dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, the nature of this ontogenetic dataset, and the inevitable lack of ontogenetic data for fossils, introduces long phylogenetic branches that may result in inaccurate ancestral state estimations. 115, 116  However, based on our result of ancestral marsupial cranial development, we hypothesize that placental mammal cranial ontogeny more closely reflects the condition of the ancestral therian mammal, while the marsupial cranial shape development reflects the most derived state of mammal cranial development, characterized by pedomorphosis. This new understanding provided by our analysis is consistent with a few recent studies, which have suggested that the highly altricial reproductive mode of marsupials is in fact a highly derived state. [117] [118] [119]  Our hypothesis is further supported by new fossil evidence that precociality evolved early in placental mammals (as in the Palaeocene taxon Pantolambda bathmodon).
Data and code availability All original code is available at a public GitHub repository. 121  All code and raw data are available to download, complete with MIT license. Please cite this paper and the Zenodo https://doi. org/10. 5281/zenodo. 7850303 when using the data or raw code. Further details are listed in the key resources table. All R package version details available at: https://github. com/HeatherEWhite/ mammal_cranial_development.
EXPERIMENTAL MODEL AND SUBJECT DETAILS The comparative dataset comprises 22 species that span the phylogenetic breadth of Mammalia including representatives of the four major placental superorders (Xenarthra, Afrotheria, Laurasiatheria, and Euarchontoglires), the three most diverse marsupial orders (Didelphimorphia, Dasyuromorphia, and Diprotodontia), and Monotremata (Table  S1 ; Figure  1A ). For each of the species, multiple developmental stages were collected, in most cases ranging from fetus to adult, with from 4-13 specimens per species (total: n=165; adults: n=22), to produce the largest three-dimensional comparative ontogenetic framework for Mammalia to date (Table  S1 ). Specimens within the ontogenetic dataset were limited to those with at least 75% of the total number of cranial bones present, which balances the need for inclusion of fetal specimens with a reduction of missing data, although it necessarily excludes earlier embryonic stages with little cranial ossification.
By limiting specimens to fetal stages with more than 75% of cranial bone present, this reduced the quantity of missing geometric morphometric data, which can be problematic in morphological analyses. 120  This requirement resulted in the dataset capturing only fetal specimens that have undergone prenatal cranial ossification, 122 whilst also reducing the quantity of missing geometric morphometric data (see below for details on treatment of missing morphometric data). Therefore, several fetal specimens were excluded from the dataset (n=7), reducing the dataset to 165 specimens. Further details on the characterisation of ontogeny can be found below. Specimens were collected from global museum collections and labreared colonies, including Natural History Museum, London. Full specimen details including museum location and colony information (license numbers) can be found in Table  S1.
Deposited data Trees for all phylogenetic analyses https://github. com/HeatherEWhite/mammal_cranial_ development White and Goswami  121  Upham et al. 72  N/A MicroCT scans https://www. phenome10k. org/ N/A isosurface segmentation, allowing the extraction of 3D surface mesh models exported as. ply files. Meshes were decimated in Meshlab version 2020. 7 123 to 1,000,000 faces, to reduce the computational demand without compromising mesh resolution. The resulting simplified 3D meshes were cleaned and prepared for subsequent geometric morphometric analysis using Geomagic Wrap (3D Systems). Cleaning involved the filling of any artefactual holes, the removal of vertebrae leaving only the skull elements within the 3D model. As the geometric morphometric analysis was performed on the left-hand side of the skull only, specimens that had an incomplete or damaged left-hand side of the skull (n=23) were mirrored 124 along the midline using the 'mirror' tool in Geomagic Wrap (3D Systems).
Characterisation of ontogeny Centroid size (CS) is commonly used as a proxy for developmental age  43, 125  when details regarding developmental age are unavailable. The species in this dataset span a large centroid size range from Mus musculus (adult CS = 87. 58) to Phacochoerus africanus (adult CS = 1465. 47). Thus, when all specimens are plotted together using CS (or logged CS) the small adult specimens of Mus musculus plot alongside fetal or infant specimens of larger species. Therefore, logged CS does not reflect these small adult specimens as adults (Figures  S2A  and S2B ). To correct for differences in overall body size, we normalised CS for each species to their respective adult size (percentage of adult CS) (Equation  1 ). This percentage of adult CS was used as a continuous proxy for developmental age of each specimen (relative adult size) (Table  S1 ).
