dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are grateful to  Luke Chamberlain  for the gift of the EGFP-DNAJC5 expression plasmids, to  Simona Barzu  for the ExoU antibody,  Michel Ragno  for ExoU purification. The authors thank  Prof S. Lory  ( Microbiology Department, Harvard Medical School ) for providing PA14 and Tn_exoU strains. This work, as well as V. D. 's PhD fellowship, were supported by a grant from the  Fondation pour la Recherche Medicale (Team FRM   2017 ,  DEQ20170336705 ). Confocal microscopy was performed at the µLife facility of the  Interdisciplinary Research Institute of Grenoble (IRIG)/Department of Structural and Cellular Integrative Biology (DBSCI) , funded by  CEA Nanobio  and Labex GRAL. Part of this project has been performed at the CMBA molecular screening platform and received funding from  GRAL , a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ).
Introduction Although frequently used, a clear definition of "surgical technique" was not found in either the literature or dictionary. Based on the Oxford dictionary definitions of "surgical" and "technique" (1) and the scenarios in which "surgical technique" is used in the literature, we define "surgical technique" as "the specific way and skills of performing a particular medical operation". Surgical technique is taught and inspired by a master, the so-called "apprenticeship model" introduced by William Halsted a century ago. As an essential component of surgery, a new surgical technique is introduced and disseminated through medical reports, books, live or recorded surgical videos at academic conferences, videos shared on social media and databases, medical labs, etc. However, many books cannot represent the best cutting-edge surgical technique due to the nature of the time-consuming publication.
Live or recorded surgical videos at academic conferences are also constrained in time, geography, funding, need for active participants, and high requirements for network speed. Videos that share surgical technique on social media platforms such as LinkedIn (2), YouTube (3) and databases such as CTSNet (4) are not peer-reviewed. In contrast, surgical technique literature is cutting-edge, widely accessible, and peer-reviewed, and serves as a vital form of surgical technique presentation, dissemination, implementation, evaluation and improvement. Standardized reporting of surgical technique in the literature by reporting guidelines is the cornerstone of effective dissemination and implementation, objective evaluation and continuous improvement. It is gratifying that several reporting guidelines that could be applied to surgical technique have been developed since the publication of CONSORT  (5).
Generation of the initial checklist Based on the scoping review of existing reporting guidelines and review of representative articles from the fifteen journals, the working group will debrief in order to reach consensus on the data to be extracted. Two teams will separately extract item entries from matched guidelines and articles. Both teams must include at least one surgeon, one journal editor, and one methodologist. Before the formal extraction, training will be performed to ensure a consistent understanding of each item entry in the extraction table and consistent extraction results from the same articles. The SUPER working group will also debate to identify additional item entries. Finally, the two teams will have a face-to-face discussion to summarize all included items and generate the initial SUPER checklist.
During limb bud outgrowth, HOXA13/D13 and GLI3 ChIP-seq peaks are detected in all CRMs of the Grem1 but not Fmn1 TAD (E11. 5, Fig. 1d , Supplementary Fig. 1 ; HOX13 datasets from ref. 27  ). This shows that the SHH pathway and HOX13 impact Grem1 cis-regulation globally rather than via specific CRMs, which points to potential cis-regulatory redundancy (Fig. 1c, d , Table in Supplementary Fig. 2 ). Multiple enhancers orchestrate Grem1 expression in limb buds. Previous genetic analysis showed that several larger genomic deletions overlap a genomic region that could be required for Grem1 expression (termed GCR)  13  , which encompasses the CRM2 to CRM4 enhancers (Fig. 1b-d , Table  1 )  22, 23. Together with Grem1 intra-TAD interactions (Fig. 1a ) this led us to assign these three CRMs to one putative enhancer cluster, called EC1, while the more closely spaced CRM5 to CRM8 regions were assigned to a second cluster, EC2 (Fig.
Scale bar: 1 mm. f Direct comparison of the Grem1 expression domain in forelimb buds of the most relevant CRM loss-of-function alleles (E11. 0, 40-42 somites). These forelimb buds belong to the same group of biological replicates as the ones shown in Figs. 2  and 3. Arrowheads indicate the posterior domain. Bar-ended lines point to the stunted crescent domain. The forelimb buds are representative of the spatial distributions in the respective genotype. The relative Grem1 transcript levels in comparison to the wild-type (set at 100%, see before) and schematics of the distal limb skeletons are shown below. Skeletons in black: pentadactyly maintained, grey: pentadactyly altered/disrupted. RT-qPCR source data are provided in a Source data file. insights into the spatial plasticity underlying Grem1 expression in mammalian limb buds (Fig.
