dataesr/scientific-paragraphs-categorization huggingface dataset:
• hco:MR_Node: This concept denotes any hco:Gray_matter_part in brain images where a connection assessed by diffusion tractography begins or ends. This concept is an adaptation of the node 13  term, defined in the FMC thesaurus  (Swanson and Bota, 2010) , dedicated to MRI datasets. • hco:White_matter_part: Any cell part cluster constituting a part (i. e. , fma:constitutional_part_of, fma:regional_part_of ) of a white matter region. • hco:MR_Route: Any physical route of white matter fiber bundles reconstructed by diffusion tractography that links two hco:MR_Node in brain images. This concept is an  The "fma:" prefix denotes terms originating from the Foundational Model of Anatomy. The "hco:" prefix denotes terms defined in the HCO. adaptation of the route 14 term, defined in the FMC thesaurus  (Swanson and Bota, 2010) , dedicated to the brain images domain as diffusion tractography does not probe the path of white matter bundles directly, but water diffusion in brain.
Neuroimaging Data and Preprocessing The analysis was performed for five subjects of the NMR public database  (Poupon et al. , 2006). This database provided T1 (voxel size 0. 9 × 0. 9 ×1. 2 mm) and diffusion-weighted datasets (voxel size of 1. 9 × 1. 9 ×2. 0 mm) acquired with a GE Healthcare Signa 1. 5 Tesla Excite II scanner. The diffusion datasets presented a high angular resolution (HARDI) based on 200 directions and a b-value of 3000 s/mm 2. The use of a twice refocusing spin echo technique was used to compensate the echoplanar distortions due to eddy currents  (Reese et al. , 2003) , at the first order. Susceptibility artifacts were corrected using a phase map acquisition. See "MRI acquisitions" on Figure  5. The Freesurfer pipeline was applied on T1-weighted datasets producing cortical  (Desikan et al.
An automatic linear (12 DOF) registration based on the correlation ratio between the JHU white matter tractography atlas and the Freesurfer structural space was computed using the FLIRT tool. See "JHU white matter atlas" on Figure  5. A registration between the Freesurfer structural space and the diffusion dataset space was computed using a rigid registration (6 DOF) based on mutual information implemented in FLIRT. The registration was performed considering the average of five B0 volumes (i. e. , volumes with b-value = 0) and the brain volume in the Freesurfer structural space.
Automatic Annotation of MRI Connectomics Datasets The HCO was populated with instances describing fiber bundles assessed by diffusion tractography between different gray matter regions of the five healthy subjects. This was achieved using a Java program based on OWL API. First, each gray matter region was represented as an instance of the hco:Gray_matter_part class. If the gray matter region was a high resolution cortical parcel, then the cortical parcel was related to the instance of the overlapping gyrus via the fma:regional_part_of object property. If two instances of fma:Anatomical_structure were found to be nearest neighbors then they were related together using the hco:continuous_with object property. Finally, each binary region-wise connectivity matrix (11 × 1004) was used to encode the connectivity reconstructed by diffusion tractography between the 11 cortical regions defined in the first target mask and the 1004 brain regions defined in the second target mask (cf.
Semantic Annotation of MRI Connectomics Datasets The HCO and its NEURO-DL-FMA module contained 811 classes, 11 object properties, no data property and a mean of 2321 instances per subject. The HCO was classified in less than 6 s per subject using the FaCT++ reasoning engine on a dual core processor, 3. 06 GHz, 3. 9 Go RAM workstation. The reasoning engine was used both to keep ontologies in a logically consistent state, and to infer new axioms between brain regions. An example of the use of some part-whole, spatial and connectivity relationships using the HCO terms is provided on Figure  6. The part-whole relationship was expressed using the fma:regional_part_of object property denoting the fact that the high resolution cortical parcel 10 (i. e. , hco:s1_gray_matter_of_left_superior_frontal_gyrus_10) was a regional part of the gray matter of the left superior frontal gyrus of the subject 01.
