dataesr/scientific-paragraphs-categorization huggingface dataset:
These building blocks include data structures and algorithms for operationalising constructions, construction inventories, heuristic search processes, meta-level learning operators, entrenchment processes, and networks of grammatical categories. FCG strives to be a flexible system for exploring novel construction grammar ideas. Apart from adhering to the basic tenets of construction grammar, it thereby imposes as few theoretical assumptions as possible. • Dynamic Construction Grammar (DCG)  [56]  and Template Construction Grammar (TCG)  [57]  are neuro-computational approaches to construction grammar. DCG makes use of artificial neural networks to find regularities in the mappings between utterances and their argument structure. TCG was developed to investigate how language and vision processing interact on a neural level in the human brain.
A wide range of tools have been developed to support grammar engineering using these formalisms, in particular TuLiPA  [87]  for TAG, XLE  [88, 89]  for LFG, DotCCG  [90]  and GF  [91]  for CCG, ALE  [92] , TRALE  [40]  and LKB  [39]  for HPSG, and Hdrug  [93]  for a variety of formalisms, including flavours of HPSG, TAG and CCG. These tools have not only made it possible to implement grammars of considerable size for a large number of languages, but have also facilitated the exchange of grammars between researchers in the computational linguistics community. For a comprehensive overview of available grammars implemented in these formalisms, we refer the reader to  [94]. Most solutions that have resulted from research on grammar engineering in the generative grammar tradition can directly be reused for construction grammar engineering, as many challenges, including verification, collaborative grammar writing and grammar debugging, remain the same.
With Babel installed on their machines, FCG users thus directly interact with its source code. They typically make use of general-purpose text editors that implement an interactive Lisp environment, such as Emacs in combination with SLIME. In this environment, users can then write their own grammars and use these in language comprehension and production tasks. FCG comes with elaborate web-based visualisations of all aspects involved in constructional language processing. As Babel is a software library rather than an integrated development environment, it takes a considerable amount of time and effort to become a proficient FCG user as a consequence of the overhead of needing to learn a new programming environment. Overall, the ECG workbench and the Babel platform have played an important role in the early development of computational construction grammars.
These include on the one hand the basic ones that are natively provided by the chosen operating system, and on the other hand the standard Emacs key bindings where they do not conflict with native ones. Main features of the FCG Editor. Advanced text editing. The editor pane provides advanced text editing functionalities, combining the look-and-feel of modern source code editors with the possibility to use the Emacs-inspired key bindings to which long-time FCG users are used. Basic functionalities, including open-save-close, undo, cut-copy-paste and find-replace, are available through the toolbar and menu bar, as well as through their native key bindings. On a more advanced level, the editor pane provides syntax highlighting, auto-completion and auto-indentation functionalities for FCG grammars as well as for Common Lisp source code, improving the readability and ease-of-development of constructs in both syntaxes.
Constructions and construction application processes can be inspected in an orderly yet detailed fashion using an interactive web interface that was especially designed for creating "visualisations for complex data and control structures"  [111]  through the ubiquitous use of expandable/collapsible elements. The web interface allows users to track the dynamics of the FCG engine during processing, thereby offering an indispensable tool for profiling and debugging complex grammars  [112]. The FCG Editor features a complete version of the Babel Interactive Web Interface that is pre-configured to trace FCG processes. The web interface is automatically initialised when the FCG Editor is launched and can be consulted at the address http://localhost:8008 using a web browser. The 'Launch web interface' button from the toolbar in the editor's main interface can be used to automatically open a web browser at the right address.
Technical implementation. Technically, the FCG Editor connects a graphical user interface to an executable form of Babel's Fluid Construction Grammar software library  [104, 114]. The editor was built using LispWorks' [115] delivery functionality and is distributed for macOS, Microsoft Windows and Linux in the form of an executable binary. The interface was built using LispWorks' portable GUI toolkit CAPI (Common Application Programming Interface)  [116] , which provides a native look-and-feel on each operating system. The editor includes Lispworks' Common Lisp interpreter, so that source code can be evaluated at runtime. All FCG source code is pre-loaded into the runtime image in its compiled form, and the input to the FCG listener, as well as the contents of all files created by the user, are automatically interpreted within the FCG-EDITOR namespace.
