dataesr/scientific-paragraphs-categorization huggingface dataset:
MR IMAGES. MR images were collected using a 3 Tesla Siemens TRIO 32-channel TIM system, and included a 3D T1-weighted magnetization-prepared rapid gradientecho (MPRAGE), two-dimensional proton density, T2weighted (T2-w) imaging, 3D fluid-attenuated inversion recovery (FLAIR), and pre-and postgadolinium T1 spinecho sequences (Supplementary Table  1 ). Native Space Postprocessing. For each patient, T2-w and T1 spin-echo images were registered onto the corresponding 3D-T1 MPRAGE image using a fully affine transformation (12 parameters; performed with FLIRT, FMRIB's Linear Image Registration Tool, which is part of FSL, FMRIB's Software Library; http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/), and the derived transforms were used to align lesion masks with the individual 3D-T1 MPRAGE scans. After performing a "lesion-filling" procedure,  23   (5) black holes, defined on T1-weighted spin-echo scans as hypointense areas compared to the NAWM; and (6) Gd1 lesions.
Acknowledgment The study was funded by specific grants from  ELA  ( European Leukodystrophy Association ; grant no. :  2007-0481 ) and  INSERM-DHOS  (grant no. : 2008-recherche clinique et translationnelle). B. B. was supported by  ANR   MNP2008-007125  and by the  ECTRIMS  postdoctoral research fellowship. M. V. and F. T. were funded by the  MRC-UK PET   Methodology Programme Grant  No. G1100809/1  and an  ARSEP travel grant. A. C. was supported by the  FRC , L. F. was supported by  JNLF  and  INSERM , and C. L. was supported by  ARSEP. The study was sponsored by  APHP  ( Assistance Publique des Hôpitaux de Paris ). The research also received funding from the  " Investissements d'avenir "  ANR-10-IAIHU-06  grant. We thank  D. Galanaud  and  S. Lehericy  ( ICM ) for technical help,  C. Comtat ,  P.
HPLC Analysis All extract analyses were performed using a LaChrom HPLC-Diode Array Detector instrument controlled by EZChrom Elite workstation software (VWR, Fontenay-sous-Bois, France). The DAD was set from 200 to 600 nm to record absorbance spectra (Figure  1 ). Chromatograms were visualized at 280 (λ maxDHRob ), 310 (equivalent absorbance of DHRob and Rob) and 366 nm (λ maxRob ). Separations 2016, 3, 23 3 of 12 was discarded (filtered out) to avoid further interferences with the extraction process. The size selected wood particles were freeze-dried (Christ alpha 1-4 lyophilisator) (Grosseron, Coueron, France) just before extraction.
HPLC Analysis All extract analyses were performed using a LaChrom HPLC-Diode Array Detector instrument controlled by EZChrom Elite workstation software (VWR, Fontenay-sous-Bois, France). The DAD was set from 200 to 600 nm to record absorbance spectra (Figure  1 ). Chromatograms were visualized at 280 (λmaxDHRob), 310 (equivalent absorbance of DHRob and Rob) and 366 nm (λmaxRob). The acquisition system used was the Ezchrom software, 3-2-1 version. Separation of flavonoids was performed using reversed-phase liquid chromatography  [20]. The column used was an Altima C18 (Grace, Epernon, France), 150 mm × 4. 6 mm, with a particle size of 5 µm. The mobile phase at a flow of 1 mL•min -1 was made up of 0. 1% formic acid in water (phase A), and 0. 1% formic acid in methanol/acetonitrile (50/50) (phase B).
The power spectra of the half-mission half-sum (HMHS) data are shown as thick lines; those of the half-mission half-difference (HMHD) data are shown as thin lines. The HMHS spectra contain signal and noise, whereas the HMHD spectra contain only noise and potential systematic effects. The variations in the temperature noise spectra at low to intermediate multipoles are caused by the component separation methods optimizing the trade-off between foreground signal and noise as a function of scale. At high mutipoles, the same spectra are smooth because the relative weighting of the frequency channels is set by the noise levels. The breaks in the Commander noise spectra are caused by the hybridization of maps at different resolutions to make the final map. Figure  14  shows the power spectra of the signal in the maps estimated using XFaster  (Rocha et al.
