Field pea(Pisum sativum L.) is an important protein-rich pulse crop produced globally. Increasing the lipid content of Pisum seeds through conventional and contemporary molecular breeding tools may bring added value t...Field pea(Pisum sativum L.) is an important protein-rich pulse crop produced globally. Increasing the lipid content of Pisum seeds through conventional and contemporary molecular breeding tools may bring added value to the crop. However, knowledge about genetic diversity and lipid content in field pea is limited. An understanding of genetic diversity and population structure in diverse germplasm is important and a prerequisite for genetic dissection of complex characteristics and marker-trait associations. Fifty polymorphic microsatellite markers detecting a total of 207 alleles were used to obtain information on genetic diversity, population structure and marker-trait associations. Cluster analysis was performed using UPGMA to construct a dendrogram from a pairwise similarity matrix. Pea genotypes were divided into five major clusters. A model-based population structure analysis divided the pea accessions into four groups. Percentage lipid content in 35 diverse pea accessions was used to find potential associations with the SSR markers. Markers AD73, D21, and AA5 were significantly associated with lipid content using a mixed linear model(MLM) taking population structure(Q) and relative kinship(K) into account. The results of this preliminary study suggested that the population could be used for marker-trait association mapping studies.展开更多
The changes in total polyphenolics in elderberry (Sambucus nigra) following treatment with various doses of pulsed ultraviolet rays (UV) were investigated. Four pulsed UV durations (5, 10, 20, 30 seconds) at three ene...The changes in total polyphenolics in elderberry (Sambucus nigra) following treatment with various doses of pulsed ultraviolet rays (UV) were investigated. Four pulsed UV durations (5, 10, 20, 30 seconds) at three energy dosages (4500, 6000, 11,000 J/m2/pulse) were considered for the research. All treated elderberry fruits were incubated for 24 h at room temperature (25℃) following treatment to ensure enough response duration for enhanced development of polyphenols by the berries. The highest increase in total phenolics around 50% was found with 11,000 J/m2/pulse for a 10 seconds treatment while nearly 40% increase in total phenolics was found at an energy dosage of 11,000 J/m2/pulse after 5 seconds exposure. Even though most of the treatments indicated an increase in total polyphenols, some treatment expressed a decrease in phenolics content when compared to untreated fruits.展开更多
Reliable and automated 3-dimensional(3D)plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level.Combining deep learning and point clouds can provide effective w...Reliable and automated 3-dimensional(3D)plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level.Combining deep learning and point clouds can provide effective ways to address the challenge.However,fully supervised deep learning methods require datasets to be point-wise annotated,which is extremely expensive and time-consuming.In our work,we proposed a novel weakly supervised framework,Eff-3DPSeg,for 3D plant shoot segmentation.First,high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system,and the Meshlab-based Plant Annotator was developed for plant point cloud annotation.Second,a weakly supervised deep learning method was proposed for plant organ segmentation.The method contained(a)pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and(b)fine-tuning the pretrained model with about only 0.5%points being annotated to implement plant organ segmentation.After,3 phenotypic traits(stem diameter,leaf width,and leaf length)were extracted.To test the generality of the proposed method,the public dataset Pheno4D was included in this study.Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting.Our method achieved 95.1%,96.6%,95.8%,and 92.2%in the precision,recall,F1 score,and mIoU for stem–leaf segmentation for the soybean dataset and 53%,62.8%,and 70.3%in the AP,AP@25,and AP@50 for leaf instance segmentation for the Pheno4D dataset.This study provides an effective way for characterizing 3D plant architecture,which will become useful for plant breeders to enhance selection processes.The trained networks are available at https://github.com/jieyi-one/EFF-3DPSEG.展开更多
基金supported by the Natural Sciences and Engineering Research Council of Canada Collaborative Research and Development and Lefsrud Seeds (CRDRJ385395-09)
文摘Field pea(Pisum sativum L.) is an important protein-rich pulse crop produced globally. Increasing the lipid content of Pisum seeds through conventional and contemporary molecular breeding tools may bring added value to the crop. However, knowledge about genetic diversity and lipid content in field pea is limited. An understanding of genetic diversity and population structure in diverse germplasm is important and a prerequisite for genetic dissection of complex characteristics and marker-trait associations. Fifty polymorphic microsatellite markers detecting a total of 207 alleles were used to obtain information on genetic diversity, population structure and marker-trait associations. Cluster analysis was performed using UPGMA to construct a dendrogram from a pairwise similarity matrix. Pea genotypes were divided into five major clusters. A model-based population structure analysis divided the pea accessions into four groups. Percentage lipid content in 35 diverse pea accessions was used to find potential associations with the SSR markers. Markers AD73, D21, and AA5 were significantly associated with lipid content using a mixed linear model(MLM) taking population structure(Q) and relative kinship(K) into account. The results of this preliminary study suggested that the population could be used for marker-trait association mapping studies.
文摘The changes in total polyphenolics in elderberry (Sambucus nigra) following treatment with various doses of pulsed ultraviolet rays (UV) were investigated. Four pulsed UV durations (5, 10, 20, 30 seconds) at three energy dosages (4500, 6000, 11,000 J/m2/pulse) were considered for the research. All treated elderberry fruits were incubated for 24 h at room temperature (25℃) following treatment to ensure enough response duration for enhanced development of polyphenols by the berries. The highest increase in total phenolics around 50% was found with 11,000 J/m2/pulse for a 10 seconds treatment while nearly 40% increase in total phenolics was found at an energy dosage of 11,000 J/m2/pulse after 5 seconds exposure. Even though most of the treatments indicated an increase in total polyphenols, some treatment expressed a decrease in phenolics content when compared to untreated fruits.
基金supported by the Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants Program(grant no.G256643)Fonds de Recherche du Québec Nature et technologies(FRQNT)Programme de recherche en partenariat—Agriculture durable(grant no.G259806 FRQ-NT 322853 X-Coded 259432)FRQNT Emerging project(2022-AD-309895).
文摘Reliable and automated 3-dimensional(3D)plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level.Combining deep learning and point clouds can provide effective ways to address the challenge.However,fully supervised deep learning methods require datasets to be point-wise annotated,which is extremely expensive and time-consuming.In our work,we proposed a novel weakly supervised framework,Eff-3DPSeg,for 3D plant shoot segmentation.First,high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system,and the Meshlab-based Plant Annotator was developed for plant point cloud annotation.Second,a weakly supervised deep learning method was proposed for plant organ segmentation.The method contained(a)pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and(b)fine-tuning the pretrained model with about only 0.5%points being annotated to implement plant organ segmentation.After,3 phenotypic traits(stem diameter,leaf width,and leaf length)were extracted.To test the generality of the proposed method,the public dataset Pheno4D was included in this study.Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting.Our method achieved 95.1%,96.6%,95.8%,and 92.2%in the precision,recall,F1 score,and mIoU for stem–leaf segmentation for the soybean dataset and 53%,62.8%,and 70.3%in the AP,AP@25,and AP@50 for leaf instance segmentation for the Pheno4D dataset.This study provides an effective way for characterizing 3D plant architecture,which will become useful for plant breeders to enhance selection processes.The trained networks are available at https://github.com/jieyi-one/EFF-3DPSEG.
