dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic Analysis of ST212 and ST3274 Isolates In the RefSeq database, last accessed on 11 April 2023, 32,776 E. coli genome assemblies were available. Twenty-three (0. 07%) genomes belonged to ST212 and 11 (0. 03%) to ST3274 (Figure  3 ), indicating that these STs are very much in the minority. All ST3274 refer to China, except one that was isolated in Japan, but no further metadata is available. Metadata on ST212 isolates showed that they originated from all around the world, from China to Europe, Brazil, and Canada. Ten were from farm animals (mostly poultry), six from wild animals, and five from humans. Among the six wild animals, five were collected from wild birds, including one vulture in Brazil. SNP analysis showed identity (0-2 SNP differences) between isolates from the same species and the same geographical origin, but also between two bird isolates in Brazil (a black vulture and a striped owl) with different ecological behaviors but brought to the same rehabilitation center  [26].
On the contrary, the AmpC phenotype observed in the sixth ESC-resistant isolate was due to a chromosomal mutation in the AmpC promoter and was thus not transferable. The STs associated with ESC-resistant isolates in this study were rare STs, found in less than 0. 1% of the RefSeq E. coli database. Consequently, it was difficult to draw hypotheses about their origin. Nevertheless, when reported, both STs were often associated with an animal, including wild birds, or an environmental source. Indeed, in addition to the isolates found in the RefSeq database, ST3274 was identified in a rook (Corvus frugilegus) in Poland  [31] , and ST212 was reported from a guinea fowl in Gambia  [32] , an ibis (Threskiornis spinicollis) in Australia  [33] , a primate in Senegal  [34] , and wastewater in Germany  [35].
Illumina Short-Read Sequencing and Data Analyses DNA was extracted using the NucleoSpin Microbial DNA extraction kit (Macherey-Nagel, Hoerdt, France), and sequencing was performed on a NovaSeq 6000 instrument (Illumina, San Diego, CA, USA). Quality control of the reads was performed using FastQC v. 0. 11. 9, and low-quality sequences were trimmed using Trimmomatic v. 0. 32. De novo assembly was performed using Shovill v. 1. 0. 4, and the quality of assemblies was assessed using QUAST v5. 0. 2 (Table  1 ). Identification was performed using Kraken (https://ccb. jhu. edu/software/kraken/, accessed on 26 May 2023), sequence types (STs) according to Achtman's MLST scheme, and resistance genes and virulence factors were determined using the CGE online tools (http://www. genomicepidemiology. org/, accessed on 26 May 2023).
Phylogenetic Analysis The E. coli A, B1, B2, or D phylogenetic groups were identified according to Doumith et al. [41]. The clonality of the isolates was estimated by multiple-locus variable-number tandem-repeat (MLVA) analysis  [42]. The three isolates sequenced in the frame of this study, as well as all ST212 and ST3274 available in the RefSeq database of the NCBI database, were downloaded. The cgMLST was determined through the pyMLST pipeline v. 2. 0. 1 (https://github. com/bvalot/pyMLST, accessed on 26 May 2023) using the scheme available on the www. cgmlst. org/ncs webpage(accessed on 26 May 2023), which comprised 2513 targets for E. coli. Cut-offs for highly related strains were <10 allelic variants. The resulting tree for both analyses was visualized using iTol v. 6 (http://itol. embl.
Acknowledgments: We thank staff from  Vautours en Baronnies  ( C. Tessier ,  J. Traversier , and  G. Foileret ) and  Parc Naturel Regional du Vercors  ( J. Andrieux-Argirakis ,  B. Betton , and  L. Augoyard ) and from  CEFE  ( Y. Sassi ,  P. Dufour , and  S. Tillo ) for helping to catch and sample vultures.
1d ). Consequently, before seeking ecological explanations for observed patterns of range dynamics across environmental gradients, these patterns should be evaluated against appropriate null models  27, 33, 35. However, very few studies of climate-related range shifts use null models to interpret patterns of range limit shifts or to fully account for geometric constraints  10, 35. Here, we analysed data from a standardized survey of non-native plant distributions (developed by the Mountain Invasion Research Network (MIREN); www. mountaininvasions. org, ref. 36  ) to quantify and compare temporal patterns of invasions in mountains across the globe. We surveyed elevational gradients along mountain roads and adjacent seminatural vegetation in 11 mountain ranges from 5 continents, ranging from 68° N (Norway) to 37° S (Australia).
