dataesr/scientific-paragraphs-categorization huggingface dataset:
To account for trait non-independence and reveal allometric scaling between traits, we performed phylogenetic generalized least squares (PGLS)  144, 150  regressions implemented in nlme 151. Regressions were performed for cerebellar volume, regressed on cerebral volume for all species (1) and those with complete (including ansiform) data (2); and ansiform volume regressed on rest of cerebellar volume (3) and cerebral volume (4), all taking species medians. To better understand potentially divergent patterns, we tested for differences in cerebello-cerebral scaling across two primate bifurcations: strepsirrhines and haplorhines, and apes and nonapes. Using a phylogenetic version of ANCOVA (pANCOVA; github. com/JeroenSmaers/evomap), separate regressions assessed the effects of allowing different intercepts (grade shifts), slopes (evolutionary rate of y relative to x), or both.
To improve comparability with a previous study that reported a grade shift of cerebellar-toneocortical scaling in apes  35  , in a haplorrhine-only dataset, we repeated our PGLS analyses within haplorhines (N = 25 species). Replication and robustness analyses. We repeated our main cerebellar-cerebral PGLS and ACE analyses in the Stephan collection  82. Cerebellar and cerebral volumes were matched to primates in 10kTrees 142. This led to a partially overlapping (species-wise) dataset of 34 species. This dataset corrected brain area volumes for shrinkage. To examine the robustness of our main results, we ran a trio of additional analyses. First, we examined data provenance. Most data came from the MNHN, containing brains conserved for several decades. To assess whether unequal shrinkage between these and other data may have driven our results, we ran PGLS on MNHN and non-MNHN datasets separately.
Secondly, we specifically examined shrinkage across areas, rerunning cerebellar-cerebral PGLS on 10. 000 simulated datasets, in which we introduced random shrinkage factors between cerebellum and cerebrum of 0. 91 to 1. 1 (assuming 10% different shrinkage difference). In a connected step, we introduced uncertainties in volumetric estimates, consistent with potentially unaccounted-for shrinkage  82  , compounding intraspecific variability  79, 80. We again simulated 10. 000 datasets, this time introducing random factors of 0. 5 to 2. 0 in PGLS. Thirdly, in lieu of intraspecific sample sizes for most species, we excluded potential outlier cerebellar volumes based on the literature  31, 68, 82, 95, 115, 116  , rerunning PGLS in a more conservative subsample. This analysis excluded observations differing more than a factor two from previous observations.
Acknowledgements The authors would like to thank  Dr. Carol MacLeod  and  Dr. Alexandra de Sousa  for enthralling discussions about the cerebellum. We would furthermore like to thank  Dr. Joshua Grant  for their proofreading of the final version of the manuscript before acceptance. RT and KH are supported by the  French Agence Nationale de la Recherche , projects  NeuroWebLab  ( ANR-19-DATA-0025 ) and  DMOBE  ( ANR-21-CE45-0016 ). KH received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant  agreement  No101033485  ( Individual Fellowship ). Last, this work was funded in part by  Helmholtz Association's Initiative and Networking Fund  under the  Helmholtz International Lab  grant agreement  InterLabs-0015 , and the  Canada First Research Excellence Fund  ( CFREF Competition 2 ,  2015-2016 ), awarded to the  Healthy Brains, Healthy Lives initiative   at McGill University , through the  Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) , including NM, SBE, and  SLV.
Data availability Data obtained in the current project, including volumes and ratios, but also intermediate text files and figures, are uploaded to the GitHub repository accompanying this project: github. com/NevMagi/34primates_cerebellum. Input files necessary to replicate our results can also be found here, alongside full-size figures to aid legibility. The source data behind the graphs can be found in Supplementary Data 2. MRI data have been collated from different sources, and published Open Access  89. The Stephan collection (https://doi. org/10. 1159/000155963) was not published Open Access. Therefore, access to the data was requested by the authors through the Copy Clearance Center Marketplace and facilitated through the Central Library of Forschungszentrum Jülich GmbH. Reuse permission for these data may be requested from the publisher.
