[Objective] Expressions of key enzymatic genes involved in phenyl-propanoid metabolic pathway in potato and StR2R3-MYB and StTGA transcripters were investigated in the present study. [Method] The primitive cultivar Ya...[Objective] Expressions of key enzymatic genes involved in phenyl-propanoid metabolic pathway in potato and StR2R3-MYB and StTGA transcripters were investigated in the present study. [Method] The primitive cultivar Yan was the materials for replicated trials and total RNA extracted from tissues of seedlings. Re-al-time florescent quantification PCR, multiple intervals of air temperature, light-il umi-nation and time-duration were factors of treatments in the experiment. Data on gene expressions were obtained and proceed to asses and compare effects based on statistical analysis. [Result] The results showed negative correlations between tem-perature degrees and expressions of StPAL, StDFR and StR2R3-MYB genes but not StTGA. Positive correlations, however, were derived between those of StCHS, StDFR and StR2R3-MYB and light-intensity. Significant interactive effects between expressions of StPAL and StDFR and treatments, light intensity and temperature degree, along the phenylpropanoid pathway were observed. Transcription regulator of StR2R3-MYB showed significant positive effect on the expression of StCHS of potato. StTGA transcription factor, on the other hand, gave significant negative ef-fects on the expression of StDFR. [Conclusion] Results from present study reveal the role of environmental factors and complicate interactions between such condi-tions as temperature-light il umination and mRNA function of target genes.展开更多
The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food industry.The k-nearest neighbor(k-NN)method of...The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food industry.The k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR data.The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables.The objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour procedures.In this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator data.Specifically,we propose three models for this kind of data which are Functional Nonparametric Regression,Functional Robust Regression,and Functional Relative Error Regression,with both kernel and k-NN approaches to compare between them.The experimental result shows the higher efficiency of k-NN predictor over the kernel predictor.The predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.展开更多
基金Supported by the Natural Science Foundation of China(31371683)the National Key Technology R&D Program of China(2012BAD02B05-8) during 12th Five-year Plan PeriodEarmarked Fund for China Agriculture Research System(CARS-10-P19)~~
文摘[Objective] Expressions of key enzymatic genes involved in phenyl-propanoid metabolic pathway in potato and StR2R3-MYB and StTGA transcripters were investigated in the present study. [Method] The primitive cultivar Yan was the materials for replicated trials and total RNA extracted from tissues of seedlings. Re-al-time florescent quantification PCR, multiple intervals of air temperature, light-il umi-nation and time-duration were factors of treatments in the experiment. Data on gene expressions were obtained and proceed to asses and compare effects based on statistical analysis. [Result] The results showed negative correlations between tem-perature degrees and expressions of StPAL, StDFR and StR2R3-MYB genes but not StTGA. Positive correlations, however, were derived between those of StCHS, StDFR and StR2R3-MYB and light-intensity. Significant interactive effects between expressions of StPAL and StDFR and treatments, light intensity and temperature degree, along the phenylpropanoid pathway were observed. Transcription regulator of StR2R3-MYB showed significant positive effect on the expression of StCHS of potato. StTGA transcription factor, on the other hand, gave significant negative ef-fects on the expression of StDFR. [Conclusion] Results from present study reveal the role of environmental factors and complicate interactions between such condi-tions as temperature-light il umination and mRNA function of target genes.
基金funding this work through the Research Groups Program under Grant Number R.G.P.1/189/41.I.M.A.and M.K.A.received the grant.
文摘The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food industry.The k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR data.The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables.The objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour procedures.In this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator data.Specifically,we propose three models for this kind of data which are Functional Nonparametric Regression,Functional Robust Regression,and Functional Relative Error Regression,with both kernel and k-NN approaches to compare between them.The experimental result shows the higher efficiency of k-NN predictor over the kernel predictor.The predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.