Eggs of the brown-banded bamboo shark were obtained from the Osaka Aquarium Kaiyukan and incubated in artificial seawater at 26 °C; embryos were collected and staged using morphological criteria such as fin bud shapes and eye pigmentation to identify stages 29 and 30  61. The comparative analysis of Grem1 expression in limb buds of different species was done using orthologous developmental stages whenever possible. ATAC-seq analysis. About 75000 mouse limb bud cells (E9. 75; E10. 5 and E11. 5) were used for ATAC-seq  62  analysis and per limb bud stage, n ≥ 2 biological replicates were analysed. The ATAC-datasets for mouse forelimb buds at E10. 5 and E11. 5 have been previously published and the datasets for all three stages have been validated as described  16. This revealed the high correspondence with the ENCODE DNase-hypersensitivity sites for mouse limb buds (R-values: 0.
76-0. 79). The ATAC-seq tracks count both 5′-ends of each fragment in bins of size 10. These numbers are divided by the sum across all bins (library size) and the bin size, and then multiplied by 1e9 to obtain the reads per kilobase per million mapped reads (RPKM) value per bin. The previously unpublished ATAC-seq datasets for mouse forelimb buds at E9. 75 and chicken wing buds (HH24) are available under the GEO accession number GSE151488. ChIP-seq analysis. The HOXA13 and HOXD13 ChIP-seq datasets have been published previously and are publicly available (GEO: GSE81358)  27. The H3K27ac ChIP-seq  63  was performed using mouse forelimb buds and the datasets including inputs are available under the GEO accession number GSE151488. The SMAD4 ChIP-seq was performed using mouse embryos at E9. 5-9.
75 and the GLI3 ChIP-seq from mouse limb buds at E11. 5. These two datasets including inputs are available under the GEO accession number GSE151647. The SMAD4 ChIP-seq was generated using a FLAG epitope-tag inserted in-frame into the endogenous Smad4 locus (Smad4 3xF allele). The GLI3 ChIP-seq was performed using limb buds from mouse embryos homozygous for an N-terminal FLAG epitope-tag inserted into the endogenous Gli3 locus (Gli3 3XF allele)  40  and processed for ChIP as previously described  64. Briefly, for all ChIP-seq analysis, limb buds were isolated at defined embryonic stages (E9. 75 to E11. 5). Tissue was then crosslinked for 10-20 min in 1% formaldehyde/PBS at room temperature, quenched with glycine (125 mM) and subsequently lysed in a hypotonic buffer. Sonication was used to shear chromatin and immunoprecipitation was performed overnight at 4 °C using mouse monoclonal anti-Flag antibody (Sigma F1804, 5 µg per sample) for the GLI3 and SMAD4 ChIP-seq analysis and the anti-histone H3 (acetyl K27) antibody (ChIP Grade Abcam ab4729, 5 µg per sample) for H3K27ac ChIP-seq analysis.
For the GLI3 ChIPseq analysis single-end reads of 76 bp were mapped to the mouse genome assembly GRCm38 (mm10) using bwa. Peaks were called relative to input controls using the MACS2 callpeak function with the following parameters: --Call-summits -B --trackline. For genome browser visualization, each ChIP-seq dataset was uniformly processed to generate tracks of fragment pileup per million reads using the -B --SPMR parameters within the macs2 callpeak utility of MACS2. 4C chromatin conformation capture. The 4C analysis was done as previously described  65  and the datasets are available under GEO accession number GSE151647. The following changes were implemented: for one biological replicate 2-4 × 10 6 cells were isolated from ~20 forelimb buds (E11. 0, 40-42 somites). A suspension of single nuclei was made and crosslinked in 2% formaldehyde for 10 min at room temperature.
For the 4C analysis of EC1 Δ/Δ and Cis Δ/Δ forelimb buds, the libraries were generated by adding adapters and barcodes by PCR amplification (30 cycles, primers listed in Supplementary Table  3 ). After column purification (QIAquick PCR purification kit, Qiagen) the libraries were sequenced to generate single-end reads of ≥76 bp read length. To achieve overall high quality, raw sequencing reads that did not match the primer sequence were discarded from all samples. Filtered reads were aligned to the mouse reference genome (mm10) using Bowtie v2. 2. 9. To identify the valid restriction fragments, the mouse reference genome was in silico digested using DpnII and NlaIII. Restriction fragments that did not contain a cutting site for NlaIII or were smaller than 20 bp were filtered out. This yielded the library of valid restriction fragments used for quantitative analysis of experimental 4C-seq datasets.
