dataesr/scientific-paragraphs-categorization huggingface dataset:
It is very important to proceed with this test, since approximately 50% of Solar-type field stars are binary or multiple systems  (Raghavan et al. 2010; Moe & Di Stefano 2017) , and most of them are photometricaly unresolved. To run StarHorse we only select stars with available masses and limit the distance to 1 kpc. Figure  8   4 ), the effect being larger for the secondary members. This is probably a direct result of the systemic magnitudes we are using, since each star is assigned a brighter magnitude than it actually is, the amplitude of the effect being larger for secondaries. Therefore, our code will tend to match it with models at nearer distances. The masses estimated by StarHorse are in reasonable agreement with the reference sample, as shown in Table  4 : 50% of the stars present relative errors smaller then 10%.
RELEASED DATA PRODUCTS We applied StarHorse to a few spectroscopic surveys together with astrometric and photometric measurements to deliver public distances and extinction catalogues. For the moment we are not releasing ages and masses because their estimates are still subject to considerable improvement. Among the several spectroscopic surveys available to the community, we chose among those that have been more widely used and which have high to medium resolution. All catalogues are available to the community via the LineA web page 4. As our method is subject to further improve- ments (e. g. incorporating new Gaia information, new stellar tracks with denser model grids, future releases of the adopted databases, and new extinction priors), this will lead to new versions of the released data, therefore, we suggest the reader to check for next releases at the LineA web page.
APOGEE Catalogues The APOGEE-2 survey  (Majewski et al. 2017 ) is a program from the Sloan Digital Sky Survey (SDSS-IV,  Blanton et al. 2017). It is a spectroscopic survey conducted in the near infrared, with high resolution (R ∼ 20, 500), and high signalto-noise (S /N > 100). It maps the Galaxy through all populations and it has targeted, as of its latest release (DR14;  Abolfathi et al. 2017) , about 270,000 stellar spectra. As a near infrared survey of high resolution, APOGEE has the advantage to study with more detail the stars located in the dusty regions of our Galaxy, such as the Bulge and Disk. We applied StarHorse to the latest APOGEE release, DR14.
APOGEE DR14 Cannon As the APOGEE DR14 also contains stellar parameters and chemical abundances derived by the data driven method called Cannon  (Ness et al. 2015; Casey et al. 2016) , we also applied StarHorse with this input. We used the APOGEE DR14 7  allStar summary catalogue (allStarCannon-l31c. 2. fits). Which contains the atmospheric stellar parameters, chemical abundances and its uncertainties. Starting from this file we did the following steps to prepare a input to StarHorse: (i) Cross-match with photometry and astrometry We carried out a positional cross match, using TOPCAT  (Taylor 2005) , with photometry from APASS  (Henden & Munari 2014 ) and from 2MASS  (Cutri et al. 2003). (ii) Av Prior As priors for the V-band extinction we used the APOGEE targeting extinction values  (Zasowski et al.
The Gaia-ESO sample The Gaia-ESO survey (GES  Gilmore et al. 2012 ) is a large public spectroscopic survey with high resolution that covers all Milky Way components and open star clusters of all ages and masses. The final GES release is expected to include about 10 5 stars. We downloaded the Gaia-ESO data release 3 (DR3) from the ESO catalogue facility 8. This catalogue contains a total of 25533 stars, including the Milky-Way field, open clusters, and calibration stars. The catalogue contains 100 columns, from which we selected the necessary information to run StarHorse: atmospheric parameters, elemental abundances, and 2MASS JHK s pass-bands  (Cutri et al. 2003). From this catalogue we proceeded with the following steps, before applying StarHorse. (i) Select only field stars and reliable sources: From this catalogue we selected only Milky Way field stars,  which are the main targets intended for StarHorse.
The RAVE DR5 catalogue The data release 5 (DR5  Kunder et al. 2017)  is the lastest RAVE data release. It contains spectra for 483,330 stars. We downloaded the publicly released catalogue called RAVE DR5, which contains spectral parameters and radial velocities derived by the SPARV pipeline  (Zwitter et al. 2008; Siebert et al. 2011). The catalogue also contains astrometry from Gaia-DR1 (215,590), and photometry from 2MASS and APASS. We note that very recently,  McMillan et al. (2017)  updated the DR5 catalogue parameters and derived distances using feedback from the Gaia DR1 parallaxes, but our VAC presented here is based on the public DR5 data. From this catalogue, we proceeded with the following steps before running StarHorse: (i) Spectral parameters We use the calibrated atmospheric parameters, which are named in the catalogue as Teff NK , logg NK , Met NK.
