dataesr/scientific-paragraphs-categorization huggingface dataset:
We used BS-Seeker2  (Guo et al. , 2013)  to map RRBS data to the human genome hg19/GRCh37 or Mouse genome mm10 and retrieve the number of methylated and unmethylated cytosines at each covered CpG site. Methylation rates were then integrated across CpG island (CGI)-based and gene-based features. CGI-based features were defined as follows: CpG islands (from UCSC database hg19/GRCh37 or Mouse mm10), shores (2 kb on each side of the island) and shelves (2 kb on each side of the shores). DNA methylation outside CpG islands was analyzed by grouping CpG sites not located in CGI-based features every 100kb window. Gene-based features were defined based on Ensembl Homo sapiens GRCh37. p13 or Mus musculus GRCm38. 78 assembly. We calculated for each gene the methylation rate across the promoter region (TSS ± 500bp) and the gene body (Figure  S1A ).
Differential (hydroxy)methylation analysis We compared methylation rates across all CGI-based and gene-based features (covered by at least 50 reads). q values were computed by comparing the number of (hydroxy) methylated and un(hydroxy)methylated reads in each condition using a logistic regression and the SLIM method for pvalue adjustment, as implemented in the methylKit package  (Akalin et al. , 2012). We also calculated the methylation rate difference (delta) between each pair of test and reference sample. We considered as differentially methylated every region with a q-value < 0. 05 and a methylation delta >0. 05 or <À0. 05 in at least 80% of test-reference pairs.
Gene set enrichment analysis We used the GSEA method  (Subramanian et al. , 2005)  to identify gene sets overrepresented among up/down-regulated and hyper/hypomethylated genes. The GSEA method calculates an enrichment score that reflects the degree to which genes belonging to a given gene set are overrepresented at the top or bottom of a gene list, ranked for example by their expression fold-change between two conditions. The statistical significance (nominal p value) of the enrichment score is derived using a permutation test procedure, and p values across gene sets are adjusted for multiple testing. We used the R code downloaded from the GSEA website (https://www. gsea-msigdb. org/gsea/index. jsp) to calculate enrichment scores, significance p values and q-values, which we adapted to be able to provide any ranked gene list as input.
For RNA-seq analyses, genes were ranked according to their expression fold-change between the two conditions (e. g. , SdhbÀ/À versus wild-type cells). For MeDIP-seq analyses, genes were ranked according to the fold-change of methylation signal within their TSS region (TSS ± 500 bp) between the two conditions. For RR(ox) BS analyses, genes were ranked according to the methylation rate difference (delta) within their TSS region (TSS ± 500 bp) between the two conditions. We downloaded gene sets from the MSigDB database (version 5. 1) and restricted our analysis to the hallmark (H), curated (C2) and oncogenic (C6) gene sets.
Wound scratch assay Wound scratch assays were performed as described elsewhere  (Loriot et al. , 2015)  and analyzed using ImageJ software or using Nikon videomicroscope and NIS-advanced imaging software. The experiments were performed in triplicates. shRNA stable transduction Lentiviral constructs were purchased at Sigma-Aldrich. pLKO. 1 is the parental viral vector without shRNA. WT imCC (30000) were infected in a serum-free medium containing 8 mg/mL polybrene by either 10 MOI of Tet1 (TRCN0000341849) and 10 MOI of Tet2 anti-TET2 (Proteintech #21207-1-AP 1/400), anti-TET3 (Active motif #61744 clone 23B9 1/400), anti-HIF2a (Abcam #ab8365 1/1000; Figure  6 ) or, anti-HIF2a (Abcam ab109616, 1/500; Figure  7 ). For H3K27me3 and H3-Cterminal western blots, proteins were extracted by acidic lysis with 0,2M H2SO4, and precipitated with trichloroacetic acid.
