dataesr/scientific-paragraphs-categorization huggingface dataset:
A higher fraction of closed states led to a high peak score, while a higher fraction of extruding states led to a higher background and thus a lower peak score (Figure  2D ). We then systematically compared simulated and experimental peak scores for fractions of open, extruding and closed states ranging from 0 to 100%. For this purpose, we first verified the consistency of our metric with experimental findings that cohesin depletion leads to elimination of chromatin loops using Hi-C maps of auxin-treated cells  (16). As expected, only simulations without closed states and with a large majority of open states (>70-80%) had peak scores statistically consistent with the Hi-C data in these cells (Figure  2C , right; P > 0. 05). We then used this peak score to compare simulation predictions to Hi-C maps in cells without auxin treatment.
Comparing polymer simulations to imaging data Having determined the fractions of loop states consistent with experimental Hi-C data, we next compared our simulations to published measurements of distances between chromatin loci from experimental imaging data. We first compared simulations of 504 kb loops with or without extrusion to recent live-cell imaging data from a study by Gabriele et al. (35)  that specifically targeted a pair of loop anchors in mouse embryonic stem cells (mESC) with and without cohesin and found good agreement between experiments and simulations in both cases (Supplementary Figure  S4 ). Next, we compared our simulations to a larger experimental data set where DNA FISH was used to measure distances between pairs of loci in mESC for a large range of genomic distances between these loci  (31).
Quantifying closed loop lifetimes from live-cell trajectories While the analysis of static data can provide important quantitative information on loop states, as shown above, a full characterization of the dynamic aspects of loop extrusion, in particular extrusion speed and the lifetime of closed loops, requires dynamic data. Here and in the next section, we assess to what extent these parameters can be estimated from live-cell tracking of loop anchors. For this purpose, we generated 1000 independent Langevin dynamics simulations of a polymer undergoing extrusion and recorded the trajectories of the two anchors as function of time. We considered a polymer with two loop anchors separated by 150 kb and simulated trajectories of 1330 s each. We assumed that extrusion started 83 s after the beginning of each simulation at a random position between the two anchors, and that extrusion proceeded until the loop was closed.
Biological parameters include the fractions of closed, open and extruding states, the speed of loop extrusion and the genomic size of the loops. Experimental parameters include the reporter-anchor separation, the localization precision of fluorescent reporters, and the sample size (number of measured anchor-anchor distances). To estimate plausible ranges of loop state fractions, we compared simulations to Hi-C data of ≈300 kb large loops (Figure  2 ), and found that only small (15-25%) fractions of closed states are consistent with the data, in line with previous studies  (20, 35). Despite this relative rarity of closed loops, we showed that in most cases the presence of loop extrusion can be detected from static images alone for realistic localization precisions, provided that the sample size N (i.
DATA AVAILABILITY The LAMMPS parameter files used for the molecular dynamics simulations and the Python code used to estimate loop state fractions from static imaging data are available at https://github. com/imodpasteur/LoopExtrusion and through Zenodo at  (88). Numerical data of static and dynamic imaging simulations are available through Zenodo at  (89). All other numerical data are available on request to the corresponding authors. Hi-C and ChIP-Seq data used in this work were taken from Rao et al. (16)  with accession number GSE104334.
Transmission Electron Microscopy (TEM) and Energy Dispersive Spectroscopy (EDS) Scanning transmission electron microscopy (STEM) was performed using a JEOL JEM-ARM200F 200kV probe (Tokyo, Japan) at the University of Wollongong Institute for Superconducting and Electronic Materials (ISEM, Wollongong, NSW, Australia). Highresolution images were taken to visualize the gold core structure, and Energy Dispersive Spectroscopy (EDS) was taken to qualitatively assess the element distribution of the particles. The sample was left to partially dehydrate over two days to increase the concentration of the material before TEM preparation. The aqueous suspension was deposited dropwise to a copper-coated lacey carbon grid and left to dry overnight before analysis. The images were processed using Gatan Digital Microscope, and EDS maps were produced using Noran System 7 (NSS) software.