Read counts were computed for each valid fragment and the resulting 4C profile visualized using the UCSC genome browser. To visualize the data, bedGraph formatted files of the read counts for each fragment or a specified window of fragments were generated. 4C-seq contacts were analysed for the mouse region on chr2:113326224-113894862 that encompasses the Grem1-Fmn1 landscape. The viewpoint, adjacent undigested fragments and fragments 10 kb up-and downstream were excluded. Finally, a range of 5 informative fragments was used to normalize the data per million reads (RPM) over a sliding window using custom scripts that are available at Zenodo (https://doi. org/10. 5281/zenodo. 5181231) and these continuous-valued profiles displayed in the UCSC genome browser tracks. These were then used to generate the panels for figures.
The subtractions were computed by subtracting fragment reads for all positions of the locus between wild-type and mutant forelimb bud samples. Generation of CRM LacZ reporter transgenic mouse founder embryos. The mouse CRM1-13 core regions and the mouse CRM2 deletion constructs were amplified by PCR from mouse genomic DNA. The primers for PCR amplification of the target CRM regions were designed with Primer3 (https://bioinfo. ut. ee/primer3-0. 4. 0/) are listed together with the genomic coordinates in Supplementary Table  4. The rabbit, bovine, pig, chicken, python and elephant shark CRM2 and CRM5 orthologous regions were amplified by PCR from genomic DNA of their respective species. Python regius and Anolis sagrei lizard genomic DNAs were isolated by the Cohn group  60. Elephant shark tissue stored in 100% ethanol was used to isolate genomic DNA with the Wizard® Genomic DNA Purification kit (Promega Inc).
Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Germany). A minimum of seven biological replicates per genotype were generated. RT-qPCR analysis was done using Grem1 specific primers listed in Supplementary Table 6  10. The relative Cq values of the Grem1 transcripts were normalized to the Cq values of the RPL19 control and normalized fold transcript levels (2 -ΔΔCq ) are shown as mean ± SEM. The data used for analysis are provided in the Source data file. The p-values were determined in Prism using a two-tailed Man-Whitney test. Whole-mount Grem1 in situ hybridization in mouse embryos and other species. The mouse, pig, bovine and chicken Grem1 riboprobes have been used previously and a standard whole mount in situ hybridization protocol was used for all experiments  10. Briefly, embryos were fixed in 4% paraformaldehyde (PFA) in PBS at 4 °C overnight, dehydrated into 100% methanol and stored at -20 °C until further use.
Lizard embryos were fixed in 4% PFA for one hour at room temperature. Python embryos were fixed overnight in 4% PFA at 4 °C. Whole mount in situ hybridization was performed using the Grem1 probes from the lizard Anolis sagrei (accession number MT124663) and Python regius (accession number KX778825) as previously described  60  , with the following modifications for lizard embryos: the methanol dehydration step was skipped and embryos were treated with 10 µg/mL proteinase K in PBT for 15 min, blocked with 25% goat serum in KTBT. After hybridization and washing, the embryos were incubated with anti-digoxigenin antibody for 4 h at room temperature. Then, the embryos were washed two times for 15 min in KTBT solution, which was followed by an overnight wash and three additional 15 min washes prior to starting the procedure to detect the in situ signal.
ucsc. edu/cgi-bin/hgGateway). Agreement between these two topologies was evaluated using tree topology tests, which have been implemented in IQ-TREE to generate the phylogenetic trees as shown in Supplementary Fig. 7a, b. The phylogenetic trees generated for visualization of evolutionary relationships and shown in Figs. 4, 6 , 8, and Supplementary Fig. 6  were generated with phyloT (https://phylot. biobyte. de/) based on NCBI taxonomy (https://www. ncbi. nlm. nih. gov/taxonomy) and visualized with iTOL (https://itol. embl. de/). Mutation of conserved Gli and Hox13 binding sites in the CE of bamboo shark CRM2. Gli and Hox13 binding sites in the bamboo shark CRM2 were identified by scanning the genomic sequence using the PWMscan tool (https://ccg. epfl. ch/ pwmtools/pwmscan. php#). For Gli binding sites, the position weight matrix (PWM) defined by Homer was used in combination with the limb GLI3 ChIP-seq dataset (E11.