Cosmological parameters from the component separated maps in both temperature and polarization are determined using XFaster power spectra, coupled to CosmoMC (Lewis & Bridle 2002) using a correlated Gaussian likelihood. Specifically, we include multipoles between min = 50 and max , where max = 1000, 1500, or 2000 for temperature, and 1000 or 1500 for polarization. We adopt a standard six-parameter ΛCDM model, and, since low-data are not used in the likelihood, impose an informative Gaussian prior of τ = 0. 07 ± 0. 006. As mentioned above, we construct foreground templates for each CMB map by propagating the simulated full-sky FFP8 foreground maps through the respective pipeline and estimating the resulting power spectra normalized to some pivotal multipole. The resulting cosmological parameters are summarized in Fig.
1-point statistics The variance, skewness, and kurtosis of the maps, and the preprocessing steps needed to compute them, are described in detail in Planck Collaboration XVI (2015),  Monteserín et al. (2008), and Cruz et al. (2011). The procedure is to normalize the data, d p , in pixel p by its expected rms, dp = d p /σ p. The rms is calculated from 1000 FFP8 realizations (Planck Collaboration XI 2015), for both CMB anisotropies and instrumental noise. To the extent that σ 2 p represents an accurate description of the data variance, and both the sky signal and the instrumental noise are Gaussian-distributed quantities, d will be Gaussian distributed with zero mean and unit variance. In temperature, the variance of the instrumental noise is subtracted in order to determine the variance of the CMB signal in the data.
Once the variance of the CMB signal is estimated, it is used to extract the skewness and Planck Collaboration: Diffuse component separation: CMB maps kurtosis from the normalized map. This procedure is well established, and provides a direct test for the presence of residual foregrounds and of CMB Gaussianity. The temperature analysis reveals that the Planck 2015 CMB maps and the FFP8 fiducial CMB maps are fully compatible with the Monte Carlo simulations, therefore they can be used for further statistical analyses. For more details about the temperature results and the FFP8 validation analysis please refer to Appendix E. 2 and Planck Collaboration XVI (2015). For polarization, since the Q and U maps are not rotationally invariant, we consider the polarized intensity P = Q 2 + U 2. P is not Gaussian-distributed with zero mean, and its skewness and kurtosis are non-vanishing; however, by comparing the data with the Monte Carlo ensemble, we can test for residual foregrounds and quantify the performance of the component separation methods.
Noise in the P maps is complicated; rather than trying to remove the noise contribution (as is done in the case of temperature), we compare the P maps with the Monte Carlo simulations of CMB and noise together. Table  3  gives the resulting lowertail probabilities, that is, the percentage of simulations that show a lower variance, skewness, or kurtosis than the P maps. This is done at three different resolutions (N side = 1024, 256, and 64) for both the data maps (using mask UPB77) and the fiducial FFP8 maps (using mask FFP8-UPA76). At lower resolutions, Table  3  shows good agreement between the fiducial CMB maps and the Monte Carlo simulations for all methods. At high resolution, skewness, and kurtosis are mostly in agreement with the simulations, with NILC and SMICA showing slightly high values for the kurtosis (lower tail probabilities of 98.
We conclude that the anomalous statistics seen in analysis of the high-resolution component-separated FFP8 CMB maps are due to the foreground components, which could plausibly be caused by the additional complexity of the FFP8 foreground model with respect to the real sky. The Monte Carlo sim-Table  3. Percentage of simulations showing a lower variance, skewness, or kurtosis than the P maps, the "lower-tail probability". The top half of the table shows results for the data, and the bottom half for the FFP8 fiducial map, using the UPB77 and FFP8-UPA76 masks, respectively. In brackets, in the variance column, we report the excess variance of the data in percent with respect to the mean of the variance distribution of the Monte Carlo simulations. ulations are compatible with the CMB and noise components of the fiducial map, as they should be by design.
In the variance column of Table  3 , in parentheses, we report the excess variance of the data in percent relative to the mean variance of the Monte Carlo simulations. The excess is 3-4 % at N side = 1024, increasing to 10-20 % at N side = 64. Moreover, we see that there are some extreme values for both the skewness and kurtosis, in particular at low resolution for Commander and at high resolution for NILC. However, as long as the variance is inaccurate, it is difficult to decide whether residual foregrounds, a true non-Gaussian feature in the map, or noise underestimation is the cause. For now, we simply conclude that the simulations are inconsistent with the data, and this effectively prevents studies of higher-order statistics of this kind for the polarization maps. For temperature, the same is not true because of the much higher signal-to-noise ratio, although care is warranted even then when probing into the noise-dominated regime above ≈ 1500-2000.
