dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The laboratory is supported by  EU   FP 7 Collaborative Project SYB ARIS  grant agreement number  242220 ; the  Equipe labelisée Ligue Nationale Contre le Cancer ;  Agence Nationale de la Recherche  grants  ANR 10-BLAN-1236 ,  ANR 10-BLAN-1308 ,  ANR-12-BSV2-0025-01 , and  ANR-FCT 12-ISV3-0002-01 ;  DCBiol Labex   ANR-11-LAB EX-0043  grant  ANR-10-IDEX-0001-02 PSL* , and  A*MID   EX  project  ANR-11-IDEX-0001-02  funded by the " Investissements d'Avenir" French government program. S. Terawaki  is a  Uehara Memorial Foundation fellow. S. Terawaki and F. Prete are Fondation de la Recherche Médicale (FRM)  fellows. T. Wenger is a von Humboldt foundation and  Institut National du Cancer  fellow. T. -P. Vu Manh  was supported by the  PhylogenDC ANR  grant. We acknowledge support from no.
ANR-10-INBS-04-01   France Bio Imaging  for advanced microscopy. Part of the research was also supported by  FCT  through the  Institute for Biomedicine-iBiMED  contract  UID/BIM/04501/2013  and  PTDC/ IMI-IMU/3615/2014. The authors declare no competing financial interests. Submitted: 15 January 2015 Accepted: 18 August 2015.
Mo-MLV vectors were produced using pCFG2-eYFP and pHIT60. HIV-1 vectors were prepared with pCSGW and p8. 91. SIVmac vectors were generated with pAd-SIV3+ and GAE-CMV-GFP, which were kindly provided by F. L. Cosset (ENS Lyon, France). EIAV vectors were produced using the ONY8. 9 construct (Oxford Biomedica). All viral stocks were titrated on MDTF cells and analyzed by flow cytometry. The multiplicity of infection (MOI) is defined as MOI = -2 ln(1-fp), where fp is the fraction of GFP/YFP-positive MDTF cells. Cells were transduced with increasing doses of retroviral vectors and the percentage of GFP-expressing cells was determined by flow cytometry 48 h post-transduction and analyzed using FlowJo software (Treestar). The lentiviral vector expressing a PML-specific shRNA (pLV-PMLi) and its non-targeting control pLV-shCon were kindly provided by Yasuo Ariumi  [59].
Immunofluorescence analyses Cells were grown on 12mm slides and fixed with 4% paraformaldehyde for 15 min, rinsed in PBS, incubated in NH 4 Cl 50 mM for 10 min and permeabilized with 0. 5% BSA / 0. 3% Triton X100 / 2% Normal Goat Serum for 30 min. Cells were then incubated with primary antibodies at room temperature for 30 min. Slides were rinsed in PBS 0. 5% BSA and incubated at RT in the dark for 30 min with secondary antibodies and rinsed in PBS 0. 5% BSA. Finally, slides were washed in PBS, counterstained with Hoechst 33342 and mounted in Fluoromount-G medium. Images were digitally acquired with a Zeiss LSM 710 confocal Microscope. For quantitative analyses of PML CBs, images were converted to binary mode, nuclei were selected and PML CBs were quantified and measured using the "Analyze particles" module of ImageJ software.
Student T test statistical analyses were performed using Statistica software. For Duolink Proximity Ligation Assay (PLA), PFA-fixed MEF cells were pre-incubated with blocking agent for 1 h. After washing in PBS, cells were incubated with primary anti-Daxx and anti-HIV-1-p24 antibodies for 1 h in a pre-heated humidity chamber at 37°C. Slides were washed three times in PBS for 10 min and then they were incubated with Duolink PLA probes (Olink Biosciences) diluted in the blocking agent (1:5) for 1 h in a pre-heated humidity chamber at 37°C. Ligation of the connector oligonucleotides, rolling-circle amplification and detection of the amplified DNA products were done with Duolink Detection Reagents Red according to the manufacturer's instructions. Nuclei were labeled with Hoechst.
