dataesr/scientific-paragraphs-categorization huggingface dataset:
Task and stimuli All but CH2 participants performed an overt speech production task. Participants P2, P3 and P5 were asked to read aloud short French sentences, which were part of a large articulatoryacoustic corpus acquired previously (Bocquelet et al 2016b) and made freely available (https://doi. org/10. 5281/zenodo. 154083). Participant P5 also took part in a protocol involving speech perception, where she was exposed to the sound of computer-generated vowels delivered by a loudspeaker positioned about 50 cm on her left. This second dataset involving P5 also contains speech production segments as she was interacting with the experimenters. The first (speech production) and the second (speech perception and production) datasets are labeled as sessions a and b, respectively. Participant CN was asked to listen and repeat aloud individual sentences of an ancient Chinese poem.
Rat recording In order to consider data recorded in a different condition, we also analyzed an electrophysiological recording obtained over the left auditory cortex of a ketamine (90 mg/kg)-xylazine (2 mg kg -1 ) anesthetized 600 g adult Sprague Dawley rat using a 64-electrode micro-ECoG array (E64-500-20-60-H64; NeuroNexus Inc. USA). This data was obtained in compliance with European (2010-63-EU) and French (decree 2013-118 of rural code articles R214-87 to R214-126) regulations on animal experiments, following the approval of the local Grenoble Table  1. Assessment of the presence or absence of contamination for 20 datasets from 5 different research centers. The durations reported refer to the time segments kept for analysis after the data selection step (detailed in section 2. 4. 1). For all datasets, the estimated risk to wrongly consider the existence of contamination (P, see section 2.
4. 5) is reported in the last column. Note that this value depends on the frequency range considered in the contamination matrix (indicated in the second to last column). Contaminated datasets are marked with an asterisk (P < 0. 05). Their contamination matrices are presented in figures 7, 8 and 9. ethical committee ComEth C2EA-12 and the ministry authorization 04815-02. A bone screw was used for the ground and a stainless-steel wire inserted below the skin ahead of Bregma was used for the reference. Signals were acquired using the RHD2000 acquisition system and two 32-channel RHD2132 headstages (Intan Technologies, USA). To avoid any possible crosstalk inside the Intan acquisition system, the sounds delivered to the rat were recorded on an independent CED Micro1401 (Cambridge Electronic Design, UK).
Both acquisition devices were interfaced and synchronized by the Spike2 software with the IntanTalker module (CED programs) and signals were digitized at 33. 3 kHz. The time jitter between sound and neural signals was checked to be below 2 ms. Pure tones (3 ms rise, 167 ms plateau and 30 ms fall times) with frequencies ranging from 0. 5 to 16 kHz were presented with pseudo-random interstimulus intervals of 1. 8-2. 2 s. Sounds were delivered at about 80-90 dB SPL in open field configuration using a MF1-S speaker (Tucker Davis Technology Inc. USA). The three lowest tone frequencies that were further considered in the present study were 0. 5, 1 and 2. 5 kHz.
In vitro recordings in PBS solution In order to further identify the origin of acoustic contamination, an in vitro setup was used. A 24-electrode ECoG array (DIXI Medical SAS) was placed in a plastic container filled with 1X phosphatebuffered saline (PBS). Two of the electrodes were used as the ground and reference electrodes, respectively. All electrodes were plugged into a clinical headbox (64-Channel Splitter Box, Blackrock Microsystems, USA) connected by shielded cables to a FEA and a NSP used for human recordings. A microphone was placed close to the plastic container. The audio data was acquired using the same hardware as for human P2-P3-P5 recordings (see section 2. 1. 3). Data was acquired at 30 kHz. Sounds were delivered either through a loudspeaker (M-Audio BX5-D2 loudspeaker used with participant P5) located about 1-2 m from the electrodes and recording chain, or very locally using a MF1-S speaker (Tucker Davis Technology Inc.
Data pre-processing A built-in analog band-pass filter was applied to the data recorded with the NSP (0. 3-2500 Hz for 10 kHz sampling rate and 0. 3-7500 Hz for 30 kHz sampling rate). Common average reference (CAR) was applied on the recording of participants P3 to lower the influence of the intrinsic spatial correlation of LFPs stemming from the close spacing of the electrodes of the Utah array, and of participant D1 for whom the reference electrode was itself highly contaminated making in turn all channels highly contaminated. CAR was also applied on participant P2 recording to analyze its effect as shown in figure  2(d). In these cases, the average neural signal, computed on the signals of all selected electrodes, was subtracted to each electrode signal. To center audio signals, a moving average was computed over 1 s windows and subtracted.