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 ,  MICINN , and  JA (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. Some of the results in this paper have been derived using the HEALPix (Górski et al. 2005) package.
We present Spitzer Infrared Array Camera (IRAC) photometric observations of the Ultra-luminous X-ray Source (ULX, X-1) in Holmberg IX. We construct a spectral energy distribution (SED) for Holmberg IX X-1 based on published optical, UV and X-ray data combined with the IR data from this analysis. We modeled the X-ray and optical data with disk and stellar models, however we find a clear IR excess in the ULX SED that cannot be explained by fits or extrapolations of any of these models. Instead, further analysis suggests that the IR excess results either from dust emission, possibly from a circumbinary disk or from a variable jet.
If the Ho IX X-1 nebula is indeed powered by jets as is the case with its SS 433 'cousin', direct evidence of these jets has yet to surface. However infrared photometry -an underused tool in the field of ULX astronomy -has the potential to produce a wealth of continuum information about the ULX that can be used to directly characterize the ULX SED. This has been done only once before for ULXs: in a study of Holmberg II X-1. In this study  Berghea et al. (2010a,b)  used Spitzer mid-infrared data to constrain the ULX SED and found the data to be consistent with a broken power law typical of jet emission. The model was substantiated when  Cseh et al. (2014)  used the Karl G. Jansky Very Large Array (JVLA) to image the ULX and found signatures of jets emanating from the ULX. On the other hand, infrared photometry has been widely used in the X-ray binary community to uncover excess from irradiated disks, circumbinary disks, and dusty shells from companions stars  (Rahoui et al.
2010; Muno & Mauerhan 2006). Thus Spitzer infrared photometry can be a powerful tool for uncovering structures and environments of ULXs, that optical and X-ray data cannot. In this paper we present Spitzer IRAC images of the ULX in Holmberg IX. Combining these images with multi-wavelength optical, UV, and X-ray data from the literature, we model the Ho IX X-1 SED to determine the source of the infrared emission in this ULX. In Section 2 we provide the details of the Spitzer IRAC data analysis. In Section 3 we provide the fits to the full SED. In Section 4 we discuss the origin of the infrared emission. Finally we summarize our results in Section 5.
IRAC OBSERVATIONS AND DATA ANALYSIS For this analysis, we used two sets of archival Spitzer Infrared Array Camera (IRAC) data of the dwarf galaxy Holmberg IX taken on  November 15, 2007 (Astronomical Observation Request, AORs 22537472 and 22537728). IRAC is a four-channel camera that provides 5. 12 × 5. 12 arcmin images at 3. 6, 4. 5, 5. 8 and 8. 0 microns. We processed the data both manually and using the Post-processed Basic Calibration Data (PBCD) available for download from the archive, but found no difference between the two methods. We used the Spitzer MOsaicker and Point Source Extractor (MOPEX) APEX package 1 to extract the photometric fluxes from the four channels. We followed the standard extraction procedure and conversion factors provided in the IRAC Instrument Handbook 2. The pipeline mosaics for IRAC are accurate to within 20% 3 of the flux.
The extinction curves used for this correction are taken from  Cardelli et al. (1989). This multi-wavelength data sample spans 5 years. We note that Ho IX X-1 is known to show X-ray variations of factors of 4-5 over days to years  (Vierdayanti et al. 2010; Luangtip et al. 2016). Unfortunately, the only available multi-wavelength dataset that was taken of this object includes X-ray data from Chandra that suffers from severe pile-up and is therefore unusable for the X-ray fitting portion of this analysis  (Grisé et al. 2011 ). However we were able to use this Chandra data set to establish an estimate for the approximate X-ray flux during the same epoch as the HST data used here. Indeed we find that the Chandra flux is very close to the mean between the two sets of XMM X-ray observations. Therefore, in the following, note that the actual HST-epoch X-ray flux is likely somewhere between the two XMM X-ray data sets and that the plotted data set provided here is not simultaneous.
