No abstract

Three polyurethanes (PRX1500Me-PU, PRX4000Me-PU, and PRX6000Me-PU) crosslinked by polyrotaxanes (PRXs), which consist of half-methylated α-cyclodextrins (CyDs) and poly(oxyethylene)glycols with different chain lengths (PEG1500, PEG4000, and PEG6000), were synthesized. The filling ratios of CyD in PRX1500, PRX4000and PRX6000 are 75, 63 and 37 %, respectively. A polyurethane crosslinked by half-methylated CyD (CDMe-PU) was also prepared for comparison of their structure and properties. ATR-FT-IR spectra of the PUs showed that the formation ratio of hydrogen bond between the PU chains around PRXs increased with increase in the filling ratio. DSC and dynamic viscoelastic measurements and tensile tests for the PUs revealed that (i) reorganized-crystallization of the soft segment chains of PRX1500Me-PU easily occurred because of formation of a pure phase for them; (ii) the thermal and physical behaviors of PRX6000Me-PU are similar to those of CDMe-PU because CyDs as the crosslink points disperse in a similar fashion in the PUs; (iii) the PRX4000 with the moderate filling ratio of CyD in PRX4000Me-PU makes slow reorganized-crystallization of the soft segment chains in the PU as well as improves the tensile performance among the PUs.

No abstract

In the evaluation of smooth pursuit eye movements (SPEMs), recording the stimulus onset time is mandatory. In the laboratory, the stimulus onset time is recorded by electrical signal or programming, and video-oculography (VOG) and the visual stimulus are synchronized. Nevertheless, because the examiner must manually move the fixation target, recording the stimulus onset time is challenging in daily clinical practice. Thus, this study aimed to develop an algorithm for evaluating SPEMs while testing the nine-direction eye movements without recording the stimulus onset time using VOG and deep learning–based object detection (single-shot multibox detector), which can predict the location and types of objects in a single image. The algorithm of peak fitting–based detection correctly classified the directions of target orientation and calculated the latencies and gains within the normal range while testing the nine-direction eye movements in healthy individuals. These findings suggest that the algorithm of peak fitting–based detection has sufficient accuracy for the automatic evaluation of SPEM in clinical settings.

Calculating the angle of objective cyclodeviation has been based on the difference between the fovea and center of the optic nerve head (ONH) in fundus images. An examiner defines the location of the fovea and center of ONH manually; thus, there is an influence of bias, and the analysis time per fundus image is long. Therefore, in this study, we developed a machine learning-based model for automatic cyclodeviation estimation using fundus images. Our model is divided into two steps: detecting macular and ONH regions using the single shot multibox detector (SSD) and estimating cyclodeviaiton using gradient boosting on decision trees of CatBoost. The SSD detected the macular and ONH with a 75% average precision of 99.7% and 99.4%. Then, CatBoost estimated the objective manual cyclodeviation with a mean absolute error of 0.804. These findings suggest that the machine learning-based algorithm estimate cyclodeviation with the same accuracy as a human from fundus images.