Yoshihara, S. | bioRxiv Science

bioRxiv2023

Functional and long-lived melanocytes from human pluripotent stem cells with transient ectopic expression of JMJD3

BackgroundMelanocytes are an essential part of the epidermis, and their regeneration has received much attention because propagation of human adult melanocytes in vitro is too slow for clinical use. Differentiation from human pluripotent stem cells to melanocytes has been reported, but the protocols to produce them require multiple and complex differentiation steps. MethodWe differentiated human embryonic stem cells (hESCs) that transiently express JMJD3 to pigmented cells. We investigated whether the pigmented cells have melanocytic characteristics and functions by qRT-PCR, immunocytochemical analysis and flow cytometry. We also investigated their biocompatibility by injecting the cells into immunodeficient mice for clinical use. ResultWe successfully differentiated and established a pure culture of melanocytes. The melanocytes maintained their growth rate for a long time, approximately 200 days, and were functional. They exhibited melanogenesis and transfer of melanin to peripheral keratinocytes. Moreover, melanocytes simulated the developmental processes from melanoblasts to melanocytes. The melanocytes had high engraftability and biocompatibility in the immunodeficient mice. ConclusionThe robust generation of functional and long-lived melanocytes are key to developing clinical applications for the treatment of pigmentary skin disorders.

cell biology↗

bioRxiv2022

Do training with blurred images make convolutional neural networks closer to humans concerning object recognition performance and internal representations?

AO_SCPLOWBSTRACTC_SCPLOWIt is suggested that experiences of perceiving blurry images in addition to sharp images contribute to the development of robust human visual processing. To computationally investigate the effect of exposure to blurry images, we trained Convolutional Neural Networks (CNNs) on ImageNet object recognition with a variety of combinations of sharp and blurry images. In agreement with related studies, mixed training on sharp and blurred images (B+S) makes the CNNs close to humans with respect to robust object recognition against a change in image blur. B+S training also reduces the texture bias of CNN in recognition of shape-texture-cue-conflict images, but the effect is not strong enough to achieve a strong shape bias comparable to what humans show. Other tests also suggest that B+S training is not sufficient to produce robust human-like object recognition based on global con-figurational features. We also show using representational similarity analysis and zero-shot transfer learning that B+S-Net does not acquire blur-robust object recognition through separate specialized sub-networks, each for sharp and blurry images, but through a single network analyzing common image features. However, blur training alone does not automatically create a mechanism like the human brain where subband information is integrated into a common representation. Our analyses suggest that experience with blurred images helps the human brain develop neural networks that robustly recognize the surrounding world, but it is not powerful enough to fill a large gap between humans and CNNs.

neuroscience↗