dataesr/scientific-paragraphs-categorization huggingface dataset:
Isolation and identification In 2017, as part of a study of gut microbiota, the strain LF1 T was first isolated from a stool sample from human using culturomics approach. It was cultivated on under blood culture flask enriched with 5% rumen fluid sterilised by filtration at 0. 2 μm then inoculated on 5% sheep blood Columbia agar (bioMérieux, Marcy L'Etoile, France) after two days of incubation at 37°C. Bacterial identification was carried out using MALDI-TOF mass spectrometry (Bruker, Daltonics, Bremen, Germany) and the reference spectrum generated was added in the local database (https://www. mediterranee-infection. com/urms-database). After several unsuccessful identification attempts with MALDI-TOF instrument, the 16S rRNA gene was amplified using universal primers pairs fD1 and rP2 (Eurogentec, Angers, France) and sequenced with the Big Dye® Terminator v1.
1 cycle sequencing kit and the 3500xL capillary sequencer Genetic Analyser (Thermo Fisher, Saint-Aubin, France), as previously described  [9]. The nucleotide sequences obtained were assembled and edited with the Codoncode Aligner software (http://www. codoncode. com). The consensual corrected sequence was used as a reference sequence and compared against the 16S gene sequences of the type strains existing in the NCBI database to assess the phylogenetically closest species. For this purpose, a Blastn method was used since the NCBI database, to compare the 16S rRNA gene sequences (https:// blast. ncbi. nlm. nih. gov/Blast. cgi).
Genome extraction and sequencing Genomic DNA was extracted using the EZ1 biorobot (Qiagen, Courtaboeuf, France) with the EZ1 DNA tissue kit preceded by a pre-treatment with lysozyme and incubation at 37°C for 2 hours. Sequencing of the gDNA was performed using MiSeq technology using the Nextra Mate Pair sample preparation kit and the Nextera XT Paired (Illumina) tip, as described previously  [13]. The assembly was performed using a pipeline incorporating different softwares (Velvet  [14] , Soap Denovo  [15]  and Spades  [16] ). Data from Illumina MiSeq were trimmed using Trimmomatic software or untrimmed using MiSeq software only  [17]. GapCloser was used to reduce assembly deviations. Scaffolds <800 base pairs (bp) and scaffolds with a depth value less than 25% of the average depth were removed.
The best assembly was selected using different criteria (number of scaffolds, N50, number of N). The degree of genomic similarity of this strain with closely related species was estimated using OrthoANI software  [18]. Then, an online server named Genome-to-Genome Distance Calculator (http://ggdc. dsmz. de) [19]  was used to calculate the DNA-DNA hybridisation (DDH) values shared between strain LF1 T and its closest related species.
Phylogenetic analysis Because there is no spectrum in the MALDI-TOF database that corresponds to the strain LF1 T , the latter cannot be identified correctly. Therefore, a similarity analysis based on 16S rDNA of the strain LF1 T showed that N. massiliamazoniensis strain LF1 T had 98. 32% sequence identity with Neobacillus bataviensis strain IDA1115 (Accession number: NR_036766. 1). This value is lower than the recommended threshold value (<98. 65%) to delineate new bacterial species  [20, 21]. Given this result, we therefore propose to classify strain LF1 T as a new species within the genus Neobacillus which belongs to the family Bacillaceae and the phylum Firmicutes  [5]. The performed phylogenetic tree (Fig. 1 ) shows the position of N. massiliamazoniensis sp. nov. strain LF1 T among closely related species with a validly published name.
They are Gram-positive bacilli and show positive oxidase and negative catalase activities. The strain grows both under aerobic and anaerobic conditions at temperatures ranging from 28°C to 37°C. The optimum grow occurs under aerobic condition at 37°C. N. massiliamazoniensis is able to ferment D-glucose, D-mannose, N-acetyl-glucosamine, Dmaltose, starch and glycogen. Alkaline phosphatase, leucine arylamidase, valine arylamidase, cystine arylamidase, acid phosphatase, α-galactosidase, α-glucosidase and β-glucosidase are present. The major fatty acids are 13-methyl-tetradecanoic acid (56%) and 12-methyl-tetradecanoic acid (17%). The genome size of strain LF1 T is approximately 4. 6 Mbp with a G+C content of 34. 1 mol%. The 16S rRNA gene sequence and the whole genome sequence of N. massiliamazoniensis have been deposited in GenBank under accession numbers LK021124 and CVRB00000000, respectively.
