dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We warmly thank all the participants and community leaders who generously contributed to the study. We are also grateful to  Kylie Jenkins  of  Australian Aid 's  Fiji Health Sector Support Programme ,  Teheipuaura Mariteragi-Helle  at the  Institut Louis Malarde , and  Dr Ketan Christie  at the  University of the South Pacific. We thank the staff of the  Ministry of Health clinical services and Mataika House Fiji Centre for Communicable Disease Control  for providing the surveillance data underpinning this study. We would also like to acknowledge the work of the field teams:  Dr. Kitione Rawalai ,  Jeremaia Coriakula ,  Ilai Koro ,  Sala Ratulevu ,  Ala Salesi ,  Meredani Taufa , and  Leone Vunileba  (2013);  Meredani Taufa ,  Adi Kuini Kadi ,  Jokaveti Vubaya ,  Colin Michel ,  Mereani Koroi ,  Atu Vesikula , and  Josateki Raibevu  (2015).
PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability.
Background The field of metabolomics has seen remarkable progress over the last decade and has enabled fascinating discoveries in many different research areas. Metabolomics is the study of small molecules in organisms that can reveal detailed insights into metabolic biochemistry, e. g. , changes in concentrations of specific molecules, metabolic fluxes between cells or compartments, identification of molecules that are involved in the pathogenesis of a disease, and the study of the biochemical phenotype of animals, plants, and even soil microorganisms  [1] [2] [3]. The principal metabolomics technologies of mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) typically generate large datasets that require computationally intensive analyses  [4]. Biomedical investigations can involve large cohorts with many thousands of metabolite profiles and can produce hundreds of gigabytes of data  [5] [6] [7] [8].
With such large datasets, processing becomes impracticable and unmanageable on commodity hardware. Cloud computing can offer a solution by enabling the outsourcing of calculations from local workstations to scalable cloud data centers, with the possibility to allocate thousands of central processing unit (CPU) cores simultaneously. Furthermore, cloud computing allows for resources to be instantiated on-demand (CPUs, random access memory, network, storage) and allows access to computational tools in the form of microservices that can dynamically grow or shrink. MS and NMR data processing usually involves selection of parameters (that are often specific to the analytical instrumentation), algorithmic peak detection, peak alignment and grouping, annotation of putative compounds, and extensive statistical analyses  [9, 10].
Scientific workflows A scientific workflow is a set of computational steps that are carried out to process and analyze data  [22]. Usually, a workflow is comprised of several linked software tools that are each executed during a particular step of the workflow. In order to manage and automate scientific workflows, in PhenoMeNal the wellestablished dedicated workflow management system Galaxy can be deployed, which presents the user with an easy-to-use graphical user interface as well as providing a programmatic interface  [20, 23]. Galaxy facilitates collaborative exchange, reproducibility, and traceability of data analysis by enabling users to share entire workflows and analysis histories  [24]. In addition to Galaxy, programmatic executable notebooks (Jupyter) and the workflow tools exposed as programmatic interfaces Luigi and Pachyderm are also supported  [25].
In order to cover typical use cases in metabolomics and to illustrate the usage and applicability of given analytical pipelines and software tools, five representative scientific workflows are available in the PhenoMeNal Galaxy (Table  1 ), each having different computational demands and purposes. More than 250 individual modules have been integrated in Galaxy (see the subsection Scientific Workflows in the Methods section). [  26, 27, 28]  Fluxomics Quantifies steady-state fluxes following 13 C metabolic flux analysis. The workflow was first based on the analysis of the MTBLS412 dataset with 13C tracer data of human umbilical vein endothelial cells under hypoxia. [29, 30]  LC-MS/MS Processes, quantifies, and annotates/identifies features in mass spectra using MetFrag -a tool that annotates molecules from compound databases of tandem mass spectrometry (MS/MS) spectra.
