dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors thank the LMDz developers for maintaining the dynamism and creativity of this climate model. The present work was granted access to the HPC resources of TGCC under the allocation  A0030102201. It was funded by the  Copernicus Atmosphere Monitoring Service , implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission. We also gratefully acknowledge the many people who contributed atmospheric observations (aircraft and surface air sample measurements). OCO-2 retrievals were produced by the  OCO-2  project at the  Jet Propulsion Laboratory, California Institute of Technology , and obtained from the  OCO -2 data archive maintained at the  NASA Goddard Earth Science Data and Information Services Center. We also thank  Martin Steinbacher ,  Luciana Gatti ,  Antoine Berchet  and  Matthew Lang  for their comments on an earlier version of the text.
Introduction Interaction of RNA-binding proteins (RBPs) with target mRNA is necessary for every aspect of its control, including its processing, export, localization, stability, and translation into protein  [1]. These events are collectively defined as post-transcriptional control of gene expression and are essential to determine the proteome of a cell. While the role of transcription factors in vertebrate organogenesis is established-for example, a detailed transcriptional regulatory network governing lens development is now derived  [2] -that of RBPs, functioning in post-transcriptional gene expression control, is not well defined  [3]. This represents a significant knowledge gap, especially considering that vertebrate genomes encode similar numbers of transcription factors and RBPs  [4].
This is of significance given that elevated levels of p21 Cip1 has been linked to cataract  [38]. We also performed Celf1 CLIP-RTqPCR on mouse lenses for p21 Cip1 , but did not detect p21 Cip1 transcripts to be enriched in the Celf1 pulldowns. This may be because the lens does not normally express p21 Cip1 transcripts  [27]. However, the direct interaction of Celf1 with p21 Cip1 has been investigated in HeLa cells that have abundant expression of p21 Cip1 mRNA  [39] , and in this dataset CLIP identifies p21 Cip1 as a direct binding target of Celf1. Together, our findings indicate that Celf1 is necessary to negatively control the expression of the cell cycle regulators p27 Kip1 and p21 Cip1 and the phosphorylation status of lamin A/ C to orchestrate fiber cell nuclear envelope breakdown in lens development.
These findings demonstrate a critical function for Celf1 in maintaining fiber cell cytoskeletal structure in lens development. Further, in mouse, scanning electron microscopy (SEM) was performed to analyze cortical fiber cells (located in the lens outer cortex) and nuclear fiber cells (located near the core of lens) at stage P15. In control lenses, both cortical and nuclear fiber cells are arranged in a discrete parallel arrangement that interlink with the neighboring fiber cells through membrane protrusions (Fig 5G  and 5H ). In contrast, both cortical and nuclear fiber cell Collectively these data suggest that Celf1 deficiency causes severe alterations in mRNA levels or specific isoform abundance of key genes Actn2 and Sptb that results in abnormal fiber cell morphology.
Data in the present manuscript establish that the RBP Celf1 mediates post-transcriptional regulation to control known (p27 Kip1 , p21 Cip1 , Dnase2b), as well as novel (Actn2, Sptb), factors thereby orchestrating fiber differentiation during lens development. Although other post-transcriptional RBPs such as Tdrd7 and Caprin2 are known to function in lens development  [16, 46] , their gene knockout mouse models do not exhibit nuclear degradation defects that are observed in the Celf1 cKO/lacZKI lens. This indicates that different RBPs have distinct function in lens development. From these new data, and in light of previous findings  [14, 15, 28, 38] , a model is defined for the molecular mechanism of Celf1 function in lens development (  Fig 6). In one pathway, Celf1 positively regulates a lysosomal nuclease Dnase2b in fiber cells while also indirectly facilitating its access to nuclear DNA by negatively regulating p27 Kip1 and p21 Cip1.
