dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiments on DeepWeeds dataset 4. 2. 1. DeepWeeds description For the training and evaluation of the learning algorithms we have used the DeepWeeds dataset first  (Olsen et al. , 2019) , which consists of in-situ images of eight di↵erent weed species (target classes) native to eight locations in northern Australia, and various plant life (negative class) indigenous to those locations. The images were collected from June 2017 to March 2018, under realistic environmental conditions, to capture the challenges related to the classification of weed species, stemming from several factors of variation such as: geographical and seasonal variation of plants, dynamic complex backgrounds, illumination variation that results in highly dynamic range scenes with bright reflectance and dark shadows. With the intent of attaining the required variability and generality of the dataset, over 1000 images were gathered of each target species, with approximately a 50:50 split of positive to negative class images from each location, leading to a total of 8403 positive images of weed species and 9106 negative images of neighboring flora and non-target backgrounds, all the images of the DeepWeeds dataset were labeled by botanists of Figure  8  and 9.
Experiments on 4-Weeds dataset 4. 3. 1. 4-Weeds description For the purpose of carrying out an extended assessment of the learning algorithms, with a particular focus on the two model types: ConvNeXt-Base, and ConvNeXt-Base-SSL, we proceeded with the training and evaluation on the 4-Weeds dataset  (Aggarwal et al. , 2022). We used the same settings applied during the previous three sets of experiments completed on DeepWeeds with all the skip-connections utilized, in terms of optimal hyperparameters, stratified cross-validation, data partitioning, and unlabeled data generation. And with respect to the 4-Weeds dataset, we used the latest updated version, which consists of 618 labeled images of four commonly weed species found in corn and soybean, these images were acquired under complex field conditions during summer months at Purdue's University ACRE farm, as for winter months at Purdue's University greenhouse.
Classification results on 4-Weeds The results of evaluation using 4-Weeds dataset are depicted in Figure  17 , through these results we can observe that ConvNeXt-Base-SSL achieved a strong classification performance (F1-Score of 95. 02%), with a very limited size of labeled training images, which is equal to 20%. This performance is greater than the classification results attained by ConvNeXt-Base in terms of both metrics, with a significant margin of 8. 58% in Accuracy and 9. 51% in F1-Score. As for when the size of the labeled training subset is higher, we can observe that we continue to have an important margin between the semi-supervised and supervised performances, which is approximately equal to 2% for both metrics. These results demonstrate that the semi-supervised models are more capable of producing a consistent and high classification performance, despite of the limitation in terms of labeled training samples, which is in accordance with what we have seen in the experiments conducted using DeepWeeds.
In this subsection, we build upon our previous findings by providing additional experimental results on the CIFAR-10 dataset. CIFAR-10 is a collection of 60000 32x32 RGB images, divided into 10 categories (classes) with 6000 images per category. The dataset is split into a training subset of 50000 images and a test subset of 10000 images. We used the same hyperparameters and initialization method for the encoder-decoder parameters (weights) as in the training on DeepWeeds and 4-Weeds. However, in this case, we did not apply the cross-validation technique. We have used random seeds for the stratified sampling of 1000, 2000, and 4000 labeled samples, respectively. The results of evaluation on the test subset of CIFAR-10 are shown in Table  3 , along with a comparison to the consistency regularization-based methods mentioned earlier, in addition to two semi-supervised methods namely: FixMatch  (Sohn et al.
Raw reads have been uploaded to ENA (accession number PRJEB58186). The presence of extrachromosomal circular copies of HOPPLA (RLC_BdisC024) was validated by an inverse PCR using 7 ng/μL total DNA. Input quantities of DNA were controlled using primers specific to the S-adenosylmethionine decarboxylase (SamDC) gene  (Hong et al. , 2008). Sequences of primers are listed in S5 Table.
mRNA-sequencing and small RNA northern blotting Leaves of 4-week-old B. distachyon plants were ground in liquid nitrogen and 500 μL of this powder was subjected to TRIzol extraction following the supplier instructions (Invitrogen, CA, USA). 20 µg of total RNA was treated with DNase I for 30 min. , then repurified via phenol-chloroform extraction and ethanol precipitation. DNase-treated total RNA samples were sent to Fasteris/Genesupport (Plan-les-Ouates, Switzerland), subjected to poly(A)-tail selection, and then aliquoted for library construction via the Illumina TruSeq Stranded mRNA Library Prep kit. Resulting stranded polyA+ RNA-seq (mRNA-seq) libraries were sequenced on an Illumina NovaSeq 6000. The raw paired-end read data were deposited at the NCBI Gene Expression Omnibus (GEO accession: GSE243693).
