In this paper, a real-time obstacle avoidance approach and a trajectory planning method are proposed to avoid collisions to move an object jointly by a human and an omnidirectional mobile robot. Different from many existing approaches of local trajectory planning, the proposed method called Multiscale Local Perception Region Approach (MLPRA) is specially designed for obstacle avoidance of omnidirectional wheeled robots with direction constraints of human guidance, which can respond fast to dynamic obstacles and guarantee the safety of the robot. To solve the problem that the position of the human hand is difficult to obtain by visual sensing due to visual occlusion, a simple mechanism that can indirectly measure the change of the position of the human hand is designed, and further a method to follow the human’s intention based on this mechanism is proposed. Finally, the simulation environment on the Gazebo simulation platform is built to verify the feasibility and effectiveness of our proposed methods. Experimental results show that after embedding proposed methods into the omnidirectional mobile robot, obstacles can be effectively avoided in co-transporting processes.Note to Practitioners—The motivation of this paper is focusing on obstacle avoidance of omnidirectional mobile robots in human-robot co-transporting, application scenarios concentrated in factories and logistics warehouses, such as the mobile robot collaborates with a human (a robot teleoperated by human) transporting a table. The existing obstacle avoidance algorithm can be regarded as a local trajectory planning problem, which cannot directly adapt to co-transporting tasks in real-time. Considering the specific task if the robot cannot cooperate with human in real-time, the carried object will fall. In this paper, a local trajectory planning method is proposed, regarding the obstacle generating virtual repulsive force, regarding the human action on the robot as the virtual gravitational force, and the mobile robot under two virtual forces not only follows human action in co-transporting, but also avoids obstacles. The method shows quick calculation and good real-time performance. The method proposed is evaluated by co-transporting in Gazebo and experiments based on omnidirectional mobile robots, and ensured that the object being carried does not fall and robot can avoid collisions in the unknown environments without positioning systems.

Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.

Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.

MOTIVATION: Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity. RESULTS: We developed a tissue-specific pathway interaction enrichment analysis algorithm (TPIEA). TPIEA was applied to two large Caucasian and Chinese genome-wide association study summary datasets of bone mineral density (BMD). TPIEA identified several significant pathways for BMD [false discovery rate (FDR) < 0.05], such as KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, which had been demonstrated to be involved in the development of osteoporosis. We also compared the performance of TPIEA and classical pathway enrichment analysis, and TPIEA presented improved performance in recognizing disease relevant pathways. TPIEA may help to fill the gap of classic pathway association analysis approaches by considering tissue specificity. AVAILABILITY AND IMPLEMENTATION: The online web tool of TPIEA is available at https://sourceforge.net/projects/tpieav1/files CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

UNLABELLED: : Pleiotropy is common in the genetic architectures of complex diseases. To the best of our knowledge, no analysis tool has been developed for identifying pleiotropic pathways using multiple genome-wide association study (GWAS) summaries by now. Here, we present PAPA, a flexible tool for pleiotropic pathway analysis utilizing GWAS summary results. The performance of PAPA was validated using publicly available GWAS summaries of body mass index and waist-hip ratio of the GIANT datasets. PAPA identified a set of pleiotropic pathways, which have been demonstrated to be involved in the development of obesity. AVAILABILITY AND IMPLEMENTATION: PAPA program, document and illustrative example are available at http://sourceforge.net/projects/papav1/files/ CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.