Our data includes nearly 15,000 observations of 616 non-native plant species from 651 sampling transects resurveyed every 5 years across a 5-year (five regions) or 10-year (six regions) period. We analysed temporal changes in the richness and upper elevational limits of non-native plant species, introducing a null-model approach to interpret patterns of range limit shifts across elevational gradients after accounting for geometric constraints and compare these patterns across regions. Specifically, we ask: (1) has the richness of non-native plant species in mountains increased during the last decade? and (2) are the upper elevation limits of non-native species moving upslope and, if so, are upslope shifts occurring faster than expected at lower elevations?.
1a  and Supplementary Table  2 ). These results were also robust to applying stricter filters excluding less common species to examine the influence of low species frequency in some of the regions: retaining only species with >5 (filter 1) and >10 (filter 2) occurrences per region over all years resulted in identical trends with upslope shifts in all regions, except Hawaii and Montana (United States), and significant upslope changes in three regions. As the overall occurrence of non-native plant species was very low in Norway, the upslope shift was no longer significant for filter 1 and the region was excluded for analysis with filter 2, as fewer than five species remained in the dataset (Supplementary Table  3 ). When only retaining species with >10 occurrences per region and year (filter 3), three regions (Norway, Switzerland and Central Chile) had fewer than five species remaining and were therefore excluded.
Data collection Data were collected by MIREN using a standard protocol  36. Sampling started in 2007/2008, is repeated every 5 years and is still ongoing. We used data from 11 mountainous regions around the world: the Australian Alps (two regions, New South Wales and Victoria); the Swiss Alps; the Andes (two regions, Central and South Chile); the Montana-Yellowstone National Park (United States); the Blue Mountains (Oregon, United States); Hawaii (United States); Tenerife (Canary Islands, Spain); Kashmir (India); and the Northern Scandes (Norway). For information about geolocation, climate, elevational range and sampling period, see Supplementary Table  4. In each region, three roads were selected (two in Central Chile, four in Hawaii and five in Victoria), all of them open to vehicle traffic for at least part of the year.
Sampling was repeated during the peak growing season at 5-year intervals (Supplementary Table  4 ). The identity of non-native vascular plant species according to the World Flora Online (http://www. worldfloraonline. org) and their abundance (scale 1, 1-10 individuals or ramets; scale 2, 11-100 individuals; and scale 3, >100 individuals) was recorded in every plot. Data from the three plots at each sample transect (elevation) were combined as presence-absence data for each transect for the analyses presented here. The two seminatural plots together accounted for only 35% of the total observations of non-native plant species across years and only 11% of unique observations of non-native plant species within sample transects (observations of species away from but not at the roadside). Further analyses conducted with data separated into road and seminatural plots revealed no consistent differences in average upper limit shifts (Supplementary Table  9 ).
Data analysis All analyses were carried out in R v. 4. 0. 3, using the lme4 package  44. The map in Fig. 4  was created on the basis of the R packages sf  45  and ggmap  46. All models were checked for compliance with model assumptions. To  Each point shows the change in upper elevation limit (90th quantile of elevational distribution) of a single species, as a function of its limit in the first survey (darker shading of points corresponds to greater total log(frequency of occurrence) of a species in both surveys, ranging from n = 2 (40 species in 10 regions) to n = 187 (Hypochaeris radicata in Victoria, Australia)). Red regression lines are fitted relationships for observed range limit shifts, weighted by species' frequency of occurrence. Grey triangles indicate shifts that could not have been observed on the basis of the elevational extent of the field survey (compare Fig.
