dataesr/scientific-paragraphs-categorization huggingface dataset:
For this purpose, Blast was implemented using the NCBI tool  (24) , based on the latest available versions of the reference genome (UMD3. 1. 1 for bovine, ARS1 for caprine and Oar v4. 0 for ovine). The parameters used for the blast analyses were the NCBI Blastn tool ('Somewhat similar sequences') and general default parameters (short queries; expected threshold, 1000; word size, 7)  (24). In the event of multiple alignments, with a whole query cover and 100% identity on the genome, the position of the microRNA sequence could be identified from a Blast of the precursor sequence. Multiple positions were obtained for 344 sequences, even after applying the precursor sequence, when this was available in the publication. These microRNAs have therefore been listed in the RumimiR database without their position, but the number of positions on the genome is shown in the 'multi-mapping' column (de facto the chromosome, start and end columns are therefore blank).
Characterization of a new microRNA was refined by comparing its sequence with small nucleolar RNA (snoRNA), transfer RNA (tRNA), ribosomal RNA (rRNA) and small nuclear RNA (snRNA) sequences. Indeed, part of the snoRNA structure, the stem loop, is almost identical to that of microRNA  (25) , and tRNAs with three hairpin loops might also be confused with microRNA. To prevent this, the sequences were therefore compared with all species snoRNA and bovine tRNA present in snoRNABase v3  (26)  and GtRNAdb 2. 0  (27) , respectively, and with the bovine, caprine and ovine rRNA and snRNA sequences present in BioMart, an Ensembl tool  (28). When a microRNA displayed 100% identity with a snoRNA, tRNA, rRNA or snRNA over its entire length, it was retained in the RumimiR database but this information is indicated in the 'small RNA' column.
A home-made Python script was applied, which compared the microRNA sequences Figure  1. Procedure followed to include microRNAs and their associated features in the RumimiR database. From each publication (black rectangle), the microRNA sequences and their features (orange rectangles) were extracted and included in the RumimiR database. Several analyses were performed to expand the description of the microRNAs collected (blue rectangles): determination of chromosomal position in the latest reference genome of each species (using the NCBI Blastn tool), search for redundancy with microRNAs already present in the RumimiR database, search for identity with other small microRNAs to compare the sequences, search for identity with human or mouse microRNAs present in miRBase. * , using a home-made Python script (Supplementary Figure  2 ).
with those extracted from the above-mentioned database (Supplementary Figure  2 ). Finally, each microRNA was assigned a RumimiR identification number: 'Rum-species ID-XXXXX'. The species IDs for cattle, goats and sheep are BTA, CHI or OAR, respectively, and XXXXX is an incremental five-digit number. To add information about a microRNA listed in the RumimiR database, a search was done to determine its identity with human or mouse microRNA. If it had a 100% sequence match with a known microRNA over the full length of the shorter sequence, using the miRBase database (release 22), it was considered identical  (14). These identities were verified by applying a Python script to compare the sequences with other microRNA sequences and those extracted from miRBase (Supplementary Figure  2 ) and to indicate any 'strict' homology with human or mouse microRNA.
This step was included because of the large number of publications, and hence details, available for human and mouse microRNAs. All these processes for adding microRNAs to the RumimiR database (Figure  1 , blue box) were used to create a fourdigit false-positive code representing four positions, each with a value of '0' or '1' (0000 to 1111). A '1' in first position means that the microRNA has multiple genomic locations, and a '1' in second position means that the microRNA is not already listed in the RumimiR database (a microRNA occurring in different publications is more likely to be a true microRNA). A '1' in third position means that the microRNA has homology with small RNAs, and a '1' in fourth position means that the microRNA has no identity with either human or mouse microRNA present in miRBase.
One or more values of this four-digit code can be filtered out. A microRNA with a code of 1111 is most likely to be a false positive. This indication enables RumimiR users to select the microRNAs with acceptable false-positive scores. All microRNAs, and most of the features described in the literature, have therefore been documented in the RumimiR database. All the microRNA sequences are noted, as is the presence (or not) of isomiRs (microRNA sequences at the same genomic position, with almost identical sequences). These almost identical sequences are highlighted in a specific column ('isomiRs') in RumimiR and also if the microRNA sequence belongs to a known microRNA family. The database also contains details of the number of animals studied, their breeds, ages and lactation stage, as well as the tissue of origin, if this was mentioned in the publication.