StarHorse FLAGS All released data products catalogues have two columns that describe the StarHorse input data, SH INPUTFLAGS, and the StarHorse output data, SH OUTPUTFLAGS, as shown in Table  5. The input flags specify which parameters were used in the likelihood calculation to estimate the distances and extinctions given. For example, if the temperature was available for that star a TEFF flag will appear, and if TEFF was not available in the calculation a uncalTEFF flag will appear. The other parameters are specified as follows in the input flag: LOGG (surface gravity), PARALLAX (parallax), MH (metallicity), JHKs (2Mass filters) and BVgri (APASS filters). If the input flag contains ALPHAM it means that the alpha elements were available in the calculation of the total metallicity of the star.
SUMMARY AND FUTURE PERSPECTIVES We have presented a code that computes distances, ages, masses, and extinctions for field stars with photometric, spectroscopic, and astrometric data. It is based on Bayesian inference, computing the marginal posterior distributions for the data given a set of stellar models. The code represents a significant improvement over the one presented by  Santiago et al. (2016)  in several aspects. The most important one is the ability to estimate ages, masses, and extinction, in addition to the spectrophotometric distances presented by those authors. The updated code, which we call StarHorse, is also capable of incorporating the parallax as an additional observational quantity in the statistical analysis (Figure  1 ). Updated spatial, metallicity, and age priors for the Galactic components, now including the bulge as well, are presented (Table  1 ).
In addition, StarHorse is now more flexible in terms of the input data and the choice of observational quantities to be used within them. The new code was validated using simulated and real stars. These latter are samples with reliable parallax (or distance) data, including field giants with asteroseismic data, EBs used as benchmarks for stellar evolutionary codes, or cluster stars with well-known distances and ages, usually from isochrone fitting, often in combination with spectroscopic data. For EBs that are not detached, the distances present an offset in relation to the reference ones, our distances being usually smaller by 20% in this specific case. The discrepancy is larger for secondary stars than for the primary ones, which is what one expects from using systemic photometric measurements.
These parameters, in turn, may be used in connection to APOGEE abundances and radial velocities, to study the properties of the main Galactic populations, and their spatial variations, as was previously done by  Anders et al. (2014), and Fernández-Alvar et al. (2016)  using distances from  Santiago et al. (2016). For more local samples, such as Gaia-TGAS and RAVE, reliable parallax information can be included in the Bayesian method to yield masses and ages, as validated in this paper, allowing for a more detailed modelling of the chemo-dynamical history of our Galaxy  (Anders et al. 2017). Finally, we have run StarHorse on different public catalogs from the RAVE collaboration, as well as on GES, and GALAH public data releases. These are available for download at the LIneA web site. 11.
ACKNOWLEDGEMENTS The StarHorse code is written in python 2. 7 and makes use of several community-developed python packages, among them astropy (Astropy Collaboration et al. 2013), ezpadova 12 , numpy and scipy (Oliphant 2007), and matplotlib (Hunter 2007). The code also makes use of the photometric filter database of VOSA (Bayo et al. 2008), developed under the  Spanish Virtual Observatory  project supported from the  Spanish MICINN  through grant  AyA2011-24052. We thank  Eddie Schlafly  (LBL) as well as the anonymous referee for various useful comments that helped improving the manuscript. We thank  Laia Casamiquela  for providing data from the OCCASO survey. Funding for the SDSS Brazilian Participation Group has been provided by the  Ministério de Ciência e Tecnologia (MCT) ,  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) , and  Financiadora de Estudos e Projetos (FINEP).
Statistical Analysis To guarantee standard operating procedures, all dogs were operated by the same surgeon (OZ). Clinical evaluations at any follow-up time were performed in each center independently. Results are expressed as mean and standard deviation. Statistical analysis was performed using Graphpad Prism v7. 04 software (GraphPad Software Inc. , La Jolla, CA). One-way analysis of variance (ANOVA) for repeated measures with Dunnett's post hoc test was used to analyze the data obtained at the different time points and for the comparison of data sets from each time point with the baseline values. Two-way ANOVA for repeated measures was applied to test the influences of animal size and OA severity on owner's (HCPI) and orthopedic (OS) scores at the different time points. A p <. 05 was considered statistically significant.
