dataesr/scientific-paragraphs-categorization huggingface dataset:
Time estimation The time for surface pre-processing and initial projection was measured by the value displayed in 'Edgewarp' log. The time required by 'Edgewarp' for the three iterations of sliding against Procrustes consensus was measured using a timer. The time required to run the 'Morpho' package in R was assessed by running the timestamp R function at the beginning and end of tasks and calculating the difference. These durations do not include time required for writing the script (scripts for data formatting for 'Edgewarp' , and R scripting for 'Morpho'). All analyses were run on a laptop computer (Asus k55vj) with a Intel ® i7-3630QM CPU, 4 Gb of memory, running on Linux Ubuntu V14. 04.
Geometric morphometrics and visualization Data analysis and visualization were performed using the R software (R Core  Team, 2014). In order to superimpose geometries and isolate size and shape, a generalized Procrustes analysis (GPA) was performed  (Gower, 1975; Rohlf & Slice, 1990; Dryden & Mardia, 1998)  for the 'Edgewarp' and the 'Morpho' dataset separately using the 'Rmorph' package  (Baylac, 2013). We reduced dimensionality of the datasets by keeping the nine first non-null axes of a Principal Component Analysis (PCA) performed on the coordinates in the tangent space. In order to asses differences between the dataset produced by each software, a PROTEST analysis was performed using the 'vegan' package  (Oksanen et al. , 2015)  run with 100,000 iterations  (Peres-Neto & Jackson, 2001).
Global shape variability The PROTEST analysis highly supports the congruence of the two datasets (m 12 = 0. 00325; P < 10 -5 ). The Procrustean superimposition plot (Fig. 2 ) shows the differences between the two datasets. It becomes immediately clear that the between-methods variability is smaller than the inter-specimens variability, even within species. The Procrustes residuals of specimens analyzed with both methods vary from one specimen to the other (Table  4 ). The specimens showing the maximal Procrustes residuals are Gulo gulo 1983-946, Mustela putorius 1991-605, and Enhydra lutris 1935-124 (respectively c, e, and i; Fig. 2 ). The first axis separates Enhydra lutris (i and j; Fig. 2 ) and Martes martes (a and b; Fig. 2 ). On the two first axes Mustela putorius and Gulo gulo show intra-specific distances greater than the inter-specific ones with Mustela putorius 2004-639 (f; Fig.
Differences As they are computed from the same dataset, morphospaces obtained by the two software where expected to be the same. The two morphospaces (relative distances between specimens) are indeed generally similar, which is validated by the significant PROTEST. Nevertheless some specimens show slightly different positions in the morphospace depending on the software (Gulo gulo 1983-946, Mustela putorius 1991-605, and Enhydra lutris 1935-124, respectively c, e, and i; Fig. 2 ). Specimens showing the greatest variation in their relative position between the two software are bones with areas where a structure interrupts the main curvature of the surface resulting in strong local deformations (Fig. 4 ). The fact that these local deformations are of a size similar to the distance between surface sliding landmarks appears to play a role.
Time 'Edgewarp' appears more time-consuming than 'Morpho' for the same sliding task. 'Edgewarp' requires 30-300 times the time required by 'Morpho' for the same tasks. Two reasons can be put forward to explain these differences: computational efficiency and workflow complexity. 'Edgewarp' is a non-parallelized software whereas many 'Morpho' functions can be run on several cores, thus improving processing time. The effect of computation power remains low on a small dataset like the one presented here, but can lead to a considerable time difference in the case of the analysis of larger datasets. In addition to the number of steps constituting the workflow, the number of actions performed by the user should also be taken into consideration. The use of 'Edgewarp' requires one to open each surface, curve, and landmark file manually.