Acknowledgements We are grateful to  A. Baur ,  J. Gamart ,  T. Oberholzer ,  N. Riesen ,  O. Romashkina  and  J. Stolte  for support concerning different aspects of the study. V. Tschan participated in the gene expression analysis of the CRM2 mutation as part of her master thesis under supervision of A. Z. E. Terszowska  and  A. Offinger  are thanked for excellent mouse care,  C. Viebahn  and  B. Püschel  for providing access to rabbit embryos and  B. Kessler  and  E. Wolff  for pig embryos. P. Pelczar  and the members of  University of Basel Center for Transgenic Models (CTM)  are thanked for generating all LacZ reporter embryos and most of the genome-edited single and compound mutant Grem1 alleles. Dr. S. Kuraku  and the  Osaka Kaiyukan Aquarium  are thanked for providing bamboo shark embryos (to K.
O. ). Sequencing was done at the  Quantitative Genomics Facility of the University of Basel  and  ETH. Florian Geier  from the  DBM Bioinformatics Core Facility  is thanked for analysing the ATAC-seq and H3K27ac ChIP-seq datasets. Calculations were performed using the  Scientific Computing Center sci-CORE  (http://scicore. unibas. ch/) at  University of Basel. The sciCORE team is thanked for support in curation and storage of the genome-wide datasets. G. Andrey  is thanked for expert advice on 4C analysis and input into the manuscript together with P. Tschopp. This research was initiated with support from the  Bonus-of-Excellence SNF  grant  310030B_166685  (to A. Z. and R. Z. ) and then supported by the  ERC  advanced grant INTEGRAL ERC-2015-AdG; Project  ID 695032  (to R. Z. ) and the  University of Basel  provided core funding (to A.
Data availability The ATAC-seq, ChIP-seq and 4C-seq datasets generated for this study have been deposited in the gene expression omnibus (GEO) database. The chicken wing (HH24) and mouse forelimb bud ATAC-seq datasets (E9. 75), and the mouse forelimb bud H3K27ac ChIP-seq datasets (E10. 5, E11. 5 and corresponding inputs) are available under the GEO accession number GSE151488. The SMAD4 3xF and GLI3 3xF ChIP-seq datasets (including inputs), and all 4C datasets are available under the GEO accession number GSE151647. The publicly available HOXA13 and HOXD13 ChIP-seq datasets can be found under the GEO accession number GSE81358. The Anolis sagrei Grem1 mRNA (partial) is available under the GenBank accession number MT124663 and the Python regius Grem1 mRNA (partial) under the GenBank accession number KX778825.
[2000]. The preparation of the samples and the analytical procedures are detailed in Supplementary Text S1. (n=92; [Gerbert-Gaillard, 2002; Godard et al. , 2000; Hanghoj et al. , 2010] ), and the grey field indicates ± 1 standard deviation (calculated as the 16th and 84th percentile) about the median (Supplementary Table  S2 ). Major oxides are plotted on a volatile free basis. Symbols are in inset. Listvenites, fuchsite-bearing listvenites (Fu-listvenites), serpentinites and ophicarbonates (grouped as Serpentinites) from Listvenite domains I, II and III are noted I, II and III respectively. Samples from the metamorphic sole (noted sole) are grouped as M1, M2 and M3. The main lithological characteristics of the analyzed samples and their major, trace and volatile element concentrations are reported in Supplementary Dataset S1.
, 2014; Prigent et al. , 2018a; Yoshikawa et al. , 2015] ). The greenschist facies BT1B metamorphic sole has basaltic compositions similar to that of the alkali metabasalts from the underthrusted Hawasina assemblages (Figures  4 5 ) from which they likely derive (e. g. ,  [Searle and Malpas, 1980] ). It displays progressively less evolved compositions away from the ophiolite contact (from M1 to M3 metabasalts), a characteristic previously documented in the amphibolitic sole  ([Ishikawa et al. , 2005] ; Fig. 2 ). There is no compositional evidence of interlayered metasediments, in contradiction to what initially hypothesized for the BT1B cores  [Kelemen et al. , 2020]. Hereafter, we will use our new high resolution geochemical database to evaluate the mass and volume changes associated with the formation of the listvenite series and discuss elemental mobility during these processes and its linkages to the composition of the protolith(s) and neighboring lithologies, the localization of fluid pathways along the BT1B cores and the interplay between solute transport and reaction kinetics during CO 2 metasomatism.