Motif analysis The consensus sequence of HOPPLA was screened for known transcription factor binding sites obtained from the PlantTFDB  (Jin et al. , 2017)  using FIMO (v 5. 1. 1)  (Grant et al. , 2011). To functionally annotate transcription factors that could bind to HOPPLA, we used GO-terms of the Gramene (release 50) database  (Tello-Ruiz et al. , 2021)  downloaded from the platform agriGO (v 2. 0)  (Tian et al. , 2017). Generic, TF specific GO terms (GO:0003700, GO:0006355, GO:0005634, GO:0003677, GO:0043565, GO:0046983, GO:0003682 GO:0045893) such as 'positive regulation of DNA-templated transcription' were removed from the list of GO terms as they would interfere with the downstream analysis. The remaining GO terms of transcription factors potentially binding to HOPPLA were visualized with REVIGO  (Supek et al.
Detection of novel HOPPLA insertions in Bd nrpd1-2 (-/-) DNA of adult plants was extracted using the DNeasy plant kit (Qiagen, Venlo, Netherlands) according to the protocol of the manufacturer subjected to whole genome sequencing. Reads were trimmed using the BBDuk tool of  BBtools (BBMap (v 38. 75, Bushnell B. , sourceforge. net/projects/bbmap/) with the parameters qtrim = rl and trimq = 20. Trimmed reads were aligned to the reference assembly of Bd21 (v 3. 0) using BWA-MEM (v 0. 7. 17-r1188)  (Li & Durbin, 2009)  with the -M parameter set. Samtools (v 1. 13)  (Danecek et al. , 2021)  was used to obtain sorted and indexed bam files. TIPs were detected with detettore (v 2. 0. 3) (https://github. com/cstritt/detettore) with the options -require_split, -q 30 and using the consensus sequences of LTR-RTs of B.
distachyon (TREPdatabase) and the annotation of all full-length LTR-RTs of Bd21  (Stritt et al. , 2020). Because both Bd nrpd1-2 lines were in the Bd21-3 background we were able to exclude all Bd21-3 specific TIPs by removing those insertions that were detected in multiple individuals with more than one genetic background. Remaining TIPs were manually curated using the genome browser IGV (v 2. 15. 4. 12)  (Robinson et al. , 2011). HOPPLA TIPs were visualized with JBrowse 2 (v 2. 6. 1)  (Diesh et al. , 2023). Raw genomic reads of the re-sequencing of Bd nrpd1-2 (-/-), Bd NRPD1 (+/+) and Bd21-3 have been uploaded to ENA (accession number PRJEB64053).
26. 0)  (Quinlan & Hall, 2010)  intersect command using the version 3. 1 of the B. distachyon annotation file (https://phytozome-next. jgi. doe. gov) that are contained in the significant regions. The UpSetR  (Conway et al. , 2017)  R package was used to visualize the intersections of significant genes between the variables. Protein constituents of the Pol IV and Pol V enzymes (see S4 Table ) were downloaded from the plant RNA polymerase database http://rna. polymerase. eu/.
Acknowledgements and author contributions We thank the  Genetic Diversity Center Zürich  for providing the infrastructure for the mobilome-seq. M. T. and A. C. R. conceived the study and planned experiments. M. T. , B. K. , K. R. , C. H. , B. R. and M. B. generated data. M. T. , A. C. R. , N. M. and W. X. analysed data. D. L. , J. V. and R. S. provided mutagenized germplasm and online TILLING/T-DNA browsing tools. C. H. , B. R. and T. B. identified and screened for the pol IV null mutants. M. T and A. C. R wrote the paper with contributions from T. B. , C. S. All authors read the manuscript. The authors declare that they have no conflict of interest. Professional editing performed by  Emanuelle Botté.