Spectrogram computation In the present study, a spectrogram refers to the timevarying power spectral density (PSD) computed over a recording channel. For all analyses, spectrograms of neural and audio data were computed using shorttime Fourier transform with 200 ms time windows (after Hamming windowing). The window overlap was chosen to obtain spectrograms sampled at 50 Hz. The mean sound PSD (or mean power spectrum) of a recording was computed by averaging the audio spectrogram over all time samples (after selection like described in section 2. 4. 1). For display purposes, the spectrograms in figures 2(a), (b) and 10(c) were computed with higher frequency and time resolutions. These spectrograms were also z-scored within each frequency bin using artifact-free data segments containing the displayed extracts.
Objective assessment of contamination To determine whether a dataset is contaminated, we developed a specific statistical approach. First, a contamination matrix was built by computing the maximum of the correlation matrices across all electrodes. Each maximum was thus computed separately for each element of the matrix (i. e. each pair of frequency bins). Then, we evaluated the values on the diagonal of the contamination matrix in relation to the rest of the matrix. The mean value on the diagonal was computed to obtain a contamination index. To evaluate the statistical significance of this original index, a distribution of surrogate index was then built by computing the mean diagonal 10 000 times on as many shuffled versions of the contamination matrix. Each shuffled matrix was built by randomly shuffling either the lines or the columns.
Neural decoding ECoG data from participants P2 and P5 (session a) were used to predict acoustic mel-cepstral coefficients of overt speech produced by the participants. Both participants were visually presented with a series of short sentences or vowel sequences written on a screen positioned about 50-100 cm in front of them, and asked to repeat them overtly. The number of sentences was 118 for participant P2 and 150 for participant P5, corresponding to an overall duration of 230 and 329 s of speech, respectively. The participants' speech audio signals were decomposed into 25 melcepstral coefficients using the SPTK toolkit (mcep function). Spectrograms of the ECoG data were computed as described in paragraph 2. 4. 3 but at a rate of 100 Hz. Neural features were the spectrogram amplitudes in 10 Hz bands (i.
7 to increase visibility of diagonal correlations). The white lines are a visual aid to assess the presence of high correlations on the diagonal. They are parallel to the diagonal of the matrix and cross the X and Y axes at 75 Hz. (b) Statistical assessment of contamination for the same datasets. The mean of the diagonal of the contamination matrix (vertical colored bar, red when statistically significant, green when not) is compared to the distribution of such values in 10 000 shuffled contamination matrices (see section 2. 4. 5 for details). The estimated risk to wrongly consider the existence of contamination (P) is shown in square brackets for each dataset. exacerbated after common average re-referencing of the ECoG signals.
As detailed in section 2. 4. 5, the statistical criterion we propose compares the diagonal of the original contamination matrix to the diagonal of shuffled matrices. This allows to distinguish whether the high correlations are (1) limited to the diagonal, in which case they are significantly higher in the original matrix compared to the shuffled ones or (2) part of a larger patch of high correlations, in which case they are not significantly higher in the original matrix compared to the shuffled ones. We used this approach to evaluate 20 different speech production and sound perception datasets. Figure  7 (a) shows the contamination matrices for six speech production datasets and figure 7(b) the corresponding statistical evaluation by randomization. According to our statistical criterion P (see section 2.
4. 5), four datasets were contaminated (red vertical bars in figure 7(b)) and two were not (green vertical bars). The audio-neural correlations that were observed for participants P2 (figure  2 ) and P3 (figure  3 ) can be observed on the diagonal of the contamination matrices (figure  7 (a), top row). Very clear and highly statistically significant contaminations appear in these cases. Weaker but still visible and statistically significant contaminations also appear for P5 session b (ECoG) due to frequencies above 200 Hz and for D1 due to frequencies around 100 Hz. In recordings of participants P2, P3 and D1, lines of high correlation can also be observed outside the diagonal. They are due to the correlations between the voice's fundamental frequency and its harmonics. Figure  8 (a) shows the contamination matrices for eight sound perception datasets, five of which are contaminated according to the corresponding statistical evaluation shown in figure  7(b).
Contamination-specific patterns appear on the matrices for participants P5 (ECoG) and D2 (ECoG), for the p. m. session of participant CN (ECoG), for the rat recording (µ-ECoG) and for the in vitro experiment (ECoG). No contamination was found for participants P5 (UEA), CH1 (ECoG) and the a. m session of participant CN. The full assessment of all datasets is summarized in table 1.
Potential influence of contamination on speech decoding We assessed the potential influence of contamination of electrophysiological signals by sound on the performance of neural decoding to predict acoustic features of produced speech. For this purpose, we considered ECoG data from participant P2 and P5 (session a). As can be seen in figure  9  (panels a-b), the P2 recording was found to be contaminated in the 0-200 Hz range while the P5a recording was not. It should also be noted that the fundamental frequency of both participants exceeds 90 Hz and thus acoustic contamination cannot be observed below. The decoding performances of models using only neural features from 0 to 90 Hz were evaluated (figure  9 , panels c-d, top graphs). Models using neural features up to 200 Hz were then evaluated, and compared to the previous one in order to estimate the contribution of 90-200 Hz features (figure  9 , panels c-d, bottom graphs).
