dataesr/scientific-paragraphs-categorization huggingface dataset:
Conventions and notations The relations between the observed Stokes parameters (I, Q, and U) and the polarization fraction (p) and polarization angle (ψ) are given by p = Q 2 + U 2 I , (1) and ψ = 0. 5 × arctan(U, Q), (2) where the two arguments function arctan(Y, X) is used to compute atan(Y/X) avoiding the π ambiguity, such that Q = p × I × cos(2ψ), U = p × I × sin(2ψ). ( 3 ) For the Stokes parameters provided in the Planck data, the angle convention above is with respect to Galactic coordinates with -90 • < ψ < +90 • and ψ = 0 • towards the Galactic north and positive towards the west (clockwise). Note that this convention is the one used in the HEALPix 2 software  (Górski et al. 2005) , but is different from the IAU convention  (Hamaker & Bregman 1996) , which is ψ = 0 • towards north and positive towards the east (counterclockwise).
Bandpass mismatch leakage correction Owing to the way the polarization maps are constructed, any instrumental difference between detectors of the same channel may produce a fake polarization signal, even for unpolarized sky signal inputs. This is the case for the bandpass mismatch (BPM) between detectors that affects Planck polarization maps. In practice, the effect corresponds to a leakage term from intensity I into polarization Q and U. The BPM polarization leakage effect is therefore strongest in regions of high intensity, i. e. , along the Galactic plane, and affects both p and ψ. Note that, since the 353 GHz intensity data used here are calibrated on the CMB 2 http://healpix. jpl. nasa. gov signal, no BPM leakage is produced by the CMB anisotropies. Other astrophysical emission sources, however, produce BPM polarization leakage.
Deriving polarization parameters The polarization parameters I, p, and ψ are derived from the observed Stokes parameters I, Q, and U using the Bayesian method described in Planck Collaboration Int. XXIII (2014). This method extends that described in Quinn (2012) by using the full 3 × 3 noise covariance matrix of each pixel. The effective ellipticity, as defined in Planck Collaboration Int. XXIII (2014), characterizes the shape of the noise covariance matrix and couples all the terms in Q and U. eff = 1 corresponds to the case described in Quinn (2012), whereas eff > 1 means that the relation between C QQ , C QU , C UU is not trivial, and there are asymmetries in the noise covariance matrix. We calculated eff for the Planck data used here. At 1 • resolution it is normally distributed with a mean value of 1.
External data In Sect. 4. 4, we compare the Planck HFI polarization maps with low-frequency radio and microwave observations that are dominated by synchrotron emission over most of the sky. These include: the 408 MHz total intensity map of  Haslam et al. (1982)  from the LAMBDA 3  site; the 1. 4 GHz total intensity map of the northern  (Reich 1982; Reich & Reich 1986 ) and southern  (Reich et al. 2001 ) sky; the 1. 4 GHz polarized intensity maps of the northern  (Reich 1982 ) and southern  (Reich & Reich 1986 ) sky; For the analysis in Sect. 4. 4, the Planck HFI and LFI maps are smoothed to 1 • FWHM resolution to match these radio data and downgraded to N side = 256. Most of the 1. 4 GHz maps are available on the Bonn survey site 4 as FITS images in Cartesian coordinates. They are converted into HEALPix using the procedure described in  Paradis et al.
Therefore, derived values of p max can only be lower limits to the intrinsic polarization fraction p 0. Here, we use the Planck maps at 353 GHz to evaluate p max. Since p is a biased quantity and since noise depends upon the data resolution, the observed maximum polarization fraction depends upon resolution. It is therefore crucial to take uncertainties into account. Figure  17  shows the sky fraction, f sky (p > p v ), where the observed polarization fraction is higher than a given value p v as a function of that p v. The various curves are for data resolutions of 1 • , 30 , and 15. The coloured area shown correspond to f sky (p ± 4σ p > p v ) for the various resolutions. Table  2. Statistics of the percentage polarization fraction p at various data resolutions, θ. The table gives the data resolution (column 1) and the median and maximum values of p (columns 2 and 3).
