dataesr/scientific-paragraphs-categorization huggingface dataset:
e. Radick et al. 1998; Lockwood et al. 2007; Radick et al. 2018). Stellar photometric variability amplitudes generally increase with mean chromospheric activity levels, but the Sun appears to have a lower photometric variability compared to other stars with similar mean chromospheric activity  (Radick et al. 2018; Reinhold et al. 2020). Young, active stars tend to become darker as their chromospheric emissions increase throughout activity cycles, while mature and less active stars, including the Sun, become brighter with increasing chromospheric emissions. This suggests a transition from spot dominated to faculae dominated photometric variability at around middle age  (Radick et al. 2018). In this paper we analyse time-series spectropolarimetric observations for a sample of main sequence and youthful F-M stars from the PolarBase 1  database  (Petit et al.
2014). Spectropolarimetry uniquely allows for the simultaneous measurement of the strength of the surface-averaged, large-scale stellar magnetic field (  ) and the chromospheric emissions in the cores of the Ca H&K lines. Thus, the data provide an opportunity to compare large-scale magnetic fields with the properties of smaller-scale magnetic surface features. These are known to be related  (Lehmann et al. 2018 ) but the nature of the relation is not yet clear. The PolarBase data span the years 2005 through to 2019, allowing for long-term monitoring of magnetic and chromospheric activity variability. We present both the average chromospheric activity and magnetic field strengths, and their variability amplitudes. We also combine our data with archival log  HK measurements from Gomes da  Silva et al.
(2020)  and Boro  Saikia et al. (2018a) , as well as details of the magnetic field geometries for ∼ 60 of our PolarBase targets which have been the subjects of previous Zeeman Doppler Imaging  (ZDI, Semel 1989 ) studies. The key aims of this work are to (i) compile a database of chromospheric activity measurements and surface-averaged large-scale magnetic field strengths for cool stars in the PolarBase sample, (ii) compare the distributions of chromospheric activity,   and their variability across the sample, (iii) search for distinct activity groups in the activity distribution, as well as any   counterparts, and (iv) use published ZDI results to gain new insights into the relationships between chromospheric activity, activity variability, magnetic field geometry and stellar properties.
Binary/multiple systems We identified binary and multiple systems using the database. We also inspected the LSD Stokes I spectral line profiles to identify any additional binary/multiple systems. The presence of multiple stellar signals in spectra and LSD profiles can result in the overestimation of the log  HK and/or   because of blending between components. Systems for which the measured   is impacted by the presence of companion stars are flagged in Table  1. We assume that the log  HK for all binaries/multiples may be contaminated by contributions from companions. This is because the cores of the Ca H and K lines are intrinsically very broad compared to most metallic lines, so the contributions of companions are usually blended.
Data reduction and calibration PolarBase provides ESPaDOnS and NARVAL observations in their reduced and calibrated format. The automatic reduction software package -, based on ('Echelle Spectra Reduction: an Interactive Tool',  Donati et al. 1997) , is applied to reduce and calibrate each sub-exposure using bias and flat-field exposures taken during the observing runs. A first wavelength calibration is carried out using a Thorium-Argon arc-lamp exposure taken on the observing night, and is then refined using telluric lines as radial velocity (RV) references. This results in a final RV accuracy of ∼ 20 to 30 m s -1  (Moutou et al. 2007). Stokes I spectra are extracted from each sub-exposure, continuum normalized, and are made available on Po-larBase at this point. Stokes V spectra are extracted from the series' of calibrated sub-exposures by dividing sub-exposures with orthogonal polarization states, which removes instrumental polarization signals  (Donati et al.
The factors  0 ,  0 and  0 are the normalization parameters from Table  2  (an extension of Table  4  from  Marsden et al. 2014). The line pattern function can thus be deconvolved from the observed spectrum to determine the mean Stokes I or V profile, as described in  Donati et al. (1997)  and  Kochukhov et al. (2010). We performed LSD using the automated technique of  Donati et al. (1997). We generated synthetic spectral line masks using stellar atmospheric models from the Vienna Atomic Line Database (VALD,  Kupka et al. 2000) , for stellar temperatures ranging between 3000 and 6500 K in steps of 500 K, and log  between 2. 0 and 5. 0 cm s -2 in steps of 0. 5 cm s -2. Line masks excluded lines with depth ≤ 10 per cent of the continuum. For each target we selected the line mask with the nearest  eff and log , and used the line normalization parameters from Table  2.
