The periodic motion of the lower trunk region during human locomotion is analyzed from a kinematic perspective for different gaits using inertial measurement units (IMUs). Experiments are performed on subjects who walked or ran on a treadmill, each equipped with an IMU attached to their lower trunk. The treadmill speed was externally controlled according to a fixed protocol consisting of speed-up and speed-down phases. Time-series data of acceleration and angular velocity were collected and analyzed. We propose a method to estimate the initial elevation angle and velocity associated with the periodic motion, and we focus on the three dynamical aspects of the subject’s movements: the trajectory of the lower trunk in the sagittal plane, the excess kinetic energy ratio, and the position of the least acceleration fluctuation point. The results revealed that the rotation direction of the trajectory in the sagittal plane in the moving frame differed between walking and running for almost all subjects. This difference is interpreted kinematically in terms of the direction and timing of the ground reaction force. The excess kinetic energy ratio as a function of Froude number also well characterizes the gait differences. Furthermore, it is found that the point of least acceleration fluctuation is located consistently behind the body during running.

Mammalian spermatozoa navigating through the female reproductive tract to reach the egg comprises a fundamental step in achieving successful fertilization. Within the oviduct, sperms encounter oviductal mucus, which is a highly viscous and shear-thinning non-Newtonian fluid whose rheological characteristics strongly influence sperm motility and guidance. During their journey, sperms are subjected to fluid flow generated by the peristaltic motion of the oviductal wall, prompting them to employ behavioral mechanisms such as thigmotaxis and rheotaxis to efficiently migrate toward the oocyte. In the present study, we experimentally examined the interplay between rheotactic behavior and collective swimming of sperm in shear-thinning fluids. To reproduce the physiological flow conditions in the oviduct, we designed and fabricated microchannels. These microchannels mimic the geometry and flow environment of the oviduct using a combination of high-resolution 3D printing and soft lithography techniques. These microfluidic systems allow precise control of the flow field and viscosity distribution, thereby enabling quantitative analysis of sperm motility under conditions relevant to in vivo reproduction. Our results demonstrate that spermatozoa exhibit pronounced rheotaxis within regions near the channel wall, where strong velocity gradients are present. The shear-thinning nature of the medium enhances this orientation response, which results in improved alignment of swimming directions and spontaneous formation of sperm clusters. Furthermore, sperms engaged in collective motion exhibit significantly higher swimming velocities than those swimming individually, suggesting hydrodynamic cooperation among neighboring cells. These findings provide new insights into the physical and biological mechanisms underlying sperm transport in complex reproductive environments. The enhanced rheotactic alignment and collective behavior observed in shear-thinning fluids may confer evolutionary advantages by increasing the probability of successful fertilization under physiological flow conditions.

This study investigated the low-dimensional spatiotemporal structure of overground walking using displacement-based principal component analysis (PCA). Four healthy young male subjects participated in the walking measurement experiment. Three-dimensional marker trajectories were collected during overground walking at three different walking rates, and marker displacements were analyzed to reduce the influence of translational components. Cumulative variance of the first two principal components (PC1 and PC2) consistently accounted for more than 85% of the total variance across all walking rates. Temporal analysis showed that PC1 exhibited a regular sinusoidal waveform corresponding to the fundamental gait frequency, whereas PC2 showed a periodic structure at approximately twice that frequency. In contrast, higher-order components displayed complex composite waveforms. Principal component loadings indicated that PC1 primarily represented alternating anterior–posterior displacements of the left and right lower limbs, while PC2 reflected coordinated in-phase displacements associated with the double-support phase. The variance explained by PC1 slightly increased with walking rate, likely due to increased step length. Sinusoidal models of PC1 and PC2 were used to successfully reconstruct walking movements with high temporal fidelity. The temporal and spatial characteristics of these components were preserved across all walking rates. Within this preliminary dataset of four healthy young males, overground walking was characterized by low-dimensional periodic coordination patterns that were relatively consistent across individuals. These findings highlight the effectiveness of displacement-based PCA for revealing fundamental walking movement organization.

The three-dimensional morphogenesis of epithelial tissues is governed by robust physical mechanisms. Examples of this process include the folding of insect imaginal discs and cerebral convolutions. Reaction-diffusion theories have historically focused on chemical aspects to explain these structures. However, recent mechanobiological studies indicate that geometric constraints are equally pivotal. In this study, we investigate how the intrinsic curvature of a tissue substrate directs the buckling morphogenesis driven by cell division. Using a three-dimensional cell-center model, we simulated epithelial proliferation on spherical and ellipsoidal surfaces. Our results reveal a curvature-dependent mode transition on spherical surfaces. Specifically, the buckling pattern shifts from a dot-like state to a labyrinthine state as the radius increases. Crucially, we observed that tissue geometry regulates the spatial orientation of the buckling patterns. On prolate ellipsoids, the tissue spontaneously generates “Yoshimura pattern-like folds” (zig-zag patterns). This topological structure is similar to the folding of beetle horns. Conversely, on oblate ellipsoids, the tissue robustly forms concentric ring patterns. This morphology resembles the folding of Drosophila leg discs. We propose that this physical pattern selection arises from geometric confinement rather than solely from molecular pre-patterning. Specifically, the high-curvature poles of prolate ellipsoids generate effective axial compression. In contrast, the high-curvature equator of oblate ellipsoids induces radial compression. These findings suggest that morphogenetic outcomes are robustly “encoded” in the macroscopic geometry of the developing primordium. Therefore, this macroscopic geometry serves as a geometric bifurcation parameter to regulate the transition between ordered and disordered patterns.

This study presents a new method for evaluating the natural frequencies of bone-conducted sounds using spectrograms obtained by short-time Fourier transform (STFT) and machine learning (ML) and examines its validity for practical applications. The natural frequencies of bone-conducted sounds is conventionally evaluated using fast Fourier transform (FFT) analysis. Spectrograms obtained by STFT are used to visualize bone-conducted sounds as a two-dimensional time-frequency representation. Another advantage of spectrograms is that they facilitate the development of a convolutional neural network based ML model for predicting natural frequency. Forty-five healthy college students (20.36 ± 1.53 years) participated in this study, and five hammer impacts were applied to the right medial malleolus to record bone-conducted sounds generated at the right medial tibial condyle. Spectrograms were obtained from the recorded waveforms using STFT and input into a ML model trained with natural frequencies evaluated by FFT as a ground truth. The natural frequencies predicted by the ML model were compared with the natural frequencies evaluated by the FFT analysis. The mean absolute error was 12.92 ± 4.05 Hz, and the mean coefficient of determination (R²) was 0.845 ± 0.163, demonstrating the high evaluation accuracy of the proposed method. Feature analysis conducted using gradient-weighted class activation mapping revealed that the developed model focused on the peak sound pressure of the spectrogram when predicting the natural frequency. Further, it was found that the predicted values were large discrepancies from those evaluated by FFT analysis when the ML model failed to capture the peak sound pressure.