The RumimiR website was built using HTML5 technology (https://dev. w3. org/html5), the bootstrap frontend framework (https://getbootstrap. com--v4. 0. 0) with additional jQuery user interface elements (http://jquery. com--v3. 2. 1) and DataTables jQuery plug-in for the data table (https://datatables. net--v1. 10. 16), and the plots were implemented by using the Highcharts JavaScript library (http://www. highcharts. com--v6. 2. 0). RumimiR has been successfully tested on Chrome (version 49 and later) and Firefox (version 57 and later). The data, in JSON format, were provided to the DataTables jQuery plug-in by setting the ajax option to the address of the JSON data source. All statistics, charts and drop-down lists have been built into the data-based fly to make any updates as easy as possible.
Content of the RumimiR database The RumimiR database currently contains 10 715 different microRNAs for 3 ruminant species: 2887 for cattle, 2733 for goats and 5095 for sheep. The average numbers of microRNAs described per publication for bovine, caprine and ovine species are 66, 136 and 340, respectively. The microRNA data were collected from 44 publications for bovines, 19 for caprines and 15 for ovines (Figure  2 ). The difference in numbers of microRNAs reported for cattle and goats, as compared to sheep, is due to the wider use of NGS technologies in ovine species. More precisely, 16 041 sequences, corresponding to 10 715 different mature microRNAs, are present in the RumimiR database, the difference being due to the presence of isomiRs. A total of 344 microRNA sequences are indicated as having multiple locations (multi-mapping).
The microRNAs listed in RumimiR have identities with 889 human or mouse microRNAs: 11. 78% of the described microRNAs show sequence identities with both human and mouse microRNAs and 4. 66% present sequence identity with only 1 human or 1 mouse microRNA. Comparisons with snoRNA, tRNA, rRNA and snRNA sequences revealed that 48 microRNAs displayed sequence identities with part of a tRNA, 47 with part of a snoRNA, 34 with part of an rRNA and 9 with part of an snRNA. The importance of each microRNA is evaluated from the false-positive score. Around 50% of the microRNAs (56. 1% in bovine, 51. 2% in caprine and 49. 8% in ovine species) have a score with a low risk of being false positive (code containing 0 or 1 '1'). Moreover, almost every microRNA has a code containing 0-2 '1' (98. 3% in bovine, 92.
The proportion differs if only those microRNAs with weak false-positive score (code containing 0 and 1 '1') are considered, but the percentage of microRNAs with lengths of 17-18 nt is still high (∼25%; Figure  4B ). Most of the ovine sequences with lengths of 17 and 18 nt were obtained from a single paper (4). The authors explained, in their discussion, that they had discovered a large number of new microRNAs because they had not applied the commonly used restricting prescreening. All sequences detected at a low-count level or with a low-sample frequency were considered. Most of the ovine sequences with lengths of 17 and 18 nt correspond to new microRNAs. The majority of microRNAs in the database were taken from studies based on differences between breeds, developmental stages (mainly in a context of meat production) or immune response (comparison of healthy animals with those suffering from mastitis, a widely-studied pathology in ruminants; Figure  5 ).
The different issues addressed in the publications were implemented as filtering options in the RumimiR database. Thus, 40 different breeds (17 bovine, 14 caprine and 9 ovine breeds, both meat and dairy) are represented along with about 30 tissues and body fluids such as milk  (29, 30) , adipose tissue (4), mammary gland  (31) (32) (33)  and ovaries  (22, 34, 35). Around half of the microRNAs described in the RumimiR database are associated with a single tissue (these 'specific' microRNAs are highlighted by the symbol ' '). While the probability of a microRNA being a false positive decreases if it is described in several studies, some of the microRNAs described only once can be specific to a breed, tissue, physiological stage or particular condition. Age was mentioned in 38% of the publications, and more than 30 different ages have been listed.
