A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots.

There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students' learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students' learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students' creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students' learning outcomes and stimulate their active learning and creativity.

Bioinformatic analysis reveals prognostic value and immunotherapy potential of Siglec-15 in laryngeal squamous cell carcinoma.

Background: Laryngeal squamous cell carcinoma (LSCC) is the ultimate common malignant head and neck cancer with dismal prognosis. The expression pattern and clinical significance of Siglec-15 (Sialic acid-binding immunoglobulin-like lectin 15) in LSCC are poorly understood. In order to lay the groundwork for future immune-related research on Siglec-15 in LSCC, we set out to study its expression and prognostic importance in the disease, as well as to use bioinformatics to investigate the immune features modulated by Siglec-15 in LSCC. Methods: ① In order to get the gene expression profile and clinical data for TCGA head and neck cancer (TCGA-HNSC), you may access the relevant data from UCSC xena and use 110 cases of laryngeal cancer as a training set. Two datasets, GSE27020 and GSE25727, were obtained from the GEO databank and utilized as validation sets. These datasets include expression profiles and clinical information. The Siglec-15 gene and immune characteristics were analyzed by bioinformatics methods. ② Retrospectively collected routine paraffin specimens from patients with pathological diagnosis of squamous cell carcinoma from December 2012 to November 2015 in Sun Yat-sen Memorial Hospital and fresh frozen tissue of patients from June 2021 to March 2022. Immunohistochemistry method, immunofluorescence technique and real-time quantitative PCR was used to examine the difference of Siglec-15 appearance in LSCC tissue and adjacent tissue, and its correlation of prognosis, clinic pathological characteristics and CD8+T lymphocyte infiltration. Using human laryngeal cancer cell line (LCC), we studied the influence of Siglec-15 in cell proliferation and invasion. Results: We identified Siglec-15 was upregulated in LSCC. The patients in Siglec-15 high expression group had a poor overall survival (OS) based on the clinical information from TGCA and 111 LSCC patients that hospitalized in Sun Yat-sen Memorial Hospital. The COX regression analysis indicated Siglec-15 as an independent predictor for poor prognosis of LSCC. Bioinformatic analysis suggested that the high expression of Siglec-15 shape an immune suppressive tumor microenvironment (TEM), leading to poor response to immunotherapy in LSCC. Siglec-15 enhanced cell invasion and proliferation, as we showed in vitro. Conclusion: Our study support Siglec-15 as a potential predictor for LSCC prognosis and an attractive target for LSCC immunotherapy.

Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random Tree.

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis. Methods: The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method. Results: Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage). Conclusion: The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.