dataesr/scientific-paragraphs-categorization huggingface dataset:
The method of self-holding programming is also used to translate SFC program into algebraic equations. The translation of the test case of Figure  19  is depicted in Figure  20. This translation should be applied to each test case. To verify if the PLC program of the Transformer Group satisfies all testing procedures, it is enough to check if there exist at least one blocking instruction in the recipe book. An instruction is blocking if, after its execution on the controlled Transformer Group, the corresponding expected result is not obtained after a certain duration. This property can be easily checked in Uppaal by to the following "query" which checks whether or not the timeout of the "RecipeBook" timer is signaled: E <> RecipeBook. timeout The "RecipeBook" timer counts, for each instruction of recipe book, the elapsed time between the execution of the instruction and the obtainment of the corresponding expected result.
The timeout represents an elapsed time which cannot be attained if the program is valid. If the timeout is attained, the program presents an error because this means that the output transition of the current active step of the corresponding SFC program cannot be fired. The delay of the timeout is quantified by the upper threshold of the test case that has the longest execution time. The satisfaction of this query will therefore mean that there exists at least one blocking instruction. Then, the model checker Uppaal provides the patha witness -(a witness in this case) leading to the blocking instruction and the corresponding testing procedure. The verification of functional requirements of the Transformer Group's control program were performed on a "core i5" windows machine with 4Gb RAM. It takes at most 70 milliseconds for the model checker to return a blocking instruction if the program contains errors.
However, even if this testing procedure is satisfied by the control program, it does not imply that property 1 will always be satisfied, because there may exist other scenarios of the control program that violate this property. A more exhaustive approach for formal verification of safety requirements is therefore needed. For this, the dangerous states should be expressed as queries in Uppaal. Then, the models in Figure  22  are used to generate arbitrary values of the system's inputs for exhaustive verification (as has been explained in Section 4. 1).
Guards are therefore associated with the transitions of these models. For example, the guard "flag" of the first model of Figure  22  is a Boolean variable which allows the activation of the fault "b" only if it will make the control program evolve during the current PLC cycle. "flagCO" and "flagCF" are used in a similar way for the second model of Figure  22. The formal verification of safety requirements consists in verifying in Uppaal (through a query) "if there exists a scenario (or inputs vector) that can occur in the system and leads it to violate the safety requirements". When the properties are violated, the model checker returns the scenario(s) leading to the dangerous state(s) to help the engineer correct the errors. Formal verification results showed that the safety requirements are not formally satisfied by the control program of the given example.
Main program execution and outputs computation using a function named "computing()". "computing()" is a stateless function which emulates the execution of PLC program by using internal variables, without interfering with the other Uppaal models; 5-Evolution of the PSEEL's state according to the new output values of the control programs through the signal "PO!" which is sent to the PSEEL elements (such as the one in Figure 15); 6-Recipe book execution through signal The test cases contained in the recipe book are sequentially executed on the controlled PSEEL for verification of functional requirements. The state of recipe book's execution is updated during each cycle.
Figure17presents the SFC program of the switch "sect1". The translated program, written in algebraic equations, is depicted in Figure18. The function of a transition, ft, is given by a conjunction of the state of the previous steps and the logical condition associated with the transition. A step is activated if the function of one of its input transitions is true. It remains active as long as the function of one of its output transitions is false. The actions associated with a step are active when the step is active. A tool has been developed for automatic translation of SFC and LD control programs into algebraic equations in Uppaal. This tool is based on the structured methodology proposed in (Machado et al. , 2006). Figure18corresponds to the translation (into Boolean equations) of the SFC control program of a switch.
4- Main program execution and outputs computation using a function named "computing()". "computing()" is a stateless function which emulates the execution of PLC program by using internal variables, without interfering with the other Uppaal models; 5-Evolution of the PSEEL's state according to the new output values of the control programs through the signal "PO!" which is sent to the PSEEL elements (such as the one in Figure 15); 6-Recipe book execution through signal The test cases contained in the recipe book are sequentially executed on the controlled PSEEL for verification of functional requirements. The state of recipe book's execution is updated during each cycle.
0. The pressure at the inlet is set to zeroGradient and specified by zero gradient. At the outlet, the pressure is specified as the atmospheric pressure, and the gas hold up is set to inletOutlet with zero gradient for outflow and fixed value for backward flow. The no-slip condition is applied at the walls for the velocities and Dirichlet condition for the gas holdup. Moreover, for the one-equation model we apply wall functions. The numerical simulations were carried out with the open source CFD package OpenFOAM library  (Weller et al. , 1998). The governing equations of continuity and momentum as well as the transport equation for   are solved by the two-phase flow solver twoPhaseEulerFoam available in 𝐌 𝐿 𝑇𝐷 = 𝐶 𝑇𝐷 𝜌 𝐿 𝑘∇𝛼 𝐺 (14) OpenFOAM 3. 0. 1. The solver is based on a finite volume formulation to discretise the model equations which has shown to be stable for transient calculations  (Weller, 2005).
