dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank  Dr. Carol MacLeod  and  Dr. Alexandra de Sousa  for enthralling discussions about the cerebellum. 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.
Overview We first used a computational model based on a hierarchical Kuramot-based computational model to generate brain dynamics in a Griffiths Phase (GP) and in the classical criticality framework and to investigate the relationship between oscillatory inter-areal synchronization and LRTCs across wide range operating points near the critical point. We then estimated inter-areal synchronization and LRTCs in source-reconstructed resting-state magnetoencephalography (MEG) data recorded from healthy subjects and resting-state stereo-electroencephalographic (SEEG) data recorded from epilepsy patients. Simulated, MEG, and SEEG data were filtered into narrow-band frequency time series. Synchronization was computed between all pairs of brain regions/electrodes and LRTCs were analysed with detrended fluctuation analysis (DFA) for each parcel.
Diffusion tensor imaging (DTI) data Briefly, we computed structural connectomes for 57 unrelated subjects randomly selected from the WU-Minn dataset (1200 subjects) of the Human Connectome Project (HCP, https://www. humanconnectome. org). From each subject's minimal-preprocessed Diffusion Weighted Imaging (DWI) data, we generated a preliminary tractogram of 50 million streamlines using Multi-Shell Multi-tissue (MSMT) spherical deconvolution and probabilistic tractography (maximum tract length = 250 mm; fiber orientation distribution (FOD) amplitude cutoff, 0. 01; seeding from the gray matter-white-matter interface; application of the anatomically constrained tractography (ACT) framework  84. Using spherical-deconvolution informed filtering (SIFT), we filtered the initial tractogram in order to reduce bias derived from longer, thicker tracts.
Acquisition of MEG and MRI Data We recorded MEG data from 52 healthy subjects (age: 31 ± 9. 2, 27 male) during a 10-min eyes-open resting-state session with a Vectorview/ Triux (Elekta-Neuromag/MEGIN, Helsinki, Finland) 306-channel system (204 planar gradiometers and 102 magnetometers) at the Bio-Mag Laboratory, HUS Medical Imaging Center, Helsinki. Overall, 192 sessions of MEG data were obtained, with subjects contributing, on average, 3. 7 ± 4 sessions each. Subjects were instructed to focus on a cross on the center of the screen Bipolar horizontal and vertical electrooculography (EOG) were recorded for the detection of ocular artifacts. MEG and EOG were recorded at a 1 kHz sampling rate. For each subject, T1-weighted anatomical MRI scans (MP-RAGE) at a resolution of 1 × 1 × 1 mm with a 1.
MEG data preprocessing and source modeling Temporal signal space separation (tSSS) in the Maxfilter software (Elekta-Neuromag) was used to suppress extracranial noise from MEG sensors and to interpolate bad channels. Independent components analysis (ICA) adapted from the Fieldtrip toolbox) was used to extract and identify components that were correlated with ocular artifacts (ocular, heartbeat, or muscle artifacts). Volumetric segmentation of MRI data, flattening, cortical parcellation, and neuroanatomical labeling with the 400-parcel Schaefer atlas was carried out with the Free-Surfer software. The MNE software was then used to create cortically constrained source models with 5-mm inter-dipole separation, for MEG-MRI colocalization, and for the preparation of the forward and inverse operators.
Acquisition of SEEG data We recorded 10 min of stereo-EEG (SEEG) neuronal signals from 68 drug-resistant focal epileptic patients (age: 30 ± 9. 4, 38 male) during the clinical assessment of the epileptogenic zone (EZ) at the "Claudio Munari" Epilepsy Surgery Centre in the Niguarda Ca' Granda Hospital, Milan. These resting-state recordings were free of seizure activity, and there were no seizures within one hour prior to or after the recording. During the recording, the patient was asked to lay down in their bed in a quiet resting state with their eyes closed. Visual inspection of delta amplitude profiles from EEG channel pair C3-P3 and video recordings of the patient were used by trained technicians to ascertain the absence of signs of sleep or drowsiness. Intracranial monopolar (with contacts sharing the reference to a single white-matter contact) local-field potentials were acquired from brain tissue with platinum-iridium multi-lead electrodes.
