dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of NaviCell map with NaviCell factory. CellDesigner map annotated in the NaviCell format is converted into the NaviCell web-based front-end, which is a set of html pages with integrated JavaScript code that can be launched in a web browser for online use. HUGO identifiers in the annotation form allow using NaviCell tool for visualization of omics data. A detailed guide of using the NaviCell factory embedded in the BiNoM Cytoscape plugin 47 is provided at https://navicell. curie. fr/doc/NaviCellMapperAdminGuide. pdf. Depositing maps at several web-based platforms. Cell-type specific maps and the meta-map of innate immune response in cancer were made available at other platforms such as MINERVA and NDEx. To integrate maps within NDEx, Cell-Designer maps were first loaded in Cytoscape using the BiNoM Cytoscape plugin and then uploaded on NDEx using the CyNDEx Cytoscape plugin.
Databases content comparison. Pathways related to the human innate immune system were selected from the InnateDB 5. 4 version, except Complement Cascade (Human), NOD-like Receptor Signaling Pathway, Regulation of Autophagy (Human), and RIG-I-Like Receptor Signaling Pathway (Human). The excluded pathways represent virus and bacterial infection-specific pathways that do not correspond to TME signaling. The innate immune-related pathways from KEGG 84. 1 version were retrieved from the list 5. 1-Immune System. The pathways obtained from REACTOME 63rd version cover Class I MHC Mediated Antigen Processing & Presentation, MHC Class II Antigen Presentation from Adaptive Immune Branch, and all pathways from Innate Immune Branch. All together 666 gene names from InnateDB 5. 4, 563 gene names from KEGG 84.
1, and 2156 gene names from REACTOME 63rd were selected. These lists were compared with the innate immune response meta-map that contains 683 gene names. The complete list of selected pathways with gene names is available in the Supplementary Data 2). The selected InnateDB pathways contain altogether, nearly the same number of objects as the innate immune response meta-map (Supplementary Data 4). The content of selected KEGG or REACTOME pathways is richer than in the innate immune response meta-map, due to the fact that KEGG and REACTOME are generic databases, describing all innate immune-related interactions, whereas the meta-maps is rather oriented to cancer signaling. The overlap between the metamap and the three selected databases represents 61% for InnateDB, 58% for KEGG, and 30% for REACTOME.
It is important to note that there are 188 genes that present exclusively at the innate immune response meta-map (Supplementary Fig. 4A , Supplementary Data 2). These unique genes are relatively homogeneously distributed across the meta-map, indicating that the depicted processes are described in more depth on the meta-map compared to the other three databases (Supplementary Fig. 3A ). Several modules are significantly enriched by unique genes on the meta-map (Supplementary Fig. 3B ). Thus, the modules Tumor Growth and Immunosuppressive Checkpoints contain signaling that are very well studied in cancer cells and therefore represented in great details on the meta-map. Two additional modules, entitled MIRNA TF Immunostimulatory and MIRNA TF Immunosuppressive, contain the latest information of miRNA involvement in the innate immune system control in cancer and unique for the meta-map, compared to other databases.
It was concluded that the content of the meta-map is not redundant with the other pathway databases and that several functional modules directly related to TME functions are unique to the meta-map. Databases annotation literature comparison. In addition, the sets of publications used to annotate the InnateDB resource and aforementioned preselected pathway from REACTOME resource were compared to the set of publications used in the meta-map. The overlap of the literature body from the meta-map with references from InnateDB and REACTOME databases is relatively small, because 785 papers out of 820 papers that were used to annotate the meta-map are unique (Supplementary Fig. 4B ). It confirms that the meta-map is not a mechanical compilation of existing databases, but rather an independent resource.
It formalizes the part of biological knowledge which was not annotated before and highlights the difference between reconstruction of generic and cell-type specific pathways in terms of literature sources. Although the median age of the literature references in the meta-map is only one year-younger compared to InnateDB and REACTOME, there is a 27% of papers dating 2010-2017 in the literature body annotating the meta-map. The literature set in the meta-map contains more papers published after year 2010 than in InnateDB and REACTOME, indicating that the meta-map represents the most recent discoveries in the corresponding fields (Supplementary Fig. 4C ). Finally, the journal types represented in the three databases were also compared. The choice of the journals used for annotating the meta-map and the other two databases is similar; however, the distribution of the papers from different types of journals is not even.