Acknowledgments We are very grateful to  Thierry Heidmann  for sharing material with us. We thank  Ron Hay  and  Gianfranco Pancino  for helpful discussion. FACS analyses were performed at the FACS and cell-sorting platform (S2C) of the  Saints-Pères Center, Paris Descartes University. We also thank the Microscopy platform (SCM) of the Saints-Pères Center and  Jean-Maurice Petit  for his help in image analyzes.
Nanobodies l-15. 3 and l-19 bound CDTb independently of each other and of the other two nanobodies. Nanobody families l-3 and l-15. 3 blocked one another. In case of CDTa, for which a 3Dstructure is available (pdb code 2wn7), molecular modeling analyses yielded plausible binding modes for nanobodies l18, l-14, and l118 at the active site of CDTa (Supplementary Fig. S4 ). Five of five CDTa-specific nanobodies inhibit the enzymatic activity of CDTa. In order to assess whether the CDTa-selected nanobodies can interfere with the enzymatic activity of CDTa, we analyzed CDTa-catalyzed ADP-ribosylation of actin in HEK cell lysates in the presence of excess nanobodies (Fig. 5 ). The results show that all five CDTa-specific nanobody families completely blocked ADP-ribosylation of actin catalyzed by CDTa.
TEER values were determined every hour (n53) and background values (medium alone) were substracted. Molecular modeling. Molecular modeling of the binding epitopes of three VHHs to CDTa (pdb code 2wn7) were performed using BioLuminate (version 1. 7, Schrodinger, New York, NY)  63. Homology models were built with the antibody prediction module in BioLuminate using framework templates obtained from the curated antibody database. CDR loops were grafted from the database based on a combination of structure clustering, sequence similarity and stem residue geometry matching. BioLuminate automatically preserved the extra disulfide bond in nanobodies l18 and l118 that connects CDR2 and CDR3 if the template also had one in the aligned position. In other cases, the formation of the disulfide bond was forced manually before de novo loop prediction.
Acknowledgments This work was supported by grants No  310/4  and  No310/5  from the  Deutsche Forschungsgemeinschaft  and by grant  01DN12005  from the  Bundesministerium fu ¨r Bildung und Forschung  to FKN and FG, by grants  AK6/20  and  AK6/23  to AK, by a stipend from the  Studienstiftung des Deutschen Volkes  to MU, and by stipends from the  Graduiertenkolleg Entzu ¨ndung und Regeneration  to LS and MS. We thank  Fabienne Seyfried , antibody core unit of the  UMC-Hamburg , for excellent technical assistance. We thank  Dr. Antonio Virgilio Failla , microscopy core facility of the  UMC-Hamburg  for assistance with the microscopy. We thank  Guillermo Vila Melo  and  Gonzalo Perez Zabala  for help with llama immunizations. We thank  Drs. Bernhard Fleischer  and  Eva Tolosa, Hamburg  for critical reading of the manuscript.
CDTb was modeled onto the 3D structure of iota toxin (pdb code 2j42) using SwissModel and assembled into the multimer by alignment with anthrax toxin (pdb code 3hvd) using PyMol. (B) Three llamas (Lama glama) were immunized subcutaneously with purified recombinant CDTa or CDTb emulsified with Specol adjuvant, boosted and bled as indicated in the time scale.
Experimental Procedures Coordinates for all crystal structures have been deposited in the Protein Data Bank. Two separate cloning, expression, and purification schemes were used. PDB codes 4YW0, 4YW1, 4YW2, 4YW3, and 4YW5 were obtained from scheme A, and PDB codes 4YZ1, 4YZ2, 4YZ4, and 4YZ5 were obtained from scheme B as described below. Differences between the two schemes will be highlighted and referred to as A or B throughout the methods description. Cloning-For A, the S. pneumoniae TIGR4 nanC gene was expressed in the pet21b vector  (28). The construct encompassed residues 28 -740. For B, genomic DNA purified from S. pneumoniae TIGR4 (kindly supplied by Dr. Samantha King, Nationwide Children's Hospital, Columbus, Ohio) was used as a template for PCR amplification of the NanC protein-coding region.
