the effects of reciprocating mechanical stimulation on root growth,providing a new way for engineering root phenotype.展开更多
Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,e...Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,especially topological trait extraction,is rarely performed.In this study,an image-based semi-automatic root phenotyping method for field-grown crops was developed.The method consisted of image acquisition,image denoising and segmentation,trait extraction and data analysis.Five global traits and 40 local traits were extracted with this method.A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method,with R^(2)=0.97.Using the method,we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th-7th nodes,and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models(e.g.,OpenSimRoot)that simulate root growth,solute transport and water uptake.展开更多
A novel approach,the Algorithmic Root Trait(ART)extraction method,identifies and quantifies computationally-derived plant root traits,revealing latent patterns related to dense root clusters in digital im-ages.Using a...A novel approach,the Algorithmic Root Trait(ART)extraction method,identifies and quantifies computationally-derived plant root traits,revealing latent patterns related to dense root clusters in digital im-ages.Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm,27 ARTs were extracted reflecting dense root cluster size and spatial location.These ARTs were then used independently and in combination with Traditional Root Traits(TRTs)to classify wheat genotypes differing in drought tolerance.ART-based models outperformed TRT-only models in drought classification(e.g.,96.3%vs.85.6%accuracy).Combining ARTs and TRTs further improved accuracy to 97.4%.Notably,4 selected ARTs matched the per-formance of all 23 TRTs,offering 5.8×higher information density(0.213 vs.0.037 accuracy/feature).This superiority reflects the ability of ARTs to capture richer,more complex architectural information,evidenced by higher internal variability(35.59±11.41 vs.28.91±14.28 for TRTs)and distinct data structures in multi-variate analyses;PERMANOVA confirmed that ARTs and TRTs provide complementary insights.Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes,ART offers a scalable,customisable toolset for high-throughput phenotyping of plant roots.By bridging conventional,visually derived traits with autonomous computational analyses,this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception.ART thus emerges as a promising framework for revealing hidden features in plant imaging,with broader applications across plant science to deepen our understanding of crop adaptation and resilience.展开更多
Beyond its fundamental roles in nutrient uptake and plant anchorage,the root system critically influences crop development and stress tolerance.Rhizobox enables in situ and nondestructive phenotypic detection of roots...Beyond its fundamental roles in nutrient uptake and plant anchorage,the root system critically influences crop development and stress tolerance.Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil,serving as a cost-effective root imaging method.However,the opacity of the soil often results in intermittent gaps in the root images,which reduces the accuracy of the root phenotype calculations.We present a root inpainting method built upon Generative Adversarial Networks(GANs)architecture In addition,we built a hybrid root inpainting dataset(HRID)that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps.Compared with computer simulation root images,our dataset provides real root system architecture(RSA)and root texture information.Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology.The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels.Additionally,Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally,we remove the skip connections in U-Net and introduce an edge attention module(EAM)to capture more detailed information.Compared with other methods,our approach significantly improves the recall rate from 17.35%to 35.75%on the test dataset of 122 cotton root images,revealing improved inpainting capabilities.The trait error reduction rates(TERRs)for the root area,root length,convex hull area,and root depth are 76.07%,68.63%,48.64%,and 88.28%,respectively,enabling a substantial improvement in the accuracy of root phenotyping.The codes for the EU-GAN and the 8922 labeled images are open-access,which could be reused by researchers in other AI-related work.This method establishes a robust solution for root phenotyping,thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.展开更多
This article describes an immersive virtual reality reconstruction tool for root system architectures from 3D scans of soil columns.In practical scenarios,experimental conditions will be adapted to fit the need of the...This article describes an immersive virtual reality reconstruction tool for root system architectures from 3D scans of soil columns.In practical scenarios,experimental conditions will be adapted to fit the need of the data analysis pipeline,including sieving and drying the soil before scanning.Based on previous reports of automatic systems that do not represent what experts would annotate,we developed a virtual reality system to assist with the extraction of root systems in cases in which automated approaches fall short of expert knowledge.The aim of the present study is to evaluate whether our immersive method is superior to classical annotation approaches when tested on synthetic data sets using untrained participants.Our laboratory user study consists of evaluating the root extractions of participants,along with their rating on central user experience and usability measures.We show significant improvement in F1 score across conditions(noisy or clear data)as well as an improved usability.Our study highlights that using virtual reality in root extraction improves accuracy,and we perform an in-depth evaluation of biases that occur when users trace roots in soil volumes.展开更多
