dataesr/scientific-paragraphs-categorization huggingface dataset:
Parameter settings DREAM4 in-silico data is generated with GeneNetWeaver  [31] ,  [32]  and is based on true networks. A perfect knowledge of TFs is thus available and simulated gene expressions are considered more reliable. Hence, we have more confidence in strong edge weights for the cluster fusion task. Higher merging criterion levels τ are therefore preferred to drive cluster fusion on simulated data. With real data, conversely, uncertainty in experimental gene expressions and partial knowledge of TFs are an incentive for lower levels. The latter ones tend to redeem lower weights, affected by experimental biases and variability. Therefore, τ is set to either 0. 8 (in-silico datasets) or 0. 3 (real datasets). Satisfactory trade-offs are obtained with the same parameter setting for all experiments, whatever the data (size, weights, number of TFs) and the methods.
Results on DREAM4 The DREAM4 multifactorial challenge  [5]  is composed of five datasets, corresponding to simulated gene expression data. Each dataset is composed of 100 genes simulated in 100 conditions. The validation procedure uses the reference graph for each network. We first compute the classical thresholding reference on CLR and GENIE3 weights (T-CLR and T-GENIE3). We then refine these results with BRANE Cut and BRANE Clust (hard and soft). Table  1  gathers corresponding AUPR and gains. It demonstrates the advantage of BRANE Clust over both classical thresholding and BRANE Cut, and of the soft over the hard version. Initialized either with CLR or GENIE3 weights, BRANE Clust (soft) ameliorates simple threshold results (Table  1 ). An improvement is observed over the five networks: about 12 % using CLR and 13 % with GENIE3.
Results on DREAM5 Gene/condition and TF/gene ratios for DREAM4 networks do not reflect real datasets properties. A second evaluation is therefore performed on DREAM5, containing more realistic data. This challenge  [6]  is composed of four networks. Three networks only (1, 3 and 4) present an associated ground truth. The synthetic dataset 1 is composed of 1643 genes (195 TFs) and 805 conditions. The two others (3 and 4) come from real data: Escherichia coli and Saccharomyces cerevisiae, respectively. The E. coli dataset contains 805 conditions for 4511 genes (334 TFs). The S. cerevisiae dataset encompasses 5950 genes (333 TFs) and 536 conditions. AUPR for these networks are given in Table  2  with the corresponding curves in Figure  5. The tininess of the AUPR for both CLR and GE-NIE3 on Network 4 impedes a relevant interpretation of gains using BRANE Clust.
Inference and insights on predictions From a biological view point, network inference methods should be evaluated in terms of reliability and predictive power as well. Hence, in addition to numerical performance (AUPR), we analyze the specific additional information acquired by BRANE Clust on real data. For this purpose, we used GENIE3 weights from the third DREAM5 dataset, a compendium of transcriptomic data from Escherichia coli. The BRANE Clust network with the best compromise in size and improvement is composed of 236 edges. We compare it to the equally-sized T-GENIE3 network, against the validation. BRANE Clust infers more true edges than T-GENIE3: 106 compared to 92 (Precision of 45 % vs. 39 %). Amidst 43 edges specific to BRANE Clust, 18 are known to be true links, while only 4 are correctly inferred by T-GENIE3 alone.
We are interested in the predictive power of our inference. We thus assess the biological interpretation of the remaining specific 25 novel interactions given by BRANE Clust. We use the STRING database  [33]. It contains both known and predicted protein-protein interactions (direct or indirect) from 2031 organisms. Interactions are derived from five main sources: genomic context predictions, high-throughput experi-ments, (conserved) co-expression, automated textmining and previous knowledge in databases. Several criteria address evidence suggesting a functional link: co-occurrence across genomes (Co-O), co-expression (Co-E), co-mentioned in PubMed abstracts (Co-M), neighborhood in the genome (N), gene fusion, experimental and biochemical data and association in curated databases. A combination of their probabilities, corrected from the chance of randomly observing an interaction, leads to a combined score (CS) per link  [34].
