dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 2 Figure 2 : 22 Figure 2 : Summary of the different steps of the EggVio pipeline needed for read annotation. A. The reads from different samples from the dataset are co-assembled into contigs B. Genes are detected on the contigs and extracted in fasta. C. Reads mapped on the contigs (previous section) are mapped on the genes and their respective coverage across the dataset is computed. D. Reads which mapped successfully on the genes and the genes themselves are functionally annotated. E. The annotations of the genes (considered as a 'gold standard') are compared to the annotations of their respective mapped reads. If the annotations of reads and genes are different, the read annotation is considered as spurious (False Positive = FP) and if they are identical, then they are considered as true positives (TP) F.
The API architecture, described in this paper, is directly applicable for any software regardless of its language since it is an easy task to build a wrapper in other programming language as presented in the following for Fortran and Python. The main difficult part is to split the software main program. When a solver is an interoperable component, it can be part of an assembly of elements in permanent interaction that work in a coordinated way. To be considered as an elementary reusable component, a computation model must: • possess APIs, which can be viewed as new functionalities or entry points in the computational model to provide new services on demand; • have a resolution algorithm comprising three main functionalities: problem initialization, simulation and finalization of the computation; • allow instantiation of several clearly separated and identifiable problems; • be usable in dynamic library form.
Design A computer programming model like the object-oriented programming (OOP) aims to organize the software design around data grouped into objects with attributes and methods. This help the developper to focus on the data rather than the logic required to manipulate them. Most of the OOP concepts (encapsulation, interfaces, polymorphism, modularity, instantiation,. ) are derived from software engineering patterns but relate to the real world. As shown in Figure  2 , the first concept used here is Encapsulation with a classic Design Pattern "Facade"  [24]. Since the telemac-mascaret system is written in Fortran 77 and 90, the design pattern has been adapted to this non object-oriented language as OOP is well-suited for the design of large and complex programs. The main benefits of this approach are that it is easier to use and understand, limits the library user dependencies, includes integrity constraints and validation rules and avoids some unnecessary calculations  [41].
Technology To maintain efficient execution, a native code compiler (for example, GNU Compiler Collection, licensed under GNU GPL) running on most systems and hardware architectures, and supporting Fortran, is used and recommended. Moreover, the calculation code of the telemac-mascaret system numerically solves a model based on a system of partial differential equations (PDE). The computational solution relies on an implicit time scheme, removing stability constraints on the time frame, but leading to a linear-system solution of considerable size. The resulting increased complexity of the model structures poses strong limitations in terms of practical implementation and computational requirements. Consequently, High-Performance Computing (HPC) is necessary to tackle realistic applications. The solution deployed in the hydro-informatic system is to decompose the domain of computation.
CALL R UN _S ET _C ON FI G_ T2 D ( ID , LU , LNG , COMM , IERR ) where all parameters are scalars of type integer and ID is an output value giving the instantiation number. The input parameter LU is used to redirect the standard output, LNG has only two possible values for the choice of the english or french language, and COMM is the MPI communicator for the distributed-memory parallelism. The output IERR has to be considered with care as a non-null value stands for the occurrence of errors. A systematic test on this return value is recommended, for instance one may want to stop the program accordingly: IF ( IERR. NE. 0) EXIT IERR After the initialization phase, the model has to be read from files with the following instruction: CALL RU N_READ _CASE_ T2D ( ID , CAS_FILE , DICO_FILE , INIT , IERR ) The name of the steering file is given with CAS_FILE as a character string, it stores several input data of the model with key-value pairs.
Python wrapping The use of API is not limited to Fortran programing but can also be used by a scripting language. To this end, a Python wrapper is also available. It is relatively easy to use the Fortran API routines directly in Python using the f2py tool of the Python Scipy library  [60]. This tool will make it possible to compile Fortran code for use in Python. The only limitations are on the type of arguments of the functions to wrap. Python is a portable, dynamic, extensible, free language, which allows (without requiring) a modular approach and object-oriented programming  [73]. In addition to the benefits of this programming language, Python offers a large number of interoperable libraries for scientific computing, image processing, data processing, machine learning and deep learning.
The link between various interoperable libraries with telemac-mascaret system APIs allows the creation of an ever-more efficient calculation chain capable of responding finely to various complex problems such as the application case presented in this article (see Section 4). Moreover, the Python scripting language makes it possible to implement a wrapper in order to provide user-friendly functioning of the Fortran API. Thus, a Python overlay was developed to encapsulate and simplify the different API calls. For instance, a Python "get" function can encapsulate all API Fortran type-dedicated function get type (where type can be replaced by Double, Integer, Boolean and String). File management is also made easier with Python (copy, move,. , and reducing the number of arguments). This Python wrapping of telemac-mascaret system API constitutes a package called TelApy distributed in the official version of the hydro-informatic system (Figure  5 ).
