冰裂隙是冰川动态变化的重要特征,通过研究冰裂隙,可以有效监测冰川的变化情况。然而,传统的冰裂隙遥感监测方法往往存在人工干预多、效率低下或对复杂冰川环境适应性不足等问题。为解决以上问题,以GeoScene Pro 3.1软件为平台,利用高...冰裂隙是冰川动态变化的重要特征,通过研究冰裂隙,可以有效监测冰川的变化情况。然而,传统的冰裂隙遥感监测方法往往存在人工干预多、效率低下或对复杂冰川环境适应性不足等问题。为解决以上问题,以GeoScene Pro 3.1软件为平台,利用高分辨率遥感影像,选择长江源各拉丹冬冰川区为试验区,结合深度学习技术,探讨了U-Net像素分类模型、HED边缘检测器和BDCN边缘检测器在冰裂隙检测与提取中的应用。首先,通过调整不同的训练样本集组合与训练参数,对以上3种模型分别进行了5次模型训练和冰裂隙检测分析。检测结果表明,U-Net像素分类模型和HED边缘检测器都存在检测效果不佳的问题,而BDCN边缘检测器能够有效抑制检测噪声干扰,并能准确地检测并识别出冰裂隙的分布位置和宽度。其次,利用BDCN边缘检测器对各拉丹冬4条冰川进行冰裂隙检测,冰裂隙检测率分别达到90%、92%、89%、93%,模型检测宽度与人工采样宽度的最小差值仅为0.3 m,充分证明了该模型在不同冰川区域应用时具备良好的鲁棒性和泛化能力。研究表明BDCN边缘检测器更适合冰裂隙的检测识别,可为各拉丹冬冰川及类似区域的冰裂隙周期性监测提供快速有效的技术支持。展开更多
Rice,a critical global staple crop,relies heavily on heading date,a key agronomic trait marking the transition from vegetative to reproductive growth.Understanding the genetic regulation of heading date is vital for e...Rice,a critical global staple crop,relies heavily on heading date,a key agronomic trait marking the transition from vegetative to reproductive growth.Understanding the genetic regulation of heading date is vital for enhancing the adaptability of high-quality rice varieties across diverse geographical regions and for bolstering local food security.In this study,we uncovered a novel role for OsCATA,a catalase gene,in the regulation of photoperiodic flowering in rice.We identified a novel allele of OsELF3.1,whose mutation resulted in delayed heading.Further analyses revealed that OsELF3.1 physically interacted with OsCATA.Notably,OsCATA exhibited rhythmic expression patterns similar to OsELF3.1 and,when mutated,also delayed flowering.Expression analyses showed that the delayed heading phenotype could be attributed to elevated Ghd7 expression under both long-day and short-day conditions,with OsCATA expression positively regulated by OsELF3.1.Double mutants of OsELF3.1 and OsCATA displayed a heading delay similar to that of oself3.1 single mutants.Additionally,OsELF3.1 could interact with Ghd7 in vivo,alleviating its suppression of Ehd1.Luciferase assays confirmed that Ghd7 repressed Ehd1 expression,while OsELF3.1 mitigated this repression.Collectively,our findings reveal that OsCATA is critical in suppressing Ghd7 expression through the OsELF3.1-OsCATA-Ghd7 transcriptional pathway,thereby regulating rice heading.展开更多
Grain size and weight are closely related traits determining yield in rice(Oryza sativa L.).Since indica and japonica rice varieties differ significantly in multiple traits,a high-generation recombinant inbred line(RI...Grain size and weight are closely related traits determining yield in rice(Oryza sativa L.).Since indica and japonica rice varieties differ significantly in multiple traits,a high-generation recombinant inbred line(RIL)population derived from the crossing LH9(indica)and RPY(japonica)was used to map grainrelated traits in six environments.Pyramiding of the quantitative trait loci(QTL)for thousand-grain weight showed that combinations of multiple QTL significantly increased the phenotypic effect.A novel gene named GSW3.1 controlling grain size and weight was discovered using the major QTL for the colocalization of grain width and thousand-grain weight on chromosome 3.Gene editing revealed that GSW3.1(LOC_Os03g16850)was pleiotropic,positively regulating grain size and weight while affecting several other agronomic traits.Haplotype analysis indicated that some traits,including grain width and weight,were highly correlated with indica-japonica differentiation.展开更多
As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.I...As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.展开更多