Fourier-Transform Infrared Spectroscopy (FTIR) Fourier-transform infrared spectroscopy (FTIR) was taken to confirm the successful synthesis and cross-linking of the AF647 fluorophore. The spectra of the ligand coating were analyzed by a comparison between the molecular structures present in the samples with and without the cross-linked fluorophore. The data were taken using the Bruker VERTEX 80 infrared spectrometer (Bruker, Germany), within the range of 4000 cm -1 to 400 cm -1 at a resolution of 0. 5 cm -1. In each case, the background spectrum was measured before the raw transmittance was recorded, and the solid samples were compressed onto the surface of the reflection unit (quartz). The background spectrum was removed from the raw data, and the new spectra were normalized using the S-S transmittance percentage (%) as a benchmark at 501 cm -1.
Our recent studies of kites in various regions propose to bridge this gap by applying a multi-proxy approach to the worldwide expansion of these spectacular types of traps, drawing on diverse disciplines such as archaeology and social anthropology, bioarchaeology and geoarchaeology, geomatics and statistics, and geochronology  [6]. The whole dataset is based on a large body of archaeological structures initially detected from satellite imagery, and then studied in the field. These massive structures visible from airplanes were first recognized in the 1920s and were quickly interpreted as hunting traps, which was confirmed by recent archaeological excavations  [6, 7, [10] [11] [12]. Until recently, almost no in-depth studies had been carried out to enhance our understanding of their function, functioning, chronology or why they were so widespread in many regions ranging from Arabia to Uzbekistan.
Site identification, survey and excavation At Jibal al-Khashabiyeh, kites were discovered in 2013 and recorded and excavated in 2015 and 2016 as part of the South-Eastern Badia Archaeological Project (SEBAP), in cooperation with the Department of Antiquities (DoA) of the Jordanian Ministry of Tourism and Antiquities. They were recorded with two systems: the one used in the field by SEBAP (JKSH 01 to 08) and the one used on global satellite imagery interpretation by the Globalkites Project (JD1088 to 1095; for Globalkites methodology see  [6, 7, 13] ). At Jebel az-Zilliyat, kites were discovered in 2014 and excavated in 2015 as part of joint fieldwork by the Dumat al-Jandal archaeological project and the Saudi Heritage Commission. Kites were recorded in two systems: the Dumat al-Jandal database (DAJ137, DAJ138, DAJ139, DAJ140) and the one used by the Globalkites Project (respectively: AB135, AB136, AB547, AB549).
In Jordan, excavation and survey permits for the 2015 and 2016 seasons were issued by the DoA (Num. 2015/31, 2016/48). In Saudi Arabia, the Saudi Heritage Commission issued the excavation and survey permits for the 2014 and 2015 seasons. The locations of each archaeological structure and feature were recorded in the field using high-precision Trimble Pathfinder Pro XRS Differential Global Positioning System (DGPS). Geomatic analyses of both fieldwork data and satellite imagery went through a Geographic Information System (GIS; using Esri ArcGIS software). A regularly updated online interactive map shows the current number of recorded kites (available at www. globalkites. fr). DGPS mapping and kite aerial photography (KAP; using HD digital cameras triggered by intervalometer or radio control system) led to Digital Surface Models (DSM) and photogrammetric rendering of topography and archaeology (with Agisoft PhotoScan software), documenting archaeological features visible on the surface, and archaeological excavations.
The 3D model was processed using PhotoScan (Metashape) Agisoft software v. 1. 6. 1. (with the following settings: Align accuracy: High; Dense cloud quality generation: High; Mesh build: custom 4 000 000 faces; Texture size/count: 12288 x 1). In view of the quality of the photos, the 3D model underwent minimal standard cleaning processing during the various building steps. The model was set to scale through measurements taken on the marker points set on the stone prior to photography. The 3D model was then set into a defined orthonormal frame, with its origin set at the center of the stone, the x axis through the width of the stone, y axis through its length and z axis through its thickness. Orthomosaics were generated for six predefined views (front, back, left, right, bottom and top), producing a basic plate with all faces of the stone (Fig  4C ).
org/10. 34969/cnd3d/ 737849. o. 2022. The stone was weighed on a scale (Health-o-meter Professional): its weight is 92 kg, which roughly corresponds to an estimation based on an average limestone density of 2. 6 g/ cm 3 and a volumetric measurement of the monolith on the 3D model of 36,768 cm 3. The engraved boulder at Jebel az-Zilliyat has not been removed from its original location. Several photosets (total 170 photos) have been made in the field under different lights and angles, with one 50 cm photo scale for geometric point of reference. Two photosets were dedicated to the engraved details, and one to the general context; all have been merged into the general 3D model of the entire setting. The 3D model was processed using PhotoScan (Metashape) Agisoft software v. 1. 6. 1. using two chunks, one for the details and one for the modeling of the whole block.
