dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluorescent staining signals were captured with a DM6000 microscope (Leica microsystems) equipped with a CDD CoolSNAP-HQ camera (Roper Scientific) or using an Olympus FV1000 confocal microscope equipped with standard PMTs, high-sensitivity GaAsP detectors. AlexaFluor-488, AlexaFluor-594, and AlexaFluor-647 were excited by using a 488 nm argon ion laser line, 559 nm, and 635 nm laser diodes lines, respectively. Control of the confocal microscope and image acquisitions were conducted by using the Olympus Fluoview software version 4. 2 at a resolution of 1024 × 1024 pixels, with a scan rate of 8-10 μs pixel -1. Images were acquired sequentially, line by line, in order to reduce excitation and emission crosstalk, step size was defined according to the Nyquist-Shannon sampling theorem. Exposure settings that minimized oversaturated pixels in the final images were used.
Twelve-bit images were processed with ImageJ or FIJI, Z-sections were projected on a single plane using maximum intensity under the Z-project function. Quantification of the number of total cells in the presumptive ONL (DAPI) and cells immunoreactive for specific markers was performed by manual counting using the FIJI/ImageJ software. A minimum of nine retinal organoids from three independent differentiation experiments were counted for each condition. The counted areas corresponded to onequarter of the area of the retinal organoid cryosection. To analyze RPE cell shape parameters, images of cells outlined by ZO-1 were first modified using several filtering steps (Maximum filter, Gaussian Blur) and binarized in order to apply a Voronoi function. Then, a threshold was performed, converted as a mask, inverted, and connectedcomponents labeling was done before applying the Glasbey LUT to colorize each Voronoi cell.
Supernatants were then collected to determine protein concentrations using a Bradford protein assay with the Infinite M1000 PRO (Tecan). Afterwards, 12 μg of proteins were denatured for 5 min at 98 °C before migration on an SDS-polyacrylamide gel (Mini-PROTEAN TGX Precast Gel, Bio-Rad) for 55 min at 150 V and transferred to a nitrocellulose membrane (Trans-blot Turbo Transfer 0. 2 μm nitrocellulose, Bio-Rad). After transfer, membranes were saturated using a blocking solution (PBS, 0. 1% Tween-20 with 5% non-fat powder milk) for 1 h at RT, and incubated with the primary antibody (Supplementary Data 6) overnight at 4 °C. After 4 wash with PBS-Tween 0. 1%, membranes were incubated 1 h at RT with the respective anti-HRP mouse or rabbit secondary antibodies (Jackson, 1:10000). Revelation was performed using the Pierce ECL Plus (Thermo Fisher Scientific) according to the manufacturer's recommendations.
ACKNOWLEDGEMENTS We are grateful to  Drs. S. Mohand-Said  and  D. Dagostinoz  ( CIC1423, Hôpital des Quinze-Vingts ) for their help in patients recruitment and  M. Cailleret  ( IStem, Corbeli-Essones, France ) for his help in CRISPR/Cas9 strategies. We acknowledge  V. Bazin  and  G. Toutirais  from the electron microscopy facility ( Sorbonne Université ) for performing SEM and TEM analysis,  C. Condroyer  for sequencing analysis, and  E. Vanoni  for her help with the phagocytosis assay. We thank  N. Oudrhiri  and  A. Bennaceur  ( GHU Paris-Sud APHP ) for the conventional cytogenetic analysis,  Dr. O. Feraud  and professor  F. Griscelli  ( ESTeam Paris-Sud/U935 INGESTEM ANR Program Investissement d'Avenir)  for the teratoma assay,  Dr. Edwin Hernandez-Garzon  ( Institut de la Vision ) for his help with RPE shape analysis and  Dr.
C. Craft  ( Mary D. Allen Laboratory for Vision Research, USC ROSKI Eye Institute ) for the cone arrestin antibody. This study was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Sorbonne Université ,  LABEX LifeSenses  ( ANR-10-LABX-65 ),  IHU FOReSIGHT  ( ANR-18-IAHU-01 ),  ANR  grants ( ANR-15-RHUS-001  and  ANR-17-CE18-0004-01 ),  Paris Ile-de-France Region (DIM Gene Therapy) ,  Fondation Maladies Rares , Retina France association and by the  Foundation Fighting Blindness  ( Individual Investigator Award  to LCB). The funding organizations had no role in the design or conduct of this research.
