dataesr/scientific-paragraphs-categorization huggingface dataset:
Details on these discarded components are given in the COMMENT column of As already stated in Section 2. 2, the future Gaia data releases will provide solutions for a large number and type of binary (astrometric, spectroscopic, and eclipsing) with periods from 0. 2 d to more than 5 yr, in amounts of hundreds of thousands in the third Gaia data release (Gaia DR3) and millions in the fourth Gaia data release (Gaia DR4), as predicted by the Gaia Universe Model Snapshot  (Robin et al. 2012). Within the 10 pc sphere, one can expect very good forthcoming astrometric solutions, including orbital parameters. Such new astrometry would complete the characterisation of the systems, even the closest ones; the expected limit is 0. 12 arcsec, but Gaia astrometry will provide information on binarity even for objects it cannot resolve.
For nearby objects in multiple systems, the orbital motion induces large residuals in the single star solution that the pipeline marks as errors and a full solution is not provided. Future Gaia data release will employ multiple star solutions so we expect full astrometric solutions for these brown dwarfs. We completed our compilation of brown dwarfs with those presented by  Kirkpatrick et al. (2019) ,  Best et al. (2020) , and, especially,  Kirkpatrick et al. (2021) , from where we added 38 ultra-cool objects with new or more precise parallaxes. We expect the 10 pc census to be further supplemented with cool Tand Y-type dwarfs in the near future. Of the 19 candidates with NB_OBJ ≥ 1001 (Sect. 2. 1), 15 are T and Y dwarfs, while Kirk-A&A proofs: manuscript no. aa-corr.
Exoplanets The existing exoplanet catalogues, such as the Extrasolar Planets Encyclopaedia 8 , the NASA Exoplanet Archive 9 , the Exoplanet Orbit Database 10 , or the Open Exoplanet Catalogue 11 , are not fully consistent. Discrepancies are partly due to different selection criteria, notations, and diligence in updating their data bases, and they are also due to the heterogeneity of information provided in discovery papers that different catalogues capture in different ways. As a consequence, it is almost impossible to achieve full homogeneity, and any direct comparison between catalogues can be difficult depending on the specific application. This is the case even for the sample of exoplanetary systems nearest to the Sun. We cross-matched the Extrasolar Planets Encyclopaedia and the NASA Exoplanet Archive in order to select the most reliable set of stars with exoplanets within 10 pc, and we added those that we considered to be confirmed to our catalogue.
The most recent discovery added to our catalogue is the transiting rocky planet GJ 486 b  (Trifonov et al. 2021 ). The astrometry given in Table  A. 1 is the one of the host star, and the discovery reference is given in the SYSTEM_BIBCODE field. Non-listed candidate, unconfirmed, or controversial exoplanets are enumerated, though, in the corresponding COMMENT field of the host star. For them, we employ the term 'candidate' when the publication reported the companion with that terminology or when the statistical evidence was not strong for the presence of the signal. A notable example is the second long-period planet candidate orbiting Proxima Centauri c  (Damasso et al. 2020). We use the term 'unconfirmed' or 'controversial' when the radial-velocity or imaging signal has not been seen by different groups analysing the same datasets, when different groups use different datasets and do not find the same signals, or when the radial-velocity signal can be explained in terms of stellar activity variations.
g. Lindegren et al. 2021). Indeed, the much expected catalogue of tens of thousands of exoplanets will be mostly populated by gas giants in the 1-4 au separation regime (e. g. Sozzetti & de Bruijne 2018, and references therein). However, there are over a dozen exoplanet candidates for which we expect to see the astrometric signature. Some of these exoplanets may have already been detected with the threeyear time baseline of Gaia DR3, and most of them should be detected in Gaia DR4. However, their actual detectability in future Gaia data releases relies on the effectiveness of a successful calibration of the astrometric data in the very bright star regime (G 9 mag). We list below the radial-velocity exoplanet candidates that should be detected by Gaia: -GJ 15 Ac is expected to induce α > 570 µas, but it has a period in the neighbourhood of 20 yr  (Pinamonti et al.
