dataesr/scientific-paragraphs-categorization huggingface dataset:
Metagenomics The first forays into metagenomics were based on genomic fragments cloned into bacterial artificial chromosomes (BACs)  [8] , while later metagenomic approaches used random shotgunsequencing of DNA molecules (e. g. marine seawater  [24]  or acid mine drainage  [25] ), befitting the definition of metagenomics as ''the application of modern genomics techniques to the study of communities of microbial organisms directly in their natural environments, bypassing the need for isolation and lab cultivation of individual species"  [26]. The acid mine drainage study is noteworthy, because this environment is so extreme that few microbes can survive here, with the two most abundant species being a) undescribed and b) at 75% and 10% abundance  [25]. This enabled the de novo assembly of microbes not represented in databases, being the first example of a MAG and, in hindsight, predicting the future of the metagenomic field.
It should be noted that authors of these studies usually highlight more complete mapping of metagenomic reads following inclusion of new references, such as with gene catalogues  [38, 51]  or reference genomes for the human gut  [41, 50]. These improved databases are essential for reference-based taxonomic profiling of metagenomes (e. g. MetaPhlAn2, mOTUs2 and Kraken2  [52] [53] [54] ). Indeed, reference-based taxonomic profiling approaches have a much better computational performance, are straightforward to use and do not require much expert knowledge compared to de novo taxonomic profiling, which is instead completely referenceindependent and relies on de novo assembled metagenomes, constructed gene catalogues or MAGs. Despite these drawbacks, we argue that the de novo metagenomic approaches should become the standard in microbiome research, because the experience of the past decade has shown an ever-expanding microbial diversity; diversity often not captured in reference-based approaches.
For example, in antibiotic-treated patients, we found a community entirely dominated by a novel order of bacteria  [55] , which was entirely missed with reference-based approaches because the novel species was too distinct from known bacteria in reference databases. This problem is only exponentially amplified in microbiotas that are less well characterised, like in the soil  [5]. Lastly, to move beyond a 'bag-of-genes' approach, or entirely ignoring the metabolism happening in these environments, MAGs and the gene functions associated to these are important building blocks in what is considered next-generation metagenomics.
Functional annotation of bacterial genomes Extensive publicly available databases of reference genomes provide a rich background for comparing microbial genomes. Among these are NCBI (www. ncbi. nlm. nih. gov), JGI's IMG/M  [56]  and proGenomes2  [57]  that host each 241 993, 80 295 and 87 920 prokaryotic genomes as of March 2020, respectively. All of these draft genomes are curated to different degrees in the databases with automatic algorithms, greatly advancing the availability and ease-of-use for reference genomes. Yet, functional annotations of predicted genes remain limited, with usually less than half of a genome being functionally annotated. It says something of our ability to annotate genomes, that the proportion of a genome functionally annotated is often correlated to the genetic distance to the very well researched Escherichia coli (anecdotal observation).
However, progress has been made and several databases exist today that can be used to infer functions assigned to taxa, some focusing on giving broad spectrum functional annotations, others on specialised functions such as transporters. Broadly used resources relevant for functional annotation are for example KEGG  [58] , UniProt  [59]  or eggNOG  [60] , each having different goals. KEGG (Kyoto Encyclopedia of Genes and Genomes) relies mostly on well-annotated reference organisms, hence it offers well-annotated metabolic pathways and modules. However, KEGG Kyoto Encyclopedia of Genes and Genomes was initially conceived for the description of eukaryotic pathways, and is of limited taxonomic range. UniProt (Universal Protein resource) is a protein database, offering different layers of information, such as functional annotations, subcellular location, catalytic activities, protein-protein interactions, variants or protein structures.
It is organised into four main databases that differ in their level of expert curation, literature mining and computational annotations. eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) is largely developed from computer-based predictions, but offers wide taxonomic range. It is not organised in metabolic pathways and functions. The underlying principle of eggNOG is to calculate clusters of orthologous groups  [61]  among prokaryotic and eukaryotic reference genomes. This evolutionary approach is powerful, because orthologous genes are functionally stable, occasionally even between different species  [62]. These represent major taxonomic groups in the eggNOG database that allow for taxonomic annotations even in novel genera or families, such as implemented in the program eggNOG-mapper  [63].
