dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the Participating Codes In the following subsections, we provide a brief description of the various codes. We first describe codes based on the finite volume method (YACC, Plaatjes, CHIC, GAIA, StreamV, and StagYY), then those based on the finite element method (FEniCS, Fluidity, ELEFANT, and ASPECT), and finally the only spectral code involved (MC3D). Furthermore, in Table  S1  of supporting information, we listed a number of geodynamic benchmark studies indicating whether each code has been validated or not against them. Geochemistry, Geophysics, Geosystems   [Tosi et al. , 2010; King et al. , 2010]. For each cell of the domain, the Stokes equation (  2 ) is solved for horizontal and vertical velocity at staggered nodes together with the continuity equation (  1 ), which is explicitly integrated for the pressure located at cell centers [e.
CHIC CHIC (Coupling Habitability, Interior and Crust) is a Fortran 90 code developed at the Royal Observatory of Belgium by L. Noack and colleagues  [Noack et al. , 2015]. It treats 2-D and 3-D Cartesian boxes as well as 2-D spherical geometries (spherical annulus and cylindrical geometry) in Cartesian or polar coordinates, respectively. The finite volume method is used to solve the partial differential equations on staggered grids. For this benchmark, the continuity and momentum equations are solved implicitly in one linear system where a viscosity-dependent penalty term is applied in the continuity equation. The thermal energy equation is solved implicitly with second-order accuracy. Both linear systems (continuity-momentum and energy) are solved iteratively in each time step until the temperature converges.
GAIA The Generic Automaton for planetary Interior Analysis GAIA is a Stokes-flow and energy solver intended for mantle convection applications written in C11 by C. H€ uttig. The code allows high-performance computing with a domain-decomposition technique  [H€ uttig and Stemmer, 2008a ] that enables GAIA, in combination with MPI, to efficiently run on thousands of compute cores. It is based on a finite volume discretization of the conservation equations (1-3) expressed in Cartesian coordinates. It can handle different geometries, from 2-D and 3-D rectangular boxes to 2-D cylindrical and 3-D spherical shells or full spheres. The computational domain can be meshed with structured or fully unstructured grids representing Voronoi diagrams obtained from a Delaunay triangulation of given sets of generator points  [H€ uttig and Stemmer, 2008b].
StreamV StreamV is a finite volume thermochemical convection code written in Fortran 90 by H. Samuel  [Samuel and Evonuk, 2010; Samuel, 2012 Samuel, , 2014]]. The code solves for the conservation of momentum subject to the incompressibility constraint and the conservation of energy and/or composition in various 2-D geometries (Cartesian, cylindric, 3-D axi-symmetric) and in 3-D Cartesian domains. The code has been successfully benchmarked against analytical and numerical solutions of the governing equations  [Blankenbach et al. , 1989; van Keken et al. , 1997]  including variable (linear) viscous rheology. For 2-D geometries, a pure stream function formulation is used to solve the Navier Stokes equation, resulting in a biharmonic equation for the stream function. The latter is defined at nodal points, leading to a natural control volume formulation on a staggered grid, with velocity components located at the center of each control volume face, and satisfying automatically mass conservation.
StagYY StagYY is a finite-volume/finite difference Fortran 90/MPI convection code developed by P. Tackley and coworkers  [Tackley, 1993; Tackley and Xie, 2003; Hernlund and Tackley, 2008; Tackley, 2008]. The Stokes, continuity, and advection-diffusion equations are solved on a staggered grid in Cartesian or spherical geometry, Geochemistry, Geophysics, Geosystems 10. 1002/2015GC005807 in two or three dimensions. The velocity components are defined at the cell boundaries perpendicular to their direction and pressure is defined at cell centers. In this study, a SIMPLER-like method  [Patankar, 1980]  is used to iterate on velocity and pressure fields, and is incorporated into multigrid F-cycles to increase convergence rate. The MPDATA scheme  [Smolarkiewicz, 1984]  has been used for the advection of temperature, and diffusion is solved through an explicit scheme, with a Courant number fixed to 0.