All dates were calibrated with Chronomodel v. 2  [69]  using IntCal20 atmospheric calibration curve  [70]. OSL dating was carried out on sediment samples taken from the filling of trap pits. These ages will post-date the age of construction and final use. Small aliquots of quartz (1 mm) were employed for determination of Equivalent Dose (D e ) using a modified Single Aliquot Regenerative Dose (SAR) protocol  [71]. Mean D e values were calculated using the Minimum Age Model  [72]  due to the presence of partial bleaching as observed in the D e distributions. The concentration of dose rate relevant elements was determined using high-resolution gamma spectrometry (cf. [73] ), annual dose rates and ages were calculated using ADELE v. 2017 software  [74]  (see S11 Fig and  S4 Table).
Acknowledgments In Jordan, we thank the  Department of Antiquities , as well as the  French Institute for the Near East (Ifpo)  and  Al Hussein Bin Talal University  for their support. In Saudi Arabia , we thank the  Saudi Heritage Commission ,  Guillaume Charloux  and  Romolo Loreto  codirectors of the Dumat al-Jandal archaeological project. We thank  Charly Poliakoff  who discovered the engraving in Saudi Arabia during a rock art survey, and for sharing his photographs and analyses of the kite representation from Jebel az-Zilliyat. We thank  Sabine Sorin  and the CND3D team for publishing the 3D models online.
the  Near East (Ifpo, Amman ) and is expected to be soon housed in the  Hussein Bin Talal University exhibition  room of the South-The  South-Eastern Badia Archaeological  Project (SEBAP; research at Jibal al-Khashabiyeh) is funded by grants from the  French Ministry of Foreign Affairs, Al-Hussein Bin Talal University  (project No. 164/2016 ) and the  CNRS National Institute for Humanities and Social Sciences (WAA, MT). The  GLOBALKITES  project (research at Jebel az-Zilliyat) was funded by a  French National Research Agency  grant  ANR-12-JSH3-0004-01  (RC). The Dumat al-Jandal archaeological project (research at Jebel az-Zilliyat) was funded by grants from the  Saudi Heritage Commission , the  French and Italian Ministries of Foreign Affairs, CNRS UMR-8167 Orient & Mediterranee , the  University L'Orientale of Naples (Guillaume Charloux and Romolo Loreto).
In contrast, with liposomes, the decoherence times are fast enough to decay to the background well within the evolution time of 1. 2 μs used in the liposome DEER measurements. The decays do not show oscillations, as is frequently observed for spin pairs with narrow distance distributions  [68] [69] [70] [71]. As a control to rule out lateral aggregation  69  , we attached a single spin-label to residue 37C and did not observe a dipolar evolution (DEER shape) that could indicate a pair with a shorter than ~80 Å separation based on only slightly concave shape of the signal (Fig. 4e ). The DEER data thus suggest that conspicuous dipolar signals in the doubly labeled proteins result from intramolecular interactions, rather than being caused by intermolecular interactions on membranes. Distance distributions could be obtained in the presence of liposomes.
Circular dichroism Circular dichroism (CD) spectra of purified PLIN3 full length and its constructs were measured on Spectropolarimeter (Jasco, J-715). A total of 0. 12~0. 24 mg/mL of protein was incubated with 1~2 mM liposomes containing 4ME-PC/PE/PA for 40 mins prior to measurement. Liposomes/protein mixture was prepared in the buffer containing 20 mM Tris pH 7. 5 and 150 mM NaCl. CD spectra were measured between 190 nm and 260 nm in increments of 1 nm, with a bandwidth of 50 nm and an averaging time of 1 min at 25 °C. 10 iterations of spectra were averaged and was reported into a mDeg, which was converted to molar ellipticity (m°× M/(10 × L × C)) of which unit is expressed in degree cm 2 dmol -1. Molar ellipticity of each protein fragment in the absence or presence was plotted using GraphPad prism software.
