The basic leucine zipper(bZIP)is an important class of transcription factors in plants,playing a critical role in plant growth and development and responses to biotic and abiotic stress.Due to gene presence/absence va...The basic leucine zipper(bZIP)is an important class of transcription factors in plants,playing a critical role in plant growth and development and responses to biotic and abiotic stress.Due to gene presence/absence variations,it is limited to identify bZIP genes based on the reference genome.Therefore,we performed the bZIP gene family analysis in the rice pan-genome.By employing a rice pan-genome,ninety-four OsbZIPs(72 core genes and 22 variable genes)were identified and divided into 11 groups in a phylogenetic tree.Based upon Ka/Ks values in 33 accessions,OsbZIPs were subjected to different selection pressures during domestication.The analysis of the effects of structural variations(SVs)on gene expression,gene structure,and conserved domains showed that SVs could significantly alter the expression levels of certain OsbZIPs,leading to gene truncation and the emergence of numerous atypical genes.Thirty-four differentially expressed OsbZIPs were identified by analyzing RNA-seq data of the Xanthomonas oryzae pv.oryzae(Xoo)infection susceptible(IR24)and resistant(IRBB67)lines under high temperature,and by counting the number of differentially expressed OsbZIPs in different subgroups.These Osb-ZIPs were found to respond to Xoo infection at an early stage and may not be involved in the mechanism of Xa4 and Xa7 resistance to Xoo.The multiple variation patterns of OsbZIP genes provide new insights into the OsbZIP genes in rice.These results provide new resources and offer new directions for functional studies of OsbZIPs.展开更多
Weeds are mainly spread by weed seeds being mixed with agricultural and forestry crop seeds,grain,animal hair,and other plant products,and disturb the growing environment of target plants such as crops and wild native...Weeds are mainly spread by weed seeds being mixed with agricultural and forestry crop seeds,grain,animal hair,and other plant products,and disturb the growing environment of target plants such as crops and wild native plants.The accurate and efficient classification of weed seeds is important for the effective management and control of weeds.However,classification remains mainly dependent on destructive sampling-based manual inspection,which has a high cost and rather low flux.We considered that this problem could be solved using a nondestructive intelligent image recognition method.First,on the basis of the establishment of the image acquisition system for weed seeds,images of single weed seeds were rapidly and completely segmented,and a total of 47696 samples of 140 species of weed seeds and foreign materials remained.Then,six popular and novel deep Convolutional Neural Network(CNN)models are compared to identify the best method for intelligently identifying 140 species of weed seeds.Of these samples,33600 samples are randomly selected as the training dataset for model training,and the remaining 14096 samples are used as the testing dataset for model testing.AlexNet and GoogLeNet emerged from the quantitative evaluation as the best methods.AlexNet has strong classification accuracy and efficiency(low time consumption),and GoogLeNet has the best classification accuracy.A suitable CNN model for weed seed classification could be selected according to specific identification accuracy requirements and time costs of applications.This research is beneficial for developing a detection system for weed seeds in various applications.The resolution of taxonomic issues and problems associated with the identi-fication of these weed seeds may allow for more effective management and control.展开更多
基金The National Natural Science Foundation of China(31871964,31801738,and 32100352)the Major Science and Technology Projects in Anhui Province(202003a06020009)+2 种基金Earmarked Fund for China Agriculture Research System(CARS-01-40)the Special Funds for Supporting Innovation and Entrepreneurship for Returned Oversea-Students in Anhui Province(2020LCX035)Anhui Provincial Natural Science Foundation(Youth Project,2008085QC148).
文摘The basic leucine zipper(bZIP)is an important class of transcription factors in plants,playing a critical role in plant growth and development and responses to biotic and abiotic stress.Due to gene presence/absence variations,it is limited to identify bZIP genes based on the reference genome.Therefore,we performed the bZIP gene family analysis in the rice pan-genome.By employing a rice pan-genome,ninety-four OsbZIPs(72 core genes and 22 variable genes)were identified and divided into 11 groups in a phylogenetic tree.Based upon Ka/Ks values in 33 accessions,OsbZIPs were subjected to different selection pressures during domestication.The analysis of the effects of structural variations(SVs)on gene expression,gene structure,and conserved domains showed that SVs could significantly alter the expression levels of certain OsbZIPs,leading to gene truncation and the emergence of numerous atypical genes.Thirty-four differentially expressed OsbZIPs were identified by analyzing RNA-seq data of the Xanthomonas oryzae pv.oryzae(Xoo)infection susceptible(IR24)and resistant(IRBB67)lines under high temperature,and by counting the number of differentially expressed OsbZIPs in different subgroups.These Osb-ZIPs were found to respond to Xoo infection at an early stage and may not be involved in the mechanism of Xa4 and Xa7 resistance to Xoo.The multiple variation patterns of OsbZIP genes provide new insights into the OsbZIP genes in rice.These results provide new resources and offer new directions for functional studies of OsbZIPs.
基金the National Natural Science Foundation for Young Scientists of China(No.31801804)the projects subsidized by the Special Funds for Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District(No.PT202001-06)+1 种基金the Key Research and Development Program of Nanning(No.20192065)Science Foundation of Nanjing Customs District P.R.China(No.2020KJ10).
文摘Weeds are mainly spread by weed seeds being mixed with agricultural and forestry crop seeds,grain,animal hair,and other plant products,and disturb the growing environment of target plants such as crops and wild native plants.The accurate and efficient classification of weed seeds is important for the effective management and control of weeds.However,classification remains mainly dependent on destructive sampling-based manual inspection,which has a high cost and rather low flux.We considered that this problem could be solved using a nondestructive intelligent image recognition method.First,on the basis of the establishment of the image acquisition system for weed seeds,images of single weed seeds were rapidly and completely segmented,and a total of 47696 samples of 140 species of weed seeds and foreign materials remained.Then,six popular and novel deep Convolutional Neural Network(CNN)models are compared to identify the best method for intelligently identifying 140 species of weed seeds.Of these samples,33600 samples are randomly selected as the training dataset for model training,and the remaining 14096 samples are used as the testing dataset for model testing.AlexNet and GoogLeNet emerged from the quantitative evaluation as the best methods.AlexNet has strong classification accuracy and efficiency(low time consumption),and GoogLeNet has the best classification accuracy.A suitable CNN model for weed seed classification could be selected according to specific identification accuracy requirements and time costs of applications.This research is beneficial for developing a detection system for weed seeds in various applications.The resolution of taxonomic issues and problems associated with the identi-fication of these weed seeds may allow for more effective management and control.