文摘冰裂隙是冰川动态变化的重要特征,通过研究冰裂隙,可以有效监测冰川的变化情况。然而,传统的冰裂隙遥感监测方法往往存在人工干预多、效率低下或对复杂冰川环境适应性不足等问题。为解决以上问题,以GeoScene Pro 3.1软件为平台,利用高分辨率遥感影像,选择长江源各拉丹冬冰川区为试验区,结合深度学习技术,探讨了U-Net像素分类模型、HED边缘检测器和BDCN边缘检测器在冰裂隙检测与提取中的应用。首先,通过调整不同的训练样本集组合与训练参数,对以上3种模型分别进行了5次模型训练和冰裂隙检测分析。检测结果表明,U-Net像素分类模型和HED边缘检测器都存在检测效果不佳的问题,而BDCN边缘检测器能够有效抑制检测噪声干扰,并能准确地检测并识别出冰裂隙的分布位置和宽度。其次,利用BDCN边缘检测器对各拉丹冬4条冰川进行冰裂隙检测,冰裂隙检测率分别达到90%、92%、89%、93%,模型检测宽度与人工采样宽度的最小差值仅为0.3 m,充分证明了该模型在不同冰川区域应用时具备良好的鲁棒性和泛化能力。研究表明BDCN边缘检测器更适合冰裂隙的检测识别,可为各拉丹冬冰川及类似区域的冰裂隙周期性监测提供快速有效的技术支持。
基金funded by the Biological Breeding-National Science and Technology Major Projects,China(Grant No.2023ZD04066)the Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LZ24C130006,LTGN24C130007)+5 种基金the Open Project Program of the State Key Laboratory of Rice Biology and Breeding,China(Grant No.20240107)the Xi’nan League Science and Technology Project,China(Grant No.2023DXZD0001)the Joint Research and Development Program on Rice Breeding in Inner Mongolia Autonomous Region,China(Grant No.YZ2023004)the China Agriculture Research System(Grant No.CARS-01)the Central Public-interest Scientific Institution Basal Research Fund,China(Grant No.CPSIBRF-CNRRI-202301)the Agricultural Science and Technology Innovation Program(ASTIP).
文摘Rice,a critical global staple crop,relies heavily on heading date,a key agronomic trait marking the transition from vegetative to reproductive growth.Understanding the genetic regulation of heading date is vital for enhancing the adaptability of high-quality rice varieties across diverse geographical regions and for bolstering local food security.In this study,we uncovered a novel role for OsCATA,a catalase gene,in the regulation of photoperiodic flowering in rice.We identified a novel allele of OsELF3.1,whose mutation resulted in delayed heading.Further analyses revealed that OsELF3.1 physically interacted with OsCATA.Notably,OsCATA exhibited rhythmic expression patterns similar to OsELF3.1 and,when mutated,also delayed flowering.Expression analyses showed that the delayed heading phenotype could be attributed to elevated Ghd7 expression under both long-day and short-day conditions,with OsCATA expression positively regulated by OsELF3.1.Double mutants of OsELF3.1 and OsCATA displayed a heading delay similar to that of oself3.1 single mutants.Additionally,OsELF3.1 could interact with Ghd7 in vivo,alleviating its suppression of Ehd1.Luciferase assays confirmed that Ghd7 repressed Ehd1 expression,while OsELF3.1 mitigated this repression.Collectively,our findings reveal that OsCATA is critical in suppressing Ghd7 expression through the OsELF3.1-OsCATA-Ghd7 transcriptional pathway,thereby regulating rice heading.
基金supported by the National Key Research and Development Program of China(2016YFD0100400)the National Special Key Project for Transgenic Breeding(2016ZX08001001)。
文摘Grain size and weight are closely related traits determining yield in rice(Oryza sativa L.).Since indica and japonica rice varieties differ significantly in multiple traits,a high-generation recombinant inbred line(RIL)population derived from the crossing LH9(indica)and RPY(japonica)was used to map grainrelated traits in six environments.Pyramiding of the quantitative trait loci(QTL)for thousand-grain weight showed that combinations of multiple QTL significantly increased the phenotypic effect.A novel gene named GSW3.1 controlling grain size and weight was discovered using the major QTL for the colocalization of grain width and thousand-grain weight on chromosome 3.Gene editing revealed that GSW3.1(LOC_Os03g16850)was pleiotropic,positively regulating grain size and weight while affecting several other agronomic traits.Haplotype analysis indicated that some traits,including grain width and weight,were highly correlated with indica-japonica differentiation.
文摘As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.
