dataesr/scientific-paragraphs-categorization huggingface dataset:
In a similar vein, we point out that while "onze" and "treize" are frequently used in French according to the Lexique database  [59] , "onze" is more frequent than "treize" (in every media source including news, film and twitter), which should make its processing faster. Finally, we created a follow-up Number Identification Task (NIT), under conditions similar to the NMT but without the inferior/superior task. This provides mean identity times -for each number and per participant -that can be subtracted essentially from their RT's on the NMT, resulting in a cleaner decision time measure. We note here that our OSF registration, https://osf. io/kz7b6/, was focused chiefly on our lengthy efforts to transform Szucs & Csepé's  [29]  Numerical Magnitude Task into French as we developed the Oral condition.
The follow-up NIT presented participants with a number (in the same form as the NMT) while asking for an identification as-quickly-as-possible (by tapping the space bar). A three-step signal (a racecar style colored countdown over one and a half seconds) prepared participants for the number's arrival. Every four to six trials, participants were asked to specify the last number heard (or seen) through an on-screen scale (ranging from 8 to 16, sans 12). A scaling trial would conclude when the participant clicked on the box below the scale (see Figure  2 ), which contained the number (shown in symbolic form) the participant identified. The NIT task ended when each number was scaled 4 times. Both tasks were prepared with 16 Psychopy software  [60]. The task's duration was on average 35 minutes.
Results We are concerned with two Dependent variables -Accuracy and Reaction Times -and begin with the former. The accuracy data -determined by participants' responses to the inferieur/superieur question --stem from the 36 participants (twelve participants across three modality conditions, which will be referred to as Oral, Symbol, and Letters) and the 5760 data points that each condition generates (making for a total of 17280). Accuracy was quite high, 97. 03% overall. We tested whether Accuracy was significantly different across the provided eight numbers and across modalities while using a mixed logistic regression model (see, for instance,  [61] ) that included two fixed factors, Number and Modality (see Figure  3 ). Analyses were carried out with R software  [62]  using the glmer-() function from the lme4 package  [63].
Second, we removed data points that were 2. 5 standard deviations above or below the participant's mean (this amounted to 2. 8% of the data) by using the LMERConvenienceFunctions package  [64]. Finally, to arrive at a cleaner measure of decision reaction times while also normalizing the data to adjust for the positive skew of the NMT's RT, we performed a log-transformation on each RT on the NMT divided by each participant's mean RT per Number on the NIT. This amounts to the following formula: log(RTNMT/ RTNIT by-participant-mean per number). We will refer to this as the Normalized Decision Reaction Time (or NDRT). In this way the NIT speed per participant and per number is removed in a principled way from each reaction time on the NMT. A summary of the overall results with this calculated mean can be seen in Figure  4.
Acknowledgments The authors wish to express their gratitude to  Maria Spychalska ,  Walter Schaeken ,  Kevin Demiddele ,  Ugo Boscain ,  Olivier Morin , and  David Barner  for various exchanges on topics related to this work. The authors also wish to thank  Thomas Holtgraves  and to two anonymous reviewers whose detailed comments on two prior versions of this manuscript greatly improved the paper. Finally, for underwriting the costs of this open-access publication, the authors express their appreciation to the Chaire d'excellence grant awarded to I. N. from the  Université de Paris  and a public grant overseen by the  French National Research Agency (ANR)  as part of the program " Investissements d'Avenir " (reference:  ANR-10-LABX-0083 ).
5 ). Mapping of the paramagnetically-broadened residues on the surface of PhPFD (PDB: 2ZDI, Fig. 2f ) allows to locate the binding interface between IAPP and PhPFD (Fig. 2g-j ). Whilst no PREbroadening is observed for residues which are located on the top of the β-barrel body, broadening inside the cavity and along the sides of coiled-coil helices is detected. More specifically, strong interaction is observed in the middle part of the coiled-coil helices corresponding to residues 8-31 and 118-135 of the α-subunit. On the β-subunits the first 32 N-terminal residues are the most strongly broadened by the Gd-IAPP. These two regions face each other in the complex. All coiled-coil regions are especially enriched with glutamine. Interestingly, the regions strongly affected by IAPP paramagnetic relaxation present a globally negatively charged surface (-2) which can complement the net positive charge (+2) of the IAPP sequence.
