dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, groups that exhibit a high accumulation of residual deviations provide more variation for selection to act upon and can thereby be considered to be more evolutionarily flexible  [20]. We assembled a brain endocast dataset sampling 284 extant bird species, 22 extinct bird species, and 12 non-avian theropod dinosaurs, which we combined with a sample of >1,900 extant species from the recent study of Sayol et al. [14]  (Figure  S1 ). The inclusion of fossil data has been shown to improve inferences of trait evolution  [21, 22]  and further allows us to answer questions about patterns of evolution in deep time. Our analyses utilize a two-phase approach. First, we use bivariate multi-regime Ornstein-Uhlenbeck (OU) methods  [23] [24] [25]  to identify where shifts in slope and intercept occur in the phylogeny.
METHOD DETAILS Brain-volume and Body-mass Data We assembled a dataset of CT-rendered virtual endocasts to estimate brain volume, so as to facilitate sampling of rare and fossil taxa. Endocasts serve as a reliable proxy of the shape and volume of the brain in both birds and crownward non-avian theropods  [50, 51]. We then combined this dataset with a recently published dataset based on lead-shot measurements of braincase volume  [14]. Raw data and sources for taxa we sampled directly are provided in the electronic file Data S1. We obtained body mass data from a compendium  [52]  for most extant taxa. If the sex of a specimen was known, we used the average body mass of the appropriate sex when available. Otherwise, the species average was taken. For extinct birds where no body mass data were available from the literature, we applied body mass regressions from femur circumference  [53].
Phylogeny and Divergence Dating As a phylogenetic backbone for the analysis of the endocast dataset (Data S2), we used a phylogeny based on whole genomes from nearly all 40+ avian orders  [29]. In order to reconcile the taxa sampled in the constraint trees, the supermatrix, and our dataset, we substituted closely related species in a few cases. These are listed in Data S3. We generated a tree sampling $6000 species using a pipeline approach  [55]  (Data S4). This tree was then dated using a penalized likelihood approach in r8s v. Unpruned, undated tree from ML analysis used as input for dating in r8s This paper calibrations (Methods S1). We then pruned extant taxa not represented in our dataset. Finally, extinct taxa for which no molecular data were available were grafted onto the tree based on a recent phylogeny for non-avian theropod taxa  [59]  or recent molecular/ morphological phylogenies for each extinct bird species (see Data S1).
Assessing Differential Changes in Brain and/or Body Size To assess whether changes in the brain$body allometry were driven primarily by increase or decrease in either brain or body size, we calculated phylogenetic means for both brain size and body size for each of the allometric regimes identified by the best-fit allometric regime analysis described above using a procedure to calculate phylogenetic means  [26] , and implemented in the 'evomap' R package  [27]. These analyses identify differences in mean brain and/or body size between groups of species. Results reveal the population averages in brain size and body size for the different allometric regimes. Comparing shifts in mean average brain size and body size across regimes provides an indication whether either shifts in brain size or body size primarily characterize shifts in allometric groups (Table  3 ).
For example, in the analysis of the endocast dataset the allometric grade comprising corvids indicates a shift in (log) brain size of 1. 5 and a shift in (log) body size of 1. 7 relative to its ancestral grade (the ratio of unlogged size changes relative to their ancestral grade is 4. 48 for brain size and 5. 47 for body size, see Table  3 ). Considering that both the corvid grade and their ancestral grade indicate negative allometry (with slopes of 0. 66 and 0. 65; Table  1 ), the general expectation is that brain size changes at a slower pace relative to body size. Results for the shifts in brain and body size in corvids, however, indicate that brain size changes more than body size in this clade, even though there is also considerable change in body size. Given that changes in brain size and body size are both positive, these results prompt the interpretation that crows and ravens have increased both brain size and body size, but brain size more than body size given allometric expectations.
ACKNOWLEDGMENTS We thank  Ruger Porter  and  Loic Costeur  for processing additional endocasts and  Josef C. Uyeda  for advice using the bayou R package. This project was supported by the  NESCent (NSF   EF-0905606 )  Catalysis Meeting grant  ' 'A Deeper Look into the Avian Brian: Using Modern Imaging  to  Unlock Ancient Endocasts. ''Additional support was derived from awards  NSF   DEB 1457181  to A. M. B. , G. S. B. , P. M. G. , and M. A. N. ;  NSF   DEB 1655736  to D. T. K. ;  NSF   DEB 1655683  to E. L. B. and R. T. K. ;  NSF   DEB 0949897  to J. A. C. ;  PIP 0437  and  0059  to C. P. T. and F. J. D. ; HHMI support to E. D. J. ;  CGL2013-42643-P  to J. M. -L. ;  NERC NE/H012176/1  to S. A. W. ; and  Marsden  grant  CTM1601  to V. L. D. P. and R. P. S.
