dataesr/scientific-paragraphs-categorization huggingface dataset:
Fixed Sample Imaging Fluorescent imaging of fixed samples was performed on Leica Sp5 and Sp8 inverted confocal microscopes. Images of discs that had not been within the stretcher device were acquired with 40x objective and depth resolution of 1mm. Images obtained of anchored and stretched discs from clones and phosphor-MyoII experiments were acquired with 40x objective and depth resolution of 0. 35mm. High resolution actin images were obtained on Leica SP8 using 100X objective, pixel resolution of 49-51nm and depth resolution of 0. 13-0. 15 mm as calculated for optimal deconvolution with Huygens Professional software.
Analysis of Junctional Intensities and Cell Aspect Ratio Images were segmented and analyzed with EpiTools software as reported before  (Heller et al. , 2016). In short, image was segmented (Epitools) after background subtraction (rolling ball radius 20, Fiji) and underlying junctional intensities (E-cad::GFP and Sqh::mCherry) were extracted with Cell Graph (EDGE_COLOR_TAG overlay) (Figure  S2 ). The following parameters were applied: 2 pixel edge intensity buffer around the junctions, selection mode 1 (tricellular signal removed), upper 50% (0. 5) of the signal intensity. Junctions were selected manually and color-coded as horizontal (junction angle <45 ) and vertical (junction angle >45 ); resultant intensity data was exported. Polarity was defined as fluorescent mean intensity on horizontal (stretched or equivalent in anchored) junctions divided by intensity on vertical (non-stretched or equivalent in anchored) junctions.
ACKNOWLEDGMENTS We are grateful to  Nic Tapon , in whose lab the development of the stretching device was initiated, for his support. We thank  GREM (Griffon Gravure)  for building the first prototype of the stretching/compression device. We thank  Duncan Farquharson ,  Simon Townsend , and  Piotr Sienkiewicz  from the Mechanical and Electronic Workshops at the  University College London  for the design and execution of the final version of the stretching/compression device. We thank  Davide Heller  for help with junctional myosin intensity measurements. We thank  John Zhang  for his help drawing the photomasks and  JDPhoto-Tools  for printing. We thank  Pedro Gaspar  for his kind gift of the ubi-Dia-GFP line. We thank all the authors and  Shiladitya Banerjee ,  Martin Raff ,  Robert Tetley ,  Franck Pichaud ,  Nic Tapon , and  David Ish-Horowicz  for revision of this manuscript.
M. D. is funded by a  Marie Sk1odowska-Curie Horizon 2020 Individual Fellowship  (MRTGS). N. J. K. is funded by a  MRC  PhD studentship. N. K. is funded by the  Rosetrees Trust , the  UCL Graduate School , the  EPSRC -funded doctoral training program CoMPLEX, and a  UCL Overseas Research Scholarship. M. T. is funded by a  Sir Henry Wellcome Fellowship  (Grant No:  103095 ). M. P. and N. C. are funded through  CNRS and Institut Curie. A. B. is funded through  EPSRC. G. C. is supported by a consolidator grant from the  European Research Council  (MolCellTissMech, agreement  647186 ). B. B. is supported by  CRUK   17343 ,  BBSRC BB/K009001/1 , and  BBSRC BB/J008532/1  grants. Y. M. is funded by an  MRC  Fellowship  MR/L009056/1 , a  UCL Excellence Fellowship , and a  NSFC   International Young Scientist Fellowship 31650110472.
, 2009). Y. Mao N/A N/A Software and Algorithms Gift from B. Baum Ecad::tomato Epitools (Huang et al. , 2009) Heller et al. (2016) https://epitools. N/A N/A mRFP. nls,w,hsF,Frt19A Bloomington Drosophila Stock Centre github. io/wiki/ #31418 Rho-1::GFP GraphPad Prism 7 Kyoto Stock Centre GraphPad Software DGRC ID: 110833 N/A Sqh::GFP, enGal4 ImageJ 1. 51w Y. Mao Schneider et al. (2012) https://imagej. N/A N/A Sqh::GFP, enGal4; UAS-Act5C::RFP Y. Mao nih. gov/ij/docs/install/osx. html N/A Sqh::mCherry Vertex model E. Wieschaus C++, as in Mao et al. (2013). Bespoke model code developed in N/A N/A SqhAx3;;Sqh::GFP Jordan and Karess, (1997) N/A UAS-Act5C::RFP K. Roper (Roper et al. , 2005) N/A UAS-AIP1 RNAi Vienna Drosophila RNAi Centre VDRC, ID: 22851 UAS-arp2 RNAi Vienna Drosophila RNAi Centre VDRC:KK10199 Fly stocks of National Institute NIG, ID: 9901-R of Genetics UAS-Cd8::GFP, nubGal4 Mao et al.