FEniCS FEniCS (P. Maierov a) is a freely downloadable software package for automated solution of partial differential equations using finite element methods  [Logg et al. , 2012]. Its main component DOLFIN is a C11 library with a Python interface that provides tools for definition, discretization, and solution of the equations. An important feature is the Unified Form Language (UFL), which is used for the definition of finite element spaces and expression of equations in a nearly symbolic notation. Different solvers and preconditioners can be applied to the linear equation systems. Provided that appropriate solvers and mesh partitioners are chosen, DOLFIN can run in parallel (with MPI or OpenMP). The applicability of FEniCS to the modeling of thermochemical mantle convection was demonstrated by  Vynnytska et al.
Fluidity Fluidity is a general purpose computational fluid dynamics code, which was recently extended to solve the Stokes and accompanying field equations that are relevant to geodynamics by  Davies et al. [2011]  and  Kramer et al. [2012]. The code uses unstructured simplex meshes, which enable the straightforward representation of complex geometries. Fluidity supports both continuous and discontinuous Galerkin finite element discretizations, in addition to control-volume discretizations. However, for the purposes of this benchmark, the governing equations are discretized using a continuous Galerkin finite element approach. In all Fluidity results that follow, a P2-P1 velocity-pressure element pair is used for the Stokes system, which consists of piecewise quadratic basis functions (P2) for velocity and piecewise linear basis functions (P1) for pressure.
g. , SUPG  [Hughes and Brooks, 1982] ) was required. The GMRES Krylov subspace method is used to solve for temperature with successive over relaxation (SOR) preconditioning and a Crank-Nicholson scheme is used for time stepping. Nonlinear coupling between temperature and velocity is handled via a Picard iteration. The mesh resolution adopted for Cases 1-5a was of 128 triangular elements in both vertical and horizontal directions. 4. 9. ELEFANT ELEFANT borrows largely from the FANTOM code  [Thieulot, 2011] , but it also brings a number of critical improvements compared to its predecessor  [Thieulot, 2014]. It is a finite element code, based on bilinear Geochemistry, Geophysics, Geosystems 10. 1002/2015GC005807 velocity-constant pressure (Q1-P0) elements and a penalty formulation. Markers are used to track material properties and are advected by means of a fourth-order Runge-Kutta scheme in space.
The direct solver MUMPS  [Amestoy et al. , 2001 [Amestoy et al. , , 2006 ] is used to solve the large linear systems arising from the discretized equations. The Streamline Upwind Petrov-Galerkin method is used to stabilize the advection term of the energy equation. ELEFANT has been extensively benchmarked. A mesh consisting of 100 3 100 quadrilateral elements was used for the benchmark Cases 1-5a. 4. 10. ASPECT ASPECT (short for Advanced Solver for Problems in Earths ConvecTion) is a new open source finite element code  [Kronbichler et al. , 2012]. The code is directly built upon deal. ii [Bangerth et al. , 2007] , which is a general-purpose finite element library, TRILINOS  [Heroux et al. , 2005] , which provides scalable and parallel solvers, and p4est  [Burstedde et al. , 2011] , which builds distributed adaptive meshes in parallel.
Benchmark results from MC3D exhibit the largest discrepancy with respect to the other codes. This is not surprising because, as described in section 4. 11, MC3D is a spectral code in which the viscosity is averaged laterally before being used to solve the conservation equations. Although the actual values of the diagnostic quantities-of the average and surface velocities in particular-differ considerably from those obtained when using the actual viscosity distribution, it is worth noting that, despite this simplified approach, the code reproduces the expected convective regime and predicts average mantle temperatures that generally differ by less than 1% from the solutions obtained with the other codes.
