dataesr/scientific-paragraphs-categorization huggingface dataset:
The top 10% of all identified links (wild-type and knockout) when ranked by ABC score were then selected as 'strong' links. In our data, this corresponded to an ABC threshold of ~0. 12. Based on this threshold, unique link IDs could be assigned as wild-type-unique, knockout-unique or common depending on the conditions in which we observed that link. These thresholds were chosen to maximize the precision of identified links while retaining a large number of links to analyze although we acknowledge that maximizing precision comes at the expense of recall using the ABC model. Finally, after identification of wild-type-unique, knockoutunique and common links, enrichment analysis was performed by associating each link with a promoter and performing χ 2 analysis using the statsmodels Python module.
CUT&RUN CUT&RUN experiments were carried out according to Meers et al. 73  using a Drosophila genomic DNA spike-in and 'input' controls consisting https://doi. org/10. 1038/s41594-023-01095-4 of samples processed in parallel with the CUT&RUN samples but with untethered MNase. Western blots of nuclear lysates were also carried out  67  to measure the relative levels of CTCF and SMC3 in wild-type and Mbd3-knockout ES cells (Extended Data Fig. 1a ). For the antibodies used see Supplementary Table  2. We carried out 50 bp paired-end sequencing on a Novaseq instrument, with three biological replicates per sample obtaining 8-16 million mapped reads per replicate, respectively, whilst the input samples had 8-23 million mapped reads per replicate, respectively. All CUT&RUN data was trimmed using trim_galore (https://www.
bioinformatics. babraham. ac. uk/projects/trim_galore/) and then aligned using Bowtie2 (ref. 74) with standard parameters to the GRCm38. p6 Mus musculus reference genome (https://www. ncbi. nlm. nih. gov/assembly/ GCF_000001635. 26/). Heatmaps of CUT&RUN enrichment were made using Deeptools v. 2. 5. 0 (ref. 75). CUT&RUN bigwig tracks were calculated using BedTools and bedGraphToBigWig with standard parameters. The coverage was calculated with computeMatrix reference-point with options-binSize 10. The heatmap of standardized signal was then plotted using plotHeatmap. Peaks were called using MACS2 (ref. 76) so as to give a false discovery rate of 1% and above fivefold enrichment. All Venn diagrams were plotted using the matplotlib-venn library in Python v. 3. 6.
3D single-molecule image processing, generation of trajectories and determination of experimental precision Single molecules were localized from 3D videos using the easy-DHPSF software  80  with a relative localization threshold of 100 for all six angles for the 20 ms data and relative thresholds of 116, 127, 119, 99, 73 and 92 for the 500 ms data. The trajectories of individual molecules were then assembled using custom Python code for connecting localizations in subsequent frames if they were within 800 nm for 20 ms trajectories and within 500 nm for 500 ms trajectories (https:// github. com/wb104/trajectory-analysis). This code also outputs average signal intensity per trajectory and trajectory lengths (OPTION -savePositionsFramesIntensities) and a summary of these data is reported in Extended Data Fig.
org) and the data were displayed using the ImageJ software  85, 86. For image processing, PeakFit 87 was used to localize genomic loci from the images using the filter settings: 'shiftFactor':1. 0, 'signal-Strength':5. 0, 'minPhotons':30. 0, 'precisionThreshold':40. 0, 'minWidth-Factor':0. 5, 'maxWidthFactor':0. 5 and 'precisionMethod':'MORTENSEN'. Trajectories were then tracked in 2D using custom Python code for connecting foci in subsequent frames if they were within 500 nm (https:// github. com/wb104/trajectory-analysis). Trajectories were classified as for single molecules using a GMM (see Single-molecule trajectory analysis in the Supplementary Data and Methods).
Immunofluorescence and DNA-FISH image acquisition and analysis Imaging was carried out using a Zeiss LSM Airy Scan 2 super-resolution microscope set for imaging of DAPI (405 nm laser, 0. 8%), Cy3 (514 nm laser, 10%) and AF647 (639 nm laser, 90%). Three stacks of horizontal plane images (38,04 × 3,804 pixels corresponding to 136. 24 × 136. 24 μm 2 ) with a z-step of 150 nm were acquired for each field of view. CZI image files were then imported into IMARIS v. 9. 6 (Bitplane) for 3D modeling. Quantitative analysis of interprobe distances within nuclei was carried out using the Surfaces and Spots modules of Imaris v. 9. 6.
