dataesr/scientific-paragraphs-categorization huggingface dataset:
Atmospheric parameters From our high-resolution spectra, we determined the stellar atmospheric parameters: the effective temperature T eff , logarithmic surface gravity log g, metallicity [Fe/H], microturbulent velocity v micro , and the total non-microturbulent velocity broadening 5  , v broad. Given that the targets are all EBs, we cannot directly derive the parameters from the optical spectra. We wanted to make use of all the spectra we had (about 15 spectra per star). However, we first needed to deal with the fact that each observed spectrum is a combination of the spectra of two stars that are in motion. For our SB2 systems, we disentangled our observed spectra with the FDBinary software described by  Ilijic et al. (2004). Then, we analyzed the resulting spectra of the giant component with the Grid Search in Stellar Parameters (GSSP) code  (Tkachenko 2015).
The atmospheric parameters derived from the spectra are given in Table  1. The effective temperatures of the secondary components are given later in the paper (see Table  4 ). These temperatures were obtained through our JKTEBOP dynamical analysis (see  Sect. 3. 3). To ensure the reliability of these values, we compared stars in our sample with the results of the APOGEE  (Gunn et al. 2006; Blanton et al. 2017; Majewski et al. 2017; Wilson et al. 2019 ) DR14  (Abolfathi et al. 2018; Holtzman et al. 2018 ) and DR16  (Ahumada et al. 2020 ) catalogs where data was available. The APOGEE values were measured from infrared spectra through an automated analysis. The discrepancies between ARCES and APOGEE temperatures were on the order of 50 K, within the uncertainties, except for KIC 7293054, 7768447, and KIC 10015516.
056 -0. 002 log ∆ν) and d 02 = (0. 131 -0. 033 log ∆ν)  (Mosser et al. 2011b). The code then cuts the spectrum into ±0. 6∆ν wide ranges around the expected l = 0 position and it picks the highest peak, assuming that it corresponds with the actual position of l = 0. It eventually searches for the mean position of the l = 1 ridge with the assumption of no mixed modes, which is expected to be separated by d 01. The routine then looks for the actual closest significant peaks to the expected ridge, which in other words allows for only one l = 1 mixed mode per overtone. As a final step it looks for l = 2 modes by searching for significant peaks near the l = 0 ridge in a separate H0 testing. Once the modes are identified in terms of degree and order, each individual frequency is fitted with a Lorentzian function.
Dynamical analysis We used the JKTEBOP software  (Southworth 2013)  to compute the orbital parameters of the binary systems. This code uses the Levenberg-Marquardt algorithm to determine the best fit to the data. Error bars are then estimated with a Monte Carlo algorithm ("task 9" of JKTEBOP). We simultaneously fitted both the eclipse-photometry light curves and the radial velocities obtained in section 2. We adopted the same convention as  Gaulme et al. (2016a) , who defined the primary component as the giant and the secondary as its companion. This is why the radii ratios we obtained are smaller than 1. We had to make two exceptions to this rule for the eccentric systems 7293054 and 9904059, for which only the eclipse of the companion is visible. Following Brogaard et al. (  2018 ), we assumed a quadratic limb-darkening law for the giant components of our binary systems.
Ages from stellar evolution models We provide proxies of the system ages by fitting the measured parameters of each binary system individually against theoretical isochrones from the YaPSI database (see  Spada et al. 2017, for details). The YaPSI database covers a wide range of metallicity and initial helium abundance; the isochrones were constructed from stellar evolution tracks for stars in the mass range 0. 15 M to 5 M , covering the evolutionary phases from pre-MS to the tip of the RGB. It should be noted that the YaPSI tracks and isochrones do not cover the helium burning phases (horizontal branch and asymptotic giant branch). Consequently, the uncertainty on the age of the stars whose evolutionary state is undetermined or classified as RC might be underestimated by our methodology. Indeed, the observed mass of a RC star may be  Fig.
3. Phase-folded eclipses and radial velocities for SB2 stars in our sample. For both eclipses, the processed Kepler data is shown in light blue dots and the solid black line corresponds to the fit from JKTEBOP. For the radial velocities, red squares (resp. green diamonds) represent the observed radial velocity of the giant (resp. the companion) and the solid red line (resp. the green line) indicates the fit from JKTEBOP. ω e i R2 R1 R1+R2 a T2 T1 4 L2 L1 † K 1 K 2 γ 1 γ 2 α LD,lin α LD,quad (days) (KJD) ( • ) ( • ) (km s -1 ) (km s -1 ) (km s -1 ) (km lower than its initial mass owing to mass loss near the tip of the RGB (see for instance  Casagrande et al. 2016). This introduces an uncertainty on the age of the concerned stars. Quantifying this effect in binary stars is out of the scope of the present paper.
