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Taniguchi, K.

Publications and source records attributed to Taniguchi, K..

2 recordsLinked to original sources

A chromosome-level genome sequence of a model chrysanthemum: evolution and 1 reference for hexaploid cultivated chrysanthemum

Chrysanthemums are one of the most industrially important cut flowers worldwide. However, their segmental allopolyploidy and self-incompatibility have prevented the application of genetic analysis and modern breeding strategies. We thus developed a model strain, Gojo-0 (Chrysanthemum seticuspe), which is a diploid and self-compatible pure line. Here, we present the 3.05 Gb chromosome-level reference genome sequence, which covered 97% of the C. seticuspe genome. The genome contained more than 80% interspread repeats, of which retrotransposons accounted for 72%. We identified recent segmental duplication and retrotransposon expansion in C. seticuspe, contributing to a relatively large genome size. Furthermore, we identified aretrotransposon, SbdRT, which was enriched in gene-dense genome regions and had experienced a very recent transposition burst. We also demonstrated that the chromosome-level genome sequence facilitates positional cloning in C. seticuspe. The genome sequence obtained here can greatly contribute as a reference for chrysanthemum in front-line breeding including genome editing.

plant biology↗

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↗