Frog celf1-knockdown Xenopus laevis embryo manipulations, embryo staining and histological methods were performed as previously described according to approved protocols  [21]. Briefly, the celf1-specific morpholinos 5'-TTGTGCCATTCATTATCTTAGAAAT-3' and 5'-ATTGTGCCATTCAT TTTCTTGGAAA-3' (NCBI Accession number BG513033), were used for generating celf1knockdown. The specific nucleotides complementary to the ATG initiation codon are underlined. The morpholino 5'-CCTCTTACCTAGTTACAATTTATA-3' was used as standard control.
iSyTE-based analysis Celf1 transcript expression in developing lens was investigated in previously generated wildtype mouse lens microarrays (E10. 5, E11. 5, E12. 5) (GSE32334) in the iSyTE database  [17]. Scatter plot analysis was performed, as previously described  [59] , to investigate if DEGs in Cel-f1 lacZKI/lacZKI lens microarrays showed preferential expression in the lens. Briefly, iSyTE assigns lens-enrichment scores to genes based on their comparison with mouse whole embryonic tissue (WB)  [17]. For iSyTE lens-enrichment (fold-change), P0 lens microarrays (GSE165333) were compared to WB (GSE32334). In a quadrant plot, Celf1 lacZKI/lacZKI lens DEGs (±2. 0 foldchange, p-value < 0. 05) were plotted against their iSyTE lens enrichment fold-change.
This work was supported by  NIH  Grant  R01EY021505  (to SAL) and a grant from  Retina France  to LP. SAL is a  Pew Scholar  in the Biomedical Sciences. ADS received a  Fight For Sight Scholarship. LPC was supported by  Ministerio de Ciencia, Tecnologı ´a y Telecomunicaciones ,  Costa Rica. Microarray expression experiments were performed by the  Microarray Core Facility of the Molecular Genetics Core Facility at Children's Hospital Boston  supported by  NIH -P50-NS40828  and  1U54HD090255. INBRE Program Grant   P20 GM103446 , funded by  NIH  and the  State of Delaware , supported the  University of Delaware  Core Imaging facility; and  1S10  ( RR027273-01 ) funded the acquisition of the confocal microscope used in this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Uncertainties, in the form of either non-predictable shape imperfections (manufacturing) or flow conditions which are not absolutely fixed (environmental) are involved in all aerodynamic shape optimization problems. In this paper, a workflow for performing aerodynamic shape optimization under uncertainties, by taking manufacturing uncertainties into account is proposed. The uncertainty quantification (UQ) for the objective function is carried out based on the non-intrusive Polynomial Chaos Expansion (niPCE) method which relies upon the CFD software as a blackbox tool. PCE is combined with an evolutionary algorithm optimization platform. CAD-free techniques are used to control the shape and simultaneously generate shape imperfections; next to this, a morphing/smoothing tool adapts the CFD mesh to any new shape.
Associated with any design under uncertainties is the process of Uncertainty Quantification (UQ) which quantifies the effect of the uncertain variables on the performance (F). In large-scale problems, Monte-Carlo  [1, 9]  methods are prohibitively expensive UQ techniques. A viable alternative is the Polynomial Chaos Expansion (PCE)  [11, 4]. There are two ways to implement the PCE. In the intrusive PCE, every uncertainty affecting the flow model is introduced in the governing equations, new PDEs are derived and numerically solved. In the non-intrusive PCE (niPCE), the evaluation software is used as a black-box to compute the objective function values for some data-sets (determined by the Gauss integration formulas) of the uncertain variables. In this work, the niPCE method is used together with an evolutionary algorithm to create a workflow for shape optimization under uncertainties.
Availability in the reconciliation web server http://phylotoo2. appspot. com/ (Zheng and Zhang, 2017) , in the genome simulation software Zombi (https:// github. com/AADavin/ZOMBI), both as input and output options in the adjacency history computing software DeCoSTAR  (Duchemin et al. , 2017). Furthermore, scripts have been developed to convert the reconciliations produced by ecceTERA  (Jacox et al. , 2016) , NOTUNG  (Durand et al. , 2006)  and PrIME  (A ˚kerborg et al. , 2009)  into recPhyloXML, and a script for converting reconciliations produced by RANGER-DTL  (Bansal et al. , 2018)  is currently under development. Additional scripts are also available to convert a recPhyloXML reconciled tree in the Newick format, count the different events represented in a recPhyloXML file, combine different files into one or extract specific trees from a file.