In silico simulations of off-target sites To assess the number of off-target sites for SpCas9 in Saccharomyces cerevisiae, online tools were used. CRISPOR is a software that evaluates the specificity of a guide RNA through an alignment algorithm that maps sequences to a reference genome to identify putative on-and off-target sites  (Li and Durbin, 2009). To predict off-target sites, the online tool sequentially introduces changes in the sequence of the gRNA and checks for homologies in the specified genome  (Haeussler et al. , 2016).
Analysis of paired-end Illumina reads Multiplexed libraries were loaded on a HiSeq2500 (Illumina), 110 bp paired-end reads for haploids and 260 bp paired-end reads for diploids were generated. Reads quality was evaluated by FastQC v. 0. 10. 1 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were mapped along S288C chromosome reference sequence (Saccharomyces Genome Database, release R64-2-1, November 2014), using the paired-end mapping mode of BWA v0. 7. 4-r385 with default parameters  (Li and Durbin, 2009). The output SAM files were converted and sorted to BAM files using SAMtools v0. 1. 19-44428cd (Li et al. , 2009). The command IndelRealigner from GATK v2. 4-9 (DePristo et al. , 2011)  was used to realign the reads. Duplicated reads were removed using the option "MarkDuplicates" implemented in Picard v1.
For haploids, the threshold for minimum variant allele frequency was set at 70%. Mutations less than 10 bp away from each other were discarded to avoid mapping problems due to paralogous genes or repeated sequences. To assess microsatellite mutations, we only retained reads uniquely anchored at least 20 bp on each side of the microsatellite  (Fungtammasan et al. , 2015). All detected mutations were manually examined using the IGV software (version 2. 3. 77), and compared between all sequenced libraries for interpretation. All the scripts used in order to process data are available on github (https://github. com/sdeclere/nuclease). All Illumina sequences were uploaded in the European Nucleotide Archive (ENA), accession number PRJEB16068. Under the reference locus are cartooned the different chromosomal structures observed in some of the survivors.
Fe limitation has been shown to induce the glyoxylate shunt in heterotrophic bacterial model organisms  (Smith et al. , 2010; Fourquez et al. , 2014; Koedooder et al. , 2018) , a pattern that was also observed for SAR11 in the HNLC Southern Ocean  (Beier et al. , 2015). The glyoxylate shunt bypasses two decarboxylation steps and the coupled release of CO 2 and reducing equivalents (NADH 2 ) of the TCA cycle, with important consequences on ATP production and processing of organic carbon  (Koedooder et al. , 2018). The aim of the present study was to extend these observations, by providing a detailed picture on the expression of genes responsible for Fe-uptake and Ferelated downstream processes in Southern Ocean natural prokaryotic communities. We screened the total mRNA from metatranscriptomes against a database that contained 10,411 protein sequences corresponding to seven Fe-related metabolic pathways connected to Fe transport, Fe storage and central carbon metabolism.
Ferritins are compounds that were shown to regulate the storage and the release of intracellular Fe in a number of eukaryotic microorganisms  (Marchetti et al. , 2009; Botebol et al. , 2015). Bacterioferritins are known to be involved in the storage of Fe in Bacteria; however, the regulation and the exact physiological mechanisms of these compounds are not clear  (Andrews et al. , 2003; Carrondo, 2003). In the GOS data set, bacterioferritin gene abundance was higher at coastal sites with overall high Fe concentrations  (Toulza et al. , 2012). In the present study, all prokaryotic groups contributed to bacterioferritin transcripts. The differences in bacterioferritin transcripts between HNLC and Fe-fertilized waters were far less pronounced than the other Fe-related metabolisms. This could indicate that bacterioferritin-related processes are occurring at background levels, for instance, as a control of Fe homeostasis, rather than as storage of Fe in response to episodic Fe supply.
Bioinformatic analysis The raw Illumina reads were checked with FastQC (Andrews 2010; http://www. bioinformatics. babraham. ac. uk/projects/ fastqc) and adapters were eliminated using Cutadapt  (Martin, 2011). Remaining ribosomal RNA sequences were removed by the riboPicker  (Schmieder et al. , 2012)  tool and sequences were checked by interlacing and de-interlacing paired-end reads ensuring that the same sequences were removed from each R1 and R2 files and finally retaining only R1 (performed in Galaxy,  Afgan et al. , 2016). Randomized subsets of 1% of the data were affiliated using BLASTX  (Altschul et al. , 1990)  against the non-redundant (nr) protein database followed by the visualization in MEGAN6  (Huson et al. , 2007)  and proportions of unassigned to not assigned sequences as well as prokaryotic to eukaryotic sequences were retrieved (Supporting Information Fig.
