利用生物信息学方法对目前已知的3种甲壳动物促雄性腺素前体(AGH precursor)和5种类胰岛素促雄性腺因子(insulin-like AG factor)进行分析,探讨了促雄性腺素前体的氨基酸理化特性、信号肽、跨膜结构域、二级结构、motif等,并利用Phyre...利用生物信息学方法对目前已知的3种甲壳动物促雄性腺素前体(AGH precursor)和5种类胰岛素促雄性腺因子(insulin-like AG factor)进行分析,探讨了促雄性腺素前体的氨基酸理化特性、信号肽、跨膜结构域、二级结构、motif等,并利用Phyre软件对其三级进行同源性收索。结果显示:促雄性腺素前体包含信号肽,存在跨膜结构域,并和信号肽同位。PDB库中没有找到匹配的motif。3种促雄性腺素前体的二级结构有比较高的相似性,比如都包含两个中心螺旋区。Phyre搜索显示,与8种蛋白的三级结构匹配的均为胰岛素家族的蛋白,这也进一步证实了促雄性腺素前体和胰岛素原的相似性。展开更多
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti...Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.展开更多
文摘采用生化方法制备凡纳滨对虾[Litopenaeus(Penaeus)vannamei)]眼柄粗提物,用活体注射法研究不同粗提物剂量对去眼柄凡纳滨对虾卵母细胞直径大小的影响,以检验其性腺抑制素(Gonadinhibiting hormone,GIH)的生物活性;在凡纳滨对虾仔虾后17、22和27 d,分别以不同浓度眼柄粗提物浸泡24 h,结果显示:除0.001个眼柄/mL的GIH浓度外,0.01、0.1和1个眼柄/mL组均显著降低凡纳滨对虾卵母细胞直径(P﹤0.05);浸泡处理后,仔虾后17 d GIH处理组凡纳滨对虾雌性率达70.1%～80.1%;仔虾后22 d GIH处理组雌性率达65.8%～71.7%,各浓度GIH处理组雌性率比对照组(雌雄比例约为1:1)显著提高(P﹤0.05);仔虾后27 d,0.01和1个眼柄/mL处理组雌性率(约58%)略高于对照组,但0.1个眼柄/mL处理组雌性率(73.1%)与对照组相比显著提高(P﹤0.05)。结果表明较低浓度的眼柄粗提物可明显诱导凡纳滨对虾雌性化,在仔虾后22 d前处理效果最佳。
文摘利用生物信息学方法对目前已知的3种甲壳动物促雄性腺素前体(AGH precursor)和5种类胰岛素促雄性腺因子(insulin-like AG factor)进行分析,探讨了促雄性腺素前体的氨基酸理化特性、信号肽、跨膜结构域、二级结构、motif等,并利用Phyre软件对其三级进行同源性收索。结果显示:促雄性腺素前体包含信号肽,存在跨膜结构域,并和信号肽同位。PDB库中没有找到匹配的motif。3种促雄性腺素前体的二级结构有比较高的相似性,比如都包含两个中心螺旋区。Phyre搜索显示,与8种蛋白的三级结构匹配的均为胰岛素家族的蛋白,这也进一步证实了促雄性腺素前体和胰岛素原的相似性。
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28050400)Jilin Province Key Research and Development Project(No.20230202040NC)Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites(No.2017-000052-73-01-001735)。
文摘Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.
