dataesr/scientific-paragraphs-categorization huggingface dataset:
This first paradigm provides a good test-bed for assessing the resilience of the bottom-up temporal control module in word processing. In its present stage, the COSMO-Onset model is not likely to be able to simulate this behavioral pattern in a realistic manner. Indeed, this would probably require an intrinsic temporal scale, to adapt to the stimulus speech rate, which would set limits for compression, likely related to limits of an oscillatory process. Hence, this first set of experimental data will probably require to replace the oversimplified onset detection algorithm based on loudness increase in the present version by a real neurocomputational oscillatory model such as the ones by  Hyafil et al. (2015)  and  Hovsepyan et al. (2020). A second challenge concerns the already mentioned study by  Aubanel and Schwartz (2020)  showing that natural speech is more intelligible in noise than speech rendered isochronous, while isochrony also plays a role in helping intelligibility, but to a lesser extent.
FUNDING This work is supported by the  French National Research Agency  in the framework of the  Investissements d'avenir program  ( ANR-15-IDEX-02 ; Ph. D. grant to MN from  Université Grenoble Alpes ISP  project  Bio-Bayes Predictions ). Authors also acknowledge additional support by the  Auvergne-Rhône-Alpes (AURA) Region  ( PAI-19-008112-01  grant). This work has also been partially supported by the  Multidisciplinary Institute of AI (MIAI) @ Grenoble Alpes  ( ANR-19-P3 IA-0003 ).
Metagenomic strategy and dataset To encompass as many individuals and nationalities as possible, we compiled a dataset of 5,278 fecal metagenomes, mostly longitudinally sampled. 290 samples were from an unpublished family cohort and combined with samples from 20 published studies (STAR Methods). These samples represented 2,089 individual hosts (average 2. 6 samples/individual, maximum 41). The average observation time was 131 days, and 242 individuals were observed for R365 days, and 33 individuals were observed for R1,000 days  (Figures S1A and S1C). In total, 417 individuals were part of a total of 191 healthy families. The data covered different age groups, including adults (>18 years of age, 56%) and children (defined as %3 years of age, 41%). Fecal microbiota transfers related samples (n = 59), were excluded from all analysis, except genome binning.
Linked persistence and family transmissions of gut bacteria The family association score, representing transmission of strains between family members, correlated with persistence (p = 1e-10, R = 0. 43, Figure  2B ), resilience (p = 1e-15, R = 0. 51), and annual persistence (p = 1e-4, R = 0. 26, Table  1 ). Investigating family association at the genus level, Sutterella, Duodenibacillus (Proteobacteria), and Dialister (Firmicutes_C) had strong family associations (Figure  S1 ; Tables  S2B  and S2C ). From our dataset, we determined six high-confidence, highly transmitted species (having >20 paired observations between hosts of the same family, and family associations >50%). These included: Bacteroides stercoris, Bacteroides massiliensis, Holdemanella biformis, Sutterella wadsworthensis, and Bifidobacterium bifidum (with the overall highest family transmission rate at 72%).
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: Internal Advisory Board. In total, 36 individuals were included, their average age was 25. 5 years old, 32% of samples and 52% of individuals were female. Including family cohorts, 13 individuals we <20 years old, one individual was a newborn baby (Table  S3C ). d KEY RESOURCES The cohort, sample numbers, mean age and female % per cohort are described in Table  S3A. All aggregated metadata from this project is available in Table  S3B , totalling to 5,278 metagenomic samples with 2,089 individuals.
In total, 5. 1e6 of 2. 3e8 genes in the gene catalog were merged, based on their occurrence pattern. All genes in the gene catalog were functionally annotated to the eggNOG database  (Powell et al. , 2014)  using Diamond  (Buchfink et al. , 2015)  in blastp mode using options ''-k 5 -e 1e-5 -sensitive''.
, 2018). In addition the single copy marker genes from each MGS were matched to proGenomes  (Mende et al. , 2017) , while all MGS genes were taxonomically annotated using kraken2  (Wood et al. , 2019)  (implemented in the script ''taxPerMGS. pl'' in MATAFILER, detailed in section ''MGS abundance in metagenomes''). The GTDB taxonomic assignment was primarily used, unless only proGenomes or Kraken2 species level assignments were available, matching to the GTDB genus or family level assignment. This taxonomy was imposed on a phylogeny based on 40 conserved marker genes, allowing for a least-common-ancestor approach to obtain taxonomic assignments of unplaced taxa, at higher taxonomic levels. Using this approach, we still find several unclassified genera, families, and orders, but all MGS are assigned at least at phylum level.