Additives were critical in increasing crystal thickness. To prepare derivative crystals, ligands were individually added to the reservoir solution to a concentration of 5-200 mM. Native crystals were then soaked in the solution for 2-45 min. Scheme B crystals were then transferred into a cryoprotectant solution consisting of the reservoir solution, 100 -200 mM of the same ligand, and 20% (v/v) ethylene glycol. Additional cryoprotectant was not necessary for scheme A crystals. Rigaku 007HFM rotating anode x-ray generator with a Saturn 944ϩ CCD detector; B, Beamline I04, Diamond Light Source synchrotron). Data collected in-house were reduced using the HKL2000 package  (35). Synchrotron data were processed by the xia2 automated reduction system  (36)  which makes use of Mosflm  (37) , Pointless  (38) , CCP4  (39) , and XDS  (40).
Structures were solved by PHASER molecular replacement (41) using the S. pneumoniae NanB structure (PDB code 2VW2  (24) ) as the search model. The initial models were autobuilt by ARP/wARP  (42)  and improved by iterative cycles of manual rebuilding in Coot  (43)  and refinement in Refmac5 (44) (A and B) and Buster-TNT (45) (B). The PDB_REDO  (46)  and MolProbity  (47)  servers were used to inform the refinement and for structure validation. Statistics for data collection and refinement are listed in Table  1. Isothermal Titration Calorimetry-The NanC CBM was subcloned into the pEHISTEV vector using primers GGCGCCA-TGGCTCAGGAGACTGAAACTTCTG and TGGTGCTCG-AGTTTACATCTTTTTAACAGTTTCTTC and purified according to scheme A with the addition of His 6 tag cleavage using TEV protease and reverse His tag purification  (48).
ITC experiments were performed using a VP-ITC microcalorimeter (MicroCal Inc. ) with a cell volume of 1. 54 ml. Prior to titration, protein samples were exhaustively dialyzed into 20 mM Tris, pH 8, 50 mM NaCl. The ligand was dissolved in the dialysis buffer. Analysis was performed using nonlinear regression and a single binding site model within MicroCal Origin software (version 7. 083). Heat of dilution experiments were performed, and the resulting isotherm was subtracted from the experimental conditions.
The manner of OC to NanC binding is similar to that of the previously solved NanA OC complex  (51). In particular, the OC 4-amino group forms hydrogen bonds with Asp-372, and repulsion by Arg-351 causes a shift out of the active site. In the NanA complex, the OC pentyl ether group forms hydrophobic interactions with Leu-583 and Ile-427  (51). Conversely, in NanC the pentyl ether group prevents the 396 loop adopting the constricted conformation, because of a steric clash (Fig. 7a ). In comparison with NanA and NanC, the equivalent position in NanB or Neu2 is hydrophilic  (24, 25, 56). Correspondingly, OC is a very poor inhibitor of Neu2 (K i greater than 6 mM). This is in stark contrast to the low micromolar inhibition observed for Neu5Ac2en and zanamivir, which can contribute numerous hydrogen bonding interactions to the pocket via the C6 glycerol groups (31, 56, 74) (PDB ID: 2F0Z).
09 Ϯ 0. 036 kcal/mol, T⌬S ϭ Ϫ4. 26 kcal/mol corresponding to a K d of 1. 60 mM. 2 /DoF ϭ 16. 78. d, a schematic representation of the interactions between Neu5Ac and the CBM binding site (PDB code 4YW1) visualized using Poseview (78).
43 M Ϫ1 , ⌬H ϭ 8. 09 Ϯ 0. 036 kcal/mol, T⌬S ϭ Ϫ4. 26 kcal/mol corresponding to a K d of 1. 60 mM. 2 /DoF ϭ 16. 78. d, a schematic representation of the interactions between Neu5Ac and the CBM binding site (PDB code 4YW1) visualized using Poseview (78).