(2018) , who obtained new radial velocity measurements and new effective temperatures and metallicities. Beyond small discrepancies, these authors confirmed the mass and radius overestimation reported by  Gaulme et al. (2016a)  when employing the asteroseismic scaling relations. In addition, for the first time, they tested the online software PARAM (Rodrigues et al. 2017) 9 , which uses grids of stellar evolution models to retrieve RG masses and radii from ν max , ∆ν, T eff , and [Fe/H]. They observed a good agreement between the PARAM results and the dynamical measurements. Finally,  Themeßl et al. (2018)  led a similar study with the ten systems studied by Gaulme et al. and a new system that had not been published before. For the new system and two others,  Themeßl et al. (2018)  measured new 9 http://stev.
These issues are out of the scope of the present paper. As a final comparison, we used the PARAM online tool (Rodrigues et al. 2017), which was successfully tested by  Brogaard et al. (2018)   13  on three systems, to estimate the masses and radii of all 14 of our systems. The principle, similar to that of White et al. (  2011 ) and  Sharma et al. (2016) , consists of fitting a stellar evolution model to the available data set. The basic set is the pair (∆ν, ν max ), which can be complemented by the effective temperature, metallicity, period spacing of the mixed modes, and absolute luminosity. We observed a strong discrepancy in the results of the new target KIC 4054905, one of the few systems that belong to the RC, when we opted to provide ∆Π as a constraint. The PARAM code estimates the mass at 1.
Observational constraints on tidal circularization Several teams have exploited the Kepler data to study the tidal circularization of binary systems. The two main observational works dedicated to test theoretical predictions were those of  Van Eylen et al. (2016)  and  Beck et al. (2018b). Van Eylen et al. investigated  the influence of the spectral type of the components of binary systems on their circularization timescale, denoted τ circ. These authors first computed the orbital period and e cos ω for 945 stars in the KEBC. They then used the photometric effective temperatures given in the catalog of  Armstrong et al. (2014)  to divide their sample into three groups: the systems composed of two hot stars, those composed of two cool stars, and those composed of a hot star and a cool star.
Acknowledgements. The authors thank the referee for their careful reading and constructive comments which allowed us to improve this article. We also thank  B. Mosser  and  T. Appourchaux  for verifying the mixed-mode analysis of KIC 4663623. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the  NASA Science Mission directorate. Some of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. This work is also based on observations obtained with the  Apache Point Observatory 3. 5meter telescope, which is owned and operated by the  Astrophysical Research Consortium. Part of our spectroscopic observations were done at the  Observatoire de Haute-Provence.
We acknowledge the technical team for their onsite support as well as the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  for their financial support. M. B. acknowledges support from the  "Deutscher Akademischer Austauschdienst" (DAAD)  and the  Université Paris Diderot. P. G. was supported by the  German space agency (Deutsches Zentrum für Luft-und Raumfahrt)  under PLATO data grant  50OO1501. P. G. and J. J. acknowledge  NASA  grant  NNX17AF74G  for partial support. M. B. , R. A. G. , and St. M. have been funded by the  PLATO CNES  grant. St. M. acknowledges support from the  ERC   SPIRE 647383  grant. Sav. M. acknowledges support by the  National Aeronautics and Space Administration  under Grant  NNX15AF13G , by the  National Science Foundation  grant  AST-1411685  and the  Ramon y Cajal  fellowship number  RYC-2015-17697.
The research leading to these results has (partially) received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement N • 670519: MAMSIE), from the  KU Leuven Research Council  (grant  C16/18/005: PARADISE ), from the  Research Foundation Flanders (FWO)  under grant agreement  G0H5416N  ( ERC   Runner Up  Project), as well as from the  BELgian federal Science Policy Office (BELSPO)  through  PRODEX grant PLATO. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah.
