Cotton breeding for the development of early-maturing varieties is an effective way to improve multiple cropping indexes and alleviate the conflict between grains and cotton in the cultivated fields in China.In the pr...Cotton breeding for the development of early-maturing varieties is an effective way to improve multiple cropping indexes and alleviate the conflict between grains and cotton in the cultivated fields in China.In the present study,we aimed to identify upland cotton quantitative trait loci(QTLs)and candidate genes related to early-maturity traits,including whole growth period(WGP),flowering timing(FT),node of the first fruiting branch(NFFB),height of the node of the first fruiting branch(HNFFB),and plant height(PH).An early-maturing variety,CCRI50,and a latematuring variety,Guoxinmian 11,were crossed to obtain biparental populations.These populations were used to map QTLs for the early-maturity traits for two years(2020 and 2021).With BSA-seq analysis based on the data of population 2020,the candidate regions related to early maturity were found to be located on chromosome D03.We then developed 22 polymorphic insertions or deletions(InDel)markers to further narrow down the candidate regions,resulting in the detection of five and four QTLs in the 2020 and 2021 populations,respectively.According to the results of QTL mapping,two candidate regions(InDel_G286-InDel_G144 and InDel_G24-InDel_G43)were detected.In these regions,three genes(GH_D03G0451,GH_D03G0649,and GH_D03G1180)have nonsynonymous mutations in their exons and one gene(GH_D03G0450)has SNP variations in the upstream sequence between CCRI50 and Guoxinmian 11.These four genes also showed dominant expression in the floral organs.The expression levels of GH_D03G0451,GH_D03G0649 and GH_D03G1180 were significantly higher in CCRI50 than in Guoxinmian 11 during the bud differentiation stages,while GH_D03G0450 showed the opposite trend.Further functional verification of GH_D03G0451 indicated that the GH_D03G0451-silenced plants showed a delay in the flowering time.The results suggest that these are the candidate genes for cotton early maturity,and they may be used for breeding early-maturity cotton varieties.展开更多
Drought is one of the abiotic stresses limiting the production of soybean(Glycine max).Elucidation of the genetic and molecular basis of the slow-wilting(SW)trait of this crop offers the prospect of its genetic improv...Drought is one of the abiotic stresses limiting the production of soybean(Glycine max).Elucidation of the genetic and molecular basis of the slow-wilting(SW)trait of this crop offers the prospect of its genetic improvement.A panel of 188 accessions and a set of recombinant inbred lines produced from a cross between cultivars Liaodou 14 and Liaodou 21 were used to identify quantitative-trait loci(QTL)associated with SW.Plants were genotyped by Specific-locus amplified fragment sequencing and seedling leaf wilting was assessed under three water-stress treatments.A genome-wide association study identified 26 SW-associated single-nucleotide polymorphisms(SNPs),including three located in a 248-kb linkage-disequilibrium(LD)block on chromosome 2.Linkage mapping revealed a major-effect QTL,qSW2,associated with all three treatments and adjacent to the LD block.Fine mapping in a BC_(2)F_(3) population derived from a backcross between Liaodou 21 and R26 confined qSW2 to a 60-kb interval.Gene expression and sequence variation analysis identified the gene Glyma.02 g218100,encoding an auxin transcription factor,as a candidate gene for qSW2.Our results will contribute significantly to improving drought-resistant soybean cultivars by providing genetic information and resources.展开更多
A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized f...A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.展开更多
文摘背景:腰椎小关节炎是引起下腰痛的一个主要原因,目前主要依靠MRI进行初步定性诊断,但仍有一定漏诊、误诊的概率发生,因此MR T2^(*)mapping成像技术有望成为定量检查腰椎小关节炎软骨损伤的重要检测手段。目的:探讨MR T2^(*)mapping成像技术在定量分析腰椎小关节炎软骨损伤退变中的应用价值。方法:收集南京医科大学第四附属医院2020年4月至2022年3月门诊或住院合并下腰痛共110例患者,设为病例组;同时招募无症状志愿者80例,设为对照组。对所有纳入对象L1-S1的小关节行3.0 T MR扫描,获取T2^(*)mapping横断位图像和T2WI图像,分别对所有小关节软骨进行Weishaupt分级及T2^(*)值测量,收集数据并行统计学分析。不同小关节Weishaupt分级之间小关节软骨T2^(*)值比较采用单因素方差分析。结果与结论:①经统计分析发现,病例组腰椎小关节软骨T2^(*)值(17.6±1.5)ms明显较对照组(21.4±1.3)ms降低,差异有显著性意义(P<0.05);②在病例组中,随着腰椎小关节Weishaupt分级增加,小关节软骨T2^(*)值也呈逐渐下降趋势,且这种差异有显著性意义(P<0.05);③提示T2^(*)mapping能够较好地显示腰椎小关节软骨损伤的早期病理变化,腰椎小关节软骨的T2^(*)值能够定量评估腰椎小关节的软骨损伤程度;T2^(*)mapping成像技术能为影像学诊断腰椎小关节炎软骨早期损伤提供很好的理论依据,具有重要的临床应用价值。
