dataesr/scientific-paragraphs-categorization huggingface dataset:
This channel is used to create a mask of the cell on Fiji and find its center of mass. Briefly, the 3D stack is interpolated (to obtain an isotropic voxel) and a background subtraction (based on a Gaussian filtered [radius = 4] image of the field without cell, time = 0) is applied. A Gaussian filter is applied on the resulting image (radius = 2) to remove local noise and the cell is finally segmented using an automatic threshold (Huang). Advanced analysis of centrosome trajectories was performed by using the 3D cell contour generated on Fiji, and then computing the distance of the centrosome from the center of the cell, and the angle formed with the cell-droplet axis on MATLAB, to merge this data with advanced nucleus analysis data. For experiments using nocodazole, the centrosome was visualized by expressing eGFP-cent1 and tracked in the same way.
Analysis of the Golgi apparatus This was performed on Icy Bioimage analysis software  (de Chaumont et al. , 2012). 3D masks of the Golgi apparatus and the droplet were obtained, and the average distance of the Golgi apparatus to the surface of the droplets was computed using a 3D distance map from the droplet. Characteristic times were extracted on single kinetic curves smoothed using 3R Tukey smoothing (repeated smoothing until convergence)  (Tukey, 1977)  using R and defined as the first time for which the distance is below 4 µm (only for trajectories starting at >5 µm in order to be able to truly detect the polarization process). This threshold value was chosen looking at the distribution of plateau values for BSA-or αIgG-coated droplets.
Analysis of the lysosomes This was performed using Icy Bioimage analysis software  (de Chaumont et al. , 2012). 3D masks of the lysosomes and the droplet were obtained, and the average distance of all the lysosomes to the surface of the droplet was computed using a 3D distance map from the droplet. Characteristic times were extracted on single kinetic curves smoothed using 3R Tukey smoothing (repeated smoothing until convergence)  (Tukey, 1977)  using R and defined as the first time for which the distance is below 3 µm (only for trajectories starting at >4 µm in order to be able to truly detect the polarization process). This threshold value was chosen looking at the distribution of plateau values for BSA-or αIgG-coated droplets.
Analysis of the nucleus and detection of nuclear indentation This was performed using custom Fiji macros and MATLAB software (available upon request). B cell nucleus is bean-shaped and exhibits a marked invagination. To automatically detect the invagination at each time point, we interpolated the confocal images of the nucleus to obtain an isotropic voxel, segmented the nucleus, and found the interpolating surface (isosurface function in MATLAB). We smoothed the surface to reduce voxelization and computed the mean curvature at each vertex with standard differential geometry methods. We defined the invagination as the point with the minimal mean curvature obtained on this surface. Ad hoc correction based on nearest-neighbor tracking is applied when several local minima are found (in nuclear that exhibit several lobes), the selected minimum is the nearest one to the point found in the previous frame.
Acknowledgements The authors thank  H Moreau  and  A Yatim  for their help in setting up the migration experiment,  M Bolger-Munro  for critical reading,  H Moreau ,  J Delon ,  M Théry ,  J Husson , and  C Hivroz  for fruitful discussions, and acknowledge the  Nikon Imaging Center  and  CNRS-Institut Curie , the  PICT-IBiSA, Institut Curie, Paris , member of the  France-BioImaging  national research infrastructure, and the  Cell and Tissue Imaging Platform-PICT-IBiSA  (member of  France-Bioimaging   ANR-10-INBS-04 ) of the  Genetics and Developmental Biology Department  ( UMR3215/U934 ) of  Institut Curie  (supported by the  European Research Council  [ ERC EPIGENETIX N°250367 ]) for support in image acquisition, as well as the  Recombinant Antibody Platform  (TAb-IP) of  Institut Curie. This work benefited from the technical contribution of the  Institut Pierre-Gilles de Gennes  joint service unit  CNRS   UAR 3750.