Different studies tried to convert the entire FMA (Protege frames version) into different OWL versions and to use reasoning engines  (Golbreich et al. , 2006; Golbreich et al. , 2013). Our strategy was to extract from the OWL Full version of FMA a "view" of the brain constituted only of entities which were parts of the neuraxis, following  (Turner et al. , 2010; Shaw et al. , 2011). Though the latter study achieved brain image analysis using the DXBrain software  (Detwiler et al. , 2009) , no DL reasoning engine was used, however. The HCO was designed in taking into account both the reference ontology in neuroanatomy FMA and the conceptual framework of structural connectivity FMC. If NEURO-DL-FMA was clearly grounded on a subset of FMA, however no concept of the BAMS 18 ontology was used in the HCO.
Experimental Work The experimental work was achieved in order to illustrate how the semantic annotation and the reasoning about MRI connectomics datasets could enhance the analysis of connectivity patterns present in this data. Connectivity was assessed using a probabilistic tractography method in the living human brain. It is worth saying that tractography results should be interpreted with care  (Jones et al. , 2013). Indeed, anatomical connectivity denotes the white matter fibers which physically connect brain regions, whereas connectivity assessed by diffusion tractography relies on water diffusion as an indirect probe of axon geometry. In fact, tractography infers fiber bundles pathways through the diffusion field in assuming that the direction of least hindered diffusion is aligned with axons  (Jbabdi and Johansen-Berg, 2011).
green) the cortical parcels matching the query 1 (resp. 2) criteria (cf. Table  5 ). When a parcel was the result of both queries, it was represented in orange. Column 4 represents in blue the cortical parcels matching the query 3 criteria (cf. Table  5 ). On column 3, the same color code was kept for the green and orange cortical parcels. BAMS database. Furthermore, although probabilistic tractography methods do not estimate the connection strength between two regions (as tracer-based methods actually do), they allow assessing the confidence in the pathway of least hindrance to diffusion. This is a major advantage of probabilistic tractography over deterministic tractography, since the latter cannot provide such confidence cues. So, although tractography is limited by several biases, it is currently the only available tool that gives us the opportunity to investigate anatomical connectivity non invasively and in the living human brain.
Automatic Inferences on Brain Connectivity Automatic annotation of brain images with terms of an ontology and subsequent analysis using reasoning engines enable powerful information retrieval thanks to the high level representation of the image content embedded in the ontology. As an illustration, when an investigator queries through the HCO all cortical parts of the orbitobasal segment of the right frontal lobe which are connected to some medial BA6 parts via fiber bundles assessed by diffusion tractography, the reasoning engine takes advantage of both class-level knowledge (what are the gyri included in the orbitobasal segment of the right frontal lobe cortex?) and instance-level facts derived from image evidence (which data instantiate some regional parts of these gyri classes?). Different initiatives used automatic inferences based on structured knowledge in order to represent cerebral connectivity: (1) Neurolex and (2) KEfED (Knowledge Engineering from Experimental Design) approach.
Interpretation of such tract-tracing experiments aims at describing connections between different brain regions. Spatial reasoning was used especially to process the part-whole and the overlaps relationships between regions. Basic geometric features were imported from the BAMS neuroanatomical ontology of the rat into PowerLoom, a first-order logic knowledge representation and closed-world reasoning system. Thus, the authors demonstrated how connectivity matrices could be inferred through the use of spatial reasoning and the modeling of the tract-tracing experiments using a KEfED approach. HCO and its NEURO-DL-FMA module differ from these approaches because they were expressed in the W3C standard OWL language and used the OWL DL description logics sublanguage. Moreover, the FaCT++ reasoning engine was used both to ensure the satisfiability of the ontologies, and to infer new axioms using transitive part-whole, spatial relationships, and connectivity relationships assessed by diffusion tractography.
As a result, complex queries (cf. Table  3 ) could be formulated directly via the "DL Query" tab of the Protégé ontology editor, in a more expressive way than using the SPARQL language. An interesting initiative dedicated to diffusion tractography called the "White Matter Query Language"  (Wassermann et al. , 2013)  used a textual language in order to express anatomical descriptions of white matter tracts. In selecting streamlines from a whole brain deterministic tractography using both anatomical structure terms describing where streamlines end or pass through, relative position terms of streamlines from other anatomical structures and finally logical operations terms, Wassermann et al. used different expressions to define some association, projection and commissural tracts  (Wassermann et al.
