dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing First, MRI data were converted from the 2dseq Bruker format to the standard NIFTI format using a modified version of the bruker2nifti script (original version: https://github. com/SebastianoF/ bruker2nifti; modified version: https://github. com/neuroanatomy/bruker2nifti). For a limited number of volumes, EPI trajectories were poorly corrected by the Bruker routine image reconstructor, which resulted in noisy volumes. In order to exclude these volumes, diffusionweighted directions for which their mean signal was 2 standard deviations away from the global average across all the volumes were visually inspected and removed. Three out of 200 volumes were removed following this criterion. The preprocessing steps were mainly done using MRtrix3 functions and included a local principal component analysis (LPCA) denoising  (Veraart et al.
Tractography-Based Connectivity Matrices Structural connectivity matrices were extracted from the tractography results using the number of streamlines connecting pairs of regions. The connectivity matrices are available in the Supporting Information. Matrices reporting the averaged fiber lengths between regions were also computed. Then, structural connectivity matrices were normalized using fractional scaling, such that the number of streamlines between pairs of regions were divided by the sum of the streamline counts connected to each of the regions, excluding self-connections  (Donahue et al. , 2016). The weights then represent the fraction of streamlines (FS). All MRI data analysis was performed using the MRtrix3 software (http://www. mrtrix. org/), and custom scripts for Python (http://www.
This proximity could have inflated our correlations benefiting from the ability of tractography and tract-tracing to more accurately recover connections from neighboring areas. In any case, the correlations in which distance was regressed out, which corrects for both effects of distance (proximity and remoteness), maintain statistically significant correlations for all probabilistic tractography algorithms (especially based on CSD). Our results showed a high correlation between diffusion MRI tractography and tract-tracing data; however, we note the limitations in our methodology. First, the two datasets had different origins (i. e. , the tract-tracing and tractography were not performed in the same animal) and the sample sizes were very small. Although the ferrets could all be considered mature in terms of brain development  (Jackson, Peduzzi, & Hickey, 1989; Neal et al.
Study Design and MED Analysis Technique This study was designed to identify a dynamical electrophysiological cellular phenotype associated with idiopathic ASD, as well as the gene expression correlates of this signal. To accomplish this, iPSC lines derived from eight ASD patients with early brain overgrowth and seven neurotypical controls were obtained and differentiated into neurons using a pan-cortical protocol  (Marchetto et al. , 2010). A schematic of the study design is depicted in Figure  1A , and a description of the samples is given in Table  1. Additional clinical metadata for these samples is presented in Table  S1. From the neural progenitor phase, longitudinal electrical recordings were gathered with MEA over the course of 47 days. For each recording, standard spike-related variables, as well as MED, a measure of dynamical complexity, were computed.
Additional exploration of cellular components related to the MED gene signature showed a significant overlap with neuronal components, such as the synapse, postsynaptic density, axon, and dendritic spines. Full results for gene ontology analysis testing of biological processes and cellular components are given in Tables  S4  and S5 , respectively. These findings suggest that MED encodes a measure of neuronal maturation and participation in circuit formation. Finally, to localize the set of MED genes to developmental time points and brain regions, data from the BrainSpan Atlas were used to identify enrichments in specific region-stage pairs (refer to the Experimental Procedures for details). The enrichments were visualized as a matrix in Figure  4C  for all region-stage pairs, and regional involvement during the highly enriched, late prenatal time point was mapped onto a representation of the brain in Figure  4D.
EXPERIMENTAL PROCEDURES iPSC Samples and Neuronal Differentiation iPSC lines were derived from two sources. First, 13 samples from  Marchetto et al. (2017)  comprised the bulk of the lines  (Marchetto et al. , 2017). These included eight lines derived from ASD patients with an early brain overgrowth phenotype and five age-matched neurotypical control lines. Second, two additional control lines from neurotypical adults were added to balance the groups. The samples from the first cohort included standardized clinical and functional assessments, including the Autism Diagnostic Observation Schedule, Wechsler Intelligence Scale, and Vineland Adaptive Behavior Scale. Sample metadata and clinical scores are given in Table  S1. Samples were obtained with approval from the internal review board of the Salk Institute for Biological Studies and informed consent of all subjects.