However, species with multiple specimens that had a continuous age of >95% (based on the percentage of adult CS calculation) were all considered as juvenile. The 'fetal' stage for marsupials was assigned to any specimen with a known age <20 days old, and marsupial specimens with a similar level of ossification (following visual inspection) to those <20 days old, where absolute age data was unavailable. Qualitative visual assessment of ossification level indicated that these marsupial specimens reflected similar levels of ossification to fetal placental specimens. Of the specimens collected for the only monotreme in the dataset (Ornithorhynchus anatinus), the earliest stage that reflected the 'fetal' stage was excluded from the dataset due to the absence of >25% of the cranial bones. The next youngest specimen presented with a much higher level of maturity due to the highly ossified cranial bones and thus did not reflect a 'fetal' stage.
Phylogeny The most recent and extensive published mammalian phylogeny based on molecular data, dated with extensive fossil calibrations,  72  was used here for phylogenetically informed analyses. Nevertheless, phylogenetic correction was not used for all analyses due to the presence of multiple ontogenetic stages for the same species, which could not be compiled into a single phylogeny. 43  The time-calibrated maximum clade credibility phylogeny, that included 5,911 extant taxa, was trimmed to the 22 species in this dataset (Figure  1 ) using the 'keep. tip' function in the ape R package. 126  Moreover, we cross-checked the marsupial topology of our trimmed phylogeny with that of the most recently published marsupial phylogeny. 127  Developmental strategy Mammals display a considerable range of developmental strategies, from highly altricial marsupials to precocial ungulates.
Morphometric data collection A total of 69 Type I (homology by biological definitions) and Type II (homology by geometric definitions) landmarks 128,129 were positioned, by a single assessor, on the left-hand side of the skull using the 'single point' tool in Stratovan Checkpoint (Stratovan Corporation, CA, USA) in order to capture cranial morphology (Figures  1B-1E ; Table  S4 ). Positioning of landmarks was complicated by the presence of fetal specimens within the dataset, as fetal specimens presented with incomplete bones and patchy ossification. In particular, the supraoccipital bone forms from two bilateral ossification centres, 130 resulting in early developmental specimens having a pair of bilateral supraoccipital bones that are not yet fused at the midline. In such cases (Phataginus tricuspis), landmarks 42 and 43 were positioned at the point of the supraoccipital that was closest to the midline.
Of these 69 landmarks, three were used as midline landmarks to mirror the manually positioned left-hand side landmarks onto the right-hand side of the skull (Table  S1 ) and thus increase the specimen sample size. The presence of early developmental specimens, with paired bones not adjoining at the midline, meant that a common set of midline landmarks could not be implemented across the dataset. Instead, specimen-specific midline landmarks were selected to ensure the appropriate mirroring of landmarks and obtain a bilaterally symmetrical skull dataset. Mirrored landmarks were checked for accuracy on the meshes of all specimens using 'spher-es3d' in the rgl R package. 131  A complete set of landmarks is required for every specimen within the dataset to perform geometric morphometric analyses.
132 Data collection avoided using incomplete specimens where possible; however, some instances of missing morphometric data were unavoidable to procure minimum sample sizes. Missing morphometric data was dealt with in one of two ways, depending on the type of missing data. Firstly, for damaged, broken, or missing bones due to artefacts of preservation, landmarks were positioned close to the missing region but 'toggled as missing' producing a landmark with an x, y, and z-coordinate value of 9999. The proportion of missing landmarks across the dataset was small, equating to less than 1% of the overall data. The missing landmarks were estimated using the 'estimate. missing' function and thin-plate spline (TPS) method in the geomorph R package, 132 prior to mirroring and performing Procrustes superimposition on the landmarks.
