dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors acknowledge IBM Research,  NSERC ,  Canada Research Chairs and CIFAR  for funding. The authors thank  Yang Yu ,  Bing Xiang ,  Bowen Zhou  and  Gerald Tesauro  for constructive feedback, and  Antoine Bordes ,  Nicolas Usunier ,  Sumit Chopra  and  Jason Weston  for providing the SimpleQuestions dataset. This research was enabled in part by support provided by  Calcul Qubec  (www. calculquebec. ca) and  Compute Canada  (www. computecanada. ca).
In this paper, we quantify both the absolute and relative accuracy in the calibration of the LFI instrument, and find an overall uncertainty of 0. 35 % (30 GHz map), 0. 26 % (44 GHz), and 0. 20 % (70 GHz). The level of the power spectrum near the first peak is now remarkably consistent with WMAP's. Other paper in this Planck data release deal with the quality of the LFI calibration, in particular: -Planck Collaboration X (2015) quantifies the consistency between the calibration of the LFI/HFI/WMAP channels in the context of foreground component separation, finding that the measured discrepancies among channels are of the order of a few tenths of a percent; -Planck Collaboration  XI (2015)  analyses the consistency between the LFI 70 GHz low-polarization map and the WMAP map in pixel space, finding no hints of inconsistencies; -Planck Collaboration XIII (2015) compares the estimate for the τ and z re cosmological parameters (reionization optical depth and redshift) using either LFI 70 GHz polarization maps or WMAP maps, finding statistically consistent values.
Structure of this paper Several improvements were introduced in the LFI pipeline for calibration relative to Cal13. In Sect. 2 we recall some terminology and basic ideas presented in Cal13 to discuss the normalization of the calibration, i. e. , what factors influence the average value of G in Eq. (1). Section 3 provides an overview of the new LFI calibration pipeline and underlines the differences with the pipeline described in Cal13. One of the most important improvements in the 2015 calibration pipeline is the implementation of a new iterative algorithm to calibrate the data, DaCapo. Its principles are presented separately in a dedicated section, Sect. 4. This code has also been used to characterize the orbital dipole. The details of this latter analysis are provided in Sect. 5, where we present a new characterization of the Solar dipole.
Handling beam efficiency In this section we develop a mathematical model to relate the absolute level of the calibration (i. e. , the average level of the raw power spectrum C for an LFI map) to a number of instrumental parameters related to the beams and the scanning strategy. The beam response B(θ, ϕ) is a dimensionless function defined over the 4π sphere. In Eq. (  1 ), B appears in the convolution B * (T sky + D) = 4π B(θ, ϕ) (T sky + D)(θ, ϕ) dΩ 4π B(θ, ϕ) dΩ , (2) whose value changes with time because of the change of orientation of the spacecraft. Since no time-dependent optical effects are evident from the data taken from October 2009 to February 2013 (Planck Collaboration IV 2015), we assume there is no intrinsic change in the shape of B during the surveys. In the previous data release, we approximated B as a Dirac delta function (a "pencil beam") when modeling the dipole signal seen by the LFI radiometers.
Here we briefly summarize the theory, and we introduce new equations that are relevant for understanding the normalization of the new Planck-LFI results in this data release. The Solar dipole D, due to the motion of the Solar System in the CMB rest frame, is given by D(x, t) = T CMB 1 γ(t) 1 -β(t) • x -1 , (3) where T CMB is the CMB monopole, β = u/c is the velocity of the spacecraft, and γ = (1 -β 2 ) -1/2. Each radiometer measures the signal D convolved with the beam response B, according to Eq. (  2 ); therefore, in principle, each radiometer has a different calibration signal. Under the assumption of a Dirac delta shape for B, Cal13 shows that the estimate of the gain constant G is related to the true gain G by the formula Gpen = G 1 -f sl 1 + φ D , (4) where f sl = θ>5 • B dΩ 4π B dΩ (5) is the fraction of power entering the sidelobes (i.
