dataesr/scientific-paragraphs-categorization huggingface dataset:
For each sample, ellipsometric data were collected and the dispersion law and bandgap calculated a minimum of four times, and the results averaged. The error bars displayed in Figs  2d  and 4c  were determined by generating the dispersion laws using the upper and lower bounds of the Tauc-Lorentz oscillator parameters (from their uncertainties) and finding the resultant maximum variation in the bandgap and refractive index. First-principles calculations. Calculations were performed within density-functional theory, as implemented in the Vienna ab initio simulation package  39, 40. An energy cutoff of 550 eV was used, and the set of projector-augmented wave potentials was employed to describe the electron-ion interaction. We considered the following valence electron configuration: 5d 10 6s 2 6p 3 for Bi, 3p 6 3d 6 4s 2 for Fe and 2s 2 2p 4 for O.
In both genders, three age cohorts (60-69 years, 70-79 years, 80+) were used to estimate outcomes using the software TreeAge Pro 2011 (TreeAge Pro Inc. , Williamston, MA). For each cohort, we simulated 1,000,000 microsimulations. The base year of the analysis was 2015. We therefore estimated the impact of vitamin D-fortified dairy products to all individuals for 1 year (2015 base case) and estimated the lifetime effect of this intervention. Based on the distribution of the population in each cohort that was obtained from the National Institute of Statistics and Economic Studies (INSEE)  [20]  (see Table  1 ), we then calculated using an Excel sheet, the total costs, and outcomes in the general French population aged above 60 years. Projections for the years 2025 and 2050 were also done using populations projections from INSEE  [21]  to evaluate the effects of demographic changes on the benefits of the strategy.
Presentation of results and sensitivity analyses We estimated the lifetime number of fractures prevented (per type), life years gained, and QALY gained of recommended dairy intake versus absence of appropriate intake for the year 2015. Data are presented for the overall population aged over 60 years as well as per age group and gender. Incremental cost-effective ratios (ICERs) were estimated for the recommended intake of fortified dairy products versus usual care. ICERs are defined as the differences in costs between alternatives divided by their differences in outcomes (expressed in QALYs). An ICER represents therefore, the cost per QALY gained of recommended intake of fortified dairy products versus usual care. Projections for the years 2025 and 2050 were also conducted to evaluate the effects of demographic changes on the potential benefits of the strategy.
Acknowledgments The  Department of Public Health, Epidemiology and Health Economics of the University of Liege  has received an unrestricted educational grant from  CNIEL (Paris, France). The funding agreement ensured the authors' independence in designing the study, interpreting the data, and writing and publishing the report. We thank the  Prince Mutaib Bin Abdullah Chair for Biomarkers of Osteoporosis and Deanship of Scientific Research, King Saud University  for their support.
For microarray analysis E11. 5 tip cells were manually microdissected. GFP + E17. 5 tip cells, with a small number of their immediate progeny, were collected by flow cytometry from Id2-CreER; Rosa26R-fGFP embryos exposed to tamoxifen at 150 μg/gram mother's body weight at E16. 5. RNA was isolated using the Qiagen RNeasy Mini Kit, amplified and labelled using the Ovation RNA Amplification Kit V2 and FL-Ovation Biotin V2 (NuGEN). Hybridisation to Affymetrix mouse 430. 2 microarray chips (five chips per condition). Data were analysed using the Bioconductor Package in R (https://www. r-project. org/; R Team, 2014). Gene Ontology analysis using GOToolbox (http://genome. crg. es/GOToolBox/). Raw data was deposited in GEO (http://www. ncbi. nlm. nih. gov/geo) under accession number GSE75860.
Samples were outgased at 200 ˝C overnight before measurements. Surface areas, pore size distributions and pore volumes were obtained by the DFT method using Micromeritics 2420 software considering a cylindrical pore shape. X-ray Diffraction (XRD) measurements were recorded on a X'Pert MPD (Panalytical, Lelyweg, The Netherlands) with Cu Kα radiation (1. 54 Å). Cell parameters were determined using the free software Eracel (IUCr, http://www. cristal. org/ftp/eracel. zip). TGA curves are presented in the supplementary materials (Figures  S1  and S2 ). From this technique, weight percent for chromium nitride and vanadium nitride in both nanocomposites were estimated as 21 wt % and 28 wt %, respectively. For the conventional TEM analyses, a CM200 (Philips, Amsterdam, The Netherlands) was used. A JEOL 2100F TEM (JEOL USA, Inc.
