dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, the squares in which sepultures were found are grouped together under the term "Sepulture layer", and the first 120 cm of the disturbed squares are considered as being a "Neolithic layer". However, these subdivisions are very artificial and probably correspond to a mix of Neolithic and Hoabinhian material, as evidenced by the lithic material from these contexts (H. Forestier, com. pers. ). A sample of the complete faunal assemblage collected in the Hoabinhian squares has been the object of a first zooarchaeological study by S. Frère  (Frère et al. , 2018). In this first study, among the 5885 vertebrate remains identified, turtles account for 44% of the NISP, monitor lizards for 1%, and large mammals for 37%. Unfortunately, no MNI data were reported, and the fact that most of the small fragments were not attributed to at least a size class of animal makes these results difficult to interpret.
Quantification of the Zooarchaeological data and Taphonomic Analyses The basic units of quantification considered are the Number of Identified Specimens (NISP) and the Weight of the Remains (WR)  (Lyman, 2008). The fragmentation of each bone has been recorded by describing the Percentage of Completion (PC) of the anatomical elements represented by the fragment. The anatomical side of the bones has been registered when possible for the best represented and easiest to identify anatomical elements (i. e. , peripheral plates and all paired elements of the plastron). A Minimum Number of Elements (MNE) has been calculated for each anatomical part to assess differences in skeletal element representation across various archaeological contexts. To do so, we added the PCs of a given element and divided the result by 100.
The positions of the peripheral plates have been identified but only for Indotestudo remains. The peripheral plates for which it was not possible to give a position have been posteriorly assigned to the different ranks following the distribution of those for which a position was determined. Taphonomic alterations have been identified following the atlas of Fernàndez-Jalvo and Andrews (2016). Regarding the size estimations (see below), the mean of the obtained size estimation is considered in the case several measurements were recorded on a single bone. Chi² tests were performed on the Microsoft Excel software 2007 version and other tests with the basic library Stats of the open-source software R (R Core Team, 2020).
The measurements recorded were taken on all the bones of the plastron and the carapace as well as on the long bones. Vertebrae and skull elements were not taken into account as their occurrences were too rare in the archaeological record. In addition to these measurements, in order to be able to choose a variable accounting for the "body size" of each individual, we took three measurements considered as "size variables" on the complete carapace of the modern specimens: the Carapace Straight Length (CSL), the Shell Height (SH), and the Plastron Length (PL). All the measurements collected on modern and archaeological specimens were recorded using a digital dial caliper  [IP 67 (Mitutoyo Corporation, Japan) ]. All the measurements recorded are included in the Supplementary Table  1. All statistical analyses were performed using the basic library Stats of the open-source software R (R Core Team, 2020).
Size of Indotestudo elongata individuals The measurements recorded on the I. elongata archaeological material of Laang Spean Cave (Supplementary Table  2 ) enabled the reconstruction of 688 SCL estimations, ranging from 68 to 345 mm, with a mean of 201 mm (Figure  5 -C), corresponding to at least 75 individuals (Supplementary Table  3 ). Most of the data (N=564) are from Hoabinhian layers, while the Neolithic squares only provided 124 SCL data. However, no significant difference emerged from the comparison of these two assemblages (Student t-test, p. value > 0. 05). The global distribution (across all squares) of these sizes is unimodal (Hartigans' dip test, p. value > 0. 05) with a peak of specimens around 200 mm SCL. In this site, small specimens below 170 mm represent only 16% of the population, and specimens below 140 mm only constitute 4.
, 2015; Frère et al. , 2018) , which aligns with the observation of scarcity of freshwater turtles in the site. Regarding the prevalence of Geoemydidae species in the TP1 of Khao Tha Phlai, it might suggest a stronger reliance on freshwater resources compared to other sites. However, considering that the chronology of the two test-pits of the site is not yet fully resolved and that the general importance of aquatic resources in this assemblage needs further estimation, this fact cannot be related to a cultural/chronological trend at this stage. Overall, the data on herpetofaunal assemblages point to strong similarities between assemblages of different ages and from various environmental settings. This aspect should be considered in conjunction with studies on mammal bone assemblages of the same sites to test the hypothesis of a potential homogeneity of Hoabinhian subsistence strategies in continental Southeast Asia.
