dataesr/scientific-paragraphs-categorization huggingface dataset:
For demonstration purposes a low number of rays  (10 6  ) and a narrow energy bandwitdh has been used. The workflow has been demonstrated in two ways. The first is using OASYS capability to generate the underlying Python code for running the simulation made in the GUI and then running the said code in the Jupyter notebook of SimEx. The second way is to use OASYS capability to run native Python code in a GUI widget, where the beamline output is linked directly to a series of two widgets: one exporting the wavefront to openPMD output and the other one containing SimEx code. The wavefront output, as shown in Fig. 9  (a), is generated in the format conforming the openPMD Domain-Specific Naming Conventions for Photon Raytracing Codes and then used as a input in the diffraction simulation within the SimEx framework.
The high occurrence of this type of integrase in data set 1 suggested a selective advantage of mobile elements carrying such a promiscuous integrase. A pertinent phylogenomic analysis confirmed these observations for the entire Int pT26-2 integrase data set. Integrases from data set 1 showed the capacity to target a high variety of sites on the host chromosome namely 18 out of the 46 possible tRNA genes, either at the 5 0 or at the 3 0 end (supplementary fig. S5 , Supplementary Material online). These attachment sites consisted for the vast majority of the 3 0 end of these tRNA genes, comprising the T stemloop which is significantly more conserved than the rest of the tRNA with a mean pairwise identity of 90% (supplementary fig. S4  and data file S2C, Supplementary Material online). We surmised that this conserved T stem-loop constitutes the core attachment site carrying the cleavage and strand exchange positions of the attB Â attP recombination reaction.
Detection of Mobile Elements and Integrase Homologs in Euryarchaea A classical similarity search in the protein databases to detect proteins closely related to Int pT26-2 could not be implemented because SSV-type integrase genes are often mis-annotated due to their fragmentation after integration. Instead, we used TBlastN with already known and subsequently detected Int(N) and Int(C) moieties as query. As subject sequence, we used the nr/nt nucleotide collection and our own collection of sequenced Thermococcales genomes (to be published elsewhere). The detection of genomic integrated elements is a twostep process. In the first step, disrupted integrases and their adjacent att site are located by sequence comparison. We selected hits with an e-value lower than 1e-30 and then reconstituted the complete integrase-coding gene.
In the second step, the surroundings (<30-40 kb) of these locations are scanned for the cognate att direct repeat. This arrangement is unequivocal as tandemly inserted MGEs were never observed. The sequences of integrated MGEs were obtained by extracting from GenBank files DNA segments comprised between attL and attR pairs (supplementary data file S1, Supplementary Material online). About 73 integrases were detected, 20 were already published, and 53 were newly identified, including 34 in our genome collection (to be published elsewhere). Mobile elements were assigned to a MGE family based on the presence of marker genes: core genes for the pT26-2 plasmid family (supplementary fig. S3 , Supplementary Material online) and the major capsid protein gene for the fuselloviruses  (Krupovic et al.
Thermococcales Isolation and Sequencing Thermococcales strains were isolated during the Starmer (1989), Amistad (1999),  CIR (2001 ), Extreme (2001 ), and Iris (2001)  Ifremer campaigns and originate from the Indian Ocean, the Oriental Pacific Ridge, and the Mid-Atlantic Ridge  (Badel et al. 2019). DNA sequencing was performed by Genoscope (Centre National de S equencage, France), using Illumina MiSeq. Reads were assembled with Newbler (release 2. 9) and gap closure was performed by PCR, Sanger sequencing, and Oxford Nanopore MinION. The sequences of all the integrated elements detected in these isolates are publicly available (supplementary data file S1, Supplementary Material online). 1-ml StrepTrap HP column (GE Healthcare). The STREPtagged Int pT26-2 and Int pT26-2 Y327F were eluted by the purification buffer supplemented with 2.