ACKNOWLEDGMENTS We thank  Charles Marcaillou ,  Sylvain Guibert , and Celine Vallot for technical contributions and advice and  Catherine Tritscher  for precious administrative help. This work has received funding from the  European Union  (FP7/2007-2013 no. 259735 ), The  Plan Cancer, Epigenetique et Cancer  ( EPIG201303   METABEPIC ), The  Paradifference Foundation , and the  Cancer Research for Personalized Medicine (CARPEM)  project  (Site de Recherche Integresur le Cancer [SIRIC ]). We thank all members of the  Genetics Department, Biological Resources Center and Tumor Bank Platform, Hôpital Europeen Georges Pompidou  ( BB-0033-00063 ). This work is part of the '' Cartes d'Identitedes Tumeurs (CIT) program '' funded and developed by the  Ligue Nationale contre le Cancer. S. M. is the recipient of a fellowship from  La Fondation pour la Recherche Medicale.
1/ R statistical software The R Foundation https://cran. r-project. org BS-Seeker2 Guo et al. , 2013 https://github. com/BSSeeker/BSseeker2 methylKit package Akalin et al. , 2012 https://bioconductor. org/packages/release/ bioc/html/methylKit. html R code from the GSEA website https://www. gsea-msigdb. org/gsea/index. jsp PRISM 8 Graphpad https://www. graphpad. com/scientificsoftware/prism/ NIS-advanced imaging software Nikon https://www. microscope. healthcare. nikon. com/products/software/nis-elements Other MSigDB database version 5. 1 UCSD, Broad Institute https://www. gsea-msigdb. org/gsea/msigdb/ index. jsp DATA AND CODE AVAILABILITY All raw sequence data have been deposited to the European Genome Phenome Archive. The accession number for the MeDIP-seq, RRBS, and oxRRBS data reported in this paper is EGA: EGAS00001004252.
A consolidated geomorphology-based approach for discharge prediction in ungauged basins (PUB) through deconvolution of discharge signals from gauged donor catchments, their transposition and their convolution towards target outlets, has been made available online to end users through a Web Processing Service (WPS) for the synoptic peninsular region of Brittany, France. In the spirit of hydrological services, the SIMFEN WPS allows anyone to execute the hydrological modelling package transfR online through: (1) Open Geospatial Consortium (OGC®) interoperability standards; (2) collection and use of public hydrometric data; (3) connection to the pre-existing MNTSurf WPS for geomorphometric analysis; (4) visualization using a collaboratively developed Mviewer; and (5) innovative WPS workflows. The ability to model discharge series at any ungauged outlet of the synoptic region is offered to specialists of other disciplines, non-modeller water practitioners and interested citizens to support interdisciplinarity, water monitoring and management, and related science-society-policy debates and actions.
The method allows users to simulate series of streamflow discharge (i. e. hydrograph) at the outlet of a given ungauged target basin based on series of discharge observed at the outlet(s) of one or more informationdonor basins using a geomorphology-based deconvolutiontransposition-reconvolution approach. The SIMFEN Web Processing Service (WPS) and associated interface allow end users to simulate discharge series online at any location of the river network draining at least approx. 10 km 2 of the synoptic region of Brittany (Bretagne in French), France, which has a coherent hydrographic area of 29,640 km 2 and faces serious hydrological (including hydrochemical) issues  (Cudennec et al. 2015 , Fovet et al. 2018 , de Lavenne et al. 2019). The hydrometric data required by the WPS are automatically downloaded from public databases to simplify use by end users.
Hydrogeomorphological and hydrometric data of Brittany The regional reference DEM and associated river network embedded in the SIMFEN WPS was obtained by correcting the 50-m resolution DEM provided by the French Geographical Institute (IGN) of its drainage anomalies with MNTSurf software, itself provided as a WPS  (Aurousseau and Squividant 1996, Squividant et al. 2015). River gauging stations are managed by DREAL (Direction Régionale de l'Environnement, de l'Aménagement et du Logement), the regional division of the French Ministry in charge of the Environment. There are 125 stations available in the region, the oldest one dating back to 1938. Hydrometric data and some signatures are contained in the national Banque Hydro website 1 but are currently difficult for end users to access and download.