Conclusions We have presented the Planck large-scale polarization maps at 353 GHz, where polarization is dominated by polarized thermal dust emission from elongated grains aligned with the magnetic field. These data allow us for the first time to study dust polarization over large angular scales and opens the field for many detailed studies to come. The dust polarization fraction p is observed to range from zero to more than 15 %. We derive a lower limit to the maximum polarization fraction of p max = 19. 8 %. These highest polarization fractions are observed in a handful of individual regions, usually located in intermediate to low column density parts of the sky. The large-scale spatial distribution of p shows a modulation that is consistent with predictions of the general magnetic field structure of the MW, as constrained previously from synchrotron and RM data.
At smaller scales, the variations of p appear to be related to variations in both the total dust column density and the magnetic field structure. There is a clear tendency for p to decrease with total column density. The variations associated with column density show that p starts to drop below around A V = 1 mag and show a sharp drop above A V = 10 mag. This is qualitatively consistent with the prediction of models where dust alignment results from the interaction with light. However, tangling of the B field geometry along the LOS is also very effective at suppressing the net polarization fraction and possibly plays the major role in most cases, as discussed in Planck Collaboration Int. XX (2014). The Planck polarization data at 353 GHz also allow precise measurements of the polarization direction ψ over most of the sky.
This is the first time such structures have been observed for dust polarization. These filamentary structures anti-correlate with p, in the sense that regions with maximal rotation generally correspond to the lowest polarization fractions. We demonstrate that, over a large fraction of the sky, this is not due to the noise-induced bias on ∆ψ and is therefore a real effect. We interpret the anti-correlation between ∆ψ and p as depolarization due to the magnetic field structure. This is likely produced by field rotation both in the plane of the sky below the resolution of the data and along the LOS. The filamentary structures often appear to be separating adjacent regions that have Planck collaboration: The Planck dust polarization sky different but quite homogeneous field orientations. When they can be followed down to the Planck resolution, their widths are smaller than the beam.
B. 2. Time cross-product method (method 2) This method consists in computing cross products between estimates of Q and U with independent noise properties. In the case of Planck HFI, each sky pixel has been observed at least four times and the four independent surveys can be used for this purpose. Another option is to use half-ring maps which have been produced from the first and second halves of each ring. These methods have the disadvantage of using only part of the data, but the advantage of efficiently debiasing the data if the noise is effectively independent, without assumptions about the Q and U uncertainties. In that case, p 2 db can be computed as p 2 db = i> j Q i Q j + U i U j i> j I i I j , (B. 6) where the sum is carried out either over independent survey maps or half-ring maps.
This is critical for investigating the spatial distribution of the noise-induced bias on ∆ψ. In both tests, we added correlated noise on I, Q, and U using the actual noise covariance matrix at each pixel, and computed the map of ∆ψ using Eq. 6 and the same lag value as for the Planck data. Figure  C. 1 shows the histograms of the ∆ψ values obtained for these two tests, both for the whole sky and in the mask used in the analysis of the real data. As expected, the ∆ψ = 52 • test histograms peak at the value of ∆ψ for Gaussian noise only (no signal, ∆ψ = 52 • ). The corresponding map of ∆ψ does not exhibit the filamentary structure of the actual data shown in Fig. 11. Similarly, the test histograms of ∆ψ do not resemble that of the real data shown in Fig. 13. The ∆ψ = 0 • test is important for assessing the amplitude of the noise-induced bias, as Monte Carlo simulations show that assuming a true value of ∆ψ 0 = 0 • maximizes the bias.
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. We acknowledge the use of the Legacy Archive for Microwave Background Data Analysis (LAMBDA), part of the  High Energy Astrophysics Science Archive Center (HEASARC).
Methodology for initialization of the CNRM-CERFACS decadal system Initial conditions for the CMIP5 decadal hindcasts produced with CNRM-CM5 are obtained from a preliminary simulation (hereafter referred to as NUD4IC) over 1958-2008, where the ocean component is nudged towards the NEMO-VAR-COMBINE  (Balmaseda et al. 2010 ) ocean reanalysis, while the other components (atmosphere, sea-ice, continents) are freely coupled. NEMOVAR-COMBINE (NEMO-VAR for simplicity) reanalysis is based on a 3D-VAR assimilation data system  (Weaver et al. 2005; Daget et al. 2009 ) and assimilates profiles of temperature and salinity from a version of the quality controlled EN3_v2a data set  (Wijffels et al. 2008). The choice for NEMOVAR instead of other reanalysis products is motivated by the fact that first, NEMOVAR and CNRM-CM5 share the same ocean model version and grid avoiding spurious effect introduced by interpolation, especially over the vertical dimension, and second, they are integrated with the same physical and dynamical assumptions set in the namelist.