Univariate and Multivariate Statistics Applies univariate and multivariate statistical analysis and illustrates how datasets may be explored, enabling the identification of variables of interest and the construction of predictive models. The workflow is based on MTBLS404. [  13, 34]  Eco-Metabolomics Implementation of a resource demanding metabolomics use case in ecology, used in large field experiments to describe interactions between different species of organisms in remarkable detail. The workflow is based on MTBLS520. [35] ISA-Create-Validate-Upload A workflow to create Investigation, Study, and Assay data model framework-compliant metadata files based on study design information, augmented with semantic markup as source, implementing UK Phenome center naming conventions. Following validation, the workflow also allows visualization of overall study design and deposition to EMBL-EBI.
Study design PhenoMeNal was designed to use standardized protocols and software tools and to comply with state-of-the-art dedicated specifications and data formats across the entire project. Development was geared toward implementation of open standards for tracking provenance of both data and metadata generated by clinical phenotyping projects. In PhenoMeNal, the ISA model and specifications were implemented using the ISA format to generate, annotate, validate, and deposit experimental metadata information of datasets and studies to public reposi-tories such as MetaboLights  [37, 38]. ISA-based metadata tracking is used for the different analysis pipelines that are specific to the distinct metabolomics domains. PhenoMeNal reached native support for the ISA format by developing a dedicated Galaxy composite data type.
Data deposition PhenoMeNal encourages the metabolomics data repository MetaboLights as a primary source of data deposition  [39]. Private and public datasets are supported, as are download and upload to MetaboLights. If the storage in a data repository such as MetaboLights is not possible, data can be stored locally or in the cloud e-infrastructure. Access to the data is strictly controlled and secured. To support data deposition, ISA-based Galaxy modules are available making it possible to publish and disseminate scientific results in standard compliant ways.
Reproducibility One of the challenges of cloud computing is that analyses need to be run continuously and successfully in different environments  [40]. Specifically, it has to be ensured that, given the same input, workflows and tools produce identical results regardless of the underlying environment  [4, 40]. When these requirements are fulfilled, end users can be confident that their data will be analyzed correctly. PhenoMeNal has implemented three major testing strategies to ensure technical reproducibility using a continuous integration framework  [41]. Tests were implemented for the infrastructure components, individual software containers, and data involved in computational workflows.
Sustainability PhenoMeNal is part of a number of initiatives (BioMedBridges, COSMOS, and ELIXIR) to foster the role of metabolomics and to harmonize experimental data and metadata usage  [15, 42]. Col-laborations were established with EGI  [43]  and Indigo Datacloud  [44]  infrastructure providers and initiatives  [45, 46]  to ensure that PhenoMeNal uses technologies that are well supported and ensure their widespread usage, continuity, and further development. For example, the development of KubeNow and contributions to the Galaxy and Workflow4Metabolomics community are essential for PhenoMeNal  [47]. Core development will continue on GitHub and is fostered by collaborations with tool developers. Dependencies on specific technologies and frameworks were avoided by focusing on open standards such as ISA-Tab/ISA-JSON, mzML and nmrML, and widely accepted software  [48].
Community engagement The PhenoMeNal project is open source and is hosted on GitHub  [52]. Developers can contribute tools to PhenoMeNal and are encouraged to do so. To add a tool to PhenoMeNal, it must be containerized using Docker and then integrated into the build process. Detailed documentation is available in the project's Wiki for developers who wish to add their tools to PhenoMeNal. Collaborations with other projects have been actively encouraged during the development of PhenoMeNal, including Work-flow4Metabolomics  [13]  and the developers of both nmrML and nmrProcFlow  [26]. These collaborations are essential to fostering greater standardization within PhenoMeNal and to increasing compatibility with other metabolomics data processing infrastructures.
Conclusions PhenoMeNal has succeeded in increasing the robustness and coverage of representative metabolomics data processing in scientific cloud e-infrastructures. The presented cloud einfrastructure covers a wide range of analysis pipelines including data generation and download, data pre-and postprocessing, (bio)statistics, and result deposition in data repositories. A large effort has been made to introduce lowerlevel changes to cloud e-infrastructures (e. g. , the cloud deployment software KubeNow) to meet the demands of the biomedical domain. Furthermore, Galaxy has been enriched with metabolomics data standards, in particular, the ISA format for study metadata and mzML and nmrML for acquired data files, as well as support for Kubernetes. PhenoMeNal has fostered the visibility of new metabolomics tools and has enabled the development of more sophisticated data analysis workflows.