APIs have been written to import and export in recPhyloXML for the Cþþ library Bioþþ  (Gueguen et al. , 2013) , for the python libraries ETE3  (Huerta-Cepas et al. , 2016)  and Biopython  (Cock et al. , 2009). All these scripts and APIs are available at https://github. com/ WandrilleD/recPhyloXML.
Funding This work was funded by the  Agence Nationale pour la Recherche ,  Ancestrome  project  ANR-10-BINF-01-01  and by the  Institut de Biologie Computationnelle   ANR-11-BINF-0002. G. J. Sz. , W. D. and A. A. D. received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement no. 714774. C. D and D. D. were funded by  Swiss National Science Foundation  grant  150654.
The conceptual design of AnaEE France includes five modules. Three modules gather experimental facilities along a gradient of experimental control ranging from highly controlled Ecotron facilities, semi-natural field mesocosms to in natura experimental sites covering major continental ecosystems (forests, croplands, grasslands, and lakes). In addition, AnaEE France also includes shared instruments that can be implemented in experiments and analytical platforms specifically dedicated to environmental biology. To promote reuse of data, generalize results and improve predictive models, AnaEE France further gathers modeling and information systems. The implementation of AnaEE France allowed for mutual synergies, improved the technical skills, stimulated new experiments and helped our scientific community to enter into the big data sharing era.
Most of these long term observations were not performed in coordinated networks until recently, preventing any straightforward comparisons across time and space by lack of harmonization and/or standardization of scientific practices  (Peters et al. , 2014). Efforts have been made in the last decades to develop dedicated observation networks (e. g. ,  Lindenmayer et al. , 2012). To achieve this goal, key milestones includes (1) the selection of existing or de novo construction of observational sites such that several ecosystem types can be studied and compared; (2) the standardization of existing methods to collect data across sites; and (3) the development of tools for prediction, databases, and a centralized policy management  (Baker et al. , 2000). Among existing infrastructures, LTER (Long-Term Ecological Research) is funded since 1980 and gathers 24 long-term study sites in USA encompassing diverse continental and oceanic ecosystems  (Baker et al.
, 2000). More recent initiatives include ICOS (Integrated Carbon Observation System, http://www. icosinfrastructure. eu/) dedicated to the monitoring of greenhouse gases budgets in 12 European countries since 2008  (Ciais et al. , 2014) , NEON (the National Ecological Observatory Network) designed to provide long-term ecological data on the US continental scale since 2012  (Kampe et al. , 2010; Kao et al. , 2012) , and TERN (http://www. tern. org. au) contributing since 2008 to deliver observation data on all Australian ecosystems  (Lindenmayer et al. , 2012). Other networks aim at understanding specific ecosystem types such as GLEON (Global Lake Ecological Observatory Network; http://gleon. org), which is a grassroots network of limnologists, ecologists, information technology experts, and engineers who have a common goal of building a scalable, persistent network of lake ecology observatories  (Weathers et al.
Data can be directly delivered by a database interface proper to the infrastructure, through web services or in modeling environments that integrate data in modeling activities. 6. Contributing to the agenda of the scientific community. The RI should attract a scientific community that shares an interest on the use of experimental platforms and their management. It is therefore a central place to foster discussions on a range of topics such as cutting edge science which can be done with the infrastructure, experimentation-model coupling, implementation of new technologies and data synthesis (see for example this review dedicated to fragmentation experiments produced by AnaEE France,  Haddad et al. , 2017).
