dataesr/scientific-paragraphs-categorization huggingface dataset:
This suggests that the slow rate constants measured by electrochemistry reect a kinetic control by the electron transfer step rather than by chain dynamics. The present work aims to address this question by: (i) assembling a model end-graed redox oligonucleotide system, (ii) characterizing its electrochemical response with fast scan rate cyclic voltammetry, and (iii) interpreting the results based on a realistic molecular dynamics model for DNA. These simulations, which were previously computationally inaccessible or not quantitative enough, have become possible with the renement of coarse grained sequence-dependent DNA models such as oxDNA. 29 For the present work, we have developed a code (Qbiol) dedicated to electrochemical application enabling the complete dynamics of the anchored DNA to be numerically reproduced and resolved in time.
Molecular dynamic simulations of the behavior of endattached Fc-DNA layers We have developed a molecular dynamics simulation code (Qbiol) based on the OxDNA package, to resolve the motional dynamics of end-anchored ss and ds Fc-DNA (ESI, Section 7 †). While numerical simulations considering a molecular pendulum (rod with electrostatics) have shown to nicely reproduce the dynamics of protein-binding to double-stranded DNA and a specic antibody,  44  such an approach remains too simple to capture the dynamics of protein-free DNA, and in particular for ssDNA that does not behave as a rod. In Qbiol, the 1 st base (in 5 ′ ) is considered as the anchoring site attached to the surface via a strong elastic force. The position of the Fc head, assumed to correspond to the free 3 ′ end of the DNA, can be tracked every ∼1 ps, for periods of 10 ms, using an optimized code.
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource.
Disease map development workflow The major phases of map development are illustrated in Figure  1. The planning phase includes i) determining map purpose and type, ii) defining the coverage of biological functions, iii) choosing a graphical standard (data model) and an editor, and iv) designing map architecture (Figure  1 , Steps 1-4). While these steps are shown in a sequence, they are often realised in parallel. Next, in the curation phase, available knowledge from literature and databases is encoded into a diagram with accompanying annotation of map entities (Figure  1 , Steps 5 and 6). When the map is ready for publication, it is made available online in a navigable tool and updated based on feedback from domain experts-the publishing phase (Figure  1 , Steps 7 and 8). The application phase depends on the goals of the project, expertise and resources available, and can include data visualisation, drug target exploration, computational modelling and hypothesis generation (Figure  1 , Step 9).
Intended map purpose might include one or several of the following objectives: integrating available knowledge: reviewing and visualising disease mechanisms, identifying gaps in knowledge, educational purposes; combining knowledge with external databases and tools: exploring drug targets, text mining; analysis: network-based analysis, omics and multi-omics data visualisation, interpretation and hypothesis generation; computational modelling: using maps as the basis/template for different types of models, refining hypothesis, making predictions. Clarifying the motivation, objectives and intended applications is needed for the following steps about the coverage of biological functions. This can be documented in a brief project description document (https://disease-maps. org/template), or in a web page (e.
Defining coverage of biological functions Exchange with the disease domain experts allows to define the scope and boundaries of the map. Also, key reviews and figures that can help to create an initial skeleton of the map. The main tasks at this stage include: -Identify priority pathways; -Identify cell types, tissues and organs relevant to the pathology mechanisms; -List the molecules known to be involved and drugs used in a given condition -Define disease outcomes on the molecular, cellular and tissue levels, including subdivision into disease stages and subtypes, if applicable. Such lists can be stored in a document shared and discussed with the domain experts, and maintained throughout the map development, e. g. , https://disease-maps. org/template. Often this information is available in relevant databases, like disease-focused pathways of Reactome (Reactome:R-HSA-1643685) including cancers (Reactome:R-HSA-5663202) and infectious diseases (Reactome:R-HSA-5663205, Reactome:R-HSA-9694516).