Up to now, the building of such models was almost exclusively manual, with only a few attempts to automate the construction by using existing prior-knowledge networks complemented with experimental data  [14] [15] [16]. Nevertheless, the use of existing, curated and well-annotated causal interactions as building blocks of information is expected to help facilitate and accelerate the model building process. Many initiatives in biocuration of causal statements exist today, and an overview of the various resources that this has produced will be of help to the model building community. We provide a review of publicly available resources and databases that contain 'explicit' information (i. e. directed interaction where a source entity regulates a target entity), 'implicit' information (i. e. causal relation between entities represented in the process description (PD) type of biological network) or integrated causal information (causal interactions that are incorporated as parts of logical models), as well as tools and pipelines developed to infer causality.
Molecular causal interaction data resources Molecular causal interactions can be retrieved from a wide range of resources and databases providing either explicit or implicit causality (see Figure  1  and Table  1 ), differentiated by different data representation schemes. In the following subsections, we first provide a list of the most prominent databases with explicit causal interactions, i. e. data resources that specifically describe causal interaction with at least a source entity, a target entity and the causal relationship. In addition, we present a list of resources that hold executable logical models, followed by a description of activity flow (AF) pathway resources, as these often also contain useful information for model re-engineering purposes. Next, we will present the resources with implicit causal interactions, such as PD pathway databases where causal interactions must be inferred to be of use for logical modeling.
Causal Biological Networks The Causal Biological Networks (CBNs) database  [27] , available at http://causalbionet. com/, is a manually curated signaling pathways database, currently focused on biological processes in pulmonary and vascular systems  [28] [29] [30] [31] [32] [33]. This database contains both causal and non-causal interactions and combines these Table  1. Summary of the listed explicit and implicit data resources with causal information. The number of causal interactions, models or maps is provided as recorded at the time of writing this manuscript. The 'Data provenance' column indicates whether a reference to a manuscript or information source leading to the description of a causal interaction is provided. The 'Exports' column indicates the formats in which causal interactions are accessible or in which formats the pathways can be extracted.
The 'Latest content update' column indicates, to the best of our knowledge, the last year the content of the resource has been modified. The '+' symbol indicates that the database does support a specific characteristic. CBN not only defines interactions between physical entities such as genes, RNAs, proteins, protein fusions, protein complexes and small molecules but also 9712 causal interactions between activities including biological processes, phenotypes and pathologies, as well as interactions between entities and activities. The data can be exported in a JSON Graph File (JGF, https://jsongraphformat. info/) and the Simple Interaction File (SIF). In addition, the CBN-BEL converter (https://github. com/ pybel/cbn-bel) enables the export of these networks in the Biological Expression Language (BEL,  [34] ).
Data resources of AF pathways (explicit causality) Resources of AF pathways describe the collections of causal relations between biological entities or specific activities of biological entities, connected together. Unlike causal interaction resources, where only one interaction at the time is delineated, AF pathways connect several causal interactions to depict specific biological mechanisms. This type of data representation has been standardized in the System Biology Graphical Notation (SBGN  [40] ) AF language. These interactions are regulatory and have a specific biological effect (e. g. 'source' negatively regulates 'target', see Figure  1 ). Several pathway databases represent signaling events in an AF form. For instance, the Kyoto Encyclopedia of Genes and Genomes (KEGG,  [41] ) is a curated database with several categories focusing on the understanding of functions of biological systems (e.
Causality between entities in a GO-CAM model is defined via building blocks of the form: 'a molecular function, enabled by a gene product, regulates a molecular function, enabled by the same or another gene product'. Therefore, causality is defined in an activity-centric way in these models, and gene products are the entities that execute these activities. GO-CAMs also enable the depiction of causal regulation between biological processes, and between a molecular function and a biological process. The models are created via the web-based collaborative editor Noctua, http://no ctua. berkeleybop. org. However, not all pathway databases represent information for causal interactions in an explicit format (i. e. source entity regulating a target entity). Several pathway databases adopt the 'PD' form to express biological mechanisms which may carry implicit causality as described in the following section.
