dataesr/scientific-paragraphs-categorization huggingface dataset:
Autosomal analysis. The first two PCs of the principal components analysis (PCA, Fig. 1c ) account for 38% of the variation in the studied dataset. The close overlap between eastern Polynesians and Samoans on the PC1 axis suggests similar amounts of genetic ancestry shared with New Guinea and northern Melanesia. The model-based analysis of autosomal SNPs using ADMIXTURE  35  shows that, at K = 4, 70-80% of the Leeward Society Islander genomes can be characterized by the component typical of ISEA/East Asia (Fig. 2a ); the remaining 20-30% of their genetic ancestry is best represented by Papuan speakers from New Guinea (light purple). From K = 5, Polynesians take their own ancestry (green), which, like their deflection on the PCA plot,   S1B. The colors used have been selected to be equivalent to those used in Fig.
Nomenclature followed that of Phylotree. org, mtDNA tree Build 17  56  (Supplementary Table  S2 ). This resulted in 78 individuals allocated to the hg B4a1a1 and three individuals to hg Q. The control region (nps 57-372 and nps 16024-16526) was sequenced for 36 samples that could not be allocated to the known sub-clades of these two haplogroups, and 25 samples were further selected for complete mitochondrial genome sequencing. Using information from the full sequences, additional nucleotides were typed by Sanger sequencing to complete the haplogroup assignment. The 25 newly generated complete mtDNA sequences were merged with published data (see Supplementary Table  S4 ), and a Bayesian phylogenetic approach in BEAST 1. 8. 4  57  used to analyse two data sets comprising genomes belonging to hgs B4a1a1 (n = 442) and M29/Q (n = 111), respectively (Supplementary Table  S4 ).
A Bayesian phylogenetic approach in BEAST  57  was used to analyse a final data set comprising 7669 SNPs from these 80 individuals. To correct for ascertainment bias, we added constant sites corresponding to the nucleotide composition across the remainder of the chromosome. The four demographic models and other details of the settings used in the MCMC analyses matched those used for analyses of mtDNA. The best-fitting model was exponential growth, which had a log Bayes factor of 8. 079 compared with the next-best model (Bayesian skyride). To calibrate the estimate of the timescale, a mutation rate of 8. 71 × 10 -10 mutations/site/year 66 was specified.
Autosomal analysis. A set of 713,014 SNPs was screened using the HumanOmniExpress-24 BeadChip array in 30 individuals from the Leeward Societies Isles (Supplementary Table  S1A ). Twenty-six samples yielded high genotyping success rates (<5% missing genotypes), and 686,565 autosomal SNPs, with less than 5% missing data, were kept for further analyses. Inference of cryptic relationships between samples was performed using KING v. 1. 4  67  and no first-, second-or third-degree relatives were detected. A single sample clustered together with Europeans in the fineSTRUCTURE 37 run (see below), and was excluded from all population level analyses (Supplementary Table  S1B ). The study dataset was produced by merging newly generated Societies data with samples from mainland and ISEA, Melanesia, and Polynesia  44, [68] [69] [70] [71] [72] [73]  , and with 25 random samples from multiple large continental reference populations from the 1000 Genomes Project 65 (Fig.
1a , Supplementary Table  1B ). Two independent datasets were produced. Firstly, a dataset comprised of 299,998 SNPs (after excluding SNPs with more than 5% missing data) from 570 samples was used for haplotype-based (fineSTRUCTURE, FS, and GLOBETROTTER  38  , GT) analyses, f3 and F IS statistics. FS/GT requires a much higher density of SNP coverage, which was not possible to achieve while keeping samples from Hudjashov, et al. 44  due to the overlap between the different genotyping arrays used. Secondly, a dataset comprised of 92,972 SNPs (after excluding SNPs with more than 5% missing data) from 739 samples including those from Hudjashov, et al. 44  was used for genotype-based analyses only (ADMIXTURE  35  , f3, F IS and PCA) (Supplementary Table  S1B ). For PCA and ADMIXTURE, only unlinked SNPs with R 2 < 0.