Science cases and the next upgrades Apart from multiplicity studies, mass function analyses, and long-term exoplanet surveys, there is a number of science topics that can be covered with the 10 pc catalogue. One of them is kinematics and membership in thin and thick disc populations and stellar moving groups and associations. There are on-going efforts to relate precise Galactocentric space velocities to youth features for a sample of over 2000 nearby M dwarfs (M. Cortés-Contreras, priv. comm. )  and to measure, for the first time, radial velocities of a number of late-type ultracool dwarfs (W. Cooper, priv. comm. ). These works will be presented in forthcoming publications and will complement future releases of the 10 pc catalogue. Further improvements of the 10 pc catalogue include adding the new Gaia DR3 astro-photometric data (expected in 2022), updating spectral types for poorly investigated companions and radial velocities of the faintest brown dwarfs, and adding more parameters useful for a variety of topics, such as atmospheric astrophysical parameters (T eff , log g, [Fe/H]), chromospheric (equivalent widths of Hα and Ca ii) and coronal (X-rays) activity indicators, and rotational velocity.
Obsolescence This catalogue will inevitably need to be updated when Gaia and other surveys issue their next data releases. Apart from extremely cool objects similar to WISEA J085510. 74-071442. 5, new objects probably hide in the Milky Way plane (see the discoveries by  Beamín et al. 2013; Scholz 2014; Scholz & Bell 2018; Faherty et al. 2018). Such new objects, in spite of their expected large proper motions, will likely be detected by state-of-the-art photometric surveys from the ground such as the Panoramic Survey Telescope and Rapid Response System  (Kaiser et al. 2002) , J-PLUS/J-PAS  (Solano et al. 2019) , and, specially, the Legacy Survey of Space and Time  (LSST Science Collaboration et al. 2009) , as well as from the space. In particular, the ESA medium-class Euclid space mission will cover more than 35 % of the celestial sphere in the red optical and near-infrared Y, J, and H bands with an unprecedented depth and spatial resolution  (Laureijs et al.
To aid in this outreach, we produced some divulgative material that we release with this contribution. The three-dimensional nature of the dataset makes creating maps more of a challenge than for more traditional terrestrial cartography. We generated maps in several different formats from the data, including a rotating animation of all the objects in the catalogue, a 3D fly-through JavaScript web application, a top down poster (see Fig. B. 1), and two 5 pc and 10 pc maps with 'star columns' showing distance above and below the galactic plane. All the resources are available online 14.
Conclusions We provide a catalogue of all objects closer than 10 pc from the Sun. It contains 540 objects divided between 375 stars, including 20 confirmed white dwarfs and one candidate white dwarf, 85 confirmed and three candidate brown dwarfs, and 77 confirmed exoplanets in 339 systems made up of 69 binaries, 19 triplets, three quadruplets, and two quintuplets. During the catalogue compilation, we extensively checked all individual entries from what is available in the published literature. In particular, it contains the most recent astrometry from the last Gaia data release when available. The catalogue will be used to assess the quality of the forthcoming Gaia releases to place limits on the frequency of planets and other components within multiple systems, as well as providing targets for focused planetary searches.
Acknowledgements. The authors thank the referee for useful and prompt comments. C. R. thanks  Catherine Turon  for fruitful discussions on the parallax measurements across the history. C. R. thanks  Will J. Cooper  for his contribution to plotting. This work was supported by the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  co-funded by  CEA  and  CNES. R. L. S. and A. S. acknowledge support from the  Italian Space Agency (ASI)  under contract  2018-24-HH. 0. A. S. acknowledges the financial contribution from the agreement  ASI-INAF n. 2018-16-HH. 0. J. A. C. acknowledges financial support from the  Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación y Universidades  and the  ERDF  through project  PID2019-109522GB-C51/AEI/10. 13039/501100011033. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
https://gruze. org/10pc/resources in particular the institutions participating in the Gaia Multilateral Agreement. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This research has made use of data obtained from or tools provided by the portal exoplanet. eu of The Extrasolar Planets Encyclopaedia. This research has made use of the Washington Double Star Catalog maintained at the U. S. Naval Observatory. This research has made use of the.