Inclusion Criteria The study reviewed retrospectively the results of all bone marrow aspirations performed in hematology laboratories between January 2002 and July 2014 on the requests of ICUs of 3 university hospitals (Hospices Civils de Lyon, France). All bone marrow aspiration requests for suspicion of HLH and all bone marrow aspirations that showed hemophagocytosis were further examined. All data from patients diagnosed with HLH or whose bone marrow aspirations displayed hemophagocytosis were kept for further analyses. In parallel, the study searched for all patients diagnosed with code D76. 1 (hemophagocytic lymphohistiocytosis), D76. 2 (hemophagocytic syndrome, infection-associated), or D76. 3 (other histiocytosis syndromes) of the International Classification of Diseases, 10th revision (ICD-10).
Data Collection Several clinical and biological data were collected on a standardized form. These data included the nature of the underlying disease, the therapeutic interventions, the variables required for the calculation of the HScore, the reasons for ICU admission, and the organ supportive care provided. The severity of organ failure was evaluated using the Sequential Organ Failure Assessment (SOFA) score at ICU admission. The occurrence of opportunistic infections, such as invasive aspergillosis, as defined by the EORTC/MSG Consensus Group,  12  was also checked. The study considered also some precipitating factors such as the presence of neoplasia, autoimmune disease, bacterial or viral infection, drug reaction (or rash) with eosinophilia and systemic symptoms (DRESS), or an unknown origin for HLH.
The Mann-Whitney U nonparametric test (test for comparison of medians) was used for the continuous variables and the x 2 test or Fisher test (as appropriate) for the categorical variables. All variables associated with a P value <0. 10 were included in a subsequent multivariate analysis using a binary logistic regression. Variable selection used then a backward stepwise selection method. In this multivariate analysis, a P value <0. 05 was considered for statistical significance. The dataset had very few missing values that could not significantly alter the results of the analyses. Thus, no specific imputation method was used for missingdata imputation. All the statistical analyses used R Software (version 3. 0. 2, 2013, R Development Core Team. R: A Language Environment for Statistical Computing.
Flow Cytometry Analysis. Bone marrow (BM), spleen, and peripheral whole blood cells were recovered. BM and spleen cells were prepared as previously reported  [25, 26]  and adjusted to a final concentration of 1 × 10 6 per mL in culture medium (PBS from GIBCO BRL). Cell suspensions were stained with the following appropriately labeled antibodies: CD11b, Ly6G-Ly6C, CD4, CD25, Foxp3, mPDCA-1, CD11c, CD115, MHC-II, CD80, and CD86 (BD Biosciences, Pontde-Claix, France). Samples were acquired on a FACSCanto II cytometer (BD Biosciences). Mononuclear cells were gated according to forward-and side-scatter properties and analysed using the FACS Diva Software.
The classical DNA recognition sequence of the glucocorticoid receptor (GR) appears to be present at only a fraction of bound genomic regions. To identify sequences responsible for recruitment of this transcription factor (TF) to individual loci, we turned to the high-resolution ChIP-exo approach. We exploited this signal by determining footprint profiles of TF binding at single-base-pair resolution using ExoProfiler, a computational pipeline based on DNA binding motifs. When applied to our GR and the few available public ChIP-exo data sets, we find that ChIP-exo footprints are proteinand recognition sequence-specific signatures of genomic TF association. Furthermore, we show that ChIP-exo captures information about TFs other than the one directly targeted by the antibody in the ChIP procedure.
Genome-wide binding of GR Processing of GR ChIP-seq data sets resulted in the identification of 47,630 bound loci in IMR90 (primary fetal lung fibroblast), 6,329 in K562 cells (erythromyeloblastoid leukemia cell line), and 41,402 in U2OS cells (osteosarcoma)  (Thomas-Chollier et al. 2013). Subsequent GR motif searches in these GR-bound regions showed a striking difference in the fraction of peaks with an apparent GBS between these cell lines at various P-value thresholds (Supplemental Fig. S1a ), with <42% of peaks having a high-stringency motif match. One potential explanation is that GR may bind to highly degenerate sequences. Alternatively, other sequences present at GR-bound regions may recruit GR directly or indirectly to The pipeline takes as input peak regions and ChIP-exo signal (mapped reads).
