dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental data The ambient average daily temperate and total daily rainfall readings collected over the sampling schedules of Donor 1 and Donor 2 are presented in Fig. 1. In addition to the weather data, the average daily temperature reading for each data logger probe is also shown in Fig. 1  (i. e. two probes per donor -one above and one below the individual). The trial using Donor 1 (Control) lasted from August 12th, 2020 (Day 0) until November 22th, 2020 (Day 102). The temperature recorded on the surface of the donor (data logger 1) began to increase after Day 21 and spiked at 28 °C on September 4th, 2020 (Day 23). This peak was due to the heat produced from increased larval aggregations on the remains  [38]  in addition to the rising temperature of the environment. The temperature recorded under the remains (data logger 2) experienced a delayed increase with the environmental temperature.
The highest temperature recorded by both data loggers was on October 5th, 2020 (Day 54) due to an increase in environmental temperature, with a reading of 32 °C on the surface of the remains and 30 °C underneath. The average daily temperature was 24 °C. The lasting disparity between the environmental temperature and the temperatures recorded by the data loggers is surprising, as maggot activity causing an increase in the temperature of the remains was recorded as ceased on Day 40. This indicates that the body was retaining the heat produced by the larval masses long after the activity had stopped. The readings for Donor 1 on both data loggers stop on Day 54 due battery failure of the HOBO MX2302. The trial using Donor 2 (Lime) lasted from October 2nd, 2020 (Day 0) until January 11th, 2021 (Day 101).
After the addition of hydrated lime the temperature recorded above (data logger 1) and below the remains (data logger 2) decreased to 15 °C, which was much lower than the average daily temperature of 20 °C. This was due to the lime prohibiting sun exposure and insulating the remains. Over the course of the trial, the surface of the remains experienced the same daily fluctuations as the environment, which was observed to a lesser extent underneath the remains. The temperature fluctuations recorded above and beneath Donor 1 and Donor 2 varied significantly. Donor 2 (Lime) did not experience a large increase in temperature during active decomposition, as there were not larval masses present to create excessive heat. Thus, the temperatures recorded both above and below the donor remained similar to the environmental temperature throughout the trial.
Acknowledgements We are indebted to all the donors involved in research at AFTER and to the invaluable contribution they have made to forensic science. We are also grateful to the students and staff from the  University of Technology Sydney (UTS)  and from the  University of Wollongong (UOW)  for their assistance at AFTER. Many thanks to  Sandali Alahakone ,  Amber Brown ,  Sharni Collins ,  Blake Dawson ,  Alisha Deo ,  Anya Graubard ,  Barbara Stuart  and  James Wallman  for assisting in collecting and running samples, and their valuable input throughout the project. Dr Schotsmans ' research was funded by the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  794891 ) and subsequently by the  Australian Research Council   DECRA fellowship  ( DE210101384 ).
Abundance and distribution of defensomes in bacterial MAGs We performed a defensome mapping across a large dataset of 7,759 high-quality (≥ 90% completeness, ≤ 5% contamination/redundancy, see Methods) soil, marine, and human gut MAGs  [23] [24] [25]  (Fig. 1a , Supplementary Tables  1 2 3 4 , Supplementary Fig. 1 ). For this purpose, we used the DefenseFinder pipeline  12  , which relies on a comprehensive collection of hidden Markov models (HMM) protein families and genetic organization rules targeting all major defense system families described in the literature (Fig. 1b ). Fig. 1. Defensome analysis. (a) Our analyses focused on 7,759 high-quality near-complete MAGs recovered from three distinct ecosystems: soil, marine, and human gut  [23] [24] [25]. The geographical distribution of sample collection sites is shown in the world map, as well as the percentage of gut MAGs recovered from each country (shown as colored heatmap).
(b) A collection of 1,024 HMM profiles targeting 127 families of anti-phage defense systems from DefenseFinder, was used to query the entire MAG dataset. Briefly, this was performed by means of genetic organization rules allowing for two types of genetic components: "mandatory" and "accessory" (as described previously  12  ). Given the wide diversity of genetic organization of anti-phage systems, rules were written differently for different types of systems. Shown is an example of a genomic region containing three defense systems (DS1-DS3), respectively characterized by a total sum of genes (SG), a maximum distance between defense genes (dmax), and a given number of mandatory genes (MG), which will allow disentangling between complete or incomplete defense systems based on well-established thresholds (a, d, b).