Analysis of inter-areal synchronization MEG and SEEG data were filtered into complex-valued narrow-band time series using 24 Morlet wavelets (parameter m = 5) with logarithmically increasing center-frequencies between 2-225 Hz. We computed pairwise phase synchronization for all narrow-band frequencies between all 400 cortical parcels in source-reconstructed MEG data and between all non-epileptogenic contact-pairs of SEEG data for all narrow-band frequencies. The main analysis was based on phase synchronization estimated with the phase-locking value (PLV) for SEEG data and with the weighted Phase-Lag Index (wPLI) for MEG data, but for comparison, phase synchronization using the PLV for MEG and the wPLI for SEEG are shown in Supplementary material. The wPLI, unlike PLV, discards zero-lag coupling that in MEG data is mostly spurious caused by linear mixing, but still can detect true positive coupling when there is a small phase lag  90.
Acknowledgements This work was supported by grants from the  Academy of Finland  ( SA 1266745 ,  1296304  to J. M. P. and  SA 325404  to S. P. ), from the  Finnish Cultural Foundation  to S. H. W. (postdoc fellowship  00220071 ), and from the  Sigrid Jusélius Foundation  to S. P. and J. M. P. Article https://doi. org/10. 1038/s41467-023-40056-9 Nature  Communications | (2023) 14: 4736.
Data availability Raw electrophysiological data cannot be shared publicly due to regulations imposed by the Ethical Committees but can be shared for collaborative efforts upon request. A minimal dataset that can be used to reproduce the main findings of this study, containing phase synchronization matrices and DFA exponents for MEG and SEEG cohorts along with supporting data, as well as simulated data, is publicly available at DataDryad repository (https://doi. org/10. 5061/dryad. vdncjsxzn). Source Data for the main manuscript figures are provided as a Source Data file. Source data are provided with this paper.
Influence of temperature regime on evolutionary trajectories To investigate whether temperature (as a prevalent stressor of the coral metaorganism) was a potential driver of species divergence patterns in Pocillopora and Porites, we assessed whether SNPs that aligned with past temperature regimes exist. Indeed, we could identify SNPs in Pocillopora and Porites whose local allelic frequencies could be predicted by past temperature regime (i. e. , temperature outlier SNPs; Pocillopora-38,229 out of 461,989 SNPs (8. 27%); Porites-6441 out of 57,966 SNPs (11. 11%); at RDA q value <0. 1 from the unlinked dataset, see "Methods"; Fig. 6 ). To determine those SNPs potentially involved in species divergence patterns, we queried which of the SNPs originated from a set of genomic islands of differentiation (GID) defined as the 1% of the 500 bp bins with the highest mean F ST value among SSHs for Pocillopora and Porites.
Our finding underlines the difficulty of defining morphological criteria that can be readily derived from photographs and therefore supports the well-established existence of high morphological plasticity in corals, as exemplified by the genus Pocillopora  [37] [38] [39]. Further, it reinforces that plasticity is a confounding factor to achieve effective taxonomic resolutions  37, 40, 41  and identification of species in the field. Finally, it highlights the difficulties in conducting large geographic surveys on targeted coral species due to the putative presence of cryptic species that, although potentially common, are typically not addressed in coral reef studies  42. Nevertheless, it also demonstrates what information can be acquired with respect to genetic divergence patterns even in the case of non-ideal sampling datasets (our dataset may comprise a 'typical' coral collection dataset in this regard).
Our dataset comprising samples of multiple species of Pocillopora and Porites across the same basin-wide range has enabled us to demonstrate contrasting evolutionary histories in these two genera, despite exposure to the same past thermal, and arguably, environmental regimes. The comparison of the genetic structures obtained in the two genera from samples coming from the same sites, along with the high sequencing depth (which led to a large number of genome-wide SNPs) on which the analyses were conducted, allows to at least partially offset the low local sample sizes for some SSHs. We found that although species geographic distribution for both Pocillopora and Porites are linked to past temperature variations, the underlying mechanisms driving this distribution vastly differ. Genetic distance was correlated to past thermal distance for Porites but not for Pocillopora (Supplementary Table  5 ).
