dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and inclusion criteria. For each potential IM case, a standardized data form was completed by manual review of medical records. Patients with at least "possible IM" according to the 2017 EULAR/ACR classification criteria (3) were considered as an IM case. An IM case was defined as "incident" if the patient presented with IM diagnosed between January 1, 2006, and January 1, 2013, and resided within the limits of the administration region of Alsace at the time of diagnosis. For all incident IM patients, demographic data and characteristics of the disease were recorded. Creatine kinase levels were retrieved from the patients' medical records and/or from the laboratory records. Data were verified by a physician with 10 years of experience in IM patients (AM). For all patients with suspected disease during the last 6 months of the study period (n = 9), we retrieved medical information during a follow-up period of at least 6 months by reviewing the patient's medical records and/or by contacting the patient's physician.
The 2 population-based cancer registries of the Alsace region provided information on cancer cases diagnosed among included patients. Statistical analysis. We used capture-recapture analysis to estimate the number of IM cases that were missed using any of the 4 sources. Source dependency was investigated by constructing 113 log-linear models, including single-, second-or third-order interactions between the 4 sources. The model that best fit the data and contained the fewest interaction terms was selected on the basis of the value of the Akaike information criterion (AIC). AIC, a criterion calculated from the likelihood ratio statistic of the model, represents the quality of the estimation of the variable of interest allowed by each model. The lower the AIC, the closer the estimation calculated by the model to reality  (7).
ACKNOWLEDGMENTS We thank the  Alsace (Bas-Rhin  and  Haut Rhin) Cancer Registries  and all the physicians who participate in this study. All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Meyer  had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and design. Kleinmann,  Velten, Sibilia, Meyer. Acquisition of data. Debrut, Klein, Spielmann, Mertz, Martin, Nadaj-Pakleza, Hirschi, Nespola, Lannes, Terzic, Hinschberger, Dervieux, Lipsker, Arnaud, Gottenberg, Kleinmann, Geny, Séverac, Velten, Sibilia, Meyer. Analysis and interpretation of data. Debrut,  Giannini, Klein, Spielmann, Mertz, Martin, Nadaj-Pakleza, Hirschi, Nespola, Lannes, Terzic, Hinschberger, Dervieux, Arnaud, Geny, Séverac, Velten, Sibilia, Meyer.
Zone C (6. During each season, beaches were patrolled for approximately 3-4 hrs at 30-minute intervals per night for 3-4 days per week between 8 p. m. and 4 a. m. to ensure that nesting turtles were encountered. When marine turtles were encountered, the following data were recorded on a standard datasheet: species identi cations, zone, date, time, and species activity (e. g. , track width). For those turtles that successfully laid eggs, we collected data on the number of eggs (clutch size), nest depth and measured the curved carapace length and width in centimetres (CCL n-t : curved carapace length notch to tip, and CCW max : maximum curved carapace width as in Pritchard et al. (1983)  and Bolten (1999). After checking for physical damage and the presence of tags following the methodology presented in  (Eckert et al.
, 1999). Patrollers remained with the turtle for the duration of the laying process and con rmed the nesting event outcome. In the case where a turtle observed after laying eggs or a nest is observed on a morning walk, nesting success is estimated and not con rmed. During the 2014-2017 period, data on marine turtles were collected from bycatch assessment at the landing site of artisanal sheries in Ebodje. In-person surveys were conducted by free interviews during 2014 and 2017 at the landing site of artisanal sheries in the coastal communities. Due to nancial reasons and the di culties in recruiting motivated observers from distant landing sites, we had to limit our low-cost follow-up survey only to the Ebodje shing community. The long and short questionnaires included mostly closed questions and were completed with shermen at landing sites; they included questions about bycatch of marine turtles, shing gear usage, target species catch, boat speci cations, and seasonality and location of shing effort.
Relatively short (< 30 min) closed-question surveys have generally been recommended for collecting quanti able or factual information  (Huntington, 2000; Fowler, 2009; Gomm, 2004; White et al. , 2005). Additionally, during the study period, we assessed the number of turtles killed on nesting beaches and the eggs poached either in situ or from hatchery every year. These data were used to assess the impacts of poaching threats as an indication of success (or failure) of the marine-turtle-protection program at the Ebodje nesting zone. The frequency of threats was calculated as the percentage of beaches where the threat was present. The presence/absence scoring system of poaching threat was used for cases where quantitative information was lacking, providing a score of one when the threat was present and zero if the threat was absent.
