dataesr/scientific-paragraphs-categorization huggingface dataset:
4A  and Fig. S10 ). A low detection of Archaea on seeds was observed with 51 ASVs that represented only 0. 1% of the reads (16S rRNA gene-V4 dataset), but it should be noted that the primers targeting the 16S rRNA gene V4 region are not adapted to accurately capture archaeal diversity (Taffner et al. 2020). For Fungi, ASVs were distributed across 10 phyla with two highly dominant phyla: Ascomycota (79% of reads, 61% of ASVs) and Basidiomycota (20% of reads, 31% of ASVs) that collectively represented 99. 77% of the reads in the dataset and 92% of the ASV richness (7458 ASVs, Fig. 4B ). For each ASV, we calculated the number of plant species in which they were detected to identify highly prevalent taxa that could be considered as seed core taxa (Fig. 4 ). The vast majority of ASVs were detected in only one plant species (64% for prokaryotic ASVs and 58% for fungal ASVs), were generally detected in a few samples and not abundant (i.
c) Identification of a seed core microbiota across all plant species Next, we characterized the taxonomy, prevalence and relative abundance of each seed core ASV across all samples (Fig. 5 ). In the 16S-V4 dataset, the 13 core bacterial ASVs were predominantly Proteobacteria (n=11, Fig. 5A ). One core bacterial ASV affiliated to Pantoea was highly dominant (16. 9% of all reads) and was detected in 68. 5% of samples collected from 27 plant species (Fig. 5A ). The other core bacterial ASVs were affiliated to Pseudomonas (n=5), Rhizobium (n=2), Sphingomonas, Methylobacterium and Paenibacillus. The 16S rRNA gene markers permitted to obtain a taxonomic affiliation only at the genus-level but the gyrB marker enabled the refinement of the taxonomy of core taxa at the species level. For the gyrB dataset, we identified 18 bacterial ASVs present in more than 12 plant species (lower plant diversity available than in the 16S rRNA gene dataset, Fig.
5B , Fig. 1B ). Therefore, the dominant Pantoea ASV was affiliated to the P. agglomerans species and was composed of three distinct ASVs, representing different populations. Interestingly, one of the three P. agglomerans ASVs was extremely dominant with a prevalence of 88% and a relative abundance of 24% (Fig. 5B ). The gyrB dataset also indicated that Pseudomonas core ASVs were affiliated to four distinct species: Pseudomonas viridiflava (3 ASVs) and three species belonging to the P. fluorescens subgroup 49 : P. fluorescens (3 ASVs), P. poae and P. antarctica. Additional core taxa were identified in the gyrB dataset that were not detected in the 16S dataset, such as ASVs affiliated to Cutibacterium acnes, Erwinia persicina and Stenotrophomonas sp. (Fig. 5A, 5B ). In both 16S and gyrB datasets, we observed that core ASVs affiliated to Rhizobium were present in a high number of samples but at a low abundance (except in rice).
In the ITS1 dataset, we identified 16 core fungal ASVs affiliated to the Dothideomycetes (9 ASVs), Tremellomycetes (4 ASVs), Microbotryomycetes (1 ASV), Sordariomycetes (1 ASV) and Leotiomycetes (1 ASV) classes (Fig. 5C ). The core fungal community was dominated by four extremely prevalent ASVs (>77% of samples, presence in >28 plant species) with a Cladosporium perangustum ASV being the most abundant (12% of reads), followed by an Alternaria metachromatica ASV (8. 2%), an unclassified Capnodiales ASV (7. 4%) and an Alternaria sp (4. 5%). The other core fungal ASVs were affiliated to diverse genera: Filobasidium, Vishniacozyma, Sporobolomycetes, Epicoccum, Aureobasidium, Gibberella (Fig. 5C ). The full list of core and flexible taxa across all plant species is available in Supplementary file 2.
Our results suggest that the seed core microbiota is less variable on the fungal side than on the bacterial side. Based on the threshold of a detection in 20 plant species, the core fungal ASVs presented a highest prevalence (range 49-85% for fungi and 18-69% for 16S Bacteria) and were detected in a higher number of plant species (31 out of 32 for fungi and 27 out of 43 for 16S Bacteria). The higher variability in the distribution of core bacterial ASVs can be visualized in the ridge plots representing the relative abundance of each ASV in all the samples of the dataset (Fig. 5A vs 5C ).