DNA and RNA libraries were sequenced using 151 bp paired-end read chemistry on a NovaSeq or HiSeq4000 Illumina sequencer (Illumina, San Diego, CA, USA) with a targeted yield of 10 8 reads per sample. For both DNA and RNA, we removed short (<30 bp length) reads, low-quality nucleotides (Q score <20), adapter/primer sequences, and read pairs that mapped to the Enterobacteria phage PhiX174 genome (GenBank: NC_001422. 1). For RNA, read pairs that mapped to ribosomal sequences were removed using SortMeRNA v2. 1  64. The raw sequences used for this study are deposited at the European Nucleotide Archive (ENA) at the EMBL European Bioinformatics Institute (EMBL-EBI) under the Tara Pacific Umbrella BioProject PRJEB47249. Additional methodologies for DNA isolation and sequencing are available in ref.
SNP calling and filtering For Pocillopora and Porites, we identified a set of genome-wide single-nucleotide polymorphisms (SNPs) by mapping the metagenomic reads to the Pocillopora cf. effusa and Porites lobata genomic references generated as part of the Tara Pacific expedition  65  using the Genome Analysis Toolkit software (GATK, v3. 7. 0)  66. We followed a modified version of the best practices protocol for variant discovery with GATK with manual filtering of the resulting variants. The following protocol was carried out for each species independently. We aligned Illumina-generated 150-bp paired-end metagenomic reads sequenced from each colony to the predicted coding sequences of the Pocillopora cf. effusa and Porites lobata coral host reference genomes using Burrows-Wheeler Transform Aligner (BWA-mem, v0.
7. 15) with the default settings  67. A read was considered a host contig if its sequence aligned with ≥95% sequence identity and with ≥50% of the sequence aligned. Hostmapped reads were sorted and filtered to remove sequences which contained >75% of low-complexity bases and <30% highcomplexity bases using SAMtools v1. 10. 2  68  with the resultant bam files visualized using the Integrated Genomics Viewer  69. The reference genomes were indexed with picardtools v2. 6. 0 (https:// broadinstitute. github. io/picard/) using CreateSequenceDictionary before performing local realignment around small insertions and deletions (indels) using GATK's RealignerTargetCreator and IndelRealigner to reduce false-positive variant identification and represent indels more parsimoniously. After sample pre-processing, we called DNA variants (SNPs and indels) individually for each colony (GATK, HaplotypeCaller), generating one Genomic Variant Call Format (GVCF) file per sample.
We then combined all per-colony GVCF files for a given island (ca. 9 files per island) into a single, multi-sample GVCF file (GATK, CombineGVCFs). This resulted in the generation of 11, island-specific combined GVCF files for each species. We then consolidated these 11 multi-sample GVCFs into a GenomicsDB database which allowed for subsequent variant calling across all island cohorts through a joint genotyping analysis (GATK, GenotypeGVCFs). Because the GenotypeGVCFs tool is capable of handling any ploidy level (or a mix of ploidies) intelligently, we did not specify the ploidy level in our function call. This analysis resulted in the generation of a single, "raw" Variant Call Format (VCF) file for each species in which all colonies were jointly genotyped. Because well-curated filtering resources necessary for GATK's Variant Quality Score Recalibration (VQSR) tool were not available for either coral species, we filtered the raw VCF files manually using VCFtools v0.
1. 12  70 keeping only biallelic sites with minor allele frequencies ≥0. 05, site-quality scores ≥30, and no missing data across colonies. This produced the "linked" datasets (5,937,714 and 1,971,638 variant sites with minimum read depth coverages ≥16 and 17 for Pocillopora and Porites, respectively) from which the SNPs were used for the identification of genomic islands of differentiation and the genomic signatures of selection. In order to avoid variant site linkage effects in downstream genetic analyses, we additionally filtered variants for linkage disequilibrium. We used the "prune" add-on feature in BCFtools v1. 11  71, 72  to discard variants with a linkage disequilibrium (r 2 ) ≥ 0. 2 in a window of 1000 sites. This resulted in the "unlinked" datasets (347,243 and 183,222 variant sites for Pocillopora and Porites, respectively).