Set-up of simulations For numerical simulations of our individual-based model, we use a Cellular Potts approach on a 2D spatial grid with a total of 400 × 400 lattice sites. Simulations were developed and run using the software CompuCell3D  [34]  on a standard workstation (Intel i7 Processor, 4 cores, 16 GB RAM, macOS 11. 2. 2), with one time-step chosen to be ∆t = 1 min. The computational implementation of Cellular Potts models is described in A, while full details of the model parametrisation are provided in B. Files to run a simulation example with Compucell3D software  [34]  are available at: https://plmlab. math. cnrs. fr/audebert/cc3dmodeltumourcd8. At the initial time point of the simulation, a certain number of tumour cells are already present in the domain, while CD8 + T cells arrive only when the simulation starts.
Acknowledgments The authors are grateful to  Jacqueline Marvel  for sharing her knowledge on the immune response. E. L. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon2020 research and innovation programme  (grant agreement No  740623 ). T. L. gratefully acknowledges support of the  MIUR  grant " Dipartimenti di Eccellenza 2018-2022 ".
Peptides were separated on a 75-mm inner diameter microcapillary column packed with 30 cm of Kinetex C 18 resin (1. 7 mm, 100 Å; Phenomenex) that was heated to 60°C in a Phoenix S&T NanoLC column heater. For each sample, 2-mg aliquots of peptides were loaded in buffer A (0. 1% formic acid, 2% acetonitrile) and eluted with a linear 210-min organic gradient followed by a wash and column reequilibration as follows: 0% to 2% solvent B over 27 min, 2% to 25% solvent B over 148 min, 25% to 50% solvent B over 10 min, 50% to 0% solvent B over 10 min, hold at 0% solvent B for 15 min. The flow rate was kept at 200 nL/min. MS data were acquired with Thermo Xcalibur software v4. 27. 19 using the topN method, where N can be up to 10. Data were processed in Proteome Discoverer v2. 3 (Thermo Scientific) using MS Amanda v2.
0 (71) and Percolator  (72). Spectral data were searched against the E. coli reference proteome database, and common laboratory contaminants and identifications were controlled at a false-discovery rate (FDR) of ,1% at the peptide level. Proteins were quantified according to the sum of their peptide chromatographic areas under the curve and normalized in Proteome Discoverer by the total peptide amount. To identify differential protein abundances, hypothesis testing was performed by t tests, and adjusted P values were calculated based on the background population of proteins. Log 2 protein abundance differences were determined to assess the fold change. Proteins were assigned to appropriate Gene Ontology (GO) term categories using EcoCyc. These data are available in the ProteomeXchange Consortium via the MASSIVE repository (https://massive.
Calibration was done with the samples of the stationary phase  of the WT, parCts at 25 • C and parEts at 30 • C. For all samples, ∼10 8 cells were fixed in 70% EtOH, washed, marked with propidium iodide (2-20 g/ml), washed again, and resuspended in sterile 1× PBS pH 7. 2 prior to analysis. FCSalyzer software (https://sourceforge. net/projects/fcsalyzer/) was used for data analysis. Hi-C libraries: Hi-C libraries were generated as recently described in  (41). 30 ml of culture was grown in Minimal Medium A supplemented with casaminoacids and glucose until OD 600 nm ∼0. 2. Protein-DNA interactions were crosslinked by the addition of 37% formaldehyde (3% final concentration) for 30 min at room temperature with gentle agitation. Crosslinking was quenched with 2. 5 M glycine (0. 4 M final concentration) for 20 min at room temperature with gentle agitation.
The Illumina process was performed according to manufacturer recommendation, with are 12 cycles of amplification. The size of the DNA fragment in the libraries are checked on TAE 1% agarose gel and subjected to paired-end sequencing on an Illumina sequencer (NextSeq500-550-75 cycles). Generation of contact maps: Generation of contact maps was done with E. coli analysis pipeline developed by Axel Cournac (https://github. com/axelcournac/EColi analysis). Briefly, reads recovered from the sequencing were aligned with bowtie2 on the reference E. coli genome (NC 000913), the two ends were merged and the reads were filtered to have a mapping quality strictly >30. Then each read was assigned to a HpaII restriction fragment (fragment attribution), >80% of the reads on average are conserved as informative reads.
