Millet seed oil activates ß-catenin signaling and promotes hair growth.

Alopecia, regardless of gender, exacerbates psychological stress in those affected. The rising prevalence of alopecia has fueled a research interest in preventing hair loss. This study investigates the potential of millet seed oil (MSO) in promoting the proliferation of hair follicle dermal papilla cells (HFDPC) and stimulating hair growth in animals with testosterone-dependent hair growth inhibition as part of a study on dietary treatments to improve hair growth. MSO-treated HFDPC significantly increased cell proliferation and phosphorylation of AKT, S6K1, and GSK3ß proteins. This induces ß-catenin, a downstream transcription factor, to translocate to the nucleus and increase the expression of factors related to cell growth. In a C57BL/6 mice model in which hair growth was inhibited by subcutaneous testosterone injection after shaving the dorsal skin, oral administration of MSO stimulated hair growth in the subject mice by increasing the size and number of hair follicles. These results suggest that MSO is a potent agent that may help prevent or treat androgenetic alopecia by promoting hair growth.

An image selection framework for automatic report generation.

The development of IoT technologies and social network services (SNS) are contributing to the growth of big data. However, the vast amount of data makes it difficult for users to find the information they need, and as a result, the demand for a system that provides the desired information in a well-organized form is increasing. Many studies are being conducted to extract desired information from data, and application studies such as automatic report generation are also being conducted. To generate a report for a given topic, a report generation system is required to extract essential information from big data and re-organize it in a compact form. Image selection system also plays an important role in automatic report generation as insertion of appropriate images can increase the completeness and readability of the report. In this study, we propose an image selection framework for recommending an appropriate image for a part of a report by combining textual information used in text-based image retrieval and visual features used in content-based image retrieval. In addition, the proposed image selection framework adopts an image filtering module that is specially designed for filtering out some images that are not suitable for use in reports. Through experiments on two datasets and comparative experiment with state-of-the-art work, we confirmed that our proposed method recommends images that fit the user's intention, and its practical applicability.