Acknowledgments The authors are grateful to all the excavation teams who collected the studied remains, as well as to all the technical staff who helped us in the study of the different bone assemblages. We are especially grateful to  Silpakorn University , which hosted C. B. during most of the studies, as well as to the  Fine Art Department of Thailand. We also thank  S. Coleman  and the  Florida Museum of Natural History  for welcoming us in their.
Funding This work has been done thanks to the support of several funding agencies: the  FYSSEN  foundation, the  DIM-MAP  funded by the  Region île-de-France , the  French Ministry of Europe and Foreign Affairs , and the  IRN PalBioDivASE  0846 funded by the  CNRS. It was also supported by the  Mission Préhistorique Franco-Cambodgienne , the  Mission Préhistorique Franco-Thaïe , and the  Mission Paléolithique Franco-Thaïe of the French Ministry of Europe and Foreign Affairs (MEAE, Paris).
| Western blot Cell pellets were resuspended in the NETN buffer and sonicated. Proteins (30 µg) were resolved on 4%-12% SDS-PAGE gels, transferred onto a PVDF membrane and probed with the following primary antibodies: mouse anti-Cas9; mouse anti-Zebra; rabbit antiα-tubulin and mouse anti-β-actin (Supporting Information: Table  1 ). Secondary HRP-conjugated antibodies were used to reveal bands. Western blot band intensity quantification was performed using the ImageJ software. The band intensity of a target protein was normalized to the band intensity of the indicated loading control protein.
For HCA2 with GSK256073 and MK6892, cryo-EM data were collected using a Titan Krios Gi3 (Thermo Fisher Scientific) with a BioQuantum energy filter (Gatan) and a K3 direct electron detector (Gatan), operated using EPU software (Thermo Fisher Scientific) running a 3 × 3 image shift pattern at 0°stage tilt. A nominal magnification of 105,000 x was used in CDS mode with a calibrated pixel size of 0. 83 Å, and exposures of 4. 5 s were dose-fractionated into 49 frames with an electron flux of 7. 55 e-/pix/s.
Cryo-EM data processing Datasets for HCA2-Gi and HCA3-Gi complexes were processed using CryoSPARC  22  and Relion  55. For GSK-HCA2-Gi, MK-HCA2-Gi, LUF-HCA2-Gi, acifran-HCA2-Gi, acifran-HCA3-Gi complexes, a total of 4872, 5802, 2828, 3602, and 6723 image stacks, respectively, were subjected to beam-induced motion correction using MotionCor2. 1  55  in Relion-4. 0. Contrast transfer function parameters for each micrograph were estimated by the CryoSPARC patch CTF algorithm. Particles were autopicked using reference-based picking, extracted with a box size of 288 pixels, and subjected to several rounds of 2D classification to remove contaminants. Initial maps were generated using stochastic gradient descent-based multi-ab initio reconstruction in CryoSPARC. A good class was subjected to 3D non-uniform refinement  56  and particles were re-imported to RELION-4.
0. Particles were then subjected to 3D refinement in RELION-4. 0, followed by CTF refinement and Bayesian polishing. After successive 3D refinement, we performed focused 3D classification with a mask only including the TM domain without alignment. Particles of the best class were then imported back to CryoSPARC for 3D non-uniform refinement and 3D local refinement. The final dataset contained 245,517, 181,958, 181,273, 146,577, and 95,567 particles, which generated maps with resolutions at 2. 85, 2. 97, 3. 13, 3. 17, and 3. 18 Å, respectively. The resolution of these maps was estimated internally in CryoSPARC by gold-standard Fourier shell correlation using the 0. 143 criterion. Local resolution estimation was performed with the CryoSPARC local resolution estimation algorithms using half maps.