Integrase Substrates Construction To construct plasmid pCB568, we annealed oligonucleotides BamHI-tRNAarg þ 6-EcoRI_A and _B (supplementary table  S2 , Supplementary Material online) corresponding to the T. sp. 26-2 tRNA arg gene and including six nucleotides downstream of the gene. The annealing product was digested by EcoRI and BamHI, ligated into a similarly digested pUC18 and transformed into E. coli XL1-Blue. The same method was applied for plasmids pCB590, pCB588, and pCB584 with the oligonucleotides BamHI-L56-coRI_A and _B, BamHI-L55-coRI_A and _B, and BamHI-L53-coRI_A and _B, respectively. Plasmid pCB596 was obtained by Gibson assembly of the following three fragments: 1) pCB568 digested by NdeI, 2) a PCR product amplified from pUC4K with the primers KanR-ex1 and 2 (supplementary table  S2 , Supplementary Material online) and corresponding to the KmR gene, and 3) a PCR product amplified from pCB568 with the primers tRNAarg þ 6-ex1 and 2 (supplementary table  S2 , Supplementary Material online) and corresponding to tRNA arg gene and additional six nucleotides downstream.
The assembled product was transformed into E. coli XL1-Blue. The same strategy was used to obtain plasmid pCB598 but with the primers KanR-inv1 and 2 and tRNAarg þ 6-inv1 and 2 (supplementary table  S2 , Supplementary Material online) that lead to the assembly of the tRNA arg in the opposite orientation. To obtain plasmids pCB586, pCB602, pCB604, pCB630, pCB632, pCB636, and pCB638, pUC18 was PCR amplified with the forward primer pUC18-H_FOR and the reverse primer L54-pUC18_REV or R49-pUC18_REV or R48-pUC18_REV or R47-pUC18_REV or R46-pUC18_REV or R43-pUC18_REV or R40-pUC18_REV, respectively. PCR product was digested by NcoI and HindIII, ligated, and transformed into E. coli XL1-Blue. All plasmids were verified by DNA sequencing. The plasmids used in this work are listed in supplementary table  S3 , Supplementary Material online.
Acknowledgments The authors wish to thank  Dr Philippe Oger  for kindly providing T. piezophilus sequencing reads and  Dr Damien Courtine  for communicating the sequence of Thermococcus IRI06c. This work was funded by  CNRS , the  European Research Council  under the European Union's  Seventh Framework Program  (FP/2007-2013)/Project EVOMOBIL- ERC  (Grant No. 340440 ) (P. F. ) and the  Agence Nationale de la Recherche   ANR-19-CE11-0007. C. B. is supported by  "Ecole Normale Sup erieure de Lyon. ".
All-against-all BlastP analyses were performed on all the integrases comprises in data set 1, a set of Sulfolobales integrases identified in free Fuselloviridae, and all available pTN3integrases. The all-against-all integrases BlastP results were grouped using the SiLiX (for Single Linkage Clustering of Sequences) package v1. 2. 8 (http://lbbe. univ-lyon1. fr/SiLiX) (Miele et al. 2011). This approach for the clustering of homologous sequences is based on single transitive links with alignment coverage constraints. Several different criteria can be used separately or in combination to infer homology separately (percentage of identity, alignment score or E-value, alignment coverage). For this integrase data set, the results of the all-against-all BlastP analyses were filtered with the additional thresholds of BlastP pairwise similarity >25% or >35% over 60% for the protein (fig.
The TreeSim package  (Stadler, 2019)  available in the R programming language was used here in order to simulate trees conditioned on a given number of sampled tips. The birth and death rates were set to 1. 0 and 0. 5, respectively and 20,000 trees were generated. Each tree was obtained by simulation of a birthdeath process with 100 species. The fraction of sampled taxa was chosen uniformly at random in [0. 1, 1]. The length of each edge in these birth-death trees was then multiplied by a random draw taken from an exponential distribution with the rate set to 1. 0. Tip-to-root distances were extracted from each tree using the phytools package  (Revell, 2018)  and their variance calculated. Figure  1  gives the plot of the number of tips (on the x-axis) against the variance of the tip-to-root distances in each tree (y-axis).
Indeed, these approaches take into account the site-specific variation of the substitution rate along lineages in the very same way the covarion model  (Fitch and Markowitz, 1970; Tuffley and Steel, 1998)  does, i. e. , by authorizing each site and each edge to evolve under its own rate of evolution. Despite these obvious advantages compared to alternative models, IARAs have not been used widely so far, most likely because of a lack of implementation in popular phylogenetics software (although the geometric Brownian model is implemented in PhyTime, a software program that is part of the PhyML package).