Parasitic collisions of main bunches with satellite bunches are rejected on the basis of the timing information from the neutron ZDCs. The results presented in this paper are based on a data sample of Pb-Pb collisions recorded with ALICE in November and December 2011 at √ s NN = 2. 76 TeV. The events were collected with a minimum-bias interaction trigger using information of the coincidence of signals between V0A and V0C detectors. Central and semi-central Pb-Pb collisions were selected online by applying thresholds on the V0 signal amplitudes resulting in two separate trigger classes (central and semi-central triggers). In addition to the central and semi-central data samples, events selected by the EMCal trigger are analysed. The EMCal trigger required an EMCal cluster energy summed over a group of 4×4 cells, implemented as a sliding window, larger than an energy threshold.
, 2008; Sutton, 2008; Madsen et al. , 2010). One main assumption in the treatment of DLS and XPCS data is that every detected photon has only been scattered once by the sample, i. e. there is no multiple scattering. In the case of light scattering however, especially when used for studying concentrated systems with sizes up to several micrometres or with high refractive index contrasts, multiple scattering becomes highly probable  (Segre et al. , 1995a; Moussaı ¨d & Pusey, 1999). This needs further technical development in order to either suppress the multiple scattering as in cross-correlation methods  (Segre et al. , 1995b; Aberle et al. , 1998; Block & Scheffold, 2010)  or to restrict the analysis to the strongly multiple-scattering regime by diffusing wave spectroscopy  (Pine et al. , 1988).
In the case of the Pilatus 300K, each speckle is within one pixel, while the same speckle distributes over a cluster of pixels on the FReLoN detector. This should result in a higher contrast. That this is the case can directly be demonstrated with the contrast values obtained from the g 2 ðtÞ data. The contrast obtained by using the Pilatus detector is ffi 0. 04, whereas the higher angular resolution of the FReLoN detector gives a contrast of ffi 0. 08-0. 09. Furthermore, a much smaller q min can be chosen in the data treatment process as more pixels near to the beamstop contribute to the signal (q min = 2 Â 10 À3 nm À1 for the FReLoN and q min = 3 Â 10 À3 nm À1 for the Pilatus detector). As the correlation rate is proportional to q 2 , these correlation functions exhibit also much slower decay rates.
Furthermore, it has been demonstrated that two detectors with different X-ray detection principles can successfully be used to measure XPCS data. The high time resolution of the Pilatus and the high angular resolution of the FReLoN detector are complementary for different kinds of samples. The obtained data were found to be of similar statistical quality. A further improvement in the future may be achieved with more advanced detectors, combining high time and high spatial resolution, like for example with the Eiger pixel detector.
Comparison of synthetic and separated spectra We fitted the separated spectra corresponding to the solution of Table  6  with the interpolated synthetic spectra to check the results of Sect. 3. 4. The program PYTERPOL was used again. 2 A detailed description with a simple tutorial how to use it is provided at https://github. com/chrysante87/pyterpol/wiki 3 The intervals are the following: T B eff ∈ [13 000, 14 500] K, T Aa eff ∈ [9000, 11 500] K, T Ab eff ∈ [9000, 11 500] K, log g B ∈ [4. 0, 5. 0], log g Aa ∈ [3. 5, 4. 5], log g Ab ∈ [3. 5, 4. 5], v sin i B ∈  [200, 250]       The following spectral regions were fitted: The parameters corresponding to the best-fitting synthetic spectra are listed in Table  8. The best-fit parameters were estimated with a MCMC simulation and the uncertainties reflect only the statistical part of the uncertainty.
We do not see any peak in the Fourier spectrum at f 0 = 1/P 0 = 2. 38 d -1 , even though the Nyquist frequency for our spectroscopic dataset is f Ny = 7. 1 d -1. Nevertheless, the coverage and cadence are not uniform at all and the expected amplitude is small (5 km s -1 ), which makes this particular argument weak. We would also expect to see some frequency modulation due to the (classical) Doppler effect, P obs = (1 -v c )γP src , with v 2v kepl. However, for 0. 423 d we would only obtain a change by 0. 001 d, which is one order of magnitude smaller than the observed total variation. The lower limit for the rotation period is the critical rotation, P min = 2π(GM/R 3 ) -1/2 , and the upper limit is determined by rotational broadening, P max = 2πR/(v sin i) (cf. Table  8 ). For component Aa or Ab, the admissible range is from about P rot = 0.