Some studies described the differential expression of microRNAs throughout development, and at different ages, which is why several ages might be considered in a single publication. The same was true of lactation stages. All these figures will increase as the database is updated. The numbers of microRNAs common to all three ruminant species, or sequences common to these species and to human and mouse sequences, or specific to each species, are presented in Figure  6. Between 80. 24% and 88. 82% are speciesspecific microRNAs. RumimiR is limited to three ruminant species, unlike miRBase, which is the most widely used and complete  database in terms of the number of species covered (plants, animals and viruses). However, this restriction enabled us to generate a more detailed database containing all the microRNAs described in different publications as well as numerous features.
Six times more microRNAs are listed in the RumimiR database for bovine, caprine and ovine species than in miRBase: 10 715 in RumimiR versus 1614 in miRBase. Although the latest miRBase release (v22) is quite recent (October 2018), it included only 5% of the data available on deep sequencing of small RNA  (14). The risk of assembling all microRNAs mentioned in the literature is still to include false-positive microRNAs, which is why this risk has been evaluated for each sequence. Thus, RumimiR offers a complete and more precise microRNA database for cattle, goats and sheep and should therefore be of value to scientists working on livestock species. The RumimiR database is extensive because it lists all the features mentioned in the publications (unlike miRBase and other databases), the data are homogenized (with blast on the same reference genomes for example), and because information is included about the identity, small RNAs etc.
) and then using the 'search' box to define different options such as 'by position' (chromosome, start and end), 'by source' (species, breed and tissue) or 'by feature' (conditions, method and software; Figure  7 ). The above-mentioned criteria can also be applied to filter all the mature microRNAs listed. A sequence or key word can also be entered in the 'Search' box to extract a list of the relevant data in the results table. The name of each microRNA in the 'name' column is the one given in the original publication. If specific requests are selected, the appropriate filtered data appear and can be downloaded in several formats (Excel, CSV, Fasta or GFF). Users can thus obtain all the data that they require, appropriately filtered and rapidly. They can also download the entire database without applying any filters.
For clarity, some statistics and graphs, which summarize the data presented in the database (Supplementary Figure  4 ), are provided in the online version. These include, among others, the date of the latest update, the number of sequences listed and the number of publications involved. The distribution of microRNAs by species and by breed is presented graphically, as is the distribution by tissue origin. RumimiR also includes an alignment tool so that sequences can be submitted for analysis and the corresponding microRNAs sorted according to the resulting hits.
Future extensions The NGS approach is generating extensive data, and the number of microRNAs will continue to rise in the near future. These new data will be integrated and the database regularly updated (at least twice a year) so that it remains exhaustive. Identity with human or mouse microRNAs will be checked each time that a new release of miRBase becomes available. The three ruminant species probably contain a comparable number of microRNAs as the difference between the numbers of bovine (2887), caprine (2733) and ovine (5095) microRNAs could be due to the more intense efforts assigned to ovine sequencing. However, the numbers of human or mouse microRNAs in the latest release of miRBase are 2654 and 1978, respectively. MicroRNA detection in ruminant species, as compared to humans and mice, may have reached saturation.
In addition, the numbers will probably evolve as the genome assemblies are updated and the same quality of contiguity and annotation is attained as for human and mouse species. One potential extension for RumimiR might be to add genetic variants of ruminant microRNAs ('miRSNPs' for microRNA and single nucleotide polymorphisms). First, those linked with dairy QTL, and then those associated with health or meat QTL. Publications on these genetic microRNA variations are indeed increasingly numerous. For example, in humans, the MSDD database has been created exclusively for miRSNPs linked to human diseases  (10). In the same way, the RumimiR database could be completed by including ruminant miRSNPs. Other features and filters will be added depending on the features described in the literature and the needs of the scientific community.