, 2007). This work aimed to investigate molecular mechanism(s) behind non-apoptotic osteogenic functions of FasL during intramembranous osteogenesis, where data are lacking. For this purpose, the mandibular bone adjacent to the first molar was selected as a model. Immunohistochemistry, PCR Arraybased screening and FasL deficient vs. wild type mouse bone examination were performed in vivo. These analytical approaches were completed by in vitro functional tests using a cell line to demonstrate a new relation between FasL and Mmp2 in osteoblasts.
Micro Computed Tomography (MicroCT) Specimens were scanned using a Quantum FX Caliper device (Life Sciences, Perkin Elmer, United States). The skeleton was acquired using an X-ray tube voltage of 90 kV and a tube current of 160 µA with a field of view 10 × 10 mm. Full 3D high-resolution raw data were obtained by rotating both the X-ray source and the at panel detector 360 • around the sample (scanning time of 3 min). Projections were reconstructed by Rigaku software in image blocks of 512 × 512 × 512 voxels with an anisotropic voxel size of 20 µm. Data were stored in Dicom frame. Quantification of the jaw bone microarchitecture below the first molar was made by a CTscan Analyzer (Skyscan, release 1. 13. 5. 1, Belgium). The region of interest (ROI) was drawn in the axial sections along the cortical zone between the first and second mesial roots.
The bone was segmented in the ROI, and the following parameters were calculated using the CTAnalyzer: bone volume/tissue volume (BV/TV), trabecular thickness (Trab. Th), trabecular number (Trab. N), trabecular pattern factor (Trab. Pf), and BMD. An internal density phantom, calibrated as in mg/cm 3 of hydroxyapatite, was used to scale the bone density. Selected nomenclature was described in  Bouxsein et al. (2010). Twelve animals at stage P24 were used, six gld and six wildtype mice, with both groups consisting of animals of mixed gender. The size of the group was arranged to reach the statistical significance (at least for p ≤ 0. 05). The averages and standard deviations were determined for all data sets. Normality of the data was tested by Shapiro-Wilks test. Data with a normal distribution (BV/TV, Trab.
PCR Array The heads of gld mice and wild-type mice (E15) collected into RNAlater (Ambion, United States) were washed in PBS, fixed in 4% paraformaldehyde, and histologically processed in paraffin. Sections (5 µm) were prepared, the mandibular bone was separated and RNA was extracted using RNeasy FFPE Kit (73504, Qiagen, United States) as described by  Svandova et al. (2014). Expression of osteogenic genes was assessed by a PCR Array. RNA was transcribed into cDNA and used for the PCR Array (Mouse Osteogenesis, PAMM-026Z; Qiagen, United States). Data (n = 6) were evaluated by Qiagen Data Analysis Center available at https://www. qiagen. com/kr/shop/genes-and-pathways/dataanalysis-center-overview-page/. The software provides fold regulation values and statistical significance of the results (p ≤ 0.
We also observed that ZEB1 downregulation by specific siRNA resulted in a significant decrease of OLIG2 expression (Fig. 5c ), whereas SOX2 expression was not affected (data not shown). High expression of the five genes signature: α6-integrin/ ZEB1/SOX2/OLIG2/CHK1 is prognostic of overall survival of GBM patients Finally we queried if the five genes signature: α6integrin/ZEB1/SOX2/OLIG2/CHK1 correlates with survival of GBM patients. For this purpose, we analyzed two different databases, TCGA and Rembrandt. In the TGCA database, we focused on patients treated with standard radio-chemotherapy for primary GBM, excluding patients with prior glioma history (n = 184). Based on a Cox model, risk score and risk groups for the five genes signature were significantly associated with overall survival (risk score: hazard ratio (HR) = 2.
72 [1. 34; 5. 51], p = 0. 005; high versus low risk: HR = 1. 74 [1. 10; 2. 73], p = 0. 017) when patients samples were stratified by the highest and lowest terciles (Fig. 6a  and b ). In the Rembrandt database (N = 178), the risk score and risk groups based on the same five genes were also statistically significant (risk score: HR = 2. 72 [1. 56; 4. 75], p = 0. 0004; high versus low risk: HR = 1. 92 [1. 31; 2. 82], p = 0. 0009, Fig. 6b ), indicating that higher levels of this five genes signature predicted significantly shorter overall survival as compared with patients with lower expression.
