dataesr/scientific-paragraphs-categorization huggingface dataset:
Metric extraction This study aimed to investigate how semantic information unfolds within titi monkey alarm sequences. The first step of this analysis was to unroll the sequence, and describe how its organization and structure changed with the emission of new calls. To do so, each sequence was cut into subsequences, so that a sequence of x calls was decomposed into x subsequences starting with the first call of the sequence and ranging from one to x calls long. Our final dataset was thus composed of 58 sequences, comprising a total of 2,667 subsequences. We characterized each subsequence by a set of 24 quantitative variables (henceforth, 'metrics'), following the procedure used in  Berthet et al. (2019). Metrics comprised: (1) the mean call interval, defined as the mean of the inter-call silence durations in the subsequence, (2) the coefficient of variation of call interval, defined as CV = standard deviation/mean of inter-call silence durations, (3-4) the proportion of each A-call and B-call in the subsequence, (5-8) the proportion of each possible two consecutive calls, or 2-grams (AA, AB, BA, BB) in the subsequence, (9-16) the proportion of each possible combination of three consecutive calls, or 3-grams (AAA, AAB, ABB, ABA, BBB, BBA, BAA, BAB) in the subsequence, (17,18) the N-gram slopes (here, 2-gram and 3-gram slopes), to test whether a N-gram is more present in the subsequence than the others.
We chose α = total number of events/(k * 100) as prior distribution, so that we simulated that each subsequence comprised 1% more events i. For example, to calculate the proportion of A-calls in a subsequence of 10 calls, yi is the number of A-calls in the sequence, the 'total number of events' is the number of calls in the sequence (here, 10) and k is the number of possible call types (here, two: A and B-calls). More details about the method can be found in  Alger et al. (2016) ,  Berthet et al. (2019) , and in the online scripts. These analyses were conducted using R version 3. 6. 1 (R Core Team 2020) and the cfp package  (Neumann 2018).
Statistical analysis In order to investigate semantic information as the sequence unrolls, we analysed how the structure of the sequence (its metrics) varied with the number of calls already emitted and the contextual information. To this end, we investigated what contextual parameters (if any) influenced the metrics' values of each subsequence using random forests algorithms. Random forests are machine learning algorithms that predict the value of a variable to explain (here, each metric) based on explanatory variables (here, the contextual information), using a set of multiple decision trees. We split the dataset into 50 subsets, one for each subsequence length. For each subset, we conducted the following analysis: first, for each metric, the explanatory variables that accounted for most of the metric's variance, if any, were extracted.
6. 1 (R Core Team 2020), the tidyverse package version 1. 3. 0 (Wickham et al. 2019) , the VSURF package version 1. 1. 0. (Genuer et al. 2015) , the randomForest package version 4. 6-14  (Liaw & Wiener 2002) , and the inflection package version 1. 3. 5  (Christopoulos 2019). The full dataset and the associated statistical scripts are available on a Figshare depository (https://figshare. com/projects/Animal_linguistics_inthe_making_The_Urgency_Principl e_and_titi_monkeys_alarm_system/121914).
Further work is needed to test the effect of additional factors such as the variation of distance between individuals or the latency of each individual's call emission, and conclude on the underlying mechanisms. In this article, we were able to confirm that methods from computational linguistics can be successfully applied to animal communication to process large datasets, highlight underlying structure and unroll information transfer processes. We have also shown that one of the key principles from the animal formal semantics framework (namely, the Urgency Principle) is an encoding strategy that is found in at least one species of nonhuman primates. Further verifications are needed to confirm that these methodologies are entirely reliable, such as testing whether the Informativity Principle is also relevant to nonhuman animals, and extending these investigations to other taxa.
ACKNOWLEDGEMENTS The authors thank  Philippe Schlenker  and  Emmanuel Chemla  for helpful discussions, the many field assistants and master students who took part into data collection, and the  Santuário do Caraça  for their logistic support. Guillaume Dezecache: https://orcid. org/0000-0002-9366-6287Klaus Zuberbühler: https://orcid. org/ 0000-0001-8378-088XMélissa  Berthet: https://orcid. org/ 0000-0002-3170-1315.