Subsequently, these models were compared to an intercept-only model but still including the same random effect, with a likelihood ratio test. We also fitted LMMs to the percentage change in richness, setting the intercept to zero (percentage change in species richness is always zero in the first year of the survey). To investigate range expansion of individual non-native species, we calculated for each species the change in upper elevational limit between the first and last time points (over either a 5-or 10-year period). We therefore only retained species present at least once in both the first/last sampling year. Additionally, to assess the influence of less common species on average regional range shifts, we defined three datasets with stricter filters only including species occurring >5 and >10 times per region over all years (filters 1 and 2) and >10 times per region and year (filter 3).
The upper elevation limit was defined as the 90th percentile of all species' occurrences along the elevational gradient. While the 90th percentile approximates the highest occurrence point when species are scarce, it reduces the influence of extreme outlier occurrences when sufficient data points are available to be informative and so provides more conservative estimates of range changes. Range limit changes were quantified as the difference in 90th percentiles between the first (t1) and last time point (t2 or t3) in each region (t2 - t1 or t3 - t1). To facilitate comparisons between regions with very different elevation ranges, elevation was centred and scaled (mean = 0, s. d. = 1) within regions before calculating range limit changes. We first fitted an intercept-only LMM of range limit changes across all species and regions, with region as a random intercept term, to test for overall changes in upper elevation limits across all regions combined.
Acknowledgements This research was funded through the  2019-2020 BiodivERsA joint call for research proposals , under the  BiodivClim ERA-Net COFUND programme  (project ' RangeX ') and with the funding organizations  Innovation Fund Denmark, Department of Science and Innovation Republic of South Africa , the  Research Council of Norway , the  Swiss National Science Foundation , the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning  and the  German Research Foundation. S. H. and A. R. B. acknowledge the support of iDiv funded by the  German Research Foundation  ( DFG-FZT 118 ,  202548816 ). A. L. H. was supported by an  NSERC   Discovery grant. P. K. acknowledges funding from the  Swedish Research Council (Vetenskapsrådet)  (grant no. 2015-04214 ) and J. J.
Data availability All datasets generated before 2016 (except two roads in Victoria, Australia) and analysed during the current study are available through the Global Biodiversity Information Facility (GBIF, https://www. gbif. org/ publisher/76388ab6-61ca-439a-ab09-e1fe73eb224a) and the complete dataset is available on Zenodo (https://doi. org/10. 5281/ zenodo. 7495407) (ref. 47 ). Environmental data and a summary file containing all species and range shifts are provided in Supplementary Data 1 and 2, respectively.
Data analysis All analyses were carried out in R, version 4. 0. 3, using the "lme4" package. The code produced for the analysis is available as a supplementary file. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All datasets generated before 2016 (except two roads in Victoria, Australia) and analysed during the current study are available through the Global Biodiversity Information Facility ( G Bl F, https://www. gbif. org/publisher /76388ab6-61ca-439a-ab09-e 1 fe 73eb224a) and the complete data set is available on Zenodo (https:// doi. org/10. 5281/zenodo. 5529072 ). Environmental data and a summary file containing all species with their respective range shifts are provided as a supplementary files.
Data Collection, Model Building and Structure Refinement X-ray diffraction data were collected at beamline BL17U1 at Shanghai Synchrotron Radiation Facility (SSRF). Data were processed with AUTOPROC software  [33]  with XDS  [34]  for indexing and data reduction and AIMLESS  [35]  for scaling. All structures were solved by molecular replacement using the BsPif1 apo structure (PDB 5FTD) as the search model using the PHASER module in PHENIX software  [36]. The models were manually adjusted with COOT  [37]  and refined in PHENIX, including twin law when necessary. The data and refinement statistics are summarized in Supplementary Table  S1.
Small-Angle X-ray Scattering The scattering data of apo DdPif1 were collected at beamline BL19U2 of SSRF in batch mode at 25 • C and at protein concentrations 1. 0, 3. 0, 5. 0, 7. 0, and 9. 0 mg/mL in buffer containing 25 mM Hepes (pH 7. 5), 100 mM NaCl, and 5% Glycerol. The wavelength (λ) of X-ray radiation was set as 1. 033 Å, and a sample to detector distance was set to 2. 5 m. The X-ray beam with a size of 0. 40 × 0. 15 (H × V) mm 2 was adjusted to pass through the centers of the capillaries for each experiment. The data were processed with BioXtasRAW  [38]. Then, 20 consecutive frames of 1 s exposure time recorded for each sample were averaged after checking for absence of radiation-induced protein damage. The scattering patterns of the corresponding buffer solution were recorded before and after the measurements of the protein sample, and the averaged buffer pattern was subtracted from the protein patterns.
