dataesr/scientific-paragraphs-categorization huggingface dataset:
Source reconstruction, power spectra and brain connectivity We reconstructed the MEG activity on the cortical surface by using a source imaging technique  (36, 37). We used the FreeSurfer 5. 3 software (surfer. nmr. mgh. harvard. edu) to perform skull stripping and segment grey/white matter from the 3D T1-weighted images of each single subject  (38, 39). Cortical surfaces were then modeled with approximately 20000 equivalent current dipoles (i. e. , the vertices of the cortical meshes). We used the Brainstorm software  (40)  to solve the linear inverse problem though the wMNE (weighted Minimum Norm Estimate) algorithm with overlapping spheres  (41). Both magnetometer and gradiometer, whose position has been registered on the T1 image using the digitized head points, were used to localize the activity over the cortical surface.
1c ). Hence, a node with a high M P C has the potential to facilitate communication across layers. The Matlab code for the computation of the M P C is freely available at https://github. com/devuci/BNT. We also used the standard coefficient of variation CV i to measure the dispersion of the degree of a node i across layers. A global coefficient of variation CV is then obtained by averaging the CV i values across all the nodes (Supplementary Text).
Statistical analysis We first analyzed network features on global topological scales in order to detect statistical differences between AD and HC subjects at the whole system level. Only for those conditions (e. g. , frequency bands) that resulted significantly different on the global scale, we also assessed possible group-differences on the local topological scale of single nodes. This hierarchical approach allowed us to associate brain network differences on multiple topological scales  (49). For global network features, we used a non-parametric permutation t-test to assess statistical differences between groups, with a significance level of 0. 05. For local network features, we applied a correction for multiple comparisons by computing the rough false discovery rate (FDR)  (50, 51). In both cases, surrogate data were generated by randomly exchanging the group labels 10 000 times.
When different network properties were considered (e. g. , P C and M P C), we concatenated the respective c s feature vectors allowing for all the possible combinations. To quantify the separation between the feature vectors of AD and HC subjects, we used a Mahalanobis distance classifier. We applied a repeated 5-fold cross-validation procedure where we randomly split the entire dataset into a training set (80%) and a testing test (20%). This procedure was eventually iterated 10 000 times in order to obtain more accurate classification rates. To assess the classification performance we computed the sensitivity (Sens), specificity (Spec) and accuracy (Acc), defined respectively as the percentage of AD subjects correctly classified as AD, the percentage of HC subjects classified as HC and the total percentage of subjects (AD and HC) properly classified.
Acknowledgments We are grateful to  F. Battiston  for his useful comments and suggestions. This work has been partially supported by the program " Investissements d'avenir "  ANR-10-IAIHU-06. FD acknowledges support from the  "Agence Nationale de la Recherche " through contract number  ANR-15-NEUC-0006-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
In addition, among these targets are genes encoding AP2 transcription factors, which are also up-regulated in the sporozoite, pointing to the existence of positive feedback loops for regulation of these factors (Fig. 4E , Table  S8 ). Only three CVGs are among the target genes predicted in the oocyst stage to have binding sites for the set of motifs identified by the FIRE analysis (bottom box in panel 4E). These results support the idea that AP2 transcription factors have roles in the regulation of stage-specific genes involved in immune evasion and in preparing the parasite for transmission and human host cell invasion. Expression of var genes during the sporogonic cycle. Among the set of CVGs developmentally regulated in the mosquito we identified genes belonging to the var family. P. falciparum var genes encode malaria virulence factor Erythrocyte Membrane Protein 1 (PfEMP1).
The alignment and accuracy statistics were computed using QualiMap v2. 1. 3  74 Table  S2 ). All sequencing reads were mapped to the P. falciparum 3D7 genome version 25. 0 (http://www. plasmodb. org). To validate this choice, the Plasmodium field isolates from Burkina were compared for similarities to the assembled 3D7 P. falciparum reference genome. ChIP and Input reads were first filtered by removing all sequences mapping to the Anopheles gambiae genome version AgamP4 (https://www. vectorbase. org/). Remaining reads were assembled into contigs and scaffolds using Velvet  75  and SOPRA  76. Filtered reads uniquely mapped to the P. falciparum reference genome were merged with assembled scaffolds to get all reference genomic regions matching sequences present in the Burkina dataset. This allowed us to confirm that 95.