It therefore becomes natural to drive these APIs using Python scripting language. As mentioned by Knox et al. [39] , this scripting language offers numerical and scientific libraries and is already a recognized tool in environmental resource modeling. Finally, the Python wrapping allows use of the telemac-mascaret system for a wide range of studies, such as optimization, coupling and uncertainty quantification. The Application Programing Interface (API) framework described in this work (Fortran APIs and its Python wrapper) is available for download and distributed in telemac-mascaret system (www. opentelemac. org).
Numerical configuration and available data The Gironde is a navigable estuary in southwest France formed by the confluence of the Garonne and Dordogne rivers just downstream of the center of Bordeaux. The Gironde Estuary is the largest estuary in western Europe. The hydraulic model covers approximately 195 km between the fluvial upstream and the maritime downstream boundary conditions, for an area of around 635 km 2. The finite element mesh is composed of 173, 781 nodes (Figure  6 ). The mesh size varies from 40 m within the area of interest, the navigation channel, to 750 m offshore (western and northern sectors of the model). As shown in Figure  6 , six friction areas are considered in the hydraulic model. The boundary condition along the marine border of the model was set using depth-averaged velocities and water levels from the Legos numerical model TUGO dataset (46 harmonic constants).
Surge data, describing the difference between the tidal signal and the observed water level, are taken into account using a data file generated from the Hycom2D model of the SHOM  [15]. Surface wind data are also considered to simulate the flow under windy conditions. Time-evolution flow discharge hydrographs are imposed upstream of the Gironde Estuary model on the Garonne and Dordogne rivers. The ability of the numerical model to reproduce complex physical processes such as sediment transport is presented in  [58]. The free surface flow was continuously measured (every minute) at the Verdon, Richard, Lamena, Pauillac, Fort Médoc, Ambes, The Marquis, Bassens and Bordeaux locations (Figure  6 ). For this study, observation results are used over a 36 hour period from August 12 to 14, 2015.
Tidal amplification parameter Tidal characteristics are imposed using a database of harmonic constituents to force the open boundary conditions. For each harmonic constituent, the water depth h and components of velocity u and v are calculated, at point P and time t by Eq. 6. f (P, t) = i f i (P, t) f i (P, t) = f i (t) A fi (P ) cos 2πt Ti -ϕ fi (P ) + ϕ 0 i + l i (t) (6) where f is either the water depth h or one of the components of velocity u or v, i refers to the considered constituent, T i is the period of the constituent, A fi is the amplitude of water depth or one of the horizontal components of velocity, ϕ fi is the phase, f i (t) and l i (t) are the nodal factors and ϕ 0 i is the phase at the original time of the simulation. The water level and velocities of each constituent are then submitted to obtain the water depths and velocities for the open boundary conditions (Eq.
Reduced Order Model PCA-PCE As already mentioned, Python is a language that can be used in many scientific contexts and can be adapted to any type of use based on dedicated libraries. The multivariate sensitivity analysis explained in the previous section was carried out based on an open-source library for uncertainty treatment named "OpenTURNS", standing for "Open source initiative to Treat Uncertainties, Risks'N Statistics" (www. openturns. org) [6]. A Design of Experiment (DoE) of size 1000 (n = 1000) is constructed for the PCA-PCE learning step. This number of model evaluation was determined based on a convergence study carried on the sensitivity analysis (see Section 5. 1. 4). The MPI library was used for launching and managing the telemac-2d solver computations. At the end of the calculation, a telemac-2d result file was composed of 288 time records corresponding to a physical variable record every 10 minutes (T = 288).
The deviation between these results varies respectively from 10 to 20 centimeters at Verdon and Bordeaux observation stations. This is confirmed by the average predictivity criterion close to 1 for Verdon and Bordeaux during the study period. A coefficient close to 1 shows a good fit between the validation database and the result estimated by the reduced order model. However, a less satisfactory performance of Q 2 criteria can be noticed at the Bordeaux observation station, compared to the Verdon station. The confluence of the Dordogne and Garonne rivers occurs just downstream from Bordeaux and the influence of the hydrological forcing of these two rivers is not studied here. Consequently, the upstream fluvial discharges are not considered as parameters in construction of the PCA-PCE emulator.
