dataesr/scientific-paragraphs-categorization huggingface dataset:
About 20. 7% of the ASVs could be affiliated to a genus, mainly identified as Rhizophagus, Funneliformis, and Glomus (18, 15, and 14 ASVs, respectively), but also, to a lesser extent, as Archaeospora, Paraglomus, Septoglomus, and Claroideoglomus (Table  S4 ). With assignment up to the species rank, 39 ASVs (12. 2%) could be named in line with referenced strains in the published databases. They were representative of eight species and six genera, namely Archaeospora trappei, Claroideoglomus lamellosum, Funneliformis mosseae, Glomus indicum, G. iranicum, Rhizophagus irregularis, R. vesiculiferus, and Septoglomus africanum (Figure  3B ). Among them, the prevalent ones in terms of both read count and ASV numbers were F. mosseae (10 ASVs totaling together 12 591 reads, i. e. , 2. 7% of the readings), G.
One could hypothetically suggest that this microbial core could correspond to taxa with basic functions (such as nutrient transfer, mainly C and N) and functional features (not yet identified) that confer some level of robustness, resistance, resilience, or functional redundancy in the face of high P content, environmental disturbances, and progressive soil restoration over time  [112] [113] [114]. However, data about the taxonomic affiliation of the core members are insufficient to go further about their functionalities. Moreover, data in the literature allowing functional inference to a given AMF species are still limited since the taxonomy and identification of AMF species are still to be refined.
Supplementary Materials: The following supporting information can be downloaded at https: //www. mdpi. com/article/10. 3390/microorganisms10122406/s1. Figure  S1 : A: The remaining material from past open-pit mines, B, C, D: Landscape in localization 1, 2, and 3 respectively. Figure  S2 : Rarefaction curves obtained from the 18S rRNA gene dataset A: Curves obtained from the 3,527,278 sequences yielded after quality filtering and chimera removal (24 soil samples and 24 root samples). B: Rarefaction curves obtained after a random resampling step, computed to compensate for the uneven sequencing efforts of different samples and to keep an adequate sequencing depth to capture most of the AMF diversity from soil and root samples. Thus, the curves were obtained for a standardized 6000 sequences per sample.
Funding: This work was carried out within the framework of the  ALIBIOTECH  and  BiHauts Eco de France  projects which are funded by the  European Union , the  French State , and the  French Region of Hauts-de-France  as well as the  TRIPLET  project financed by  A2U. This research was also funded by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  to MH, Grant Number  RGPIN-2018-04178. The authors wish to thank the " Université de Montréal " and the  "Région des Hauts de France"  for providing financial support to the ADD PhD thesis.
Methods: We included n = 118 healthy young adults (mean age: 23. 5 ± 3. 7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0-60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test.
Processing of speech data Recordings of the positive and negative story were transcribed by two Psychology students (M. Sc. ) according to the CHAT protocol  [18]. Audio features were automatically extracted from the audio signal. Textual features were automatically extracted from the manual transcripts. We used the proprietary speech processing engine SIGMA  [19]  to extract textual and audio features. Features were extracted separately for positive and negative stories. These include acoustic features with segmental features (e. g. , the number or length of words or pauses or the number or length of speaking segments), supra-segmental or prosodic features based on the processing of the frequency spectrum (e. g. , pitch, intensity), and textual features. Acoustic features were calculated only for the parts when the participants told the positive/negative story.
Feature extraction was similar as in our previous studies  [16, 17]. Different software packages were utilized to calculate the features. The computation of fundamental frequency related features relied on the parselmouth package  [20]  (which is a python wrapper for praat;  [21] ). The pitches were computed with the standard value (i. e. , zero) for time step, 50 Hz for pitch floor, and 250 Hz for pitch ceiling. The WebRTC Voice Activity Detector (py-webrtcvad; https:// github. com/ wisem an/ py-webrt cvad) package was used to estimate pause and speech ratio. A frame size of 10 ms was used to partition the audio data before marking them as pause or speech. A threshold of 1 s was used to determine whether consecutive frames form meaningful speech. Most of the linguistic features relied on the stanza package  [22]  with the default tokenization.