Primary purpose Details Examples Primary users Process Description (PD)  (Rougny et al. , 2019)  and Activity Flow (AF)  (Mi et al. , 2015)  are the two most used languages of the Systems Biology Graphical Notation (SBGN) standard (www. sbgn. org)  (Le Novère et al. , 2009)  (Figure  2 ). More information about the SBGN standard are available in the "SBGN Learning" materials at https://sbgn. github. io/learning and the "Figure  to  SBGN" page at https://sbgn. github. io/figuretosbgn. CellDesigner (https://www. celldesigner. org) is a diagram editor that is frequently used for developing disease maps. It supports extensive diagrams and follows SBGN logic and makes it possible to draw SBGNcompatible diagrams in Process Description and Activity Flow type of languages using the default CellDesigner's palette or its Reduced Notation palette.
Formalising knowledge from literature and databases In the knowledge formalisation step, relevant causal interactions and processes are taken from selected sources including diseaserelevant scientific papers and existing representations in pathway databases. In case of scientific papers, causal interactions and processes are manually identified, integrated and encoded into a diagrammatic representation  (Kondratova et al. , 2018). In case of pathway databases, relevant causal interactions are manually reviewed and contextualised for a specific disease. Knowledge about molecular processes relevant in the context of a particular disease need to be represented in a standard way, for example, receptor activation, phosphorylation, complex formation, positive and negative regulation of transcription, transport, glycosylation, and more.
Features Top molecules, pathways and cell types. Prioritised pathways are curated in CellDesigner from literature or pathway databases, and visualised in MINERVA for use and review, leading to updates in CellDesigner. The knowledge formalisation and integration works in a similar way to how we build a review paper but, in this case, it is structured and systematic to follow the rules of the graphical standard and to ensure the connectivity in the network. Disease map curation can be seen as a graphical review of relevant disease mechanisms and can be compared to a combination of scoping, rapid and systematic reviews  (Grant and Booth, 2009). A scoping review, corresponding to step 2 of the map development process (Figure  1 ), determines the coverage of biological functions, defines disease hallmarks, and identifies knowledge gaps.
Rapid and systematic reviews, corresponding to steps 5 and 6 (Figure  1 ), narrow down the focus and collect evidence from literature and databases for drawing and annotating diagrams in an editor. These steps are usually performed by a dedicated curator with advice and feedback from a panel of domain experts. This "graphical review" requires following diagram-specific knowledge representation formats. It demands consistency in using a standardised graphical language and describing connectivity between diagram elements that may span beyond the currently curated article. For example, if a paper states that IL6 activates epithelial cells, it is important to identify the relevant protein (IL6, UniProt:P05231), identify receptors involved (IL6RA, IL6ST, IL6RB), and describe the following signalling pathway, which may require looking for additional articles.
Annotating map entities and interactions Two objectives during the annotation step are identifying map components and providing evidence for interactions. Identification assigns stable identifiers or ontology terms to map components to uniquely define them. This ensures interoperability with systems biology tools and external databases. Providing evidence for interactions defines the context of a given interaction, e. g. , a specific cell type, tissue, organism, or relevant pathology. Contrary to components, these annotations are not unique but interaction-specific, and require evidence from a relevant source, e. g. , scientific publications, curated pathways, or interaction databases.
ebi. ac. uk/complexportal). For instance, GO CC term "Fc-epsilon receptor I complex" (GO: 0032998) for the complex above. Phenotypes or other elements representing higher order processes should be annotated with Gene Ontology (GO) Biological Process (BP) terms () (Gene  Ontology Consortium et al. , 2021)  or MeSH terms (https://www. ncbi. nlm. nih. gov/ mesh). GO BP is recommended for annotating components describing biological processes and MeSH terms are recommended for diseases-related terms. Metabolites ("simple chemical" in SBGN, or "simple molecule" in CellDesigner) are identified via the Chemical Entities of Biological Interest database (ChEBI, https://www. ebi. ac. uk/chebi)  (Hastings et al. , 2016). Alternatively, PubChem (https://pubchem. ncbi. nlm. nih. gov)  (Kim et al. , 2021) , Human Metabolome Database (https:// hmdb.