Part 2: Specific patterns associated to each plant species a) Plant-specific patterns in community composition To explore the influence of plant species on seed microbiota composition, we performed ordinations based on Bray-Curtis dissimilarities between samples. On the 16S-V4 dataset, plant species were not a significant driver of seed prokaryotic community composition (Fig. 6A ). Indeed, high variation in community composition was observed for seeds harvested from the same plant species (e. g. bean or rapeseed). In contrast, a clustering of seed microbiota per plant species was observed with ITS1. For instance, a separation between bean and rapeseed samples can be observed along the PCoA axis 1 and these two species are separated from radish samples along the PCoA axis 2. Still in many cases, an important overlap was observed between samples from different plant species, suggesting that they share similar community composition.
For the 16S-V4 and gyrB datasets, we also performed metagenome predictions based on KEGG orthologs (KOs) using PICRUSt2 to determine if we could observe a structuring of seed microbiota based on their putative functional profiles. These metagenome predictions confirmed the limited effect of plant species on seed microbial community composition data with no clear clustering of samples by plant species and a large variability in functional profiles between samples (Fig. S11 ). We next explored if specific patterns at the phylum or class levels existed by plant species (Fig. 6C, D ). At the bacterial phylum level, we observed that Proteobacteria were dominant in the majority of plant species, with the exception of several Brassicaceae species that had a high abundance of Firmicutes (e. g. Arabidopsis thaliana, Erophila verna, Fig.
b) Identification of a seed core and flexible microbiota specific to each plant species Next, we identified the seed core and flexible microbiota specific to some plant species for which a sufficient number of independent studies (n ≥ 3) and of samples (n ≥ 50) were available for a robust analysis. Only four plant species responded to these criteria for the 16S rRNA gene-V4 dataset (bean, radish, rapeseed and rice) and three plant species for the gyrB and ITS1 datasets (bean, radish, rapeseed). To identify the core taxa specific to these plant species, we used the arbitrary criteria that an ASV should be detected in a minimum of 2 independent studies and be present in at least half of the samples (minimum prevalence of 50%). Consistent with the core taxa analysis across all samples (Part 1, Fig.
4 ), we found that the core taxa identified were both extremely prevalent and abundant on seeds as this can be seen on abundance-occupancy curves (Fig. 7A, B, C ). The core bacterial microbiota identified with the 16S rRNA gene-V4 dataset was composed of a small number of ASVs ranging from 6 ASVs for bean to 15 for radish (Fig. 7D ). Still, these few ASVs collectively represented a large fraction of the entire bacterial community with a cumulative relative abundance of 28% (bean) to 85% (radish, Fig. 7D ). The four plant species shared a similar core microbiota affiliated to the Pantoea and Pseudomonas genera but some core ASVs were plant-specific, like Bacillus for bean, Sphingomonas for rapeseed and rice, or Rhizobium and Xanthomonas for rice and radish (Fig. 7G ). The core bacterial microbiota identified with the gyrB dataset presented similar patterns with the 16S dataset (Fig.
7B, E, H ). The higher taxonomic resolution of the gyrB marker enabled to identify at the species level the core ASVs (Fig. 7H ). The core bacterial microbiota was dominated by two Pantoea agglomerans ASVs and Erwinia persicina (not identified in 16S dataset) for the three plant species. Rapeseed and radish seeds possessed also two core ASVs affiliated to Pseudomonas viridiflava. Two Pseudomonas fluorescens core ASVs and a Serratia marcescens ASV were specific of rapeseed plants, while radish plants exhibited two specific Pantoea agglomerans ASVs and a Pseudomonas syringae ASV. The core fungal microbiota identified with the ITS1 dataset was generally more diverse than its bacterial counterpart, with 9 ASVs (bean) to 30 ASVs (rapeseed, Fig. 7C, F ). These core ASVs represented an important fraction of the fungal community with a cumulative relative abundance of 35% (bean) to 87% (rapeseed, Fig.
This meta-analysis provides new insights on seed microbiota diversity and composition but also highlights key knowledge gaps. This study gathered data on only 50 plant species (including 30 crops), with often only one study by plant species and few seed samples analyzed. This means that the seed microbiota of a majority of crops and natural plant communities have not been characterized. More investigations are also required to assess the influence of geographical sites and plant genotypes on seed microbiota. Additionally, this meta-analysis included mainly seed microbiota characterization from multiple seeds (>3 seeds in sample) with more than 1000 reads. More work is needed at the single seed level  37, 59, 60  to assess seed-to-seed variation and the proportion of seeds that do not have detectable microbiota (i.
