dataesr/scientific-paragraphs-categorization huggingface dataset:
The observations and data reduction We have conducted X-shooter  (Vernet et al. 2011 ) observations of five γ-NLS1s (including PKS J1222+0413) in the Southern and equatorial sky, with the aim of characterising their accretion disc continuum and emission line properties. Our near-infrared, optical and ultraviolet spectra were obtained using X-shooter which is a wide-band, intermediate resolution spectrograph developed for the Very Large Telescope (VLT). The instrument is mounted on the VLT's 8. 2 m Unit Telescope 2 at ESO Paranal in Chile. Dichroics in the instrument reflect light into ultraviolet-blue (UVB) and visible light (VIS) arms and transmit the remaining light to the near-infrared (NIR) arm. At the end of each arm is an échelle spectrograph, with the UVB spectrograph covering the 300-550 nm range, the VIS spectrograph covering 550-1010 nm and the NIR spectrograph covering 1000-2500 nm.
Thus, by operating these three spectrographs simultaneously, Xshooter offers a very wide wavelength coverage from 300 to 2500 nm. We used the 0. 9 × 11 , 1. 2 × 11 , 1. 3 × 11 slits, giving spectral resolutions R ≈ 5600, 6700 and 4000 in the IR, optical and UV spectra, respectively. The observation was conducted on 3 April 2017 under favourable weather conditions with clear skies and seeing ≈ 1 at 500 nm. The on-source integration time was 1 hour and the mean S/N achieved in the IR, optical and UV spectra were 12, 16 and 23, respectively. The data reduction was performed with the ESO reflex software which outputs wavelength-and fluxcalibrated 1D spectra. Corrections for telluric absorption in the optical and UV spectra were performed with the xtellcor general tool available as part of the spextool IDL package  (Vacca et al.
2:1. To determine the widths of the individual broad lines we model the doublet as the sum of two broad Lorentzians with their intensities and wavelength separation fixed to the theoretical values. Additionally, we include two narrow Gaussian lines to account for any contribution from the narrow emission line region. The narrow lines are fixed to the same intensity ratio and separation, and their widths are limited to be in the range 300 FWHM 900 km s - 1. The fitting is performed with a custom python script employing the lmfit package, which uses the Levenberg-Marquardt method of least-squares fitting. We find that the inclusion of the narrow lines does not improve the fit therefore we model the doublet simply as the sum of two Lorentzians (see Figure  2 ). The FWHM of these lines is ≈ 1750 km s -1.
NuSTAR We obtained an X-ray observation of PKS J1222+0413 with the Nuclear Spectroscopic Telescope Array (NuSTAR  Harrison et al. 2013 ) on 2017 June 27-18 (OBS ID: 60301018002, PI: Kynoch). The data reduction was performed following the method described in  Landt et al. (2017)  (see Section 2. 3. 1 in that paper) and employing the latest software available  (NuSTARDAS version 1. 7. 1, HEASOFT version 6. 21 and CALDB version 20170222). The sum of good-time intervals after event cleaning and filtering the event files is 24. 5 ks for both FPMA and FPMB. The signal-to-noise ratio is 25. 5 and 15. 5 per module for the 3-10, and 10-79 keV bands, respectively. The PSF-corrected source count rate was stable around 0. 10 s -1 per module throughout the observation. We analyzed the NuSTAR spectra using Xspec (Arnaud 1996).
Swift A 2 ks Swift snapshot was taken simultaneously with the NuSTAR observation. Data products from the X-ray telescope (XRT) were created using the xrtpipeline v0. 13. 2. The source extraction region was a 47 -radius circle centred on the source (corresponding to the 90% encircled energy radius at 1. 5 keV) and the background region was a 141 -radius circular region offset from the source, in an area free of field sources. The spectra were extracted using xselect and ancilliary response files were created with xrtmkarf. The source count rate was 0. 003 counts s -1. The snapshot included a UV observation with the 2600 Å UVW1 filter. Circular source and background regions of 5 and 30 radius, respectively, were analysed. Inspecting the data with the uvotsource tool, the Vega magnitude was determined to be 16.
