AIM： To investigate the expression of gastrin-releasing peptide （GRP） and GRP-receptor mRNA in non-tumor tissues of the human esophagus, gastrointestinal tract, pancreas and gallbladder using molecular biology tech...AIM： To investigate the expression of gastrin-releasing peptide （GRP） and GRP-receptor mRNA in non-tumor tissues of the human esophagus, gastrointestinal tract, pancreas and gallbladder using molecular biology techniques. METHODS： Poly A^＋ mRNA was isolated from total RNA extracts using an automated nucleic acid extractor and, subsequently, converted into single-stranded cDNA （sscDNA）. PCR amplifications were carried out using genespecific GRP and GRP-receptor primers. The specificity of the PCR amplicons was further confirmed by Southern blot analyses using gene-specific GRP and GRP-receptor hybridization probes. RESULTS： Expression of GRP and GRP-receptor mRNA was detected at various levels in nearly all segments of the non-tumor specimens analysed, except the gallbladder. In most of the biopsy specimens, coexpression of both GRP and GRP-receptor mRNA appeared to take place. However, expression of GRP mRNA was more prominent than was GRP-receptor mRNA. CONCLUSION： GRP and GRP-receptor mRNAs are expressed throughout the gastrointestinal tract and provides information for the future mapping and determination of its physiological importance in normal and tumor cells.展开更多
Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a...Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.展开更多
基金Supported by the Molecular Biology Program (Grant No.21407)Laboratory Medicine Center-LMC, University Hospital Linkoping, Swedenthe Development Foundation of Region Skane, Sweden
文摘AIM： To investigate the expression of gastrin-releasing peptide （GRP） and GRP-receptor mRNA in non-tumor tissues of the human esophagus, gastrointestinal tract, pancreas and gallbladder using molecular biology techniques. METHODS： Poly A^＋ mRNA was isolated from total RNA extracts using an automated nucleic acid extractor and, subsequently, converted into single-stranded cDNA （sscDNA）. PCR amplifications were carried out using genespecific GRP and GRP-receptor primers. The specificity of the PCR amplicons was further confirmed by Southern blot analyses using gene-specific GRP and GRP-receptor hybridization probes. RESULTS： Expression of GRP and GRP-receptor mRNA was detected at various levels in nearly all segments of the non-tumor specimens analysed, except the gallbladder. In most of the biopsy specimens, coexpression of both GRP and GRP-receptor mRNA appeared to take place. However, expression of GRP mRNA was more prominent than was GRP-receptor mRNA. CONCLUSION： GRP and GRP-receptor mRNAs are expressed throughout the gastrointestinal tract and provides information for the future mapping and determination of its physiological importance in normal and tumor cells.
基金supported by the Science and Technology on Space Intelligent Control Laboratory for National Defense(KGJZDSYS-2018-08)。
文摘Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.