CONCLUSIONS We provide a catalog of new chromospheric activity and surfaceaveraged large-scale magnetic field measurements for 954 main sequence and youthful stars with spectral types from mid-F to mid-M. This includes mean values and peak-to-peak variability amplitudes for both the log  HK and   , which were derived using time-series spectropolarimetric observations from the PolarBase database. In this work we compare chromospheric activity, magnetic field strengths and their variability amplitudes. We also use previously published chromospheric activity data and ZDI results to diagnose relationships between chromospheric activity, activity variability, large-scale magnetic field geometries and stellar properties. The data confirm that chromospheric activity, magnetic field strengths, and their variability amplitudes are generally higher for youthful stars compared to more mature, main sequence stars.
ACKNOWLEDGEMENTS We thank the referee for their constructive and thorough review, which has helped us to improve the quality of this manuscript. This work is based on observations obtained at the  TBL and CFHT. The TBL is operated by the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France (INSU/CNRS). The CFHT is operated by the the  National Research Council of Canada , the  INSU/CNRS  and the  University of Hawaii. We thank the staff at the  TBL and CFHT  for their time and data. The observations at the CFHT were performed with care and respect from the summit of Maunakea which is a significant cultural and historic site. We also acknowledge the use of the PolarBase database, which makes TBL and CFHT observations publicly available, and is operated by the  CNRS ,  Observatoire Midi-Pyrénées  and  Université Toulouse III -Paul Sabatier.
We acknowledge use of the and V R data bases operated at  CDS, Strasbourg, France. This work has also made use of the VALD database, operated at  Uppsala University , the  Institute of Astronomy RAS in Moscow , and the  University of Vienna. ELB is supported by an  Australian Postgraduate Award Scholarship. SVJ acknowledges the support of the  German Science Foundation  ( DFG) priority program SPP 1992 'Exploring the Diversity of Extrasolar Planets'  ( JE 701/5-1 ). AAV acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  817540 , ASTROFLOW). MMJ acknowledges funding from  STFC  consolidated grant  ST/M001296/1. SBS acknowledges the support of the  Austrian Science Fund (FWF)   Lise Meitner  project  M2829 -N.
We present new MMT/Hectochelle spectroscopic measurements for 257 stars observed along the line of sight to the ultrafaint dwarf galaxy Triangulum II (Tri II). Combining results from previous Keck/DEIMOS spectroscopy, we obtain a sample that includes 16 likely members of Tri II, with up to 10 independent redshift measurements per star. To this multi-epoch kinematic data set, we apply methodology that we develop in order to infer binary orbital parameters from sparsely sampled radial velocity curves with as few as two epochs. For a previously identified (spatially unresolved) binary system in Tri II, we infer an orbital solution with period 296. 0 + 3. 8 -3. 3 d, semimajor axis 1. 12 + 0. 41 -0. 24 au, and systemic velocity -380. 0 ± 1. 7 km s -1 that we then use in the analysis of Tri II's internal kinematics.
First, we present new spectroscopic data acquired with the Hectochelle spectrograph at the 6. 5-m MMT. Secondly, we combine with the previously published spectroscopic data in order to obtain a multi-epoch data set that then lets us model the orbital parameters of the likely binary star. Our orbital solution includes an inference for the binary system's centre-of-mass motion, allowing us properly to include this star in our analysis of Tri II's stellar kinematics. To date, only one star system within a dwarf spheroidal galaxy has a full orbital solution, based on 34 independent velocity measurements taken o v er a 2-yr baseline  (Koch et al. 2014 ). Here, we develop methodology for inferring orbital solutions with as few as two velocity epochs. The problem of finding orbital parameters for a binary system given a small number of radial velocity (RV) measurements has been undertaken previously by  Price-Whelan et al.
