dataesr/scientific-paragraphs-categorization huggingface dataset:
Spitzer-IRS and ISO spectra We searched the entire Spitzer Infrared Spectrograph (IRS,  Houck et al. 2004 ) archive for PNe with observations in the SL (5. 2-14. 5 µm), SH (9. 9-19. 6 µm), and LH (18. 7-37. 2 µm) modules. In this search, we found six new fullerene-containing PNe, namely, Hen2-68, IC2501, K3-62, M1-6, M1-9, and SaSt2-3. Morisset et al. (2012)  reported that IC418 shows two resonances at 17. 4 and 18. 9 µm in the ISO spectra due to C 60. We confirmed both of these C 60 lines in the Spitzer spectrum of IC418, which was not previously analyzed. We combine this data set with Spitzer/IRS spectra of  and Tc1 , which are known to show the 17. 4 and 18. 9 µm C 60 resonances. We did not include the C 60 PN  K3-54 (García-Hernández et al. 2010) , because only Spitzer/IRS SL and LL spectra are available.
To reduce the Spitzer/IRS data, we used the reduction package SMART v. 8. 2. 5 provided by the IRS Team at Cornell University  (Higdon et al. 2004 ) and IRSCLEAN provided by the Spitzer Science Center. With IRSCLEAN, we removed the bad pixels. For the SH and LH spectra, we subtracted the sky background using off-set spectra taken in the same program as the on-source spectra, if available. We scaled the flux density of the SL spectra up to, and the LH spectra down to, the SH spectra using the overlapping wavelength regions. Finally, we scaled the flux density of these combined spectra to the Wide-field Infrared Survey Explorer (WISE) band 4 photometric value (λ c =22 µm), except for IC2501. The flux density at the WISE band 4 and the adopting scaling factor matching this band for each object are listed in Table  2.
AKARI/IRC spectra Checking for the presence of the 3. 3 µm PAH feature is necessary to estimate the extent by which the 8. 5 µm C 60 band is contaminated by the 8. 6 µm PAH feature. Using the intensity ratio of 3. 3 µm to 11. 3 µm PAH bands, we can estimate the number of C atoms of each PAH grain. To that end, we analyzed the 2. 5-5. 5 µm spectra of Hen2-68, IC2501, K3-62, M1-6, M1-11 and M1-12 taken with the Infrared Camera spectrograph (IRC;  Onaka et al. 2007 ) on board of the AKARI satellite  (Murakami et al. 2007 ). The data were obtained as part of two different mission programs, AGBGA (IC2501, PI: Y. Nakada) and PNSPEC (the remainder, PI: T. Onaka). The 1 ′ ×1 ′ observing window is large enough to include the optical nebulae of these PNe as demonstrated in Section 2. 3, therefore, we assume that the loss of the light is negligibly small and we did not perform any correction in flux density.
Optical and mid-infrared images of C 60 -containing PNe To check the apparent size of the nebulae, the nebular shape, and the slit-position in the Spitzer observations, we analyzed the archival optical and mid-infrared images from HST, Gemini 8. 2-m, and ESO/NTT 3. 6-m telescopes. These images are necessary to set the outer radii of each PN in Cloudy photo-ionization models (See Section 3). We downloaded the archival data of IC2501 and K3-62 from the Canadian Astronomy Data Centre (CADC) taken by the Gem-ini telescopes with the mid-infrared imager and spectrograph T-ReCS for IC2501 and Michelle for K3-62. These data were taken through the observing programs GS-2004B-DD-8 (PI: K. Volk) for IC2501 and GN-2005A-C-4 (PI: S. Kwok) for K3-62. Both images were taken using the Si-5 filter (λ c =11.
6 µm). We reduced the data of IC2501 and K3-62 using NOAO/IRAF 6  in a standard manner. The archival HST data of Hen2-68 (WFC3/F507N), IC418, M1-6, M1-11, M1-12, and M1-20 (all WFPC2/F656N) were also downloaded from the CADC and reduced using the standard pipeline with STSDAS/MultiDrizzle to obtain higher resolution images. The NTT/EFOSC data of Tc1 taken through the program 64. H-0557(A) (PI: T. Rauch) were downloaded from the ESO archive center, and we reduced the R-band image using IRAF. The instrumental distortion was corrected with the XYXYMATCH, GE-OMAP, and GEOTRAN IRAF routines, and we aligned the positions of stars in the detector. The reduced images are presented in Fig. 1. Since our attention is mainly towards the regions where the 17. 4 and 18. 9 µm C 60 bands are detected, we also present the slit position and its size for the Spitzer/IRS SH module (slit dimension: 4.