MEA Recordings and Spike Analysis Ninety-six-well MEA plates from Axion Biosystems (San Francisco, CA, USA) were used to record electrical activity of neurons derived from all 15 subjects. Each subject's cells were plated in replicates of six and seeded with 10,000 neural progenitor cells that were induced into neuronal differentiation the next day. Wells were coated with poly-ornithine and laminin before cell seeding. Cells were fed every other day and measurements were taken twice a week before feeding. The Maestro MEA system and AxIS software (Axion Biosystems) were used to record neuronal electrical activity from the plates. Voltages were recorded at a frequency of 12. 5 kHz and bandpass filtered between 10 Hz and 2. 5 kHz. Spike detection was performed using an adaptive threshold set to 5.
5 standard deviations above the mean activity of each electrode. Following 5 min of plate rest time, recordings were performed for 10 min. A total of 15 separate recordings were performed over a time span of 47 days for each subject. Multielectrode data analysis was performed using the Axion Biosystems Neural Metrics Tool, which calculated standard spikerelated variables. Bursts were detected with an adaptive Poisson algorithm for high spiking activity that occurred on a single electrode. Variables were averaged across subject replicates and plotted by group for each day of the recordings. The 95% confidence interval around the mean was also calculated and plotted for each day to aid in the discovery of significant trends.
The second criterion models noise in the data. We introduced a threshold parameter A tol to compare the neighbor distances in dimension d + 1with the size of the attractors in dimension d. By combining these two criteria, the MED for time-series data can be effectively found, even when the data are noisy. We set T = 2, and we used the same threshold parameters as in Kennel's original study R tol = 15 and A tol = 2  (Kennel et al. , 1992). FNN was implemented in MATLAB R2017b (Natick, MA, USA) and performed on both real MEA data and simulated data from the Henon map  (Henon, 1976; MATLAB, 2017). An online code repository for the MED analysis is freely accessible to others who wish to perform a similar dynamical complexity analysis of electrical recording data. The GitHub repository is available at this link: https://github.
, 2001)  human genome using the STAR aligner version 2. 4. 0k (Dobin et al. , 2013). Mapping was carried out using default parameters (up to ten mismatches per read, and up to nine multi-mapping locations per read). The genome index was constructed using the gene annotation supplied with the hg19 Illumina iGenomes collection  (Illumina, 2015)  and sjdbOverhang value of 100. Raw gene expression was quantified across all gene exons (RNA sequencing) using the top-expressed isoform as proxy for gene expression. Transcripts per million (TPM) values were calculated for each sample: TPM i = X i l i , 0 B B @ 1 P X j l j 1 C C A 3 10 6 ; where X i is the count estimate for gene i and l i is the length of the transcript as determined by querying the Ensembl mart with dataset = ''hsapiens_gene_ensembl'', using biomaRt in R version 3.
Gene Ontology Analysis Gene ontology (GO) analysis was performed to determine which biological pathways were associated with differentially expressed genes. The statistical overrepresentation tool from PantherDB was used to perform Fisher's exact tests for a list of uncorrected differentially expressed genes and each GO term in the database  (Mi et al. , 2017; Thomas et al. , 2006). p values were adjusted for multiple comparisons using FDR correction. Biological processes with a p FDR less than 0. 05 were reported and plotted in this study.
Spatiotemporal Enrichment Analysis Spatiotemporal enrichment of the MED differentially expressed genes was assessed using gene expression data from the BrainSpan Atlas of the Developing Human Brain  (Sunkin et al. , 2013). Normalized gene transcript counts were acquired for region-stage pairs. Twelve developmental stages and 16 discrete brain structures were included. In a process similar to one described by  Krishnan et al. (2016) , representative genes sets were chosen for each region-stage pair by calculating the modified Z score of a given gene in the distribution of counts for all region-stage pairs  (Krishnan et al. , 2016). The modified Z score was calculated using the median, and the median absolute deviation (MAD) of each gene across all pairs was calculated for the ith gene and jth region-stage pair: z i;j = 0:645 3 count i;j À median i MAD i : For the jth region-stage, all genes for which z i,j R 2 were selected as representative genes.