Studies included in the Seed Microbiota Database We identified seed microbiota studies from our team and from the literature by performing keyword searches in Google Scholar and by following references in reviews and seed microbiota papers (end of search November 17th, 2020). We initially identified 100 seed microbiota studies (59 for 16S rRNA gene; 14 for gyrB gene and 27 for ITS region) that used amplicon sequencing (i. e metabarcoding), but we decided to conserve in the meta-analysis only studies using the Illumina sequencing technology that was the most common approach (90 datasets). This choice permitted us to use a similar bioinformatic pipeline for all studies and to not introduce biases in our interpretation due to sequencing technologies. Out of these 90 studies, only 63 studies were finally included in this meta-analysis based on the following criteria: i) availability of the raw sequence data on public repository (fastq files), ii) availability of metadata associated to each sample, iii) sequencing quality, and iv) the primer sets used.
For this last point, we focused on the primers or gene regions that were the most commonly studied in seed microbiota studies: for the 16S rRNA gene the V4 and V5-V6 regions and for ITS, the ITS1 and ITS2 regions. Most studies were downloaded from online repositories (e. g. ENA, NCBI-SRA) and some were obtained after personal communication with the authors. Detailed information on the 63 studies that constitute the Seed Microbiota Database can be found in Supplementary Data 1.
Bioinformatic analysis and preparation of the datasets and subsets Each study was independently re-processed with a standardized bioinformatic pipeline (code available at https://github. com/marie-simonin/Seed-Microbiota-Database) using Qiime 2 and DADA2  65, 66. In brief, primer sequences were removed with cutadapt 2. 7 and trimmed fastq files were processed with DADA2 version 1. 10, using variable truncation parameters depending on the sequencing quality of the study. If necessary, because of small differences in the primers used, a trimming parameter was added in DADA2 to have all studies targeting the exact same region of the V4, V5-V6, ITS1 or ITS2 regions. Chimeric sequences were identified and removed with the removeBimeraDenovo function of DADA2. ITS1 and ITS2 reads were processed similarly, except that only the forward reads were included in the analysis as recommended by Pauvert et al.
(2020)   67  to maximize fungal diversity detection. ASV taxonomic affiliations were performed with a scikit-learn multinomial naive Bayesian classifier implemented in Qiime 2 (qiime feature-classifier classify-sklearn  68  ). ASVs derived from 16S rRNA reads were classified with the SILVA 132 database 69 trained for the specific gene region (V4 or V5-V6). gyrB reads were classified with an in-house gyrB database (train_set_gyrB_v4. fa. gz) available upon request. ASVs derived from ITS1 and ITS2 reads were classified with the UNITE v8 fungal database  70. Unassigned ASVs or ASVs affiliated to mitochondria, chloroplasts or eukaryotes were removed from the 16S rRNA gene studies. Non-fungal ASVs were filtered from the ITS gene studies, and unassigned and parE ASVs (a gyrB paralog) were filtered from the gyrB gene studies.
After these filtering steps, samples with less than 1000 reads were excluded from the meta-analysis. For each study, two datasets were prepared:an unrarefied and a rarefied one. The rarefaction was performed at the lowest read number observed (min=1000 reads/sample, max=86108 reads/sample, Supplementary file 1). The next step was to merge all the studies targeting the same marker gene and region to form a final "dataset". This merging step between different studies was possible because all the ASVs obtained targeted the exact same region of the marker gene considered (see above). A total of 5 datasets (16S rRNA-V4, 16S rRNA-V5-V6, gyrB, ITS1 and ITS2) was included in the meta-analysis (Fig. 1B ). The unrarefied studies were merged into one dataset then further filtered to obtain a "clean database" (e.
g. Subset 1) by removing i) ASVs present in only one sample and with less than 20 reads in the entire dataset, ii) ASVs shorter than 200 bp, and iii) gyrB ASVs with a read length not compatible with codon triplets (multiple of 3). The rarefied studies were merged into one dataset (Subset 2) to look at study-specific patterns in seed microbiota diversity. To investigate community structure and composition patterns across all studies, an additional Subset was prepared by performing a global rarefaction across all samples (Subset 3, rarefaction at 1000 to 1056 reads/sample depending on the dataset, Table  S1 ).