Software and code The following tools were used for data collection: microscope image acquisition, Micro-manager (https://www. micro-manager. org); ImageJ software  85, 86. The following tools and methods were used: for Hi-C analysis -NucProcess (https://github. com/tjs23/nuc_processing), Nuc-Tools (https://github. com/tjs23/nuc_tools), Juicer (https://github. com/aidenlab/juicer), Cooler (https://cooler. readthedocs. io/en/ latest/), CscoreTool (https://github. com/scoutzxb/CscoreTool) and Lavaburst (https://github. com/nvictus/lavaburst); for enhancer-promoter analysis (https://github. com/dhall1995/Acitivity-By-Contact_ Enhancer-Promoter_Link_Prediction); for CUT&RUN analysis -Trim galore (https://www. bioinformatics. babraham. ac. uk/projects/trim_ galore/), Bowtie2 (ref. 74)   87  ; for 3D single-molecule peak fitting -easy-DHPSF  80  ; for trajectory analysis (https://github.
com/wb104/ trajectory-analysis); for trajectory overlay visualization -TrackMate; for GMM classification (https://zenodo. org/record/6497411#. YmlG-Fy8w3q0); for 3D DNA-FISH analysis -Imaris v. 9. 6. GATAD2A confirm that GATAD2A is required for CHD4 to interact with the deacetylase sub-complex. Pull-down images are representative of ≥3 independent replicates. (c) Distribution of the four biophysical parameters described in Fig. 1  for 20ms exposure tracking of MBD3 and CHD4 in wild-type ES cells, as well as CHD4 in the absence of either MBD3 or GATAD2A/B. The data for MTA2 in the presence and absence of nuclear localised MBD3 are also shown. HaloTag with a nuclear localisation sequence (HaloTag-NLS) is also shown as a control for a (mostly) freely diffusing molecule  42, 43. The grey dotted lines indicate the upper bound (at the 95 % confidence interval) of the different biophysical parameters determined for stationary JF 549 dye molecules.
Data availability The single-molecule/locus imaging videos and XYZt single-molecule/ locus trajectory data files are available at: https://zenodo. org/ deposit/7985268 (https://doi. org/10. 5281/zenodo. 7985268). The Hi-C and Cut&Run datasets reported in this study are available from the Gene Expression Omnibus (GEO) repository under accession code GSE147789, and they were processed using the GRCm38. p6 mouse reference genome: (https://www. ncbi. nlm. nih. gov/assembly/ GCF_000001635. 26/). Source data are provided with this paper.
Code availability All the code developed in this project has been made freely available (for a description, see Methods and Supplementary Methods). The software repositories are at: https://github. com/wb104/trajectory-analysis, https:// zenodo. org/record/6497411#. YmlGFy8w3q0 and https://github. com/ dhall1995/Acitivity-By-Contact_Enhancer-Promoter_Link_Prediction.
(Right) The Bayesian information criterion (BIC) was calculated for all the datasets shown in (a) and shows that two populations (Gaussians) are the best model to account for the data -that is that model has the lowest BIC value (light blue box). (e) Table showing the proportions of trajectories containing either slow (S), fast (F1), or fast (F2) chromatin bound sub-trajectories (or combinations thereof). Extended Data Fig. 7 | 2D dCas9-GFP tracking of active enhancers. (a) Representative images of 36 gRNAs targeted to the Tbx3 enhancer in the presence or absence of MBD3 with a negative control expressing no gRNAs. (Right) An example of a cell with a doublet indicating that it is in S phase (that was excluded from the analysis) is also shown. Images are representative of ≥3 independent replicates collected over ≥2 days.
(b) Distribution of the four biophysical parameters extracted from sliding windows within the 2D singlemolecule trajectories of dCas9-GFP bound at the Tbx3 enhancer in wild-type ES cells imaged using 500 ms exposures -(Top) before and (Middle) after classification based on the anomalous exponent α, the apparent diffusion coefficient D, the length of confinement Lc, and the drift magnitude, norm∥V∥ of the mean velocity. (Bottom) Distribution of the four biophysical parameters after classification for the Tbx3 enhancer in Mbd3-ko cells. (c) (Left) Fitting of 1, 2 or 3 Gaussians to the fast-moving anomalous exponent distributions of the Tbx3 enhancer tracked in either wild-type ES (Top) or Mbd3-ko cells (Bottom) -the R 2 values indicate the goodness of fit. (Right) The Bayesian information criterion (BIC) was calculated for all the datasets to determine which number of populations (Gaussians) best modelled the data, with the lowest BIC value indicated by a light blue box.
