dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation and results We ran our methods on a real dataset composed of roads and hiking paths from BD TOPO, the 1m resolution database of IGN, and routes provided by FFRP (Fédération Francaise de Randonnée Pédestre), the French hiking federation, on which a conflation pre-process had been run to reach the modeling described in Section 3. The considered dataset covers an area of 10×20km. Given the density of roads and hiking routes, the dataset could be used to generate a map at a scale of about 1:50k. At this scale the test dataset has a size of 20 × 40cm (approximately an A3 leaflet). The test area assessed is representative of what can be found on real route maps in terms of complexity of route symbol placement (number of routes carried per road, complexity of junctions). Table  1  shows the distribution of roads of our test area in terms of number of carried routes, and for each number of carried routes, the cardinality of the variable domain.
The total size of the search space, which is quite large, is shown in the bottom-right cell of the table. We implemented our model in GeOxygene, an open source framework developed in Java at COGIT laboratory  [3]. The main reason for choosing GeOxygene is that the opportunity to reuse existing generalization methods implemented in GeOxygene, more pre- cisely in its generalization module CartAGen  [16]. Regarding the work presented in this paper, we already had in GeOxygene an implementation of the static model for roads carrying routes, and an implemented method for roads strokes detection based on roads attributes and angles at junctions. We adapted this method by splitting the computed strokes if needed, so that all road segments of a stroke carry the same set of routes. We generated in advance the possible domains D n (described in Section 3.
Experimentations and assessment settings The two implemented methods (systematic/backtracking, and non-systematic/simulated annealing) were run on a system powered by an Intel Core i5-2500 CPU (3. 30 GHz) with 8GB of RAM. Both were assessed in terms of computational performance (evaluation 1, Section 5. 2). Because of the computational performance issues already described, the systematic (backtracking) method could not be run on our complete test dataset. However, as it is guaranteed to reach the best possible solution, analyzing the cartographic results obtained using this method was a way to validate the defined set of constraints (evaluation 2, Section 5. 3). Therefore the method was run on small extracts of our test dataset corresponding to complex zones-in other words, with respect to the CSP, it was run on restricted subsets of variables.
Acknowledgements The authors would like to thank  Anne Bérard ,  Didier Blaquière ,  Régine Cochet ,  Blandine Delavault  and  Christophe Pécot  for their time and the interesting discussions around the cartographic results assessment. They also would like to thank  Eve Feinblatt-Mélèze  for her copy editing work. They finally would like to thank the anonymous reviewers who helped improve the paper by their thorough comments and questions.
Electron microscopy Four microliters of purified ribosomes were applied to holey carbon grids (carbon-coated Quantifoil R2/4 grid, Quantifoil Micro Tools, GmbH, Großl öbichau, Germany) containing an additional continuous thin layer of carbon, glowdischarged using Gatan Solarus 950  (9). Grids were blotted for 4 s at 4 • C in 100% humidity and vitrified by plunging into liquid ethane cooled with liquid nitrogen, using the Mark IV Vitrobot (FEI, Hillsboro, Oregon)  (10). Data were collected on a TF30 Polara electron microscope (FEI, Hillsboro, Oregon) operating at 300 kV, set up with a K2 Summit direct electron detection camera (Gatan, Warrendale, PA). Images were recorded using the automated data collection system Leginon  (11)  in counting mode, and taken at the nominal magnification of 23 000x, corresponding to a calibrated pixel size of 1.
Image processing The dose-fractionated image stacks were first corrected for beam-induced motion, using the method of Li et al. (13) , and averages of all 20 frames were used for image processing. A total number of 329K particles were automatically extracted from 5734 selected averaged images, using the araautopick and ara-crop tools in Arachnid  (14) , a pythonencapsulated version of SPIDER. Contrast transfer function parameters were estimated using CTFFIND3  (15) , and 3D classification was performed in RELION 1. 2  (16) , to discard defective particles and identify structurally homogeneous subsets. Initial RELION 3D classification, with K = 10 classes and an angular sampling of 1. 8 • , yielded mainly three classes: rotated 80S ('rt80S' for brevity) with A/P and P/E tRNAs (22 793), slightly rotated 80S without tRNAs  (27 158) , and non-rotated 80S ribosomes (197 536).
