dataesr/scientific-paragraphs-categorization huggingface dataset:
To determine OTUs, reads were clustered using Vsearch with the AGC method at the 0. 03 level. From this point on, results were analyzed separately for birds raised during the first and second replicate experiment. Reads were aligned and classified using the Silva database (version 123). For diversity analysis, reads were subsampled or rarefied to the lowest number of reads found in a single sample. The following alpha-diversity indices were computed and compared across conditions: coverage, Sobs, Inverse Simpson, Shannon, and Shannon's evenness. Beta-diversity analysis was performed by comparing the bird's microbiota structure using Yue and Clayton diversity index and analyzed by AMOVA and HOMOVA. The composition was analyzed by LDA effect size (LEFSE)  (Segata et al. , 2011)  according to the experimental conditions.
Statistical Analysis Comparison of the chicken's body weight, C. jejuni colonization levels, seric total GPX activity, seric IgY concentrations, intestinal IgA concentrations, and alpha-diversity indices were analyzed in GraphPad Prism 5 (GraphPad Software, La Jolla, CA, USA). Prior to selecting the correct statistical analysis, the distribution of the data was inspected. When the normality was confirmed, data were analyzed using parametric tests. Otherwise, non-parametric analyses were conducted. An alpha of 0. 05 was set to assess significance. Results, with the exception of the microbiota, were analyzed separately for the first or second replicate according to the selenium-yeast or C. jejuni status before being pooled for the analysis of the global effect observed in this study.
Microbiota A total of 8,438,914 sequences were obtained after assembly. Prior to OTU clustering, a total of 4,978,059 sequences remained, representing 308,896 unique sequences. The two negative controls included in this study contained 260 and 427 sequences. Two chicken caecal samples returned numbers of sequences similar to the negative controls (328 and 348) and were discarded from the analysis. Two more samples, originating from chickens not inoculated with C. jejuni, returned unexpectedly high numbers of reads classified as Campylobacter, in similar proportions to all other samples originating from inoculated birds. Further PCR analyses confirmed the samples negative for C. jejuni while all C. jejuni inoculated birds returned a strong PCR signal (data not shown); these samples were removed from the analysis.
ACKNOWLEDGMENTS We thank the  Ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec (MAPAQ) , the  Centre de Recherche en Infectiologie Porcine et Avicole (CRIPA) , and  Jefo Nutrition Inc. for funding; without them none of these experiments could have been carried out. We also thank  Christine Maynard  at the  NRC Montreal  for the MiSeq sequencing as well as  William Thériault  from the  CRSV lab  for conducting the ELISA test. We also thank all members of  Dr. Letellier 's lab for the essential help during the in vivo experiments and the  Mothur community  for bioinformatic support. Lastly we thanks  Dr. Arnoud van Vliet  from  IFR  for scientific advices.
Computational analysis of gene expression datasets Microarray initial processing and normalisation were performed with BeadStudio software. BeadChip internal p-values (technical bead replicates) were used to identify genes significantly expressed above the background noise. To filter out genes with signals that were not significant, a p-value of 0. 05 was used as the cut-off value and only genes with a p-value<0. 05 in at least one sample passed the filter. From the original set of 34,685 gene targets, 23,356 targets met this criterion. The data were imported into GeneSpring v13, normalised using quantile normalisation and the biological replicates averaged for subsequent analysis. We performed pairwise comparison between 0 hr and 4, 8 and 12 hr. Genes showing a fold change higher than two compared to control (and both p-values were significant) were subjected to GO analysis with GeneSpring v13 (152 genes between 4 hr and 0 hr, 553 between 8 and 0 hr, 1136 genes between 12 and 0 hr).
Dynamic Boolean network analysis The Boolean network analysis was performed using RE:IN and RE:SIN software, which is designed to encode and test whether a single or set of Boolean networks are consistent with observed changes in gene expression states. The methodology has been described previously  (Dunn et al. , 2014; Yordanov et al. , 2016; Shavit et al. , 2016). Briefly, a set of possible and definite interactions comprises an Abstract Boolean Network (ABN), which implicitly defines a set of concrete Boolean networks. Each concrete network has a unique topology defined by the different combinations of the possible interactions, which can be present or absent. This allows the user to explore alternative model topologies, and to test the requirement for different interactions that are suggested by experimental data.
