dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of data and materials The structural domain predictions supporting the conclusions of this article as well as the version of the multi-scale community detection (MSCD) algorithm code used for all analyses and a wrapper MATLAB script allowing to reproduce the described pipeline can be downloaded at http://perso. ens-lyon. fr/ benjamin. audit/StructuralCommunities/. The latest version of MSCD MATLAB source code is freely available at http:// www. gipsa-lab. fr/~nicolas. tremblay/index. php?page=downloads.
RNArchitecture is a database that provides a comprehensive description of relationships between known families of structured non-coding RNAs, with a focus on structural similarities. The classification is hierarchical and similar to the system used in the SCOP and CATH databases of protein structures. Its central level is Family, which builds on the Rfam catalog and gathers closely related RNAs. Consensus structures of Families are described with a reduced secondary structure representation. Evolutionarily related Families are grouped into Superfamilies. Similar structures are further grouped into Architectures. The highest level, Class, organizes families into very broad structural categories, such as simple or complex structured RNAs. Some groups at different levels of the hierarchy are currently labeled as 'unclassified'.
The classification is expected to evolve as new data become available. For each Family with an experimentally determined three-diemsional (3D) structure(s), a representative one is provided. RNArchitecture also presents theoretical models of RNA 3D structure and is open for submission of structural models by users. Compared to other databases, RNArchitecture is unique in its focus on structurebased RNA classification, and in providing a platform for storing RNA 3D structure predictions.
The need to compare and classify protein structures has led to the development of commonly used databases and hierarchical structural classifications, such as SCOP (3) and CATH  (4). Thanks to these and other computational resources, comparing and classifying proteins was demonstrated to be of crucial importance for function inference and it continues to be used in many applications. For RNA structures several databases have been developed. However, some of them, including RNABase  (5)  and SCOR  (6)  are not updated any more, while others, that implement automated clustering, such as HD-RNAS  (7)  or RNA Structure Atlas  (8) , provide information only about very close similarities. Currently, no comprehensive database of RNA families exists that provides information about structural similarities and dissimilarities at a level analogous to superfamily or fold in SCOP, or to topology or architecture in CATH.
DATABASE CONTENT The RNArchitecture database has been developed to provide a comprehensive classification system that describes relationships between RNA families, with a focus on structural similarities (Figure  1 ). RNArchitecture uses and organizes information from several databases, including Rfam  (9)  and Protein Data Bank  (10) , and introduces a SCOP/CATH-like hierarchical classification. The central level of classification is Family, which has been largely taken from the Rfam database. RNArchitecture includes 2688 Families of which only 2. 54% (74 Families) have a structural model solved experimentally. Families group together evolutionarily related RNAs with conserved structure and detectable sequence similarity. Families whose members exhibit structural variation are further subdivided into Subfamilies.
On the other hand, Families with similar structures and functions, and likely to be evolutionarily related (or at least converged to fulfill the same role in essentially the same way) are grouped into Superfamilies (which are more extensive than clans currently defined by Rfam). Superfamilies that share a similar core structure, but which are not clear homologs, are grouped into Architectures. The highest level, Class, organizes the data into very broad structural and functional categories. The coarse-graining of sec-ondary structure in RNArchitecture is not much used at the 'evolutionary' level of classification into Superfamilies, but mostly at higher levels of Architecture and Class, which group RNAs according to structural similarities, in analogy to fold and class in SCOP. RNArchitecture also serves as a repository of theoretical models of RNA 3D structures, open for submission from users.
Dataset acquisition and processing The dataset of Families (in each case including the multiple sequence alignment, the consensus secondary structure, and the consensus sequence) was constructed based on Rfam (release 12. 3), and expanded to include known RNA families that are currently not covered by Rfam. In particular, we included group I and group II intron Families which are not included in Rfam as complete full-length sequences. Group I intron sequences and alignments were obtained from GISSD: Group I Intron Sequence and Structure Database  (11) , and group II intron sequences were obtained from The Database for Bacterial Group II Introns  (12)  and the alignments were generated by us. These Families were further subdivided into Subfamilies, as proposed in these databases. The consensus structures were used to calculate reduced shape representations, similarly to the RNA shapes approach  (13, 14).
