dataesr/scientific-paragraphs-categorization huggingface dataset:
FEA simulations This model is based on the GFtbox software, a MATLAB (The Mathworks, Inc. , USA) application developed for the simulation of a growing continuous sheet of tissue  (Kennaway et al. , 2011). The heart tube is represented as a cylindrical mesh, made of 938 nodes and 1800 finite elements, with two outside and inside surfaces and a thickness (Figure  6 , Figure  6 -figure supplement 1A). At each successive step during a simulation, each element is deformed according to a growth tensor field specified from the hypotheses of the model. The constraint of continuity of the tissue implies that the resulting growth is different from the input growth, this difference giving rise to residual strain. The output shape of the simulations is computed by minimising the energy derived from this residual strain, under the assumption of linear elasticity (see  [Kennaway et al.
, 2011]  for a detailed presentation and discussion of this modeling framework and its numerical implementation). The simulations of a tube growing between fixed poles, in a minimal hypothesis (Figure  2F -G), were generated with the MATLAB code provided in Source code 1. In a more refined model, the dorsal mesocardium was simulated as a displacement constraint on a set of nodes situated along two vertical lines on the dorsal side of the tube (Figure  6 , Figure  6 figure supplement 1B). These nodes are not allowed any displacement along the y and z axes (the axes along which a thin beam would strongly resist deformation). This constraint is released to simulate breakdown of the dorsal mesocardium. The fixed distance between the two poles is implemented by forbidding any displacement along the z axis for the nodes situated at both extremities of the tube.
4%) in the dorsal left part of the tube (Figure  6B ), to accentuate the inner curvature and produce a shape closer to biological observations. This is in agreement with proliferation gradients  (de Boer et al. , 2012)  and the observations that cell proliferation is inhibited in compressed regions (see  Mammoto et al. , 2013). Simulations were run for 90 steps. The MATLAB code containing the interaction function of the GFtbox model, and used to generate the shapes in Figure  6 , is provided in Source code 2. For exploring the parameter space of shapes shown in Figure  7 , the same model was used, excluding only the inflation of the two ventricles. For Figure  7A , the arterial pole rotation was the same as above (25 ˚) and the position of the burst at the venous pole varied between 0 ˚and 360 ˚(relative to dorsal) in increments of 90 ˚.
For Figure  7B , the position of the burst at the venous pole was fixed as in Figure  6  (270 ˚relative to dorsal). The arterial pole rotation varied between 0 ˚and 60 ˚by increasing the circumferential growth rate from 0% to 3% per time step. Asymmetry at the venous pole was increased in intensity by variations in the burst growth from 0% to 12%. Simulations were run for 80 steps. The MATLAB code used to generate the shapes is provided in Source code 3. The simulation for Figure  8A  replaced the rotation at the arterial pole by a burst of longitudinal growth, a similar mechanism to that of the venous pole asymmetry, positioned at 120 ˚relative to the burst at the venous pole (peak of growth in a ventral left position). The MATLAB code used to generate the shapes is provided in Source code 4.
Statistics Sample size was checked post-hoc, using the PS software, in order to ensure a power of at least 0. 8, with a type I error probability of 0. 05, with an effect size of 100% (Figures 2C, 3F and 4C, Figure  4 -figure supplement 1B), 50% (Figure  5E ) or 25%  (Figures 5G and 9F, G and K). All sample numbers indicated in the text refer to biological replicates, i. e. different embryos. No outlier was excluded from the data analysis. Comparisons of two centre-values were done on the average, using a Student two-tailed test. A paired Student test was used for comparing left and right angles or lengths at successive positions (Figure  4C , Figure  4 -figure supplement 1B). A Mann-Whitney test was used when a normal distribution could not be assumed. A chi-squared goodness-of-fit test, with Yates's correction for small sample size, was used to compare observed with expected distributions.
Acknowledgements We thank  F Spitz  for comments on the manuscript,  AH Kottmann  for the gift of the Shh +/-mouse line,  N Gadessaud  and the histology plateform of the  SFR Necker ,  N Goudin  and the imaging plateform of the SFR Necker,  L Guillemot ,  J Xie ,  A Murukutla ,  J Eiblwieser ,  D Darby  and  C Cimper  for technical assistance,  C Ragni  and  H Moisset  for preliminary observations,  F Prin  for advice on HREM and  R Kennaway  for adaptation of the modelling framework.