Conclusion and Perspectives In this article we have described a neuroanatomical ontology dedicated to human connectomics called the Human Connectomics Ontology (HCO) that could represent brain regions and connectivity assessed by diffusion tractography in the living human brain. Moreover, an experimental work was achieved in order to show how the HCO could be effectively used within an information system to express complex queries concerning MRI connectomics datasets and process them using a DL reasoning engine. This approach can facilitate comparison of data across scales, modalities and species. Future work will consist in the development of a visualization module in order to display macro-connectome at different levels of granularity in a matrix or a network form. This module could leverage the reasoning engine to retrieve connections assessed by diffusion tractography between different gray matter regions.
MALDI-TOF identification An isolated colony was deposited in duplicate on a MALDI-TOF target to be analysed. A matrix of 1. 5 μL (saturated solution of α-cyano-4-hydroxycinnamic acid diluted in 500 μL acetonitrile, 250 μL of acid tri-fluoro-acetic to 10%, and 250 μL of HPLC water) was used on each spot. This solution enables ionization and desorption of the homogeneous biological sample with which it crystallizes. The analysis was performed by a Microflex (Bruker Daltonics, Leipzig, Germany) device, and protein spectra were compared with those of the hospital database. A score was assigned indicating the reliability of the identification of the bacteria; above 1. 9 was considered proper identification. Conversely, if the bacterium was not referenced in the database, sequencing the 16S rRNA was used to achieve the correct identification  [34].
Phylogenetic analysis Phylogenetic analysis based on 16S rRNA of our isolates was performed to identify its phylogenetic affiliations with other near isolates, including other members of the genus Virgibacillus. MEGA 6 software (http://www. megasoftware. net/mega. php) allowed us to construct a phylogenetic tree. Sequence alignment of the different species was performed using Clustal W (http://www. clustal. org/clustal2/), and the evolutionary distance was calculated with the Kimura two-parameter model  [35].
Genome annotation and comparison Open reading frames (ORFs) were predicted using Prodigal  [36]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [37]  and the Clusters of Orthologous Groups (COGs) database using BLASTP. The tRNAScanSE tool  [38]  was used to find tRNA genes, whereas ribosomal RNAs were found using RNAmmer  [39]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [40]  and TMHMM  [41] , respectively. ORFans were identified if their BLASTP E value was lower than 1e-03 for alignment length greater than 80 aa. If alignment lengths were smaller than 80 aa, we used an E-value of 1e-05.
Such parameter thresholds have already been used in previous works to define ORFans. Artemis  [42]  was used for data management and DNA Plotter  [43]  for visualization of genomic features. Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [44]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used an in-lab pipeline software named Marseille Average Genomic Identity (MAGi) to calculate the average genomic identity of gene sequences (AGIOS) among compared genomes  [45]. Briefly, this software combines the Proteinortho software  [45]  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
Genomes from the genus Virgibacillus and closely related genera were used for the calculation of AGIOS values. Here we compared the genome sequences of Virgibacillus senegalensis strain SK-1 T (GenBank accession number PRJEB1962) with those of Virgibacillus kekensis strain YIM kkny9 (NR_042744. 1), Virgibacillus albus strain YIM 93624 (NR_109613. 1), Aquibacillus salifodinae WSY08-1 (AB859945. 1), Virgibacillus halodenitrificans DSM 10037 (AY543169), Thalassobacillus devorans MSP14 (JX518269. 1), Halobacillus dabanensis HD 02 (HG931924. 2), Halobacillus kuroshimensis DSM 18393 (AB195680. 1), Thalassobacillus devorans strain XJSL7-8 (GQ903447. 1), Bacillaceae bacterium EFN-4 (EU817569. 1), Virgibacillus marismortui strain M3-23 (GQ282501. 1), Halobacillus salinus strain GSP59 (AY505517. 1), Virgibacillus alimentarius J18 (GU202420), Pseudomonas aeruginosa PAO1 (NR_074828.
