Early defoliation,which usually occurs during summer in pear trees,is gradually becoming a major problem that poses a serious threat to the pear industry in southern China.However,there is no system for evaluating the...Early defoliation,which usually occurs during summer in pear trees,is gradually becoming a major problem that poses a serious threat to the pear industry in southern China.However,there is no system for evaluating the responses of different cultivars to early defoliation,and our knowledge of the potential molecular regulation of the genes underlying this phenomenon is still limited.In this study,we conducted field investigations of 155 pear accessions to assess their resistance or susceptibility to early defoliation.A total of 126 accessions were found to be susceptible to early defoliation,and only 29 accessions were resistant.Among them,19 resistant accessions belong to the sand pear species(Pyrus pyrifolia).To identify the resistance genes related to early defoliation,the healthy and diseased samples of two sand pear accessions,namely,the resistant early defoliation accession‘Whasan’and the susceptible early defoliation accession‘Cuiguan’,were used to perform RNA sequencing.Compared with‘Cuiguan’,a total of 444 genes were uniquely differentially expressed in‘Whasan’.Combined with GO and KEGG enrichment analyses,we found that early defoliation was closely related to the stress response.Furthermore,a weighted gene co-expression network analysis revealed a high correlation of WRKY and ethylene responsive factor(ERF)transcription factors with early defoliation resistance.This study provides useful resistant germplasm resources and new insights into potentially essential genes that respond to early defoliation in pears,which may facilitate a better understanding of the resistance mechanism and molecular breeding of resistant pear cultivars.展开更多
The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.Although the frozen-HCI method is currently a common method used t...The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.Although the frozen-HCI method is currently a common method used to evaluate stone cell content in pears,it is limited in incomplete separation of stone cell and pulp and is time consuming and complicated.Computeraided research is a promising strategy in modern scientific research for phenotypic data collect!on and is increasingly used in studying crops.Thus far,we lack a quantitative tool that can effectively determine stone cell content in pear fruit.We developed a program,Pearprocess,based on an imaging protocol using computer vision and image processing algorithms applied to digital images.Using photos of hand-cut sections of pear fruit stained with phloroglucin-HCI(Wiesner's reagent),Pearprocess can extract and analyze image-based data to quantify the stone cell-related traits measured in this study:number,size,area and density of stone cell.We quantified these traits for 395 pear accessions by Pearprocess and revealed large variation in different pear varieties and species.The number of stone cells varied greatly from value of 138 to 2866,the density of stone cells ranged from 0.0019 to 0.0632 cm2 cm-2,the distribution of stone cell area ranged from 0.06 to 2.02 cm2,and the stone cell size was between 2e-4 and 1e-3 cm2.Moreover,trait data were correlated with fruit taste data.We found that stone cell density is likely the most important factor affecting the taste of pear fruit.In summary,Pearprocess is a new cost-effective web-application for semi-automated quantification of two-dimensional phenotypic traits from digital imagery using an easy imaging protocol.This simpler,feasible and accurate method to evaluate stone cell traits of fruit is a promising new tool for use in evaluating future germplasms for crop breeding programs.展开更多
基于图神经网络的推荐系统是当前数据挖掘应用的研究热点。在异质信息网络(Heterogeneous Information Network,HIN)上结合图神经网络进行推荐,可通过用户的关联信息来学习用户的偏好,从而提升推荐性能。但现有基于HIN的推荐方法大多存...基于图神经网络的推荐系统是当前数据挖掘应用的研究热点。在异质信息网络(Heterogeneous Information Network,HIN)上结合图神经网络进行推荐,可通过用户的关联信息来学习用户的偏好,从而提升推荐性能。但现有基于HIN的推荐方法大多存在不能有效地解释高阶建模结果及人工设计元路径需要相关领域知识的问题。因此,结合层次粒化思想,在异质推荐过程中引入知识图谱,提出一种基于知识图谱的异质推荐方法(Heterogeneous Recommendation Methods for Knowledge Graphs,HKR)。该方法首先结合知识图谱,对局部上下文和非局部上下文进行层次粒化,分别学习用户特征的粗粒度表示;然后基于门控机制结合局部和非局部的属性节点嵌入,进一步学习用户和项目之间的潜在特征;最后将细粒度的特征融合用于推荐。在真实的大规模数据集上的实验结果表明,所提方法的性能在多方面评测上均优于目前的基于知识图谱的图神经网络推荐方法。展开更多
基金supported by the earmarked fund for Jiangsu Agricultural Industry Technology System,China(JATS[2021]453)the National Key Research and Development Program of China(2021YFD1200200)the earmarked fund for China Agriculture Research System(CARS-28).