Database construction A database containing sequences of genes involved into Fe-related metabolic pathway was retrieved from the study by  Toulza et al. (2012). This database was constructed by screening for bacterial sequences from NCBI with the gene name as query, as well as the protein sequences from the Moore Microbial Genome database (http://www. moore. org/microgenome/) for genes involved in Fe metabolism. For the purpose of this study, the specific sequences for the following pathways were retrieved from the database and updated by searching for these in NCBI protein clusters: Flavodoxin switch (FL), Fe 2+ -uptake (F2), Fe 3+ -uptake (F3), siderophore-uptake (SU), and storage (ST) (Supporting Information Table  S2 ). These five together are further named as 'Fe' database. In addition, protein sequences for two supplementary enzymes were chosen for pathway specific analysis.
Aconitase that catalyses the isomerization of citrate to isocitrate via cis-aconitate in the tricarboxylic acid cycle and isocitrate lyase an enzyme in the glyoxylate shunt which catalyses the cleavage of isocitrate to succinate and glyoxylate. For maximal phylogenetic coverage, all available bacterial protein sequences were retrieved from protein clusters (proteins grouped on taxonomic groups which are non-redundant) using the NCBI search tool with the protein name as query. In order to include more sequences from environmental marine bacteria the two databases were aligned using BLASTX tool  (Altschul et al. , 1990)  against the Global Ocean Sampling (GOS) protein database (retrieved from /iplant/home/shared/imicrobe/projects/26/CAM_PROJ_GOS. read_pep. fa, Yooseph et al. , 2007)  containing peptide sequences predicted from long reads from Sanger sequencing and thus more robust for the purpose of the analysis.
The retrieved GOS sequences were then checked against the KEGG  (Kanehisa and Goto, 2000; Kanehisa et al. , 2016; 2017)  and for each custom database sequences with an e-value of <1e -5 were chosen for further analysis and annotated from their KEGG-Id (Supporting Information Table  S3 ). Finally, each database contained FASTA sequences with the taxonomic affiliation in the header as well as the KEGG-Id. For SU only Fe-related siderophore KEGG-Ids were retained. Additionally, all ribosomal protein sequences (n = 261 980) stored in the KEGG database (download January 2018) were broadly (order level) annotated by retrieving taxonomic information for sequences from KEGG. These sequences were used to recruit ribosomal protein transcripts from our metatranscriptome data that served as verification of the normalization approach.
Sequence alignment The final curated databases (n = 4, Fe, aconitase, isocitrate lyase and ribosomal proteins) containing information on the annotated taxonomic levels (phylum, class, order, family and genus) were aligned to the short-read translated DNA query sequences for each station and duplicate using diamond blastx (parameters used -k 1 -e 10 -p 12)  (Buchfink et al. , 2015). Total counts per phylum, class, order, family and genus for each database, and in case of Fe, for each pathway, were summed and relative proportions to all prokaryotic reads for bacterial groups were calculated (Supporting Information Fig. S7 ). Bacterial groups defined at the taxonomic order level with the highest abundances of pathway specific transcripts were taken for further analysis.
We subsequently related mT iso to the number of metatranscriptome reads coding for SAR11 isocitrate lyase gene transcripts (mT iso ) and used this factor to estimate the absolute transcript numbers per L water or all remaining genes (mT). qPCR data were only available for Station R-2 and F-L, thus samples from A3-2 were excluded from these calculations. Operational taxonomic units (OTUs) obtained by 16S rRNA gene sequencing were retrieved for Station R-2 and F-L from an already published data set from the same sampling date  (Landa et al. , 2016). The OTUs were corrected for copy numbers of 16S rRNA gene per cell per specific taxa obtained from the ribosomal RNA database  (Stoddard et al. , 2015). Total cell numbers per bacterial group were then calculated with 16S rRNA gene relative proportions and counts from the Eub228-I, -II and -II catalysed reporter deposition-fluorescence in situ hybridization (CARD-FISH) probe from the same samples (data from the study by  Fourquez et al.