The look-and-feel of the editor is native on each operating system, and its key bindings are designed to reflect those used in other modern programming environments. The second requirement was that the editor should offer a straightforward and cross-platform installation, and that external dependencies should be limited to the bare minimum (R2). The FCG Editor is available for Linux, macOS and Microsoft Windows, and can be installed in a single click. For an optimal user experience, one external dependency needs to be installed, namely the Graphviz visualisation tool which is freely available for the three operating systems. The third requirement was that the editor should make implementing computational construction grammars more accessible to novice users (R3). This is achieved by including a manual, a didactic example grammar, a construction wizard, a grammar configurator, and a testing pane for comprehending and producing utterances from within the editor's main interface.
Acknowledgments We would like to express our gratitude to  Jens Nevens ,  Lara Verheyen ,  Jonas Doumen , Jerome  Botoko Ekila ,  Veronica Schmalz ,  Martina Galletti ,  Inès Blin  and  Luc Steels  for their valuable feedback on earlier versions of the manuscript and software. We would also like to thank all participants of earlier workshops and tutorials for their contributions to the requirements analysis, and for testing earlier versions of the software. We are equally grateful to the anonymous reviewers and handling editor of PLOS One, whose precious advise, suggestions and questions have had an important impact on the scope and quality of this paper. Any mistakes and bugs that might remain are ours alone.
Climatic conditions We defined day-and night-time temperatures based on the daily maximum and minimum soil temperatures recorded for each calendar day between 16 November 2005 and 15 April 2006 in Italy, and between 24 September 2005 and 15 June 2006 in Sweden at the sites where source populations were collected. For each site, this represents roughly the period from established seedling to fruit maturation in the local A. thaliana population. Soil temperature was recorded with temperature sensors placed 1 cm below the soil surface and connected to a HOBO Pro Data Logger Series H08-031-08 (Onset Computer Corp. , Bourne, MA, USA). The schedule for the Italian chamber ran for a total of 166 d. Since winter at the Swedish site is characterized by long periods of subzero temperatures, which strongly increases the risk that a growth chamber will fail, we shortened the 121 d between 1 December 2005 and 31 March 2006 to 31 d by using only every fourth day for the Swedish chamber  (Dittmar et al.
, 2014)  (Fig. 1c ), giving a programme with a total of 190 d. Although the durations of the two climate programmes are different, these durations were determined by the real seasonal differences and their association with the development from established seedling to fruit maturation at the two sites. We thus prioritized similarity in plant developmental stages over similarity in residence time for examining microbiome variation. We defined the transition from day to night as the times for sunrise and sunset recorded for Sundsvall (64 km south of the Swedish site) and Rome (24 km south of the Italian site) for each day in the schedules based on data taken from www. timeanddate. com (Fig. 1c ). We set temperatures to increase from night to day temperatures over 4 h around sunrise, and to decrease to night temperatures for 4 h around sunset (Fig.
Validation of soil microbiome transplantation We assessed whether microbial communities of the untreated soils (i. e. used to prepare the microbial input inocula) resemble those of soils directly harvested from the original natural sites, and of soils used in an in situ field experiment  (Thiergart et al. , 2020). For this, we retrieved demultiplexed sequencing data from soil samples of the former study and reanalysed them together with sequencing data of the untreated soil samples from the current study, following the pipeline described below for rhizosphere microbiome profiling (see 'Rhizosphere microbiome profiling and read processing' in the Materials and Methods section) (Fig. S2 ). To test the success of our microbial transplantation approach, we made two comparisons. First, we analysed microbial community diversity and composition in the two untreated soils and corresponding microbial inocula at preparation time (T0) and soils sterilized-and-repopulated with their indigenous Ó 2022 The Authors New Phytologist Ó 2022 New Phytologist Foundation.
