dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR duplicated reads were marked and removed using SAMtools. For the visualization of ChIPseq tracks, BedGraph tracks were generated using the GenomeCoverageBed function from SAMtools. IGV was used to visualize the tracks  [78]. The regions with differential enrichment of H3K4me3 in the control and treatment were identified using the csaw (v1. 26. 0) package  [79]. First, we counted count reads in the full genome using windowCounts() with spacing = 50, width = 150, ext = 530, shift = 0, filter = 10, and bin = FALSE parameters. Then, normalization factors were calculated using normFactors (). Then, estimate-Disp () and glmQLFit () from the edgeR package (v3. 34. 0)  [80]  were used to calculate the log2-fold-change, p value and FDR of the differentially enriched regions. The significantly differentially enriched regions were selected at FDR < 0.
RNA extraction and quantitative PCR Total RNA was extracted using an RNeasy Plus Mini Kit (Qiagen) according to the manufacturer's instructions. This kit includes a DNA elimination step. Reverse transcription of 1 µg of RNA was performed using an iScript ™ cDNA Synthesis Kit (Bio-Rad). The resulting cDNA was diluted 10 times and used for quantitative RT-qPCR. The primer sequences used for RT-qPCR are shown in Additional file 1: Table  S9. RT-qPCR was performed using iTaq Universal SYBR Green SuperMix (Bio-Rad) according to the manufacturer's instructions on a CFX384 Touch Real-Time PCR Detection System (Bio-Rad). The quantification cycle (Cq) values were calculated using Bio-Rad CFX Manager 3. 1. The Cq values of RPL37A cDNA were used for normalization, as this housekeeping gene showed low variation among all samples.
Immunofluorescence For immunofluorescence, cells were fixed with 1% paraformaldehyde for 1 h, washed with PBS, additionally quenched with 10 nM glycine, permeabilized with 0. 15% Triton X-100 and blocked with 4% BSA. Cells were incubated at 4 °C with either 1:500 anti-H4 hyperacetylated histone (06-956, Millipore) or 1:500 anti-H3K9me3 (ab8898, Abcam) or 1:200 anti-ESR1 (ab32063, Abcam), or 1:200 anti-p53 (ab1101, Abcam) primary antibody, washed, incubated with Alexa-conjugated secondary antibody for 45 min and mounted in Vectashield containing DAPI. The cells were then analysed using a Zeiss fluorescence microscope, and images were taken using AxioVision software. Images were obtained using an AxioImager microscope equipped with an AxioCam MRc5 camera and Axio-Vision software version 4. 8.
The Rod-Extremity and Gadolinia AnaLysis (REGAL) Program is a joint international effort to expand the nuclide inventory experimental data for irradiated nuclear fuel, with a specific focus on addressing two challenging needs associated with the characterization of modern, high duty, nuclear fuel. The first challenge is filling the gaps in experimental nuclide inventory data for gadolinia (UO 2 -Gd 2 O 3 ) fuel rods. The huge absorption cross sections of Gd-155 and Gd-157 in the Gd dopant in these rods lead to atypical spatial self-shielding patterns and have an impact on the neutronic environment within the fuel assembly compared to regular UO 2 fuel rods. The second challenge is investigating the impact of burnup gradients at rod extremities on fuel composition and neutron leakage, to provide relevant experimental data for assessing computational capabilities to model such impact.
These types of experiments require complex analytical methods (i. e. , for sample preparation, chemical separation, spectrometry), specialized instruments and expertise, state-of-the-art experimental laboratories, and facilities for handling and measuring irradiated nuclear fuel. Experimental programs for nuclide inventory measurements are challenging endeavors which, as demonstrated by past international efforts, can be successfully performed through collaborative efforts, benefitting from diverse expertise and capabilities across institutions in various countries  (Boulanger et al. , 2004; Baeten et al. , 2002). The Rod-Extremity and Gadolinia AnaLysis (REGAL) Program is a joint international effort to expand the experimental nuclide inventory data for irradiated nuclear fuel. It specifically focuses on addressing two challenging needs associated with the characterization of modern, high duty, nuclear fuel.