Our efforts were also guided by feedback from real-life test scenarios collected at workshops with users from the clinical domain. PhenoMeNal constitutes a keystone solution in cloud platforms available for metabolomics data analysis. The platform was designed to deliver optimal performance and functionality for typical use cases in the metabolomics domain. While the needs of clinicians and researchers in the biomedical and biochemical domains have been targeted, PhenoMeNal is not limited to a specific domain as the cloud infrastructure, tools, and workflows can be adapted to other use cases as demonstrated with the inclusion of the eco-metabolomics workflow. The technological advancements can be reused in other scientific cloud environments and could be integrated with solutions from other 'omics domains in the future.
Cloud e-infrastructure The PhenoMeNal CRE is designed as a microservice architecture, with services being implemented as virtual machine images and software containers. Containers are used to provide microservices for metabolomics data analysis tools and also long-running services such as workflow management systems. A container orchestrator runs containers on top of the scalable infrastructure. The orchestrator takes a group of machines that act as a distributed cluster and receives requests for tools as well as service executions. PhenoMeNal implements various layers to providea container orchestrator on top of either bare metal hardware or IaaS given by a cloud provider  [55]  (Supplementary Fig. S1 ). During the setup process and while PhenoMeNal is deployed, data storage and CPU limits can be configured and dynamically scaled to fit any cloud environment.
Deployments can be made to GCE, AWS, and OpenStack-based private clouds from the Phe-noMeNal portal. Deployments are also supported from the command line to Microsoft Azure  [56] , the European Science Cloud  [57] , and local servers (bare metal)  [58] ; we provide step-by-step instructions for these solutions. PhenoMeNal provides IaaS for three different cloud environments: "local cloud": local workstations or bare metal clusters where data are not allowed to leave the facility. "public cloud": the flexible use of commercial cloud providers such as GCE and AWS. "shared cloud": using OpenStack-a free and open-source software platform for cloud computing, ideal for custom environments and research networks.
Software tools The PhenoMeNal portal has an application library that allows users to deploy tools as microservices into the cloud infrastructure (Fig. 2 , Supplementary Table  S1 ). The portal is packaged into frontend and backend engines on top of Kubernetes. Most software tools in PhenoMeNal are compiled from source code and use a variety of programming languages. Linux versions of software tools and user interfaces such as Galaxy are supported in dedicated encapsulated Docker containers that are implemented as minimum-sized microservices. PhenoMe-Nal currently hosts 100 such projects in its GitHub repository [59] (Supplementary Table  S1 ). Projects are indicated by the trailing Àcontainer-À name and include a ruleset to build and run the containerized tools, as well as datasets for testing and other necessary files.
Galaxy The Galaxy workflow management system is widely regarded as one of the most popular scientific workflow platforms  [20, 61]. It provides a user-friendly web-based GUI to make it easy for the end user to configure and run individual modules and entire workflows without programming experience. Commandline tools and scripts are encapsulated into modules that are launched via the web interface. Galaxy also supports more powerful features such as programmatic access through a REST API and helper libraries to access the running instance of Galaxy  [62]. PhenoMeNal has been able to adapt Galaxy for use with a microservices-based architecture  [31]. To this end, modules are encapsulated into Docker containers that can be flexibly launched within the cloud e-infrastructure. Galaxy is available in all deployed PhenoMeNal CREs and contains more than 250 modules that have been implemented as part of PhenoMeNal.
Jupyter Jupyter, which started its history as the IPython notebook, is the most popular among the tools commonly referred to as exe-cutable notebooks or computational notebooks  [63]. Jupyter lets users combine executable code with results from code executions such as text, tables, and figures. Usually, Jupyter notebooks are enriched with extended information that explains what the code does. As a result, they are often used for training material and for tutorials. Also, computational notebooks can, to some extent, be used as a way to document code executions and to make executions more reproducible  [64].