2 and 4. 65 µm are the same and the line-profile of these H i lines can be represented by a single Gaussian. The resulting spectra in the range from 3. 1 to 3. 9 µm are presented in Fig. 6. In panels a-h, as a comparison, we also present the ISO/SWS spectrum of NGC7027 indicated by grey lines. The spectral resolution of NGC7027 data was reduced down to∼110 at 3. 3 µm using Gaussian convolution to match that of AKARI/IRC. For the purpose of removing the H i 3. 2 µm, we used the H i 4. 65 µm in stead of H i 3. 73 µm, because the SNR of local continuum around 4. 65 µm is better (IC418, in particular) and there is less contribution from nearby emission-lines. The positions of some aliphatic hydrocarbons seen in M1-6 and NGC7027 are indicated by the dotted lines (a). The flux density is normalized to the peak intensity of the 3.
All data points in the re-binned spectrum of IC418 were assigned a flux uncertainty of 5%, in line with typical values for the Spitzer/IRS and ISO flux calibration accuracy. Assuming that dust in PNe is found to be present at a range of temperatures, reflecting a range of distances to the central star, we follow the method put forward by  Kemper et al. (2002). This model assumes that dust is distributed around the central star following a power-law ρ(r) ∝ r -p , with the temperature distribution also follows a power-law T (r) ∝ r -q. Thus, following  Kemper et al. (2002)  we can derive, for spherical grains, that F ν = C • Q abs (λ) Tmax T min T -α B λ (T )dT (2) with α = (3 -p)/q > 0. Q abs (λ) is the absorption efficiency as a function of wavelength. Relevant species that could contribute to the continuum are amorphous carbon and graphite, as discussed before, but also SiC.
ACKNOWLEDGMENTS We are grateful to the anonymous referee for the useful suggestions which greatly improved this article. We thank  Elisabetta Micelotta  and  Anthony Jones  for numerous fruitful discussions. MO thanks the astronomy group at the  University of Western Ontario  for their kind support during his visit. FK acknowledges support from the  National Science Council  in the form of grant  NSC100-2112-M-001-023-MY3. JBS wishes to acknowledge the support from a  Marie Curie Intra-European Fellowship  within the 7th European  Community Framework Program  under project number  272820. JC and EP acknowledge support from an  NSERC   Discovery Grant  and a startup grant from the  Department of Physics and Astronomy at Western University. This work is based on archival data obtained with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA.
Support for this work was provided by an award issued by  JPL/Caltech. This work is in part based on HST archive data from the CADC and ESO archive data. This work is in part based on observations with AKARI, a  JAXA  project with the participation of  ESA. This research has made grateful use of the SIMBAD database, operated at  CDS, Strasbourg, France ; and of  NASA's Astrophysics Data System Bibliographic Services.
Robot-automated siRNA screen The automated screen was performed using a liquid handling station (STAR; Hamilton Robotics). U-2-OS GFP-RPA1/RFP-PCNA cells were reverse transfected with a custom-made siRNA library targeting 236 genes (Applied Biosystems). After 46 h, cells were treated with 3 mM HU for 2 h, preextracted with cytoskeleton buffer (CSK: 10 mM Pipes, pH 7, 100 mM NaCl, 300 mM sucrose, and 3 mM MgCl 2 ) containing 0. 5% Triton X-100, fixed in 2% paraformaldehyde, and stained by Hoechst. Five images were acquired per well (20×/0. 45 NA objective lens; IN Cell Analyzer 1000; GE Healthcare) and analyzed by IN Cell Analyzer Workstation 3. 5 software. Hits were identified based on combined activities of their targeting siRNAs. Candidate genes were selected according to both the significance assessment by redundant siRNA activity (RSA) analysis  (König et al.
5-1. 00 NA oil objective lens equipped with a chargecoupled device camera (DFC340 FX; Leica) or a DeltaVision system with U Apochromat/340 40×/1. 35 NA oil objective lens and analyzed with soft-WoRx 5. 0. 0 software. Immersion oil (n = 1. 522) was used as an imaging medium. All images in the individual panels were acquired under room temperature with the same settings and adjusted for brightness and contrast identically using Photoshop CS5 (Adobe).
