dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were then fixed on the grids with 2. 5% glutaraldehyde in 0. 1 M HEPES buffer, pH 7. 2, followed by staining with 2% aqueous uranyl acetate (for 30 s). Grids were washed three times with ultrapure water and air-dried. Dried grids were imaged with a Tecnai Spirit 120-kV TEM machine equipped with a bottom-mounted Eagle 4kx4k camera (FEI-Thermo Fisher). TEM images of MVs were taken at a magnification of Â18,500. For size distribution analysis, a total of 600 MVs for each condition were analyzed on TEM images using ImageJ software, and the diameter size was measured as per the scale bar of the image. Diameter data of MVs were size distributed with a window bin size of 10 nm to create a histogram plot. Nonlinear curve fitting using a Gaussian equation {Y = amplitude Á exp(20. 5) Á[(X 2 mean)/SD] 2 } (where SD is the standard deviation) on the histogram data was done to obtain the frequency distribution plot.
STEM analysis was carried out by using a TEM coupled-scanning TEM JEOL detector with a 1-nm probe operating in a high-angle annular dark-field (HAADF) mode. A combination of XEDS and STEM data revealed elemental composition mapping of the bacterial sections. Sample preparation and immunolabeling by the Tokuyasu technique for TEM. Bismuth-treated (2. 5 mM) and untreated cultures of H. pylori G27 wild-type strain and the Dppk mutant were fixed by mixing them with an equal volume of 2Â fixative (0. 2% glutaraldehyde [Sigma]) and 8% paraformaldehyde (EMS) for 15 min at room temperature, followed by overnight incubation at 4°C. Fixed samples were harvested and resuspended in PBS with 10% gelatin (Sigma). The samples were allowed to solidify on ice and infiltrated overnight with 2. 3 M sucrose at 4°C.
DNA was extracted using phenol-chloroform and precipitated with 2. 5 vol of 100% ethanol. Pellets were suspended in a final volume of 130 mL of 1Â TE supplemented with RNase, incubated for 1 h at 37°C, and stored at 220°C until use. DNA was extracted, purified, and processed into the sequencing library as described previously  (65). Proximity ligation libraries were sequenced using paired-end (PE) Illumina sequencing (2 Â 35 bp; NextSeq 500 apparatus). (iii) Hi-C analysis. Reads were aligned with bowtie2 v2. 3. 5. 1  (66) , and Hi-C matrices (5 kbp resolution) were generated using hicstuff v3. 0. 3 (https://github. com/koszullab/hicstuff). Balanced normalizations were performed using the ICE algorithm  (67). Comparisons between matrices (10-kbp resolution) were done using a log 2 ratio for flexible binning  (68).
Statistical analysis. Statistical analyses of the size of bacteria and of purified MVs from bismuthtreated or control conditions were performed with the nonparametric Kolmogorov-Smirnov test. Statistical analyses were carried out by one-way analysis of variance (ANOVA) or by Mann-Whitney tests. GraphPad Prism 8 was used to perform the statistical analysis. Facility, and we are grateful for support for equipment from the French government program Investissements d'Avenir France BioImaging (FBI; no. ANR-10-INSB-04-01) and the French government (Agence Nationale de la Recherche) Investissement d'Avenir program, Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases (ANR-10-LABX-62-IBEID). We are also thankful to Milica Denic, Evelyne Turlin, and Daniel Vinella from the Helicobacter Pathogenesis research unit for their interest in this work and their advice.
We thank Alejandro Tejada Arranz for manuscript reading and providing feedback. We also thank Clélie Feurtet-Zaire for her help with the Western blot experiments. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. S. K. and H. D. R. conceptualized the study, designed the experiments, and analyzed the results. S. K. carried out bacterial cultures, construction of strains, MV isolation, Western blot analysis, RT-qPCR, and sample preparation for microscopy, proteomics, and ICP-OES. C. S. and O. G. performed sample preparation for scanning and transmission electron microscopy and observations. M. D. and M. Matondo carried out proteomics analysis of purified MVs and analyzed the proteomics data, and Q. G. G. did the statistical analysis.