α6-Integrin protein expression by FACS analysis α6-Integrin protein expression on cells derived from GBM biopsy specimens was analyzed using the Flow cytometry live cell protocol from Cell Signaling Technology and the PE-conjugated anti-α6-integrin antibody from eBioscience. Stained cells were analyzed by flow cytometry (BD Accuri TM C6 cytometer) and the data were analyzed by the BD Accuri C6 software. The specific florescent index (SFI) was calculated as previously described, with the following formula: SFI = (MFI antibody -MFI isotype control)/MFI isotype control (MFI: mean fluorescent index).
γH2AX staining Cells derived from GBM biopsy specimens, transfected with a specific α6-integrin siRNA or a scramble control were exposed to a radiation dose of 4 Gy. Six hours or 24 h post-IR, cells were fixed and intracellular staining with an Alexa Fluor 488 anti-H2AX-Phosphorylated (Ser139) antibody performed according to the manufacturer's Fig. 7  Proposed mechanism for the radio-protective effect of α6integrin in GBM. We showed that α6-integrin contributes to GBM radioresistance by: controlling DNA damage checkpoint and repair via CHK1 and Cdc25c and regulating the transcriptional network ZEB1/ OLIG2/SOX2 protocol (BioLegend). Stained cells were analyzed by flow cytometry (BD Accuri TM C6 cytometer) and the data were analyzed by the BD Accuri C6 software.
Cell cycle analysis Forty-eight hours post-IR, cells were fixed in 70% icecold ethanol for 1 h at 4 ̊C. After washing, the cell pellet was resuspended in PI-staining buffer (50 μg/ml PI, 10 μg/ ml RNAse A) and incubated for 15 min at 37 ̊C. The DNA content was analyzed by flow cytometry (BD Accuri TM C6 cytometer). Percentage of cells in sub-G1, G1/S and G2/M phases were quantified by the BD Accuri C6 software. Correlation of five genes signature: α6-integrin/ZEB1/ SOX2/OLIG2/CHK1 with survival of GBM patients For survival analysis, using the GBM database of the Cancer Genome Atlas, (TCGA) (https://genome-cancer. ucsc. edu/), we focused on patients treated with standard radio-chemotherapy for primary GBM, excluding patients with prior glioma history (n = 184). Overall survival rates were estimated using Kaplan-Meier method and univariate analyses were performed using Cox proportional hazard model.
From predictors included in a Cox model, a risk score prediction was created. It is based on the linear predictor given by the model. This score was then divided into three groups by taking the terciles of the risk score. Thus, three groups were established (poor, medium and good prognostic) and correspond to the signature for this dataset. To confirm the prognostic ability of our predictors, the same strategy was applied on REMBRANDT dataset (n = 178) (http://www. betastasis. com/glioma/rembrandt/). All reported p-values were two-sided. For all statistical tests, differences were considered significant at the 5% level. Statistical analysis was performed using R. 3. 4. 2 software.
Acknowledgements We thank  Professor Vincent Lubrano  from the  Neurosurgery Department at Toulouse University Hospital, Purpan , for the human GBM specimens. We thanks  Manon Farcé  from the  Pôle technologique of Cancer Research Center of Toulouse (CRCT) ,  INSERM UMR. 1037 for her technical help in FACS analysis. This work was supported by  INSERM  ( "Institut National de la Sante et de la Recherche Medical" ) and the  "Institut National du Cancer" (Plan Cancer   2014-2015 ).
Acknowledgements The authors would like to thank  I. Low ,  T. Seng ,  S. Mustafah , and  N. Shadan  from the SIgN flow-cytometry facility for their help with cell sorting, the  SIgN Immunomonitoring Platform  for assisting with luminex processing, and the  SIgN Mutant Mouse Collection Core Facility  for providing some mice. We wish to thank  H. S. Tay  for providing technical assistance,  K. Neo  for performing mouse genotyping and  Insight Editing London  for critically reviewing the manuscript. This work was supported by a  SIgN core grant  ( A*STAR, Singapore ). J. F. was supported by  European Social Fund  and  European Regional Development Fund-Project MAGNET  (No. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000492 ).
