dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression Constructs Coding sequences to express were PCR amplified, and then inserted into Invitrogen vectors pPICZαA  (Couturier et al. , 2011a,b) , pGAPZαA and pBGP1  (Sasagawa et al. , 2011 ) (this study) inframe with N-terminal yeast α-secretion factor and C-terminal His 6 tag encoding sequences. Plasmid pBGP1 was obtained by removing the Gateway cassette from pBGP1-DEST  (Sasagawa et al. , 2011)  by XhoI/XbaI restriction digestion. The primers used were: GGGGCTAAAGAACTCGAGAAAAGAG AGGCTGAAGCTCTCCCCCAAGCACAAGGTG and TTTTC TAGACCCGCCGGGAGAGCATTGATAG for Podospora anserine GH5 (GenBank: HM357135. 1), GGGGCTAAAGAA CTCGAGAAAAGAGAGGCTGAAGCTGCCCCCGGTGAGCT TCCT and TTTTCTAGACCGTGTGTCTGGACATAAATGTC for P. anserine GH11 (GenBank: HM357137. 2), and GGGGCTA AAGAACTCGAGAAAAGAGAGGCTGAAGCTGATGTCCCA CTTTGGGGCCAATG and TCTAGACCTTCGTCCGTACGAG CACA for P.
Acknowledgments We are grateful to  Dr Frederic Carrière  ( Enzymology at Interfaces and Physiology of Lipolysis, EIPL, Marseille, France ) for the protocol for preparing frozen electro competent cells and for allowing one of us to perform preliminary experiments in his lab where we greatly appreciated the valuable assistance of  Justine Baignol. We thank  Pr Manabu Arioka  at the  Laboratory of Microbiology, Department of Biotechnology, the University of Tokyo  for providing pBGP1-DEST. This study was funded by the  French National Research Agency  ( ANR FUNLOCK ANR-13-BIME-0002-01 ).
Negative-stain electron microscopy and random conical tilt (RCT) reconstructions SMG-1-8-9 and SMG-1-8-9-UPF1 complexes were affinity purified using the SBP-tag and concentrated to 0. 05 mg/ml. Complexes were adsorbed to carbon film for 30 s, followed by 1% uranyl acetate staining for 60 s. Hundred micrograph pairs were recorded at 45 • and 0 • tilt angles under low-dose conditions at room temperature on a Biotwin CM120 electron microscope (FEI) operating at 120 kV using a 4k × 4k CCD camera and a defocus of ∼-2 m. The magnification used was 41 667× which corresponds to a pixel size of 3. 6 Å. For both samples ∼12 000 tilt pairs were extracted manually by using tiltpicker  (35). Untitled images were subjected to cycles of 2D multivariance statistical analysis and classification resulting in 400 (SMG-1-8-9) or 300 (SMG-1-8-9-UPF1) classes using IMAGIC-5 software  (36)  and XMIPP  (37).
Electron cryo-microscopy and image processing Quantifoil 300 mesh R 1. 2/1. 3 type grids (Quantifoil Mi-croTools GmbH) coated with a thin continuous carbon layer were glow discharged before usage. A total of 3. 5 l of freshly prepared SMG-1-8-9-UPF1 sample was applied to the carbon side of the grid and vitrified using a Vitrobot Mark IV (FEI, Eindhoven, Netherlands). Micrographs were recorded using the automatic data collection software EPU (FEI company) under low dose conditions on a Tecnai G2 Polara (FEI) operated at 100 kV with a Gatan 4k × 4k CCD camera and a nominal magnification of 59 000× in a defocus range between -1 and -4. 5 m. The final pixel size was 1. 86 Å (object scale). The contrast transfer function (CTF) estimation and correction of micrographs was performed in bctf (Bsoft  (39) ).