(d) Table showing the Gaussian fitted anomalous exponent values for slow-and the fast-moving chromatin bound dCas9-GFP at both enhancers tracked in the presence and absence of MBD3. https://doi. org/10. 1038/s41594-023-01095-MBD3-dependent assembly of the NuRD complex increases Mb-range chromosomal interactions. (a) UMAP projection plot showing the reproducibility between our own and previously published (E-MTAB-6591) Hi-C datasets for wild-type (blue) mESC's grown in 2i/LIF conditions, and comparison with those from the Mbd3-ko (green). (b) The fold change in numbers of contacts at a range of genomic distances shows an increase in intermediate-range (~1 Mb) contacts in the presence of the intact NuRD complex (p < 10 -8 , for all tested length scales, two sided Mann-Whitney U-test). Error bands depict the 95 % confidence interval.
Study design We conducted a prospective study enrolling all consecutive patients who underwent ERCP for moderate-to-severe cholangitis between November 2021 and June 2023 at the Gastroenterology Unit of the Hospital of Imola, Italy. This "proof-of-concept" study was conducted in accordance with the principles of the Declaration of  Helsinki (1975 Helsinki ( , revised in 2008)). Informed consent for endoscopic procedures and anonymous data analysis for research purposes was obtained from each patient. Because of lack of a control group or any kind of treatment allocation or randomization, no registration on dedicated repositories has been performed. Inclusion criteria were moderate-to-severe acute cholangitis according to Tokyo Guidelines 2018  [22, 23]  requiring urgent ERCP, age ≥18 years, and ability to understand and sign informed consent.
The exclusion criterion was refusal to undergoing ERCP. For each patient, more than 30 variables were recorded, including patient demographic characteristics, concomitant medications, laboratory tests on admission or at the time of diagnosis of cholangitis (white blood cell [WBC] count, C-reactive protein, procalcitonin), previous biliary interventions (cholecystectomy, ERCP), post-ERCP pancreatitis prophylaxis, ERCP procedure details, early and late adverse events (AEs), and mortality. Data were collected at the time of the procedure, before discharge, and 3 months after the procedure. Patients were followed up during hospitalization and for 3 months after index ERCP; during follow-up, any additional biliary event or intervention was recorded.
Statistical analysis Categorical variables were reported as number and percentage and compared using the Fisher exact test or the Chi-square test. Continuous variables were reported as means±standard deviation (means±SD) or median and interquartile range (median IQR), depending on the distribution, and compare with the Student's t-test or Mann-Whitney test, when appropriate. Univariate and multivariate logistic regression have been used to identify factors related to incidence of AEs and 30-day and 3-month mortality. P <0. 05 was considered statistically significant. MedCalc Statistical Software version 20. 115 (MedCalc Software, Ostend, Belgium; https://www. medcalc. org; 2022) was used.
While most of these studies included elective procedures, a recent French study confirmed those outcomes also in the setting of emergent ERCPs performed for several indications  [14]. Finally, the present study contributes data in a homogeneous, albeit small population. In this setting, in November 2021, we began an ERCP program at the Gastroenterology Unit of the Hospital of Imola, Italy. Due to the expected ERCP volume of 150 procedures/year, a single duodenoscope with disposable detachable tip (TJF-Q190V, Olympus) was available in our facility. In order to reduce the risk of bacterial contamination of the reusable duodenoscope, we designed an internal protocol for adopting SUD use in patients with proven biliary infection. Therefore, we prospectively enrolled all patients with moderato-to-severe acute cholangitis requiring urgent ERCP with SUD in order to assess the performance of urgent ERCP and patient outcomes.
We speculate that leukopenia could be a surrogate marker for anergic response to biliary sepsis, justifying the unfavorable long-term outcome observed in this group, independent of clinical resolution of acute biliary infection. This study presents several limitations. First, the relatively small sample size (35 cases) cannot allow us to draw definitive conclusions. However, we prospectively collected and analyzed those data to provide one of the few pieces of evidence in the field of SUD use for urgent ERCP. To date, a similar French experience has been published, but in a relatively inhomogeneous group of patients (the authors included not only acute cholangitis but also progressive jaundice due to malignant biliary obstruction)  [14]. Another limitation is related to the performance of all procedures by a single operator.
In our targeted observing programme with XMM-Newton and NuSTAR, we aimed to cover an interesting binary phase range in which we expect strong changes in absorption while the accretion and ionisation wakes are crossing the line of sight of the observer  (Grinberg et al. 2017). In our previous work with NuSTAR  (Diez et al. 2022) , we observed strong absorption variability along the orbital phase. However, an in-depth study of the varying absorption cannot be explored with NuSTAR alone as we need coverage at lower energies, which is possible with XMM-Newton. On the other hand, XMM-Newton alone is not sufficient and we need NuSTAR to constrain the continuum, hence the necessity for simultaneous observation. We introduce the datasets and their reduction in Sect. 2 followed by a presentation of the light curves extracted in different relevant energy bands in Sect.