We tested for significant differences in alpha diversity across conditions using the Kruskal-Wallis test, with a Dunn's post hoc test (P < 0. 05, Fig. S4a ). We calculated Bray-Curtis distances between samples using the rarefied ASV tables (vegdist function within the VEGAN R package) and performed principal components analyses (PCoAs) using the cmdscale function (VEGAN R package) (Figs S4b, S5b). To quantify community differentiation between soil treatments (sterilized, sterilized-and-repopulated, untreated), we computed Bray-Curtis distances to untreated soil centroids, and then tested for significant differences using the Kruskal-Wallis test, with a Dunn's post hoc test (P < 0. 05, Fig. S5c ). To assess which ASVs were affected by the microbial transplantation procedure, we compared the relative abundances of total ASV counts in untreated soil and in soil sterilized-and-repopulated with its indigenous microbial inoculum using the R package DESEQ2 (P < 0.
Plant fitness recording and data analysis At the end of the climate schedule, we recorded the proportion of plants that had survived to flower, the number of fruits per flowering plant and, as an estimate of fitness, the mean number of fruits per seedling planted for each genotype in each block. We analysed the effects of experimental treatments on fitness of the two plant genotypes with three different mixed-model analyses of variance, addressing separate questions. Since wells within the same block are not independent, all analyses of variation in fitness were conducted using genotype-block means, giving us eight replicates per genotype 9 soil matrix 9 inoculum 9chamber treatment combination. All models included block (nested within chamber, soil matrix and inoculum) and its interaction with genotype as random factors.
S6 ; Table  S1 ). Finally, to determine whether fitness variation among plants grown on untreated soil paralleled that observed when plants and soil were reciprocally transplanted in the field by  Thiergart et al. (2020) , we conducted an analysis restricted to plants grown on untreated soil (referred to as 'field comparison', Fig. S7a-d ; Tables  S2, S3 ). Models were fitted with restricted maximum likelihood (REML) with the software JMP 15. 1 (SAS Inc. , Cary, NC, USA).
, 2020). Sequencing reads were demultiplexed using QIIME2 (qiime demux emp_paired,  Bolyen et al. , 2019)  and merged using FLASH2 (Mago c & Salzberg, 2011). Reads were denoised and dereplicated using DADA2  (Callahan et al. , 2016) , and remaining individual reads were denoted as ASVs. Chimeras were removed using QIIME2 (vsearch uchime-denovo). Taxonomic classification was done via the QIIME feature classifier using the silva_138 database for bacteria and the unite database for fungal sequences. For bacteria, sequences classified as mitochondrial or chloroplast were removed from the dataset. Remaining ASVs were included in count tables.
3b, c ; Table  2 ). We further inspected the effects of genotype and climatic chamber using a constrained PCoA using the capscale function (VEGAN R package) (Fig. S8 ). In addition, and to ensure that different normalization methods would not provide different results to those obtained with rarefied ASV tables, we generated PCoAs and calculated the variance explained by different variables using either ASV tables with raw counts (not-normalized) or relative abundances (percentage of each ASV relative to the total read count in a given sample) (Fig. S9 ). To assess which microbial families were affected by the experimental treatments, we aggregated the ASV table at the family level, so that read counts of all ASVs in a given family were summed. The effect of experimental treatments on the abundance of these families was calculated with aggregated ASV counts using the R package DESEQ2  (Love et al.
, 2014 ) (Fig. 3d ; Table  S4 ). In a second analysis (i. e. 'field comparison'), we determined whether rhizosphere microbiome variation of plants grown on untreated soil in chambers mimicking conditions at the Italian and Swedish sites paralleled that observed when plants and soil were reciprocally transplanted at the Italian and Swedish locations. We estimated the variance in pairwise Bray-Curtis distances explained by soil, plant genotype and chamber/location, and by their two-and three-way interactions with 999 iterations of a permutation-based test (PERMANOVA, adonis function, VE- GAN R package) (Fig. S7a ,b,e,f; Tables  S5, S6 ). We visualized the results using the GGPLOT2 R package (Wickham, 2016). All analyses were performed with R v. 4. 0. 2 (R Core Team, 2021), except when stated otherwise.
Acknowledgements This work was supported by funds from a  European Research Council  starting grant (MICRORULES) to SH and grants from the  Swedish Research Council  to  J A. SH  was also supported by funds from the  Max Planck Society , the ' Priority Programme 2125 DECRyPT ' funded by the  Deutsche Forschungsgemeinschaft and the 'Cluster of Excellence on Plant Sciences ' programme funded by the  Deutsche Forschungsgemeinschaft. We thank  Anna Lisa Roth  and  Brigitte Pickel  who helped in processing microbiome samples for microbial community profiling. We thank  Guillaume Chesneau  for constructive comments on the manuscript. Open Access funding enabled and organized by Projekt DEAL.