XMM-Newton A short observation (12 ks) of PKS J1222+0413 was made by XMM-Newton on 2006 July 12. The observation data        The profiles are shown in velocity space relative to the expected rest-frame wavelength and have been Fe iiand continuum-subtracted and normalized to the same peak intensity. In the top left panel, the Mg ii profile has been modelled as the sum of two Lorentzians (magenta) and the total modelled profile is shown in green. Since the narrow line region likely makes a negligible contribution to the Balmer line fluxes, the FWHM of the broad Hβ and Hα lines may be estimated directly from the observed line profiles, as shown. files (OBS ID: 0401790601) were obtained from the XMM-Newton Science Archive and reduced using the Science Analysis System (SAS, v16. 0. 0). The observation was strongly affected by background flaring, so after filtering the EPIC event files the good exposure times were 1.
8, 3. 8 and 3. 6 ks for the pn, MOS1 and MOS2 detectors, respectively. Circular regions of radius 40 and 120 were used to extract the source and background spectra, respectively. The source extraction regions were centred on the source whereas the background extraction regions were placed on a blank patch of sky on the same chip as the source. The XMM-Newton optical monitor (OM) observed PKS J1222+0413 using only the UVM2 filter. The photometry for this filter was extracted using the SAS omichain and omsource tasks and standard procedures. Data analysis was performed with the X-ray spectral fitting package Xspec v12. 9. 0n (Arnaud 1996). We fitted all three EPIC (pn, MOS1 and MOS2) spectra simultaneously, allowing for a cross-normalisation factor between them; these did not vary by more than ≈ 6%.
Photons in a circular region of interest (RoI) of radius 10 • , centred on the position of PKS J1222+0413, were considered. A zenith-angle cut of 90 • was applied. The analysis was done using the Fermi Science Tools software package version v10r0p5, in combination with the PASS 8 instrument response functions (event class 128 and event type 3) corresponding to the P8R2 SOURCE V6 response and the gll iem v06. fits and iso P8R2 SOURCE V6 v06 models for the Galactic and isotropic diffuse background, respectively. The spectral model of the region included all sources located within the RoI with spectral shapes and initial parameters as in the 3FGL catalogue  (Acero et al. 2015). The extraction of the Fermi-LAT data is complicated by the presence of two bright, nearby γ-ray sources in the RoI (see Figure  5 ).
The fractional effect of each of these operations is given in Table  3  of  Angelakis et al. (2019). The observed dataset has been finally calibrated with reference to standard sources listed in Table  3  of  Angelakis et al. (2015). Table  3  in this paper summarises the flux densities at all observing frequencies weighted averaged over a period roughly from 2011 May 19 to 2012 February 18. The uncertainty in the average flux densities is limited to less that a couple of percent. The error-weighted mean radio flux densities and their 1σ errors, the number of detections N in the observation period starting on the given date and ending on 2012-02-18.
Swift BAT In the broad-band SED we also include data from the Swift Burst Alert Telescope (BAT) 105-month all-sky hard X-Ray survey  (Oh et al. 2018)  spanning the period 2004 December -2013 August. The time-averaged spectrum and response file were obtained from the online archive. The spectrum was well-fit in Xspec with a single power-law of index Γ = 1. 5 ± 0. 1 and normalisation 9 +5 -3 × 10 -4 , giving χ 2 ν = 0. 32.
The hot dust temperature is well-determined from the WISE W1 and W2 points and changes little between fits. As can be seen in Figure  9 , our data do not strongly discriminate between zero-and high-spin models. However, the high-spin model slightly overpredicts the soft X-ray flux (although the modelled ROSAT spectrum has been scaled down by 20 per cent to match the flux level of the Swift XRT data) and cannot accommodate soft Comptonisation or coronal components. We note that this model conserves energy between the outer and inner accretion flow, producing the optical and UV/X-ray emission, respectively. The UV/X-ray flux would be lower if power were lost between outer and inner radii as the result of an accretion disc wind which could be present if the accretion rate were super-Eddington as in the high-spin model.