From this set of marker genes (some binned in MGS, others not), we used a similarity-based approach to identify known species within these, mapping all predicted genes with Lambda 1. 9. 3  (Hauswedell et al. , 2014)  onto MGs clustered to specI's in the proGenomes database  (Mende et al. , 2017)  and using a MG specific similarity cutoff  (Mende et al. , 2013). Hits were immediately accepted, if a metagenomic MG was hitting a single proGenomes specI at the required similarity threshold. However, since several metagenomic MGs had valid hits to multiple proGenomes MG's, we supplemented the identification of a species using a coabundance approach, in a concept similar to canopy clustering  (Nielsen et al. , 2014). Briefly, for ''complete'' specI's (>30 MG present or based on binned MGS annotations) we calculated the average profile across all MGs.
The remaining MGs that were not uniquely assigned to a single specI (with >30 MG's, or binned MGS), were correlated to existing specI profiles. Correlated MG's were then tested for taxonomic similarity (being assigned to the same species or the same genus), and were combined to a new cluster if the pearson correlation coefficient was >0. 9. Additionally, at different phases in the clustering algorithm, within each specI, MGs were checked to correlate with the average profile (Spearman) < 0. 9, or were removed as false positive assignments and iteratively tried to be added to different specI's, as described above. This is implemented in the script ''annotateMGwSpecIs. pl'' available in MATAFILER. For analysis, mean abundance refers to non-zero mean abundance, excluding samples that do not show presence of a given species, unless otherwise mentioned.
This was chosen to reflect a population of a given bacterium better, that the species usually reaches in normal ecosystems, excluding the prevalence of a species observed in our sample set. Prevalence was calculated as samples with >1% abundance of a given species Characterisation of sporulation-related and oxygen-resistance genes in MGS Core genes of MGS were annotated with EggNOG-mapper v2. 0. 0 based on eggNOG orthology data  (Huerta-Cepas et al. , 2019). Sequence searches were performed using Diamond v0. 9. 24. 125  (Buchfink et al. , 2015). KO annotations were retrieved from Eggnog-mapper annotations for each MGS. KOs related to spores or to oxygen resistance (catalases, peroxidases, super-oxide dismutases according to  (Browne et al. , 2017; Miller and Britigan, 1997; Rolfe et al. , 1978 )) were identified.
Determining nucleotide variants per sample Reads were mapped against the complete metagenomic co-assembly of a given sample group. From the filtered bam files (see above), nucleotide variants per biological sample were computed using Bcftools mpileup 1. 9  (Li, 2011)  with the following additional options ''-count-orphans -min-BQ 30 -d 12000 -skip-indels -min-MQ 20 -a DP,AD,ADF,ADR,SP ''. The output vcf file was processed using a custom Perl script  (Hildebrand et al. , 2019) , to calculate the consensus fasta sequence for each assembled contig in a given sample. The minimum coverage was set to 2 reads per position. A 0. 501 consensus threshold was used, to avoid introducing a reference bias in the consensus sequence and to filter reads assignments at a depth of 2, that have a 50% allele frequency.
Alignment and phylogeny From ingroup and outgroup genes, multiple sequence alignments (MSA's) per gene category were created (independently of each other), using AA sequences and mafft  (Katoh and Standley, 2013)  with default parameters. MSA's suffer from their own technical idiosyncrasies, which we tried to mitigate through extensive automatic filtering. First, MSA's were back-translated and filtered using trimal  (Capella-Gutierrez et al. , 2009) , with the parameters ''-ignorestopcodon -gt 0. 1 -cons 60''. Second, we observed sporadically occurring ''frameshifts'' in few MSA's: whole sections of a gene were shifted by 3 nucleotides (typically the 3 0 or 5 0 part), leading to very low sequence identities on part of the MSA. To correct for these, a custom C++ program was implemented (available on https:// github.
From these filtered MSA's, a partitioned phylogeny was created using IQ-tree  (Nguyen et al. , 2015)  in fast mode, to handle the computational complexity of this dataset (with some trees containing >4,000 tips), with the options ''-m GTR+F+I+G4 -seed 678 -alrt 1000 -fast''. To capture statistics on the tree structure imbalances, we measured Sackins index. To measure these on our rooted trees, we used the R-package ''apTreeshape'', using pda normalization to account for tree size.
