dataesr/scientific-paragraphs-categorization huggingface dataset:
De novo genome assembly Genome assembly for each of the 12 genomes followed a five-step procedure as shown in (Figure  2 ): Step 1: PacBio subreads were assembled de novo into contigs using three genome assembly programs: FALCON  (Chin et al. 2016) , MECAT2  (Xiao et al. 2017 ) and Canu1. 5  (Koren et al. 2017). The number of de novo assembled contigs obtained varied from 51 (e. g. NATEL BORO::IRGC 34749-1 and KETAN NANGKA::IRGC 19961-2) to 1,473 (CHAO MEO::IRGC 80273-1) for the 12 genomes (Table  S2 ). Step 2: Genome Puzzle Master (GPM) software  (Zhang et al. 2016c ) was used to merge the de novo assembled contigs from the three assemblers, using the high-quality O. sativa vg. indica cv. Minghui 63 reference genome MH63RS2  (Zhang et al. 2016a,b)  as a guide. GPM is a semi-automated pipeline that is used to integrate logical relationship data (i.
Step 3: The sequence quality of each contig was then improved by "sequence polishing": twice with PacBio long reads and once with Illumina short reads. Briefly, PacBio subreads were aligned to GPM edited contigs using the software blasr  (Chaisson and Tesler 2012). All default parameters were used, except minimum align length, which was set to 500-bp. Secondly, the tool arrow as implemented in SMRTlink6. 0 (Pacific Biosciences of California, Inc) was used for polishing the GPM edited contigs. The bwamem program  (Li 2013 ) was then used for mapping short Illumina reads onto assembled contigs, and the tool pilon  (Walker et al. 2014 ) was used for a final polishing step with default settings. Step 4: The polished contigs for each accession were arranged into pseudomolecules using GPM, using MH63RS2  (Zhang et al.
2016a,b)  as the reference guide. The program blastn  (Altschul et al. 1997 ) with a minimum alignment length of 1 Kb and an e-value < 1e -5 as the threshold was used to align the corrected contigs to the reference guide. In doing so, the corrected contigs were assigned to chromosomes, as well as ordered and orientated in the GPM assembly viewer function. The number of contigs after step 4 ranged from a minimum of 15 contigs (GOBOL SAIL (BALAM)::IRGC 26624-2) to a maximum of 104 contigs (IR64) (Table  3 ). The assembly size for the 12 accessions ranged from 376. 86 Mb (CHAO MEO::IRGC 80273-1) to 393. 74 Mb (KHAO YAI GUANG::IRGC 65972-1) (Table  3 ) and the length of individual chromosome varied from 23. 06 Mb (chromosome 9 of CHAO MEO::IRGC 80273-1) to 44. 96 Mb (chromosome 1 of LIMA::IRGC 81487-1) (Table  S4 ).
BUSCO evaluation The Benchmarking Universal Single-Copy Orthologs (BUSCO3. 0) software package  (Simao et al. 2015)  was employed to evaluate the gene space completeness of the 12 genome assemblies. These genomes captured, on average, 97. 9% of the BUSCO reference gene set, with a minimum of 95. 7% (IR64) and a maximum of 98. 6% (LARHA MUGAD::IRGC 52339-1 and KHAO YAI GUANG::IRGC 65972-1) (Table  3 ). Of note, when performing this analysis, we observed that on average 30 out of the 1,440 conserved BUSCO genes tested (https://www. orthodb. org/v9/index. html) were missing from each new assembly, 16 of which were not present in all 12, plus the IRGSP, ZS 97, MH 63 and N 22 RefSeqs (Figure  S5 ). This result suggested that these 16 "conserved" genes may not exist in rice, or other cereal genomes, thereby artificially reducing the BUSCO gene space scores for our 12 assemblies.
To test this hypothesis, we searched for all 16 genes missing in maize, which diverged from rice about 50 million years ago (MYA)  (Wolfe et al. , 1989 , Gale et al. , 1998 and Guo et al. , 2019). We found that 13 of the 16 genes in question could not be found in 3 high-quality recently published maize genome assemblies (Figure  S5 ) and therefore, concluded that 13 of the 16 "conserved" genes in the BUSCO database are not present in cereals, and should be excluded from our gene space analysis. Taking this into account, we recalculated the BUSCO gene space content for each of 12 assemblies and found that 10 of 12 assemblies captured more than 98% of the BUSCO gene set (Table  3 ).