, 2010). The vegetation module was based on modifications to the flow field resulting from three-dimensional drag, in-canopy waveinduced streaming, and production of turbulent kinetic energy in the hydrodynamics model (Regional Ocean Modeling System -ROMS), along with energy dissipation and resultant hydrodynamic feedback from the wave model (Simulating WAves Nearshore -SWAN). The vegetation module requires the user to input a given set of plant properties (stem density, height, diameter, and thickness). These vegetation properties can be highly variable depending on the season and environment, yet obtaining a full set of measurements in realistic settings is impractical. Identifying which properties have the greatest influence on the resulting flow dynamics can reduce the amount of observational data required to robustly parameterize the model and/or reduce the number of runs required in a model ensemble to quantify the uncertainty associated with data gaps.
All these studies highlight various approaches to perform sensitivity analysis. Saltelli et al. (2008)  provided a comparison of different sensitivity analysis methodologies and the optimal setup for specific combinations of parameters and model. Ultimately, the choice of sensitivity analysis methodology depends on multiple factors such as the computational cost of running the model, the characteristics of the model (e. g. , nonlinearity), the number of input parameters, and/or the potential interactions between parameters. Saltelli et al. (2008)  described variance-based techniques as providing the most complete and general pattern of sensitivity for models with a limited number of parameters, such as the vegetation module in COAWST. Sobol' indices, as a form of variance-based sensitivity analysis, provide a decomposition of the variance of a model into fractions that can be assigned to inputs or combinations of inputs.
Conclusions The coupled wave-flow-vegetation module in the COAWST modeling system provides a tool to study vegetated flows in riverine, lacustrine, estuarine, and coastal environments. The resulting flow field in the presence of vegetation depends on its properties, including vegetation stem density, height, diameter, and thickness. The sensitivity of the hydrodynamic and wave conditions to changes in vegetation parameters is investigated. The sensitivity analysis helps in understanding the multi-parameter/multi-response of various interactions within the model. We use an existing tool that formulates the Effective Quadratures method to quantify the sensitivity of plant input properties for the vegetation module in COAWST model. The decomposition of the variance of the model solution given by the Sobol' indices is assigned to plant parameters.
Code availability. The Effective Quadratures methodology is an open-source Python-based tool designed to perform sensitivity analysis for a given physical system. The instructions to install the code along with all the open-source files for this tool are detailed here: https://github. com/Effective-Quadratures/ Effective-Quadratures  (Seshradi, 2017; Seshadri and Parks, 2017). For any further inquiries about the Effective Quadratures methodology, please contact the corresponding author, Pranay Seshadri (ps583@cam. ac. uk). The COAWST model is an open-source coupled hydrodynamics and wave model containing vegetation effects mainly coded in Fortran 77. This model provided the physical setting to perform the sensitivity analysis. The code is available from https://coawstmodel-trac. sourcerepo.
Acknowledgements. We thank  Jeremy Testa  at the  University of Maryland Center for Environmental Science  for providing us guidance on the ranges of vegetation for sensitivity studies in the early stages of work. We also thank  P. Soupy Daylander  at the  US Geological Survey, St. Petersburg Coastal and Marine Science Center , for providing her feedback to improve the clarity of the paper. We thank the anonymous reviewers for their careful reading of our manuscript and their insightful comments and suggestions. Edited by: Paul Halloran Reviewed by: two anonymous referees.
, 2005). Additional modules allow us to simulate sedimentation and the decay of particle mass, but they were not used here. The model is particularly suited for large-scale simulations on supercomputers due to its efficient Message Passing Interface (MPI) / Open Multi-Processing (OpenMP) hybrid parallelization. Trajectory calculations are based on numerical integration of the kinematic equation of motion, dx dt = v(x, t), (1) where x denotes the position and v the velocity of an air parcel at time t. The air parcel position x is defined by geographic latitude φ and longitude λ as horizontal coordinates as well as pressure p as vertical coordinate. The horizontal wind (u, v) and vertical velocity (ω = dp/dt) at position x and time t are obtained by linear spatial and temporal interpolation of the meteorological data.
