dataesr/scientific-paragraphs-categorization huggingface dataset:
Patterns of Ponto-Caspian invasions To test our second hypothesis, i. e. the relationship between the numbers of these Ponto-Caspian species and the distance to their native ranges, we assigned Ponto-Caspian macroinvertebrate species to major groups (i. e. Amphipoda, Annelida, Bivalvia, Gastropoda, Hirudinea, Hydrozoa, Isopoda, Mysida, Oligochaeta and Tricladida; Table  S2 ) to identify the presence of potential taxonomic patterns in Ponto-Caspian invasions over time. We first calculated the distance between each site and the Ponto-Caspian region, following the river network (including canals) using the riverdist function of riverdist R package  (Tyers 2022). To standardize distances, we used the Danube delta as the native range for all time series, except for those from Latvia, for which we used the Dnieper delta  (Karatayev et al.
2008; Lipinskaya et al. 2020 ) and for those from the United Kingdom, which were calculated following the marine route that connects Great Britain with the Netherlands and subsequently via the invasion corridors along the river networks  (Gallardo and Aldridge 2015). Then, we evaluated the relationship between the number of Ponto-Caspian species recorded in each time series and the distance to their native range using Generalized Linear Mixed Models (GLMMs) with negative binomial family using the glmer function of the lme4 R package  (Bates et al. 2015). In order to take into account the dependency between observations (i. e. distances to the native region) within each country, we included 'country' as a random effect in the GLMMs. We also included the starting year of each time series to account for the potential effect of the heterogeneous database (i.
Discussion Overall, we identified 29 Ponto-Caspian macroinvertebrate species in our European dataset, which is higher than the numbers reported in previous studies  (Bij de Vaate et al. 2002; Gallardo and Aldridge 2015 ; reporting n = 22 and 23 species, respectively). However, differences in sampling effort could have created biases and delays in detecting Ponto-Caspian macroinvertebrates at local and regional scales, particularly as the available time series began at variable times relative to actual invasion histories  (Aikio et al. 2012; Dawson et al. 2017 ). The countries represented in our dataset were between 1312 and 4670 km away from the Ponto-Caspian region, whereas several countries were not covered by our data (e. g. Russian Federation, Romania, Bulgaria, Ukraine, Italy, Belarus, Ireland).
The vast majority of the Ponto-Caspian macroinvertebrates recorded were found in countries through which the Rhine-Main-Danube (RMD) canal flows (e. g. Hungary and Germany, whose exclusion did not affect the direction of identified trends; Supplementary Material 2). There are two other invasion corridors in Europe, i. e. the southern and the central invasion corridors, connecting the Ponto-Caspian region to other countries (e. g. Latvia; Bij de  Vaate et al. 2002; Morton 1997). Although the presence of many Ponto-Caspian macroinvertebrates has been facilitated across these two corridors (see e. g. Jazdzewski et al. 2004; Grabowski et al. 2006 ; Son 2019), we were unable to identify any suitable (i. e. comparable and sufficiently long) time series data containing entire macroinvertebrate communities from either corridor.
e. 2015-2019). Additionally, a lag time in reporting could have influenced observed trends, despite potential increases in invasive species propagule or colonization pressures. In combination with the increase in the number of Ponto-Caspian species over time, we observed a slight increase in the abundance trend of Ponto-Caspian species in central and western Europe. This increase was also supported by trends within bivalves and gastropods (albeit represented entirely by L. naticoides), which are well-represented in our dataset. Ponto-Caspian amphipod invader trends, however, decreased over time (albeit non-significantly), highlighting that changes over time manifest differently among taxa. This phenomenon can also be observed in the North American Great Lakes, where Ponto-Caspian fishes and invertebrates have become dominant components of food webs  (Ricciardi 2001; Vanderploeg et al.
Data Sets The Hubble XDF  (Illingworth et al. 2013 ; see Figure  1 ) is a small field of the sky with the deepest Hubble Space Telescope (HST) observations ever taken since this telescope started operations more than 30 years ago. This field has been the main window to study the early universe before the JWST advent, with numerous works scientifically exploiting its unique possibilities. Now in the JWST era, the HST data in the XDF and surroundings are being enhanced with deep imaging and spectroscopy obtained with the JWST/NIRCam and MIRI, extending the wavelength coverage of high-spatial-resolution observations to the mid-infrared. In particular, the background image has been obtained by combining the HST and JWST/NIRCam filters. The zoom-in shows the region covered by MIRI/ F560W. In this case, to create the RGB image, we adopted the JWST filters only where R: F560W; G: F430M, F444W, F460M, and F480M; B: F182M and F210M.