Fig. S5  provides Western blot analysis of replication fork components in FLASH-and SLBP-depleted cells as well as control cells treated with CHX, HU, and the Chk1 inhibitor UCN-01. Table  S1  shows antibodies used in this study. Table  S2  shows siRNA sequences used in this study. Table  S3  shows quantitative RT-PCR primer sequences. Table  S4  shows a list of genes used for the siRNA screen. Table  S5  shows a candidate gene list from the siRNA screen. Online supplemental material is available at http://www. jcb. org/cgi/content/full/jcb. 201305017/DC1. Additional data are available in the JCB DataViewer at http://dx. doi. org/10. 1083/ jcb. 201305017. dv.
Analysis of gene expression in rat C6 glioma xenografts Total RNA was isolated from rat C6 glioma xenografts harvested from vehicle and MNF-treated mice (n = 3 per group, cohort 1). This analysis was repeated in a second cohort of animals (n = 3 per group, cohort 2). Total cellular RNA was extracted using an RNeasy plus mini kit (Qiagen, Valencia, CA), and then hybridized to Illumina's Sentrix Rat  San Diego, CA). Raw data were subjected to Z-normalization, as described elsewhere  (Cheadle et al. 2003; Lee et al. 2012). The raw data file and the filtered, normalized results are available online in the Gene Expression Omnibus, Accession Number GSE45307 (http://www. ncbi. nlm. nih. go/geo/query/acc. cgi?acc=GSE45307). Additional information can be found in the Supplemental information.
2012). The detection of immunoreactive bands was performed by chemiluminescence using the ECL Plus Western Blotting Detection System (GE Healthcare, Piscataway, NJ). Quantitation of the protein bands was done by volume densitometry using ImageJ software (National Institutes of Health, Bethesda, MD). Primary antibodies used in this study were raised against cyclin A (sc-751, 1:500 dilution; Santa Cruz Biotechnology, Inc. , Santa Cruz, CA), cyclin D1 (sc-8396; 1:500 dilution; Santa Cruz), caspase 3 (sc7148, 1:500, Santa Cruz), and b-actin (mouse; 1:10,000 dilution; Abcam, Cambridge, MA). Detection of Hsp90 with a monoclonal antibody (1:1000; Santa Cruz) was carried out to control for equal protein loading.
Statistical analysis Western blot data from both sets of tumor tissues were analyzed together. The Shapiro-Wilk test was used to assess if the values (protein expression levels) followed a Gaussian distribution  (Shapiro and Wilk 1965). Outliers were removed from further analysis as they were preventing the population to pass the normality test. The results from rat C6 cells in culture were analyzed using the Student t-test. Repeated two-way analysis of variance (ANO-VA) was used to compare induction of changes as a function of time. Data were expressed as means AE standard error of the mean (SEM) and were considered significant when the P-value was less than 0. 05.
4A ). Computational analysis of the datasets derived from this experiment led to the identification of a number of cell cycle-associated GO terms, such as "DNA replication," "Cell cycle" "Mitosis," and "Cell division" that are likely involved in the control of tumor growth during MNF treatment (Table  1 ). The analysis of the overrepresented GO terms upon MNF treatment in the xenograft transcriptome revealed significant negative regulation of cell division-related genes together with those involved in control of metabolism of nucleic acids (Fig. 4B ). The impact of MNF on the expression of genes implicated in the GO term "Cell cycle" (GO:0007049) can be found in Table  S2. Using parameterized analysis of gene set enrichment (PAGE), we provided additional insight into regulated signaling pathways and biological processes affected by MNF.
We have developed two independent methods for measuring the one-dimensional power spectrum of the transmitted flux in the Lyman-α forest. The first method is based on a Fourier transform and the second on a maximum-likelihood estimator. The two methods are independent and have different systematic uncertainties. Determination of the noise level in the data spectra was subject to a new treatment, because of its significant impact on the derived power spectrum. We applied the two methods to 13 821 quasar spectra from SDSS-III/BOSS DR9 selected from a larger sample of over 60 000 spectra on the basis of their high quality, high signal-to-noise ratio (S/N), and good spectral resolution. The power spectra measured using either approach are in good agreement over all twelve redshift bins from z = 2.
(1998 Croft et al. ( , 2002) )  and  Viel et al. (2004). The second approach uses a likelihood method to compute the covariance matrix of δ in real space (or line-of-sight correlation function) as a function of the pixel pair separation in the spectra  (McDonald et al. 2006 ). The 1D power spectrum is the FT of the Lyα correlation function obtained in this way. The two methods have their own advantages and drawbacks in terms of, for example, robustness, processing speed, accounting of instrumental effects, precision, etc. To benefit from their complementarity, we have developed independent analysis pipelines based on either technique. In this paper we present and compare the results obtained with the two approaches. The outline of our paper is as follows. In section 2, we present the BOSS data and explain how we calibrate the level of noise in the spectra and determine the spectrograph resolution.
