A new medical microrobot to drive endoscopes into endocoeles is presented. The structure of the microrobot is very simple; it consists of a right spirally grooved micromotor, a left spirally grooved cylinder and a flexible coupling. When the micromotor rotates, a hydrodynamic mucus film is formed because highly viscous mucus exists in endocoeles. The mucus film can prevent direct contact between the microrobot and the endocoele, and the injury to organic tissues may be avoided. The locomotion speed and the hydrodynamic mucus film thickness formed when the microrobot drives endoscopes into endocoeles have been calculated according to hydrodynamic lubrication theory. The results indicate that the microrobot can be suspended to drive endoscopes quickly into endocoeles. This has been confirmed by experiments.
AbstractGenome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer’s Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.
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