Case 5b: Bifurcation Analysis Case 5b is an extension of Case 5a. Here we varied the yield stress from r Y 53 to 5 with the goal of identifying the critical values at which the system transitions from a steady, mobile lid regime (for low values of r Y ), to a periodic regime first (for intermediate values of r Y ), and to a steady, stagnant lid regime afterward (for high values of r Y ). The results of this analysis are shown in Figure  7  for the six finite volume codes and in Figure  8  for three finite element codes and for the spectral code MC3D. In order to assess the impact of the mesh resolution on the estimate of these critical values, each participant performed the analysis using three to five meshes of increasing resolution. At the highest resolutions tested (between 100 3 100 and 160 3 160 cells used by GAIA and StreamV, respectively) and, again, with the exception of MC3D, all codes predicted the first transition from mobile lid to periodic regime to occur for 3:7 < r Y < 3:9.
g. , Plaatjes was tested on meshes from 8 3 8 cells to 512 3 512 cells). We found that the nonlinearity introduced by the dependence of the viscosity on the strain rate does not significantly impact on the accuracy of the numerical solutions, which, on meshes of $1003100 cells/elements, typically differ by less than 1% from those obtained with the same code on grids with $2003200 cells/elements or more. In addition, we performed a bifurcation analysis (Case 5b) aimed at identifying the critical values of the yield stress for which the convective regime changes from mobile lid to periodic and from periodic to stagnant lid. Also in this case, we found a good agreement among the different codes with finite volume codes generally exhibiting a more pronounced sensitivity to the mesh resolution with respect to finite element codes.
A spectral code (MC3D) capable to treat only a depth-dependent viscosity obtained from laterally averaging temperature and strain rate was also used to carry out all tests. Although, with this approach, reasonably accurate solutions could be achieved in the steady state cases (for the average temperature and Nusselt numbers in particular), the critical yield stresses at which the transition to different regimes was observed were remarkably different from those obtained with the other codes. The majority of the codes involved in this benchmark effort can also work with more complex geometries using the same solution framework adopted here. Additional benchmarks will certainly be needed to reliably assess the accuracy of numerical implementations in geometries other than a simple 2-D box. Nevertheless, we expect that our tests will be a useful tool to at least partially validate new implementations of viscoplastic thermal convection in cylindrical and spherical domains that are more appropriate for modeling mantle dynamics.
Acknowledgments Input files to reproduce the benchmark tests with the codes YACC (https://bitbucket. org/7nic9/yacc-yetanother-convection-code),FEniCS (http://fenicsproject. org),and ASPECT (http://aspect. dealii. org)are available upon request from  N. Tosi ,  P. Maierov  a, and  A. Glerum , respectively. The data used to plot the figures of this paper are either contained in Tables 2 andS2-S23 or available upon request from  N. Tosi. We thank the Editor  Cin-Ty Lee ,  Harro Schmeling , and an anonymous reviewer for their thoughtful and constructive comments, which helped improve an earlier version of the manuscript. N. Tosi  was supported by the  Helmholtz Gemeinschaft  (project  VH-NG-1017 ). C. Stein is grateful for funding from the  DFG  (grant  HA1765/ 24-1 ). L. Noack has been funded by the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy Office  through the Planet Topers alliance.
P. Maierov a was supported by the  Czech Ministry of Education, Youth and Sports  (project  LK11202 ). H. Samuel acknowledges support from the  Deutsche Forschungsgemeinschaft  (project  SA   2042 ) and from the  CNRS-INSU Programme National de Plan etologie. D. R. Davies was supported by  ARC   Future Fellowship   FT140101262. C. R. Wilson was supported by the  National Science Foundation  (grants  OCE-0841079  and  EAR-1141976 ). A. Rozel  and  P. Tackley  were funded by the  ERC  project iGEO. All Fluidity simulations were undertaken on the  NCI National Facility in Canberra, Australia , which is supported by the  Australian Commonwealth Government. GAIA simulations have been carried out on the  High-Performance Computing Center Stuttgart (HLRS)  through the project  Mantle Thermal and Compositional Simulations (MATHECO ).
