dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). For each tooth, three landmark schemes were defined as well as one linear measurement for the tooth length (TL). The FL scheme (Fig. 2 ) only considers the fixed landmarks (m1FL; M3FL). The variability of the anterior (AL) and posterior loops (PL) was observed with 2 fixed landmarks combined with all semilandmarks (m1AL: lm n 7 and n 11, 20 slm; M3PL: lm n 4 and n 7, 50 slm). Finally, the overall shapes of the teeth were analysed with the SL datasets, which consider semi-and fixed landmarks altogether (Fig. 2 ). In these SL datasets, the fixed landmarks overlapping the semi-landmarks were removed to avoid any redundancy information (m1: lm n 8, 9 and 10; M3: lm n 5 and 6 in Fig. 2 ).
Geometric morphometrics All landmark configurations of modern specimens were superimposed with generalized Procrustes analysis (GPA), which isolates the shape variables from size, orientation and position parameters  (Dryden and Mardia, 1998; Rohlf and Slice, 1990). Semi-landmarks were placed along specific outlines and were allowed to slide to minimize the bending energy between the mean shape and the specimen under consideration  (Bookstein, 1997). The obtained coordinates were then projected into the Euclidean tangent space. Putative outliers were then identified independently in each dataset as the individuals having a high Procrustes distance to the mean shape (lying beyond the third quartile increased by 1. 5 times the interquartile range, see Table  2 ). Fossil specimens were then aligned on present day meanshapes for each dataset, and rotated according to the rotation computed on the modern datasets.
The number of PCs that has the lowest prediction error is then selected between 1 and p, p being the number of specimens comprised in the smallest group (Myopus   1  and Suppl. Data 1). in our case), thus reducing putative over-fitting issues  (Kovarovic et al. 2011). The issues of imbalanced datasets were also investigated following the method of  Evin et al. (2013) , which is especially important since the modern referential of this work has more Lemmus specimens than Myopus specimens. This effect was tested by iteratively sampling a subset of Lemmus containing the same number of specimens as the Myopus group, and then building the model with it. Finally, a dataset simulating the null hypothesis of an inefficient model was simulated, by randomizing the attribution of the modern specimens prior to building the model.
Both balanced dataset and null hypothesis datasets were iterated 100 times allowing a precise calculation of the prediction error. The quality of all predictive models was evaluated by checking at the posterior probabilities. The final classification of a specimen is decided by looking at the most efficient models for the teeth, meaning the ones that are successful in the modern record and for which a fossil individual is classified with high posterior probability (post. prob. > 0. 9). A specimen will be correctly classified if all models have the same.
Description of the morphospace For both m1 and M3, the two genera seem to be well separated along the PC1 axis. Looking at the m1, the first two PCs respectively explain more than 50% of shape variation, except for m1FL for which it barely reaches 25%. The main morphological differences along this axis seem to be focused on the anterior loop for all datasets. The m1FL scheme is characterized by a reduction of the width of the anterior loop with the antero-posterior displacement of the lm 10 and 9, which are closer on Lemmus than on Myopus. This tendency is even clearer on m1SL and m1AL, where re-entrant angles of the anterior loop seem to be more pronounced on Lemmus than on Myopus. The m1TL measurement shows that Myopus tends to have smaller teeth than Lemmus, especially when compared to L.
0%; M3TL ¼ 19. 8%) (Table  2 ). All models are performing similarly either on balanced or unbalanced datasets when less than x 50 PCs are included in the analysis (Fig. 4 ). Beyond that point, both m1SL, M3SL and M3PL seem to have an increased prediction error rate for balanced samples, resulting from the bias induced by a larger number of predictor variables compared to the number of specimens in each group. Each model was built on an optimal number of PCs that is smaller than 50 for all datasets (Fig. 4 , Table  2 ), in order to avoid any over-fitting issue.
Acknowledgments The authors are grateful to  Nicolas Navarro  and  R emi Laffont  for their help in the methodology development and the statistical analyses. The authors are also grateful to the different persons and institutions that gave access to their collection for modern referential:  J erôme Thomas  ( ReColNat, Paleontology collection, Biog eosciences ),  Hanneke Johanna Maria Meijer  ( Department of Natural history, University Museum of Bergen ),  Daniela C. Kalthoff  and  Anna Perrson  ( Department of Zoology, Swedish Museum of Natural History ),  Maria Mostadius  ( Biological Museum, Lund University ) and  Lars Erik Johannessen  ( Bird and mammal Collections, Natural History Museum, University of Oslo ). We also thank  Jan Wagner  ( Palaeontological department, National Museum Prague ) for giving us access to the fossil collections.