04% of the de novo assembly is mappable to the reference, and confirm the presence of nearly all annotated Pf3D7 genes, including var genes as well as other relevant clonally variant multigene families (rifin, stevor, and pfmc-2TM), (Figure  S1 ). The percentage of the 3D7 genome reference covered by the de novo Burkina assembly was calculated using the intersectBed tool in Bedtools. Table  S10  includes coverage data corresponding to CVGs. In order to estimate sequence similarity between Burkina isolates and the Pf3D7 reference genome sequence, variant calling was performed using SAM-tools in combination with vcflib (https://github. com/vcflib/vcflib) to obtain variants with a quality score greater than 20. In order to examine coverage bias in the Burkina assembly, for example in telomeric and sub-telomeric regions that are AT-rich and repetitive, we first calculated theoretical mappability of the Pf3D7 reference genome.
For this purpose, we used BEADS v2. 1  77. First, we obtained all possible 50 bp sequence tags from the P. falciparum 3D7 genome (PlasmoDB v25) and mapped them back against the same reference using Bowtie. Coverage plots were generated by counting the number of overlapping uniquely mapped tags in a 10 bp window and plotted them as a percent of maximum. The resulting mappability track was visualized together with the gene track for the 3D7 reference genome and the scaffolds of de novo assembly in IGV (Figure  S2 ). In addition, we performed further coverage analysis using deepTools  78. First, we plotted the fraction of the genome covered by the ChIP-seq and input data (Figure  S6A ). Then, we applied the computeGCbias module of deepTools to quantify the G + C bias defined as ratio of the expected/observed GC fraction (GC fraction is defined as the number of G's or C's in a genome region of a given length) (Figure  S6B ).
embl. de/users/anders/ HTSeq/) configuration parameters were set for a strand-specific assay to separate between sense and antisense transcripts. The matrix of raw read counts was used as input for the R/Bioconductor DESeq2 package  86  , that performs library normalization and uses negative binomial generalized linear models to identify differentially expressed genes. Statistical significance was set to P adj < 0. 001 and > 2-fold differential expression. In the case of var genes we employed the R/Bioconductor package DEXSeq 87 , which tests for differential expression at the exon level. The method uses generalized linear models to estimate an overall expression level for each exon, and then infers differential gene expression by assessing the changes of expression at the exon level between genes.
Sets of differentially expressed genes between parasite stages were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to identify significantly represented functional groups (P < 0. 05). Gene annotation and functional enrichment analysis of GO terms were performed using the set of tools available at the PlasmoDB database. Heatmaps of gene expression data were constructed using the "heatmap. 2. " function implemented in the "gplots" package of R. Statistical tests and plots were performed in R 3. 1. 0 (http://www. r-228 project. org/) using Bioconductor (http://www. bioconductor. org). For comparative purposes, gene expression data corresponding to RNA-seq normalized counts obtained from DESeq2 was log2 transformed and we added a pseudocount (0. 1) to avoid dividing by 0.
For ChIP-seq data, we performed square root transformation and used the value of enrichment level as a measurement of histone modification abundance. To explore the relationship between ChIP-seq signal and RNA levels in each stage of P. falciparum development in the mosquito, we first divided genes based on their expression into high, intermediate or low. Then we obtained the distribution of each histone modification enrichment levels in the ±1 kb region surrounding the ATG translation start codon. To measure the quantitative association between histone modifications and mRNA levels we fitted a linear regression model for all genes using the R package lm, following similar studies by others  88, 89. For the linear regression model, we used the R2 decomposition implemented in the calc. relimp function of the relaimpo R package.
nbcr. net/meme/doc/tomtom. html) using as reference the database of validated AP2 motifs previously published  33. Sequence logos were constructed using WebLogo (http://weblogo. berkeley. edu). Cytoscape network analysis (http://www. cytoscape. org/) was used to investigate links between target genes identified by FIRE that have binding motifs for significantly enriched AP2 transcription factors and their expression levels in different stages during sporogonic development. For the motif analysis at var genes we conducted motif scan using FIMO  90  to validate the occurrence of the PF3D7_1466400 AP2 transcription factor binding sites on var gene sequences encompassing a 2 kb region upstream of the ATG start codon. In a subset of UPSB var genes, the motif is located near the ATG start codon  33.