The computation of the rates of parts of speech (POS) is straightforward with stanza's provided POS-tagging. Graph related features were calculated with the NetworkX package  [23]. Semantic clusters were computed using transcript's word embedding (provided by the flair package  [24] ) by applying sklearn's affinity propagation. More precisely, word vectors of the tokens in the transcripts (without any processing like stemming) were computed using word embedding provided by the flair package. Then affinity propagation clustering on word vectors was computed with default Euclidean affinity and default damping of 0. 5. The coherence metric was estimated following the work by Iter and colleagues  [25] : the cosine similarity between consecutive sentences was computed and then the mean of the similarity value was taken.
In addition, we tested the significance of each model by comparing its' performance to an extra trees model trained while permuting the true depression scores 1000 times. Next, we tested whether we would find significant differences in Trail Making Test performance between individuals below and above cut-off using multivariate ANOVA, with group as between-subject factor and performance as dependent variable. Finally, we correlated, in each group (i. e. , below or above cut-off ), the scores achieved in the depression score with time to complete the Trail Making Test (A and B), using Pearson correlation or Spearman correlation (to account for potential outliers). We used SPSS (version 26. 0; IBM Inc. ; USA) and R (version 1. 4. 1106) for statistical analyses and GraphPad Prism (version 9. 0.
, 2008) , S362ANI  (Kustowski et al. , 2008) , GyPSuM-S  (Simmons et al. , 2010) , S40RTS  (Ritsema et al. , 2011) , Savani  (Auer et al. , 2014) , and SEMUCB-WM1  (French and Romanowicz, 2014). TP stands for "true positive", indicating a high-temperature cluster for mantle flow models and a low-velocity cluster for seismic tomographic models; similarly, TN ("true negative") corresponds to a low-temperature cluster and a high-velocity cluster for flow models and seismic tomographic models, and FN ("false negative") corresponds to a low-temperature cluster and low-velocity cluster for flow models and seismic to-mographic models, respectively. A is Earth's total surface area. The third is the time-averaged median angular distance θ between volcanic eruptions and basal mantle structures. We considered three databases of volcanic eruptions: (1) large igneous provinces (LIPs) from  Ernst and Youbi (2017) , hereafter referred to as EY17.
2 ). We used the pyGPlates Python API (https://www. gplates. org/pygplates, last access: 30 November 2020) to identify the centroid of LIP polygons in databases EY17 and J18. We only considered LIPs covering an area greater than 20 000 km 2. We resampled databases EY17, J18, and T18 to obtain the same temporal resolution (20 Myr) as in the mantle flow models. Volcanic eruptions that occurred within (aa w ) < a ≤ (a + a w ), where a w = 10 Myr is an age window, were assigned the age a. We combined 1168 centroids from EY17, J18, and T18 to measure the distance between volcanic eruptions and BMSs from 960 Ma. We created maps of angular distances from the edge of BMSs, with distances positive inside and negative outside BMSs  (Davies et al. , 2015b; Doubrovine et al. , 2016; Flament et al. , 2022).
The last is the statistical significance of the median distance between volcanic eruptions and BMSs, expressed as a fraction f s. For each mantle flow or tomographic model, we created 1000 distributions of spatially random points, each containing 1168 points with the same temporal distribution as in the database (Fig. 2 ). A set of 1000 "random" EDFs was created for each case. We computed the mean of absolute values of distances (|θ |) for each sample and random EDF. We carried out a Kolmogorov-Smirnov (KS) statistics test  (Kolmogorov, 1933)  based on the distance between two EDFs (the sample EDF and each random EDF). We used the scientific Python implementation of the KS test for two samples (https://docs. scipy. org/doc/scipy/reference/ generated/scipy. stats. ks_2samp. html, last access: 19 February 2021).