Conclusion The RumimiR database contains an exhaustive list of all microRNAs described in publications pertaining to three livestock species: cattle, goats and sheep. This database supplements miRBase, one of the most widely used microRNA databases, by including various features mentioned in the literature, which are important in the context of animal production and dairy traits, notably the breeds of animals studied or the tissues in which the microRNAs were described. RumimiR can be used to retrieve specific microRNAs and thus provide additional information about the livestock species and a clearer understanding of the context in which these microRNAs were discovered. The database will be regularly updated and will continue to be exhaustive. RumimiR, by standardizing and centralizing information from a large number of publications, constitutes a unique tool that presents and describes all known microRNAs in ruminants.
1 M phosphate buffer saline, 500 mM NaCl, 5% dextran sulfate, pH 7. 2) supplemented with 0. 12-0. 25 nM of the corresponding PAINT imager strand coupled to Atto650 (Metabion, 40,000 frames at 33 Hz). No chromatic aberration correction was necessary as both channels were acquired using the same spectral channel, and translational shift was corrected by autocorrelation and manual refinement. The N-STORM software (Nikon Instruments) was used for the localization of single fluorophore activations. After filtering out localizations to reject too low and too high photon counts, the list of localizations was exported as a text file. Image reconstructions were performed using the ThunderSTORM ImageJ plugin  50  in Fiji software  51. Custom scripts and macros were used to translate localization files from N-STORM to ThunderSTORM formats, as well as automate images reconstruction for whole images and detailed zooms.
Quantification of the scaffold periodicity on SMLM images was performed using autocorrelation analysis:  7  if an intensity profile is periodically patterned, its autocorrelation curve will exhibit regular peaks at multiples values of the period (here 190 nm, 380 nm, 760 nm etc). The position of the first peak of the autocorrelation curve can be used to retrieve the average spacing (s), and its height estimates how marked the periodicity of the profile is. Axons were manually traced in ImageJ using polyline ROIs on 16 nm/pixel reconstructed images. The normalized autocorrelation curve of the corresponding intensity profile was calculated and plotted. Autocorrelation curves of all tracings for a given labeling and condition were then averaged. The first non-zero peak of the averaged autocorrelation curve was fitted in Prism (Graphpad software) to estimate its position, providing the spacing values and the corresponding error.
Five lines were traced between consecutive braids (for braid spacing) or across a single braid, before and after eventual splitting (for braid width) and averaged to obtain each distance data point. Correlative SMLM/PREM. For correlative SMLM/PREM, unroofed and stained samples were first imaged by epifluorescence, then a STORM image was acquired (see above). At the end of the acquisition a objective-style diamond scriber (Leica) was used to engrave a ~1 mm circle around the imaged area, and a low-magnification, ×10 phase image was taken as a reference for relocation. After replica generation and prior to its release, the coated surface of each coverslip was scratched with a needle to make an EM grid sized region of interest containing the engraved circle. Grids were imaged with low-magnification EM to relocate the region that previously imaged by epifluorescence and SMLM, and highmagnification EM views were taken from the corresponding axonal region.
A highresolution SMLM reconstruction was mapped and aligned by affine transformation to the corresponding high-magnification EM view using the eC-CLEM plugin in ICY software  53. Data representation and statistical analysis. Intensity profiles, graphs and statistical analyses were generated using Prism. On bar graphs, dots are individual measurements, bars or horizontal lines represent the mean, and vertical lines are the SEM unless otherwise specified. Significances were tested using two-tailed unpaired non-parametric t tests (two conditions) or one-way non-parametric ANOVA followed by Tukey post-test (three or more conditions). In all figures, significance is coded as: ns non-significant, *p < 0. 05, **p < 0. 01, ***p < 0. 001. The number of data points and experiments for quantitative results is presented in Supplementary Tables  1  and 2.