Genomic comparison of Neobacillus massiliamazoniensis strain LF1 T between their closely related species using GGDC Neobacillus massiliamazoniensis LF1 (Genome accession number: CVRB00000000); N. fu, Neobacillus fumarioli DSM 18237 (BCUZ00000000); N. jd, Neobacillus jeddahensis DSM 28281 (CCAS000000000); N. ba, Neobacillus bataviensis IDA 1115 (AJLS00000000); N. cu, Neobacillus cucumis DSM 101566 (PGVE00000000); N. de, Neobacillus drentensis DSM 15600 (BCUX00000000); N. so, Neobacillus soli DSM 15604 (BCVI00000000); GGDC, Genome-to-Genome Distance Calculator.
These results suggest that CPAP-CC1 is largely unfolded while CP110-CC2 and a mixture of CPAP-CC1 and CP110-CC2 forms α-helical coiled-coil structures. To assess whether the CPAP-CC1/CP110-CC2 complex forms a canonical, extended coiled coil and to further probe the dimerization of CP110-CC2, we performed SEC coupled with small angle X-ray scattering (SEC-SAXS) experiments. Following buffer subtraction, the SAXS data were consistent with the presence of a monodisperse species in solution (Figure  3g , Supp. FigureS5c) with a radius of gyration, Rg, of 3. 5 nm as estimated by Guinier approximation. To gain insight into the overall shape of CPAP-CC1/CP110-CC2 and CP110-CC2 in solution, we derived the pairwise distance distribution function, P(r), of these molecules (Figure  3h , Supp. Figure  S5d ), which suggested the presence of elongated particles in both cases, with a maximum dimension (interatomic distance, Dmax) of approximately 12.
Digested peptides were separated using a 50 cm reversed-phase column packed in-house (Agilent Poroshell EC-C18, 2. 7 µm, 50cm x 75 µm) and were eluted at a flow rate of 300 nl/min using a linear gradient with buffer A (0. 1% FA) and buffer B (80% ACN, 0. 1% FA) ranging from 13-44% B over 38 min, followed by a column wash and re-equilibration step. The total data acquisition time was 55 min. MS data were acquired using a DDA method with the following MS1 scan parameters: 60,000 resolution, AGC target equal to 3E6, maximum injection time of 20 msec, the scan range of 375-1600 m/z, acquired in profile mode. The MS2 method was set at 15,000 resolution, with an AGC target set to standard, an automatic maximum injection time, and an isolation window of 1. 4 m/z. Scans were acquired using a fixed first mass of 120 m/z and a mass range of 200-2000, and an NCE of 28.
Precursor ions were selected for fragmentation using a 1-second scan cycle, a dynamic exclusion time set to 10 sec, and a precursor charge selection filter for ions possessing +2 to +6 charges. Raw files were processed using Proteome Discoverer (PD) (version 2. 4, Thermo Scientific). MSMS fragment spectra were searched using Sequest HT against a human database (UniProt, year 2020) that was modified to contain protein sequences from our cloning constructs and a common contaminants database. The search parameters were set using a precursor mass tolerance of 20 ppm and a fragment mass tolerance of 0. 06 Da. Trypsin digestion was selected with a maximum of 2 missed cleavages. Variable modifications were set as methionine oxidation, and protein N-term acetylation and fixed modifications were set to carbamidomethylation.
Percolator was used to assign a 1% false discovery rate (FDR) for peptide spectral matches, and a 1% FDR was applied to peptide and protein assemblies. An additional filter requiring a minimum Sequest score of 2. 0 was set for PSM inclusion. MS1 based quantification was performed using the Precursor Ion Quantifier node with default settings applied. Precursor ion feature matching was enabled using the Feature Mapper node. Proteins matching the common contaminate database were filtered out from the results table.
Circular dichroism (CD) spectroscopy Far-UV CD spectra of proteins samples were recorded at 5°C using a Chirascan-Plus spectrophotometer (Applied Photophysics Ltd. ), equipped with a computer-controlled Peltier element. A 400 µl of protein sample with the final concentration of 0. 2 mg/ml in PBS was loaded into a quartz cuvette of 1 mm optical path length. The thermal stability of each protein sample was analyzed by monitoring their CD spectrum at 222 nm using constant heating from 5 to 85°C with 1°C min -1 intervals. The apparent midpoint of the transition, referring to the melting temperature, Tm, was determined by fitting the data points with the GraphPad Prism 7 by choosing the nonlinear leastsquare fitting function based on a sigmoid model.