At this time, South America is located about 20 • farther westward in FIX_LLSVP com-   (Ernst and Youbi, 2017) , J18  (Johansson et al. , 2018) , and T18  (Tappe et al. , 2018). The number of volcanic eruptions from 960 Ma is given in brackets for each database, as is the total number of volcanic eruptions. pared with all other reconstructions, keeping in mind that there are only two tie points for a longitudinal-correctionbased aligning of plume products with the African LLSVP: the Skagerrak central LIP and a Scandinavian kimberlite  (Torsvik and Cocks, 2019). At 400 Ma the NNR, OPT, and ORTHO reference frames are still very similar but deviate substantially from both PMAG and FIX_LLSVP reference frames, in particular the longitudinal continental positions in the FIX_LLSVP frame, which ties kimberlites and LIPs in Siberia to the Pacific LLSVP  (Torsvik and Cocks, 2019).
Our Solid Earth Evolution Model for the last 1000 million years (SEEM1000) can be analysed, tested, and modified to provide insights into the history of magmatism, the history of the Earth's geodynamo and magnetic field reversals, mineral resources, sea level change, and biological evolution, forming the foundation for a multitude of spatio-temporal data analysis approaches. Code availability. The codes used for this paper are available on the public EarthByte and GPlates GitHub sites. This includes absolute plate motion model optimization code at https://github. com/ EarthByte/optAPM  (Tetley et al. , 2019) , CitcomS software for mantle convection modelling at https://github. com/EarthByte/citcoms (Bower et al. , 2015) , and GPlates/pyGPlates at https://github. com/ gplates  (Müller et al.
, 2019)  for viewing and manipulating plate reconstructions. Data availability. The optimized plate model, including paleooceanic crustal age grids, used in this paper is available on the Earth-Byte webdav site at https://www. earthbyte. org/webdav/ftp/Data_ Collections/Muller_etal_2022_SE/  (Müller et al. , 2022). Mantle flow model configuration files and outputs are available on Zenodo (see below). Video supplement. The data and video supplements are available on Zenodo at https://doi. org/10. 5281/zenodo. 6622194 (Müller et al. , 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-13-1127-2022-supplement. Author contributions. RDM conceived and coordinated the research underpinning the paper and wrote the paper. JC contributed to the design of the absolute plate model optimization code, executed the optimization runs, and contributed to the paper.
Acknowledgements. This research was supported by the  Australian Research Council  under grants  LP210100173 ,  LP170100863 , and  DE210100084  and supported by the  Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS) , with access to computational resources provided by the  National Computational Infrastructure (NCI)  through the  National Computational Merit Allocation Scheme  and through the  Sydney Informatics Hub HPC Allocation Scheme , which is supported by the  Deputy Vice-Chancellor (Research) ,  University of Sydney , and  ARC  grant  LE190100021. Financial support. This research has been supported by the  Australian Research Council  (grant nos. LP210100173 ,  LP170100863 ,  DE210100084 , and  LE190100021 ). Review statement. This paper was edited by  Susanne Buiter  and reviewed by  D.
Conversely, Bulk soil specimens were enriched predominantly for functions implicated in photosynthesis and sporulation, which are congruent with microbial adaptation to a lack of organic resources, such as the case in unplanted soils. To gain a finer view of the functional diversification of the Bulk and rhizosphere microbiotas, we performed a cluster analysis of individual GO terms on the top 10 clusters differentiating between samples (Fig. S4 ). For each cluster, we determined the significance of individual terms in pairwise comparisons between Bulk soil and rhizosphere samples and, within the latter, between genotypes (Wald test, individual P values of ,0. 05, FDR corrected, Data Set S1 at https://doi. org/10. 5281/zenodo. 7119900). This allowed us to implicate nitrate transporters with functions putatively underpinning multitrophic interactions, such as response to reactive oxygen species and the type VI secretion system.
These two functions were also significantly enriched in and differentiated between Elite and Desert communities (Wald test, individual P values of ,0. 05, FDR corrected, Data Set S1 at https://doi. org/10. 5281/zenodo. 7119900, cluster 6). Conversely, ammonium transporters were identified as a depleted function in rhizosphere communities (Wald test, individual P values of ,0. 05, FDR corrected, Data Set S1 at https://doi. org/10. 5281/zenodo. 7119900, clusters 5 and 8) as were functions implicated in phosphate metabolism, including "cellular  ). The overarching picture emerging in this investigation was that, at the metagenomic resolution we obtained, the major effect on the functional potential of the microbiota is exerted by the microhabitat (i. e. , Bulk versus rhizosphere). Conversely, the effect of the host genotype appears confined to a limited number of individual GO terms and, congruent with the 16S rRNA gene survey, manifested predominantly in the comparison between Elite and Desert genotypes.