Selection and Organization of the Resources Experimental facilities in AnaEE France focused on continental ecosystems (aquatic and terrestrial) and were selected for their originality, large community of users, and open access to the international research community. They were also chosen on the basis of their complementary tools and approaches to offer opportunities for scaling up and down from in vitro to in natura approaches within a unique infrastructure and with the same access rules, and to allow feedbacks between models and experiments. In order to compare data by using a common framework, and to use comparable measures and standards across platforms, we anticipated the need for a common set of analytical platforms and a common procedure for collecting data using the same instruments with a mobile laboratory.
General Organization, Types of Services, and Access Policy The first goal of the construction of the infrastructure was to build a coherent set of experimental, analytical and data-model platforms with respect to the different key conditions defined in sections Main objectives and Implementation of ecosystem experimental research infrastructure. The second goal was to ensure a smooth infrastructure management and an open access policy. We first defined the three main kinds of AnaEE France services, which include (1) access to experimental and analytical facilities, instruments, and modeling platforms; (2) access to archived samples and collections of biological resources; and (3) access to data sets. The two first kinds of services require matching the demands with the offers in terms of technical, financial and logistic needs, while the data access is regulated by a general license defining the rights and duties associated with the use of data.
To implement the data access policy, users are asked to register and commit to properly cite the source and authorship of the downloaded data sets. We developed a single access point with updated information on services and fees, open calls for projects, and a review process to evaluate the scientific and technical relevance of proposed projects (see Figure  3 ). The general policy for access fees (i. e. , the economic model of the RI) is to ask users to cover part of the running costs of the services for projects coordinated by academics and to charge additional fees corresponding to manpower and renewal of instruments for projects coordinated by private partners. Approved users are requested to sign an agreement with AnaEE France including information on data and patent protection, copyright and ownership.
Information Systems The AnaEE France information system is based on a distributed architecture gathering information about databases located in different centers, some modeling platforms and a portal for metadata and access to the resources (Figure  5 ). The databases information system was designed to store data, to make them available and to manage access rights. Two complementary systems were developed in parallel for the management of data generated by experimental platforms, including one dedicated to long-term experiments (i. e. , decades) conducted with in natura platforms (accessible for instance at https://si-acbb. inra. fr for experiments on agrosystems) and one dedicated to shortterm (i. e. , months or years) research projects operated with  Ecotrons and field mesocosms. Since the goal of the information systems for long term experiments is to deliver core data, tools were developed for on line data stream, the standardization of variables and metadata across platforms, data querying and the management of rights.
For short-term experiments, a dedicated web application, called ISIA (Information System for FIGURE 4 | Information system for the administration of AnaEE RI including dedicated web applications for project submission (Project Proposal Administration), project implementation (Experiment Design Event Measures) and data management (Management Analysis Sample). The diagram shows the flow path from project submission to data production and storage, and associated databases dedicated to project and exploitation metadata, database structure and raw data. Infrastructure Administration), was designed and implemented (see Figure  4 ). The aim of ISIA is to collect the information of the whole experimental cycle within a single environment and database including the project submission, the experimental design, the experimental protocols and the raw data.
Metadata The annotation of resources using metadata (data about data) is a key to promote their discovery and reuse. When based on common standards, it directly contributes to build an efficient interoperability between raw data, models and experiments. Metadata is used in three main mechanisms including discovery (identification of the data sources that contain a given information), exploration (evaluation of the match between data and users' needs) and exploitation (use and access conditions of the data sources). Exploitation of metadata are usually managed within the data information systems, whereas discovery metadata are most frequently managed in a dedicated environment allowing compatibility with international standards and the harvesting by different catalogs (e. g. , GEOBON at a global scale in the field of biodiversity research).
We used two international metadata standards developed for environmental sciences and ecology: the geospatial metadata standard ISO19115/19139 compatible with the EU INSPIRE directive (Anonymous, 2013), and the Ecological Metadata Language (EML,  Fegraus et al. , 2005). Currently, the description of all resources of AnaEE France (http://w3. avignon. inra. fr/geonetwork_anaee) follows uses of the GeoNetwork (http://geonetwork-opensource. org/) software. In addition to these standards, there are international initiatives to develop metadata standards such as OGC's Sensor Web Enablement (SWE) to implement data flow from sensors to web interface and a series of Metadata Language (e. g. , SensorML) to describe sensors and associated data sets. However, these standards do not fix the vocabulary and semantic links between concepts, and there exist several initiatives led by different groups of ecologists, agronomists or geoscientists to develop thesaurus and ontologies.