Size exclusion chromatography coupled with multi-angle light scattering (SEC-MALS) SEC-MALS was done at 20°C using a Superdex S75 10/30 or a Superdex S200 10/30 column (Cytiva). The system was purged and equilibrated overnight using an Agilent UltiMate3000 HPLC in a buffer containing 20 mM HEPES, pH 7. 5, 150 mM NaCl, 2 mM DTT with a flow rate of 0. 5 ml/min. For each experiment, 15 µl of protein sample was loaded onto the respective SEC column at a concentration of ~ 7 mg/ml. The molecular mass of protein samples was determined using the miniDAWN TREOS and Optilab T-rEX refractive index detectors (Wyatt Technology). For the data fitting, the Zimm model was selected in the ASTRA 6 software.
SAXS data collection and analysis SAXS data were collected at the small-angle scattering beamline B21 of the Diamond Light Source (Harwell, UK). Protein samples in 50 mM HEPES, pH 7. 5, 100 mM NaCl, 1 mM DTT, 1 mM MgCl2 were passed through a Shodex (Munich, Germany) KW402. 5-4F SEC column in line to the X-ray scattering measurement cell. Samples of 20 and 10 mg/ml protein concentrations and 80 l volume were used; however, only data from the lower concentration samples were analyzed due to superior homogeneity as judged by the SEC profile. Buffer subtraction, summation of scattering intensities across peaks in size-exclusion chromatograms, calculation of the radius of gyration (Rg) from Guinier plots, estimation of molecular weight from scattering volume-of-correlation (Vc) plots, and evaluation of distance distribution functions (P(r)) were performed using Scatter3  60.
Ab initio calculation of molecular volumes from P(r) distributions was performed using DAMMIF  61. For each dataset, 23 bead-based models were derived using random starting seeds and assuming no internal volume symmetry (P1). Pairwise cross-correlation and averaging of models was performed by DAMAVER  62. The final CP110-CC2 envelope derives from averaging of 22 calculated models with NSD 0. 67 ±0. 05, while the CPAP-CC1/CP110-CC2 envelope is the average of 22 models with NSD 0. 68 ± 0. 04. Bead models were converted to volumetric envelopes using Situs 3 (ref  63  ); graphical representations were created in UCSF Chimera  64.
Digestion was performed with lysyl endopeptidase (1:50 w/w, Wako) for 2 h followed by a second digest with trypsin at 35°C overnight at 1200 rpm (1:50 ratio w/w, Promega). Proteolysis was stopped by the addition of 1% (v/v) trifluoroacetic acid (TFA). Crosslinked peptides were purified by reversed-phase chromatography using C18 cartridges (Sep-Pak, Waters) and enriched on a Superdex Peptide PC 3. 2/30 column (300 × 3. 2 mm). Fractions of crosslinked peptides were analyzed by liquid chromatography coupled to tandem mass spectrometry using an LTQ Orbitrap Elite (Thermo Scientific) instrument  65. Crosslinked peptides were identified using xQuest  66. The results were filtered with an MS1 tolerance window of -4 to 4 ppm and score ≥ 22 followed by manual validation. The intensities of the identified crosslinks were extracted and normalized by using a modified protocol of the previously published software xTract  43.
Analysis of MT plus end dynamics in vitro Kymographs were generated using the ImageJ plugin KymoResliceWide v. 0. 4 (https://github. com/ekatrukha/KymoResliceWide). MT dynamics parameters were obtained from the kymographs. For the experiments determining the proportion of MTs blocked or paused, we manually observed the kymographs for the complete blocking, occasional pausing, and no visible effects on dynamic MTs by the added proteins. Values reported are fractions of the total MT population expressed in percentages. The quantitative data reported for each experiment were collected in at least two independent assays.
Single molecule counting and fluorescence intensity analysis The single molecule counting and fluorescence intensity analysis was done as described in  55. Briefly, parallel flow chambers were made on the same plasma cleaned coverslip containing the appropriate dilutions of purified GFP, GFP-EB3 and CEP97^CP110-GFP in MRB80 buffer. After protein addition, the flow chambers were washed with MRB80 buffer, sealed with vacuum grease, and immediately imaged with a TIRF microscope. Images (about 40) of unexposed coverslip areas were acquired with 100 ms exposure time and low laser power. Single-molecule fluorescence spots were detected and fitted with 2D Gaussian function using custom-written ImageJ plugin DoM_Utrecht v. 1. 2. 2 (https://github. com/ekatrukha/DoM_Utrecht). The fitted peak intensity values were used to build fluorescence intensity histograms.