Drug dilutions were prepared in assay buffer, and 30 μl was immediately added to plated cells. Plates were read using a SpectraMax i3x multi-plate reader (Molecular Devices) using 410-and 515-nm emission filters with a 1-s integration time per well. The computed BRET ratios (GFP2/RLuc8 emission) were normalized to a ligand-free control (net BRET). Data were analyzed using "Sigmodal, 4PL, X is concentration" in GraphPad Prism 10. 0.
01% BSA was added to each well; the plates were incubated for 10 min. Then, a 10-μL volume of 10-uM forskolin (Sigma-Aldrich) (final 1 μM) was added and the plates were incubated for 20 min at room temperature. The luminescence of the cells was measured using a SpectraMax i3x multi-plate reader. Data were analyzed using "Sigmodal, 4PL, X is concentration" in GraphPad Prism 10. 0.
Statistics and reproducibility All functional study data were analyzed using GraphPad Prism (GraphPad Software) and are presented as means ± SEM from at least n = 3 independent experiments performed. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Acknowledgements This work was supported by  Grants-in-Aid for Scientific Research  (A) under Grant Number  20H00451  the  Japan Agency for Medical Research and Development (AMED)  under Grant Number  JP21ae0121028  (Y. F). This work is partially supported by  Platform Project for Supporting Drug Discovery and Life Science Research  ( Basis for Supporting Innovative Drug Discovery and Life Science Research [BINDS] ) from the  Japan Agency for Medical Research and Development (AMED)  under Grant number  JP21ama0101074. Grantsin-Aid for Scientific Research  (B) and a  Grant-in-Aid for Challenging Exploratory Research  (A. O. ).
Data availability All data generated or analyzed in this study are included in this article and Supplementary Information. The cryo-EM density maps and corresponding coordinates have been deposited in the Electron Microscopy Data Bank (EMDB) and the PDB, respectively, under the following accession codes: PDB-8IHB and EMDB-35442 (GSK256073), PDB-8IHF and EMDB-35443 (MK6892), PDB-8IHH and EMDB-35444 (LUF6283), PDB-8IHI and EMDB-35445 (Acifran), and PDB-8IHJ and EMDB-35446 (HCA3-Gi with Acifran), and PDB-8IHK and EMDB-35447 (HCA3-Gi with Acifran (local)). Source data are provided with this paper.
Sample collection. Ethical considerations. Partners were in charge of navigating national guidelines and regulations regarding ethical approval (such as institutional review boards, ethical review boards or other) of their participation in the study. The Danish National Scientific Ethics Committee was consulted with regards to The Technical University of Denmark leading the study, and based on their assessment of the study protocol, the committee concluded that the samples were not human and therefore the study did not require ethical approval. No patient material was transferred with the samples, and no patient identifiers were shared with the project. Only minimal metadata pertaining to the infection and bacterial isolates or their DNA were sampled. Isolate selection. Partners collected samples according to their availability to do so, during 2020.
Sample handling and processing. Logging of received samples. Upon arrival in Denmark, samples were logged together with received metadata. Validation of the metadata was performed prior to database submission. Validation of metadata is explained in detail under "Technical Validation". Logging entailed entering sample names (as written on the labels provided to partners), registration of unique sample id's, original as well as validated metadata and processing information with regards to culturing and freezing of isolates. Once validated, all information resulting from logging samples and their metadata was submitted to the MySQL database. Culturing of received isolates. Isolates received on coal swabs were cultured on blood agar or chocolate agar, in presence of CO2 if necessary, and sub-cultured until the expected (as submitted by sampling partner) species were (presumedly) isolated (visual recognition by experienced laboratory professionals).