The TPS approach uses a reference specimen with a complete landmark dataset against which specimens are aligned to using the common set of landmarks where the 'space' between the homologous landmarks is calculated and used to interpolate the missing landmarks for the target specimen. 61  Whilst interpolation may introduce error, the vast majority of missing landmarks were closely surrounded by other placed landmarks, reducing the magnitude of error that may be introduced from interpolation. Moreover, following interpolation, landmarks were mirrored and visually inspected on specimen meshes to confirm appropriate positioning. Secondly, variably present bones (jugal, premaxilla, ventral premaxilla, interparietal, paraoccipital, and basisphenoid) (Table  S1 ), that were either biologically absent in the species, developmentally absent (had not yet developed in young specimens), or were seamlessly fused with adjacent cranial bones were still represented by their homologous landmarks associated with the absent bone defined in Table  S4.
This approach followed, the protocol outlined by  Bardua et al. 124  and ensured homology of landmark placement across the dataset. The most prominent variably present bone across the dataset was the interparietal which was either genuinely absent through ontogeny, for example in Sminthopsis macroura, 127 or commonly fused with the supraoccipital, for example in Monodelphis domestica and Cyclopes didactylus. 133  When these variably present bones were either genuinely absent or fused with no visible suture to the adjacent bone, the bone was represented by all associated landmarks being placed onto a fixed position, creating a ''zero area'' for the absent or seamlessly fused bone. The fixed position where landmarks were placed was determined as the point that best reflected the location of the absent region, usually the region from which the absent bone developed.
QUANTIFICATION AND STATISTICAL ANALYSIS All analyses were performed in R v. 3. 6. 1. 134  Generalised Procrustes Analysis Generalised Procrustes analysis (GPA)  135  was performed on all morphometric data to align the specimens by removing non-shape components (rotation, translation, and size). 136  GPA was performed on the mirrored landmark dataset using the 'gpagen' function in the geomorph R package, 132 as a bilaterally symmetrical skull dataset produces a more accurate alignment of the specimens. 137, 138  The mirrored right-side landmarks were removed following Procrustes superimposition, to reduce the dimensionality of the geometric morphometric dataset. All downstream analyses were therefore conducted on the left and midline only coordinate dataset.
Cranial morphology We performed principal components analyses (PCA) on the Procrustes superimposed coordinate data, using the 'gm. prcomp' function in the geomorph R package, 132 to assess the morphological variation across both the adult-only (n=22) and full developmental datasets (n=165). Extreme shapes were plotted along each principal component (PC) to visualise morphological variation. Firstly, a phylogenetic principal components analysis was performed for the dataset. Cranial variation across the major clades (Marsupialia, Xenarthra, Afrotheria, Laurasiatheria, and Euarchontoglires), for the adult-only dataset (n=22), was quantified using allometry-corrected Procrustes variances, by implementing the 'morphol. disparity' function in the geomorph R package. 132  A second PCA for the full developmental dataset (n=165) was then conducted.
This PCA for the full dataset was plotted indicating discrete and continuous age to assess the role of ontogeny on the evolution of cranial morphology. Similarly, allometry-corrected Procrustes variances were calculated to quantify multivariate morphological variation across the discrete age categories (fetal, infant, juvenile, and adult). Finally, PCs 1 and 2 from the PCA for the full developmental dataset were plotted against continuous developmental time (% of adult centroid size) (on the z-axis), with discrete developmental age also displayed as symbols, to visualise the dynamics of shape variation through ontogeny (note that this univariate comparison was not used in any statistical analysis, all of which used the full multivariate shape analyses).
Influences on cranial shape The influence of developmental strategy on allometry-corrected multivariate skull shape was assessed by implementing ordinary least squares (OLS) regression in both a MANOVA for the entire developmental dataset ('procD. lm' function in geomorph) and phylogenetic MANOVA for the adult dataset ('procD. pgls' function in geomorph). 132  In both cases this was a regression between the residuals of the allometry fit (Procrustes shape variables $ log(Centroid size)) (i. e. , allometry corrected shape) and the developmental strategy categories.
Allometry and ontogenetic trajectories To test whether allometry (size-related shape change) explained a significant degree of shape variation in this dataset, a multivariate Procrustes regression was performed using the 'procD. lm' function in the geomorph R package, 132 regressing Procrustes superimposed coordinates against their log-transformed centroid size (Procrustes shape variables $ log(centroid size)). Multivariate allometry was then compared pairwise across species (two species Procrustes shape variables $ two species log(centroid size)) and subject to Bonferroni correction to account for simultaneous multiple comparisons and reduce the risk of type 1 error. In order to visualise shape change through ontogeny for specific components of cranial shape, ontogenetic trajectories were computed for each PC separately, following the protocol outlined in Morris et al.
