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Abstract We investigate the chemical abundance distributions of the Fornax, Sculptor, Ursa Minor, and Draco dwarf galaxies using Subaru/Hyper Suprime-Cam (HSC) photometric data. The HSC data set, which includes broadbandgandifilters and the narrowband NB515 filter, offers sensitivity to iron and magnesium abundances, as well as surface gravity, enabling the identification of giant stars and foreground dwarfs. For analysis, we selected a total of 6713 giant candidates using a random forest regressor trained on medium-resolution (R∼ 6000) Keck/Deep Imaging Multi-Object Spectrograph spectroscopic data. Our analysis reveals the extent of radial metallicity gradients in the galaxies. Such trends, not detectable in earlier studies, are now captured owing to the substantially enlarged sample size and areal coverage provided by the HSC data. These results are also consistent with chemical abundance patterns previously observed in the central regions through spectroscopic studies. Furthermore, we infer that Fornax underwent extended star formation, whereas Sculptor formed both metal-poor and metal-rich stars over a shorter time. Ursa Minor and Draco appear to have experienced brief, intense star formation episodes leading to nearly extinguished star formation. This study underscores the critical role of the expanded HSC data set in revealing chemical gradients that were previously inaccessible. Future work incorporating additional spectra of metal-poor stars and age-sensitive isochrone modeling will enable more accurate maps of chemical abundance distributions. 

Background:The limited diagnostic accuracy of prostate-specific antigen screening for prostate cancer (PCa) has prompted innovative solutions, such as the state-of-the-art 18-gene urine test for clinically-significant PCa (MyProstateScore2.0 (MPS2)).Objective:We aim to develop a non-invasive biomarker test, the simplified MPS2 (sMPS2), which achieves similar state-of-the-art accuracy as MPS2 for predicting high-grade PCa but requires substantially fewer genes than the 18-gene MPS2 to improve its accessibility for routine clinical care.Methods:We grounded the development of sMPS2 in the Predictability, Computability, and Stability (PCS) framework for veridical data science. Under this framework, we stress-tested the development of sMPS2 across various data preprocessing and modeling choices and developed a stability-driven PCS ranking procedure for selecting the most predictive and robust genes for use in sMPS2.Results:The final sMPS2 model consisted of 7 genes and achieved a 0.784 AUROC (95% confidence interval, 0.742–0.825) for predicting high-grade PCa on a blinded external validation cohort. This is only 2.3% lower than the 18-gene MPS2, which is similar in magnitude to the 1–2% in uncertainty induced by different data preprocessing choices.Conclusions:The 7-gene sMPS2 provides a unique opportunity to expand the reach and adoption of non-invasive PCa screening.