Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is inf...Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.展开更多
To estimate the impact of crop rotation on the pathotype and genetic structure of Phythophthora sojae in fields, 372 isolates of P. sojae were obtained from long-term localisation experimental fields in Heilongjiang P...To estimate the impact of crop rotation on the pathotype and genetic structure of Phythophthora sojae in fields, 372 isolates of P. sojae were obtained from long-term localisation experimental fields in Heilongjiang Province of China. The hypocotyl inoculation method was used to characterize the virulence of P. sojae on 13 differential cultivars, and the amplified fragment length polymorphism（AFLP） technique was used to analyze difference in the genetic structure of P. sojae. The results indicated that an abundant diversity of genetic structures and pathotypes of P. sojae, a more uniform distribution of pathotypes and less dominance of pathotypes occurred in corn-soybean and wheat-soybean rotation fields than in a continuous soybean mono-cropping field. These findings suggested that P. sojae did not easily become the dominant race in rotation fields, which maintain disease resistance in soybean varieties. Therefore, the results of this study suggested that Phytophthora stem and root rot of soybeans could be effectively controlled by rotating soybeans with non-host crops of corn and wheat.展开更多
【目的】蛀茎夜蛾Sesamia spp.是伊朗和其他国家(包括印度、巴基斯坦、斯里兰卡和日本)甘蔗上最具破坏性的害虫。蛀茎夜蛾幼虫钻蛀茎秆,对茎秆产生为害,降低重量和含糖量并降低甘蔗汁品质。应用抗性品种是一种有效的工具,对环境无不利...【目的】蛀茎夜蛾Sesamia spp.是伊朗和其他国家(包括印度、巴基斯坦、斯里兰卡和日本)甘蔗上最具破坏性的害虫。蛀茎夜蛾幼虫钻蛀茎秆,对茎秆产生为害,降低重量和含糖量并降低甘蔗汁品质。应用抗性品种是一种有效的工具,对环境无不利影响。本研究旨在评价甘蔗商业品种对蛀茎夜蛾的抗性。【方法】在伊朗Ahwaz的Salman-Farsi Agro-industry Farms于2013-2014和2014-2015两个连续年份,采用随机区组设计进行5次重复试验。应用了CP69-1062,CP48-103,CP57-614,CP73-21,SP70-1143,IRC99-01,IRC99-02和L62-96 8个品种。在收获前,随机取20株完整的甘蔗茎秆进行蛀茎夜蛾的为害评估。记录受害茎秆百分比、被钻蛀的节间百分比(percent of internodes bored,IB)、出口孔的数目、活的蛀茎夜蛾的数目、每品种每公顷面积中蛾的繁殖量(moth production per hectare of each variety,MP)。【结果】各测定参数在品种间均存在显著差异。从被钻蛀的节间百分比和蛾的繁殖指数判断,L62-96是最敏感的品种(2014年:14.58%IB,95 200 MP/ha;2015年:16.76%IB和111 300 MP/ha),其次是CP69-1062和CP48-103;CP57-614是这两年中抗性最强的品种(2014年:1.24%IB,8 400 MP/ha;2015年:1.02%IB,7 000 MP/ha)。【结论】建议限制敏感品种的栽培,并应用其他控制措施,结合品种抗性,以治理敏感品种中的蛀茎夜蛾。展开更多
文摘Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.
基金Supported by the Special Fund for Agro-scientific Research in the Public Interest(201303018)the National Natural Science Foundation of China(31370449)
文摘To estimate the impact of crop rotation on the pathotype and genetic structure of Phythophthora sojae in fields, 372 isolates of P. sojae were obtained from long-term localisation experimental fields in Heilongjiang Province of China. The hypocotyl inoculation method was used to characterize the virulence of P. sojae on 13 differential cultivars, and the amplified fragment length polymorphism（AFLP） technique was used to analyze difference in the genetic structure of P. sojae. The results indicated that an abundant diversity of genetic structures and pathotypes of P. sojae, a more uniform distribution of pathotypes and less dominance of pathotypes occurred in corn-soybean and wheat-soybean rotation fields than in a continuous soybean mono-cropping field. These findings suggested that P. sojae did not easily become the dominant race in rotation fields, which maintain disease resistance in soybean varieties. Therefore, the results of this study suggested that Phytophthora stem and root rot of soybeans could be effectively controlled by rotating soybeans with non-host crops of corn and wheat.
文摘【目的】蛀茎夜蛾Sesamia spp.是伊朗和其他国家(包括印度、巴基斯坦、斯里兰卡和日本)甘蔗上最具破坏性的害虫。蛀茎夜蛾幼虫钻蛀茎秆,对茎秆产生为害,降低重量和含糖量并降低甘蔗汁品质。应用抗性品种是一种有效的工具,对环境无不利影响。本研究旨在评价甘蔗商业品种对蛀茎夜蛾的抗性。【方法】在伊朗Ahwaz的Salman-Farsi Agro-industry Farms于2013-2014和2014-2015两个连续年份,采用随机区组设计进行5次重复试验。应用了CP69-1062,CP48-103,CP57-614,CP73-21,SP70-1143,IRC99-01,IRC99-02和L62-96 8个品种。在收获前,随机取20株完整的甘蔗茎秆进行蛀茎夜蛾的为害评估。记录受害茎秆百分比、被钻蛀的节间百分比(percent of internodes bored,IB)、出口孔的数目、活的蛀茎夜蛾的数目、每品种每公顷面积中蛾的繁殖量(moth production per hectare of each variety,MP)。【结果】各测定参数在品种间均存在显著差异。从被钻蛀的节间百分比和蛾的繁殖指数判断,L62-96是最敏感的品种(2014年:14.58%IB,95 200 MP/ha;2015年:16.76%IB和111 300 MP/ha),其次是CP69-1062和CP48-103;CP57-614是这两年中抗性最强的品种(2014年:1.24%IB,8 400 MP/ha;2015年:1.02%IB,7 000 MP/ha)。【结论】建议限制敏感品种的栽培,并应用其他控制措施,结合品种抗性,以治理敏感品种中的蛀茎夜蛾。