Acknowledgements We would like to thank all children and their parents who participated in this study, as well as the local authorities in  Burkina Faso  for their support. We are very grateful to  B. Yameogo ,  S. Tamboula ,  R. Hien ,  J. Bazié ,  B. Dabiré ,  F. Da ,  A. Diasso  and  F. Yao in Burkina Faso  for technical assistance. We thank The  Genomic Services Lab at the HudsonAlpha Institute for Biotechnology , specially  Braden Boone ,  Angela Jones , and  Terri Pointer , for their help in preparing RNA-seq libraries and performing Illumina sequencing of RNA-seq and  ChIP-Seq  samples. This work was supported by  U. S. Public Health Service  Award  R01GM035463  from the  National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
In the same way, all ICEs, which encode a FA T4SS and a TcpA CP, encode a relaxase belonging to the noncanonical MobT superfamily. The MobT relaxases are related to a family of RCR initiators involved in the intracellular replication and maintenance of small plasmids  (Guglielmini et al. , 2013; Ambroset et al. , 2016). MobT encoded by ICEBs1 is involved in both conjugative transfer and in replication of the excised ICE [for a review, see  (Auchtung et al. , 2016) ]. It should be mentioned that most families of relaxases involved in the RCR of small plasmids or viruses are clearly distinct from and perhaps unrelated to the superfamilies of relaxases found in conjugative and mobilizable elements. Besides the MobT relaxases encoded by the ICEs with FA T4SS and TcpA CP, the only other exception corresponds to PF01446 RCR initiators of some mobilizable plasmids of firmicutes that are involved in both plasmid replication and mobilization by ICEs encoding TcpA CPs  (Naglich and Andrews, 1988; Showsh and Andrews, 1999; Lee et al.
Genomes Examined and Database of Reference Proteins The dataset of the 124 complete chromosomes from Streptococcus species available at the start of the present study was taken from Genbank 1. This initial database of reference proteins contains signature proteins from ICEs and the few IMEs reported for Firmicutes in the literature. It includes protein sequences from 50 tyrosine integrases, 13 serine integrases, 2 DDE transposases, 50 relaxases, 37 CPs, and 26 VirB4 proteins (a T4SS ATPase). This last protein has never been found in any mobilizable elements and is used here as the main criterion for differentiating between ICEs and IMEs at the detection step.
g. , identification of an authentic gene as a pseudogene most frequently due to the presence of a type II intron within the gene or mis-identification of the "start" codon). Because before this work, the known IMEs did not encode a CP, those elements carrying an integrase gene, a relaxase gene, and a CP pseudogene were considered as IMEs. 1 http://www. ncbi. nlm. nih. gov/genome/browse/, last accessed December 2013 Moreover, our workflow detects elements containing only an integrase and a CP CDS but apparently no relaxase gene or pseudogene. In such cases, an exhaustive manual analysis was performed to search for new relaxase genes, in particular for genes encoding proteins related to RCR initiators. Newly found relaxases were added to the database of reference proteins and step (i) was reiterated on all genomes.
Domain Composition Analysis and Tree Construction The retrieval of the domain composition of all IME signature proteins from Uniprot annotations was done in batch using the BioMart Central Portal 2. De novo conserved domain search (CD-search 3  ) and/or PSI-Blast analyses were performed when no data was available through BioMart. The correspondence with Mob families was established using the CONJscan-T4SSscan program 4   (Guglielmini et al. , 2011). This tool is no longer accessible but should soon be available on the Pasteur Galaxy server. The signature proteins were aligned using Clustal omega with default parameters  (Sievers et al. , 2011). The trees of signature proteins were built with MEGA  (Tamura et al. , 2013)  using (i) maximum likelihood (ML) based on the JTT (Jones-Taylor-Thornton) model including amino acid empirical frequencies (partial deletion of gaps and missing data at 80% cutoff, Gamma distribution in five categories, allowance for invariant sites), and (ii) BioNJ methods with the Poisson model  (Gouy et al.
Prevalence of IMEs within Streptococcal Chromosomes The prevalence of IMEs was studied in a large set of species (27 Streptococcus species) and strains (n = 124). This exhaustive examination led to the identification of 144 IMEs. Their sizes ranged from 5 to 18 kb (Supplementary Table  S1 ) except for IME_Sparas15912_rpsI which was 53 kb. The larger size of IME_Sparas15912_rpsI has likely resulted from a tandem ICE-IME insertion in the element. Within streptococcal genomes, this study showed that IME sizes (mean = 10 kb) were generally smaller than those of ICEs (mean = 41 kb). This difference is reminiscent of that observed between mobilizable and conjugative plasmids  (Smillie et al. , 2010). Ten IMEs were found to be integrated in tandem accretion with other IMEs (IME1-IME2) or with ICEs (ICE-IME or ICE-IME1-IME2) (Supplementary Table  S1 ) and many with decayed elements (data not shown).