Research in context Evidence before this study We searched PubMed for research articles published in English from database inception to Nov 21, 2017, using the terms "latent tuberculosis", "haematopoietic stem cell", "CD34", "PCR", and "human". We found one report of Mycobacterium tuberculosis DNA being detected in CD34-positive peripheral blood mononuclear cells (PBMCs) of 15 HIV-uninfected adults with latent tuberculosis living in Austria. We did not find reports of this observation being investigated in a high tuberculosis burden setting, or in HIV-infected people.
Categorical QuantiFERON status was assigned on the basis of IFN-γ concentrations in super natants in antigen-stimulated tubes and positive and negative controls using QuantiFERON-TB Gold Plus Analysis Software (Qiagen). IFN-γ concentrations in super natants of antigen-stimulated whole blood were also analysed as continuous variables when testing for correlations with IS6110 and rpoB copy numbers. Fisher's exact.
Acknowledgments This work was supported by a grant from the  UK Medical Research Council  to ARM ( MR/P024548/1 ). SF and STR acknowledge support from the  Cambridge National Institiute of Medical Research Biomedical Research Centre  antibiotic resistance theme. JM acknowledges support from the  Developing Excellence in Leadership, Training and Science Africa Initiative  ( DEL-15-011 , through THRiVE-2). JFH, GMJ, and DMO'S acknowledge support from the  UK government Department for Business, Energy & Industrial Strategy. MN acknowledges support from the  Wellcome Trust  ( 207511/Z/17/Z ) and from  National Institute for Health Research Biomedical Research Funding to University College London  and  University College London Hospital. We thank all the people who participated in the study, members of the field and laboratory teams who enrolled participants and processed samples,  Susan Barton  ( Miltenyi Biotec ) for assistance with flow cytometry,  Eloise Busby  ( National Measurement Laboratory, LGC ) for design of the femA_SE dPCR assay, and  Pawan Dhami  and  Heli Vaikkinen  for assistance with dPCR assays done at the  Genomics and Genome Engineering Facility, University College London , which was supported by  Cancer Research UK.
5 mm) and sealed. An empty capillary and the capillary containing DI water were also recorded for solvent subtraction  [35]. The probed q-range was 0. 005-0. 2 Å -1 , where q is the scattering vector. SASView software (version 4. 1. 2) was used to fit the SAXS data using either a rigid and flexible elliptical cylindrical model  [35]. The intensity I(q) can be written as follows: ( ) ∝ ( ) ( ) (1) with P(q) the form factor of the objects studied, giving information about their shape and S(q) is the structure factor associated with the interactions between the objects probed. The structure factor, S(q), was obtained by dividing the pattern obtained in the presence of salt by the pattern from OCNF without salt, where fibril-fibril interactions could be neglected: ( ) = ( ). ( ) (2) where S(q) is the structure factor of the gel prepared in the presence of salt (NaCl = 0.
Detailed data fitting for the blend hydrogels was not conducted due to the multicomponent nature of the gel system and the fact that the differences in the data were only seen at the low-q limit of the recorded data. Proper representation of all components and cross-interactions would inevitably lead to over parametrisation, undermining confidence in the fitting. The SAXS patterns of the individual OCNF (1 wt%) and starch (1 wt%) suspensions at various NaCl concentrations were also assessed. Patterns taken after the addition of NaCl to the OCNF gels displayed similar high-q scattering (see Figure  9a ) associated with the OCNF cross-section. In the low-q region (0. 004 ≤ q ≤ 0. 01 Å -1 ), scattering from the pure OCNF (1 wt%) gels showed a slope of q -1 , the expected slope at a smaller angle for noninteracting rigid elongated objects like rods or cylinders  [35].
Acknowledgments: The authors would like to thank the EPSRC for funding this project (Grant numbers EP/N033310/1 (Bath) and EP/N033337/1 (UEA)). V. C. thanks the University of Bath for supporting his PhD studies. The authors thank Diana Lednitzky of the Material and Chemical Characterisation Facility (MC 2 ) at the University of Bath for helping with the TEM imaging. Professor Nick Terrill, Andy Smith, and Tim Snow are thanked for their assistance with the SAXS experiment (Experiment no. SM20409-1) at the I22 beamline, Diamond Light Source, Didcot. This work benefited from the use of the SasView software (developed under NSF Award DMR-0520547) containing code developed under the EU Horizon 2020 programme (the SINE2020 project Grant 654000).