However, DREAL has an OGC-compliant data flux WFS of daily discharges, 2  which to date has remained for confidential use by a regional consortium for inter-agency coordination (GéoBretagne 3  ) to monitor the regional hydrological status and display it in summaries (e. g. https://bretagne-environnement. fr). The SIMFEN WPS uses the DREAL database and data flux WFS to update inputs and -consistent with the principles of Web Service interoperability and reusability, and the INSPIRE directive (EC 2018) -will be easily switched to the complete public hydrometric data flux Web Service once it is made available at the national level.
System and architecture The system is organized into four parts: client, calculation, data and web servers (Fig. 2 ); and development followed the INSPIRE-directive architecture, which is organized into three layers: service, data and application (Fig. 3 ). The service layer is the core of the SIMFEN WPS service-oriented architecture, which has two types of service functions. First, classical and wellknown functions, which are used in many other Spatial Data Infrastructures, are discovery, spatial view and spatial download functions, to find, view and download geographical data, respectively. Second, more specific functions are (i) a temporal download function, to share hydrological series; (ii) connection to the pre-existing MNTSurf WPS, which includes geomorphometric functions; and (iii) the SIMFEN WPS itself, which executes the hydrological modelling functions using the transfR package.
The data layer describes persistent data storage associated with each service, using different types. For example, the temporal function is associated with a PostgreSQL database, MNTSurf WPS with a simple "directories and files" data organization and the spatial download function with both a PostGIS database and raster GeoTIFF files or vector shapefiles. The application layer  connects end users to the functions and services of the service layer. All of the latter developments can be reused by any OGCcompliant application. The SIMFEN WPS can be accessed directly using a GIS software, a script or URL request at http://wps. geosas. fr/sim fen. For ease of use by non-specialist end users, however, we developed a public web interface (http://bit. do/simfen).
Description of the SIMFEN WPS To make it easy to use, the SIMFEN WPS allows users to execute the transfR package, with a minimum of input data (Fig. 4 ). It is operationalized through a process developed with PyWPS (version 4. 3. dev0), which is an implementation of the OGC® WPS standard written in Python  (Čepický and De Sousa 2016)  that supports WPS version 1. 0. 0. Modelling results follow the OGC® WaterML standard (Kadlec and Ames 2011) and, for those using the web interface, are provided in. csv format.
Web application and interface The web application and bilingual interface (Fig. 5 ) on the web server are based on the cartographic viewer "MViewer" collaboratively developed by the GéoBretagne consortium, 4 to which we have contributed for several years as a research unit committed to providing open results and products to the public. It was developed using HTML5, CSS3, JavaScript, JQuery, OpenLayers and BootStrap. An add-on has been developed to send a form to the SIMFEN WPS with user inputs, to track progression and to collect modelling results.
Pre-treatments and processing time The hydrological modelling is composed of three steps: deconvolution of observed discharge series of the donor basin(s), transposition using ensemble weighting towards the target basin and simulation of discharge at the target outlet via convolution (Fig. 1 ). The last two steps depend upon user choices at execution, but the deconvolution step is pre-computed (Fig. 4 ). It estimates the R n of a gauged basin as a function of the latter's contour, geomorphometric function and the entire history of streamflow discharge series at the outlet. These characteristics are calculated in advance for every gauged basin (i. e. potential donor basin) in the domain and stored in the PostgreSQL database on the data server. Thus, deconvolution, which requires the most time because of its underlying inverse modelling, does not need to be performed at the time of simulation, increasing the speed of service delivery.
The R n is updated automatically every week by a script that determines whether new discharge measurements are available from the data flux WFS. In addition, processing time has been optimized by using the OGC® WPS, which allows processes to be executed asynchronously. During the execution of the modelling functions, the WPS returns an iterative XML document with a tag status. The web interface controls this tag to know when the process has finished, allowing users to view and optionally download the results.