Annual survival rate was estimated using survival statistics. Here the R-package ''survival'' and function ''Surv'' was used on the longest timeframe within each individual, where we could detect a strain (right censoring). To estimate the percentage of strains that survived for 365 (or other times) days, we used the stepfun and survest R functions. Family persistence was estimated from the same within-MGS phylogenies, using the same algorithm described above. This was used to obtain strains shared between individuals of the same family (any timepoint was valid for this). If the family members were both adults, this was defined horizontal, if an adult and infant/teenager shared a strain, this was defined as vertical strain sharing. Geographic stability was tested twofold, but in either case we reduced the sample set to include only one sample per individual, or per family if other family members were within out dataset.
This representative sample was chosen randomly, as long as it contained the MGS, even if we detected >1 strain per sample, to avoid any false positive signal due to incomplete strain delineation. We tested the association to a specific country, by testing if within-country genetic distance was significantly smaller than between-country distance, using a perMANOVA, as implemented in the vegan R package  (Oksanen et al. , 2014). We tested the geographic association by testing for a significant correlation between geographic distance and genetic distance, using a mantel test. As genetic distance we used the cophenetic distance between leaves of the within-MGS phylogeny. As all metagenomic samples were from anonymised individuals, we inferred GPS coordinates for either the sampling hospital (if detailed in original publications) or the country the study was conducted in.
Estimates were inferred either directly from the MSAs (Tajima's D, Q, p, d N /d S ) or MSAs plus derived phylogeny to estimate p N /p S  (Murrell et al. , 2013 ). Tajima's D, Q, p and p N /p S were calculated solely from nucleotide multiple sequence alignments within a MGS, while for d N /d S sequences from an outgroup MGS were included. However, within-MGS sequences were later downsampled to account for potential biases (see further). Tajima's D, Q, p were calculated using the R packages PEGAS and APE  (Paradis, 2010; Paradis et al. , 2004) , using the functions  ''tajima. test'', ''theta. s'' and ''nuc. div''. Tajima's D, Q, p at synonymous and non-synonymous sites (Tajima's D S , Q S , p S , Tajima's D N , Q N , p N ) were calculated based only on sites of the MSA, for simplicity represented by either 4-fold (synonymous) or 0-fold (non-synonymous) degenerate coding DNA positions, as determined with subfunctions from ''dnds'' (R-package APE).
To estimate the strength of purifying selection acting on of polymorphisms potentially segregating within the bacterial species (p N /p S ), we chose to use a Bayesian approach to estimate p N /p S , using a maximum likelihood approach implemented in FUBAR  (Murrell et al. , 2013). After consultation with one of the software's author (SK Pond), we used grid-estimated p N and p S values, corrected by their posterior probability. We found that some of our estimates correlated strongly to the number of samples included per MGS (Figure  S4 ). While this could be a genuine biological signal it may be possible that the number of sequences in an analysis might bias results for technical reasons. To address this, we corrected for differences in sampling depth (i. e. number of genomes available per MGS) and autocorrelation in time series data, by 1) randomly selecting 1 sample per host-individual and 2) randomly selecting from this set n=10,20,30,100,200 or 500 genes.
ACKNOWLEDGMENTS We are grateful to three anonymous reviewers for their suggestions, and to  Sebastian Waszak ,  Lindsay Hall ,  Yan Yuan ,  Rebecca Ansorge ,  Vladimir Benes ,  Rajna Hercog ,  Judith Pell ,  Rachel Gilroy , members of both the Hildebrand and Bork groups, and the  European Molecular Biology Laboratory (EMBL) Information Technology Core Facility  for proofreading, discussions, technical support, and metadata generation. This project has received funding from  Biotechnology and Biological Sciences Research Council (BBSrC) Institute Strategic Programme Gut Microbes and Health   BB/r012490/1  and its constituent project  BBS/e/F/000Pr10355 ,  EMBL , the  European Research Council  (ERC, erc-adg-669830; MicrobioS), the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no.
Data availability Metagenomics sequences from 290 samples are deposited in the European Bioinformatics Institute-Sequence Read Archive (SRA) database under accession SRA: PRJEB41102. A list of all public metagenomic studies used and their accession numbers is provided at http://vm-lux. embl. de/$hildebra/Drama_GC. The gene catalogue nucleotide and amino acid sequences, aggregate metadata, and study lists have been deposited at http://vm-lux. embl. de/$hildebra/Drama_GC.