Phenotypic description Strain SK-1T was isolated in February 2014 (Table  1 ) by aerobic culture on a homemade culture medium at 37°C after 48 hours. No significant MALDI-TOF result for the strain SK-1 T against our MALDI-TOF database was obtained, suggesting that our isolate was not a previously known species. We added the specter of SK-1T strain to our database (Fig. 1 ). The gel view allowed us to observe the spectral differences with other members of Virgibacillus genus (Fig. 2 ). PCR-based identification of the 16S rRNA of our new isolate (GenBank accession number LK021111) yielded 96. 3% 16S rRNA sequence similarity with the reference Virgibacillus kekensis (GenBank accession number NR042744), the phylogenetically closest validated Virgibacillus species (Fig. 3 ). After growth for 24 hours on our homemade culture medium at 37°C, the surface colonies were circular, greyish, shiny and smooth, with a diameter of 1 to 2 mm.
Genome comparison The draft genome of V. senegalensis SK-1 T   (AB681753. 1), Virgibacillus byunsanensis strain ISL-24 (FJ357159. 1), Virgibacillus massiliensis strain Vm-5 (HG931931. 1) and Paenibacillus polymyxa strain KCTC3717 (AY359637. 1). GenBank accession numbers are indicated in parentheses. Sequences were aligned using Clustal W (http://www. clustal. org/ clustal2/), and phylogenetic inferences were obtained using maximum-likelihood method within MEGA 6 (http://www. megasoftware. net/mega. php). Paenibacillus polymyxa was used as outgroup. Scale bar = 0. 005% nucleotide sequence divergence.
19 Nucleotide transport and metabolism H 93 2. 49 Coenzyme transport and metabolism I 102 2. 73 Lipid transport and metabolism P 202 5. 40 Inorganic ion transport and metabolism Q 74 1. 98 Secondary metabolites biosynthesis, transport and catabolism R 448 11. 99 General function prediction only S 287 7. 68 Function unknown - 352 9. 06 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on total number of protein-coding genes in annotated genome. NMNI Seck et al. Virgibacillus senegalensis sp. nov.
The fact that a single matrix inversion is sufficient to simulate the vector a from known A and b (instead of a full ODE system solution) is of major importance, since it enables a radical simplification of parameter estimation in the inverse problem in the RPP labeling experiment. We illustrate the feasibility of this approach by simulating the propagation of a label in Escherichia coli metabolism upon addition of uniformly labeled glucose according to RPP label input. The metabolic network was taken from Supplementary Data 3 of  [21]  corresponding to the reference network from the Fig 5 of the cited paper. It is defined in an FTBL file which stands for Flux Table format. This format was introduced in  [3]  and originally used as input format for 13CFlux software. In this plain text file, several sections describe the network topology, measurement data and some additional information.
24 amino acid fragments resulting from 11 distinct amino acids. Note that using uniformly labeled substrate as sole carbon source makes the data redundant as the mean label on any fragment of a molecule is the same as the mean label for the whole molecule. We kept all the fragments in the original dataset to stay close to real experimental conditions and because redundant measurements make it possible to reduce the confidence intervals (CI) of estimated parameters. All metabolic pool sizes were considered as known. After an ODE simulation of the label propagation in a RPP experiment with t 1 = t 2 = 30 min and "sampling" at 2-min intervals, a centered Gaussian noise with a standard deviation of 0. 01 was added to the mean label to simulate an experimental noise. The chemostat was simulated as running for a period of 10 hours and the data were "sampled" during the last hour.
This work was supported by  Agence Nationale de la Recherche  (' METABOHUB '  ANR-11-INBS-0010-01 )  Région Midi-Pyrénées , the  European Regional Development Fund , the  French Ministry for Higher Education and Research , the SICOVAL, the  Institut National de la Recherche Agronomique , and  IBiSa. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Results A target-based microRNA complementary network. To evaluate how microRNAs could act together on cellular processes, we intended to infer networks based on microRNA target sharing. We used the genome-wide DIANA-microT v3. 0 prediction database  13  , comprising 555 human microRNAs, 18,986 genes and nearly 2 million interactions. We considered only Homo sapiens interactions and did not take into account any score but rather used all available information. In consequence, around 60% microRNAs are predicted to have between 2,000 and 5,000 different target sites and four microRNAs (miR-495, miR-548c-3p, miR-590-3p and miR-603) are predicted to exhibit affinity towards more than 10,000 target sites -which represents around 6000 genes (Supplementary Figures  S1a  & S1b ). Furthermore, 193 genes are targeted by more than 293 microRNAs (Supplementary Figure  S1c ), which is the 99 th quantile of the microRNA-mediated target regulation histogram.
