The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.展开更多
Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In ord...Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.展开更多
A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the p...A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.展开更多
This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to ...This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to obtain the sampling distributions of the outputs for the positive class and the negative class respectively. As a result, the ROC curve is a plot of all the (False Positive Rate (FPR), True Positive Rate (TPR)) pairs by varying the decision threshold over the whole range of the boot- strap sampling distributions. The advantage of this method is, the bootstrap based ROC curves are much stable than those of the holdout or cross-validation, indicating a more stable ROC analysis of Fisher classifier. Experiments on five data sets publicly available demonstrate the effectiveness of the proposed method.展开更多
A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensi...A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.展开更多
The generalized Riccati equation vational expansion method is extended in this paper. Several exact solutions for the generalized Burgers-Fisher equation with variable coefficients are obtained by this method, and som...The generalized Riccati equation vational expansion method is extended in this paper. Several exact solutions for the generalized Burgers-Fisher equation with variable coefficients are obtained by this method, and some of which are derived for the first time. It is concluded from the results that this approach is simple and efficient even in solving partial differential equations with variable coefficients.展开更多
A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der ...A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.展开更多
AIM： To identify the risk factors in predicting the out- come of acute-on-chronic hepatitis B liver failure pa- tients. METHODS： We retrospectively divided 113 patients with acute-on-chronic liver failure-hepatitis ...AIM： To identify the risk factors in predicting the out- come of acute-on-chronic hepatitis B liver failure pa- tients. METHODS： We retrospectively divided 113 patients with acute-on-chronic liver failure-hepatitis B virus （ACLF-HBV） and without concurrent hepatitis C or D virus infection and hepatocellular carcinoma into two groups according to their outcomes after anti-HBV therapy. Their demographic, clinical, and biochemical data on the day of diagnosis and after the first week of treatment were analyzed using the Mann-Whitney U test, Fisher＇s exact test, and a multiple logistic regres- sion analysis. RESULTS： The study included 113 patients （87 men and 26 women） with a mean age of 49.84 years. Fifty- two patients survived, and 61 patients died. Liver failure （85.2%）, sepsis （34.4%）, and multiple organ failure （39.3%） were the main causes of death. Mul- tivariate analyses showed that Acute Physiology and Chronic Health Evaluation （APACHE） Ⅱ scores ≥ 12 [odds ratio （OR） = 7.160, 95% CI： 2.834-18.092, P 〈 0.001] and positive blood culture （OR = 13.520, 95% CI： 2.740-66.721, P = 0.001） on the day of diagnosis and model for end-stage liver disease （MELD） scores 28 （OR = 8.182, 95% CI： 1.884-35.527, P = 0.005） after the first week of treatment were independent predictors of mortality. CONCLUSION： APACHE II scores on the day of diag- nosis and MELD scores after the first week of anti-HBV therapy are feasible predictors of outcome in ACLF- HBV patients.展开更多
Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussi...Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.展开更多
The authors investigate the comparative classification performance of the two groups linear classification techniques. They compared the Fisher linear classification analysis, its robust version based on the minimum c...The authors investigate the comparative classification performance of the two groups linear classification techniques. They compared the Fisher linear classification analysis, its robust version based on the minimum covariance determinant with the Filter linear classification rule and the linear combination linear classification technique. These procedures are investigated using laboratory reared aedes albopictus mosquito data set and simulated data set generated based on heteroscedastic covariance matrices with various proportion of contamination. The evaluation procedure is based on the effect of contamination on the mean probabilities of correct classification obtain for each technique. The comparative analysis revealed that the robust Fisher linear classification rule and the linear combination linear classification rule are robust and comparable than the other procedures.展开更多