ACKNOWLEDGMENTS This study was funded by the  Fondation pour la Recherche Médicale  by grant  DBF20161136767  to H. D. R. and support to S. K. as well as by  Agence Nationale de la Recherche  grant  ANR-19-CE12-0003-02. We thank  Flavien Bourdreux  from the  Institut Lavoisier ,  UMR CNRS 8180  ( Université de Versailles-Saint-Quentin en Yvelines ), for the ICP-OES measurements. We thank  J. Pablo Radicella  from the  CEA/Fontenay aux Roses  for the gift of anti-DNA antibodies and careful reading of the manuscript. We thank the  Ultrastructural BioImaging Core.
| MATERIALS AND METHODS Ten sites participated in the study (one Philips 1. 5-T scanner, nine Siemens 3-T scanners). Phantoms were produced at a single site and shipped to participating sites. Data were acquired using a standardised protocol prescribing both product and custom DTI sequences. A second scan was performed from 1-9 days following the first scan. Data were uploaded to a central server, and analysed by a single site using a standardised processing pipeline.
| Data acquisition To verify consistent temperature, a fast diffusion-weighted imaging (DWI) scout scan was performed using the product sequence (i. e. pulsed gradient spin echo [PGSE] with single-shot EPI readout). The mean apparent diffusion coefficient (ADC) was measured in a region of interest (ROI) in Sites performed experiments using the product PGSE sequence  13  and a custom DTI sequence of their choice optimised for cardiac-specific applications. The scan parameters for the product sequence were TR/TE = 3000/85 ms, coronal view, field of view (FOV) = 300 Â 230 mm, in-plane resolution = 2. 5 mm, slice thickness = 8 mm, gap = 8 mm, slices = 3, bandwidth $ 3000 Hz, parallel imaging acceleration = 2x, ECG-triggered with simulated heart rate = 60 beats per min. Specified parameters such as TE and resolution were fairly conservative, to ensure that all sites could meet the sequence specifications in the presence of different hardware capabilities.
| Data analysis Image reconstruction was performed on the scanners using standard vendor reconstruction software. Data storage and management were performed using the Human Heart Project, an online platform for heart imaging research. 40, 41 Data were analysed centrally using open source Matlab F I G U R E 2 Schematic pulse sequence diagrams describing the diffusion waveforms used. These include the pulsed gradient spin echo (PGSE) product sequence and custom sequences as follows: motion-compensated spin echo (M2SE), convex optimised diffusion encoding (CODE), motion-compensated optimised diffusion encoding (MODE), motion-compensated symmetric spin echo (M2B1resist) and stimulated echo acquisition mode (STEAM). All custom spin echo sequences were motion-compensated up to the second order, and all sequences used a singleshot echo planar imaging readout.
For clarity, preparation pulses, navigators, crushers and spoilers are not shown. Gradient waveform parameters, including diffusion gradient duration (δ n ) for each unique gradient lobe, time (b -a), slew time (ζ) and diffusion time (Δ) where applicable, are given in Table  1. δ n was measured from the start of each unique gradient lobe (G n ) to the end of its plateau. Pulse sequences are not shown to scale Abbreviations: CODE, convex optimised diffusion encoding; M2B1resist, M2SE with improved robustness to B code (https://github. com/vigente/gerardus). The first step was semiautomatic tube segmentation, where tube centres were defined manually, and ROIs generated up to a fixed radius from the tube centres. Tensors were fit directly to the data using linear least squares, using all image repetitions without prior averaging.
MD and FA were calculated as follows (Equations 1 and 2, respectively). T A B L E 1 Relevant MD ¼ λ 1 þ λ 2 þ λ 3 3 ð1Þ FA ¼ 1 ffiffiffi 2 p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi λ 1 À λ 2 ð Þ 2 þ λ 1 À λ 3 ð Þ 2 þ λ 2 À λ 3 ð Þ 2 λ 1 2 þ λ 2 2 þ λ 3 2 s , ð2Þ where λ 1 , λ 2 and λ 3 are the principal eigenvalues of the diffusion tensor. To obtain the reference diffusivity of water (D ref ), the self-diffusion of water, as measured using an independent non-MR technique, was extrapolated to 0 C using a second-order polynomial fit. 42 To assess the effect of b-values and rationalise an appropriate choice of b-values, average values of MD and FA across an ROI in tube 1 using data acquired with different b-value combinations are reported.