3F ). Notably, no footprint profile was observed for sequences that have only one half-site matching the consensus sequence at all important positions, indicating that GR is unable to bind such sequences in this cell line. Together, these results suggest that GR can bind to highly degenerate sequences. However, in contrast to high-scoring motifs within GR peaks that are typically bound, additional inputs are likely needed to specify which of the loosely defined sequences, for which a much smaller fraction is bound (data not shown), are occupied. Depending on the cell-type examined, different fractions of the ChIP-seq peaks appear to have a GBS sequence (Supplemental Fig. S1a ). Especially for K562 cells, fewer GBS-like sequences are found (313 vs. 4496 in IMR90 and 6236 in U2OS) (Supplemental Fig.
ExoProfiler identifies profiles for non-GBS motifs in GR ChIP-exo data ExoProfiler was systematically applied to all motifs resulting from de novo motif discovery in GR ChIP-seq peaks and motifs from JASPAR. The motifs to study further were selected by ranking on their coverage P-value (Supplemental Data 2 for IMR90), choosing a representative motif for groups of related/redundant motifs, and visual inspection of the profile, to ensure coherent peak-pairs with forward reads density upstream of reverse reads density. Interestingly, several non-GBS motifs produced footprint profiles with significantly enriched ChIP-exo coverage. The shape of these profiles is distinct from the one for GBSs, indicating that other proteins are recruited by these sequences or that GR binds such sequences in a distinct manner.
This reporter showed a hormone-dependent activation that was lost when positions in the GR half-site or the TTCC sequence were mutated, in both IMR90 (Supplemental Fig. S4c ) and U2OS cells (Fig. 5B ). A search for the TTCC motif in the JASPAR database identified several candidate binding TFs, including TEAD proteins, components of the NFKB complex, and members of the ETS family of TFs, for example, ETS1 and ETS2, that have been shown to physically interact with GR  (Mullick et al. 2001). Structural alignments for ELK1 (ETS family), ETS1, and TEAD1 indicated that these factors are capable of cobinding with GR (Supplemental Fig. S4d ); however, this was also true for other candidate proteins (STAT and NFKB components) we aligned (Supplemental Data 3). To assess the role of candidate factors in GR-dependent activation from the combi motif, we prioritized proteins whose complete recognition sequence is present in the combi motif (true for members of the ETS and TEAD family of TFs), and for ETS members we chose to test proteins that interact with GR (ETS1 and ETS2).
FOX motif footprint profile Motif discovery in IMR90 cells also revealed motifs bound by forkhead box (FOX) TFs sharing a similar DBD. For these motifs, ExoProfiler uncovered footprint profiles with significantly enriched ChIP-exo coverage that are markedly different from the one observed for GBSs (Fig. 4B ). The distinct profile suggests that a protein other than GR binds and is consistent with this; EMSAs showed no increased affinity of the GR DBD for the FOX consensus sequence when compared to randomized control sequences (Supplemental Fig. S5a ). The most obvious candidates for binding are members of the FOX family of TFs. Because no structure is available for FOXA1, we examined the structure of a homologous protein, FOXK1  (Tsai et al. 2006) , and found a conserved lysine residue, K328, in the wing domain that maps to the cross-linking point, which is locat-ed in between the forward and reverse peaks (Supplemental Fig.
The resulting footprint profiles and associated plots are not a novel concept  (Rhee and Pugh 2011). Here however, we systematically tested hundreds of motifs, rather than just the one for the TF of interest, thereby revealing extra information contained in the ChIP-exo data, which can serve to elaborate new testable hypotheses to uncover binding mechanisms. We provide our method as a free and usable open source tool for the community, named ExoProfiler (https://github. com/ ComputationalSystemsBiology/ExoProfiler). In principle, DNase I footprinting could give similar information to that provided by the ChIP-exo procedure. However, recent studies have revealed that DNase I footprints, for example, for GR  (Sung et al. 2014) , reflect sequence bias of the nuclease. In contrast, the ChIP-exo footprints we observe here are specific to the protein precipitated (Supplemental Fig.
