Ross Riggs (crimepolice1)

This article has been retracted please see Elsevier Policy on Article Withdrawal (https//). This article has been retracted please see Elsevier Policy on Article Withdrawal (http//). This article has been retracted at the request of the Editor-in-Chief. Concern has been raised by a reader about both the inappropriateness of certain methods used to prepare Figures 1A and 3A; as well as the lack of important information including the exact age of the mice and details of the ELISA used. These issues could undermine the scientific grounds of the article. Apologies are offered to readers of the journal that this was not detected during the submission process.The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ε-constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm. To undertaken a systematic review of the technical success and technique efficacy rates of percutaneous image-guided radiofrequency ablation (RFA) for adrenal tumours. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the electronic databases MEDLINE, EMBASE, and PubMed were searched for relevant studies from inception to the third week of January 2020. Only studies reporting effectiveness rates of percutaneous RFA for adrenal tumours were included. Data regarding sample size, tumours, effectiveness rates, outcomes, and complications were extracted in duplicate and recorded. A total of 15 studies evaluating 292 individuals with 305 tumours were included. Patient selection criteria included age ≥18 years, contraindication to surgical intervention, and no uncorrected coagulopathy. Cumulative technical success, primary technique efficacy, and secondary technique efficacy rates were 99%, 95.1% and 100%, respectively, indicating optimal immediate control of adrenal tumours. Technical success and technique efficacy rates of primary adrenal tumours were higher than adrenal metastases; however, formal statistical analyses were precluded due to lack of comparative studies. Local tumour progression rates for adrenal metastases were 20.3% at 3 months, 26.3% at 6 months, and 29.3% at 12 months. Overall survival rates for adrenal metastases were 81.8% at 6 months, 59.6% at 12 months, and 62.9% at 18 months. The intraprocedural complication rate was 30.2%, with the most frequency reported complication being procedural hypertensive crisis. The findings of this study suggest percutaneous image-guided RFA is a safe and efficacious procedure. Further studies are warranted to define patient selection criteria and long-term outcomes. The findings of this study suggest percutaneou