New Relaxase Families Related to RCR Initiator Proteins in IMEs A total of 154 relaxase genes were detected within IMEs (including three pseudogenes in IMEs harboring two relaxases). Based on their domain composition, the relaxases were classified in nine distinct superfamilies (Table  1 ). Among the four most prevalent relaxase superfamilies, only the Rel_PF02486 superfamily was recognized by the CONJscan-T4SSscan analysis as belonging to a known type of relaxase (MobT). The three others were novel superfamilies of relaxases characterized by the following domains: PF01719 (Rel_PF01719), PHA00330 (Rel_PHA00330) and by the combination of PF001719 and PF00910 (Rel_PF001719-PF00910). The Rel_PF02407 superfamily was the fourth novel superfamily identified in this study. All these new relaxase superfamilies discovered in IMEs harbored domains (PF01719, PHA00330, or PF02407) that were previously found exclusively in RCR initiators from viruses or plasmids  (Ebisu et al.
, 1995; Bachrach et al. , 2004; Gibbs et al. , 2006; Lorenzo-Diaz et al. , 2014). They were assumed to correspond to novel relaxase superfamilies since all these proteins were associated with an integrase and a large majority of these non-canonical relaxases (or all for PF02407) were associated with a CP in our IME collection. Four other relaxase superfamilies described in Table 1 (Rel_PF03389, Rel_PF01076, Rel_PF13814, and Rel_PF03432) were recognized by the CONJscan-T4SSscan server as the MobQ, MobV, MobC, and MobP superfamilies, respectively. On the basis of their relaxase content, the IMEs were grouped in nine classes (IME_Class_1 to 9) encoding a unique relaxase, one for each superfamily of relaxase (Table  2 ). An additional class (IME_Class_10) contained 10 IMEs that carry 2 relaxases: one belonging to the Rel_PF02486/MobT and the other to the Rel_PF01076/MobV.
Each of the two relaxase superfamilies present in this class also exists as standalone relaxases in IME_Class_1 and IME_Class_6. Within each superfamily of relaxases, the diversity was estimated by phylogenetic analyses and 40% sequence identity clustering. The most abundant superfamily, Rel_PF02486/MobT (Supplementary Figure  S2 ), includes six families (Rel_PF02486_1 to 6), among which the family PF02486_3 was associated with a Rel_PF01076/MobV in IME_Class_10. In contrast, the 35 relaxases from the Rel_PF01719 superfamily are closely related and were therefore clustered in a unique family (Supplementary Figure  S3 ). The same was true for the 12 relaxases of the Rel_PF03389/MobQ superfamily (data not shown). The 21 members of the Rel_PHA00330 superfamily were grouped into three families (Supplementary Figure  S4 ), and the Rel_PF01719-PF00910 superfamily (15 members) analysis yielded four families (Supplementary Figure  S5 ).
Finally, the superfamily Rel_PF01076 was clustered in two families, with the Rel_PF01076_1 being always found in IME_Class_10. Overall, the IME relaxases showed a great diversity: they were classified in nine distinct superfamilies subdivided in 20 families (Table  1 ). It should be noted that the diversity of IME relaxases largely exceeds that of ICEs, since ICEs are classified in three superfamilies/eight families according to the same criteria  (Ambroset et al. , 2016). Only two superfamilies of relaxases corresponding to Rel_PF02486/MobT and Rel_PF03432/MobP are encoded by both IMEs and ICEs. However, our analysis showed that within these superfamilies, the relaxases of IMEs always belong to clearly distinct families from those including ICE relaxases. Moreover, whereas the number of Rel_PF02486/MobT relaxases found for ICEs and IMEs are similar, it can be stressed that Rel_PF03432/MobP relaxases constitute the most abundant superfamily in ICEs (62/105 relaxases) and the least abundant in IMEs (only 1/154 relaxases).
