A Rv map of the Milky Way revealed by LAMOST

The total-to-selective extinction ratio, Rv, is a key parameter for tracing the properties of interstellar dust, as it directly determines the variation of the extinction curve with wavelength. By utilizing accurate color excess measurements from the optical to the mid-infrared range, we have derived Rv values for approximately 3 million stars from the LAMOST data release 7 (DR7) using a forward modeling technique. This extensive dataset enables us to construct a comprehensive two-dimensional Rv map of the Milky Way within the LAMOST footprint at a spatial resolution of ~27.5arcmin. Based on reliable sightlines of E(B-V) > 0.1, we find that Rv exhibits a Gaussian distribution centered around 3.25 with a standard deviation of 0.25. The spatial variability of Rv in the Galactic disk exhibits a wide range, spanning from small scales within individual molecular clouds to large scales up to kiloparsecs. A striking correlation is observed between the distribution of Rv and molecular clouds. Notably, we observe lower Rv values within the regions of nearby molecular clouds compared to their surrounding areas. Furthermore, we have investigated the relationships between Rv and various parameters, including dust temperature, dust emissivity spectral index, column density of atomic and molecular hydrogen, as well as their ratios and the gas-to-dust ratio. We find that these relationships vary with the level of extinction. These analyses provide new insights into the properties and evolution of dust grains in diverse interstellar environments and also hold significant importance for achieving accurate extinction corrections.Comment: 16 pages, 12 figures. Accepted for publication in the Astrophysical Journal Supplement Serie

Learning Invariant Visual Representations for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen pairs. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize the attributes attached to any object reliably. Similarly, attribute-invariant features can also be learned when recognizing the objects with attributes as domains. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. Experiments on two CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art

Stellar loci IV. Red giant stars

In the fourth paper of this series, we present the metallicity-dependent Sloan Digital Sky Survey (SDSS) stellar color loci of red giant stars, using a spectroscopic sample of red giants in the SDSS Stripe 82 region. The stars span a range of 0.55 -- 1.2 mag in color g-i, -0.3 -- -2.5 in metallicity [Fe/H], and have values of surface gravity log g smaller than 3.5 dex. As in the case of main-sequence (MS) stars, the intrinsic widths of loci of red giants are also found to be quite narrow, a few mmag at maximum. There are however systematic differences between the metallicity-dependent stellar loci of red giants and MS stars. The colors of red giants are less sensitive to metallicity than those of MS stars. With good photometry, photometric metallicities of red giants can be reliably determined by fitting the u-g, g-r, r-i, and i-z colors simultaneously to an accuracy of 0.2 -- 0.25 dex, comparable to the precision achievable with low-resolution spectroscopy for a signal-to-noise ratio of 10. By comparing fitting results to the stellar loci of red giants and MS stars, we propose a new technique to discriminate between red giants and MS stars based on the SDSS photometry. The technique achieves completeness of ~ 70 per cent and efficiency of ~ 80 per cent in selecting metal-poor red giant stars of [Fe/H] $\le$ -1.2. It thus provides an important tool to probe the structure and assemblage history of the Galactic halo using red giant stars.Comment: 18 pages, 13 figures. Accepted for publication in Research in Astronomy and Astrophysic

Chronic jet lag alters gut microbiome and mycobiome and promotes the progression of MAFLD in HFHFD-fed mice

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide. Circadian disruptors, such as chronic jet lag (CJ), may be new risk factors for MAFLD development. However, the roles of CJ on MAFLD are insufficiently understood, with mechanisms remaining elusive. Studies suggest a link between gut microbiome dysbiosis and MAFLD, but most of the studies are mainly focused on gut bacteria, ignoring other components of gut microbes, such as gut fungi (mycobiome), and few studies have addressed the rhythm of the gut fungi. This study explored the effects of CJ on MAFLD and its related microbiotic and mycobiotic mechanisms in mice fed a high fat and high fructose diet (HFHFD). Forty-eight C57BL6J male mice were divided into four groups: mice on a normal diet exposed to a normal circadian cycle (ND-NC), mice on a normal diet subjected to CJ (ND-CJ), mice on a HFHFD exposed to a normal circadian cycle (HFHFD-NC), and mice on a HFHFD subjected to CJ (HFHFD-CJ). After 16 weeks, the composition and rhythm of microbiota and mycobiome in colon contents were compared among groups. The results showed that CJ exacerbated hepatic steatohepatitis in the HFHFD-fed mice. Compared with HFHFD-NC mice, HFHFD-CJ mice had increases in Aspergillus, Blumeria and lower abundances of Akkermansia, Lactococcus, Prevotella, Clostridium, Bifidobacterium, Wickerhamomyces, and Saccharomycopsis genera. The fungi-bacterial interaction network became more complex after HFHFD and/or CJ interventions. The study revealed that CJ altered the composition and structure of the gut bacteria and fungi, disrupted the rhythmic oscillation of the gut microbiota and mycobiome, affected interactions among the gut microbiome, and promoted the progression of MAFLD in HFHFD mice