Root mean squared differences (RMSDs) between scans 1 and 2 were assessed. MD and FA across ROIs in all tubes were reported using data from all repetitions, and also expressed as a time course reconstructed from subsampled single repetitions. The reference diffusivity for water was defined as D ref (H 2 O) = 1. 113 x 10 À3 mm 2 /s at 0 C extrapolated from Mills. 42  calculate drift, the MD and FA across repetitions were first smoothed with a sliding window of five repetitions. Drift was determined by the difference between final and initial values. The stability of MD and FA measurements across repetitions were expressed in terms of the standard deviation (SD) across repetitions. As the non-DW data had variable b-values, image SNR was calculated based on the b = 100 s/mm 2 images averaged across six DW directions: SNR = mean/SD over repetitions.
| Data quality All sites acquired DTI data at two time points. The custom sequence acquisitions were split evenly into two groups, SE (N = 5) and STEAM (N = 5). While the product sequence acquisitions were standardised apart from small scanner variations in diffusion gradient duration (25. 6 ± 2. 9 ms) and diffusion time (39. 9 ± 3. 5 ms; mean ± SD across sites), there was greater variation across the custom sequence acquisitions. The main difference was that of all those sites that acquired STEAM data, only one site (site H) was able to acquire three slices of data as stipulated in the protocol, by slice interleaving and using TR = 6000 ms. To maintain consistent scan time, 15 repetitions were used in the analysis. The other four sites were unable to acquire multislice data in an interleaved fashion because of implementation limitations, and therefore only acquired single-slice data.
As TR = 2000 ms was used, 45 repetitions were acquired to match scan times (sites F, G and I), although one site acquired 30 repetitions (site J). In the custom SE data, 30 repetitions were acquired with TR = 3000 ms, matching the product sequence. There were, however, instances in the custom SE data where not all 30 repetitions were available for all DW directions: 27 (site A, scan 1), 29 (site A, scan 2) and 28 (site E, scan 2) repetitions. Where a given repetition did not include all required DW directions, that repetition was excluded from analysis. In one site (site H), the data were exported at a reconstructed in-plane resolution of 0. 94 x 0. 94 mm, and had to be resampled to the nominal 2. 5 x 2. 5 mm resolution. Variations in parameters such as TR, TE and diffusion gradient timings are given in Table  1.
Reasons that could increase CV (ROI) include noise and image artefacts. From Figure  3 , Gibb's ringing can be seen in all datasets due to the hard edges of the phantom. Additional artefacts are visible in the STEAM data (sites G and H), potentially increasing the CV (ROI). Furthermore, as the tube number and PVP concentration increased, we observed that the mean FA and CV (ROI) in MD and FA were increasingly elevated, particularly in STEAM. This may be associated with (i) effects of concomitant fields away from the isocentre (site E), (ii) localised image artefacts in tubes 5, 6 and 7 possibly caused by vibration (site G), and (iii) the increased effect of Gibb's ringing in the tubes with higher PVP concentrations due to the increased differential in DW image intensities relative to the surrounding ice-water bath.
ACKNOWLEDGMENTS This study was supported by the  British Heart Foundation  (grant nos PG /19/1/34076 ,  SI/14/1/30718 ,  FS/13/71/30378 ,  PG/17/28/32943  and  RG/19/1/34160 ), the  German Ministry of Education and Research (BMBF ; grant no. 01EO1504 ), the  Swiss National Science Foundation  (grant no. PZ00P2_174144 ),  National Institutes of Health  (grant nos  R21-EB024701 ,  R01-HL151704 ,  R01-HL135242 ,  R01-HL124096 ,  R01-HL131823 ,  R01-H131975  and  R01-HL141563 ) and the  Oxford NIHR Biomedical Research Centre.
( Step 2) When replication of the other strand catches up, it will be mostly populated by new nucleosomes (2a) and RNAPII will then "switch" from the early replicating strand to the late one and direct H3K4 and H3K36 methylation of new histones by Set1p and Set2p, respectively, on the second gene copy (2b). (B) Modulation of the replication timing of replicated gene copies determines the pattern of old and new nucleosome segregation. In addition to temporal maps of nucleosome and RNAPII binding dynamics after replication, our analysis of NChAP data sets has also uncovered new information on replication dynamics in S. cerevisiae. NChAP fractions revealed dozens of discrete smaller peaks that surround one or two dominant peaks that are, as a rule, the ones closest to previously identified origins (cf.