The radius of gyration (Rg) and maximum particle dimension (Dmax) were extracted using BioXtasRAW and GNOM program from ATSAS suite  [39]. Ab initio envelopes were determined using 50 models calculated either by DAMMIF  [40]  or by DENSS  [41] , with experimental Rg and Dmax values as constraints. The quality and uniqueness of the results were assessed further by averaging with DAMAVER for DAMMIF models and with the internal procedure of DENSS. The agreement between the replicated models was quantified by the mean normalized spatial discrepancy (NSD). The atomic model of apo DdPif1 was generated by flexible modelling using DADIMODO  [42]  where flexibility was introduced in the N-terminal tail and between domains 1A and 2A. Atomic models were fitted in ab initio envelopes with the Supcomb algorithm  [43].
Modeling and Molecular Dynamics Simulations As DdPif1 structure is not available in the Alphafold database, the full-length DdPif1 structure (residues 1-510) was modelled using Alphafold  [44]  and RosettaFold  [45]  installed on a local computer. From the Alphafold model, domains 2A and 2B were manually moved in order to mimic the conformation of BsPif1 in complex with ssDNA (PDB 5FTE). ssDNA was built in the model using the path provided by the structure 5FTE inside the helicase core and the binding mode of ssDNA to WYL the domain in the structure of the C. crescentus DriD C-domain (PDB 7U02). The ATP nucleotide was modelled using DdPif1-AMPPNP complex information. The DdPif1-ATP-ssDNA was then subjected to a molecular dynamics simulation. The inputs were prepared using CHARMM-GUI  [46]  with the Solution Builder module.
Overall Structure of DdPif1 and SsPif1 The helicase core of Pif1 from thermophilic bacteria is very similar to other bacterial Pif1 structures already solved (Figure  2 ). The HC contains the features of previously described SF1B helicases: two RecA-like domains 1A and 2A containing insertions 1B and 2B, respectively (Figure  1A ). Conserved motifs are present in DdPif1 and SsPif1 domains with motifs I, IA, II, and III in domain 1A and motifs IV, V, and VI in domain 2A (Figure  1B ). Domains 1A and 2A have the characteristics α/β fold of the RecA domains with 1A folded as a parallel β-sheet with five β-strands flanked by three and four α-helices at each side and 2A folded as a parallel β-sheet with three β-strands surrounded by seven α-helices. The 2B domain is folded as the SH3 domain, as shown in RecD2 from Deinococcus radiodurans (PDB ID 3GPL) and Dda from T4 bacteriophage (PDB ID 3UPU), with a long β-hairpin protruding, named loop-3 in BsPif1  [20].
Before starting the experiment, written instructions were given to participants, with the recommendation to respond as accurately and quickly as possible, positioning the index, middle, and ring finger of their right (dominant) hand above the keys 1, 2, and 3, respectively. All participants took part in two experimental sessions. At the beginning of each session, participants were presented with initial training to get familiar with the task. The training comprised 10 new sentences, 8 covering the four conditions included in the experimental dataset plus 2 sentences with coordination in which each subject was matched with a predictable action. The two experimental sessions were held at a minimum distance of 2 days from each other and a maximum distance of 7 days (online participants received the link to the second session a few days after completing the first one).
The experimental material was therefore divided into two lists to avoid a long session (A and B; one list per experimental session) containing 96 sentences each, 24 per type. In each list, sentences were divided into three blocks, each comprising 8 sentences per type. Blocks were separated by a break, whose length was decided by the participants, who could resume the experiment whenever they wanted. Lists and blocks presentation order was counterbalanced across participants, whereas items order was randomized within each block. Each session lasted about 25 minutes, and students received course credits for their participation. Accuracy and reaction times (RTs) were recorded. Due to the SARS-COVID-19 pandemic, data collection occurred initially remotely and then in lab. Online participants (data collected between March and July 2021) performed the experiment using the E-prime Go platform built for E-Prime 3 (Psychology Software Tools Inc.
