Sweetpotato[Ipomoea batatas(L.)Lam.],a food crop with both nutritional and medicinal uses,plays essential roles in food security and health-promoting.Chlorogenic acid(CGA),a polyphenol displaying several bioactivities...Sweetpotato[Ipomoea batatas(L.)Lam.],a food crop with both nutritional and medicinal uses,plays essential roles in food security and health-promoting.Chlorogenic acid(CGA),a polyphenol displaying several bioactivities,is distributed in all edible parts of sweetpotato.However,little is known about the specific metabolism of CGA in sweetpotato.In this study,IbPAL1,which encodes an endoplasmic reticulum-localized phenylalanine ammonia lyase(PAL),was isolated and characterized in sweetpotato.CGA accumulation was positively associated with the expression pattern of IbPAL1 in a tissue-specific manner,as further demonstrated by overexpression of IbPAL1.Overexpression of IbPAL1 promoted CGA accumulation and biosynthetic pathway genes expression in leaves,stimulated secondary xylem cell expansion in stems,and inhibited storage root formation.Our results support a potential role for IbPAL1 in sweetpotato CGA biosynthesis and establish a theoretical foundation for detailed mechanism research and nutrient improvement in sweetpotato breeding programs.展开更多
The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case.Recent advances in deep learning frameworks inspire us to propose a two-step m...The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case.Recent advances in deep learning frameworks inspire us to propose a two-step method to address this problem.To obtain a better understanding and more specific representation of the legal texts,we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment documents.By formalizing prison term prediction as a regression problem,we adopt the linear regression model and the neural network model to train the prison term predictor.In experiments,we construct a real-world dataset of theft case judgment documents.Experimental results demonstrate that our method can effectively extract judgment-specific case features from textual fact descriptions.The best performance of the proposed predictor is obtained with a mean absolute error of 3.2087 months,and the accuracy of 72.54%and 90.01%at the error upper bounds of three and six months,respectively.展开更多
With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.Howe...With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.However,different people have different language expressions and legal professional backgrounds.This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation.How to accurately understand the true intentions behind different users’legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services.Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data,and are not scalable,and often require taxing manual annotation work.This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users’intention to legal consulting.First,we present the users’legal consulting statements as a tensor.And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users’intention of legal consultation,namely the core tensor.The core tensor relies less on the lexical and semantic information of the original users’legal consulting statements data,it reduces the dimension of the original tensor,and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm.Furthermore,we use a large number of core tensors obtained by the tensor decomposition layer with users’legal consulting statements tensors as inputs to continuously train Bi-LSTM,and finally derive the users’legal consultation intention classification model which can comprehensively understand the user’s legal consultation intention.Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks.展开更多
The pathogen and characteristics, infection cycle, occurrence regularity and damage symptoms of sweet potato stem nematode disease were introduced in the paper. Moreover, the comprehensive prevention measures were put...The pathogen and characteristics, infection cycle, occurrence regularity and damage symptoms of sweet potato stem nematode disease were introduced in the paper. Moreover, the comprehensive prevention measures were put forward, including plant quarantine, agricultural control and chemical control. The study provided certain basis for reducing damages of sweet potato stem nematode disease and improving yield and quality of sweet potato.展开更多
基金supported by the National Key Research and Development Program of China(2019YFD1001302 and 2019YFD1001300)National Natural Science Foundation of China(31701483 and 31601382)+2 种基金Jiangsu Agricultural Science and Technology Independent Innovation Fund[CX(19)3063]the National Technical System of Sweetpotato Industry(CARS-10-C3)Jiangsu Province Science and Technology Support Program(BK20171325)。
文摘Sweetpotato[Ipomoea batatas(L.)Lam.],a food crop with both nutritional and medicinal uses,plays essential roles in food security and health-promoting.Chlorogenic acid(CGA),a polyphenol displaying several bioactivities,is distributed in all edible parts of sweetpotato.However,little is known about the specific metabolism of CGA in sweetpotato.In this study,IbPAL1,which encodes an endoplasmic reticulum-localized phenylalanine ammonia lyase(PAL),was isolated and characterized in sweetpotato.CGA accumulation was positively associated with the expression pattern of IbPAL1 in a tissue-specific manner,as further demonstrated by overexpression of IbPAL1.Overexpression of IbPAL1 promoted CGA accumulation and biosynthetic pathway genes expression in leaves,stimulated secondary xylem cell expansion in stems,and inhibited storage root formation.Our results support a potential role for IbPAL1 in sweetpotato CGA biosynthesis and establish a theoretical foundation for detailed mechanism research and nutrient improvement in sweetpotato breeding programs.
基金This work is supported in part by the National Key Research and Development Program of China under grants 2018YFC0830602 and 2016QY03D0501in part by the National Natural Science Foundation of China(NSFC)under grants 61872111,61732022 and 61601146.
文摘The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case.Recent advances in deep learning frameworks inspire us to propose a two-step method to address this problem.To obtain a better understanding and more specific representation of the legal texts,we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment documents.By formalizing prison term prediction as a regression problem,we adopt the linear regression model and the neural network model to train the prison term predictor.In experiments,we construct a real-world dataset of theft case judgment documents.Experimental results demonstrate that our method can effectively extract judgment-specific case features from textual fact descriptions.The best performance of the proposed predictor is obtained with a mean absolute error of 3.2087 months,and the accuracy of 72.54%and 90.01%at the error upper bounds of three and six months,respectively.
基金This work is supported by the National Key Research and Development Program of China(2018YFC0830602,2016QY03D0501)National Natural Science Foundation of China(61872111).
文摘With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.However,different people have different language expressions and legal professional backgrounds.This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation.How to accurately understand the true intentions behind different users’legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services.Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data,and are not scalable,and often require taxing manual annotation work.This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users’intention to legal consulting.First,we present the users’legal consulting statements as a tensor.And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users’intention of legal consultation,namely the core tensor.The core tensor relies less on the lexical and semantic information of the original users’legal consulting statements data,it reduces the dimension of the original tensor,and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm.Furthermore,we use a large number of core tensors obtained by the tensor decomposition layer with users’legal consulting statements tensors as inputs to continuously train Bi-LSTM,and finally derive the users’legal consultation intention classification model which can comprehensively understand the user’s legal consultation intention.Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks.
基金Supported by Jiangsu Agricultural Science and Technology Independent Innovation Fund(CX(12)2030)Special Fund Project for Establishment of Modern Agricultural Industry System (CARS-11-C-03)
文摘The pathogen and characteristics, infection cycle, occurrence regularity and damage symptoms of sweet potato stem nematode disease were introduced in the paper. Moreover, the comprehensive prevention measures were put forward, including plant quarantine, agricultural control and chemical control. The study provided certain basis for reducing damages of sweet potato stem nematode disease and improving yield and quality of sweet potato.