MRI preprocessing fMRI data preprocessing was performed using the fMRI Expert Analysis Tool (FEAT) from the FSL's [FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library; version 6. 0. 0; Woolrich et al. , 2009; Jenkinson et al. , 2012]. Images were motion-corrected using mc-FLIRT (Motion Correction-FMRIB Linear Image Registration Tool;  Jenkinson et al. , 2002) , slice-time corrected using Fourier-space timeseries phase-shifting, skull stripped using Brain Extraction Tool (BET;  Smith, 2002) , and smoothed using a Gaussian kernel of FWHM ϭ 5 mm. The 4D dataset was grand-mean intensity normalized by a single multiplicative factor and was high-pass filtered ( ϭ 31 s). As part of the denoising process, independent component analysis of each participant's native space preprocessed data fMRI was performed using  Me-lodic (version 3.
This was done using the Greedy symmetric diffeomorphic normalization (GreedySyN) approach  (Avants et al. , 2011). We used this approach to normalization as it has been shown to be the joint top-performing normalization method in a comparison of 14 common methods  (Klein et al. , 2009). The template was created using the buildtemplateparallel. sh script distributed with the Advanced Normalization Tools software (http://stnava. github. io/ANTs/; Avants et al. , 2010). After an initial affine template was created, four iterations of the nonlinear template creation were performed, to allow template convergence. A t1-weighted image-to-template mapping was then computed for each participant using ANTS/GreedySyN. This mapping was then composed with the affine fMRI-to-t1-weighted-image mapping obtained with BBR.
Structural MRI processing Cortical and subcortical reconstruction was performed using FreeSurfer  (Fischl, 2012). This involved removal of nonbrain tissue using a hybrid watershed/surface deformation procedure  (Segonne et al. , 2004) , segmentation of subcortical structures and ventricles  (Fischl et al. , 2002) , intensity normalization, tessellation of the white/gray matter border, and topology correction to remove any "holes" or "handles" in the cortical surface  (Fischl et al. , 2001; Segonne et al. , 2007). The initial white/gray surface was then progressively deformed until it encountered areas of sharpest image contrast, which indicate the borders between white matter and gray matter and between gray matter and CSF  (Dale et al. , 1999). The cerebral cortex was then automatically parcellated using geometric information and inference from a handdissected training dataset  (Fischl et al.
Diffusion-weighted MRI processing Diffusion-weighted and b0 images were processed as follows. Brain masks were created using FSL's BET  (Smith, 2002). Motion and eddycurrent correction was performed on the masked diffusion data using ExploreDTI  (Leemans et al. , 2009). A spherical deconvolution approach was chosen to allow for the estimation of multiple fiber directions within a single voxel  (Tournier et al. , 2004). This was calculated using a damped version of the Richardson-Lucy algorithm  (Dell'acqua et al. , 2010). We optimized parameters to find the best balance between resolving multiple fiber directions and creating minimal spurious (false positive) fiber orientation distribution (FOD) components. After carefully altering the parameters and testing on multiple subjects, we used a regularization threshold of ϭ 0.
The dynamic approach developed in this study is an implicit approach to take into account particlephase diffusion with a low number of layers and to keep a computation time as low as possible in order to be used by 3-D air quality models. It does not describe particle-phase diffusion as thoroughly as KM-GAP  (Shiraiwa et al. , 2012). The code can be downloaded at http://cerea. enpc. fr/en/ modeles. html.
Bioinformatics Variant calling was performed against the GRCh37 human reference genome, using LifeScope™ 2. 5 (exome sequencing) and Torrent Suite V 3. 6. 2 (NSES) (Life Technologies). Data was filtered using an ANNOVAR annotation software suite. Variants were annotated using the EncodeGencode gene annotation set. Variants were filtered against the 1000 Genomes database (2012 release,  [27] ) and the dbSNP137 common database, and variants with a frequency of >0. 5 % were excluded. Variants were then filtered against an in-house common variant list and were checked against the HGMD professional database to identify any known disease-causing mutations. The frequencies of candidate disease variants in the 1000 Genomes Project, Exome Variant Server (http://evs. gs. washington. edu/EVS/) and ExAC Browser (http://exac.