Network analysis of cold cognition and depression in middle-aged and elder population: the moderation of grandparenting

BackgroundCognitive decline and negative emotions are common in aging, especially decline in cold cognition which often co-occurred with depression in middle-aged and older adults. This study analyzed the interactions between cold cognition and depression in the middle-aged and elder populations using network analysis and explored the effects of grandparenting on the cold cognition-depression network.MethodsThe data of 6,900 individuals (≥ 45 years) from the China Health and Retirement Longitudinal Study (CHARLS) were used. The Minimum Mental State Examination (MMSE) and the Epidemiology Research Center Depression Scale-10 (CESD-10) were used to assess cold cognition and depressive symptoms, respectively. Centrality indices and bridge centrality indices were used to identify central nodes and bridge nodes, respectively.ResultsNetwork analysis showed that nodes “language ability” and “depressed mood” were more central nodes in the network of cold cognition and depression in all participants. Meantime, nodes “attention,” “language ability” and “hopeless” were three key bridge nodes connecting cold cognition and depressive symptoms. Additionally, the global connectivity of the cold cognition and depression network was stronger in the non-grandparenting than the grandparenting.ConclusionThe findings shed a light on the complex interactions between cold cognition and depression in the middle-aged and elder populations. Decline in language ability and depressed mood can serve as predictors for the emergence of cold cognitive dysfunction and depression in individuals during aging. Attention, language ability and hopelessness are potential targets for psychosocial interventions. Furthermore, grandparenting is effective in alleviating cold cognitive dysfunction and depression that occur during individual aging

Photometric calibration of the Stellar Abundance and Galactic Evolution Survey (SAGES): Nanshan One-meter Wide-field Telescope g, r, and i band imaging data

In this paper, a total of approximately 2.6 million dwarfs were constructed as standard stars, with an accuracy of about 0.01-0.02 mag for each band, by combining spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 7, photometric data from the corrected Gaia Early Data Release 3, and photometric metallicities. Using the spectroscopy based stellar color regression method (SCR method) and the photometric-based SCR method (SCR' method), we performed the relative calibration of the Nanshan One-meter Wide-field Telescope imaging data. Based on the corrected Pan-STARRS DR1 photometry, the absolute calibration was also performed. In the photometric calibration process, we analyzed the dependence of the calibration zero points on different images (observation time), different gates of the CCD detector, and different CCD positions. We found that the stellar flat and the relative gain between different gates depend on time. The amplitude of gain variation in three channels is approximately 0.5%-0.7% relative to the other channel, with a maximum value of 4%. In addition, significant spatial variations of the stellar flat fitting residual are found and corrected. Using repeated sources in the adjacent images, we checked and discovered internal consistency of about 1-2 mmag in all the filters. Using the PS1 magnitudes synthesized by Gaia DR3 BP/RP spectra by the synthetic photometry method, we found that the photometric calibration uniformity is about 1-2 mmag for all the bands, at a spatial resolution of 1.3 degree. A detailed comparison between the spectroscopy-based SCR and photometric-based SCR method magnitude offsets was performed, and we achieved an internal consistency precision of about 2 mmag or better with resolutions of 1.3 degree for all the filters. Which is mainly from the position-dependent errors of the E(B-V) used in SCR' method.Comment: 15 pages in Chinese language, 8 figures, Chinese Science Bulletin accepted and published online (https://www.sciengine.com/CSB/doi/10.1360/TB-2023-0052), see main results in Figures 6, 7 and

Load transference with the gain of excessive body mass: A two-year longitudinal study

Previous studies investigating the effect of excessive weight on the foot have commonly been cross-sectional; therefore, it is still unclear how the foot function gradually changes with the increased body mass that is physiologically gained over time. This study aimed to use a load transfer method to identify the mechanism of how the foot function changed with the increased excessive body mass over two years. Taking normal weight as the baseline, fifteen children became overweight or obese (group 1), and fifteen counterparts maintained normal weight (group 0) over the two years. Barefoot walking was assessed using a Footscan® plate system. A load transfer method was used based upon the relative force–time integral (FTI) to provide an insight into plantar load transference as children increased in weight. Significantly increased FTIs were found at the big toe (BT), medial metatarsal (MM), lateral metatarsal (LM), and lateral heel (HL) in group 1, while at BT, MM, medial heel (HM), and HL in group 0. Foot load showed a posterior to anterior transferal from midfoot (2.5%) and heel (7.0%) to metatarsal and big toe in group 1. The control group, however, shifted the loading within the metatarsal level from LM to HM (4.1%), and equally relieved weight from around the midfoot (MF) (3.0%) to BT, MM, HM and HL. Earlier weight loss intervention is required to prevent further adverse effects on foot functions caused by excessive weight-bearing. © 2021 by the authors. Licensee MDPI, Basel, Switzerland