Population estimates and threats For the present study, the population of nesting turtles was calculated over each season from 2013-2017. It was based on the reproductive characteristics and track counts of the nesting season for the duration of this study. The total number of nests in the area was determined by counting crawls (tracks), nests, and recording the date of the species' rst and last nests. Crawls (tracks) are counted as representing reproductive effort and nesting females with assumptions of constant nesting success (nests and crawls) and constant clutch frequency (the number of clutches deposited by an individual turtle in a nesting season) (Godley et al. , 2001). Recorded data were later analysed through population models to examine variation in turtle nesting density. The knowledge of absolute numbers of turtles used as a guide to understand trends in species populations when compared with historical studies in the area.
Body size and nesting trend variation The biometric and nesting parameters for the two most predominant species are provided in Table  1. During the four years of data collection, the carapace lengths of 193 olive ridley and 11 leatherback turtles were measured. After the exclusion of misrepresentative records, 32 data points of average CCL with corresponding clutch sizes remained for the analysis. For CCW, however, 28 data points remained, as no correction for the incompleteness of caudal projections had to be applied. For the total nesting females, their clutch size (yolked eggs) ranged from 30 to 178 for the corresponding CCL (mean ± S. D. ; 76. 45 ± 20. 12, n = 30) and CCW (mean ± S. D. ; 72. 35 ± 10. 55, n = 30). Clutch size for olive ridley increased with CCL (R 2 = 0. 014, F (1, 30 df) = 6.
Acknowledgements This study was facilitated by the involvement of many individuals and the community-based association Tube Awu and organizations during its 5-year lifespan within the Ebodje nesting zone, Atlantic coastline of southern Cameroon. We would particularly like to thank all those who assisted in monitoring surveys of the species and tagging, including  Denis Gnamaloba ,  Felix Mpinde ,  Nestor Kowe ,  Tobie ,  Yannick  and a few other individuals of the Tube Awu Association who are of blessed memories;  Abba Abba  and  Paul Manga. We are grateful for the constructive comments by  Dr. Ohiana Revulta  on an earlier draft manuscript. This work was supported by  Rufford Small  Grants 1 & 2, UK. Data surveys in 2013 were provided by  Dr Jacques Fretey  through his participation in the project;  ESIA GDF-Suez Cameroon LNG.
Amplification specificity was checked using light cycler melting-curve analysis once the PCR product size was confirmed. The Light Cycler quantification software quantified the transcripts in samples, and the data were calibrated from repeated dilutions of purified PCR products containing known amounts of cDNA molecules from each gene (Roche Diagnostics). The ratio of LMP1 mRNA to actin mRNA was calculated to determine the relative abundance of each mRNA. This experiment was replicated three times. The Relative quantification analysis is a method used to compare the ratios of two DNA sequences. The first ratio is the target DNA sequence to a reference DNA sequence in an unknown sample. The second ratio is the same two sequences in a standard sample called a "calibrator". The "target" is the specific RNA or DNA sequence of interest, while the "reference" is a nucleic acid found in all samples at a constant copy number that serves as an endogenous control.
Statistical analysis The statistical analysis for the tests involved the use of the Mann-Whitney HSD test with a significance level of p<0. 05. The tests were performed in triplicate, and the results were presented as mean standard deviation after one-way ANOVA and Tukey's HSD test with a significance level of p<0. 05. The statistical software used for all analyses was SPSS Statistics, version 25. 0. A previous study  (8)  found that EBV-LMP1 oncoproteins isolated from NPC patients' sera had strong mitogenic activity in vitro. In this present study, mice were left untreated and tumors were observed to grow rapidly, reaching a diameter of 4mm by day 4, 7-8mm (c666-1) or 12-15mm (AGS) by day 8, and 1. 5-1. 6cm (c666-1) or 1. 8-2cm (AGS) by day 14 (Fig. 1 -a; Fig. 2 ). EBV-AGS tumors were significantly larger than c666-1 tumors.