After a global minimum was found, we split our data and optimised the ephemeris, relative luminosity of component B, and surface potentials using only observations from the MOST satellite, after which we optimised the effective temperature of component Ab and the relative luminosity of component B using the Johnson UBV photometry. The epoch of the primary minimum was also fitted for the UBV dataset to slightly adapt it for the ETVs discussed in Sect. 4. 3. The parameters corresponding to the best-fitting model are listed in Table  9. Our model is unable to account for either the rapid light oscillations or the ETVs; therefore we raised the uncertainty of observations from the MOST satellite to deal with B V 0. 622 ± 0. 060 L B B 0. 639 ± 0. 069 L B U 0. 713 ± 0. 071 L B MO 0. 634 ± 0. 024 L B Hp 0.
If they were set free, the solution would converge to unrealistic values ( 1. 0 mas), therefore they had to be fixed at values given by the parallax of the system and the light-curve solution (see Table  9 ). Convergence of the orbital parameters of orbit 1 was in general slow because the bulk of observations (NPOI, AMBER) was taken at low spatial resolution, at which this orbit is almost mainly on observations from VEGA/CHARA. Our model allows fitting separate sets of relative luminosities L R for each passband because the visibilities were estimated in narrow passbands: four for CHARA/VEGA, sixteen for NPOI, and ≈40 for VLTI/AMBER. It was not possible to divide the data into a larger number of small groups and to densely sample the relative luminosity of components Aa, Ab, and B as a function of the wavelength.
After a set of trial attempts, we split the data into two subsets: visible (MARKIII, NPOI, CHARA/VEGA) and infrared (AMBER). This sampling is justified by the very low variability of the luminosity ratios with wavelength of all stars within the visible and infrared regions, which we checked using synthetic spectra from the PHOENIX grid  (Husser et al. 2013). The relative luminosities of components Aa and Ab did not converge to plausible values for the infrared subset (it generally predicted a too low luminosity ratio between the two components of orbit 1), therefore we decided to use the estimate based on the PHOENIX grid and radii obtained from the light-curve analysis for components Aa and Ab, and the radius of component B was taken from  Harmanec (1988). The best-fit set of parameters is listed in Table  11  and a plot of the model vs.
The latter effect was almost overlooked if it had not been for the indication in the very accurate photometric data from the MOST satellite. However, the analytic models above give only limited insights into dynamical effects in a four-body system such as ξ Tau. Nonetheless, they provide very good results that are also needed as a starting point for a more sophisticated solution based on an approach that includes dynamical evolution in a more complete way. We proceed in two steps. In Sect. 8 we develop a numerical model that consistently takes into account the gravitational interaction of all stars in the ξ Tau. We use a fully numerical implementation, basically a standard N-body integrator, which we extend by subroutines that allow us to model several types of observables relevant for the ξ Tau dataset.
In our N-body model we do not fit the observed spectra using synthetic ones, individual light curve points, or interferometric fringes. We use higher-level observational data instead that were reduced and derived in previous sections. Hence we fit RV measurements for the three components Aa, Ab, and B, altogether N rv = 843, minima timings for the eclipses in the inner binary (Aa+Ab), N etv = 35, and astrometric observations for components B and C, N sky = 49. The latter is a subset of measurements from NPOI and WDS, for which it was possible to convert fringe visibilities (averaged over one night) into distance-angle values. The individual uncertainties of the observations used in this section were modified as follows: σ rv ≥ 2 km s -1 due to calibration uncertainties, σ etv ≥ 0. 001 d = 1.
The eclipse duration is then given by a simple geometry, A = 2 (R Aa + R Ab ) 2 -∆ 2 /v, where v denotes the average velocity between the points. We thus straightforwardly account for disappearing eclipses and their durations, but we do not model (possible) eclipse depth variations at this stage. To remove minor systematics in minima timings and eclipse duration, we attempted to suppress quasi-periodic oscillations visible in the MOST light curve by subtracting a function of the following form: f (t) = C 0 + C 1 (t -T 1 ) -[A 0 + A 1 (t -T 1 )] sin 2π(t -T 1 ) P 0 + P 1 (t -T 1 ) • (19) Its coefficients (C 0 , C 1 , T 1 , A 0 , A 1 , P 0 , P 1 ) were always determined by a local fit in the surroundings of the given minimum. The resulting data are reported in Table  14. The relative luminosities for photocentre computations were set to L Aa = 0.
