【目的】评价意大利蜜蜂 Apis mellifera ligustica (简称“意蜂”)及中华蜜蜂 Apis cerana cerana (简称“中蜂”)对黄沙白柚 Citrus maxima (Burm) Merr. cv. huangsha Yu的访花行为及授粉效果。【方法】于重庆市垫江县开展2种蜂对黄...【目的】评价意大利蜜蜂 Apis mellifera ligustica (简称“意蜂”)及中华蜜蜂 Apis cerana cerana (简称“中蜂”)对黄沙白柚 Citrus maxima (Burm) Merr. cv. huangsha Yu的访花行为及授粉效果。【方法】于重庆市垫江县开展2种蜂对黄沙白柚的访花行为及授粉效果试验,比较2种蜂访问黄沙白柚花朵的行为及其与环境因素和花朵泌蜜规律的相关性,并评价意蜂与中蜂授粉对黄沙白柚座果率、产量及果实品质的影响。【结果】意蜂与中蜂访问黄沙白柚花朵的数量呈先上升后下降的趋势,在13:00-17:00时间段中蜂访问黄沙白柚花朵的数量显著高于意蜂( P <0.05),但意蜂的单花访问时间显著高于中蜂( P <0.05);意蜂访问黄沙白柚花朵的数量受花朵泌蜜量和光照显著影响( P <0.05),中蜂的访花数量受花蜜糖浓度和光照显著影响( P <0.05)。经意蜂与中蜂授粉的黄沙白柚果实座果率、单果重、果型及果实品质显著高于自花授粉组( P <0.05),且意蜂授粉组的座果率及2种蜂授粉组的总糖含量和糖酸比均显著高于人工授粉组( P <0.05);意蜂授粉组和中蜂授粉组总果重无显著差异( P >0.05),但意蜂授粉组显著高于人工授粉组与自花授粉组( P <0.05);与中蜂授粉相比,意蜂授粉能够显著提高黄沙白柚的果实座果率。【结论】综合比较2种蜂对黄沙白柚的访花行为及授粉效果,黄沙白柚可采用蜜蜂授粉代替人工授粉,且意蜂授粉效果更好。展开更多
The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few...The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few important achievements in chemistry that have been aided by ML techniques.The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations.Important two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly introduced.Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios.Three important fields of ML in chemistry are discussed:(1)retrosynthesis,in which ML predicts the likely routes of organic synthesis;(2)atomic simulations,which utilize the ML potential to accelerate potential energy surface sampling;and(3)heterogeneous catalysis,in which ML assists in various aspects of catalytic design,ranging from synthetic condition optimization to reaction mechanism exploration.Finally,a prospect on future ML applications is provided.展开更多
文摘【目的】评价意大利蜜蜂 Apis mellifera ligustica (简称“意蜂”)及中华蜜蜂 Apis cerana cerana (简称“中蜂”)对黄沙白柚 Citrus maxima (Burm) Merr. cv. huangsha Yu的访花行为及授粉效果。【方法】于重庆市垫江县开展2种蜂对黄沙白柚的访花行为及授粉效果试验,比较2种蜂访问黄沙白柚花朵的行为及其与环境因素和花朵泌蜜规律的相关性,并评价意蜂与中蜂授粉对黄沙白柚座果率、产量及果实品质的影响。【结果】意蜂与中蜂访问黄沙白柚花朵的数量呈先上升后下降的趋势,在13:00-17:00时间段中蜂访问黄沙白柚花朵的数量显著高于意蜂( P <0.05),但意蜂的单花访问时间显著高于中蜂( P <0.05);意蜂访问黄沙白柚花朵的数量受花朵泌蜜量和光照显著影响( P <0.05),中蜂的访花数量受花蜜糖浓度和光照显著影响( P <0.05)。经意蜂与中蜂授粉的黄沙白柚果实座果率、单果重、果型及果实品质显著高于自花授粉组( P <0.05),且意蜂授粉组的座果率及2种蜂授粉组的总糖含量和糖酸比均显著高于人工授粉组( P <0.05);意蜂授粉组和中蜂授粉组总果重无显著差异( P >0.05),但意蜂授粉组显著高于人工授粉组与自花授粉组( P <0.05);与中蜂授粉相比,意蜂授粉能够显著提高黄沙白柚的果实座果率。【结论】综合比较2种蜂对黄沙白柚的访花行为及授粉效果,黄沙白柚可采用蜜蜂授粉代替人工授粉,且意蜂授粉效果更好。
基金financial support from the National Key Research and Development Program of China(2018YFA0208600)the National Natural Science Foundation of China(12188101,22033003,91945301,91745201,92145302,22122301,and 92061112)the Tencent Foundation for XPLORER PRIZE,and Fundamental Research Funds for the Central Universities(20720220011)。
文摘The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few important achievements in chemistry that have been aided by ML techniques.The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations.Important two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly introduced.Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios.Three important fields of ML in chemistry are discussed:(1)retrosynthesis,in which ML predicts the likely routes of organic synthesis;(2)atomic simulations,which utilize the ML potential to accelerate potential energy surface sampling;and(3)heterogeneous catalysis,in which ML assists in various aspects of catalytic design,ranging from synthetic condition optimization to reaction mechanism exploration.Finally,a prospect on future ML applications is provided.