As in Section 3. 3, it should be judicious to compare the 236edge network inferred by SIMoNe to the one obtained with BRANE Clust from the third dataset of DREAM5. The resulting clustering of SIMoNe and BRANE Clust may also be analyzed. Unfortunately, with our com-putational capabilities, SIMoNe failed to return a 236edge network and the underlying estimated clustering. These limitations did not allow us to pursue the comparison. On the other hand, WGCNA determines a clustering from a correlation network  [24]. Its correlation values are often concentrated in an extremely small interval close to 1. Hence, neither classical nor improved thresholding (e. g. BRANE Clust) is well suited to inference. However, clusters may be more informative -although in a different way -than the network itself.
21), thus the least similarity. The best partition overlap (2. 82) is observed between WGCNA and BRANE Clust, despite the gap in cluster amount. An external validation with biologically-sound groups of genes from a validated database may be more pertinent. It is built from operons 1 identified in RegulonDB  [41]. This primary database for transcriptional regulation in Escherichia coli K-12 contains manually curated knowledge from original scientific publications. All significant operons, containing at least 5 genes, compose the ground truth. It splits a subset of 803 genes into 123 groups. We compare this partitioning to those of BRANE Clust, WGCNA and X-means on the same gene subset in Table  4. A smaller VI (higher similarity) is found for BRANE Clust, suggesting that its partitioning is nigher in terms of operon structure.
Stimuli For the fMRI paradigm, subjects were presented with stimuli belonging to the categories of houses, objects, faces, bodies, words, and numbers (see  Fig 2 and S2 Fig). Two additional categories of high-frequency and low-frequency grids with variable orientations were also presented. Each category comprised 60 different exemplars, either using black and white pictures (for houses, objects, faces, and bodies) or 4-character strings (for letters and numbers). The words were frequent, regular words encountered by young readers, as specified in Manulex, a lexical database compiling the frequency of occurrence of words in 54 scholarly French reading books  [47]. Faces were front views of male and female children's faces. Bodies were standing male and female adult bodies; to avoid face responses, their head fell outside the picture frame (see an example in  Fig 2).
Experimental procedure After training to remain still in a mock scanner (only for the first session), children were brought to the 3T MRI scanner (Siemens Trio). They were protected with noise-protection earphones, and a mirror system above their head allowed them to see the visual stimuli presented on a screen at the end of the tunnel. The images were viewed from a distance of 120 cm with an approximate view angle of 6 degrees. Stimulus presentation and behavioral responses collection were performed using PsychToolbox (http://psychtoolbox. org), a free Matlab toolbox (Math-Works, Natick, MA, US). In each session, T1 images were first acquired (voxel size = 1 × 1 × 1 mm), then functional images were collected (100 EPI volumes with TR = 2. 4 seconds, TE = 30 millisecond matrix 64 × 64 × 40, voxel size = 3 × 3 × 3 mm in each run).
Preprocessing To correct for motion within each run, images were first realigned using the corresponding tool provided by SPM8 (http://www. fil. ion. ucl. ac. uk/spm/), including both estimation and reslicing steps. The target image was the mean of all images, except if movement during acquisition corrupted the mean image. In that case, the first image was used. S1 Fig shows the average amount of detected movement, computed as the maximum absolute value of the three translation and three rotation parameters provided by SPM. Average movement amounted to a few millimeters in translation and a fraction of a degree in rotation, often due to a sudden movement (e. g. , cough), whereas the child was quiet most of the time. Each fMRI volume was then visually inspected one by one. Using the Matlab toolbox ArtRepair (http://cibsr.