JWST/NIRCam In this work, we made use of the recent JWST/NIRCam images collected by  Williams et al. (2023)  in a General Observers Cycle-1 program across the Hubble eXtreme Ultra Deep Field (HUDF; PID: 1963; PI: Christina C. Williams). Observations have been taken in five JWST/NIRCam medium bands: F182M, F210M, F430M, F460M, and F480M. In particular, 7. 8 hr of the total integration time have been dedicated to F182M, F210M, and F480M. Instead, only 3. 8 hr of observations have been collected for F430M and F460M. In order to complement these data sets, we also made use of the imaging data taken as part of The First Reionization Epoch Spectroscopic COmplete Survey (FRESCO;  Oesch et al. 2021 , 2023 , PID: 1895; PI: Pascal Oesch). On the one hand, this GO program allowed us to add more depth to F182M and F210M; on the other hand, it gave us the opportunity to include F444W in our analysis.
All JWST/NIRCam images have been reduced by adopting a modified version of the official JWST pipeline 24 (based on jwst 1. 8. 2 and Calibration Reference Data System pipeline mapping (CRDS; pmap) 1018). More detailed information about the reference files is available on the official STScI/ CRDS website. 25  Compared to the official JWST pipeline, our version includes different procedures, following some of the ideas presented in  Bagley et al. (2023) , to deal with the unresolved problems that still affect the official software. In our data reduction, we minimized the impact of the so-called "snowballs," the 1/f noise, the "wisps," 26 and the residual cosmic rays. After reducing all the JWST/NIRCam images from Williams's and FRESCO programs, we drizzled all the NIRCam calibrated files to 0 03 pixel -1 , as the final pixel scale we adopted in this work.
All the final images have been aligned to the Hubble Legacy Fields (HLF) catalog. 27  As a sanity check, we compared the photometry for the brightest sources (<24 mag) in all the NIRCam filters. To do that, we produced two versions of our final images, with and without the extra steps we employed in our modified version of the official pipeline. Then, we extracted the sources by using the software SOURCE EXTRACTOR (SEXTRACTOR;  Bertin & Arnouts 1996 ) and compared their photometry. This test demonstrated that our extra steps do not introduce any kind of systematic effect in the photometry.
Ancillary HST Data We obtained all of HST images over the HUDF from the Hubble Legacy Field GOODS-S (HLF-GOODS-S). 28 The HLF-GOODS-S provides 13 HST bands covering a wide range of wavelengths (0. 2-1. 6 μm), from the UV (WFC3/ UVIS F225W, F275W, and F336W filters), optical (ACS/ WFC F435W, F606W, F775W, F814W, and F850LP filters), to near infrared (WFC3/IR F098M, F105W, F125W, F140W, and F160W filters). See  Whitaker et al. (2019)  for more detailed information on these observations.
Photometric Analysis We used the software SEXTRACTOR to detect the sources and measure their photometry in all the 20 filters available from the HST and JWST, covering a wide range of wavelengths (0. 2-5. 6 μm). We used SEXTRACTOR in dual-image mode adopting a superdetection image that we created by combining photometric information from different bands. In order to maximize the number of the detected sources, we opted to use a hot-mode extraction, as presented in  Galametz et al. (2013) , which is well suited to find very faint sources. We combined aperture photometry, adopting circular apertures (i. e. , MAG_APER) of 0 5 diameter, and Kron apertures (i. e. , MAG_AUTO, Kron 1980) following the same prescription we adopted in Rinaldi et al. (2022, see Section 3. 2). We chose a circular-aperture flux over a Kron flux when the sources were fainter than a given magnitude.
In this case, as we were dealing with very deep images, we decided to consider mag lim = 27 as our faint limit for the Kron aperture. This final decision has been taken after several tests we performed with the HST photometry, comparing our fluxes with the HLF photometric catalog from  Whitaker et al. (2019). We corrected the aperture fluxes to the total. For the HST, these corrections are well known. 29,30,31 For the JWST, instead, we estimated the aperture corrections using the software WEBBPSF. 32  Moreover, we adopted a minimum error of 0. 05 mag for all the HST photometry because SEXTRACTOR typically underestimates photometric errors (e. g. ,  Sonnett et al. 2013). We decided to adopt this minimum error value for the JWST images as well to account for possible uncertainties in the NIRCam and MIRI flux calibrations.