The kinematic equation of motion is solved with the explicit midpoint method, x(t + ∆t) = x(t) + v x(t) + ∆t 2 v (x(t), t) , t + ∆t 2 ∆t. (2) The time step ∆t mainly controls the trade-off between accuracy and speed of the calculations. For our simulations we selected ∆t = 30 s, which is sufficiently small so that truncation errors can be neglected. This time step is also consistent with the sampling rate of the balloon data. The diffusion module of MPTRAC considers two processes. Turbulent diffusion is modelled by means of uncorrelated, Gaussian random displacements of the air parcels with zero mean and standard deviations √ D x ∆t and √ D z ∆t, where D x and D z are the horizontal and vertical diffusion coefficients, respectively. Typical values for the stratosphere are D x = 0 and D z = 0.
The FLEXPART model uses a default value of α = 0. 16 for downscaling of the grid-scale variances (or 40% in terms of standard deviations). Meridional wind and vertical velocity fluctuations are calculated in the same way. For this study we implemented a new module in MPTRAC that allows us to simulate the vertical motions of the balloons more realistically. This module is called at each time step and adjusts the pressure of the air parcels so that vertical motions are constrained to either (i) an isobaric surface (constant pressure), (ii) an isopycnic surface (constant density), (iii) an isentropic surface (constant potential temperature), or (iv) the pressure time series measured by the balloon. In a first approximation the balloons move on isopycnic surfaces, which is represented by option (ii).
We varied the starting days for the analysis of the different flights to homogenize temporal coverage. As there are data gaps in the GPS and TSEN data of the balloon measurements, we imposed the requirement that each sample should have at least 90% coverage. Based on these criteria we obtained a set of 104 samples of 15-day time windows from the 19 Concordiasi balloon flights. Absolute horizontal transport deviations (AHTDs) and relative horizontal transport deviations (RHTDs) are standard measures to compare trajectory calculations with observations or to evaluate results for different model configurations  (Kuo et al. , 1985; Rolph and Draxler, 1990; Stohl et al. , 1995; Stohl, 1998). While other measures of trajectory error have also been defined, AHTDs and RHTDs are most often reported because they can be compared easily to other studies.
4 K (ECMWF OA) to 2. 1 K (NCEP/NCAR). Zonal wind biases are in the range of -0. 3 m s -1 (NCEP/NCAR) to 0. 5 m s -1 (MERRA). Meridional wind biases are about 0. 1 m s -1 for all data sets. Standard deviations vary between 0. 5 K (ECMWF OA) and 1. 4 K (NCEP/NCAR) for temperature, 0. 9 m s -1 (ECMWF OA) and 2. 3 m s -1 (NCEP/NCAR) for the zonal wind, and 0. 9 m s -1 (ECMWF OA) and 1. 9 m s -1 (NCEP/NCAR) for the meridional wind. Skewness and excess kurtosis values found here indicate that the distributions are quite symmetric and not affected by many or rather large outliers. We confirmed these findings by calculating additionally more robust and resistant statistical measures of location, spread, and symmetry, namely the median, the interquartile range, and the Yule-Kendall index  (Wilks, 2011).
5 K at 26 -27 km. Gobiet et al. (2005)  speculate that the assimilation of microwave radiances from satellite measurements into the ECMWF analyses may be a reason for the temperature bias. We note that a warm bias is still present in Antarctic winter 2010 in all data sets, but its magnitude is significantly reduced for ECMWF OA, which may be attributed to improvements of the forecast model, data assimilation scheme, and observations used to produce this analysis. Although many factors influence the accuracy and precision of meteorological analyses, Table  3  indicates that the spatiotemporal resolution is a rather important factor. Both bias and standard deviations are lowest for ECMWF OA, which has the highest resolution, followed by ERA-Interim, MERRA, and finally NCEP/NCAR, which has the lowest resolution.
For the high resolution ECMWF OA data the standard deviations removed by filtering are largest and about the same size as the standard deviations related to the differences of meteorological data minus balloon data. We found that ECMWF OA reproduces about 60% and ERA-Interim and MERRA about 30% of the standard deviations of the temperature and wind fluctuations of the balloons. NCEP/NCAR reproduces about 15% for temperature and 30% for the winds. This is in good agreement with the studies of Jewtoukoff.