Data Generation and Proceeding The KIT D816V 3D model (sequence I516-R946) was derived by homology modelling from the KIT WT full-length CD model with its transmembrane helix  [43]  (Figure  S1 ) and studied by all-atom MD simulation in its natural environment (the protein was embedded into a membrane through the TM helix and submerged in water). Three independent 2-µs MD simulation replicates were generated to enhance conformational sampling and examine the consistency and completeness of KIT D816V conformations produced upon strictly identical conditions. The generated MD trajectories were analysed for a full-length construct and per domain/region using unique and concatenated trajectories. To avoid rigid body motions, KIT D816V trajectories were normalised by least-square fitting on the initial structure (t = 0 µs).
KIT Folding in Inactive (Wild Type) and Constitutively Active (Mutant) States The KIT D816V TKD folding (2D structure) is generally well-conserved along the MD simulations (Figure  2 ). The average structural folding of TK domain N-and C-lobes corresponds well to the empirically (X-ray) characterised structures of inactive (PDP ID: 1T45) and active states of KIT WT (PDB ID: 1PKG)  [48, 49]. The fold of four intrinsically disordered regions (IDRs)-JMR, KID, A-loop, and C-tailalong the MD trajectories of KIT D816V show either partially conserved secondary structures or structures reversibly transitioning from folded to alternatively folded or unfolded (αH ↔ 3 10 -helix ↔ β-strand ↔ turn ↔ bend ↔ coil), and their overall folding content, estimated on the average structure, is close to that of the KIT WT , except A-loop (Figure  S3 ; Table  S1 ).
The secondary structures-αH-(red), 310-helices (light blue), and β-strands (dark blue)-assigned for a mean conformation of each MD trajectory (1-3) of KIT D816V and KIT WT , and for the crystallographic structures of inactive (PDP ID: 1T45) and active (PDB ID: 1PKG) KIT WT. (c) The increase in helical folding of KIT D816V compared to KIT WT was estimated by the difference in probability of folding per couple of residues i from KIT WT and KIT D816V and illustrated by the thickness of the coloured ribbons (large ones are red, small ones are white). The fold of four intrinsically disordered regions (IDRs)-JMR, KID, A-loop, and Ctail-along the MD trajectories of KIT D816V show either partially conserved secondary structures or structures reversibly transitioning from folded to alternatively folded or unfolded (αH 310-helix β-strand turn bend coil), and their overall folding content, estimated on the average structure, is close to that of the KIT WT , except A-loop (Figure  S3 ; Table  S1 ).
The effect caused by the D816V mutation on A-loop folding is in concordance with the early reported observations in a KIT D816V partial construct, studied by MD simulation  [26]. In particular, the A-loop helical folding increased significantly in KIT D816V (13%) compared to KIT WT (3%), mainly through a folding up-or downstream from the point mutation. At the same time, the A-loop β-hairpin in KIT D816V is decreased compared to KIT WT. Consequently, according to the Define Secondary Structure of Proteins (DSSP) assignment, D816V mutation significantly influences A-loop folding only. However, an estimation of the difference in the probability of formation of secondary structures for each pair of residues i from KIT WT and KIT D816V showed that the effects caused by the The secondary structures-αH-(red), 3 10 -helices (light blue), and β-strands (dark blue)-assigned for a mean conformation of each MD trajectory (1-3) of KIT D816V and KIT WT , and for the crystallographic structures of inactive (PDP ID: 1T45) and active (PDB ID: 1PKG) KIT WT.
KIT Plasticity: Mutation-Induced Effects on the Conformational Space Besides the IDRs' reversible transitions at the folding level, conformational plasticity KIT D816V appears from mutual linear and rotational displacements of local structures in each ID region, or/and global displacements of regions/domains, both ordered and disordered, relative to each other. These effects also lead to an intrinsic disorder at the conformational level. To capture the conformational heterogeneity of KIT D816V , and, particularly, to compare the conformational spaces of KIT D816V and KIT WT , we analysed the conformational ensemble of each KIT by Principal Component Analysis (PCA), a multivariate statistical technique employed to reduce the dimensionality of large datasets, leading to increase in interpretability and minimising information loss  [50].
The projection of KIT D816V MD conformations from three independent replicates onto the two or three first principal components (PCs) shows essential overlap. The overlapping areas are formed by the conformations reproduced in the replicates and consist of statistically rich 'cloned' data, while the non-overlapping areas represent novel conformations observed in only a single trajectory that complement the 'cloned' data, yielding a generic dataset. Obviously, the generic conformational space combined from the generated subspaces does not represent a complete conformational space of the disordered KIT but more exhaustively reflects its conformational properties than a single subspace. The extensively overlapped three KIT D816V MD replicates, as justified by PCA, supports their merging and further calculations on the concatenated trajectories.