2 were kept; 57,825 SNPs passed this criterion. Although there is a substantial overlap between the two datasets used here (including populations from East and Southeast Asia, Philippines, Indonesia and Melanesia) some important differences need to be mentioned. The dataset used for the FS and GT analyses does not include samples from Taiwan, Tonga, Samoa and Tahiti. These four populations are, therefore, only in the dataset used for allele-frequency based analyses. However, the Kankanaey of northwestern Luzon in the Philippines are proposed as a proxy for early Austronesian speakers from Taiwan  71. Polynesian populations from Hudjashov, et al. 44  (Tonga, Samoa and Tahiti) were further controlled for the presence of cryptic relatedness between samples as described above by using the full SNP dataset from the original publication.
4. 7. 0  77. A series of f3 tests were performed with ADMIXTOOLS v. 4. 1  36. Firstly, standard f3 statistics were used as a formal test for admixture between all possible combinations of populations in the comparative dataset. Secondly, the outgroup f3 test was implemented as a measure of the shared branch length between each of Polynesian groups and all other populations. For outgroup f3, the Yoruba population (YRI) from Africa was employed as the outgroup. To assess the potential bias introduced by two different SNP subsets and sample clustering procedures used here, f3 and F IS were calculated as follows: (a) using the dataset of ca 93k SNPs and 739 samples (with data from Hudjashov, et al. 44  ) and the original population affiliation; (b) using a dataset of ca 300k SNPs and 570 samples (without data from Hudjashov, et al.
Acknowledgements We gratefully acknowledge the participation of the people of the  Leeward Society Isles and Maori communities of New Zealand  whose collaborations made this study possible. In particular, we owe  Paeata Clark  and  Father "Pa" Anthony Brown  many thanks for their support throughout this study. This research was supported by the  European Union  through the  European Regional Development Fund  (Projects Nos. 2014-2020. 4. 01. 15-0012 ; 2014-2020. 4. 01. 16-0271 ;  2014-2020. 4. 01. 16-0125 ;  2014-2020. 4. 01. 16-0030 ;  2014-2020. 4. 01. 16-0024 ). P. E. , G. H. , M. K. and M. M. were supported by  NEFREX grant  funded by the  European Union  ( People Marie Curie Actions; International Research Staff Exchange Scheme ; call FP7-PEOPLE-2012-IRSES-number  318979 ). N. N. and R.
J. M. were supported by the  National Geographic Society, IBM  and the  Waitt Family Foundation , through the  Genographic  Project. S. Y. W. H. was funded by the  Australian Research Council. M. P. C. was supported by the  Royal Society of New Zealand  through a  Rutherford Fellowship  ( RDF-10-MAU-001 ). Computational resources were provided by  Massey University  and the  High Performance Computing Center, University of Tartu , Estonia.
Data Availability. The genotyping SNP and STR data for mitochondrial and Y chromosomal DNA generated during the current study are included in the published article and its Supplementary Information files. The complete mitochondrial genome sequences generated during the current study are available from GenBank (https://www. ncbi. nlm. nih. gov/genbank/) under the accession numbers MG244202-MG244226. The seven novel Y chromosome sequences are available from European Nucleotide Archive (https://www. ebi. ac. uk/ena) under the accession number PRJEB22729. The Autosomal data produced from 30 Leeward Society Islanders is available from the corresponding author on reasonable request.
patens, we assign it to Eukaryotes. We confirm that the trends remain unaffected (Supplementary Table  2 , Supplementary Figure  2 ). Finally, to determine if D. melanogaster CAPs are enriched at certain ages, we used ProteinHistorian  (Capra et al. 2012 ) (http://lighthouse. ucsf. edu/proteinhistorian/). ProteinHistorian regroups databases of D. melanogaster proteomes with protein age assigned by different methods. We calculated enrichment using five different sets of protein family prediction of the Princeton Protein Orthology Database  (Heinicke et al. 2007 ) (DROME_PPODv4 clustered with OrthoMCL, Multiparanoid, Lens, Jaccard and Panther7) and two different methods  (Wagner and Dollo parsimony)  to account for the expected differences according to the different phylogenies and data sets (Supplementary Table  3 ).
Intron/exon extraction and analysis We extracted genome-wide exon data from Ensembl Biomart (https://www. ensembl. org, v93, and Ensembl Metazoa v40, both released July 2018) for 4 species  (D. melanogaster, A. gambiae, H. sapiens, M. musculus). Introns were computed by subtracting exons from the coding sequence. For each gene, introns were divided into two groups: the first two 5' introns, named "first", and any other introns as "rest". We verified our method against the Drosophila pre-computed intron data of FlyBase and detected no major differences in our results. Note that the pre-computed intron data includes introns in the 5' UTR. The chromatin type of genes (and their introns) in Drosophila was determined by the "color" of their TSS as done in  (Filion et al. 2010 ). In the 3 other species, genes were assigned the chromatin color of the ortholog in D.