Generation and analysis of SILAC LC-MS/MS datasets A pre-confluent keratinocyte culture was split into two separate cultures with FAD -lysine-arginine medium (Sigma) differentially supplemented either with K 0 R 0 or with K 8 R 10 (stable isotopes of amino acids Lysine and Arginine; Cambridge Isotope Laboratories)  (Ly et al. , 2014). FCS used for SILAC medium was also depleted of Lysine and Arginine (Sigma). Cells were grown in SILAC medium for 5-6 days to reach 70-80% confluence and later harvested for downstream assays. Light labelled cells served as the 0 hr sample whereas heavy labelled cells were suspended in methylcellulose and harvested at 4, 8 and 12 hr. Cell extracts prepared from 0 h cells were mixed individually with 4, 8 and 12 hr samples at a 1:1 ratio of total protein. The mixed samples were then subjected to mass spectrometry (MS).
Beads were washed with loading buffer, and two wash buffers, composed of (1) 70% ACN + 3% TFA and (2) 20% ACN + 0. 5% TFA. Phosphopeptides were eluted in two steps, first with 4% of ammonium hydroxide solution (28% w/w NH 3 ) in water for 1 hr and again with 2. 6% of ammonium hydroxide solution in 50% ACN overnight. Elutions were collected, dried, resuspended in 5% FA and analysed by LC-MS/MS. The LC-MS analysis was performed similarly to above with the following modifications: peptides were chromatographed on an EasySpray PepMap 75 mm x 50 cm column, and a 'Top30' method was used where the top 30 most intense ions were chosen for MS/MS fragmentation. Raw data were then processed using MaxQuant, which implements the Andromeda search engine, for peptide and protein identification and SILAC quantitation.
For the proteomics dataset, 1155 out of 2024 proteins had a significant p-value<0. 05 (calculated in MaxQuant). To identify differentially expressed proteins between time 0 and 4, 8 and 12 hr, we collected per time point the proteins whose absolute ratios were >0. 5. For each time point, the proteins were then split according to whether the ratio was positive or negative. Six lists were annotated for GO terminology using GeneSpring. The extensive list of GO-terms was submitted to Revigo  (Supek et al. , 2011)  to reduce complexity and the resulting GO-categories depicted in Figure  1 figure supplement 1g Figure  1f. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD003281.
Skin reconstitution assays Pre-confluent keratinocyte cultures (km) were disaggregated and transfected with SMART pool siRNAs or non-targeting control siRNAs, or with lentiviral shRNAs. 24 hr post-siRNA transfection or >7 days post-shRNA infection, keratinocytes were collected and reseeded on irradiated de-epidermised human dermis  (Sen et al. , 2010)  in six-well Transwell plates with feeders and cultured at the air-liquid interface for 3 weeks. The cultures were then fixed in 10% neutral buffered formalin (overnight), paraffin embedded and sectioned for histological staining. 6 mm thick sections were labelled with haematoxylin and eosin or appropriate antibodies. H and E stained images were acquired with a Hamamatsu slide scanner and analysed using NanoZoomer software (Hamamatsu).
Automated analysis of skin reconstitution assays H and E stained sections were scanned with a Hamamatsu slide scanner and analysed in Python. Briefly, tissue sections were automatically detected based on k-means segmentation, and colours were rebalanced. The epidermis was then isolated by colour separation (extraction of haematoxylin color channels by a deconvolution algorithm)  (Ruifrok and Johnston, 2001). After performing an automatic thresholding, epidermal area was measured by pixel counting. To measure epidermal thickness, the area was divided by a 1-pixel wide reduced-topology  (van der Walt et al. , 2014). Full script is available as Supplementary Source Code 1.
Antibodies Antibodies against the following proteins were used: P-ERK (Cell Signaling # 9101; western blot -1:1000 dilution), ERK (Cell Signaling # 9102; western blot -1:1000), P-p38 (Cell Signaling # 9211; western blot -1:1000), p38 (Cell Signaling # 9212; western blot -1:1000), Cyclophilin B (R and D # MAB5410; western blot -1:4000), MKP-3/DUSP6 (Abcam # ab76310; western blot -1:1000 and R and D Systems # MAB3576-SP; immunostaining -1:200), PPTC7 (Abcam # ab122548; western-blot 1:250 and Sigma # HPA039335; immunostaining -1:200), PTPN1/PTP1B (Sigma # HPA012542; western-blot -1:500; R and D Systems # AF1366-SP; immunostaining -1:200), PTPN13 (R and D Systems # AF3577; western-blot -1:300 and immunostaining -1:200), PPP3CA/Calcineurin A (Sigma # HPA012778; western-blot 1/1000 and R and D Systems # MAB2839-SP; immunostaining -1:200), DUSP10 (Abcam # 140123; western-blot -1:1000 and immunostaining -1:200), TP63 (SCBT # sc-8431 or Abcam # ab735; immunostaining -1:100), Involucrin (SY5, in-house; immunostaining -1:500, or DH1, in-house; immunostaining -1:200), Ki67 (Abcam # ab16667; immunostaining -1:100) and b1-Integrin (clone P5D2, in-house; immunostaining -1:200).