Pre-processing of Fossil Data Fossil data is another source of information commonly used for molecular dating. It consists in a fairly intricate combination of time and morphological information. Time information is only indirect. It is derived from the estimated age of the sediments in which the extinct taxon was collected. The age of these sediments is itself often derived indirectly from that of rock bodies that "bracket" the sediments of interest  (Sterli et al. , 2013). Moreover, little information is available about the way the different stratigraphic ranges were sampled in general. The data produced by paleontologists generally consist in the combination of a fossil description and the stratigraphic layer in which this fossil was found. Additional information about the experimental design, including the number and types of geological layers surveyed or the excavation techniques that were used, is often difficult to access.
That lack of information is problematic. For instance, from a mathematical modeling perspective, finding a particular fossil after searching a single stratigraphic layer is very different from finding the same fossil after examining multiple layers. Finally, the rock record itself is highly heterogeneous in space because of plate tectonics and net erosion, thereby complicating even further the task of finding and interpreting fossil data  (Benton et al. , 2009). Morphological information is also difficult to deal with. First, the analysis of one or multiple specimens of a given fossil taxon by paleontologists leads to a selection of "informative characters. " These morphological characters are selected based on the phylogenetic signal they convey and result from complex taphonomic processes.
The fact that the selection of a subset of morphological characters is not random (i. e. , characters are selected based on their variability across sampled species) may also be considered problematic (see section 3. 2. 3). Finally, it is commonplace to assemble morphological characters from multiple specimens of fossils that are deemed to belong to the same ancient species  (Parham et al. , 2011). Here again, this step relies on the interpretation of evidence by paleontologists. In this context, it is important to stress that the validity of molecular dating analyses relies heavily on the ease with which the whole scientific community can access raw fossil data along with detailed information about how this data was processed in order to define calibration constraints. Without open and systematic access to well-curated and extensive databases of raw fossil data, dating experiments will not be fully reproducible, thereby harming our field of research.
Fortunately, rich sources of information about the way fossil data is prepared prior to dating analysis can be found online. For instance, the Fossil Calibration Database 1  (Ksepka et al. , 2015)  set out to use the rigorous set of guidelines defined in  Parham et al. (2011). It provides useful, if partly outdated, information to generate calibration constraints for more than 200 clades and does so on a transparent forum that is open to the whole scientific community. Note however that knowledge about fossil data is constantly evolving (see e. g. ,  Marjanović, 2019)  and databases such as the Fossil Calibration Database require ongoing and constant efforts in order to remain relevant.
Expert-Based Analysis of Fossil Data A fossil provides a minimum age for the smallest extant clade to which it belongs, i. e. , the youngest node from which it as well as any two or more extant taxa are descended. The phylogenetic position of a fossil is determined either by a phylogenetic analysis of (a subset of) its parsimony-informative characters or by a manual comparison with a list of apomorphies derived from a prior phylogenetic analysis or from prephylogenetic taxonomic work. Both approaches leave varying amounts of uncertainty, depending in part on how fragmentary the fossil in question is. Every fossil-based calibration thus contains age uncertainty and phylogenetic uncertainty. Fossils that branch near the tips in the calibrated clade define looser younger ages for that clade compared to older fossils that branch closer to the basal node.
Linking a given fossil to a group of taxa as is done here involves a considerable amount of work and discussions among multiple experts, as demonstrated by the wealth of information provided in the journal Palaeontologia Electronica for animals, for instance (see https://palaeo-electronica. org/content/fc-1). Note however that only taxa considered as important receive high levels of scrutiny. Hence, existing databases can be used to find wellcurated calibration information for "popular" taxa, in which cases researchers rely on previous rigorous work by paleontologists in order to calibrate their own analysis. Calibrating an analysis of less well-characterized taxa generally involves a thorough search of the primary literature followed by an in-depth comparative analysis of raw morphological data as briefly explained in the previous paragraphs (see also section 3.
Using the same data set as in their article, comprising 109 dated extinct taxa covering Amniota and Diadectomorpha, I used the software DateFBD (available from https://github. com/ gilles-didier/DateFBD) to infer the age of the Amniota clade. The estimation is conditioned here on a particular tree topology, in which Diadectomorpha is placed as outgroup. This topology is one of thousands of equally-most-parsimonious trees obtained from the analysis of a matrix of morphological characters  (Didier and Laurin, 2020). Although the three distributions are not conditioned here on having the same younger bound (i. e. , by forcing the three densities to have the same 95% upper quantile for instance), substantial differences in their modes and the variances are observed depending on the value of the fossilization rate.