The authors would like to thank the engineers of this unit for their advice during the development of the microfluidic setup. PP and AMLD were supported by  CNRS  and  INSERM , respectively. JP was funded by the  Ecole Doctorale FIRE-Programme Bettencourt ,  Université de Paris  and by funding from the  Agence Nationale de la Recherche  ( ANR-21-CE30-0062-01 IMPerIS ) and LP was funded by the  IPV.
Data availability All data generated or analyzed during this study are included in the source data files and supporting files. Custom image analysis scripts are available online at https://github. com/PierobonLab/Paper-Pineau2022,  (Pineau, 2022 , copy archived at swh:1:rev:85d6b03e630656509de21146c6b1992 58de70659). The following source codes from the GitHub repository were used to analyze the images: Antigen_recruitment Fiji macros to quantify antigen recruitment. Masks can be generated from the fluorescent or the transmission channel (less resolved). ActinLive_Analysis Fiji macros to obtain masks of the cell and the droplet, count the number of actin maxima and their distance to the immune synapse, cell shape characteristics and measure the actin enrichment within 2 µm of the immune synapse.
, 2016 S11225 resource Cell line (Mus Recombinant DNA Chemical compound, drug reagent Designation Streptavidin Alexa Fluor 647 Thermo Fisher Source or reference Identifiers Cellosaurus A20. IIA S32357 C1δ-GFP Botelho et al. , 2000 Additional information musculus) Genetic reagent (M. Chemical compound, drug Recombinant DNA reagent IIA1. 6 Biotin labeled bovine albumin GEF-H1 Yuseff et al. , 2011 Sigma-Aldrich Origene (CVCL_0J27) A8549-10MG RG204546 IgG + B lymphoma cell line musculus) Recombinant DNA Chemical compound, drug reagent LifeAct-GFP mice/C57BL/B6 Riedl et al. , 2008 Schindelin et al. , Addgene, PDMS-RTV 615 Neyco pRK5myc RhoA L63 Nobes and Hall, 1999 15900 MGI:4831036 RTV615 1:9 ratio Software, algorithm Fiji Software, algorithm Icy bioimage analysis Software, algorithm MATLAB Recombinant DNA Chemical compound, drug PVP K90 reagent RhoA WT EGFP Recombinant DNA Chemical compound, drug Latrunculin A reagent RhoA T19N EGFP 2012 de Chaumont et al.
e RNase H2A and Pol II ChIP-seq profiles of RNU1-4 in HeLa cells. Numbers in brackets indicate the viewing range (FPKM). f RNase H2A ChIP-qPCR for RNU1 in HeLa cells. Values are expressed as percentage of input. n = 4 biologically independent experiments (means ± SEM). p-values were calculated using two-tailed unpaired t-test. All subsequent gene diagrams depict snRNA coding region or protein-coding gene exons (black), UTRs (white), introns (lines), TSS, TES and qPCR amplicons (below the diagram). PSE is proximal sequence element. g, i, k Meta-analysis of read density (FPKM) for RNase H2A ChIP-seq on different categories of genes in HeLa cells, with the window size of the presented plots adjusted to the gene size. Data are represented as in d. h, j, l RNase H2A ChIP-qPCR in HeLa cells transfected with siLuc (white bars) and siRNASEH2A (green bars) for different gene categories.
Values represent percentage of input (means ± SEM). p-values were calculated using two-tailed unpaired t-test. h Intron-containing genes (ACTB, DDX1 and TFF1). n = 2 (n = 4 for ACTB Prom) biologically independent experiments. j Histone HIST1H1E gene. n = 3 (n = 4 for 5' and TES) biologically independent experiments. l Intronless JUNB gene. n = 3 biologically independent experiments. Horizontal dotted line indicates background signal. Source data are provided as a Source data file. levels upon RNase H2 depletion (Fig. 4f  right panel and Supplementary Fig. 5g ). This suggests that despite RNase H2 binding to these genes, either its R-loop-resolving activity is not necessary or it can be compensated by other factors. Consistent with short genes being affected stronger by RNase H2 depletion, proteincoding genes positive for RNase H2A binding in ChIP-seq are significantly shorter compared to RNase H2A-negative genes (Supplementary Fig.