Data S4 DATA AND CODE AVAILABILITY Scripts for the R package Evomap are archived at GitHub (https://github. com) and can be downloaded in R using the code: require(devtools) install_github(''JeroenSmaers/evomap'') Scripts for Bayou are archived at GitHub (https://github. com/uyedaj/bayou). The input file for dating the tree in r8s is provided at Data S4 and the input data for the analyses of allometry are provided as Data S5.
The Poisson's ratio of the middle lamella is not known but it should be lower than 0. 5 like most amorphous polymer blends. Thus, in this case, the indentation modulus is quite close to the Young's modulus. In previous papers, a protocol to study the mechanical properties of plant cells with the AFM PF-QNM method was already tested and the results were validated  [36] [37] [38] [39]. In the present work, AFM PF-QNM was used to investigate the area of the middle lamellae at the cell-wall level and to obtain information on their indentation modulus. For that purpose, a panel of plant fibers commonly used in biocomposites, i. e. , flax, hemp, jute, kenaf, nettle, and date palm leaf sheath, was investigated. A database of middle lamella indentation modulus was addressed on this range of plant fibers to implement the knowledge of this layer and help future works in computational modeling.
Gwyddion software (Department of Nanometrology, Czech Metrology Institute, Brno, Czech Republic, http://gwyddion. net [75] ) was used to process the images acquired. For each sample, at least three images of 512 × 512 pixels were used and several images of 256 × 256 pixels were acquired and considered to have good image representability and a statistically significant average. A small area of each image was selected according to the morphology of the middle lamella, and only the cell corner (tricellular junctions) was considered, as shown in Figure  8. A mean of the indentation modulus of the selected area was calculated by using the "statistical quantities" tool of the software.
Conclusions The results obtained in this paper show differences in both the elastic mechanical behavior and the morphology of the middle lamellae in fiber bundles from six different plants commonly used as biocomposite reinforcements: flax, hemp, jute, kenaf, nettle, and date palm leaf sheath. Atomic force microscopy mechanical characterization mode (AFM PF-QNM) was used here as it is probably the only technique available today capable of providing semi-quantitative mechanical values of very thin layers at the nanoscale level, such as middle lamellae. This allowed us to propose a comparative database of the indentation modulus of the cell corner middle lamella of the six plant fibers considered with three different groups: the first one (flax, jute and kenaf) with intermediate indentation modulus (on average between 8 and 12 GPa) close to values already obtained in previous studies on wood fiber middle lamellae, the second one (hemp and nettle) with very high values (in average between 13 and 17 GPa), surprisingly close to those of the secondary cell wall, and the last one (date palm leaf sheath) with low values (in average between 6 and 9 GPa), close to the lowest values obtained on wood fiber middle lamella.
Results A piece of mandible injected with gelatin-barium appears on Fig. 1. The 3D image was prepared by over imposing a first 3D model containing bone and teeth; the mandibular canal was exposed with a cutting box. A second 3D model was prepared by thresholding only the vessels filled with bariumgelatin that absorbed X-rays more than the calcified tissues. This second model was painted in an orange pseudo color. A video movie is added to this article as a supplementary material. The same stack of 2D images was imaged by the three 3D volume-rendering softwares and the results were markedly different as illustrated on Fig. 2. On the CTVox images, a large number of small vessels and capillaries were missing; on ImageJ,more details were available but with a strong background noise. The best results were obtained with VG Studio MAX that allowed the identification of very thin vessels and capillaries.
Here we present a high-performance software for proteome analysis that combines different mass spectrometric approaches, such as, top-down for intact protein analyses and bottom-up, for proteolytic fragment characterization. ProteoCombiner capitalizes on the data arising from different experiments and proteomics search engines and presents the results in a user-friendly manner. Our tool also provides a rapid and easy visualization, manual validation and comparison of the identi ed proteoform sequences, including post-translational modi cations (PTM) characterization. Thus, ProteoCombiner is recommended for studies dealing with different proteomics strategies, in order to increase con dence in proteoform identi cation including PTMs.