Data resources of PD pathways that carry implicit causality Pathway databases are commonly built to provide a comprehensive representation of known biological mechanisms, which are portrayed as biochemical reactions. The reactions usually represent the transformation of molecules (i. e. reactants) that are consumed to produce other molecules (i. e. products), catalyzed by enzymes. These biochemical reactions are visualized with the SBGN PD standard language  [46]. SBGN PD pathways commonly refer to metabolic and signaling representations of biological events (i. e. networks and interactions) where the mechanistic details are preserved: entities go through physical or locational state changes, sometimes because of the action of a catalyst or regulator (e. g. A catalyzes the reaction transforming B1 in B2, where B2 is a phosphorylated form of B1).
While these networks can be used for Ordinary Differential Equation (ODE) modeling, the causality associated with these ODE relationships makes them interesting for logical modeling as well. However, the causal aspect between the entities of these types of networks is buried in the PD representation, which is why we name them 'implicit causalities'. In this case, an inference step is required to make the causal relation between two entities explicit, based on the knowledge provided by the reaction as described in Figure  1. Popular pathway databases with signaling and gene regulatory information include the already mentioned KEGG (its PD pathways), Protein ANalysis THrough Evolutionary Relationships (PANTHER)  [47, 48] , Reactome  [49] , Atlas of Cancer Signaling Network (ACSN)  [50]  and Disease Maps  [51, 52].
These manually curated resources are used by a wide range of scientists for understanding the mechanistic aspects of regulatory pathways and to interpret the outcome of experimental results. The PANTHER resource contains 177 pathways with relationships between interacting biological entities, constructed with modeling tools such as CellDesigner  [53]. The Reactome pathway database is a resource of biological mechanisms, analysis algorithms and predictive computational models. ACSN is a repository of biological networks focused on the representation and analysis of cancer regulatory networks (i. e. molecular processes found in cancer cells and tumor microenvironment). Disease Maps represent a growing community effort that builds a collection of biological networks representing mechanisms affecting a wide range of diseases (e.
g. COVID-19 Disease Map  [54] , Parkinson's Disease Map  [55] , AsthmaMap  [56]  and RA-map  [57, 58] ). The Disease Maps are archived in the Minerva platform  [59]  and involve clinicians and domain experts to validate the annotated pathways. In addition to these resources, several more narrowly focused yet valuable efforts on specific diseases of processes have been published and their results are made available as pathway maps describing specific normal or diseased biological conditions  [60] [61] [62] [63] [64] [65] [66]. The extraction of causal interactions from these pathway databases needs a conversion of the data structure in order to explicitly represent causality for logical modeling. Several studies have been performed to identify the patterns of various biological interactions that would allow their translation into causal interactions between biological entities.
For instance, Vogt et al. [67]  proposed rules to translate maps from the SBGN PD language to SBGN AF to obtain smaller and more manageable maps, which has been implemented in the SBGN-ED tool  [68]. Recently, Aghamiri et al. developed CaSQ,  [15]  a tool to automatically infer executable Boolean models from molecular interaction maps represented in the CellDesigner  [53]  format. The researchers proposed a framework of graph conversion for PD representations including SBGN schemes, concerning various biological scenarios such as complex formation, phosphorylation, ligandreceptor interaction, etc. , with simultaneous inference of logical formulae describing regulation. The logical rules are inferred Downloaded from https://academic. oup. com/bib/article/22/4/bbaa390/6055722 by guest on 06 January 2022 based on the topology and semantics already encoded in the original maps, resulting in Boolean models with an AF-like layout.
Integrated resources to access causal interactions and build models While data resources presented above have their own curation efforts and allow the download of causal interactions through different data formats to access the curated knowledge, several databases focus less on curation but integrate information from other resources to combine knowledge describing causal interactions into a single platform. These resources usually provide powerful querying interfaces, via command line, web services, graphical interfaces or scripting, which facilitates data retrieval for a particular study (see Supplementary File S1 available online at https://academic. oup. com/bib). OmniPath is a comprehensive integrated resource that combines a vast range of curated and computed signaling resources (full list available at http://omnipathdb.
org/), to offer a single endpoint for querying data  [69, 70]. They can be accessed and analyzed through pypath (http://saezlab. github. io/pypath), a Python module enabling the construction of models from the multiple supported resources. BioGateway  [71]  is a semantic knowledge base with curated information from a variety of Elixir core data resources  [72]  and recently augmented with a knowledge graph containing an exhaustive set of regulatory interactions between transcription factors and target genes (98 768 interactions, albeit only a minority with regulatory type), which can be queried using the SPARQL query language. The BioGateway app  [73]  provides access from the Cytoscape network editor and allows the export of data in all the formats supported by Cytoscape, including the SIF format.