RNA extraction and RT-qPCR Total RNA was isolated using the RNeasy kit (Qiagen). Complementary DNA was generated using the QuantiTect Reverse Transcription kit (Qiagen). qPCR analysis of cDNA was performed using qPCR primers (published or designed with Primer3) and Fast SYBR green Master Mix (Life Technologies). RT-qPCR reactions were run on a CFX384 Real-Time System (Bio-Rad). Heatmaps of RT-qPCR data were generated by Multiple Expression Viewer (MeV_4_8) or GraphPad Prism 7. Sequences of qPCR primers are provided in Supplementary file 13.
Acknowledgements This research was funded by the  Medical Research Council  and the  Wellcome Trust. The authors also gratefully acknowledge the use of Core Facilities provided by the financial support from the  Department of Health  via the  National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's and St Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust. Benedicte Oule  `s is the recipient of fellowships from  SocieteFrancaise de Dermatologie, College des Enseignants en Dermatologie de France and Fondation d'Entreprise Groupe Pasteur Mutualite. We thank  Giacomo Donati ,  Arsham Ghahramani  and  Klaas Mulder  for helpful advice and discussions and are grateful to  Fredrik Ponte  ´n for providing anti-phosphatase antibodies.
First, this data is in a kinematic regime where the heavier quark flavors (such as strange and charm) contribute substantially. Second, by comparing the proton W ± /Z data with the heavy ion results we have an ideal environment to precisely characterize the nuclear corrections. The combination of the above can not only improve the nuclear PDFs, but also the proton PDFs which are essential for any LHC study. In this work we present predictions for vector boson production in pPb and PbPb collisions at the LHC obtained using nCTEQ15 nuclear parton distributions, and perform a comprehensive comparison to the available LHC data. We also identify the measurements which have the largest potential to constrain the nPDFs, and perform a reweighting study which allows us to estimate the effects of including these data in an nPDF fit.
The rest of the paper is organized as follows. Sec. 2 is devoted to predictions of vector boson production at the LHC in nuclear collisions. In particular, we provide an overview of the kinematic range probed by the W ± /Z data and discuss the tools we will use for the calculation. Then we present our predictions for pPb and PbPb collisions at the LHC and compare them with the experimental data and other theoretical predictions. In Sec. 3 we perform a reweighting using nCTEQ15 distributions to assess the impact of the nuclear data on the nPDFs. Finally, Sec. 4 summarizes our results and observations.
Comparison to Proton-Lead (pPb) data We first consider the LHC pPb collisions at √ s = 5. 02 TeV. The distributions are shown in the CM frame, and include the appropriate rapidity shift according to Eq. (  2 ). In Fig. 2 , we display the kinematic range of the pPb  data bins (central values) in the plane (y, x 2 ) where y is the rapidity in the CM frame of the relevant vector boson or lepton, and x 2 the lead parton momentum fraction. As expected, there is little data below x ∼ 10 -3 and most of the constraints from these LHC data are in the low-to mid-x region. Figs. 3, 4 and 5 show our predictions for the AT-LAS  [1] , CMS  [3]  and LHCb  [5]  Z boson production measurements, respectively. In all three cases, results obtained with the nCTEQ15 nPDFs are shown along with those obtained with a lead nucleus composed by Z protons and A -Z neutrons, assuming isospin symmetry and using CT10 PDFs; the ratio of predictions over the data is shown in the lower panel.
Note that the errors shown for the nCTEQ15 predictions are for nuclear uncertainties only (and only for the beam with momentum fraction x 2 ) which means that the PDF error of the proton beam is not accounted for. 3 Furthermore, the errors shown for the pPb predictions using lead nuclei constructed from CT10 and MSTW2008 proton PDFs are only for the beam with momentum fraction x 2. By comparing the proton uncertainties (CT10 and MSTW2008) to the nuclear uncertainties, we see that the nuclear uncertainties are much larger. Examining Figs. 3, 4 and 5, it is interesting to note the following. 1. The data and theory are generally compatible (without significant tension) both with and without nuclear corrections; this situation may change as the experimental errors and nuclear uncertainties are reduced.