Interestingly, binding is not restricted to the region 20-29, which is strongly involved in fibril formation and found buried in recently elucidated cryo-EM structures  [11] [12] [13] [14] [15]. The spectral distribution in the proton dimension remains narrow, which suggests that IAPP does not fold upon binding, but remains essentially disordered. This confirms the PRE-results obtained upon monitoring the interaction of paramagnetically tagged IAPP on PFD which show an extended binding surface (Fig. 2 ). More quantitative analysis of binding strength was performed with the TITAN lineshape fitting program  54  , assuming a 1:1 stoichiometry. Line-shapes and change of signal frequencies were fitted for Ala13, Val17, His18, Ser19, Leu27, Phe23, Ala25, and Ser28, as these residues are important for the binding and analysis is not hampered by peak overlap.
To characterize this interaction further at a sub-molecular level, we have docked PFD (PDB: 2ZDI) on the previously determined cryo-EM structure of IAPP fibrils, based on experimental PRE-broadening on PFD and chemical shift perturbation detected by solution NMR between PFD and IAPP (Fig. 5d, e ), using the software HADDOCK  55, 56. The docking procedure was either directed to the fibril surface or to the fibril ends, thereby creating two representative complex-models (Fig. 5d, e ). Analysis of these models indicates that IAPP-PFD binding is strongly mediated by the N-terminal part of IAPP, which remains flexible and solution-exposed also in the fibrillar state, and that protruding N-termini of several neighboring IAPP molecules can interact with the same prefoldin (up to six). This model rationalizes the pattern observed in the negative stain micrographs, as well as the inhibitory effect of PFD on IAPP fibrillation in the kinetic assays.
The fibril core is composed of residues 13-37 with residues 1-12 protruding from the fibril core which are therefore not resolved in the cryo-EM map due to their dynamic behavior. This polymorph (PDB: 6Y1A) was used to model the PFD-IAPPfibril interaction as observed by EM, utilizing interacting IAPP and PFD residues identified by NMR to dock PFD and the fibril. This allowed us to get a sub-molecular insight into the interaction observed at low resolution by negative stain EM. From the fibril structure, it is clear that only the N-terminal binding epitope determined on IAPP is available for PFD binding when a fibril is formed. The PFD-IAPP fibril interaction through the N-terminal epitope was confirmed by the model obtained by NMR-guided docking  55, 56  (Fig. 5d, e ). Yet, ca. up to six N-termini of IAPP in its fibrillar state can fit into the PFD cavity when PFD is binding to the fibril surface or end, possibly leading to a higher affinity as Fig.
f Model of PhPFD (PDB: 2ZDI) with α-subunits in blue, β-subunits in green. g No line-broadening was observed for residues on the top of the PFD complex. Conversely, a strong effect was observed inside the cavity (j) and along the elongated coiled-coil helices of (h) β/β'-subunits (two views related by 180°rotation) and (i) α-subunits dimer (side view). compared to monomer binding. On the fibril end, the second binding site in the middle IAPP segment is additionally available to stabilize the interaction. According to EM, the PFD density at the fibril end was higher than on the lateral fibril surface. This suggests that the fibril end has a particularly high affinity for PFD, which can be explained by the fact that both IAPP segments important for interaction are exposed at the fibril end. The preference of PFD to bind to fibril ends correlated with the strong inhibition of fibril elongation observed in IAPP aggregation assays (Fig.
1a ), which results from the formation of less aggregates with an altered morphology (Fig. 1b ). The inset zoom represents a model of IAPP fibril structure with unfolded residues 1-12 in yellow and the structured fibril core is represented in purple (from residues 13-37). d, e present docking models of PhPFD (PDB: 2ZDI) on IAPP fibril (PDB: 6Y1A) surface and extremities, respectively, integrating structural information obtained by NMR (Figs. 2  and 3 ) and EM (Fig. 4 ); the black arrows indicate the fibril axis with the tips pointing towards the fibril ends. amyloidogenic protein IAPP. Solution NMR enabled us to identify two binding regions on IAPP (from residues 10 to 19 and 24 to 28), and to map the corresponding binding sites within the PhPFD cavity. We established, using IAPP fibrillation assays and electron microscopy, that the substoichiometric inhibition of IAPP fibrillation by PFD is mainly due to binding to fibril surface and ends, thereby inhibiting both secondary nucleation and elongation.