文摘Early defoliation,which usually occurs during summer in pear trees,is gradually becoming a major problem that poses a serious threat to the pear industry in southern China.However,there is no system for evaluating the responses of different cultivars to early defoliation,and our knowledge of the potential molecular regulation of the genes underlying this phenomenon is still limited.In this study,we conducted field investigations of 155 pear accessions to assess their resistance or susceptibility to early defoliation.A total of 126 accessions were found to be susceptible to early defoliation,and only 29 accessions were resistant.Among them,19 resistant accessions belong to the sand pear species(Pyrus pyrifolia).To identify the resistance genes related to early defoliation,the healthy and diseased samples of two sand pear accessions,namely,the resistant early defoliation accession‘Whasan’and the susceptible early defoliation accession‘Cuiguan’,were used to perform RNA sequencing.Compared with‘Cuiguan’,a total of 444 genes were uniquely differentially expressed in‘Whasan’.Combined with GO and KEGG enrichment analyses,we found that early defoliation was closely related to the stress response.Furthermore,a weighted gene co-expression network analysis revealed a high correlation of WRKY and ethylene responsive factor(ERF)transcription factors with early defoliation resistance.This study provides useful resistant germplasm resources and new insights into potentially essential genes that respond to early defoliation in pears,which may facilitate a better understanding of the resistance mechanism and molecular breeding of resistant pear cultivars.
基金This work was supported by the National Natural Science Foundation of China(31725024),the National Key Research and Development Program of China(2018YFD1000200),the earmarked fund for China Agriculture Research System(CARS-28),and the earmarked fund for Jiangsu Agricultural Industry Technology System,China(JATS 2018-277).
文摘The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.Although the frozen-HCI method is currently a common method used to evaluate stone cell content in pears,it is limited in incomplete separation of stone cell and pulp and is time consuming and complicated.Computeraided research is a promising strategy in modern scientific research for phenotypic data collect!on and is increasingly used in studying crops.Thus far,we lack a quantitative tool that can effectively determine stone cell content in pear fruit.We developed a program,Pearprocess,based on an imaging protocol using computer vision and image processing algorithms applied to digital images.Using photos of hand-cut sections of pear fruit stained with phloroglucin-HCI(Wiesner's reagent),Pearprocess can extract and analyze image-based data to quantify the stone cell-related traits measured in this study:number,size,area and density of stone cell.We quantified these traits for 395 pear accessions by Pearprocess and revealed large variation in different pear varieties and species.The number of stone cells varied greatly from value of 138 to 2866,the density of stone cells ranged from 0.0019 to 0.0632 cm2 cm-2,the distribution of stone cell area ranged from 0.06 to 2.02 cm2,and the stone cell size was between 2e-4 and 1e-3 cm2.Moreover,trait data were correlated with fruit taste data.We found that stone cell density is likely the most important factor affecting the taste of pear fruit.In summary,Pearprocess is a new cost-effective web-application for semi-automated quantification of two-dimensional phenotypic traits from digital imagery using an easy imaging protocol.This simpler,feasible and accurate method to evaluate stone cell traits of fruit is a promising new tool for use in evaluating future germplasms for crop breeding programs.
文摘基于图神经网络的推荐系统是当前数据挖掘应用的研究热点。在异质信息网络(Heterogeneous Information Network,HIN)上结合图神经网络进行推荐,可通过用户的关联信息来学习用户的偏好,从而提升推荐性能。但现有基于HIN的推荐方法大多存在不能有效地解释高阶建模结果及人工设计元路径需要相关领域知识的问题。因此,结合层次粒化思想,在异质推荐过程中引入知识图谱,提出一种基于知识图谱的异质推荐方法(Heterogeneous Recommendation Methods for Knowledge Graphs,HKR)。该方法首先结合知识图谱,对局部上下文和非局部上下文进行层次粒化,分别学习用户特征的粗粒度表示;然后基于门控机制结合局部和非局部的属性节点嵌入,进一步学习用户和项目之间的潜在特征;最后将细粒度的特征融合用于推荐。在真实的大规模数据集上的实验结果表明,所提方法的性能在多方面评测上均优于目前的基于知识图谱的图神经网络推荐方法。