Numerical tests In this section we demonstrate numerically the behavior of the scheme in terms of convergence with mesh and time-step refinement. The tests presented hereafter correspond to the internal energy/pressure function (6. 1) U (r) = P (r) = r 2 , for which the Euler equations (1. 1) yield the shallow water equations without rotation and the gradient flow (1. 4) yields the porous medium equation with a quadratic nonlinearity. Note, however, that in tests below the vector field ∇U (ρ) is not Lipschitz (in fact, it is discontinuous at the boundary of the support of ρ), so they are outside the limits of applicability of our theorems. For all the tests the discrete initial condition are determined by optimal quantization with respect to the Wasserstein distance as in Remark 5. 4. For the computation of the Moreau-Yosida regularization, we used the open-source library sd-ot, which is available at https://github.
Genome data We used 193 annotated genomes  [41]  belonging to a culture collection of bacteria isolated from A. thaliana roots grown in the Cologne Agricultural Soil (Germany) and representing a taxonomically diverse core set of bacteria of the host plant  [41]. Annotated genomes  [41, 42]  were downloaded from the At-SPHERE database (http:// www. at-sphere. com/). Both the taxonomy and phylogeny of the whole set of genomes  [41]  were used. The phylogenetic tree was inferred by maximum likelihood  [41]  from a multi-alignment of 31 bacterial AMPHORA  [43]  genes obtained with Clustal Omega v1. 2. 1  [44]  passed into FastTree v2. 1  [45]. The genomes were sequenced on a HiSeq2500 (Ilumina Inc, USA) and were considered as high-quality drafts genomes (see Bai et al. [41] ). Pairedend reads were trimmed using Trimmomatic v0.
Reference database The reference used to link genome annotation to metabolism was the MetaCyc database, a collection of organismspecific Pathway/Genome Databases (PGDBs). MetaCyc contains ~2 500 metabolic pathways from many organisms  [52]. Two criteria motivated the choice of this database: first, it is manually curated; second, our tools, which are designed for the reconstruction of the metabolic network (mpwt and AuReMe)  [53, 54]  are pre-configured to work with this curated database.
Reconstruction of the metabolic networks (GEMs) Metabolic networks of each genome were simulated with genome-scale metabolic models (GEMs) reconstructed with an automated command-line version of Pathway-Tools  [55] [56] [57]  using the mpwt program of the meta-ge2metabo tool suite  [52]  , then converted in padmet and sbml format with AuReMe and padmet-utils  [54]. GEMs in sbml format were parsed with the Python lxml package when needed. All GEMs were drafts, used without gap-filling or manual curation. These steps are usually required to improve the quality of a GEM  [58]  , but are likely to introduce false positives, particularly in the case of poorly known organisms, thereby masking potential metabolic dependencies. Consequently, we chose to rely on drafts of GEMs, i. e. , we chose false negatives (due to flaws in genome annotation) over false positives.
Genomes and GEM metrics In order to detect patterns between strains and metabolism, a set of metrics (the full definitions are listed in Table  1  above) were used and applied to a single GEM (i. e. , single strains) and to random combinations ("Syn-Coms") comprising two to 20 GEMs. The Python API of Miscoto scopes  [59]  was used to compute all the predicted producible metabolites, (PPM) for a single GEM, SynComs, and the whole community under simulated nutritional constraints. AuReMe reports were parsed to record constraint-free PPM. Phylogenetic distances were computed based on the phylogenetic tree with the Python package ete3  [60]. Genome sizes were available in the annotation data. For each SynCom, the core predicted producible metabolites (CPPM) were also computed using sets in Python 3 and AuReMe and Miscoto scopes outputs (Fig.
Vitamins were chosen according to their importance in metabolism. Phyto-hormones were chosen with respect to the root-associated trait of the microbial community under study. In addition, according to the reference database, these metabolite biosynthesis pathways and the genes encoding them are relatively well-known, easily predictable, and are expected in the studied taxa, thus reducing the risk of misses in the genome assembly, annotation errors, and false negatives in the reconstructed metabolic networks. When under a nutritional constraint (see below), TPPM production was computed for each GEM with the Python API of Miscoto scopes  [59]. In the absence of a nutritional constraint, TPPM production was assessed with their absence/presence in AuReMe reports.
