dataesr/scientific-paragraphs-categorization huggingface dataset:
b Correlation between root sample coverage (left panel) measured in ITS1 (n = 169) and ITS2 (n = 158) datasets for each of the 41 fungi (n = 41, Spearman's rank correlation). Right panel: same correlation but based on log2 RA values (n = 41, Spearman's rank correlation). c RA profiles of naturally occurring fungi (class level) detected in A. thaliana roots across 17 European sites  18  ("all ASVs", left) and the corresponding distribution of the ITS1 sequences of the 41 selected fungi ("41 fungi", right). Note that the cumulative RA of the 41 fungi accounts for 35% of all sequencing reads detected in A. thaliana roots across European sites. trajectories when applied to Wagner-predicted ancestral genomic compositions (see pie charts, Fig. 2a ). This probabilistic approach corroborated that recent ancestors of the beneficial root endophyte Colletotrichum tofieldiae were likely pathogenic  28  , whereas those of beneficial Sebacinales-like those of ectomycorrhizal Agaricomycetes-were predicted to be saprotrophs  25, 48  (see arrows numbered 1 and 2 on Fig.
e. , xylan: GH10, GH16, CE1) were identified, together with others encoding peptidases, transporters and proteins involved in amino acid metabolism (Fig. 3b  and Supplementary Data 4a). These 84 gene families were analyzed for coexpression in published fungal transcriptomic datasets gathered in the database STRING  52. An MCL-clustered co-expression network built on families enriched in known endophytes and A. thaliana mycobiota members revealed six clusters of co-expressed genes (Fig. 3c ), including carbohydrate membrane transporters, and genes involved in carbohydrate metabolism (e. g. , GH10) and amino acid metabolism. These functions are likely to be essential for endophytic root colonization. Root colonization capabilities explain fungal outcome on plant growth. Root-colonizing fungi can span along the endophytismparasitism continuum  25, 53.
Further phylogenetic instability analysis based on duplication and mutation rates (MIPhy  56  ) identified PL1_9 and PL1_10 as slow-evolving clades in the gene family tree (instability = 30. 94 and 18. 86 respectively, Fig. 6c ), contrasting with most PL1_7 genes that were located in two rapidly evolving clades (index = 85. 30 and 66. 12). Of note, genomic counts of PL1_7, but not PL1_9/10, remained significantly associated to detrimental host phenotypes after correction for the phylogenetic signal in our dataset (PhyloGLM  50  , FDR = 0. 03). PL1_7 was also part of the core transcriptional response activated in planta by six non-detrimental fungi (Fig. 5c ) and was enriched in mycobiota members and endophytes in comparison to saprotrophs and mycorrhizal fungi (Supplementary Fig. 14d ). Therefore, degradation of pectin by root mycobiota members is likely crucial for penetration of-and accommodation in-pectin-rich A.
Here, we showed that Fig. 6  content polysaccharide lyase PL1_7 links colonization aggressiveness to plant health. a Spearman's rank correlation between the number of genes encoding secreted PL1_7 in fungal genomes and the plant performance index at low Pi in recolonization experiments. b Minimal set of 11 gene families discriminating detrimental from non-detrimental fungi at low Pi (SVM-RFE R 2 = 0. 88). The first heatmap on the left shows the SVM coefficients, reflecting the contribution of each orthogroup to the separation of the two groups, whereas the heatmap on the right shows the enrichment of these gene families in fungi identified as detrimental in recolonization experiments at low Pi. Gene family sizes and representation in the different lifestyles are shown on the barplots in the context of the whole fungal dataset (n = 120).
Asterisks indicate significant difference to T. reesei WT, according to ANOVA (P = 1. 45e-12) and a two-sided TukeyHSD test (WT vs. D1: adjusted P = 0. 28; WT vs. B1: adjusted P = 3. 75e-2; WT vs. R1: adjusted P = 1. 19e-2). e Fungal colonization measured by qPCR in colonized roots at low Pi, conducted with T. reesei WT and three pel12 overexpression mutant lines. Asterisks indicate significant difference to T. reesei WT, according to Kruskal-Wallis (P = 6. 25e-4) and a two-sided Dunn test (WT vs. D1: adjusted P = 2. 4e-3; WT vs. B1: adjusted P = 1. 6e-3; WT vs. R1: adjusted P = 1. 5e-2). For both d and e, three independent biological replicates were performed resulting in n = 15 data points per condition. Boxes are delimited by first and third quartiles, central bars show median values, whiskers extend to show the rest of the distribution, but without covering outlier data points (further than 1.