Model building by molecular dynamic flexible fitting An atomic model was built for the nrt80S E-tRNA complex starting from the published models for the P. falciparum 40S and 60S ribosomal subunits (5) (PDB codes: 3J79, 3J7A). Since the published models of the P. falciparum 40S and 60S subunits were refined separately, their interface contains elements that violate stereochemistry. We found a large number of spatial clashes when we attempted to put the 40S and 60S models together as a full 80S model. We first resolved the clashes manually, and then applied MDFF flexible fitting to generate the complete structure of the 80S from P. falciparum. (For details on major clashes see Supplementary Figure  S6  and Table  S2 ). More specifically, we fitted the structures of the 40S and the 60S subunits into the segmented maps of the nrt80S-E ribosome using the molecular dynamics flexible fitting program MDFF  (20).
The E-site tRNA from PDB 3J7A was also fitted into the segmented map. The initial system was prepared for MDFF  (20)  using Visual Molecular Dynamics (VMD)  (21)  and run in the NAMD program  (22)  for 0. 5 ns of simulation time. Details of the simulation are as follows: Each of these fittings was run for a length of 0. 5 ns, which stabilized the structures to closely match the maps. These runs removed most of the initial clashes, and the remaining ones were removed manually. The fitted structures of the 40S, 60S and E-site tRNA were then combined for a collective fitting of the map of the nrt80S E-tRNA complex. All of the runs used the generalized-Born implicit solvent model, as implemented in NAMD  (22). The above fitted structure for the nrt80S E-tRNA complex was used as the starting model for modeling for three maps: nrt80S-P, nrt80S, and rt80S-A/P-P/E.
Specifically, the inputs for ES10S were (i) the RNA sequence, UAUGCUUAUAUUUGUAUCUUUGAUGCU-UAUAUUUUGCAUA, and (ii) the RNA secondary struc-  ture, ((((. ((((. (((((. ))))). )))). ))))). The inputs for ES6BS were (i) the RNA sequence, AAAUCCCCACUUUUGCU-UUUGCUUUUUUUGGGGAUUU, and (ii) the RNA secondary structure, (((((((((. ((. )). ))))))))). The inputs for h16 were, (i) RNA sequence, UGCAAGGC-CAAUUUUUGGUUUUGUA, and (ii) the RNA secondary structure,  (((((((((((. ))))))))))). The program 3dRNA generated ten models for each expansion segment. In order to select a good model for further analysis, we rigid-body fitted each of these models into the segmented map of nrt80S-P using UCSF Chimera  (25) , and chose the one that had the highest cross-correlation (CC) value (details see Supplementary Figure  S5  and Table  S1 ).
In our work we used a combination of different strategies to build complete models for ES10S and ES6BS. We identified the region of ES10S and ES6BS based on Wong et al. 's work (5) and used the 3dRNA software  (23) , an automated RNA tertiary structure-building program, to complete the tertiary structure of these components (for details, see Figure  3 , Supplementary Figure  S5  and 'Materials and Methods'). Both ES10S and ES6BS fulfill the requirements for application of the 3dRNA software as the length of these expansion segments is within the benchmarked length, and the secondary structures are known  (5). Considering the good quality of cryo-EM map and the biologically functional meaning of the state, we chose to use the map of the nrt80S-P complex for further detailed analysis and model building.
The absence of RACK1 Many studies have established RACK1 as a key player in multiple signaling pathways, some of which connect signaling with the translation machinery  (34, 35, (39) (40) (41) (42). Surprisingly, RACK1 displays a dramatically different behavior in P. falciparum than other species, such as yeast and mammalian cells. From yeast  (7, 35, 43)  to mammalian cells  (29, 37) , RACK1 or its counterpart cpc2 is a well-established component of the small ribosomal subunits. In contrast, it is absent from its conserved binding site on the small 40S subunit head in cryo-EM reconstructions both in our work and previous work  (5) , even at low density threshold settings (data not shown). Early studies pointed out that RACK1 has both ribosome localization-dependent and -independent functions in vivo  (34).