This article presents the study of a passive acoustic dataset recorded on the Chukchi Shelf from October 2016 to July 2017 during the Canada Basin Acoustic Propagation Experiment (CANAPE). The study focuses on the lowfrequency (250-350 Hz) ambient noise (after individual transient signals are removed) and its environmental drivers. A specificity of the experimental area is the Beaufort Duct, a persistent warm layer intrusion of variable extent created by climate change, which favors long-range acoustic propagation. The Chukchi Shelf ambient noise shows traditional polar features: it is quieter and wind force influence is reduced when the sea is ice-covered. However, the study reveals two other striking features. First, if the experimental area is covered with ice, the ambient noise drops by up to 10 dB/Hz when the Beaufort Duct disappears.
A. The Canada Basin acoustic propagation experiment The CANAPE was conducted in 2016 and 2017 in the Canada Basin (deep water, hereinafter DW-CANAPE) and on the Chukchi Shelf (shallow water, hereinafter SW-CANAPE). Many acoustic assets, both receivers and sources, were deployed as part of the experiment. This paper relies on a year-long passive acoustic dataset collected by Woods Hole Oceanographic Institution (WHOI) on the Chukchi Shelf (SW-CANAPE). Five "several hydrophone recording unit" (SHRU) moorings were deployed on the shelf edge. Each mooring consists of a vertical line hydrophone array (four channels) with temperature and pressure sensors. Although the study focuses on the acoustic data collected by the SHRUs, the proposed method also relies on information collected using various DW-CANAPE and SW-CANAPE assets.
Acoustic data This study uses passive acoustic data collected on a mooring called SHRU5, located at 72. 908 N, 157. 484 W, where the water depth is 445 m. This specific SHRU has been chosen as it was the mooring with the lowest selfnoise. The four SHRU5 hydrophones were placed at depths from 163 to 170. 5 m with a regular sensor spacing of 2. 5 m. Here, only the deepest hydrophone is used. The study also considers data collected on a mooring called SHRU1, located at 72. 907 N, 159. 018 W, with water depth equal to 302 m. SHRU1 also has four hydrophones, but a single channel at depth 163 m is used. All the acoustic data were recorded continuously from October 22, 2016 to July 30, 2017, with sampling frequency f s ¼ 3906:2 Hz. Most of the study focuses on the SHRU5 data. The data from SHRU1 will exclusively be used to assess the consistency of distant cryogenic noise drivers, and SHRU1 has been chosen to maximize the distance with SHRU5.
, 2010). In this article, we consider exclusively IC at the acoustic receiver locations. WS data are available online from the European Centre for Medium-Range Weather Forecasts (ERA5 dataset) (ECMWF, 2019). The data have a time resolution of 3 h and a spatial resolution of 0. 25 Â 0. 25. WS (in m/s) are obtained by a combination of models and data assimilation to "reanalyze" archived observations. Here, again, our study focuses on local data at the receiver location. Global ice drift over the Arctic is available online from the National Snow and Ice Data Center  (Tschudi et al. , 2019). The data are a daily two-dimensional (2D) vector field (in cm/s), with a spatial resolution of 25 km Â 25 km. The data are obtained by satellite remote sensing (passive microwave or scatterometer) and a multi-sensor merging algorithm.
Spectral analysis of acoustic data The acoustic data analysis relies on the assumption that the ambient sound can be described as the superposition of a diffuse broadband ambient noise and various individual transient signals. The acoustic data analysis is performed in the spectral domain. First, power spectral densities (PSD) (in dB re 1 lPa 2 /Hz) are computed to characterize the overall ambient sound. Further, ANLs (in dB re 1 lPa 2 /Hz) are computed to characterize the underlying diffuse noise  (Carey and Evans, 2011). Note that ANL has been used in previous polar underwater acoustic studies  (Kinda et al. , 2013; Roth et al. , 2012). It can be estimated by manually removing data snapshots with transient signals  (Roth et al. , 2012). Here, we use an automated algorithm, as proposed by  (Kinda et al.