42. akin to a BL Lac, albeit at a much higher luminosity than BL Lacs. We can better fit the data by turning down the magnetic field strength. (With 1H 0323+342 it was necessary to turn down both the magnetic field strength and the power injected into the relativistic electrons. ) As with 1H 0323+342, a typical NLS1 SED model fits the observed NIR-X-ray SED well, if a (jet) power-law is added to account for hard X-rays. In 1H 0323+342  Kynoch et al. (2018)  found that the X-ray photon index below ≈ 2 keV was very soft, typical of (non-jetted) NLS1s, whereas above 2 keV, the photon index was much harder, typical of nonthermal jet emission. This was interpreted as a transition from accretionflow-dominated to jet-dominated emission. We do not find such strong evidence of a soft, low-energy photon index in PKS J1222+0413 (Sections 3.
ACKNOWLEDGEMENTS DK acknowledges the receipt of an  STFC studentship  ( ST/N50404X/1 ). DK, HL, MJW and CD acknowledge support from the  STFC  ( ST/P000541/1 ). Thanks to  Gerard Kriss  for providing electronic versions of the Telfer et al. (2002) composite spectra. Thanks to  Luigi Foschini  and  Matt Lister  for useful discussions. In this paper we have made use of the following: • the ESO reflex data reduction software (Freudling et al. 2013); • data from  Fermi , a NASA mission operated and funded  NASA , the  U. S. Department of Energy and institutions in France, Germany, Japan, Italy and Sweden ; • data from Spitzer,  WISE, Planck  and  2MASS  which are available from the  NASA / IPAC Infrared Science Archive , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  NASA ; • data from  NuSTAR , a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory  and funded by  NASA ; • data from Swift, and its XRT Data Analysis Software (XRTDAS) developed under the responsibility of the  ASI Science Data Center (ASDC), Italy ; • data from and software developed for  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA ; • data and software (including the ftools  11 Blackburn 1995 ) provided by the  High Energy Astrophysics Science Archive Research Center (HEASARC) , which is a service of the  Astrophysics Science Division at NASA/GSFC  and the  High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory ; • data from SDSS: funding for the SDSS, SDSS-II and SDSS-III has been provided by the  Alfred P.
Triple negative breast cancer (TNBC) represent 15% of breast cancers. Histoclinical features and marketed prognostic gene expression signatures (GES) failed to identify good-and poor-prognosis patients. Tyrosine kinases (TK) represent potential prognostic and/or therapeutic targets for TNBC. We sought to define a prognostic TK GES in a large series of TNBC. mRNA expression and histoclinical data of 6379 early BCs were collected from 16 datasets. We searched for a TK-based GES associated with disease-free survival (DFS) and tested its robustness in an independent validation set. A total of 1226 samples were TNBC. In the learning set of samples (N = 825), we identified a 13-TK GES associated with DFS. This GES was associated with cell proliferation and immune response. In multivariate analysis, it outperformed the previously published GESs and classical prognostic factors in the validation set (N = 401), in which the patients classified as "low-risk" had a 73% 5-year DFS versus 53% for "high-risk" patients (p = 1.
Supervised analysis of expression profiles between the "high-risk" and "low-risk" TNBC according to our 13-gene classifier. (A) Volcano plot showing the 480 genes differentially expressed in the learning set (Metabric), including 333 overexpressed in "low-risk" samples and 147 overexpressed in "high-risk" samples. (B) The metagene-based prediction score is significantly higher (Student t-test) in the "high-risk" samples than in the "low-risk" samples in the learning set as expected (left), but also in the independent validation set (right). (C) Gene ontology (GO) biological processes of the DAVID database associated with the 480-gene list. The barplot indicates the -log(pvalue) (y-axis) of the top 15 biological pathways (x-axis) that are enriched for genes overexpressed in the "high-risk" samples versus the "low-risk" samples (in red) and that are enriched for genes overexpressed in the "low-risk" samples versus the "high-risk" samples (in green).
(B) The metagene-based prediction score is significantly higher (Student t-test) in the "high-risk" samples than in the "low-risk" samples in the learning set as expected (left), but also in the independent validation set (right). (C) Gene ontology (GO) biological processes of the DAVID database associated with the 480-gene list. The barplot indicates the -log(p-value) (y-axis) of the top 15 biological pathways (x-axis) that are enriched for genes overexpressed in the "high-risk" samples versus the "low-risk" samples (in red) and that are enriched for genes overexpressed in the "low-risk" samples versus the "high-risk" samples (in green). The p-value threshold is indicated by the orange horizontal line.