ACCESSION NUMBERS The cryo-EM maps for rt80S-A/P-P/E, nrt80S-P and nrt80S-E have been deposited with the accession numbers EMD-6452, EMD-6456 and EMD-6454, respectively. The MDFF-fitted atomic models of rt80S-P/E, nrt80S-P and nrt80S-E have been deposited with the accession numbers 3JBO, 3JBN and 3JBP, respectively.
ACKNOWLEDGEMENTS We thank  Robert A. Grassucci  for assistance with the data collection and  Melissa Thomas-Baum  for assistance with the preparation of the illustrations. We would like to thank  Sjors Scheres  and colleagues for making a preprint and coordinates of their structure available prior to publication of their paper (5). We thank  Shobhona Sharma ,  Department of Biological Sciences, TIFR, Mumbai, India  for sharing the anti-PfP0 and anti-PfP2 antibodies. We are also indebted to other members of the  Frank lab  for help and discussions, in particular Amedee des Georges,  Harry Kao ,  Robert Langlois , and  Danny Nam Ho.
FUNDING Howard Hughes Medical Institute and  National Institutes of Health  [ R01 GM29169  to J. F. ];  National Institutes of Health  [ R01 AI102907 ,  DP2 AI112219  to J. D. ];  Swedish Research Council : dnr 2013-367 [to K. B. ]; LABEX:  ANR-10-LABX-0036   NETRNA  and the  ANR  grant  @RAction program  ' ANR CryoEM80S ' [to Y. H. ]. Funding for open access charge:  Howard Hughes Medical Institute.
Informally, a program is here an observation based query described by the observations of causal relations. Hence, the programmer does not explicitly describe the process chain supporting a function but rather the expected observations of the designed function by emphasizing causal relations. The compiler then selects a set of components in a database such that their assembly complies to the program specification. Informally, the compilation process relies on a syntactical pattern matching such that the description of the selected components tile the description of the behaviour. Hence, a functional description of the behaviour is associated to each component and the union of the selected components behaviourally covers the programmed functionality. By contrast to the standard compilation process based on the definition of a morphism 1  from syntax to objects of calculus underpinning the hierarchical assembly, the tiling is here global and orderless.
This property is called the observability of Q (obs ðQ½rÞ in Table  1 ) informally meaning that the assembly does not produce an infeasible behaviour. Rule (Inst. ) describes the fact that an observable instance of a part of a component in the library is functionally synthesized. Rule (Com. ) expresses the commutativity of the assembly. Rule (Cont. ) contracts the redundant formulation of programs. Finally, Rule (Asm. ) details the conditions for an assembly of two components, each representing a functional synthesis of a part of the designed function. Another set of rules  (Adrien 2014 ) not presented here defines the alternate possibilities to express similar behaviours in order to find components if no components in the database fit to the matching. The algorithm derived from the formal system can be assimilated to a general unification algorithm  (Knight 1989 ) called the ACI-unification  (Baader and Bu ¨ttner 1988; Baader and Snyder 2001 ) (Associative, Commutative, Idempotent).
Compilation Algorithm ACI-Unification is an NP-Complete problem and the algorithm explores all the possibilities until an unification is found or fail  (Baader and Bu ¨ttner 1988; Baader and Snyder 2001). To improve the algorithm, we account the specificity of GUBS language. The performance of the algorithms actually depends on the size of the database where the components are stored. For a small set of components, the ACIunification algorithm selects a subset of components behaviourally covering the Table  1  Functional synthesis rules VA(P) stands for the set of variables of the program and rj V is the restriction of the substitution on a set of variables V. C is a set of components representing the library. P T Q denotes the textual inclusion of P in Q i. e. , Q ¼ ðQ 1 ; P; Q 2 Þ where Q 1 or Q 2 may be an empty program initial program P in the whole database.