AIM： To explore epigenetic changes in the gene encod- ing X chromosome-linked inhibitor of apoptosis-associ- ated factor 1 （XAF1） during esophageal carcinogenesis. METHODS： Methylation status of XAF1 was detected ...AIM： To explore epigenetic changes in the gene encod- ing X chromosome-linked inhibitor of apoptosis-associ- ated factor 1 （XAF1） during esophageal carcinogenesis. METHODS： Methylation status of XAF1 was detected by methylation-specific polymerase chain reaction （MSP） in four esophageal cancer cell lines （KYSE30, KYSE70, BICl and partially methylated in TE3 cell lines）, nine cases of normal mucosa, 72 cases of pri- mary esophageal cancer and matched adjacent tissue. XAF1 expression was examined by semi-quantitative reverse transcriptional polymerase chain reaction and Western blotting before and after treatment with 5-aza- deoxycytidine （5-aza-dc）, a demethylating agent. To investigate the correlation of XAF1 expression and methylation status in primary esophageal cancer, immu- nohistochemistry for XAF1 expression was performed in 32 cases of esophageal cancer and matched adjacent tissue. The association of methylation status and clini-copathological data was analyzed by logistic regression. RESULTS： MSP results were as follows： loss of XAF1 expression was found in three of four esophageal cell lines with promoter region hypermethylation （com- pletely methylated in KYSE30, KYSE70 and BIC1 cell lines and partially in TE3 cells）; all nine cases of normal esophageal mucosa were unmethylated; and 54/72 （75.00%） samples from patients with esophageal can- cer were methylated, and 25/72 （34.70%） matched adjacent tissues were methylated （75.00% vs 34,70%, z2 = 23.5840, P = 0.000）. mRNA level of XAF1 mea- sured with semi-quantitative reverse transcription poly- merase chain reaction was detectable only in TE3 cells, and no expression was detected in KYSE30, KYSE70 or BIC1 cells. Protein expression was not observed in KYSE30 cells by Western blotting before treatment with 5-aza-dc. After treatment, mRNA level of XAF1 was detectable in KYSE30, KYSE70 and BIC1 cells. Protein expression was detected in KYSE30 after treatment with 5-aza-dc. Immunohistochemistry was performed on 32 cases of esophageal cancer and adjacent tissue, and demonstrated XAF1 in the nucleus and cytoplasm. XAF1 staining was found in 20/32 samples of adjacent normal tissue but was present in only 8/32 samples of esophageal cancer tissue （Z2= 9.143, P = 0.002）. XAF1 expression was decreased in cancer samples compared with adjacent tissues. In 32 cases of esophageal can- cer, 24/32 samples were methylated, and 8/32 esopha- geal cancer tissues were unmethylated. XAF1 staining was found in 6/8 samples of unmethylated esophageal cancer and 2/24 samples of methylated esophageal cancer tissue. XAF1 staining was inversely correlated with XAF1 promoter region methylation （Fisher＇s exact test, P = 0.004）. Regarding methylation status and clinicopathological data, no significant differences were found in sex, age, tumor size, tumor stage, or metas- tasis with respect to methylation of XAF1 for the 72 tis- sue samples from patients with esophageal cancer. CONCLUSION： XAF1 is frequently methylated in eso- phageal cancer, and XAF1 expression is regulated by promoter region hypermethylation.展开更多
Sensory evaluation was performed on 32 commercial Malbec wines （2008 and 2009 vintages） produced in five provinces of Argentina. Wines from different areas in Mendoza （the most important producer of Malbec） were a...Sensory evaluation was performed on 32 commercial Malbec wines （2008 and 2009 vintages） produced in five provinces of Argentina. Wines from different areas in Mendoza （the most important producer of Malbec） were also included to test possible differences within this province. Ten key attributes were first recognized by descriptive analyses and then carefully evaluated by a trained sensory panel composed of 10 judges. Among the aroma and flavour attributes the analyses focused on plum, red fruits, white pepper, bell pepper, and floral. Three attributes of taste （acidity, astringency, and bitterness） and two attributes of color （red and blue-purple hues） were also analyzed. Statistical differences and similarities in sensory data were tested using analysis of variance （ANOVA）, multiple means comparisons by least significant difference test （Fisher LSD）, and principal component analysis （PCA）. ANOVA and Fisher LSD tests of sensory data showed significant differences （P 〈 0.05） for 6 out of 10 wine attributes： plum, floral, red fruits, astringency, red and blue- purple hues.展开更多
基金Project (50934006) supported by the National Natural Science Foundation of ChinaProject (2010CB732004) supported by the National Basic Research Program of ChinaProject (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China
文摘The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.
