dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Milk data obtained during the differential milking period and during the twice daily milking switchback period were analysed separately using the same statistical model. The average daily milk yield per udder half and milk composition data were analysed by ANOVA using the SAS MIXED procedure (SAS Institute, 1999). The data obtained during the pretreatment period were averaged and used as covariate per udder half. The effects of milking frequency (once or twice daily) and cow were tested. The averaged milk data and composition obtained during the pretreatment period were compared between udder halves using a paired Student's t-test. Statistical analysis to determine the differential accumulation of transcripts was performed using the R software version 3. 0. 0 (R Development Core Team, 2013) and the Bioconductor package DESeq2 version 1.
0. 0  [30]. DESEQ2 utilizes a negative binomial distribution to model read counts per mRNA and implements a method to normalise the counts. This normalisation procedure uses the library median of the ratios between the read count and the geometric mean of each gene as a scaling factor for each library. Fold changes were estimated with an empirical Bayes shrinkage procedure. This helps to moderate the broad spread in fold changes for genes with low counts, while it has negligible effects on genes with high counts  [30]. The test for differential transcript levels was only performed with respect to genes for which the sum of counts across all four samples counts was higher than 50 (16864/22715 genes). The pvalues were adjusted for multiple testing using the Benjamini and Hochberg method  [31].
This RNA-Seq approach limited to four animals revealed a two-fold reduction in the levels of all other CSN transcripts (CSN1S1, CSN1S2 and CSN3; Table  8 ) with q-values ranging from 0. 06 to 0. 096. These results are in agreement with those obtained by RT-qPCR. RNA-Seq data further revealed that all CSN1S1 transcripts in the bovine mammary gland during lactation did not share the same 59ends. Some 59ends were located around BT6: 87. 141. 556, as described for the CSN1S1 transcrip- tional start point in Ensembl release 75, February 2014, others were located further downstream, whereas most transcripts were initiated at position 87. 141. 591, 24 nucleotides away from a classical TATA box, as previously described  [34]. In this work we used that site as a reference when describing the CSN1S1 regulatory region.
CSN transcripts in total RNA (1. 25 mg) were evaluated using RT-qPCR. Data were normalized relative to CCPL transcript (DCt). Differences between ODM and TDM were evaluated as DDCt and fold changes calculated. Differences were estimated by a paired t-test. doi:10. 1371/journal. pone. 0111556. t007 pregnancy or in the liver (Figure  5A ), and this methylation state is not modulated by milking frequency. This CpG is located within a potential STAT5 binding site (D1) and close to a second potential binding site (D2) (Figure  1A ). The binding activities of these tandemly arranged potential sites were evaluated using EMSA. Mammary gland nuclear extracts incubated with the 32 P labelled double strand D2 probe ( 32 P-D2, corresponding to the more distal potential STAT5 sequence), formed a complex that migrated at a position similar to that observed for the Consensus STAT5 probe, indicated as C in Figure  1B  (Figure  6A ).
The methylation of the CpG within the D1 sequence at most only marginally interferes with this weak in vitro interaction. The specificity and relative binding activities of probes for mammary nuclear extracts were further analysed by competition assays. The binding of nuclear extracts to 32 P C or 32 P D2 labelled probes was impaired in the presence of increasing amounts of C, D2, D1, D1 m unlabelled double strand oligonucleotides (Figure  6B , upper and lower panels, respectively) but not in the presence of irrelevant unlabelled double strand oligonucleotides (data not shown). The amounts of unlabelled C and D2 required to observe half-maximal inhibition (IC 50 ) with 32 P C (Figure  6B  upper panel) or 32 P D2 (Figure  6B  lower panel) ranged from 0. 2 to 0. 7 ng (1. 6 to 5. 9 nM, respectively).