The LAI observations strongly correlated with the PAR measurements (See Fig. S2 ). For the leaf trait analysis, we collected 17 leaf samples from the healthy unshaded top of the canopy of each individual plot. Prior visual and geometric inspection guided choices to select the most appropriate samples in terms of areal representativeness. As such, the collected samples approximated a representative composite trait mean of the plots' upper canopy layer. Collected leaves were transported on ice after which they were either dried for LMA analysis or stored in a ≤ -18 • C freezer until CAB analysis in the lab. LMA was calculated based on the dry leaf weight and fresh leaf area (as determined in Image J 1. 52a software  (Schneider et al. , 2012) ). CAB was derived using a protocol based on  Lichtenthaler (1987).
, 2002; Jay et al. , 2017; Spitters et al. , 1986). We used PROSPECT-4 coupled with 4SAIL in order to simulate canopy reflectance. The value, range, and distribution followed for each input parameter of the models are provided in Table  1. For the inversion, we used a non-normalized "least-squares estimator" cost function  (Rivera et al. , 2013; Rossi et al. , 2020)  and implemented the LUT inversion with the ARTMO toolbox V1. 14 in Matlab  (Verrelst et al. , 2011). We applied the LUT inversion approach to estimate leaf CAB, LMA, and LAI. For optimization of its performance, we applied Gaussian noise (0-18%) to account for model and measurement uncertainties  (Rivera et al. , 2013). We used ARTMO's default parameterization for the noise and the multiple solution binning.
Sentinel application platform biophysical processor. The Sentinel Application Platform (SNAP) biophysical processor  (Weiss and Baret, 2016)  is based on a hybrid approach combining physical modeling and machine learning. This type of approach consists of training the machine learning algorithm with a LUT to produce regression models for the estimation of a set of parameters. SNAP uses an artificial neural network (ANN) inversion pre-trained on a PROSAIL simulated database including canopy reflectance and the corresponding set of input parameters. The value, range and distribution followed for each input parameter of the models are described in  Weiss and Baret (2016). SNAP can be considered as the standard approach and first port of entry for the estimation of vegetation biophysical parameters, as it is publicly available and easily applicable without strong expertise.
SNAP includes an unreleased version of PROSPECT prior to PROSPECT-4, coupled with the SAIL model  (Fourty and Baret, 1997). Traits retrieved in SNAP are scaled at canopy-level and include LAI, canopy chlorophyll (CAB*LAI) and canopy water (EWT*LAI). We reversed the multiplication by LAI to arrive at leaf trait estimates (CAB, EWT). In order to derive LMA, we coupled LMA to EWT (fixed factor; EWT*0. 79 = LMA), a strategy adopted more commonly in RTM inversion exercises given the large spectral overlap of LMA and EWT (e. g. Combal et al. , 2003; Weiss et al. , 2000; Kattenborn et al. , 2017). The strong association between EWT and LMA has been found repeatedly in relevant datasets; in our own in-situ observations, but also the LOPEX/ANGERS, and NEON datasets  (Hosgood et al. , 1994; Jacquemoud et al.
PROSAIL-D/support vector regression hybrid approach. We tested an alternative hybrid approach including a physical modeling layer and a machine learning algorithm differing from those used in SNAP. The physical modeling layer included the newer leaf model PROSPECT-D  (Féret et al. , 2017)  coupled with the 4SAIL canopy model  (Verhoef et al. , 2007). The Support Vector Regression (SVR,  Vapnik, 1998)  algorithm was used as a regression model. This hybrid inversion consists of bagging prediction of biophysical properties: a LUT is simulated with PROSAIL and resampled in order to produce multiple datasets including a limited number of samples. Then a set of individual support vector regression (SVR) models is trained from each reduced dataset. In our study, we produced 5000 samples and trained 50 SVR models of 100 samples with repetition for each biophysical property.
