dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, sequence was filtered for quality (Q-score ≥ 9) and length (L ≥ 500 bp) using NanoFilt v. 1. 1. 0 (De Coster et al. , 2018). Finally, 13. 75 Gb of trimmed long reads (coverage of 137×) were selected for further analysis. Reads from the Illumina Technology were obtained from 3 µg of gDNA using the HiSeq3000 platform as described by  Phan et al. (2020). Paired-end reads of 150 bp were generated (mean insert size = 452 bp), yielding 43. 08 million reads (64. 6 Gb; ca. 153-fold genome coverage). The quality of Illumina raw reads were assessed using FastQC  (Andrews, 2010 ). Spades v. 3. 14. 1 (Bankevich et al. , 2012)  and Blobtools v. 2. 1  (Kumar et al. , 2013)  were used to identify possible contamination; however, no potential contamination was detected. The Skewerv. 0. 2. 2 software  (Jiang et al.
graminicola (NC_024275. 1) and M. chitwoodii (KJ476150) was used to confirm the prediction and to manually check the position of start/stop codons. Fourteen protein coding genes  (atp6, nad5, cox1, nad1, nad2, cox3, nad6, nad4L, cox2, rrnL, nad3, cob, and nad4) , two ribosomal RNA (rRNA) genes (rrnS and rrnL), two repeated regions (102 R and 313 R), and 21 transfer ribonucleic acid (tRNA) genes  (trnM, trnW, trnQ, trnA, trnR, trnV, trnE, trnS, trnT, trnY, trnL 2 , trnI, trnN, trnF, trnG, trnK, trnC, trnH, trnL  1 , trnP, and trnD) were finally annotated from the mitogenome sequence. The mitogenome structure was visualized using the CIRCOS software (http://circos. ca/) (Fig. 1 ). The reads that mapped to the mitogenome (with 100% identity; CIGAR = 100 M) were removed from the cleaned long and short reads datasets and the remaining sequences used for assembly of nuclear genome.
The Canuv. 1. 8 software  (Koren et al. , 2017)  was first used for the assembly. Subsequently, Racon v. 1. 4. 3  (Vaser et al. , 2017)  and Pilon v. 1. 23 (Walker et al. , 2014)  were used to correct bases and homopolymer lengths. Contigs that had low readcoverage (<10×) were eliminated from the assembly to avoid artifacts and possible contamination. Finally, the assembled genome consisted of 206 contigs with a maximum contig length of 3,958 Kb and N50 of 1,882 Kb (Table  1 ). The total length of the assembly is 42. 10 Mb, which matches the estimated haploid genome length of 43. 2 Mb based on k-mer analyses (at k = 21) using Jellyfish v. 1. 0 (Marçais and  of the short-read data. The genome is estimated to be diploid (AB) with heterozygosity of 0. 03% (Fig. 2B ). Blobtools  (Laetsch and Blaxter, 2017)  was used to assess contaminant DNA presence on the final genome assembly (Fig.
Acknowledgments This research was funded by the  Consultative Group for International Agricultural Research Program  on rice-agrifood systems (CRP-RICE,  2017-2022 ). The sequencing was performed at the  GeT-PlaGe core facility ,  INRAE Toulouse, France. Ngan Thi Phan was supported by a PhD fellowship from  French Embassy in Vietnam  and by the  LABEX Agro coordinated by Agropolis Fondation  ( ANR-10-LABX-001-01 ). Guillaume Besnard  is member of the EDB lab that is supported by the  LABEX TULIP  ( ANR-10-LABX-0041 ) and  CEBA  ( ANR-10-LABX-25-01 ). The authors are grateful to  J. Aribi  ( PHIM ) for his technical assistance.
Data availability and accession number(s) Procedural information concerning the genome assembly and analysis presented in this paper can be found at the GitHub repository at https://github. com/ PhanNgan/genome_assembly_mex. The mitogenome and nuclear genome sequences have been deposited in DDBJ/ENA/GenBankunder the accession numbers MZ359281 and JAGUQR000000000, respectively. The nuclear genome version described in this paper is version JAGUQR010000000.
