dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis and quantifications of smFISH images Quantification of the brightness of transcription sites was made with a modified version of HotSpot 40. This is a user-friendly MatLab software that allows to quantify smFISH signals in very noisy images, unlike FISH-quant  53, 54. Briefly, the user can navigate in 2D or 3D bicolor images to manually select transcription sites and single RNAs, and the script finds the local maxima and fits 2D or 3D gaussians to the selected transcription spots. The images of single RNAs are averaged and also fitted to 2D or 3D gaussians, and the integrated intensity of the transcription sites are divided by the intensity of average of single RNAs, yielding the exact number of molecules present at transcription sites. In this work, quantifications were done in 2D on MIPs using 100-500 single RNA per image to generate the average.
HotSpot is available on GitHub (https://github. com/muellerflorian/hotspot). To calculate initiation rates, we assumed that initiation occurs at random with a constant rate. This produces a uniform distribution of RNA polymerases along the SPX1 gene following arithmetic series. Because the MS2 and SPX1 probes are distributed homogeneously along the corresponding pre-mRNAs, incomplete nascent RNAs are labelled with only a fraction of the probes. If one assumes that the pre-mRNAs immediately leave the transcription site once polymerases reach the 3'-end of the gene (in agreement with experimental data), the barycenter of the polymerase distribution is the middle of the gene, and the average brightness of a nascent pre-mRNA is thus half that of a full-length pre-mRNA. In this case, the number of polymerase on the gene, Npol, is thus twice the brightness of transcription sites, expressed in number of full-length pre-mRNA (noted TSRNA).
15 NA; 0. 6 mm DT). Z-stacks were made every 3 minutes with a scan size of 100-150µm (0. 5µm step size; ~200 steps per Z-stack). The data was analyzed with the ImageJ software using the TrackMate plugin to track transcription sites on 2D MIPs. The intensity of SPX1 transcription sites was assessed for several nuclei per sample. In each nucleus, the mean intensity of the nuclear background was subtracted to the maximum intensity of SPX1 transcription site, and the resulting intensities were plotted against time. Figure Legends.
Garcia  for critical reading of the manuscript. We express our gratitude to the  Dr J. M. Escudier  for the synthesis of the SPX1 set of fluorescent probes. We acknowledge the  MRI imaging facility  (belonging to the  National Infrastructure France-BioImaging  supported by the  French National Research Agency ,  ANR-10-INBS-04 ), and the ZoOM platform (supported by the  Région Provence Alpes Côte d'Azur , the  Conseil General of Bouches du Rhône , the  French Ministry of Research , the  Centre National de la Recherche Scientifique  and the  Commissariat à l'Energie Atomique et aux Energies Alternatives ). The authors declare no competing interests.
Introduction The coronavirus disease 2019 (COVID-19) pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already resulted in the infection of over 250 million people worldwide, of whom almost 5 million have died (https://covid19. who. int, accessed on  05. 10. 2021). This global challenge motivated researchers worldwide to an unprecedented effort towards understanding the pathology to treat and prevent it. To date, over 170 thousand articles have been published in relation to  or sars-cov-2[Title/Abstract]", accessed on 01. 07. 2021). The reported molecular pathophysiology that links SARS-CoV-2 infection to the clinical manifestations and course of COVID-19 is complex and spans multiple biological pathways, cell types and organs  (Gagliardi et al, 2020). Resources such as Protein Data Bank repository of viral protein structures (preprint:  Lubin et al, 2020)  or the IMEx coronavirus interactome  (Perfetto et al, 2020)  offer detailed information about particular viral proteins and their direct binding partners.
However, the scope of this information is limited. To gain insight into the large network of molecular mechanisms, knowledge from the vast body of scientific literature and bioinformatic databases needs to be integrated using systems biology standards. A repository of such computable knowledge will support data analysis and predictive modelling. With this goal in mind, we initiated a collaborative effort involving over 230 biocurators, domain experts, modellers and data analysts from 120 institutions in 30 countries to develop the , an open access collection of curated computational diagrams and models of molecular mechanisms implicated in the disease  (Ostaszewski et al, 2020). The C19DMap is a constantly evolving resource, refined and updated by ongoing biocuration, sharing and analysis efforts.