The annotations of meta-map mainly contain papers from immunological journals such as Journal of Immunology, Immunity, Nature Immunology, and cancer-specific journals, such as Cancer Research and Oncogene, comparing to the other two databases. The annotations of InnateDB and REACTOME are rather oriented towards more generic molecular biology journals as JBC, MCB, Nature, and PNAS (Supplementary Fig. 4C  and D ). High-throughput data analytical pipeline. Normalized melanoma data sets from GEO (GSE72056)  45  were transformed into log expression levels and mean centered. The exploratory analysis and statistical testing was performed and visualized using R packages (ggplot2, stats, pheatmap)  [56] [57] [58]  then MATLAB ICA implementation of FastICA algorithm  46  and icasso package  59  to improve the stability.
Colored map images were obtained using function "Stain CellDesigner map" from BiNoM Cytoscape plugin 47 using. xml map files and the mean expression from the analysis described below. Analytical pipeline. The single-cell molecular profiles are characterized by high variability that have both biological and technical origin. A common practice is to group single cells in order to make an aggregated representative profile that minimizes the technical biases but still represents finer level of granularity than a bulk sample. In order to define cell groupings that would lead to functional interpretation we used a matrix factorization technique called ICA ICA is a matrix factorization-based technique aiming at defining statistically independent hidden factors shaping gene expression. Stability-based analysis revealed only one sufficiently stable independent component in the case of both Macrophage and NK data subsets.
Acknowledgements We thank  Daniel Rovera  for help with network structure analysis and  L. Cristobal Monraz Gomez  for help with data visualization and critical reading of the paper. We thank  Marek Ostaszewski  and  Piotr Gawron  for integration of the resource into the MINERVA platform. This work has been funded by  INSERM Plan Cancer  No. BIO2014-08 COMET grant  under  ITMO Cancer BioSys program. This work received support from  MASTODON program  by  CNRS  (project  APLIGOOGLE), COLOSYS  grant  ANR-15-CMED-0001-04 , provided by the  Agence Nationale de la Recherche  under the frame of ERACoSysMed-1, the  ERA-Net  for Systems Medicine in clinical research and medical practice and by IMI2-IMMUcan grant. ITMO cancer (AVIESAN) provided 3-year PhD grant and foundation  Bettencourt Schueller  and  Center for Interdisciplinary Research  supported the training of the Ph.
NaviCell, MINEVRA and integrated into the repository NDEx. The maps exist and can be downloaded in several exchange formats (CellDesigner SBML level 2 version 4, SBGN-ML 0. 2, SBML level 3 version 1, Cytoscape CX version 3. 4. 0). In addition, the composition of map signaling pathways, modules, and meta-modules is provided in a form of GMT files (Supplementary Tables2 and 3, respectively) suitable for further functional data analysis. A network of binary relations between proteins generated from the meta-map and the complete list of references annotating the maps are also available.
In previous work we have shown how the interpretation of reporter gene data can be formulated as an input estimation problem and solved in a mathematically sound and practically useful manner using linear inversion  [22]. Most existing approaches first smooth the primary data and then inject the resulting approximating functions into the gene expression model, which may lead to estimation errors that are difficult to control. Instead of this indirect approach, we formulated a global optimization problem with a regularization term directly applying to the quantities to be estimated. This problem was solved using efficient linear inversion methods  [24] [25] [26]  adapted to the structure of the problem. The methods have been implemented in the Python package Well-FARE and made accessible through the web application WellInverter.
If the reporter protein is stable as well, the default choice of γ p = γ r usually leads to good results, in the sense of returning a protein concentration that is a smoothed version of the reporter concentration. The system of Eqs. 5-7 is again linear, where the equations for the reporter and host protein quantity are coupled by a shared input a(t). The linear inversion procedure can be adapted for linear equations with a shared input, resulting in an estimate of p(t) from absorbance and fluorescence measurements, given values for γ p , γ r  [22]. The linear inversion procedures for estimating the growth rate, promoter activity, and protein concentration from population-level absorbance and fluorescence data have been implemented in the Python package Well-FARE  [22]. A new version correcting several (minor) bugs and some new functionalities has been released since the original publication (see "Availability and requirements" section).
Distributed computing architecture The WellFARE package has been integrated into the web application WellInverter, thus allowing non-expert users to analyze their data in a user-friendly way without installing the package and developing Python scripts. WellInverter has a client-server architecture: the client can access the application through a web browser and the estimation queries are handled by a dedicated server running WellFARE. The estimation procedures may be computationally complex, because the algorithms involve numerical simulation of the models and the multiplication and inversion of large matrices  [22]. The complexity is determined by the number of so-called control points, a high number of control points corresponding to a high temporal resolution of the estimates of promoter activities and protein concentrations.