Through the use of NUD4IC outputs as initial conditions datasets, our initialization strategy differs from the traditional full-field one, where raw initial conditions from reanalysis datasets are simply used. The rationale for NUD4IC is to try minimizing the initial shock when forecasts begin but also, on a practical side, to get states for components for which there is no available reanalyses such as land and sea-ice (thickness, surface albedo etc. ) for initial conditions. Two different approaches are combined for nudging in the NUD4IC experiment. At the surface, a restoring is applied in terms of heat and fresh water fluxes, by using a flux derivative term as follows: where Q ns and Q o ns are the net non solar flux at the surface, T is the sea surface temperature of the model and SST NEMOVAR for the reanalysis, and dQ/dT is a feedback coefficient between flux and temperature set to -40 W/ m 2 /K as diagnosed from  Barnier et al.
To build the decadal ensembles, only the atmosphere is perturbed by random selection of initial states within the January month produced in NOTROP_IC for the corresponding starting date. These experiments have been published in the CMIP5 database together with additional starting dates for years 0 and 5 of each decade  (1960, 1965, 1970,…, 2000). A similar protocol has been followed for the NOEQ_IC ensemble that is not published in the archive. Note that for 6 starting dates  (1960, 1961, 1980, 1981, 2005, 2006)  and for NOTROP_IC only, the 10-year members have been extended up to 30 years. External forcings (GHGs concentration, aerosols, solar irradiance and observed volcanic eruptions) are prescribed in the model and are the exact same ones as in the so-called historical experiments (HIST hereinafter) corresponding to the non-initialized runs  (Taylor et al.
2012). 10 members are also available for HIST and have been initialized in 1850 from 10 states randomly selected from a 1000-year long pre-industrial simulation (hereinafter piControl). In this paper, we investigate the physical and dynamical processes playing a role in the model adjustment towards its own equilibrium state or model attractor. In the following analyses, we make the hypothesis that the model attractor can be estimated by HIST over the same period as the forecasts. Most of the diagnostics are thus presented through differences between the decadal predictions starting from NOTROP_IC (hereafter DEC for simplicity) and HIST. The latter is partitioned into members/ leadtimes and starting dates to mimic the DEC dataset and for both, fields used to investigate the model drifts are generally averaged over all the members and all the starting dates produced for the CMIP5 CORE experiments  (Taylor et al.
From JAS of Yr2, the histograms shift towards La Niña states (negative values). At the end of Yr3, DEC hindcasts statistics are undistinguishable from HIST. Figure  4  clearly highlights that the excess of heat with respect to the model own equilibrium, which has been inserted at the subsurface through initialization (Fig. 3a ), is released during the first year of the prediction through the excitation of a El Niño events for 80 % of the 10 members × 10 starting dates of the forecast dataset at JAS Yr1. In the following winter (OND Yr2) like a boomerang, 62 % of the forecasts simulate a cold ENSO event in response to the leadtime Yr1 El Niño following the so-called recharge-discharge paradigm. Note that such a shock has been minimized in the DEC configuration because of the absence of subsurface nudging in the tropical band as presented above.
Acknowledgments The financial support of this work has been provided by the  EU   FP7  project  COMBINE , under contract  GOCE-CT-2006-037005  and from  EPIDOM GICC French National  project under contract  10-MCGOT-GICC-7-CVS-131. The authors wish to thank  Gurvan Madec  ( LOCEAN ) for the discussion and help about the choice of damping parameters for nudging experiments; and  Anthony Weaver  (CERFACS) and  Magdalena Alonso Balmaseda  ( ECMWF ) for their interesting discussions about NEMOVAR-COM-BINE reanalysis. Special thanks are given to  Aurore Voldoire  and  Stephane Senesi  (CNRM) as well as to the entire  CNRM-CERFACS group  for their great effort in the preparation and set up of the coupled model and decadal framework. Finally, we will wish to thank  Eric Greiner  from  Mercator-Ocean  for his helpful discussion on ocean processes in the North Atlantic.