To this aim, we computed the average activity evoked by each of the 4 nonsymbolic image categories (tools, houses, faces, and bodies) relative to rest in the RR voxels and in each of the 5 initial sessions. We submitted these data to an ANOVA with categories and sessions as within-subject factors. A main effect of category was present For each child, we subdivided left ventral temporal voxels into the ROI formed by those selective for a given category during the final sessions 6 and 7 (category > others, p < 0. 001) and the remaining voxels. We then went back in time and asked how these 2 sets of voxels differed in their responsivity to the various categories of stimuli in the preceding sessions 1-5. Each plot shows the difference in activation evoked by a given stimulus (e. g. , words) relative to other categories (excluding words), in voxels within and outside the specified ROI.
Most importantly for theories of reading acquisition, the data allowed us to retrospectively examine what the VWFA voxels were responding to prior to reading acquisition. Reading did not recruit voxels that were selective for faces but systematically encroached on slightly more lateral sectors of cortex, within the left occipitotemporal sulcus, in a region that was not strongly selective to any category but did respond more to tools than to other visual categories. The emergence of the VWFA occurred without radically altering the preferences or the topographical organization of ventral visual responses to faces, bodies, houses, or tools, although at the group level we detected a positive correlation between reading performance and the amount of right fusiform activation to faces. Those results suggest that the VWFA emerges quickly, at a fixed and constrained location within a well-organized mosaic of preferences, and by superimposing itself onto existing preferences while minimally altering them.
Fast emergence of the VWFA The first question that our study aimed to answer is how quickly the VWFA emerges in children during the course of reading acquisition. In our subjects, the VWFA was undetectable (B) Temporal evolution across sessions of the r coefficient for words and for the average of nonsymbolic images (tools, bodies, faces, and houses) in the left ventral mask. (C) and (D), same as (A) and (B), restricted to the visual word form area. (E) and (F), similarity of the word-induced activation pattern in each ROI selective for a given category relative to others, respectively within the left and right ventral masks. All comparisons within versus between category are significant, except those indicated as ns. Underlying data found in S4 Data. FFA, fusiform face area; ns, nonsignificant; ROI, region of interest; VWFA, visual word form area.
Our results suggest that, by 2 to 4 months after school onset, a change in the volume of selective activation to words is already detectable (Fig  5 ) and is associated with the establishment of a stable pattern of subject-specific activation for words relative to other categories (Fig  7 ). This finding is entirely coherent with another longitudinal study  [17]  in which left occipitotemporal responses emerged in fMRI and event-related potentials once preschoolers had been exposed to a few weeks of training with the GraphoGame, a software game that teaches grapheme-phoneme correspondences. Lochy et al. (  2016 ) and Maurer et al. [21, 55, 56]  likewise observed a rapid growth of left-lateralized event-related potentials evoked by letter strings (N170) in the course of reading acquisition, both in children and in adults acquiring a new script.
Developmental trajectory of the reading circuit Most previous studies of reading acquisition have either been cross-sectional  [14, 23, [65] [66] [67]  or longitudinal with few data points spread over a long time period, typically 6 months to several years  [19, 21, 68, 69]. Here, we aimed to clarify the developmental trajectory of the VWFA and other areas of the reading circuit by obtaining detailed single-subject measurements spaced every 2 months around the onset of literacy. Theories of child development have contrasted several accounts of the emergence of functional brain specialization (reviewed in  [70] ): according to a maturational account, the adult reading circuit should progressively emerge at its normal location, with increasing size and intensity as increasingly larger populations of neurons become tuned to written words.
Acknowledgments The authors thank the children and their parents for their enthusiasm and their numerous questions during this long and demanding study, as well as the medical and MRI team of  UNI-ACT at Neurospin , who help to welcome the children every 2 months. We are also grateful to  Isabelle Denghein  for the flat maps presented in Fig 3 and S4 and S5 Figs and to  Koke Oteo  from www. mijnfotoshoot. nlfor his generosity in providing free pictures to be used as stimuli.
ACKNOWLEDGEMENTS The authors thank the referee for his/her/their comments and suggestions towards improving the manuscript. The research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement N • 670519: MAMSIE). The computational resources and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by the  Research Foundation -Flanders (FWO)  and the  Flemish Government -department EWI.