Data availability Sequencing reads of microbiome samples (MiSeq 16S rRNA and ITS reads) have been deposited in the European Nucleotide Archive (ENA) under accession nos. ENA: PRJEB50402 (bacteria) and ENA: PRJEB50404 (fungi). Data and R scripts for computational analyses are available at https://github. com/duranpa/ reciprocaltransplantsweita. git.
carotovorum subsp carotovorum. However, even in a given species, the gene may be present or not (presence of a homologue of svfB in 10 out of the 23 P. brasiliense strains present in the ASAP data bank (https://asap. ahabs. wisc. edu/asap/home. php). svf genes are absent from M. paradisiaca. Outside Pectobacteriaceae, homologues of svfB can be found in a few Gammaproteobacteriaceae, i. d. in some Photorhabdus, Luteibacter and Pseudoalteromonas strains. yoaJ is present in all Dickeya and Pectobacterium strains except D. poaceiphila. virK is present in all Dickeya strains except in D. aquatica and absent in all Pectobacterium strains tested. We also examined the presence or absence of genes of other non-pectinolytic proteins known to be secreted by a T2SS in Dickeya or Pectobacterium: ibpS, nipE, xynA, avrL/avrM (Table  1 ).
Subcellular localization prediction algorithms, available at the ExPASy portal (https: //www. expasy. org; accessed on 28 January 2022), UniProt  [53]  and the 'SUBcellular location database for Arabidopsis proteins' (SUBA4, http://suba. live; accessed on 28 January 2022)  [54] , indicated the presence of a predicted 24-amino acid mitochondrial targeting sequence in the N-terminal region of MISF2 (Figure  S1a ). In silico 3D structure prediction, using the AlphaFold server  [55] , suggested that MISF2 harbors a typical PPR helical fold (Figure  S1a ), with an inner basic core representing the RNA binding surface, as previously indicated from the analysis of the plant PPR10 protein  [29].
MISF2 Encodes a Lowly-Expressed P-Type PPR Protein That Is Localized in Mitochondria Expression analysis of MISF2 was performed using publicly available microarray and high-throughput sequencing databases. The Arabidopsis Information Resource (TAIR) (http://www. Arabidopsis. org; accessed on 28 January 2022) (Figure  S2a ) and 'Genevestigator analysis toolbox'  [56]  (Figure  S2b ) databases indicated differential expression of the MISF2 gene throughout development, with MISF2 expression being dominant in embryonic organs, young developing leaves, apical root tissues, flowers, and the shoot apex. To further investigate the intracellular location of MISF2, a fragment comprising the first 203 amino acids of MISF2 was fused in-frame to GFP (MISF2-GFP) expressed in Arabidopsis cells and the subcellular localization of the resulting fluorescence examined by confocal microscopy (Figure  2 ).
Production of Embryo-Rescued misf2 Mutant Plants Although misf2 is not found among the 32 Arabidopsis embryo-defective ppr mutants of the 'SeedGene' database  [57] , our genetic and microscopic analyses indicate that MISF2 is essential for proper embryo development (Figure  1b ). Embryo rescue by in vitro culture allows to establish certain Arabidopsis mutants showing germination-defective phenotypes  [58]. Among these are a few mutants affected in mitochondria biogenesis and function, such as the cod1  [59] , ndufv1  [60]  cal1/cal2 [61,62], nmat3  [63] , or rfl8  [33]  mutants. Therefore, white seeds contained in young siliques of heterozygous misf2 plants (i. e. , 10~12 days post-anthesis, DPA), were sown on MS-agar plates supplemented with 1% sucrose and various vitamins (see Section 4, Materials and Methods) and then transferred.
