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Abstract Based on a template-matching method, we estimate the barium (Ba) abundances for stellar spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Medium-Resolution Spectroscopic Survey (MRS). The Ba abundances of 198,011 stars have been determined from MRS spectra with signal-to-noise ratios (S/N) > 40 combined with the stellar atmospheric parameters from the LAMOST Low-Resolution Spectroscopic Survey DR9 by the LAMOST Stellar Parameter Pipeline. The uncertainties in the Ba abundances from the LAMOST MRS spectra are less than 0.3 dex when S/N exceeds 40, which align closely with the results based on the high-resolution UVES spectra from the Gaia-ESO survey obtained by spectral synthesis. Further analysis of Ba abundances from repeated observations of the same stars reveals that random errors related to spectral quality remain below 0.3 dex at the same S/N, with a systematic overestimation for the low-S/N spectra. This extensive sample of stellar Ba abundances will enhance studies of thes-,i-, andr-processes, and deepen our understanding of the chemical-evolution history of the Milky Way. 

Living tissues display fluctuations—random spatial and temporal variations of tissue properties around their reference values—at multiple scales. It is believed that such fluctuations may enable tissues to sense their state or their size. Recent theoretical studies developed specific models of fluctuations in growing tissues and predicted that fluctuations of growth show long-range correlations. Here, we elaborated upon these predictions and we tested them using experimental data. We first introduced a minimal model for the fluctuations of any quantity that has some level of temporal persistence or memory, such as concentration of a molecule, local growth rate, or mechanical property. We found that long-range correlations are generic, applying to any such quantity, and that growth couples temporal and spatial fluctuations, through a mechanism that we call “fluctuation stretching”—growth enlarges the length scale of variation of this quantity. We then analyzed growth data from sepals of the model plant Arabidopsis and we quantified spatial and temporal fluctuations of cell growth using the previously developed cellular Fourier transform. Growth appears to have long-range correlations. We compared different genotypes and growth conditions: mutants with lower or higher response to mechanical stress have lower temporal correlations and longer-range spatial correlations than wild-type plants. Finally, we used theoretical predictions to merge experimental data from all conditions and developmental stages into a unifying curve, validating the notion that temporal and spatial fluctuations are coupled by growth. Altogether, our work reveals kinematic constraints on spatiotemporal fluctuations that have an impact on the robustness of morphogenesis. 

Abstract Arabidopsis leaf epidermal cells have a wide range of sizes and ploidies, but how large cells are spatially patterned alongside smaller cells remains unclear. Here, we demonstrate that the same genetic pathway that creates giant cells in sepals is also responsible for their formation in the leaf epidermis. In both sepals and leaves, giant cells are scattered among smaller cells; therefore, we asked whether the spatial arrangement of giant cells is random. By comparing sepal and leaf epidermises with computationally generated randomized tissues we show that giant cells are clustered more than is expected by chance. Our cell-autonomous and stochastic computational model recapitulates the observed giant cell clustering, indicating that clustering emerges as a result of the cell division pattern. Overall, cell size patterning is developmentally regulated by common mechanisms in leaves and sepals rather than a simple byproduct of cell growth. TeaserThe spatial pattern of giant cells becomes non-random as the surrounding cells divide. 

Abstract R-process-enhanced (RPE) stars are rare and typically metal-poor ([Fe/H]  < −1.0), primarily found in the Milky Way halo system and dwarf galaxies. This study reports the discovery of two relatively bright, highly RPE stars ([Eu/Fe]  >  +0.70) located in the Milky Way disk, with [Fe/H] of −0.34 and −0.80, respectively. These two stars are selected from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope medium-resolution (R ∼ 7500) spectroscopic survey. Follow-up high-resolution (R ∼ 25,000) observations were conducted with the High Optical Resolution Spectrograph installed on the 10.4 m Gran Telescopio Canarias. We perform the determination of elemental abundances and calculate the orbital parameters. We find that they arer-II stars with elemental abundances in agreement with the solarr-process pattern. These two objects are chemically and dynamically consistent with membership in the Galactic disk and exhibit no evidence of being part of accreted systems. 