We used an in-house program to convert full secondary structure representation to Level1 reduced representation, in which a single pair of brackets corresponds to two uninterrupted segments of paired residues, and then to Level2 reduced representation, in which a single pair of brackets corresponds to two series of mutually paired segments that can be interrupted by bulges and loops, but are not interrupted by residues paired with other segments (Figure  2 ). Level2 reduced representations were compared and the most common shapes were used, along with the 3D structural information, wherever available, to define the Architectures. For each Family, a representative member was identified to illustrate exemplary structural information. First, for each Family with a member that has an experimentally determined 3D structure (according to Rfam annotation), the structural coordinates were obtained from the Protein Data Bank  (10).
For these structurally characterized RNAs, sequences were extracted from the PDB file using do x3dna  (15) , and secondary structure was annotated using ClaRNA  (16). Additional data, such as header, title, molecule, and information about ligands, cofactors and hetero atoms, were derived from the associated PDB file using pypdb  (17). Second, for each Family without an experimentally determined 3D structure, we selected the closest sequence to the consensus sequence, in terms of sequence identity. Its secondary structure was assigned based on the Rfam consensus secondary structure. In a few selected cases (e. g. , in the case of RNAs with additional structural and functional information), a different family representative was identified, and secondary structure was assigned manually, based on literature and database information.
Search Options for database searching and querying have been implemented, including search using PDB IDs, names of Class, Architecture, Superfamily, Family, Subfamily, Rfam accession number and RNA type. The database also includes a powerful and dedicated RNA shape search tool for the exploration of the Families that contain a particular architectural motif, e. g. , a simple pseudoknot, by querying the database with '([)]'. Currently such searches are either not possible or very difficult to make with other databases of RNA families or RNA structures.
Database implementation An object-relational PostgreSQL database management system is used to store all the information. The web server is implemented for the Linux operating system under Django. Programs to export, to import and to visualize have been implemented in Python 3, including our in-house package rna-pdb-tools (freely available https://github. com/ mmagnus/rna-pdb-tools/). The secondary structure visualizations are generated with VARNA (21), the tertiary structure visualizations are rendered with JSmol  (22).
Future prospects The number of experimentally determined RNA molecules is increasing rapidly, in line with recent discoveries and growing interest in RNA functions. The number of experimentally determined RNA structures is also growing, albeit at a much slower rate. Therefore, RNArchitecture is expected to be updated systematically with new information. In particular, we envisage updating it, following all major updates of the Rfam database. The catalog of Families is intended to be systematically expanded, to include additional sequences e. g. , from the RNAcentral database  (23). Structural information will be updated with new experimentally determined RNA structures, as well as with improved theoretical models. The generation of 3D structure representatives for all Families that are predicted to form stable 3D structures is an ongoing process and we intend to expand the current dataset of models to maximize the coverage.
We encourage the users of RNArchitecture to submit models of RNA 3D structures, preferably ones with experimental support, to be included in the database, as well as suggestions for improvement of the existing classification. For the next release, we plan to expand the structural repertoire to include multiple structures (e. g. , for different members of the same Family, for different functional/structural states of the same RNA, or for alternative theoretical models) and structural superpositions. New structural data will be used to update and potentially revise the classification system, in particular, the assignment to Superfamilies and Architectures, and the diversification of Classes. Another envisaged next step of the RNArchitecture database development is to link it with databases on other aspects of RNA structure, such as RMDB  (24).
ACKNOWLEDGEMENTS We would like to thank all members of the  Bujnicki laboratory  and all participants of the RNA Puzzles experiment for Nucleic Acids Research, 2018, Vol. 46, Database issue D205 fruitful discussions. We are indebted to the authors of primary databases and services, whose content could be reused or linked to by RNArchitecture, in particular, Rfam, PDB and RNAcentral.