Elements plotting above and below the isocon are enriched and depleted, respectively, in the altered rock compared to the model protolith. The compositions of the model protoliths were determined on the basis of the published geochemical studies of the Semail peridotites for which structural (localization, distance to the Moho and/or to the sole) and petrological (lithology, mineralogy) information were available. They were calculated including the volatile compositions of the variously serpentinized Semail peridotites. We defined two endmember protolith compositions (Supplementary Table  S3 ): (1) a refractory protolith, modelled using the compositions of MMS harzburgites and dunites  [Gerbert-Gaillard, 2002; Godard et al. , 2000; Hanghoj et al. , 2010; Lippard et al. , 1986]  due to the lack of data on the composition of basal harzburgites and dunites; (2) a fertile protolith, calculated using the compositions of basal lherzolites, including amphibole-bearing samples  [Khedr et al.
, 2014; Lippard et al. , 1986; Takazawa et al. , 2003]. The model refractory and fertile protoliths are noted MMS and basal lherzolite respectively in Fig. 6. For some trace elements, the published chemical database is limited (e. g. , Ga) and/or highly variable (e. g. , LREE, Cs) resulting in a large uncertainty in their distribution in the model protoliths, in particular for basal lherzolites; however, these elements represent a minor subset of the chemical database and therefore had little impact on the evaluation of the overall mass changes resulting from CO 2 metasomatism. The mass balance calculations comparing the composition of listvenites and serpentinites to the refractory protolith show co-linearity (within uncertainty) on the isocon diagrams for most major and trace elements (Fig.
These results indicate that these elements are immobile at the scale of the listvenite domains despite their meter-scale scattered downhole distribution. It should be noted however that the most incompatible elements, even those generally considered as fluid immobile, display minor differences from one rock type and domain to the other (e. g. , depleted LREE in Serpentinite I). In particular, Nb, Ta and Th are systematically depleted relative to the model mantle protoliths. This could reveal trace element depleted protoliths compared to the models, but we posit that it most likely relates to the uncertainty due to the limited dataset on the composition of these elements in the Semail peridotites. The same consideration could explain the apparent systematic loss of Ga observed for all calculations.
Determination of Fe(II) The FeO concentrations of 8 serpentinites and 21 Listvenites was quantified at the University of Lausanne (ISTE) following the Fe-titration protocol of  Wilson [1960]. 200 mg of non-ignited sample powder was dissolved in a H2SO4/HF solution with ammonium vanadate (NH4VO3) to oxidize and complex all Fe(II) to Fe(III) with vanadium under controlled conditions. The complexed Fe(III) molecules were then reduced and the resulting Fe(II) complexed with a red indicator solution of 2,2'-dipyridil (C10H8N2) in a H3BO3/CH3COONa solution. The colored solution was analyzed using a UV/Vis Perkin Elmer Lambda 25 spectrophotometer and the Fe(II) concentration quantified with UV WinLab Software. Calibration was determined from a blank solution and 4 standards having FeO composition of 1.
Data preparation Inputs for mass balance calculations consist of the average composition of a potential protolith and the altered rock. These average compositions were specified as log means and standard deviations, since data are log normally distributed for many elements: log mean = mean [log10(concentration)] log SD = SD [log10(concentration)] Regardless of this, in the case of normally distributed data, the log normal mean and standard deviation converge on their normal counterparts. All anhydrous major element data were recalculated to include the contribution of volatiles (measured as LOI) in their totals since volatile elements are considered as mobile components in the mass balance calculation. H2O and CO2 were directly measured in BT1B samples while for the Semail ophiolite database we have assumed all LOI constitutes H2O.
XRF bead data for Cr2O3, MnO and TiO2 were omitted in favour of data from XRF pellets or ICP-MS with the latter always taken were both were available. For Semail ophiolite samples from the literature where ICP-MS trace element data was not available in the Oman ophiolite database, XRF pellet data was substituted where possible (including Sc, Ti, Cr, V, Co, Cu, Zn and Mn). Individual rare-earth elements were not included in the calculations to avoid giving undue weight to this group of elements. Instead, totals for light, middle and heavy rare-earths (LREE, MREE, and HREE) were used in the mass balance calculations.