Production of Embryo-Rescued misf2 Mutant Plants Although misf2 is not found among the 32 Arabidopsis embryo-defective ppr mutants of the 'SeedGene' database  [57] , our genetic and microscopic analyses indicate that MISF2 is essential for proper embryo development (Figure  1b ). Embryo rescue by in vitro culture allows to establish certain Arabidopsis mutants showing germination-defective phenotypes  [58]. Among these are a few mutants affected in mitochondria biogenesis and function, such as the cod1  [59] , ndufv1  [60]  cal1/cal2 [61,62], nmat3  [63] , or rfl8  [33]  mutants. Therefore, white seeds contained in young siliques of heterozygous misf2 plants (i. e. , 10~12 days post-anthesis, DPA), were sown on MS-agar plates supplemented with 1% sucrose and various vitamins (see Section 4, Materials and Methods) and then transferred to a controlled growth chamber.
The MISF2 Protein Associates with nad2 Intron 1 In Vivo A scheme of nad2 transcripts indicating the six typical stem-loop domains (D1-D6) within nad2 intron 1 (nad2 intron 1) is indicated in Figure  5a. PPR proteins are known to be sequence-specific RNA-binding factors  [28, 34, 39, [65] [66] [67]. A combinatorial code for RNA-recognition by PPR proteins was proposed, based on combinations of amino acids found at positions 5 and 35 of each PPR repeat  [39, 67, 68]. The code applied to the 10 PPR repeats of MISF2 (Figures Figure  1  and   5b ). Atomic structural model of MISF2 (Figure  S1b ) was predicted by the AlphaFold server  [55]. A BLAST search along the updated Arabidopsis mtDNA (BK010421) revealed an eight-nucleotide matching sequence within the D1 stem-loop of nad2 intron 1 (Figure  5b ).
e. , barley, maize, rice, sorghum, and wheat) plant species (Figure  S5b ). The Arabidopsis SeqViewer database (https://seqviewer. arabidopsis. org; accessed on 28 January 2022), which uses the outdated TIGR 4. 0 version of the Arabidopsis genome, suggests that the 5 untranslated region (UTR) of RDM1 (At3g22680) may overlap with the coding sequence of MISF2 (encoded on the opposite strand). Such different-strand overlapping of genes is especially untypical when considering the 2152 nucleotide-long 5 UTR suggested for the RDM1 gene by the SeqViewer database in the compact genome of A. thaliana. However, this occurrence is not supported by the updated TAIR10 genome assembly that indicates a 76-nt-long 5 UTR for RDM1 gene (Figures  S6  and S7a ). Likewise, the annotated RDM1 genes in other Arabidopsis species (i.
Funding: This work was supported by grants to O. O. B from the  'Israeli Science Foundation ' ISF grants No. 1834/20  and  3254-2020  and by grants to H. M. from the  French National Research Agency  No. ANR-16-CE11-0024-01. The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). This work has benefited from the support of IJPB's Plant Observatory technological platforms. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Both approaches are non-parametric and they use the information provided by entire datasets. The test statistic is based on the 2-Wasserstein distance, which integrates the geometry of the underlying space and provides strong theoretical guarantees and attractive empirical performance  [11]. Here, we implemented the first of the testing procedures defined in  [10] , called N g -geod, to detect differences between the codon-specific Ramachandran plots provided in  [1]. For each amino acid, we tested all the pairwise differences between all the (ϕ, ψ) distributions of synonymous codons. To facilitate the comparison with the results in  [1] , we kept only pairs of samples with sizes n, m ≥ 30 and we divided all conformations according to their secondary structure according to DSSP  [12] : Extended strand (E) and α-helix (H).
This indicates that codon-specific effects have similar significance in different areas of Ramachandran space, although the effect may be stronger for a particular type of secondary structure, such as β-strands compared to α-helices. Rosenberg et al. considered codon-specific Ramachandran plots corresponding to amino acids with arbitrary neighbors. However, the invalidity of Flory's Isolated Pair Hypothesis  [13]  and the interdependence of neighbor effects have been demonstrated in several experimental  [14, 15]  and theoretical/computational 4 s e e m a n u s c r i p t D O I f o r d e t a i l s W I T H D R A W N studies  [16, 17]. The consideration of neighboring residues particularly relevant here, because the dataset in  [1]  important discrepancies in the proportion of left and right neighboring amino acid types among synonymous codons.