A Benjamini-Hochberg (BH) correction was used to account for multiple testing hypothesis correction  29  , even though we did not consider the corrected P-values as pure decision-making values (see the Methods section for a brief discussion). Only the genes that were shared by at least 50% of all members of the clusters were used in this analysis (4 microRNAs). Using the package TopGO  30  to calculate the GO enrichment, we found enrichment in mRNA processing, transcription and gene expression on the biological process (BP) level of GO (Table  1 : assorted club 1), for which BH corrected P-values (P BH ) ranged from 10 25 to 10 28. When considering less generic annotations, we found enrichment for protein modification (P BH 5 3 3 10 24 ), endosomal transport (P BH 5 5 3 10 24 ) and regulation of locomotion (P BH  5 10 22  ).
On the other hand, only miR-612 is expressed in normal colorectal mucosa (Supplementary Figure  S7  and S8 ). The three microRNAs seem rarely co-expressed in the different tested tissues. This is consistent with the phenotypic outputs that we observed after ectopic overexpression of these microRNAs in RPE1 cells. Differential expressions of the three microRNAs between healthy and cancerous tissues were then carried out on Gene Expression Omnibus (GEO) data. Interestingly, miR-940 was found consistently downregulated in breast cancer (log fold change of 20. 26 on GSE44124, -0. 53 on GSE31309, and -0. 57 on GSE38867). Moreover, two out of the three datasets tested showed a statistically very significant differential expression (Figure  5 ). These data are consistent with our previous results on the role of miR-940 in cell dynamics and might shed light on a probable role of this microRNA in breast cancer progression.
We chose to limit this study to the non-conserved version of the TargetScan algorithm, as no real proof indicates that targets which are conserved across species are more accurate than non-conserved targets  45  , and also to reduce the number of false negative prediction. Due to the differences in the prediction algorithms, the two databases predict different microRNA targets. These differences in target prediction are further illustrated in Supplementary Figure  S9 , where we observe that the coverage of microRNA targets between the two databases is -on averageonly approximately 60% (meet/min, Supplementary Figure  S9 ). The coverage is slightly higher when considering only the members of the assorted clubs but is still below 70%. The most covered microRNA is miR-513a-3p, with a meet/min value of 84%, whereas the least covered microRNA is miR-543, with a meet/min value of 59% (Supplementary Figure  S9a ).
The 11 hubs identified by DIANA-microT were compared with the two prediction algorithms. Interestingly, the two assorted clubs are connected with density profiles that are almost identical in both networks (Supplementary Figure  S12a  and b ). This proves that the connections between the microRNAs are robust across database changes. A major difference is that the two clubs are now connected in the TargetScan network. This connection is made via miR-548c-3p (Supplementary Figure  S12c ). Importantly, when looking at gene ontology enrichment for the two assorted clubs with TargetScan predictions, the results are equivalent to DIANA-microT target ontology enrichment (Supplementary Tables  XII  and XIII ). Surprisingly, even more significant P-values are observed for the targets predicted by TargetScan for the assorted club 2 (typically by one order of magnitude), again with a focus on GTPase signalling.
For example, only one target is validated for miR-612 in the latest miRTarBase release v4. 5  49  , and none of those in miRecords v4 are validated  50. Not only do the predictions have a high false positive rate, but the coverage between different algorithms is Expression of miR-940 on three datasets of human breast cancer taken from GEO (GSE38867, GSE44124 and GSE31309). The expression of the microRNA is consistently downregulated in breast cancer tissues on the three experiments. On two out of three microarray sets, miR-940 is differentially expressed with high significance based on limma analysis (p-Value , 0. 001). also low (approximately 60% between TargetScan v6. 2 and DIANA-microT v3: Supplementary Figure  S9 ). Regardless of the aforementioned issues, the networks built independently with the two algorithms gave almost equivalent results, both in terms of network architecture and in terms of hypotheses for the biological implications of the two groups of interconnected hubs.