The dominance of bacterial sequences over other members of the microbiota is not unusual in soil metagenomes  (52) , although both the protocol used for microbial DNA preparation and the databases used for sequencing annotation (53) can artificially inflate the proportion of bacteria among the analyzed metagenomes. Despite this potential caveat, we demonstrated with an independent quantification that bacterial gene copy number exceeded that from fungal sources by several orders of magnitude, as previously reported  (54, 55). It is however important to consider that PCR-based approaches may fail to provide an accurate estimation of fungal biomass due to nuclear exchanges on these filamentous microorganisms  (56). A closer inspection of the bacterial and fungal abundances revealed no significant differences among samples for arbuscular mycorrhizal fungi (AMF) symbionts of barley.
The remainder of the reads was of high quality; consequently, no 39 trimming was deemed necessary. The dada2::learn_errors() method was run to determine the error model with a MAX_CONSIST parameter of 20, following which the error model converged after 9 and 12 rounds for the forward and reverse reads, respectively. The dada2::dada() method was then run with the resulting error model to denoise the reads using sample pooling, followed by read merging and chimera removal using the consensus method. Taxonomy assignment was performed using the Ribosomal Database Project (RDP) naive Bayesian classifier through the dada2::assignTaxonomy() method with the SILVA database (94) (version 138) using a minimum bootstrap confidence of 50. The DADA2 outputs were finally converted to a Phyloseq object (version 1.
e. , Desert, North, and Elite) from specimens maintained in Quarryfield soil under N0% conditions as described above. These 12 new preparations were quantified and submitted to the LGC Genomics sequencing service (Berlin, Germany) where they were used to generate DNA shotgun libraries using the Ovation Rapid DR multiplex system 1-96 NuGEN (Leek, The Netherlands) kit following the manufacturer's recommendations. These libraries were run simultaneously into an individual Illumina NextSeq500 run following the manufacturer's recommendations with 2 Â 150 bp chemistry and generated a total of 412,385,413 read pairs. After sequencing, read pairs were demultiplexed according to the samples' barcodes using the Illumina bcl2fastq2. 17. 1. 14 software. Metagenome analysis was conducted according to the general approach of Hoyles et al.
(98) , using updated tools where appropriate. Sequence reads were quality assessed using FastQC and quality/ adapter trimmed using TrimGalore (99), using a quality cutoff of 20, a minimum sequence length of 75 bp, and removing terminal N bases. Taxonomic classification of the sequence reads was performed using Kraken 2. 0. 9 (100) with the Kraken PlusPFP database  (101) , which incorporates protozoa, fungi, and plants in addition to the archaea, bacteria, and viruses present in the standard database. Host contamination was removed by alignment against the Morex V2 barley genome sequence (102) using BWA MEM  (103) , and nonaligning reads were extracted from the resulting BAM files using SAMtools  (104). Metagenome assembly was conducted using MegaHit version 1. 2. 9 (105) with the "meta-large" preset.
Predicted proteins were produced from all assemblies using Prodigal version 2. 6. 3  (106) , which were then clustered using MMseqs2 version 11. e1a1c (107) and the "easy_cluster" method. Abundance of predicted proteins in each sample was determined by alignment of sequence reads against the representative cDNA sequences of the clusters using the Burrows-Wheeler Aligner Maximal Exact Matches (BWA MEM) and determining the read counts associated with each sequence using a custom PySAM (108) script. Functional annotations of the protein sequences were performed using InterProScan 5-50-84. 0  (109)  and Interpro version 84. 0. GO terms were enumerated using a custom Python script, which assessed the number of occurrences of each term in each sample based on the previously determined abundance of each annotated sequence.