The result of such transformations is a new timeline V out. Under the hypothesis of perfect knowledge of the beam B and of the dipole signal D, these steps are enough to estimate the true calibration constant without bias 6  (unlike Eq. 4): G4π = G, (9) which should be expected; since no systematic effects caused by the shape of B affect the estimate of the gain G. In order to see how Eq. (  7 ) changes in this case, we write the measured temperature Tsky as Tsky = B * T sky + M = B main * T sky + B sl * T sky + M. ( 10 ) Since in this 2015 data release we remove B sl * T Gal , the contribution of the pickup of Galactic signal through the sidelobes (Planck Collaboration II 2015), the equation can be rewritten as T 4π sky = B main * T sky + B sl * (T CMB + T other ) + M. (11) If we neglect details at angular scales smaller than the main beam size, then B main * T sky ≈ (1 -f sl ) T sky , (12) so that T 4π sky = (1 -f sl ) T sky + B sl * (T CMB + T other ) + M.
(13) We modify Eq. (  8 ) in order to introduce a new term φ sky : φ sky = B sl * (T CMB + T other ) Tsky ; ( 14 ) solving for T sky , Eq. (  13 ) can be rewritten as T sky = T 4π sky 1 -φ sky 1 -f sl + T 0 , (15) where T 0 = M/ 1f sl is a constant offset that is of little relevance for pseudo-differential instruments like LFI. Eq. (  15 ) is the equivalent of Eq. (  7 ) in the case of a calibration pipeline that takes into account the 4π shape of B, as is the case for the Planck-LFI pipeline used for the 2015 data release. Since one of the purposes of this paper is to provide a quantitative comparison of the calibration of this Planck data release with the previous one, we provide now a few formulae that quantify the change in the average level of the temperature fluctuations and of the power spectrum between the 2013 and 2015 releases.
The code used to estimate K, named DaCapo, has been completely rewritten; it is able to run in two modes, one of which (the so-called unconstrained mode) is able to produce an estimate of the solar dipole signal, and the other one (the constrained mode) which requires the solar dipole parameters as input. We have used the unconstrained mode to assess the characteristics of the solar dipole, which have then been used as an input to the constrained mode of DaCapo for producing the actual calibration constants. We smooth the calibration constants produced by DaCapo by means of a running mean, where the window size has a variable length. That length is chosen so that every time there is a sudden change in the state of the instrument (e. g. , because of a change in the thermal environment of the front-end amplifiers) that discontinuity is not averaged out.
Panel A: calibration constant K estimated using the expected amplitude of the CMB dipole. Note that the uncertainty associated with the estimate changes with time, according to the amplitude of the dipole as seen in each ring. Panel B: expected peak-to-peak difference of the dipole signal (solar + orbital). The shape of the curve depends on the scanning strategy of Planck, and it is strongly correlated with the uncertainty in the gain constant (see panel A). Note that the deepest minima happen during Surveys 2 and 4; because of the higher uncertainties in the calibration (and the consequent bias in the maps), these surveys have been neglected in some of the analyses in this Planck data release (see e. g. , Planck Collaboration XIII 2015). Panel C: the calibration constants K used to actually calibrate the data for this Planck data release are derived by applying a smoothing filter to the raw gains in panel A.
Analysis When running DaCapo in "constrained" mode to compute the calibration constants K (Eq. 1), the code needs an estimate of the Solar dipole in order to calibrate the data measured by the radiometers (see Sect. 3 and especially Fig. 3 ), since the signal produced by the orbital dipole is ten times weaker. We have used DaCapo to produce this estimate from the signal produced by the orbital dipole. We limited our analysis to the 70 GHz radiometric data, since this is the cleanest frequency in terms of foregrounds. The pipeline was provided by a self-contained version of the DaCapo program, run in unconstrained mode (see Sect. 4. 2), in order to make the orbital dipole the only source of calibration, while the Solar dipole is left in the residual sky map. We bin the uncalibrated differenced time-ordered data into separated rings, with one ring per pointing period.
This sky map is assumed to be unpolarized, but since radiometers respond to a single linear polarization, the data will contain a polarized component, which is not compatible with the sky model and thus leads to a bias in the calibration. An estimate of the polarized signal, mostly CMB E-modes and some synchrotron, needs to be subtracted from the timelines. To bootstrap the process we need an intial gain estimate, which is provided by DaCapo constrained to use the WMAP dipole. We then used the inverse of these gains to convert calibrated polarization maps from the previous LFI data release (which also used WMAP dipole calibration) into voltages and unwrap the map data into the timelines using the pointing information for position and boresight rotation. This polarized component due to E modes, which is ∼ 3.