Listeria genus comprises two opportunistic pathogenic species, L. monocytogenes (Lm) and L. ivanovii, and several non-pathogenic species. All can thrive as saprophytes, whereas only pathogenic species cause systemic infections in human and cattle. Identifying Listeria species' respective biotopes is critical to understand the ecological contribution of Listeria pathogenic potential. Here, we aimed at detecting Listeria in samples of diverse origins, to highlight ecological differences between pathogenic and non-pathogenic species. We retrieved 16S rDNA datasets from the metagenomics MG-RAST database and determined the prevalence and abundance of Listeria species in various sources. Overall, Listeria was detected in 14% of datasets. Lm was the most prevalent species, most abundant both in soil and host-associated environments, including in 5% of human stools.
Results Ecological sampling is influenced by a priori assumptions about potential niches  28, 29. Here we circumvented this limitation by assessing Listeria species distribution in publicly available metagenomic datasets from the large MG-RAST database  30  , to which high quality metadata are associated, and retrieved 2,490 full metagenomes and 11,907 16S rDNA high quality datasets (see Materials and methods). We assessed the impact of Listeria pathogenic potential on its ecological distribution by comparing the relative abundance (proportion of a species in a given sample, x axis, Fig. 1 ) and prevalence (frequency of a species in samples of a given category, y axis, Fig. 1 ) of the Listeria pathogenic species Lm and L. ivanovii to that of the non-pathogenic species L. innocua, L. seeligeri and L.
welshimeri  31. Listeria was detected in 14. 05% 16S datasets (Fig. 1a ). Note that no positive result could be obtained using our approach (see Materials and Methods) analysing full metagenomes, in line with the relative low abundance of Listeria species  32  and consistent with a higher sensitivity of 16S sequencing compared to full metagenome sequencing for a given sequencing depth  33. Lm was most frequently present in soil (673/1,700; mean relative abundance 1. 2x10 -4 ), sludge (70/309), sediment (32/170) and host-associated samples (854/7,695; mean relative abundance 9. 0x10  -5  ). Only few water (42/1,980) samples and no air sample (0/53) were positive for any Listeria species (Fig. 1a , and Fig. S1a  for normalised data per category). Lm was the most prevalent Listeria species in both soil and host-associated environments (Fig.
ivanovii was detected, consistent with the known enrichment of L. ivanovii in small ruminants  34. A high Lm prevalence in pigs and wild boars has been reported  [35] [36] [37]  , and pigs might constitute an underappreciated niche for Lm. We next investigated the human sampling sites in which Lm was present. As expected for a foodborne pathogen, Lm was detected in faecal samples (108/2,238), but also in oral (7/108) and sputum (3/50) samples (Fig. 1c ), consistent with reports that Lm may colonise both the gut and the oral cavity  38, 39. Lm was rarely present in skin samples (2/56) and absent in vaginal samples (0/30), but for both categories only few datasets were available for analysis. The non-pathogenic species L. innocua and L. seeligeri were not detected in any human-associated samples, while L.
ivanovii, the only other pathogenic Listeria species, was detected, albeit far less than Lm, second most frequently in human stools (Fig. 1c ). We aimed to replicate the result of frequent Lm carriage in human independently, and assessed Lm presence by hly PCR in the stools of a cohort of 900 healthy and a cohort of 125 diarrheic individuals (see Material and methods). It was detected in 10% (90/900) of healthy human stool samples and 20. 8% (26/125) of diarrheic stools samples (Fig. S1c , Table  S1 ). The enrichment of Lm in diarrhoea samples (χ 2 =11. 702, P=0. 0018, Benjamini-Hochberg correction) is consistent with the observation that Lm can induce diarrhoea  40, 41. The two-fold higher carriage level in healthy asymptomatic donors in this cohort from France, relative to the 16S datasets from MG-RAST may be due to the different sensitivities of the two methods (targeted hly amplification versus total 16S amplification), and sample selection bias reflecting a potential differential exposure to Lm-contaminated food  30.
Neither age nor gender was associated to asymptomatic carriage (Table  S2 ). The gut microbiota is a major line of defence against foodborne pathogens, and several commensals exert a protective effect against enteropathogens  42  , including Lm  43. Lm also produces bacteriocins that can alter microbiota composition  44, 45. In order to assess if microbiota composition has an impact on Lm faecal carriage in human and vice versa, we investigated the relative abundance of microbiota taxonomic groups in MG-RAST human faecal samples. To take into account the compositional nature of data of different origins  46  , we calculated the ratios between microbiota phylogenetic groups and Lm abundance in the human microbiome datasets where Lm is present (Fig. 2 , Supplementary Tables  S3  and S4 ). Lm abundance correlated with the ratio of abundance of Firmicutes to Bacteroidetes phyla (Fig.