基金Supported by the National Basic Research Program of China （2013CB733600）, the National Natural Science Foundation of China （21176073）, the Doctoral Fund of Ministry of Education of China （20090074110005）, the Program for New Century Excellent Talents in University （NCET-09-0346）, Shu Guang Project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.
基金Supported by the National 11th Five-Year Science and Technology Supporting Plan of China(2006BAB02A02)Central South University Innovation funded projects (2009ssxt230, 2009ssxt234)
文摘A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.
基金the Natural Science Foundation of Zhejiang Province of China (No. Y104540)the Foundation of the Key Laboratory of Advanced Information Science and Network Technology of Beijing, China (No.TDXX0509).
文摘This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to obtain the sampling distributions of the outputs for the positive class and the negative class respectively. As a result, the ROC curve is a plot of all the (False Positive Rate (FPR), True Positive Rate (TPR)) pairs by varying the decision threshold over the whole range of the boot- strap sampling distributions. The advantage of this method is, the bootstrap based ROC curves are much stable than those of the holdout or cross-validation, indicating a more stable ROC analysis of Fisher classifier. Experiments on five data sets publicly available demonstrate the effectiveness of the proposed method.
文摘A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.
基金Supported by the National Basic Research Project of China (973 Program No. 2006CB705500)by the National Natural Science Foundation of China under Grant Nos. 10975216, 10635040by the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20093402110032
文摘The generalized Riccati equation vational expansion method is extended in this paper. Several exact solutions for the generalized Burgers-Fisher equation with variable coefficients are obtained by this method, and some of which are derived for the first time. It is concluded from the results that this approach is simple and efficient even in solving partial differential equations with variable coefficients.
基金Supported by the National Natural Science Foundation of China (60504033)the Open Project of State Key Laboratory of Industrial Control Technology in Zhejiang University (0708004)
文摘A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.
基金Supported by Tri-Service General Hospital,No.TSGH-C101-137
文摘AIM： To identify the risk factors in predicting the out- come of acute-on-chronic hepatitis B liver failure pa- tients. METHODS： We retrospectively divided 113 patients with acute-on-chronic liver failure-hepatitis B virus （ACLF-HBV） and without concurrent hepatitis C or D virus infection and hepatocellular carcinoma into two groups according to their outcomes after anti-HBV therapy. Their demographic, clinical, and biochemical data on the day of diagnosis and after the first week of treatment were analyzed using the Mann-Whitney U test, Fisher＇s exact test, and a multiple logistic regres- sion analysis. RESULTS： The study included 113 patients （87 men and 26 women） with a mean age of 49.84 years. Fifty- two patients survived, and 61 patients died. Liver failure （85.2%）, sepsis （34.4%）, and multiple organ failure （39.3%） were the main causes of death. Mul- tivariate analyses showed that Acute Physiology and Chronic Health Evaluation （APACHE） Ⅱ scores ≥ 12 [odds ratio （OR） = 7.160, 95% CI： 2.834-18.092, P 〈 0.001] and positive blood culture （OR = 13.520, 95% CI： 2.740-66.721, P = 0.001） on the day of diagnosis and model for end-stage liver disease （MELD） scores 28 （OR = 8.182, 95% CI： 1.884-35.527, P = 0.005） after the first week of treatment were independent predictors of mortality. CONCLUSION： APACHE II scores on the day of diag- nosis and MELD scores after the first week of anti-HBV therapy are feasible predictors of outcome in ACLF- HBV patients.
基金Supported by the National Natural Science Foundation of China(61273167)
文摘Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.
文摘The authors investigate the comparative classification performance of the two groups linear classification techniques. They compared the Fisher linear classification analysis, its robust version based on the minimum covariance determinant with the Filter linear classification rule and the linear combination linear classification technique. These procedures are investigated using laboratory reared aedes albopictus mosquito data set and simulated data set generated based on heteroscedastic covariance matrices with various proportion of contamination. The evaluation procedure is based on the effect of contamination on the mean probabilities of correct classification obtain for each technique. The comparative analysis revealed that the robust Fisher linear classification rule and the linear combination linear classification rule are robust and comparable than the other procedures.