The SNPs from this curated set were used to identify Secondary Species Hypotheses (SSHs) using individual-based phylogenies, genetic hierarchical clustering and species trees, and to test for population structure, admixture, and Genotype Environment associations, as described below. For the detection of clonal lineages, we had to further reduce the size of these unlinked datasets to avoid unreasonable computing times. This was performed in Pocillopora by first further filtering on linkage disequilibrium (r 2 ≥ 0. 02) by BCFtools to a 27,382 variant site dataset for a first round of clone detection, and then by partitioning the individuals by SVDquartet lineages (see below). The coalescent testing of SSHs needed a further size reduction of the datasets, again on linkage disequilibrium, and keeping only one individual per putative genetic lineage per island (two replicates of 25 individuals and 636 variant sites and of 24 individuals and 1032 variant sites for Pocillopora and Porites, respectively).
Due to the absence of a reference genome at the time of SNP calling for Millepora cf. platyphylla, a 'target gene' approach was followed. Trimmed RNA-Seq metatranscriptomic sequences were subjected to de novo whole transcriptome assembly using Trinity  73  , after being further reduced to 23,755,336 reads by in silico normalization. The Trinity assembly produced 302,299 transcripts, which were then clustered into 40,560 unigenes (i. e. , uniquely assembled transcripts) with an N50 of 1053 bp. Based on BUSCO, the assembled transcriptome was largely highly complete with 85. 6% of the ortholog genes from the Eukaryota database being present with low fragmentation (3. 6%), missing (10. 8%), and duplication (16. 7%) metrics. Within this transcriptome, we identified Millepora orthologs for an arbitrary set of stress and/or environment response genes from scleractinians and actinarians or genomic fragments from Pocillopora and Porites that contain temperature outlier SNPs obtained within the present study.
The Millepora orthologs were obtained using QuickParanoid that takes a collection of files produced by InParanoid  74  as input and finds ortholog clusters among species. The procedure used strictly followed the instructions published at http://pl. postech. ac. kr/ QuickParanoid/. Briefly, for a given set of target genes, QuickParanoid first preprocesses each InParanoid output file by using BLASTall and then computes ortholog clusters. As for the other coral genera, metagenomic reads were aligned to this set of target genes using Burrows-Wheeler Transform Aligner (BWA-mem, v0. 7. 15) with the default settings  67. Host-mapped reads were then sorted and processed using SAMtools v1. 10. 2  68  to generate respective bam files, and SNPs were identified as detailed above using GATK. The list of orthologs for the Millepora transcriptome contigs that contained SNPs is part of the resource files and scripts deposited in ref.
75. The SNP dataset was then filtered for biallelic status, SNPs only, quality, missingness, and minimum allele frequency with the same thresholds as for the other coral genera resulting in a final set of 446 variant sites. One individual with an excess of missing data (I09S03C026) was not included in the sNMF analysis. Filtering on linkage disequilibrium (r 2 > 0. 2) for further genetic analyses reduced this dataset to 243 unlinked variant sites in 56 individuals.
Parallel computing on 50 threads was performed to analyze all possible quartets and 100 bootstrap replicates. The bootstrap consensus tree was then transferred to MegaX for graphical annotation. To select which of the SVDquartet clades formed pertinent SSH, a hierarchical genetic clustering was also performed using the program sNMF  81  with the snmf function from the LEA v2. 8. 0 82 R package. However, as this analysis is sensitive to clonality (B. Porro, pers. comm. ), we first identified clonal lineages in the Pocillopora, Porites, and Millepora "unlinked" datasets using the R package Rclone v1. 0. 2  83  to identify the pairwise threshold distance between clonal individuals, and then used the mlg. filter function from the R package poppr v2. 8. 6  84, 85  to assign individuals to clonal, or, more properly, multilocus lineages (MLL).
We ran this clonal analysis on all samples for each of the 3 genera. In Pocillopora and Porites, due to a considerable lineage-correlating variation in pairwise distances, we also ran further analyses on SVD-grouping-defined subsets of samples. This analysis highlighted the presence of six MLL shared among 14 specimens in Pocillopora and five MLL shared among 12 specimens in Millepora. No shared MLL were found among Porites (Supplementary Table  3 ). The subsequent sNMF analyses were performed keeping only one ramet per clonal genet except for the 3 M. intricata samples that were identified as being a single MLL. sNMF determines the optimal number of ancestral populations from which the actual dataset could be issued through admixture. These analyses were performed on 50 cores and 10 repetitions for a number of possible ancestral populations that varied from 1 to 20, with an alpha parameter value of 10.