In this study we found 43,263 defense systems and 764,507 defense genes pertaining to a total of 70 defense families across our full MAG dataset (Supplementary Tables  3, 4 ). The relative distribution of defense systems differed considerably across environments, with R-M, Cas and the SoFIC AMPylase being the most predominant (Fig. 2a ). When the distribution of total defense genes was represented instead, we observed multiple solitary genes / incomplete systems (e. g. , Gabija, Gao and Dodola) consistently present across most MAGs (Supplementary Fig. 2 a-c ), which suggests possible non-defensive roles. While defense system distribution across human and soil MAGs followed a typical binomial distribution (with most genomes encoding between 2-3 defense systems), that observed in marine. CC-BY-NC-ND 4.
2d , Supplementary Fig. 4 ; Supplementary Tables  3, 5 ). No MAG was entirely devoid of defense genes, with maximum densities (per MAG and per kb) ~8. 5×10 -3 across the different biomes (Fig. 2d , Supplementary Fig. 4a ; Supplementary Table  3 ). Apart from MAG size, the abundance of defense genes was expected to depend on phylogenetic depth, as deeper lineages accumulate more events of HGT exchanges presumably leading to defensome buildup. We ran stepwise linear regression analyses to assess the role of these variables in explaining the variance of the defensome (Supplementary Table  6 ). These showed that MAG size had the strongest direct effect on defensome abundance, and that phylogenetic depth had a significant but less important explanatory role. We found in our dataset multiple WYL and CARD-like domain-containing genes (Supplementary Figs.
4b-d ) which have been shown to code for transcription regulators of phage defense systems, namely BREX, CRISPR-Cas, CBASS and gasdermins  [28] [29] [30] [31]. Interestingly, we found here a significant colocalization between these domains and multiple defense genes belonging to additional families involved in regulated cell death, such as Lamassu, RosmerTA, and Rst_PARIS. Very few WYL and CARD-like domains were found in genes from marine MAGs (< 0. 75% of the dataset), in agreement with the latter's more limited defensome. The patterns of colocalization differed across soil and human gut environments (Supplementary Figs. 4c-d ). For example, WYL preferentially colocalized with CBASS and RosmerTA, respectively in soil and human gut environments. We also found in the Bacteroidetes bacterium UBA1952, instances of an operon with some similarity to the recently described Pedobacter rhizosphaerae CARD-encoding defense system  31.
This prompted us to examine how the defensome's abundance and diversity correlated with bacterial biogeography. The top five most represented Classes in our dataset for each environment are Gammaproteobacteria (soil, marine, human gut), Alphaproteobacteria (soil, marine), Dehalococcoidia (marine), Bacteroidia (soil, human gut), and Clostridia (human gut) (Supplementary Table  2 ). Such different patterns in species richness, and relative phylogenetic diversity across environments, necessarily impact genetic flux and concomitantly, defensome profiles. In soil environments, the highest and lowest densities of defense systems were respectively observed in MAGs recovered from serpentine-hosted ecosystems and contaminated or regular soils (Fig. 3a ). These observations are consistent with the fact that serpentine environments are among the most challenging niches on Earth, characterized by low cellular abundances, limited microbial diversity, high VPRs  34, 35  , and consequently, the likely need for additional anti-phage systems.
2a ). While R-Ms (and to a lesser extent Cas) are largely ubiquitous, our results are supportive of a 'second line' of defense systems (SoFIC, CBASS, Rst_Paris, etc) that is also mostly non-species-specific, differentially favored across distinct environments, and privileged by distinctive strategies of genetic mobility (Fig. 4c , see below). As we move down the ladder of defense system abundance, we face an increasing variety of cryptic, presumably highly specialized, and more species / population-specific systems. By further splitting our dataset into sub-environments or by geographic location, we observed significant differences in defense system abundance (Fig. 3 ). And while the increased densities observed at serpentine systems and across the arctic ocean can be explained by the extreme conditions experienced at such environments and a subsequent phage-bacteria imbalance, the more subtle variations in defense system density in human gut MAGs across multiple countries and the panoply of confounding variables associated, preclude the identification of more explanatory scenarios.
