A budding yeast CAGE dataset comprising two cell types.

The budding yeast Saccharomyces cerevisiae is an excellent model organism for studying chromatin regulation with high-resolution genome-wide analyses. Since newly generated genome-wide data are often compared with publicly available datasets, expanding our dataset repertoire will be beneficial for the field. Information on transcription start sites (TSSs) determined at base pair resolution is essential for elucidating mechanisms of transcription and related chromatin regulation, yet no datasets that cover two different cell types are available. Here, we present a CAGE (cap analysis of gene expression) dataset for a-cells and α-cells grown in defined and rich media. Cell type-specific genes were differentially expressed as expected, ensuring the reliability of the data. Some of the differentially expressed TSSs were medium-specific or detected due to unrecognized chromosome rearrangement. By comparing the CAGE data with a high-resolution nucleosome map, major TSSs were primarily found in +1 nucleosomes, with a peak approximately 30 bp from the promoter-proximal end of the nucleosome. The dataset is available at DDBJ/GEA.

Gender Inequality Improvement in Medical School Admissions in Japan.

Purpose: To investigate the effects of gender discrimination in Japan's medical school admission process and to assess whether the situation has improved since the disclosure of such discrimination in 2018. Materials and Methods: A cross-sectional study was conducted using secondary data from the Ministry of Education, Culture, Sports, Science, and Technology. The proportions of male and female applicants vis-à-vis all successful candidates admitted from 2016 to 2021 were analyzed; four medical schools were found to be systematically guilty of discriminatory admission practices. Acceptance rate ratios (ARRs) were estimated, and difference-in-differences (DID) analysis was used to examine the differences in ARRs between the two groups-the 4 and 75 medical schools that were and were not reported, respectively-in the predisclosure (2016-2018) and postdisclosure (2019-2021) periods. Results: Female applicants were subjected to discriminatory admission practices at the four reported medical schools in the predisclosure period. However, postdisclosure, those four medical schools had higher female than male acceptance rates in all 3 years. DID analysis revealed a statistically significant estimated average treatment effect on the treated of 0.25148 (95% confidence interval [0.00455-0.49840]), indicating a 0.25-point increase in ARRs relative to the other 75 medical schools. Conclusions: Discriminatory practices against female applicants have decreased since the disclosure in 2018, with the acceptance rate of female students exceeding that of male students for the first time in 2021. In response to these findings, we propose recommendations to further promote gender equality in medicine.