32  Regressions were performed for each species between PC scores and log-transformed centroid size using the 'procD. lm' function in the geomorph R package (PC axis scores $ log(centroid size)). 132  These univariate ontogenetic trajectories were then plotted for PC1 and PC2, although slope and intercept values were calculated for all 164 PCs (Table  S7 ). As noted above, statistical comparisons were not performed on these univariate trajectories, due to the issues associated with univariate analysis. 75  Ancestral estimation: Skull shape development Using the phylogenetic framework described above, we estimated ancestral allometries, using species' multivariate allometries, for each major placental superorder (Xenarthra, Afrotheria, Laurasiatheria, and Euarchontoglires) and for the ancestral therian, marsupial, placental, and common ancestor of placentals, marsupials, and the monotreme (crown Mammalia) (Figure  S3 ).
ACKNOWLEDGMENTS This work was funded by the  Biotechnology and Biosciences Research Council  through the  London Interdisciplinary Doctoral Training Programme (BBSRC LIDo DTP)  (grant no. BB/ M009513/1 ) to H. E. W. CT scan data collection was also supported by the  Leverhulme Trust, UK  (grant no. F/09364/I ) to L. H. and the  National Science Foundation  (grant nos. NSF 1854469 ;  NSF 2017803 ) to K. E. S. In addition to our co-authors (L. H. , A. H. , and K. E. S. ),  Vera Weisbecker  and  Robert Asher  kindly provided CT data for many marsupial and Macroscelides proboscideus specimens, respectively, for this study. Additionally,  Jessie Maisano  kindly gave access to CT data from MorphoSource. We would also like to thank  Anne-Claire Fabre ,  Ryan Felice ,  Andrew Knapp ,  Julien Clavel ,  Agnese Lanzetti ,  David Button ,  Thomas Raven ,  Carla Bardua ,  Ellen Coombs , and  Eve Noirault , who offered invaluable discussions, methods training, and support with code throughout the process of this study.
The innovative medicines initiative 'AMYPAD' study It is within this context, that the Innovative Medicines Initiative (IMI) funded the ' Amyloid Imaging to Prevent Alzheimer's Disease' (AMYPAD) study. Since its original kick-off in October 2016, the AMYPAD consortium is a unique collaboration of a wide range of partners, including nine academic institutes, three industry/ European Federation of Pharmaceutical Industries Associations (EFPIA) (GE Healthcare [GEHC], Life Molecular Imaging [LMI] and Janssen Pharmaceuticals), 2 'Small and Medium-sized enterprises' (SME's) (IXICO and SYNAPSE), and 1 patient organization (Alzheimer Europe) (www. amypad. eu). With the funding formally ended in at the end of September 2022, AMYPAD has formed new collaborations with ARIDHIA and Alzheimer's disease Data Initiative (ADDI) to maintain, curate, and provide access to the large database of biomarkers collected from nearly 2400 subjects who have been included in the study at large.
However, to facilitate timely recruitment into the study, other cohorts with similar aims and readily collected data across Europe were invited to participate as parent cohorts (PCs). In return, AMYPAD PNHS provided the newly collaborating PCs with the opportunity to perform amyloid-PET imaging. Effectively, this framework boosted the recruitment for PNHS and resulted in the availability of longitudinal data in a significant proportion of participants in several studies across Europe. To date, 17 centres have contributed to the PNHS across 11 PCs [EPAD  (15) , EMIF-AD 60++ (16) and 90+, ALFA+  (17) , FACEHBI  (18) , FPACK (19), UCL-2010-412, Microbiota, AMYPAD DPMS [via the VUmc] (13), DELCODE  (20) , and H70 (21)], with several additional PCs expressing interest in joining forces after the IMI-funding period.