Discussion Data considered in this study includes human and animal recordings during speech production and/or sound perception tasks. Using these different setup conditions, we observed statistically significant correlations between the spectrograms of electrophysiological and simultaneously recorded audio signals. These correlations occurred at frequencies most present in the sound signal, thus encompassing the high-gamma range and also frequencies above 300 Hz. This contamination effect was observed in recordings from ECoG and µECoG grids and intracortical micro-electrode arrays, interfaced with different data acquisition systems. The phenomenon was observed in data collected by three out of the five centers worldwide who participated in this study. Thus, this variety of situations suggests that acoustic contamination of neural signals, although not systematic, is a widespread phenomenon.
However, for in vitro PBS recordings, the contamination was small at the level of the FEA connector and more important at the level of the headbox and cables situated upstream (figure  13 ). Moreover, the same setup used with different participants (or even the same participant in two different sessions) may sometimes exhibit contamination and sometimes not (as here with participant CN). Thus, a given setup should likely not be granted for clean once and for all. Rather, we suggest that any dataset should be objectively tested for any contamination before being considered for further analysis. The extent to which the acoustic noise spectrally overlaps with the measured brain activity depends on the nature of the sound and on the studied activity. In the case of ECoG recordings during speech production paradigms (see section 3.
While these results demonstrate what could be seen as a relatively trivial contamination of electrophysiological recordings by surrounding acoustic signals, the implication of the study is important in both the neuroscience and neuro-engineering domains. In particular, the common investigation performed here on several datasets acquired in various research places worldwide (France, China, Germany, Switzerland, and the United States) suggest that decoding analyses should be performed after having excluded any potential microphonic effect. Yet and importantly, this report does not question the existence of relevant physiological neural information in high-gamma frequency signals underlying speech production or sound perception. Several groups have shown that spectral features of imagined speech or silent articulation can be predicted to some extend from low or high-gamma signals recorded in participants that are not overtly speaking  (Pei et al 2011 , Ikeda et al 2014 , Martin et al 2014 , 2016 , Bocquelet et al 2016a , Gehrig et al 2019 , Anumanchipalli et al 2019).
Acknowledgments This work was supported by the  FRM Foundation  under grant No  DBS20140930785 , the  French National Research Agency  under Grant Agreement No. ANR-16-CE19-0005-01  (Brainspeak), and by the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreements No. 732032  (BrainCom), No. 696656  ( GrapheneCore1 ), and No. 785219  ( GrapheneCore2 ), as well as the  Swiss National Science Foundation  under Grant No. 167836. C K is supported by the  German research foundation , Grant  KE1514/2-1. K J M is supported by  NIH /CTSA/KL2 TR002379  ( NCATS) Grant , the  Brain and Behavior Research Foundation , and the  Brain Research Foundation.
We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 Database, Vol. 2020, Article ID baaa017 genes, 106 RNA entities, 2 ions and 7 simple molecules.
The RA map serves as an interactive knowledge base but also as a template for omic data visualization. Omic datasets can be superimposed on the map, pinpointing affected areas in different samples. Furthermore, the map is a good starting point for the development of a computational model, providing an intermediate step between a conceptual, mechanistic graph and an executable mathematical model  (12). The article comprises three parts. In the first part, we present the process of constructing the RA map, highlighting the most critical pathways. In the second part, we transform the RA map into a state-of-the-art interactive knowledge base for the disease, which interfaces with various databases for content annotation and enrichment analysis of experimental results. In the third part, we use bioinformatics tools such as BioInfoMiner (16) (https://bioinfominer.
Construction of the RA map CellDesigner (  18 ) is a structured diagram editor for the creation of gene-regulatory and biochemical networks. Networks are drawn using the Process Description visual language of SBGN, and are stored using the Systems Biology Markup Language (SBML)  (20) , a standard for representing models of biochemical and gene-regulatory networks. In a CellDesigner diagram, nodes represent species like proteins, genes, complexes and other molecules, and the edges denote the interaction between the nodes, which can be activation, inhibition, catalysis and state transition among other possible interactions  (21, 22). A comprehensive molecular interaction map for RA was published in 2010  (23)  with information derived from highthroughput data combined with interaction data from the KEGG pathway database (24-27) (http://www.
genome. jp/ kegg/pathway. html). The researchers of this study used 28 published studies for the construction of the first RA Figure  1. Workflow for the construction and use of the RA map. The assembly of the signalling and molecular pathways implicated in RA involves exhaustive manual curation and information mining from literature, public databases and repositories and the use of the software CellDesigner  (18). The RA map contains mechanisms reported in the most recently published studies, after validation from RA experts. The map can be transformed into an online interactive knowledge base using the platform MINERVA  (19). Functional enrichment and topological analysis is possible using the software BioInfoMiner (  16 ) (https://bioinfominer. com) and Cytoscape  (17) , respectively. map that included experiments performed in different cell types/tissues/fluids such as the peripheral blood mononuclear cells, synovial fibroblasts, macrophages, chondrocytes, synovial tissues, bone, blood, and synovial fluid (Figure  S1 , Table  S1 ).