A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution o...A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution of the different root types in the soil. The ability to image,track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system,while allowing for aeration,solution replenishment and the imposition of nutrient treatments,as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modi fications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity（detection of fine roots and other root details）,higher ef ficiency,and a broad array of growing conditions for plants that more closely mimic those found under field conditions.展开更多
The availability in the soil of potassium(K^(+)),a poorly mobile macronutrient required in large quantities for plant growth,is generally suboptimal for crop production in the absence of fertilization,making improveme...The availability in the soil of potassium(K^(+)),a poorly mobile macronutrient required in large quantities for plant growth,is generally suboptimal for crop production in the absence of fertilization,making improvement of the ability of crops to adapt to K^(+)deficiency stress a major issue.Increasing the uptake capacity of the root system is among the main strategies to achieve this goal.Here,we report an integrative approach to examine the effect of K^(+)deficiency on the development of young plant entire root system,including root hairs which are known to provide a significant contribution to the uptake of poorly mobile nutrients such as K^(+),in two genetically distant wheat varieties.A rhizobox-type methodology was developed to obtain highly-resolved images of root and root hairs,allowing to describe global root and root hair traits over the whole root system via image analysis procedures.The two wheat varieties responded differently to the K^(+)shortage:Escandia,a wheat ancestor,reduced shoot biomass in condition of K^(+)shortage and substantially increased the surface area of its root system,specifically by increasing the total root hair area.Oued Zenati,a landrace,conversely appeared unresponsive to the K^(+)shortage but was shown to constitutively express,independently of the external K^(+)availability,favorable traits to cope with reduced K^(+)availability,among which a high total root hair area.Thus,valuable information on root system adaptation to K^(+)deficiency was provided by global analyses including root hairs,which should also be relevant for other nutrient stresses.展开更多
Nitric oxide(NO)is an essential reactive oxygen species and a signal molecule in plants.Although several studies have proposed the occurrence of oxidative NO production,only reductive routes for NO production,such as ...Nitric oxide(NO)is an essential reactive oxygen species and a signal molecule in plants.Although several studies have proposed the occurrence of oxidative NO production,only reductive routes for NO production,such as the nitrate(NO_(3)^(-))-upper-reductase pathway,have been evidenced to date in land plants.However,plants grown axenically with ammonium as the sole source of nitrogen exhibit contents of nitrite and NO3−,evidencing the existence of a metabolic pathway for oxidative production of NO.We hypothesized that oximes,such as indole-3-acetaldoxime(IAOx),a precursor to indole-3-acetic acid,are intermediate oxidation products in NO synthesis.We detected the production of NO from IAOx and other oximes catalyzed by peroxidase(POD)enzyme using both 4-amino-5-methylamino-2′,7′-difluorescein fluorescence and chemiluminescence.Flavins stimulated the reaction,while superoxide dismutase inhibited it.Interestingly,mouse NO synthase can also use IAOx to produce NO at a lower rate than POD.We provided a full mechanism for POD-dependent NO production from IAOx consistent with the experimental data and supported by density functional theory calculations.We showed that the addition of IAOx to extracts from Medicago truncatula increased the in vitro production of NO,while in vivo supplementation of IAOx and other oximes increased the number of lateral roots,as shown for NO donors,and a more than 10-fold increase in IAOx dehydratase expression.Furthermore,we found that in vivo supplementation of IAOx increased NO production in Arabidopsis thaliana wild-type plants,while prx33-34 mutant plants,defective in POD33-34,had reduced production.Our data show that the release of NO by IAOx,as well as its auxinic effect,explain the superroot phenotype.Collectively,our study reveals that plants produce NO utilizing diverse molecules such as oximes,POD,and flavins,which are widely distributed in the plant kingdom,thus introducing a long-awaited oxidative pathway to NO production in plants.This knowledge has essential implications for understanding signaling in biological systems.展开更多
基金supporting from Shanghai Pujiang Program(23PJ1400400)DHU startup grant,the Fundamental Research Funds for the Central Universities,DHU Distinguished Young Professor Program.