Enrichment using knowledge from databases and text mining The knowledge of COVID-19 mechanisms is rapidly evolving, as shown by the growth of the , a source of manuscripts and metadata on COVID-19related research (preprint: Lu  Wang et al, 2020). CORD-19 currently contains almost 480,000 articles and preprints, over ten times more than when it was introduced more than a year ago (accessed on 05. 10. 2021). In such a quickly evolving environment, manual biocuration needs to be supported by automated procedures to identify and prioritise crucial articles, molecules and their interactions to be included in the C19DMap. Potential knowledge sources for such assisted biocuration are interaction and pathway databases, especially those with dedicated COVID-19 content  (Licata et al, 2020; Perfetto et al, 2020).
Their structured and annotated information on protein interactions or causal relationships was generated using separate biocuration guidelines and formats. Nevertheless, their comparable identifiers and references to source publications make them plausible building blocks for constructing the C19DMap (see Materials and Methods). Text mining approaches are another source of information that can direct the biocurators towards the most recent and relevant findings. They automatically extract and annotate biomolecule names and their interactions from abstracts, full-text documents or pathway figures  (Bauch et al, 2020; Hanspers et al, 2020). Networks of molecule interactions constructed by text mining can carry substantially more noise than the contents of interaction databases but offer broader literature coverage.
Second, we developed routines for programmatic queries of the interaction databases, providing automated and reproducible exploration of the selected databases. This was realised using data endpoints: Application programming interfaces (API) for INDRA, AILANI or Pathway Studio, and SPARQL for BioKB. This automated exploration retrieved functions, interactions, pathways or drugs associated with submitted queries, e. g. gene lists. This way, otherwise timeconsuming tasks such as an assessment of completeness of a given diagram or search for new literature evidence were automated.
Adaptive immune response While the interactions of SARS-CoV-2 with various host cell types are vital determinants of COVID-19 pathology  (Hui et al, 2020; Mason, 2020; Ziegler et al, 2020) , the current C19DMap represents an infection of a generic host cell. Several pathways included in the map are shared between different cell types; for example, the IFN-1 pathway is active in dendritic and lung epithelial cells and in alveolar macrophages  (Hadjadj et al, 2020; Lee & Shin, 2020; Sa Ribero et al, 2020). Continued annotations of emerging expression datasets  (Delorey et al, 2021)  and other sources of information will allow the construction of cell-specific versions of the C19DMap to provide an integrated view of the effects of SARS-CoV-2 on the human organism. An example workflow to construct such a focused version of the map was proposed in Section "Case study: analysis of cellspecific mechanisms using single-cell expression data".
Exploration of the networked knowledge The diagrams of the C19DMap were curated in a distributed manner across various platforms and tools. In order to coordinate such an effort and get a systematic overview of the contents of the map, we programmatically analysed the content of the diagrams, benefiting from their standard encoding and annotation (see  Materials and Methods). This allowed us to identify crosstalk and functional overlaps across pathways. Then, we linked the diagrams to interaction and text mining databases to fill the gaps in our understanding of COVID-19 mechanisms and generate new testable hypotheses. (Mogensen, 2009). Downstream, IFN-1 activates Tyk2 and Jak1 protein tyrosine kinases, causing STAT1:STAT2:IRF9 (ISGF3) complex formation to promote the transcription of IFN-stimulated genes (ISGs).
Perturbations in these pathways may impair the IFN response against SARS-CoV-2 and explain persistent blood viral load and an exacerbated inflammatory response in COVID-19 patients  (Hadjadj et al, 2020). New crosstalk from interaction and text mining datasets New relationships emerging from associated interaction and text mining databases (see Section "Exploration of the networked knowledge" of the Results) suggested new pathway crosstalk (see Figs  4B  and EV3 ). One of these was the interplay between ER stress and the immune pathways, as PPP1R15A regulates the expression of TNF and the translational inhibition of both IFN-1 and IL-6  (Smith, 2018). This finding coincided with the proposed interaction of pathways responsible for protein degradation and viral detection, as SQSTM1, an autophagy receptor and NFKB1 regulator, controls the activity of cGAS, a double-stranded DNA detector  (Seo et al, 2018).