Generation of the hybrid quasi-atomic model of SMG-1-8-9-UPF1 The homology models of SMG-1 N-terminal HEAT repeats and kinase domain were generated using the HHpred server  (43). In each case, best hits were used to generate the homology models. SMG-1 residues 150 to 782 were modeled based on their homology to importins (PDBs: 3W3W, 2BPT), TIP120 (PDB: 1U6G), Protein Phosphatase 2A (PDB: 4lAC) and one exportin (PDB: 3M1I). SMG-1 residues 1038 to 2436 and 3593 to 3661 were modeled using the mTOR kinase domain structure (PDB: 4JSN). The antibody mapping results for SMG-8, SMG-9 and the SMG-1 FATC domain  (23, 31)  were taken into account for fitting the SMG-1 N-terminal HEAT domain into the arm and the kinase domain into the back of the head. In both cases, Chimera (44) yielded unambiguous fittings with high correlation coefficients (cc = 0.
Surface plasmon resonance experiments Surface plasmon resonance (SPR) experiments were performed on a BIAcore3000 using streptavidin-coated sensor chips (GE-Healthcare). The first flow-cell was not functionalized and used as a control; the second flow-cell was functionalized with purified SMG-1-8-9 to a density of about 2000 RU. The assay buffer contained 20 mM Tris pH 9. 0, 100 mM KCl, 25 mM glycine, 1 mM DTT, 0. 01% Tween-20. UPF1 variants were injected at 30 l/min during 3 min followed by a 10 min dissociation phase. To determine the binding sites, all UPF1 variants were injected at 1 nM on both flow-cells. Between each UPF1 injection, the surface was regenerated by 30 s injection of assay buffer containing 0. 5 M KCl. Data were analyzed by subtracting both the control flow cell of any UPF1 injection and the buffer injection curve.
03 to 500 nM for SMG-1 and SMG-1-8-9 and to 1 M for SMG-1-9 and SMG-1 C-8-9. About 3-5 l of each sample were loaded into hydrophilic-treated glass capillaries (Nanotemper) and the thermophoresis analysis was performed using a Nanotemper Monolith NT115 instrument (20% LED power and 80% IR-laser power). The MST curves were fitted using the Nanotemper analysis software to obtain K D values. In order to be able to superimpose the three curves the data were represented in terms of UPF1-bound fractions. SEC-MALLS experiments are described in the Supplementary Material and Methods part.
ACKNOWLEDGEMENTS We are grateful to  Guy Schoehn ,  Marc Jamin ,  Wim Hagen  and  Maria Bacia  for technical assistance and advice, and  Imre Berger  for discussions. We thank  Stephen Cusack  for plasmids pPROEXHtb-UPF1( 115-287 ) and pPROEXHtb-UPF1( 115-914 ). This work used the platforms of the Grenoble InstructCenter (ISBG :  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The EM facility is supported by the  Rh ône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the fonds  FEDER , the  Centre National de la Recherche Scientifique (CNRS) , the  CEA , the  University of Grenoble, EMBL , and the  GIS-Infrastructures en Biologie Sante et Agronomie (IBISA).
FUNDING We thank the  EC FP7 I3  project  P-CUBE  for financial support. A. D. was supported by an  EC-EMBL CoFUNDS EI-POD fellowship. C. S. is supported by the  European Research Council   Starting grant  (ComplexNMD, 281331); and by a  Sinergia grant  from the  Swiss National Science Foundation  ( CRSII3 136254 /1 ).
The amount of information and data necessary for comprehensive hazard and risk assessments present many challenges regarding the lack of accessibility, comparability, quality, organisation and dissemination of natural hazard spatial information. In order to mitigate these limitations an interoperability framework (IF) for natural hazard spatial data has been developed and is the basis of the DS document  (European Commission 2013). The IF has been developed following the INSPIRE Generic Conceptual Model  (INSPIRE 2013b ) and the common methodology  (INSPIRE Drafting Team Data Specifications 2008; Toth et al. 2012). In INSPIRE, interoperability means ''the possibility for spatial data sets to be combined, and for services to interact, without repetitive manual intervention, in such a way that the result is coherent and the added value of the datasets and services is enhanced'' (CEC 2007a: L 108/5).
Therefore, interoperability is seen as providing access to spatial datasets through the Internet. The IF provides means for facilitating access, integration, harmonisation and dissemination of natural hazard data from different domains and sources. The objective of this paper is twofold. Firstly, the paper shows the applicability of the IF and highlights the key aspects of interoperability to data users and data providers in the natural hazard domain. Secondly, the paper ''translates'' into common language the main features and potential of the IF for a wider audience of scientists and practitioners in the natural hazard domain but not necessarily involved in their day to day activity in spatial data interoperability issues. Additionally, the paper identifies gaps, limitations and further potential improvements in the DS.