HDX-MS Protein digestion and MS/MS data collection Protein samples were rapidly thawed and injected onto an integrated fluidics system containing a HDx-3 PAL liquid handling robot and climate-controlled (2 °C) chromatography system (LEAP Technologies), a Dionex Ultimate 3000 UHPLC system, as well as an Impact HD QTOF Mass spectrometer (Bruker). The protein was run over either one (at 10 °C) or two (at 10 °C and 2 °C) immobilized pepsin columns (Trajan; ProDx protease column, 2. 1 mm x 30 mm PDX. PP01-F32) at 200 μL/min for 3 mins. The resulting peptides were collected and desalted on a C18 trap column (Acquity UPLC BEH C18 1. 7 mm column (2. 1 x 5 mm); Waters 186003975). The trap was subsequently eluted in line with an ACQUITY 1. 7 μm particle, 100 × 1 mm 2 C18 UPLC column (Waters), using a gradient of 3-35% B (Buffer A 0.
HDX-MS peptide identification Peptides were identified from the non-deuterated samples of PLIN3 using data-dependent acquisition following tandem MS/MS experiments (0. 5 sec precursor scan from 150-2000 m/z; twelve 0. 25 sec fragment scans from 150-2000 m/z). MS/MS datasets were analyzed using PEAKS7 (PEAKS), and peptide identification was carried out by using a false discovery based approach, with a threshold set to 1% using a database of known contaminants found in Sf9 and Escherichia coli  85. The search parameters were set with a precursor tolerance of 20 ppm, fragment mass error 0. 02 Da, charge states from 1-8, leading to a selection criterion of peptides that had -10logP scores of 22. 8 for the pulse experiment and 20. 9 for the liposome experiment.
HDX-MS mass analysis of peptide centroids and measurement of deuterium incorporation HD-Examiner Software (Sierra Analytics) was used to automatically calculate the level of deuterium incorporation into each peptide. All peptides were manually inspected for correct charge state, correct retention time, appropriate selection of isotopic distribution, etc. Deuteration levels were calculated using the centroid of the experimental isotope clusters. Results are presented as relative levels of deuterium incorporation, with the only correction being applied correcting for the deuterium oxide percentage of the buffer utilized in the exchange (63 and 72%). For the experiment with a fully deuterated sample, corrections for back exchange were made by dividing the pulse %D value by the fully deuterated %D value and multiplying by 100.
The raw %D incorporation for the fully deuterated sample is included in the source data, with the average back exchange being 33%, and ranging from 10-60%. Differences in exchange in a peptide were considered significant if they met all three of the following criteria: ≥5% change in exchange and ≥0. 4 Da difference in exchange. The raw HDX data are shown in two different formats. Samples were only compared within a single experiment and were never compared to experiments completed at a different time with a different final D 2 O level. The data analysis statistics for all HDX-MS experiments are in Supplementary Table  1  according to the guidelines of  86. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025717  87.
Dynamic light scattering The size of liposomes was measured by dynamic light scattering (Brookhaven Instruments NanoBrook Omni), with or without fulllength PLIN3. Liposomes were prepared in the buffer containing 50mM HEPES pH 8. 0 and 50 mM NaCl by repeating the freeze/thaw cycles to prevent multilamellar vesicle formation. Each protein construct was incubated with liposomes in 1:200 of molar ratio at 25 °C for 40 mins. DLS was conducted using a scattering angle of 90°, and the liposome/protein mixture was equilibrated for 5 mins after loading into the instrument to get a uniform temperature and minimize any loading effects prior to measurement. The number average size distribution (%) was considered as a relative concentration of particles with a certain size. For analysis, measurements with large outlier peaks, which were suspected to be due to dust particles or aggregated vesicles, were discarded, and three runs that did not contain outlier peaks were used for the data analysis.
5 ns in subsequent measurements, i. e. by the quarter period of the 26. 2 MHz nuclear Zeeman frequency for protons at 615 mT corresponding to the working frequency. The four signal traces were summed to achieve deep suppression of nuclear ESEEM  91. The solution samples had phase memory times, T m 's, of about 2. 5 μs, so the data could be recorded up to 3 μs evolution time (t m ). Such evolution times did not provide sufficient decay of the dipolar coherence to the background level, providing however clear indication of the unstructured nature of the protein in solution. Any residual secondary structure could be probed by using multiple labeling sites, however, other techniques did not encourage this undertaking. For solutions, the DEER data were acquired in less than 12 h. Faster phase relaxation times (~1 μs) and low spin concentrations in liposome samples required signal averaging for ~24 h to facilitate good reconstruction by Tikhonov regularization using MATLAB  92, 93.
