The Pristine survey II: a sample of bright stars observed with FEROS

Extremely metal-poor (EMP) stars are old objects formed in the first Gyr of the Universe. They are rare and, to select them, the most successful strategy has been to build on large and low-resolution spectroscopic surveys. The combination of narrow- and broad band photometry provides a powerful and cheaper alternative to select metal-poor stars. The on-going Pristine Survey is adopting this strategy, conducting photometry with the CFHT MegaCam wide field imager and a narrow-band filter centred at 395.2 nm on the CaII-H and -K lines. In this paper we present the results of the spectroscopic follow-up conducted on a sample of 26 stars at the bright end of the magnitude range of the Survey (g<=15), using FEROS at the MPG/ESO 2.2 m telescope. From our chemical investigation on the sample, we conclude that this magnitude range is too bright to use the SDSS gri bands, which are typically saturated. Instead the Pristine photometry can be usefully combined with the APASS gri photometry to provide reliable metallicity estimates.Comment: AN accepte

Titanium abundances in late-type stars I. 1D non-LTE modelling in benchmark dwarfs and giants

The titanium abundances of late-type stars are important tracers of Galactic formation history. However, abundances inferred from Ti I and Ti II lines can be in stark disagreement in very metal-poor giants. Departures from local thermodynamic equilibrium (LTE) have a large impact on the minority neutral species and thus influences the ionisation imbalance, but satisfactory non-LTE modelling for both dwarfs and giants has not been achieved in previous literature. The reliability of titanium abundances is reassessed in benchmark dwarfs and giants using a new non-LTE model and one-dimensional (1D) model atmospheres. A comprehensive model atom was compiled with a more extended level structure and newly published data for inelastic collisions between Ti I and neutral hydrogen. In 1D LTE, the Ti I and Ti II lines agree to within $0.06$ dex for the Sun, Arcturus, and the very metal-poor stars HD84937 and HD140283. For the very metal-poor giant HD122563, the Ti I lines give an abundance that is $0.47$ dex lower than that from Ti II. The 1D non-LTE corrections can reach $+0.4$ dex for individual Ti I lines and $+0.1$ dex for individual Ti II lines, and reduce the overall ionisation imbalance to $-0.17$ dex for HD122563. However, it also increases the imbalance for the very metal-poor dwarf and sub-giant to around $0.2$ dex. Using 1D non-LTE reduces the ionisation imbalance in very metal-poor giants but breaks the balance of other very metal-poor stars, consistent with the conclusions in earlier literature. To make further progress, consistent 3D non-LTE models are needed.Comment: 9 pages plus appendix, 6 figures; accepted for publication in Astronomy & Astrophysic

An artificial neural network to discover hypervelocity stars: candidates in Gaia DR1/TGAS

The paucity of hypervelocity stars (HVSs) known to date has severely hampered their potential to investigate the stellar population of the Galactic Centre and the Galactic Potential. The first Gaia data release gives an opportunity to increase the current sample. The challenge is the disparity between the expected number of hypervelocity stars and that of bound background stars. We have applied a novel data mining algorithm based on machine learning techniques, an artificial neural network, to the Tycho-Gaia astrometric solution (TGAS) catalogue. With no pre-selection of data, we could exclude immediately $\sim 99 \%$ of the stars in the catalogue and find 80 candidates with more than $90\%$ predicted probability to be HVSs, based only on their position, proper motions, and parallax. We have cross-checked our findings with other spectroscopic surveys, determining radial velocities for 30 and spectroscopic distances for 5 candidates. In addition, follow-up observations have been carried out at the Isaac Newton Telescope for 22 stars, for which we obtained radial velocities and distance estimates. We discover 14 stars with a total velocity in the Galactic rest frame > 400 km/s, and 5 of these have a probability $>50\%$ of being unbound from the Milky Way. Tracing back their orbits in different Galactic potential models we find one possible unbound HVS with velocity $\sim$ 520 km/s, 5 bound HVSs, and, notably, 5 runaway stars with median velocity between 400 and 780 km/s. At the moment, uncertainties in the distance estimates and ages are too large to confirm the nature of our candidates by narrowing down their ejection location, and we wait for future Gaia releases to validate the quality of our sample. This test successfully demonstrates the feasibility of our new data mining routine.Comment: Published in MNRAS, 17 pages, 10 figure

