A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessi...A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.展开更多
Two classification and identification methods based on pattern discrimination models and the majority-vote technique were investigated for implementing a World Wide Web-based system for the identification of rice dise...Two classification and identification methods based on pattern discrimination models and the majority-vote technique were investigated for implementing a World Wide Web-based system for the identification of rice diseases. The experiment was carried out using color and shape patterns in 425 images of three rice diseases, which were classified into four classes： two classes of leaf blast, and one class each of sheath blight and brown spot. A method consisting of two discrimination steps involving application of multiple discrimination models of a support vector machine gave the best result because of its capacity to evaluate the similarity of disease types. This accuracy of the method was 88% for leaf blast （A-type）, 94% for sheath blight, and 80% for leaf blast （B-type） and brown spot; on average, the accuracy of this method was 5% greater than that of the other method when three classes were used in the model. Although the accuracy of both methods was inadequate, the results of this study show that it is possible to estimate the least number of possible or similar diseases from a large number of diseases. Therefore, we conclude that there is merit in grouping classes into subgroups rather than attempting to discriminate between all classes simultaneously and that these methods are effective in identifying diseases for web-based diagnosis.展开更多
Primary malignant lymphoma of the thyroid gland is a rare disease comprising about 1%–3% of the thyroid malignancies, and this uncommon lymphoma represent less than 1% of all non-Hodgkinlymphomas (NHL). According to ...Primary malignant lymphoma of the thyroid gland is a rare disease comprising about 1%–3% of the thyroid malignancies, and this uncommon lymphoma represent less than 1% of all non-Hodgkinlymphomas (NHL). According to the modified Ann-Arbor-Classification primary thyroid lymphoma by definition is a lymphoma that is restricted to the thyroid gland (stage I E ) or involves the thyroid gland and supradiaphragmatic predominantly adjacent thyroid lymph nodes (stage II E ). Primary thyroid lymphoma is a heterogenous disease encompassing a wide variety of lymphoma entities. The diagnosis and treatment of this lymphoma are emphasis of this article. Key words thyroid gland - lymphoma - diagnosis - treatment展开更多
Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods...Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.展开更多
Objective： The aim of this study was to investigate the association between neuronal nitric oxide synthase （nNOS） and the expression level of Cytochrome C （Cyt-c） in mitochondria. Methods： The pathological diagn...Objective： The aim of this study was to investigate the association between neuronal nitric oxide synthase （nNOS） and the expression level of Cytochrome C （Cyt-c） in mitochondria. Methods： The pathological diagnosis of glioma and tumor classification was by HE staining, and we use immunohistochemistry method to analyse the level of nNOS in different pathological grade glioma and the expression level of Cyt-c in mitochondria meanwhile. Results： The levels of nNOS were highest in grade Ⅲ tumors, moderate in grade Ⅱ tumors, and lowest different in grade I tumors. There was significant difference of the nNOS levels among different pathological grade tumors （P 〈 0.05）. Furthermore, the similar phenomenon was observed in the expression level of Cyt-c in mitochondria （P 〈 0.05）. Conclusion： The expression level of nNOS and Cyt-c in mitochondria was significantly related to the pathological grade of glioma.展开更多
基金Project(Z132012)supported by the Second Five Technology-based in Science and Industry Bureau of ChinaProject(YWF1103Q062)supported by the Fundemental Research Funds for the Central Universities in China
文摘A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.
文摘Two classification and identification methods based on pattern discrimination models and the majority-vote technique were investigated for implementing a World Wide Web-based system for the identification of rice diseases. The experiment was carried out using color and shape patterns in 425 images of three rice diseases, which were classified into four classes： two classes of leaf blast, and one class each of sheath blight and brown spot. A method consisting of two discrimination steps involving application of multiple discrimination models of a support vector machine gave the best result because of its capacity to evaluate the similarity of disease types. This accuracy of the method was 88% for leaf blast （A-type）, 94% for sheath blight, and 80% for leaf blast （B-type） and brown spot; on average, the accuracy of this method was 5% greater than that of the other method when three classes were used in the model. Although the accuracy of both methods was inadequate, the results of this study show that it is possible to estimate the least number of possible or similar diseases from a large number of diseases. Therefore, we conclude that there is merit in grouping classes into subgroups rather than attempting to discriminate between all classes simultaneously and that these methods are effective in identifying diseases for web-based diagnosis.
文摘Primary malignant lymphoma of the thyroid gland is a rare disease comprising about 1%–3% of the thyroid malignancies, and this uncommon lymphoma represent less than 1% of all non-Hodgkinlymphomas (NHL). According to the modified Ann-Arbor-Classification primary thyroid lymphoma by definition is a lymphoma that is restricted to the thyroid gland (stage I E ) or involves the thyroid gland and supradiaphragmatic predominantly adjacent thyroid lymph nodes (stage II E ). Primary thyroid lymphoma is a heterogenous disease encompassing a wide variety of lymphoma entities. The diagnosis and treatment of this lymphoma are emphasis of this article. Key words thyroid gland - lymphoma - diagnosis - treatment
文摘Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.
基金Supported by grants from the Education Office of Liaoning Province Foundation (No. 20061008)Liaoning Provincial Science and Technology Foundation (No. 2006401013-3)Dr. Start Fund of Liaoning Province(No. 20072099)
文摘Objective： The aim of this study was to investigate the association between neuronal nitric oxide synthase （nNOS） and the expression level of Cytochrome C （Cyt-c） in mitochondria. Methods： The pathological diagnosis of glioma and tumor classification was by HE staining, and we use immunohistochemistry method to analyse the level of nNOS in different pathological grade glioma and the expression level of Cyt-c in mitochondria meanwhile. Results： The levels of nNOS were highest in grade Ⅲ tumors, moderate in grade Ⅱ tumors, and lowest different in grade I tumors. There was significant difference of the nNOS levels among different pathological grade tumors （P 〈 0.05）. Furthermore, the similar phenomenon was observed in the expression level of Cyt-c in mitochondria （P 〈 0.05）. Conclusion： The expression level of nNOS and Cyt-c in mitochondria was significantly related to the pathological grade of glioma.