Draft reconstruction The reconstruction of good-quality GSMs is an iterative process. Initial draft reconstruction is fairly straightforward: involving the extraction of reaction stoichiometries and reversibility from the organism specific biochemical databases, such as BioCyc  [67, 68] , KEGG  [58, 69] , BIGG  [70, 71] , and BRENDA  [72]  or use of tools, such as Pathway Tools  [73, 74] , ModelSEED  [75] , KBase  [76]  and Car-veMe  [77] , that take either a sequence or an annotated genome to automatically reconstruct the draft model. However, these automatically-constructed draft models can be of poor quality with inconsistent naming of metabolites and reaction identifiers, incorrect reaction stoichiometries and reversibility, missing or incorrect GPR associations and gaps in the metabolic network  [78].
Curation and validation Curation of GSMs is usually an iterative process where individual reactions are ensured to be atomically balanced, names of identifiers are made consistent, reaction reversibility is corrected so that the model is not able to generate energy or mass in the absence of relevant substrate import  [82] , and missing reactions are identified for ''gap-filling", with the help of a wide range of tools  [83] [84] [85]. Experimental data such as growth on defined media and biomass composition are used for gap-filling and refinement of the network which often lead to updated GPR associations. The curated model, thus ensures the laws of mass and energy conservation, is free from stoichiometric inconsistencies  [86]  and is able to produce biomass components in experimentally observed proportions from the defined media known to support the growth.
Pitfalls of genome-scale metabolism reconstruction Since the publication of the first GSM in 1999  [90] , thousands of GSMs have been reconstructed (either manually or using automated tools) and made available to the community in the previously cited databases. They concern diverse organisms belonging to Bacteria, Archaea, and Eukaryotes. They have been useful in analysing cellular behaviour under different genetic and environmental conditions, designing defined growth media and drug targets, and investigating metabolic interactions in microbial communities (reviewed in  [91] [92] [93] [94] ). However, there are still limitations in the field. The foremost limitation includes the requirement for manual intervention and curation. As detailed in the above section, there are metabolic modelling tools that supports the automated construction of GSMs however, the need for extensive manual curation has not yet been fully replaced by these automated methods which includes defining transporters, biomass components and gapfilling  [95, 96].
Top-down approaches suitable to large metagenomes Most approaches that model communities with metabolic networks focus on a small number of members and highly-curated GSMs. When using culturable organisms, it is advisable to sequence their genomes, or use the genome of closely related strains from genome databases, to de novo reconstruct GSMs. Another possibility is to use curated GSMs from databases and simulate the community with adequate environmental settings, but strain-specific genome differences likely exist  [158]  and the proper strain-specific integration of GSMs into metagenomes is still an active field of research. In the gut microbiota, 818 curated GSMs are available for simulation (AGORA)  [159] , together with diet information and the human metabolism  [160] , constituting a remarkable resource for tests and validations of algorithms.
For the intestinal microbiota, AGORA already provides a large number of GSMs for community simulations. Despite this, it is a considerable challenge to choose a well-fitting GSM, depending on proximity between the 16S rDNA genes of both species, as often OTUs can represent genera or families that are not well represented in GSMs databases. The second possibility is to identify close genomes for each OTU available in public genome databases, and subsequently de novo build draft GSMs from these genomes  [117, 162]. The obtained GSMs will undoubtedly be of lower quality due to the lack of manual curation, but one asset of the method is to target genomes that are closer to the OTU. A common problem is that functions in the resulting GSMs can differ from the actual functions in the community, as it is well documented that even between strains substantial differences exist of member from the same species  [28].
Therefore, it might be the safer choice to follow an analysis of community GSMs, starting from MAGs obtained via shotgun metagenomics (Fig. 2 ). While this is limited by potentially incomplete or chimeric MAGs, and missing manual GSM curation, this path will very likely become more prevalent in the next few years, as the availability of MAGs reconstructions increases. The asset of such methodology is to free the modelling procedure from the availability of genomes or GSMs in databases, obtain host-specific GSMs (automatically constructed) and understand the possible metabolic role of unknown genera, not represented by cultured GSMs. Currently one obstacle to overcome is that few top-down approaches are able to meet the demand of larger metagenomic studies, often in the scale of hundreds or thousands of species' metabolic networks and potential interactions.