Prognostic and/or Predictive Value of Our13-Gene Classifier To determine the link of our classifier with metastatic risk and/or with response to chemotherapy, we analyzed, within our series of 1226 patients with operated TNBC, the 668 cases who had not received any adjuvant systemic therapy. In this set, the "low-risk" patients had a longer DFS than the "high-risk" patients with 5-year DFS of 79% (95%CI 74-84) versus 60% (95%CI 54-66) respectively (p = 2. 02 × 10 -8 , log-rank test; data not shown). Then, we assessed the ability of our classifier to predict for the pathological complete response (pCR) to anthracycline-based neoadjuvant chemotherapy. The pCR was defined as absence of invasive residual cancer in breast and lymph nodes removed during post-chemotherapy surgery. Information was available in our dataset for 257 patients with TNBC, including 78 cases (30%) with pCR samples and 105 "low-risk" and 152 "high-risk".
Discussion Here, we have identified a robust prognostic 13-TK gene expression signature in early-stage TNBC. The 13-gene model divided TNBC into two classes ("high-risk" and "low-risk") with different post-operative DFS and OS. Despite its association with prognostic clinicopathological features, such classification remained an independent prognostic feature in multivariate analysis in the validation set. We analyzed a retrospective pooled set of 1226 pre-therapeutic samples of non-metastatic and invasive primary TNBC, all informative for DFS. Such figure allowed to use a learning set and a validation set thus avoiding the problem of overfitting and allowed to apply the multivariate analysis in the validation set only. Moreover, the whole-genome transcriptional data allowed to test the prognostic value of several published gene signatures and modules relevant to breast cancer, and to search for biological alterations associated to our 13-gene classifier.
Tumor Samples and Kinase Genes Clinicopathological and mRNA expression data were collected from 6379 primary BC patients included in 16 retrospective data sets, including ours (Supplementary Table  S4 ). All samples were extracted from surgery specimen of non-pretreated patients, with histologically proven non-metastatic invasive TN carcinoma, and with available clinicopathological data, and had been previously profiled using DNA microarrays or RNA-sequencing. We analyzed a total of 86 genes coding for tyrosine kinases (Supplementary Table  S1 ), selected within the list of 771 kinase and kinase-interacting genes, based on an update of the initial kinome description  [21, 59].
Gene Expression Data Processing Data sets were processed as previously described  [60]. Briefly, for the Agilent sets, we applied quantile normalization to available processed data. Regarding the Affymetrix sets, we used Robust Multichip Average (RMA) with the non-parametric quantile algorithm as normalization parameter  [61]. Quantile normalization or RMA was done in R using Bioconductor and associated packages. Data analysis required pre-analytic processing. First, we normalized each DNA microarray-based data set separately, by using quantile normalization for the available processed Agilent data, and Robust Multichip Average (RMA)  [61]  with the non-parametric quantile algorithm for the raw Affymetrix data. Normalization was done in R using Bioconductor and associated packages. Then, we mapped hybridization probes across the different technological platforms.
We used SOURCE and NCBI EntrezGene to retrieve and update the Agilent annotations, and NetAffx Annotation files  [62]  for the Affymetrix annotations. The probes were then mapped according to their EntrezGeneID. When multiple probes represented the same GeneID, we retained the one with the highest variance in a particular dataset. For the TCGA data, we used the available normalized RNA-Seq data that we log 2 -transformed. Next, the batch effects were corrected across the 16 studies using z-score normalization. Briefly, for each expression value in each study separately, all values were transformed by subtracting the mean of the gene in that dataset divided by its standard deviation, mean and standard deviation (SD) being measured on luminal A samples. Thanks to the bimodal distribution of respective mRNA expression levels and in order to avoid biases related to trans-institutional immunohistochemistry (IHC) analyses, the estrogen receptor, progesterone receptor, and ERBB2 statutes (negative/positive) of tumors were defined on transcriptional data of ESR1, PGR, and ERBB2 genes respectively, as previously described  [17].