The subsequent iterative relaxation requires several manual operations per iteration as does the data saving after sliding. The use of a programming language such as python  (Van Rossum & De Boer, 1991) , bash (Free Software Foundation, 2013), or even R (R Core Team, 2014) is useful to automate some parts of the workflow such as the '. sav' generation. Once written, scripts can be run several times or adapted, reducing the time required to perform these repetitive tasks. Furthermore 'Edgewarp' does not perform the Procrustes superimposition needed for the computation of the mean shape, entailing the use of another software package (here R with the 'Rmorph' library). Therefore the time needed cannot be spent doing other things. Conversely using 'Morpho,' the principal active component is the writing of the script.
' This 'rail' (named '. cur' in 'Edgewarp') is constituted of a collection of points digitized on the bone surface, linked by segments. Consequently the definition of the 'rail' needs a large number of points to achieve a good description. However, when two curves are close, points can 'jump' from one curve to the other if the relaxed position is close to the second curve. Two strategies can counterbalance this effect in 'Morpho'; first using a higher density of curve sliding landmarks  (Gunz, Mitteroecker & Bookstein, 2005)  and second, by reducing the relaxation step size. This last solution presents the advantage of being applicable on the initial dataset and does not require new data collection. We also noticed points being projected on the wrong side of the supracondylar lateral crest and points projecting through bone from the olecranon fossa to the coronoid fossa while using 'Edgewarp.
' The projection of surface sliding semi-landmarks from the template to the specimen surface is a crucial step preceding the actual sliding process. The main principle used in both software packages is a TPS deformation of the template constrained by anatomical and curve sliding landmarks followed by a projection onto the specimen surface. For some areas such as the lateral supracondylar crest, surface points can be projected onto the wrong side of the crest, if projected to the closest surface after TPS deformation. The 'Morpho' function 'placePatch' implements additional steps to avoid such effects. Inflation/deflation along the normals (vector giving the orientation of the mesh vertex) is performed; then the normals of projected points are compared to those of the template to avoid inside-out effects.
In 'Edgewarp,' the graphical interface allows users to manually displace and correct wrongly projected points. The 'Morpho' strategy presents the advantage to be automated and integrated as a part of the 'placePatch' function. In contrast, 'Edgewarp' requires the manual displacement of the points, implying the correct identification of wrongly projected points prior to moving them to the correct side. This requires the investigation of all points on every specimen which is time consuming. The use of surface sliding semi-landmarks at the very vicinity of the curves appears to be a factor leading to projection on the wrong side of crests. Therefore, this problem could be circumvented by taking this fact into account during the design of the template. Aside of the own properties of these software, the help file determines a part of the global ergonomy of any software package.
CONCLUSIONS This study shows that the results obtained by two software packages for 3D shape quantification, 'Edgewarp'  (Bookstein & Green, 1994)  and 'Morpho'  (Schlager, 2013b) , are similar and that biological variability is similarly described by both software packages. We highlight the fact that small structures jutting out of the surface as well as depressions may have an impact on the results and underline the importance of identifying these structures prior to the sliding operation. An increase in the density of the surface sliding landmarks in these areas or the removal of structures that do not contain biologically relevant information for the study are possible solutions for avoiding strong impact of these structures on the morphospace. The present study focuses on a small number of species, showing a range of size and shape variation.
This experimental design highlights the kind of structures that drive divergence between the two software packages. However, this design does not allow us to anticipate possible biases for different datasets such as those used in intra-specific studies where the variability between specimens is often quite subtle. Beyond the similarity in results, 'Morpho' is faster than 'Edgewarp' for the same sliding task, notably as a result of the computational power of 'Morpho' and the complexity of the workflow in 'Edgewarp. ' 'Edgewarp,' on the other hand provides a visual approach to the sliding procedure. Thanks to the exploitation of modern computational power, 'Morpho' library, provides a powerful tool for the treatment of larger datasets.