For all 41 Fungi, sequences of the internal transcribed spacer 1 and 2 (ITS1/ITS2) were retrieved from genomes (https://github. com/fantin-mesny/ Extract-ITS-sequences-from-a-fungal-genome) or, in the cases where no sequences could be found, via Sanger sequencing (4 of 41). All ITS sequence variants were directly aligned to the demultiplexed and quality filtered reads from previously published datasets  18  using USEARCH 74 v10. 0. 240 at a 97% similarity cut-off. A count table across all samples was constructed using the results from this mapping and an additional row representing all the reads that did not match any of the reference sequences was added. This additional row was based on the count data from the amplicon sequence variant (ASV) analysis from the original study, whereas the read counts from the new mapping were subtracted sample wise.
To have coverage-independent information on the RA of each fungus, we calculated RA only for the root samples where the respective fungi were found (RA > 0. 01%). The sample coverage was calculated across all root samples (>1000 reads, n = 169). Enrichment in roots was calculated for all root and soil samples (>1000 reads, n = 169 / n = 223) using the Mann-Whitney U test (FDR < 0. 05). In the same way the RA and coverage across leaf samples from two A. thaliana populations  39  was calculated (two locations in Germany, samples n = 51). For this specific analysis of leaf samples, only ITS2 sequences were used and no fold change was calculated. In order to estimate the presence of the 41 fungi across worldwide collected samples, we used the GlobalFungi database  38  (https://globalfungi. com/, version August 2020).
The most prevalent ITS1 sequences from each genome were used to conduct a BLAST search on the website. Sample metadata for the best matching representative species hypothesis sequences were then used to determine the global sample coverage. Appearance across samples from type root/shoot was counted for each fungus and compared to the total number of root/shoot samples for each continent. Whole-genome sequencing and annotation. Forty-one fungal isolates from a previously assembled culture collection 2 were revived from 30% glycerol stocks stored at -80 °C. Genomic DNA extractions were carried out from mycelium samples grown on Potato extract Glucose Agar (PGA) medium, with a previously described modified cetyltrimethylammonium bromide protocol  31. Genomic DNA was sequenced using PacBio systems.
Species assignment was conducted by extracting ITS1 and ITS2 sequences from genome assemblies, performing a similarity search against the UNITE database 78 (https:// unite. ut. ee, version February 2021) and a phylogenetic comparison to fungal genomes on MycoCosm 77 (https://mycocosm. jgi. doe. gov). Comparative genomics dataset. In addition to our 41 fungal isolates from A. thaliana roots, we used 79 previously published fungal genomes in a comparative genomics analysis (Supplementary Data 2). While 77 genomes and annotations were downloaded from MycoCosm, the genome assemblies of fungal strains Harpophora oryzae R5-6-1  34  and Helotiales sp. F229  30  were downloaded from NCBI (GenBank assembly accessions GCA_000733355. 1 and GCA_002554605. 1 respectively) and annotated with FGENESH 79 v8.
8. 0. Lifestyles were associated to each single strain by referring to the original publications describing their isolation, and consulting the FunGuild  40  database with the species and genus names associated to each strain. Orthology prediction was performed on this dataset of 120 genomes by running OrthoFinder 46 v2. 2. 7 with default parameters. From this prediction, we used the generated orthogroups data, the species tree, and gene trees. OrthoFinder was also run on our 41 newly sequenced fungi to obtain a second species tree, for this subset. Predicting ancestral lifestyles. To identify gene family gains and losses events, GLOOME  80  gainLoss. VR01. 266 was run using the species tree and presence/absence of each orthogroup in the 120 genomes. To obtain reconstruction of ancestral genomes using the Wagner parsimony approach, Count 47 v10.
04 was run using these same inputs. To associate a lifestyle to each reconstructed ancestral genome, a Random Forest classifier was trained on the copy numbers of each orthogroup in the comparative genomics dataset excluding A. thaliana mycobiota members, and the fungal lifestyles associated to these 79 genomes. This was performed using the RandomForestClassifier() function of the Python library sklearn 81 v0. 20. 3. The accuracy of the model was estimated by a leave-one-out cross-validation approach, computed using the function cross_val_score(cv=KFold(n_splits=120)) in sklearn. Finally, the probabilities of ancestors to belong in each lifestyle category were retrieved using function predict_proba(). Genomic feature analyses. Statistics of genome assemblies (i. e. , N50, number of genes and scaffolds and genome size) were obtained from JGI MycoCosm  77  , and assembly-stats (https://github.