文摘The root system actively reacts to mechanical stimuli in its environment,transmitting mechanical signals to optimize the utilization of environmental resources.While the mechanical impedance created by the growth medium serves as the primary source of stimulation for the roots,extensive research has focused on the roots'response to static mechanical stimulation.However,the impact of dynamic mechanical stimulation on root phenotype remains underexplored.In this study,we utilized a low acyl gellan gum/polyacrylamide(GG/PAM)double network elastic hydrogel as the growth medium for rapeseed.We constructed a mechanical device to investigate the effects of reciprocating extrusion stimulation on the growth of the rapeseed root system.After three weeks of mechanical stimulation,the root system exhibited a significant increase in lateral roots.This branching enhanced the roots'anchoring and penetration into the hydrogel,thereby improving the root system's adaptability to its environment.Our findings offer valuable data and insights into the effects of reciprocating mechanical stimulation on root growth,providing a new way for engineering root phenotype.
基金supported by the National Key Research and Development Program of China (2016YFD0300202)the Science and Technology Project of Yunna, China (2017YN07)the Science and Technology Major Project of Inner Mongolia, China (2019ZD024 and 2020GG0038)
文摘Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,especially topological trait extraction,is rarely performed.In this study,an image-based semi-automatic root phenotyping method for field-grown crops was developed.The method consisted of image acquisition,image denoising and segmentation,trait extraction and data analysis.Five global traits and 40 local traits were extracted with this method.A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method,with R^(2)=0.97.Using the method,we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th-7th nodes,and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models(e.g.,OpenSimRoot)that simulate root growth,solute transport and water uptake.
基金This study was funded by Agriculture Victoria Research,Victoria state government,Australia.
文摘A novel approach,the Algorithmic Root Trait(ART)extraction method,identifies and quantifies computationally-derived plant root traits,revealing latent patterns related to dense root clusters in digital im-ages.Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm,27 ARTs were extracted reflecting dense root cluster size and spatial location.These ARTs were then used independently and in combination with Traditional Root Traits(TRTs)to classify wheat genotypes differing in drought tolerance.ART-based models outperformed TRT-only models in drought classification(e.g.,96.3%vs.85.6%accuracy).Combining ARTs and TRTs further improved accuracy to 97.4%.Notably,4 selected ARTs matched the per-formance of all 23 TRTs,offering 5.8×higher information density(0.213 vs.0.037 accuracy/feature).This superiority reflects the ability of ARTs to capture richer,more complex architectural information,evidenced by higher internal variability(35.59±11.41 vs.28.91±14.28 for TRTs)and distinct data structures in multi-variate analyses;PERMANOVA confirmed that ARTs and TRTs provide complementary insights.Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes,ART offers a scalable,customisable toolset for high-throughput phenotyping of plant roots.By bridging conventional,visually derived traits with autonomous computational analyses,this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception.ART thus emerges as a promising framework for revealing hidden features in plant imaging,with broader applications across plant science to deepen our understanding of crop adaptation and resilience.
基金supported by the National key Research and Development plan(2021YFD1201500,2022YFD2002304)National Natural Science Foundation of China(U21A20205),Key Agricultural Core Technology Research Project in Hubei Province(HBNYHXGG2023-9)Fundamental Research Funds for the Central Universities(2662024ZKPY003).