For each sample we generated at least 100 million paired-end reads, except for the IFNG+TNF-stimulated BMDM samples that were sequenced on a single end. Although all the samples were sequenced once, due to the unique recombination of the parental alleles and homozygosity at each of the informative genetic markers (934), there are at least 4 replicates (the minimum number of mice having the same allele at each marker) for each marker. Thereafter, we processed the RNA-seq data as previously described  [25]. Briefly, we aligned the RNA-seq reads to the mouse reference genome (NCBI build GRcm38, downloaded from Illumina iGenomes; https://support. illumina. com/ sequencing/sequencing_software/igenome. html) using TopHat  [66]. To avoid read alignment bias due to sequence polymorphisms between AJ and B6 genomes, we made a synthetic reference genome in which all the polymorphic nucleotides between AJ and B6 were converted to a neutral nucleotide  [67].
However, and consistent with a recent report  [68] , allele bias did not significantly affect read alignment to the genome. On average, about 70% of reads in each sample uniquely mapped to the synthetic genome, which was about 1% less than the number of reads uniquely aligned to the iGenome. Henceforth, unless otherwise stated, all RNA-seq data presented herein were processed using the synthetic genome. Transcript abundance was estimated using Cufflinks  [69]  and reported as fragment per kilobase exon per million reads (FPKM). Next, using the FPKM values from each of the 26 RI BMDM and the corresponding AXB/ BXA genetic map, we mapped the genomic loci that modulate gene expression (expression QTL, eQTL)  [32]  using R/qtl  [57]. As previously described  [25] , we performed 1000 permutations to correct for multiple testing across the 934 informative genetic markers in the AXB/ BXA cross.
Next, we used a false discovery rate (FDR) 10%, calculated in the qvalue package  [70] , to correct for multiple testing on the transcripts and to nominate significant eQTL. Finally, to allow for meaningful comparisons, we only included in the downstream analyses, for each stimulation condition, eQTL for transcripts with an average FPKM ! 5 across the 26 RI BMDM. The linkage analyses and the subsequent filtering steps were performed separately in resting, IFNG+TNF-stimulated, CpG-stimulated, and Toxoplasma-infected BMDM and identified, 131, 367, 688 and 1008 significant eQTL, respectively, dispersed throughout the genome (Fig 2A and 2B and S1A-S1D Dataset). Thus, consistent with previous studies  [24, 71] , the genetic background influences macrophage gene expression profile. Importantly the different stimuli induced transcriptional programs that were modulated by distinct eQTL hotspots, which can be exploited to unravel the complex molecular networks that regulate macrophage activation states.
206 trans) BMDM (S1A-S1D Dataset). Suppose a common locus was to modulate the expression of multiple genes in trans, then in linkage analysis, we should expect the eQTL for these genes to co-localize in the vicinity of the common locus forming a trans-eQTL hotspot (trans-band). Indeed, similar to previous studies  [23, 24, 73] , and indicative of a common variant regulating the expression of multiple genes in trans, we detected trans-eQTL hotspots, within a 10 Mb window, in all the samples (Fig 2A  and 2B  and S1A-S1D Dataset). Because it is possible for eQTL to co-localize by chance alone, we used Bonferroni-corrected p-values and Poisson distribution to compute the number of trans-eQTL that can co-localize in a 10 Mb genomic window by chance. Using these cutoffs, we identified 2, 3, 5, and 15 trans-eQTL hotspots in the resting, IFNG+TNF-stimulated, CpGstimulated, and Toxoplasma-infected BMDM, respectively (S1A-S1D Dataset).
Leveraging eQTL analysis to identify candidate genes that modulate macrophage response to exogenous stimuli Transcriptional networks capture the connectivity between genes modulating complex phenotypes and may provide a means to unravel the molecular mechanisms underlying complex traits. Because cis genetic variants account only for the phenotypes related to the gene they modulate, we reasoned that they are not good prototypes to illustrate how transcriptional, linkage and network analyses can be leveraged to systematically elucidate the genetic basis of a complex trait. Therefore, we used trans genetic variants, which potentially modulate multiple phenotypes, and followed a step-wise procedure  [34]  to identify the relationship between transcript levels, QTL, and BMDM phenotypes. First, to gain insight into the transcriptional architecture that modulate macrophage response to stimuli, we constructed gene co-expression network modules for each macrophage stimulation condition using the topological overlap matrix (TOM) in the weighed gene co-expression network analysis (WGCNA) program  [88].