Calibration Results The calibration algorithm (Figure  7 ) is performed by coupling telemac-2d and the data assimilation library ADAO in Python context through the component TelApy of the telemac-mascaret system. ADAO, A module for Data Assimilation and Optimization, provides modular data assimilation and optimization features in Python (https://pypi. org/project/adao) [3]. It can be coupled with other modules or external codes while providing a number of standard and advanced data assimilation or optimization methods. The ADAO library also covers a wide variety of practical applications, from real engineering to experimental methodologies. Its architecture and numerical scalability adapt to the field of application. As shown by Eq. 18, the optimal search for the control vector X takes a minimization form of an objective or cost function (given by the expression inside the exponential term) which must satisfy the background error statistics (prior term) and the equivalent observation error (likelihood term).
For the telemac-mascaret system the transformation towards the concept of interoperability required several years of development. A recommendation is to take into account these criteria at the early stage of development considering the fact that the code will probably have to interact with the outside world. The implementation of APIs makes it possible to extend the scope of the software by facilitating the use for different types of applications. For instance, based on fluid exchange of information, APIs can be used to couple the telemac-mascaret system with a Computational Fluid Dynamics (CFD)-type software (for example Code Saturne  [2] ; www. code-saturne. org) to take into account atmospheric and groundwater flows. The development of standards for publishing interoperable softwares in forms suitable for community interactions remains a major issue  [43].
The present work allows the telemac-mascaret system to be integrated in different environments. However, the wrapper for each specific environment must be maintained. A lean standard has been proposed with the OpenMI environment  [29]. The integration of the telemac-mascaret system in this environment could constitute an outlook to this work. Since Python is really easy to pick up and learn, a wrapper for this language is distributed in the telemac-mascaret system official version (TelAPy). This choice aims to favorise the dissemination of the hydro-informatic system as an environmental modeling tool. However, the system compilation is a cumbersome process which can slow down its dissemination. To overcome this issue, and based on the Python Package Index (PyPI, https://pypi. org), a compiled version of TelApy might be envisaged to provide all dynamic libraries needed to run telemac-mascaret system APIs.
Conclusion The Application Programming Interface (API) of the open source telemac-mascaret system was developed to convert a heterogeneous set of open-source, user-contributed models into a suite of plug-and-play modeling components that can be reused in many different contexts. The APIs provide a user-friendly development framework that can be easily understood, allowing seamless integration of base codes, and do not invalidate existing institutional software development practices. The API development framework does not compel the use of, or supply, specific environmental modeling standards, as its services standardize at a more basic level of internal communication. This gives researchers and model developers more freedom to customize services for the problems they are facing. The telemac-mascaret system API seeks to assist the hydrodynamic community in resolving new challenges, such as uncertainties, real-time data assimilation, and multi-physics simulations.
Many model developers have limited skills in software development and architecture. Recognizing this and seeking to promote the widest possible adoption of the telemac-mascaret system API by the user community, a scripting feature of modeling is privileged with the Python language, as recommended by  Knox et al. [39]. This forms a new module of the system called TelApy. Work performed with Python can be transposed to other scripting languages such as R and Julia. Python is a language that can be used in many contexts and adapted to any type of program based on dedicated libraries. However, it is particularly used to automate tedious tasks, such as the calibration process of a numerical model, saving a significant amount of time in realizing a project. Use of Python is widespread in the scientific community, and it has many libraries optimized and intended for numerical computation.
Acknowledgements The authors gratefully acknowledge contributions from the open-source community, especially that of  OpenTURNS (Open source initiative for the Treatment of Uncertainties, Risks'N Statistics ) and ADAO (a module for Data Assimilation and Optimization). In particular, we would like to thank  Angelique Ponçot  and  Jean-Philippe Argaud  from  EDF R&D  for their constructive discussion on data assimilation,  Regis Lebrun  from  Airbus  for the POD construction based on OpenTURNS and all of the EDF R&D OpenTURNS team. The authors also would like to address special thanks to  Kamal El Kadi Abderrezzak  for his support and deep reading that greatly enriched this paper. Finally, we would like to thank the anonymous reviewer and  Arnald Puy , whose comments and suggestions helped improve the manuscript.