文摘Beyond its fundamental roles in nutrient uptake and plant anchorage,the root system critically influences crop development and stress tolerance.Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil,serving as a cost-effective root imaging method.However,the opacity of the soil often results in intermittent gaps in the root images,which reduces the accuracy of the root phenotype calculations.We present a root inpainting method built upon Generative Adversarial Networks(GANs)architecture In addition,we built a hybrid root inpainting dataset(HRID)that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps.Compared with computer simulation root images,our dataset provides real root system architecture(RSA)and root texture information.Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology.The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels.Additionally,Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally,we remove the skip connections in U-Net and introduce an edge attention module(EAM)to capture more detailed information.Compared with other methods,our approach significantly improves the recall rate from 17.35%to 35.75%on the test dataset of 122 cotton root images,revealing improved inpainting capabilities.The trait error reduction rates(TERRs)for the root area,root length,convex hull area,and root depth are 76.07%,68.63%,48.64%,and 88.28%,respectively,enabling a substantial improvement in the accuracy of root phenotyping.The codes for the EU-GAN and the 8922 labeled images are open-access,which could be reused by researchers in other AI-related work.This method establishes a robust solution for root phenotyping,thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.
基金The authors would like to acknowledge funding provided by the German government to the Gauss Centre for Supercomputing via the InHPC-DE project(01-H17001)This work has partly been funded by the EUROCC2 project funded by the European High-Performance Computing Joint Undertaking(JU)and EU/EEA states under grant agreement No 101101903+2 种基金This work has partly been funded by the German Research Foundation under Germany's Excel-lence Strategy,EXC-2070-390732324-PhenoRobthe German Federal Ministry of Education and Research(BMBF)in the framework of the funding initiative Soil as a Sustainable Resource for the Bioeconomy BonaRes,the project BonaRes(Module A):Sustainable Subsoil Management-Soil3subproject 3(grant 031B1066C).
文摘This article describes an immersive virtual reality reconstruction tool for root system architectures from 3D scans of soil columns.In practical scenarios,experimental conditions will be adapted to fit the need of the data analysis pipeline,including sieving and drying the soil before scanning.Based on previous reports of automatic systems that do not represent what experts would annotate,we developed a virtual reality system to assist with the extraction of root systems in cases in which automated approaches fall short of expert knowledge.The aim of the present study is to evaluate whether our immersive method is superior to classical annotation approaches when tested on synthetic data sets using untrained participants.Our laboratory user study consists of evaluating the root extractions of participants,along with their rating on central user experience and usability measures.We show significant improvement in F1 score across conditions(noisy or clear data)as well as an improved usability.Our study highlights that using virtual reality in root extraction improves accuracy,and we perform an in-depth evaluation of biases that occur when users trace roots in soil volumes.
基金the support of the Biotechnology and Biological Sciences Research Council and Engineering and Physical Sciences Research Council funding to the Centre for Plant Integrative Biologyfunding in the form of a Biotechnology and Biological Sciences Research Council Professorial Research Fellowship+1 种基金European Research Council Advanced Investigator Grant funding(FUTUREROOTS)the Distinguished Scientist Fellowship Program(DSFP)at King Saud University
文摘A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution of the different root types in the soil. The ability to image,track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system,while allowing for aeration,solution replenishment and the imposition of nutrient treatments,as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modi fications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity（detection of fine roots and other root details）,higher ef ficiency,and a broad array of growing conditions for plants that more closely mimic those found under field conditions.
基金supported in part by a doctoral grant from the Algerian Ministry of Higher Education and Scientific Research(“bourse d’excellence du gouvernement algérien”to IM)by an ERANET EU Arimnet2 grant(no.618127)(to HS)by the French Institut National de Recherche pour l’Agriculture,l’Alimentation et l’Environnement(INRAE grant“Phenopili”from Biologie et Amélioration des Plantes Department)(to HS).