genes that exhibit transcriptional correlation across the 26 IFNG+TNF-stimulated BMDM). Of the 4 modules, the "white" module showed the greatest association with NO, hence we used it to illustrate our approach. Because the module-trait relationship is based on the correlation of the module eigenvalue with the amount of NO, not all the genes in the module will show significant association with the trait (amount of NO produced in the BMDM). Therefore, we further filtered the genes in the "white" module based on their individual relationship with NO (gene significance) (S1F Dataset). Expectedly, the eQTL for most of the genes in the module were localized on chromosome 12 and chromosome 4, the locations for NO QTL. To identify the potential regulator for cellular amounts of NO, we reasoned that if two genetic traits are both modulated by the same genetic variant, then the QTL for the two traits will co-localize at the common genetic variant  [34].
Therefore, to further narrow down the significant genes and to identify the transcriptional network that likely modulate genetic differences in cellular amounts of NO produced after IFNG+TNF-stimulation, we searched for genes with eQTL that overlapped with the NO QTL on chromosome 12 (4. 4-13. 1 Mb) and found 11 eQTL (S1F Dataset). These eQTL were functionally enriched for "oxidoreductase activity" (p = 8. 322e-04) and "nitrogen compound metabolism" (p = 1. 345e-03) in gene ontology. Because the common regulator for both NO levels and the chromosome 12 trans-band can be a polymorphic or differentially expressed transcription factor or signaling receptor physically located at the chromosome 12 trans-band locus, we narrowed the putative regulators by searching for cis-eQTL or expressed polymorphic genes at the chromosome 12 locus.
Similar to NO, Ddx1 transcript abundance in IFNG+TNF-stimulated macrophages mapped to two loci on chromosome 4 and 12. However, Ddx1 transcript abundance exhibited an inverse relationship with the amount of macrophage NO at both loci (S4A-S4E Fig). Thus, we concluded that the expression of Ddx1, which is higher in B6 compared to AJ BMDM, inhibited NO production in the B6 macrophages. Due to the important role macrophages play in the pathology of various intracellular pathogens  [90] , we investigated whether there were overlaps between our trans-eQTL hotspots and other disease QTL in the AXB/BXA RI mice available in webQTL  [91]  and found several (S1A-S1D Dataset). For example the QTL for Listeria monocytogenes proliferation, which is attributed to macrophage inflammatory response  [92] , and susceptibility to hepatitis virus  [93]  overlapped the trans-eQTL hotspots on chromosome 15 and 7, respectively, in the IFNG +TNF-stimulated macrophages.
Quantitative Trait Locus (QTL) mapping To map QTL, we used 934 AXB/BXA genetic informative markers obtained from http://www. genenetwork. org. For all the in vitro measurements and gene expression linkage analysis, a genome-wide scan was performed using R/qtl  [57]. Significance of QTL logarithm-of-odds (LOD) scores was assessed using 1000 permutations of the phenotype data  [114]  and the corresponding p-values reported. For the cellular phenotypes, QTL significance was reported at a genome-wide threshold corresponding to p < 0. 05. However, for eQTL mapping, we further corrected for multiple testing on the multiple transcripts by using the p-values to estimate false discovery rate (FDR) in the qvalue package  [115]  and reported significant eQTL at FDR 10%. To identify cis-and trans-eQTL, we computed the distance from the position of the eQTL and the start of the physical location of the corresponding gene and designated any eQTL located <10 Mb from the corresponding gene as cis, otherwise trans eQTL.
D) Conversely, the AJ allele at the Ddx1 eQTL on chromosome 4 is linked with higher expression of Ddx1 and E) lower cellular amounts of NO in IFNG+TNF-stimulated BMDMs (EPS) S1 Dataset. Individual eQTL positions and candidate gene knockdown expression phenotypes. Individual eQTL in: A) Resting macrophages, B) IFNG+TNF-stimulated macrophages, C) CpG-stimulated macrophages, and D) Toxoplasma-infected macrophages. Datasets E) shows a set of perturbed genes in macrophages with Irak4, Apobec1, Eya1, Rb1cc1, and Odc1 knockdown after IFNG+TNF stimulation, and F) shows the genes in the "white" module that show correlation with the amount of NO produced in IFNG+TNF-stimulated macrophages. (XLS).