In the second step, we analysed whether the selected type of assessments could be applicable to all types of natural hazards. And in the final step, we developed a common model (Fig. 2 ) for providing results of the each type of selected assessment. The common assessment model provides means for qualitative and quantitative evaluations. In both cases, qualitative or quantitative assessment results, the provision of the ''background'' contextual information-the methodological framework the evaluation was made within-is a fundamental principle. The reference to the methodology used for an assessment is obligatory information that uses a reference data model (DocumentCitation) common to all INSPIRE data themes (INSPIRE 2013b). When there is a qualitative assessment (e. g. when the level of risk is reported as being ''high''), the common assessment data model uses a ''character string'' data type (free text).
Extensible classification of natural hazards Classifying natural hazards is a difficult task due to the complexity of the processes involved and relations that a hazard phenomenon poses within the human system with which it interacts. It usually implies professional competency disputes  (Olcina and Ayala 2002)  which can be seen as another symptom of how varied can a hazard be seen and studied. Most classifications used so far are objective driven or very specific, e. g. disaster databases for insurance companies, landslide studies, and none of them proved to be flexible enough for interoperable means due to the presence of duplicated or void categories, thematic bias, or other more complex issues such as chained consequences or multitype concurrent hazards. Providing a classification aims at easing the process of cataloguing data and facilitating interoperability, by identifying the most used and accepted terminology in the highest levels.
Therefore, a unique classification was adopted as the backbone to which any term describing a type of natural hazard must be attached, and thus enabling interoperability between different datasets. The second option is specifying a new term. Thus, it is feasible for a data provider to add a specific new term that is not in the classification or for which not a single parent term can be found. In such a case, the data provider is allowed to provide his specific term, but he should also provide a link (set as a URI) where one could find more information about the value entered. This latter possibility is not a bypass to the natural hazard classification, to which a reference must be made anyhow: it is considered as an additional possibility to provide further information about the nature of the natural hazard type entered.
Applicability of the interoperability framework This chapter illustrates the applicability of the IF in three steps: first, applying the framework to a selected EU-wide information system. Second, summarising existing EU projects that use the IF or part of it. And finally, evaluating the input from the public consultation conducted during the development of the DS (European Commission 2013) and reporting the submission of natural hazard national data sets to the INSPIRE GeoPortal (INSPIRE 2014c) to date.
However, most short-term fire danger systems, as EFFIS, assess fire danger using numerical indices not producing a probability per se. A fire danger index can be defined as a quantitative indicator of one or more facets of fire danger, expressed in a relative sense or as an absolute measure  (Merrill and Alexander 1987 ). An example is the Fire Weather Index (FWI) (  Van Wagner 1987)  from the Canadian Forest Service adopted in EFFIS. The FWI consists of six components that account for the effects of fuel moisture and wind on fire behaviour. In EFFIS internal workflow, meteorological data and remotely sensed MODIS satellite imagery are acquired during the fire season for computing several fire indicators and statistics. Meteorological data from three providers is used for producing up to 8 days forecasted fire danger maps on a daily basis.
MODIS imagery is used to map burned areas with 250-m spatial resolution. Daily imagery from MODIS satellites is acquired and processed few hours after the acquisition by MODIS. EFFIS provides the daily update of the perimeter of burnt areas for fires of about 40 ha or larger. Fire danger (hazard) maps and fire perimeter maps are provided in EFFIS via map viewer and WMS (Fig. 4 ). As indicated in Fig. 4 , the implementation of the IF will provide means for making EFFIS data delivered using WMS service interoperable with data from other data providers. The generic provider in Fig. 4  can supply different data typologies of interest for fire emergency services such as for example probabilistic national and regional level fire hazard or exposure data (e. g. properties, wood resources, protected areas, power networks, land cover, etc.