To compare the genomic compositions of the different lifestyle categories while taking into account phylogenetic signal, we first generated a matrix of pairwise phylogenetic distances between genomes (i. e. sum of branch lengths) using the function tree. distance() from package biopython Phylo 87 , then computed a principal component analysis using the PCA(n_components=4) function of sklearn 81 v0. 20. 3. Components PC1, PC2, PC3 and PC4 (Supplementary Fig. 3 ) were then used to compare the per-genome numbers of CAZymes, proteases, lipases, SSPs, PCWDEs, and FCWDEs in the different lifestyles with an ANOVA test and a TukeyHSD post hoc test. R function aov() was used with the following formula specifying the model: GeneCount PC1 þ PC2 þ PC3 þ PC4 þ Lifestyle þPC1 : Lifestyle þ PC2 : Lifestyle þ PC3 : Lifestyle þ PC4 : Lifestyle Differences in subfamily composition of the groups of genes of interest were then carried out using a PERMANOVA-based approach (https://github.
com/fantinmesny/Effect-Of-Biological-Categories-On-Genomes-Composition). This approach relies on Jaccard distances calculation (best suited for discrete variables such as copy numbers) then a PERMANOVA testing with function adonis2() from R package Vegan v2. 5-7 (https://github. com/jarioksa/vegan), with the model specified by the following formula: JaccardDistanceMatrix PC1 þ PC2 þ PC3 þ PC4 þ Lifestyle þPC1 : Lifestyle þ PC2 : Lifestyle þ PC3 : Lifestyle þ PC4 : Lifestyle Post hoc testing with function pairwise. perm. manova() from package RVAide-Memoire v0. 9-77 (https://cran. r-project. org/web/packages/RVAideMemoire) was then performed to compare pairs of lifestyles. For each Jaccard matrix, we used the function dbRDA() from the R package Vegan, to calculate two distance-based redundancy analyses (dbRDA), respectively constrained by phylogenetic variables (formula Jaccard~Condition(Lifestyle)+PCs) and by lifestyle groups (formula Jaccard~Condition(PCs)+Lifestyle).
This was performed with functions from sklearn SVC(kernel = 'linear') and RFECV(step=10, cv=KFold(n_s-plits=120, min_features_to_select=10)), which implement a leave-one-out crossvalidation allowing the estimation of the classifier accuracy at each step of the recursive orthogroup elimination. PhyloGLM models  50  were built with R package phylolm v. 2. 6. 2 on the two groups of interest and orthogroup gene counts, with parameters btol = 45 and log. alpha. bound = 7, and the logistic_MPLE method. Further analysis of the gene families segregating fungi of interest from others (n = 84) was carried out by identifying a representative sequence of each orthogroup in our SVM-RFE model, and studying both its annotation and coexpression data in databases. To identify representative sequences, all protein sequences composing an orthogroup were aligned with FAMSA 88 v1.
6. 1. Using HMMER 89 v3. 2. 1, we then built a Hidden Markov Model (HMM) from this alignment with function hmmbuild, then ran function hmmsearch looking for the best hit matching this HMM within the proteins composing our orthogroup. We then considered this best hit as a representative sequence of the orthogroup and analyzed its annotation. GO enrichment analysis was performed by running GOATOOLS 51 v1. 0. 3 using the GO annotations associated to the representative sequences. To obtain coexpression data linking the orthogroups retained in our SVM-RFE model, we searched the String-db 52 website (https://string-db. org, version August 2020) for COG protein families matching our set of representative protein sequences in fungi. Each protein was associated to one COG (Supplementary Data 4a), and coexpression data were downloaded.
The outer circle shows orthogroup enrichment/depletion as described in panel a (see Supplementary Data 4a for associated ANOVA P-values). The inner circle depicts the SVM coefficients, reflecting the contribution of each orthogroup to lifestyle differentiation. In the center, links between orthogroups indicate coexpression of associated COG families in fungi (STRING database52). c Coexpression network of gene families across published fungal transcriptomic datasets, built on discriminant orthogroups enriched in endophytes and mycobiota members and clustered with the MCL method.