Data access Raw gene expression profiling (BeadChip) and raw and processed ChIP-seq and ChIP-exo data have been submitted to EBI ArrayExpress (http://www. ebi. ac. uk/arrayexpress) under accession numbers E-MTAB-2954, E-MTAB-2955, and E-MTAB-2956, respectively. We provide the source code for the ExoProfiler method in the Supplemental Material as a free and open source tool to the community (https://github. com/ComputationalSystemsBiology/ ExoProfiler).
Acknowledgments We thank  Bernd Timmermann  for sequencing of the ChIP experiments,  Edda Einfeldt  and  Katja Borzym  for excellent technical support, and  Alejandra Medina-Rivera ,  Samuel Collombet , and  Benjamin Bardiaux  for contributing valuable technical ideas. J. I-S. was funded by an  Erasmus  fellowship; M. J. by  DFG  grant ( ME4154/1-1 ). Author contributions: S. R. S. and M. J. performed and analyzed experiments. J. I-S. , M. T-C. , M. I. L. , and H-R. C. conducted the computational analysis of the data. M. J. performed the structural alignments. C. H. prepared ExoProfiler for public release. S. H. M. , M. V. , and  M. T-C. supervised the study. M. T-C. and S. H. M. designed the study and wrote the manuscript with input from other authors.
Characterization of the isolate The isolate was serogrouped using a panel of 32 specific antisera provided by the Royal Tropical Institute (KIT, Amsterdam;  Faine et al. , 2000). Furthermore, its DNA was extracted and a partial sequence of the rrs  (Merien et al. , 1993)  and secY genes  (Ahmed et al. , 2006)  were amplified by PCR and sequenced. The latter procedure was done at the Genotyping of Pathogens and Public Health Platform (Institut Pasteur, Paris, France). All molecular epidemiological data were stored and analyzed with Bionumerics software (Version 6. 5; Applied-Maths, Sint-Martens-Latem, Belgium). Genotyping was also performed by multiple-locus variable-number tandem repeat analysis (MLVA) using the loci VNTR4, VNTR7, and VNTR10, as described  (Salaun et al. , 2003). According to the analysis of partial sequencing of the gene secY, PFGE was conducted using Not I restriction enzyme  (Herrmann et al.
Patients In 2008, P. pulmonicola was identified as a nonfermentative Gram negative organism, responsible for chronic lung colonization that started in 2000 in one of our CF patients. During 2009, five of the 243 patients attending our CF center contracted P. pulmonicola. In order to analyze the clinical impact of P. pulmonicola in the 6 patients, the following data were collected: age, sex, type of CFTR mutations, date of first P. pulmonicola isolation, associated pathogens, best forced expiratory volume in one second (FEV1) value per year, number of intravenous antibiotic courses per year, clinical status, transplantation and clinical outcome.
apista LMG 16407 type strain as a positive control. Moreover, the 16S rDNA-based speciesspecific PCRs were performed as described by Coenye  [16]. Identification with the 16S rDNA gene sequencing was performed as previously described  [17]. Sequence alignment used the NCBI/BLAST (www. ncbi. nlm. nih. gov/blast) and BIBI (https://umr5558-bibiserv. univ-lyon1. fr/lebibi/ lebibi. cgi) programs. Sample preparation for MALDI-TOF MS analysis was conducted using MicroFlex LT with the Biotyper v2. 3 database (Bruker Daltonics). Briefly, colonies from overnight bacterial cultures were smeared onto the target plate (1 spot per strain), and 1 μL of α-cyanohydroxycinnamic acid was added. For strain comparison, one strain from each patient was tested with pulsed-field gel electrophoresis (PFGE) using XbaI, as previously described  [18].