SED Fitting We performed the SED fitting and derived the properties of our sources by making use of the code LEPHARE  (Arnouts & Ilbert 2011). We constructed the libraries for LEPHARE by adopting the same configuration we used in  Rinaldi et al. (2022, see Section 4). Briefly, we considered the stellar population synthesis (SPS) models proposed by  Bruzual & Charlot (2003, hereafter BC03) , based on the Chabrier IMF  (Chabrier 2003). We made use of two different star formation histories (SFHs): a standard exponentially declining SFH (known as "τ-model") and an instantaneous burst adopting a simple stellar population (SSP) model. In particular, we adopted two distinct metallicity values, a solar metallicity (Z e = 0. 02) and a fifth of solar metallicity (Z = 0. 2Z e = 0. 004). Moreover, to take the strong contribution from the nebular emission lines that can occur at very young ages into account, we also considered STARBURST99 templates  (Leitherer et al.
Acknowledgments In memoriam to the MIRI European Consortium members  Hans-Ulrik Noorgard-Nielsen  and  Olivier Le Fèvre. The authors would like to acknowledge an anonymous referee for a careful reading and useful comments on this manuscript. This work is based on observations made with the  NASA/ESA/CSA James WEBB Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes  at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for the JWST. These observations are associated with programs GO #1963, GO #1895, and GTO #1283. The authors acknowledge the team led by  coPIs C. Williams ,  M. Maseda  and  S. Tacchella , and  PI P. Oesch , for developing their respective observing programs with a zero-exclusive-access period.
K. I. C. and E. I. acknowledge funding from the  Netherlands Research School for Astronomy (NOVA). K. I. C. , R. N. C. , and V. K. acknowledge funding from the  Dutch Research Council (NWO)  through the award of the  Vici Grant   VI. C. 212. 036. The  Cosmic Dawn Center  is funded by the  Danish National Research Foundation  under grant No. 140. L. C. acknowledges financial support from  Comunidad de Madrid  under  Atracción de Talento  grant  2018-T2/TIC-11612. S. G. acknowledges the support of the  Cosmic Dawn Center of Excellence  funded by the  Danish National Research Foundation  under grant  140. G. Ö. , A. B. , and J. M. acknowledge support from the  Swedish National Space Administration (SNSA). A. A. H. acknowledges support from  PID2021-124665NB-I00  funded by the  Spanish Ministry of Science and Innovation  and the  State Agency of Research   MCIN/AEI/10.
To measure chromatin dynamics at maximum temporal resolution, we then used this line for triple-label tracking over ve-minute windows. Over this timeframe, the majority of movies were either always transcribing or always silent, but transition points were occasionally observed (Fig. 5b ). When comparing transcribing and nontranscribing alleles, there were negligible differences in median promoter-enhancer separation (182 nm for transcribing, 175 nm for non-transcribing; p = 0. 6; Wilcoxon rank sum test; Fig. 5c ) or apparent anomalous exponent of the Sox2 promoter (median 0. 134 for transcribing, 0. 111 for non-transcribing; p = 0. 37; Wilcoxon rank sum test; Fig. 5d ; full data given in Table  S2 ). Strikingly, however, we found that the enhancer is speci cally more constrained on average when transcriptionally active (median α app 0.
The DNA was then circularized by ligation with 200 U/μg T4 DNA ligase under dilute conditions (3 ng/μL DNA), and puri ed by phenol/chloroform extraction and isopropanol precipitation. For musculus-speci c 4C pro les, samples of the DNA were digested with AvaII, cutting speci cally at the region between the Csp6I site and the nonreading primer annealing site on the castaneus allele. 100 ng aliquots of treated DNA were then used as template for PCR with bait-speci c primers containing Illumina adapter termini (primer sequences in Table  S6 ). PCR reactions were pooled, primers removed by washing with 1. 8× AMPure XP beads, then quanti ed on a Bioanalyzer (Agilent) before sequencing with a HiSeq 4000 (Illumina). Sequencing read fastq les were demultiplexed with sabre (https://github. com/najoshi/sabre) and aligned to the mm10 genome with Bowtie  73  , and intrachromosomal reads were assigned to DpnII fragments by utility tools coming with the 4See package  54.
4See was also used to visualize the 4C pro les. Interactions were called for each replicate with peakC  74  with window size set to 21 fragments, and were then ltered to only include the regions called as interacting across all wild-type replicates. For statistical comparison of speci c interactions, the 4C read counts within 1 Mb of the bait for all replicates and conditions (from the same bait) were quantile normalized using the limma package  75. The means of summed normalized 4C scores over tested interacting regions were taken as "interaction scores", and were compared across conditions by two-tailed t-tests.
