Lodging is still the key factor that limits continuous increases in wheat yields today,because the mechanical strength of culms is reduced due to low-light stress in populations under high-yield cultivation.The mechan...Lodging is still the key factor that limits continuous increases in wheat yields today,because the mechanical strength of culms is reduced due to low-light stress in populations under high-yield cultivation.The mechanical properties of the culm are mainly determined by lignin,which is affected by the light environment.However,little is known about whether the light environment can be sufficiently improved by changing the population distribution to inhibit culm lodging.Therefore,in this study,we used the wheat cultivar“Xinong 979”to establish a low-density homogeneous distribution treatment(LD),high-density homogeneous distribution treatment(HD),and high-density heterogeneous distribution treatment(HD-h)to study the regulatory effects and mechanism responsible for differences in the lodging resistance of wheat culms under different population distributions.Compared with LD,HD significantly reduced the light transmittance in the middle and basal layers of the canopy,the net photosynthetic rate in the middle and lower leaves of plants,the accumulation of lignin in the culm,and the breaking resistance of the culm,and thus the lodging index values increased significantly,with lodging rates of 67.5%in 2020–2021 and 59.3%in 2021–2022.Under HD-h,the light transmittance and other indicators in the middle and basal canopy layers were significantly higher than those under HD,and the lodging index decreased to the point that no lodging occurred.Compared with LD,the activities of phenylalanine ammonia-Lyase(PAL),4-coumarate:coenzyme A ligase(4CL),catechol-O-methyltransferase(COMT),and cinnamyl-alcohol dehydrogenase(CAD)in the lignin synthesis pathway were significantly reduced in the culms under HD during the critical period for culm formation,and the relative expression levels of TaPAL,Ta4CL,TaCOMT,and TaCAD were significantly downregulated.However,the activities of lignin synthesis-related enzymes and their gene expression levels were significantly increased under HD-h compared with HD.A partial least squares path modeling analysis found significant positive effects between the canopy light environment,the photosynthetic capacity of the middle and lower leaves of plants,lignin synthesis and accumulation,and lodging resistance in the culms.Thus,under conventional high-density planting,the risk of wheat lodging was significantly higher.Accordingly,the canopy light environment can be optimized by changing the heterogeneity of the population distribution to improve the photosynthetic capacity of the middle and lower leaves of plants,promote lignin accumulation in the culm,and enhance lodging resistance in wheat.These findings provide a basis for understanding the mechanism responsible for the lower mechanical strength of the culm under high-yield wheat cultivation,and a theoretical basis and for developing technical measures to enhance lodging resistance.展开更多
Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new sour...Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new source of individual sentiment,emojis,which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations.We examined popular sentiment analysis algorithms,including rule-based and classification algorithms,to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance.Emojis were also translated into corresponding sentiment words when con-structing features for comparison with those directly generated from emoji label words.In addition,considering different functions of emojis in texts,we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance.We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms,and the algorithm performance can be further increased by taking different emoji usages into consideration.In this study,we developed an improved emoji-embedding model based on Bi-LSTM(namely,CEmo-LSTM),which achieves the highest accuracy(around 0.95)when analyzing online Chinese texts.We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1,2019 to March 20,2020 to understand the sentiment evolution of online users during the COVID-19 pandemic.We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions(e.g.,horror and sadness).Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.展开更多
基金the National Natural Science Foundation of China(32071955)the Natural Science Foundation of Shaanxi Province,China(2018JQ3061).
文摘Lodging is still the key factor that limits continuous increases in wheat yields today,because the mechanical strength of culms is reduced due to low-light stress in populations under high-yield cultivation.The mechanical properties of the culm are mainly determined by lignin,which is affected by the light environment.However,little is known about whether the light environment can be sufficiently improved by changing the population distribution to inhibit culm lodging.Therefore,in this study,we used the wheat cultivar“Xinong 979”to establish a low-density homogeneous distribution treatment(LD),high-density homogeneous distribution treatment(HD),and high-density heterogeneous distribution treatment(HD-h)to study the regulatory effects and mechanism responsible for differences in the lodging resistance of wheat culms under different population distributions.Compared with LD,HD significantly reduced the light transmittance in the middle and basal layers of the canopy,the net photosynthetic rate in the middle and lower leaves of plants,the accumulation of lignin in the culm,and the breaking resistance of the culm,and thus the lodging index values increased significantly,with lodging rates of 67.5%in 2020–2021 and 59.3%in 2021–2022.Under HD-h,the light transmittance and other indicators in the middle and basal canopy layers were significantly higher than those under HD,and the lodging index decreased to the point that no lodging occurred.Compared with LD,the activities of phenylalanine ammonia-Lyase(PAL),4-coumarate:coenzyme A ligase(4CL),catechol-O-methyltransferase(COMT),and cinnamyl-alcohol dehydrogenase(CAD)in the lignin synthesis pathway were significantly reduced in the culms under HD during the critical period for culm formation,and the relative expression levels of TaPAL,Ta4CL,TaCOMT,and TaCAD were significantly downregulated.However,the activities of lignin synthesis-related enzymes and their gene expression levels were significantly increased under HD-h compared with HD.A partial least squares path modeling analysis found significant positive effects between the canopy light environment,the photosynthetic capacity of the middle and lower leaves of plants,lignin synthesis and accumulation,and lodging resistance in the culms.Thus,under conventional high-density planting,the risk of wheat lodging was significantly higher.Accordingly,the canopy light environment can be optimized by changing the heterogeneity of the population distribution to improve the photosynthetic capacity of the middle and lower leaves of plants,promote lignin accumulation in the culm,and enhance lodging resistance in wheat.These findings provide a basis for understanding the mechanism responsible for the lower mechanical strength of the culm under high-yield wheat cultivation,and a theoretical basis and for developing technical measures to enhance lodging resistance.
基金the National Natural Sci-ence Foundation of China(82041020,72088101,91846301)XL ac-knowledges support from the National Natural Science Foundation of China(72025405,71771213)+3 种基金the Hunan Science and Technol-ogy Plan Project(2020JJ4673,2020TP1013)JL was supported by the National Natural Science Foundation of China(61773248)the Major Program of National Fund of Philosophy and Social Sci-ence of China(20ZDA060)TC and XT were supported by the Shen-zhen Basic Research Project for Development of Science and Technology(JCYJ20200109141218676).
文摘Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new source of individual sentiment,emojis,which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations.We examined popular sentiment analysis algorithms,including rule-based and classification algorithms,to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance.Emojis were also translated into corresponding sentiment words when con-structing features for comparison with those directly generated from emoji label words.In addition,considering different functions of emojis in texts,we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance.We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms,and the algorithm performance can be further increased by taking different emoji usages into consideration.In this study,we developed an improved emoji-embedding model based on Bi-LSTM(namely,CEmo-LSTM),which achieves the highest accuracy(around 0.95)when analyzing online Chinese texts.We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1,2019 to March 20,2020 to understand the sentiment evolution of online users during the COVID-19 pandemic.We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions(e.g.,horror and sadness).Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.