As the following sections will demonstrate, the multi-epoch nature of these data makes generating a good fit difficult. X-ray spectra exist in the archives from XMM-Newton, Swift, and NuSTAR. Luangtip et al. (2016)  found two main types of spectra: one hard ultra-luminous (HUL)-like and one more disk-like (see also  Sutton et al. 2013). We selected the XMM-Newton datasets 0112521101 and 0693851701 (refered to as obs 1 and obs 2 respectively from here forward), which represent each of the two, typical ULX spectra regimes seen in this object.
2009; Walton et al. 2014). We obtain good fits for both spectra. The parameter fit results are shown in Table  2. The Self-IrRadiated Funnel model (SIRF in XSPEC), describes the emission from a "supercritical funnel", similar to SS 433  (Abolmasov et al. 2009 ). The SIRF model provides acceptable fits for both sets of X-ray spectra. The accretion rate was fixed at 350 × Eddington, the funnel opening angle was set to 30 degrees and the inclination angle was set to zero. The inner radius for this model is calculated in units of the "spherisation radius"  (Abolmasov et al. 2009). In order to better compare them with the other models we convert them in units of km using the luminosity. We obtained estimates for the bolometric luminosities from the fitted X-ray models plus the jet model fit to the IRAC data (see Section 4).
g. Berghea & Dudik 2012). Following  Gierlinski et al. (2008 Gierlinski et al. ( , 2009) )  and  Berghea & Dudik (2012)  we set the fraction of the flux thermalized in the inner disk at 0. 1 and the radius of the Compton irradiated disk at 1. 1 of the inner disk radius. A good fit requires we set a large Comptonized luminosity (≥ unilluminated disk luminosity), and an outer disk of radius 1000 R in (where R in is the inner disk radius), but this parameter is not well constrained. The fits are not acceptable statistically, but we obtained estimates for the irradiated flux fraction. It is large for both X-ray obs 2 (HUL-like, >6. 8%), and X-ray obs 1 (disk-like, >5. 7%). Typical values of 2-4% have been found for previous fits of ULX X-ray/optical datasets with the DISKIR model  (Tao et al. 2012; Grisé et al.
Extending the models to Longer Wavelengths In the following we construct the SED by extending the X-ray models we fit in the previous section to the optical/UV and IR data. Extrapolated SIRF Model: The extrapolated SIRF models are plotted in Figure  2. Neither of these X-ray models fit the optical data well, however the optical data is located between the two SIRF models. When the X-ray spectrum is disk-like, it over-predicts the optical data by a factor of 6. 2. When the X-ray spectrum is HUL-like it under-predicts the optical data by a factor of 3. 2. Taking into account the variability of the Ho IX X-1 and the multi-epoch nature of this data set we conclude that a funnel model is capable of reproducing the optical and X-ray data. However Figure  2  clearly shows that the SIRF model does not fit any of the IR data.
DISKIR model: Per Section 3. 1 we began fitting the DISKIR data to the optical + X-ray data since it makes physical sense to do so when optical data is available. This extrapolated DISKIR model, described in Section 3. 1, was a very good fit to the optical data but unfortunately not statistically acceptable overall for either X-ray data set. Here too, the DISKIR model is also not a good fit the IRAC data. Based on these fits and the multi-epoch nature of this dataset, we conclude that all three models are plausible models for the optical/UV/X-ray data in Ho IX X-1. To definitively rule out any one model would require good, simultaneous optical and X-ray data  (Grisé et al. 2016, in prep). However, most importantly, none of these models fit the IR -12excess we see in Ho IX X-1, suggesting another mechanism is responsible for this emission.
2001). Rahoui et al. (2010)  suggests that the dust component could be related to the dusty shell often found surrounding red giants. We fit a spherical blackbody model to the Ho IX X-1 IRAC data. This blackbody, shown in Figure  2 , gives a dust temperature of ∼ 1100 K and a radius of ∼ 1400 R ⊙. However, the fit is quite poor, the χ 2 ν being much greater than two, thereby precluding error estimates for the parameters. The dust temperature is normal for dusty shells around red giants, however if the companion star is a blue supergiant, as the optical HST data suggests, it is unlikely to be surrounded by dust. In addition this blackbody emission does not fit the 5. 8 and 8. 0 µm data well. We therefore rule out this option as a viable model for the infrared emission in the ULX. Circumbinary Disk: An alternative explanation for a dust component is the circumbinary disk proposed by Perez M.