Operational use sequence and options The web interface displays a default configuration to allow anyone to run the SIMFEN WPS, with standard options. The advanced mode, which allows users to customize certain options, is useful for those who wish to use their hydrological expertise in the modelling exercise or to explore alternate options and assess the sensitivity and quality of simulations. This feature, visualization options, accompanying documentation and metadata aim to help end users understand the modelling principles, epistemology, data-dependence and uncertainties. Online documentation includes the rationale, scientific references, credits and disclaimers. A tutorial video is also available. 5 The entire interface has been tested and finely tuned with a focus group of end users. To proceed, the user indicates the location of the target outlet and the period over which to simulate streamflow discharge series (i.
These data are available using WFSs, essentially of the national public service SANDRE, 6  and they are displayed in the SIMFEN web interface (Fig. 5  provides an illustration) using an on-demand workflow, with no data storage in the SIMFEN database and thus no updating or maintenance issues. The "xy2watershed" function of MNTSurf WPS  (Squividant et al. 2015)  is used to precisely locate the target outlet on the river network and delineate the contour of the target basin. Then, the "xy2hl" function of MNTSurf WPS analyses the river network within the target basin to extract the geomorphometric function, which then yields the TF when the regional streamflow velocity is applied (Fig. 4 ). Next, the process builds a WFS request to identify all gauging stations available in the DREAL database in the geographic neighbourhood of the target basin over the given period.
5  displays the example of an enveloped simulation for a target basin based on five donor basins). The combined display of the hydrographs and mapping of the basin contours and networks help users understand the basins' hydrological behaviour, hydrological similarity, as well as the modelling epistemology, data-dependence and uncertainties. They also allow advanced users to explore options and gain expertise. The simulated hydrographs are accompanied by the processing metadata, which include the location of the target outlet, the period simulated and the basin(s) used as a source of information. The metadata allow users of the SIMFEN WPS and those with whom simulations are subsequently shared to replicate the simulations and understand the data. In addition, following the WPS standard, a function (GetCapabilities request) allows users to access the Web Service metadata themselves.
Conclusion Web Service technology and interoperability standards were used to develop a hydrological service that provides a consolidated geomorphology-based modelling approach online to simulate streamflow discharge series (hydrographs) at ungauged target outlets of societal or technical interest. The SIMFEN Web Service allows anyone to execute the hydrological modelling package transfR online using a dedicated WPS 7  with the following rationale: (1) respect of OGC interoperability standards; (2) collection and use of public hydrometric and hydrogeomorphological data; (3) connection to the preexisting MNTSurf WPS for geomorphometric analysis; (4) visualization through a web interface using a collaborative MViewer 8  ; and (5) facilitation of future innovative WPS workflows. The SIMFEN WPS is currently available for the synoptic peninsular region of Brittany, France.
It benefits from the combination of a consolidated geomorphology-based hydrological modelling approach and a proactive collaborative strategy for open access to research data and products of the research unit SAS. Its ergonomics and options have been designed in cooperation with a focus group of end users. The Web Service is tuned to current data flux WFSs offered by the regional public hydrometric authorities and is switchable to upcoming national WFS data fluxes, which will allow expanding the geographic domain and may open up new opportunities, especially as the data will have hydrometric time steps shorter than one day. The SIMFEN Web Service provides modelling abilities to end users, such as specialists of other disciplines interfacing with hydrology, non-modeller water practitioners and interested citizens.
g. Jade  [4] , NetLogo  [5] , GAMA  [6] , or Janus  [7]. On one hand, these solutions are highly interesting because they frame and provide tools for the development of agent-based systems. On the other hand, these systems are very complex to implement, and the conventional programming languages (Java, etc. ) are not suited. There is therefore a real need for programming languages dedicated to MAS, which would offer more clarity to developers, and simplify developments. Several agent-programming languages have been proposed. Most of them are domain-specific languages, e. g. GAML  [6] , MARS  [8] , or Jason  [9]. Several of these languages are general-purpose, e. g. SARL  [10]. SARL  [10]  is a new general-purpose agent-oriented programming language (APL). SARL tries to set up adaptive and modular principles for developing multi-agent systems.