Acknowledgements The sequencing project was funded by the  U. S. Department of Energy (DOE) Joint Genome Institute , a  DOE Office of Science User Facility , and supported by the  Office of Science of the U. S. DOE  under Contract No. DE-AC02-05CH11231  within the framework of  CSP   1974  " 1KFG: Deep-sequencing of ecologically relevant Dikaria ". This work was supported by funds to  S. Hac  from a  European Research Council   starting grant  ( MICRORULES 758003 ), the ' Priority Programme: Deconstruction and Reconstruction of the Plant Microbiota (SPP DECRyPT 2125 )' and the  Cluster of Excellence on Plant Sciences (CEPLAS) , both funded by the  Deutsche Forschungsgemeinschaft. F. M. salary was covered by the  DECRyPT 2125 programme. This research was also supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ), the  Region Lorraine , the  European Regional Development Fund , and the  Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program , the  Office of Biological and Environmental Research in the US DOE Office of Science  (to F.
Data availability The genomic data generated in this study have been deposited in the GenBank database under the following BioProject accession codes: PRJNA371205 (assembly JAHBNJ000000000), PRJNA347188 (assembly JAHBNI000000000), PRJNA441695 (assembly JAHBNH000000000), PRJNA370201 (assembly JAGJXA000000000), PRJNA571620 (assembly JAGIZQ000000000), PRJNA370120 (assembly JAHBOE000000000), PRJNA347200 (assembly JAHBOF000000000), PRJNA371203 (assembly JAGPYM000000000), PRJNA370196 (assembly JAGMUU000000000), PRJNA500112 (assembly JAGMUV000000000), PRJNA370194 (assembly JAGMWT000000000), PRJNA455444 (assembly JAHBOG000000000), PRJNA370199 (assembly JAHBOO000000000), PRJNA347190 (assembly JAHEWL000000000), PRJNA455442 (assembly JAHEVI000000000), PRJNA347185 (assembly JAGMUX000000000), PRJNA370198 (assembly JAGTJS000000000), PRJNA347189 (assembly JAGPXF000000000), PRJNA455443 (assembly JAGMVH000000000), PRJNA500113 (assembly JAGMVI000000000), PRJNA347186 (assembly JAGPNQ000000000), PRJNA347191 (assembly JAHLEZ000000000), PRJNA370195 (assembly JAGTJR000000000), PRJNA370119 (assembly JAGTJQ000000000), PRJNA347187 (assembly JAGSXK000000000), PRJNA500111 (assembly JAHEWK000000000), PRJNA347192 (assembly JAGTJP000000000), PRJNA347193 (assembly JAGMWG000000000), PRJNA538399 (assembly JAGMVJ000000000), PRJNA459235 (assembly JAGTJN000000000), PRJNA347194 (assembly JAGMVK000000000), PRJNA371204 (assembly JAGPXD000000000), PRJNA570880 (assembly JAGSXJ000000000), PRJNA347196 (assembly JAGTJM000000000), PRJNA347195 (assembly JAGTJL000000000), PRJNA371202 (assembly JAGTJO000000000), PRJNA370118 (assembly JAGPNK000000000), PRJNA370200 (assembly JAGPNJ000000000), PRJNA347197 (assembly JAGPXC000000000), PRJNA519173 (assembly JAHEWJ000000000), and PRJNA370197 (assembly JAHEWH000000000).
The outer circle shows orthogroup enrichment/depletion as described in panel a (see Supplementary Data 4a for associated ANOVA P-values). The inner circle depicts the SVM coefficients, reflecting the contribution of each orthogroup to lifestyle differentiation. In the center, links between orthogroups indicate coexpression of associated COG families in fungi (STRING database52). c Coexpression network of gene families across published fungal transcriptomic datasets, built on discriminant orthogroups enriched in endophytes and mycobiota members and clustered with the MCL method. and might therefore represent key genetic components explaining adaptation to-and accommodation in-A. thaliana roots. It is important to note that although the 41 isolates are representative of naturally occurring A.
Cloud controller modules Pre-Allocator (PA). This module (see Algorithm 1) reads general cloud information from the Information database, creates a complete graph of ECs and complete graph of CNs inside each EC, calls the appropriate sub-modules to pre-allocate the ACs, and finally sends the determined placement information to the Allocator (Level 1 and 2) modules. The PA module includes several sub-modules that we will describe as follows: Trying each vertex as a starting point is important because the subgraph formed starting from will often be biased towards vertices in the proximity of ; taking the best one of the candidate subgraphs helps to find a globally optimal one. In principle, it would also be possible to consider all subgraphs of with sufficient total capacity. However, the number of all such subgraphs can be exponential, making this approach intractable in practice.