Acknowledgments We thank  Susan Forsburg ,  Paul Kaufman ,  Traci Lee ,  Etienne Schwob ,  Karim Labib , and  Zhiguo Zhang  for yeast strains. We also thank  Marta Gut  and  Julie Blanc  from  CNAG (Barcelona, Spain ) and  Veronique Pantesco  ( Plateforme Transcriptome-IRMB Montpellier ) for massive parallel sequencing services, and  Robert Feil  for critical reading of the manuscript. This work was supported with the  ERC-Consolidator  grant-NChIP 647618 (M. R. L. ). Author contributions: R. Z. optimized  ChIP-NChAP  and performed all the experiments except the ones in Figure 3 (done by M. R. L. ) and Supplemental Figures S5 andS13 (done by A. C. ). A. C. assisted R. Z. in the cell culture and ChIP steps of the experiments. R. Z. and A. C. constructed the yeast strains. M. R. L. , R. Z. , and A.
Data compilation We compiled assessment data primarily through regional in-person and remote (that is, through phone and email) workshops with species experts (9,536 species) and consultation with IUCN Species Survival Commission Specialist Groups and stand-alone Red List Authorities (442 species, primarily marine turtles, terrestrial and freshwater turtles, iguanas, sea snakes, mainland African chameleons and crocodiles). We conducted 48 workshops between 2004 and 2019 (Supplementary Table  1 ). Workshop participants provided information to complete the required species assessment fields (geographical distribution, population abundance and trends, habitat and ecological requirements, threats, use and trade, literature) and draw a distribution map. We then applied the Red List criteria  34  to this information to assign a Red List category: extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concern and data deficient.
Habitat preferences Where known, species habitats were coded using the IUCN Habitat Classification Scheme (v. 3. 1) (https://www. iucnredlist. org/resources/ habitat-classification-scheme). Species were assigned to all habitat classes in which they are known to occur. Where possible, habitat suitability (suitable, marginal or unknown) and major importance (yes or no) was recorded. Habitat data were available for 9,484 reptile species.
All species were assessed according to their recognized taxonomic circumscription at the time of assessment. Taxonomic uncertainty therefore did not result in a data-deficient assignment, although some species were listed as data deficient because they are morphologically indistinguishable from another species and therefore estimates of distribution and abundance are not feasible. Time span of assessments. The assessments were completed between 1996 and 2020, with 1,503 assessments completed before 2011. The IUCN Rules of Procedure (https://www. iucnredlist. org/resources/ rules-of-procedure) recommend reassessment every 10 years and thus, as of 2020, 15% of the assessments can be considered outdated. Of the species assessed 1996-2010, slightly more species were threatened (23. 0%) than the species assessed more recently (20.
Restricting analyses to terrestrial species ensured more-consistent analyses and avoided wide variation in summary values caused by small numbers of species. Analyses of the distribution maps used polygons either with the following IUCN map code designations or with no codes indicated: Presence = extant (code 1) and probably extant (code 2) Origin = native (code 1), reintroduced (code 2) and introduced (code 6) Seasonality = resident (code 1), breeding season (code 2), non-breeding season (code 3) and passage (code 4). Ranges for species categorized as critically endangered (possibly extinct) are coded as possibly extinct (code 4) and excluded from the spatial analyses. All spatial analyses were conducted on a global 0. 5° by 0. 5 ° latitudelongitude grid (approximately 50 km at the Equator).
In the main text, we report results for the 100% or more threat level. Results for all thresholds are included in Extended Data Tables  1, 2. Conservation strategies. We identified global conservation priorities for each tetrapod class using two alternative strategies: strategy 1 prioritized areas containing many threatened species with relatively highly restricted ranges, whereas strategy 2 prioritized areas core to the most range-restricted threatened species globally. We implemented both conservation strategies within the spatial conservation-planning software Zonation  45  and the R package zonator  46  , using the additive benefit function and the core-area Zonation algorithms for strategies 1 and 2, respectively, at 50-km and 100-km resolutions for threatened reptiles. The additive benefit function algorithm prioritizes areas by the sum of the proportion of the global range size of all species included in a given grid cell-a quantity similar to weighted species endemism (as defined previously  47  ) and endemism richness (as defined  48  ).
Estimating surrogacy. To assess the degree to which conserving the diversity of threatened species of birds, mammals and amphibians (individually or combined) serves as a surrogate for conserving threatened reptile diversity, we calculated a species accumulation index (SAI) of surrogate effectiveness. The SAI is derived from the comparison of three curves: (1) the 'optimal curve' represents the accumulation of the diversity of threatened reptile species when conservation is planned using data for threatened reptiles directly; (2) the 'surrogacy curve' represents the accumulation of the diversity of threatened reptile species when conservation is planned using the diversity of threatened species diversity of a different class as a surrogate; and (3) the 'random curve' represents the accumulation of the diversity of threatened reptile species when conservation areas are selected at random.