Acknowledgements We acknowledge  Rachel Gilroy  and  Raven Reynolds  for their constructive comments and their help in proof-reading the manuscript. FH's salary is funded by the  BBSRC   Institute Strategic Programme Gut Microbes and Health   BB/r012490/1 , its constituent project  BBS/ e/F/000Pr10355. TK is funded through the  BBSRC Core Strategic Programme Grant  ( Genomes to Food Security )  BB/ CSP17270/1  and its constituent work package  BBS/E/ T/000PR9817. DS is funded by the  BBSRC   Institute Strategic Programme Microbes  in the Food Chain  BB/R012504/1  and its constituent project  BBS/E/F/000PR10349.
graminis proteins using BlastX  (Altschul et al. 1990)  with an E-value cut-off of 1e-5. Identification of single copy genes and primer design. For SNP identification, we used candidate genes of the Funybase database  (Marthey et al. 2008 ) which contains 246 reliable orthologous genes clusters present as single copy genes in 21 fungal genomes. Since Sclerotinia sclerotiorum is the closest species relative to SNPs from an Erysiphe alphitoides draft genome -2 Erysiphe sp. in Funybase (http://genome. jouy. inra. fr/funybase/), we extracted all protein sequences of this species and searched them in the E. alphitoides contigs using TblastN  (Altschul et al. 1990)  with an E-value cut-off of 1e-20. All sequences with more than one hit in the E. alphitoides draft genome were rejected from subsequent analyses.
, 2005). The quality and quantity of the final library were measured on a Bioanalyzer 2100 using High Sensitivity DNA kit (Agilent Technologies, Santa Clara, USA), and the final pool was diluted to 18 pM before sequencing on Ion Torrent PGM (Thermo Fisher Scientific, Waltham, USA). Ion Torrent Sequencing data were analyzed to identify SNPs using two custom Python (V2. 7. 6) scripts. Using the first script, Ion Torrent reads of each isolate were grouped into different amplicons, and then aligned to the corresponding reference sequences (i. e. the sequences extracted from the E. alphitoides draft genome) using MUSCLE  (Edgar 2004 ). The second Python script was used to (i) build a consensus sequence for each amplicon, (ii) remove short indels which are often produced by the Ion Torrent Sequencing technology  (Loman et al.
alphitoides and E. quercicola), 165 pairs, targeting 79 distinct genes, produced amplicons (Table  S3 ). We investigated the causes of failure of amplification by estimating phylogenetic relationships of the reference amplicons using PhyML 3. 1  (Guindon et al. 2010) , orthologs from the 21 complete genomes present in Funybase, and protein sequences from the B. graminis genome V3. Despite of our choice to only select the 10kb contigs homolog to Blumeria for limiting DNA amplification associated with putative DNA contamination from other microorganisms or host plants, we found that 11 reference amplicons isolated from the E. alphitoides draft genome were related to Aspergillus sp. (Fig. S1 ). Our custom python-script detected 1794 SNPs in the 165 amplified sequences. These SNPs were located in 139 amplicons, corresponding to 79 distinct genes (Table  S4 ).
Validation of SNPs for E. alphitoides and population genetic studies. For SNP validation in E. alphitoides, we selected a total of 58 SNPs within 39 distinct genes and grouped in two SNP arrays (Table  3 ). A set of 54 SNPs were polymorphic among the 23 monospore isolates (Table  3 ). The comparison between genotypes obtained by the Ion Torrent Sequencing and the MassArray genotyping Technology only revealed six discrepancies in the 23 control isolates, corresponding to an error rate of 0. 0048. Most of these differences were concentrated on the isolate AG320. This isolate was probably not a monospore haploid isolate since several heterozygote SNPs were detected using the MassArray genotyping analysis. Most of monospore isolates (95%) had less than three missing data, whereas 93% of the lesion and herbarium samples had more than five missing data (mean 11.