Novel regulators of protein activity Finally, we identified potential novel regulators of proteins in the C19DMap using interaction and text mining databases (see Fig  4C ). These proteins take no part in the current version of the map but interact with molecules already represented in at least one of the diagrams. An example of such a novel regulator was NFE2L2, which controls the activity of HMOX1 in the context of viral infection  (Kesic et al, 2011). In turn, HMOX1 controls immunomodulatory haem metabolism  (Zhang et al, 2019) , the mechanisms of viral replication, and is a target of SARS-CoV-2 Orf3a protein  (Miao et al, 2020). The suggested NFE2L2-HMOX1 interaction is supported by the literature reports of NFE2L2 importance in COVID-19 cardiovascular complications due to crosstalk with the renin-angiotensin signalling pathway  (Valencia et al, 2020)  and potential interactions with viral entry mechanisms  (Hassan et al, 2020).
Data interpretation and network analysis The projection of omics data onto the C19DMap broadens and deepens our understanding of disease-specific mechanisms, in contrast to classical pathway enrichment analyses, which often produce lists of generic biological mechanisms. Visualisation of omics datasets on the map diagrams creates overlays, allowing interpretation of specific conditions, such as disease severity or cell types  (Satagopam et al, 2016). Datasets projected on the C19DMap can create signatures of molecular regulation determined by the expression levels of the corresponding molecules. Together, multiple omics readouts and multiple measurements can increase the robustness of such signatures  (De Meulder et al, 2018). This interpretation can be extended using available SARS-CoV-2-related omics and interaction datasets  (Bouhaddou et al, 2020)  to infer which transcription factors, their target genes and signalling pathways are affected upon infection  (Dugourd & Saez-Rodriguez, 2019).
Combining regulatory interactions of the C19DMap with such data collections extends the scope of the analysis and may suggest new mechanisms to include in the map. Besides the visual exploration of omics datasets, the network structure of the C19DMap allows extended network analysis of viral-human protein-protein interactions (PPIs)  (Gordon et al, 2020). It can be expanded by merging virus-host with human PPIs and proteomics data to discover clusters of interactions indicating human biological processes affected by the virus  (Messina et al, 2020). These clusters can be interpreted by visualising them on the C19DMap diagrams to reveal additional pathways or interactions to add to the map.
Mechanistic and dynamic computational modelling Diagrams from the C19DMap can be coupled with omics datasets to estimate their functional profiles and predict the effect of interventions, e. g. effects of drugs on their targets  (Salavert et al, 2016). However, such an approach has a substantial computational complexity, limiting the size of the input diagrams. Large-scale mechanistic pathway modelling can address this challenge but requires transformation of diagrams into causal networks, which, combined with transcriptomics, (phospho-)proteomics or metabolomics data  (Dugourd et al, 2021) , contextualise the networks and hypotheses about intervention outcomes. Both approaches provide a set of coherent causal links connecting upstream drivers such as stimulations or pathogenic mutations to downstream changes in diagram endpoints or transcription factor activities.
Case study: analysis of cell-specific mechanisms using single-cell expression data To investigate cell-specific mechanisms of COVID-19, we projected single-cell expression data onto the C19DMap. To this end, we calculated differentially expressed genes (DEGs) for two datasets relevant to the disease. The first dataset describes non-infected bronchial secretory cells  (Lukassen et al, 2020; Data ref: Lukassen et al, 2020) , where we selected DEGs from three different subtypes of secretory cells dubbed (i) secretory1, (ii) secretory2 and (iii) secretory3 (transient) cells. The second dataset describes SARS-CoV-2-infected intestinal organoids  (Triana et al, 2021; Data ref: Triana et al, 2021) , where we selected DEGs from (iv) infected and (v) bystander immature enterocytes from the intestinal organoids infected with SARS-CoV-2.