, 2020). While earlier designs of gadolinia rods had Gd 2 O 3 loadings of up to 5-6% and natural uranium or uranium enrichments lower than that of regular UO 2 rods within the same fuel assembly  (Manzel and Dorr, 1980) , modern designs are characterized by Gd 2 O 3 loadings up to 10% and uranium enrichment closer to that of the UO 2 rods in the assembly. The local inhomogeneities induced by rod extremities or burnable poison rods are challenging for the prediction by neutron transport codes and coupled depletion codes. The assessment of their predictive qualities relies on relevant high-quality experimental data which are scarce in the open literature. The REGAL project aims to fill this gap by performing high-quality radiochemical analyses on a one-cycle irradiated gadolina fuel rod and two 'high-duty' UO 2 sibling rods, all irradiated in the Tihange 1 (Belgium) pressurized water reactor (PWR).
In the framework of REGAL, blind cross-checks on quasi-identical samples are planned, with multiple highly reputable radiochemical laboratories. These cross-check analyses will allow improving the measurement uncertainties and increase confidence in the reported results. Moreover, the nuclide inventory information averaged over the fuel pellet that is obtained by destructive radiochemical analysis will be complemented by a spatially resolved local analysis using Secondary Ionization Mass Spectrometry (SIMS) and Electron Probe Micro Analysis (EPMA) to describe the radial distribution of nuclides in the fuel pellet. SFCOMPO, the world's largest database  (OECD-NEA, 2017; Michel-Sendis et al. , 2017)  of measured nuclide inventories in spent nuclear fuel, provides RCA data for 750 samples selected from fuel irradiated in 44 reactors of different types, and with measurement data for over 90 nuclides important to the reactor and back-end fuel safety applications.
Albeit extensive and covering a large space of fuel characteristics, the database is limited in data for gadolinia fuel. Only 49 UO 2 -Gd 2 O 3 samples selected from fuel irradiated in one PWR and in five boiling water reactors (BWRs) are present in SFCOMPO. Only 5 of these 49 samples include measurements for Gd-155 and Gd-157. Two of these five samples, from Fukushima-Daini-1 (Japan) BWR fuel and with burnups of 28 and 54 GWd/t HM , were selected from the same fuel rod with an initial 5% Gd loading. The other three samples from the Ohi-2 (Japan) BWR, were selected from two fuel rods with 6% Gd initial loading and had burnups in the 21-29 GWd/t HM range. The REGAL samples fill existing data gaps for lower burnups of less than 15 GWd/t HM , of high relevance to peak reactivity in BWRs and to optimize fuel performance during operation.
The REGAL Program is coordinated by SCK CEN, the Belgian Nuclear Research Centre, who also coordinated earlier successful international spent fuel assay programs such as MALIBU  (Boulanger et al. , 2004) , and participated actively in the ARIANE program  (OECD-NEA, 2011). Phase I of REGAL, which formally started in 2016 and was completed in 2021, included measurements of ten spent fuel samples, five from a UO 2 rod and five from a gadolinia rod. The REGAL partners at the start of Phase I included, in addition to the SCK CEN lead, participants from ENGIE and Institut de Radioprotection et de Sûreté Nucléaire (IRSN) in France, and Oak Ridge National Laboratory (ORNL) in the United States. Later, other French institutions joined the program: CEA, EDF, and FRA-MATOME. Phase II of REGAL, initiated in 2021, will include crosschecks of selected samples at different measurement laboratories, measurements of radial distributions within the fuel rod of nuclide inventories, and an expanded analysis matrix.
Experimental data The fuel samples measured in the REGAL Phase I were taken from three fuel rods: two UO 2 fuel rods from the same assembly and one gadolinia fuel rod from another assembly. Both assemblies were irradiated in the Tihange 1 PWR. Each of the UO 2 rods had an estimated rod-average burnup of approximately 50 GWd/t HM and were irradiated in the reactor for two consecutive cycles. The gadolinia rod, irradiated for only one cycle, had an estimated rodaverage burnup of approximately 12 GWd/t HM  (Govers et al. , 2015). The main characteristics of these rods in comparison with those rods that were the subject of investigations under the ARIANE and MALIBU programs are presented in Table  1. As seen, the REGAL technical scope adds to and complements that of the two previous efforts. The characteristics of the rods considered under REGAL, the focus on gadolinia fuel and special attention to rod extremities, make the experimental database more applicable to modern fuel assemblies and reactor operation (longer fuel cycles, smaller number of cycles to achieve higher burnup, and the inclusion of integral burnable absorbers such as gadolinia rods with a high Gd loading).
