Evaluation of the softness and its impression of visual stimuli in VR space

To examine the softness and impression of visual objects in VR (Virtual Reality) space, the impression of the visual stimuli in VR space was measured using the subjective evaluation of a seven-rank scale by changing with each the value of the deformation resistance of the stimuli, of shapes, and colors. The value of the deformation resistance of the stimuli expresses the degree of deformation to return to the original of the object when touching it in VR space. The lower value indicates the larger deformation like pudding and the higher one is the smaller one like thick rubber they were used three types of values lower and higher, and no-deformation of the objects. The shapes of objects as the stimuli were three shapes (sphere, cube, pyramid). The colors of the stimuli were selected from five colors (red, green, green, gray, and white) and they have used two types of the feeling of materials (matte and metallic) in each color. Ten participants were asked to subjectively evaluate the softness and impression of the stimulus. In the results, the evaluation changes from soft to hard by increasing the values of deformation resistance in all the stimuli in VR space. It is suggested that the degree of the deformation to return to the original can express the softness of objects when touching them in VR space even though the user does not touch them physically. It also discussed the relationship between softness and its impression of the stimuli in VR space

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

Prediction of fatigue crack growth using convolutional neural network (2nd Report, Prediction of crack propagation on different levels)

In this paper, the prediction of crack propagation with two cracks using machine learning is described. The analysis results of crack propagation by s-version FEM (s-FEM), which combines the automatic mesh generation technique, are used for generation of training and validation datasets. Plural crack propagation with the different vertical distance between the two cracks as a variable are analyzed. The analysis cases are divided into training and validation datasets. In training process, the input parameters are the coordinates of 4 crack tip, the output data are crack propagation vectors, the number of cycles for crack propagation of 0.25 mm. Initial crack configurations should be specified. After the specification, the predictor iteratively predicts crack propagation direction and the number of loading cycles. A prediction accuracy depends on the training datasets, which contains 0.25 mm length of each crack propagation in this study. To improve prediction accuracy, the data augmentation is effectively applied. In case of plural crack interaction, when the crack tips close each other, the accuracy gets worse and worse. Reducing datasets which satisfy the crack coalescence condition, it is shown that the prediction accuracy is improved. Even if training datasets are not enough number for accurate prediction, it is shown that the prediction accuracy is improved by the data augmentation