It is between 1 and 0 when the surrogate curve lies above the random curve (positive surrogacy), zero when the surrogate and random curves coincide (no surrogacy) and negative when the surrogate curve lies below the random curve (negative surrogacy). We calculated the SAI using R code modified from a previous study  49. For each reptile-surrogate combination, we report median and 95% confidence intervals across all combinations of optimal, surrogate and random curves (5 target and surrogate curve iterations and 100 random curve iterations). Although not strictly a measure of surrogacy  25  , we also calculated the spatial congruence (Spearman's rank correlation, analogous to a previously published approach  9  ) of Zonation priorities for each conservation strategy and spatial resolution. Coverage by protected areas.
We overlayed protected areas (polygons, categorized as IUCN I-VI from the World Database of Protected Areas 50 ) over the ranges of all threatened tetrapods and classified species with ranges completely outside any protected area as unprotected. Phylogenetic diversity. To calculate phylogenetic diversity  15  , we used published time trees of mammals  51  , birds  52  and amphibians  53. For reptiles, we combined two time trees: a comprehensive squamate time tree containing 9,755 squamate species, including the species Sphenodon punctatus  16  , and a turtle and crocodilian tree containing 384 species  17. The time trees contain some species lacking genetic data, added by taxonomic interpolation  54  to maximize taxonomic coverage. In total, we analysed 32,722 tetrapod species including 10,139 reptiles, 5,364 mammals, 9,879 birds and 7,239 amphibians.
For squamates, and for turtles and crocodiles, 10,000 fully resolved trees were available. For each group, we randomly sampled 100 trees and combined them to obtain 100 fully resolved reptile time trees, to accommodate for uncertainty. Similarly, we randomly sampled 100 amphibian and 100 mammal time trees over the 10,000 available. We thoroughly compared the species name mismatches between geographical and phylogenetic data to match synonyms and correct misspelled names. We also imputed species for which the genus (but not the species) was already present in the tree, for example newly described species (262 amphibian, 1,694 bird, 236 mammal and 777 reptile species). Imputed species were randomly attached to a node within the genus subtree. Because polytomies can result in an overestimation of the phylogenetic diversity, we randomly resolved all polytomies using a previously published method  54  implemented in R code.
Following this method, we calculated evolutionary distinctiveness scores  56  for each cell from the subtree including all species present in the focal cell with the R package caper  57. For genera with interpolated species, the mean evolutionary distinctiveness score of non-interpolated species was assigned to interpolated species of that genus. For those genera, we computed a second evolutionary distinctiveness score corresponding to the mean evolutionary distinctiveness score of the focal genera (including interpolated species). For species belonging to genera with no interpolated species, the first and second evolutionary distinctiveness scores were identical. Next, we calculated the mean of the two evolutionary distinctiveness scores and reported this value as the evolutionary distinctiveness score of each species.
Acknowledgements We thank the 961 Red List assessors (listed in Supplementary Note 1); the organizers of workshops in  United Arab Emirates (D. Mallon), Fiji (H. Pippard)  and  Colombia  ( B. Bock  and  V. Páez ); the co-principal investigators of  NSF  award  1136586  ( G. Costa ,  C. Graham  and  V. Radeloff ); and  J. Chanson ,  D. Bennett ,  N. Garcia ,  H. Hamilton ,  D. Hole ,  D. Juhn ,  A. Kenlan ,  M. Mascia ,  R. Mittermeier ,  M. Ormes ,  J. P. Ross ,  J. P. Rodriguez ,  J. Rodriguez ,  J. Smart ,  S. Stuart ,  P. Uetz  and  J. -C. Vié. Funding was provided by  Agencia Nacional de Promoción Científica y Tecnológica-Fondo para la Investigación Científica y Tecnológica   RC 2014-0116  (L. J. A. ); the  Australian Department of Agriculture, Water  and the Environment; the  Australian Research Council ; B.
Code availability Python scripts used for the spatial analyses are permanently available at https://transfer. natureserve. org/download/Publications/ Global_Reptiles/. No code was used for the Fisher's exact tests, which were performed in Excel and available with the tabular data at https:// transfer. natureserve. org/download/Publications/Global_Reptiles/. Code used for the phylogenetic diversity, areas of disproportionate threat and surrogacy analyses are available at Zenodo (https://zenodo. org/record/5974891).