Transcripts were evaluated by RNA-Seq. The ODM and TDM data were normalised, the fold change evaluated between paired values of ODM and TDM (ODM/TDM), and the adjusted p-value (q-value) was analysed as described in Materials and Methods. doi:10. 1371/journal. pone. 0111556. t008Evaluation of transcripts in the bovine mammary gland during pregnancy using RT-qPCR analyses. CSN transcripts in total RNA (1. 25 mg) were evaluated using RT-qPCR. Data were normalized relative to CCPL transcript (DCt). Differences between pregnancy and lactation were evaluated as DDCt and fold changes calculated. Differences were estimated by a Wilcoxon Mann-Whitney test. doi:10. 1371/journal. pone. 0111556. t009 important role in pregnancy and lactation during which PRL levels in the blood vary.
Acknowledgments This work benefited from the facilities and expertise of the high throughput sequencing IMAGIF platform ( Centre de Recherche de Gif  -www. imagif. cnrs. fr) and we are grateful to  Claude Thermes ,  Erwin van Dijk  et  Yan Jaszczyszyn  for their constructive advice. We also wish to acknowledge the  MIMA2  facility ( INRA, UMR1198, Jouy-en-Josas, France ) for its technical support with confocal imaging,  S. Marion  and  N. Huchet  ( INRA UMR 1348 Saint Gilles, France ) for their technical assistance, and  L. Galio  ( INRA UMR1198 Jouy-en-Josas, France ),  P. Lamberton  ( Mejusseaume Experimental farm, France ) for their help with sampling and animal experiments, respectively and  S. Truchet  for her help during the revision of the manuscript. Our special thanks to  J. P. Renard  for his great help in obtaining funds for the project.
52 0. 096 CSN2 ENSBTAG00000002632 193841 448346 0. 48 0. 029 HSTN ENSBTAG00000048250 3021 6642 0. 46 0. 013 STATH ENSBTAG00000024683 42 67 0. 65 0. 581 CSN1S2 ENSBTAG00000005005 162642 392410 0. 48 0. 065 ODAM ENSBTAG00000006810 9 49 0. 44 0. 090 CSN3 ENSBTAG00000039787 304260 602687 0. 52 0. 078 LALBA ENSBTAG00000005859 135456 357379 0. 43 0. 019 RPS9 ENSBTAG00000006487 1646 1939 NS 0. 879 RPS15 ENSBTAG00000019718 1199 1330 NS 0. 969.
HFI data processing overview The processing of HFI data proceeds according to a series of levels, shown schematically in Fig. 1. Level 1 (L1) creates a database of the raw satellite data as a function of time (TOI objects). The full set of TOI comprises the signals from each HFI bolometer, ancillary information (e. g. , pointing data), and associated housekeeping data (e. g. , temperature monitors). Level 2 (L2), the subject of this paper, uses these data to build a model of the HFI instrument, the Instrument Model (IMO), produces cleaned, calibrated timelines for each detector, and combines these into aggregate products such as maps at each frequency. Level 3 (L3) takes these instrument-specific results and derives various products: component-separation algorithms transform the maps at each frequency into maps of separate astrophysical components; source detection algorithms create catalogues of Galactic and extragalactic objects; finally, a likelihood code assesses the match between a cosmological and astrophysical model and the frequency maps.
In somewhat more detail, L1 software fills the database and updates, daily, the various TOI objects. Satellite attitude data, sampled at 8 Hz during science data acquisition Unpolarized SWBs are used alone, while PSBs (with individual bolometers denoted "a" and "b") are used by pair; "MIX" denotes a combination of detector types. Here we consider only the temperature map extracted from the analysis of four bolometers (two pairs of PSBs). The weights indicate the relative weighting used in producing maps out of the TOI of several detectors. The relative weights in a set are given in the numerical order of each detector  (e. g. , 1a, 1b, 2a, 2b, 3a, 3b,. for the 100-ds0 set). The last column details the specific products created for each set. "B": beam information; "C": detector-set spectra, corrected for the beam transfer function, for the detector sets used in the high-likelihood; "F": frequency band information; "H": pixel hit-count maps; "HR1", "HR2": maps made from the first or second half of each ring; "N": nominal mission maps; "S1", "S2": survey maps generated from the data collected during the first six months or the next six months; and "Z": ZLE/FSL-corrected maps.