Luigi and pachyderm Luigi is a Python workflow programming library that was originally developed by the company Spotify. It manages pipelines of computations primarily on "big data" systems such as Hadoop and Apache Spark but also supports local execution  [63, 64]. Luigi is a very flexible library that facilitates building complex pipelines of batch jobs handling dependency resolution, workflow management, and visualization. Similarly, Pachyderm makes it possible to process distributed data and to keep track of the data from every stage of the analysis pipeline  [25]. With Pachyderm, it is possible to track the provenance of results and to accurately reproduce scientific workflows. Luigi and Pachyderm are well suited for complex scientific tasks and are easy to use from the python environment in Jupyter notebooks without additional integration tooling needed.
Standardization PhenoMeNal has implemented several dedicated Galaxy modules that directly retrieve and store ISA-Tab data set descriptors from and to MetaboLights, and can convert between other formats. Native Galaxy composite data types to support ISA-Tab and ISA-JSON have also been integrated, building upon the ISA API  [38, 48]. The ISA data type allows for the upload of an ISA-Tab archive (a zip file containing the ISA set of files and raw   S1 ). PhenoMeNal also advanced the specification of the nmrML standard data format  [27]  and contributed a dedicated composite data type for nmrML to Galaxy. nmrML is used extensively throughout the NMR 1D workflow and conversion from raw format into nmrML is supported via dedicated Galaxy modules (Table  1 ). Throughout the entire analysis pipeline, modules of computational workflows were designed to accept standard formats such as mzML, XML or CSV whenever possible.
Privacy PhenoMeNal supports fully anonymized data, which cannot be traced back to individuals in any way  [50]  and treats pseudonymized data as identifiable. As pseudonymized data are anonymous to the investigator, third parties may be able to link pseudonymized data back to identifiable individuals through mappings such as a hash or code  [49]. In these cases, e. g. , in a hospital environment, users must deploy PhenoMeNal within a private cloud or bare metal cluster behind their institution's firewall. PhenoMeNal provides guidance on ethical and technical frameworks to regulate and secure the use of private or sensitive data  [49, 50]. It is possible to combine data and metadata within an ELSI compliant framework  [50]  and in such cases users can follow the example of the European Genome Phenome Archive (EGA)  [71].
Availability of supporting data The following MetaboLights datasets are integrated into Phe-noMeNal and are used to demonstrate the cloud integration and reproducibility of Galaxy workflows: MTBLS1 (NMR1D), MT-BLS404 (Uni-and multivariate statistics), MTBLS412 (Fluxomics), MTBLS520 (Eco-Metabolomics), MTBLS558 (MetFrag). Datasets are available at https://www. ebi. ac. uk/metabolights. Snapshots of the code and additional supporting data are available in the GigaScience repository, GigaDB  [76].
pDC-IRF7-induced potent downstream ISG responses in absence of detectable IFN-I To determine whether IFN-I response to viral stimuli was restored in pDC:Irf7 + mice, animals were infected with DENV systemically (intravenously, i. v. ), and IFNa/b expression was assessed. High levels of IFNa were detected in both the spleen and plasma of infected WT mice (Figure  3C-D ), but not Irf3/7 DKO mice, in agreement with previous results  (Chen et al. , 2013). IFNb levels displayed the same pattern of expression (data not shown). pDC:Irf7 + mice had undetectable IFNa/b levels in both the plasma and spleen at all analyzed times post-infection (p. i. ) (Figure  3C-D ). We considered the possibility that the pDC response to DENV may occur in a localized manner, with insufficient type I IFN in plasma or spleen to allow its detection.
