Discovery of the shell structure via break radii in the outer halo of the Milky Way

Based on the \textit{Gaia} DR3 RR Lyrae catalog, we use two methods to fit the density profiles with an improved broken power law, and find that there are two break radii coinciding with the two apocenter pile-ups of high-eccentricity Gaia-Sausage-Enceladus (GSE) merger. Also, there is a break caused by the Sagittarius (Sgr) stream. Combining the positions of all breaks, we briefly analyze the metallicity and its dispersion as a function of $r$ as well as its distribution in cylindrical coordinates. For the clean sample, the $z\text{-to-}x$ ellipsoid axial ratio $q$ in 36\,{\rm kpc}\,\textless\,r\,\textless\,96\,{\rm kpc} becomes much smaller than that of the inner halo (r\,\textless\,36\,{\rm kpc}), while the major axis has a large uncertainty in the region of $36-66\,{\rm kpc}$ and the one in the region of $66-96\,{\rm kpc}$ is obviously different from that dominated by the Hercules-Aquila Cloud (HAC) and the Virgo Overdensity (VOD) in the inner halo, which indicates that there is an over-density structure distributed at low zenithal angles. Finally, we found that the over-density structure in the outer halo (r\,\textgreater\,50\,{\rm kpc}) is shell-shaped and relatively metal-rich compared to the outer background halo. We conclude that the shells could be the apocenter pile-ups of the high-eccentricity GSE merger, which is supported by previous numerical simulations.Comment: 16 pages, 14 Figures, accepted for publication in MNRA

Photometric Metallicity Calibration with SDSS and SCUSS and its Application to distant stars in the South Galactic Cap

Based on SDSS g, r and SCUSS (South Galactic Cap of u-band Sky Survey) $u$ photometry, we develop a photometric calibration for estimating the stellar metallicity from $u-g$ and $g-r$ colors by using the SDSS spectra of 32,542 F- and G-type main sequence stars, which cover almost $3700$ deg$^{2}$ in the south Galactic cap. The rms scatter of the photometric metallicity residuals relative to spectrum-based metallicity is $0.14$ dex when $g-r<0.4$, and $0.16$ dex when $g-r>0.4$. Due to the deeper and more accurate magnitude of SCUSS $u$ band, the estimate can be used up to the faint magnitude of $g=21$. This application range of photometric metallicity calibration is wide enough so that it can be used to study metallicity distribution of distant stars. In this study, we select the Sagittarius (Sgr) stream and its neighboring field halo stars in south Galactic cap to study their metallicity distribution. We find that the Sgr stream at the cylindrical Galactocentric coordinate of $R\sim 19$ kpc, $\left| z\right| \sim 14$ kpc exhibits a relative rich metallicity distribution, and the neighboring field halo stars in our studied fields can be modeled by two-Gaussian model, with peaks respectively at [Fe/H]$=-1.9$ and [Fe/H]$=-1.5$.Comment: 8 pages, 7 figures, Accepted for publication in MNRA

DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era

IntroductionThe DNA N4-methylcytosine (4mC) site levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting accurate features from DNA sequences is the key to establishing a prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites.MethodsThe model adopted multi-scale channel attention to extract features and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby, distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF, and DRSN-CW.Results and DiscussionThe results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species. This paper will potentially provide support for the diagnosis and treatment of gastrointestinal cancer based on artificial intelligence in the precise medical era