Dependencies The code provided is a stand-alone C++ code, available at https://doi. org/10. 5201/ipol. 2017. 179. It uses the C++ standard template library extensively. The user can choose between the single-threaded implementation and its parallel version. The single-threaded version does not rely on any external libraries. The parallelization is done through OpenMP 3  , a standard API for shared memory multiprocessing programming. The code was tested successfully on Ubuntu 16. 04 and 14. 04 with g++ 4. 7. The compilation is performed through the CMake build system.
Individuals with an ambiguous sex code, excessive missing genotypes (failure rate >2%), and outlying heterozygosity (heterozygosity rate of 3 SDs from the mean) were also excluded. Identity by state similarity was used to estimate cryptic relatedness for each pair of individuals using the PLINK software (pngu. mgh. harvard. edu/∼purcell/plink/). Closely related individuals with identity by descent >0. 1875 were eliminated from the subsequent B. Xu et al. analysis. Population stratification for the GWAS was examined using multi-dimensional scaling (MDS) analysis with HapMap populations as reference groups. Individuals with divergent ancestry (from Utah residents with ancestry from northern and western Europe) were excluded through visual inspection of the first two components. Supplementary Figure  S1  shows that our sample is genetically homogeneous (overlapping with the CEU population).
Acknowledgements This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  FP7  projects  IMAGEMEND ( 602450 ;  IMAging GEnetics  for MENtal Disorders),  AGGRESSOTYPE  ( 602805 ) and MATRICS ( 603016 ), the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  Medical Research Council Grants  '' Developmental pathways into adolescent substance abuse '' (93558) and  Consortium on Vulnerability  to Externalizing Disorders and Addictions [c-VEDA] ( MR/N000390/1 ), the Swedish funding agencies VR, FORTE and FORMAS, the  Medical Research Council  and the  Wellcome Trust  ( Behavioural and Clinical Neuroscience Institute, University of Cambridge ), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-1 ,  SM 80/7-2 ,  SFB 940/1 ), the  National Institutes of Health, U.
Method Participants Participants of the study were tested at eight assessment centres (London, Nottingham, Dublin, Mannheim, Berlin, Hamburg, Paris, and Dresden). Local ethics research committees at each site approved the study. On the day of assessment, written consent was obtained from the parent or guardian, and verbal assent was obtained from the adolescent. A detailed description of the recruitment and assessment procedures and inclusion/exclusion criteria have been published elsewhere  18. After quality control, complete and reliable datasets were available from 1129 participants during the reward anticipation phase of the monetary incentive delay task (MID), and from 1227 participants for the successful inhibition in the stop signal task (SST). Demographics information is displayed in Table  1.
Next 32 or 64 channels silicon microelectrodes (Neuronexus technologies, Ann Arbor MI) were slowly inserted into in deep layers of the cortex (depth 600-1200 mm; lowering speed ~1 mm/hour). Probes had either eight shanks each with eight staggered recording sites per shank (model Buzsaki64-A64), or four shanks with two tetrode configurations in each (model A4 Â 2tet-5mm-150-200-312-A32). Neuronal signals were high-pass filtered (1 Hz) and amplified (1,000X) using a 64-channel amplifier (Sensorium Inc. , Charlotte, VT), recorded at 20 kHz sampling rate with 16-bit resolution using a PC-based data acquisition system (United Electronic Industries, Canton, MA) and custom written software (Matlab Data Acquisition Toolbox, MathWorks) and stored on disk for further analysis.
Analysis of spiking network simulations The spiking network model was implemented in C++, and the code is available at ModelDB (https:// senselab. med. yale. edu/ModelDB, Jercog, 2017). Model equations were numerically integrated using second-order Runge Kutta, where integration step was defined as dt = 0. 05 ms and the total simulation length was 5000 s (50 simulations of 100 s each, where data from first D and U detected periods for each simulation was discarded to eliminate possible initial transient effects). For the analysis of U and D statistics and the rate dynamics in the spiking network simulations we used methods analogous to those applied on the experimental data. In addition, U and D period detection was obtained by applying the HMM  (Chen et al. , 2009)  on 100 randomly selected neurons, using the same parameters as for the experimental data.