Control samples were treated with the vehicle only. siRNA transfections. siRNA transfections were performed for 72 h (HeLa) or 48 h (HEK293T) using Lipofectamine 2000 (Thermo Fisher) according to the manufacturer's instructions. HeLa cell transfections were performed in 6-well plates 24 h Fig. 4  RNase H2 depletion induces R-loop accumulation. a Distribution of genes in the indicated genic compartments defined by DRIP-seq peaks in control and RNase H2A-depleted HeLa cells. Unchanged (left), increased/gained (middle) and decreased/lost (right) peaks in RNase H2A-depleted HeLa cells are shown. b Meta-analysis of DRIP-seq peaks (blue) enriched over both input and RNase H-treated control in siLuc samples (+/-250 bp around peak center). RNase H2A ChIP-seq data (red) are superimposed on DRIP peaks (+/-5 kb).
Fluorescence intensity was analyzed with ImageJ (version 1. 52p) as described previously  70. Pol II signal per nucleus was represented by using boxand-whisker plot with GraphPad Prism 8. a ring region around the nuclei and identifying the micronuclei in this region by using the "B" method with Columbus software (version 2. 8. 2). Antibodies used in this study can be found in Supplementary Information file. I F N G R 1 O A S 1 P T G S 2 T N F I S G 2 0 S T I N G S E L E N B P 1 5'-EU labeling. HeLa cells were seeded on coverslips and incubated with 1 mM 5-ethylnyl uridine (EU) for 1 h to label newly synthesized RNA. For automated EU analysis HeLa and HEK293T cells were seeded in 96-well plates (CellCarrier; PerkinElmer). 96-well plates were coated with poly-L-lysine (Sigma-Aldrich) for HEK293T.
52p) as described previously  70. In 96-well plates, nuclei were stained with 1 µg/ml Hoechst 33342 for 15 min, washed twice with PBS and stored at 4 °C until analysis. 96-well plates were scanned with a 20X objective using an Operetta High-Content Imaging System (PerkinElmer) with Harmony software (version 4. 9). Analysis and quantifications were carried out by defining the nucleoli by using the "B" method with Columbus software (version 2. 8. 2). Data were plotted by using box-andwhisker plot with GraphPad Prism 9. 1. 0 software with the following settings: boxes: 25-75 percentile range; whiskers: 10-90 percentile range; horizontal bars: median. Cellular fractionation and immunoblotting. Whole cell extracts (WCE) were prepared by lysing the cells for 20 min on ice in RIPA buffer (25 mM Tris-HCl pH 7.
Acknowledgements We thank the  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  203141/Z/16/Z ) for the generation and initial processing of the sequencing data. We gratefully thank the  Micron Advanced Bioimaging Unit  (supported by  Wellcome Strategic  Awards  091911/B/10/Z  and  107457/Z/15/Z ) and the  CRCT Cell Imaging Platform (Cancer Research Center of Toulouse)  for the support and assistance with microscopy. We thank  N. Proudfoot  for critically reading the manuscript. We thank  T. Nojima ,  L. Palazzo ,  K. Kamieniarz-Gdula  and  G. Dujardin  for technical advice. N. G lab is supported by a  Royal Society   University Research Fellowship  ( UF150656 ),  EPA Research Fund  ( Sir William Dunn School of Pathology, University of Oxford ), an  MRC New Investigator Research Grant  ( MR/J007870/1 ), a  John Fell award  ( BVD07340 ,  Ref 133/090 ) to N.