Lm shedding from infected tissues back in the intestinal lumen may favour long-term faecal carriage and account for the purifying selection of its virulence genes  6, 9, 10  , in line with the finding that the most virulent Lm clonal complexes are the most adapted to mammalian gut  8  , and the present observation that non-pathogenic species are not found in stool datasets retrieved from MG-RAST. To study Lm faecal carriage and its determinants experimentally, we inoculated mice intravenously with 5x10 3 CFUs of Lm belonging to the hypervirulent clonal complex-1  8, 54. We observed a cage-dependent asymptomatic faecal carriage in 3/7 cages (11/26 mice). Lm could be detected over 30-days post-inoculation. We classified faecal carriage as either heavy (>10 6 CFU/g, 6 mice in 2 cages) or light (<10 6 CFU/g, 4 mice in 1 cage, together with one non-carrier mouse) (Fig.
Screening of Listeria sp. in 16S rDNA datasets A summary of the study workflow is represented in Fig. S4. We collected 13,749 16S rDNA amplification datasets from MG-RAST from studies with >5 and <50 samples, for studies containing host samples <250 (last accessed: November 2017) as described in  59. When more samples were available, we randomly selected 50 or 250 samples, respectively. We removed those containing non-ribosomal data or less than 2000 sequences using SSU-align v. 1. 01  60. This left us with a total of 11,907 rDNA datasets (Supplementary Table  S5 ). Sequences shorter than 60 bp were removed. 16S rDNA sequence datasets were re-aligned using mafft v. 7. 407  61  , and trimmed using trimal v. 1. 4 62 using the 'automated1' algorithm. The resulting trimmed sequences were then clustered within each sample at 99% identity and 90% coverage using the uclust algorithm from usearch v.
10. 0. 240  63. A representative sequence of each cluster was defined according to the distance to the cluster centroid. Henceforth, we will call these our environmental dataset. To identify Listeria ssp. in the environmental dataset we used a maximum likelihood approach. First, Lm 16S rDNA reference sequences were aligned using mafft with the 'linsi' algorithm. The resulting multiple sequence alignment was trimmed using trimal v. 1. 4. A phylogenetic reconstruction was then performed using IQ-tree v. 1. 6. 5 64 using the GTR model (according to the model test) and 1000 rapid bootstrap iterations. The resulting tree was manually pruned to leave only one representative member of each clade. Environmental sequences were then classified as potential Listeria candidates by mapping them against the multiple sequence alignment using the '-addfragments' algorithm of mafft.
We did not include the closest non-pathogenic relative of Lm, L. marthii since it is only rarely sampled in any environment  66. The remaining representative sequences were used to construct a global catalogue of operational taxonomic units (OTUs). To do so, the representative sequences of all datasets were grouped and clustered together at 97% identity using usearch, and the frequency of each OTU was calculated on each dataset. Finally, OTU representatives were taxonomically classified at genus level using the RDP classifier  67. At the same time, we defined the α-diversity of each dataset as the Expected Number of Species (ENS). To do so, we did calculate the Shannon diversity index (H'): 𝐻 ′ = -∑ 𝑝 𝑖 𝑙𝑛 (𝑝 𝑖 ) 𝑅 𝑖=1 where pi is the relative frequency of a specific species in the dataset (the number of sequences associated with the species divided by the total number of sequences assigned to species), and R is the number of datasets.
Sequence variants were classified with RDP  72  , which uses VSEARCH to match sequences with the reference database  74. Statistical were performed with R and the phyloseq, vegan and microbiome libraries  75, 76. α-diversity has been calculated by number of observed species, abundance-based coverage estimator (ACE)  77  and Shannon index  78. β-diversity between samples has been calculated with MDS of Bray-Curtis dissimilarities  79. Figure legends Log 10 relative abundance of Listeria spp. -       Lm carriage correlates with a specific microbiota signature in humans All significant correlations with more than 75 associated samples and rho>0. 2 between Lm and commensals relative abundance in 108 healthy carrier (left panels) and comparison between carriers and non-carriers for the same groups (right panels): a.