For all assays, the samples were prepared as triplicates in protein LoBind tubes (Eppendorf) on ice and then transferred into a 96-well half-area polystyrene nonbinding surface (NBS) microplate, black with flat transparent bottom (3881, Corning). ThT FI values were recorded in a BMG FLUOstar Omega microplate reader controlled by Reader Control software from BMG (BMG LABTECH, Ortenberg, Germany) at 37 °C, using excitation wavelengths of 448-10 nm and emission of 482-10 nm, each data point corresponds to an orbitally averaged value over 3 mm with 20 flashes per well. In the de novo assays, 20 s orbital shaking at 300 rpm before each cycle was applied as additional agitation. Kinetic analysis was performed and various parameters were extracted using numerical approximations of first and second derivatives with respect to time of the ThT FI curve (Fig.
1b. Extracting the kinetic parameters was done automated using a custom script, processing the data uniformly. First, data was smoothened via moving averaging over 11 data points. Then, numerical approximations of first and second order time derivatives were calculated as the symmetric difference quotient of the ThT FI data and first order derivative accordingly, using a derivative depth window of 4 points. Growth phase duration was defined as time between growth start and end (time points where first derivative has a value of 5% of its maximum); maximal growth rateas maximal value of first order derivative; average growth rateas the difference between ThT FI values at end and start of growth, divided by the growth phase duration; initial growth rate was calculated as mean value of first 20 values of the first order derivative; final plateau heightas a mean of 50 points following after the growth end; acceleration maximumas the maximal value of second order time derivative.
The full text of the used script is linked to this publication. AFM imaging. For the AFM imaging, after recording de novo aggregation assays, the samples were taken out of the plate (96-well Half Area Black/Clear Flat Bottom Polystyrene NBS Microplate; 3881, Corning). The solution was extensively mixed in order to include the aggregates potentially sticking to the walls of the well. 2 µl of each sample were put onto a freshly cleaved muscovite mica surface and dried during the incubation over 10 min under the clean bench. Subsequently, the samples were washed 3 times with 100 µl Milli-Q H 2 O and dried with a steam of N 2 gas. Imaging was performed in intermittent contact mode (AC mode) in a JPK NanoWizard 3 atomic force microscope equipped with NanoWizard Control Software v. 5 version 5. 0.
35 N/m and resonance frequency around 65 kHz. The images were processed using JPK DP Data Processing Software (version spm-5. 0. 84). For the presented height profiles, a polynomial fit was subtracted from each scan line, first independently and then using limited data range. Cell viability assay (MTT test). In order to examine the cytotoxic effects of IAPP with or without (different) concentrations of PhPFD, an MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide) based cell viability assays was accomplished with RIN-m5F cells (rat pancreatic beta cells). RIN-m5F cells (ATCC, CRL-11605, Manassas, VA, USA) were cultured in RPMI 1640 medium (Thermo Fisher ATCC modification, Germany) supplemented with 10% fetal calf serum, 1% antibiotics (Penicillin/Streptomycin) (all Sigma-Aldrich, Germany) on tissue culture flasks (Avantor, Germany).
7 mm cryogenic probe and operating at a 1 H frequency of 850 MHz. For 2D titration experiments with an acquisition time longer than 5 h, the NMR data acquisitions were split to control that the IAPP NMR signals did not decrease over time, ensuring that the IAPP state did not change over the course of the NMR experiment. Data were acquired using TopSpin (Bruker) and NMRlib 71 processed using nmrPipe/nmrDraw  72  , CcpNmr and the K D and k off were extracted using TITAN global lineshape fitting 54 using NMR signals of residues A13, V17, H18, S19, F23, A25 and L27, assuming a 1:1 stoichiometry, therefore using the twostate binding model. IAPP was assigned using 3D BEST-TROSY HNCO, HNCA, HN(CO)CACB, HN(CA)CB and HN(CA)CO 71 experiments (BMRB accession code 51259). PhPFD assignment. Backbone atoms and methyl groups resonances of both αand β-PhPFD subunits were previously assigned  45  , using a combination of (i) 3D BEST-TROSY HNCO, HNCA, HN(CA)CB, HN(CO)CA, HN(COCA)CB and HN(CA)CO experiments 71 ; (ii) structure-based analysis of inter-methyl NOEs 73 ; (iii) mutagenesis  74  and (iv) 3D HCC-relay experiments  75, 76  connecting backbone to methyl moieties.