ACKNOWLEDGMENTS Y. K. is supported by a grant from the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare  ( HI18C1077 ), and a grant from the  National Research Foundation  ( 2018R1A2B003640 ), Republic of Korea. M. C. M. would like to acknowledge the support from the  National Institutes of Health  grant number  R03 MH115426-01A1. D. N. A. would like to acknowledge support from the  National Institutes of Health   F30 MH115584. F. H. G would like to thank  The Leona M. and Harry B. Helmsley Charitable Trust , The  Robert and Mary Jane Engman Foundation , and  JPB Foundation. Salk core facilities are supported by the  Salk Cancer Center  with a grant from the  NCI  ( P30.
DNA Sampling, Extraction, and Sequencing One female mountain hare (Lepus timidus hibernicus) specimen (NCBI BioSample ID SAMN12621015) was captured from the wild for scientific research purposes by the Irish Coursing Club (ICC) at Borris-in-Ossory, County Laois under National Parks & Wildlife (NPWS) license no. C 337/2012 issued by the Department of Arts, Heritage and the Gaeltacht (dated October 31, 2012). Genomic DNA was extracted from kidney, muscle, and ear tissue using the JETquick Tissue DNA Spin Kit (GENOMED), with RNAse and proteinase K to remove RNA and protein contamination. Genomic libraries of different insert lengths were generated following the standard ALLPATHS-LG protocol  (Gnerre et al. 2011 ): one Illumina TruSeq DNA library of 180 bp fragments was sequenced with overlapping paired-end (OPE) reads, and three Illumina TruSeq DNA mate-pair (MP) libraries of 2.
Quality control of the assembly at different stages was assessed based on metrics obtained with QUAST v. 3. 2  (Mikheenko et al. 2016). The completeness of the L. timidus re-scaffolded genome was evaluated using BUSCO v. 3. 0. 2  (Simão et al. 2015) , based on the presence and absence of core single-copy genes (from mammalia_odb9 database). We then checked consistency of gene content in the larger chromosome-like scaffolds and rabbit chromosomes using blastp from NCBI BLAST v2. 7. 1þ  (Camacho et al. 2009) , considering the best hit per gene with similarity above 90% over 500 bp. The 22 rabbit chromosomes were aligned against inferred corresponding L. timidus re-scaffolded scaffolds using D-Genies v. 1. 2. 0 Mashmap  (Cabanettes and Klopp 2018). Repetitive regions were identified using RepeatModeler v.
1. 0. 11 (Smit and Hubley 2008)  and masked using RepeatMasker v. 4. 0. 7  (Smit et al. 2013 ). The masked genome was used as input for gene prediction in MAKER v. 3. 01. 02  (Cantarel et al. 2008) , using ab-initio predictions, L. timidus transcriptome data, and rabbit protein annotations (O. cuniculus) (supplementary text, Supplementary Material online). Functional inference for genes and transcripts was performed using the translated CDS features of each coding transcript. Each predicted protein sequence was based on blastp searches against the Uniprot-Swissprot database to retrieve gene name and function, and InterProscan v5. 30-69  (Jones et al. 2014)  to retrieve Interpro, Pfam v31. 0  (Finn et al. 2016) , GO  (Mi et al. 2017) , KEGG  (Kanehisa et al. 2016) , and Reactome  (Fabregat et al.
Analyses of Whole-Genome Re-Sequencing Data To compare the performance of using the L. timidus genome or other strategies based on the rabbit genome for wholegenome analyses, we analysed re-sequencing data from the mountain hare and another hare species, the Iberian hare, L. granatensis, mapping the reads to 1) the new L. timidus re-scaffolded genome, 2) the rabbit reference genome (available from Ensembl-OryCun2. 0, release 80), and 3) a hare pseudo-reference genome built through iterative mapping of hare sequence reads on the rabbit genome, followed by reference updating (from  Seixas et al. 2018). For the resequencing data (NCBI Sequence Read Archive Biosamples SAMN07526960 and SAMN07526963;  Seixas et al. 2018) , adapters were removed using cutadapt version 1. 8  (Martin 2011 ) and low quality bases (quality < 20 at the end of reads, and 4 consecutive bp with average quality < 30) were trimmed using Trimmomatic v0.