A second Scientific advice was conducted in 2019, providing further input, particularly in the area of quantitative methodology for measuring amyloid load using PET. Specifically, focus was given to EMA's view on the opportunity for quantitative metrics, such as the Centiloid measure, to assist with both subject selection and therapy monitoring, as well as for prediction of cognitive progression and measuring small early changes over time. During this period, quantitation was added to the Summary of Product Characteristics (SmPCs) of both tracers used in AMYPAD, as a result of data packages presented to EMA showing the value of quantitation as an adjunct to the visual read of a adjunctive diagnostic scan. AMYPAD aims to continue discussions with various regulators, such as EMA and FDA, even beyond its IMI period, to facilitate a wider appreciation of both the robustness and value of quantitative methodology to measure amyloid PET burden.
Interacting with other IMI partners and external collaborators AMYPAD had a close working relationship with EPAD (https://ep-ad. org/) in that a large number of the initial PNHS participants were recruited from the Longitudinal Cohort Study (LCS). EPAD has also developed data access models that AMYPAD benefited from (see section 8 below). Additionally, AMYPAD has been an active member of coordination and support action (CSA) NEURONET (https://www. imi-neuronet. org/), which was created to set up an efficient platform to boost synergy and collaboration across IMI's wider neurodegenerative disorders (ND) portfolio. Here, members of AMYPAD were represented on the NEURONET Scientific Coordination Board, the Working Group on sustainability and the NEURO Cohort Task Force. Furthermore, AMYPAD's cohorts, datasets and algorithms were signposted in the NEURONET Asset Map.
This in turn was held on the NEURONET Knowledge Base, which also signposts and reports further information about the AMYPAD project, including its deliverables, partners and publications (https://kb. imi-neuronet. org/). Other close relationships have developed with other global consortiums. Collaborations with IDEAS, ALFA, AIBL and ADNI are ongoing, whilst data sharing with additional cohorts such as OASIS, EMIF-AD, ABIDE, and ADC yielded the highly cited work on the pooled multi-tracer amyloid staging model  (26)  (see section 6. 1). Amyloid burden in Centiloid units for DPMS and PNHS The goals of AMYPAD rely on the assumption that amyloid burden can be accurately quantified irrespective of the radiotracer that was used for the acquisition of the PET scans. In this regard, the Centiloid (CL) method has been proposed as an absolute scale to quantify amyloid burden, allowing the pooling and comparison of data across tracers and quantification pipelines.
Pan-European scientific collaborations In addition to scientific data generated from both the DPMS and PNHS, AMYPAD researchers have significantly contributed to the AD field by furthering our understanding of amyloid deposition in the brain and the optimal methodology to measure this process. Several key papers have been published using either locally readily available datasets, open-access sources, or the academic collaborations established under the AMYPAD umbrella. From a methodological perspective, the consortium has validated the implementation of the coffee-break or dual-time acquisition protocol for both the FMM and FBB radiotracers  (25). This acquisition protocol results in fully quantitative data (i. e. , BP ND ) and additionally provides a surrogate measure of cerebral blood flow (i.
A major collaboration was the development of a multi-tracer staging model, which included over 3,000 amyloid-PET scans from six cohorts, including historical data of several PCs aligned with the PNHS  (26). Taking these findings, we performed preliminary analyses in predicting changes in cognitive functioning in a preclinical population of the OASIS-3 open-access dataset. We showed that regional and longitudinal amyloid-PET improved the prediction of cognitive decline in specific domains (mean follow-up period was 4. 0 ± 1. 9 years)  (32). This is considered the groundwork for the primary end-point of the PNHS trial. The third line of research has been to optimize the use of amyloid-PET in the clinical setting. Firstly, from a regional perspective, implementing the results of the previously mentioned quantitative studies in our approach to performing visual assessments.
A dual regression approach will then be used to compute the mean time series and functional connectivity strength of each RSN. Similarly, bold time series will be summarized within atlases region of interest. Functional connectivity matrices in atlas space and graph properties will be derived. For DWI, preprocessed data will first be fed into the FSL Brain Extraction Toolbox (BET) (41) and then into FSL DTIFIT, to fit the diffusion tensor model to the data and produce diffusion tensor imaging (DTI) scalars maps (fractional anisotropy (FA), and mean (MD), axial (AD) and radial (RD) diffusivity). On these data, the Tract-based spatial statistics (TBSS) pipeline will be used to compute global and regional FA features from the JHU ICBM-DTI-81 atlas  (42). For ASL, mean cerebral blood flow (CBF) and spatial coefficient-of-variation will be computed as described in Mutsaerts et al.