Results The Drosophila chromatin landscape is dominated by eukaryotic and metazoan-age proteins Taking the dataset from  (Filion et al. 2010; van Bemmel et al. 2013) , we searched for homologs across 53 species and clustered the resulting phylogenetic profile in order to gain insight into the conservation and evolution of the D. melanogaster chromatin landscape. The clustering reveals six clusters (Figure  1 , left side, I -VI). We associated these clusters to five major age groups: pre-eukaryotic CAPs (I & II), eukaryotic CAPs (III), multicellular plant and metazoan CAPs (IV), metazoan-specific CAPs (V) and arthropod CAPs (VI), with ages assigned on the basis of stable blocks of conserved CAPs in multiple species. For phylogenetic positioning and dates of our five age groups, see Supplementary Figure  1.
GREEN evolves fast and expanded in a lineage-specific way in Drosophila GREEN chromatin is best characterized as constitutive, classic heterochromatin, and encompasses regions with high content in repetitive DNA and transposable elements  (Sun et al. 1997; Filion et al. 2010). It is marked by HP1, a protein family that is involved in chromatin packaging and that binds di-and trimethylated histone H3 (H3K9me2/3)  (Bannister et al. 2001). Classic proteins linked with HP1 heterochromatin are conserved  (Saksouk et al. 2015 ) and indeed we find HP1 (SU(VAR)205 in Fig. 1 ), HP1c, and SU(VAR)3-9 across metazoa (cluster IV). Yet, 11 GREEN proteins, from a total of 25 in the whole dataset, are assigned to the arthropod cluster, the youngest gene cluster VI. Thus, as opposed to YELLOW and RED, the fraction of proteins bound in GREEN increases through evolutionary times (Figure  5B ).
By studying protein domains, we found evidence that a subset of young GREEN proteins are part of the family of proteins with BESS domains that is expanding in the Drosophila lineage. BESS domains direct protein-protein interactions, including with itself. Among all known proteins (not just the ones in our data set) with an inferred BESS domain (InterPro identifier IPR004210), more than 80% are restricted to insects and more than 50% are restricted to diptera. A comparison among drosophilids has shown that the BESS domain family expanded through duplications in a lineage-specific way approximately 40 million years ago  (Shukla et al. 2014 ). In our dataset, five of 107 proteins have a BESS domain (SU(VAR)3-7, LHR, BEAF-32, CC20, and CC25). They are all found in the arthropod cluster (VI), and with the exception of CC20, they are GREEN-associated.
Comparing different species is crucial to determine if the evolutionary scenarios that we propose indeed hold true and how they may need to be refined or reconsidered. One future breakthrough we hope for, is that such studies could provide insight into new BLACK-associated proteins and perhaps lead to a better molecular and evolutionary characterization of this type. Moreover, some classes of proteins are better studied in species other than Drosophila. For instance, in our dataset, five proteins are responsible for histone acetylation/deacetylation, but substrate specificity and links with previously inferred chromatin states are not well-investigated in fly species. Contrastingly, acetylases (HAT) and deacetylases (HDAC) specificity are well-characterized in human  (Seto & Yoshida 2014)  and thus H.
Acknowledgements We thank  Joke van Bemmel  for help interpreting the genome-wide binding data and the network model of chromatin organization. We thank  Guillaume Filion  for critical and stimulating comments and additional insight into the data, and  Christian Rödelsperger  for critical and encouraging comments. We thank  Carole Knibbe  for statistical advice. AC kindly acknowledges the support by  LabEx Memolife  and  Fondation Bettencourt Schueller.