Using the multi-object adaptive optics demonstrator RAVEN to observe metal-poor stars in and towards the Galactic Centre

The chemical abundances for five metal-poor stars in and towards the Galactic bulge have been determined from the H-band infrared spectroscopy taken with the RAVEN multi-object adaptive optics science demonstrator and the Infrared Camera and Spectrograph at the Subaru 8.2-m telescope. Three of these stars are in the Galactic bulge and have metallicities between −2.1 < [Fe/H] < −1.5, and high [α/Fe] ∼ +0.3, typical of Galactic disc and bulge stars in this metallicity range; [Al/Fe] and [N/Fe] are also high, whereas [C/Fe] < +0.3. An examination of their orbits suggests that two of these stars may be confined to the Galactic bulge and one is a halo trespasser, though proper motion values used to calculate orbits are quite uncertain. An additional two stars in the globular cluster M22 show [Fe/H] values consistent to within 1σ, although one of these two stars has [Fe/H] = −2.01 ± 0.09, which is on the low end for this cluster. The [α/Fe] and [Ni/Fe] values differ by 2σ, with the most metal-poor star showing significantly higher values for these elements. M22 is known to show element abundance variations, consistent with a multipopulation scenario though our results cannot discriminate this clearly given our abundance uncertainties. This is the first science demonstration of multi-object adaptive optics with high-resolution infrared spectroscopy, and we also discuss the feasibility of this technique for use in the upcoming era of 30-m class telescope facilities

Hypervelocity star candidates in Gaia DR1/TGAS

Galaxie

Beyond Gaia DR3: tracing the [{\alpha}/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia RVS and Convolutional Neural-Networks

Gaia DR3 has provided the community with about one million RVS spectra covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200M spectra. Thus, stellar spectra are produced on an "industrial scale" with numbers well above those for current and anticipated ground based surveys. However, many of these spectra have low S/N (from 15 to 25 per pixel), such that they pose problems for classical spectral analysis pipelines and therefore alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility/capabilities of machine learning techniques for supercharged stellar parametrization, by combining Gaia RVS spectra with the full set of Gaia products and high-resolution, high-quality spectroscopic reference data sets. We develop a hybrid Convolutional Neural-Network (CNN) which combines the Gaia DR3 RVS spectra, photometry (G, Bp, Rp), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g), and overall [M/H]) and chemical abundances ([Fe/H] and [$\alpha$/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 841300 stars, that remarkably compared to external data-sets. The CNN is robust against noise in the RVS data, and very precise labels are derived down to S/N=15. We managed to characterize the [$\alpha$/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine-learning with such diverse datasets (spectroscopy, astrometry, and photometry), and paves the way for the large scale machine-learning analysis of Gaia-RVS spectra from future data releases.Comment: 24 pages, 24 figures, submitted to A&