Interphase nuclei were imaged on a LSM 880 AxioObserver (Olympus) microscope, using a x63/1. 4 objective lens (C plan-Aprochromat 63x/1. 4 Oil DIC UV-VIS-IT M27), under 2x zoom and line-averaging 4 settings. The interval between z-slices was 0. 37 µm and the x-and y-pixel size was 132 nm. Nuclei were segmented and inter-probe 3D distances were measured by custom scripts in ImageJ  77. Distance distribution differences were assessed by Wilcoxon rank sum tests. Single-molecule RNA FISH MS2v6 and Sox2 probes were designed with Stellaris Probe Designer and labeled with Cy3 and Cy5, respectively, on the 3' ends. Glass coverslips were placed into a 12-well plate and coated overnight with Poly-d-lysine. 400,000 cells were seeded per well and allowed to attach for 3 h to 70-80% con uency at 37ºC, 5% CO 2.
0 V3 717 (Hamamatsu). A 40x oil objective lens (NA 1. 4) with 1. 6x Optovar was used. 27 z-stacks with 300 nm slices and 1x1 binning were taken with an exposure time of 500 ms for Cy3, 750 ms for Cy5 (100% LED power for both), and 25 ms for DAPI (10% LED power). Micromanager 1. 4 software was used. A custom Python script was used to detect, localize and classify the spots (https://github. com/Lenstralab/smFISH). Cells and nuclei were segmented using Otsu thresholding and watershedding. Spots were localized by tting a 3D Gaussian mask after local background subtraction 78 and counted per cell. Western blot ~ 3 million cells were collected by trypsinization, washed with PBS and lysed in 100 µL RIPA buffer (50 mM Tris-HCl pH 8, 150 mM NaCl, 1% (v/v) NP-40, 0. 5% (w/v) sodium deoxycholate, 0. 1% (w/v) SDS, 1 mM EDTA) for 30 min at 4ºC.
MSD analysis MSD analysis was performed using the msdanalyzer utility in Matlab 81. For each individual trajectory, the time-averaged MSD was computed (time steps of 0. 5-120 s for most experiments; 1-300 s for experiments with the Sox2-SCR MS2 line), using either the trajectories uncorrected or corrected by GPTool for substrate movement, as stated. Because some trajectories exhibited large deviations from the average, especially at long time, we chose to calculate the ensemble median of all individual MSD and the median absolute deviation as a measure of the variability. For testing the t of GPTool-measured parameters to experimental data, theoretical 2D MSD curves were made from these measured parameters for each individual trajectory, giving either for parameters called without substrate correction, or for the particle and substrate parameters called when substrate motion correction is applied.
The ensemble medians of these MSD curves were plotted with the experimental MSD curves. Radial MSD was computed in exactly the same way as single-particle MSD, but using the evolution of Euclidean distance between two particles instead of the displacement of a single particle. The diffusive parameters were t to the rst 60 s of the log-log plots of radial MSD using linear least squares with bisquare weights, implemented in Matlab. 95% con dence intervals were estimated as two standard deviations of the point estimates. Diffusive parameter differences between Sox2-SCR and Inter-Down radial MSD were assessed by ANCOVA.
This renders extrusion dependent on CTCF dynamics. Finally, cohesin has LJ a nity for PolII both in the bound ( = 2. 5k B T) and unbound state ( = 2k B T, higher a nity for bound PolII mimicking its suggested role in cohesin loading on chromatin 86 ). PolII and loop extrusion dynamics are performed using a python script driving the EspressoMD library. The polymer initializes as a random walk and its dynamics rst evolves in the absence of extrusion and   suggesting that there is a stricter limit on the area that is explored.
ACKNOWLEDGEMENTS ANCHOR constructs were provided by NeoVirTech. The MS2v6 constructs were built by  Biljana Atanasovska. Sequencing was performed by the  Institute of Genetics and Molecular and Cellular Biology (IGBMC) GenomEast platform , a member of the  France Génomique consortium  ( ANR-10-INBS-0009 ). This study was made possible because of the  IGBMC Imaging Center  (ici. igbmc. fr), ow cytometry and molecular biology platforms. BioRender was used for generating Figs 7d and e. This study was funded from grants by  LabEx INRT  ( ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame program  Investissements d'Avenir   ANR-10-IDEX-0002-02 ),  ERC  ( Starting Grant   678624 -CHROMTOPOLOGY ) and  Agence Nationale de Recherche (HUBDYN).