Lipid-Based Nutritional Supplements. Two types (SQ-LNS1 and SQ-LNS2) of SQ-LNS sachets were obtained from Nutriset (Malaunay, FR). The nutrient composition and ingredients of SQ-LNS1 and SQ-LNS2 were identical (Table  S1 ), with the exception of the adherence marker added. SQ-LNS1 contained 8. 6 mg of saccharin and SQ-LNS2 contained 5 mg of trans-resveratrol and 5 mg of sucralose. Sucralose was initially evaluated as an adherence marker; however, preliminary results indicated that sucralose concentrations in urine were very low, and therefore sucralose was eliminated from consideration as an adherence marker, and the data are not reported. Calibration Curve and Internal Standards. Individual stock solutions of trans-resveratrol, trans-resveratrol-d 4 , trans-resveratrol-3-O-β-D-glucuronide, trans-resveratrol-4′-O-D-glucuronide, resveratrol-3-O-sulfate, saccharin, and saccharin-d 4 were prepared by dissolving standards in methanol/water 1:1 at 4 mg mL -1.
3d ). These results (See figure on previous page. ) Fig. 1  FGF-2 increases SRSF1, SRSF3 and SRPK1 protein levels in primary endothelial cells. a, b xCELLigence (a) or MTS (b) assay was used to assess HUVEC and HDMEC cellular adhesion, proliferation, and viability in response to FGF-2 at 1 or 3nM. Data are representative of at least 2 independent experiments performed in at least triplicate (mean ± SD, unpaired t test, **p<0. 01; ***p<0. 001). c Representative immunoblots for SRSF1, SRSF3, SRPK1 (left panel), and phospho-SR (p-SR) (mAb104, right panel, two exposure times) protein levels in HUVEC and HDMEC, respectively, treated with 1 or 3nM FGF-2 for 72 h. GAPDH was used as a loading control. NT: nontreated. d Semi-quantification using ImageJ software of SRSF1, SRSF3, P-SRSF3, or SRPK1 signal relative to GAPDH signal.
It has been shown that the 8p12 locus (containing the FGFR1 gene) is frequently amplified in approximately 20% of squamous lung carcinoma  [40]. To assess whether the link we identified in endothelial cells between FGF-2 signaling and sVEGFR1-ex12 could also be relevant in cancer cells, we took advantage of a publicly transcriptomic database, the TCGA research network initiated by NIH, including 411 squamous lung carcinoma patients with clinical annotations. In this cohort, we found a correlation between the mRNA level of either FGF-2 or FGFR1 and sVEGFR1-ex12 usage value, with patients displaying high FGF2 or FGFR1 mRNA level also exhibiting high sVEGFR1-ex12 level and inversely (Fig. 10, a, b ). In addition, we found that patients with high sVEGFR1-ex12 levels display a worse prognosis compared to patients exhibiting low levels (Fig.
a Dose effects of FGF-2 in HUVEC and HDMEC treated or not (NT) for 72 h with increasing concentrations of FGF-2. Left panel: representative RT-PCR analyses of VEGFR1 and sVEGFR1-ex15a, sVEGFR1-i13, and sVEGFR1-ex12 splice variants. GAPDH was used as an internal control. Right panel: semi-quantification using ImageJ software of PCR-specific signals related to GAPDH signal in 3nM FGF-2-treated cells. Ratio obtained in nontreated (NT) condition was arbitrarily assigned the value 1 (mean ± SD, unpaired t test, *p<0. 05, **p<0. 01, ns: not significant). Numbers below the graph indicate the number of biological replicates for each condition. b, c RT-qPCR analyses of VEGFR1 (black bars), sVEGFR1-ex15 (hatched bars), sVEGFR1-i13 (white bars), and sVEGFR1-ex12 (gray bars) mRNA levels in HUVEC (b) or HDMEC (c) treated or not with 3nM FGF-2 for 48 or 72 h as indicated.
8  FGF-2 mediates accumulation of sVEGFR1 splice variants through a SRSF1/SRSF3/SRPK1-dependent axis. a HDMEC were transfected with control siRNA (mis) or specific SRSF1 (Srsf1) or SRSF3 (Srsf3) siRNA and treated with 3nM FGF-2 for 72 h. Left panel: representative immunoblots of SRSF1 or SRSF3 protein (WB) or PCR analyses (RT-PCR) of sVEGFR1-i13 and sVEGFR1-ex12 mRNA levels. GAPDH was used as a loading/internal control. Right panel: PCR data semi-quantification by ImageJ software. The sVEGFR1/GAPDH ratio obtained in control condition was arbitrarily assigned the value 1. Data are the mean ± SD of 3 and 4 biological replicates for sVEGFR1-ex12 and sVEGFR1-i13, respectively (unpaired t test, **p<0. 01, ***p<0. 001, ns: not significant). b sVEGFR1-i13 and sVEGFR1-ex12 mRNA levels were quantified by RT-qPCR in HUVEC transfected with either control siRNA (mis) or specific SRSF1 or SRSF3 siRNA and treated with 3nM FGF-2 for 48 h.