33  (Bolger et al. 2014). Mapping was done using BWA-MEM v0. 7. 15  (Li 2013). Mapped reads were sorted with samtools v1. 3. 1  (Li et al. 2009 ) and read duplicates removed using Picard Markduplicates (Picard toolkit 2019). Realignment around INDELs was performed using GATK v3. 2-2 (Van der Auwera et al. 2013). Genotype calling was performed for each species independently using bcftools v. 1. 5 (Li 2011) , with minimum site (QUAL) and RMS mapping (MQ) qualities of 20, coverage (FMT/DP) between 6X and twice the average genomic coverage, and minimum genotype quality (FMT/GQ) of 20. Indels and flanking 10 bp were coded as missing data. Only sites covered in the two analysed individuals were retained. Heterozygosity was calculated in sliding windows of 50 kb, using the popgenWindows. py script available at https:// github.
Acknowledgments In Ireland, a license (no. C 337/2012  dated October 31, 2012) to take animals for scientific purposes under the Wildlife Acts 1976--2012 Sections 9,  23 , and 34 was issued by  Laura Claffey ,  Wildlife Licensing Unit ,  National Parks & Wildlife Service  (NPWS),  Department of Arts, Heritage & the Gaeltacht (DAHG). Thanks to  Jimi Conroy , the local NPWS Conservation Officer, who consented to sampling within his jurisdiction. The Irish Coursing Club (ICC) captured animals from the wild and we thank  D. J. Histon ,  Chief Executive  and Secretary, who supported the research. William Fitzgerald,  Department of Agriculture, Food  and the  Marine (DAFM)  dispatched animals in a humane manner and provided dissection skills. This work was funded by the project HybridAdapt, supported by  Portuguese National Funds through the Fundac¸ão para a Ciência e a Tecnologia (FCT)  (reference  FCT-ANR/BIA-EVF/0250/2012 ), and by  French Agence Nationale de la Recherche (ANR)  (reference  ANR-12-ISV7-0002-01 ).
Additional support was obtained from  Programa Operacional Potencial Humano -Quadro de Referência Estrat egica Nacional (POPH-QREN)  funds from the  European Social Fund (ESF)  and  FCT  ( IF/00033/2014/ CP1256/CT0005 FCT Investigator grant  to J. M. -F. , and  SFRH/BD/115089/2016  and  SFRH/BD/87126/2012  PhD grants to J. P. M. and F. A. S. , respectively), and from a  National Science Foundation (NSF)   EPSCoR grant  ( OIA-1736249  to J. M. G. ). Instrumentation, laboratory, and/or computational support were provided by CIBIO NEW-GEN sequencing platform, supported by the  European Union  under grant agreement no. 286431 , by the  Montpellier Bioinformatics Biodiversity  platform supported by  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ), and by the  University of Montana Genomics Core.
Indeed, within two hours, reciprocal data are not equivalent to the normal data collected two hours before since we induce some changes in the aquifer. One way of partially solving this is to acquire the reciprocal measurement right after the normal one. However, in this case, the multichannel acquisition optimization is not optimal anymore. We then collected reciprocals only for a subset of sequences and during more steady-state phases as the background profiles. We were able to maintain the reciprocal error below 2%, except for a few outliers that were removed from all data sets. We also used this 2% value as the stopping criterion for the inversion process. Before proceeding to data inversion, it is crucial to ensure that our data set actually contains information about the physical process that is monitored.
Data Inversion We used the RES3DINV software (https://www. geotomosoft. com/) to perform our inversions. We used a robust data constraint (L1 norm) to deal with eventual bad data that would remain and a standard smoothness model constraint (L2 norm) corresponding to Occam's inversion  [44, 45]. Although there are different regularization techniques allowing to recover sharper and more focused thermal plumes such as the blocky inversion  [46, 47]  or the minimum gradient support  [48, 49] , we believe the standard smoothness-constraint scheme is well adapted to the smooth changes that are expected with injecting heated water in the aquifer. Although data difference inversion seems the most robust time-lapse inversion technique  [8, 50] , when data quality is good, simple independent inversions can provide reliable results when the forced electrical resistivity contrast is high enough and when data quality is good.
Bioinformatic analyses All bioinformatic analyses were performed using a Unix shell script  (Brandt et al. , 2019) , available on Gitlab (https://gitlab. ifremer. fr/abyss-project/), on a home-based cluster (DATARMOR, Ifremer). Pairs of Illumina reads were corrected with DADA2 v. 1. 10 (Callahan et al. , 2016)  following the online tutorial for paired-end data (https://benjjneb. github. io/dada2/tutorial. html). The details of the pipeline, along with specific parameters used for all metabarcoding markers, are given in  Table S 3  and in Brandt   et al. (2019). Micro-eukaryote and prokaryote diversity was evaluated with Amplicon Sequence Variants (ASVs), while metazoan data was further clustered into OTUs with the FROGS pipeline  (Escudié et al. , 2018). ASVs and OTUs were taxonomically assigned via BLAST+ (v2.