Kinematics with Gaia DR2: the force of a dwarf

We use Gaia DR2 astrometric and line-of-sight velocity information combined with two sets of distances obtained with a Bayesian inference method to study the 3D velocity distribution in the Milky Way disc. We search for variations in all Galactocentric cylindrical velocity components (Vϕ, VR, and Vz) with Galactic radius, azimuth, and distance from the disc mid-plane. We confirm recent work showing that bulk vertical motions in the R–z plane are consistent with a combination of breathing and bending modes. In the x–y plane, we show that, although the amplitudes change, the structure produced by these modes is mostly invariant as a function of distance from the plane. Comparing to two different Galactic disc models, we demonstrate that the observed patterns can drastically change in short time intervals, showing the complexity of understanding the origin of vertical perturbations. A strong radial VR gradient was identified in the inner disc, transitioning smoothly from 16 km s−1 kpc−1 at an azimuth of 30° < ϕ < 45° ahead of the Sun-Galactic centre line to −16 km s−1 kpc−1 at an azimuth of −45° < ϕ < −30° lagging the solar azimuth. We use a simulation with no significant recent mergers to show that exactly the opposite trend is expected from a barred potential, but overestimated distances can flip this trend to match the data. Alternatively, using an N-body simulation of the Sagittarius dwarf–Milky Way interaction, we demonstrate that a major recent perturbation is necessary to reproduce the observations. Such an impact may have strongly perturbed the existing bar or even triggered its formation in the last 1–2 Gyr

Development of trofinetide for the treatment of Rett syndrome: from bench to bedside

Rett syndrome (RTT) is rare neurodevelopmental disorder caused by mutations in the MECP2 gene that encodes methyl-CpG-binding protein 2 (MeCP2), a DNA-binding protein with roles in epigenetic regulation of gene expression. Functional loss of MeCP2 results in abnormal neuronal maturation and plasticity, characterized by loss of verbal communication and loss of fine and gross motor function, among others. Trofinetide, a synthetic analog of glycine-proline-glutamate, was approved by the US Food and Drug Administration for the treatment of RTT in adult and pediatric patients aged 2 years and older. Here, we present the development of trofinetide from bench research to clinical studies and emphasize how the collaboration between academia, the pharmaceutical industry, and patient advocacy led to the recent approval. The bench-to-bedside development of trofinetide underscores the value of collaboration between these groups in the development and approval of treatments for rare diseases

CARD15/NOD2 Is Required for Peyer's Patches Homeostasis in Mice

BACKGROUND: CARD15/NOD2 mutations are associated with susceptibility to Crohn's Disease (CD) and Graft Versus Host Disease (GVHD). CD and GVHD are suspected to be related with the dysfunction of Peyer's patches (PP) and isolated lymphoid follicles (LFs). Using a new mouse model invalidated for Card15/Nod2 (KO), we thus analysed the impact of the gene in these lymphoid formations together with the development of experimental colitis. METHODOLOGY/PRINCIPAL FINDINGS: At weeks 4, 12 and 52, the numbers of PPs and LFs were higher in KO mice while no difference was observed at birth. At weeks 4 and 12, the size and cellular composition of PPs were analysed by flow cytometry and immunohistochemistry. PPs of KO mice were larger with an increased proportion of M cells and CD4(+) T-cells. KO mice were also characterised by higher concentrations of TNFalpha, IFNgamma, IL12 and IL4 measured by ELISA. In contrast, little differences were found in the PP-free ileum and the spleen of KO mice. By using chamber experiments, we found that this PP phenotype is associated with an increased of both paracellular permeability and yeast/bacterial translocation. Finally, KO mice were more susceptible to the colitis induced by TNBS. CONCLUSIONS: Card15/Nod2 deficiency induces an abnormal development and function of the PPs characterised by an exaggerated immune response and an increased permeability. These observations provide a comprehensive link between the molecular defect and the Human CARD15/NOD2 associated disorders: CD and GVHD

Tight junctions and the modulation of barrier function in disease

Tight junctions create a paracellular barrier in epithelial and endothelial cells protecting them from the external environment. Two different classes of integral membrane proteins constitute the tight junction strands in epithelial cells and endothelial cells, occludin and members of the claudin protein family. In addition, cytoplasmic scaffolding molecules associated with these junctions regulate diverse physiological processes like proliferation, cell polarity and regulated diffusion. In many diseases, disruption of this regulated barrier occurs. This review will briefly describe the molecular composition of the tight junctions and then present evidence of the link between tight junction dysfunction and disease