Modeling nutritional constraints (growth media) Nutritional constraints were established by simulating different growth media. We used two online resources to choose the growth media: MetaCyc (previously mentioned), which contains few growth media with their detailed list of nutrients, and the KOMODO database  [61]  , which contains a huge number of growth media, together with their composition, and their MetaCyc IDs. We modeled nine growth media (five poorly nutritive with mainly mineral nutrients and four highly nutritive, with more carbon sources, Table  2 ), to which a common list of cofactors  [62]  was added. Each medium has an "artificial root exudate" enriched version (giving a total of 22 media, Table  2 ), based on Baudoin et al. [63]. Growth media, except rich media, were chosen based on two conditions: (1) none of the TPPM should be included in a medium's nutrients and (2) the nutrients should not contain any "mixture compounds, " such as tryptone or yeast extract, whose composition is not described in MetaCyc.
Scripting Data acquisition and links between tools inputs and outputs (Fig. 1 ) were organized using homemade Python 3 scripts. All Miscoto outputs were stored in json format, and the relevant data they contained (PPM and TPPM, number of genomes producing a TPPM under a given nutritional constraint, etc. ) were parsed and stored as csv tables. Figures and data analysis were performed with R 4 with the ggplot2 package  [67]  and Python 3 with the matplotlib and seaborn packages. Scripts and data are available at https:// gitlab. com/ matai vic/ artic le_ metab olic_ model ling_ thali ana_ micro biome.
Most SynComs, particularly big SynComs, were concentrated around this phylogenetic distance rather than spread equally along all possible distances (Fig. 3  and S4, A1 to A4). This corresponds to the values of most combinations of Proteobacteria and Actinobacteria (Figure  S7  A to B) and is explained by the fact that these two phyla were the most frequent in the dataset. Genome sizes were positively correlated with both PPM (coefficient=0. 23, p < 0. 001) and CPPM (coef-ficient=0. 46, p < 0. 001, Fig. 3C, D ). Thus, genome sizes compensated for the negative effect of phylogenetic distance on metabolic redundancy. At a fixed phylogenetic distance, SynComs with bigger genomes displayed both higher metabolic diversity and a bigger core metabolism than SynComs with smaller genomes (Figure  S7 C1&C2 ).
8978 without ARE and p < 0. 001, Cliff 's delta effect size = -0. 5102 with ARE, Fig. 4A ). Supplementing media with compounds that artificially mimicked the exudate composition of plant roots (ARE) was predicted to significantly increase the number of PPM compared to non-supplemented media (green vs. orange in Fig. 4A , Table  3 , p < 0. 001). Notably, the composition of PPM was more similar (i. e. , similar sets of producible compounds) across media containing ARE, irrespective of whether the original media were poor or rich (Fig. 4B ). Hence, the addition of ARE is predicted to unlock the production of the same metabolites across media. Most of the GEMs have the potential to produce most of the TPPM in the absence of nutritional constraints (27 out of 30 being producible by a single GEM in the full dataset) but cannot complete the entire pathways under most of the nutritional constraints, Fig.
5B ). Among vitamins and phytohormones, only nicotinic acid (vitamin B3) and salicylic acid pathways were predicted as complete, with metabolic exchanges required between two GEMs under severe nutritional constraints. The frequency of GEMs in the solutions was highly varied with a few GEMs occurring in several hundred solutions (Fig. 6A ). The other GEMs were much less frequent with only a few occurrences. Thus, for all TPPM, most solutions can be aggregated with a set of seven GEMs (Supplementary Table  S1 , seven first rows), belonging to strains of the phyla Proteobacteria and, surprisingly, Firmicutes (despite being very underrepresented in the dataset). When TPPM are split according to the category, the majority of solutions can be aggregated with a set of 15 GEMs (Supplementary Table  S1 ).
TPPM and nutritional constraints are ordered in the same way as in panel A. Results are given for each nutritional constraint (black: poor, red: rich), without (plain) or with (bold) ARE. The list of nutritional constraints varies depending on the TPPM considered, in accordance with the no-TPPM-in-media condition (i. e. , growth media containing TPPM in their composition were excluded when necessary). On all axes in all the figures, poor media are labeled in black, rich media in red, ARE-enriched media in bold, and standard media in plain text (See figure on next page. ) this pathway are also recorded in the database. The first reaction is encoded into five GEMs only, which are part of the aforementioned seven main GEMs. The second reaction is encoded into 47 GEMs. Most of the combinations for the completion of this pathway of salicylic acid are then built with these GEMs.