Disruption of the actin cytoskeleton by purified recombinant toxins A monolayer of Vero cells was obtained by seeding 1 mL of a 1x10 5 cells mL -1 suspension per well in a 24-well plate (TPP), followed by incubation for 24 h at 37°C, 5 % CO 2. Sub-confluent cells were then incubated for 18h with 4 µg. mL -1 of native toxins (positive controls) or purified recombinant toxins. In addition, a negative control was also added by keeping cells in DMEM and 0. 1% of BSA only. After incubation, cells were fixed and stained with Rhodamine Phalloidin (reference: P1951, Sigma) and DAPI contained in the Fluoromount-G TM mounting medium (reference: 00495952, Invitrogen). Five random fields were acquired for each condition, and FIJI software was used to determine the mean area per cell  [29]. For each field randomly selected, using a thresholding method and particle analysis, we used the actin channel to determine the area of the field occupied by cells, expressed in µm 2 and the DAPI channel to count the number of nuclei in the field.
Phylogenetic analysis Phylogenetic analyses were conducted on the following 39 genomes of C. difficile strains: 630Δerm, A685, CD015, CD196, CIP1079324, E1, E7, E12, E14, VPI 10463, E15, E16, E23, E24, E25, E28, NAP07, NAP08, QCD_23m63, QCD_32g58, QCD_37x79, QCD-63q42, QCD_66c26, QCD_76w55, QCD_97b34, T3, T5, T6, T10, T11, T15, T17, T19, T20, T22, T23, T42, T61 and R20291. Selected genomes and protein sequences were obtained from Marc Monot  [33]. In addition, protein sequences of large clostridial toxins from Paeniclostridium_sordellii were obtained from NCBI, for TcsH (AGK40891. 1) and TcsL (Q46342. 1). See supplementary Table  S4  for genome accession numbers. For phylogenetic trees, protein sequences were first aligned with MUSCLE provided by EMBL-EBI (http://www. ebi. ac. uk/Tools/msa/muscle/)  [34].
Acknowledgments We thank  J. Peltier  for providing the knowledge on the obtention of the mutant by allele exchange in Clostridioides difficile,  P. Monassa  and  L. Henger  for the construction of plasmids pADS1 and pADS2. We also thank  Pr. M. Popoff  ( Pasteur Institute ) for generously providing C. difficile native toxins. We are also grateful to  V. Nicolas  (platform  MIPSIT of Paris Saclay University ) Finally, we thank our colleagues for their insightful review of the manuscript, particularly  J. Malet-Villemagne  who also provided us with the pJV7 plasmid. Finally, we thank  C. Janoir , whose laboratory this work was conducted in, for her constant interest and support.
The ability to reproduce (at basin scale) the combined effects of hydro-climatological processes, including sediment transport, in the absence of distributed spatial and temporal data, relies on the possibility of considering a drainage basin as a homogeneous ecosystem unit, counting only on two stations with continuous and homogeneous data. The sediment loss time-series, with a total of 28 sediment samples from Sasso d'Ombrone station (derived from the former SIMN dataset  [54] ), offered a unique opportunity to explore erosion processes in the basin. Thus, for the first time, it was possible to apply this approach to reconstruct the sediment loss from the Ombrone River Basin (ORB), over the 1942-2020 period.
Data collection Continuous sediment loss measurements made between 1949 and 1977 at the Sasso d'Ombrone turbidometric station (Fig 3B, red triangle) are a valuable source of information for sediment erosion modelling. In particular, for the pluviometric data, we refer to the data provided by the Tuscany Functional Centre (https://www. cfr. toscana. it). The percentage vegetation cover was, in both the calibration and reconstruction stages, extracted from the HYDE 3. 2 dataset  [58].
Model parameterisation and evaluation The parameters that are required to carry out the applications of our model are defined by the model structure that best represents the processes at work, in a way that model conforms to the 'real world data', and that it discerns the processes and their interactions  [53]. The constrained model parameters were derived from physical considerations, translated into empirical correlations base on observed data. The upscaling issue formulated here was optimised using both the runoff indicator, Eq  (5) , and the exponential vegetation function of Thornes  [49] , which has also been integrated to ensure that the formulation of a process at a point-location is somehow approximated over an area as large as a homogeneous basin unit. Througout the calibration process, special attention was paid to the use of sensitive parameters, which were carefully calibrated against high quality datasets to ensure that the resulting model would produce reliable estimates even over historical periods.