ACKNOWLEDGEMENTS The authors would like to thank  S. Ivan Perez  and an anonymous reviewer for their comments on the original manuscript and their suggestions,  S Schlager  ( University of Freiburg, Germany ) for advice and his reactivity,  J Cuisin ,  G. Véron ,  J. Villemain , and  C Bens  for the access to specimens from the collections Mammifères et Oiseaux, MNHN, Paris and the  UMS 2700 OMSI MNHN/CNRS  for the access to the scanner. LB-D thanks the doctoral school 'Frontières du vivant' and the Bettencourt Schueller foundation. LB-D thanks  M Depraetere  ( MNHN, Paris ) for her useful comments on the manuscript.
Mappix is a pixelated method and, as such, it is limited by the finite optical size of the camera pixels. Two emitters can be distinguished providing that they are separated by a distance corresponding to at least 1. 5 pixels even in situations where their individual emission can hardly be selectively observed in time. However, it should be noted that where signals overlap, the shape of the processed signal is changed and cannot anymore be approximated by a sum of Gaussian PSF. Mappix images can be estimated considering full movie data (K frames) or repeating the procedure for shorter image sequences. In the latter case, multiple images are output and the results are averaged to produce the final Mappix image. For a short image sequence, dissimilarity is calculated in reference to the mean taken over the images composing the sequence.
However, it should be noted that the final image might be affected by the fact that dissimilarity is assessed to different mean vectors. This approach was not explored further in this work. The routines used for Mappix were written in MATLAB version 7. 4. 0 (R2007a) or higher. 23 These routines are available on request to the authors. SOFI images were obtained using Localizer,  24  a freely available and open source software package that implements the computational data processing inherent to several types of super-resolution fluorescence imaging.
A key aspect of the design of any software system is its architecture. One issue for perpetually designing good and robust architectures is the new security concepts. Many new applications are running on powerful platforms that have ample rich architecture models to support multiples security techniques and to explicit several security constraints. The design of an architecture meta-model that considers security connectors is required in order to ensure a realistic secure assembly and to address the problems of vulnerability of exchanging data flow. Our research proposes a generic meta-modelling approach called SMSA (Security Meta-model for Software Architecture) for describing a software system as a collection of components that interact through security connectors. SMSA metamodel is modeled as a UML SMSA profile.
components diagram in UML 2. 0 consisting of four applications, each application as a components sets. These applications deployed on different devices in a target environment, connected by wireless or fixed line communication networks. Basically, a main component (e. g. Consolidate GUI) receives its results from a Treatment Consolidate component with itself receives its data from all database components (e. g. Consolidate DataBase, personal and human resource DataBase, Immobilier DataBase, Invoice and Finance DataBase). Simple connectors are used between those components to exchange data and informations.
a components diagram in UML 2. 0 consisting of four applications, each application as a components sets. These applications deployed on different devices in a target environment, connected by wireless or fixed line communication networks. Basically, a main component (e. g. Consolidate GUI) receives its results from a Treatment Consolidate component with itself receives its data from all database components (e. g. Consolidate DataBase, personal and human resource DataBase, Immobilier DataBase, Invoice and Finance DataBase). Simple connectors are used between those components to exchange data and informations.
AC K N OW L E D G E M E N T S This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France , and of the Tycho-2 catalogue by  E. Høg  et al. The C code for the CuReD algorithm that we used was developed and provided by  Matthias Rosensteiner  and  Andreas Obereder  ( MathConsult GmbH, Linz, Austria ). We also thank  Matthias and Andreas  for many useful discussions!We appreciate the comments on the paper draft by  Aglaé Kellerer. In UK this research project is supported by the  Science and Technology Facilities Council  (grant no. ST/I002871/1 ). The French authors acknowledge the support from  CNRS/INSU, Observatoire de Paris  and  EU OPTICON  program  WP1 , grant agreement no. 312430.
Land use patterns for use in LPJmL LPJmL needs irrigated and rain-fed physical (as opposed to harvested) areas for each simulated crop. Physical and harvested areas differ through multiple cropping practices; that is, when one area is used twice in a year, the harvested area is twice as large as the physical area. For the present model development a new land use data set had to be compiled from different sources:  Portmann et al. (2011) , hereafter MIRCA,  Monfreda et al. (2008) , hereafter  MON, Klein Goldewijk et al. (2011), hereafter HYDE, and Ramankutty et al. (2008) , hereafter RAM. The first step for the compilation of the land use data set was determining the harvested areas of all LPJmL classes, including the new Mediterranean crops, for the present time. For crops present in MIRCA, which differentiate between irrigated and rain-fed areas, all values in that study were used directly.