Graphical user interface The client part of WellInverter consists of the graphical user interface (GUI) of the application, running in a web browser. The GUI of the original WellInverter was functional but limited, in the sense that it did not support the entire workflow and the whole range of options of the WellFARE algorithms. We therefore designed and developed a completely new GUI, written in JavaScript and communicating with the server using JSON-encoded data and Ajax (Asynchronous JavaScript) calls  [33, 34]. The GUI is structured around a visual representation of the microplate and guides the user through the process of analyzing reporter gene data in an intuitive manner, as Fig. 2  Distributed architecture of WellInverter. User queries are received by a front-end web server that distributes them over several back-end Python application servers running WellFARE, using the load balancer.
We have chosen ANTLR  [38] , which allows the data format to be formulated as a grammar from which a parser is generated that is capable of interpreting files specified in the language defined by the grammar. In ANTLR, grammars are specified in the EBNF formalism  [39]. An excerpt of the grammar for parsing data files produced by a Tecan microplate reader is shown in Fig. 3. When defining a grammar for a new data file format, it is usually convenient to start from existing grammars that have been developed already and that come with WellInverter. The grammar specification is transformed by ANTLR into Java code, the parser, which can be called by WellInverter when an experiment data file is uploaded. The parsers are plug-ins of WellInverter, declared on the server side of the application, thus making their development, addition, and maintenance entirely modular.
Data export The GUI offers general file management operations for loading and saving the results of (the analysis of ) a reporter gene experiment. For many purposes, it is desirable to export the data in formats that allow them to be analyzed by other software, such as general purpose scientific programming languages like Matlab, Python, and R. In the new version of WellInverter, the contents of the Plots screen can be exported in CSV and XLS format, in addition to several graphical formats (PDF, JPEG, PNG, SVG). Moreover, the user can export the data and/or the final or intermediate analysis results in the JSON format, a format that is widely used in the scientific computing community. The precise contents of the JSON export file can be customized in the Export screen. The file structure is defined in the WellInverter manual.
1% glucose (blue) or 0. 1% galactose (red) Fig. 8  Computation of reporter concentrations. Mean absorbance and reporter concentration curves and an uncertainty band defined by the standard deviation for a group of wells containing WT pRM-gfp cultures growing in M9 minimal medium with 0. 1% glucose (blue) or 0. 1% maltose (green). Upper: Reporter concentration estimates obtained by dividing fluorescence by absorbance. Lower: Smoothed estimates of reporter concentrations obtained by computing the protein concentration while setting the degradation constant of the host protein equal to the degradation constant of the GFP reporter in the Parameters window (0. 00065 min -1 ). Notice that the two estimates are not shown on the same scale and integrate the results over different conditions, etc. This will require the use of more flexible, general-purpose scientific software, such as Matlab, R or Python.
Moreover, in some cases one may wish to relax the simplifying assumptions that underlie the models presented in the "Estimation algorithms" section. For example, as explained in  [22] , the models can be generalized so as to distinguish between the transcription and translation processes and to explicitly take into account the maturation of the reporter protein. The WellFARE package contains more sophisticated and time-consuming variants of the estimation algorithms implemented in WellInverter to analyze such extended models. In order to allow further analysis of the data, beyond the functionalities supported by WellInverter, the data and the analysis results can be exported. The Export screen allows the user to define the wells and analysis results to export (Fig. 9 ).
Growth-rate dependence of the expression of a constitutive gene The data for the reporter gene experiments have been exported and analyzed separately using Python to determine the relation between the estimated growth rates and reporter protein concentrations. For each medium, we computed the mean and the standard deviation of the steady-state growth rate estimated by WellInverter for (background-corrected) absorbance values between 0. 01 and 0. 1. In this interval the growth rate of the cultures is observed to be constant and maximal (see Fig. 6  and the discussion in the previous section). Similarly, we computed the mean of the steady-state concentration of the reporter protein produced from the pRM-controlled gene. A scatter plot of pairs of steady-state growth rate and reporter concentration for the different media is shown in Fig.
Most of the methods have in common that they are based on an explicit model of the process of gene expression, although they differ in the details of the biochemical processes considered (see, for example,  [11, 17, 23]  for a discussion of gene expression models used). The models implemented in WellInverter are the simplest possible, reducing gene expression to a single step lumping transcription and translation. While the methods underlying the tool admit multi-step models  [22] , in our experience one-step models are sufficient in most situations and avoid difficult issues with the parametrization of intermediate transcription, translation, and maturation steps. Moreover, if not sufficient, one-step models at least allow for an initial exploration of the results, before exporting the background-corrected data to tailored analysis scripts for multi-step models.
Moreover, the approach can be easily generalized to the estimation of growth rates and protein concentrations. It should be mentioned though that validation studies with indirect approaches have shown that the latter can also yield excellent results  [17, 23]. The simulation study in  [23]  comparing a direct with an indirect method shows better performance of the latter for data with low time resolution and no bias for low absorbance values. This latter point suggests further work to improve the implementation of regularization in the WellFARE package. Source code implementing some of the abovementioned methods is publicly available, but until now few user-friendly computer tools, equipped with a graphical user interface, have been developed to assist biologists in the analysis of reporter gene data.