However, the aggregation of SynComs around the same phylogenetic distance is more interesting. Closely related bacteria have similar needs and hence face strong competition for resources  [15]  despite many cross-feeding opportunities. Conversely, very distant bacteria avoid competition, i. e. , are adapted to colonize different niches, and their needs only overlap to a limited extent. The observed optimal phylogenetic distance in our results could be driven by the taxonomic composition of the dataset (mostly composed of Proteobacteria and Actinobacteria), and the composition is itself partially determined by the host plant  [78, 79]. More importantly, this optimum can be interpreted as a trade-off between metabolic similarity and dissimilarity. Optimum niche overlap would limit competition for resources among closely related species while being sufficient to promote exchanges of metabolites.
This observation can be interpreted as community-level functional redundancy  [59]  and as an insurance of the completion of metabolic processes for nutrition or interaction with host plants under a range of environmental constraints  [91]. These putative redundancies in metabolic completions could play a key role in maintaining stability under variable environmental constraints. Beyond these core metabolic functions, it would be interesting to extend the analysis of minimal communities to secondary, more specific metabolism. The software used, "Miscoto mincom"  [59]  , worked in a way such that only the most parsimonious solutions are calculated, the simplest combinations of GEMs that fit the applied constraints. However, these putative solutions do not mean more complex combinations do not exist to produce the TPPM.
Funding Open Access funding enabled and organized by Projekt DEAL. This work was supported by a grant from the French Ministry for Research and Innovation, by a grant from the CNRS (EC2CO), and also by a starting grant to S. Hacquard from the European Research Council (MICRORULES 758003), the Max Planck Institute, the Cluster of Excellence on Plant Sciences (CEPLAS), and the "Priority Programme: Deconstruction and Reconstruction of the Plant Microbiota (SPP DECRyPT 2125), " both funded by the Deutsche Forschungsgemeinschaf.
Regarding climate projections, the IPCC concluded in its 5th assessment report (AR5)  [7] , that "Future [human-induced] warming trends cannot be predicted precisely, especially at local scales". In the IPCC AR6  [2] , a new generation of climate models  [8]  has been used to provide a range of projections in response to different socio-economic scenarios  [9]. Based on this new dataset, various studies have recently shown that uncertainty in global mean warming can be considerably reduced by using the information provided by recent observed warming trends via so-called "constraint" methods  [10, 11, 12, 13]. These studies consistently point towards a downward revision of the expected warming in all emission scenarios  [12, 10] , with a decrease in model uncertainty of nearly 40% for end of century projections  [11] , and even more at shorter lead times.
5 • C by 2100, with a reduction in model uncertainty of nearly 40%  [11]. Figure  1a  offers an update of this result using GMST instead of GSAT (see Methods for the difference between GSAT and GMST): the warming of 5. 3 • C projected by CMIP6 is in this case revised downward by 0. 6 • C. Differences with Ribes et al. [11]  are explained by the addition of several CMIP6 models, which affects the prior, and by the lower warming observed in GMST compared to GSAT  [24, 25]. In the following, we consider the GMST metric to be consistent with the local observation dataset used (see Data).
Observational dataset and models The temperature observations are from the Cowtan and Way  [26]  (hereafter CW) dataset which complements the HadCRUT4  [42]  data by filling in the missing data over the 1850-2019 period. The temperature field comes from a blending of nearsurface temperature and sea surface temperature using land sea mask and sea ice concentration. The measurement uncertainty of the CW dataset is estimated from a ensemble of 100 equiprobable realisations. Most of the other observational products are included in the temperature range estimated by this ensemble, which confirms our choice to consider the CW dataset as a reference. CMIP6 models are selected according to the availability of the following data: at least 200 years of pre-industrial control simulation; at least one member of a historical simulation and one member of a projection simulation for the SSP5-8.
Modelling of observational uncertainty Given equation 4, we assume that ǫ ∼ N (0, Σ obs ), where Σ obs = Σ meas. + Σ iv is the observation error covariance matrix. Σ meas. and Σ iv are both of size 2n y × 2n y and describe the measurement error and internal variability, respectively. Σ meas. is estimated as the sample covariance matrix over the 100-member ensemble of the CW dataset  [26]. We define Σ iv as a matrix of size 2n y × 2n y of the following form: Σ iv =    Σ iv,loc Σ iv,dep Σ ′ iv,dep Σ iv,glo    , (8) where Σ iv,loc and Σ iv,glo are the covariance matrices of size n y ×n y modelling local and global internal variability within y loc and y glo , respectively. Σ iv,dep is the covariance matrix modelling the dependence between global and local temperature. To compute Σ iv , we take into account decadal internal variability that exists in the global  [51] , regional  [52] , and even local  [53]  observations, by using a mixture of two autoregressive processes or order 1 (AR1), hereafter MAR, as done by Ribes et al.