We determine the global seismic parameters of the solar-like oscillations using two different methods: one using the A2Z pipeline and the other the Bayesian apollinaire code. With the latter, we also perform the peak-bagging of the modes to characterize their individual frequencies. By combining the frequencies with the Gaia DR2 effective temperature and luminosity, and metallicity for five of the targets, we determine the fundamental parameters of the targets using the IACgrids based on the MESA (Modules for Experiments in Stellar Astrophysics) code. We find that four of the stars are on the main sequence, two stars are about to leave it, and two stars are more evolved (a subgiant and an early red giant). While the masses and radii of our targets probe a similar parameter space compared to the Kepler solar-like stars, with detailed modeling, we find that for a given mass our more evolved stars seem to be older than previous seismic stellar ensembles.
g. ,  Vanderburg & Johnson 2014; Lund et al. 2015; Aigrain et al. 2016; Luger et al. 2016). For this work, we used two of those pipelines. The first is the EVEREST 3  (Ecliptic Plane Input Catalog Variability Extraction and Removal for Exoplanet Science Targets) pipeline  (Luger et al. 2016 (Luger et al. , 2018) )  that has been shown to provide calibrated data that are well suited to asteroseismic analyses of red giants (e. g. ,  White et al. 2017; Zinn et al. 2020 Zinn et al. , 2022)). However, for the short-cadence data, only light curves up to C13 are available with this calibration. Hence, we also used an adapted version of the Lightkurve Python package 4  (Lightkurve Collaboration 2018) to generate the short-cadence light curves for C17. We first extracted the Target Pixel File (TPF) downloaded from the Mikulski Archive for Space Telescopes (MAST).
2004a,b). The longest living p modes in the stars analyzed in this work have lifetimes of around 3. 7 days. Therefore, during the 2. 5 year gap, all the modes are re-excited hundreds of times and there is therefore no effect in the characterization of the central frequencies of the limit spectrum. A detailed Monte Carlo simulation of the longlived modes in EPIC 212485100 is described in Appendix B, where we justify this choice. As C17 LK is noisier than C6 EV , we apply a correction factor to scale the two campaigns on a similar level. To do so, we multiply the C17 LK flux by the ratio of the noise in the PSDs above 6 mHz between C6 and C17, as follows: flux C17 LK = flux C17 LK √ PSD C6>6 mHz √ PSD C17>6 mHz , (1) where the symbol <> indicates the mean of the PSD. Light curves from both EVEREST and Lightkurve are then processed with the Kepler Asteroseismic Data Analysis  Correction Software pipeline (KADACS,  García et al.
Global seismic parameters with A2Z and apollinaire We ran both codes on the EVEREST calibrated light curves (C6), the Lightkurve data (C6 and C17), and on the stitched C6 and C17 (C6+C17) time series. We compared the results obtained from the two pipelines for the different observing campaigns (C6 EV , C6 LK , C17 LK ) as well as for the combined campaigns (C6+C17), finding that for all the analyzed light curves, the values of ∆ν and ν max obtained with A2Z and apollinaire agree within 3σ, and within 1σ between C6 EV and C6+C17. In addition we compared the absolute uncertainties provided by each method. The error bars on ν max are generally smaller for A2Z by up to a factor of 2, while for ∆ν, the uncertainties are significantly smaller for apollinaire by a factor of 5 to 6. Given the results of the comparison between the two pipelines, and for compatibility with previous published values of A2Z for other stars, we decided to use the A2Z global seismic parameters obtained with the combined C6+C17 calibrated light curves for the stellar modeling.
For one star, HD 119038 (H), with a mass M 1. 3 M , we consider a fit to a grid of models with overshooting, as indicated in Sect. 4. 3. As can be seen in Table  2 , stellar parameters from the grid with overshooting are consistent with those derived from the grid without including it. Finally, we compute results considering T eff and log g from the Gaia DR3 database as mentioned in Sect. 3. The ages, masses, and radii change on average by 13%, 6%, and 3%, respectively. These figures are slightly higher than the statistical A106, page 7 of 25 errors given in Table  1. As mentioned previously, these results should be taken with caution because the spectroscopic values of log g in DR3 require further analysis.
Although most of the stars in our sample may be too evolved or too hot for Skumanich-type spin down to take place, it is nonetheless interesting to compare the gyrochronology predictions with analyses based on the empirical light curves. Estimations of surface rotation have been carried out for large samples of stars observed by Kepler and K2 (e. g. ,  Nielsen et al. 2013; García et al. 2014b; McQuillan et al. 2014; Santos et al. 2019 Santos et al. , 2021;; Reinhold & Hekker 2020; Gordon et al. 2021 ). This measurement relies on the passage of active regions and/or faculae on the visible stellar disk with a periodicity related to the rotation of the star. To study the surface rotation of our targets, we only use the C6 EVEREST light curves as the Lightkurve data (the only ones available for C17) are heavily filtered at low frequency.