Acknowledgments We thank  Georges Michel Haustant  and  Cédric  Fund,  Biomics Platform , C2RT,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and  IBISA  for 16S sequencing,  Auguste Fourneau  and  Amandine Brunet  for PCR assays, and  Sandrine Isaac ,  Henrik Salje  and  Olivier Disson  for critical reading. LH is supported by the  Pasteur-Paris University (PPU)   International PhD Program , funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  665807 , and the " Ecole Doctorale FIRE-Programme Bettencourt " of the  CRI Paris. This work was funded by  Institut Pasteur ,  Inserm ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  and the  European Research Council.
Lm is more prevalent in host-associated enviroments than non-pathogenic Listeria species Figure 1 a L. innocua L. ivanovii L. monocytogenes L. seeligeri L. welshimeri 50 100 150 50 100 150 host soil 874 /1700 1270 /7695 50 100 150 50 100 150 sediment sludge 32 /170 99 /309 all environments 150 50 100 water 42 /1980 150 50 100 air 0 /53 b c Number of 16S samples human cow pig chicken goat 157 /2238 8 /108 4 /56 3 /50 0 28 /552 48/300 90 /1270 10 /212 /30 270 /3338 host-associated environments human-associated environments 5 -4 -3 -2 Figure 1. Relative abundance and prevalence of Listeria sensu stricto species in 16S datasets in a. different environments, b. in selected host datasets for which metadata detailing the host species were available and c. from different sampling sites of healthy human hosts for which detailed metadata on body sampling site were available.
, 2010) , CELLML  (Cuellar et al. , 2003) , or NEUROML  (Le Franc et al. , 2012)  to describe biological models. However, crop modelers generally use procedural modelling rather than a mathematical formalism like differential or reaction equations as it is commonly done in System biology. An alternative to the problem of PBM component reuse between PBM platforms is the use of a centralized framework that enables the development of PBM components regardless of the modeling platforms (Fig. 1 ). We followed this approach and developed a modeling framework called Crop2ML (Crop Modelling Meta Language) that separates the structure of a model component from its implementation. Given that the wrapping solution was excluded because of the lack of transparency and high maintenance cost and that Crop2ML does not aim at replacing existing modeling platforms or at simulating components within large modeling solutions (crop models), we created a solution that generates components, from a metalanguage, for specific PBM platforms.
Design and concepts of Crop2ML model specification Software modularity is one of the main criteria of reuse. Jones et al. , (2001)  proposed key elements for modular model structure, which is an essential first step to enhance collaborative modelling effort. Crop2ML follows and extends these principals. In most PBM, the system is decomposed into compartments such as plant parts or soil layers that interact. For each compartment, different processes are described and assembled in components to simulate the response of the compartment. These processes can be subdivided into discrete, explanatory, independent biophysical sub-processes, which could be individually modeled (ModelUnit) or composed (ModelComposite). A modular model structure requires making an objective decomposition of the system to avoid coarse granularity models, which limit reusability.
A ModelUnit is defined with the following descriptive elements (Fig. 2a ):  a model description; J o u r n a l P r e -p r o o f  a list of inputs;  a list of outputs;  an initialization step of the state variables;  a link pointing to the source of the model algorithm;  a list of usual mathematical functions;  a set of unit tests with parameterization shared between modeling platforms. A ModelComposite includes the same elements as a ModelUnit. In addition, it contains a list of Models and the links between them. (Fig. 2b ). However, if control structures are necessary to express the behavior of a ModelComposite, the Algorithm can be explicitly provided. The Crop2ML model specification is based on XML Language. XML is a widely used declarative metalanguage for describing or structuring data in a portable format with some descriptive elements.
XML format is used in several PBM platforms for template parametrization and model simulation configuration (e. g. APSIM, BioMA, RECORD, Simplace, SiriusQuality). This reinforces our choice on this format since the transformation between different XML documents or in any language is relatively straightforward, allows using XML as a bridge between heterogeneous structures and it facilitates collaborative development. Moreover, the use of XML and a formal description of model specifications and their associated metadata facilitate machine readability and model exchange. In the following sections, we describe the concepts of Crop2ML model specifications.
