Oblique effect is a phenomenon that is cardinal (horizontal and vertical) orientations are easily distinguishable from slightly tilted ones, while oblique orientations are not. In the periphery of the visual field, a phenomenon of visual crowding that is visual targets become indistinguishable when surrounded by flankers occurs. The oblique effect has been widely reported in the center of the visual field, but no notable results have been obtained in the periphery of the visual field. Although both phenomena affect perception, there has been little previous research linking the two, and no reported cases have confirmed the oblique effect during crowding. In the current study, we designed experiments that combined both phenomena using human 10 subjects in their 20s with normal vision. In experiment 1, oblique effect was confirmed in the center of the visual field. In experiment 2, oblique effect was tested in the periphery of the visual field at an eccentricity of 8° from the center of the visual field. In experiment 3, oblique effect was tested in the circumstance that evokes visual crowding at an eccentricity of 8° from the center of the visual field. Analyses based on the signal detection theory and Mann-Whitney U test showed discrimination of oblique orientations from slightly tilted ones were harder than discrimination of cardinal orientations in the periphery of the visual field than the center (center cardinal n＝20, oblique n＝20, U＝25.5, p＜0.001, periphery cardinal n＝60, oblique n＝60, U＝328, p＜0.001), and also showed the depression of the discrimination performance, but existence of the oblique effect in the circumstance of the visual crowding (cardinal n＝60, oblique n＝60, U＝586.5, p＜0.001) .

In 2024, the total fertility rate in Japan declined to 1.15, with annual births falling below 700,000 for the first time. The need to prepare for and manage diaper changes while outside the home poses a psychological burden on parents. In this study, we aimed to develop a noninvasive method to record infant bowel sounds at the onset of feeding session and predict the total number of daily diaper changes using XGBoost with a limited set of features. Fourteen healthy infants (seven boys and seven girls, aged 2-11 months) participated in the study, which was approved by the Ethics Committee of Ehime University Hospital. Bowel sounds were recorded for 10 minutes immediately after feeding session began using a small device attached inside the diaper. Our method consists of the following steps: (Step 0) Noninvasive bowel sound recording and classification into six characteristic types based on duration and maximum frequency (MF) under physician supervision; (Step 1) Calculation of the bowel sound ratio by dividing the number of occurrences of each characteristic sound by the total number of sounds and normalizing the values; (Step 2) Clustering of the feature space using bowel sound ratio and MF to derive four-dimensional effective features (4D) ;and (Step 3) Classification of the number of daily diaper changes (6-10 times/day, five classes) as the target variable using XGBoost, with SMOTE applied to correct for class imbalance. The comparison conditions included 6D, 6D＋SMOTE, and 4D, with performance evaluated using accuracy and F1-score. Statistical significance was assessed using a t-test (p-value) and Cohen’s d effect size. The proposed method (4D＋SMOTE) achieved an accuracy of 0.86 and an F1-score of 0.82, with a significant difference observed between 6D and 4D＋SMOTE (t＝5.78, p＝0.0045, dz＝2.58 for F1-score) . Moreover, since features with a low proportion of bowel sounds contributed to prediction performance, the findings suggest that bowel sound analysis may serve as an effective indicator of diaper change frequency. This approach enables accurate estimation of daily diaper changes, facilitates outing planning, and may help prevent social isolation and reduce parenting-related stress.

Regenerative medicine has attracted attention as a novel therapeutic approach for restoring damaged or impaired tissue and organ functions. The advancement of regenerative medicine requires efficient, safe, and high-quality induced pluripotent stem cell (iPSC) production, along with quantitative methods for differentiation status assessment. In this study, we aimed to identify characteristic features derived from the beating dynamics of iPSC-derived beating cardiomyocyte clusters as well as to propose and validate fast and highly accurate machine learning-based method for determining their differentiation status. The method we present in this study involves cell beating behavior acceleration vector calculation via optical flow analysis to construct a 3D beating point cloud defined by time frame, angle, and acceleration. Next, we extracted four features (periodicity, periodic variance, convergence, and convergence variance) and assessed the degree of differentiation using unsupervised (k-means＋＋, GMM) and supervised (SVM, decision tree) learning. We achieved the highest accuracy of 96.9% using a decision tree. In addition, by leveraging the structural properties of the 3D beating point cloud, we reduced the number of features from four to two while maintaining accuracy and decreasing computation time by approximately 80%. Moreover, in our study, we discuss why high differentiation accuracy is preserved with fewer features. Overall, the proposed method provides an efficient and highly accurate approach for iPSC-derived cardiomyocyte cluster differentiation status evaluation using a minimal feature set.