Visualising and analysing the maps Systems biology diagrams of disease maps are developed for online interactive exploration and visual analytics to bridge the gap between the domain experts and bioinformatics experts. The maps can also be used as a part of larger bioinformatic workflows. For example, they can be transformed into a graph database for scalable exploration and analysis, for instance via Neo4j (Section 4), or integrated into a shared network repository like the NDEx platform  (Pratt et al. , 2015). Their content can be transformed with the use of the CaSQ tool for Boolean modelling (Section 2. 4; Table  3 ), or for analysis of network topology with the help of tools such as Cytoscape  (Shannon et al. , 2003). However, the reminder of the section focuses on the visual exploration aspect of disease maps, using a dedicated online platform.
The MINERVA Platform  (Gawron et al. , 2016; Hoksza et al. , 2019a; Hoksza et al. , 2019b)   Video tutorials are available for introducing key MINERVA functionalities with the AsthmaMap as an example: navigating the maps, adding comments, exploring drug targets and visualising data (https://asthma-map. org/tutorials). Maps can be uploaded to MINERVA in several formats including CellDesigner SBML, SBML + layout + render, SBGN-ML and GPML  (Hoksza et al. , 2019a). The conversion module within the MINERVA Platform allows export to these formats as well. On upload users can choose from CellDesigner's view and SBGN view. Automatic annotation can be selected on upload. As discussed above, with HGNC names used for proteins, mRNAs and genes, the MINERVA Platform automatically adds links to relevant external databases.
Map browsing and zooming works in MINERVA in the Google Maps manner. For multi-layered maps, interconnected diagrams are organised into a single entity via the submaps function and are visible from the top-level diagram in the SUBMAPS tab (https:// asthma-map. org/tutorials/#navigating-the-asthmamap). Search functionality queries all connected maps. Data can be uploaded as a tab-separated file (https://minerva. uni. lu/doc/examples). This way, omics data (for example, differential expressed genes) can be converted into a gradient of colours on the map (https://asthma-map. org/tutorials/#visualisingdata). Multiple datasets can be visualised simultaneously and compared, including a multi-omics combination (e. g. , proteomics and metabolomics), or time series data with different time points visualised as separate datasets.
Drug target search connects via online queries to DrugBank (https://www. drugbank. ca)  (Wishart et al. , 2018)  and ChEMBL (https://www. ebi. ac. uk/chembl)  (Gaulton et al. , 2012; Bento et al. , 2014)  databases and allows highlighting all entities affected by a drug (https://asthma-map. org/tutorials/#exploring-drug-targets). Moreover, MINERVA API and plugins  (Hoksza et al. , 2019b)  allow the implementation of customised visual exploration and analytics pipelines. An alternative web-based tool is NaviCell  (Kuperstein et al. , 2013; Bonnet et al. , 2015)  developed primarily to support the ACSN project. It offers functionalities for visualising omics data for performing functional analysis of the maps, and for computing aggregated values for sample groups and their visualisation on the maps (https://navicell.
Collecting feedback and updating the map When the first complete version of a map is available online, it is advisable to request feedback from domain experts regarding the coverage of disease hallmarks, the depth and quality of pathways included, accuracy and adequacy of the disease mechanisms representation. This input can be collected in the form of 1) freeformat notes, 2) suggested publications, or 3) directs comments on the map in MINERVA (https://asthma-map. org/tutorials/#commenting). It is also suggested to have an automatic check of any inconsistencies in curation and annotation. This can be done using Neo4j-based tools (see the "Disease map management in the Neo4j graph database" section) or the automated verification in the MINERVA Platform (https://minerva. pages. uni. lu/doc/ admin_manual/v16.