However, for large database, the unification may be too time-consuming to be effective. In this case, the unification will operate on a part of it. The issue is to appropriately select of a subset of components behaviourally covering P. selection is achieved by a directed evolutionary algorithm where the individuals represent a possible of components. By directed we mean that we proceed to a pre-selection, checking whether each individual (subset of components) will not obviously cause the unification failure. Hence, the functional synthesis algorithm is structured in two stages: the ACIunification algorithm and the directed evolutionary algorithm. An individual represents a subset of components. The best individuals have the minimal number of unified components and maximize the number of unified rules in the program.
Based on ACI unification, the functional synthesis takes benefit of the specificity of the language to heuristically improve the selection of subset of components forming the individuals of a population. The heuristics implies to define a relation between causal relations of P and the components Q j of a database. The relation accounts the structure of the causal rule and the nature of the agents. P is assumed to be a set of causes P ¼ fp i g i. The relation qualifies a local causal association of P where each cause of P can be associated to a cause of a subset of components Q ¼ S j Q j without accounting the interplays between the unification of different causal rules. If the causes p j is not related to some Q j s then the ACI-unification surely fails. Constraints on causal relation. A causal rule p i is related to a component Q j if and only if there exists a causal rule q j of Q j such that: 1.
Example: Compilation of the Repressilator In this section, we sketch the application of the algorithm to the Repressilator  (Elowitz and Leibler 2000)  example (Fig. 4 ). The Repressilator circuit is one of the first synthetic system leading to oscillation of fluorescent protein (GFP) monitored by a circuit of three genes, each one inhibiting another. To simplify the presentation we apply the algorithm on a piece of the database used for the effective compilation (Fig. 5 ). In the sequel,the causal relations are labelled by p H as follows: p 1 : g1 ! !g2, p 2 : g2 !!TetR_lite, p 3 : TetR_lite ! !g1. 1. Define the local causal relation; 2. initialize a counter n (for small database n 1); 3. generate a population of individuals where each individual covers of P locally (for small database, the single individual is the database); 4.
Benchmarks and Numerical Results In order to empirically validate the functional synthesis algorithm, we executed Ggc on several data sets. Those data sets are based on databases containing randomly generated 100 components. In order to insure a solution for the tested program and the fact that the compiling time-out is due to the lack of time finding a solution, each program is generated by selecting a subset of components in the database. Execution time on the following curves correspond to program containing respectively 10 and 25 causal relations. In order to show the impact of the number of agents and constants on the compiling time, those numbers evolve from 1 to 10 on each program (Fig. 6 ). Those curves confirm the theoretical hypothesis. In fact, we observe that the time evolve exponentially function of the number of variables and not of the agents because constants restrict the possibility of choices.
In conclusion, the efficiency of the compilation directly depends on the number of constants corresponding to the known agents. In this article, we have the main features of Gubs, a language for synthetic biology based on a behavioural description of the designed biological function. The compilation principle relies on the covering of the behaviour of a programmed function by the behaviour of a collection of components. The behavioural covering corresponds to a matching between component specification and the programmed function specification. The compiler combines the ACIunification algorithm with a directed evolutionary algorithm enabling to analyse large biological database. We have demonstrated the proof-of-concept of the compilation with a prototype applied on some realistic examples.
GGC is implemented in Ocaml, using XML files for the database (the platform is freely available in open source on  Basso-Blandin et al. (2015) ). GGC has been tested on multiple randomly generated examples and databases  (Adrien 2014 ). In the future, we could imagine that the design in synthetic biology will require different programming layouts based on different language paradigms structured in a tower of languages and addressing different levels of integration in biology. From a language describing collective operations on cell colonies  (Giavitto et al. 2005; Beal et al. 2011 ) the program will be translated into different intermediate representations to end by a structural low level description programmed in a grammar based language  (Cai et al. 2010 ) of genome sequences. In this tower, the GUBS language occupies the intermediary level dedicated to cell entity behavioural programming.
Acknowledgments The funding for most this work is granted by the  ANR SYNBIOTIC  ( 030701 ). 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.