Therefore, the benchmark definition focuses on ensuring consistency among the codes rather than providing the best-estimate description of the fuel irradiation, the latter which ultimately depends on the code utilization itself. The adequacy of the proposed benchmark model is assessed by comparing the calculated nuclide inventories with the corresponding experimental data. Fuel properties and conditions during irradiation are available in data books that are based on data provided by the fuel vendor and reactor operator. In the following, the name of the samples is referred to as D05 and E14, corresponding to the rod from where the UO 2 and UO 2 -Gd 2 O 3 samples have been taken respectively. The data books available for the fuel rods D05 and E14 have served as the basis for the benchmark model definition, which required simplifications that took into account the computational aspects of the modeling and compatibility of implementation among the various codes.
Assembly and fuel data Although rods D05 and E14 were irradiated in different reactor cycles, they were hosted in the same fuel assembly type. A sketch of the fuel assembly indicating the positions of the D05 and E14 rods is shown in Fig. 2. It is a standard 15 Â 15 assembly lattice containing 188 UO 2 rods and 16 UO 2 -Gd 2 O 3 rods, 20 guide tubes, and one central lattice location containing water for instrumentation insertion. Typical rod and assembly pitch values are used, evaluated at hot core conditions, and taking into account the thermal expansion of the assembly and spacer grids. All fuel rods have identical dimensions and the M5 material is used for cladding. The guide tube and rod dimensions are evaluated at cold conditions. The as-manufactured diameter is considered when modeling the fuel pellets, therefore not taking into account fuel swelling and densification during irradiation.
ALEPH ALEPH2  (Stankovskiy et al. , 2020 ) is a Monte Carlo burnup code developed at SCK CEN, which couples the Monte Carlo particle transport codes MCNP or MCNPX with a deterministic depletion algorithm. The most recent version of ALEPH2 allows the use of two different depletion solvers: the ORIGEN 2. 2  (Croff, 1980)  solver and the RADAU5  (Hairer and Wanner, 2002)  solver. One of the strengths of the ALPEH2 code is its data consistency: the same source of nuclear data is used for particle transport and for the decay data and the energy-group cross section collapsing that feeds the depletion solver. For the current benchmark exercise, ALEPH2 version 2. 8 is used, using both the JEFF-3. 3 and ENDF/B-VIII. 0 nuclear data libraries. The assembly and depletion regions were modeled using a 3D representation of the 1/4 assembly model, taking advantage of the symmetry of the configuration.
It couples a 2D deterministic neutron transport solver that is based on the method of characteristics to evaluate self-shielding and lattice shielding effects, with the ORIGEN code used for depletion calculations. In this work, depletion simulations with SCALE were performed with each of these three approaches: (1) TRITON/KENO-VI in multi-group mode, (2) TRITON/NEWT, and (3) Polaris. The 252group built-in cross section library based on ENDF/B-VII. 1 evaluated data was used in all these three cases. Internally within TRITON or Polaris, the problem-dependent multi-group neutron flux and cross sections are post-processed at each depletion step and for each depletion material to generate a transition matrix that is used by ORIGEN to calculate the evolution of the nuclide inventories in these materials.
VESTA VESTA 2. 2  (Haeck and Dechenaux, 2017 ) is a Monte Carlo depletion code developed by IRSN, which couples a continuous energy Monte Carlo neutron transport code with a deterministic depletion module. The VESTA 2. 2 calculations performed for the REGAL benchmark used the MCNP6 Monte Carlo code and the IRSN PHOE-NIX depletion module. The nuclear data libraries used for this study are based on ENDF/B-VII. 1 and JEFF-3. 2 libraries. The symmetry of the system has been used to develop a 1/8 assembly model in a 3D representation using MCNP6 and based on the benchmark specifications. Reflective boundary conditions were applied to the outermost boundary. All fuel rods are individually depleted assuming a constant power over a time interval. According to the benchmark specifications, the UO 2 rods are modeled as a single zone, except for the investigated rod.
WIMS WIMS  (Lindley et al. , 2015)  is a general-purpose reactor physics code developed by the ANSWERS British company, which is part of the Jacobs Engineering Group. The code includes deterministic and probabilistic neutron transport solvers that can model 2D and 3D geometries. The calculations for this benchmark were performed with the code version WIMS10 ru0. A 172-group cross section library that used for the benchmark is based on ENDF/B-VII. 1 data. This library has been prepared by the code developers with standard modules of NJOY  (Macfarlane, 2017). A typical PWR weighting spectrum was used to perform the cross section condensation. In addition to the broad-group data, the library does also contain fine group data for resonant nuclides which have been produced by a set of NJOY modules developed specifically for this purpose.