Separation of EVs on sucrose gradients First, 500 µL of lEVs or sEVs (corresponding to a total protein content of 120 µg) was loaded on top of a sucrose density gradient in an SW40 tube. The density gradient was prepared by layering successive sucrose solutions (31 fractions of 350 µL) of decreasing density (2. 0 M to 0. 4 M) on top of 700 µL 2. 5 M sucrose, as previously described  [26]. After ultracentrifugation at 200,000 × g for 18 h at 4°C, 12 fractions of 1 mL were collected. Fraction 1 (1 mL from the top of the gradient) was not further analysed, since proteins sticking to the tube wall were recovered in this fraction in an unspecific manner (data not shown). Sucrose density was measured on an aliquot of each fraction using a refractometer. Five millilitres of phosphate-buffered saline (PBS) was added to each 1 mL fraction collected, before recentrifugation at 100,000 × g for 70 min at 4°C.
Nanoparticle tracking analysis EV samples were diluted in sterile NaCl 0. 9% before nanoparticle tracking analysis (NTA). NTA was undertaken using the NanoSight NS300 (Malvern Instruments, Malvern, UK) equipped with a 405 nm laser. Ninety-second videos were recorded in five replicates per sample with optimised set parameters (the detection threshold was set to 7 and 5, respectively, for lEVs and sEVs). Temperature was automatically monitored and ranged from 20°C to 21°C. Videos were analysed when a sufficient number of valid trajectories was measured. Data capture and further analysis were performed using the NTA software version 3. 1. At least three independent biological samples of each EV subtype were analysed, and the presented results correspond to the mean of the five videos taken for a given biological sample.
Data processing and bioinformatics analysis The Mus musculus database was downloaded from the UniProt KB database site (www. uniprot. org, January 2015, 24,721 protein entries). This database was merged and, in conjunction with reverse and contaminant databases, searched by X!Tandem (Sledge Hammer version 2013. 09. 01. 1, http://www. thegpm. org/tandem/) using X!TandemPipeline (version 3. 3. 3) developed by the PAPPSO platform (http://pappso. inra. fr/bioinfo/). Enzymatic cleavage was declared as a trypsin digestion with one possible miscleavage. Cys carboxyamidomethylation and Met oxidation were set to static and possible modifications, respectively. The precursor mass was 10 ppm and the fragment mass tolerance was 0. 02 Da. A refinement search was added with similar parameters except for semi-trypsic peptide and possible N-ter proteins.
In this analysis, a difference of five spectra between each EV subpopulation for a protein was set as the filter. The second method, XIC analysis, involves the quantification of the ion precursor (surface area) of a given peptide, the intensity of which is correlated with the protein abundance. The XIC-based approach has higher accuracy, sensitivity and specificity as it can detect low peptide quantities in a sample, but it cannot support qualitative variations in a given protein (presence/absence). MassChroQ software  [30]  was used for the XIC approach. All the previously identified peptides were selected for quantification. XIC extraction was performed using a peak detection threshold between 30,000 and 50,000 and a range of 10 ppm. XIC analyses were performed on log 10 -transformed data.
The distribution of EV markers is then studied in the collected fractions following lEV and sEV sucrose density gradient separation. By this means, we observed that lEVs were mainly recovered in density fractions between 1. 20 and 1. 22 g/mL, according to caveolin-1 and flotillin-2 signals (Figure  2(C) ). The sEVs floated at lighter densities, between 1. 14 and 1. 20 g/mL, as illustrated by CD9, CD63 and Alix detection (Figure  2 (D), in agreement with densities and markers generally described in the literature  [16]. Mfge8 presented a large pattern of expression over the lEV and sEV-derived fractions (Figure  2(C, D )). By contrast, Alix was undetectable in lEV-derived sucrose fractions (data not shown) and CD63 was only weakly expressed (Figure  2(C) ). Taken together, these results show different biochemical properties for lEV and sEV subpopulations associated with differential expression of classical EV markers and distinct sucrose flotation properties.