DEGs (i), (ii) and (iii) can serve as an illustration of pathway activity across normal lung cells, while datasets (iv) and (v) demonstrate a comparison of molecular activity in cells of infected intestinal tissue. These selected datasets are available as overlays in the C19DMap and can be interactively explored, showing cell-type-specific dysregulation of particular diagrams (see Materials and Methods). Visual exploration of the differential expression profiles in the C19DMap revealed that transient secretory cells specifically express molecules associated with the virus replication cycle (TMPRSS2). This suggests that these cells are more susceptible to viral entry than the other types of bronchial secretory cells. Also, the interferon 1 signalling pathway was up-regulated in both secretory1 and transient secretory cells.
However, transient secretory cells showed upregulation of elements up-and downstream of the pathway (IFNAR1-JAK1, and ISG15 or OAS1); in secretory1 cells, the upregulated proteins were downstream (transcription factor AP-1). In the intestinal organoid dataset, the comparison of infected and bystander immature enterocytes confirmed the downregulation of the ACE2 receptor reported by the original article  (Triana et al, 2021; Data ref: Triana et al, 2021) , as visualised in the virus replication cycle diagram. In addition, exploration of other affected pathways may suggest the context of this observationfor instance, the C19DMap demonstrated the differential activity of the pyrimidine deprivation pathway, which could suggest a reduction of transcriptional activity as a host response to the viral infection.
Enrichment analysis of diagrams indicated that mitochondrial dysfunction, apoptosis, and inflammasome activation were dysregulated in infected enterocytes. The enrichment analysis of the cell-typespecific overlays was obtained by the GSEA plugin of the C19DMap. These results can be replicated and examined directly by the users via the visual interface of the C19DMap (see https://covid. pages. uni. lu/minerva-guide/ and Materials and Methods). Case study: RNA-Seq-based analysis of transcription factor activity As discussed above, the diagrams of the C19DMap can be coupled with omics datasets. Here, we highlight how the map systematically reveals the transcription factors (TFs) related to SARS-CoV-2 infection. To do so, we conducted differential expression analysis between SARS-CoV-2 infected Calu-3 human lung adenocarcinoma cell line and controls.
Case study: RNA-Seq-based analysis of pathway signalling The diagrams of the C19DMap allow for a complex analysis of how the infection may affect signalling sequences in encoded pathways based on available omics data. To demonstrate this approach, we applied a mechanistic modelling algorithm that estimates the functional profiles of signalling circuits in the context of omics datasets. We used expression profiles from nasopharyngeal swabs of COVID-19 patients and controls  (Lieberman et al, 2020; Data ref: Lieberman et al, 2020)  to calculate the differential expression profiles and derive the pathway signalling activities (see Materials and Methods). To illustrate this approach, we focused on the results of the analysis of the apoptosis pathway, also shown in Fig  6  and Table EV2. We observed an overall downregulation of both the CASP3 and CASP7 subpathways and an inhibition of the circuit ending in effector protein CASP3, possibly due to the downregulation of AKT1 and BAD and the downstream inhibition of BAX.
Discussion Our knowledge of COVID-19 molecular mechanisms is growing at a great speed, fuelled by global research efforts to investigate the pathophysiology of SARS-CoV-2 infection. Keeping an overview of all the findings, many of which focus on individual molecules, is a great challenge just one year after the start of the pandemic. The C19DMap aggregates this knowledge into molecular interaction diagrams, making it available for visual exploration by life science and clinical researchers and analysis by computational biologists. The map complements and interfaces with other COVID-19 resources such as interaction databases  (Licata et al, 2020; Perfetto et al, 2020) , protein-centric resources (preprint:  Lubin et al, 2020)  and relevant omics data repositories  (Delorey et al, 2021)  by providing a context to particular pieces of information and helping with data interpretation.
In this work, we established a computational framework accompanying the biocuration process, integrating interaction databases and text mining solutions to accelerate diagram building. This allowed us not only to enrich particular diagrams but also to explore crosstalk between them and prioritise key novel regulators of the encoded pathways. Thanks to the interoperability of different systems biology formats, we performed this analysis for diagrams constructed in different biocuration environments, extending current advances in pathway interoperability  (Bohler et al, 2016). Activation levels were calculated using transcriptional data from GSE152075 and the Hipathia mechanistic pathway analysis algorithm. Each node represents a gene (ellipse), a human metabolite/viral protein (circle) or a function (rectangle).