Code availability Custom computer code was used at all stages of analysis. Some of the scripts used are adapted scripts  89. Full custom code can be obtained from GitHub (github. com/NevMagi/ 34primates_cerebellum). Code is immediately available for usage under an Apache 2. 0 License, which can be found on GitHub. A formal release of the data, as per 01-11-2023 was published to Zenodo (https://doi. org/10. 5281/zenodo. 10054902) 154. Full accreditation of R software used in the project can be found in Supplementary Table  2  and their references [155][156][157][158][159][160][161] are added at the end of the reference list, unless the software was explicitly mentioned and cited earlier in the manuscript.
Pharmacological inhibition of EHMT1/2 (using UNC0638) or shRNAmediated knockdown of EHMT1/2 led to decreased levels of H3K9me2 and, consequently, HbF reactivation  [70, 71]. However, the reactivation of HbF observed upon EHMT1/2 downregulation was associated with an additional activating event, the acetylation of H3 lysine 9 (H3K9Ac) at the HBG1/2 promoters, which is typically associated with gene activation (see paragraph "Histone acetylations")  [70]. Promisingly, UNC0638 also led to high levels of HbF expression in erythroid cells from β-thalassemia patients  [72]. FTX-6058, and inhibitor of EDD, was also tested in healthy volunteers and induced high HbF levels without major side effects (NCT04586985). Methylation of arginine 3 on H4 also plays an important role in globin gene regulation.
Funding: This research was funded by State funding from the  Agence Nationale de la Recherche  under " Investissements d'avenir" program  [ ANR-10-IAHU-01  and  ANR-21-CE18-0066-01 ], by the  European Research Council  [865797 DITSB] and by  Fondation pour la Recherche Médicale  [ FRM PLP202110014595 ]. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
The placental skull has evolved into myriad forms, from longirostrine whales to globular primates, and with a diverse array of appendages from antlers to tusks. This disparity has recently been studied from the perspective of the whole skull, but the skull is composed of numerous elements that have distinct developmental origins and varied functions. Here, we assess the evolution of the skull's major skeletal elements, decomposed into 17 individual regions. Using a high-dimensional morphometric approach for a dataset of 322 living and extinct eutherians (placental mammals and their stem relatives), we quantify patterns of variation and estimate phylogenetic, allometric and ecological signal across the skull. We further compare rates of evolution across ecological categories and ordinal-level clades and reconstruct rates of evolution along lineages and through time to assess whether developmental origin or function discriminate the evolutionary trajectories of individual cranial elements.
Differences in methodologies across studies complicate agreement on a single pattern of cranial modularity for any dataset. Ultimately, it is likely that there exists a hierarchical organization of the cranium where a broadly facial-neurocranial division can be decomposed into smaller modules, e. g. rostrum, orbit, vault, base, etc. themselves composed of multiple individual elements or structures  [25, 31, 43, 44]. While cranial modules are often described in terms of function, developmental patterning has long been considered a primary cause of cranial integration and modularity  [27, 28, 45, 46] , providing a direct link between associations among phenotypic traits and their developmental origins. Two distinct cranial mesenchymal stem cell populations give rise to the bones of the skull: the cranial neural crest (CNC) and the paraxial mesoderm (PM)  [47, 48].
Previous studies of the evolutionary dynamics of cranial modules have suggested that these developmentally (and likely functionally) complex regions may show lower integration and higher variation in birds and mammals  [14, 25, 66, 67] , although this is not supported in other vertebrate clades  [19, 22, 81]. Here, we use a large three-dimensional dataset of 322 living and extinct eutherian mammals to quantify and compare macroevolutionary dynamics across the elements forming the cranium. Our sample ranges from the smallest living mammal, the bumblebee bat (Craseonycteris thonglongyai), to the largest, the blue whale (Balaenoptera musculus). Using a high-dimensional geometric morphometric approach to discriminate 17 cranial elements (representing 12 individual cranial bones, as some are subdivided into different aspects or structures), we quantify variation, phylogenetic, allometric and ecological signals across the skull and reconstruct the tempo of evolution and disparity for each region through time.
Although these elements certainly form higher-level modules  [25, 34] , we treat them here independently to maximize the ability to link observed patterns to developmental origin and function. With this high-resolution dataset that is more capable of discriminating patterns for each cranial element compared to previous approaches (e. g. landmarks only or lengths), we assess whether elements of the mammalian cranium that share a common developmental origin or function also share a common pattern of evolution. In particular, we assess the hypothesis that elements formed from CNC cells show greater variation, faster rates of evolution, and stronger ecological signal, relative to those derived from the PM.