Quantitative Real-Time RT-PCR (qRT-PCR) Total RNA was extracted from harvested cell culture using Zymo Research Quick-RNA Viral kit (Irvine, CA, USA, cat. no. R1035) according to the manufacturer's instructions. qRT-PCR experiments were performed using the One Step Prime Script Syber Green RT-PCR kit from TAKARA (Cat. No RR066A). qRT-PCR was performed in a final volume of 5 µL containing 1 µL of extracted total RNA per reaction, 2. 7 µL of enzyme mix, and 1. 3 µL of primer mix with a final primer concentration of 250 nM. Primer pairs were designed in-house using the NCBI Primer Designing Tool (http://www. ncbi. nlm. nih. gov/tools/ primer-blast/ accessed on 3 January 2020). The specific primers used are listed in Table  1. qRT-PCR was carried out using a Quant Studio 5 thermocycler from Applied Biosystems (Thermo Fisher Scientific) with the following steps: a reverse transcription at 42 • C for 5 min followed by 40 cycles of a denaturation step at 95 • C for 5 s, an annealing step at 58 • C for 15 s, and an extension step at 72 • C for 15 s.
The coverslips were mounted with Vectashield (Vector Labs; Clinisciences, Nanterre, France) and fluorescence was observed using a Nikon Eclipse E2000-U microscope (Nikon, Tokyo, Japan). Images were captured and processed using a Hamamatsu ORCA-ER camera and the imaging software NIS-Element AR (Nikon, Tokyo, Japan). Chemokine concentrations in SC supernatants were measured using commercially available ELISA kits for CCL2 (Peprotech, London, UK: cat. no. 900-K31), CXCL8 (Peprotech; cat. no. 900-K18), and CCL5 (Peprotech; cat. no. 900-K33), according to the manufacturer's instructions. Samples were analyzed from four independent experiments.
Statistics Statistical analyses were performed with GraphPad Prism software version 6. 01 using one-way ANOVA followed by the Bonferroni's test for multiple comparisons. p-values ≤ 0. 05 were considered statistically significant. Significance was indicated in the figures as follow: p-values ≤ 0. 05 (*), p-values ≤ 0. 01 (**), p-values ≤ 0. 001 (***), and p-values ≤ 0. 0001 (****). Results are expressed as mean ± standard error "SEM" and as percentages.
| Method for dnDSA IgG subclass quantification by mass spectrometry Relative quantification of the IgG subclasses was performed as previously described. 32 Peptides and proteins were identified using the ProteinPilot software v. 5. 0 (Sciex). Each IgG subclass was quantified using two unique proteotypic peptide sequences (Table  S1 ). Only proteotypic peptides with a confidence score ≥99 were retained. To evaluate the relative abundance of each IgG subclass in a sample, the spectral counts of each subclass-specific peptide were normalized to the sum of the spectral counts of all IgG subclasses.
| Purification of total IgG3 from sera We first purified the IgG3 using protein A and protein G columns (Thermo Scientific, Pierce) to analyze the specific IgG3 subclass glycosylation profile as previously described by Yuan et al. 33  Briefly, 2000 μl of serum was loaded in Protein A columns which bind IgG1, IgG2, and IgG4. The flow-through was preserved and then loaded three more times on the same protein A column after column regeneration to further increase the purity of IgG3. The last flow-through was loaded on a protein G column, and then washed and eluted with elution buffer to recover the IgG3 fraction. Collected fractions were then desalted through a PD10 column and vacuum concentrated. A high quality of IgG3 purification was obtained for all samples: the median IgG3/ (IgG1 + IgG2 + IgG3 + IgG4) ratio was 94% (data not shown).
Quantification of the different glycoforms of iDSA IgG3 The PeakView™ v1. 2 software (Sciex) with the XIC Manager add-in was used for targeted high-resolution MS and MS/MS glycopeptide data processing. The XIC Manager consisted of a table of predefined glycosylated ions with m/z (±25 ppm) and retention time windows to generate extracted ion chromatograms (XIC) from the total ion chromatogram (TIC) for each mass of glycosylated peptides selected. 4] [35] [36] [37] [38] [39]  Details regarding the construction of this table are presented in the supplementary data section (Table  S2 ). After being extracted, the glycopeptides were assigned to MS1 peak intensities after manual inspection for the presence of specific low mass collision-induced dissociation (CID) marker ions in MS2 spectra (Table  S3 ).