Applying the resource Map applications can play a role in the development process, as analysis and computational modelling may identify missing feedback loops or analytical endpoints, key pathways, or larger gaps in the network. Therefore, considering the use-cases of a map can help with its curation, revision and refinement  (Ostaszewski et al. , 2021). One of map applications is omics data visualisation for its interpretation, hypothesis generation, and planning validation experiments. Another is exploration of drug targets in MINERVA using the corresponding functionality (https://asthma-map. org/ tutorials). Finally, computational modelling allows to transform a static diagrammatic representation into a dynamic view of disease progression to be used for refining hypotheses and for predictions.
, 2014) ; Computational modelling: creating computational models based on disease maps, for example, for rheumatoid arthritis  (Miagoux et al. , 2021; Aghakhani et al. , 2022)  and atherosclerosis  (Parton et al. , 2019) ; creating causal-interaction networks based on a disease map  (Touré et al. , 2021a) ; In preclinical studies with follow-up validation experiments: in preclinical studies in cancer  (Chanrion et al. , 2014; Jdey et al. , 2016; Monraz Gomez et al. , 2019)  with validation of the proposed hypothesis by follow-up experiments; Fusing disease maps with other solutions: integrating disease maps with machine learning inferred networks  (Miagoux et al. , 2021) ; identifying new crosstalks between pathways from combined analysis of interactions and text mining datasets  (Ostaszewski et al.
Long-term sustainability of disease maps Developing a disease map requires a significant amount of work thus it is important to ensure its long-term sustainability and use. First, the map should be deployed on a stable IT infrastructure for uninterrupted access to the content. This can happen via institutional resources or via a third party, such as the open-access map hosting service offered by ELIXIR Luxembourg (https://elixir-luxembourg. org/services/catalog/ minerva). Second, a universal programmatic interface should be set up allowing reproducible queries to the disease map contents. The performance of the already available MINERVA API can be further improved by deployment of graph databases, supporting large and complex queries. Finally, a map should be set up as a FAIR resource (https://www.
Disease map management in the Neo4j graph database Maps offer visual representations primarily intended for human comprehension. For their exchange and edition, they are stored in machine-readable formats, e. g. , CellDesigner SBML and SBGN-ML. While these formats are well suited for storing and exchanging graphical layout information, they do not offer a convenient solution for the network-based management (integration, querying, exploration) and analysis of collections of maps. The Neo4j graph database was proposed for an efficient management of large-scale highly-interconnected heterogeneous biological data  (Lysenko et al. , 2016) , including biological pathway representations in systems biomedicine like Reactome  (Fabregat et al. , 2018 ), IntAct (Del Toro et al. , 2022)  and Recon2Neo4j  (Thiele et al.
, 2013; Balaur et al. , 2016). Collections of SBGN maps may also be stored and explored in a Neo4j database using the StonPy software  (Rougny et al. , 2023) , which builds a Neo4j graph from an SBGN-ML map or a CellDesigner map converted to SBGN-ML using cd2sbgnml  (Balaur et al. , 2020). Data model of StonPy is close to the visual representation of maps and that is easy to learn. In this model, SBGN nodes (e. g. , proteins, states variables) are modelled using Neo4j nodes, while SBGN arcs (e. g. , catalyses, production arcs) and relationships between concepts are modelled using Neo4j relationships (edges). Annotations are also stored and can be easily queried. Figure  5  shows an example of a map and an excerpt of its corresponding Neo4j graph as built by StonPy. The StonPy workflow was used to perform a comparative analysis of integrated biological pathways in several major resources including the Atlas of Cancer Signalling Networks (ACSN), PANTHER, and Reactome  (Mazein et al.
, 2021b; Rougny et al. , 2021). The cd2sbgnml  (Balaur et al. , 2020)  and StonPy tools were also used to develop the dedicated C19DM-Neo4j graph database component (https://c19dm-neo4j. covid. pages. uni. lu), which integrates the biological content of the COVID-19 Disease Map diagrams available in MINERVA.