1a ). The NVJ is older, in the sense that it originates from a longer path than the NCJ, regarding its last contact with the surface. Hence, without ventilation, the DOXY of the NVJ intermediate waters is lower than the NCJ DOXY. Using DOXY as a proxy to find the difference between NVJ and NCJ,  Rousselet et al. (2016)  revealed an intrusion of NVJ waters in the NCJ pathway during the BIFURCATION cruise  (Maes, 2012). The former authors concluded that the NVJ waters were transported southwards by a mesoscale anticyclonic eddy. Here the BGC Argo float deployed at long-duration station A (LDA) of OUTPACE, north of New Caledonia, has followed the path of the NCJ. Changes in the BGC Argo float data allow us to determine whether it has encountered water masses coming from the NVJ. Further east, other Argo floats, deployed during OUT-PACE, are characterized by a main eastward displacement in the opposite direction compared to the westward SEC (Fig.
Autonomous floats In the present study we benefit from the deployment during the 2015 spring of several Argo floats (https://doi. org/10. 17882/42182, Argo, 2000)  under the auspices of the OUTPACE cruise  (Moutin et al. , 2017)  and of the THOT project  (Martinez et al. , 2015). We analyze the first 2 years of data. All OUTPACE and THOT floats are related to the Argo international program and have the same type of sampling cycle. This cycle starts with the descent of the float to a depth of around 1000 m, called the parking depth, where the floats drift for a programmed time. This time can be remotely modified in the latest generation of floats, such as those used in this study. Here we use the measurements made while the floats are rising to the surface. At the surface, these data are transmitted via satellite.
The discussion of such consideration is thoroughly made by  Ollitrault and Rannou (2013). In the case of the 656 float, we also consider the DOXY measurements. These data were calibrated (Alain Fumenia, personal communication, March 2017) based on the CTD profiles made just before the float was deployed during the OUTPACE cruise. The calibration for the 656 float is DOXY clb = 0. 97125 DOXY raw + 14. 3755, where DOXY raw is the dissolved oxygen concentration data from the float and DOXY clb is the calibrated dissolved oxygen concentration data, both expressed in micromoles per kilogram (µmol kg -1 ).
Ancillary data In order to evaluate the possible link between oxygen anomalies and surface currents in the Coral Sea, we use the AVISO altimetry products (Archiving, Validation and Interpretation of Satellite Oceanographic data; http://www. aviso. altimetry. fr, last access: 20 January 2017). We select ocean-level elevation anomaly products, reworked from all satellites, to obtain geostrophic velocity anomaly fields (u g and v g ) with a 1/4 • resolution. From those data we calculate the geostrophic velocity amplitude. We also use HYCOM (HYbrid Coordinate Ocean Model) re-analysis GLBu0. 08 of the experiment 91. 1 from March 2015 to March 2016 (https://www. hycom. org, last access: 4 June 2018). This system is a hybrid isopycnalsigma-pressure (generalized) coordinate ocean model with 1/12 • horizontal resolution and 40 vertical levels assimilating in situ data.
In order to place our analyses within the global thermohaline context, we used the ISAS13 atlas (In Situ Analysis Sys- tem;  Brion and Gaillard, 2012) , which provides a climatology of thermohaline properties. This atlas collects and processes all the profiles provided by the Argo floats from 2004 to 2012 in order to calculate a monthly global climatology over the entire depth of the oceans. More details are provided by  Gaillard et al. (2016). We apply the same methods to calculate the density as those used for the HYCOM re-analysis. In the following, whatever is the source for the temperature and salinity fields, the density of the water masses is computed with the Matlab toolbox Gibbs Seawater based on the TEOS-10 convention (http://www. teos-10. org/software. htm, last access: 10 April 2017).
The western branch of A (152 • E) forms a meridional path with southward velocities. Using the HYCOM re-analysis, we can compare the AVISO observations to the modeled velocity field at different depths. At 300 m, the depth of the DOXY minimum, we can clearly identify structures C2 and A, but C1 is harder to locate (Fig. 5b ). C1 may be located 1 • farther south. A is much more circular in the HYCOM re-analysis than in the AVISO surface data. These data also allow us to consider the densities of these water masses and thus enable us to track the potential NVJ waters down to the NCJ pathway. Figure  5b  clearly shows that the NVJ waters can be associated with C2 for instance. Otherwise, when studying the AVISO product and the HY-COM re-analysis for O1, we do not identify an eddy structure or a circulation shape that could explain that DOXY anomaly.
