BackgroundProstate cancer is a common malignancy of the male genitourinary system that occurs worldwide. The current research aims to investigate caveolin-1 expression in prostate cancer tissue and its relationship with pathological grade, clinical pathologic staging, and preoperative prostate-specific antigen (PSA) levels.MethodsFrom January 2012 to December 2014, samples from 47 patients with prostate cancer who had received transurethral prostatic resection (TURP) and 20 patients with benign prostatic hyperplasia were collected at the First Affiliated Hospital of Guangxi Medical University. Caveolin-1 was detected by streptavidin-perosidase (SP) immunohistochemical staining in pathological tissue slices. The results were statistically analyzed for pathological grade, clinical stage, and preoperative PSA level.ResultsThe expression of caveolin-1 was significantly higher in prostate cancer samples than in benign prostatic hyperplasia samples (P < 0.05), and caveolin-1 expression was significantly different among the pathological grades of poorly, moderately and well-differentiated prostate cancer (P < 0.05). The difference in caveolin-1 expression was significant for different clinical stages (T1-T2 and T3-T4) of prostate cancer (P < 0.05). The difference in caveolin-1 expression was not significant among samples with different preoperative PSA levels (0–10, 10–100 and > 100 μg/L) (P > 0.05).ConclusionsCaveolin-1 is closely related to the pathological grade and clinical stage of prostate cancer after transurethral surgery, and it may be a novel tumor marker for prostate cancer. The expression of caveolin-1 is not associated with preoperative serum PSA levels.
There are a lot of redundant data in wireless sensor networks (WSNs). If these redundant data are processed and transmitted, the node energy consumption will be too fast and will affect the overall lifetime of the network. Data fusion technology compresses the sampled data to eliminate redundancy, which can effectively reduce the amount of data sent by the node and prolong the lifetime of the network. Due to the dynamic nature of WSNs, traditional data fusion techniques still have many problems. Compressed sensing (CS) theory has introduced new ideas to solve these problems for WSNs. Therefore, in this study we analyze the data fusion scheme and propose an algorithm that combines improved clustered (ICL) algorithm low energy adaptive clustering hierarchy (LEACH) and CS (ICL-LEACH-CS). First, we consider the factors of residual energy, distance, and compression ratio and use the improved clustered LEACH algorithm (ICL-LEACH) to elect the cluster head (CH) nodes. Second, the CH uses a Gaussian random observation matrix to perform linear compressed projection (LCP) on the cluster common (CM) node signal and compresses the N-dimensional signal into M-dimensional information. Then, the CH node compresses the data by using a CS algorithm to obtain a measured value and sends the measured value to the sink node. Finally, the sink node reconstructs the signal using a convex optimization method and uses a least squares algorithm to fuse the signal. The signal reconstruction optimization problem is modeled as an equivalent ℓ1-norm problem. The simulation results show that, compared with other data fusion algorithms, the ICL-LEACH-CS algorithm effectively reduces the node’s transmission while balancing the load between the nodes.
In this article, a fuzzy-approximation-based adaptive backstepping control method for dual-arm of a humanoid robot was proposed. The purpose of this control system is to provide coordinated movement assistance to enable the humanoid robot's human-like forearm to grab objects coordinately (or track any continuous desired trajectory), even in the presence of environmental disturbances and parametric uncertainties. We analyze the proposed adaptive backstepping by mathematical modeling and actually measure the robot dual-arm motion information of a number of case when they simulate the trajectory to verify the model. We design the adaptive fuzzy-approximation control strategy and combining the synthesis of the robust design, backstepping control, and Lyapunov function method, the proposed adaptive fuzzy backstepping control does not need to know the humanoid robot's arms model precisely. In the control system proposed here, once the desired trajectories of the robot's dual-arm positions are given, the adaptive fuzzy system was closed to any unknown functions and to the derivative of the virtual control law of the humanoid robot system. In this case, a robust design scheme was utilized to compensate for any approximation errors. With the proposed trajectory tracking, not only able to generate the coordinate motions for a humanoid robot's two arms, but it can also control the arms to move to the desired positions. The proposed closed-loop system under the adaptive fuzzy backstepping control design was effective and that asymptotic stability was successfully achieved. The adaptive fuzzy-approximation backstepping control strategy should be more complete and intelligent and more actual test should be conducted to further evaluate the effect of the proposed trajectory tracking. The instability of dual-arm of humanoid robot system is systematically analyzed and a backstepping control strategy based on the adaptive fuzzy-approximation to improve the continuity of trajectory tracking of the robot's arms is proposed.
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