2. Focusing on the ATLAS and CMS comparison of Figs. 3  and 4 , we observe that the distributions peak at negative rapidities y Z ∼ -1. Referring to Fig. 1 , this corresponds to an enhancement of the q q proton-lead luminosity over the pure proton one in the x 2 region ∼ 0. 05. 3. Focusing on the LHCb data of Fig. 5 , we find good agreement for negative y, but large differences at positive y. Despite these differences, the large uncertainties will yield a reduced impact in our subsequent reweighting procedure. We now turn our attention to W + and W -production at the LHC. In Figs. 6, 7 and 8 we compare the data obtained by CMS  [4] , ATLAS  [2]  and ALICE  [6]  for W ± production with theoretical predictions obtained with nCTEQ15 and CT10 PDFs. We find the W -CMS and ATLAS data are adequately described in the negative rapidity range (y -< 0), but the tensions grow as we move to larger rapidity.
Comparison to Lead-Lead data We now consider the LHC PbPb collisions at √ s = 2. 76 TeV. As these beams are symmetric, we now have y CM = y lab. Again, we will use nCTEQ15  [21]  and CT10  [24]  PDFs for the theoretical predictions. Results from AT-LAS and CMS collaborations are available in the form of either event yields (Z boson production) or charge asymmetries (A ). In Fig. 9a  and 9b we present the comparison of the ATLAS  [7]  and CMS  [9]  data with theoretical predictions with nCTEQ15 and CT10 PDFs. Note that the differential cross sections have been normalized to the total cross section. The PbPb data generally exhibits no tension as the distributions are well described across the kinematical range; however, this is in part due to the large uncertainties due to two nuclei in the initial state.
The ellipses are calculated in the following way  [30] , X = X 0 + ∆X cos θ, Y = Y 0 + ∆Y cos(θ + φ), ( 6 ) where X, Y represent PDF-dependent observables, X 0 (Y 0 ) is the observable calculated with the central PDF, ∆X (∆Y ) is defined in Eq. (  5 ), φ is the correlation angle defined in Eq. (  4 ), and θ is a parameter ranging between 0 and 2π. From Figs. 11 and 12 one can generally observe that the ellipses for the different PDF sets overlap. Furthermore, the central predictions for all three PDF sets lie in the overlapping area of the three ellipses. However, a trend can be observed as a function of the rapidity: 1. For negative rapidities (y < -1), the central predictions from the nuclear PDFs (nCTEQ15, EPS09) are closer to the experimental data as they yield larger cross sections than the uncorrected (proton) CT10 PDFs.
1, CT10), the strange quark exhibits substantial differences while the charm (and bottom) distributions are quite similar; this pattern then feeds into the nPDFs. Therefore, the strange quark PDF will be the primary focus of the following discussion. In Figs. 13 and 14 we compare the 5 flavor and 2 flavor results using nCTEQ15, CT10+EPS09, CTEQ6. 1+ EPS09, and CT10. We have added CTEQ6. 1+EPS09 as CTEQ6. 1 was the baseline used for the EPS09 fit. Examining Fig. 13 , the shift of the 2 flavor results compared to the 5 flavor results can be as large as ∼30% and reflects, the contributions of the strange and charm quarks. For the 5 flavor case ( ), the calculations are scattered to the low side of the data in both W + and W -. The CT10 result is the closest to the data, but due to the larger uncertainties of nCTEQ15, the data point is within range of both of their ellipses.
Fig. 14  displays the same information as Fig. 13  except it is divided into rapidity bins. As we move from negative y to positive y we move from high x where the nPDFs are well constrained to small x where the nPDFs have large uncertainties (cf. , Fig. 2 ). Thus, it is encouraging that at y < -1 we uniformly find the nuclear predictions yield larger cross sections than the proton results (without nuclear corrections) and thus lie closer to the LHC data. Conversely, for y > 1 we find the nuclear predictions yield smaller cross sections than the proton results. The comparison with the LHC data varies across the figures, but this situation suggests a number of possibilities. First, the large nPDF uncertainties in this small x 2 region could be improved using the LHC data. Second, the lower nPDF cross sections are partly due to the nuclear shadowing in the small x region; if, for example, this shadowing region were shifted to even lower x values, this would increase the nuclear results.
Reweighting In this section we perform a reweighting study to estimate the possible impact of the W ± /Z data on nCTEQ15 lead PDFs. For this purpose we will use only the pPb data sets. We refrain from using PbPb data as typically the agreement of these data with current nPDFs is much better (in part due to the large uncertainties), so the impact in the reweighting analysis will be minimal. Secondly the factorization in lead-lead collisions is not firmly established theoretically  [32]  such that the interpretation may be complicated.