Coral Sea When analyzing the DOXY measurements of float 656, we were able to describe two distinct events with well-marked oxygen minima (O1 and O2, Fig. 4c ) between 150 and 400 m depth, i. e. , near the upper part of the main thermocline. Inspired by the previous study of  Rousselet et al. (2016) , we associate these oxygen minima with the NVJ waters, much less oxygenated than those of the NCJ. We propose that the NVJ waters were indeed transported by mesoscale eddies. To support this hypothesis, we compare the geostrophic currents at the surface with AVISO data and also use the HY-COM re-analysis at 300 m. In the case of O2 we have been able to identify a cyclonic structure (C2, Fig. 5 ) and an anticyclonic structure (A1) in the two sets of data. These vortex structures are involved in the transport of NVJ waters southward to the NCJ pathway.
, 2013). The best-fitting wave that we found is close to a Rossby wave in a baroclinic ocean case with a thermocline at 200 m. This case is close to the real context of the studied zone where the upper thermocline waters are located around 200 m (GLODAPv2 database;  Fumenia et al. , 2018). Moreover, this zone is precisely located where Rossby waves are frequently found, and their signatures can be observed through sea level anomalies  (Maharaj et al. , 2007). Those arguments strengthen the hypothesis of a Rossby wave close to 160 days in duration and with a 855 km wavelength. These observations lead to a large discussion of the possible interactions between waves and intermediate flows. Such processes could have a significant impact on the sampling strategy of intermediate waters by shifting the front location hundreds of kilometers from its mean location.
Moreover, the hypothesis that the waters can be transported on the edge of several eddies is a new proposed pathway for NVJ waters. To ensure the mechanisms of this meridional transport, the fronts between the described cyclonic and anticyclonic eddies could be studied using calculations of finite-size Lyapunov exponents. These calculations could be applied to both AVISO and HYCOM data to provide information at different depths. Another solution is to place the Lagrangian observations in their finer circulation context; a study based on dynamic attractors  (Mendoza and Mancho, 2010 ) could be useful to test our assumptions. Otherwise, since DOXY is the parameter that can differentiate NVJ from NCJ, resorting to biogeochemical models could help visualizing and studying the connection between the NVJ and the NCJ.
Data availability. The Argo data were collected and made freely available by the International Argo Project and the national programs that contribute to it (http://www. argo. ucsd. edu, http://argo. jcommops. org, last access: 4 June 2018). Argo is a pilot program of the Global Ocean Observing System. The altimeter products were produced by Ssalto/Duacs and distributed by AVISO, with support from CNES (http://www. aviso. altimetry. fr/duacs/, last access: 20 January 2017). The HYCOM simulation and re-analysis are sponsored by the National Ocean Partnership Program (http: //hycom. org, last access: 4 June 2018). ISAS analysis was performed by Fabienne Gaillard and made available through Coriolis operational center (http://www. coriolis. eu. org, last access: 7 March 2017).
Acknowledgements. Special thanks to the officers and crew of the  R/V L'Atalante  who operated the OUTPACE cruise (https://doi. org/10. 17600/15000900 ). Thanks to  Luiz Neto  (project:  CR2017Z2MERO0 ; AMICO biogeochemistry; PI:  Christel Pinazo ) for developing a float profile toolbox and  Alain Fumenia  for the oxygen calibration of the OUTPACE floats. The authors are grateful for the support of the  OUTPACE  project (PIs:  Thierry Moutin  and  Sophie Bonnet ) funded by the  French National Research Agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  GOPS program (IRD) , the  European FEDER Fund  under project  1166-39417  and  TOSCA/CNES  (PI:  Andrea Doglioli ,  BC T23 ZBC 4500048836 ). We also thank  ERC Remocean , the LEFE and MOM programs, and the  Contrat de Projets Etat -Polynésie française  for their financial supports to the  THOT  project led by  Elodie Martinez  (IRD).
In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid.
INTRODUCTION Statistical shape analysis methods are increasingly used in neuroscience and clinical research. Their applications include the study of correlations between anatomical structures and genetic or cognitive parameters, as well as the detection of alterations associated with neurological disorders. A current challenge for methodological research is to perform statistical analysis on large databases, which are needed to improve the statistical power of neuroscience studies. A common approach in shape analysis is to analyse the deformations that map individuals to an atlas or template (e. g. ,  Ashburner et al. , 1998; Chung et al. , 2001; Vaillant et al. , 2004; Durrleman et al. , 2008; Lorenzi, 2012). The three main components of these approaches are the underlying deformation model, the template estimation method and the statistical analysis itself.