The type of Hazard area is set using the code list described in Sect. 2. 4, in the case of the EFFIS fire danger data set is ''\forest fires[''. The determination method describes the method used for delineating the hazard, i. e. modelling or indirect determination. The spatial representation was coded as ''Grid'' because the data set provided is a raster map. And finally, the likelihood of occurrence describes the chance of an event occurring, in this case measured in a qualitative scale of six levels of fire hazard. The specifications described are key for making data sets from different providers interoperable and easily accessible. The increasing number of providers fulfilling the requirements of the IF will ease integrating natural hazard data from different sources into a common framework promoting enhanced interoperability.
Based on the detailed analysis of the comments and testing reports, the final data model of the IF was produced in three steps. First, clarifying the used terminology by applying the standard UNISDR (  2009 ), second introducing the feature type observed event in the data model, and finally by applying the extensibility concept for both key classifications and providing means for both spatial data representations. The INSPIRE directive requires the commission to establish a community geo-portal, and the member states shall provide access to their infrastructures through the geo-portal as well as through any access points they themselves decide to operate. The INSPIRE geoportal (INSPIRE 2014c) published in December 2011 is becoming the central point of access to geospatial data in Europe. Currently, it provides access to over 225,000 metadata records about datasets or services.
Discussion and conclusions A multidisciplinary interoperable approach for natural hazards is a key instrument for disaster-risk reduction (United Nations 2005). The amount of spatial data available necessitate a framework for fully exploiting the potential for decision-making in the natural hazard domain. This covers all the phases of the disaster cycle, from prevention and preparedness to disaster recovery. Defining a common framework for making data interoperable is a key component of the instrument. The main results of this paper are the illustration of the key aspects of the IF for natural hazards and the assessment of its applicability in different contexts. The results confirm that the IF is flexible and can be applied in the natural hazard domain facilitating harmonisation of multi-source datasets and making them comparable and ready for crossdomain assessments.
Acknowledgments The authors would like to thank their host organisations for funding their involvement in the  TWG-NZ  and the  Joint Research Centre of the European Commission  for facilitating and hosting the process and outcomes. We would like also to thanks the anonymous reviewers for useful suggestions during the review process. The authors would also like to thank other members of the  TWG-NZ :  Raquel Canet Castella ,  George Exadaktylos ,  Cristiano Giovando ,  Andrea Camia  and  Venco Bojilov. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Two possible values: ''modelling'' or ''indirect determination'' Life span of the The date the object was entered in the dataset and beginLifeSpanVersion: \ 05/08/2014[ object the date the object was removed or superseded endLifeSpanVersion: \ 06/08/2014[ from the dataset. Dates are represented in the attributes ''beginLifeSpanVersion'' and ''endLifeSpanVersion'' Validity period It is the future finite time frame where the hazard validityPeriod: applies \for 1 day [.
The measurement of ongoing ice-mass loss and associated melt water contribution to sea-level change from regions such as West Antarctica is dependent on a combination of remote sensing methods. A key method, the measurement of changes in Earth's gravity via the GRACE satellite mission, requires a potentially large correction to account for the isostatic response of the solid Earth to ice-load changes since the Last Glacial Maximum. In this study, we combine glacial isostatic adjustment modelling with a new GPS dataset of solid Earth deformation for the southern Antarctic Peninsula to test the current understanding of ice history in this region. A sufficiently complete history of past ice-load change is required for glacial isostatic adjustment models to accurately predict the spatial variation of ongoing solid Earth deformation, once the independently-constrained effects of present-day ice mass loss have been accounted for.
2005) , although limitations on seismic tomography resolution in the region pose a significant problem. In addition to the uncertainties outlined above, implementing lithosphere thickness or mantle viscosity directly from the wider geophysical literature in a GIA model is not appropriate due to the necessarily simplified Maxwell rheology assumed within a GIA model. Instead, we use the information above to guide our choice for the range of parameters that can be considered plausible, given the poor state of knowledge of the rheology of the region. In this paper, we first describe the GPS data collection and processing, GIA modelling methods and our data-model fitting algorithm. We then report the GPS results with corrections for elastic deformation and tectonic plate rotation, followed by output from the GIA modelling.