GO terms were mapped to the metagenomics GO slim subset dated 2020 March 23 (110) using the Map2Slim function of OWLtools  (111). Functional enrichment analysis was performed using DESeq2 (112) version 1. 26. 0. Metagenome-assembled genomes (MAGs). MAGs were created using the MegaHit-assembled contigs described above and MetaBat2 version 2. 15 to create contig bins representing single genomes. Contig bins were dereplicated using dRep version 3. 2. 0 followed by decontamination with Magpurify version 2. 1. 2 (113). The resulting MAGs were assessed for completeness and contamination using checkM  (114). Annotation of the MAGs was performed with Prokka 1. 14. 6  (115)  and InterProScan 5-50-84. 0 (109) before taxonomic classification was determined using the Genome Taxonomy Database Toolkit (GTDB-Tk) version 1.
Two microliters of the fungal or bacterial standards was also used to create the respective quantification curves. DNA samples from the conditioned soil experiment were randomized in the 96-well plates, using a minimum of 11 biological replicates per treatment. Quantification was performed in a StepOne thermocycler (Applied Biosystems by Life Technologies) following the cycling protocols of each of the above-mentioned bacterial and fungal kits. Statistical analyses on the univariate data set and amplicon sequencing. Data analysis was performed in R software using a custom script with the following packages: Phyloseq (95) version 1. 36. 0 for preprocessing and alpha-and beta-diversity analyses, ggplot2 version 3. 3. 4 (118)  for data visualization, vegan version 2. 5-7  (119)  for statistical analysis of beta-diversity, PMCMR version 4.
ACKNOWLEDGMENTS We are particularly grateful to  Eyal Fridman  ( Agricultural Research Organisation, Bet Dagan, Israel ) for providing us with the B1K seeds used in this study. We thank  Lawrie Brown  ( The James Hutton Institute, Invergowrie ) for advising us in the preparation and application of the nutrient solutions and  Niamh Johnston  ( Nuffield Research Placements Student ) and  Jim Wilde  ( The James Hutton Institute, Invergowrie ) for technical assistance during the feedback experiment. We thank  LGC Genomics Gmbh (Berlin, Germany)  for generating the shot gun metagenomics reads. For the purpose of open access, the authors have applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.
Data availability. The sequences generated in the 16S rRNA gene sequencing survey and the raw metagenomics reads reported in this study are deposited in the European Nucleotide Archive (ENA) under the accession number PRJEB54874. Individual metagenomes are retrievable on the metagenomic RAST (MG-RAST) server under the IDs mgm4798244. 3, mgm4798274. 3, mgm4798349. 3, mgm4798388. 3, mgm4798507. 3, mgm4798563. 3, mgm4798641. 3, mgm4798894. 3, mgm4799467. 3, mgm4799972. 3, mgm4801514. 3, and mgm4801719. 3. The scripts used to analyze the data and generate the figures of this study are available at https:// github. com/BulgarelliD-Lab/Barley-NT-2020.
2 , Table  S1 ). Due to the distance between gardens, high fecundity, and the narrow time window in which data needed to be collected, it was not possible to quantify female fitness for every plant. For those plants for which we quantified female fitness, we counted the number of fruits on every fourth branch (ca. 20 -50 plant -1 ), starting with the lowest branch, and moving up towards the apical meristem. We subsampled branches in this way to account for variation in allocation to female reproduction across plant development. We multiplied this number by four to get a whole-plant estimate of female fitness. We also recorded fruit maturity on each group of fruits. Fruits were considered mature and given a score of one if at least one achene in the axillary cluster was brown. Plants with only immature green fruit were given a fitness score of zero, as these fruits were unlikely to mature prior to the first frost.
Hereafter 'number of fruits' refers only to mature fruits. We collected data on female fitness (number of mature fruits produced) at both 16 weeks after planting and at the end of the growing season to capture the range in life span of the different populations, day length, and the length of the growing season across gardens. This was particularly relevant at the southern gardens, IL and MO, which had longer growing seasons. We counted fruits on the same individuals at both visits, and where possible, the same branches. When fruit counts differed across visits, we kept the higher value. It was possible for an individual to have a higher fruit count at the first census because some individuals had senesced already due to a shorter lifespan by the second census, leading to the mature fruits dropping off the plant.