In this protocol, we describe the key steps for using ProteoCombiner, a powerful tool capable of integrating proteomics datasets obtained from different experiments (bottom-up and top-down proteomics) and different search engines, such as, PatternLab for Proteomics  [4] , MaxQuant  [5] , Comet  [6] , ProsightPD  [7] , pTop  [8]  and TopPIC  [9] , in order to increase the con dence of proteoform identi cation and facilitate the characterization of PTMs. A video demonstrating ProteoCombiner v1. 0 in action is available at https://proteocombiner. pasteur. fr/video.
Data les • ProteoCombiner v1. 0 is compatible with output data les from PatternLab for Proteomics  [4] , MaxQuant  [5] , Comet  [6] , ProsightPD  [7] , pTop  [8]  and TopPIC  [9]  and can work directly with Thermo®, ABSciex®, Agilent® and Waters® RAW les. The software is also compatible with mzML 1. 1. 0, MS2 and Mascot Generic Format (MGF).
Work ow The following work ow demonstrates how to combine proteomics data using ProteoCombiner. 2. 1. Execute the ProteoCombiner tool (Figure  1 ) [Figure 1] 2. 2. Specify the directory containing all result les from bottom-up proteomics experiments. In this directory can also have the database used in the search and the original RAW les in one of these formats: mzML 1. 1. 0, MS2, Mascot Generic Format (MGF), ABSciex®, Agilent®, Waters® and Thermo® RAW. 2. 2. 1. Bottom-up proteomics 2. 4. 1. Remove Contaminants: This option allows to remove all protein sequences that represent a contaminant.
3. 1. 1. 5. Search: Only results from peptides or proteins containing the sequence input to this eld will be displayed. The user can further search by ProteinID (protein accession number), protein description or le name. 3. 1. 1. 6. By clicking on Filter button, this operation is accomplished using all of the parameters described above. 3. 1. 1. 7. By clicking on Reset button, all initial values are restored. [Figure  2 ].
Combined results All identi ed proteins that contain a valid sequence* will be displayed on this tab sorted by Score followed by Sequence Coverage. The protein score is represented by the best proteoform score. *Protein sequence present in the database. 3. 2. 1. By clicking on an identi ed protein, all respective identi ed proteoforms will be displayed* below the protein table sorted by CombScore**. *If there is no identi ed proteoform for a respective protein, all possible identi ed peptides will be displayed instead. **CombScore is calculated by summing two different scores: i) a score related to the TDP identi cation software (which is normalized between 0 and 1; and ii) the percentage of the proteoform sequence coverage based on the peptides, obtained in BUP approach, that match to this proteoform.
By clicking on each item, all respective lines will be high-lighted in the right box. 3. 5. 2. 1. Bottom-up and middle-down approaches, represented by the blue color, 3. 5. 2. 2. Top-down approach is represented in three different colors: Expected proteoforms in orange, which means all identi ed proteoforms by the full theoretical mass; non-expected proteoforms in cyan, which represents all identi ed truncated proteoforms; and tagged proteoforms in red, which means all proteoforms that were identi ed by part of the protein sequence. 3. 5. 2. 3. All identi ed PTMs will also be displayed in this box. 3. 5. 3. The right box will display all identi ed proteoforms and/or peptides that will be represented by different lines. All of them will be displayed sorted by CombScore. On the top will be displayed the full protein sequence and all theoretical modi cations (present in database) as can be seen in Figure  4.
All participating models use either a form of data assimilation or nudging techniques of different degrees to improve the match with present-day observations (Table  4 ). All groups have contributed a complete set of core experiments (Table  3 ), which form the basis of the analysis for this paper. The submissions are identified by the group ID and model name (Table  2 ) and a counter to distinguish several submissions from the same group (Table 3). Four models have used the non-standard open forcing framework (BGC-BISICLES, UAF-PISM2, UCIJPL-ISSM2, VUW-PISM), which does not define the ocean sensitivity experiments exp09 and exp10. In the BGC-BISICLES case, they have been replaced by their own interpretation of high and low ocean forcing. The two models MUN-GSM1 and MUN-GSM2 have used remapped SMB anomalies  (Goelzer et al.