Standard FTS data processing techniques were employed to obtain the observed spectra of each HFI detector and that of the reference bolometer; the ratios of the detector spectra against the reference bolometer spectrum provide the relative response of each HFI detector. This method incorporates the detector throughput (i. e. , étendue AΩ, see Planck Collaboration IX 2014) in the spectral response as the reference bolometer accepts a much wider field of view, i. e. , approximately 2π sr over all frequencies. The HFI detector spectral transmission data, and their frequency band-average transmission spectra, are described more fully in Planck Collaboration IX (2014) and   Planck  Collaboration (2013) , where a description of the bandaveraging scheme is also provided. As discussed in Sect. 6. 2, HFI is calibrated using the CMB dipole for the four 100-353 GHz bands, expressed in units of K CMB , and using planets for the two 545-857 GHz bands, expressed in units of W m -2 sr -1 Hz -1 (equivalently, MJy sr -1 ).
This unit conversion is defined to translate data between units of K CMB and MJy sr -1 , the latter for spectra with a profile consistent with a spectral index of -1, following the IRAS convention  (Beichman et al. 1988). Colour-correction coefficients are provided for a variety of spectral indices (see Fig. 25 ) and other common spectral profiles including modified blackbodies and planetary spectra. It is thus important to ensure that data processing, e. g. , component separation, is conducted in a consistent manner in units appropriate for the frequency band. The HFI policy is to operate in units of MJy sr -1 , as this is valid for all of the HFI frequency bands whereas K CMB is only valid for 100-353 GHz. The K CMB to MJy sr -1 unit conversion coefficients are therefore given by the following ratios U(K CMB to MJy sr -1 ) = dν τ(ν)b ν dν τ(ν)(ν c /ν) × 10 20 MJy sr -1 K CMB , (3) where τ(ν) is the relevant spectral transmission curve, b ν = dB ν /dT | T CMB is the Planck function derivative, and ν c is the nominal band centre frequency.
More details on the unit conversion factors are provided in Planck Collaboration IX (2014), while tables of conversion factors for Planck detectors (only HFI channels for some spectral shapes, e. g. , CO lines) are provided in Planck Collaboration (2013). Figure  26  illustrates the band-averaged spectra for each of the HFI bands; similar, and related, plots for individual HFI detectors are provided in Planck Collaboration  (2013). A series of IDL scripts, called the UcCC routines, is available to allow users of Planck data to determine unit conversion and colour correction coefficients, and uncertainties, for userspecified profiles, in addition to those nominally provided in the public IMO. Further details on these scripts are provided in Planck Collaboration (2013).
Polarization Together with the intensity maps, we derive polarization maps for each HFI frequency from 100 GHz to 353 GHz (cf. Planck Collaboration VIII 2014). The Q and U Stokes parameter maps have a nearly white noise spectrum and are dominated by Galactic dust emission and CMB with a signal-to-noise ratio of roughly 100 for < 100. Nevertheless, at the present state of the reconstruction, polarization maps are dominated at large scales by systematic effects. As a consequence, they are not included in the present release. However, these data will be used in forthcoming analyses of foreground polarization, and hence we describe the major systematic effects remaining in the polarization data. The Planck scanning strategy does not allow recovery of the polarization signal for each detector independently.
We use a combination of at least four detectors oriented at 45 • in order to reconstruct the Q and U Stokes parameters. Any miscalibration between the data sets will thus lead to a mix between Stokes parameters and, in particular, a leakage from intensity to polarization. As discussed in Planck Collaboration VIII (2014), absolute calibration is derived from the solar dipole and gain variations due to ADC nonlinearities are corrected to a few tenths of a percent in the time domain. Final intercalibration uncertainties between HFI bolometers are of the order of 0. 4 % or less for the three lowest HFI frequencies (0. 39, 0. 28, and 0. 21 % respectively at 100, 143, and 217 GHz) and rise to 1. 35 % at 353 GHz. The induced leakage is dominated by the solar dipole intensity, which produces large-scale features in the polarization maps.