Statistical procedure Statistical analyses were carried out using the statistical software R (R Core Team, 2021). The complete dataset and analyses scripts are in the OSF repository of the current project (https://osf. io/7zg6m/). First, we performed a preliminary check of accuracy in both Control and Control_While sentences. Three participants showed a mean accuracy below 80% in the Control sentences (one of them also had an accuracy below 80% in the Control_While sentences, see Figure  2 ) and were removed from the subsequent analyses. Ultimately, we analyzed the results of 57 participants (24 males, mean age = 23. 5, SD = 2. 2). Two mixed model regressions were run using the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015). The first model had the dichotomous variable accuracy (0 = wrong; 1 = correct) as a dependent variable; therefore, the glmer function for generalized mixed models was used.
In particular, we aimed to see how participants answered the different types of (i) questions across the different (ii) sentence types. As we previously highlighted (see 2. 2), each question concerns the verb of a different clause in the different sentence types. To identify the maximal converging model for each dependent variable, we used the buildmer function (buildmer package,  Voeten, 2022). Participants and Items were included as random intercepts to account for participant-specific variability and item-specific idiosyncrasies  (Baayen et al. , 2008) , as well as by-item random slopes for the effect of the different sentence types. All the post-hoc comparisons were run through the testInteractions function of the phia package (De Rosario-Martinez, 2015), and p values were corrected with the False Discovery Rate (FDR;  Benjamini & Hochberg, 1995).
However, HIRA-1 depletion in spt-2 null or spt-2 HBM worms resulted in a dramatic increase in the incidence of vulvar protrusions and of burst nematodes (Fig. 5f ). The morphological defects of the spt-2 HBM were fully rescued in the reverted (A627M) strain (Fig. 5f ). Comparison of the transcriptomes of spt-2 null (this study) and hira-1 null worms (dataset from ref. 36  ) revealed a statistically significant overlap of the genes up-regulated in both mutants, the up-regulated genes being enriched for stress response transcripts (Fig. 5g ). From these data, we conclude that the CeSPT-2 and HIRA-1 histone chaperones have overlapping functions in the control of H3-H4 deposition in worms, and that HIRA-1 becomes essential for worm fitness in spt-2 mutants.
We note that our sequence conservation analysis was unable to confirm the presence of a previously described HMGbox domain (InterPro: IPR009071) in SPT2  53  , which was suggested to mediate DNA binding. Examination of the SPT2 AlphaFold models also failed to reveal an L-shape arrangement of three α-helices, which is characteristic of the HMG-box domain  54. In yeast, the first 200 amino acids of Spt2 are necessary and sufficient for Spt2 recruitment to active genes, and for association with Spt6 which recruits Spt2 to target genes  29. Whether metazoan SPT2 binds to SPT6 (and other components of the active RNA polymerase II complex) is not known, and mechanism behind SPT2 recruitment to chromatin in Metazoa remain to be elucidated. Besides SPT2, the histone chaperones FACT, SPT6, SPT5, ASF1, and HIRA have all been implicated in maintaining chromatin structure at active genes  17, 18, 20.
Computational protein sequence analysis Multiple sequence alignments were generated with the program T-Coffee using default parameters 61 , slightly refined manually and visualized with the Belvu program 62. Profiles of the SPT2 evolutionarily conserved regions as hidden Markov models (HMMs) were generated using HMMer. Profile-based sequence searches were performed against the Uniref50 protein sequence database 63 using HMMsearch 64,65.
Structural modelling and conservation analysis The crystal structure of human SPT2 (Protein Data Bank [PDB] code 5BS7)  25  was used as a search template to generate a structural homology model for C. elegans SPT-2 using the homology-model server SWISS-MODEL (swissmodel. expasy. org) 66,67. A multiple protein sequence alignment was generated by MUSCLE (ebi. ac. uk/Tools/msa/muscle) 68. UCSF Chimera 69 was used to align the atomic models of human and C. elegans Set using the "MatchMaker" function. Protein sequence conservation was mapped onto the structural model and figures generated in Chimera.