Images were recorded on a JEM3200FSC microscope (JEOL) equipped with a K2 Summit direct electron detector (Gatan) and an in-column energy filter operated in zero-loss imaging mode with a 30 eV slit. Images were recorded at 300 kV with a nominal magnification of 10000, resulting in a pixel size of 3. 668 Å at the specimen level. Imaging was performed using SerialEM software  67  , recording bidirectional tilt series starting from 0° ±60°; tilt increment 2°; total dose of 80-100 e-/Å2; target defocus -4 µm.
3D volume reconstruction and analysis Tomographic tilt-series were processed as outlined in Suppl. Figure  S7  (analysis flowchart). Direct electron detector movie frames were aligned using MotionCor2  68  and then split into full, even and odd stacks. Tilt series alignment and tomographic reconstructions were performed on sums of full stacks with the IMOD software package using gold beads as fiducial markers  69. Final tomographic volumes were binned by two, corrected for contrast transfer function, and the densities of gold beads were erased in IMOD. 3D volumes were subsequently denoised using the cryoCARE procedure  37. For this, 3D reconstruction was performed on odd and even aligned stacks with the IMOD parameters identified for full stacks. We trained 2-3 denoiser models for each acquisition series and then applied one model to the rest of the tomograms in this series.
Splitting of movie frames, reconstructing even and odd volumes, training data generation, model training and denoising was performed on a cluster of graphics processing units (GPU) using python scripts (available at https://github. com/NemoAndrea/cryoCARE-hpc04). Subvolumes containing MT ends were manually extracted from denoised tomographic volumes and processed further. First, the polarity of MTs was determined on summed projections using moiré patterns of images Fourier-filtered at the origin using Fiji  39, 70. Following the previously published procedure to obtain protofilament coordinates  38  , 3D models were manually built for each MT end in 3dmod  69. Each protofilament was stored as a separate contour, the first point in a contour was placed on a MT wall, the second point at the last segment of the protofilament that was still in the MT cylinder, and the following points were placed every 2-4 nm along the bending part of the protofilament.
Accuracy of manual segmentation was constantly monitored in the Isosurface view of 3dmod, which contained both the rendered 3D representation of the tomographic volume and the manually built 3D model. This procedure resulted in 3D models such as those presented in Figure  2b, d. Coordinates of the protofilaments were then extracted using the 'howflared' program in IMOD. Protofilament coordinates were further analyzed using Matlab scripts available at https://github. com/ngudimchuk/Process-PFs. These scripts are based on the previously published ones  38, 57  , but they were modified to account for protofilament shapes that deviated from 2D planes. As reported previously, the sampling along the protofilament was made uniform by interpolation and then smoothed using quadratic LOESS with a window of 10 points.
Curvature was calculated as the angle between consecutive pairs of line segments in LOESS-smoothed traces. To segment the denoised densities into 'tubulin' and 'cap', we used the tomoseg module of EMAN2. 2  71. Using a full denoised tomogram containing MTs grown in the presence of CEP97^CP110, we boxed reference regions sets containing (1) MT walls, (2) bent protofilaments at MT ends and soluble tubulin oligomers, (3) caps at MT ends and (4) 'bad' regions containing carbon support, gold particles, ice contamination etc. These boxed sets were then manually segmented, and three neural networks were trained: 1 vs 4, 2 vs 4 and 3 vs 4. The resulting neural networks were applied to subvolumes containing MT ends, and the resulting segmentations were used to mask tomographic densities in UCSF Chimera  64.
Acknowledgements We thank  R. Stucchi  and  A. F. M. Altelaar  ( Utrecht University ) for the help with mass spectrometry experiments and  W. Evers  and  A. Jakobi  lab ( TU Delft ) for help with cryoET and valuable discussions. We also thank  J. R. McIntosh  ( University of Colorado ) for feedback on protofilament shape analysis and  D. Mastronarde  ( University of Colorado ) for modifying the 'howflared' program to extract 3D coordinates from protofilament models. This work was supported by the  European Research Council Synergy  grant  609822  to M. D. and A. A. and the  Netherlands Organization for Scientific Research (NWO)   ALWOP. 440grant  to A. A, the  Medical Research Council UK  ( MR/N009274/1 ) grant to I. V. ,  EMBO Long Term Fellowship   ALTF-840-2018  to F. O. ,  Marie Curie COFUND Fellowship (Marie Skłodowska-Curie grant agreement   701647 ) to S.