5 300 cycles, Cat. nr 20024905). Gram-negative samples were run 96 isolates in parallel, and Gram-positive samples were run 192 isolates in parallel. Few flow cells were run with mixed Gram-negative and Gram-positive samples with approximately 100 samples on a single flow cell. Eight samples were sequenced on an Illumina MiSeq platform, paired-end sequencing, 500 cycles (2 × 251) on a V3 flowcell. Data processing and analytics. Sequencing data was downloaded from BaseSpace (Illumina's customer cloud platform) and transferred to the Danish National Supercomputer for Life Sciences 11 , a high-performance computing cluster, where it was both stored and processed, and all downstream analytics took place. Raw read quality control (QC). An in-house bioinformatics pipeline, called FoodQCPipeline v.
1. 5  12  , was used at default settings to quality assess the raw sequence data, trim the raw reads according to predefined quality thresholds and perform de-novo assembly on the genomes. The quality assessment and trimming of raw sequencing data is further described under "Technical Validation". Given the '-spades' option, FoodQCPipeline performs de-novo assembly with SPAdes v. 3. 11. 0  13. After running the FoodQCPipeline, both trimmed fastq data and fasta (draft assemblies) are available for downstream analyses. QC summary data was submitted to the MySQL database after genome validation, which is explained in detail under "Technical Validation". Species prediction with KmerFinder v. 3. 0. 2. KmerFinder  14  , was used as one of two species prediction programs. KmerFinder assesses species identity by matching k-mers from the query sequence to a kmer-based database of reference strains.
KmerFinder was run on the draft assemblies with default settings, the evaluation was done on total query coverage, which is calculated as the number of unique k-mers shared between the query and the template, divided by the number of unique k-mers in the query, with the first hit being accepted if it had more than 80% total query coverage. Species prediction with rMLST. The other species prediction software used, was rMLST  15. In contrast to KmerFinder, rMLST identifies species based only on ribosomal multi-locus sequence typing, which includes the 53 genes that encode subunits of the bacterial ribosome. rMLST was run on assembled genomes through the open access API at https://pubmlst. org/species-id/species-identification-via-api. The first hit was accepted if it had more than 90% support.
Final species identification. The conclusion of the in silico identified species was based on either species or genus level concordance between the top hits for KmerFinder and rMLST, or an acceptable hit from only one of the two software. The point of using two different species prediction software was to allow for a sensitive assessment of whether the genomes were contaminated (KmerFinder), while complementing with a more robust but less sensitive species prediction software (rMLST). Species that could not be exactly identified are given as NA, if the genome was validated. The genome validation is described under "Technical Validation". As with QC summary data, species prediction data was submitted to the MySQL database upon genome validation, and concordance between the KmerFinder and rmlst is given.
Identification of resistance-conferring genes with ResFinder 4. 1. In order to identify acquired resistance genes in the validated bacterial genomes, ResFinder version 4. 1 16 was run on the assemblies. All samples were run with the '-s "other"' option, meaning that the samples were not run as specific species. ResFinder has the option to run the samples as specific species, in which case a secondary program, PointFinder, is run. This analysis is omitted when running as '-s "other"' , and allows for complete cross-comparability of the output data resulting from our in-house ResFinder summary script, which in this case only encompasses "acquired" resistance genes. The ResFinder summary script produces different overviews of the ResFinder data, with both a class level and a drug level overview of acquired resistance genes, as well as the query coverage, percent identity to reference and position in the assembly of the hit.
The ResFinder summary script is submitted as supplementary material, and is available as Supplementary file 1 Phylogeny. Genetic distance-based phylogeny was inferred for sequencing runs that passed the technical validation (see below), using Evergreen COMPARE  [17] [18] [19]  (commit b512e6e). The reference database was the complete bacterial chromosomal genomes from the refseq collection of National Center for Biotechnology Information (NCBI), last fetched in April 2021, homology reduced to 98 percent sequence identity, using kma_index from KMA with the settings for homology reduction -hr 0. 769 and -ht 0. 769. Consequently, the threshold for accepting a matching reference was also lowered to 98% (76. 90% k-mer identity), and the inclusion criterium for consensus sequence completeness reduced to 80%.