Absolute calibration In this section we provide an assessment of the change in the absolute level of the calibration since the first Planck data release, in terms of its impact on the maps and power spectra. Generally speaking, a change in the average value of G in Eq. (  1 ) of the form  value of the variation δ G from the previous Planck-LFI data release to the current one. We have done this by comparing the level of power spectra in the = 100-250 multipole range consistently with Cal13. G → G (1 + δ G ), with δ G 1 ( 40 There have been several improvements in the calibration pipeline that have led to a change in the value of G : Table  4  lists the impact of such effects on the amplitude of fluctuations in the temperature T of the 22 LFI radiometer maps. The numbers in this table have been computed by rerunning the calibration pipeline on all the 22 LFI radiometer data with the following setup: 1.
This is discussed in more detail in Planck Collaboration IV  (2015). Given the broad usage of beam shapes B in the current LFI calibration pipeline, it is extremely important to assess their accuracy and how errors in B propagate down to the estimate of the calibration constants K in Eq. (  1 ). In the previous data release we did not use our knowledge of the bandpasses of each radiometer to produce an in-band model of the beam shape, but instead estimated B by means of a monochromatic approximation (see Cal13). In that case, we estimated the error induced in the calibration as the variation of the dipole signal when using either a monochromatic or a bandintegrated beam, as we believe the latter to be a more realistic model. In this data release, we have switched to the full bandpassintegrated beams produced using GRASP, which represents our best knowledge of the beam (Planck Collaboration IV 2015).
Null tests In Cal13 we provided a study of a number of null tests, with the purpose of testing the quality of the calibration. In this new data release, we have moved the bulk of the discussion to Planck Collaboration III (2015). We just show here one example, which is particularly relevant in the context of the LFI calibration validation. Figure  12  shows the variation in the quality of the maps due to the use of the full 4π convolution versus a pencil beam approximation, as discussed in Sect. 2. The analysis of many similar difference maps has provided us with sufficient evidence that using the full 4π beam convolution reduces the level of systematic effects in the LFI maps. 13 To reduce the impact of the Galactic signal we have masked 60 % of the sky, since we found that less aggressive masks produced significant biases in the ratios.
Measuring the brightness temperature of Jupiter The analysis of the flux densities of planets for this Planck data release has been considerably extended. We only use Jupiter data for planet calibration, so we will focus the discussion on observations of this planet. The new analysis includes all seven transits of Jupiter through each main beam of the 22 LFI radiometers. The analysis pipeline has been improved considerably by taking into account several effects not included in Cal13. Planets provide a useful calibration cross-check; in particular, the measurement of the brightness temperature of Jupiter can be a good way to assess the accuracy of the calibration, as Jupiter is a remarkably bright source with a S/N per scan as high as 50 and a relatively well known spectrum. Furthermore, at the resolution of LFI beams it can be considered a point-like source.
Estimation of the brightness temperature In Cal13, we computed the brightness temperature T B from the antenna temperature T A by means of the following formula (as-suming monochromatic radiometers): T B = B -1 Planck T A f sl Ω b Ω p ∂B Planck ∂T T CMB , (47) where B Planck is Planck's blackbody function, Ω b and Ω p the beam and planet solid angles, and T CMB = 2. 7255 K is the temperature of the CMB monopole. In this 2015 Planck data release, we have also introduced corrections to account for the bandpass. The accuracy of the T B determination is affected by confusion noise (i. e. , noise caused by other structure in the maps), which we estimate from the standard deviation of samples taken between a radius of 1 • and 1. 5 • (depending on the beam) and 5 • from the beam centre. These samples are masked for strong sources or other defects.
Determination of the solid angle of Jupiter The solid angle Ω p of Jupiter for a given planet-spacecraft distance ∆ p and planeto-centric latitude δ P is given by is the solid angle of the planet as seen from its pole at the fiducial distance, and e p (< 1) the ratio between the polar and equatorial radii of the planet. Ω p (δ P ) = Ω Our new pipeline does not use the ellipticity, FWHM, and orientation of the elliptical beam parameters provided by Planck Collaboration IV (2015), as they were derived from a marginalization over T A,p on the same Jupiter data used in this analysis. However, because of a degeneracy between T A,p and the beam parameters, the results were sensitive to details of the fitting procedure (up to about 1 %), such as the radius of the area being analysed and the minimization method.