Methods: We searched the relevant literature using English terms combined in a tested search string in two publication databases (Scopus and Web Of Science Core Collection). The search string combined terms describing the population (tropical reef-building corals) and the exposure (chemicals). We searched for additional literature through three search engines, three dissertations repositories, 11 specialist websites, and through a call to local stakeholders. Titles, abstracts, and full-texts were successively screened using pre-defined eligibility criteria. A database of all studies included in the map with coded metadata was produced. The evidence was described and knowledge clusters and gaps were identified through the distribution and frequency of studies into types of exposure and/or types of outcomes and/or types of study.
Estimating the comprehensiveness of the search To assess the comprehensiveness of the search string, we used a test list of 58 articles considered by the review team as relevant to answer our question and spanning a wide range of chemicals (see Additional file 3 in  [31] ). The search string retrieved 56 of the 57 articles of the test list indexed in the WOS CC and/or Scopus databases. The article of the test list that was not retrieved on either WOS CC or Scopus was a review about the impact of UV filters on aquatic biota that does not contain the term "coral" in its title, abstract, or keywords. This review reviewed only one article about corals that was nevertheless retrieved by the search string (Additional file 2 in  [31] ).
Bibliographic databases We performed searches on two online multidisciplinary publication databases: Scopus (Elsevier) and WOS CC (Clarivate Analytics) that we can access through a CNRS (the French National Centre for Scientific Research) subscription. Searches were performed on March 19th 2020. The abovementioned search string was adapted to fit the search facilities of the Scopus database (Additional file 2). All search strings used for the different sources are provided in Additional file 2. We had access to the following WOS CC Citation Indexes: We had access to all Scopus database (1788-present). No time restriction was applied during searches. -.
Internet searches Additional searches of literature were performed using three search engines: -CORE (https:// core. ac. uk/); -Google Scholar (https:// schol ar. google. fr/); -GreenFILE (www. green infoo nline. com). Searches were performed on July 7th 2020 for CORE, and July 8th 2020 for Google Scholar and GreenFile, and the search string developed during the scoping exercise on WOS CC database was adapted to fit the search facilities of these search engines (Additional file 2). In particular, the search string had to be split into six search strings for Google Scholar. Searches were performed on titles, then the results were sorted by relevance and the first 400 hits were extracted. Extraction of results from CORE was done one by one into Zotero using the Zotero connector for web browser.
Results from Google Scholar were extracted using the software Publish or Perish (version 7. 15. 2643. 7260, https:// harzi ng. com/ resou rces/ publi sh-or-perish, version accessed 16 March 2020). Results from GreenFile were extracted using the offered export facilities (results can be sent by email in various bibliographic formats e. g. RIS format). Additionally, we also searched on September 15th 2020 for dissertations in ProQuest Dissertations and Theses (https:// search. proqu est. com/, Publicly Available Content Database), Open Access Theses and Dissertations (https:// oatd. org/) and the French thesis repository (https:// www. theses. fr/). The search string was adapted to fit the specificities of each repository (Additional file 2). Searches were performed on titles and all hits were extracted.
Data coding strategy All articles included in the map were split into studies, i. e. the combination of a taxon, an exposure, and an outcome, and the following information was recorded in Microsoft Excel sheet from full-texts (details are given in Additional file 6): -Bibliographic information (unique identifier assigned by the review team, source, title, authors, journal, year, DOI, language and publication type); -General description of the study (type of study, ISO 3166 country or territory name, latitude and longitude or location); -Description of the population (taxon and taxon level); -Description of the exposure (as described by the authors and as an exposure category defined by the review team); -Description of the type of outcome(s) (as described by the authors and as an outcome category defined by the review team).
Data mapping method We produced a database (Microsoft Excel sheet) of all included studies and their coded data (Additional file 6). We mapped the evidence at two levels. First, we described the source, document type, and chronological and geographical distribution of the articles (article level). Then, we described the distribution of studies in taxa, types of exposure and outcome (study level). The description was made separately for experimental and observational studies because whether the exposure was controlled by the researchers (experimental studies) or not (observational studies) is an important criterion for the subsequent exploitation of the studies. Finally, through the distribution and frequency of studies by types of exposure and/or types of outcomes and/or types of study, we presented four clusters for which a full synthesis of evidence (systematic review) should be possible and relevant for stakeholders, and knowledge gaps.