Conclusions The first phase of the REGAL international program has provided valuable high-quality experimental data to fill in existing gaps in the database of nuclide inventories for spent fuel that can be used as a validation basis for computational tools and nuclear data. REGAL specifically adds data to investigate the impact of burnup gradients at rod extremities for characterization of modern, high duty, nuclear fuel and the impact of gadolinium as a burnable neutron poison. Preliminary evaluations of measurements performed for two fuel samples during the first phase of the program are discussed in this paper. The computational assessments presented here are based on an initial benchmark that was designed to serve as a consistent investigation basis for the experimental data evaluation using multiple computational tools and nuclear data.
The primary goal of this assessment was to identify any improvements that would be necessary to develop adequate best-estimate models, as well as to identify any unexpected behavior in the comparison calculation-experiment for the considered nuclides. The latter would be instrumental in pointing out potential gaps in the nuclear data used in the simulation or in the level of detail necessary in a best-estimate model, as well as in indicating qualitative impacts of various modeling approximations used with different codes. Both measured samples discussed here were selected from fuel rods irradiated in PWR fuel assemblies that contained gadolinia rods as integral burnable absorbers. The measured UO 2 -Gd 2 O 3 sample is unique, having the highest Gd 2 O 3 loading (10 wt. %) and the lowest burnup (relevant for peak reactivity) among all gadolinia samples for which destructive assay data are available in the open literature.
Provisional city-specific costs are computed by aggregating by city the costs filled in the claims data provided by the cedents. Because these claims data are not exhaustive, the sum of all the provisional city-specific costs is smaller than the estimated overall cost. The (final) city-specific costs are proportional to the provisional city-specific costs in such a way that the sum of all the (final) city-specific costs equals the estimated overall cost. Figure  1  illustrates the gaps between the estimated overall costs across France and the sum of the provisional city-specific costs. The ratios of the latter to the former range from 20% to 90%. About the city-level SWI. For every year and every city, we derive a collection of 36 city-level SWIs, one for each ten-day period (a décade in French) that make up a year.
POS taggers As for the corpora, different morphosyntactic tagging tools specific to the French language have been proposed, each with its own specificities in terms of approaches and tags granularity. The list of the most popular POS taggers for the French language is detailed in Table  2. We can first note that all POS taggers are based on statistical approaches and are, for the most part, trained on open corpora. Only LIA-Tagg relies on a private corpus, on which we found no description. Nevertheless, if we look at the open corpora, we see that French TreeBank is mostly used: its license being non-commercial (i. e. CC-BY-NC-ND, as seen in Table  1 ), only the spaCy tool, under a non-restrictive MIT license, is completely license free right of use. # spaCy however suffers from a limited number of tags (only 18).
Extended corpus proposal Each existing corpus for French language has interesting specificities, whether in relation to the size of the annotated data, their license free right of use, or the large number of labels offered, but no corpus combines all these advantages at the same time. However, we found that although the associated tagset is often small, a lot of data related to linguistic information is available. This is particularly the case for UD corpora. We have chosen to focus on the annotated French corpus UD French-GSD  [11]  because it includes all the necessary features to implement a linguistic enrichment of POS labels. Moreover, it is one of the few corpora completely free of use (see Section 2. 1), allowing a complete redistribution of its improvements. It contains 16 POS tags and is composed of 16, 341 documents, for approximately 400k manually annotated word occurrences.
It also offers data integrating the morphological characteristics of words (gender, number, tense, verbal form, person, etc. ) which has been automatically annotated and then partially manually corrected. The UD French-GSD corpus follows the standard UD  [19]  annotation scheme. The new annotation scheme that we propose follows the morphosyntactic tags proposed by the LIA-Tagg tagger, as they allow a complete and deep representation of the spelling as well as the grammar of French language. We achieve this enrichment by transforming the CoNLL-U tags (UPOS) and the features (FEATS) to our 65 new tags in order to give information on morphological characteristics such as the gender (feminine and masculine), number / person (first person, etc. ), tense (past participle, etc. ), types of pronouns (relative, indefinite, demonstrative, etc.