Functional and taxonomic diversity estimations The inversion of PROSAIL with Sentinel-2 spectra allowed us to estimate pixel-based trait values of aggregated plant canopies. For both insitu and remote sensing datasets, functional diversity metrics were calculated for each location. While numerous functional diversity metrics exist, we opted for two commonly used metrics of functional diversity: the convex hull volume (CHV) and Rao's quadratic entropy (Rao's Q)  (Dahlin, 2016; Gholizadeh et al. , 2018; Rocchini et al. , 2017; Torresani et al. , 2019). Both metrics are straightforward to compute with relatively few observations, relatively easy to interpret and particularly equipped in characterizing multivariate trait space. The CHV, a construct from computational geometry, provides an ndimensional measure of the volume of canopy plant trait space within a community.
We relied on the commonly applied Shannon's H diversity index as an indicator of local taxonomic diversity of each location; H = - ∑ s i=1 (p i × log 2 (p i ) ) (2) where s is the number of species IDs and p i is the proportion of the community represented by species i. These calculations were conducted using the scikit-bio 0. 5. 6 (http://scikit-bio. org/) package in Python. Min-max normalization of all metrics provided all values to be scaled between 0 and 1.
To account for autocorrelation, we ran Lee's L test (instead of Pearson's r). Lee's L statistic captures the spatial co-patterning by integrating a univariate spatial autocorrelation (Moran's I) and their bivariate point-to-point association (Pearson's r)  (Kim et al. , 2018; Lee, 2001). In addition, we ran a spatial error regression model implemented in the "spdep" R package  (Bivand et al. , 2011) , which controls for the bias of spatially autocorrelated errors. We evaluated the model's performance for spatial autocorrelation by reassessing the residuals with Moran's I, which showed that spatial autocorrelation was no longer significant (Suppl. Mater. ; S4). In addition, we evaluated the correspondence of satellite and in-situ observations at the location-level. The means and standard deviations at the location-level gave insight in local variability of estimates and potential robustness of aggregation against noise and misregistration.
Differences in performance between the three algorithms is in part due to parameterization and the mathematical intrinsic properties of the individual inversion methods. However, also the role of 'a priori' information deserves further attention  (Verrelst et al. , 2015; Verrelst et al. , 2019a). In this study, the ARTMO LUT-based and SVR hybrid approaches both rely on PROSAIL simulations that are generated from the minimum and maximum trait ranges found in the field, while the SNAP biophysical processor runs completely independently of ancillary data  (Weiss and Baret, 2016). Databases like TRY  (Kattge et al. , 2020)  can aid future analyses to generate locally optimized trait ranges based on species occurrences without the need for dedicated field campaigns. The optimized LUTs, taking into account the trait ranges of the ecosystem under study, enhanced performance in terms of the relative prediction of in-situ trait and the absolute bias.
In agreement with this, functional diversity (based on LAI, LMA, and CAB) captured from satellite remote sensing indeed also showed a relationship to in-situ community taxonomic diversity (Fig. 4 ). Fig. S8 -S11 indicate that the inclusion of LAI as a key trait of functional diversity actually weakens the relationship between trait diversity and taxonomic diversity both in in-situ and satellite-derived observations. Diversity in LAI seems to be relatively orthogonal to the taxonomic diversity in the dataset of our study area. As such, functional diversity measures only involving the leaf traits (LMA and CAB) resulted in stronger significant relationships to taxonomic diversity, both for in-situ and satellite-based observations (See Fig. S8-S11 ). Given the dominance of LAI on the canopy reflectance signal, proper estimation of LAI and diversity thereof is still highly important to accurately obtain canopylevel traits and structural diversity  (Asner et al.
Complementary, wall-to-wall (i. e. spatially explicit) landscape retrieval approaches of functional diversity from remote sensing would allow further assessment of the validity of remotely sensed functional diversity metrics along well-studied ecological gradients  (Durán et al. , 2019). Finally, interpolation of species-mean trait estimates based on carefully curated trait and species occurrence databases may complement the extensive sampling of field observations presented here to build a more complete validation dataset, possibly covering multiple ecosystem types, regions and or biomes (e. g. :  Aguirre-gutiérrez et al. , 2021; Ma et al. , 2019; Serbin et al. , 2019).
Across a representative selection of different inversion approaches, the functional diversity metrics (CHV in particular) appear relatively robust against errors in trait retrievals. Taken together, the study provides an important step towards maturation of operational, scalable, spatially and temporally explicit methods and, hopefully, inspires further validation and assessment of satellitebased functional diversity metrics across ecological gradients and larger datasets. A validated assessment will allow monitoring large-scale patterns of plant functional diversity to better understand the dynamics of functioning of our ecosystems.