Acknowledgements This work was supported by the  European Union 's  Horizon 2020 Research and Innovation Programme in the framework of the   EUCP project  (Grant Agreement  776613 ) and  Météo-France. The authors thank  Hervé Douville  for fruitful discussions about this work. The authors thank the climate modeling groups involved in CMIP6 exercises for producing and making available their simulations. The authors thank  Kevin Cowtan  for providing the blended temperature data, and the  ETH Zurich  for providing CMIP6 data through their cmip6-ng interface (http: //dx. doi. org/10. 5281/zenodo. 3734128). The analyses and figures were produced with the R software (https://www. R-project. org/) and the  NCAR  Command.
Demographic and clinical characteristics of study participants. A total of 2213 samples were availed for WGS, 1987 samples were processed for WGS, and 1573 samples made it to lineage assignment (Supplementary Table  1 ). From the 1573 samples, 31% (496/1573) had frameshift mutations on several viral proteins such as the ORF7a/b, ORF8, ORF6, ORF1a/b, E, S, N, and ORF3a. Sixty-nine percent (1077/1573) of these sequences have been submitted to the GISAID and European Nucleotide Archive (ENA) (Supplementary Data 1), and are analysed herein alongside 46 genomes from Ngoi et al. ; the rest (496/1573) are under investigation before submission to GenBank/GISAID. Thus, a total of 1123 COVID-19 PCR positive samples from ten regions in Ghana between March 2020 and September 2021 were successfully sequenced.
From our dataset, B. 1. 1. 318 appeared to replace Alpha as the predominant lineage in community infections in March 2021 and remained dominant until June 2021, when it was replaced by Delta lineages. The combination of spike substitutions T95I, E484K, D614G, P681H, and D796H in B. 1. 1. 318 may have allowed it to escape Alpha-induced immunity in the Ghanaian population and replace the Alpha variant. This analysis further solidifies the evidence for E484K, L5F, A27S and D614G as spike substitutions that increase infectiousness with minimal impacts on severity  14. Our dataset showed that Delta lineages dominated in Ghana from June 2021, and remained dominant as at September 2021. The worldwide dominance of Delta variants  15, 16  has been linked to the P681H/R substitutions 3 as well as additional mutations in the viral RNA-dependent RNA-polymerase coding sequence.
The purified barcoded library was quantified using the Qubit TM DNA HS Assay Kit (Thermo Fisher Scientific TM , USA) with about 75 ng of barcoded libraries ligated to the AMII sequencing adaptors (Oxford Nanopore Technologies, UK) using the Quick ligation kit (New England Biolabs, UK). The adaptor-ligated library was finally purified and quantified using Ampure XP beads (Beckman Coulter) and Qubit TM DNA HS Assay Kit (Thermo Fisher Scientific TM , USA) respectively. About 20 ng of the purified adaptor-ligated libraries were loaded on an R9. 4. 1 flow cell (FLO-MIN106). The sequencing was carried out using a MinION Mk1b or the Mk1c device (Oxford Nanopore Technologies, UK). Our previously published data from March to May 2020 were included in the study to help provide continuity to SARS-Cov-2 genomic epidemiology in Ghana  9.
Methods Generation of SARS-CoV-2 genomes. Base-calling and demultiplexing of MinION Fast5 files were performed using Guppy (from version 3. 4. 3-5. 0. 7) according to the ARTIC bioinformatic protocols  23. Sequencing QC was asessed using pycoQC, and demultiplexed reads were aggregated and length filtered using ARTIC guppyplex for a minimum of 400 reads and a maximum of 700 reads to remove chimeric reads. Read QC was assessed using NanoPlot before read alignment, variant calling and consensus generations using ARTIC MinION software (ARTIC version 1. 2. 1). Alignment metrics, amplicon coverage analysis, variant annotation, and consensus assessment were performed using samtools, mosdepth, BCFtools, SnpEFF, and Quast according to the nfcore/viralrecon pipeline (version 2. 2)  24, 25. Variant annotation, validation and quality assessment of the consensus sequence were performed using Viral Annotation DefineR (version 1.