Transposable element (TE) prediction To determine the pan-transposable element content of cultivated Asian rice we analyzed the 12 new reference genomes, presented here, along with the MH 63, ZS 97, N 22 PacBio reference genomes. In addition, we also included a reanalysis of the IRGSP author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi. org/10. 1101/2019. 12. 29. 888347 doi: bioRxiv preprint RefSeq as it is conventionally considered the standard rice genome for which all comparisons are conducted. This 16 genome data set was used to represent the K=15 population structure of cultivated Asian rice. A search for sequences similar to TEs was carried out using RepeatMasker  (Smit AFA et al, 2013)  run under default parameters with the exception of the options: -no_is -nolow.
RepeatMasker was run using the library "rice 7. 0. 0. liban", which is an updated in-house version of the publicly available MSU_6. 9. 5 library  (Ou et al. 2019) , retrieved from https://github. com/oushujun/EDTA/blob/master/database/Rice_MSU7. fasta. std6. 9. 5. out. The average TE content of this 16 genome data set was 47. 66% with a minimum value of 46. 07% in IRGSP RefSeq and a maximum of 48. 27% in KHAO YAI GUANG::IRGC 65972-1 (Table  4 ). The major contribution to this fraction was composed of long terminal repeat retrotransposons (LTR-RTs, min: 23. 55%, max: 27. 27% and average: 25. 96%) followed by DNA-TEs (min:14. 87%, max, 16. 18% and average: 15. 26%). Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs) were identified as on average 1. 43% and 0.
Structural Variants Each genome assembly (n=16), as described above, was fragmented using the EMBOSS tool splitter  (Rice et al. 2000)  to create a 10x genome equivalent redundant set of 50kb reads. These reads were then mapped onto every other genome assembly using the tool NGMLR  (Sedlazeck et al. 2018). Finally, the software SVIM  (Heller and Vingron 2019)  was run under default parameters to parse the mapping output. Only insertions, deletions and tandem duplications up to a maximum length of 25 Kb were considered in this analysis. The results of this analysis identified several thousand insertions and deletions whenever an assembly was compared to any other. Greater variability was found between varieties belonging to different major groups (e. g. Geng-japonica vs. Xian-indica) than occurred between those within these groups.
Data Records Data for all 12 genome shotgun sequencing projects have been deposited in Genbank (https://www. ncbi. nlm. nih. gov/), including PacBio raw data, Illumina raw data, Bionano optical maps and the twelve PSRefSeqs. The BioProjects, BioSamples, Genome assemblies, Sequence Read Archives (SRA) accession and supplementary files (Bionano optical maps) of 12 genomes are listed in Table  3.
Acknowledgements This research was supported by the  AXA Research Fund (International Rice Research Institute) , the  King Abdullah University of Science & Technology , and the  Bud Antle Endowed Chair for Excellent in Agriculture (University of Arizona ) to R. A. W. , the  Startup Fund of Huazhong Agricultural University  to J. Z. , and funding from the  Taiwan Council of Agriculture  to IRRI. The BUSCO analysis data for maize was kindly provided by  Dr. Wu  and  Dr. Li  from the  Institute of Plant Physiology and Ecology , and  Dr. Wang  from  Shanghai Jiao Tong University. One of two TE libraries used for repeat analysis was provided by  Dr. Eric Laserre  ( University of Perpignan, France ).
But those techniques have specificities that can put some restraints on their utilization. On the one hand EDX is fast and enables the measurement of all elements simultaneously. However, routine EDX is less adapted to a quantitative approach due to the peak overlap and a relatively low signal to noise ratio. On the other hand, WDX provides more accurate chemical composition (no peak overlap and better S/N ratio) but requires a longer acquisition time. -the quantitative mineralogy SEM-EDX and EPMA techniques provide X-ray data cubes and many software programs (PETROMAP®, XRMapAnal®, QEMSCAN®, Zeiss®, MAPS Mineralogy®, XMapTools®) or published thresholding methods  (Tovey & Krinsley, 1991) ;  (Berrier, Hallaire, & Curmi, 1999) ;  (Pret, et al. , 2010)  allow us to construct phase maps and to retrieve the texture of grains.
