BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dim...The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal compo...There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.展开更多
Objective： In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma （HNSCC）-associated long noncoding RNAs （IncRNAs）, and to discover potential IncRNA prognostic biomarkers for...Objective： In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma （HNSCC）-associated long noncoding RNAs （IncRNAs）, and to discover potential IncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq. Methods： RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas （TCGA）, including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated IncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify IncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified IncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. Results： We identified nine HNSCC-relevant IncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CY-I-OR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival （OS） and disease free survival （DFS） of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum （DDP）. HCG22, the most dramatically down-regulated IncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values in- dependent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. Conclusions： Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated IncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these IncRNAs in HNSCC as well as clinical applications.展开更多
Objective To investigate the endoscopic and pathological features and the independent risk factors for early esophageal cancer combined with multiple primary cancer.Methods Endoscopic and pathological features of 324 ...Objective To investigate the endoscopic and pathological features and the independent risk factors for early esophageal cancer combined with multiple primary cancer.Methods Endoscopic and pathological features of 324 patients diagnosed as having early esophageal cancer from January 2013 to January 2022 in Beijing FriendshipHospital wereretrospectivelycollected.Independent risk factors for early esophageal cancer combined with multiple primary cancer were selected by multivariate logistic regression analysis.Results Among the 324 patients with early esophageal cancer,47(14.51%)patients(29 metachronous and 18 synchronous)had multiple primary cancer.Multivariate logistic regression analysis showed that alcohol drinking≥5 standard drinks/day(OR=6.23,95%CI:2.49-15.57,P<0.001),submucosal layer invasion(0R=2.80,95%Cl:1.07-7.30,P=0.036),lesion location at lower esophagus(0R=4.18,95%CI:1.98-8.97,P<0.001)and multiple lesions in esophagus(0R=3.30,95%CI:1.57-6.92,P=0.002)were independent risk factors for early esophageal cancer combined with multiple primary cancer.Conclusion Alcohol drinking≥5 standard drinks/day,submucosal layer invasion,lower lesions location,and multiple lesions in the esophagus are independent risk factors that are more likely to develop multiple primary cancer in patients with early esophageal cancer.It is recommended to prioritize monitoring patients with these factors,and enhance endoscopic follow-up and assessment.展开更多
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
文摘The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
文摘There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.
基金Project supported by the National Natural Science Foundation of China(Nos.31471226 and 91440108)the Fundamental Research Funds for the Central Universities(Nos.WK2070000044 and WK2070000034),China
文摘Objective： In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma （HNSCC）-associated long noncoding RNAs （IncRNAs）, and to discover potential IncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq. Methods： RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas （TCGA）, including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated IncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify IncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified IncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. Results： We identified nine HNSCC-relevant IncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CY-I-OR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival （OS） and disease free survival （DFS） of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum （DDP）. HCG22, the most dramatically down-regulated IncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values in- dependent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. Conclusions： Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated IncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these IncRNAs in HNSCC as well as clinical applications.
文摘Objective To investigate the endoscopic and pathological features and the independent risk factors for early esophageal cancer combined with multiple primary cancer.Methods Endoscopic and pathological features of 324 patients diagnosed as having early esophageal cancer from January 2013 to January 2022 in Beijing FriendshipHospital wereretrospectivelycollected.Independent risk factors for early esophageal cancer combined with multiple primary cancer were selected by multivariate logistic regression analysis.Results Among the 324 patients with early esophageal cancer,47(14.51%)patients(29 metachronous and 18 synchronous)had multiple primary cancer.Multivariate logistic regression analysis showed that alcohol drinking≥5 standard drinks/day(OR=6.23,95%CI:2.49-15.57,P<0.001),submucosal layer invasion(0R=2.80,95%Cl:1.07-7.30,P=0.036),lesion location at lower esophagus(0R=4.18,95%CI:1.98-8.97,P<0.001)and multiple lesions in esophagus(0R=3.30,95%CI:1.57-6.92,P=0.002)were independent risk factors for early esophageal cancer combined with multiple primary cancer.Conclusion Alcohol drinking≥5 standard drinks/day,submucosal layer invasion,lower lesions location,and multiple lesions in the esophagus are independent risk factors that are more likely to develop multiple primary cancer in patients with early esophageal cancer.It is recommended to prioritize monitoring patients with these factors,and enhance endoscopic follow-up and assessment.