For all CAPGIA sites (including FOS1) we use data from late 2009 until the most recent site download in late 2013 or early 2014. Earlier campaign data exist for FOS1 but they are subject to different elastic corrections, which are less well known than for 2009-2014; they may also be subject to unknown offsets due to equipment changes. The BREN antenna was changed in 2012 and hence has an offset in the time-series at that point. As such we only make use of data up to a data gap in mid 2010 for BREN noting that the data used to calculate the elastic correction at this site were specific to the BREN epoch. Further details of the record from each site are provided in Table  1. Readers not interested in the specific details of the GPS analysis may wish to advance to Section 2. 2. We processed undifferenced raw data for BREN and the CAP-GIA sites in GIPSY/OASIS v6.
2011a)  installed at all CAPGIA sites other than FOS1. We modelled elastic effects associated with the pole tide, Earth body tide and ocean tide loading according to the IERS2010 Conventions. The ocean tide loading displacements were computed in the centre of mass of the entire Earth system (CM) frame  (Blewitt 2003)  based on the TPXO7. 2 ocean tide model  (Egbert et al. 2009 ) using the SPOTL software  (Agnew 1997). Ocean tide loading displacements in our region are dominated by tides in the Weddell Sea. The TPXO7. 2 model was chosen because a comparison to independent GPS measurements of ice shelf tidal motion showed it to be the most accurate of the models tested  (King et al. 2011b,c). We did not correct for time-variable elastic effects associated with recent polar motion as they are small at these locations  (King & Watson 2014).
We estimated site coordinates once per day, alongside tropospheric zenith delays and horizontal gradients every 5 min, receiver clocks at each measurement epoch, and real-valued ambiguity parameters. Ambiguities were fixed to integers using JPL's tabulated wide-lane phase biases using the approach of  Bertiger et al. (2010). Typical GIPSY analysis involves weighting all data uniformly but this does not reflect the error distribution of typical GPS observations for which scatter tends to decrease with increasing satellite elevation angle. Instead, we performed an iterative analysis similar to that widely used in GAMIT software  (Herring et al. 2010) , whereby an initial analysis with uniform weighting was performed and then an elevation-dependent model was fit to each of the phase and code residuals.
Daily estimates more than three standard deviations from the model in any one component were regarded as outliers and removed. Some of our sites exhibit rapid transient behaviour during the wintertime that is most evident in the horizontal coordinate components. Similar transients have been identified in previous studies (e. g. Jaldehag et al. 1996; Johansson et al. 2002)  and attributed to frost or rime ice, which may form on the side of the antenna radomes in autumn and winter. Following the processing approach of  Argus et al. (2014) , we remove the affected time intervals from our GPS data before analysis. The only sites where such transient behaviour is not evident are FOS1 and BREN-we note that FOS1 is much closer to sea level than the other sites and BREN does not have a radome-so the observations are consistent with the rime ice hypothesis but the source of this signal clearly requires further investigation.
After removal of outliers, we computed three-component site velocities and uncertainties using the CATSv3. 20 software  (Williams 2008) , estimating constant, annual, semi-annual and velocity terms and adopting a time-variable white plus power-law noise model. We estimated the power-law spectral index  (Agnew 1992)  of the sites for each coordinate component, with a mean value of -0. 81 ± 0. 22 for the vertical coordinates and -0. 25 ± 0. 09 for the horizontal coordinates. Velocities in ITRF2008 have their origin at an approximation to CM over the period 1983. 0-2009. 0. For SUGG and HAAG we did not analyse raw data but instead adopted the  Argus et al. (2014)  velocities and uncertainties, using their tabulated frame translation parameters and plate rotations to shift their horizontal and vertical components back into ITRF2008.
Elastic modelling We modelled the effects of elastic deformation of the solid Earth due to ice load changes occurring over the GPS data collection epoch. For sites that have GPS data in the 2010-2014 epoch (all sites including SUGG and HAAG but excluding BREN), we adopted 5 km gridded ice elevation trends for West Antarctica and Alexander Island based on a combination of CryoSat-2 (CS2) data (November 2010-September 2013 inclusive) as described by  McMillan et al. (2014)  and the detailed loading changes of  Nield et al. (2014)  for the region north of Marguerite Bay. We extrapolated the CS2 data to the grounding line, although this has negligible effect on our solutions. CS2 mass trends and uncertainties were derived using the method of  McMillan et al. (2014) , whereby CS2 elevation trends were converted to mass trends using a density model to separate regions changing mass at a density of snow or ice.