List of assigned chemical shifts is available online (BMRB accession code 50845) and annotated 2D  13   For each sample 2D  15  N-BEST-TROSY  77  or 2D SOFAST-METHYL-TROSY spectra  74  were recorded at 30 °C for ~0. 5 day/sample on a Bruker Avance III HD spectrometer equipped with a cryogenic probe and operating at a 1 H frequency of 950 MHz. For long PRE experiments, the NMR data acquisitions were split and 1D control NMR experiments were inserted before and after each 2D experiment. These control experiments were enabling to monitor IAPP NMR signals over time, ensuring that the IAPP state did not change over the course of the NMR experiment. As a control experiment to exclude false positive interaction of PFD with the DOTA-cycle, we verified that addition of Gd 3+ -loaded DOTA to methyl-labeled PFD does not lead to specific residue broadening (Supplementary Fig.
Images were acquired using Digital Micrographs 1. 85. 1535 and analyzed with Gwyddion software  79. For cryo-EM imaging of PhPFD, 3. 5 μl of PFD solution in 50 mM Tris, 100 mM NaCl pH 8. 5 buffer at 100 μM was blotted on a Quantifoil R2/2300 mesh gold grid coated with carbon and ionized by glow discharge. A FEI Vitrobot (automated vitrification machine) MARK IV was used at 6. 5 sec blotting time, 100% humidity at 20 °C. About 1000 movies were recorded with a Thermo Scientific GLACIOS 200 kV FEG with a Falcon II electron counting direct detection camera with EPU (automatic data collection) with a pixel size of 1. 206 Å. About 700,000 particles were extracted using guided particle picking, using the PhPFD crystal structure (PDB: 2ZDI). 2D classification was performed using relion standard procedures  80.
Modeling of IAPP-PhPFD complexes. Interaction models were created by HAD-DOCK rigid model docking  55  , using the HADDOCK 2. 4 web server  56. For the model of the complex between monomeric IAPP and PhPFD, the basic docking protocol was used with the interaction residues on IAPP defined as the residues which showed above average chemical shift displacement upon PFD addition for most of the ratios (residues 6, 10-13, 17-19, 24, 26-28). The PhPFD interaction site was defined as residues which showed more than 50% of signal intensity loss upon interaction with paramagneticlabeled IAPP (α-subunit:   5 ). The solution structure of IAPP in SDS micelles (PDB: 2L86) determined by NMR  81  was used as the starting IAPP monomer conformation. Residues on the N-terminus of β-subunits missing in the PhPFD crystal structure (PDB: 2ZDI)  82  were obtained from molecular modeling (MODELLER  83  ).
For the complex involving PhPFD and IAPP fibril, the cryo-EM structure of the polymorph 1 of IAPP fibrils (PDB: 6Y1A)  13  was used. The 12 missing N-terminal residues were added using molecular modeling (MODELLER  83  ), whereby a disulfide bond between residue two and seven was enforced. The same residues in PhPFD-IAPP monomer docking were used as restraints in PhPFD-IAPP fibril docking. For the fibril end interaction model, an IAPP fibril construct of 16 chains was used, and all the according residues on all the chains were given as docking restraints. To direct the docking towards the middle of the fibril, a longer IAPP fibril construct of 26 chains was used, and the interaction restraints were only set on the 18 chains in the middle of the construct. The interacting residues of IAPP on the fibril surface or on the fibril end with PFD residues in the docking models are presented on Supplementary Fig.
Acknowledgements The authors thank  Drs. Rida Awad  and  Leandro Estrozi  for help and advice,  Drs. K. Miki ,  M. Fujihashi  and  J. M. Valpuesta  for providing clones of Prefoldin. This work used the high field NMR and EM facilities at the  Grenoble Instruct-ERIC Center (ISBG ;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology.
Data availability The NMR assignments data together with corresponding NMR experiments used in this study are available in the Biological Magnetic Resonance Data Bank under accession code 51259 for IAPP  84  and 50845 for PhPFD. The titration and PRE NMR experiments used in this study are available in the Biological Magnetic Resonance Data Bank under accession code bmrbig32. All other experimental data (EM, AFM, BLI, ThT and Cell viability assays) used in this study are available in Zenodo repository under accession code 6319386.