1% or better). We note here that since the nCTEQ15 error PDFs correspond to the 90% confidence level (c. l. ) we need to convert the obtained uncertainties such that they correspond to the 68% c. l. 6  The conversion is done using the following approximate relation between the 68% c. l. and 90% c. l. Hessian uncertainties: ∆ H 90 O ≈ 1. 645 ∆ H 68 O. In Fig. 16  we perform the above exercise and determine if our procedure is self consistent. Specifically, in Fig. 16a  we display the central value and uncertainty bands for the original gluon PDF and those generated from the replicas; they are indistinguishable. Additionally, in Fig. 16b  we demonstrate the convergence of  Having defined the replicas we can apply the reweighting technique to estimate the importance of a given data set on our current PDFs.
( 11 ) For our reweighting analysis we will only use the pPb data sets. Because the uncertainty of the nuclear PDFs dominates the proton PDFs, it is sufficient to only vary the lead PDFs. Consequently, the pPb cross sections are linear in the lead uncertainties, and we can compute the reweighting by evaluating cross sections only on the Hessian error PDFs (32+1 in case of nCTEQ15) instead of the individual replicas (N rep = 10 4 ) σ k = f p ⊗ σ ⊗ f Pb 0 + N i f Pb(+) i -f Pb(-) i 2 R ki. ( 12 ) A similar decomposition can be used for pp or PbPb data to reduce the number of necessary evaluations. However, because of the quadratic dependence on the PDFs, the reduction is smaller and does not necessarily lead to lower computational costs. We will compare the χ 2 for each experiment calculated with the initial PDFs (before reweighting) and with the PDFs after the reweighting procedure; this will allow us to estimate the impact of each individual data set.
This is true for both the W + (left figure) and W -(right figure) cases. We can quantify the improvement of the fit 10 We note here the difference of PDF uncertainties compared to the plots presented in Sec. 2; this is caused by the fact that now we use the 68% c. l. errors whereas in Sec. 2 we have used the 90% c. l. errors that are provided with the nCTEQ15 PDFs. This holds for all plots in Sec. 3. by examining the χ 2 /N data for the individual distributions. For the W + case, the χ 2 /N data is improved from 5. 07 before reweighting to 3. 23 after reweighting. Similarly, for W -the χ 2 /N data is improved from 4. 57 to 3. 44. The amount of change due to the reweighting procedure should be proportional to the experimental uncertainties of the incorporated data; this is the same as we would expect from a global fit.
Since u v (x 1 ) > d v (x 1 ) in the proton, we expect a positive asymmetry in the kinematic region of large positive rapidities. Furthermore, the reweighting of the nuclear PDFs will have very little impact on the charge asymmetry in this limit even if the precision of the data will increase in the future. A → u v (x 1 )d(x 2 ) -d v (x 1 )u(x 2 ) u v (x 1 )d(x 2 ) + d v (x 1 )u(x 2 ) u v (x 1 ) -d v (x 1 ) u v (x 1 ) + d v (x 1 ) , ( 20 Another asymmetry used by CMS is the forwardbackward asymmetry. This is defined as a ratio of the number of events in the forward and backward region in a given rapidity bin: A ± FB (y ) = dN l ± (+y lab ) dN l ± (-y lab ). ( 21 ) This asymmetry is defined separately for the W + and W -cases. It can also be combined into a single quantity, the forward-backward asymmetry of charge-summed W bosons: A FB (y ) = dN l + (+y lab ) + dN l -(+y lab ) dN l + (-y lab ) + dN l -(-y lab ).
Including all the data sets Due to large experimental uncertainties, the effect of individual data sets presented in Sec. 2 on the lead PDFs is rather limited. The largest constraint is obtained from the CMS W ± data  [4]  (Secs. 3. 2), and from (preliminary) ATLAS W ± data  [2]. In order to maximize the effects on the PDFs, we now employ all protonlead data sets from Tab. 1 to perform the reweighting of the nCTEQ15 lead PDFs. Note that we use both the rapidity distributions and the asymmetries; although this can be regarded as "double counting", it is a common practice in proton PDF analyses, e. g. [19]. As the impact of the reweighting on the theory predictions for ALICE W ± production data  [6] , LHCb Z data  [5]  and both ATLAS  [1]  and CMS  [3]  Z production data is very small, we will not show the corresponding comparisons of theory predictions before and after the reweighting.