To fulfill our specific needs, we therefore developed our own semantic referential tools by using vocabularies derived from different existing thematic thesauri (GEMET, EnvThes, AGROVOC, TAXREF, etc. ) and by collecting the vocabularies used in the different distributed platforms of the infrastructure. These terms were then gathered in an AnaEE France Thesaurus, which is supported by the VOCBENCH software and shared with the whole AnaEE community. The AnaEE thesaurus is publicly accessible through the AgroPortal semantic repository (http://agroportal. lirmm. fr/;  Jonquet et al. , 2016). In order to provide an accurate description of data resources and to allow their interoperability with modeling platforms, existing databases and models were semantically annotated using ontologies. We used the OBOE ontology (https://github.
com/NCEAS/oboe/; Madin et al. , 2007; Schildhauer et al. , 2016)  developed for scientific observations and measurements in ecology as the core element of our ontology. Extensions to OBOE are developed for the specific needs of AnaEE France and others thematic ontologies are also used such as wgs84_pos for spatially-located objects to describe their spatial patterns and SSN-SensorML to describe sensors and methods. The AnaEE ontology is developed using the Protégé collaborative software and will be accessible through AgroPortal as well.
Harmonization Current monitoring, measurements and experimental protocols in AnaEE France are not standardized due to various historical, logistical or monitoring constraints. Substantial improvements are expected on the short-term in order to improve data traceability and facilitate comparisons across experiments. Harmonization of data and protocol description is an outcome of the use of the shared information systems, which provide an integrated, semantically correct and shared description of each data set. Standardization of protocols and measurements, including accuracy assessment, curation procedure and the use of shared instruments will be a key objective to improve data quality and make them comparable across datasets. For example, methodological developments are implemented on different platforms to improve methods as for soil moisture sensor calibration or soil gas sampling techniques.
For example, working groups were established to address some of them as for instance the review of concepts and methods to measure biodiversity, dedicated sensors, and tools to analyse and model biodiversity data. The RI also raises opportunities to develop both training and teaching based on technologies and data available in all platforms. In AnaEE France, the infrastructure was used to organize and offer some training on best techniques or practices, and to foster or develop experiment-oriented teaching. Outreach activities were also developed for the general public. By their very nature, research infrastructures offer also other possibilities of developing synthesis works such as comparisons of ecosystem indicators or reviews of management practices. In the field of ecosystem science, an infrastructure can help developing general measures of the state of the environment such as CO 2 storage capacities, or tools to characterize and measure biodiversity.
CONCLUSION AND PERSPECTIVES There are still too few attempts to network together relevant experimental facilities from the same country in the scientific fields of biodiversity, agronomy and ecology. Learning from grand challenges in ecological research, we propose guidelines for the construction and operation of such a research infrastructure. Frontiers in Ecology and Evolution | www. frontiersin. org In the case of AnaEE France, experimental set-ups were selected from a range of control capacity and a capacity to handle a representative set of ecosystem types. The infrastructure included analytical and modeling platforms, and dedicated information systems. Standardized methods and practices, solutions for data storing and access, modeling platforms, and training activities were developed to increase the quality of all services and promote synergies among existing platforms.
ACKNOWLEDGMENTS The authors acknowledge CNRS,  INRA ,  Université Grenoble Alpes  and colleagues from  AnaEE France  for their support. Cécile Callou  and  Aurélien Maire  coordinated the construction of the ISIA software and helped draw Figure 4 in this paper. AnaEE France  is a project supported under the program Investissements d'Avenir launched by the  French government  and implemented by  ANR  with the reference  ANR-11-INBS-0001.