ACKNOWLEDGEMENTS We thank  Sylvie Rimsky ,  Adrien Camus ,  Martial Marbouty ,  Théo Foutel Rodier  for insightful discussions. Axel Cournac ,  Etienne Jean ,  Lyam Baudry ,  Vittore Scolari ,  Cyril Downloaded  from https://academic. oup. com/nar/article/ 50/5/2635/6536896  by  Institut Pasteur -CeRIS user on 07 June 2022 Nucleic Acids Research, 2022 ,  Vol. 50 , No. 5 2649 Matthey Dorey ,  Remi Montagne  for Computational analysis tips. We thank  Ivan Matic ,  Arnaud Gutierrez  and  Wei Lin Su  and  Agnès Thierry  for technical advices. We thank the CIRB imaging and cytometry facility. We thank  Jean-Yves Bouet ,  Pat Higgins ,  Frédéric Boccard  and  Ken Marians  for strains and plasmids.
| Taxonomic and functional structures of weed communities For each field, weeds were sampled on ten 1 m 2 located in the field core, at least 20 m from the field margin (sampling designs for each data set are provided in Figure  S3 ). We sampled 20 pairs of fields in 2019 and 17 pairs of fields in 2020, in June and July before crop harvest. Pairs of fields sampled in 2019 were different from those sampled in 2020, but evenly located along the same range of landscape gradients. We visually estimated the cover of each species found in quadrats, with an accuracy of ±5%. Ten quadrats per field were necessary to obtain a fairly accurate estimate of weed diversity, as indicated by species accumulation curves (Figure  S4 ). Thus, we calculated the mean cover (%) of each species over the 10 quadrats for each field.
| Indicators of plant spillover and environmental heterogeneity To estimate plant spillover, we used the phi coefficient of association, which distinguishes 'transient' species that rely on regular recolonization from neighbouring non-crop habitats, from 'resident' species typical of arable habitats and buffered by persistent seedbanks  (Metcalfe et al. , 2019). To avoid circular analysis, we used data from vegetation surveys at national scale. Weed frequency in arable fields was obtained from the 'Biovigilance Flore' network 2002-2012  (Fried et al. , 2008) , whereas plant frequency in uncropped field margins came from the '500 ENI' network  (Fried et al. , 2018). The fidelity score was calculated as follows, where N = 7257, the total number of plots (including arable fields and uncropped field margins), N p = 1875, the number of plots in the habitat of interest (uncropped field margins), n is the total number of plots in which the species is found, and n p is the number of plots where the species is found in the habitat of interest:   (Julve, 1998).
| Environmental factors Kermap (https://kermap. com/en/) generated hedgerow mapping, using Computer Assisted Photo-interpretation based on the IGN orthophotograph of 2017. We rasterized vector maps with a resolution of one pixel for 5 m × 5 m to compute the 'bocage' metric, i. e. , the density and complexity of hedgerow networks, within circular buffer radii of 250, 500, 750 and 1000 m around each field, using Chloe software  (Boussard & Baudry, 2017). These buffer sizes correspond to the weed dispersal distances usually reported in the literature (e. g. Martin et al. , 2020). 'Bocage' was calculated using the following formula based on landscape grain  (Burel & Baudry, 1990) , where n is the total number of pixels in a buffer zone and distance i is the distance from the pixel i to the nearest hedgerow: Assuming that hedgerows are on average about 10 m high in the region  (Boinot & Alignier, 2022) , this index is based on the hypothesis that hedgerows located more than 100 m from a pixel have no or very little influence on it  (Forman & Baudry, 1984).
| Data analysis We used generalized additive models (GAMs) to assess the effect of 'bocage', farming system at field and landscape scales, and two-way interactions on weed communities in field cores (n = 74 fields). The spatial coordinates of each field were included in GAMs to account for spatial autocorrelation, thanks to a smooth term modelling the interaction between latitude and longitude  (Fried et al. , 2018). 'Year' was also included as a covariate. We computed Gamma GAMs for total weed cover, Hill-Shannon diversity, CWV indices, and CWM Seed mass, and Gaussian GAMs for other CWM indices, using the r package mgcv  (Wood, 2017). We added a small constant (10 -6 ) to total weed cover and Hill-Shannon diversity to allow for 0 values in Gamma GAMs. For CWM SLA, CWM EIV-L, and CWV EIV-L, we removed one CF field with extreme value that was unduly influencing the results.
All explanatory variables were standardized Fidelity = N • n p -n • N p √ n • N p • (N -n) • N -N p. Bocage = 1 - ∑ n i=1 � min � distance i , 100 �� n × 100. (mean-centred and divided by two standard deviations) to compare their relative importance  (Schielzeth, 2010). The degree of collinearity between explanatory variables was checked before data analyses using generalized pairs plots (Figures  S14-S17 ) and variance inflation factors (VIF). All explanatory variables had VIF values lower than 3 and thus were included in full models  (Zuur et al. , 2013). We proceeded to a model averaging procedure based on information-theoretic approach  (Burnham et al. , 2011) , using the r package MuMIn  (Barton, 2020). We used the 95% confidence model set (i. e. summed Akaike weights from largest to smallest until reaching 0.