Community and Taxa-level analyses Diversity and community structure analyses were performed in R 3. 6. 2 using the phyloseq (v1. 28. 0), vegan (v2. 5-7) and microbiome (v1. 7. 21) packages  [71] [72] [73]. The code and files to reproduce the analyses and figures are available at https://github. com/marie-simonin/Seed-Microbiota-Database. Seed microbiota diversity across all studies was explored using ASV richness, Shannon and Pielou's evenness indexes. Changes in seed microbial community composition were assessed using Bray-Curtis dissimilarity and principal coordinate analysis (PCoA) was used to plot the ordination. Seed core and flexible microbiota across all plant species or specific to some plant species were identified. For the analyses across all plant species, we used the arbitrary criteria that ASVs detected in at least 20 plant species would be considered as core taxa, as they are prevalent in seed microbiota from diverse plants and countries.
For the analyses by plant species, we focused on plants for which a sufficient number of independent studies (minimum 3 studies) and samples (minimum 50 samples) was available for a robust analysis. Four plant species responded to these criteria for the 16S rRNA gene-V4 dataset (bean, radish, rapeseed and rice) and three plant species for the gyrB and ITS1 datasets (bean, radish, rapeseed). To identify the core ASVs specific to these plant species, we used the arbitrary criteria that an ASV should be detected in a minimum of 2 independent studies and be present in at least half of the samples (minimum prevalence of 50%). For the 16S-V4 and gyrB datasets (using Subset 1), we used PICRUSt2 74 to predict metagenome functions of seed microbiota from metabarcoding data. All figures were prepared using the ggplot2 (v3.
75  and ggpubr (v0. 4. 0) packages and the data management was done using the dplyr (v1. 0. 4) and tidyverse (v1. 3. 0) packages in R and in DB Browser for SQLite (v3. 12. 2). Pays de laLoire", supported by the French Region Pays de la Loire, Angers Loire Métropole and the European Regional Development Fund. The Seed Microbiome project (Barret_M_2020 study) was funded by USDA 2019-67019-29305, the Brassica Div Patho project was funded by the P10037 INRAE -Metaomics and Ecosystems Metaprogram, Plant Health division (Mougel_C_2017 study). The RhizoSeed project (Sarniguet_A_2016 study) was funded by the INRAE -Metaomics and Ecosystems Metaprogram, Plant Health division.
ACKNOWLEDGEMENTS We are deeply grateful to the authors whose data contributed to this database and metaanalysis. Without their efforts to make their datasets available with good metadata, this metaanalysis would not have been possible. We thank all our collaborators that contributed unpublished datasets to this study, in particular  Josiane Le Corff ,  Muriel Marchi  and  Marie-Anne Le Moigne  for the  Endowild  project ( Le Corff_J_2018 study ),  Simon Goertz  ( Goertz_S_2020 study ),  Christophe Mougel  for the  Brassica Div Patho project (Mougel_C_2017 study ) and  Brice Marolleaux  for the  Seed Microbiome project (Barret_M_2020 study ).
DATA AVAILABILITY The Seed Microbiota Database is available on the Data INRAE portal (https://doi. org/10. 15454/2ANNJM). The database includes all the metadata tables, ASV tables, taxonomy tables, fasta files and phylogenetic trees associated to the 5 datasets corresponding to the different molecular markers. Two scripts are available to query the database: 1) the "getASVInfosInDb. pl" to search the database for a specific ASV using its sequence; 2) the "getASVFoundOnSpecies. pl" to get the list of all ASVs associated with a plant species. More details are available on the README file in the repository. The code and files to reproduce the bioinformatic analyses and figures are available at https://github. com/marie-simonin/Seed-Microbiota-Database. The list of accession numbers (e. g. NCBI-SRA, ENA) to download the raw data of the studies included in the database is available in the Supplementary file 1 -Study description.
3. 3 ) 33 75  and ggpubr (v0. 4. 0) packages and the data management was done using the dplyr (v1. 0. 4) and tidyverse (v1. 3. 0) packages in R and in DB Browser for SQLite (v3. 12. 2). Pays de laLoire", supported by the French Region Pays de la Loire, Angers Loire Métropole and the European Regional Development Fund. The Seed Microbiome project (Barret_M_2020 study) was funded by USDA 2019-67019-29305, the Brassica Div Patho project was funded by the P10037 INRAE -Metaomics and Ecosystems Metaprogram, Plant Health division (Mougel_C_2017 study). The RhizoSeed project (Sarniguet_A_2016 study) was funded by the INRAE -Metaomics and Ecosystems Metaprogram, Plant Health division.