When compared across environments, defense islands did not show significant differences in terms of size, relative abundances of major defense families, or at the topmost abundant COG functional categories for genes classified as 'non-defensive' (Fig. 5a, c, d ). While many of these genes seem to encode factors involved in genetic mobility, others have hitherto unknown functions. In this line, an interesting next step would be to build upon our precise delimitation of defense islands in such a large and phylogenetically diverse MAG dataset and use a previously developed colocalization framework 3 to leverage the identification of novel defense systems. A significant overrepresentation of several defense families (e. g. , Hachiman, R-M, Thoeris) was observed in defense islands (compared to nonisland ones).
The first, concerns the imbalance in our dataset between the number of samples recovered from each biome, as well as their geographic distribution. While the number of soil and marine MAGs analyzed was essentially the same, human gut MAGs were roughly one order of magnitude greater. From the geographic standpoint, marine samples have a global representation, but soil and human gut microbiome data are greatly biased towards the US and China. These observations highlight a critical need for thorough geographic sampling, more global representation of participants in microbiome studies, and a fairer access to genomics resources especially in resource-poor countries. The second confounding variable, likely more relevant, concerns the fact that MAG binning methods using short reads tend to miss certain low-abundance or difficult to resolve MGE families.
The fact that defense genes are often carried or colocalize with MGEs, necessarily indicates that our results i) may have a bias in the ratio of defense genes inside versus outside the mobilome, and ii) are most likely a partial underestimated picture of the real defensome abundance. Future inclusion of long-read data will enable reference-quality genome reconstruction from metagenomes, and further improve our observations. Still, the stringent dataset filtering used in our study in terms of MAG completeness and N50 (with associated controls shown in Supplementary Fig. 1 ), together with previous demonstration on the accuracy of MAG size estimates (that are part of our dataset) compared with reference genomes  25  , makes us have good reasons to think that our analyses constitute a reasonable proxy of the defense landscape diversity carried by such populations, and of the complex interplay underlying their interactions at the intra-and inter-environment level.
Data In this study we built upon a large dataset of 7,759 high-quality soil, marine, and human gut MAGs  [23] [24] [25]  (Supplementary Table  1 ). These MAGs were filtered based on the Minimum Information about a Metagenome Assembled Genome (MIMAG) standard (≥ 90% completeness, ≤ 5% contamination/redundancy, ≥ 18/20 tRNA genes and presence of at least one class of 5S, 16S and 23S rRNA genes). When not clearly stated in the original study, we performed identification of rRNA genes using both Infernal 50 v1. 1. 4 (options: -Z 1000 -hmmonly --cut_ga --noali -tblout) and RNAmmer 51 v1. 2 (options: -S bac -m tau,ssu,lsu -h -fgff) (Supplementary Table  3 ). Since defense systems are often i) multigenic and ii) clustered in defense islands, we further selected for highly contiguous MAGs to more accurately reflect the defensome abundance and distribution.
2. 1. 9 (default parameters). To test for colocalization of defense families in defense islands, we computed their odds ratio and associated Fisher's exact test P value. For this purpose, we considered all colocalized defense genes distanced of 5 genes or less both inside and outside defense islands. Genes belonging to the same defense system are necessarily colocalized, so we deliberately eliminated such hits to avoid inflating true same-system colocalization frequencies. To determine the presence of putative defense system regulators harboring WYL or CAspase Recruitement Domains (CARD), all MAG proteomes were scanned against the Pfam-HMMs PF13280 (WYL) and PF00619 (CARD) using HMMER3  56  and a cut-off e-value of 0. 01.
Statistical and graphical analyses of data All statistical and graphical analyses were conducted using R v. 4. 3. 1. Geographical representation of metagenome sampling locations was generated using the mapdata package. Visualization of genomic contexts was performed with the package gggenes. Colocalizations heatmaps were created using the ComplexHeatmap package. Stepwise linear regression analyses were performed by using the step function from the stats package. given defense family by the fraction of defense genes of that family colocalized with a given domain. P values correspond to the χ 2 -test. To avoid performing analyses with weak statistical power, we omitted the marine MAG dataset due to their low CARD and WYL abundance (detected in as much 0. 75% of the dataset).
Acknowledgements This work was supported by the  Genoscope , the  Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA)  and  France Génomique  ( ANR-10-INBS-09-08 ). We thank  Eduardo P. C. Rocha  ( Institut Pasteur, Paris ) for critical reading of the manuscript, and  Hadrien Guichard  ( CEA, Genoscope ) for initial efforts in defensome analyses.