The exceptions that we are aware of, such as WellReader  [46]  and BasyLICA  [19] , do not support the same broad scope of functionalities, or are based on proprietary software, or are platform dependent, or a combination of the above. One of the motivations for the development of the original WellInverter software was to make the tool available as a web application, thus avoiding technical issues associated with the cross-platform installation of the software and the supporting libraries. Table  1  summarizes some useful features of available tools. The new version of WellInverter presented in this paper improves upon the shortcomings of the original application in a number of important ways. The graphical user interface has been entirely redesigned and rewritten, scalability has been ensured by the development of a parallel computing architecture based on a generic load balancer, and the use of a grammar-based parser generator has made it possible to easily adapt the tool to the import of input files in multiple formats.
The new version of WellInverter has been deployed on publicly accessible web servers hosted by the Institut Français de Bioinformatique and Inria, but can also be locally installed (see "Availability and requirements" section). The capabilities of the tool have been illustrated on the analysis of an E. coli dataset. The expression of a green fluorescent reporter protein from a phage promoter has been related to the growth rate, and the resulting curve compared with the predictions from a theoretical model. This has provided further evidence that the phage promoter is constitutive. The illustrations given above concern fluorescence reporter gene data, but WellInverter is also directly applicable to the analysis of luminescence reporter gene data, since the underlying gene expression models are the same  [17].
Funding This work was supported in part by a grant from the  Institut Français de Bioinformatique (IFB) ,  in the framework of its call Development of innovative bioinformatics services for life sciences , project  WellInverter  ( ANR-11-INBS-0013 ), and by the  Programme Investissements d'Avenir (PIA) ,  Bioinformatique , project  Reset  ( ANR-11-BINF-0005 ). The funding body did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Ensuring extensive experimental reproducibility in this context requires the platform to allow for thorough hardware modifications, as well as the sharing of said modifications within the scientific community. Therefore, we chose to develop Reachy's design on the following principles and technical solutions: 3D-printed plastic skeleton parts; off-the-shelf actuators, mechanical components and electronics; free and open-source sharing of both software and hardware resources. Reachy was designed by the creators of the Poppy project  (Lapeyre et al. , 2014) , a family of robots for research, art and education that rely on a common software and hardware architecture, but display a variety of shapes, features and purposes. In particular, the first robot of this family, Poppy Humanoid, was originally designed to investigate the role of morphology in biped locomotion  (Lapeyre et al.
The fourth and fifth motors operate respectively elbow flexion-extension and forearm pronation-supination, the latter being mounted with a ball bearing. Finally, the last two motors operate the wrist joint by performing consecutively radial-ulnar deviation and flexion-extension. Their respective rotation axes are orthogonal, however they do not intersect; instead, the two motors are linked by a short piece joining the forearm and end-effector. This interval between rotation axes shares some resemblance to that which separates a human's wrist joint axes, even though it is slightly too large because of the actuator size. We chose to employ Robotis Dynamixel servomotors 1  to actuate Reachy's seven DoF. These motors are all-in-one modules that provide a good trade-off between accuracy, speed and robustness in mechanical terms, as well as embedded sensors monitoring angular speed and position.
Electronics and Software Reachy's motors are connected with each other in a series using three-pin connectors and powered by a pair of 12 V×5 A power supply units, for a total power of 120 W. At one end of the series, a USB adapter allows for plugging into a computer. The robot is then controlled through a serial port with a software interface called Pypot, which handles the communication with Dynamixel servomotors to drive the robot, e. g. , sending motor commands, retrieving data from embedded sensors. This architecture is illustrated in Figure  1A. Developed as part of the Poppy project, this software base is common to the whole Poppy-Reachy family of Dynamixelpowered robots. Following an open-source approach, Pypot was entirely written in Python in order to enable cross-platform deployment, as this language is compatible with most desktop operating systems as well as some embedded systems for singleboard computers.
Python programming also allows for fast development by emphasizing code readability and conciseness, so that developers can efficiently produce clear programs whether their project is of small or large scale. Besides, Reachy users can take advantage of numerous Python libraries dedicated to scientific computing, and already in use within the scientific community. This allows them to combine Reachy's features with techniques such as signal processing or machine learning, without having to resort to other languages or software. While its open-source nature provides expert programmers with extensive freedom over the system, Pypot is also intended to be accessible to beginners. In particular, it provides highlevel motor commands over the joints' angular positions and mechanical compliance, so that any user can program a trajectory and put the robot in motion with only a few lines of code (see Supplementary Material).