Ages and kinematics of chemically selected, accreted Milky Way halo stars

We exploit the [Mg/Mn]-[Al/Fe] chemical abundance plane to help identify nearby halo stars in the 14th data release from the APOGEE survey that have been accreted on to the Milky Way. Applying a Gaussian Mixture Model, we find a `blob' of 856 likely accreted stars, with a low disc contamination rate of ~7%. Cross-matching the sample with the second data release from Gaia gives us access to parallaxes and apparent magnitudes, which place constraints on distances and intrinsic luminosities. Using a Bayesian isochrone pipeline, this enables us to estimate new ages for the accreted stars, with typical uncertainties of ~20%. Our new catalogue is further supplemented with estimates of orbital parameters. The blob stars span a metallicities between -0.5 to -2.5, and [Mg/Fe] between -0.1 to 0.5. They constitute ~30% of the metal-poor ([Fe/H] < -0.8) halo at metallicities of ~-1.4. Our new ages are mainly range between 8 to 13 Gyr, with the oldest stars the metal-poorest, and with the highest [Mg/Fe] abundance. If the blob stars are assumed to belong to a single progenitor, the ages imply that the system merged with our Milky Way around 8 Gyr ago and that star formation proceeded for ~5 Gyr. Dynamical arguments suggest that such a single progenitor would have a total mass of ~1011Msun, similar to that found by other authors using chemical evolution models and simulations. Comparing the scatter in the [Mg/Fe]-[Fe/H] plane of the blob stars to that measured for stars belonging to the Large Magellanic Cloud suggests that the blob does indeed contain stars from only one progenitor.Comment: 14 pages, 9 figures, 2 tables, submitted to MNRAS. Comments welcome

seestar: Selection functions for spectroscopic surveys of the Milky Way

Selection functions are vital for understanding the observational biases of spectroscopic surveys. With the wide variety of multi-object spectrographs currently in operation and becoming available soon, we require easily generalisable methods for determining the selection functions of these surveys. Previous work, however, has largely been focused on generating individual, tailored selection functions for every data release of each survey. Moreover, no methods for combining these selection functions to be used for joint catalogues have been developed. We have developed a Poisson likelihood estimation method for calculating selection functions in a Bayesian framework, which can be generalised to any multi-object spectrograph. We include a robust treatment of overlapping fields within a survey as well as selection functions for combined samples with overlapping footprints. We also provide a method for transforming the selection function that depends on the sky positions, colour, and apparent magnitude of a star to one that depends on the galactic location, metallicity, mass, and age of a star. This `intrinsic' selection function is invaluable for chemodynamical models of the Milky Way. We demonstrate that our method is successful at recreating synthetic spectroscopic samples selected from a mock galaxy catalogue.Comment: MNRAS, revised version contains significant improvements to the model and more rigorous statistical test

Discrimination, Challenge and Response: People of North East India

Pulla, V., Bhattacharyya, R., &amp; Bhatt, S. (eds). Discrimination, Challenge and Response-People of North East India, Palgrave Macmillan, 2020, 203 pp., ISBN 978-3-030-46250-5, eBook:£87.50; Hardcover: £109.9

Non-Minimal Inflation with a scalar-curvature mixing term 12ξRϕ2\frac{1}{2} \xi R \phi^2

We use the PLANCK 2018 and the WMAP data to constraint inflation models driven by a scalar field $\phi$ in the presence of the non-minimal scalar-curvature mixing term $\frac{1}{2}\xi R \phi^2$. We propose four scalar field potentials $\phi^p e^{-\lambda\phi},~(1 - \phi^{p})e^{-\lambda\phi},~(1-\lambda\phi)^p$ and $\frac{\alpha\phi^2}{1+\alpha\phi^2}$ in the non-minimal scenario. We calculate the slow-roll parameters and predict the scalar spectral index $n_s$, the tensor to scalar ratio $r$ and tensor spectral index $n_T$ in the parameters($\lambda, p, \alpha$) space of the potential. We compare our results with the PLANCK 2018 and WMAP data and found that the non-minimal parameter $\xi$ lies between $10^{-3} \sim 10^{-5}$.Comment: 17 pages, 7 figures, 6 table

Alzheimer’s Protective A2T Mutation Changes the Conformational Landscape of the Aβ1–42 Monomer Differently Than Does the A2V Mutation

AbstractThe aggregation of amyloid-β (Aβ) peptides plays a crucial role in the etiology of Alzheimer’s disease (AD). Recently, it has been reported that an A2T mutation in Aβ can protect against AD. Interestingly, a nonpolar A2V mutation also has been found to offer protection against AD in the heterozygous state, although it causes early-onset AD in homozygous carriers. Since the conformational landscape of the Aβ monomer is known to directly contribute to the early-stage aggregation mechanism, it is important to characterize the effects of the A2T and A2V mutations on Aβ1–42 monomer structure. Here, we have performed extensive atomistic replica-exchange molecular dynamics simulations of the solvated wild-type (WT), A2V, and A2T Aβ1–42 monomers. Our simulations reveal that although all three variants remain as collapsed coils in solution, there exist significant structural differences among them at shorter timescales. A2V exhibits an enhanced double-hairpin population in comparison to the WT, similar to those reported in toxic WT Aβ1–42 oligomers. Such double-hairpin formation is caused by hydrophobic clustering between the N-terminus and the central and C-terminal hydrophobic patches. In contrast, the A2T mutation causes the N-terminus to engage in unusual electrostatic interactions with distant residues, such as K16 and E22, resulting in a unique population comprising only the C-terminal hairpin. These findings imply that a single A2X (where X = V or T) mutation in the primarily disordered N-terminus of the Aβ1–42 monomer can dramatically alter the β-hairpin population and switch the equilibrium toward alternative structures. The atomistically detailed, comparative view of the structural landscapes of A2V and A2T variant monomers obtained in this study can enhance our understanding of the mechanistic differences in their early-stage aggregation

AlphaFold Distillation for Improved Inverse Protein Folding

Inverse protein folding, i.e., designing sequences that fold into a given three-dimensional structure, is one of the fundamental design challenges in bio-engineering and drug discovery. Traditionally, inverse folding mainly involves learning from sequences that have an experimentally resolved structure. However, the known structures cover only a tiny space of the protein sequences, imposing limitations on the model learning. Recently proposed forward folding models, e.g., AlphaFold, offer unprecedented opportunity for accurate estimation of the structure given a protein sequence. Naturally, incorporating a forward folding model as a component of an inverse folding approach offers the potential of significantly improving the inverse folding, as the folding model can provide a feedback on any generated sequence in the form of the predicted protein structure or a structural confidence metric. However, at present, these forward folding models are still prohibitively slow to be a part of the model optimization loop during training. In this work, we propose to perform knowledge distillation on the folding model's confidence metrics, e.g., pTM or pLDDT scores, to obtain a smaller, faster and end-to-end differentiable distilled model, which then can be included as part of the structure consistency regularized inverse folding model training. Moreover, our regularization technique is general enough and can be applied in other design tasks, e.g., sequence-based protein infilling. Extensive experiments show a clear benefit of our method over the non-regularized baselines. For example, in inverse folding design problems we observe up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity, while still preserving structural consistency of the generated sequences.Comment: Preprin