Data Collection Between March 2019 and April 2020, we conducted questionnaires in Portuguese among health professionals FIGURE 1 | Criteria used by health professionals to classify a dog as "suspect" of rabies or "rabid. " Reported criteria used by health professionals to (A) define a "rabid" and "suspect" dog, and (B) distinguish between a "suspect" and "rabid" dog. The y-axis represents the percentage of professionals that included that criterion (out of 147 respondents). Respondents could mention more than one criteria. responsible for filling the SINAN form and prescribing PEP including doctors, nurses and technicians in public health centers of the states of RS, SC and RN. In each state, we first obtained a list of all public health centers in each municipality administering PEP and completing the SINAN form from the state's health secretary.
We interviewed all available professionals until the 1-month period was completed. Data were entered using the free open source KoBoToolbox software (https://www. kobotoolbox. org/) on a mobile phone. All participants were given a document describing the study objectives and a data confidentiality statement before confirming their voluntary enrolment in the study. Professionals were given the team's contact information for further inquiries. Our questionnaire included 45 questions divided into 4 sections. Section 1 included 13 questions on the criteria used to assess the dog's health condition (6 questions), the 10-day observation period (4 questions), bite severity (1 question) and the professional's trust in correctly assessing the dog's health condition (2 questions). Section 2 included 12 questions on PEP administration, with four questions related to specific simulated scenarios of dog bites where the professional was asked to choose the appropriate PEP according to the Ministry of Health guidelines  (20).
Data Analyses We built a PEP Knowledge Score (KS) using four questions aiming to assess whether the health professional could identify the appropriate PEP according to the Ministry of Health and state guidelines under four different bite patient scenarios, which were discussed with professionals from the health secretary of RS. The PEP KS ranged from 0 (four incorrect answers) to 4 (four correct answers). The four questions related to hypothetical scenarios are given in Supplementary Table  S1  (questions  [15] [16] [17] [18]. We then tested the correlation between the PEP KS and several variables obtained during the questionnaires including the state ID, whether the professional was in a high or low notification health center, the number of years working in that center, if he/she had received PEP training, the number of colleagues at the center that can complete the SINAN form or administer PEP, and the professional's level of confidence in PEP decisions.
ACKNOWLEDGMENTS We would like to thank the  Ministry of Health of Brazil  and all collaborators from the  Health Departments of Rio Grande do Sul ,  Santa Catarina  and  Rio Grande do Norte  that facilitated data collection and logistics. We thank all health practitioners that participated in this study. This work belongs to the research project entitled 'Piloting a One health surveillance approach to support elimination of rabies from Brazil' , a collaboration between  UNESP-Botucatu , and the  University of Glasgow. We thank  FUNDUNESP  for financial support.
Collectively the transient absorption data show that the steric effects imparted by methylation have a slightly greater deactivating influence on the  3  MLCT state lifetime (i. e. 1 compared to 2/3 versus 1 compared to 4/6) but that this is largely comparable to the electronic effect of replacing pyridine by triazole donors. Photochemical reactivity. The photochemical ligand release reactivity of the complexes was investigated by UV-visible absorption spectroscopy in proteo-acetonitrile and also by  1  H NMR spectroscopy in d3-acetonitrile for photoproduct identification. For NMR experiments, photolysis was conducted using irradiation with the mercury emission lines from a 23 W fluorescent light bulb, whilst for optical spectroscopic experiments a blue LED with an emission maximum at 446 nm was used.
Genetic analysis The PSAP coding regions were fully sequenced using Molecular Inversion Probes (MIPs) as previously described  [15, 20]. A detailed description of the MIPs library and protocols is available online (https://github. com/gan-orlab/MIP_protocol). Variant annotation was performed with ANNOVAR  [21]. The frequency of each variant was extracted from the Genome Aggregation Database (gnomAD)  [22]. Post-alignment quality control and variant calling were done using the Genome Analysis Toolkit (GATK, v3. 8)  [23]  as previously described  [24]. Full code is available at https://github. com/gan-orlab/MIPVar/.