= 1 - •. (41) where and are the maximum possible cost and carbon emission for running the new requests, respectively. Finally, the subgraph with highest membership value is selected. Allocator (Level 1). Based on AC allocation information from the Pre-Allocator module, this module sends each AC request to the chosen EC controller. Monitoring. Cooperating with EC controllers, this module continuously monitors the cloud environment and updates the Cloud Information Database. Information Database. All information related to the distributed EC is stored in this module. Inter-EC AC Migration. The energy price and carbon emission rate not only can be different from EC to EC (i. e. , in different locations) but can also vary with time (e. g. , even on an hour-tohour basis). Therefore, the overall energy cost or carbon emission may be reduced by shifting portions of the ACs to ECs that currently offer better energy prices and/or carbon emission rates  [25].
EC controller modules The EC controller receives AC requests along with target CNs from the cloud controller. The EC controller allocates the ACs on the selected CNs (Allocator level 2), monitors the CNs (EC Monitoring) and gathers information of the EC and sends it to the cloud controller. To keep optimizing cost and carbon emission while preventing SLA violations, a Migration module is also used. Allocator (Level 2). This module executes the AC allocation, as determined by the cloud controller. Monitoring. Cooperating with CN controllers, this module continuously monitors the EC and updates the Information Database. 42 )) Inspired by  [20] , we consider three thresholds: (low), (middle) and (high). These thresholds divide a CN's possible workload situations into four states: under-utilized (under ), light (between and ), moderate (between and ), and over-utilized (above ).
], copy array Index[][] to array Temp[][] 21 copy sets S to S and D to D, m++ 22 migrate based on array Temp information Information Database. All information related to the EC is stored in this module. Intra-EC AC Migration. The aim of this module is twofold: (i) to prevent SLA violations of over-utilized CNs, and (ii) to optimize energy consumption (and thus costs and carbon emission) by emptying and switching off under-utilized CNs.
The GC module creates a complete graph of ECs and complete graph of CNs inside each EC based on general cloud information in the Information database. AC Mapper (ACM). The ACM module (see Algorithm 2) receives a candidate vertex (which may represent an EC or a CN) with its current capacity and a set of ACs with their traffic matrix as input. Starting from each AC ∈ , ACM 9 /* return the with highest inter-AC traffic */ Candidate Subgraph Creator (CSC). As Algorithm 3 shows, this module receives a weighted graph = ( , , , ), a list of ACs M[ ] and their traffic information TR as input and returns | | subgraphs (i. e. , ) with allocated ACs on them (i. e. , ).
GCM intercomparisons for Earth and beyond No object in space is more well studied and has as significant a dataset regarding its extant and past state as the Earth. Indeed, much of the understanding of planets in our solar system and exoplanets has been informed by principles gleaned from the study of different processes on the Earth. This is especially true when considering the impact of geophysics on the modeling of exoplanets using GCMs. The development of GCMs for Earth Science studies has enabled their use for other planets. But, there are also key ways in which exoplanet GCM simulations can in turn improve our understanding of the Earth system and its evolution. At a fundamental level, exoplanet GCM simulations inherently act as a means of stress testing and phase space exploration that can then be applied to Earth-tuned models.
2019) , in the future. Once JWST data for TRAPPIST-1e are available, GCM output will be compared to observational data. We expect such comparison will lead to a new set of simulations and to further validation of model performance against terrestrial exoplanet data. It is important to maintain and improve the level of collaboration between the exoplanet GCM community (including THAI) and the observational community. The "TRAPPIST-1 JWST Community Initiative"  (Gillon et al. 2020)  is particularly relevant as it aims to develop a coordinated framework to study TRAPPIST-1 planets with JWST from the both the observational and theoretical/modeling levels.
Radiative transfer models were also compared for specific test cases (as announced by  Harri et al. 2003). These efforts showed that, most of the time, significant differences could always be attributed to different settings in the models, and that it was difficult to reach profound scientific conclusions from such organized intercomparisons. Mars GCM teams later focused on comparing their models with the numerous observational datasets that became available in the 2000s. Nevertheless specific intercomparison studies have continued being conducted to study phenomena or altitude ranges for which not much observational data are available (e. g. ,  González-Galindo et al. 2010; Wilson et al. 2014)  or to compare model predictions at a mission landing site (e. g. ,  Kass et al. 2003; Newman et al.