After centrifugation for 20 min at 12. 000 rpm at 4°C, supernatants were collected, and protein amount was quantified for each sponge using BCA. All sponge samples (6 sponges/each group) were equalized in lysis buffer at a final concentration of 3. 5mg/ml. For each group, a pool of 6 sponge samples was done and 30-50 μg proteins were analyzed by immunoblotting after loading on NuPAGE 4-12% gels (Life Technologies, USA). Three independent pooled samples for PBS (n=6) and FGF-2 (n=6) group were analyzed. The intensity of specific band was measured by ImageJ (NIH software).
In total, 1 μg of total RNA was subjected to reverse transcription using iScript RT supermix (Bio-Rad). RT reaction (3. 5 μl; 2. 5 ng/μl cDNA) was then amplified by PCR for a total of 40 cycles using the following conditions: 94°C for 2 min followed by 5 cycles at 94°C for 30 s, 64°C for 30 s, 72°C for 1 min, followed by 5 cycles at 94°C for 30 s, 61°C for 30 s, 72°C for 1 min, followed at 5 cycles of 94°C for 30 s, 59°C for 30 s, 72°C for 1 min, followed by 25 cycles at 94°C for 30 s, 56°C for 30 s, 72°C for 1 min. DNA amplicons were loaded on 2% agarose gels containing UView loading dye (Bio-Rad) and visualized using a ChemiDoc apparatus (Bio-Rad). The intensity of each band was determined using ImageJ (NIH software). The number of biological replicates for each experiment is indicated in the figure legends.
Embryos were then fixed at 28hpf and 42hpf in 4% paraformaldehyde overnight at 4°C or 4 h at room temperature and transferred in 100% methanol for storage at -20°C until use. Whole-mount immunostaining were performed as previously described  [55]. Briefly, embryos were permeabilized in PBS-T (1% Triton X-100 in PBS) and then incubated with proteinase K (Roche) for 20 min. Embryos were incubated overnight at 4°C with rabbit monoclonal anti-GFP (1:100, Clinisciences, # TP401) and then with anti-rabbit IgG coupled to AlexaFluor-488 (1:500, Invitrogen, # ALL034). Embryos were stored at 4°C until observation. Images of embryos from 3 independent experiments were obtained with a Leica SP8 confocal or a Leica DM6000 epifluorescence microscope. Movies were done using the LasX software from LEICA. Analysis of TCGA RNA-Seq dataset FGF-2 and FGFR1 gene expression values as well as sVEGFR1-exon12 usage value were retrieved from publicly available TCGA Lung Squamous Cell Carcinoma (TCGA LUSC) dataset and downloaded using the TCGA Splicing Variants DB database (http://www.
Fundings This work was supported by the  "Fondation ARC pour la Recherche sur le Cancer " ( ARC_PGA1_20160203791 ) and the  "Institut National du Cancer" (INCA-PLBIO  # 2016-155 ) grants to BE, JLC and FR. Sponges assays were performed thanks to the Optimal platform supported by  France Life Imaging (French program  " Investissement d'Avenir " grant; " Infrastructure d'avenir en Biologie Santé ,"  Agence Nationale de la Recherche ,  ANR-11-INBS-0006 ) and the  IBISA French consortium "Infrastructures en Biologie Santé et Agronomie. ".
Progression largely occurs because of the emergence of secondary mutations in KIT or PDGFRA  [5] [6] [7] [8]. Ripretinib, a switch-control TKI, inhibits a broad range of known primary and secondary KIT and PDGFRA mutations that drive drug resistance by regulating the kinase switch pocket and activation loop  [9]. Clinical data suggest the efficacy of ripretinib in advanced GIST as second-line, third-line, and at least fourth-line therapy  [10, 11]. In the dose-escalation phase of the ripretinib phase I study (NCT02571036), the maximum tolerated dose (MTD) was not reached among the doses tested, including ripretinib 200 mg b. i. d. Based on the safety, pharmacokinetic (PK), and pharmacodynamic results of the phase I study, ripretinib 150 mg once daily (QD) was established as the recommended phase II dose (RP2D)  [10].