Measurements of the normalized inclusive 2-, 3-, and 4-jet cross sections in ∆φ 1,2 and ∆φ min 2j The normalized inclusive 2-, 3-, and 4-jet cross sections differential in ∆φ 1,2 and ∆φ min 2j are corrected for the finite detector resolution to better approximate the final-state particles, a procedure called "unfolding". In this way, a direct comparison of this measurement to results from other experiments and to QCD predictions is possible. Particles are considered stable if their mean decay length is cτ > 1 cm. The bin width used in the measurements of ∆φ 1,2 and ∆φ min 2j is set to π/36 = 0. 087 rads (5 • ), which is five to ten times larger than the azimuthal angular separation resolution. The corrections due to the unfolding are approximately a few per cent. The unfolding procedure is based on the matrix inversion algorithm implemented in the software package ROOUNFOLD  [39]  using a 2-dimensional response matrix that correlates the modeled distribution with the reconstructed one.
The MADGRAPH generator matched to PYTHIA 8 provides a reasonable description of the measurements in the 3-jet case, but shows deviations in the 4-jet case. The predictions from MADGRAPH + PYTHIA 8 and PYTHIA 8 are very similar for the normalized cross sections as a function of ∆φ min 2j in the four-jet case. It has been checked that predictions obtained with the MADGRAPH matrix element with up to 4 partons included in the calculation without contribution of the parton shower are able to reproduce the data very well. Parton shower effects increase the number of events with low values of ∆φ min 2j. Figures  14  and 15  illustrate the ratios of predictions from PH-2J matched to PYTHIA 8 and HER-WIG++, PH-3J + PYTHIA 8, and HERWIG 7 to the normalized inclusive 3-and 4-jet cross sections differential in ∆φ min 2j , for all p max T regions.
Acknowledgments We thank  Simon Plätzer  and  Simone Alioli  for discussion and great help on setting up, respectively, the HERWIG 7 and the PH-3J simulation. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;.
A small column in 2 mL polypropylene (PP) including frits (pore size = 20 µm) (PE: polyethylene) was used with internal diameter: 7 mm and a filling height of resin > 39 mm, reference AC-142-TK from Triskem. All water used for the dilutions had a resistivity of 18 MΩ and was prepared using Purelab (Veolia) (Ultrapure Water Purification System). Activated samples were diluted and put in a tube in a determinated geometry. Activity measurements were assessed by ϒ-ray spectrometry with an HPGe (High-Purity Germanium) detector. The HPGe detector was calibrated in energy and efficiency for different geometries with certified standard radioactive sources (Cerca, France). For activity measurement, γ-ray spectrum analysis software package, Genie 2000 (Canberra, France) was used. The concentrations of Er Ho, Cr and Mn were estimated following peaks centered at 53 keV for 165 Er, 80 keV for 166 Ho, 320 kev for 51 Cr and 935 for 52 Mn (744 and 1434 can be used too).
For activity measurement, γ-ray spectrum analysis software package, Genie 2000 (Canberra, France) was used. The concentrations of Er Ho, Cr and Mn were estimated following peaks centered at 53 keV for 165 Er, 80 keV for 166 Ho, 320 kev for 51 Cr and 935 for 52 Mn (744 and 1434 can be used too). All activities were determined at the end of beam (EOB). For polishing the chromium target, Model ES 300GTL has been used. The magnetic putty (1, 3 and 6 µm) carpet for pre-polishing (graduation 120, 320, 600 and 1200) and carpet for polishing (1, 3 and 6 µm) came from ESCIL (France). 165 Er (an Emitter Auger Electron and Bimodality).
37 g of LN2 resin (1 mL) were made first by the gravimetry method then with variation of flow (1 mL/min and 3 mL/min) at atmospheric pressure using a peristaltic pump in the outlet of the column. A gradient of concentration on HNO 3 was used: 0. 4 M 0. 8 M, 1 M and 2 M or/and 5 M if necessary. Tests at 1 bar of Nitrogen (maximum pressure of solenoid valves from Bio-Chem (used in semi-automated system) is 1. 4 bars) were made. Around 4 g of LN2 (for a maximum of mass target of 140 mg, around 11 mL) 50-90 mg of the target were used in semi-automated tests. Four tests on a homemade automation system (drive with the homemade software ACCRA (Automatisme & Contrôle Commande Radiochimie)) were made using the remote control sketch below (Figure  3 ) from the dissolution step of the target until the final evaporation step of HNO 3.