Biology subjects

Kondo, Y.

Publications and source records attributed to Kondo, Y..

2 recordsLinked to original sources

bioRxiv2019

Origination of the circadian clock system in stem cells regulates cell differentiation in Arabidopsis thaliana.

The circadian clock regulates various physiological responses. To achieve this, both animals and plants have distinct circadian clocks in each tissue that are optimized for that tissues respective functions. However, if and how the tissue-specific circadian clocks are involved in specification of cell types remains unclear. Here, by implementing a single-cell transcriptome with a new analytics pipeline, we have reconstructed an actual time-series of the cell differentiation process at single-cell resolution, and discovered that the Arabidopsis circadian clock is involved in the process of cell differentiation through transcription factor BRI1-EMS SUPPRESSOR 1 (BES1) signaling. In this pathway, direct repression of LATE ELONGATED HYPOCOTYL (LHY) expression by BES1 triggers reconstruction of the circadian clock in stem cells. The reconstructed circadian clock regulates cell differentiation through fine-tuning of key factors for epigenetic modification, cell-fate determination, and the cell cycle. Thus, the establishment of circadian systems precedes cell differentiation and specifies cell types.

plant biology↗

bioRxiv2019

Automated acquisition of knowledge beyond pathologists

Deep learning algorithms have been successfully used in medical image classification and cancer detection. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Herein, fully automated acquisition of explainable features from annotation-free histopathological images is achieved via revealing statistical distortions in datasets by introducing the way of pathologists examination into a set of deep neural networks. As validation, we compared the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by an expert pathologist using established criteria on 13,188 whole-mount pathology images. Our method found not only the findings established by humans but also features that have not been recognized so far, and showed higher accuracy than human in prognostic prediction. This study provides a new field to the deep learning approach as a novel tool for discovering uncharted knowledge, leading to effective treatments and drug discovery.

bioinformatics↗