The author of MIRCA (F. Portmann, personal communication, 2013)  assumes that most of these inconsistences are due to scale, the grid-cell differences being potentially large but presenting a good agreement at the administrative level. Grasslands, representing meadows, were taken directly from RAM and assumed to always be rain-fed. After deriving harvested areas for around the year 2000, the calculation followed the flowchart shown in Fig. S1  in the Supplement. Harvested areas were compared with cropland and grassland areas from RAM in order to exclude multiple cropping and derive physical cultivation areas. Decadal cropland data from HYDE were interpolated to derive annual values and then used for extrapolating the land use patterns of ∼ 2000 to the past, until 1700 (see below, Eqs. 1 to 5).
Historical irrigation fractions were determined as explained in  Fader et al. (2010). Due to inconsistences between HYDE and the data set combining MIRCA, MON and RAM (hereafter MMR), proportionally changing cell fractions using the HYDE historical trend would have given an unrealistic overall trend of cropland areas. For this reason, crop-specific bias corrections had to be performed in between, as follows. First, the global (g) area difference (D) was calculated: This procedure yields a gridded global data set at 30 arcmin spatial resolution of the cultivation areas of 24 crops from 1700 to 2010. Table  1  shows the resulting areas for each crop class of LPJmL. Expanding LPJmL for modelling Mediterranean crops made it possible to account for ∼ 88 % of irrigated areas of the Mediterranean instead of ∼ 50 %.
The agreement is high (MAPE < 30 %) for all classes except for olives and dates  (MAPE 47 and 46 %, respectively)  where our data set has mainly smaller areas. This is due to the fact that MIRCA, RAM and MON have been compiled for the year 2000 and the FAO shows strongly an accelerated expansion of areas from 2000 to 2010, for example 54 % for olives in Morocco and 45 % for dates in Egypt and Turkey. Overall, the input data set compiled here has no better alternatives and appears to be broadly suitable for applications until newer versions of the land use data used as sources are released.
There are insufficient quantitative data on this issue, and we therefore assumed this parameter to be 4 years for all agricultural trees. After these years, a plant-specific portion (HI, the "harvest ratio" or "harvest index") of the net primary productivity (NPP) of the tree is harvested every year. Thus, fruit growth is represented by a carbon accumulation that equals the multiplication of HI and NPP. An additional tree-specific parameter determines the replanting cycles of trees. Since there are no data available on this, we assumed that plantations are renewed (replanted) after 40 years. Most agricultural trees have chilling requirements; that is, they need a period of low temperatures before flowering. This is modelled using the parameter T lim shown in Table  2. A 20-year running average of the coldest month maximum and minimum temperatures is compared with these values and defines the bioclimatic limits of each species.
Infiltration depends on the soil water content of the first layer (water that does not infiltrate runs off), and percolation between the layers was simulated following the storage routine technique (see  Schaphoff et al. , 2013 , for more details). Excess water over the saturation level is assumed to feed subsurface runoff. LPJmL has two soil carbon pools, with intermediate and fast turnover (0. 001 and 0. 03 rate of turnover per year at 10 • C). The maximum decomposition rate is reached around field capacity and decreases afterwards due to decreased soil oxygen content. A simple energy balance model is used for the thermal soil module. It includes a one-dimensional heat conduction equation, convection of latent heat, thawing and sensible heat (see  Schaphoff et al. , 2013 , for more details).