6. 0) based on minimum similarity and minimum coverage (-perc_identity 70 and -qcov_hsp 80). The Silva132 reference database was used for taxonomic assignment of rRNA marker genes  (Quast et al. , 2012) , and MIDORI-UNIQUE  (Machida, Leray, Ho, & Knowlton, 2017)  was used for COI. Molecular inventories were refined in R v. 3. 5. 1 (R Core Team, 2018). A blank correction was made using the decontam package v. 1. 2. 1  (Davis, Proctor, Holmes, Relman, & Callahan, 2018) , removing all clusters that were more prevalent in negative control samples than in true or mock samples. Unassigned and non-target clusters were removed. Additionally, for metazoan loci, all clusters with a terrestrial assignment (groups known to be terrestrial-only) were removed. Samples with less than 10,000 target reads were discarded.
Statistical analyses Sequence tables were analysed using R with the packages phyloseq v1. 22. 3 (McMurdie & Holmes, 2013) , following guidelines in online tutorials (http://joey711. github. io/phyloseq/tutorials-index. html), and vegan v2. 5. 2  (Oksanen et al. , 2018). Alpha diversity between molecular processing methods was estimated with the number of observed target clusters in rarefied datasets. Cluster abundances were compared via analyses of deviances (ANODEV) on generalized linear mixed models using negative binomial distributions, as the data were overdispersed. Pairwise post-hoc comparisons were performed via Tukey HSD tests using the emmeans package. Homogeneity of multivariate dispersions were evaluated with the betapart package v. 1. 5. 1  (Baselga & Orme, 2012) , and statistical tests performed on balanced datasets for COI as dispersions were different between 2 g and 10g datasets (Table  S 4 ).
Data were rarefied for metazoans and Hellinger-normalised for microbial data. Differences in community compositions resulting from molecular processing were evaluated with Mantel tests (Jaccard and Bray-Curtis dissimilarities for metazoan and microbial taxa respectively; Pearson's product-moment correlation; 1000 permutations). Permutational multivariate analysis of variance (PERMANOVA) was performed on normalised datasets to evaluate the effect of molecular processing and site on community compositions, using the function adonis2 (vegan) with Jaccard dissimilarities (presence/absence) for metazoan and Bray-Curtis dissimilarities for prokaryotes and microeukaryotes. The rationale behind this choice is that metazoans are multicellular organisms of extremely varying numbers of cells, organelles, or ribosomal repeats in their genomes, and can also be detected through a diversity of remains.
The number of reads can thus not be expected to reflect the abundance of detected OTUs. Significance was evaluated via marginal effects of terms, using 10,000 permutations with site as a blocking factor. Pairwise post-hoc comparisons were performed via the pairwiseAdonis package, with site as a blocking factor. Differences between samples were visualized via Principal Coordinates Analyses (PCoA) based on abovementioned dissimilarities. Finally, taxonomic compositions in terms of cluster and read abundance were compared between molecular processing methods on balanced datasets.
High-throughput sequencing results A total of 70 million 18S-V1 reads, 61 million COI reads, 30 million 18S-V4 reads, and 45 million 16S-V4V5 reads were obtained from four Illumina HiSeq runs of pooled amplicon libraries built from triplicate PCR replicates of 75 sediment samples, 2 mock communities (for 18S-V1 and COI), 3 extraction blanks, and 2-4 PCR negative controls(Table  S 5 ). One to seven sediment samples failed amplification in each dataset. These were always coming from the same sampling sites (MDW-ST117 and MDW-ST38), and predominantly comprised RNA samples (Table  S 5 ). After bioinformatic processing, read numbers were reduced to 44 million for 18S-V1, 45 million for COI, 16 million for 18S-V4, and 24 million for 16S-V4V5 (Table  S 5 ). For eukaryote markers, less reads were retained in negative controls (2-64%) than in true or mock samples (49-83%), while the opposite was observed for 16S-V4V5 (62% of reads retained in control samples against 49-57% in true samples).