Conclusions We have described the method used to calibrate the Planck/LFI data for the 2015 Planck data release, and we have provided a quantitative analysis which shows in detail the amount of change in the calibration level due to every improvement we implemented in the pipeline since the previous data release. Compared to our 2013 release, we have improved the LFI calibration in several ways, most notably in the use of an internally consistent dipole signal as a calibrator, as well as more accurate data analysis algorithms. As a result, we have improved by a factor of 3 the accuracy of the LFI calibration, which for the 2015 release ranges from 0. 20 % to 0. 35 %, depending on the frequency. An important byproduct of our analysis is a novel estimate of the solar dipole signal; using LFI data only, we have estimated the amplitude of the dipole to be 3 365.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES , and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO ,  JA  and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck_collaboration.
Flow Cytometry The cells were harvested, and 2 3 10 5 cells were placed in aliquots in phosphate-buffered saline (PBS) containing 1% BSA (PBS/BSA) and centrifuged (200g) for 5 minutes. Antibodies were added directly to cell pellets that were then resuspended to a final volume of 100 ml, incubated for 30 minutes on ice, washed in 5 ml PBS/BSA, and resuspended in 300 ml. The samples were analyzed using an LSRFortessa (BD Biosciences, Franklin Lakes, NJ, http://www. bdbiosciences. com) using FACSDiva acquisition (BD Biosciences) and FlowJo analysis software (FlowJo, Ashland, OR, http://www. flowjo. com), or sorted on a FACSARIA (BD Biosciences). Anti-human antibodies included CD71-FITC (catalog no. 11-0719; eBioscience, San Diego, CA, http://www. ebioscience. com), CD43-APC (catalog no. 17-0439-42; eBioscience), glycophorinA (M) (CD235a), efluor450 (catalog no.
5D ). Globin profiles and enucleation rates were also analyzed in cultures that were activated with HOXB4 throughout the differentiation protocol (i. e. , from day 0 to 24), with similar results (data not shown). Because one of the aims of the present study was to determine whether HOXB4 activation could enhance the number of RBCs that could be generated, we also assessed the effects of HOXB4 on cell expansion throughout the culture period. A 10,000-fold expansion occurred in cell numbers during the 24-day culture period in the control cells and in the hESCs carrying the HOXB4-ER T2 transgene in the absence of tamoxifen. However, a significant reduction in expansion was observed with HOXB4 activation (Fig. 6 ). Previous studies have indicated that HOXB4 enhances the proliferation of HPCs ex vivo without significant effects on their differentiation potential  [27, 28].
ACKNOWLEDGMENTS We thank the following funding agencies:  Wellcome Trust, Scottish Funding Council , the  Australian Stem Cell Centre, Stem Cells Australia , the  National Health and Medical Research Council (NHMRC) of Australia , and the  Victorian Government's Operational Infrastructure Support program. A. G. E. and E. G. S. are senior research fellows of the  NHMRC. The work was performed as part of the  Novosang Consortium  (available at http://www. novosang. co. uk).
After a few hours of incubation with (R/W) 9 peptide, a change in EF cells morphology (already visible using an optical microscope) and a significant reappearance of stress fibers were indeed observed by immunofluorescence. In addition, a decrease in EF cells motility was observed by videomicroscopy. Finally, the ability of EF tumoral cells to grow without anchorage was also shown to be impaired in the presence of the peptide. These results indicate a possible reversion of the EF cells tumoral phenotype in the presence of (R/W) 9 CPP  [19]. To help filling the gap in proteomic data for Ewing sarcoma and to progress in the understanding of (R/W) 9 mode of action on EF tumoral cells, we performed two sets of quantitative differential proteomic experiments. First, we compared tumoral EF and nontumoral 3T3 cells to see how the oncogenic protein EWS-FLI1 was affecting proteins expression levels.