LMNA encodes for Lamin A/C, type V intermediate filaments that polymerize under the inner nuclear membrane to form the nuclear lamina. A small fraction of Lamin A/C, less polymerized, is also found in the nucleoplasm. Lamin A/C functions include roles in nuclear resistance to mechanical stress and gene regulation. LMNA mutations are responsible for a wide variety of pathologies, including Emery-Dreifuss (EDMD) and LMNA-related congenital muscular dystrophies (L-CMD) without clear genotype-phenotype correlations. Both diseases presented with striated muscle disorders although L-CMD symptoms appear much earlier and are more severe. Seeking for pathomechanical differences to explain the severity of L-CMD mutations, we performed an in silico analysis of the UMD-LMNA database and found that L-CMD mutations mainly affect residues involved in Lamin dimer and tetramer stability.
In this study, we performed in silico analyses of the UMD-LMNA locus specific mutation database and used fibroblasts from EDMD and L-CMD patients as well as myoblasts from EDMD and L-CMD mouse models to seek differences between these two muscular dystrophies. We found that L-CMD patients harbor significantly more mutations in residues involved in Lamin dimer and tetramer interactions and stabilization. In line with this, we showed an increased proportion of Lamin A/C within the nucleoplasm of L-CMD patient and mouse cells. Additionally, using mouse primary myoblasts derived from EDMD  [19] , and L-CMD  [20]  mouse models, we showed that the absence of Lamin A/C from the nuclear periphery and its accumulation in the nucleoplasm are highly detrimental for myoblast differentiation. This is in part due to the inability of L-CMD post-mitotic myocytes to sequester muscle specific NET in the nuclear envelope required for chromatin remodeling.
The UMD-LMNA Locus Specific Database The UMD-LMNA database (www. UMD. be/LMNA/) was developed since 2001 using the "Universal Mutation Database" (UMD) tool  [21]. It aims to gather all published LMNA definitely mutated patients (probands as well as their relatives) in order to provide up-to-date information about mutations for the scientific community. The mutations are collated from published articles, congress abstracts and from personal communications transmitted to the curators (R. Ben Yaou and G. Bonne). If patients are reported several times, they are captured only once in UMD-LMNA. The database also includes the LMNA mutations submitted to the LMNA Leiden open database (https://databases. lovd. nl/shared/ genes/LMNA) that are freely and publicly available. For each reported patient, several data levels are captured including mutation details at nucleotide level (exon and codon number, wild type and mutant codon, mutational event, mutation name), amino acid level (wild type and mutant amino acid), RNA level when RNA consequence studies were performed by the authors and at the clinical level.
Immunofluorescence Human control and patient fibroblasts were grown on glass coverslips. Proliferating and 4-day-differentiating myoblasts were grown on Permanox lab-tek culture chambers (Nunc) or ibi-Treat coated µ-slides (iBidi). All cell types were fixed for 10 min in 4% PFA, permeabilized for 6 min in 0. 5% Triton and then blocked for 30 min in blocking solution (5% BSA in PBS). Primary antibodies were incubated overnight at 4 • C in blocking solution (Table  1 ). After 3 PBS washes, secondary antibodies were added in blocking solution for 45 min. Mounting medium containing DAPI (4 ,6-diamidino-2-phénylindole; Vectashield, Eurobio Scientific, Les Ulis, France) was added following 3 additional PBS washes. Images were taken on Olympus FV-1200 confocal microscope. Color profile plot throughout nuclei was obtained using "RGB Profiler" plugin in FiJi (Image J) software.
In Silico Analysis of the UMD-LMNA Database To gain insight into the differences between EDMD and L-CMD, we first analyzed all mutations reported for EDMD and L-CMD in the UMD-LMNA locus specific database (http://www. umd. be/ LMNA/). For this analysis, we only considered pure EDMD and L-CMD clinical presentations and excluded overlapping phenotypes with other laminopathic extramuscular traits. Among the 1678 index cases reported, 28% are EDMD (464 index cases representing 189 different mutations, corresponding to 452 heterozygous, 7 homozygous and 5 compounds heterozygous individuals) and only 9% are L-CMD (150 index cases, representing 66 different mutations, corresponding to 146 heterozygous, 1 homozygous and 3 compound heterozygous individuals) with similar representation of men and women in both conditions.