& Blundell (2010),  Rahoui et al. (2010)  and  Muno & Mauerhan (2006)  to explain the IR emission in X-ray binaries (SS 433, GRS1915+105, A0620-00 and XTE J1118+480). Here the circumbinary disk is distinct from the accretion disk described in Section 3, and is composed of material that may have been lost through the L2 point (e. g. SS433, see Perez M. & Blundell 2010). If Ho IX X-1 is more like these X-ray binaries, we might expect to see such a circumbinary disk in the infrared. In this case the black body model that we fit to the infrared data would represent emission from the inner circumbinary disk, while the redder 5. 8 and 8. 0 µm excess is likely emanating from the outer portion of the circumbinary disk as lower temperature blackbody emission. The four IRAC data points that comprise the IR excess in Ho IX X-1 are not sufficient to fit a complicated circumbinary disk model (e.
We also find that the IR data is not well fit by a single black body, such as that expected from a dusty shell. However, in this case the simultaneity of the data will not significantly improve the fit, since the single black body fits neither the optical data nor the longer wavelength IR data as well. The degeneracy between the circumbinary disk and transient jet theory underscores the need for simultaneous observations when observing ULX structure and environments. In this case, very sensitive radio observations of Ho IX X-1 may solidify these findings and help detect or constrain the power law break and slope needed to confirm jet activity. Indeed such radio observations have recently been obtained using the JVLA B-array and will be published in a follow-on study to this paper. However, as this study also indicates, deep radio monitoring observations with JVLA are also critical to providing information about the IR excess in this source in the event that the jet (if one exists) is variable.
CONCLUSIONS Using Spitzer IRAC observations of Ho IX X-1, we have constructed an SED of the ULX. Two contiguous IRAC observations of Ho IX X-1 were made. The datasets at 5. 8 and 8. 0 microns are at the sensitivity limit of the IRAC instrument, however combining these measurements yields a statistically significant detection in both bands. The combined measurements coupled with detailed fits to previous optical/UV and X-ray data of Ho IX X-1 suggest that the IR excess in this object is due either to a circumbinary disk such as those seen in SS 433 and other standard X-ray binaries or a variable jet such as that seen in Ho II X-1 by  Cseh et al. (2014). Future high-sensitivity radio monitoring observations would be needed to break the degeneracy between the two models and determine if the IR excess seen in Ho IX X-1 results from either mechanism.
-24 - -SED constructed in Section 3. The HST data is taken from  Grisé et al. (2011) , the XMM OM UV data from  Berghea & Dudik (2012). The two representative X-ray datasets (XMM-Newton datasets 0112521101 and 0693851701 shown as obs1 in blue and obs 2 in green, respectively), are taken from  Luangtip et al. (2016). Error bars are 1-sigma.
We thank the anonymous referee for the very helpful suggestions that have greatly improved this paper. The authors thank Luangtip et al. for providing the X-ray data presented here and for their useful discussions. We also thank  Michel Hillen ,  Hans Van Winckel , and  Rachel Akeson  for the useful discussions concerning circumbinary disk models and their application to this dataset. TPR acknowledges financial support from  STFC  as part of the consolidated grant  ST/L00075X/1. This work is based on observations obtained from multiple telescope facilities including 1 ) XMM-Newton , an  ESA  science mission with instruments and contribution directly funded by  ESA Member States  and  NASA , 2) The  NASA/ESA Hubble Space Telescope  and obtained from the  Hubble Legacy Archive , which is a collaboration between the  Space Telescope Science Institute (STScI/NASA) , the  Space Telescope European Coordinating Facility  ( ST-ECF/ESA ) and the  Canadian Data Centre  ( CADC/NRC/CSA ), and finally, 3) The  NASA Spitzer Space Telescope  which is managed by the  Jet Propulsion Laboratory  with data obtained from the  Spitzer Heritage Archive  which is maintained by the  Spitzer Science Center (SSC) , located on the campus of the  California Institute of Technology  and part of  NASA 's Infrared Processing and Analysis Center (IPAC) The Spitzer Space Telescope is a NASA mission managed by the  Jet Propulsion Laboratory.