W1 is composed of KIT WT (60%) and KIT D816V (34%) making a 2:1 population ratio, while W2 is primarily composed of KIT D816V (90%). Representative major conformations of W1 and W2 differ in the folding of C-and P-loop and the relative position of the P-loop and αC-helix. An observable approach of P-loop toward αC-helix is seen in KIT D816V , but not to the extent observed in the crystallographic structure of the active KIT WT (PDB ID: 1PKG). The D816V mutation appears mostly to affect αC-helix orientation and increase C-loop fold in its N-end containing the catalytic residue D792 from the conserved catalytic (DFG) motif, which is in DFG-in conformation in both major conformations. As the PCA of the full-length CD reflects all structural and dynamic effects together, (i) reversible folding-unfolding ID regions, (ii) local (intrinsic) flexibility, and (iii) positions of IDRs relative to the kinase domain derived from extrinsic disorder (global plasticity), it remains challenging to definitively determine the D816V effects on each individual domain of KIT.
The analogue of these drugs, inhibitor 3G8 co-crystallised with KIT (PDB ID: 6HH1), shows its location in the ATP-binding area adjacent to the αC-helix, similar to the ripretinib position  [61]. This pocket was previously described in the comparative analysis of the crystallographic structure 1T45 and MD conformations of the inactive and constitutively active KIT states in which the JMR, KID, and C-tail regions are undefined or only partially defined  [26]. We expect the comprehensive dynamic model of the full-length KIT cytoplasmic domain to significantly expand the search for new pockets. We used our long-scale unbiased MD simulations of the two proteins, KIT WT or KIT D816V , to explore and characterise their pockets, and provide the best perspective for the development of selective allosteric modulators.
50, dissociates into three separated pockets-the larger P1, and the smaller P1 and P1. The pocket P1 corresponds to the ATP-binding site that binds imatinib and other type 2 antagonists  [22, 23, 38, 49, 64]. It represents a 'path-of-pockets 'with an unusually large binding volume, which is likely related to high adaptability of KIT's active site upon receptor activation and ATP binding. Generally, the flexibility of the active site leads to rearrangements of binding site groups, which are dependent on different ligands. For instance, next-generation drugs that have improved selectivity and reduced side effects, and are also effective in combating mutation-induced resistance, are also located in P1 pocket but close to the αC-helix  [61] , as documented by the structure of KIT co-crystalised with the 3G8 inhibitor (PDB ID: 6HH1).
As a single transmembrane, α-helix was not found in the Orientation of Protein in Membrane (OPM) database  [91] , but the orientation of the double-helix of PDGFR-β, a cousin of KIT, was considered. Suggesting that the single helix in the monomer of KIT D816V can have an alternative orientation, KIT L521-R946 was oriented manually so that its transmembrane helix was positioned perpendicularly to the bilayer with polar residues next to the phospholipids' polar heads and apolar residues among their tails. Finally, to reduce the number of residues located in the extracellular area, the N-terminal extremity of KIT D816V was reduced to I516-R946 (KIT I516-R946 ).
Molecular Dynamics Simulation System set-up. KIT I516-R946 , wrapped in a 23 Å-width leaflet lipid bilayer of POPC (2), was solvated with TIP3P water model in a rectangular box with the PACKMOL-Memgen  [92]  and LEaP modules of AmberTools20 (http://ambermd. org/AmberTools. php, accessed on 14 September 2020) using the ff14SB all-atom force field  [93]  for protein and Lipid17 for membrane: (i) hydrogen atoms were added; (ii) protonation states of amino-acids at physiological pH were assigned, and the histidine residues were protonated on their ε-nitrogen atoms; (iii) no counter-ions were added, as the system is already of neutral charge. The systems, KIT T544-R946 in the water solution (1), and KIT I516-R946 wrapped in a 23 Å-width leaflet lipid bilayer of POPC (2), contained 71,980 atoms in total with 6873 atoms of protein and 64,647 atoms of water (1), and 172,368 atoms in total with 6869 atoms of protein, 30,144 atoms of the lipid membrane, 123,030 atoms of water (2), and one Cl -ion.