NuSTAR We used the NuSTARDAS pipeline (nupipeline) v2. 1. 2 with the calibration database (CALDB) v20220608 applied with the clock correction. We proceeded to the extraction of the products as in  Diez et al. (2022) , using the updated pipeline and A147, page 2 of 18  Diez, C. M. , et al. : A&A proofs,  Table  1. Observations log.
Instrument Obs ID Time start Time stop Exposure Orbital phase Orbital phase MJD (day) binarycor (a) MJD (day) binarycor (a)  (ks) (with T 90 ) (with T ecl ) Notes. ( a) Stands for 'binary-corrected' times. The BinaryCor tool in ISIS removes the influence of the double-star motion for circular or elliptical orbits. The Git repository of the isisscripts where the BinaryCor function is described at https://www. sternwarte. uni-erlangen. de/ gitlab/remeis/isisscripts/-/blame/6216a4ab8307f5825e17109db3cfb5c317a7ab08/share/isisscripts. sl. The start and stop times are given as modified Julian dates (MJD). Fig. 1. Sketch of Vela X-1 showing the orbital phases covered during this XMM-Newton EPIC-pn observation. In this image, the observer is located facing the system at the bottom of the picture. calibration files mentioned above.
XMM-Newton The observation was setup in pn-timing mode with a thin filter. For the generation of the event lists file, we used the Science Analysis System (SAS) software v20. 0 with the Current Calibration Files (CCF) as of April 2022 starting from Observation Data Files (ODFs) level running epproc 1. The default calibration uses withrdpha='Y' and runepfast='N' as of SASv14. 0 2. However, this default timing mode calibration led us to an offset of the instrumental and physical lines of the source towards higher energies of ∼+140 eV (see Appendix A. 1). Therefore, after consulting with the XMM-Newton calibration team (S. Migliari, priv. comm. ), we decided to instead turn off the Rate-Dependent PHA (RDPHA) correction (withrdpha='N') and apply the Rate-Dependent CTI (RDCTI) correction using epfast (runepfast='Y'), resulting in satisfactory spectra.
No filtering for flaring particle background was necessary. Because the source was so bright that it illuminated the whole CCD detector, no background was extracted. The event times were barycentred using the barycen tool from the SAS pipeline and were deleted if found to be on bad pixels with the #XMMEA_EP argument in the evselect step. As XMM-Newton EPIC-pn and NuSTAR observations were simultaneous, we were able to reuse the pulse period previously derived on the FPMA light curve, P = 283. 4447 ± 0. 0004 s  (Diez et al. 2022) , to extract the XMM-Newton light curves in order to avoid intrinsic pulse variability. For a sanity check, we still performed epoch folding on the XMM-Newton light curve with 1 s binning and indeed derived the exact same pulse period as in our previous work. We selected single and double pixel events (PATTERN<=4 in the evselect step) in the source region from RAWX=32 to RAWX=44.
As an additional sanity check, we performed an energy test on the iron line region (for more details, see Appendix A. 2). According to the results of these tests, we decided to exclude the three centremost columns of the PSF. Moreover, we excluded spectral channels below 0. 5 keV and above 10 keV from the analysis where the S/N was very low. We use this work to also report some cross-calibration issues between NuSTAR and XMM-Newton EPIC-pn. Cross-calibration issues between XMM-Newton EPIC-pn and NuSTAR are a known phenomenon when combining datasets from both observatories 3 (see e. g. Gokus 2017) and the XMM-Newton Science Operations Centre is working on this issue with an upcoming update of the SAS software (S. Migliari, priv. comm. ). At the time of writing, no release is available, and so we describe the cross-instrumental issues in Appendix A.
362 keV  (Garcia & Mack 1965)  blends Mg XI Heα, and the O VIII RRC at 0. 871 keV  (Garcia & Mack 1965)  with the Ne IX Heα lines (see e. g. Sako et al. 1999). Finally, our final and best-fit model, which we use for the time-resolved spectroscopy in this work, is: We show the last and most absorbed NuSTAR-orbit of our observation. We indicate the individual model components including all lines detected in this dataset (blue dot-dashed Gaussians) and the absorbed continuum (blue dotted line). We refer to Sect. 4. 2 for a detailed description of the model and to Table  2  for the soft lines. I(E) = N H,2 × [(CF × N H,1 + (1 -CF)) × (F(E) × CRSF, F × CRSF, H) + FeKα + FeKβ + 10 keV + Soft lines]. (4).