NDEx  [74]  is an online network data exchange platform that provides a 'commons' of biological networks. The resource aims to be a collaborative platform where scientists can deposit and share their networks, as well as use resources from others with multiple application possibilities such as visualization and analysis of the networks in Cytoscape. Likewise, the Pathway Commons resource incorporates knowledge from 22 pathway and interaction databases  [75]  and enables to export data in BioPAX and SIF, among other formats. PathMe aligns and integrates pathways from KEGG, Reactome and WikiPathways in BEL  [76] , and the greater Bio2BEL  [77]  ecosystem which it is part of integrates more than 50 biological databases that can be exported to the different formats supported by PyBEL  [34].
Causal inference in software and pipelines In addition to resources providing causal molecular interaction information, a broad set of pipelines have been produced to infer causality from various types of biological datasets. This inferred knowledge is generally coupled with prior knowledge extracted from literature. We provide a brief overview of these tools. The INDRA-IPM modeling web interface enables the assessment of causality by translating biological mechanisms from natural language processing  [78]  and enabling the export of the models in various standard formats (e. g. SBML  [25]  and SBGN  [40] ). The CAusal Reasoning for Network identification using Integer VALue programming (CARNIVAL  [79] ) is a pipeline that integrates gene expression data to identify upstream regulatory signaling pathways.
CARNIVAL implements a reverse engineering process and uses a prior knowledge network obtained from OmniPath  [69, 70]. Similarly, CausalR  [80]  is an R package that extracts causality from genome expression datasets. CoRegNet  [81]  is an R package that infers co-regulatory networks of transcription factors and target genes by analyzing transcriptomics datasets and estimating activities of transcription factors. Whistle  [82]  implements the Reverse Causal Reasoning algorithm to discover upstream causal regulators from transcription profiles. It uses prior knowledge described in the BEL format to identify possible molecular mechanisms that explain the gene expression data. CausalPath  [83]  infers causal interactions from prior knowledge resources (i. e. Pathway databases) combined with proteomics cell lines profiles.
This technique enables the automatic contextualization of causal interactions for diseases in which they are experimentally observed. CausalPath uses mainly the Pathway Commons data to assess causality  [75]. CANDis is a web server that explores possible causal relations between diseases, drugs and drug targets  [84]. Bayesian methods have been used in several studies to infer causality: SAGA  [85]  infers transcriptional regulatory relationships and ARACNE  [86]  enables the reconstruction of mammalian transcriptional regulatory networks using both biological data (i. e. microarray datasets) and mathematical methods (i. e. Relevance Networks and Bayesian Networks algorithms). Martin et al. [87]  developed a scoring method applied in their study to the biomedical field (e. g. impact of disease, drug treatment and environmental agents on humans), the Network Perturbation Amplitude (NPA).
When combined with high-throughput data and prior knowledge, the NPA algorithm identifies change of activities in targeted biological processes and thereby helps to better understand biological mechanisms leading to diseases. TETRAD (http://www. phil. cmu. edu/tetrad) is a software implementing algorithms, such as Fast Greedy Equivalence Search and Greedy Fast Causal Inference, to enable causal inference. The application has been applied to projects inferring causality for biological and clinical molecular interactions  [88]. Finally, Causaly (www. causaly. com) is a commercial interface that uses artificial intelligence, machine learning and text mining to infer evidence and causality from biomedical data. A variety of data sources, including Biomedical Literature, Clinicaltrials. gov and several sideeffect databases are machine-read and integrated in the Causaly Knowledge Graph.
Data exchange formats Formats have been developed from multiple perspectives, each supporting the expression of causal interactions with different aspects that answer specific use cases. They enable the retrieval of causal interactions through the download of files for different usage purposes. These formats can range from the simplest form with two entities and the regulation sign (e. g. SIF) and more complex representations with the storage of additional metadata [e. g. the BEL, GO-CAM using the Web Ontology Language (OWL), and PSI-MITAB]. The following section aims at providing a short description of the most prominent formats (detailed description can be found through the links provided in Supplementary File S2, available online at https://academic. oup. com/bib), the type of metadata these formats can handle and the list of databases providing causal interactions in these formats.
g. P01106, for a UniProt entry  [90] ) of the matching biological entity. The interaction type is annotated with terms such as 'activates'/'inhibits', 'increases'/'decreases', 'up-regulates'/'down-regulates' or even symbols such as '→'/'-|' to represent the causal effect of the interaction. Still, the data annotation should be consistent throughout each individual data resource. As the SIF file contains only the most basic yet important information about an entity-based causal interaction (i. e. no contextual details are stored), it can be seen as a format for data users once they have filtered for data of interest to their study (e. g. selected for appropriate biological context) from original knowledge sources. A SIF file can be easily used in visualization tools for displaying and analyzing networks (e.