RNA was sent to SeqMatic (Fremont, CA) for DNase treatment, ribodepletion, and cDNA library construction. Libraries were sequenced on a NextSeq500 Illumina platform with 75 bp single end reads. A total of 17,472,800 raw reads were obtained and trimmed using CLC v20. 0. 3 (Qiagen). Post-trimmed reads were assembled into 7,603 contigs using CLC and were aligned using BLASTx against a reference virus database. Three contigs had nucleotide similarity to banana mild mosaic virus, one to garlic virus H, and one to cassava alfa exivirus. The contigs overlapped into a scaffold of 7,051 bp. A total of 8,436 reads were mapped against the ve contigs. The 5' terminus was determined using the Invitrogen 5' RACE System kit (Waltham, MA). The 3' end was obtained by using oligo dT cDNA synthesis followed by PCR and Sanger sequencing of amplicons in both directions.
After nishing the complete genome, 8,630 reads mapped to the complete genome. The amino acid (aa) open reading frames (ORFs) of the replicase (RNA-dependent RNA polymerase or RdRp) and the coat protein (CP) sequences of representative viruses in Alpha exiviridae and Beta exiviridae were retrieved from NCBI GenBank for comparative analyses. A multiple sequence alignment of the replicase (aa), and the CP (aa) were conducted using MAFFT v. 7. 450 with the FFT-NS-2 algorithm and JTT200 (aa) or 200PAM (nt) scoring matrix with a gap open penalty of 1. 53 and offset value of 0. 123. The best t model was determined using ModelTest-NG v0. 1. 7 for both nucleotide and amino acid alignments. Phylogenetic trees were constructed using RAxML-NG v. 1. 1. 0 for the RdRp (aa) and CP (aa), using the LG +G4 +I and VT +G, models, respectively, with 1,000 bootstrap replicates.
The complete genome of the virus detected in the Miscanthus is 7,388 nt excluding the poly-A tail. The 5' and 3' untranslated regions were 69 and 153, respectively. This virus is tentatively named Miscanthus virus M (MiVM), and its complete sequence was submitted to the NCBI database under GenBank accession no. ON986335. 1. Five ORFs were predicted using NCBI's ORF Finder (https://www. ncbi. nlm. nih. gov/or nder/) and the domains were identi ed using NCBI's Conserved Domain Search (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi) (Fig. 1A ). ORF1 encodes a 1,773 aa (M r : 204 kDa) viral replicase polyprotein. Five putative domains were identi ed in the replicase: a viral (superfamily 1) methyltransferase (aa residues 44 to 355), ovarian tumor (OTU)-like cysteine protease (aa residues 671-778), carlavirus endopeptidase (aa residues 778-863), viral (superfamily 1) RNA helicase (aa residues 949-1190), and RdRp (aa residues 1386-1700).
, 2009) ; <http://broadinstitute. github. io/picard/>; to remove optical duplicates, secondary alignments, reads with a mapping quality below 30 and to keep only pairs in which both reads met quality checks. SNP calling was done with Freebayes v0. 9 using the option --report-monomorphic  (Garrison & Marth, 2012 ) and all positions detected as low complexity regions or TEs were excluded. Low complexity regions and TEs were detected using RepeatMasker with default settings (RepeatMasker Open-4. 0 <http://www. repeatmasker. org>) and REPET  (Flutre et al. , 2011) , respectively. Quality filters were applied to VCF files using in-house scripts. Depth of coverage (DP) calculated at each position followed a normal distribution around the depth main peak. Hence, a DP filter was set at twice the standard deviation of the mean.
In case more than one gene is assigned to the candidate region, we picked the closest one from the lead SNP having pathogenicity-like properties (e. g. secretion signal, differential expression in planta). If no gene fell in the LD region, we selected the closest gene to the lead SNP. LD heatmaps were constructed using Haploview v4. 2 (J. C. Barrett et al. , 2005). Functional annotation. We inferred gene models from the ab initio Z. tritici annotation  (Grandaubert et al. , 2015)  in the candidate genomic regions identified by GWAS. We used RNAseq datasets of the reference isolate IPO323  (Rudd et al. , 2015)  to check for missing genes or manually refined gene models (e. g. splice junctions, missing exons). N-terminal secretion signals were predicted by using the Hidden Markov Model (HMM) scoring method implemented in SignalP 3.
