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Reaching the boundary between stellar kinematic groups and very wide binaries. III. Sixteen new stars and eight new wide systems in the beta Pictoris moving group

Aims. We look for common proper motion companions to stars of the nearby young beta Pictoris moving group. Methods. First, we compiled a list of 185 beta Pictoris members and candidate members from 35 representative works. Next, we used the Aladin and STILTS virtual observatory tools, and the PPMXL proper motion and Washington Double Star catalogues to look for companion candidates. The resulting potential companions were subjects of a dedicated astro-photometric follow-up using public data from all-sky surveys. After discarding 67 sources by proper motion and 31 by colour-magnitude diagrams, we obtained a final list of 36 common proper motion systems. The binding energy of two of them is perhaps too small to be considered physically bound. Results. Of the 36 pairs and multiple systems, eight are new, 16 have only one stellar component previously classified as a beta Pictoris member, and three have secondaries at or below the hydrogen-burning limit. Sixteen stars are reported here for the first time as moving group members. The unexpected large number of high-order multiple systems, 12 triples and two quadruples among 36 systems, may suggest a biased list of members towards close binaries or an increment of the high-order-multiple fraction for very wide systems.Comment: A&A in pres

Extracting Topics from Open Educational Resources

In recent years, Open Educational Resources (OERs) were earmarked as critical when mitigating the increasing need for education globally. Obviously, OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 123 lectures from Coursera and Khan Academy in the area of data science related skills, 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to these skills, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 79% of F1-score.Comment: Editted version of this paper has been accepted to be published in the proceedings of The European Conference on Technology-Enhanced Learning (EC-TEL) 2020 by Springer (Lecture Notes in Computer Science (LNCS) Series

CARMENES input catalogue of M dwarfs. I. Low-resolution spectroscopy with CAFOS

Context. CARMENES is a stabilised, high-resolution, double-channel spectrograph at the 3.5 m Calar Alto telescope. It is optimally designed for radial-velocity surveys of M dwarfs with potentially habitable Earth-mass planets. Aims. We prepare a list of the brightest, single M dwarfs in each spectral subtype observable from the northern hemisphere, from which we will select the best planet-hunting targets for CARMENES. Methods. In this first paper on the preparation of our input catalogue, we compiled a large amount of public data and collected low-resolution optical spectroscopy with CAFOS at the 2.2 m Calar Alto telescope for 753 stars. We derived accurate spectral types using a dense grid of standard stars, a double least-squares minimisation technique, and 31 spectral indices previously defined by other authors. Additionally, we quantified surface gravity, metallicity, and chromospheric activity for all the stars in our sample. Results. We calculated spectral types for all 753 stars, of which 305 are new and 448 are revised. We measured pseudo-equivalent widths of Halpha for all the stars in our sample, concluded that chromospheric activity does not affect spectral typing from our indices, and tabulated 49 stars that had been reported to be young stars in open clusters, moving groups, and stellar associations. Of the 753 stars, two are new subdwarf candidates, three are T Tauri stars, 25 are giants, 44 are K dwarfs, and 679 are M dwarfs. Many of the 261 investigated dwarfs in the range M4.0-8.0 V are among the brightest stars known in their spectral subtype. Conclusions. This collection of low-resolution spectroscopic data serves as a candidate target list for the CARMENES survey and can be highly valuable for other radial-velocity surveys of M dwarfs and for studies of cool dwarfs in the solar neighbourhood.Comment: A&A, in pres

Fidelity of Phenylalanyl-tRNA Synthetase in Binding the Natural Amino Acids

Aminoacyl-tRNA synthetases guard the fidelity of cognate amino acid incorporation during protein biosynthesis; for example, phenylalanyl-tRNA synthetase (PheRS) activates and transfers only Phe to its tRNA. Since we are interested in using a computational protocol to identify nonnatural amino acids that are incorporated by wild-type PheRS, it is critical to understand the fidelity of PheRS in binding the 20 natural amino acids. To this end, HierDock, a computational protocol for predicting binding sites and relative binding affinities, was used for testing the natural amino acids in PheRS. Scanning the entire ligand-accessible protein surface for the best binding region, we find that HierDock correctly identifies the active site of Phe in PheRS and predicts Phe within 0.61 Å RMSD of the crystal structure. HierDock also successfully shows PheRS discriminates for Phe, as the noncognate amino acids bind less favorably in the binding site of Phe. However, we find that Met, Cys, and Tyr bind competitively but at positions distant from the Phe binding site. This result corroborates in vitro measurements of aminoacyl adenylate formation, which show Met competes with Phe at the amino acid binding stage. We predict that the binding site of Met would not activate PheRS, as the noncognate amino acid cannot establish suitable hydrogen bonds with the PheRS reaction center. These results validate the use of HierDock in predicting the binding sites of the cognate amino acids in PheRS. The HierDock procedure calculates the discrimination of aminoacyl-tRNA synthetases at the stage of binding the cognate amino acid and offers a molecular level understanding of the mistakes made in protein biosynthesis that are not readily uncovered through experiments. This technique is also useful for predicting the binding of a selected nonnatural amino acid analogue, thereby indicating whether the molecule would be incorporated into a wild-type aminoacyl-tRNA synthetase

Molecular mechanisms underlying differential odor responses of a mouse olfactory receptor

The prevailing paradigm for G protein-coupled receptors is that each receptor is narrowly tuned to its ligand and closely related agonists. An outstanding problem is whether this paradigm applies to olfactory receptor (ORs), which is the largest gene family in the genome, in which each of 1,000 different G protein-coupled receptors is believed to interact with a range of different odor molecules from the many thousands that comprise “odor space.” Insights into how these interactions occur are essential for understanding the sense of smell. Key questions are: (i) Is there a binding pocket? (ii) Which amino acid residues in the binding pocket contribute to peak affinities? (iii) How do affinities change with changes in agonist structure? To approach these questions, we have combined single-cell PCR results [Malnic, B., Hirono, J., Sato, T. & Buck, L. B. (1999) Cell 96, 713–723] and well-established molecular dynamics methods to model the structure of a specific OR (OR S25) and its interactions with 24 odor compounds. This receptor structure not only points to a likely odor-binding site but also independently predicts the two compounds that experimentally best activate OR S25. The results provide a mechanistic model for olfactory transduction at the molecular level and show how the basic G protein-coupled receptor template is adapted for encoding the enormous odor space. This combined approach can significantly enhance the identification of ligands for the many members of the OR family and also may shed light on other protein families that exhibit broad specificities, such as chemokine receptors and P450 oxidases