Most of the non-amplified sequences were associated to Aspergillus sp. , likely present on the oak leaves from which E. alphitoides was sampled  (Unterseher et al. 2007 ). The phylogenetic analysis, such as presented in our study, should be performed before the final choice of candidate regions to better remove genes from contaminating taxa. Our method may also be applied to any other fungus since genomic resources can easily be generated today using high throughput sequencing. Moreover, gene databases are available for several phyla  (Grigoriev et al. 2014). Our approach may provide an alternative to the RAD-seq method since, on one hand, it does not require complex laboratory protocols  (Harvey et al. 2016 ) and, on the other hand, it allows to easily control both the phylogenetic origin and duplications of the targeted sequences.
The choice of focusing on a set of conserved SNPs from an Erysiphe alphitoides draft genome -5 genes, generally present in single copy in most fungal genomes should prevent the risk of amplifying paralogs, reducing errors in estimation of allelic frequencies  (Gayral et al. 2013). Controlling for gene duplicates with our method is dependent on the selected threshold for the Blast analysis (see for example  Feau et al. 2011) , and quality of the genome assembly. However, the results obtained from the MassArray genotyping showed no evidence of copy number variations for the chosen SNP (data not shown), and especially, we detected heterozygote SNPs for only one of the 23 monospore haploid isolates used as controls, consistent with absence of duplication of the chosen genes. Although it is likely that large parts of the E.
alphitoides genome, especially regions of low complexity, have probably not been accurately assembled  (Frantzeskakis et al. 2018) , our results suggest that our targeted single-copy genes are frequently located in the easiest regions to assemble, greatly helping their isolation in draft genomes. In addition, we showed that a small quantity of DNA (which is usually what is obtained for obligate fungal biotrophs, like OPM) is not a limitation for the isolation of these targeted markers by using this method. This method might further be improved by increasing the depth of sequencing, by using long-read sequencing technologies which would allow better assembly of full genomes  (Faino et al. 2015). Using additional gene databases to identify conserved genes such as OrthoMCL-DB  (Li et al. 2003 ) might also allow to increase the number of targeted genes.
Nevertheless, even a limited number of SNPs (i. e. around 40, such as obtained in this study) may allow to get a first insight on population genetic structure (e. g. Dutech et al. 2017 , Tsykun et al. 2017 ). The study published by  Tsykun et al. (2017)  showed no major difference in population structure characterized with SSR or SNP markers. DNA amplification using the Fluidigm methodology made it possible to obtain numerous SNPs for a preliminary population genetic study in E. alphitoides, and its sister species E. quercicola. A limitation of our study was due to the use of the Ion Torrent sequencing methodology which generates a large number of spurious indels linked to homopolymers  (Loman et al. 2012). Our in-house script designed to removed these indels from the consensus sequences built for each individual, seemed to efficiently solve this problem.
Actually, the validation of more than 90 % of the selected SNPs following MassArray genotyping confirmed that our method is robust. In addition, 72 % of the detected SNPs are fixed in each species, as expected in absence of recent gene flow between the two Erysiphe species  (Feau et al. 2011) , and support they are not false positive SNPs. By contrast, we also used another method implemented in DiscoSNP  (Uricaru et al. 2015)  which detected less than 300 SNPs, suggesting that numerous SNPs are missed relative to our method of SNP calling. Additional tests should be performed to define the best set of parameters of DiscoSNP on such sequence data with a large presence of indels generated by Ion Torrent sequencing, but these results suggest that this automatic method of SNP calling is unadapted to these sequence data.
The patterns were indexed automatically using AZtechHKL software package from Oxford Instruments. Three areas (Areas 1, 2 and 3) were mapped with a step size of 250 nm covering an area of 249. 5 mm Â 197. 0 mm. Grain reconstruction and grain misorientation boundaries were determined using MTEX open source toolbox (https://doi. org/ 10. 4028/www. scientific. net/SSP. 160. 63) for MATLAB  (Hielscher & Schaeben, 2008; Mainprice et al. , 2014). The recent inverse pole figure (IPF) coloring scheme developed by  Nolze & Hielscher (2016)  was used for orientation mapping coloring.