Link between acoustic and environmental data Correlation between ANL 300 and environmental data are computed to estimate the main noise drivers. The various datasets have different sampling periods: 7 min (ANL 300 ), 3 h (WS), and 24 h (IDM and IC). To compute correlation with a given environmental variable, ANL 300 is first decimated to the relevant sampling frequency (same as the environmental variable). Correlations are computed between ANL 300 and the local environmental variable (IC and WS) at different time scales. The correlation between ANL 300 and IC is computed for the whole dataset, while the correlation between ANL 300 and WS is computed over 31-day sliding windows, without overlap. Correlation between ANL 300 and IDM is computed for an area that covers the whole Arctic ocean (north of the iceedge), as in  Kinda et al.
At other times (November 2016, June and July 2016), the ID data are too scarce (i. e. , with a lot of missing points) for the correlation to be meaningful. All the correlation maps for SHRU5 are presented in Fig. 7. Over this whole period, the position of the correlation maximum stays between latitudes 75 N and 77 N, while its longitude is gradually moving eastward from 180 W to 150 W. The distance between this point and the acoustic receivers is between 270 and 770 km. Last but not least, an animated figure that shows all the results (correlation maps and time series) for SHRU1 and SHRU5 is provided as supplementary material. 2 When comparing the results between the two acoustic datasets, the IDM grid point that maximizes the correlation is the same for SHRU1 and SHRU5 four times out of nine.
IV. DISCUSSION This study demonstrates that the low-frequency (250-350 Hz) ambient noise on the Chukchi Shelf is driven by local (WS, IC), distant (IDM), and pervasive (Beaufort Duct) oceanographic features. As in many polar areas, the ambient noise largely depends on the local ice cover; it shows a classic quiet-under-ice pattern  (Dziak et al. , 2015; Kinda et al. , 2013; Menze et al. , 2017). Further, ambient noise is also driven by local WS: it tends to increase when WS increases. Although our dataset covers a very short period (about 3 weeks) with open-sea conditions, it still shows that the relationship between ambient noise and WS differs based on ice condition, the impact of WS being smaller when the area is fully covered with ice. This is a well-known polar phenomenon that can be found both in the Arctic (e.
Further, we demonstrate for the first time how, over the 6 months of the Arctic winter, the location of an important cryogenic noise driver drifts by more than 900 km. This analysis has been performed on two datasets that were collected on moorings separated by $50 km. Both datasets show very similar results. We thus hypothesize that the cryogenic sound source that is observed here radiates noise over most of the Alaskan Chukchi Shelf. Additionally, our result is the first to demonstrate the profound impact of the Beaufort Duct on the ambient sound. If the experimental area is ice-covered, the low-frequency ambient noise drops by 5-10 dB/Hz when the Beaufort Duct disappears. This confirms that a significant part of the ambient noise is driven by distant sound sources. Interestingly, some of the ambient noise variability remains driven by distant cryogenic events, even when the duct is absent from the experimental area (March to May), and thus the environment is less favorable for long distance propagation.
ACKNOWLEDGMENTS This research was supported by the  Independent Research and Development Program at WHOI  and by the  Office of Naval Research (ONR)  under Grant Nos. N00014-19-1-2627  and  N00014-18-1-2811. J. B. warmly acknowledges  D. Cazau  ( ENSTA Bretagne, France ) for helpful discussion and code sharing. The acoustic data collection effort was supported by the  ONR  under Grant No. N00014-15-1-2196  (Principal Investigator:  Y. -T. Lin , WHOI). Thanks also go to crew members of the  R/V Sikuliaq and USCGC Healy  for assisting in mooring operations. The ITP data were collected and made available by  WHOI.
Simulated data To validate the proposed method, simulated earthquake waveforms are produced for a simple 2D velocity model representing an underwater sedimentary basin. A basin was simulated to generate the commonly observed interactions of waves propagating in opposite directions. A spectral-element-based numerical simulation is done using the SPECFEM2D 7. 0. 0 code published under the CECILL V2 License  (Komatitsch et al. , 2012). The simple, though adequate, physical model is composed of two linear elastic sub-domains: a trapezoidal sedimentary layer with maximum depth of 100 m, characterized by P-and S-wave velocities of 1600 and 400 m s -1 , respectively, and density of 2000 kg m -3 (yellow in Fig. 2a ), overlying a bedrock with P-and S-wave velocities of 2500 and 1200 m s -1 , respectively, and density of 2200 kg m -3 (orange in Fig.