The second star, HD 117779 (F), has a mass of 1. 4 M and is close to being a subgiant. Our analysis shows different fast possible rotation periods of 1. 9 days, 4. 4 days, and 9. 6 days. For such a massive star, the magnetic braking is weaker than a typical low-mass solar-like star below the Kraft break  (Kraft 1967) , and it can therefore retain a fast rotation even at this late stage of the main sequence. Longer datasets would be necessary to confirm its rotation period.
76 µHz up to l=0 at ν = 1679. 24 µHz) in the same conditions as in the K2 data analyzed in this paper, that is a total time span of 1029 days consisting of a first series of 79 days (similar to the C6 observations), a gap of 883 days and a final segment of 67 days (as in C17). The properties of the modes and the background used to build the limit spectrum were taken from the fit of the C06 EVEREST dataset. In Fig. B. 1, the limit spectrum used to compute the Monte Carlo simulation is shown. Once this limit spectrum is calculated, we convert the PSD into an amplitude spectrum (AS) and we multiply the real and imaginary parts of the AS by two Gaussian noise distributions. By calculating the inverse Fourier transform, we obtain the 1079 day light curve associated with a single noise realization.
Acknowledgements. This paper includes data collected by the  Kepler mission. Funding for the Kepler mission is provided by the  NASA Science Mission directorate. Some of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. L. G. C. acknowledges support from grant  FPI-SO  from the  Spanish Ministry of Economy and Competitiveness (MINECO) , (research project  SEV-2015-0548-17-2  and predoctoral contract  BES-2017-082610 ). S. M. and L. G. C acknowledge support from the  Spanish Ministry of Science and Innovation (MICINN)  with the grant no. PID2019-107061GB-C66  for PLATO. S. M. and D. G. R. acknowledge support from the  Spanish Ministry of Science and Innovation (MICINN)  with the grant no.
Ong et al. (2021)  obtained high-resolution spectra, providing T eff and [Fe/H]. Their effective temperatures agree with the Gaia DR2 values within 2σ, and so we kept the Gaia constraints, while we used the metallicity from Ong et al. well as the potential systematic errors in the metallicity scale when compared to other surveys (Gaia Collaboration 2023). For five of our targets, 5 https://gitlab. com/rgarcibus/k2_multicampaignk2_ gonzalezcuesta2023.
Structure determination by cryo-EM of RIPK2-XIAP BIR2 complex Exhaustive crystallisation trials failed to produce crystals containing either the RIPK2 1-317 -XIAP BIR2 154-240 or the RIPK2 1-317 -XIAP BIR2 AG 154-240 complex, resulting only in RIPK2 dimer crystals. Therefore, we attempted to solve the structure of RIPK2 1-317 -XIAP BIR2 154-240 by cryo-EM. To make the complex, we purified both WT domains to homogeneity. We added ATP-MgCl 2 to the kinase sample to promote full phosphorylation  (Pellegrini et al, 2017)  and we combined it with an excess of XIAP BIR2 protein. The sample was further purified by sizeexclusion chromatography and the most enriched complex fraction was selected for vitrification  (Fig S4A and B). We performed data collections with a Titan Krios, combining untilted and tilted data.
We obtained two-dimensional (2D) class averages showing structural features and finally obtained a map at a nominal resolution of 3. 15 Å (Figs S4-S6 and Table  S2 ). In this map, we were able to unambiguously dock two RIPK2 kinase molecules, arranged as an antiparallel dimer, as in RIPK2 crystal structures (PDB ID: 5NG0, 4C8B), bound to one molecule of XIAP BIR2 (PDB ID: 4J3Y)  (Lukacs et al, 2013; Canning et al, 2015; Pellegrini et al, 2017 ) (Figs 3A-D and S7A and B and Table  S2 ). We immediately observed that one of the BIR2 mutations (the C213G) known to improve protein quality is at the interaction interface  (Fig 3F). Therefore, we did not pursue the structure determination of the RIPK2 1-317 -XIAP BIR2 AG 154-240 complex. Both kinase chains are in the active-like conformation based on their αC-helix position (IN conformation) and density can be observed for nucleotide in the active site  (Figs 3A and S7C and D).