Acknowledgements We thank  Matthew Rasband  and  Vann Bennett  for antibody gifts;  Fanny Boroni-Rueda  for help with neuronal cultures; the  NCIS imaging facility and Nikon Instruments  for SMLM equipment; the IBPS cryo-EM facility for EM equipment;  Subhojit Roy ,  Jeanne Lainé ,  Marc Bitoun ,  Ricardo Henriques ,  Manuel Théry ,  Laurent Blanchoin ,  Emmanuel Nivet , and the NeuroCyto team members for discussions and critical reading of the manuscript. This work has been funded by the  CNRS ATIP-AVENIR program  (grant ATIP  AO 2016  to CL); by  Sorbonne Université ,  INSERM ,  Association Institut de Myologie  core funding and the  Agence Nationale de la Recherche  (grant  ANR14-CE12-0001-01  to SV).
Surface sediment reworked areas Additional images of the sediment surface were collected under ambient light on days 10, 15, 20, 25 and 30 (starting from the beginning of the acclimation period) using a Nikon ® D7000 Reflex 16. 2 Mpixels camera fitted with a 18-105 mm lens. A special care was taken to insure that the image plan was strictly parallel to the sediment surface. For all worms, a zonation of the sediment surface became rapidly visible. This included: (1) a faecal mound area near the tube opening, (2) a prospected area devoid of surface particles, and (3) undisturbed sediment (Fig. 2A  and B ). The faecal mound and the total affected areas were manually drawn on each collected image and their surface was assessed using the Image J® software (USA National Institutes of Health) after appropriate calibration.
Resulting electrophoregrams were analysed using the Applied Biosystems® Peak Scanner software. Peak sizes inferior to 200 bp and superior to 1200 bp were considered as background noise and eliminated. Then, an "optimal divisor" (Od) was determined to remove fluorescence background within remaining peaks  (Osborne et al. , 2006). Peaks contributing less than 0. 1% (i. e. Od value) of the total amplified DNA (as determined by relative fluorescence intensity) were indistinguishable from baseline noise and eliminated. Binning was carried out under the R software (available on http://cran. rproject. org) using the algorithm "Interactive binner" (available on http://www. ecology-research. com -  Ramette, 2009). This allowed for the assessment of the relative abundance of each Operational Taxonomic Unit (OTU) in each sediment replicate.
0 software. Oxygen profiles were performed with a vertical resolution of 100 µm and an equilibration time of 7 s down to a 1 cm depth in the sediment column. Two to four sets of 4 profiles were achieved in each aquarium. Overall, 13 micro-profiles were located in faecal mound areas, 18 in prospected areas and 21 in undisturbed areas. Oxygen micro profiles were processed using the PRO2FLUX software  (Deflandre and Duchêne, 2010)  to assess oxygen penetration depths.
These tables were computer controlled, which allowed for precise (i. e. , ±1. 5 µm) positioning  (Duchêne, 2012). A laser telemeter (Sick OD80) was attached to the lower Y table. The raw data (in volts) generated by the telemeter were converted in linear distances using an appropriate calibration  (Maire et al. , 2007b ). The whole system was used for assessing the microtopography of 5 cm side square surfaces centered on tube openings and including faecal mound areas. Each scan was achieved within a 20 min time period and with a 15 µm vertical resolution. Surface sediment reworking rates were assessed by summing the differences in microtopography between two consecutive scans of the faecal mound area divided by the time duration between the beginnings of these two scans. Positive differences corresponded to an elevation, whereas negative ones corresponded to a digging of the sediment-water interface.
Methylome analysis reveals great epigenomic diversity in C. difficile From an ongoing Pathogen Surveillance Program at Mount Sinai Medical Center, 36 C. difficile isolates were collected from fecal samples of infected patients (Supplementary Table 1). A total of 15 different MLST sequence types (STs) belonging to clades 1 (human and animal, HA1) and 2 (so-called hypervirulent or epidemic)  19  are represented in our dataset (Fig. 1a ). Using SMRT-seq with long library size selection, de novo genome assembly was achieved at high quality (Supplementary Table  1 ). Methylation motifs were found using the SMRTportal protocol. We found a total of 17 unique high-quality methylation motifs in the 36 genomes (average of 2. 6 motifs per genome) (Fig. 1a , Supplementary Table  2a ). The large majority of target motifs were of 6mA type, one motif (TAACTG) belonged to the 4mC type, and no confident 5mC motifs were detected (Supplementary Notes).