2009 ) in the southern Galactic sky and in a manner identical to the original SDSS imaging. This increased the total imaging SDSS footprint to 14 055 square degrees, with 7 600 square degrees at Galactic latitude |b| > 20 deg in the northern Galactic cap and 3 000 square degrees at |b| > 20 deg in the southern Galactic cap. All of the imaging was reprocessed and released as part of SDSS Data Release 8  (Aihara et al. 2011). BOSS is a spectroscopic survey primarily designed to obtain spectra and redshifts over a footprint covering 10 000 square degrees for 1. 35 million galaxies, 160 000 quasars, and approximately 100 000 ancillary targets. The quasars, whose spectra cover the Lyα forest of interest for this work, are selected with several algorithms based on the SDSS imaging  (Yèche et al.
Sky line masking Sky lines affect the data quality by increasing significantly the pixel noise. The procedure used to identify them is detailed in  Lee et al. (2013). We briefly summarize it here. We use the sky calibration fibers and compute the mean and the rms of the residuals measured on the sky-subtracted spectrum obtained with the standard BOSS pipeline. We define a 'sky continuum' as the running average of the residual rms fluctuation centered on a ±25 pixel window, and generate a list of sky lines from all the wavelengths that are above 1. 25 × the sky continuum. The continuum, measured with the unmasked pixels, and the sky line list are iterated until they converge. To this list, we add the calcium H and K Galactic absorption lines near λ = 3933. 7 Å and λ = 3968. 5 Å. We then mask all pixels that are within 1.
(5) The window function corresponding to the spectral response of the spectrograph is defined by W (k, R, ∆v) = exp - 1 2 (kR) 2 × sin(k∆v/2) (k∆v/2) , (6) where ∆v and R are the pixel width and the spectrograph resolution, respectively. Both quantities are in km/s, and R should not be confused with the dimensionless resolving power of the spectrograph. We illustrate in Fig. 10  the spectrograph resolution on the window function W 2 (k, R, ∆v) for different values of R. We compute the FT using the efficient FFTW package 1. Compared to the likelihood approach described in the next section, the Fourier transform is much faster, but it requires some simplifying hypotheses in the treatment of the noise and of the spectrograph resolution. We explain these simplifications below. Sky emission lines are also treated in a Palanque-Delabrouille, N.
Extraction of P 1D (k) We extract the P 1D (k) power spectrum from the likelihood L(P(z)) that combines several spectra in the same redshift bin. We use the MINUIT  (James & Ross 1975)  package to minimize the term -2 ln(L). This minimization provides the value and the error of each P ℓ (z) and the covariance matrix between the different P ℓ (z). The method implemented in MINUIT package may be slow but it is robust, because it seldom falls into secondary minima. As the minimization can take a few hundred iterations, we have optimized our fitting procedure. The computation time of the likelihood is limited by the inversion of the covariance matrix C. Therefore, to reduce the size of the matrix C (number of pixels), we do the computation on the 'z-sectors' defined in Sec 3. 1, instead of on the entire Lyα forest.
Biases in the analyses and related systematics We study here the biases and systematic uncertainties introduced at each step of the data analysis. We estimate their impact using mock spectra. We compute the 'bias' of the method as the ratio of the measured flux power spectrum to the flux power spectrum that was generated in the mock spectra. We generated mock spectra with the following procedure. First, a redshift and a g-magnitude are chosen at random from the real BOSS spectra. Second, an unabsorbed flux spectrum is drawn for each quasar from a random selection of PCA amplitudes following the procedure of  Pâris et al. (2011)  and flux-normalized to the selected g magnitude. Third, the Lyα forest absorption is generated following a procedure adapted from Font-Ribera et al. (2012). They provide an algorithm for generating any spectrum of the transmitted flux fraction F (λ) from a Gaussian random field g(λ).
In addition, we introduce nuisance parameters to take the correlation between H i and Si iii into account (parameter a in Eq. 14), and the imperfection of our resolution and noise models. We choose a pivot point in the middle of our measurements, k 0 = 0. 009 (km/s) -1 and z 0 = 3. 0. The results of the fits are summarized in Table  3. The agreement between the different methods and datasets is good. All five parameters are within 1 or 2σ of one another. Tables  4  and 5  summarize, for each redshift bin, the results for the 1D Lyα forest power spectrum. They are available in their entirety in the online edition. 2 The different components (P 1D , P noise and P metals ) are given in these tables. In P metals we consider only the uncorrelated background computed in Sec. 6. 1. The last two columns represent the statistical and systematical uncertainty on P 1D.