METHODS We used the scaffold-level genome assemblies of O. vulgaris, O. bimaculoides, A. dux, and N. pompilius, publicly available under GenBank accession numbers GCA_003957725. 1, GCA_001194135. 1, GCA_006491835. 1, and GCA_018389105. 1, respectively. A chromosomal-scale assembly generated with LACHESIS  (Burton et al. , 2013)  was used for E. scolopes (Schmidbaur et al. , in review, http://metazoa. csb. univie. ac. at/data/ v2/). Completeness of genomes was assessed with BUSCO 5. 2. 2  (Manni et al. , 2021)  by considering the 954 conserved orthologs of the metazoa_odb10 database and with technical statistics supplied by Quast 5. 0. 2  (Gurevich et al. , 2013)  (Supplementary Table  S1 ). For each assembly, the same repeat annotation workflow was employed: a family library was generated with RepeatModeler 2.
0  (Flynn et al. , 2020)  and used to annotate and mask each starting assembly with RepeatMasker 4. 0. 9 (Smit et al. , 2020) ; in order to uncover further sequences that were not detected in the first masking round, these steps were performed a second time on the previously hard-masked genome (double-masking, as employed in  Meyer et al. , 2021) ; a defragmentation step of all the obtained sequences was then carried out with RepeatCraft in the "strict" merge mode  (Wong and Simakov, 2019). Custom Bash, Python, and R scripts were used to filter and parse the data for the assessment of repeat content. Because the "Unknown" and "Simple_repeat" categories constituted a significant portion of the total repeats (see Supplementary Table  S1 ) but were not of interest for our purpose, they were discarded to obtain a clearer landscape of the known TEs.
Any repeat content that is henceforth referred to is therefore intended as deprived of unknown and simple repeats. Total repeat composition was assessed for every assembly in terms of subclass and family raw counts. Such content was then split into three contiguous intervals of divergence from consensus, namely, 0-10, 10-30, and >30%, as defined by RepeatMasker estimation with the Kimura distance-based method. We then looked for expression evidence by comparing RNA-seq data from different tissues with the repeat annotations to have an overview of the repeat complement activity of every species, except A. dux, for which transcriptomic data are not available (for data accessions, see Supplementary Table  S3 ). After adapter and quality trimming (TrimGalore 0. 6. 5, Krueger, 2015) , the reads were mapped to their genome with Hisat2 2.
Despite the effort made to make inter-and intraspecies sequence hit proportions as balanced as possible, exactly even retention of both in the search output was not reached (Supplementary Figure  S5 ). The possibility that the marked bias favoring same-species matches could affect to some extent the net plot arrangement should be taken into consideration. The research in Dfam gave significant hits for 12 DNA and 3 LINE families, with TcMar-Tc1, Mariner, and Tigger having the highest number of hits in the database and A. dux being the species with the highest number of overall matches (Supplementary Table  S4 ).
Conclusion and Next Steps The family repeat content was outlined in five cephalopod species, and a preliminary assessment of an ancestral TE set was made by considering the most divergent repeat sequences. This allowed us to distinguish between lineage-specific, shared, and stem-coleoid expanded repeat elements. An additional sequence similarity-based analysis of some ancestrally shared families revealed more accurate patterns of independent and interspecies expansions, therefore highlighting a possible partially shared history of such repeat families. The comparative profiling here described is preliminary work, and the inclusion of new key species and chromosome-level data will be essential for making the coleoid and cephalopod TE landscape more robust. Indeed, the recent genome sequencing of Nautilus added an important comparative point to our study as the only coleoid outgroup, and future acquisition of new data regarding nautiloids and new coleoid species will be fundamental for investigating the cephalopod repeatome evolution.
New information The ATBI datasets from the Parc national du Mercantour in France are now publicly available. Between 2007 and 2020, 247 674 occurrences were recorded, checked and published in the INPN information system. All this information is available in open access in the GBIF web site. With 12 640 species registered, the ATBI is the most important inventory in France. This data paper provides an overview of main results and its contribution to the french National Inventory of Natural Heritage. It includes a list of 52 taxa new to science and 53 species new to France discovered thanks to the ATBI.