Methods (a) Material and methods (i) Specimens and morphometric data Our dataset samples 322 crown and stem placental mammals, comprising 207 extant and 115 extinct species (electronic supplementary material, table  S1 ), from a recently published analysis  [2]. Each species is represented by a single adult specimen. Sixty-six three-dimensional landmarks and 69 semilandmark curves were collected for the left side of the skull using Stratovan Checkpoint (Stratovan, Davis, CA, USA) (figure  2 ; electronic supplementary material, table  S2 ). A total of 754 three-dimensional landmarks and semi-landmarks were then imported into R  [82]  for analysis and curves were resampled to a common number of semi-landmarks using the 'SURGE' package  [83]. Specimens were selected for completeness, but some structures were missing or incomplete from preservation (as opposed to biologically absent).
Missing structures were estimated using fixLMtps, which uses weighted averages from the three most morphologically similar and complete configurations to estimate missing landmarks, in the R package 'Morpho' v. 2. 9  [84] , after which they were slid to minimize bending energy. Landmarks and semi-landmarks for biologically absent elements were moved to a single landmark position, as described in Bardua et al. [22, 85]. Morphometric data were mirrored across the midline plane to create bilaterally symmetrical landmark configurations, and then registered with Generalised Procrustes Analysis in the R package 'geomorph' v. 4. 04  [86]. Mirrored (right-side) data were then removed to reduce dimensionality. Finally, the full morphometric dataset was separated into the following 17 cranial regions for analysis: dorsal premaxilla, ventral premaxilla, nasal, dorsal maxilla, ventral maxilla, palatine, pterygoid, jugal, frontal, parietal, zygomatic region of the squamosal, glenoid fossa of the squamosal ( jaw articulation), vault region of the squamosal, supraoccipital, occipital condyles, basioccipital and basisphenoid.
(ii) Phylogenetic and ecological data Data on diet and locomotion were collected from the published literature using palaeoecological reconstructions for fossil taxa as detailed in Goswami et al. [2]  and electronic supplementary material, table S1. In the absence of a well-resolved phylogenetic hypothesis that samples all living and extinct taxa in our dataset, we constructed composite trees from molecular and morphological analyses by grafting fossil taxa onto a recent species-level molecular analysis of placental mammal relationships  [87] , as described in Goswami et al. [2]. Specifically, we binned the posterior distribution of dated trees from that study by estimated origin of placental mammals and randomly selected one tree from the bin ranging from 80-85 Ma, consistent with the most recent analyses  [88].
(b) Macroevolutionary analyses To examine the overall pattern of variation in individual cranial regions across placentals, we conducted principal components analyses using Procrustes-aligned three-dimensional data. We further conducted phylogenetic principal components analysis for use in the branch-specific rate analyses described below. We quantified phylogenetic signal using K mult , disparity as Procrustes variance, and evolutionary allometry using Procrustes ANOVA of shape (Procrustes coordinates) against log(centroid size), incorporating phylogeny under a Brownian motion model of evoution, as implemented in the R package 'geomorph' using procD. pgls. We also calculated evolutionary rates for each cranial region under a Brownian motion model using the compare. multi. evol. rates function in 'geomorph' (with 999 iterations for significance testing), in order to facilitate direct comparison of rates across regions  [90, 91].
We then used the estimates of disparity and evolutionary rate to assess whether elements of cranial neural crest or paraxial mesoderm origin differ significantly in evolutionary capacity, using non-parametric analyses of variance. We further assessed the association of life-history and ecological traits with shape variation and evolutionary rate for each cranial region with multivariate phylogenetic regressions. We conducted type II phylogenetic MANOVAs with Pagel's lambda by penalised likelihood on the Procrustes coordinates with log centroid size, locomotion, and diet as predictors, using the functions 'mvgls' and 'manova. gls' as implemented in the R package 'mvMORPH' v. 1. 16  [92]. Pillai's statistic and 1000 permutations were used to assess significance. Note that Pagel's lambda corresponds to fitting a phylogenetic mixed model which can provide increased flexibility in estimating the error structure and allows for departures from a Brownian motion model.