Cells and reagents Huh7. 5. 1 cells  (Zhong et al. , 2005)  (RRID:CVCL_E049), Vero cells (CLS Cat# 605372/p622_VERO, RRID:CVCL_0059), and BHK-21 cells (ATCC Cat# CCL-10, RRID:CVCL_1915) were maintained in DMEM (Dulbecco's modified Eagle medium; Life Technologies) supplemented with 10% FBS, 100 units (U)/ml penicillin, 100 mg/ml streptomycin, 2 mM L-glutamine (Life Technologies). Cells were maintained at 37 ˚C in 5% CO 2. MEFs were maintained in supplemented RPMI growth media. The work involved cell line, with routinely verify status for contamination (e. g. Mycoplasma). Cell culture reagents included the TLR7 agonist (IMQ; imiquimod) and TLR7/8 agonist (R848) (Invivogen, San Diego, CA); TLR7 antagonist, IRS661 (5'-TGCTTGCAAGCTTGCAAGCA-3') synthesized on a phosphorothioate backbone (MWG Biotech, Ebersberg, Germany); TLR3 agonist (poly-I: C) (Invivogen); TLR9 agonist (CpG) (Invivogen); recombinant IFNb (PBL Interferon Source, Piscataway, NJ).
Acknowledgements We thank  A Davidson  ( Bristol University ) for providing the DENV-2 strain  New Guinea C, P Despres (Pasteur Institut, Paris, France)  for the anti-E DENV 3H5 antibody,  V Lotteau  ( CIRI, Lyon, France ) for Influenza A virus stocks,  FV Chisari  ( Scripps Research Institute, La Jolla, CA ) for the Huh-7. 5. 1 cells,  T Taniguchi  ( University of Tokyo, Tokyo, Japan ) for the Irf3/7 DKO mice,  J Tschopp  for the cardif -/- mice, and  S Akira  for the Tlr7 -/-mice. We are grateful to  Y Jaillais  for critical reading of the manuscript,  F Fusil  for assistance with mice and to our colleagues for encouragement and help. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facilities, PBES and cytometry for mice housing and technical assistance.
This work was supported by grants from the  'Agence Nationale pour la Recherche' (ANR-JCJC-EXAMIN) , the '' Agence Nationale pour la Recherche contre le SIDA et les Hepatites Virales '' ( ANRS-AO 2016-01 ) and the  LabEx Ecofect  Grant  ANR-11-LABX-0048  to  MD ,  ANR-14-CE14-0015-02   CHIKV-Viro-Immuno  to.
postdoctoral fellowship was sponsored by  EMBO  ( ALTF 1466-2014 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Agence Nationale de la Recherche ANR-JCJCde la Recherche   ANR-11-LABX-0048 Marlene Dreux Agence Nationale de la Recherche   ANR-14-CE14-0015-02   CHIKV-Virofunders had no role in study design , data collection and interpretation, or the decision to submit the work for publication.
Saline solution was infused into the bladder at a flow rate of 90 µl/min. The bladder was filled with saline up to 0. 6 ± 0. 2 ml and the grade of bladder filling was kept stable during the stimulation protocol. The intravesical pressure was recorded continuously using a disposable IBP pressure transducer (DPT-6000, Codan pvb Medical GmbH, Germany) and the MP150 data acquisition system equipped with an amplifier module and acquired with the AcqKnowledge software (AcqKnowledge 4. 2 version, BIOPAC system Inc. , Goleta, CA, USA). Simultaneously, stimulation was applied by connecting animals to a stimulus isolator (DS100, WPI Europe, Germany) driven by a digital stimulator (DS8000, WPI Europe, Germany). After bladder stabilization, electrical stimulation was given with constant current biphasic square wave pulses of 0.
, USA) and rinsed following the same protocol as for brain sections. Subsequently, a 2-h incubation with the secondary antibody Alexa Fluor 488-conjugated donkey anti-mouse (1:100 dilution, Thermo Fisher Scientific, NY, USA) was followed by three washing steps with TBS and by coverslipping in 80% glycerol/TBS). Photographs of the stained brain sections were taken (4× and 10× magnification) using a U-CMAD-2 digital camera mounted on an Olympus AX70 bright-field microscope (analySIS, Imaging System, Germany) and evaluated with ImageJ analysis software (ImageJ software, NIH, USA). We assessed results for the caudal and rostral regions of the PAG separately. In the rostral part of the PAG no ventrolateral (vl) column can be found and therefore results are based on the dorsomedial (dm), dorsolateral (dl) and lateral column of the PAG.