0  (Dyrløv Bendtsen et al. , 2004). Transmembrane domains were identified with TMHMM 2. 0  (Krogh et al. , 2001). Effector probabilities were predicted through EffectorP 3. 0  (Sperschneider & Dodds, 2021). Conserved protein domains were assigned using HMM scan against the PFAM 35. 0 database and blastp was used to search for homologs from other fungi. Finally, we used PHIB-BLAST, a blast server for PHI-base (Pathogen-Host Interactions), to search for homologs against functionally characterized pathogenicity factors. Gene expression analysis. RNAseq data from in vitro and in planta (1-, 4-, 9-, 14-and 21-days post infection; dpi) were obtained from a previous study  (Rudd et al. , 2015). We computed gene expression quantified as fragments per kilobase of exon per million fragments mapped (FPKM) using the improved gene models with Cuffdiff v.
Acknowledgements We thank  David Gouache  ( ARVALIS Institut du Végétal, Boigneville, France ),  Marc-Henri Lebrun ,  Henriette Goyeau ,  Frédéric Suffert  and  Anne-Sophie Walker  ( INRAE UR BIOGER, Palaiseau, France ) for their help in establishing the fungal collection;  Robert King  and  Jason Rudd  ( Rothamsted Research, Harpenden, United Kingdom ) for providing RNAseq data;  Gabriel Scalliet  ( Syngenta Crop Protection AG, Stein, Switzerland ) for providing the vectors pNOV2114, pNOV_3Gate_SUL and pNOV_3Gate_HYG used to clone the candidate pathogenicity genes;  Martin Willigsecker  and  Béatrice Beauzoone  ( INRAE UR BIOGER ) for their help in the pathology assays; and  Thierry Rouxel  ( INRAE UR BIOGER ) for critical reading of the manuscript and valuable feedback.
Financial Disclosure This work was supported by the  French National Research Agency  (program  BIOADAPT ,  ANR-12-ADAP-0009-04 ), the  FSOV 2008 B  project and  BASF France SAS  for R. A. postdoctoral fellowship. T. C. M. was supported by a  Short-Term Scientific Mission  granted by the  COST SUSTAIN  action  FA1208. A. S. V. was supported by the  Ministerio de Ciencia e Innovación (Ramón y Cajal program , grant no. RYC2018-025530-I ). INRAE BIOGER  benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
FUNDING This paper was part of the PhD and postdoc of CL. The Cifre thesis ( Conventions Industrielles de Formation par la Recherche ) was supported by the  ANRT (Association Nationale de la Recherche de la Technologie , convention Cifre N°  2018/0179 ). It was funded by  Invenio , the  Région Nouvelle-Aquitaine  ( Regina  project N°  22001415-00004759  and chestnut pollination project N°  2001216-00002632 ), by  INRAE  ( Institut National de Recherche pour l'Agriculture et l'Environnement)  and by  ANR  (project  ANR-21-PRRD-0008-01 ).
MS/MS data was acquired in the ion trap with rapid scan mode, a 20% normalized AGC target and a maximum injection time in dynamic mode. Selected precursors were excluded for 60 seconds. Protein identification was done in Proteome Discoverer 2. 5. Mascot 2. 5 algorithm was used for protein identification in batch mode by searching against a Uniprot Homo sapiens database (75 793 entries, release September 3, 2020). Two missed enzyme cleavages were allowed for the trypsin. Mass tolerances in MS and MS/MS were set to 10 ppm and 0. 6 Da. Oxidation (M) and acetylation (K) were searched as dynamic modifications and carbamidomethylation (C) as static modification. Raw LC-MS/MS data were imported in Proline Studio for feature detection, alignment, and quantification  (45). Protein identification was accepted only with at least 2 specific peptides with a pretty rank=1 and with a protein FDR value less than 1.
0% calculated using the "decoy" option in Mascot. Label-free quantification of MS1 level by extracted ion chromatograms (XIC) was carried out with parameters indicated previously. The normalization was carried out on median of ratios. The inference of missing values was applied with 5% of the background noise. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (46)  partner repository with the dataset identifier : PXD043841.