Funding University of Zurich Research Priority Programs  (URPP) Evolution in Action to M. T. and A. C. R. ;  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. Grant Number:  31003A_182785  to A. C. R. , W. X. , N. M. and B. K. ;  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. Grant Number:  PZ00P3_154724  to C. S. ; T. B. was supported by the  Interdisciplinary Thematic Institute IMCBio  ( ITI 2021-2028 program ), including funds from  IdEx Unistra  ( ANR-10-IDEX-0002 ),  SFRI-STRAT'US  ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) in the framework of the  French Investments for the Future Program.
genoscope. cns. fr/microscope/) derived from the in-house genome sequences (section 2. 5) were also added to the database, as well as common contaminants (118 entries) and decoy sequences. The concatenated target/decoy database contains 2 x 5,934,749 protein entries. Spectra were searched with a mass tolerance of 5 ppm in MS mode and 0. 07 Da in MS/MS mode. One trypsin missed cleavage was tolerated. Carbamidomethylation of cysteine residues was set as a fixed modification. Oxidation of methionine residues and acetylation of proteins' N-termini were set as variable modifications. Identification results were imported into Proline software (http://proline. profiproteomics. fr/)  (Bouyssié et al. 2020)  for validation. Peptide Spectrum Matches (PSM) with pretty rank equal to one were retained.
False Discovery Rate was then optimized to be below 1% at PSM level using Mascot Adjusted E-value and below 1% at Protein Level using Mascot Mudpit score. To obtain unambiguous bacterial identifications, peptide sequences shared between A. thaliana (or contaminant proteins) and bacterial sequences were removed, as well as shared spectra (leading to close but different sequences). Quantification methods and statistical analysis are presented in the statistical methods section 2. 7. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. 2022)  partner repository with the dataset identifier PXD041221 and 10. 6019/PXD041221.
Statistical methods Analysis of chlorophylls accumulation. and FDR was optimized for each pairwise comparison. Functional annotation enrichment analysis of differential proteomics data was performed in an unbiased way using the desktop version of DAVID (Ease v2. 0) and an updated version of gene ontology (GO) and KEGG pathway databases (18 th of October 2021)  (Huang et al. 2009b (Huang et al. , 2009a)). The list of differential proteins from each comparison of the label free experiment was compared to the list of all protein matches identified. Enriched GO terms and KEGG maps were filtered by only considering those with an Ease score lower than 0. 1 and a Benjamini p-value lower than 0. 05. Enriched GO terms and KEGG maps were thereafter grouped together into broad functional categories, which were considered as enriched broad functions.
Peaks selected for fragmentation were automatically put on a dynamic exclusion list for 60 sec and peptide match selection was turned on. MS data were saved in. raw file format using XCalibur software 3. 1. 66. 10 (Thermo Fisher Scientific, Waltham, MA, USA). LC-MS/MS data interpretation and validation. Raw files were converted to. mgf peaklists using MSconvert and were submitted to Mascot database searches (version 2. 6. 2, MatrixScience, London, UK) against an A. thaliana protein sequences database downloaded from The Arabidopsis Information Resource TAIR site (TAIR10 gene model, 27,416 entries) (Lamesch et al. 2012) concatenated with a selection of bacterial sequences downloaded on 10 th of July 2020 from UniprotKB (only Streptomyces tagged proteomes with more than 1000.
4. S. cocklensis DSM 42063 and A. bryophytorum DSM 42138 colonize A. thaliana and have differential effects on host growth S. cocklensis or A. bryophytorum occur in the microbiota of other plants, we compared the 16S rRNA sequences obtained from genomic data of these two strains with those found in plant metagenomic datasets using the Joint Genome Institute (JGI) BLAST tools (https://img. jgi. doe. gov/cgi-bin/mer/main. cgi (accessed 17 August 2020)). We found several sequences sharing 100% identity with the 16S rRNA sequences of S. cocklensis DSM 42138 and A. bryophytorum DSM 42063 (with a coverage of more than 350 nt), in the Arabidopsis metagenomic datasets but also in the metagenomic data of Populus, Miscanthus, Switchgrass, Corn, or Agave microbiota. Consequently, S. cocklensis and A.
Database searches suggest that bacteria affiliated to S. cocklensis or A. bryophytorum are found in the microbiota of other plants. It is therefore most likely that the bacterial effect observed here in the case of Arabidopsis could also be observed with other plants. Overall, the work presented here provides a better understanding of the molecular mechanisms involved in the interaction between Streptomycetaceae and A. thaliana and, more broadly, the selection mechanisms of the plant microbiota. These capacities were ascertained using genomic data. Blue, Plant-bacteria interaction capacities of S. cocklensis DSM 42063; Green, Plant-bacteria interaction capacities of A. bryophytorum DSM 42138; Red, Carbon and phosphate metabolism capacities of both bacteria. Created with BioRender. com. LEGENDS TO FIGURES Figure 1.
