NSK (Japan)

<bold>Background:</bold> Emphysema and overinflation are associated with airflow limitation, but their regional distribution within the lungs is less understood. <bold>Objective:</bold> To examine the association between regional emphysema (LAA-950%), overinflation (expiratory/inspiratory volume ratio) and gas trapping (DPM gas trapping) with FEV₁/FVC. <bold>Methods:</bold> Post-bronchodilator spirometry and HRCT (inspiratory and expiratory) scans were collected in TRAIT, a prospective 3-year observational study. Quantitative HRCT metrics were derived by VIDA software in the upper and lower lobes, and their relationship with FEV₁/FVC was tested using ANCOVA (adjusting for age, height and sex). The strength of these associations was expressed as η². <bold>Results:</bold> Subjects with a smoking history included: ACO (n=85; mean age=70 years; FEV₁, 70% predicted; FEV₁/FVC, 0.58) or COPD (n=104; mean age=71 years; FEV₁, 69% predicted; FEV₁/FVC, 0.55). Upper lobar volumes were slightly greater (≤6%) than lower lobar volumes. More gas-trapping, overinflation and a trend for more emphysema were seen in the upper lobes, but a stronger association between these measures and FEV₁/FVC was seen in the lower lobes (<bold>Table</bold>). <bold>Conclusions:</bold> In this cross-sectional analysis, more structural damage was observed in the upper lobes, but damage in the lower lobes was more strongly associated with airflow limitation, regardless of lobar volume. <fig><object-id>erj;66/suppl_69/OA4434/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>

In ophthalmic surgeries, the light probe is responsible for providing safe intraocular illumination and ensuring the visibility of the instrument and its shadow as the only available reference for qualitative depth estimation and landing point prediction in fundus microscopic images. To achieve sustainable shadow-based estimation during surgeries, we propose controlling the light probe automatically to limit the shadow position around the instrument tip using only 2D information from the microscope. We also integrate an intensity balancing sub-module to guarantee the normal intensity distribution and the safe depth of light-tip placement. Without motor-based pose coordination between the light probe and the instrument, experiments analyze the performance of our image-based shadow maintenance with only image information under the constraints of RCM and discuss the working volume and segmentation limitations during simulation and real-robot tests.

Syringe pump has been applied to actuate soft pneumatic robots. Most previous works focus on designs of the syringe pump, its applications, and improvement of its problems such as leaking air, inefficient motions, etc. This paper introduces dynamical modeling and parametric analysis of a syringe pump. The syringe pump is made of a commercial syringe and a linear actuator. The dynamic equation is derived from the motions of the linear actuator, the air dynamics in the syringe, and the airflow inside the soft actuator. Because of the high-elastic materials, the volume of the soft actuator is a time-varying parameter. Therefore, the variation of volume is estimated by the Kalman filter instead of relying on the traditional design method. The dynamic model is also utilized to select optimal parameters which are verified by the experiments for the syringe pump. Two system controllers are designed with and without consideration of the pressure dynamics. The controller considering pressure dynamics outperforms. This work shows the benefits of pressure dynamic of the syringe pump for both system design and advanced controller design.

Many robotic systems have emerged in the recent past as cutting-edge solutions to enhance the capabilities of ophthalmic surgeons in order not only to increase the quality of conventional operations but also to enable new and advanced interventions such as gene-and stemcell-based therapies. Some of these operations require precise and stable delivery of therapeutics into the sub-retinal domain and therefore, automatic procedures with micron precision at the tooltip are essential. One of the most critical parameters to precisely maintain the tooltip in automated robotic retinal surgery is the appropriate configuration and control of the Remote Center of Motion (RCM). The RCM precision might be affected by any physical uncertainties, such as instrument assembling, or minor kinematic changes. Therefore, an accurate RCM identification requires an extensive calibration plan before each operation. This paper presents a novel preoperative evaluation-calibration method for kinematic-adjustable software-based RCM robots. Our key concept is decomposing the 3D workspace into two orthogonal working planes in order to reduce the complexity while adding robustness to RCM evaluation and software calibration. First, we propose an ablation-based RCM-related analysis method of the kinematics of the robot. Using a Convolutional Neural Network (CNN), we analyze image-based RCM along the instrument during a predefined RCM motion maneuver. Utilizing software calibration protocol by prior-analyzed RCM-related kinematic parameters, the software calibration is done automatically. The process is repeated until the RCM accuracy is set within a clinically acceptable range. Evaluation of the method on a highly accurate 5-DOF-Software-RCM robot demonstrated significant optimization in RCM error within an average of 4 minutes for each plane and 0.300 ± 0.20 mm accuracy.

Diagnosis of rolling bearing damage is one of the essential maintenance tasks to improve the operational efficiency of rotating machinery. Recent studies on bearing diagnostic models have been based on deep learning. However, there has been little discussion of how the performance of deep learning-based diagnostic models is affected by changes in the train-test data split rules of the CWRU dataset used in those studies. Motivated by this question, we examined the diagnostic performance of deep learning models under multiple conditions of data split rules and a number of diagnostic classes in the CRWU dataset. The frequency-domain and time-domain characteristics of the train-test data were also compared, and their relationship to diagnostic performance was discussed. We found that differences occurred mainly in the frequency-domain for the train-test data split conditions that could be set for the CWRU dataset. The more differences exist between train and test data in the frequency-domain, the worse the diagnostic performance of the deep learning model tended to be. On the other hand, there were no split conditions with apparent differences in time-domain characteristics. Therefore, to construct a more robust diagnostic model, it is desirable to use data with various frequency and time domain characteristics for train-test, such as using more diverse datasets together.

Genetically modified mice are commonly used in biologic, medical, and drug discovery research, but conventional microinjection methods used for genetic modification require extensive training and practical experience. Here we present a fully automated system for microinjection into the pronucleus to facilitate genetic modification. We first developed software that automatically controls the microinjection system hardware. The software permits automatic rotation of the zygote to move the pronucleus to the injection pipette insertion position. We also developed software that recognizes the pronucleus in 3-dimensional coordinates so that the injection pipette can be automatically inserted into the pronucleus, and achieved a 94% insertion rate by linking the 2 pieces of software. Next, we determined the optimal solution injection conditions (30 hPa, 0.8–2.0 s) by examining the survival rate of injected zygotes. Finally, we produced transgenic (traditional DNA injection and piggyBac Transposon system) and knock-in (genomic editing) mice using our newly developed Integrated Automated Embryo Manipulation System (IAEMS). We propose that the IAEMS will simplify highly reproducible pronuclear stage zygote microinjection procedures.

ABSTRACTThe different mechanisms of fretting wear in oil and grease lubrication and methods to reduce fretting wear were examined by means of thrust ball bearings in this study. Tests of fretting wear under oil lubrication were conducted. It was confirmed that high-viscosity oil can reduce fretting wear at high velocity (i.e., high frequency) through oil film formation. In the case of grease lubrication, the influence of velocity on fretting wear was significantly different for low- and high-viscosity greases. Grease with low-viscosity base oils could reduce fretting wear at high velocity. In contrast, grease with high-viscosity base oils could reduce fretting wear at low velocity. Grease thickeners were found to be effective in forming a layer that could prevent fretting wear. These results highlight the large differences in effective fretting wear reduction mechanisms between oil and grease lubrication.