Parameter Value Unit Note -0. 0073 and m C = 0. 90 +0. 73 -0. 04. Component B is on its lower limit m B min. The mass of component C is very poorly constrained, it is more of a distant test mass compared to the others. The 3σ uncertainties of the elements were determined by a simplified one-dimensional χ 2 mapping, assuming a relative increase of χ 2 by a factor of 1. 13, i. e. suitable for the number of degrees of freedom we have (ν = 908). The uncertainty values were verified using the bootstrap method with 100 random selections of datasets and corresponding simplex optimisation, but realistic uncertainties are likely to be larger than that because there are a number of local minima with statistically equivalent χ 2 values. We do not report a full correlation matrix of our solution here.
The complex visibilities V and their triple products T 3 were computed assuming uniform disks for individual components. Relative luminosities L i j at a given effective wavelength λ were computed by a black-body approximation. This extended model minimises χ 2 = χ 2 rv + χ 2 etv + χ 2 edv + χ 2 sky + χ 2 vis + χ 2 clo and has nine additional free parameters: distance d to ξ Tau , uniform-disk radii R j , and effective temperatures T eff j of all the components, even though the contribution of component C is only minor (clearly lower than 10% at the longest wavelength, λ = 2. 6 µm). We used all observational data from the MARKIII, NPOI, CHARA/VEGA, and VLTI/AMBER spectro-interferometers, and N clo = 4856 measurements of the closure phase arg T 3 (from NPOI and VLTI/AMBER). The total number of degrees of freedom is thus ν = N data -M free = 28 019 -32 = 27 987.
-Eclipse-timing variations -orbit 1: Eqs. (  29 )-(  31 ) provide amplitudes of the ETVs (assuming that component B is at periastron) of orbit 1 δt ETV,long = 0. 0162±0. 0007 d, δt ETV,short = 0. 0068 ± 0. 0003 d. Their sum agrees with the detected amplitude of ETVs (δt ETV,OBS = 0. 025 ± 0. 010 d). We also note that the two primary eclipse minima in the MOST data were found to be phase-shifted by 0. 0003 in Sect. 4. 3. This is about 0. 1 • in orbital longitude of inner orbit 1. Combining results in Eqs. (  29 )-(  31 ) and taking into account 2 86 • and λ 1 F 2 from Table  15 , we obtain very good agreement with the observed shift.
For each row, N is the number of observations in each of the filters used, ∆T is the time span covered by each dataset, column "Passbands" shows the photometric filters used, column "Comp/Check" lists the names of comparison and check star used. UBV denote the Johnson filters, and MO denotes the broad-band filter of the MOST satellite. (1) Only three observations were taken before RJD = 54 116, all at RJD = 46 324. (2) The original Hipparcos H p broad-band observations were transformed into the Johnson V filter following Harmanec (1998). However, for the light-curve solutions the limb-darkening coefficients corresponding to the original Hipparcos passband were used. Instruments: HVAR -Hvar Observatory 0. 65 m Cassegrain reflector, photoelectric photometer; HIPP -The ESA Hipparcos Astrometric Mission; SAAO -South African Astronomical Observatory 0.
Acknowledgements. The research of J. N. , P. H. , M. W. , and P. Z. was supported by grants  P209/10/0715  and  GA15-02112S  of the  Czech Science Foundation. The research of J. N. and P. H. was also supported by grant No. 678212  of the Grant Agency of the  Charles University. The Navy Prototype Optical Interferometer is a joint project of the  Naval Research Laboratory  and the  US Naval Observatory , in cooperation with Lowell Observatory and is funded by the  Office of Naval Research and the Oceanographer of the Navy. The authors thank  Jim Benson  and the NPOI observational support staff, whose efforts made this project possible. This research has made use of the SIMBAD astronomical literature database, operated at  CDS, Strasbourg, France. The  CHARA Array  is operated with support from the  National Science Foundation  through grant  AST-0908253 , the  W.
A. F. J. M. is grateful for financial assistance to  NSERC (Canada)  and  FQRNT (Quebec). The observations with the MPG 2. 2 m telescope were supported by the  Czech Ministry of Education, Youth  and  Sports  project  LG14013  (" Tycho Brahe: Supporting Ground-based Astronomical Observations ") during run P2 in May 2015. We acknowledge the use of the electronic database from the  CDS, Strasbourg  and electronic bibliography maintained by the  NASA /ADS system. We acknowledge the constructive criticism by the referee  Peter P. Eggleton , which helped us to improve the paper.