Adipocyte-derived lEVs and sEVs exhibit a specific protein signature To further characterise the protein composition of adipocyte EV subpopulations, we performed label-free quantitative proteomic analysis from four biological replicates of total EVs, lEV and sEV populations using MS. We identified 533 proteins, corresponding to 408 groups of proteins (0. 268% FDR peptides) from all EV subgroups (Dataset S1). This qualitative analysis allowed the identification of 217, 480 and 168 proteins in total EV, lEV and sEV fractions, respectively (Dataset S2). A more conservative filtering was applied to consider proteins present in EV fractions based on the detection of at least two distinct peptides in two different biological replicates (see the Methods section). A list of the identified proteins, categorised according to gene ontology (GO) annotations in the UniProt KB database, is presented in Dataset S3.
Identification and validation of specific protein markers of adipocyte-derived lEVs and sEVs To identify and validate specific EV markers of lEVand sEV-derived adipocytes, we performed a quantitative analysis of our proteomic data based on SC and XIC analysis of selected peptide ions (see Methods), two methods of analysis that have been shown to correlate well with protein abundances in complex biological samples  [39, 40]. Based on SC and XIC statistical analysis of the results (p < 0. 05), we were able to identify proteins that display significant enrichment in the lEV (Table  1 ) and sEV (Table  2 ) fractions. Proteins that were specifically enriched in the lEV fractions were more abundant than in sEVs; this is likely to be related to the higher diversity of proteins recovered in the lEV fractions (Dataset S2).
The SC ratio corresponds to the median of SC detected for one protein in the four independent replicates of sEVs to the median of SC detected for the same protein in the four independent replicates of lEVs. Proteins were considered enriched in sEVs when p < 0. 05 following XIC and/or SC analysis. Proteins exclusively found in sEV samples are shown in bold. Those p values not found to be statistically different are in grey. Acc. no. , accession number; NA, not available. association of the tetraspanins CD9 and CD63 with sEVs, whereas the membranous proteins flotillin-2 and 1 were preferentially retrieved in lEVs. These results highlight the ability of in vivo adipocytes to secrete non-negligible amounts of EVs, which can be separated into small and large subpopulations. The lEVs   (A, B ) Western blot analysis of selected proteins identified by the quantitative analysis of proteomic data.
In the light of our study, showing that lEVs carry many proteins from diverse origins and are specifically enriched in metabolic enzymes (mainly of mitochondrial origin), one can advance the hypothesis that they may modulate metabolic pathways in recipient cells, such as fatty acid oxidation, a feature already attributed to adipocyte exosomes  [21]. Moreover, high annexin V binding of adipocyte-derived lEVs could predict specific pro-coagulant properties of this fat cell EV subtype, in agreement with the detection of tissue factor proteins only in the lEV fractions (see Dataset S2), and may participate in the cardiovascular complications associated with obesity. Therefore, our results highlight the need to distinguish adipocyte-derived subtypes to delineate their respective functional properties.
PCA of identified proteins for each EV subtype shows a clear separation of lEVs from sEVs and total EVs. (C-E) Gene ontology (GO) terms of protein families enriched in lEV and sEV preparations. Identified proteins in lEV and sEV fractions, respectively, were classified according to cellular component (C), biological process (D) and molecular function (E) categories defined by the GO consortium using the Panther classification system database. (C) Cellular component GO analysis reveals an enrichment of membrane and organelle components for lEVs, and in the extracellular region and extracellular matrix for sEV. (D,E) GO categories with greater than five-fold enrichment are presented for lEV and sEV populations following classification according to biological process (D) or molecular function (E).