During mixing, the preferred empiric therapy was rotated for every new patient needing treatment. Patients could be treated with different antibiotic courses during admission. Readmissions were included. To safeguard optimal patient care, protocol allowed for physicians to change patient therapy on an individual basis at any time (e. g. , de-escalation, combination therapy, allergic reactions or patient safety). There was no blinding of intervention allocation for physicians during admission, for those responsible for data collection or the patient. For the current analysis we had access to individual microbiological culture data from seven of eight participating European ICUs from Belgium, France, Germany, Portugal, and Slovenia. These were ICUs with mixed, medical or surgical patient populations.
Patients were included if a first culture was negative for Gram-negative ARB in cultures from the respiratory-or gastrointestinal tract (e. g. feces, rectum, perineum or gastric contents). Enrolment of patients was preceded by approvement of the study and a waiver for individual informed consent, by all local Interne Review Boards of each participating center. This trial is registered with ClinicalTrials. gov, number NCT01293071. There were no changes to the study design after trial commencement. The primary endpoint was the first clinical culture with Gram-negative ARB, defined as non-susceptibility to 3 rd -or 4 th generation cephalosporins and piperacillin-tazobactam in Enterobacterales species, and piperacillin-tazobactam or-meropenem resistance in Pseudomonas aeruginosa and Acinetobacter species.
Results During the cycling and mixing intervention periods, there were 8,267 admissions overall in 7 ICUs in 5 countries (  Fig 1). For this nested cohort study, 1,613 (19. 5%) admissions were eligible. Data was collected from June 27, 2011, to February 16, 2014. Baseline demographics and ICU characteristics of interventions were comparable (Tables  1  and 2 ). In these patients 16,437 microbiological cultures were collected (mean of 10. 27 cultures per admission); upper-or lower respiratory materials (%), blood-or intravascular cultures (34. 8%), enteric cultures (e. g. gastric fluid, bile, feces, rectum swabs, 29. 9%) and urine (12. 2%) (Appendix Table  1  in S1 Appendix). In total 8. 3% of the cultures was taken as part of surveillance for carriage, not for diagnosis and treatment of infection (missings 12.
Data collection All clinical cultures taken during the study periods were included for endpoint analysis. If the primary endpoint was reached in an individual patient, subsequent culture results were excluded. Microbiological procedures were performed according to local laboratory practices, including local Minimum Inhibitory Concentration cutoff values. Participating microbiological laboratories did not change protocols for antibiotic susceptibility testing during the study. Likewise, infection prevention-and control measures did not change during the study period. Systemic antibiotic use was collected at the individual or aggregate level. Consumed quantities of antibiotics were converted to WHO Defined Daily Dose (DDD). Use of different antibiotic groups were represented as subdivided 6-week periods for cycling, and divided over the 9-month intervention period for mixing.
Previously published T. gallinae data from 2011 to 2012, with samples collected from the same sites using the same methods  (Lennon et al. , 2013; Stockdale et al. , 2015) , were also included in subsequent analyses. Sample collection at breeding sites in western France was undertaken at two locations: Chizé Forest (46°6′N, 0°21′W) and Oléron Island (45°55′N, 01°16′W). Birds were caught using baited potter traps during a two week period (24 May-7 June 2014) and sampled for T. gallinae as above. Using a combination of whoosh and mist nets, over-wintering turtle doves were caught in Oursi, Burkina Faso (14°41′N, 0°27′W) from November 2012 to April 2013, and at a site near Sandiara, Senegal (14°24′N, 16°47′W) from January to March in 2014 and 2015, and sampled as above. Issues with export permits led to samples from Burkina Faso being stored in refrigerated conditions (4°C) for approximately 18 months before being imported.