Notes. For each row, N is the number of observations in each of the filters used, ∆T is the time span covered by each dataset, column "Passbands" shows the photometric filters used, column "Comp/Check" lists the names of comparison and check star used. UBV denote the Johnson filters, and MO denotes the broad-band filter of the MOST satellite. (1) Only three observations were taken before RJD = 54 116, all at RJD = 46 324. (2) The original Hipparcos H p broad-band observations were transformed into the Johnson V filter following Harmanec (1998). However, for the light-curve solutions the limb-darkening coefficients corresponding to the original Hipparcos passband were used. Instruments: HVAR -Hvar Observatory 0. 65 m Cassegrain reflector, photoelectric photometer; HIPP -The ESA Hipparcos Astrometric Mission; SAAO -South African Astronomical Observatory 0.
Blood samples were drawn from patients before and after 1, 2, 3, and 4 cycles of ipili mumab treatment. Patients' samples were provided by C. Robert, N. Chaput, C. Coutzac, and S. Cotteret (Gustave Roussy Institute, Villejuif, France). Retrospective German cohort of 4 patients. Blood was collected, before and after 2 ipilimumab injections, from 4 patients partici pating in a phase II study evaluating the safety and efficacy of com bined ipilimumab and intratumoral IL2 treatment in pretreated patients with stage IV melanoma (ClinicalTrials. gov identifier: NCT01480323). Chemokine receptors were assessed in patients at baseline and after 2 injections of ipilimumab with concomitant treatment consisting of 7 or 8 injections of intratumoral IL2 (9 mIU/injection). Retrospective Italian cohort of 10 patients.
Blood samples were collected from patients before and after 1 and 2 injections of ipili mumab at the University Hospital of Siena between July 2011 and June 2015. The chemokine receptor profile was assessed after the samples were thawed. Retrospective American cohort of 11 patients. Blood samples were collected from patients before and after 2 injections of ipilimumab (ClinicalTrials. gov identifier: NCT00495066). The chemokine receptor profile was assessed after the samples were thawed. Clinical characteristics of these 4 cohorts are described in detail in Supple mental Table  3.
Methods Characteristics of patients and cohorts SORAFTEM. The SORAFTEM (European Union Drug Regulating Authorities clinical trial EudraCT 2007-000527-18) cohort has been described previously  (63). Patients over 18 years of age with histolog ically confirmed metastatic or unresectable melanoma, measurable disease, an Eastern Cooperative Oncology Group performance status below 2, and adequate hematologic, renal, hepatic, and coagulopathic medium, which consisted of RPMI 1640, 1% penicillinstreptomy cin, type IV collagenase (50 IU/ml), hyaluronidase (280 IU/ml), and DNAse I (30 IU/ml) (all from SigmaAldrich) and were run on a gen tleMACS Dissociator (Miltenyi Biotec). The cell samples were diluted in PBS and then passed through a cell strainer and centrifuged for 5 minutes at 450 g. Next, cells were resuspended in PBS and stained for flow cytometric analysis.
Statistics Data were analyzed and presentations were performed within the R statistical environment  (66). Flow cytometric parameters were con verted to Zscores prior to any correlation analyses. The estimation of metastatic group effect (human data) and organ distributions (mouse data) on FACS parameters was performed by beta regression mod eling, with the dispersion allowed to differ between groups. For the other continuous parameters, generalized leastsquares regression was applied to logtransformed data and contrasts of interest tested by Wald tests. Parameter distributions between the blood and tumor were assessed by mixedeffects linear modeling. OS, determined from the date of diagnosis, and progressionfree survival (PFS), deter mined from the date of sampling, were used as the primary endpoints.
Acknowledgments The authors thank  C. Robert ,  N. Chaput ,  C. Coutzac , and  S. Cot  teret ( Gustave Roussy Cancer Center ) for patients' specimens. We are indebted to  P. Rameau  and  Y. Lecluse  from the flow cytometry and imaging platform; to the  Department of Biology and Pathology (Gustave Roussy  and  CHU of Lyon) ; and to the  Plateforme d'Eval uation Préclinique of Gustave Roussy. The  Institute for Research in Biomedicine  is supported by the  Helmut Horten Foundation. This work was supported by the  Institut National du Cancer , the  Agence Nationale pour la Recherche , the  Ligue contre le Cancer  (équipe labellisée de L. Zitvogel), the  European Union (Inflammation and Cancer Research in Europe [INFLACARE]) , the  Site de Recherche Intégrée sur le Cancer (SOCRATES) ,  LABEX OncoImmunology , the  Direction Générale de l'Offre de Soins (Université ParisSud) , and the  Paris Alliance for Cancer Research Institutes.