Acknowledgments: The authors thank  Christophe Beroud  for maintaining the online release of the UMD-LMNA database,  Eric Schirmer  for fruitful discussions and antibodies against NETs,  Colin L. Stewart  for the Lmna-KO myoblasts,  Ohad Medalia  for DARPin plasmids and  Michio Hirano  for providing cells and clinical details of EDMD patient. The authors thank the  Pitié-Salpêtrière Imaging Platform (PICPS)  for microscopy assistance and the  Animal facility of Pitié-Salpêtrière campus (Centre d'Expérimentation Fonctionnelle, Sorbonne Université ).
Statistical analysis Statistical analysis was performed using SPSS version 15. 0 software. Categorical variables were presented as frequencies, and continuous variables were presented as the median [25-75 percentiles]. Comparisons between groups were performed using Mann-Whitney test for quantitative variables and Fisher's exact tests for frequencies. Associations between continuous variables were analyzed using Spearman's correlation tests. Multiple linear regression was performed to determine the impact of cardiovascular risk factors and comorbidities on CEC levels. Statistical significance was defined as p<0. 05.
DATA A N D M E T H O D S Following the M w 7. 8 16 April 2016, Pedernales Earthquake, an international response coordinated by the IG-EPN, deployed seismic instruments immediately following the main event  (Meltzer et al. 2019; Fig. 1). These instruments recorded continuously for approximately one year following installation. Data from this deployment has already been used to study the aftershock seismicity  (Agurto-Detzel et al. 2019; Meltzer et al. 2019; Soto-Cordero et al. 2020)  and velocity structure across the rupture area and Ecuador  (León-Ríos et al. 2019; Lynner et al. 2020). We incorporate data from the temporary deployment as well as permanent stations from the Ecuadorian national network  (Alvarado et al. 2018)  to construct P-wave receiver functions (RFs) along the margin. We then jointly invert our calculated RFs with Rayleigh wave phase velocity measurements from  Lynner et al.
Extracting RFs from a CCP volume rather than using individual station RFs has the advantage of more accurately accounting for variations in backazimuth and ray parameter, thereby minimizing artefacts associated with imperfect data coverage and lateral heterogeneity beneath a station. CCP stacks can, however, suffer from unrealistic sharp changes in amplitude that may result from: (1) the discretization of the model into constant thickness bins, which will incorporate different time windows of the receiver functions based on the velocity model or (2) introducing new data into a bin, as rays spread out and number of receiver functions in a bin generally increases as a function of depth. These artefacts cannot be predicted by the receiver function forward operator. In order to mitigate these artefacts, the CCP-derived RFs are filtered with a Gaussian filter following the method described by  Delph et al.
Joint inversion Cross sections through the upper 50 km of our shear wave velocity model from our joint inversion at the same locations as the ACCP stacks are shown in Fig. 4. As in the ACCP stacks, we separate each profile into four along-strike segments based on the crustal shear wave velocities and RF discontinuities. Side by side comparisons between the ACCP stacks and shear wave velocity model can be seen in Figs  S7  and S8. Fits of the predicted data compared against the observations are shown in Fig. 5. In general, the final model fits the dispersion curve data well across the study area, while RF fits are degraded in regions with complicated shallow structure, such as around the Manabí and Borbon basins (Fig. 5 ). The fit for individual gridpoints at each of the stations shown in Fig.
A statistical analysis of 295 cloud samples collected at the Puy de Dôme station in France (PUY), covering the period 2001-2018, was conducted using principal component analysis (PCA), agglomerative hierarchical clustering (AHC), and partial least squares (PLS) regression. Our model classified the cloud water samples on the basis of their chemical concentrations and of the dynamical history of their air masses estimated with back-trajectory calculations. The statistical analysis split our dataset into two sets, i. e. , the first set characterized by westerly air masses and marine characteristics, with high concentrations of sea salts and the second set having air masses originating from the northeastern sector and the "continental" zone, with high concentrations of potentially anthropogenic ions.