Biology subjects

Sugiura, T.

Publications and source records attributed to Sugiura, T..

2 recordsLinked to original sources

bioRxiv2025

Cultivated Beef Meat Has the Potential to Maintain Original Characteristics of Beef Meat with Customizable Features

Cultivated meat (CM) is currently attracting much attention because of its promise to become a solution to issues of environmental sustainability, animal welfare, and decarbonization. While the fabrication process and materials are still the main focus, it is not yet clear what the characteristics of the constructed CMs are and how they take on the original characteristics of beef meat. In this study, the biological, physicochemical and sensory characteristics of three types of beef (Wagyu, Crossbreed and Holstein) meat were systematically analyzed and compared to muscle and fat fibers constructed by three-dimensional printing using satellite and adipose-derived stem cells isolated from these beef meats. The different characteristics of each beef meat were largely taken on by the CM fibers composed of bSC and bADSC of each meat. In addition, some differing properties from those of the respective beef meat were observed in CM fibers such as one of the omega-3 fatty acids, docosahexaenoic acid (DHA; Wagyu fat fibers showed the highest amount). Furthermore, we also found the important possibility to increase the composition of oleic acid to over 80% in monounsaturated fatty acid (Wagyu has around 50% oleic acid). This study revealed the importance of using cells isolated from each beef meat to provide CM that closely resemble the original texture and taste of each meat, and further the possibility of more carefully arranging their properties.

bioengineering↗

bioRxiv2024

Task-guided Generative Adversarial Networks for Synthesizing and Augmenting Structural Connectivity Matrices for Connectivity-Based Prediction

Recent machine learning techniques have improved connectome-based predictions by modelling complex dependencies between brain connectivity and cognitive traits. However, they typically require large datasets that are costly and time-consuming to collect. To address this, we propose Task-guided GAN II, a novel data augmentation method that uses generative adversarial networks (GANs) to expand sample sizes in connectome-based prediction tasks. Our method incorporates a task-guided branch within the Wasserstein GAN framework, specifically designed to synthesize structural connectivity matrices and improve prediction accuracy by capturing task-relevant features. We evaluated Task-guided GAN II on the prediction of fluid intelligence using the NIMH Health Research Volunteer Dataset. Results showed that data augmentation improved prediction accuracy. To further assess whether augmentation can substitute for increasing real-world sample sizes, we conducted additional validation using the HCP WU-Minn S1200 dataset. Task-guided GAN II improved prediction performance with limited real data, with gains of up to twofold augmentation observed. However, excessive augmentation did not result in further improvements, suggesting that augmentation complements, but does not fully replace, real data augmentation. These results suggest that Task-guided GAN II is a promising tool for harnessing small datasets in human connectomics research, improving predictive modelling where large-scale data collection is impractical.

neuroscience↗