FAIR principles for disease maps available in MINERVA Disease Maps approaches evolve together with other systems biology communities such as COMBINE  (Hucka et al. , 2015) , CoLoMoTo  (Niarakis et al. , 2021) , SysMod  (Dräger et al. , 2021) ,  SBGN (Le Novère et al. , 2009) , SBML  (Keating et al. , 2020)  and FAIR  (Wilkinson et al. , 2016)  to enhance the accessibility and reusability of the resources  (Niarakis et al. , 2022). The FAIR (Findable, Accessible, Interoperable and Reusable) principles (https://www. go-fair. org/fair-principles) establish criteria to assess and improve the reuse of scientific resources including data, metadata and infrastructure. The underlying MINERVA Platform provides a set of functionalities that addresses several elements of FAIR: unified API calls to access the standardised content of all diagrams on the platform; functionality for translating between systems biology standards; authentication and authorisation procedures for user management; explicitly mentioning the reuse licence (for example, the COVID-19 Disease Map diagrams are released in MINERVA under the CC-BY 4.
Interoperability: The biological content in MINERVA follows XMLbased systems biology standards such as CellDesigner SBML; translation to other systems biology standard formats is also possible via MINERVA. Moreover, the content of maps is rich in metadata through annotations with information from initial publications connected to biomedical resources as recommended in Table  3 , and there are ongoing efforts to connect the content to the Bricks Ontology  (Rougny et al. , 2021). Reusability: The biological content of disease map diagrams in MINERVA is released under a specific data usage licence, e. g. , under the CC-BY 4. 0 usage licence for COVID-19 Disease Map  (Ostaszewski et al. , 2021). The biological diagrams are primarily edited using CellDesigner, thus complying with the incorporated rules for molecular mechanism representation, and are annotated with various types of information including references to publications (PMIDs) and standard identifiers for molecular entities (UniProt IDs for proteins, ChEBI IDs for metabolites, etc.
The closest similar visualisations are figures with an overview of disease mechanisms in review papers, representations with cell types, tissues and organs shown. By free-format here we mean flexible representation with one necessary function: all lower-layer sub-pathways in a hierarchically-organised structure are represented by an element on the toplevel view and this way integrate them into a single virtual map. layer: top-level view Intermediate layer: AF Detailed layer: PD SBGN language Activity Flow or free format b Activity Flow Process Description Connectivity Interactions Interactions Processes Granularity Least detailed: focus on key molecules Intermediate level of details Most detailed, the level of processes and cell types Compatible Not available a KEGG signalling pathways, BioCarta, OmniPath, SIGNOR Reactome, PANTHER, Recon X, KEGG databases metabolic pathways Application/Use Communication with domain experts for omics visualisation, network analysis, Boolean modelling, omics visualisation, network analysis, ODE defining disease hallmarks integration with machine learning inferred networks modelling, Boolean modelling (via CaSQ) a b.
enables easy online access to and visual interactive exploration of disease maps. Examples are: -Parkinson's Disease Map (https://pdmap. uni. lu); -AsthmaMap (https://asthma. uni. lu); - Rheumatoid Arthritis Map (https://ramap. elixir- luxembourg. org); -Cystic Fibrosis Map (https://cysticfibrosismap. github. io); -COVID-19 Disease Map (https://covid19map. elixir- luxembourg. org).
Systematic literature review The SLR was designed to evaluate the overall HRQoL in patients with axSpA. It was conducted in accordance with the Centre for Reviews and Dissemination methodological guideline and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. [18] [19] [20]  Here, we report the results of a subset of studies from the SLR that present workplace productivity in patients with axSpA, as measured by the WPAI questionnaire at Weeks 12-16 following treatment. S2. Database searches were supplemented by cross-referencing data in the bibliographies of relevant SLRs and grey literature searches of clinical trial registries to identify eligible studies for inclusion. In August of 2022, data on file for the WPAI results from BE MOBILE 1 and BE MOBILE 2 studies were added to the evidence base.