Of note, ripretinib 150 mg b. i. d. in the dose-escalation phase of the phase I study was well tolerated, without significant dose-limiting toxicity (DLT) in patients with advanced GISTs. Given the acceptable safety profile of ripretinib 150 mg b. i. d. , patients in the phase III INVICTUS study (NCT03353753) were offered the option of ripretinib intrapatient dose escalation (IPDE) to 150 mg b. i. d. after PD on ripretinib 150 mg QD  [11]. The primary analysis of the INVICTUS study has previously been reported  [11]. Here, we present results focused on patients randomized to ripretinib 150 mg QD in the INVICTUS study who received ripretinib IPDE to 150 mg b. i. d. after PD assessed by blinded independent central review (BICR).
ACKNOWLEDGMENTS We thank the patients, their families and caregivers, the investigators, and the investigational site staff of the INVICTUS study. Medical writing was provided by  Uma Chandrasekaran, Ph. D. ( Deciphera Pharmaceuticals, LLC ); editorial support was provided by  AlphaBioCom, LLC (King of Prussia, PA, USA ) and was funded by  Deciphera Pharmaceuticals, LLC  in compliance.
Forecast systems Subseasonal forecasts delivered by CNRM have been routinely feeding the S2S database  (Vitart et al. , 2017)  since 2015 with forecasts issued every Thursday. Lately, the CNRM forecast system version 1  (Ardilouze et al. , 2017)  has been superseded by a version 2 used in this study. Unlike the ECMWF extended range forecast system (which also feeds the S2S database), the CNRM upgraded system has been designed for research purposes and is not intended for operational aspects. Since the ECMWF system is often acknowledged as the most skillful system in several intercomparison comparison studies (e. g. ,  Zheng et al. , 2019; Specq et al. , 2020) , it will serve as a benchmark in the present work to assess the performance of the new CNRM system. The main characteristics of both forecast systems are described in Table 1.
Reference dataset and forecast skill metrics The ERA5 reanalysis  (Hersbach et al. , 2020)  serves as the reference for daily sea-level pressure and daily mean 2 m temperature. This reanalysis, although resulting from a model output, assimilates a wide array of observations and will therefore be considered as our observational reference. For simplicity, we use the term "observation" -albeit loosely -to refer to ERA5 in the rest of the paper. Because ERA5 and ECMWF reforecasts are derived from two versions of the same model, one may object that ERA5 is not a suitable reference for this study. To verify if the assessment of the ECMWF predictions is favored by the choice of this reanalysis, we have compared some of the results obtained with ERA5 to results using the JRA-55  (Kobayashi et al. , 2015)  reanalysis as a reference.
Limited changes in the number of significantly positive ACCs per year and per forecast system be-  fore and after detrending confirm the minimal influence of the warming trend on the forecast skill. In any case, 2009-2010 stands out as the winter with the maximum number of skillful forecasts of the 20-year period for both CNRM and ECMWF systems considered either jointly (green bars) or separately (blue and red bars). Since that winter is characterized by a record-breaking neg-ative NAO index  (Cattiaux et al. , 2010) , we have computed the correlation between the yearly number of skillful forecasts and a winter mean NAO index (December to March) derived from our daily NAO index datasets. The correlation is not significant if the NAO index is computed by averaging daily NAO indices (not shown).
Such screening selects the time series with an initial atmospheric flow characterized by intense NAO+ and NAO-conditions. Despite different datasets and methodology, the NAO decorrelation compares well to other studies when keeping the whole sample  (Keeley et al. , 2009) , with a characteristic decorrelation time of 8 to 10 d. However, the decorrelation is much slower when considering only the subsample with intense NAO initial conditions. Keeley et al. (2009)  also identified a similar "shoulder" or "rebound" in the NAO decorrelation function between 10 and 30 d and find it largely related to interannual variability as opposed to intraseasonal. The decorrelation timescale and behavior are consistent when evaluated over a wide range of different NAO indices (Fig. 3b  in  Domeisen et al. , 2018).
Consequences on forecast skill outside Europe In the previous section, we identify a statistical link between wintertime temperature anomalies over a number of regions of the Northern Hemisphere extratropics and the NAO index 3 weeks prior. We now return to the forecast skill evaluation, but this time, we proceed to a subsampling of the reforecasts based on two conditions: the initial weather regime and its intensity. More precisely, we select all the reforecasts initiated in NAO+ and NAO-and evaluate their initial NAO index from the NOAA dataset. We then retain only the "initial NAO+" ("initial NAO-") reforecasts for which the initial NAO index belongs to the upper (lower) quartile of the distribution. The choice of this percentile results from a trade-off between the strength of the initial NAO signal and a sufficient sample size.