We have estimated the leakage due to calibration mismatch using simulations. Figure  27  shows that the leakage is dominant over the CMB signal up to = 100 in the EE angular power spectrum, as predicted by the best-fit cosmological parameters measured from Planck temperature data (Planck Collaboration XVI 2014). In addition to the calibration mismatch, differences in bolometer spectral transmissions will also introduce a leakage from dust emission intensity into polarization. Using the spectral transmission as measured on the ground (Planck Collaboration IX 2014), we estimated the level of leakage introduced by bandpass mismatch, as shown in Fig. 27. Polarization angles and efficiency have been assessed during ground measurements. Values and uncertainties are reported in  Rosset et al. (2010).
Consistency with a severe selection of data Another check based on real data consists of comparison with data in which the pointing periods have been much more severely censored. As mentioned in Sect. 3. 10, the normally discarded pointing periods represent less than 1 % of the total integration time for the full mission and 0. 2 % for the nominal mission, the difference being due to some solar flare events and the HFI end-of-life operations during the fifth survey. NB: the next 2 paragraphs and the figure were revised. Following much more severe criteria, 30 % of the data of the full mission are rejected. For example, very conservative thresholds on the stationarity of the noise are applied (see the details in Appendix B). We then create a map at each frequency with this selection and compute the pseudo-spectra of both the Figure  27.
Difference map consistency tests We perform a series of difference map consistency tests to evaluate the contribution of residual systematic effects to the angular power spectrum of the temperature anisotropies. We generate difference maps of halves of the nominal mission data in which the signal is expected to fully subtract out in the absence of systematic effects. The resulting maps are then subtracted from systematics-free simulated "Yardstick" realizations (Appendix A) of the same differences propagated through the same mapmaking pipeline as that used for data processing. The amplitude of the power of the remaining signal in each likelihood bin can thus be used as an estimate of the level of the residual systematic contamination in this bin. This estimate is derived from cross-spectra between different detector sets to allow for a direct comparison with the spectra used as inputs by the likelihood code (Planck Collaboration XV 2014).
Many differences of halves of the data can be expected to be compatible with an absence of signal. Given Planck's scanning strategy, we expect the bulk of the remaining systematic contamination to be captured in survey difference maps. In particular, transfer function errors, far-sidelobe pickup, gain instability, pointing drifts, and residual glitches will leave an imprint (Survey 2 -Survey 1)/2 consistency test result from the ds1 × ds2 cross-spectra at 100, 143, and 217 GHz, with the f sky 0. 30 mask derived from that used in the primary cosmological analysis (see text). Left: The residual signal in each bandpower up to the highest multipole used in the likelihood code  (Planck Collaboration XV 2014). For clarity, the bins used in the likelihood analysis are grouped in sets of four for > 60 and sets of eight for > 1250.
Unlike in the survey difference test shown in Fig. 29 , no significant null test failure is detected here. The detector combinations used in the example plots above are (5 -8) × (1 -4), (7 -8) × (3 -4), and (ds1-3)×(ds2-4) for, respectively, the (Left-Right), (In-Out), and (PSB-SWB) tests. As in Fig. 29 , these power spectra are divided by four to allow for a direct comparison of the amplitude of the residuals to that of the components in the nominal mission power spectrum. Finally, to best address any remaining concerns about systematic errors resulting from data selection and differences in the first-half and second-half spectra, we present additional, high precision, tests of systematics performed since the March 2013 data release on the 217 GHz Planck channel. Indeed, this channel has the highest angular resolution within the "CMB frequency" range 100 -217 GHz used for most of the cosmological results.
As a consequence, it has the least degree of redundancy of the CMB frequencies, since one cannot cross-check the 217 GHz spectrum with the lower frequency spectra at high multipoles  ( > ∼ 2000). Appendix C presents tests of the fidelity of the Planck power spectra using additional data from the full mission (29 months, 4. 8 sky surveys, with the same processing than for the nominal mission). By using the full mission it is possible to perform more extensive and higher signal-to-noise tests than those described by  Spergel et al. (2013) , which were based exclusively on the 2013 nominal mission data release. Reassuringly, the outcome lends further support to the claimed robustness and accuracy of the cosmological results based on the current processing of HFI data.