In other cases, the historical run is not specifically forced, rather representing the background evolution arising as an artefact of the initialization. In any case, representing the historical mass loss accurately was not a strong priority for our experi-  mental set-up, where any background evolution is effectively removed by subtracting results of experiment ctrl_proj. The control experiment (ctrl_proj) is in most cases the result of competing tendencies to (1) continue the mass trend before 2014 and (2) relax toward an unforced state as a result of removing the anomalies at the start of the projection period in 2015. The ensemble range of sea-level contribution due to that drift in experiment ctrl_proj is -50 to 15 mm (Table  B1 ). -ISSM1 x x x x x x x x x x x AWI-ISSM2 x x x x x x x x x x x AWI-ISSM3 x x x x x x x x x x x BGC-BISICLES x x x 1 x 1 x 1 x 1 x 1,2 x 1,2 x 1 x 1 x 1 GSFC-ISSM x x x x x x x x x x x ILTSPIK- x x x x x x x x x x x SICOPOLIS1 ILTSPIK- x x x x x x x x x x x SICOPOLIS2 IMAU-IMAUICE1 x x x x x x x x - - - IMAU-IMAUICE2 x x x x x x x x x x x JPL-ISSM x x x x x x x x x x x JPL-ISSMPALEO x x x x x x x x - - - LSCE-GRISLI x x x x x x x x x x x MUN-GSM1 x x x x x x x x - - - MUN-GSM2 x x x x x x x x x x x NCAR-CISM x x x x x x x x x x x UAF-PISM1 x x x x x x x x x x x UAF-PISM2 x x x 1 x 1 x 1 x 1 - - x 1 x 1 x 1 UCIJPL-ISSM1 x x x x x x x x x x x UCIJPL-ISSM2 x x x 1 x 1 x 1 x 1 - - - - - VUB-GISM x x x x x x x x x x x VUW-PISM x x x 1 x 1 x 1 x 1 - - - - - We further evaluate the initial model state at the end of 2014 in comparison to ice-sheet-wide observational datasets (Fig.
5 ). We calculate the root mean square error (RMSE) of the modelled data compared to observations of ice thickhttps://doi. org/10. 5194/tc-14-3071-2020 The Cryosphere, 14, 3071-3096, 2020 ness  (Morlighem et al. , 2017)  and horizontal surface velocity magnitude  (Joughin et al. , 2016). The diagnostics are calculated for subsampled data to reduce spatial correlation in the error estimates, and we show median values for different offsets. The comparison shows a wide diversity of the models in terms of their match with the observed ice thickness distribution (Fig. 5a ) and velocity (Fig. 5b ). We include a comparison with the logarithm of the velocity magnitude (normalized by 1 m yr -1 ), which reduces the emphasis of errors in high velocities at the margins (Fig. 5c ). These diagnostics are complemented by the absolute ice thickness change in ctrl_proj that serves as a measure of the model drift (Fig.
A5 IMAU-IMAUICE The model  (de Boer et al. , 2014)  is initialized to a thermodynamically coupled steady state with constant, presentday boundary conditions for 61 kyr using the average  1960 -1990 surface temperature and SMB from RACMO2. 3 (van Angelen et al. , 2014). Bedrock data are from  Morlighem et al. (2017)  and geothermal heat flux from  Shapiro and Ritzwoller (2004). The model is run in SIA mode with ice sheet margins evolving freely within the observed ice mask, outside of which a negative SMB is applied. For IMAUICE1 (16 km resolution) we continue with fixed temperature for 11 kyr to get a dynamic steady state that we assign to the year 1959. The historical run (1960 The historical run ( -2014) )  is forced with SMB anomalies from a downscaled RACMO product  (Noël et al. , The Cryosphere, 14, 3071-3096, 2020 https://doi.
A6 JPL-ISSM The JPL-ISSM ice sheet model  (Larour et al. , 2012)  configuration relies on the data assimilation of present-day conditions followed by a model relaxation and a historical spinup, similar to  Schlegel et al. (2016). For the calculation of stress balance, L1L2  (Hindmarsh, 2004; Schoof and Hindmarsh, 2010 ) is used over the entire domain with a resolution varying between 250 m in the areas of strongest gradients in surface velocity and along the margins to a resolution of 15 km in the interior. Bedrock topography is interpolated from BedMachine  (Morlighem et al. , 2017) , and initial ice surface is from the GIMP dataset  (Howat et al. , 2014). Basal heat flux is from  Shapiro and Ritzwoller (2004)  and air temperature from RACMO2  (van Angelen et al. , 2014). We use observed surface velocities  (Rignot and Mouginot, 2012)  to infer unknown basal friction at the base of the ice sheet  (Morlighem et al.