基金Supported by Grants from the National Basic Research Program(973 Program), No. 2012CB934002, 2010CB912802National Key Scientific Instrument Special Programme of China, No.2011YQ03013405National Science Foundation of China,No. 81121004, 81071953 and 81161120432
文摘AIM： To explore epigenetic changes in the gene encod- ing X chromosome-linked inhibitor of apoptosis-associ- ated factor 1 （XAF1） during esophageal carcinogenesis. METHODS： Methylation status of XAF1 was detected by methylation-specific polymerase chain reaction （MSP） in four esophageal cancer cell lines （KYSE30, KYSE70, BICl and partially methylated in TE3 cell lines）, nine cases of normal mucosa, 72 cases of pri- mary esophageal cancer and matched adjacent tissue. XAF1 expression was examined by semi-quantitative reverse transcriptional polymerase chain reaction and Western blotting before and after treatment with 5-aza- deoxycytidine （5-aza-dc）, a demethylating agent. To investigate the correlation of XAF1 expression and methylation status in primary esophageal cancer, immu- nohistochemistry for XAF1 expression was performed in 32 cases of esophageal cancer and matched adjacent tissue. The association of methylation status and clini-copathological data was analyzed by logistic regression. RESULTS： MSP results were as follows： loss of XAF1 expression was found in three of four esophageal cell lines with promoter region hypermethylation （com- pletely methylated in KYSE30, KYSE70 and BIC1 cell lines and partially in TE3 cells）; all nine cases of normal esophageal mucosa were unmethylated; and 54/72 （75.00%） samples from patients with esophageal can- cer were methylated, and 25/72 （34.70%） matched adjacent tissues were methylated （75.00% vs 34,70%, z2 = 23.5840, P = 0.000）. mRNA level of XAF1 mea- sured with semi-quantitative reverse transcription poly- merase chain reaction was detectable only in TE3 cells, and no expression was detected in KYSE30, KYSE70 or BIC1 cells. Protein expression was not observed in KYSE30 cells by Western blotting before treatment with 5-aza-dc. After treatment, mRNA level of XAF1 was detectable in KYSE30, KYSE70 and BIC1 cells. Protein expression was detected in KYSE30 after treatment with 5-aza-dc. Immunohistochemistry was performed on 32 cases of esophageal cancer and adjacent tissue, and demonstrated XAF1 in the nucleus and cytoplasm. XAF1 staining was found in 20/32 samples of adjacent normal tissue but was present in only 8/32 samples of esophageal cancer tissue （Z2= 9.143, P = 0.002）. XAF1 expression was decreased in cancer samples compared with adjacent tissues. In 32 cases of esophageal can- cer, 24/32 samples were methylated, and 8/32 esopha- geal cancer tissues were unmethylated. XAF1 staining was found in 6/8 samples of unmethylated esophageal cancer and 2/24 samples of methylated esophageal cancer tissue. XAF1 staining was inversely correlated with XAF1 promoter region methylation （Fisher＇s exact test, P = 0.004）. Regarding methylation status and clinicopathological data, no significant differences were found in sex, age, tumor size, tumor stage, or metas- tasis with respect to methylation of XAF1 for the 72 tis- sue samples from patients with esophageal cancer. CONCLUSION： XAF1 is frequently methylated in eso- phageal cancer, and XAF1 expression is regulated by promoter region hypermethylation.
文摘Sensory evaluation was performed on 32 commercial Malbec wines （2008 and 2009 vintages） produced in five provinces of Argentina. Wines from different areas in Mendoza （the most important producer of Malbec） were also included to test possible differences within this province. Ten key attributes were first recognized by descriptive analyses and then carefully evaluated by a trained sensory panel composed of 10 judges. Among the aroma and flavour attributes the analyses focused on plum, red fruits, white pepper, bell pepper, and floral. Three attributes of taste （acidity, astringency, and bitterness） and two attributes of color （red and blue-purple hues） were also analyzed. Statistical differences and similarities in sensory data were tested using analysis of variance （ANOVA）, multiple means comparisons by least significant difference test （Fisher LSD）, and principal component analysis （PCA）. ANOVA and Fisher LSD tests of sensory data showed significant differences （P 〈 0.05） for 6 out of 10 wine attributes： plum, floral, red fruits, astringency, red and blue- purple hues.