This is consistent with hospital transmission of a new Klebsiella quasipneumoniae subsp. quasipneumoniae (K. qpq) clone exhibiting both resistance and virulence genes. Excluding K. pne, only three isolates from other species harboured a carbapenemase gene; these were all isolated from the hospital environment and carried bla VIM-1. Two of these (K. mic SPARK_1816_C1 and K. gri SPARK_1652_C1) presented nearly identical genotypic and phenotypic resistance profiles to each other and to five isolates of K. pne. This resistance profile is characterized by the presence of the bla SHV-12 , bla VIM-1 , mph(A) and qnrS genes, harboured by a class 1 integron (GenBank accession no. MN783743) associated with the conjugative IncA plasmid pR210-2-VIM  37. This plasmid is known to circulate in multiple Enterobacteriaceae species in Italy  38  and the re-emergence of VIM-1 in this region is thought to reflect the increased use of ceftazidime-avibactam against K.
Article https://doi. org/10. 1038/s41564-022-01263-0 the K. pne isolates bearing bla CTX-M-15 were from humans, the exceptions being from hospital surfaces and companion animals. In contrast, only 51 out of 170 (30%) of the K. pne isolates bearing bla SHV-27 variants were from human sources compared to 87 out of 170 (51%) from cows. Of the 175 K. pne isolates harbouring carbapenemase genes, all were isolated from the hospital environment and the majority (n = 161; 92%) carried bla KPC and corresponded to the healthcare-associated clones ST258/512 or ST307. K. pne_ST307_SC1 was the most abundant clone in the dataset and was isolated from hospital surfaces and companion animals as well as hospital patients, although none of the ST307 isolates from non-human sources harboured bla KPC. Eleven K.
Besides the 12 phenotypically resistant isolates harbouring an mgrB mutation, Kleborate did not detect a mechanism for colistin resistance in the other cases, including three K. pne isolates from pigs and a single K. aer isolate from a goat. This was not unexpected since many mcr variants are not included in the Kleborate database and colistin resistance can also be conferred through mutations responsible for membrane synthesis  40. The final non-human colistin-resistant isolate was a single K. pne isolate from a cow that harboured mcr-1.
These figures contrasted with K. pne_SC2_ST17, the second most common lineage in our dataset (n = 128), for which the equivalent figures were 20% and 57%, respectively. Other K. pne SCs were associated with cows rather than humans (for example, SC5_ST661, SC9_ ST3068, SC10_ST2703, SC17_ST3345). Some intermingling occurred, particularly in SC5_ST661, which contained clonal expansions of both bovine and human isolates. This lineage was previously observed from both human and bovine sources  16  and may represent a more generalist clone that is adapted to and able to transmit between both cows and humans. Fewer statistically significant SC enrichments were apparent for other species, likely due to smaller sample sizes. However, a number of observations were notable. As discussed, K. mic was enriched within hospital carriage (Supplementary Table  4 ) but this was not due to the expansion of a single SC.
pne lineage recovered from dogs in our data, have both previously been associated with companion animals  45. A more complex question relates to the emergence of high-risk lineages that combine heightened virulence and/or resistance attributes with the ability to move between, and spread within, different settings. A full understanding of the emergence of such clones requires a consideration of likely anthropogenic drivers, such as inappropriate use of antibiotics or other environmental stresses, but also of the underlying ecology since free transmission will be less likely if the selective landscape is characterized by local adaptation and specialization. Our data revealed low levels of resistance and virulence genes outside of clinical settings and within species other than K. pne. Given the caveat that this may in part reflect biases in the database used by Kleborate towards well-characterized genes common in K.
Sampling A summary of the sampling and subsequent methodology is provided in Extended Data Fig. 1. In this study, two strategies were used to obtain samples: 5,900 samples were collected in the city of Pavia (Northern Italy) and the surrounding province between June 2017 and November 2018. Information on the samples collected is given in Supplementary Table  1. To summarize, the following types of samples were collected: stool and rectal swabs from hospital inpatients and outpatients (four different hospitals) and from a nursing home; stool from healthy volunteers; stool and rectal swabs from companion animals, farm animals and animals admitted to veterinary clinics (dogs, cats, cattle, pigs, poultry, turtles, rabbits and wild birds); invertebrates; samples of edible and ornamental plants, both wild and purchased from grocery shops, garden centres and wholesale distribution; soil samples; samples of drinking water (drinking fountains) and surface water (rivers and irrigation ditches); surface swabs from hospital, anthropic surfaces (including ATM keypads, ticket machines, buses, benches, supermarket trolleys) and farm surfaces (including enclosure, buckets and milking machines).