Pull-down data show that mutant R36L in RIPK2-Kinase_A reduces the complex interaction dramatically, whereas mutants R41L, R36L/R41L, and XIAP mutant Y176A abort complex formation completely. The structure shows that the two arginines also contribute to the kinase dimerization interface (Fig  3E ). In the apo-structure (PDB ID: 5NG0,  [Pellegrini et al, 2017] ) R41 of Kinase_A is H-bonded to D291 of Kinase _B, whereas R36L makes polar contacts with waters located in between the two kinase molecules. Therefore, in the case of the arginine mutants, we cannot distinguish whether the mutation affects complex formation or kinase dimerization or both. XIAP H178 potentially makes a hydrogen bond with the oxygen group of D39, which is also part of the R36/R41 patch. However, the mutations XIAP H178A and RIPK2 D39L do not impair complex formation  (Figs 3E and 4B and C).
The XIAP BIR2 loop 209-214 makes electrostatic interactions with the C-lobe of Kinase_B, which comprises residues belonging to the αH-helix (E279, S282, and K285) and to the regulatory region (K209 and I208). N209, E211, and D214 from XIAP BIR2 loop 209-214 form a negatively charged binding site that appears to lock Kinase_B residues K209-I208. Mutations of these XIAP loop residues blocks complex binding, except for C213A, which agrees with the fact that the XIAP BIR2 AG construct can still bind the kinase (Fig  4C ). Mutations of RIPK2 K209, E279, S282, and K285 destroy the binding, validating the interaction interface (Fig  4B ). In the apo structure (PDB ID: 5NG0,  [Pellegrini et al, 2017] ), K209, E279, S282, K285 are not contributing to the kinase dimerization interface, which implies that their mutation likely affects BIR2 binding only.
The cryo-EM reconstruction of the RIPK2 1-317 -XIAP BIR2 154-240 complex shows the RIPK2 as a similar antiparallel dimer to that seen in crystal structures. Consistent with our NMR results, only the folded BIR2 domain binds to RIPK2. Importantly, the bound XIAP BIR2 domain engages both monomers with two different loops, highlighting the importance of the RIPK2 dimer as a scaffold for downstream ubiquitination. The resulting interface is different from the one described for the caspase 3 and 7-binding sites (PDB IDs: 1I3O, 1l51 and Fig  S10A  and B )  (Chai et al, 2001; Riedl et al, 2001). All the residues previously described to disrupt the RIPK2-XIAP BIR2 interaction contribute directly to protein-protein interaction, except I212, which is located deeper in the Kinase B structure  (Damgaard et al, 2013; Cavallari et al, 2017; Hrdinka & Gyrd-Hansen, 2017).
(B) Surface representation of the XIAP BIR2 structure (PDB ID: 1C9Q,  Sun et al, 1999). The N-and C-terminal protein segments that are not involved in the interaction with RIPK2 are highlighted in red. (C) { 1 H}-15 N heteronuclear NOE (HETNOE) ratios measured for XIAP BIR2 at 25°C and 850 MHz 1 H frequency. The protein segments that remain visible in the NMR correlation spectrum of (A) upon interaction with RIPK2 are highlighted by red bars. These N-and C-terminal segments show reduced HETNOE ratios (≤0. 6), indicative of significant fast (sub-ns) time scale backbone mobility, whereas for the central part, an average HETNOE of 0. 8 is measured, in agreement with a globular protein domain. The flexibility of the N-and C-terminal segments is preserved in the complex, which makes them NMR-observable despite their high molecular weight.
In agreement with the biophysical data, we could not identify any 2D classes with double XIAP BIR2, suggesting that the presence of a second copy of XIAP BIR2 in the RIPK2-XIAP BIR2 AG 124-240 sample might be related to the ability of XIAP BIR2 domain to homodimerise  (Lukacs et al, 2013). Interestingly, the complex reconstruction does not show any anti-parallel β-sheet interaction of the kinase N-termini, which appear to be flexible. Moreover, the two kinase monomers are closer in comparison to the original active structure (PDB ID: 5NG0, Fig  S8A  and B )  (Pellegrini et al, 2017). From these observations, we speculate that binding of XIAP BIR2 slightly perturbs the kinase dimer, which might be the reason for which only one XIAP BIR2 molecule can be bound. Based on the RIPK2-XIAP BIR2 structure here described, we can now provide a molecular explanation for the mechanism of XIAP BIR2 and RIPK2 inhibitors that prevent complex formation.
