为探索可得然胶添加量对大豆分离蛋白(Soy Protein Isolate,SPI)凝胶冻融前后理化性质及结构的影响。文章采用傅里叶变换红外光谱探究大豆分离蛋白的结构变化,并通过分析冻融循环过程中SPI凝胶水分分布状态、持水性、质构特性及微观结构...为探索可得然胶添加量对大豆分离蛋白(Soy Protein Isolate,SPI)凝胶冻融前后理化性质及结构的影响。文章采用傅里叶变换红外光谱探究大豆分离蛋白的结构变化,并通过分析冻融循环过程中SPI凝胶水分分布状态、持水性、质构特性及微观结构,探讨其作用机理。结果表明,随着可得然胶添加量增加,SPI凝胶硬度、持水性和结合水含量呈先增后降趋势。5次冻融循环(5 FTCs)后,SPI凝胶硬度、持水性和水分分布状态均有显著变化。当可得然胶添加量为4 g/100 g时,与未冻融前相较,SPI凝胶硬度增加109.13 g,持水性降低24.05%。添加可得然胶后,SPI凝胶水分子可移动性减弱,维持原本水分分布状态。同时,SPI凝胶网络结构增强,有效延缓冻融循环过程中品质劣变。傅里叶变换红外光谱分析结果表明,添加可得然胶可增强SPI凝胶持水性,促进蛋白质中α-螺旋转化为β-折叠。展开更多
Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of p...Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.展开更多
In plants and mammals,non-homologous end-joining is the dominant pathway to repair DNA doublestrand breaks,making it challenging to generate knock-in events.In this study,we identified two groups of exonucleases from ...In plants and mammals,non-homologous end-joining is the dominant pathway to repair DNA doublestrand breaks,making it challenging to generate knock-in events.In this study,we identified two groups of exonucleases from the herpes virus and the bacteriophage T7 families that conferred an up to 38-fold increase in homology-directed repair frequencies when fused to Cas9/Cas12a in a tobacco mosaic virus-based transient assay in Nicotiana benthamiana.We achieved precise and scar-free insertion of several kilobases of DNA both in transient and stable transformation systems.In Arabidopsis thaliana,fusion of Cas9 to a herpes virus family exonuclease led to 10-fold higher frequencies of knock-ins in the first generation of transformants.In addition,we demonstrated stable and heritable knock-ins in wheat in 1%of the primary transformants.Taken together,our results open perspectives for the routine production of heritable knock-in and gene replacement events in plants.展开更多
Phenomic selection is a recent approach suggested as a low-cost,high-throughput alternative to genomic selection.Instead of using genetic markers,it employs spectral data to predict complex traits using equivalent sta...Phenomic selection is a recent approach suggested as a low-cost,high-throughput alternative to genomic selection.Instead of using genetic markers,it employs spectral data to predict complex traits using equivalent statistical models.Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted.However,for hybrid breeding,the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation.Here,we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding.We performed predictions for various traits in a structured population of 410 test hybrids,grown in multiple environments,using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models.We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height,even when spectral data was collected at single locations,while being less affected by population structure.Furthermore,we demonstrate that phenomic prediction across generations is feasible,and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection.We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.展开更多
Dear Editor,Rauvolfia tetraphylla(aka the Devil pepper)(Supplemental Figure 1)is a well-known medicinal plant that produces monoterpenoid indole alkaloids(MIAs).This MIA biosynthesis occurs in several organs,including...Dear Editor,Rauvolfia tetraphylla(aka the Devil pepper)(Supplemental Figure 1)is a well-known medicinal plant that produces monoterpenoid indole alkaloids(MIAs).This MIA biosynthesis occurs in several organs,including leaves,stems,fruit,and roots,which accumulate the famous antiarrhythmic ajmaline(Kumar et al.2016a,2016b;Kumara et al.,2019).MIAs are natural products notably involved in plant adaptation to the environment and defense against aggressors.This mainly results from their high biological activities,which also explain their pharmacological properties.展开更多
基金Global wheat was directly supported by Analytics for the Australian Grains Industry(AAGI).
文摘Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
基金funded by grant no.031B0548 in the frame of the program"Crop plants of the future"from the Bundesministerium fur Bildung und Forschung to A.T.funded by the Investissement d’Avenir program of the French National Agency of Research for the project GENIUS(ANR-11-BTBR-0001_GENIUS).
文摘In plants and mammals,non-homologous end-joining is the dominant pathway to repair DNA doublestrand breaks,making it challenging to generate knock-in events.In this study,we identified two groups of exonucleases from the herpes virus and the bacteriophage T7 families that conferred an up to 38-fold increase in homology-directed repair frequencies when fused to Cas9/Cas12a in a tobacco mosaic virus-based transient assay in Nicotiana benthamiana.We achieved precise and scar-free insertion of several kilobases of DNA both in transient and stable transformation systems.In Arabidopsis thaliana,fusion of Cas9 to a herpes virus family exonuclease led to 10-fold higher frequencies of knock-ins in the first generation of transformants.In addition,we demonstrated stable and heritable knock-ins in wheat in 1%of the primary transformants.Taken together,our results open perspectives for the routine production of heritable knock-in and gene replacement events in plants.
基金funded by Federal Ministry for Food and Agriculture grant 281B200416.
文摘Phenomic selection is a recent approach suggested as a low-cost,high-throughput alternative to genomic selection.Instead of using genetic markers,it employs spectral data to predict complex traits using equivalent statistical models.Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted.However,for hybrid breeding,the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation.Here,we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding.We performed predictions for various traits in a structured population of 410 test hybrids,grown in multiple environments,using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models.We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height,even when spectral data was collected at single locations,while being less affected by population structure.Furthermore,we demonstrate that phenomic prediction across generations is feasible,and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection.We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.
基金supported by the EU Horizon 2020 research and innovation program (MIAMi project-grant agreement N°814645)the ARD CVL Biopharmaceutical program of the Region Centre-Val de Loire (ETOPOCentre project)the ANR (project MIACYC–ANR-20-CE43-0010).
文摘Dear Editor,Rauvolfia tetraphylla(aka the Devil pepper)(Supplemental Figure 1)is a well-known medicinal plant that produces monoterpenoid indole alkaloids(MIAs).This MIA biosynthesis occurs in several organs,including leaves,stems,fruit,and roots,which accumulate the famous antiarrhythmic ajmaline(Kumar et al.2016a,2016b;Kumara et al.,2019).MIAs are natural products notably involved in plant adaptation to the environment and defense against aggressors.This mainly results from their high biological activities,which also explain their pharmacological properties.
