Muroran Institute of Technology

Rapid and reliable identification of microplastics in complex biological matrices remains challenging due to limited throughput or insufficient chemical specificity of existing techniques. Here, we demonstrate a high-speed and high-throughput mid-infrared hyperspectral imaging approach based on sub-cycle mid-infrared pulses. The system enables acquisition of centimeter-scale hyperspectral images covering the infrared fingerprint region with a spectral resolution of approximately 3cm-1 within seconds. Microplastic particles composed of five common polymers-polypropylene, polystyrene, polyethylene, polyvinyl chloride, and polyethylene terephthalate-were accurately identified and spatially mapped. The method was further validated in mouse embryo tissue sections, where micrometer-scale microplastics were reliably distinguished against highly complex and spatially heterogeneous infrared backgrounds. This approach combines the intrinsic chemical specificity of infrared absorption spectroscopy with large field-of-view, high-throughput, and rapid imaging, enabling efficient in situ microplastic analysis in complex biological environments.

Amyloid β (Aβ) and α-synuclein (α-syn) have traditionally been recognized as the major causative proteins in Alzheimer's disease (AD) and Parkinson's disease (PD), respectively. However, AD and PD share many common pathogenic mechanisms and exhibit overlapping pathological features. Furthermore, multiple studies have reported the coexistence of Aβ and α-syn within the same pathological regions in individual patients, suggesting that such pathological coexistence is involved in disease progression and pathogenesis. However, the detailed mechanisms by which Aβ and α-syn influence each other and modulate their aggregation dynamics remain unclear. We previously established a method to observe the aggregation processes of Aβ and α-syn in two and three dimensions by utilizing the affinity between quantum dots (QDs) and amyloid aggregates, using fluorescence microscopy and confocal laser scanning microscopy. In this study, we used QD imaging, thioflavin T (ThT) fluorescence assays, and transmission electron microscopy (TEM) to evaluate in detail how Aβ42 and α-syn affect each other's aggregation behaviors. We found that 1 μM Aβ42 monomers did not affect the aggregation of 20 μM α-syn, whereas 1 μM Aβ42 oligomers significantly promoted 20 μM α-syn aggregation. In contrast, 1-10 μM α-syn inhibited the aggregation of 20 μM Aβ42 in a concentration-dependent manner, with α-syn polymers showing a stronger inhibitory effect than α-syn monomers. Taken together, these results demonstrate an asymmetry in their mutual effects on aggregation under the experimental conditions examined in this study: Aβ42 oligomers promote α-syn aggregation, whereas α-syn inhibits Aβ42 aggregation, particularly in its polymeric form.

This paper presents a domain decomposition finite element method (DD-FEM) to accelerate topology optimization of photonic devices. In the proposed approach, the structure invariant region is precomputed and transformed into a reduced boundary conditions for the design region, allowing only the design region to be reanalyzed during optimization. The applicability of the method is demonstrated through the analysis and optimal design of a matching coupler between a strip waveguide and a subwavelength-grating (SWG) waveguide, as well as a waveguide lens. Under the same optimization settings, DD-FEM reduces the computational time per-analysis by approximately a factor of 2-3 and the memory requirement to about one third of that of a standard FEM analysis, making it particularly advantageous when the design region is small relative to the overall computational domain. In addition, we investigate a thresholding method for the dense interface matrix, where small nonzero entries are discarded to further reduce the computational cost of DD-FEM. If an error of about 0.5% in transmitted-power evaluation is acceptable, the linear-system solving time can be reduced to approximately 77% of that without thresholding.

We propose a method to analyze bowling throwing posture by extracting nine new features focusing on feet, hands, and waist. Using motion capture data and similarity metrics, our approach identifies points for correction. Experiments show high accuracy by the proposed methods, enabling targeted posture improvement and safer, more consistent throws.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that accounts for the majority of dementia cases. The accumulation of amyloid-β (Aβ) aggregates on neuronal surfaces is a known important event that typifies AD. Although cell membrane architecture and cytoskeletal tension are thought to be involved in the process of Aβ aggregation, it remains unclear how cytoskeleton-derived tension alters the function of cell membranes, which serve as a scaffold for Aβ aggregation. In this study, we investigated whether cytoskeletal tension promotes Aβ aggregation on neuroblastoma, SH-SY5Y cells. Cytoskeletal tension was enhanced by jasplakinolide, an actin depolymerization inhibitor, and calyculin A, a serine/threonine phosphatase inhibitor that promotes myosin II activation. Real-time imaging with quantum-dot-labeled Aβ nanoprobes revealed that both pharmacological treatments significantly increased Aβ deposition on the surface of living cells. Our findings suggest that cytoskeletal tension promotes Aβ aggregation over the membrane barrier, providing new insights into the biophysical mechanisms underlying Aβ accumulation in AD pathogenesis.

Waveguide lenses based on mosaic structure are proposed and experimentally demonstrated. The devices are designed by an adjoint method based on the finite-element method (AM-FEM). In AM-FEM, it is easy to calculate the gradient of the objective function with respect to the refractive index distribution, and it is especially useful for multi-port and multi-mode problems. First, to demonstrate the effectiveness of the AM-FEM, simple 1 × 2 power splitters are designed, and the importance of the initial structure is demonstrated together with experimental results. And then, the design of single-mode waveguide lenses is demonstrated. After passing through the mosaic lens, the wavefront of the light is aligned, and the light can traverse a slab region, which has no confinement mechanism in the transverse direction. Also, multimode waveguide lenses are presented for the first time, showing low-loss and almost wavelength-independent characteristics. The designed devices are fabricated, and the measured results are in good agreement with the theory. These devices can be useful for various wavefront controlling devices.

Thermally induced deformation is a major source of dimensional error in end-milling, especially under high-speed or high-load conditions. Direct measurement of workpiece deformation during machining is impractical, while temperature signals can be obtained with good stability using embedded thermocouples. This study proposes an indirect method for predicting milling-induced thermal deformation based on temperature measurements. A three-dimensional thermo-mechanical finite element model is established to simulate the transient temperature field and corresponding deformation of the workpiece during milling. The numerical model is validated using cutting experiments performed under the same boundary conditions and machining parameters. Based on the validated results, the relationship between deformation at critical machining locations and temperature responses at candidate monitoring points is analyzed. To improve applicability to complex workpieces, a statistical prediction model is developed. Temperature monitoring points are optimized, and significant temperature–deformation correlations are identified using multiple linear regression combined with information-criterion-based model selection. The final model is constructed using simulation-derived datasets and provides stable deformation prediction over the entire milling process.