miR-150 is a factor of survival in prostate cancer patients.

PURPOSE: Prostate cancer (PC) is the most common malignant disease in males and the second leading cause of cancer related deaths in men in developed countries. The purpose of this study was to investigate whether microRNA (miR)-150 is a factor influencing survival in prostate cancer patients. METHODS: miR-150 mRNA and protein expression levels in prostatic cancer cell lines and healthy tissues were determined by quantitative (q) RT-PCR and Western blotting. Additionally, the protein expression of miR-150 was detected by immunohistochemistry. RESULTS: High miR-150 expression was positively correlated with tumor recurrence or metastasis (p=0.010). In addition, PC patients with high miR-150 expression had significantly poorer overall survival/OR (hazard ratio/HR, 1.87; 95% confidence interval/CI, 1.19-2.94; p=0.006) and poorer disease-free survival/DFS (HR, 1.90; 95% CI, 1.21- 2.98; p=0.005) than those with low miR-150 expression. The cumulative 5-year OS was only 35.19% (95% CI, 26.18- 44.20) in the high miR-150 expression group, whereas it was 55.93% (95% CI, 43.26-68.60) in the low miR-150 expression group (p<0.05). Multivariate Cox regression analysis demonstrated that the expression of miR-150, tumor size, and number of tumor lesions were independent prognostic predictors for OS in PC patients. CONCLUSION: miR-150 was overexpressed in PC at both the mRNA and protein levels, and high expression of miR-150 could serve as a novel and reliable prognostic biomarker for PC patients.

Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas.

Purpose: To investigate the association between clinic-radiological features and glioma-associated epilepsy (GAE), we developed and validated a radiomics nomogram for predicting GAE in WHO grade II~IV gliomas. Methods: This retrospective study consecutively enrolled 380 adult patients with glioma (266 in the training cohort and 114 in the testing cohort). Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. The semantic radiological characteristics were assessed by a radiologist with 15 years of experience in neuro-oncology. A clinic-radiological model, radiomic signature, and a combined model were built for predicting GAE. The combined model was visualized as a radiomics nomogram. The AUC was used to evaluate model classification performance, and the McNemar test and Delong test were used to compare the performance among the models. Statistical analysis was performed using SPSS software, and p < 0.05 was regarded as statistically significant. Results: The combined model reached the highest AUC with the testing cohort (training cohort, 0.911 [95% CI, 0.878-0.942]; testing cohort, 0.866 [95% CI, 0.790-0.929]). The McNemar test revealed that the differences among the accuracies of the clinic-radiological model, radiomic signature, and combined model in predicting GAE in the testing cohorts (p > 0.05) were not significantly different. The DeLong tests showed that the difference between the performance of the radiomic signature and the combined model was significant (p < 0.05). Conclusion: The radiomics nomogram predicted seizures in patients with glioma non-invasively, simply, and practically. Compared with the radiomics models, comprehensive clinic-radiological imaging signs observed by the naked eye have non-discriminatory performance in predicting GAE.

Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization.

In the EMO (evolutionary multi-objective, EMO) algorithm, MaOPs (many objective optimization problems, MaOPs) are sometimes difficult to keep the balance of convergence and diversity. The decomposition based EMO developed for MaOPs has been proved to be effective, and BBO/Complex (the biogeography based optimization for complex system, BBO/Complex) algorithm is a low complexity algorithm. In this paper, a decomposition and adaptive weight adjustment based BBO/Complex algorithm (DAWA-BBO/Complex) for MaOPs is proposed. First, a new method based on crowding distance is designed to generate a set of weight vectors with good uniformly. Second, an adaptive weight adjustment method is used to solve MaOPs with complex Pareto optimal front. Subsystem space obtains a non-dominated solution by a new selection strategy. The experimental results show that the algorithm is superior to other new algorithms in terms of convergence and diversity in DTLZ benchmark problems. Finally, the algorithm is used to solve the problem of NC (numerical control machine, NC) cutting parameters, and the final optimization result is obtained by AHP (Analytic Hierarchy Process, AHP) method. The results show that the cutting speed is 10.8m/min, back cutting depth is 0.13mm, the cutting time is 504s and the cutting cost is 22.15yuan. The proposed algorithm can effectively solve the practical optimization problem.

Research on hot rolling scheduling problem based on Two-phase Pareto algorithm.

Under the background of excess capacity and energy saving in iron and steel enterprises, the hot rolling batch scheduling problem based on energy saving is a multi-objective and multi constraint optimization problem. In this paper, a hybrid multi-objective prize-collecting vehicle routing problem (Hybrid Price Collect Vehicle Routing Problem, HPCVRP) model is established to ensure minimum energy consumption, meet process rules, and maximize resource utilization. A two-phase Pareto search algorithm (2PPLS) is designed to solve this model. The improved MOEA/D with a penalty based boundary intersection distance (PBI) algorithm (MOEA/D-PBI) is introduced to decompose the HPCVRP in the first phase. In the second phase, the multi-objective ant colony system (MOACS) and Pareto local search (PLS) algorithm is used to generate approximate Pareto-optimal solutions. The final solution is then selected according to the actual demand and preference. In the simulation experiment, the 2PPLS is compared with five other algorithms, which shows the superiority of 2PPLS. Finally, the experiment was carried out on actual slab data from a steel plant in Shanghai. The results show that the model and algorithm can effectively reduce the energy consumption in the process of hot rolling batch scheduling.