ADC nonlinearity impact We check the impact of including the effect of ADC nonlinearity through a comparison of data and simulations of the two PSB pairs at 143 GHz. Note that this effect had been suspected to be detectable, but is partially degenerate with time-dependent gain variations in the detectors. Beyond correcting for such gain variations, the effect was not accounted for in the nominal mission products, nor included in the corresponding Yardstick simulation, since the relevant modelling information was only obtained after the end of the HFI cryogenic phase through dedicated data gathering during the warm phase. Figure  31  shows that the inclusion of this effect leads to similar < ∼ 100 deviations from the Yardstick compared to those observed in the survey difference consistency test of the 143 GHz data, which suggests this is one of the main limiting factors for measuring the low-polarization with the current processing of the data.
This demonstrates that indeed the effect of ADC nonlinearity can explain most of the coherent deviation seen at < ∼ 100. given the prior information on the effective beam uncertainties and calibration. At each frequency, an estimate of an adapted power spectrum model is processed with the SMICA likelihood (Planck Collaboration XV 2014). At 143 and 217 GHz where the CMB is dominant, the spectrum on the measured sky is modelled as aa † + n , a sum of the the free signal power, C , plus Comparison of the high-part of the power spectra of the sum and difference of half-ring maps for the 143-1a detector, both for flight data and simulations where glitches were introduced and deglitched with the actual processing pipeline. The comparison shows that the simulations provide a good description of this effect (which is not included in the Yardstick simulations).
Top: The sum and difference spectra. Bottom: Ratios of the sum and difference spectra for both data and simulations, which differ by about 2 %. a free independent noise spectrum, n , where the known vector a accounts for the fixed CMB emissivity at each frequency (bold-faced vectors have elements at each frequency). At higher frequencies, dust emission begins to dominate the CMB, and some foreground modelling is necessary. Thus the CMB power is fixed to the angular spectrum determined at 143 GHz, the noise is kept free and a free foreground is added, with colour correction determined from a greybody spectral shape integrated though the measured spectral response of each detector [40 % of the sky is used to estimate the binned empirical power spectrum with spice  (Szapudi et al. 2001) ]. Furthermore, beam errors are modelled as a linear combination of templates, thanks to a beam error-mode analysis (Planck Collaboration VII 2014).
Consistency of the calibration across frequencies In the previous section, we looked at the consistency of detector spectra within a given frequency band. A similar ana- lysis of the spectra used in the high-part of Planck likelihood Planck Collaboration XV  (2014)  shows that the best relative recalibration (for the mask considered and accounting for the first beam eigenmode), between all 13 detectors maps in the 100, 143 and 217 GHz channels, would be less than 0. 2 %. This level has an insignificant effect on the constraints on cosmological parameters. Finally, we can further broaden our consistency checks by using the SMICA component separation method on both LFI and HFI data from 44 to 353 GHz, in effect intercalibrating on the common CMB anisotropies themselves, taking 143 GHz as the reference (see  Sect.
The impact of the transfer function convolution and deconvolution is in any case accounted for. Finally, TOI are propagated through the DPC mapmaking and calibration pipelines following the procedures described in this paper, including gain correction and application of the various flags  (Planck Collaboration VIII 2014). This allows for a one-to-one comparison of the simulated and data maps. Each simulation leads to the generation of per-detector and perdetector-set maps for each survey, and for the nominal mission on which the results in this release are based.
Figure1. Overview of the data flow and main functional tasks of the HFI Data Processing Centre. Level 1 creates a database of the raw satellite data as a function of time. Level 2 builds a data model and produces maps sky maps at the six frequency of the instrument. This flow diagram illustrates the crucial role of the Instrument Model (IMO), which is both an input and an output of many tasks, and is updated iteratively during successive passes of the data. Level 3 takes these instrument-specific products and derives final astrophysical products. This paper is mostly concerned with Level 2 processing and its validation.
Figure32. Comparison of the high-part of the power spectra of the sum and difference of half-ring maps for the 143-1a detector, both for flight data and simulations where glitches were introduced and deglitched with the actual processing pipeline. The comparison shows that the simulations provide a good description of this effect (which is not included in the Yardstick simulations). Top: The sum and difference spectra. Bottom: Ratios of the sum and difference spectra for both data and simulations, which differ by about 2 %.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, 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) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project=planck&page=Planck_Collaboration.