It is also a necessary object for a great deal of physical simulations, notably those based on finite elements methods. We consider then the problem of converting a 3-dimensional image stack of segmented cell tissue into a triangular mesh representing the surfaces the cells. Our work is focused on images of shoot apical meristems (SA M) of Arabidopsis thaliana acquired by confocal laser scanning microscopy (C LSM) such as the one shown in Figure  1 , and segmented using the M A RS software pipeline  (Fernandez et al. , 2010). SA Ms constitute small niches of dividing cells where all the aerial organs of a plant (inflorescence, leaves or branches), originate from. The study of sequences of cell tissue on the SA M is therefore a key step for a better understanding of morphogenesis in plants, and the growth of organs over time.
Implementation Details The algorithms detailed above were implemented in Python 2. 7, using the standard NumPy and SciPy libraries, and wrappers for the C++ C G A L library. An implementation constraint comes from the fact that 16-bit labels are not handled by C G A L the mesh generation function. Consequently, the processing of images with more that 255 cells requires a re-labeling step before the mesh generation, and the re-affectation of original labels to the connected components in the resulting tetrahedral complex. Concerning execution times, the complexity proves to be linear with respect to the number of cells in the image, as the number of triangles varies proportionally with it. The typical computation times for a 2000 cell image is of 125s for the mesh generation and 130s for its optimization, provided the costly filtering of the image necessary for the gradient and the cell corner extraction (depending on the image size) has been computed beforehand.
g. , remote sensing sensors and ground measurements device). In contrast, indirect comparison uses high resolution (HR) images that are later aggregated to the scale of MR products  [25, 26]. This technique helps to overcome the time-space ground data limitation. Using HR images as an intermediate scale of measurement improves spatial coverage but requires an upscaling step that introduces new problems related to sampling strategy  [27]  and the processing of HR time series with irregular image acquisition frequencies  [21]. An alternative to direct validation is indirect validation  [28, 29]. This approach consists of benchmarking similar remote sensing products for one target variable via intercomparison. Working at the same spatial scale avoids the issue of sampling strategy and the potential use of a large dataset improves the ability to cover a wide range of pedoclimatic conditions and to better estimate uncertainties.
Forage Production Index (FPI) Processing Methodology The FPI processing chain is a fully automatic process (Figure  1 ). Biophysical parameters, in particular fCover and fAPAR, were calculated using the Overland image processing software developed by Airbus Defence and Space. This tool extracts vegetation parameters by inverting a radiative transfer model that couples scene and atmospheric models  [41]. Overland integrates most recently developed models  [42]  (Figure  1A ), Low Resolution Transmission (LOWTRAN)  [43]  for the atmospheric component, a model of leaf optical Properties Spectra (PROSPECT)  [44]  and Scattering by Arbitrarily Inclined Leaves (SAIL)  [45]  for the vegetation component. The vegetation model has been upgraded by adding a "brown" contribution (for non-productive vegetation) to the foliage.
Because one MR pixel may contain different land cover types, a disaggregation method based on a statistical approach originally applied to reflectances  [47, 48]  was used to determine fCover values for grassland (Figure  1C ). This method estimates fCover values for each land cover class from the input of fCover estimates for a population and the a priori knowledge of each land cover class's contribution to each pixel (local aspect). Consequently, fCover was calculated at an Elementary Statistical Unit (ESU) scale of 6 km × 6 km. The land cover database used for disaggregation was provided by farmer declarations in the 2012 Common Agricultural Policy (RPG Anonyme ASP 2012) and completed with the Corine Land Cover Database 2006 (European Environment Agency, 2012). Surfaces of each land cover in the ESU are combined and form the final land cover database.
Method Biomass production measurements during 2012, 2013 and 2014 were collected from 28 farms (Figure  2 ). The complete dataset contained 290 (679 ha), 768 (1739 ha) and 557 (1203 ha) natural and temporary grassland plots in 2012, 2013, and 2014, respectively. Yields (in tons of dry matter per hectare) for each plot were estimated by multiplying the total number of harvested hay bales by the average plot-specific bale yield. To obtain the latter estimate, measurements of weight and moisture were taken with a pallet scale and portable moisture meter from approximately 5% of the bales harvested in each plot (Figure  4A ). Because grasslands have several growing cycles in a year, the annual biomass estimation of the plot corresponds to the sum of the yield after each mowing. The difficulty of acquiring ground data over large areas and the need to create a representative grassland sample required resizing the original ESU (e.