Gene duplication is a fundamental mechanism that determined genetic novelty in species by providing new material for gene function innovation  [12, 13]. The majority of duplicated genes will vanish over time, while a smaller subset may evolve into novel or more complex functions  [14]. Currently, more than 3400 eukaryotic genomes are available in public databases  [15]. The availability of such a huge amount of genomic data provides relevant source of information to investigate the major mechanisms that shaped genome evolution, such as genome/gene duplication, diploidization, reshuffling, gene reduction, fusion/fission  [16] [17] [18] [19]. These studies are mainly based on sequence comparisons and aim to the detection of orthologs and paralogs from available gene annotations  [8]. Consequently, the reliability of the results from these approaches is strongly related to the quality of gene and protein coding regions annotations  [20].
The co-expression analysis (Fig. 4 ) highlighted that cluster 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22 show a similar profile for the three correspondent stages of the two species. By contrast, some of the clusters highlighted an opposite pattern between the two species. For example, about 500 BBHs, from clusters 2 and 4, showed an evident grapevine preferential expression in the tested fruit stages and conversely, 800 BBHs, from clusters 19 and 24, showed a preferential expression in tomato. About 27% of the BBHs belonging to the first group (BBHs of cluster 2 and 4) and 43% of the BBHs belonging to the second group (BBHs of the cluster 19 and 24) come from the consensus dataset (homology confirmed by gene-transcript-protein levels). Details about the number of loci per cluster and the BBHs datasets included are reported in Additional file 5.
The analysis of the 24 clusters of BBHs based on the Gene Ontology enrichment highlighted that the preferentially expressed genes in grapevine fruit stages from clusters 2 and 4 are mainly related to cell wall biogenesis and organization as well as to transmembrane transporter (see Additional file 6). Further investigations on these genes could provide relevant information for understanding specificities of the maturation process of climacteric versus non-climacteric species. By contrast, no enriched GO classes were detected for preferentially expressed BBHs in tomato fruit stages from clusters 19 and 24. In order to highlight possible differences associated with the metabolic processes of the two species, the BBHs were also mapped onto the metabolic pathways using the Mapman (v 3. 6. 0) software.
Discussion In order to provide a representative cross-comparison between S. lycopersicum and V. vinifera gene loci, we considered the most used gene annotations from tomato  and grapevine, i. e. iTAG 2. 4 for tomato and CRIBI v1 for grapevine. These are also the most recent versions available in public comparative databases (Table  3 ). We performed all-against-all similarity searches using the proposed multilevel approach. We updated the functional descriptions and functional domains assignments for both collections to favor comparisons, and organized all the data into a dedicated web accessible platform to support user friendly explorations of the entire collection. The multilevel approach here proposed consisted in independent reciprocal cross-comparisons of genes, translated mRNAs and proteins.
In order to provide an updated and uniform description of the two gene annotations of tomato and grapevine, and to contribute to the functional annotations, we compared the two species transcripts collections with the Swiss-Prot database. Based on a blast similarity searches (see Methods section for details), we associated each transcript with the functional annotation of the most similar protein, also considering the EC number in case of similarities to enzyme. We exploited the transcripts versus protein similarity search instead of comparisons between protein sequences to overcome possible protein coding gene miss-annotation. Indeed, in association with what we previously discussed for orthologs and paralogs detections, similarity searches based exclusively on protein sequences can be limited by the lack of a correct and exhaustive definition of the protein coding regions in preliminary gene annotations from recently sequenced genomes  [25, 30].
4C) shows a relationships inferred by our approach only at gene and transcript level, not at protein level. The need of not neglecting such relationships in the list of orthologs is confirmed also by the high similarity level in terms of both score and e-value here detected, confirming the usefulness of a multilevel approach when single protein based comparisons fail to detect relevant similarities hiding relationships at genome level. Homology comparisons in EggNog  [45]  are based on the Smith-Waterman algorithm  [59] , combined with composition-based score adjustment such as in BLAST  [21]. After grouping in-paralogous proteins, orthology between proteins are assigned by joining triangles of reciprocal best hits initially involving three different species  [45]. Unfortunately, the EggNog database provides lists of multi-species clusters, making a gene by gene comparison of the orthologs collections impossible.