Biocuration platforms Individual diagrams were encoded in systems biology layout-aware formats (see below) by biocurators using CellDesigner  (Matsuoka et al, 2014) , Newt (https://newteditor. org), SBGN-ED  (Czauderna et al, 2010)  and ySBGN (https://github. com/sbgn/ySBGN). This community-based curation was coordinated by sharing curation topics, e. g. relevant pathways or particular SARS-CoV-2 proteins across the community to cover the available literature and identify synergies. Curation guidelines (https://fairdomhub. org/documents/ 661) were established to ensure proper representation and annotation of the key features of the diagrams. Curation guidelines for logical models (Niarakis et al, 2020) were followed. Regular technical reviews of the diagrams were performed following a previously established checklist to harmonise their content.
The diagrams are stored and versioned in a GitLab repository (https://gitlab. lcsb. uni. lu/covid/models). Individual diagrams are visualised in the MINERVA Platform  (Gawron et al, 2016). The entry-level view is based on Fig 2. Reactome  (Jassal et al, 2020)  biocuration efforts initially focused on SARS-CoV-1 and its proteins, and their functions are extensively documented in the experimental literature. Reactome curators were assigned a subpathway from the viral life cycle, a host pathway or potential therapeutics. Curators were supported by an editorial manager and a dedicated SARS literature triage process. The resulting set of pathways for SARS-CoV-1 provided the basis for computational inference of the corresponding SARS-CoV-2 pathways based on structural and functional homologies between the two viruses.
Interaction databases The biocuration process was supported by interaction and pathway databases storing structured, annotated and curated information about COVID-19 virus-host interactions. The IMEx Consortium  (Meldal et al, 2019)  dataset  (Perfetto et al, 2020)  contains curated Coronaviridae-related interaction data from reviewed manuscripts and preprints, resulting in a dataset of roughly 7,300 interactions extracted from over 250 publications, including data from SARS-CoV-2, SARS, CoV, and other strains of Coronaviridae. The dataset is updated with every release of IMEx data and is open access (https://www. ebi. ac. uk/intact/resources/datasets#coronavirus). The SIGNOR 2. 0  (Licata et al, 2020)  dataset contains manually annotated and validated signalling interactions related to the host-virus interaction, including cellular pathways modulated during SARS-CoV-2 infection.
The dataset was constructed from the literature on causal interactions between SARS-CoV-2, SARS-COV-1, MERS proteins and the human host and is openly available (https://signor. uniroma2. it/covid/). The Elsevier Pathway Collection  (Daraselia et al, 2004; Nesterova et al, 2020)  COVID-19 dataset comprises manually reconstructed and annotated pathway diagrams. Statements about molecular interactions are extracted into a knowledge graph by a dedicated text mining technology adapted for extracting facts about viral proteins and viruses from the literature. These interactions were filtered for experimental evidence, used for pathway reconstruction and made openly available (http://dx. doi. org/ 10. 17632/d55xn2c8mw. 1). Information from OmniPath (T€ urei et al, 2021) on existing interactions gathered from pathway and interaction databases was used in a programmatic way to suggest cellspecific interactions and cell-cell interactions specific to immune reactions.
Text and figure mining Text mining was performed on the  CORD-19: COVID-19 Open Research Dataset dataset (preprint: Lu Wang et al, 2020). INDRA  (Gyori et al, 2017) , AILANI COVID-19 (https://ailani. ai) and BioKB processed CORD-19 dataset (https://biokb. lcsb. uni. lu/topic/DOID: 0080599), with their results available programmatically via REST API and SPARQL interfaces. An OpenNLP-based (https://opennlp. apache. org/) text mining workflow using GNormPlus  (Wei et al, 2015)  was applied to the CORD-19 dataset and the collection of MEDLINE abstracts associated with the genes in the SARS-CoV-2 PPI network  (Gordon et al, 2020)  using the Entrez GeneRIFs, https://www. ncbi. nlm. nih. gov/gene/about-generif. (https://gitlab. lcsb. uni. lu/covid/models/-/tree/master/Resources/Text%20mining). Also, we used data from 221 CORD-19 dataset figures using a dedicated Figure Mining Workflow  (Hanspers et al, 2020) , with results available at https://gladstone-bioinformatics.