broadinstitute. org) were also determined. Pathogenicity predictions were made using online prediction software programs: SIFT, PolyPhen  [28] , and MutationTaster  [29]. The LSBFG has a cut-off of 90 % of on-target regions covered to 20-fold or greater for the neuromuscular panel (NSES) and 80 % covered to 20-fold or greater for exome sequencing, however some samples, especially early samples, did not achieve these cut-offs (Additional file 1: Table  S1 ). There was no significant difference in the average coverage (mean ± SEM) of exome sequencing data for genetically resolved (80 ± 14-fold; n = 15) versus unresolved cases (70 ± 8-fold; n = 23). For the NSES panel, average coverages were 220 ± 23-fold (n = 6) for resolved cases versus unresolved cases (195 ± 13-fold, n = 9). Thus coverage is unlikely to contribute to the lack of a genetic diagnosis in most cases.
Sanger confirmation and co-segregation studies PCR amplification and Sanger sequencing was performed to verify potential mutations identified by next generation sequencing. Co-segregation was also verified for all existing family members where available. Primers were based on genomic and cDNA sequences obtained from the UCSC Human Genome Browser (http://genome. ucsc. edu/) and Ensembl (http://www. ensembl. org/). Primer sequences and conditions are available upon request. Sanger sequencing data was processed by LSBFG and results viewed using CodonCode Aligner software.
1315delG, p. Val439Trpfs*11). To our knowledge neither of these mutations have been previously reported and are not listed in the CHRND locus-specific database (http:// www. dmd. nl/nmdb/home. php?select_db=CHRND). Sanger sequencing confirmed the presence of the mutations in the affected individual and showed each parent was a carrier of one of the variants. The mother conceived a second time, naturally. Prenatal diagnosis was performed and the fetus had both variants. The pregnancy was terminated. Studies in HEK cells, found that cell surface expression levels of AChRs harbouring the δC257R subunit to be approximately 20 % of wild-type (Fig. 4 ). This result is consistent with the c. 769T>C mutation (in combination with c. 1315delG, p. Val439Trpfs*11 on the second allele) underlying a congenital myasthenic syndrome due to AChR deficiency.
Genes involved in axonal function should therefore be considered as candidates for arthrogryposis, in addition to skeletal muscle contractile proteins. Our study highlights the widening spectrum of phenotypes associated with mutations in known fetal akinesia, arthrogryposis and myopathy genes, as is increasingly demonstrated for other neuromuscular disorders  [26, 59, 60]. As sequencing of targeted gene panels or exome sequencing becomes the mainstay of genetic diagnostics  [61, 62] , it is likely that there will be greater broadening of genotype-phenotype correlations for neuromuscular diseases. With the overwhelming amount of genetic information obtained via next generation sequencing, the reliability of meticulously curated locus-specific databases, the availability of large exome datasets from ethnically matched reference populations and appropriate functional and/or protein studies will be critical to obtaining accurate genetic diagnosis.
Acknowledgements We are grateful to the patients and their families for their participation in this research. This research was supported by the  National Health and Medical Research Council of Australia  ( Early Career Researcher Fellowship  # 1035955  to GR, Research Fellowship  APP1002147  to NGL and Project Grant  APP1022707 ;  EU Collaborative  grant  APP1055295 ); the  Association Francaise contre les Myopathies  (# 15734 ) and a  UWA Collaborative Research Award. EJT and KSY were supported by  University Postgraduate Awards.
Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents.
Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth sys-tem modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land-atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe.
The LC_CCI conversion tool The LC_CCI land cover and seasonality products are initially downloaded in full spatial resolution, i. e. , 300 m grid cells for land cover, and 1 km grid cells for the seasonality products, at global extent in plate carrée projection. In order to fulfill a range of ESM requirements, the LC_CCI project team developed the LC_CCI user tool to allow users to adjust parameters of the LC products in a way that is suitable to their model setup, including modifying the spatial resolution and converting the LC_CCI classes to fractional PFT area. The BEAM Earth Observation Toolbox and Development Platform, designed for visualization and analysis of ENVISAT products, was selected to provide the basis of the conversion software. A list of resampling resolution and coordinate system options is provided in Table  3.