More sequences are added to the group if there are sequences in the two proteomes that are closer to the corresponding seed ortholog, than to any sequence in the alternative proteome. These members of an orthology group are called inparalogs  [44]. Also the Inparanoid database, however, provides lists of group of orthologs, making a gene by gene comparison of the orthologs collections impossible. A substantial difference between all the presented pipelines and our approach is their exclusive use of protein sequences to infer the orthology relationships. Our multilevel analysis, instead, exploits all the levels of a gene information content detecting a consensus and a comprehensive approach to get a more reliable overview of gene loci similarities especially in still preliminary gene annotations.
As an example, 4465 of our partially confirmed sequence similarities based on transcript and/ or genes do not reveal a sequence similarity when the encoded proteins are compared. Moreover, in our analysis, the definition of networks of related genes allows to investigate not only which are the associated genes, but also the level at which the relationships were confirmed (Fig. 7 ). None of the four considered resources, finally, allows the immediate identification of species-specific genes for each species or presents reconciled functional descriptions on the same protein database reference to straightforward compare genes and their annotations. Since orthologs are expected to share the same functionality in the species in which they are detected, this should be also confirmed, presumably, by a similar expression patterns.
The subsequent mapping of the BBHs on the metabolic pathways confirmed the presence of grapevine preferentially expressed genes associated with cell wall, and showed the presence of a group of tomato preferentially expressed genes associated with ethylene (see Additional file 7) and protein G. This supports the idea that evolutionary changes affected the pattern of expression, presumably differentiating some specific processes, although preserving a sequence similarity, allowing in fact to still infer an orthology relationships. Divergence in gene regulation, rather than on the protein encoding loci, should be also investigated as one relevant driving force affecting species differentiation  [62] [63] [64]. We organized a public database which contains all the presented data, available at  [51].
Data sets Gene, transcript and protein collections for S. lycopersicum  [33]  (release iTAG 2. 4) and V. vinifera  [66]  (release CRIBI V1) were downloaded from the Solanaceae Genomics Network website  [67]  and from the CRIBI website  [68] , respectively. Predefined Orthology relationships for tomato and grapevine were downloaded from Ensembl Plants (Release 39)  [27] , Gramene (build 57)  [31] , EggNog (v4. 5)  [45]  and Inparanoid (v8)  [44].
Orthology and paralogy prediction All-against-all sequence similarity searches between S. lycopersicum and V. vinifera genes, mRNAs and proteins collections were independently performed using the appropriate BLAST analysis (BLASTn, tBLASTx and BLASTp, respectively) (v2. 2. 29+)  [21]. With the aim of including the widest significant subset of relationships, all the similarity searches were carried out setting an expect-value (e-value) cut-off at e -3. In order to identify orthologs based on each analysis, we implemented a dedicated Python (v3. 3. 3) based software evolving from Transcriptologs  [25] , a transcriptome-based approach to predict orthology relationships. The software takes as input the output of the different BLAST based similarity searches, accordingly to what described in Additional file 1.
Networks construction and species-specific gene collections Networks of genes based on their similarity relationships were built. The network construction process relies on the BBHs and on the putative paralog genes detected with a stringent e-value threshold of e -50 , defining related gene-families between the two species. This procedure organizes all the groups of connected components into distinct graphs by using the NetworkX package (v1. 9) (see Additional file 3)  [70]. Each node represents a gene and each edge represents an orthology or paralogy relationship based on gene and/or mRNA and/or protein similarity. Cytoscape software (v3. 4)  [71]  was used to show the networks organization. The tomato-specific and grapevine-specific genes were defined filtering out all the genes, mRNAs and proteins that share at least one orthology relationship with the counterpart, or a paralogy relationship with an ortholog.