| Trichomonas gallinae Fe-hydrogenase region The full 1000 bp length of the Fe-hyd gene region was targeted using the primers TrichhydFOR and TrichhydREV (Table  2 ). Samples from 2013 were amplified as per TFR1 and TFR2, but with 3 mM MgCI₂, 0. 25 µM dNTP mix, 0. 25 µM forward and reverse primer and 5 U Go Taq Hot Start Taq DNA Polymerase in a 50 μl reaction volume. To allow HTS using an Illumina MiSeq, new primer sets were designed to amplify the 1000 bp Fe-hyd region in four overlapping sections 300-350 bp in length (Table  2 ). These primer sets were designed using Primer 3 v. 0. 4. 0  (Koressaar & Remm, 2007; Untergasser et al. , 2012)  based on a consensus sequence of all the available Fehyd sequences on GenBank (accessed 13 April 2015), using the search terms "Trichomonas gallinae" and "Fe-hydrogenase" (n = 26).
A StepOnePlus Real-Time PCR system (Applied Biosystems) was used to run the qPCR. The concentration of each library was calculated using the KAPA data analysis template and normalised to 4 nM. The library was sequenced using 250 paired-end reads on a MiSeq benchtop sequencer (Illumina). Illumina samples were sequenced over four different Miseq runs, in combination with other samples, as part of a larger project  (Thomas, 2017). Each run contained at least 10% duplicates (i. e. , 10 duplicates per 96 well plate).
, 2014). A variant had to be present in at least 50 copies within an amplicon to be retained within the analysis. This value was chosen to minimize the risk of false positives. All remaining sequences were queried using the ncbi-blast algorithm  (Altschul et al. , 1997)  to determine the closest sequence match. In the case of the fragmented Fehyd gene, the four fragments from a sample were overlapped to form the full sequence. Partial Fe-hyd sequences were identified where possible by aligning them with full length Fe-hyd and sequences downloaded from GenBank for primer design using clustalw  (Larkin et al. , 2007)  in bioedit  (Hall, 2005). A neighbour-joining tree based on this trimmed alignment was constructed in mega6  (Tamura et al. , 2013). If a query sequence grouped with a recognized strain (either from GenBank or the full length sequences from this study) and that group had bootstrap support >50%, it was identified as that strain.
| Coinfection To test whether coinfecting parasite strains (of Trichomonas sp. n = 5 strains; Haemoproteus sp. n = 3; and Leucocytozoon sp. n = 6) occurred together at random, we used the cooccur package  (Griffith et al. , 2016)  in R version 3. 3. 3 "Another Canoe" to analyse data from UK birds. This tests whether the observed frequency of strain cooccurrence is greater or less than expected given the overall prevalence of each strain in the population  (Griffith et al. , 2016). We also ran the same analysis on the entire data set for which Trichomonas sequence data were available (n = 128 birds) to quantify the expected levels of Trichomonas coinfection given the prevalence of different parasite strains: whilst this data set included sequences identified using Sanger sequencing (where potential coinfections might be missed) no Sanger sequences returned overlapping double peaks in the chromatogram, indicating an absence of coinfection in these samples.
Post hoc power analyses were conducted using the pwr package  (Champely, 2018)  in R, using nonscaled parameters to estimate effect sizes, to test the statistical power of both of these analyses. Seven distinct ITS sequences were identified. Five were identical to existing strains in GenBank (Figure  1 ). The sixth sequence was found in one turtle dove from Senegal, representing a new strain with 99% similarity to the "GEO" strain (GenBank accession number (A/N) JQ755287), and is hereafter named GEO-TD (A/N MN587098). This same new strain was later detected in Laughing Doves Streptopelia senegalensis in Senegal (n = 2; Thomas, 2017).