Species identification and antimicrobial susceptibility testing Yellow and mucoid colonies on SCAI plates suspected to belong to the Klebsiella genus were identified at the species level through matrix-assisted laser desorption/ionization (MALDI)-time-of-flight (TOF) mass spectrometry (Microflex LT/SH; Bruker Daltonik GmbH) equipped with the Bruker biotyper 3. 1 software. Once confirmed to be members of this genus, the isolates were subcultured on MacConkey agar for antibiotic susceptibility testing and DNA extraction. Antibiotic susceptibility was tested for all the isolates using the BD Phoenix 100 automated system and the dedicated panels NMIC-402 for all the diagnostic routine samples and NMIC-417 for all the other samples. The antibiotics and range of antibiotic concentrations present in the two panels are listed in Supplementary Table  7.
0. 33 (ref. 52 ). SPAdes v. 3. 9. 0 (ref. 53 ) was used to generate de novo assemblies from the trimmed sequence reads using k-mer sizes of 41, 49, 57, 65, 77, 85 and 93 and with the --cov-cutoff flag set to 'auto'. The assemblies were annotated using Prokka v. 1. 12 (ref. 54 ). Kleborate v. 2. 0. 0 (ref. 26 ) was used to group the isolates into Klebsiella species using Mash distances and assign multilocus sequence typing (MLST) types to several species. It was also used to detect antimicrobial resistance and virulence genes in the assemblies. Mob-Suite v. 2. 1. 0 (ref. 55 ) and Prokka v. 1. 4. 5 were also used to identify and annotate plasmids; Abricate v. 1. 0. 0 (https://github. com/tseemann/abricate) was used to assign resistance genes to the plasmid contigs (PlasmidFinder and ResFinder databases downloaded on 21 May 2021 from https://bitbucket.
org/ genomicepidemiology/plasmidfinder_db and https://bitbucket. org/ genomicepidemiology/resfinder_db, respectively). Two phylogenetic trees were generated to show species relationships across the whole genus. The first was a neighbour-joining tree of all isolates generated by RapidNJ from Mash v. 2. 0 (ref. 24 ) distances. The second was a maximum-likelihood tree of a subset of 703 isolates generated by RAxML v. 8. 2. 8 (ref. 25 ) (GTR+Γ substitution model) from an alignment of core genes generated by Roary v. 3. 12. 0 (ref. 56 ). Individual species trees were generated by PopPunk v. 2. 0. 2 (ref. 31 ) core distances using the neighbour-joining method.
MALDI-TOF All of the sequenced isolates were initially assigned to 1 of 7 Klebsiella species by MALDI-TOF and, apart from 1 exception, subsequently assigned to 1 of 15 species on the basis of the genome sequences using Kleborate and phylogenetics. The accuracy of the MALDI-TOF assignments varied according to species; 88. 4% of the isolates assigned as K. pneumoniae by MALDI-TOF were confirmed by WGS, whereas only 30% of the isolates assigned as K. oxytoca were confirmed as this species. These discrepancies reflect the fact that reference databases are currently unable to distinguish K. oxytoca from closely related species (Supplementary Table  8 ).
Inferring transmission events To quantify the frequency of transmission events within and between different sources, we aggregated data over all Klebsiella species and identified transmission events using a threshold-based approach based on SNP distances using a mapping procedure. Briefly, for each SC in the dataset we used the 'sample_n' function from the dplyr v. 0. 8. 3 package in R v. 3. 6. 1 to randomly choose a reference isolate to represent the SC. This was an objective and practical solution, given the high diversity of the dataset (1,168 SCs across 15 species). We mapped all isolates from a given SC to this random SC-specific reference using Snippy v. 4. 6. 0 (https:// github. com/tseemann/snippy), with variant calling by freebayes v. 1. 3. 2 and the parameters --mapqual 60, --basequal 13, --mincov 10, --minfrac AUTO, --minqual 100, --maxsoft 10.
Article https://doi. org/10. 1038/s41564-022-01263-0 For each SC, we created a network where all isolates were connected to all other isolates and then pruned this network into 'putative transmission clusters' by removing all the links between isolates where the SNP distance was greater than the chosen threshold SNP distance (an example is given in Extended Data Fig. 10 ). For each of these putative transmission clusters, we recorded every unique source pair (including same source pairs) connected by a pair of isolates differing by an SNP distance ≤ threshold. We excluded pairs of samples from the same individual hosts. Each unique source pair (not isolate pair) within each cluster, which satisfied these criteria, was recorded as a single transmission event. This approach is conservative because multiple transmission events occurring within a cluster between any given source pair are only counted once, but this avoids double counting of single transmission events.