, 2010). We then calculate ice temperature, assuming that the ice sheet is in a steady-state thermal equilibrium  (Seroussi et al. , 2013). This is followed by a relaxation of 4. 2 kyr to reduce the initial unphysical transient behaviour resulting from errors and biases in the datasets and forcing  (Schlegel et al. , 2016)  using a climatological mean surface mass balance from 1979 to 1988  (Box, 2013). Finally, we run a historical spin-up from 1840 through 1979 using the  Box (2013)  reconstruction of surface mass balance. Grounding-line migration is based on hydrostatic equilibrium and a sub-element scheme  (Seroussi et al. , 2014; Seroussi and Morlighem, 2018, SEM2 parameterization) , and basal melting rates from the literature  (Rignot, 2001; Rignot and Steffen, 2008; Seroussi et al. , 2011; Prescott et al.
A7 JPL-ISSMPALEO Initialization procedures are after  Cuzzone et al. (2019). Bedrock topography is interpolated from the BedMachine dataset  (Morlighem et al. , 2014) , which combines a mass conservation algorithm for the fast-flowing ice streams and kriging in the interior of the ice sheet. Initial ice thickness is from the GIMP dataset  (Howat et al. , 2014). Geothermal flux is from  Shapiro and Riztwoller (2004) , and air temperature is from  Box (2013). We assimilate surface horizontal velocities derived from published 2008-2009 surface velocities  (Rignot and Mouginot, 2012)  to derive basal sliding on grounded ice and ice viscosity on floating ice. The model uses the higher-order ice flow approximation of  Blatter (1995)  and  Pattyn (2003) , which is extruded to five layers and uses higher-order vertical finite elements  (Cuzzone et al.
A9 MUN-GSM The two models GSM1 and GSM2 are from a two-glacialcycle run (starting at 240 ka) with 30 ensemble model parameters set to values from an ongoing calibration against various palaeo constraints (including relative sea level data https://doi. org/10. 5194/tc-14-3071-2020 The Cryosphere, 14, 3071-3096, 2020 and cosmogenic dates) along with topography fit to present day at the end of the transient run. The grid resolution is 0. 25 • × 0. 125 • longitude-latitude, which translates to about 13. 9 km in the latitudinal direction and goes down to about 5 km in the longitudinal direction near the northern edge of the ice sheet. For this intercomparison, the model was nudged to observed 2000 CE ice margins during the 1500 to 2000 CE interval. The model uses a 4 km deep permafrost-resolving bed thermal model with the deep geothermal heat flux set to a partially calibrated mix from  Rogozhina et al.
A12 UCIJPL-ISSM The ice sheet configuration is set up using data assimilation of present-day conditions  (Morlighem et al. , 2010). The bed topography is interpolated from the BedMachine Greenland v3 dataset  (Morlighem et al. , 2017). The initial ice surface topography is from the GIMPdem  (Howat et al. , 2014). For the thermal model, surface temperatures from  Fausto et al. (2009)  and geothermal heat flux from Shapiro and Ritzwoller (2004) are used. A higher-order model (HO) is used for the entire domain. The model for UCIJPL-ISSM1 has 14 vertical layers and a horizontal resolution varying between 0. 5 km along the coast and 30 km inland, while UCIJPL-ISSM2 has 4 vertical layers with a horizontal resolution between 0. 2 and 20 km. We perform the inversion of basal friction assuming that the ice is in a thermo-mechanical steady state.
The ice temperature is updated as the basal friction changes, and the ice viscosity is changed accordingly. At the end of the inversion, basal friction, ice temperature and stresses are all consistent. After the data assimilation process, the model for UCIJPL-ISSM1 is relaxed for 50 years using the mean surface mass balance of  1961 -1990 from RACMO (van Angelen et al. , 2014)  while keeping the temperature constant. The historical run was performed with SMB anomalies of MIROC5 provided by ISMIP6, with the fixed ice front for UCIJPL-ISSM1 and with the moving ice front for UCIJPL-ISSM2.
Acknowledgements. We thank the  Climate and Cryosphere (CliC)  project for providing support for ISMIP6 through sponsoring workshops, hosting the ISMIP6 website and wiki, and promoting IS-MIP6. We acknowledge the  World Climate Research Programme  and its Working Group on Coupled Modelling for coordinating and promoting  CMIP5  and  CMIP6. We thank the climate modelling groups for producing and making available their model output; the  Earth System Grid Federation (ESGF)  for archiving the CMIP data and providing access; the  University at Buffalo  for ISMIP6 data distribution and upload; and the multiple funding agencies who support  CMIP5 ,  CMIP6  and the  ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining IS-MIP6.