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factortarget databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
Once the core GRN is obtained, mathematical modeling is then applied to simulate the gene expression dynamics  [14] [15] [16] [17] , which helps elucidate the potential gene regulatory mechanism driving the biological process in question. The current practice for synthesizing a core GRN is by compiling data via an extensive literature search, e. g. , in these studies  [18] [19] [20]. While this works well for systems where sufficient knowledge has been gained and accumulated, it is less effective in cases where key component genes and regulatory interactions have yet to be discovered. Due to the rapid increase of biomedical publications, manual synthesis of literature information has become extremely time-consuming and prone to human error in data interpretation. One way to address the labor-intensive issue is to rely on existing manually curated databases, such as KEGG  [21]  and Ingenuity Pathway Analysis (IPA)  [22].
However, these databases often compile gene regulatory interactions from different tissues, species, or diseases. Therefore, it is hard to obtain context-specific interactions directly from these types of databases. The second approach adopts a top-down perspective, in which researchers apply bioinformatics and statistical methods on genome-wide transcriptomics and/or genomics data to infer large-scale GRNs  [13]. These data-driven methods are ideal for obtaining a global picture of gene regulation and the overall structure of gene-gene interactions. This approach can also be used to characterize key regulators and regulatory interactions between genes that are specific to the biological context of the study. However, conventional bioinformatics methods for gene network inference are usually not designed to identify an integrated working system.
So far, to the best of our knowledge, there is no computational platform available that utilizes this combined approach for systematic GRN inference and modeling. In this study, we introduce a computational platform, named NetAct, for inferring a core GRN of key transcription factors (TFs) using both transcriptomics data and a literature-based TF-target database. Integrating both resources allows us to take full advantage of the existing knowledgebase of transcriptional regulation. NetAct adopts the combined top-down bioinformatics and bottom-up systems biology approaches, designed specifically to address the following two major issues. First, many network inference methods rely on correlations of gene expression data, yet the actual transcriptional activities of many master regulators may not be reflected in their gene expression.
RACIPE has been applied to model the dynamical behavior of gene regulatory networks of different biological processes, such as epithelialmesenchymal transition  [23, 29] , cell cycle  [37] , and stem cell differentiation  [38]. The new NetAct platform addresses the abovementioned issues by (1) inferring the activities of TFs for individual samples using the gene expression levels of their targeted genes, (2) identifying the regulatory interactions between two TFs based on their activities rather than their expressions, and (3) subsequently simulating the constructed core GRN with RACIPE to validate and evaluate the gene expression dynamics of the core GRN. In this paper, we describe in detail the NetAct platform, extensive benchmark tests for TF-target databases, TF activity inference, network construction, and two examples of applications to model GRNs with time series gene expression data.
Results We developed a computational systems biology platform, named NetAct, to construct transcription factor (TF)-based GRNs using TF activity. The method uniquely integrates both generic TF-target relationships from literature-based databases and context-specific gene expression data. NetAct also integrates our previously developed mathematical modeling algorithm RACIPE to evaluate whether the constructed network functions properly as a dynamical system. It evaluates the roles of every gene in the network by in silico perturbation analysis. NetAct has three major steps: (1) identifying the core TFs using gene set enrichment analysis (GSEA)  [39]  with an optimized TF-target gene set database (Fig. 1a ), (2) inferring TF activity (Fig. 1b ), and (3) constructing a core TF network (Fig. 1c ).
Then, the network is validated and analyzed by simulating its dynamics using mathematical modeling by RACIPE. Details of each step are given in the "Methods" section and Additional file 1: Supplementary Note 5. Below, we demonstrate how we optimized the NetAct algorithm, compared its performance of activity inference with Fig. 1  Schematics of NetAct. a First, key transcription factors (TFs) are identified using gene set enrichment analysis (GSEA) with a literature-based TF-target database. b Second, the TF activity of an individual sample is inferred from the expression of target genes. From the co-expression and modularity analysis of target genes, we find target genes that are either activated (blue), inhibited (red), or not strongly related to the TF (gray). The activity is defined as the weighted average of target genes activated by the TF minus the weighted average of target genes inhibited by the TF.
c Lastly, a TF regulatory network is constructed according to the mutual information of inferred TF activity and literature-based regulatory interactions. d Performance of GSEA for various TF-target gene set databases. The plot shows the sensitivity and specificity with different q-value cutoffs. The gene set databases in the benchmark include the combined literature-based database (D1); FANTOM5-based databases (D2) with 20, 50, and 100 target genes per TF; the combined experimental-based database (D3, ChIP); and RcisTarget databases (D4), one with 10 targets per TF binding motif and another with 50 total number of targets per TF three existing methods using in silico gene expression data, and applied the network modeling approach to two biological datasets.