SARL Tool-chain The SARL tool-chain is the set of programming tools that are used to perform a multi-agent system with SARL. As illustrated by Figure  3 , three types of tools are used in sequence in order to create and run an agent-based system: • SARL Compiler: The SARL compiler transforms the source SARL language to the target language. Several target languages may be considered by this compiler. Because most of the agent frameworks are written with the Java language, the SARL compiler targets this object-oriented programming language by default, but not restricted to (a Python generator is also provided as proof-of-concept). The SARL compiler translates SARL statements into their object-oriented equivalent statements. Three different implementations of the SARL compiler are provided: a specific command-line tool (sarlc), the Eclipse development environment plugin, and a Maven plugin.
• Java Compiler: The files generated by the SARL compiler are standard Java files. They must be compiled with one of the standard tools that are available: eclipsec 2  , javac 3  , gcj  4  , or jikes 5. The result of the Java compilation is a collection of binary files (a. k. a. byte-code files) that may be run by a virtual machine. • SARL Run-time Environment: The SARL Run-time Environment (SRE) is a collection of tools that enables running of an agent-based application written with SARL. Such an SRE must provide the implementation for each service and feature that are assumed to be provided by the runtime environment. Usually, a Java-based SRE is composed by the Java Run-time Environment (JRE) and a Java framework that supports the execution of the agents upon the JRE. In order to create a valid SRE, it is mandatory to define the core features that are expected into a SRE implementation.
Selection of an Agent Framework as SRE Candidate SARL language specifies a set of concepts and their relations. It defines on top of them a collection of Built-In Capacities for agents. However, the SARL project does not impose a particular execution infrastructure. We consider that many different implementations of these concepts can be provided, and it can help SARL be developed faster. In this section, four Java-based agent platforms are considered as candidates for implementing a SRE: Jade  [4] , NetLogo  [5] , GAMA  [6] , and Janus  [7]. According to the key points that are described in Section 4, several criteria are used to compare these agent frameworks: • Application type: Multi-agent systems have a very wide scope of application, including 3D simulation, geography simulation, social simulation, video game.
e. agents are run in a loop, or multi-threaded. • Hierarchical Multiagent System: Hierarchical systems, and specifically holonic systems are a key principle behind the SARL agent programming language. This criterion indicates if, and how, a hierarchy of agents is supported by the agent framework. Table  1  provides an overview of our agent framework comparison. Jade and Janus are both general purpose platforms: they could be used for building any agent-based application, including embedded system and simulation applications. NetLogo and GAMA are simulation platforms because they enables to build simulation software. We advocate that a general purpose platform fits better the needs for the SARL agents. Indeed, SARL does not target a specific application domain. It is defined for enabling the building of embedded and simulation-based applications, for instances.
SARL Key Concepts Jade  [4]  NetLogo  [5]  GAMA  [6]  Janus  [7]  Application Type  All the existing platforms that are included in this comparison provide a message-based interaction mechanism: agents are sending messages to other agents. The receiving agents decide to get the messages from their mail boxes. This approach corresponds to a pro-active behavior: consuming a message is a direct and explicit decision of the agent. The messages are not given to the agent as part of its reactive behavior. It does not fit the reactive interaction behavior, based on events, that is expected by the SARL developers. The creation of an SRE, based on all the above frameworks needs a complete recast of the data exchange mechanism. The agent's life-cycle is almost supported in the same way by all the above platforms.
Initialization and destruction functions are invoked at the beginning and end of the agent's life. In between, the agent's life is supported by a specific function that is called regularly by the agent framework. SARL specification does not assume the existence of this function. In order to make each platform compliant with SARL, this agent running function should read the agent mailbox and fires corresponding events, for example. Agent execution is another key feature of an agent framework. The existing frameworks use a single thread to each agent. According to the execution mechanism that is expected by SARL, the execution mechanism should be adapted to enable threaded execution of the event handlers. Or, this parallel execution should be simulated in order to give the illusion to the agent's code that the event handlers are executed in parallel.