The observ ations follo w a similar strategy and goal as presented by  Starkenburg et al. ( 2017 )  in the Pristine surv e y but correspond to a single CFHT MegaCam field centred on Tri II. The field co v ers the full extent of Tri II and the integration amount to 4800 s in total from the CaHK images. Additional observations were also obtained in the MegaCam g and i bands (4140 and 4050 s, respectively) to benefit from the full depth of the CaHK photometry is deeper than the Pan-STARRS1 data that were used to discover Tri II  (Laevens et al. 2015 ). All were observed in service mode by the CFHT observing staff between 2015 July 18 and 2016 February 13. After pre-processing of the images with ELIXIR by CFHT (debiasing, flat-fielding, and de-trending;  Magnier & Cuillandre 2004 ) , the images were astrometrically calibrated, stacked, and processed for photometry with the CASU pipeline  (Irwin & Lewis 2001 ) , as described in detail by  Starkenburg et al.
2015 )  from the Large Binocular Camera. In general, the field of Tri II is sparse enough that most candidate member stars in the field of the slitmask could be observed. As a result, the samples have little selection bias due to colour (or stellar age or metallicity). Kirby et al. ( 2017 )  were mainly interested in quantifying RV variability, not in finding new members. As a result, they designed their slitmasks to target stars already identified as members by  Kirby et al. ( 2015 )  and  Martin et al. ( 2016 ). Velocities were measured in slightly different ways. Kirby et al. ( 2015 Kirby et al. ( , 2017 ) )  reduced the spectra with custom modifications to the spec2d data reduction pipeline  (Cooper et al. 2012 ). They matched empirical spectral templates observed with DEIMOS to the Tri II spectra and varied the velocity until χ 2 was minimized.
Zero-point offset In this section, we describe the calculation of the zero-point velocity offset between Keck and MMT. Although our complete set of Tri II K eck observ ations is a concatenation of the  Kirby et al. ( 2017 )  and  Martin et al. ( 2016 )  data sets, the zero-point offset between the two appears consistent with zero (see section 3. 2 of  Kirby et al. 2017 ). For this analysis, we have defined the offset as the MMT zero-point minus the Keck zero-point. There are eight objects in our data set around the Tri II galactic centre with RV measurements in both MMT and Keck catalogs. Measurements for a given object and instrument are combined into a single weighted mean value. We then calculate the offset between the sets of combined instrument velocities assuming Gaussian errors. The methods described here and in Section 3 are based on a Gaussian likelihood function, which is described in terms of model prediction μ, model error σ , given data in the form of the observed velocity v: G( v | μ, σ ) = 1 2 π ( v 2 error + σ 2 ) exp - ( v -μ) 2 2( v 2 error + σ 2 ) (1) This is the general likelihood for a given velocity prediction, and the total likelihood function is the product of the velocity likelihoods.
The offset error is taken as the width of this offset posterior distribution. We derive the offset value from the resulting posterior, sampled using Markov Chain Monte Carlo (MCMC) by the Metropolis-Hastings algorithm via the EMCEE PYTHON package (F oreman-Macke y et al. 2013 ). Applying this procedure to the eight stars in the o v erlapping MMT-Keck data sets observed around the Tri II galactic centre, we find an offset value of δ v = -0. 11 ± 1. 02 km s -1. While this value could be determined using only our Tri II data set, the large error means that the offset is not resolved within 1 km s -1 and the introduction of a large offset error can make it more difficult to resolve the Tri II velocity dispersion. To impro v e the error on our zero-point offset between the instruments, we also include Keck/DEIMOS  (Pace et al.
2020  ) and MMT/Hectochelle  (Spencer et al. 2018 )  observations from an additional 288 Ursa Minor objects. The combined Tri II and Ursa Minor data set results in an offset correction of δ v = -1. 33 ± 0. 33km s -1. We opt to use the offset that is calculated while including Ursa Minor observations, as the larger amount of data gives a more precise value for the offset. Thus, we bring all observations on to a common zero-point by subtracting a fixed amount of 1. 33 ± 0. 33 km s -1 from each velocity measurement obtained with Keck/DEIMOS. We do not propagate the offset error as the total error is dominated by the combination of instrument-specific systematics and random errors.