, 2016, Supporting  Information Table  S6 ). In order to obtain the number of transcripts per cell, we divided mT for the inspected genes derived from a certain bacterial group (based on the number of hits from the specific databases) by the total cell numbers per L for the respective bacterial group shown in Supporting Information Table  S6. While we tried to minimize biases introduced by the qPCR approach, it should be considered that our method is not fully free of biases, also because additional biases might be introduced by combing multiple techniques, such as qPCR, CARD-FISH and amplicon sequencing data. Accordingly, the retrieved and presented values (Fig. 6 ) represent a rough estimate of transcripts per cell. Analysis and visualization of data were performed in the R language [https://cran.
r-project. org/, version 3. 4. 0 (21 April 2017)] using customized colour palettes. Following packages and versions were used: phyloseq_1. 22. 3, gdtools_0. 1. 7, gridExtra_2. 3, cowplot_0. 9. 3, gtable_0. 2. 0, ggpubr_0. 2, magrittr_1. 5, ggplot2_3. 0. 0, plyr_1. 8. 4. Figures were using the open-source vector graphics editor Inkscape (http://inkscape. org/). Codes and databases are provided through the following link https://github. com/ PavlaDe/emi14621/.
Acknowledgements We thank the chief scientist  Bernard Quéguiner  for leading the KEOPS2 expedition. We also thank the officers and the crew aboard the  R/V Marion Dufresne  for their help in the successful completion of the cruise. We thank  Marine Landa  for her help with the seawater collection and  Francois-Yves Bouget  for financial support for the sequencing of the samples. The authors thank the  Roscoff Bioinformatics platform ABiMS  (http://abims. sb-roscoff. fr)for providing computational resources. Extremely important comments on this data set have come from  Barbara Bayer ,  Laurie Bousquet ,  Coco Koedooder  and  Yan Liu  during multiple discussions and have greatly improved this work. Two anonymous reviewers provided insightful comments on a previous version of the manuscript.
The KEOPS 2 project was supported by the  French Research program of INSU-CNRS LEFE-CYBER ('Les enveloppes fluids et l'environnement' -'Cycles biogéochimiques , environnement et ressources'), the  French ANR ('Agence Nationale de la Recherche ',  SIMI-6 program ,  ANR-2010-BLAN-614 KEOPS2  and,  ANR-10-JCJC-606 ICOP ), the  French CNES program  (' Centre National d'Etudes Spatiales')  and the  French Polar Institute IPEV (Institut Polaire Paul-Émile Victor ). This work was additionally funded by the  ANR  project  PhytoIron   11BSV7 018 02  and the  Austrian FWF  grant under the number  P28781-B21 , and is part of the PhD thesis of PD.
To assess the usefulness of Foldit for cryo-EM, the players were provided with cryo-EM densities corresponding to 4 segmented subunits of the antefeeding prophage (AFP, from a soil bacterium Serratia entomophila)-Afp1, Afp5, Afp7, and Afp9  [34]. For ease of comparison, the maps were filtered to 3. 2-Å resolution to avoid local quality variation and contain information up to the same resolution of 3. 2 Å, which is currently considered fairly high by the cryo-EM community but still arduously low for fully automated model-building algorithms. The players tried to achieve the highest possible Foldit score, which combines the Rosetta force field with map fitting  [26, 27]. The structures generated by players were compared with those produced by a cryo-EM expert who created models using the manual model-building and real-space refinement software Coot  [28] , followed by additional real-space refinement in Phenix  [29].
This observation suggests that by collaborating with Foldit, only minimal work will be required by microscopists to obtain an accurate, highquality model. Building models of large molecules into low-resolution data can be a time-consuming process for microscopists building structures by hand. However, in the 4 datasets presented here, For each of the 4 different proteins, any method that outperformed the other 5 for a particular metric is shaded in green, with any method outperformed by the other 5 shaded red. CC mask , CC box , CC peaks , and CC volume are correlation coefficients calculated between the model and the map. The differences between these correlation coefficients arise from whether the entire map is used (CC box ), only the map around the atomic centers (CC mask ), the molecular envelope defined by the model (CC volume ), or the strongest peaks in the model and map (CC peaks )  [14].