Electrospray emitters were 360/20 mm o. d. Â10 mm i. d. fused-silica tips (PicoTip Emitter, Standard Coated SilicaTip, New Objective, Woburn, MA, USA). The heated capillary temperature and spray voltage were 200 C and 1. 5 kV, respectively. Orbitrap spectra (automated gain control (AGC) 2. 10 5 ) were collected from m/z 500-2000 at a resolution of 30,000 in the profile mode followed by data dependent sequential CID MS/MS spectra of the ten most intense ions with a normalized energy of 35. A dynamic exclusion time of 60 s was used to discriminate against previously analyzed ions.
Data treatment, statistical analysis and interpretation The peptides were identified and quantified using the Proteome Discoverer 1. 3 software (Thermo Scientific) with carbamidomethylation (C), oxidation (M), 13 C 6 (K) and 13 C 6 15 N 4 (R) as variable modifications. The database used for protein identification is the Uniprot protein database for the taxonomy Mus musculus (downloaded on http://www. uniprot. org/, 30th October 2013, 74,249 entries). False Discovery Rate (FDR) calculated at the peptide level with a decoy database (reversed peptides sequences) was set to 1%, MS tolerance was 10 ppm and MS/MS tolerance was 0. 8 Da. Protein ratios were corrected using the median protein ratio normalization. The statistical analysis of the proteins lists obtained for each experiment was performed with MyProMS software  [23].
The threshold values for absolute fold change in protein expression and p-value for the ratio calculation were determined with a control experiment mixing identical EF cells grown in light and heavy medium at a ratio of 1:1. Proteins were found to be significantly over-or under-expressed from an absolute fold change of 1. 3 and a p-value < 0. 05. Three biological replicates of the experiment comparing (1) EF versus 3T3 and (2) EF treated with photo (R/W) 9 versus EF untreated were performed. Only proteins with a ratio corresponding to a fold change greater than 1. 5, with a p-value < 0. 05 and quantified in at least two of the three replicates with at least two peptides were kept for data interpretation. Analysis of the protein networks for significantly over-or under-expressed proteins was performed using the STRING software available online (http://string-db.
Comparison of tumoral (EF) and non-tumoral (3T3) cell proteomes, using a SILAC experiment EF cells have been labeled with heavy Arg and Lys whereas 3T3 cells were grown with light amino-acids. Quantification of the protein levels (heavy/light) was done using the software Proteome Discoverer 1. 3 (Thermo Fisher Scientific) (Supplemental file S9) and the statistical analysis of the lists of under-or over-expressed proteins obtained for each quantification experiment was performed using MyProMS software  [23]. Based on the filtering criteria determined with control experiments (fold change >1. 5, pvalue < 0. 05 and presence in at least two of the three replicates experiments) we were able to obtain a list of 94 proteins (among 1700 proteins quantified) with a significant variation of expression level between EF and 3T3.
Study population The International Takotsubo Registry (InterTAK Registry , www. takotsuboregistry. com) was established at the University Hospital Zurich in 2011. Patient recruitment and inclusion criteria have been recently published. 18 n brief, data were collected from the leading hospital Zurich and 25 collaborating centres from 9 different countries (Austria, Finland, France, Germany, Italy, Poland, Switzerland, UK, and USA) between 2011 and 2014. Takotsubo syndrome was defined according to the modified Mayo Clinic Diagnostic Criteria. 18, 19 hen eligibility for inclusion was uncertain, cases were reviewed by the core team at the leading centre to reach a consensus. 18 he InterTAK Registry consists of currently 1750 patients, of which 485 presented with an emotional event leading to a TTS episode.
The remaining patients had a physical, a combination of an emotional and physical, or no identifiable trigger. 18 atients with a preceding pleasant emotional event prior to TTS were classified as 'happy hearts', while patients with a preceding negative emotional event were categorized as 'broken hearts'. The complete medical records of the cardiovascular history preceding the episode of TTS were reviewed in detail and documented as well as the acute index TTS event by standardized forms. Data were collected on clinical presentation, precipitating factors, cardiovascular risk factors, electrocardiographic findings, cardiac biomarkers, inflammatory markers, medications, demographics, angiographic and echocardiographic parameters, in-hospital complications, acute cardiac care. Furthermore, follow-up was obtained through telephone interviews, clinical visits or medical records.