Biology subjects

Aoto, S.

Publications and source records attributed to Aoto, S..

2 recordsLinked to original sources

bioRxiv2025

High-Coverage Jomon Genomes Provide Insights into Population Structure and Genetic Traits of Ancient Japanese Hunter-Gatherers

We analyzed eight high-coverage Jomon genomes spanning 9,000-2,800 years ago across the Japanese archipelago. Population genomic analyses revealed substantial genetic homogeneity across time and space reflecting post-glacial isolation, yet temporal stratification emerged: Initial Jomon (Iyai) individuals formed the earliest branch with weaker affinities to present-day Japanese. Using genomes from Todoroki and Chidorikubo sites, we estimated Jomon ancestry in mainland Japanese at [~]20%, substantially higher than previous estimates ([~]13%). Polygenic score analyses, based on the direct analysis of Jomon genomes, provided the genetic evidence for their phenotypic predispositions, including elevated BMI and metabolic trait scores, reduced immune-related scores, and larger cardiac dimensions, patterns consistent with hunter-gatherer adaptations. These ancestral genetic influences, including elevated risks for obesity and dyslipidemia, persist in present-day Japanese. Our findings demonstrate that Jomon populations maintained genetic continuity despite ecological diversity, while their greater-than-expected contribution to present-day Japanese reshapes population history models and reveals how ancient adaptations continue influencing contemporary disease susceptibility.

genomics↗

bioRxiv2020

Collection of 2429 constrained headshots of 277 volunteers for deep learning

Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired numerous studies in this area. Hardware and software technologies as well as large quantities of data have contributed to the drastic development of the field. However, the application of deep learning is often hindered by the need for big data and the laborious manual annotation thereof. To experience deep learning using the data compiled by us, we collected 2429 constrained headshot images of 277 volunteers. The collection of face photographs is challenging in terms of protecting personal information; we established an online procedure in which both the informed consent and image data could be obtained. We did not collect personal information, but issued agreement numbers to deal with withdrawal requests. Gender and smile labels were manually and subjectively annotated only from the appearances, and final labels were determined by majority among our team members. Rotated, trimmed, resolution-reduced, decolorized, and matrix-formed data were allowed to be publicly released. Moreover, simplified feature vectors for data sciences were released. We performed gender recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset.

animal behavior and cognition↗