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region,China(2022D01B222)the China Agriculture Research System(CARS-15-06)the Key R&D Project of Eight Division of Xinjiang Production and Construction Corps,China(2021NY01)。
文摘Cotton breeding for the development of early-maturing varieties is an effective way to improve multiple cropping indexes and alleviate the conflict between grains and cotton in the cultivated fields in China.In the present study,we aimed to identify upland cotton quantitative trait loci(QTLs)and candidate genes related to early-maturity traits,including whole growth period(WGP),flowering timing(FT),node of the first fruiting branch(NFFB),height of the node of the first fruiting branch(HNFFB),and plant height(PH).An early-maturing variety,CCRI50,and a latematuring variety,Guoxinmian 11,were crossed to obtain biparental populations.These populations were used to map QTLs for the early-maturity traits for two years(2020 and 2021).With BSA-seq analysis based on the data of population 2020,the candidate regions related to early maturity were found to be located on chromosome D03.We then developed 22 polymorphic insertions or deletions(InDel)markers to further narrow down the candidate regions,resulting in the detection of five and four QTLs in the 2020 and 2021 populations,respectively.According to the results of QTL mapping,two candidate regions(InDel_G286-InDel_G144 and InDel_G24-InDel_G43)were detected.In these regions,three genes(GH_D03G0451,GH_D03G0649,and GH_D03G1180)have nonsynonymous mutations in their exons and one gene(GH_D03G0450)has SNP variations in the upstream sequence between CCRI50 and Guoxinmian 11.These four genes also showed dominant expression in the floral organs.The expression levels of GH_D03G0451,GH_D03G0649 and GH_D03G1180 were significantly higher in CCRI50 than in Guoxinmian 11 during the bud differentiation stages,while GH_D03G0450 showed the opposite trend.Further functional verification of GH_D03G0451 indicated that the GH_D03G0451-silenced plants showed a delay in the flowering time.The results suggest that these are the candidate genes for cotton early maturity,and they may be used for breeding early-maturity cotton varieties.
基金The study was supported by the National Natural Science Foundation of China(32101795,32301782)National Key Research and Development Program of China(2016YFD0100201-01)+2 种基金Liaoning Provincial Major Special Project of Agricultural Science and Technology(2022JH1/10200002,2021JH1/10400038)Key Research and Development Plan of Liaoning Science and Technology Department(2021JH2/1020027)Shenyang Seed Industry Innovation Project(22-318-2-12).
文摘Drought is one of the abiotic stresses limiting the production of soybean(Glycine max).Elucidation of the genetic and molecular basis of the slow-wilting(SW)trait of this crop offers the prospect of its genetic improvement.A panel of 188 accessions and a set of recombinant inbred lines produced from a cross between cultivars Liaodou 14 and Liaodou 21 were used to identify quantitative-trait loci(QTL)associated with SW.Plants were genotyped by Specific-locus amplified fragment sequencing and seedling leaf wilting was assessed under three water-stress treatments.A genome-wide association study identified 26 SW-associated single-nucleotide polymorphisms(SNPs),including three located in a 248-kb linkage-disequilibrium(LD)block on chromosome 2.Linkage mapping revealed a major-effect QTL,qSW2,associated with all three treatments and adjacent to the LD block.Fine mapping in a BC_(2)F_(3) population derived from a backcross between Liaodou 21 and R26 confined qSW2 to a 60-kb interval.Gene expression and sequence variation analysis identified the gene Glyma.02 g218100,encoding an auxin transcription factor,as a candidate gene for qSW2.Our results will contribute significantly to improving drought-resistant soybean cultivars by providing genetic information and resources.
基金supported by the National Key Research and Development Program of China(2021YFB3901205)the National Institute of Natural Hazards,Ministry of Emergency Management of China(2023-JBKY-57)。
文摘A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.