(2011)  and the value from Planck (H Planck 0 = 67. 4 ± 1. 4) given in Planck  Collaboration et al. (2013)  differ by over 2σ, we write this H 0 constraint so as to give equal weight to both measurements: χ 2 ext (H 0 ) =        (H0-H Planck 0 ) 2 σ 2 Planck if H 0 < H Planck 0 0 if H Planck 0 < H 0 < H HST 0 (H0-H HST 0 ) 2 σ 2 HST if H HST 0 < H 0. The purpose of this section is to compare the SDSS and BOSS measured power spectra. We therefore use the same binning in k and z and the same strategy for the fit as in  McDonald et al. (2006). In this study, the minimization of χ 2 (P, σ; Θ) was performed with the MINUIT package  (James & Ross 1975). Fig. 23  shows the P (k i , z j ) measurements of the power spectrum with the P th (k, z) function adjusted by the minimization of χ 2 (P, σ; Θ).
Results on σ 8 and n s Table  6  shows the results for the cosmological and the astrophysical parameters. Since the hydrodynamical simulations are preliminary versions of simulations that we are currently developing, we only focus here on the measurement of the two cosmological parameters σ 8 and n s in the case of ΛCDM, assuming a flat Universe with three neutrinos with m ν = 0. In this interpretation section, the other parameters should be considered as nuisance parameters, and the constraints obtained are only indicative. They will be refined in future work. Figures  25  and 26  present the confidence level obtained with Eq. 16 on n s and σ 8. The central values of the fits on the SDSS measurements of  McDonald et al. (2006)  and on the BOSS measurements agree within 1σ. The comparison of the confidence level curves shows an improvement by a factor 2-3 in the constraint on these parameters with the BOSS data compared to the SDSS data.
Conclusions We have developed two independent methods for measuring the 1D power spectrum of the transmitted flux in the Lyα forest. The first method is based on a Fourier transform, and the second approach relies upon a maximum likelihood estimator. The two methods are independent and present different systematic uncertainties owing to the techniques used to mask pixels contaminated by sky emission lines or to take the spectrograph resolution and the noise contribution to the Lyα power spectrum into account, which differ in the two approaches. Determining the noise level in the data spectra was subject to a novel treatment, because of its significant impact on the derived power spectrum. We applied these two methods to 13 821 quasar spectra from SDSS-III/BOSS, that were selected from a larger sample of almost 90 000 DR9 BOSS spectra on the basis of their high quality, high S/N, and the low value of the spectral resolution.
Acknowledgements. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U. S. Department of Energy. The  SDSS-III  web site is http://www. sdss3. org/. The  BOSS French Participation Group  is supported by  Agence Nationale de la Recherche  under grant  ANR-08-BLAN-0222. A. B. , N. P. -D. , G. R. , and Ch. Y. acknowledge support from grant  ANR-11-JS04-011-01  of  Agence Nationale de la Recherche. MV is supported by  ERC-StG "CosmoIGM". SDSS-III  is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the  SDSS-III Collaboration  including the  University of Arizona , the  Brazilian Participation Group ,  Brookhaven National Laboratory, University of Cambridge, University of Florida , the  French Participation Group , the  German Participation Group , the  Instituto de Astrofisica de Canarias , the  Michigan State/Notre Dame/JINA Participation Group ,  Johns Hopkins University ,  Lawrence Berkeley National Laboratory ,  Max Planck Institute for Astrophysics, New Mexico State University ,  New York University , the  Ohio State University , the  Penn State University, University of Portsmouth ,  Princeton University, University of Tokyo , the  University of Utah, Vanderbilt University, University of Virginia, University of Washington , and  Yale University.
D-HÁ Data collection: CrysAlis PRO (Oxford Diffraction, 2009); cell refinement: CrysAlis PRO; data reduction: CrysAlis PRO; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and PARST  (Nardelli, 1995).
Computing details Data collection: CrysAlis PRO (Oxford Diffraction, 2009); cell refinement: CrysAlis PRO (Oxford Diffraction, 2009); data reduction: CrysAlis PRO (Oxford Diffraction, 2009); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and PARST  (Nardelli, 1995).