Abstract Highlyr-process-enhanced (RPE) stars are rare and usually metal poor ([Fe/H] < −1.0), and they mainly populate the Milky Way halo and dwarf galaxies. This study presents the discovery of a relatively bright (V= 12.72), highly RPE (r-II) star ([Eu/Fe] = +1.32, [Ba/Eu] = −0.95), LAMOST J020623.21+494127.9. This star was selected from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope medium-resolution (R∼ 7500) spectroscopic survey; follow-up high-resolution (R∼ 25,000) observations were conducted with the High Optical Resolution Spectrograph installed on the Gran Telescopio Canarias. The stellar parameters (T_eff= 4130 K, $\log \mathrm{g}$= 1.52, [Fe/H] = −0.54,ξ= 1.80 km s⁻¹) have been inferred taking into account nonlocal thermodynamic equilibrium effects. The abundances of [Ce/Fe], [Pr/Fe], and [Nd/Fe] are +0.19, +0.65, and +0.64, respectively, relatively low compared to the Solarr-process pattern normalized to Eu. This star has a high metallicity ([Fe/H] = −0.54) compared to most other highly RPE stars and has the highest measured abundance ratio of Eu to H ([Eu/H] = +0.78). It is classified as a thin-disk star based on its kinematics and does not appear to belong to any known stream or dwarf galaxy. 

Abstract Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], logg, andT_eff, based on a combination of medium-band and broadband photometric observations. Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. We combine theuv-band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for logg, and 70 K forT_eff. Furthermore, by incorporating Two Micron All Sky Survey and Wide-field Infrared Survey Explorer infrared photometric and Galaxy Evolution Explorer ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational data set for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGES such DDO51 and Hα, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets. 

Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this paper, we focus on learning representations for objects and scenes that preserve the structure among them. Motivated by the observation that visually similar objects are close in the representation space, we argue that the scenes and objects should instead follow a hierarchical structure based on their compositionality. To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is used to learn object representations and a hyperbolic loss is used to encourage representations of scenes to lie close to representations of their constituent objects in a hyperbolic space. This novel hyperbolic objective encourages the scene-object hypernymy among the representations by optimizing the magnitude of their norms. We show that when pretraining on the COCO and OpenImages datasets, the hyperbolic loss improves downstream performance of several baselines across multiple datasets and tasks, including image classification, object detection, and semantic segmentation. We also show that the properties of the learned representations allow us to solve various vision tasks that involve the interaction between scenes and objects in a zero-shot fashion. 

Although DNAN⁶-adenine methylation (6mA) is best known in prokaryotes, its presence in eukaryotes has recently generated great interest. Biochemical and genetic evidence supports that AMT1, an MT-A70 family methyltransferase (MTase), is crucial for 6mA deposition in unicellular eukaryotes. Nonetheless, the 6mA transmission mechanism remains to be elucidated. Taking advantage of single-molecule real-time circular consensus sequencing (SMRT CCS), here we provide definitive evidence for semiconservative transmission of 6mA inTetrahymena thermophila. In wild-type (WT) cells, 6mA occurs at the self-complementary ApT dinucleotide, mostly in full methylation (full-6mApT); after DNA replication, hemi-methylation (hemi-6mApT) is transiently present on the parental strand, opposite to the daughter strand readily labeled by 5-bromo-2′-deoxyuridine (BrdU). In ΔAMT1cells, 6mA predominantly occurs as hemi-6mApT. Hemi-to-full conversion in WT cells is fast, robust, and processive, whereas de novo methylation in ΔAMT1cells is slow and sporadic. InTetrahymena, regularly spaced 6mA clusters coincide with the linker DNA of nucleosomes arrayed in the gene body. Importantly, in vitro methylation of human chromatin by the reconstituted AMT1 complex recapitulates preferential targeting of hemi-6mApT sites in linker DNA, supporting AMT1's intrinsic and autonomous role in maintenance methylation. We conclude that 6mA is transmitted by a semiconservative mechanism: full-6mApT is split by DNA replication into hemi-6mApT, which is restored to full-6mApT by AMT1-dependent maintenance methylation. Our study dissects AMT1-dependent maintenance methylation and AMT1-independent de novo methylation, reveals a 6mA transmission pathway with a striking similarity to 5-methylcytosine (5mC) transmission at the CpG dinucleotide, and establishes 6mA as a bona fide eukaryotic epigenetic mark.