文摘The availability in the soil of potassium(K^(+)),a poorly mobile macronutrient required in large quantities for plant growth,is generally suboptimal for crop production in the absence of fertilization,making improvement of the ability of crops to adapt to K^(+)deficiency stress a major issue.Increasing the uptake capacity of the root system is among the main strategies to achieve this goal.Here,we report an integrative approach to examine the effect of K^(+)deficiency on the development of young plant entire root system,including root hairs which are known to provide a significant contribution to the uptake of poorly mobile nutrients such as K^(+),in two genetically distant wheat varieties.A rhizobox-type methodology was developed to obtain highly-resolved images of root and root hairs,allowing to describe global root and root hair traits over the whole root system via image analysis procedures.The two wheat varieties responded differently to the K^(+)shortage:Escandia,a wheat ancestor,reduced shoot biomass in condition of K^(+)shortage and substantially increased the surface area of its root system,specifically by increasing the total root hair area.Oued Zenati,a landrace,conversely appeared unresponsive to the K^(+)shortage but was shown to constitutively express,independently of the external K^(+)availability,favorable traits to cope with reduced K^(+)availability,among which a high total root hair area.Thus,valuable information on root system adaptation to K^(+)deficiency was provided by global analyses including root hairs,which should also be relevant for other nutrient stresses.
基金supported by grants AGL2014-52396,AGL2017-86293-P,and PID2022-142968NB-I00 from MCIN/AEI/10.13039/501100011033/FEDER,UE,and a grant from the Public University of Navarre(PID-2020-117703GB-I00)(to J.F.M.)and the UPV/EHU-GV IT-1018-16 program(Basque Government)(to R.E.).M.U.is a recipient of a predoctoral fellowship from the Government of Navarre,Spain.J.B.and P.L.-G.have received pre-doctoral fellowships from the Public University of Navarre,Spain.P.L.-G is currently financed by a postdoctoral contract funded by the Spanish National Research Council(20224AT017).J.B.is also a recipient of the"Requalification of the Spanish University System for 2021-2023,Public University of Navarra"fellowship,funded by the European Union-Next Generation(EU).
文摘Nitric oxide(NO)is an essential reactive oxygen species and a signal molecule in plants.Although several studies have proposed the occurrence of oxidative NO production,only reductive routes for NO production,such as the nitrate(NO_(3)^(-))-upper-reductase pathway,have been evidenced to date in land plants.However,plants grown axenically with ammonium as the sole source of nitrogen exhibit contents of nitrite and NO3−,evidencing the existence of a metabolic pathway for oxidative production of NO.We hypothesized that oximes,such as indole-3-acetaldoxime(IAOx),a precursor to indole-3-acetic acid,are intermediate oxidation products in NO synthesis.We detected the production of NO from IAOx and other oximes catalyzed by peroxidase(POD)enzyme using both 4-amino-5-methylamino-2′,7′-difluorescein fluorescence and chemiluminescence.Flavins stimulated the reaction,while superoxide dismutase inhibited it.Interestingly,mouse NO synthase can also use IAOx to produce NO at a lower rate than POD.We provided a full mechanism for POD-dependent NO production from IAOx consistent with the experimental data and supported by density functional theory calculations.We showed that the addition of IAOx to extracts from Medicago truncatula increased the in vitro production of NO,while in vivo supplementation of IAOx and other oximes increased the number of lateral roots,as shown for NO donors,and a more than 10-fold increase in IAOx dehydratase expression.Furthermore,we found that in vivo supplementation of IAOx increased NO production in Arabidopsis thaliana wild-type plants,while prx33-34 mutant plants,defective in POD33-34,had reduced production.Our data show that the release of NO by IAOx,as well as its auxinic effect,explain the superroot phenotype.Collectively,our study reveals that plants produce NO utilizing diverse molecules such as oximes,POD,and flavins,which are widely distributed in the plant kingdom,thus introducing a long-awaited oxidative pathway to NO production in plants.This knowledge has essential implications for understanding signaling in biological systems.