We then estimated ancestral states for locomotion and diet using stochastic character mapping with an 'All Rates Different' (ARD) model, which allows different transition rates between character states, in the 'phytools' v. 0. 7-70 package  [93]. We then used a statespecific Brownian motion (BM) model in 'mvMORPH' to estimate rates of evolution for each locomotory and dietary state for each cranial region. Model fitting jointly estimated measurement error and intraspecific variation, which is again flexible to departures from Brownian motion. For each cranial region, we further assessed 10 alternative evolutionary models (variable-and single-rate models for Brownian motion, single-optimum Ornstein-Uhlenbeck, and variableand single-rate BM models with lambda, kappa or delta tree transformations).
A lambda tree transformation captures fit to phylogenetic structure, a kappa tree transformation reflects punctuational processes, and a delta transformation equates to an early burst model. Each analysis used phylogenetic PC scores representing 95% of the total variation in the dataset (ranging from three PCs in the ventral premaxilla to 14 for the dorsal maxilla) and a reversible-jump Markov Chain Monte Carlo (MCMC) algorithm implemented in BayesTraits v. 3  [94]. Models ran for 500 000 000 iterations, and convergence of the chains was assessed using Gelman and Rubin's convergence diagnostic implemented in the R package 'coda' v. 0. 19-3  [95]. Bayes Factor (BF) was used to compare models and identify the best supported model. For the best supported model, we plotted rates of evolution on branches to visualize rate variation across the phylogeny.
Acknowledgements. We are indebted to the numerous colleagues, curators and collections staff at international museums that provided access to specimens for this project, in particular  Roberto Portela Miguez ,  Pip Brewer  and  Roula Pappa  ( NHM ),  Pierre-Henri Fabre  ( Université de Montpellier ),  Judy Galkin  ( AMNH ),  Gertrud Rößner  ( BSP ),  Bill Simpson  ( FMNH ),  Desui Miao  ( KU ),  Judith Chupasko  ( MCZ ),  Sam McLeod ,  Xiaoming Wang  and  Jorge Velez-Juarbe  ( LACM ),  Jack Ashby  and  Paolo Viscardi  ( LDUCZ ),  Marcelo Reguero  ( MLP ),  Guillaume Billet ,  Jacques Cuisin  and  Geraldine Veron  ( MNHN ),  Pat Holroyd  ( UCMP ),  Amanda Millhouse  and  Suzanne Peurach  ( USNM ),  Chris Norris  and  Dan Brinkman  ( YPM ) and  Loic Costeur  (NMB). We are grateful to  Vincent Fernandez  and  Brett Clark  ( NHM ) and  Jonathan Keller  ( UMN ) for support in microCT scanning and to the 'plate-forme de morphometrie' of the UMS 2700 ( CNRS, MNHN)  for access to the surface scanner.
Funding. This work was supported by funding from  European Research Council  grant  STG-2014-637171  (to A. G. ),  National Science Foundation SF-EAR  grant  1349607  (J. G. , B. B. , A. G. , M. C. ),  Gerstner Scholar Postdoctoral Research Fellowship  (A. C. , N. S. ),  Natural Environment Research Council Doctoral Training Partnership  training grant  NE/L002485/1  (E. C. ),  National Science Foundation   EAR 1338262  (D. L. F. ),  Horizon 2020 MCSA Fellowship IF 797373-EVO-TOOLS  (to J. C. ) and  Labex BCDiv   10-LABX-0003  (to A. C. F. ).
Data accessibility. Three-dimensional meshes for all specimens are available for free download on Phenome10k. org and/or Morphosource. org, unless specifically restricted by specimen repositories. All data and novel code, as well as interactive PCAs, are available at https://github. com/anjgoswami/Goswami_et_al_Placental_evol-ution_2022. All specimen and species details, including repository information and trait data, are provided in electronic supplementary material, table  S1. The data are provided in electronic supplementary material [163].
