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| 1 | Overexpression of IbPAL1 promotes chlorogenic acid biosynthesis in sweetpotato显示文摘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. | Yang Yu Yingjie Wang Yue Yu Peiyong Ma Zhaodong Jia Xiaoding Guo Yizhi Xie Xiaofeng Bian | 2021 | The Crop Journal2021，9，1： | 5 |
| 2 | Prison Term Prediction on Criminal Case Description with Deep Learning显示文摘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. | Shang Li Hongli Zhang Lin Ye Shen Su Xiaoding Guo Haining Yu Binxing Fang | 2020 | Computers, Materials & Continua2020，，3： | 2 |
| 3 | Molecular dynamics study of the structures and properties of RDX/GAP propellant显示文摘 | Miaomiao Li Fengsheng Li Ruiqi Shen Xiaode Guo | 2010 | Journal of Hazardous Materials2010，，2： | 1 |
| 4 | Study on Sweet Potato Stem Nematode Disease显示文摘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. | Xiaoding GUO Yizhi XIE Zhaodong JIA Peiyong MA Xiaofeng BIAN | 2012 | Plant Diseases and Pests2012，3，2： | 0 |
| 5 | Field Evaluation System for Resistance of Sweet Potato Stem Nematode显示文摘[Objective] The paper was to explore the field evaluation system for resistance of sweet potato stem nematode. [Method] The resistance of 525 accessions was evaluated using naturally induced identification method in diseased field from 2004 to 2009,and the accessions with resistance were selected. [Result] The field evaluation system for resistance of sweet potato stem nematode was affected by many factors. Non-uniform incidence in fields led to unstable identification results of certain materials. For test problems,some parameters of the existing evaluation system were corrected to reduce the experimental error. [Conclusion] The study provided the reference for further improvement of field evaluation system for resistance of sweet potato stem nematode. | Guo Xiaoding Xie Yizhi Jia Zhaodong Ma Peiyong Bian Xiaofeng | 2013 | Plant Diseases and Pests2013，4，3： | 0 |
| 6 | TdBrnn:An Approach to Learning Users’Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM显示文摘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. | Xiaoding Guo Hongli Zhang Lin Ye Shang Li | 2020 | Computers, Materials & Continua2020，，4： | 0 |
| 7 | STRUCTURE AND FERROMAGNETIC RESONANCE OF Fe/Cu SUPERLATTICES显示文摘Metallic Fe/Cu superlattice films on glass substrates were prepared by a dc-magnetionsputtering system.The modulation behaviors and the crystal structures of the films were ex-amined by X-ray diffraction and transmission electron microscopy(TEM)respectively.Their magnetic properties were studied by means of ferromagnetic resonnance spectrometerand the vibrating sample magnetometer.The results show that there exists a strong magneticcoupling between the neighbouring Fe layers and it is the coupling that affectes the magneticproperties of these superlattice films. | BI Siyun MA Xiaoding SONG Ruitian MEI Liangmo ZHAO Jiangao GUO Yicheng Shandong University,Jinan,China | 1991 | Acta Metallurgica Sinica(English Letters)1991，4，8： | 0 |
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