Traditional interaction perception of parallel robots relies on a six-dimensional force sensor for contact sensing at their distal end. However, the sensor body occupies the space of moving platform and also increases the load on the robot actuations. To enable both minimization and embodied intelligence, this paper proposes an external interaction estimation method with embodied mechanical intelligence by embedding two single-axis force sensors in each leg of 6-PSS parallel robot. The method uses a backward propagation neural network optimized by sparrow search algorithm, and it can simultaneously estimate the external force and its position using information from multiple single-axis force sensors and the encoder of driving motor. The experimental platform is established to collect the data and train the network. The result shows that the force estimation mean error is 2.4% and the position estimation error is 2.9%. A demonstration with a virtual display interface showing the reconstructed parallel robot pose, and the interaction force and its pose using the proposed estimation method, indicates the effectiveness of the proposed interaction method with embodied mechanical intelligence for 6-PSS parallel robot

SUMMARY: The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. AVAILABILITY AND IMPLEMENTATION: diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

Most Chinese EMRs, text mining over which enable compilation of specific disease databases, are stored as unstructured text, natural language in the field of medicine is distinct and differs from general Chinese. word segmentation of EMRs, pattern matching and feature mining methods were used to find the quantity of disease characteristics in case histories. a full-scale, comprehensive medical database was compiled. After construction of a semantic analysis model based on clinical natural language processing, this study perform pattern matching and disease feature mining and complete mining and analysis of diseases, realizing a semantic parsing and analysis system for diseases.