This method aims to produce quantitative chemical maps with improved spatial resolution in combining the EDX spectrum, density and proxy EPMA analysis for each pixel. The method includes the following main steps: -To decrease the volume of interaction and reach a higher spatial resolution, the hyperspectral maps are acquired with a lower accelerating voltage than conventional SEM-EDX working conditions. -Complementary deconvolution procedures are added to the clustering method allowing us to obtain accurate modal abundances. These supplementary procedures deal with the problem of extreme mixing of micrometer-sized assemblages and the compositional variation existing in some mineral phases due to solid solutions. -Quantitative chemical compositions of each phase are obtained by coupling SEM-EDX datasets with EMP point analyses (normal conditions 15 keV) allowing more accurate standard calibrations and correct φ(ρz) corrections.
We set a process time of 3 on the Aztec software in order to obtain a full width at half maximum (FWHM) of the manganese K∝ peak of ~ 135 eV for the different maps. Monte Carlo simulations were performed using NIST-DTSAII software  (Ritchie, 2009)  to estimate the interaction volume at 5 keV. Depth resolution variations with those conditions are contained in a range between 100 nanometers (for Fe-rich metal grains) and up to ~300 nanometers (based on the Fe L-alpha line) for porous phyllosilicates (compared to 300 nm and 1. 5 µm respectively for a 15 keV accelerating voltage). Again the comparison of the probed volumes is interesting: it is contained between [0. 003 -0. 040 µm3] for the 5 keV conditions while the range becomes [0. 3 -10 µm3] for the 15 keV conditions (also based on the Fe L-alpha line).
Analyzing the Composition, the modal Abundance and the Density using Electron MicroscopY (ACADEMY) We used low-voltage EDX hyperspectral maps combined with EPMA and established a method to obtain modal abundance, density maps and bulk composition of heterogeneous phase assemblages. The procedure can be divided into five parts steps which are summarized in Fig. 1. These steps include background modelling and peak fitting of EDX spectra, phase map, EPMA chemical quantification, density map and quantification. -Elemental maps were created from the raw EDX hyperspectral data thanks to the development of a background model which is fitted together with Gaussians for the different X-ray lines. -Elemental maps were analyzed by a classification algorithm in order to obtain a high-resolution phase map.
Linear combination of end-member spectra was used to account for mixing of grains smaller than the pixel size. We thus obtained accurate modal abundances. -These data were turned into quantitative maps using EPMA data as calibration. -The density of each phase was determined by analyzing the bremsstrahlung background, which is a function of density. Determining the density is a requisite for obtaining bulk composition. -The bulk composition of the entire region of mixed fine-grained materials was calculated by combining modal abundance, phase composition and density information. Most data are processed using Hyperspy, an open source library for analysis of multidimensional data  (De la peña, et al. , 2017). A first phase classification is performed using XmapTools  (Lanari et al. , 2014)  and corrected afterwards by manual thresholding.
We used the mass absorption coefficients database of  Chantler et al. (2005). Weight fractions are known a priori, and two options are available in the Hyperspy module of our model: i) by default, weight fractions are estimated by integrating EDX peaks or ii) when they are known, the phase compositions can be provided as inputs to obtain a more precise model (see section 3. 3). Absorptions due to the thin coating layer (C) and by the polymer window (W) must be taken into account because the efficiency of low-energy X-ray collection has a major impact on background modeling. The coating layer parameter is computed using the Love and Scott model: C = % & '()*7 , -. We used values for ρ and x of 1. 9 g/cm 3 and 15 nm for the sample coating (100 wt% of C). For the W parameter we used the ultra-thin polymer window curve proposed in  (Schlossmacher, Klenov, Freitag, Harrach, & Steinbach, 2010).
Phase recognition and pixel classification The phase map is established in two steps. The first one consists of identifying the various phases present using compositional fields. It is a "training stage" which is necessary to define "reference" grains. The second step uses those reference grains for the supervised classification of all pixels, performed thanks to the XMapTools software  (Lanari et al. , 2014). The X-ray intensities are plotted to reveal compositional endmembers and mixing lines (Fig. 3A ). Those composition fields are directly extracted from the elemental count map, where each datapoint corresponds to one pixel. Each cluster represents a phase of a given composition (but from randomly distributed grains). We use these plots (Matlab®) to select pixels of similar composition and reveal the localization of the different "objects" (minerals, mineral boundaries and fractures).