(B, C, D, E, F) Assignment according to the cryo-EM density features of (B) Kinase_A αE-helix residues (120-137), (C) Kinase_B αE-helix residues (120-137), (D) Kinase_A and Kinase_B αL-helices and their interaction (residues 299-312), (E) XIAP BIR2 interaction with Kinase_A, (F) XIAP BIR2 interaction with Kinase_B. et al, 2005). Comparison between our structure and that of XIAP BIR2 in complex with peptide AVPI (PDB ID: 4J46)  (Lukacs et al, 2013)  shows that the BIR2 inhibitor prevents complex formation with RIPK2 occluding the IBM groove (Fig  S10C ). Indeed the BIR2 inhibitor engages polar contacts with residues of the XIAP loop 209-214, which we have here described to guide the interaction between the BIR2 domain and the C-lobe of Kinase_B. This structural observation is in agreement with the fact that XLP-2 BIR2 mutations that either destabilise the domain or belong to the IBM groove abolish the RIPK2-BIR2 interaction  (Sun et al, 1999; Damgaard et al, 2013; Lukacs et al, 2013).
Translational diffusion constants of the proteins in solution were measured by 1D 1 H DOSY experiments  (Johnson, 1999). NMR data processing and analysis were performed using TopSpin 3. 5 (Bruker BioSpin) and CCPNMR V3 software tools. Heteronuclear { 1 H}-15 N nOe (HETNOE) data were recorded as interleaved 2D data sets with and without 1 H saturation before 15 N excitation. The inter scan (recycle) delay was set to 5 s, and 1 H saturation was applied for 3 s. The HETNOE values are computed as the peak intensity ratio measured in the 1 H-saturated and reference spectra.
Cryo-EM specimen preparation Cryo-EM specimens were prepared on UltrAuFoil R1. 2/1. 3 holey gold grids (Quantifoil) that were glow-discharged for 20 s at 25 mA on both sides (PELCO easy glow). Vitrobot (Vitrobot Mk IV; Thermo Fisher Scientific) was set to 4°C and 100% humidity. A drop of 2 μl of the sample was applied to each grid side and blotting was run at force 0 for a total time of 3. 5-4. 5 s. Grids were then vitrified by plunging into liquid ethane at liquid N2 temperature. Grids were clipped into autoloader cartridges and screened using a Glacios cryo electron microscope (Thermo Fisher Scientific) equipped with a Falcon 3 detector. Promising grids showing visible particles by eye at -1 μM defocus were used for data collection on CM01, ESRF Grenoble France (Fig  S4C ).
Cryo-EM data collection and processing The RIPK2 1-317 -XIAP BIR2 154-240 dataset was acquired using a Titan Krios operating at 300 keV, equipped with K2 Quantum detector (Gatan) and a GIF Quantum energy filter (Gatan) at CM01 (ESRF)  (Kandiah et al, 2019). 7,178 movies were collected in untilted mode, at 165K magnification, corresponding to a pixel size of 0. 87 px/ Å for a total dose of 47. 8 (1. 2 e -/A2 per frame, fractionated in 40 frames). A total of 3,698 movies were recorded in tilted mode (1,870 micrographs at 20°, 1,825 micrographs at 25°), at the same magnification, for a total dose of 45. 7 (1. 1 e -/A2 per frame, fractionated in 40 frames) (Fig S4D and Table  S2 ). Movies were imported into Relion, aligned and dose-weighted using MotionCor2 in Relion 3. 1  (Fernandez-Leiro & Scheres, 2017; Zheng et al, 2017).
Micrographs were then imported into Cryo-SPARC  (Punjani et al, 2017)  where CTF estimation has been run with Patch CTF and tilted micrographs were manually curated according to CTF-based estimated resolution (<10 Å) and estimated ice thickness (<1. 1). Any attempts of processing the untilted dataset alone or in combination with the tilted datasets with a series of 2D and 3D classifications resulted in poorly resolved cryo-EM maps indicating preferred particle orientations. Therefore, we modified our processing approach. We firstly processed only the tilted data. The two tilted datasets (collected at 20°and 25°respectively) were kept separately and processed similarly  (Fig S5). Particles were firstly picked on 100 representative micrographs using topaz  (Bepler et al, 2019)  and they were extracted with a box size of 300 × 300 pixels.
We applied 2D classification with a tight mask (100 Å) and select 2D classes showing particle features. These particles were used to train topaz and the resulting model was used to pick particles in the remaining micrographs. After extraction, 2D classification was applied to eliminate bad particles and to select 2D class averages with lower background noise and stronger features. We then performed ab initio reconstruction with two models followed by non-uniform (NU) refinement using the best model as reference and all the particles. The resulting map denoted the tilted map, showing a better orientation distribution than for previous maps. To maintain a balance between the number of particles from the tilted dataset and the number of particles from the untilted dataset, only 2,000 micrographs of the latter were selected.