We focus on the local hydraulic system in the study area. The movement of moisture in partially saturated media is simulated using a 3D diphasic flow, double-permeability model, with the soil moisture data recorded during the period of 2010-2019 as surface boundary and the Rhône River as edge boundary conditions. We will finally discuss the possible triggering mechanism by comparing the results of the hydraulic model with those of a mechanical model that simulates the mass withdrawal due to the quarry exploitation in a similar geological configuration. We will try to answer two key questions: (1) How large is the hydraulic overpressure due to the meteoric water recharge compared to the Coulomb stress change due to the mass withdrawal from the surface quarry? (2) Is the estimated location of the maximum hydraulic overpressure consistent with the estimated hypocenter location? 2019 ) that contains the 25 km SMOS L2 cell of the EASE equal-area grid (black square).
Differential Synthetic-Aperture Radar Interferometry (DInSAR) Using Sentinel-1 Data Spaceborne differential SAR interferometry (DInSAR) has been widely used during the past decades to track land subsidence or uplift related to groundwater extraction or underground gas storage  [37]. The same method has also been successfully used for identifying ruptures due to earthquakes and quantifying the co-seismic motion  [38, 39]. Sentinel-DInSAR was applied to measure the surface displacement along the sensor's Line of Sight (LOS), which is the sensor-to-target direction. This method measures the projection of real surface motion along the LOS and provides 1D displacement measurements. These displacements are relative in space and time, as they are spatially related to a reference point, and temporally to the date of the first available satellite acquisition.
In order to reduce the loss of coherence effect, four interferograms with short six-day timespans around the date of 11 November 2019 were produced using Sentinel-1 data (Table  S1 ). The processing is based on the Gamma processing software (https://gamma-rs. com, accessed on 16 January 2021). Only one interferogram (named A059-orbit 59 in ascending mode) resulted as interpretable because of a poor coherence on the surface rupture zones for the three others. In order to interpret this interferogram for identifying and quantifying surface ruptures, an additional unwrapping step is required (Figure  2 ). For this step, we used the Minimum Cost Flow procedure implemented in Gamma  [40]. 2019 ) that contains the 25 km SMOS L2 cell of the EASE equal-area grid (black square). (b) Simplified bedrock geology modified from the BRGM geological map at the 1:50,000 scale  [36] , showing the observed faults (light-blue solid lines) and the hypothetical faults (light-blue dashed lines).
In order to reduce the loss of coherence effect, four interferograms with short six-day timespans around the date of 11 November 2019 were produced using Sentinel-1 data (Table  S1 ). The processing is based on the Gamma processing software (https://gamma-rs. com, accessed on 16 January 2021). Only one interferogram (named A059-orbit 59 in ascending mode) resulted as interpretable because of a poor coherence on the surface rupture zones for the three others. In order to interpret this interferogram for identifying and quantifying surface ruptures, an additional unwrapping step is required (Figure  2 ). For this step, we used the Minimum Cost Flow procedure implemented in Gamma  [40]. The visual examination allowed a first estimation of the LRF rupture location and the positions of the extremities of a candidate for the BRF rupture (Figure  3 ).
In order to obtain additional candidates, we added the positions of faults extracted from a 1:50,000 geological map  [36]. These faults were imported in the tool for profile stacking and displacement estimation using the Cosi-corr software  [41, 42] , in order to be applied to our unwrapped interferogram. Figures  S1-S3  illustrate the use of the tool on different parts of LRF and BRF. Twenty lateral profiles were automatically generated by the tool, perpendicularly to the candidate on LRF/BRF, and three were manually added on BRF. Our objective was first to validate points on the fault candidate as reliable observations if significant differential motion between each side of the profile was observed, and then to quantify this motion. In addition, if the "jump" on the displacement profile is not exactly on the candidate's position, this procedure allows to adjust the position by displacing the candidate according to the jump's position.
These faults were imported in the tool for profile stacking and displacement estimation using the Cosi-corr software  [41, 42] , in order to be applied to our unwrapped interferogram. Figures  S1-S3  illustrate the use of the tool on different parts of LRF and BRF. Twenty lateral profiles were automatically generated by the tool, perpendicularly to the candidate on LRF/BRF, and three were manually added on BRF. Our objective was first to validate points on the fault candidate as reliable observations if significant differential motion between each side of the profile was observed, and then to quantify this motion. In addition, if the "jump" on the displacement profile is not exactly on the candidate's position, this procedure allows to adjust the position by displacing the candidate according to the jump's position.