NuSTAR-orbit-by-orbit analysis We can now perform the analysis on shorter timescales with both XMM-Newton EPIC-pn and NuSTAR to access the variability of the stellar wind at low energies. Combining both datasets -and thus increasing the covered spectral range -limits the impact of possible degeneracy between the power-law slope and absorption strength. NuSTAR data are especially crucial at high absorption, when it is especially difficult to constrain the continuum with XMM-Newton only. In  Diez et al. (2022) , we extracted a spectrum for every orbit of NuSTAR around the Earth, which is referred to here as 'NuSTAR-orbit' for the remainder of the paper. This should A147, page 8 of 18  Diez, C. M. , et al. : A&A proofs,  not be confused with the duration of a binary orbit of the neutron star around its companion.
We do not use C NuSTAR , gain shift, CF NuSTAR , or C Fe as there is no need to correct for crosscalibration because no coverage from NuSTAR is available for those NuSTAR-orbits. Thirdly, for the NuSTAR-orbit that is covered by NuSTAR only (NuSTAR-orbit number 1), the multiple soft emission lines and the FeKβ line identified with XMM-Newton EPIC-pn are not resolved by NuSTAR alone. However, those emission lines do not impact the shape of the overall continuum of Vela X-1, and therefore they can be safely ignored for fitting the data for simplicity. We tried to fix them to the values of the closest following NuSTAR-orbit but no significant difference could be highlighted when comparing the residuals. We present the results of the NuSTAR-orbit-by-orbit analysis in Fig. 8 , focusing on the parameters of interest.
While these problems should not affect FPMB, together with the high variability of the CF as deducted from the NuSTAR data alone, they imply a reduced reliability of the NuSTAR data for our observation in this energy range. Moreover, looking at Fig. 6 , it is obvious that a CF of less than 0. 97 would not describe the data. We therefore decide to focus our discussion on XMM-Newton-driven CF values for the remainder of the paper.
Pulse-by-pulse: XMM-Newton only The sensitivity of XMM-Newton EPIC-pn allows us to extract a spectrum down to the pulse period of the neutron star (283 s). A147, page 10 of 18 Fig. 9. Results of the pulse-by-pulse analysis as a function of time, together with the corresponding binary orbital phase. The panels show (from top to bottom) photon index (Γ), energy of the FeKα line in keV, unabsorbed flux F 0. 5-10 keV in keV s -1 cm -2 , CF with XMM-Newton EPIC-pn (CF XMM ), and absorption from the stellar wind N H,1 in 10 22 cm -2. We performed a spectral analysis for every pulse of the neutron star to explore further variability on shorter timescales. Given the cross-instrumental issues between XMM-Newton and NuSTAR and the different coverage of the overall observation (due to both LEO of NuSTAR and the loss of data), this analysis is performed on XMM-Newton data only.
While for the NuSTARorbit-by-orbit analysis it was still possible to safely determine the individual contribution of each instrument for each GTI and exclude outliers when needed, such an approach is not feasible for the pulse-by-pulse analysis. The pulse-by-pulse results for this observation with NuSTAR only are presented in  Diez et al. (2022). We again use the model from Eq. (  4 ), setting up the initial parameters for each pulse spectrum from the results of the corresponding NuSTAR-orbit of the NuSTAR-orbit-by-orbit spectral analysis. Given the low signal of the individual datasets, we fix all parameters but the photon index Γ, the covering fraction CF XMM , the absorption column density from the stellar wind N H,1 , the energy of the fluorescent FeKα line, and the flux of all the emission lines.
To expand the frequency search range to larger values, we also performed a Lomb-Scargle periodogram using the routine of Astropy v5. 1 Timeseries software (Astropy Collaboration [photons/s/cm 2 ] Fig. 13. Fluxes in photons s -1 cm -2 of some soft lines obtained within the pulse-by-pulse analysis as functions of time, together with the corresponding binary orbital phase. The panels show (from top to bottom) the flux of the FeKα, the FeKβ, the Ne IX, and the S XV line. 2022) (see panel d of Fig. 14 ). This technique only finds the binning size of our database (and following harmonics) as a possible period but no other periodicity could be found. It does not allow the shape of the power spectrum to be assessed. This search could be enhanced with an extended sample of the N H along one or more binary orbits.