We added proteins, genes and cellular phenotypes to the map and used databases like KEGG pathway (24-27) (http://www. genome. jp/kegg/pathway. html), and Ingenuity Pathway Analysis (IPA)  (28)  to retrieve connections among them, where it was not possible to retrieve the links directly from the corresponding articles. All added factors were discussed thoroughly with RA experts before addition to the map and advice was taken for the best possible representation of their mechanism of action.
Annotation and curation criteria We carried out an exhaustive literature search for new proteins, genes and other molecules involved in the pathogenesis of RA. Relevant keywords and key phrases like 'Pathogenesis of RA', 'Cytokines in the pathogenesis of RA', 'Therapeutic targets in RA' among many others were used to filter the literature abstracts and studies in PubMed and Google Scholar. Along with it, we used peer reviewed articles concerning RA and searched their bibliographies to mine relevant information. We focused only on studies based on cells, fluids and tissues of human origin using small-scale experiments, in an attempt to limit false positives from gene expression data used to construct the first RA map. New RA mediators were added and referenced with at least two PubMed IDs. However, we made some exceptions during the building of the map.
SBGN standards and process description map validation The SBGN (  15 ) is a standard for the visual representation of biological/biochemical processes as networks. Three types of SBGN languages cover different ways to represent biological networks, Process Description (PD), Entity-Relationship (ER) and Activity Flow (AF)  (30). The RA map is a PD map showing the detailed biological processes implicated in RA. We systematically checked the compliance to the SBGN standard. For keeping the diagram compact and avoid repeating the same pattern multiple times (activation of protein production from an empty set), we used the translation connectors. VANTED (Visualisation and Analysis of Networks containing Experimental Data)  (31) , is a framework for systems biology applications with functionalities ranging from network reconstruction, data visualization, integration of various data types to network simulation using systems biology standards for visualization and data exchange.
We used SBGN-ED (an add-on for VANTED for editing, validating and translating of SBGN maps)  (32)  to validate our SBGN PD encoding of the RA map. As this tool works with SBGN-ML file format, we utilized the CellDesigner to SBGN converter (https://royludo. github. io/cd2sbgnml) for converting the CellDesigner XML file into SBGN-ML format and subsequently import the file to VANTED for further analysis.
Web-based MINERVA map The RA map is available as an online interactive map using MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform  (19). MINERVA is a web service that supports curation, annotation and visualization of molecular interaction networks in the SBGNcompliant format. MINERVA provides automated content annotation and verification, along with mapping of drug targets and overlaying experimental data on the visualized networks. Automated annotations (HGCN) and curator's annotations for every component and reaction are displayed in the left panel (see Figure  3A ). The user can also visualize cell-specific data based on curated overlays or analyse patients' omic datasets (see Figure  8 ). Moreover, MINERVA provides an interface for interrogating several other databases such as DrugBank (33) (https:// www.
drugbank. ca/), CHEMBL (34) (https://www. ebi. ac. u k/chembl/), CTD  (35)  (http://ctdbase. org) and miRTarBase (36) (http://mirtarbase. mbc. nctu. edu. tw). Overlays We provide three different types of overlays with the RA map. The first type corresponds to cell, tissue and fluid specific overlays. The RA map is a global map, integrating data and information from various sources. As a result, it has reactions and components that come from different cell or tissue types. We have grouped the sources into seven distinct groups that we provide as overlays. The groups are synovial fibroblasts, synovial tissue, peripheral blood mononuclear cells, blood, synovial fluid, chondrocytes and macrophages (Table  S1 ). These overlays allow visualizing cell or tissue-specific interactions and molecules.
BioInfoMiner analysis The algorithm performs a topological analysis of semantic networks, derived from ontologies (Gene Ontology  (37, 38) , Human Phenotype Ontology  (39)  and Mammalian Phenotype Ontology  (40) ) and pathway databases with hierarchical structure, like REACTOME  (41) (42) (43). It employs a graph-theoretical method that corrects the annotation bias of community ontologies, performs enrichment analysis to assess the over-representation of terms and ranks the related genes according to their connectivity in the corrected semantic network  (44, 45). Systemic processes are clusters of terms that share maximum semantic similarity among them, but minimal similarity among other clusters. The highly ranked genes are those associated with many systemic processes, and thus, they are considered hub genes in the semantic network, assuring cross-talking among distinct, orthogonal (interindependent) processes.