AIM: To explore the association of methylation of the CpG island in the promotor of the p16 tumor suppressor gene with the clinicopathological characteristics of the colorectal cancers. METHODS: Methylation-specific P...AIM: To explore the association of methylation of the CpG island in the promotor of the p16 tumor suppressor gene with the clinicopathological characteristics of the colorectal cancers. METHODS: Methylation-specific PCR (MSP) was used to detect p16 methylation of 62 sporadic colorectal cancer specimens. RESULTS: p16 methylation was detected in 42% of the tumors.Dukes'staging was associated with p16 methylation status.p16 methylation occurred more frequently in Dukes'C and D patients (75.9%) than in Dukes'A and B patients (12.1%). CONCLUSION: p16 methylation plays a role in the carcinogenesis of a subset of colorectal cancer, and it might be linked to poor prognosis.展开更多
The co-existence of multiple cell components in tissue samples is the main obstacle for precise molecular evaluation on defined cell types. Based on morphological examination, we developed an efficient approach for pa...The co-existence of multiple cell components in tissue samples is the main obstacle for precise molecular evaluation on defined cell types. Based on morphological examination, we developed an efficient approach for paralleled RNA and protein isolations from an identical histological region in frozen tissue section.The RNA and protein samples prepared were sufficient for RT-PCR and Western blot analyses, and the results obtained were well coincident each other as well as with the corresponding parameters revealed from immunohistochemical examinations. By this way, the sampling problem caused by cell-cross contamination can be largely avoided, committing the experimental data more specific to a defined cell type. These novel methods thus allow us to use single tissue block for a comprehensive study by integration of conventional cytological evaluations with nucleic acid and protein analyses.展开更多
Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a ...Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.展开更多
The current growing demand for Conservation Agriculture(CA)at the national level in the countries of the Asia-Pacific region presents an opportunity to promote its widespread adoption and up-scaling through national p...The current growing demand for Conservation Agriculture(CA)at the national level in the countries of the Asia-Pacific region presents an opportunity to promote its widespread adoption and up-scaling through national policy and institutional support that appears necessary.Despite the obvious benefits of CA,it does not spread automatically unless the constraints that hinder adoption are understood and addressed in specific situations.These can include a combination of intellectual,social,financial,biophysical,technical,infrastructure constraints,or policy related support.Knowing what the bottlenecks are is important in developing strategies to overcome them.This paper presents:(a)some of the generic policy opportunities that exist for the adoption and uptake of CA;(b)a summary proceedings and outcome of the Regional Expert Consultation Workshop held in Beijing and sponsored by FAO Regional Office for Asia-Pacific which describes the status of CA in the Asia-Pacific region;(c)the challenges to CA adoption and uptake in the Asia-Pacific region;and(d)the conditions that need to be taken into account in designing and promoting policy and institutional support strategies for up-scaling CA.展开更多
基金Supported by the grants from Mjnistry of Public Health of China,No.98-1-303The Educational Committee of Shanghai,No.2000B02.
文摘AIM: To explore the association of methylation of the CpG island in the promotor of the p16 tumor suppressor gene with the clinicopathological characteristics of the colorectal cancers. METHODS: Methylation-specific PCR (MSP) was used to detect p16 methylation of 62 sporadic colorectal cancer specimens. RESULTS: p16 methylation was detected in 42% of the tumors.Dukes'staging was associated with p16 methylation status.p16 methylation occurred more frequently in Dukes'C and D patients (75.9%) than in Dukes'A and B patients (12.1%). CONCLUSION: p16 methylation plays a role in the carcinogenesis of a subset of colorectal cancer, and it might be linked to poor prognosis.
基金This work was supported in part by NationalNatural Science Foundation of China(No.39470767and No.396101300419)and by a special grantfrom Liaoning Provincial Committee for Science andTechnology,China(No.963010)
文摘The co-existence of multiple cell components in tissue samples is the main obstacle for precise molecular evaluation on defined cell types. Based on morphological examination, we developed an efficient approach for paralleled RNA and protein isolations from an identical histological region in frozen tissue section.The RNA and protein samples prepared were sufficient for RT-PCR and Western blot analyses, and the results obtained were well coincident each other as well as with the corresponding parameters revealed from immunohistochemical examinations. By this way, the sampling problem caused by cell-cross contamination can be largely avoided, committing the experimental data more specific to a defined cell type. These novel methods thus allow us to use single tissue block for a comprehensive study by integration of conventional cytological evaluations with nucleic acid and protein analyses.
基金supported by the National Natural Science Foundation of China (No.60970081)the National Basic Research Program (973) of China (No.2010CB327903)
文摘Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.
基金the Program for Changjiang Scholars and Innovative Research Team in University of China(Grant No.IRT13039).
文摘The current growing demand for Conservation Agriculture(CA)at the national level in the countries of the Asia-Pacific region presents an opportunity to promote its widespread adoption and up-scaling through national policy and institutional support that appears necessary.Despite the obvious benefits of CA,it does not spread automatically unless the constraints that hinder adoption are understood and addressed in specific situations.These can include a combination of intellectual,social,financial,biophysical,technical,infrastructure constraints,or policy related support.Knowing what the bottlenecks are is important in developing strategies to overcome them.This paper presents:(a)some of the generic policy opportunities that exist for the adoption and uptake of CA;(b)a summary proceedings and outcome of the Regional Expert Consultation Workshop held in Beijing and sponsored by FAO Regional Office for Asia-Pacific which describes the status of CA in the Asia-Pacific region;(c)the challenges to CA adoption and uptake in the Asia-Pacific region;and(d)the conditions that need to be taken into account in designing and promoting policy and institutional support strategies for up-scaling CA.