Half of the Streptococcal IMEs Encode a CP Prior to the present study, none of the previously known or predicted IMEs encode a CP  (Bellanger et al. , 2014) , and the few CPs that are known to be encoded by mobilizable plasmids belong to the VirD4 superfamily (Garcillan-  Barcia et al. , 2009). Surprisingly, our results show that more than half of the streptococcal IMEs encode a CP (85/144 including 11 pseudogenes of CP) and that almost all these CPs do not belong to the canonical VirD4 family. Indeed, only two IMEs encode a VirD4 CP characterized by a C-terminal VirD4 domain (COG3505 in the NCBI CDD classification). All others (72 proteins excluding pseudogenes) were found to display a unique PF01580 "FtsK-SpoIIIE" catalytic domain and to be more closely related to FtsK (a DNA translocase involved in DNA segregation during cell division) than to the canonical VirD4.
Organization of Conserved CDSs in IMEs: Predominance of a Common Compact Structure For a better characterization of the IMEs, a search for conserved CDSs in IMEs encoding the same superfamily of relaxases was undertaken. The various conserved CDS architectures are schematized in Figure  4. Apart from IME_Class_10, the IMEs with a tyrosine integrase encode a unique non-canonical relaxase (Rel_PF02486/MobT, Rel_PF01719, Rel_PHA00330, Rel_PF01719-PF00910, and Rel_PF02407). These IMEs display a compact structure composed of successive genes (relaxase, excisionase, and integrase), generally preceded by a TcpA gene (83/118). Interestingly, the same compact structure was All other classes of IMEs encode other types of canonical relaxases and do not encode a CP. In IME_Class_10, the two genes encoding relaxases (a canonical Rel_PF01076/MobV and a non-canonical Rel_PF02486/MobT) are located far upstream of the tyrosine integrase gene in the opposite orientation.
As well as the two relaxases and a tyrosine integrase, these IMEs encode a protein with a repA_N domain that is also found in proteins that initiates the theta replication of plasmids and of ICEs belonging to the Tn5252 superfamily from firmicutes  (Weaver et al. , 2009; Guerillot et al. , 2013; Ambroset et al. , 2016). However, repA_N proteins from IMEs are shorter (∼100 amino acids) than those found in plasmids and ICEs (∼340 amino acids), suggesting that they probably serve a different function. Apart from IME_Class_8, all IMEs encoding a serine integrase and relaxases from canonical superfamilies (Rel_PF03389/MobQ, Rel_PF01076/MobV, and Rel_PF03432/ MobP) also encode one or several proteins that could be involved in the maintenance of the excised elements. These proteins include homologs to: (i) 'replisome organizers' or 'DnaC-related' proteins involved in the initiation of theta replication of various phages from Firmicutes such as phi5218, phi4268, or phi9871  (Trotter et al.
Only 20% of our collection of streptococcal IMEs encode canonical relaxases (i. e. , belong to the Rel_PF03389/ MobQ, Rel_PF01076/MobV, Rel_PF13814/MobC, or Rel_PF03432/MobP superfamilies), already found to be encoded by conjugative and mobilizable elements from G-bacteria (Garcillan-  Barcia et al. , 2009). Among them, none encode any CP (IME_Class_5, _6, and _9) except the two IMEs with a Rel_PF13814/MobC relaxase (IME_Class_8) that encode a canonical VirD4 CP. This is reminiscent of mobilizable plasmids of G-bacteria and firmicutes (Garcillan-  Barcia et al. , 2009). Unexpectedly, the large majority (90%, n = 129/144) of streptococcal IMEs in our collection encode non-canonical relaxases (Rel_PF02486/MobT, Rel_PF01719, Rel_PHA00330, Rel_PF01719-PF00910, and Rel_PF02407) related to proteins responsible for RCR initiation involved in the maintenance of plasmids from firmicutes or viruses.
MobT was previously identified in a few predicted IMEs  (Bellanger et al. , 2014)  and in ICEs belonging to the Tn916 superfamily  (Ambroset et al. , 2016). The four other superfamilies of putative relaxases are unrelated to any known relaxase of mobilizable or conjugative elements. It should be emphasized that the MobT "relaxase" of the integrative and conjugative element ICEBs1 from the firmicute Bacillus subtilis is involved in not only the initiation of the conjugative transfer of ICEBs1 but also the initiation of RCR needed for the maintenance of ICEBs1 after excision  (Lee et al. , 2010). In the same way, another family of "RCR initiators" exhibiting a Rep_1/PF01446 domain is involved not only in the maintenance of three plasmids from firmicutes but also in their mobilization by ICEBs1  (Lee et al.