). We used the XMapTools software to build phase map  (Lanari, et al. , 2014). However, this classification could also be done using open source python libraries. XMapTools is a MATLAB©-based graphical user interface dedicated to electron microprobe X-ray image processing  (Lanari, et al. , 2014). It uses K-means clustering to classify pixels into classes of similar compositions. The K-means procedure identifies clusters and allocates pixels to these clusters by minimizing the distance in the compositional space between each pixel and the center of gravity of each cluster  (Saporta, 2006). As for other supervised classification methods, the user needs to define reference pixels as initial guesses for each group on the chemical map. The compositions of these pixels are used as starting cluster centroids.
The principal uncertainty is the variability of the emission depth. Indeed, based on Monte-Carlo simulations, the variations of mean emission depth at 5 keV can be around 20%  (Ritchie, 2009). Nonetheless, uncertainties on the mass absorption coefficients and approximation linked to the chosen model might also have a strong effect on the background modelling. At low energy (below 1 keV), values of mass attenuation coefficients found in different databases  (Heinrich, 1986)   (Henke, Gullikson, & Davis, 1993)   (Chantler, et al. , 2003)  are poorly constrained which could explain part of the uncertainties. This parameter is critical in many aspects of modeling X-ray transport and the improvement of these databases is necessary  (Lepy, Mantler, & Beckhoff, 2008). Conversely, the background model is an evolutionary code which could be easily improved in the future.
Acknowledgments We thank the two anonymous reviewers, especially the first one, for its constructive comments which helped to improve the manuscript as well as professor  Balz Kamber  ( AE ) for handling the manuscript. We thank the  Department of Mineral Sciences of the Smithsonian institution  for providing us with microbeam reference standards (catalog number:  117733-85276-111356-115900-114887-R2460 ). We thank the  SARM (Service d'Analyse des Roches et des Minéraux)  for providing us the Biotite. We thank the  Muséum National d'Histoire Naturelle (Paris)  for providing the sections of meteorites. We thank  Ahmed Addad  and  Séverine Bellayer  for their assistance with the electron microscope instruments. This work was supported by the  Programme National de Planétologie (PNP)  of  CNRS/INSU , co-funded by  CNES.
Calculation 3. 1. The first step of the method, using diviz The first step of the method compares the various mobility offers in order to establish a ranking for local authorities so that they can choose, as mobility managers, the most suitable one(s) for their territory. Each mobility offer was compared to single-occupancy car use, as the reference offer. We used a variety of sources that helped us to draw an initial proposition of criteria and indicators for the experts, then carry out the assessment: institutional reports, surveys, generalist and specialized media, the website of the mobility offer, and academic articles (see the detailed sources that were used in Appendix B). To process the assessment, we used the diviz software, as it is "a software for modeling, processing and sharing MCDA techniques and experiments"  (Meyer and Bigaret, 2012).
The Decision Deck project (which partly lies behind the diviz software), was initially aimed to enable the comparison of different analysis methods. Diviz has a number of benefits for the practitioner  (Bigaret and Meyer, 2015) : • Comparing different MCDA techniques using a unique interface; • Heterogeneous data inputs can be integrated; • Intuitive user interface; and • Open source. The diviz software performs the complex computations required by MCDA analyses through a simple design and the rapid execution of successive sequences of calculation as a workflow (Fig. 2 )  (Cailloux et al. , 2013). Before processing the assessment, a typology of mobility offers was established. We retained the most representative offer(s) in each category, namely (Appendix C): • First, the offer that regionally attracts the largest number of users (e.
Acknowledgements This study was financed by the  VEDECOM Institute. VEDECOM was involved in all parts of the project: in the study design; in the collection, analysis and interpretation of data; in the writing of the report; in the decision to submit the article for publication. We would like to thank the experts who helped us to build the list of criteria and indicators:  Mireille Apel-Muller ,  Julie Bulteau ,  Amélie Coulbaut-Lazzarini ,  Mioara Cristea ,  Anne Guillaume ,  Annie Jaecker ,  Yannick Pérez ,  Julien Pillot  and  Flore Vallet. We are also grateful to the experts who helped us to construct the typology of areas:  Délia Copel ,  Thierry Feuillet ,  Guilhem Sanmarty ,  Guillaume Vallier , and  Emmanuel Veiga. A special thanks to  Nicolas Souliman  for drawing the figures 1 and 6.