These include conditional statements (if, elif and else) to check if a condition is satisfied before addressing part of an algorithm, sequential statement (for loop) with an incremental index on a data collection, and a repetitive statement (while) used to repeat part of an algorithm while a condition is satisfied. These structures can be nested. To support modular designs and the reuse of ModelUnits and functions, CyML provides import mechanisms, which assumes that imported ModelUnits or functions are referenced. Crop2ML framework provides a source-to-source transformation system (CyMLT) which converts CyML source code into procedural (Fortran, Python, C++), object-oriented (Java, C#, C++, Python) and scripting or functional (R, Python) languages  (Midingoyi et al. , 2020). CyMLT implementation relies on the transformation of the abstract syntax tree (AST) generated from the syntax analysis of the CyML code.
Crop2ML model package In the context of large projects and collaborative work, it is useful to define some requirements or standards to facilitate common exchange. Crop2ML provides a logical, standardized but flexible support to facilitate model sharing between modeling platforms through the definition of a directory structure (Fig. 8 ). This template includes a folder that contains model description and associated algorithms, a repository of source code for each language and modeling platforms. It also includes a folder containing input data for a ModelComposite simulation, and a folder containing the unit tests. To save time and avoid omission of mandatory files or folders during package creation, we created a cookiecutter  (Roy, 2017).
Model validation Crop2ML model components can be validated by executing unit tests. It consists of using the parameter and variable values from the model specification to produce unit tests in different languages. Unit tests are generated in Jupyter notebook format, a document format for publishing source codes and reproducible computational workflows that could be executed in the appropriate kernel in Crop2ML software environment. This format is useful for code and documentation publishing and real-time collaboration when running on a remote server  (Kluyver et al. , 2016). Unit tests may also be associated with a model publication.
Model transformation The success of Crop2ML model reuse through a white box approach comes from its ability to  Transforming a Crop2ML ModelComposite: A Crop2ML ModelComposite provided as a directed graph can be transformed to source code as a sequential order of the submodels.  Visualizing a ModelComposite: Pycrop2ML provides a function to visualize a ModelComposite with the links between ModelUnits (Fig. 9 ). PyCrop2ML is written in Python and can be executed via a command-line interface, inputting either a Crop2ML package or CyML source code, as well as the target language or platform for transformation. Users with no knowledge of the Python language can easily run PyCrop2ML via the.
Interoperability between various simulation platforms The interoperability between simulation platforms is based on two transformation processes In DSSAT and Record the export process is many automatic but some aspects need to be done manually. In DSSAT, Crop2ML transformation system generates a submodule in Fortran 90 for each ModelUnit. It also generates a sequence of submodules calls for composite models. One issue that makes this transformation not completely automatic is that Crop2ML does not manage the handling of input and output files. Therefore, it requires to manually add the input and output methods into the generated submodules. The concepts of atomic and coupled models in Record are mapped with those of Crop2ML. Thus, atomic model classes are generated in C++ to correspond to ModelUnits.
However, the configuration and simulation file (VPZ) representing the ModelComposite is manually completed with further information such as the description of simulation result files. The import process (from simulation platforms to Crop2ML) is only partially automatic. Platform tools produce automatically the meta-information in Crop2ML format but algorithms are manually converted into the CyML language that leads to a semi-automatic transformation. A complete automatic transformation would require the implementation of source-to-source transformation from platforms' language into CyML. ModelUnits meta-information. The process method (algorithm) is currently translated manually in CyML. Links between the different SimComponents (Unit) stored in the SimComponentGroup (Composition) are automatically exported to the Crop2ML structure.
However, there is a loss of information since when a ModelUnit is activated or ignored it is not transferred to the Crop2ML structure. In DSSAT, unlike in the other platforms, the description of physiological processes is provided as documentation in submodules and it is not fully complete with respect to Crop2ML specifications. Inputs and outputs variables and their descriptions, units can be clearly identified, based on systematic platform guidelines. DSSAT submodules contain specific platform variables such as control variables that need to be removed to produce CyML model algorithms. In Record, as in DSSAT, there is no explicit specification of a model. The documentation of a model within its associated C++ class is used to generate partial ModelUnit meta-information. The parsing of the VPZ file, that contains the structure of composite models in Record, is used to generate a ModelComposite.