For displaying the phylogenies on the website, a custom script (Supplementary file 2) was used to select the minimum amount of phylogenetic trees that in totality contained all possible samples. spreadsheets, or simply by sending the handwritten metadata sheets. If the information given could not be validated, no validated data was registered, in which case it is omitted from down-stream analysis. The following validations were applied to the metadata: • Geographical origin of sample identifiable via openstreetmap. org • Species and genus information separated, according to validated nomenclature • Date according to specific date format (yyyy-mm-dd) • Sample source type according to 3 validated lists: 1) type of sample, 2) anatomical origin and 3) other source indicator • Age according to specific format (age in years) • Gender according to specific format ('f ' , 'm' , 'o') • Hospital-or community acquired infection according to specific format ('h' or 'c') Quality controlling the raw sequencing data.
Genome validation. The genome validation consists of two assessments: sequencing QC and genome contamination. The process is depicted in Fig. 4. Based on the quality control reports generated by FoodQCPipeline, samples were discarded at the preliminary quality assessment, if the raw data did not live up to any of four criteria: (1) >100 mega bp after quality trimming, (2) depth of coverage >20X, (3) N50 > 15,000 bp and (4) <500 contigs in the assembly (unless the species prediction was a Pseudomonas spp. , in which case up to 1098 contigs were accepted). If any of the four criteria were not met, a QC alert would be given and the genome would fail validation. Genome contamination was assessed by the following 3 criteria: 1. the 1st KmerFinder hit had >80% total query coverage 2. that the 2nd KmerFinder hit had < than 80% total query coverage 3.
that the 1st rMLST hit had >90% support. If any of these criteria were not met, a contamination alert was given for the sample and the genome validation failed. However, in the case of a KmerFinder-based contamination alert, the genome could be validated if the rMLST 1st hit had >95% support. All failed genomes were assessed manually afterwards, and in certain cases a genome could be manually validated based on various assessments. Reasons for manual validations (and failures) are indicated in the dataset. Identification of the correct bacterial isolate. When several bacterial isolates were cultured from a swab, the trained laboratory professionals attempted to correctly identify the suspected species by visual recognition. When in doubt, all isolates were brought forward for sequencing and bioinformatic analyses.
Exclusion reasons. Through the process from receiving samples to validating the genomes for the final dataset, reasons for samples to be excluded were: • sample missing (some samples are registered as being received, because they were registered in the partner's metadata, however the sample was never received/recovered) • alternative isolate (if several isolates were cultured from a swab and another isolate matched the suspected pathogen) • out of scope (if an isolate turned out to be something not bacteria (e. g. fungi)) • not viable • not isolatable (typically due to insurmountable Proteus spp. contamination) • contaminated with fungi (a bacterial pathogen was also present, but could not be isolated from the fungal contamination) • x-isolate (if several isolates were cultured from a swab but the suspected pathogen was assumed visually identified and brought forward, the remaining are x-isolates) • lab material test (if a sample was registered multiple times, simply because it was used to test laboratory material) • not enough DNA (partners sending extracted DNA didn't always send adequate amounts of DNA) • contamination with no original isolate available (partners sending DNA may have sent DNA which was contaminated -in this case the original isolate could not be regrown and re-isolated) A total 182 samples were excluded based on these reasons.
Usage Notes No unvalidated genomes have been submitted to ENA, and therefore it should be "safe" for data users to apply these genomes in bioinformatic analyses. The MySQL database, however, contains information regarding all received samples. Some samples could not be regrown in the laboratory in Denmark, and some DNA extracted by partners was contaminated and could not be re-extracted in Denmark, because the original isolate was not available. However, the metadata in the "sample" table in the mySQL still has information regarding what types of samples were collected from which places, as well as the suspected genus and species of the samples -even though the pertaining genomes do not exist on ENA.