Analysis of vertical motions In the following sections of this paper we focus on the validation of trajectory calculations using the MPTRAC model with Concordiasi superpressure balloon observations. As outlined in Sect. model to constrain the vertical motions of the air parcels. We first tried to identify the approach that is best suited to simulate the vertical motions of the balloons in a realistic manner. In our comparison we considered vertical motions based on prescribed pressure time series as measured by the balloons, isopycnic motions, isentropic motions, and vertical motions prescribed by the vertical velocities of the meteorological data sets (referred to as 'omega velocities' below). The comparison was conducted using ERA-Interim data as input for the trajectory calculations. For illustration, Fig.
(2008)  for the Vorcore campaign in 2005. For 15 days' trajectory time  Boccara et al. (2008)  found mean spherical distances of about 1650 km (with an interquartile range of 800 -3600 km) for ECMWF analyses (sampled at 0. 5 • × 0. 5 • horizontal resolution and 60 levels vertically) and 2350 km (1400 -3800 km) for NCEP/NCAR data. The transport deviations and growth rates found here also compare well with a wider range of results for the troposphere reported by  Stohl (1998). Our analysis indicates that the best accuracy of trajectory calculations in the Antarctic lower stratosphere is achieved with ECMWF OA and ERA-Interim, followed by MERRA and NCEP/NCAR. This is related to the accuracy of the horizontal winds of the meteorological data sets as discussed in Sect. 3. 1.
Impact of subgrid-scale wind fluctuations In this section we discuss the influence of diffusion on the trajectory calculations. We assessed this by means of dispersion simulations, each consisting of 1000 trajectories, and by applying the MPTRAC diffusion module described in Sect. 2. 3. Note that these simulations consider only horizontal diffusion, because vertical motions have been restricted to the pressure measurements of the balloons. Following  Stohl et al. (2005) , the turbulent horizontal diffusivity coefficient in the stratosphere was set to zero, D x = 0, i. e. , the diffusion in our simulations is related only to horizontal subgrid-scale wind fluctuations. For   Kahl (1996)  analyzed correlations between trajectory model errors and the complexity of the meteorological situation under study in more detail.
The MCFs of the four meteorological data sets of our study are shown in Fig. 11. The MCFs increase rather steadily over time. After 15 days we found values of 1300 km for ECMWF OA, 800 -900 km for MERRA and NCEP/NCAR, and 600 km for ERA-Interim. These differences in the MCFs came somewhat unexpected, as the spread of air parcels ideally should be the same in all simulations, independent of the meteorological data set and the diffusion model being applied. The differences are not directly related to the resolution of the meteorological data sets, as can be seen from the ranking of the MCFs of the data sets. The inconsistencies of the MCFs found here might be due to dynamical inconsistencies of the analysis wind fields that are introduced during the data assimilation process. Such dynamical inconsistencies may lead to more rapid dispersion and spurious mixing in Lagrangian transport model simulations  (Stohl et al.
, 2004). In principle, we may tune the scaling factor α in Eq. (3) of the MPTRAC diffusion module to achieve simulations with more consistent MCFs. However, we refrained from any tuning measures, because appropriate reference data for validation are lacking. We applied a constant scaling factor α = 0. 16 in all simulations, which is the default value used in the FLEXPART model. However, despite the different levels of MCFs found in the simulations, we conclude that the transport deviations between the simulations and the balloons are representative, because the are generally larger than the MCFs. To further confirm this result we also calculated the AHTDs between the trajectory ensembles and the balloon trajectories. We found that the transport deviations with or without diffusion are rather similar (Fig.
Best large-scale accuracy and precision are achieved by ECMWF OA (highest resolution), followed by ERA-Interim, MERRA, and NCEP/NCAR (lowest resolution). Model truncation also affects the representation of small-scale fluctuations. Standard deviations of unfiltered minus filtered temperature and wind data of the balloons are reproduced at a level of about 60% by ECMWF OA, but only by 15 -30% by the reanalyses. For ECMWF OA temperatures this is consistent with recent studies of  Jewtoukoff et al. (2015)  and  Hoffmann et al. (2016b) , providing further evidence that the ECMWF operational model explicitly resolves a significant portion of the atmospheric gravity wave spectrum. We also used the Concordiasi balloon observations to validate trajectory calculations in the Antarctic lower stratosphere.
