Identification of abnormal patterns in AR (1) process using CS-SVM

Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of eight patters are generated in experiments for verifying the performance of the proposed model. The results of comparison experiments show that the average recognition rate of the proposed model reaches 96.25% as the autocorrelation coefficient is set equal to 0.5. That is apparently higher than those of the SVM model optimized by the particle swarm optimization (PSO) or the genetic algorithm (GA). Another experiment result demonstrates that the average recognition accuracy of the CS-SVM model also reaches higher than 95% for different autocorrelation levels. At last, a lot of data streams in or out of control are simulated to measure the ARL values. The results turn out that the model has an acceptable online performance. Therefore, we believe that the model can be used as a more effective approach for identification of abnormal patterns in autocorrelation process

Is Overexpression of Ki-67 a Prognostic Biomarker of Upper Tract Urinary Carcinoma? A Retrospective Cohort Study and Meta-Analysis

Background: Upper tract urinary carcinoma (UTUC) is a relatively uncommon but aggressive disease. The Ki-67 antigen is a classic marker of cellular proliferation, but there is still controversy regarding the significance and importance of Ki-67 in tumor progression. Methods: In this study, we first detected Ki-67 expression in UTUC patients by immunohistochemistry (IHC). Subsequently, we quantitatively combined the results with those from the published literature in a meta-analysis after searching several databases. Results: IHC results demonstrated that patients with muscle-invasive tumors (T2-T4) had higher Ki-67 expression than those with non-muscle-invasive tumors (Tis-T1), suggesting that high Ki-67 expression may be associated with the aggressive form of UTUC. Kaplan-Meier curves showed that patients with high Ki-67 expression had significantly poorer cancer-specific survival (CSS) and disease-free survival (DFS). Furthermore, multivariate analysis suggested that Ki-67 expression was an independent prognostic factor for CSS (hazard ratio, HR=3.196) and DFS (HR=3.517) in UTUC patients. Then, a meta-analysis of the published literature investigating Ki-67 expression and its effects on UTUC prognosis was conducted. After searching the PubMed, Medline, Embase, Cochrane Library and Scopus databases, 12 articles met the eligibility criteria for this analysis. The eligible studies included a total of 1740 patients with a mean number of 82 patients per study (range, 38-475). The combined results showed that increased Ki-67 levels were associated with poor survival and disease progression, with a pooled HR estimate of 2.081 and 2.791, respectively. In subgroup analysis, the pooled HR was statistically significant for cancer-specific survival (HR=2.276), metastasis-free survival (HR=3.008) and disease-free survival (HR=6.336). Conclusions: In conclusion, high Ki-67 expression was associated with poor survival in patients with UTUC, as well as a high risk of disease progression, although these findings need to be interpreted with caution. Large-scale, adequately designed, prospective trials are needed to further confirm the value of Ki-67 in prognosis of UTUC patients