Details and rationales of experimental structural templates used for docking and dynamics Following earlier studies on the use of the SARS-CoV-2 Spike glycoprotein  [15, 52, 53] , the cryo-EM structure of its pre-fusion form (PDB ID: 6XR8, 2. 9 Å, 25995 protein atoms  [21] ) was used as its representative structure (receptor). For the host protease, Furin (ligand), we first made a thorough structural survey of its domains and enzyme active sites, especially that of the catalytic domain and triad). There are only a few experimental (X-ray) structures of Furin to be found presently in the Protein Data Bank  [39]  (PDB ID: 1P8J, 5JXH) which are of equivalent resolutions with insignificant structural deviations (C α -RMSD: 0. 31 Å) upon alignment. More importantly, the region spanning Catalytic residues in both are well conserved.
1P8J, (resolution: 2. 6 Å), the first Furin structure ever to be solved for Furin  [32]  is undoubtedly the most studied and best characterized Furin structure to have, also insightful in terms of proteolytic cleavage mechanisms  [48, [54] [55] [56] [57]. It is for these reasons, 1P8J was chosen as the representative host protease (Furin) structure in the current study. Again, 1P8J has 8 identical chains (pairwise RMSD < 0. 2 Å) in its asymmetric unit combining to as many as 11 bio-assemblies. The majority of the bio-assemblies (bio-assemblies: 3-10) are monomeric -which is the interactive molecular species (or, functionally effective bio-assembly) involved in the Spike-Furin interaction  [48]. This made us further retain the first chain (chain A) of 1P8J alone. Further, to set an appropriate baseline for the Spike-Furin interaction in SARS-CoV-2, a second round of docking was performed by taking the same ligand (Furin: 1P8J) and docking it onto the representative homologous Spike structure (PDB ID: 7AKJ)  [58]  of  SARS-CoV (2002 /2003).
Dataset of coronavirus Spikes For an evolutionary analysis of sequence based disorder predictions, a second dataset was compiled consisting of 44 experimentally solved (exclusively cryo-EM) structures (Table  S1 , Supplementary Materials) of the pre-fusion form CoV/CoV-2 Spike culled at a resolution of 'not worse than 3 Å' from the PDB. These coronavirus Spike structures are solved to serve different specific research objectives at various pH and other varying physico-chemical conditions  [17, 58, 60, 61]. All these structures were found not to have any experimental (primary) data for its FLCS Spike patch (located at their Spike-S1/S2 junctions) resulting in missing atomic coordinates (remarked under 'REMARK 465' in the corresponding PDB files) for the said patch (10-12 amino acids).
Modeling of missing disordered loops in Spike To explore the interaction dynamics between SARS-CoV-2 Spike (PDB ID: 6XR8) and Furin (1P8J), first we needed to have 'all-atom' atomic models for both partners. Furin (1P8J) is much smaller (468 residues) compared to the Spike homo-trimer (3 identical chains with 1149 residues per chain) and has no missing stretches in its X-ray structure but for the terminal most residues. The Spike trimeric structure, however, being a large glycoprotein, characteristically contains missing stretches of residues (of roughly similar lengths) localized at strategic positions, adding up to missing coordinates for 42 amino acids in 6XR8. As discussed in the previous subsection (section 2. 1. 2), to have such missing stretches of residues, especially at the Spike-S1/ S2 junctions, is common to all Spikes (Table  S1 , Supplementary Materials) irrespective of the particular coronavirus species.
These missing stretches map to flexible  [36]  as well as disordered  [21]  loop protrusions resulting from the extended internal packing involved in the trimerization of the monomeric S units. The missing disordered stretches in the representative SARS-CoV-2 Spike pre-fusion structure (6XR8) were then modeled by MODELLER (v. 10. 1)  [62]  using its full-length (proteomic  [63] ) sequence (https://www. rcsb. org/fasta/entry/6XR8/display) obtained from the Protein Data Bank  [39]. To account for the loop disorder in the modeled missing stretches (in 6XR8), an ensemble modeling approach was adapted. To that end, the 'automodel' module of MODELLER was implemented in an iterative cycle of 500 runs, producing that many conformationally non-redundant 'all-atom' trimeric Spike atomic models, only varying among themselves at the modeled missing stretches.