Structure prediction by AlphaFold and RoseTTAFold Structure prediction of PLIN3 was carried out by AlphaFold and RoseTTAFold. Two different confidence values were generated (pLDDT from AlphaFold and rmsd from RoseTTAFold). These values were converted using PHENIX to get relative confidence level from the two softwares, AlphaFold and RoseTTAFold 99.
Acknowledgements This work was supported by in part by the  NIGMS  ( R35GM128666 , MVA), the  Sloan Research Foundation (MVA) , the  Natural Science and Engineering Research Council of Canada  (Discovery Grant  2020-04241 , JEB) with salary support from the  Michael Smith Foundation for Health Research  ( Scholar award   17868 , JEB), the  ANR  ( 18-CE11-0012-01-MOBIL ,  CE11-0032-02-LIPRODYN  and  21-CE13-0014-LIPDROPER , ART), and the  NSF  ( CBET 1903189  and  DGE 1922639 , SRB). ESR study, conducted at ACERT, was supported by  NIH/NIGMS  grants  1R24GM146107  and  P41GM103521.
Study design This is an ancillary study of our French retrospective study and review of the literature  (10). All the methodological details of the patient recruitment and data collection are detailed in this previous publication. The inclusion criteria were patients aged less than 18 years, presenting a clinical presentation of idiopathic purpura fulminans in a post-infectious context and with evidence of a transient protein S deficiency. All data were collected retrospectively from the patient files for the patients of the retrospective French cohort and from the literature for the patients of the historical series. For this study, we focus on patients who were tested for APL and/or thrombophilia. APL was assayed according to ISTH recommendations (11), including assays for lupus anticoagulant (LA) anti-cardiolipin antibodies (ACL) (IgG or IgM), and antibeta 2-GPI antibodies (Aβ2GP1) (IgG or IgM).
Statistical analysis The continuous variables are presented as medians with the minimum and maximum values, and binary variables as the ratio of the total population. Distal necrosis leading to amputation and skin necrosis requiring skin grafting were considered to be severe complications. We used Fisher's exact test and calculated relative risk to study APL or thrombophilia association with patient outcomes. The statistical analyses were performed using GraphPad Prism version 9 for Windows software (GraphPad Software, San Diego, California, USA).
Materials and Methods Isolates. Ninety-six isolates that were either susceptible or resistant to fluconazole were selected for this study (Supplemental Data Table  S1 ). Some of the isolates used in the present study have been previously described  [9, 18]. Among the resistant isolates, 39 belong to the clonal set that recently spread in different ICUs of two hospitals located in Paris (the La Pitié-Salpêtrière hospital (PSL) and Bichat Claude Bernard hospital (BCH)). The remaining isolates were selected from the daily activity of three hospitals (PSL and BCH in Paris and the Pellegrin Hospital in Bordeaux). All Candida parapsilosis isolates were cultured in parallel on three types of culture media (Sabouraud Chloramphenicol Gentamycin (SAB-CG; Oxoid, Dardilly, France), Chromagar (CHR; BD, Le Pont de Claix, France) and Blood Agar (BLOOD; BioMérieux, Craponne, France)).
Spectra were acquired using Microflex machines located in four different Parisian laboratories: the mycology department (MYCO-PSL) and the bacteriology department (BACT-PSL) in the Pitié Salpêtrière hospital, the bacteriology department in the Bichat Claude Bernard hospital (BICHAT) and the bacteriology department in Saint-Antoine hospital (SAINT-ANTOINE). For the four mass spectrometers, the default acquisition method (MBT-AutoX) was selected. One default spectrum acquisition parameter from the Flex Control software (version 3. 4) was altered as follows: when none of the 800 shots led to a spectrum that met the manufacturer requirements, the sum of the rejected spectra was saved instead of selecting the default option (i. e. , do not save), allowing a systematic acquisition of a spectrum, regardless of the wear of the machine.