This work was supported by the following 2 National Institutes of Health grants:  R00 GM120388  (http://grantome. com/grant /NIH/R00-GM120388-03 ,SH) and 1UH2CA203780 (http:// grantome. com/grant /NIH/UH2-CA203780-02 ,SC and FK). This material is based upon work supported by the  National Science Foundation  under grant no. 1629879  (https://www. nsf. gov/awardsearch/showAward?AWD_ID=1629879, SC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
QfsR is widely conserved among RepABC plasmids carried by Rhizobiaceae The localization of qfsR gene on pTiC58 questioned its conservation. Therefore, we investigated whether QfsR homologs could also be detected within genomes available in the NCBI Nucleotide collection (nt) and in the MicroScope genome database  (23). Using the BLASTN algorithm, we identified 395 candidates showing various sequence similarities with qfsR. Using RNAfold algorithm, we predicted the secondary structures of these candidates and compared them using the RNAforester algorithm  (24). We selected 230 putative QfsR homologs according to the high conservation of their secondary structure. 88 candidates, most of which belonging to Agrobacterium genomes (Figure  1A ), present also a high percentage of sequence identities and a good sequence coverage and 142 candidates present a high conservation of their secondary structures but a low conservation level of their nucleotide sequences (Table  S1 , Figure  1B ).
Secondary structure predictions and similarity searches RNA secondary structures were predicted using the RNAfold algorithm. Webserver default options were chosen for folding. Sequence similarity searches were performed using the BlastN algorithm with BLAST+ 2. 5. 0 software  (60). Sequence similarities were searched in the NCBI Nucleotide collection (nt) and MicroScope genome database  (23). Multiple alignment of non-redundant RNA molecules were performed to obtain a consensus secondary structure using LocARNA  (61). RNAforester was then used with the local similarity model to select QfsR homolog according to their secondary structure conservation  (24).
Acknowledgements This study was supported by the  PEPS INSB  and  PEPII INSB-INSMI-INS2I  " Bio-Math-Info " funding from the  Centre National de la Recherche Scientifique. M. Dequivre  and  B. Diel  received doctoral grants from the  French Ministère de l'Education nationale de l'Enseignement Supérieur et de la Recherche. The authors thank  Dominique Loqué ,  William Nasser  and  Sylvie Reverchon  for comments on the manuscript,  Xavier Nesme ,  Denis Faure ,  Cristina Vieira  and  Yvan Rahbé  for helpful discussion,  Eliane Hajnsdorf  for providing us the E. coli Hfq mutant strain, and  Jörg Vogel  for providing us with pXF30 plasmid. The authors are grateful to  Camille Villard  and  Agnès Rodrigue  for helpful technical assistance. qPCR experiments were performed at the DTAMB platform from the  BioEnviS scientific federation.
The expected DNA maps were constructed from the known DNA sequences of the tested microbial species (downloaded from the NCBI database). Specifically, the n locations of each methyltransferase enzyme recognition sequence (in units of bp) were listed. This list, l(n), was converted into the intensity signal e(x, f ) by summing up Point Spread Functions (PSFs) centered at each recognition sequence location. The signal was sampled along the DNA with a step size equal to the camera pixel size projected in the sample: e (x, f ) = n PSF x -l (n) 0. 34 nm bp where PSF(x) is the microscope PSF and p the projected pixel size. The final matching score S was taken as the global maximum across all δ and f values, and for both orientations d: S = argmax δ, f,d XC (δ, f, d) This way, the optimal shift, orientation and overstretching factor of the measured DNA map were also found.
Simulating DNA optical mapping We developed a simulation model, implemented in MAT-LAB, covering the entire process of DNA mapping, including strand breakage, DNA labeling, DNA deposition and imaging. First, the DNA sequences of bacteriophage species were downloaded from the NCBI database. Next, DNA fragments of 35 kbp length were drawn from the full DNA sequences, with random starting position. This step simulates the random breakage of the DNA that occurs due to shearing forces from pipetting and the presence of nucleases in the sample mixture. The length of 35 kbp corresponds to a typical DNA fragment length in our experiments (see Supplementary Section S2. 2). One thousand DNA fragments were simulated per species. In earlier work, we found that the methylation enzyme does not label all recognition sites on the DNA, but instead has a labeling efficiency of ∼80%  (30).
RESULTS AND DISCUSSION To assess and validate our new method for identifying species by optical DNA mapping, we used three complementary test case-studies, two consisting of experimental data measured on bacteriophage and bacterial DNA, respectively, and one consisting of simulated data from a detailed model. The simulated data allowed us to find out what parameters are important when identifying species based on DNA maps. The experimental data were obtained by imaging DNA fragments from bacteriophages lambda and T7 and bacterium V. Harveyi. We labeled DNA fragments with fluorescent dyes using the M. TaqI methyltransferase enzyme with recognition sequence 5 -TCGA-3 and a synthetic AdoMet cofactor  (17, 32). After labeling (Figure  1A ), the DNA was linearized on a coated coverslip using a 'rolling droplet' technique (Figure  1B ).