However, a few patterns shown by  Monfreda et al. (2008)  are not shown in LPJmL simulations, including the north-south pattern of olives in Spain, the high-yielding zone of olives in southern Italy, and the grape and olive yields in Egypt. The first case is due to the extremely high management intensity in southern Spain which is not captured by the national calibration of planting densities  (Scheidel and Krausmann, 2011). The latter case is originated by differences between the MON and MIRCA land use data sets that produce a lack of irrigated areas of grapes and olives in Egypt in our data set (see Sect. 2. 2 for more details). The same is the case for the gaps in potatoes, e. g. in France. Overall there is a large agreement with no systematic differences between the spatial patterns shown by  Monfreda et al.
Also, orchards have a relatively low planting density in France (1300 trees per hectare), which reduces shadow effects and litter input that, in turn, results in lower soil carbon compared to high-density mowed grass. Validation of the new SOC patterns is very challenging since SOC measurements are spatially discontinuous as well as dependent on local conditions, sampling method and small-scale drainage conditions. Comparison with empirically based or process-based modelled SOC is also difficult due to differences in approaches, parameters, processes considered and issues of scale. Nevertheless, we compared our SOC estimates before (LPJmL Old ) and after (LPJmL New ) the implementation of agricultural trees with the organic carbon density from the HWSD database  (Hiederer and Köchy, 2012).
Code availability and technicalities LPJmL is written in the C programming language and is run mainly under UNIX-like systems. Inputs and outputs are in binary format. Depending on the size of the region analysed and on the desired spatial resolution, it may require a highperforming computational structure. The version used as the basis for the present development was 3. 5. 003 and the revision number 2133 from 17 May 2014. The main site for downloads of different model versions can be found under https://www. pik-potsdam. de/ research/projects/activities/biosphere-water-modelling/ lpjml/versions. There, downloads are free of charge and possible after registration. However, the latest model version that includes the agricultural module and the Mediterranean development is not available yet since the different working groups are still compiling a complete technical documentation and merging the last developments into one unique model version.
Acknowledgements. This work is a contribution to the  Labex OT-Med  (no. ANR-11-LABX-0061 ) funded by the  French Government Investissements d'Avenir program  of the  French National Research Agency (ANR)  through the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ). Alberte Bondeau  and  Wolfgang Cramer  receive support from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under projects  OPERAs  (grant agreement number  308393 ) and  LUC4C  (grant agreement number  603542 ). We thank the  Joint Research Centre of the European Commission  for giving as access to the  Global Soil Organic Carbon Estimates data  set of the European Soil Data Centre (ESDAC). We thank the  LPJmL group  at the  Potsdam Institute for Climate Impact  Research for discussions and technical support.
Question 6. Are the implant-choices for young patients in The Netherlands evidence-based? As many types of thAs exist to treat young patients with degenerative hip disease, we have reviewed the available literature on this topic to determine the optimal treatment (paper 6). We examined all reported outcomes of uncemented and cemented total hip arthroplasty in patients younger than 50 years of age listed in Medline between 1966 and 2009 as well as those mentioned in PubMed. in addition we scrutinised the reference lists of relevant papers and evaluated relevant data of the swedish hip Arthroplasty Register. inclusion criteria were as follows: primary total hip arthroplasty, age at index surgery younger than 50 years and a minimal mean followup of 10 years. We have identified 109 relevant articles of which 37 had a mean follow-up longer than 10 years.
2009 ). survival at 15 years was 84% with failure for all reasons as end point. for the uncemented option in the model, data were obtained from the Norwegian Arthroplasty Register. Unpublished data of 1289 patients younger than 50 years with primary hip osteoarthritis, who had received an uncemented cup, were analyzed and survival rates were determined. the most commonly. the probability of survival of primary uncemented cups was 61% at 15 years. survival of the first uncemented cup revision was based on 76 patients and was 52% at 15 years. As survival rates of the cup solely were not available for the second revision, we used the same data for both cemented and uncemented revisions as presented by Lie et al. (  2004 ), with a survival rate of 60% at 10 years. the costs of an intervention consisted of material costs, costs of operation theatre and costs of hospital admission.