Biology subjects

Hino, H.

Publications and source records attributed to Hino, H..

3 recordsLinked to original sources

bioRxiv2026

Standardizing image-derived fish length-frequency distributions to reference measurements using bin-specific error matrices

Artificial intelligence (AI)-based image analysis can efficiently estimate fish length, but differences in devices, imaging conditions, operators, and AI models limit comparability among surveys. We propose a standardization framework that estimates a bin-specific error matrix from paired reference measurements and AI-derived lengths and applies it to standardize (correct) AI-derived length-frequency distributions. The Richardson-Lucy expectation-maximization algorithm was used, with the number of iterations selected via cross-validation. Simulations based on empirical length-frequency data from 110 species showed that standardization reduced relative bias and distributional discrepancy; median relative-bias and root mean square error ratios were below 1, and the performance was more affected by the amount of paired data than by the number of cross-validation folds. In real data from 957 Japanese jack mackerel, standardized AI-derived distributions approached human-observer histograms, although discrepancies remained in the range of 160-230 mm. The proposed framework provides a practical approach for improving the comparability of image-derived length-frequency data using paired calibration data, without retraining the underlying AI model.

ecology↗

bioRxiv2023

In Vivo Imaging of Bone Collagen Dynamics in zebrafish

Type I collagen plays a pivotal role in shaping bone morphology and determining its physical properties by serving as a template for ossification. Nevertheless, the mechanisms underlying bone collagen formation, particularly the principles governing its orientation, remain unknown due to the lack of a method enabling continuous in vivo observation. To address this challenge, we constructed a method to visualize bone collagen by tagging with GFP in zebrafish and observed the interactions between the osteoblasts and collagen fiber during bone formation in vivo. When Col1a2-GFP was expressed under the control of the osteoblast-specific promoters osx or osc in zebrafish, bone collagen could be observed clearly enough to identify their localization, but collagen from other organs did not. Therefore, we determined that this method was of sufficient quality for detailed in vivo observation of bone collagen. Next, bone collagen in the scales, fin ray, and opercular bones was observed in detail in zebrafish, when bone formation is more active. By simultaneously observing bone collagen and osteoblasts, we successfully observed dynamic changes in the morphology and position of osteoblasts from the early stages of bone formation. It was also found that the localization pattern and orientation of bone collagen significantly differed depending on the choice of expression promoter. Both promoters (osx and osc) used in this study are osteoblast-specific, but their Col1a2-GFP localizing regions within bone are exclusive, with osx region localizing mainly the outer edge of bone and osc region localizing the central area of bone. This suggests the existence of distinct subpopulations of osteoblasts with various gene expression profiles, each of which may play a unique role in the osteogenic process. These findings would contribute to a better understanding of the mechanisms governing bone collagen formation by osteoblasts.

cell biology↗

bioRxiv2023

Hawkes process modeling quantifies complicated firing behaviors of cortical neurons during sleep and wakefulness

Despite the importance of sleep to the cerebral cortex, how much sleep changes cortical neuronal firing remains unclear due to complicated firing behaviors. Here we quantified firing of cortical neurons using Hawkes process modeling that can model sequential random events exhibiting temporal clusters. "Intensity" is a parameter of Hawkes process that defines the probability of an event occurring. We defined the appearance of repetitive firing as the firing intensity corresponding to "intensity" in Hawkes process. Firing patterns were quantified by the magnitude of firing intensity, the time constant of firing intensity, and the background firing intensity. The higher the magnitude of firing intensity, the higher the likelihood that the spike will continue. The larger the time constant of firing intensity, the longer the repetitive firing lasts. The higher the background firing intensity, the more likely neurons fire randomly. The magnitude of firing intensity was inversely proportional to the time constant of firing intensity, and non-REM sleep increased the magnitude of firing intensity and decreased the time constant of firing intensity. The background firing intensity was not affected by the sleep/wake state. Our findings suggest that the cortex is organized such that neurons with a higher probability of repetitive firing have shorter repetitive firing periods. In addition, our results suggest that repetitive firing is ordered to become high frequency and short term during non-REM sleep, while unregulated components of firing are independent of the sleep/wake state in the cortex. Hawkes process modeling of firing will reveal novel properties of the brain.

neuroscience↗