Reconstruction of multiple twin models Schematic 3D models of multiple twins with different crystallographic configurations were reconstructed in VESTA  (Momma & Izumi, 2011 ) by using the multiple-phase data option. Absolute orientation for each of the domains of a multiple twin was obtained by expressing in Miller notation the orientation of two perpendicular reference vectors based on the orientation data for each domain obtained from EBSD maps (recorded as Euler angles). In VESTA, the orientation of a crystal or crystal domain is given by the out-of-plane (projection) vector Z = (u,v,w) and the north direction as the pole of the (hkl) in-plane, i. e. the (upward) vector Y = (h,k,l). A Matlab script for the conversion of Euler angles obtained from EBSD maps to vectors for VESTA is given in the supporting information.
After inserting the orientation data for all domains into the VESTA file, the domains were cut along the proper {101} contact planes common to the neighboring domains. Finally, relative positioning of the domains was done manually to obtain a model with all domains in contact. Each of the domains was colored to match the EBSD colors in order to facilitate the comparison between the 3D twin model and the grain in the 2D cross-section. Grain boundaries in EBSD maps were identified by locating misorientations greater than 5 using the band contrast images as reference. It was found that a 10 threshold was not suitable for the investigated samples. The angle distribution of SnO 2 -SnO 2 grain misorientation for Area 1 is presented in Fig. 1(c ). The majority of grain contacts are random GBs with statistical distribution of angles as proposed by  MacKenzie (1958)  which is controlled by the point symmetry of cassiterite.
2  and 5. Models were reconstructed based on quantitative orientation data from EBSD, where absolute orientation of each (indexed) crystal domain relative to the referential coordination system of the stage in the electron microscope is obtained. Although grains develop spherical morphologies without distinct crystal faces in the process of solid-state sintering, we reconstructed models as idealized faceted crystals surrounded by {110}, {010}, {101} and {111} faces for a more illustrative representation of crystallographic setting of the twins. Orientation of each 3D model shown in Figs. 6 and 7 corresponds exactly to the orientation of the sectioned grain and the colors of the twin domains correspond to the colors in the EBSD map. Fig. 6 (a) shows coplanar twins with three and four domains (C3 and C4; where C stands for coplanar).
Funding information The authors gratefully acknowledge financial support by the  Slovenian Research Agency  under the Project No. J1-9177. Financial support within the bilateral project  BI-FR-PROTEUS/17-18-007  funded by the  Slovenian Research Agency , the  French Ministry for Europe and Foreign Affairs (MEAE)  and the  French Ministry of Higher Education, Research and Innovation (MESRI)  during the initial stages and the conception of this project is also greatly appreciated.
Traits related to the root system architecture (RSA) assessed from the 2D RGB images are described in Table 2, and some are illustrated in Figure  2A. Data for the trait total root length (trl), average diameter of roots (adr), number of root tips (tips), projected area (pa), root surface area (rsa) and root volume (rv) were obtained using the WinRHIZO software program  (WinRHIZO PRO 2013). The maximum root depth (mrd), which measures the depth reached by the roots, maximum root width (mrw), and total area explored by the roots (ea) were obtained using a purposely-developed macro toolset running on the ImageJ software program  (Abràmoff et al. , 2004). A macro file ("Toolset") that contains macro tools available in ImageJ was developed to streamline image analysis and facilitate the semi-automatic measurements of root traits.
Results and Discussion All the plants grown in the rhizotrons showed normal development and growth of both the shoots and the roots (Figures  1C  and 2A  for cotton, and Figure  2B  for soybean and sorghum). The positioning of the rhizotron at a 45° angle induced the roots to grow alongside the glass plate facilitating observation of the rooting system. Additional decreases in the angle (see Figure  1B ) did not increase the amount of roots that could be seen (data not shown). The photographs of the root system were taken at the end of the experiment at 21 days after sowing, when the roots of the cotton plants had reached the bottom of the rhizotrons; however, since the system was devised to allow easy handling of the rhizotrons and direct access to the root system, pictures can also be taken repeatedly over the course of the experiment to provide a dynamic view of root growth and development.