(2008)  for the Vorcore campaign in 2005. They show a significant improvement compared to early studies of  Knudsen and Carver (1994)  and  Knudsen et al. (1996) , which found transport deviations of about 20% between trajectories based on ECMWF analyses and long-duration balloon observations. We conducted the trajectory calculations with the Lagrangian particle dispersion model MPTRAC  (Hoffmann et al. , 13 Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017 -71, 2017  Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 17 February 2017 c Author(s) 2017. CC-BY 3. 0 License. 2016a); and our study provides a contribution to the validation of this new model. However, the results will be transferable also to other Lagrangian transport models for the stratosphere. We used the diffusion module of MPTRAC to conduct dispersion simulations.
Acknowledgements. Concordiasi was built by an international scientific group and is currently supported by the following agencies:  Météo-France ,  CNES ,  IPEV ,  PNRA ,  CNRS/INSU ,  NSF ,  NCAR, Concordia consortium, University of Wyoming , and  Purdue University. ECMWF also contributes to the project through computer resources and support, and scientific expertise. The two operational polar agencies PNRA and IPEV are thanked for their support at Concordia station. Concordiasi is part of the THORPEX-IPY cluster within the International Polar Year effort. The authors acknowledge the  Jülich Supercomputing Centre (JSC)  for providing computing time on the supercomputer JURECA. 14 Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017-71,2017 Manuscript under review for journal Atmos. Chem. Phys.
lmd. polytechnique. fr/VORCORE/McMurdoE. htm (last access: 21 December 2016). The ERA-Interim reanalysis and operational analyses are distributed by the European Centre for Medium-Range Weather Forecasts (ECMWF), see http://www. ecmwf. int/en/forecasts/datasets (last access: 21 December 2016). MERRA data are provided by the Global Modeling and Assimilation Office at NASA Goddard Space Flight Center through the NASA GES DISC online archive, see http://disc. sci. gsfc. nasa. gov/mdisc/data-holdings/merra (last access: 21 December 2016). NCEP/NCAR reanalysis data were obtained from the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www. esrl. noaa. gov/psd (last access: 21 December 2016). The code of the Massive-Parallel Trajectory Calculations (MPTRAC) model is available under the terms and conditions of the GNU General Public License, Version 3 from the repository at https://github.
All the micro-rearrangements here are identical to the "micro-rearrangements" in the intuitive definition of Pevzner and Tesler cited above. In this work we present five general issues that affect the identification of conserved segments: duplications of ancestral genes, micro-rearrangements, mono-genic conserved segments, overlaps of portions of diagonals and ambiguous conservation of gene order. We also introduce five strategies to solve them. All strategies are implemented in a new version of PhylDiag  [5] , which we benchmarked against simulated conserved segments. To our knowledge there is no program that specifically identifies conserved segments as opposed to synteny blocks. We thus compared PhylDiag with i-ADHoRe 3. 0  [6]  and Cyntenator  [7] , two well-known alternative approaches to identify synteny blocks  [8] , and, during the comparison, we used their synteny blocks (more precisely base_clusters and Representative Synteny Blocks, RSBs) as if they were conserved segments.
We also explain why Proportion of the number of tandem duplications among all gene duplications that occurred between the Amniota ancestor and five extant vertebrates. Panel A contains the species tree linking extant human, mouse, dog, opossum and chicken species to Amniota, their most recent common ancestor. The topology of the tree and the dates of speciation come from the Ensembl database  [11]. The graph in Panel B shows the proportion of tandem duplications among all duplications. Genes are considered duplicated in tandem if they fulfil two criteria: (i) they must belong to the same gene family, and therefore share the same ancestral Amniota gene (ii) they are separated, in the extant genome, by at most tandemGapMax genes (on the x-axis). Tandemly duplicated genes form clusters of two or more tandem duplicates of the same gene family.