We then measured the proportion of random signatures with p-value inferior to the p-value observed with our 13-gene model. We applied to each data set separately several multigene signatures. First, the Lehmann's classifier  [17] , which defines six TNBC subtypes, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype. We also applied the three major prognostic multigene classifiers of breast cancer: 70-gene Mammaprint signature  [27] , Recurrence Score  [28] , and Risk of Relapse score based on PAM50 subtype and proliferation ROR-P  [6]. Other signatures included three immune gene signatures reported as prognostic in specific molecular subtypes of breast cancer: the Immune Response (IR) signature  [12]  and the LCK signature  [13]  in ER-negative breast cancers, and the Immune 28-kinase signature [9] in basal breast cancers.
We also applied the metagenes associated with immune cell populations such as T-cells, CD8+ T-cells and B-cells defined by  Palmer et al. [65] , the transcriptional signatures of 24 different innate and adaptative immune cell subpopulations defined by Bindea et al. [29] , the cytolytic activity score  [26] , the activation score of IFNα, IFNγ, and TNFα immune-related and TP53, MYC and hypoxia biological pathways  [25]. Finally, to explore more-in-depth the biological pathways linked to our 13-gene classifier, we applied a supervised analysis by using the largest data set (Metabric: 335 samples) as learning set, and the other data sets as independent validation sets (891 samples). In the learning set, we compared the whole-genome expression profiles between the tumors classified as "high-risk" (N = 86) according to our 13-gene model and the tumors classified as "low-risk" (N = 249) using a moderated t-test with empirical Bayes statistic  [66]  included in the limma R packages.
False discovery rate (FDR)  [67]  was applied to correct the multiple-testing hypothesis and significant genes were defined by the following thresholds: p < 1. 0 × 10 -2 , q < 1. 0 × 10 -2 , and fold change FC > |1. 25×|. Ontology analysis of the resulting gene list was based on the GO biological processes of the Database for Annotation, Visualization, and Integrated Discovery (DAVID; david. abcc. ncifcrf. gov/). The robustness of the resulting 480-gene list was verified in the validation set (526 tumors classified as "high-risk" and 365 as "low-risk") by computing for each tumor a score defined as the difference between the correlation coefficient of its 480-gene expression profile with the median 480-gene expression profile of "high-risk" samples and the correlation coefficient of its 480-gene expression profile with the median 480-gene expression profile of "low-risk" samples.
2 vs. 3), and type (ductal vs. lobular vs. other). Analyses included also molecular classifications based on the Lehmann's subtypes, and six prognostic GES (good vs. poor-prognosis subgroups), and the Bindea CD8 T-cells GES. Multivariate analyses tested all variables with a p-value inferior to 0. 10 in univariate analysis. All statistical tests were two-sided at the 5% level of significance. Analyses were done using the survival package (version 2. 30), in the R software (version 2. 9. 1). Our analysis adhered to the reporting recommendations for tumor marker prognostic studies (REMARK)  [68].
A more conservative use of the term "tuning" refers to the correlation and pattern-matching to astronomical solutions only. There are many potential tuning targets in cyclostratigraphy: an astronomical solution that consists of one or more astronomical parameters  (Hays et al. , 1976; Lourens et al. 1996; Hinnov, 2018a) , insolation curves  (Köppen and Wegener, 1924; Milankovitch, 1941; Laskar et al. 2004) , glacial models  (Imbrie and Imbrie, 1980) , and astronomically tuned stratigraphic reference datasets  (Lisiecki and Raymo, 2005; De Vleeschouwer et al. , 2017a). If one chooses to tune to an astronomical solution, different constraints apply depending on the solution. The Earth's obliquity and precession solutions are not well constrained beyond 10 Ma, an issue that arises from the poor constraints on the long-term evolution of the Earth-Moon system due to the poorly known history of tidal dissipation and/or dynamical ellipticity  (Laskar et al.
Study design Each CIP core team member designed one case study, and together they discussed and refined the three cases. Each case study was presented as a data set with an accompanying text and figure with contextual information (see Sections 2. 2-2. 4). Initially, the three case studies were independently tested by a small group of researchers (A. C. Da Silva, N. J. de Winter, C. Ma) to allow potential adaptation of the design following feedback. This feedback confirmed the validity of the first and third case studies, and led to modifications of the instruction text for the second case. Therefore, initial test results for Cases 1 and 3 were included in the final CIP compilation, but were omitted for Case 2. Subsequently, all three cases were made openly accessible online for about three months to all interested scientists.