Minimisation, equilibration and data generation. The systems were equilibrated using the Sander module of AmberTools20. For system (1), 30,000 minimization steps were performed: (i) 10,000 on the all-atom fixed protein to relax the water; (ii) 10,000 with fixed Cα atoms to allow the relaxation of sidechains; and (iii) 10,000 without any constraints on the system. For (2), the positional constraints of various forces were applied and subsequently decreased at each minimisation/equilibration step to allow a smooth equilibration. The values were for: all protein atoms-10, 10, 2. 5, 1, 0. 5, 0. 1; the phosphate atom of POPC-2. 5, 2. 5, 1, 0. 5, 0. 1 kcal/mol; the dihedral angle around the double bond of the oleoyl chain of POPC (restricted to 0 • ) and the dihedral angle formed by the glycerol carbon and the oleoyl ester oxygen atoms of POPC (restricted to 120 • )-250, 250, 100, 50, 25 kcal/mol.
In (2), the membrane surface tension was set to 0 on the x-y plane. Lastly, a mini (100 ps) molecular dynamics simulation in the previous conditions was completed without any constraint on the system. All equilibration steps and molecular dynamics simulations were carried out with an integration step of 2 fs. Non-bonded interactions were calculated with the Particle-Mesh Ewald summation (PME) with a cut-off of 10 Å, and bonds involving hydrogen atoms were constrained with SHAKE algorithm  [96]. The initial velocities were reassigned according to the Maxwell-Boltzmann distribution, and the same parameters (simulation conditions) as the mini dynamics were applied. Coordinates were recorded every 1 ps. The system was simulated with AMBER18 (http://ambermd. org/AmberMD. php, accessed on 14 September 2020) using the PMED Cuda module running on the supercomputer JEAN ZAY at IDRIS (http://www.
Data Analysis All standard analyses were performed using the CPPTRAJ 4. 25. 6 program  [97]  of AmberTools20. Analysis of MD conformations (every 10 ps) was realized after least-square fitting on residues of the TK domain (W582-S688, L769-S931) or on residue-truncated trajectories of each fragment to remove rigid-body motions. (1) The RMSD and RMSF values and cross-correlations were calculated for the Cα-atoms using the initial full-length model or each separated domain/region (at t = 0 µs) as a reference. (2) Secondary structural propensities for all residues were calculated using the Define Secondary Structure of Proteins (DSSP) method  [98]. The secondary structure types were assigned for residues based on backbone -NH and -CO atom positions. Secondary structures were assigned every 10 and 20 ps for the individual and concatenated trajectories, respectively.
The cut-off was varied from 3 to 5 Å. The analysis was performed every 100 ps. (5)  The H-bonds between donor (D) and acceptor (A) atoms N, O, S were monitored according to the following geometrical parameters: d(D-A) ≤ 3. 6 Å, ∠(DHA) ≥ 120 •. Hydrophobic contacts were considered for all hydrophobic residues with side chains within a 4 Å of each other. (6)  The Principal Components Analysis (PCA) modes were calculated for the backbone atoms (N, H, Cα, C, O) after least-square fitting on the average conformation calculated on the concatenated data. The eigenvectors were visualized with NMWiz module for VMD  [99]. (7)  The relative Gibbs free energy of the canonical ensemble was computed as a function of two reaction coordinates with Equation (2) [100]: ∆G = -k B T ln P(R 1 , R 2 ) P max (R 1 , R 2 ) (2) where k B represents the Boltzmann constant, and T is the temperature.
Visualisation and Figure Preparation Visual inspection of the conformations and figure preparation were performed with PyMOL (https://pymol. org/2/, accessed on 14 September 2020). The VMD 1. 9. 3 program (accessed on 21 December 2020) [101] was used to prepare the protein MD animations. To visualise the motions along the principal components, the Normal Mode Wizard (NMWiz) plugin  [99, 102]  which is distributed with the VMD program, was used. The 3-dimensional representations of the free energy surface were plotted using Matlab (US, © 1994-2021 The MathWorks, Inc. , Natick, MA, USA).
Acknowledgments: This research was supported by  Centre National de la Recherche Scientifique (CNRS)  and  Ecole Normale Supérieure (ENS) Paris-Saclay. The authors were granted access to high-performance computing (HPC) resources at the  French National Computing Centre CINES  ( DARI A0070710973 ) by  GENCI  ( Grand Equipement   National de Calcul Intensif). Calculations were performed on the  Jean Zay  cluster at  IDRIS  (101063).