The best number of ancestral clusters was determined by the entropy criterion, and a bar chart representing the individual admixture coefficient from each of these ancestral clusters was produced. To test for possible subclustering, a second round of sNMF analyses was performed separately on each of the ancestral clusters identified in the first round (with the same analysis parameters). As a further visualization of the genetic diversity within each of the 3 genera, we performed principal component analyses (PCA) based on individual genome-wide genotypes using the glPca function of the adegenet v1. 7-15R package  86, 87. For Pocillopra and Porites, sNMF clustering and SVDquartet species-tree topology then guided the construction of secondary species hypotheses to be tested by coalescent analysis using Bayes factor delimitation with genomic data (BFD*)  88, 89.
Morphology analysis To investigate the degree to which morphology may support or confound the genetics-based SSHs, we categorized the samples into morphotypes through morphological analysis and contrasted these groupings with the genetic resolutions. The M. intricata samples were excluded from the analysis. Photographs of entire colonies were taken for all samples using an underwater camera with 20 × 20 cm quadrats as a scale (photographs available online at https://store. pangaea. de/Projects/TARA-PACIFIC/Images/). Collection of morphological parameters was conducted using ImageJ v1. 50e  91  , the image annotation plugin objectj v1. 05j (https:// sils. fnwi. uva. nl/bcb/objectj/), and custom macros with the photos corrected for perspective using the "interactive perspective" plugin with the quadrat as a reference.
The Gini coefficient was tested to measure the contribution of morphological variables in the homogeneity of the nodes of the decision trees. The proximity matrix was used as input to generate a principal coordinate analysis ordination (PCoA; stats::cmdscale) to visualize variance in morphology across the samples in accordance with the genetic lineage assignments. In addition, PERMANOVA (pairwiseAdonis::pairwise. adonis2; 999 permutations; P-adjusted Bonferroni correction applied; https://github. com/pmartinezarbizu/ pairwiseAdonis) was used to test whether variance in morphology could be significantly explained by the genetic delineations.
Among-and within-species genetic differentiation To further resolve evolutionary history in the Pocillopora and Porites species designations, we investigated introgression and pairwise divergence for the SSHs. Following ref. 97  , introgression among lineages was computed with TreeMix 1. 13  98. The unlinked SNP dataset VCF was converted to TreeMix format using vcftools  70  and PLINK  99. The individuals were regrouped in populations according to their SSH and islands of origin, and the optimal number of admixture events was estimated at this level. For this, we ran TreeMix 10 times for 0 to 10 events and estimated the optimal number of events using the optM R package  100. We then ran TreeMix 100 times for this optimal number of admixture/ introgression events to produce a consensus tree and bootstrap values using the BITE 1.
2. 0008 R package  101. The residual covariance matrix was estimated for the optimal number of admixture/introgression events and the consensus tree using TreeMix. The occurrence of the admixture/introgression events were verified by computing the relevant f 4 indices  34  and correlation in genetic content was further quantified by calculating pairwise f 2 distances using AdmixTools2 (admixtools 2. 0 R package, https://uqrmaie1. github. io/admixtools/index. html) after converting the unlinked SNP dataset VCF to the BED format using vcftools  70  and PLINK 1. 90b6. 21  99. To quantify and characterize genomic divergence between the SSHs within Pocillopora and Porites, we computed pairwise Weir and Cockerham 102 F ST along the genome (500 bp sliding window) with vcftools 0. 1. 17  70.
Environment as a predictor of genetic lineage distribution The algorithm gradientForest was used to assess whether temperature-related parameters and sampling depth could be predictors for the geographic distribution of the genetic lineage distribution found in Pocillopora and Porites. The analysis was conducted in R (v4. 2. 2) using the library gradientForest (v0. 1-32)  35. Abundance data of the lineage at each sites were normalized using the logarithm of the abundance plus the minimum abundance observed (log(y + c) where c is the minimum positive abundance y), according to ref. 38. Sea surface temperature-related variables, described in ref. 36  and the mean depth at each sampling site, calculated from individual depth data available in ref. 36  were used as predictors. The gradient forest model was computed using 300 trees, a maximum of level of split of 2 (calculated following  38  ), a correlation threshold of 0.