For example, in the PDF and HTML versions of this document, clicking on the word Species will send the reader to the section containing the definition of this class. SomeThing, otherThing: Attributes of classes, data type names, literal XML, and generally all tokens other than SBML UML class names, are printed in an upright typewriter typeface. Primitive types defined by SBML begin with a capital letter; SBML also makes use of primitive types defined by XML Schema 1. 0  (Biron and Malhotra, 2000; Fallside, 2000; Thompson et al. , 2000) , but unfortunately, XML Schema does not follow any capitalization convention and primitive types drawn from the XML Schema language may or may not start with a capital letter.
Type boolean The XML Schema 1. 0 type boolean is used for SBML object attributes that represent binary true/false values. XML Schema 1. 0 defines the possible literal values of boolean as the following: "true", "false", "1", and "0". The value "1" maps to "true" and the value "0" maps to "false" in attribute values. Note that there is a discrepancy between the value spaces of type boolean as defined by XML Schema 1. 0 and MathML: the latter uses only "true" and "false" to represent Boolean values, with "0" and "1" reserved for numbers. Software tools should take care not to attempt using "0" and "1" as Boolean values in MathML expressions. See further discussion in Section 3. 4. 4 on p. 24.
If the E or e is present, it must be followed by an integer, or else an error results. The integer exponent must consist of a decimal number optionally preceded by a leading sign ("+" or "-"). If the sign is omitted, "+" is assumed. The following are examples of legal literal double values: -1E4, +4, 234. 234e3, 6. 02E-23, 0. 3e+11, 2, 0, -0, INF, -INF, NaN As described in Section 3. 4, SBML uses a subset of the MathML 2. 0 standard (W3C, 2000b) for expressing mathematical formulas in XML. This is done by stipulating that the MathML language be used whenever a mathematical formula must be written into an SBML model. Doing this, however, requires facing two problems: first, the syntax of numbers in scientific notation ("e-notation") is different in MathML from that just described for double, and second, the value space of integers and floating-point numbers in MathML is not defined in the same way as in XML Schema 1.
(For instance, the concentrations of species, the sizes of compartments, etc. ) As SBML evolved, more objects were given id attributes but these were not always associated with a mathematical meaning. When SBML Level 3 packages were introduced, not only did further uses of id become apparent: it also became possible for Level 3 packages to add nuances of meanings that Level 3 Core did not. Thus, while Level 3 Core may not define a use for an id attribute on a given object, a Level 3 package might. Examples exist in the SBML Level 3 Graphical Layout and Hierarchical Model Composition packages. • The metaid attribute on SBase may seem to fulfill a similar role as an identifier for every object in an SBML document. However, for several reasons it is unsuitable for use as a substitute for id. First, owing to the fact that its data type is the XML type ID, it has a less restricted syntax (for instance, allowing Unicode characters).
Second, its scope cannot be ammended as needed for elements such as the UnitDefinition or LocalParameter, because XML ID is defined to have document-wide uniqueness properties. Finally, having a model -wide scope instead of a document-wide scope has been found desirable for the SBML Level 3 Hierarchical Model Composition package. • Because SBML Level 3 Version 2 Core objects may reference objects in SBML Level 3 packages directly, and because package objects themselves may reference Core objects as well as objects from other packages, the simplest means of achieving this is to put the id attribute on SBase directly. • The name attribute is logically paired with id, to provide the option of a user-readable moniker for any object with an id attribute. With id on SBase, it makes sense to put name on SBase too.
Use of SBML Level 3 Version 1 packages and implications for identifiers Packages created and designed for use with SBML Level 3 Version 1 may be used in SBML Level 3 Version 2 documents, and be interpreted in exactly the same manner as before. They may not, however, take full advantage of the new features and constructs in Version 2. This means the following: • Any object class in an SBML Level 3 Version 1 package that inherits from SBase will not inherit the new id and name attributes on SBase. • An object identifier from a SBML Level 3 Version 1 package may not be used in Math constructs in SBML Level 3 Version 2 Core, nor as the value of any SIdRef type attribute in a Core construct. • No object from a SBML Level 3 Version 1 package is considered to have mathematical meaning in core: no such object's SId may be used as the target of a rule nor of an assignment, nor may it be used in any MathML outside of that package.