These stellar radii and masses are calculated using the Isochrones PYTHON package  (Morton 2015 )  to determine stellar parameters for the binary star of age 13 Gyr, [Fe/H] of -2. 2, and distance modulus of 17. 24 using the MESA Isochrones and Stellar Tracks (MIST) grid  (Dotter 2016 ; Choi et al. 2016 ). Fig. 4  shows the rele v ant isochrone fit on a colour-magnitude diagram with Tri II members plotted. For the binary star Star46 , we determine a mass of M bin = 0. 777 M and stellar radius of r bin = 0. 027 au from an isochrone fit to Tri II stars. We equi v alently deri ve a mass and radius for the other Tri II members by finding the point on the isochrone closest to each member star using the same steps. Similarly,  Raghavan et al. ( 2010 )  looked at 259 confirmed solartype binaries in the Hipparcos catalog and found that the periods are distributed according to a lognormal distribution with μ log P = 5.
We use the mixture sub package found in scikit-learn  (Pedregosa et al. 2011 )  to fit a Gaussian mixture model (GMM) of a singular Gaussian to the resulting posterior samples. Fig. 5  shows the RV curve and resulting posteriors using this method, referred to as the Gaussian fit method, and binary model method for an example Tri II member star with multiple velocity observations. This Gaussian Fit method has the advantage in that it does not assume binarity but does retain increased error with larger variability in the RV curve, more than the weighted mean. For comparison, we also calculate the combined velocities under the weighted mean and binary model methods (Fig. 6  ). Applying the binary model to the assumed non-binary member stars takes up significantly more computation time and results in a larger uncertainty in the combined v elocity.
AC K N OW L E D G E M E N T S This work is supported in part by  National Science Foundation (NSF)  grants  AST-1813881  and  AST-1909584. ENK is supported by  NSF  grant  AST-1847909 , and he gratefully acknowledges support from a  Cottrell Scholar award  administered by the  Research Corporation for Science Advancement. EO is partially supported by  NSF  grant  AST-1815767. NC is supported by  NSF  grant  AST-1812461. MM is supported by  U. S. NSF  grants  AST -1312997 ,  AST -1726457  and  AST-1815403. ES acknowledges funding through  VIDI grant  ' Pushing Galactic Archaeology to its limits ' (with project number  VI. Vidi. 193. 093 ), which is funded by the  Dutch Research Council (NWO). CB and CMD are supported by  NSF  grant  AST-1909022. This work has made use of data from the  European Space Agency (ESA) mission Gaia  ( ht tps://www.
cosmos. esa. int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC; https: // www. cosmos. esa. int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Pan-STARRS1 Surveys ( PS1 ) and the PS1 public science archiv e hav e been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins Uni versity, Durham Uni versity , the  Uni versity of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  Grant No. AST-1238877 , the  University of Maryland, Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation. This research has made use of  NASA's Astrophysics Data System Bibliographic Services. This paper made use of the Whole Sky Database (wsdb) created by  Sergey Koposov  and maintained at the  Institute of Astronomy, Cambridge by Sergey Koposov ,  Vasily Belokurov , and  Wyn Evans  with financial support from the  Science & Technology Facilities Council (STFC)  and the  European Research Council (ERC). Observations reported here were obtained at the  MMT Observatory , a joint facility of the  University of Arizona  and the  Smithsonian Institution.
2. 88 (https://github. com/cov-lineages/pango-designation/releases/ tag/v1. 2. 88). We present the main findings of genomic surveillance from weeks 1-22, 2021 (January 4-June 6, 2021). We focused on the evolution of the weekly proportions of the 8 most frequent SARS-CoV-2 variants and examined the correlation between the weekly number of confirmed cases and the proportion of sequences identified as Beta variant (B. 1. 351). We extracted lineage distributions in other islands of the Indian Ocean and South Africa from the GISAID database (http://www. gisaid. org) to investigate the origins of the Beta variant sublineages. We identified 22 SARS-CoV-2 lineages, 71% of which were attributed to the Beta variant (sublineages B. 1. 351 and B. 1. 351. 2) (Table ). On the basis of available data in the GISAID database, lineage B.
1. 622 seems to be specific to Réunion Island; no other sequence had been reported elsewhere. Since mid-February 2021 (week 7 of 2021), the Beta variant has become dominant on Réunion Island, despite low-level circulation of the Alpha variant, another variant of concern that was dominant in mainland France and other countries in Europe at that time. We detected a correlation (Spearman ρ = 8. 4 × 10 -4 ; p<0. 001) between the number of CO-VID-19 cases in January-February 2021 and the number of sequences attributed to the Beta variant, which has been shown to have increased transmissibility (C. A. Pearson et al. , unpub. data, https:// cmmid. github. io/topics/covid19/sa-novel-variant. html). Several additional factors could explain the dominance of Beta variant; genetic and epidemiologic factors may have contributed to a founder effect, a higher frequency of virus introductions resulting from holiday travels, possible superspreading events, and local and regional contexts  (2).
Acknowledgments We thank all the partners involved in the surveillance of COVID-19 in the  Regional Health Agency  and the  Regional Health Insurance Fund. We acknowledge the key role of the network of sampling laboratories on Réunion Island  ( CHU, CHOR, Laboratoire de Saint-Benoit, Bioaustral, Réunilab, Cerballiance ). We also acknowledge and thank the members of the  EMERGEN consortium , as well as  Laetitia Ali Oicheih ,  Elsa Balleydier ,  Adeline Feri ,  Javier Castro Alvarez ,  Sibylle Bernard-Stoecklin ,  Anna Maisa ,  Justine Schaeffer ,  Bruno Coignard ,  Guillaume Spaccaferri ,  Bruno Lina ,  Marie-Alice Simbi ,  Magali Turpin ,  Christian Mériau , and  Maya Cesari. We are also grateful to the originating and submitting laboratories of the sequences from GISAID database (Appendix 2, https://wwwnc.
Furthermore, the protein sequences of GH30_2, GH43_8, GH43_28, GH43_33, and GH76 were identical to those of several strains of P. distasonis already annotated for CAZymes (http://www. cazy. org/bP. html). Homologous proteins were also found in numerous P. distasonis sequences available in the databases (not shown). Regarding gut-metabolic modules (GMM), only the cysteine pathway of methionine degradation (methionine = > L-homocysteine) changed significantly during the intervention and was increased with MF bread consumption (CD: 0. 32, small; p =. 026) (Figure  4 ). Interestingly, the mercaptan pathway of methionine degradation (methionine = > methanethiol) showed the opposite evolution with similar effect size, non-significantly after multiple test, though (p =. 04, q = 1). Shortchain fatty acids-related GMM were not modulated during the intervention.
from this subfamily are rather observed in the genome of Enterobacteriacea, and lactic acid bacteria  40, 41  but many other GH13 enzymes dedicated to the disruption of alpha glycoside linkages are found in P. distasonis according to the CAZy database, PL26 is thought to degrade rhamnogalacturonan from pectins but only one fungal enzyme from this family has been characterized so far,  42  therefore other functional characteristics may exist. Nevertheless, numerous PL26 encoding genes have been found in Bacteroides species including B. vulgatus  43  which is also significantly decreased with MF bread consumption. B. vulgatus has been reported to be associated with alteration of insulin resistance. 44 Thus, its observed decrease could partly explain the improvement of insulin sensitivity. Moreover, we showed a significant increase of Parabacteroides distasonis whose involvement in type 2 bile acid production and farnesoid X receptor (FXR) pathway activation resulting in insulin sensitivity improvement has recently been shown.
Compliance was checked by the number of returned empty packs. During the intervention periods, subjects were asked to maintain the same diet in terms of caloric and macronutrients content and were ordered to fill in a 3-day dietary record. Data from this 3-day dietary record were processed using NUTRILOG© which allowed to estimate energy and macronutrient intake. In parallel, the subjects also completed a questionnaire concerning 128 fiber-rich food items to evaluate their dietary fiber intake in terms of diversity. 12 A fiber-rich food group (breakfast and biscuits, starchy food, dried vegetables, vegetables, fruits, nuts, chocolate, and probiotics) is made of different items (for example, apple is an item belonging to fruits). For each subject, fiberrich food group was taken into account when he/ she ate at least one item/day of this group on 2 days out of three.