Sequencing was performed on a NovaSeq6000 instrument to generate 150-bp paired-end reads. MIDAS and G12873 wholegenome assemblies were generated using the nanopore-based approach based on 26 Gb (50-fold coverage) and 36 Gb (69-fold coverage), respectively. Raw nanopore reads were corrected using Canu v2. 1  [37]  and the resulting corrected reads were assembled de novo using wtdbg2 v2. 5  [38]. Draft assemblies were refined by iterative polishing using long reads (Racon v1. 4. 3 and Medaka v1. 0. 3)  [39]  and short reads (three rounds of Pilon v1. 23)  [40]. Completeness was assessed using BUSCO v4. 1. 2  [41]  and the Fabales_odb10 dataset (Supplementary Table  5c ).
Finally, the NRRs derived from the panel of 339 common bean accessions were added to the preliminary pan-genome to generate the final pan-genome. To exclude putative contaminants and/or organelle sequences, NRRs were compared to the NCBI non-redundant nucleotide database using BLASTn, considering a minimum 80% identity and 25% coverage, leading to the removal of 1,194 sequences. Overall, we identified 64,174 added sequences, 86% of which reflected the mapping of the 339 low-coverage WGS accessions. The remaining 14% was identified by comparing the reference genome independently with the other four high-quality genomes (Supplementary Table  1a ).
Pan-genome annotation Repetitive sequences were identified and soft-masked using RepeatModeler v2. 0. 2  [51]  and RepeatMasker v4. 1. 2-p1  [52]. Protein-coding genes were identified using a hybrid-approach prediction with Augustus v3. 3. 3  [53]. Proteins from P. vulgaris and correlated species (Medicago truncatula and Glycine soja) plus RNA-Seq data (unpublished data from  [18] ) were aligned to the genome and used as extrinsic evidence. Protein sequences were aligned with Hisat2 v2. 2. 1  [54]  and RNA-Seq data were aligned using Genome Threader v1. 7. 1  [55]. BUSCO genes in the Fabales_odb10 database were used to train the model for the Augustus predictor. Predicted genes were scanned with InterProScan v5. 46-81. 0  [56]  for the presence of protein domains. Using a custom script, genes with transposon-related domains were filtered out and retained in the annotation if they contained at least one known protein domain.
FST analysis involved the separate testing of PAVs in the Mesoamerican and Andean gene pools by comparing the frequency of each PAV between wild and domesticated forms. Each PAV was considered as a single locus (0/1) and FST was calculated by applying the formula FST = (H total -H within) / H total, where H is the heterozygosity  [65]. The same procedure was applied to wild accessions when comparing the Mesoamerican and Andean gene pools. Only PAVs in the top 5% of the FST distribution were considered as candidates. The functions of interesting PAVs and those associated with A. thaliana orthologs detected by OrthoFinder  [58]  were investigated manually in the NCBI database (https://www. ncbi. nlm. nih. gov/). Phylogenetic analysis involved the extraction and filtering of SNVs located in core genes and PAVs using bcftools  [59] , resulting in two final datasets: 1,451,663 SNPs for the core genes and 338,475 SNPs for the PAVs.
The datasets were used to calculate the genetic distance between individuals and compute maximum composite likelihood values with 1000 bootstraps for the NJ tree in MEGA11  [66]. The final trees were visualized in FigTree (http://tree. bio. ed. ac. uk/software/figtree/). The filtered dataset of SNPs in core genes was also used to quantify the genetic diversity within groups of accessions by estimating π. The --window-pi vcftools flag was used to obtain measures of nucleotide diversity in 250-kb windows. The windowed-pi estimates were then divided by the total number of SNPs to calculate a global estimate for each genetic group. Fisher's exact test with the false discovery rate corrected for multiple comparisons was applied in R to identify PAVs that differed significantly in frequency between the Mesoamerican and Andean gene pools for the American and European accessions.
Data Availability The 109 raw sequencing reads generated and analyzed in this study have been deposited in the Sequence Read Archive (SRA) of the National Center of Biotechnology Information (NCBI) under BioProject number PRJNA1042929. Additional data comprising 230 raw sequencing reads have been sourced from Frascarelli et al. [11]  and Bellucci et al. [13]. The pan-genome assembly and its annotation are publicly accessible via this link: https://doi. org/10. 6084/m9. figshare. 24573874.
Acknowledgments The authors thank  Didier Tropée  and  Gaël Vieceli  for their valuable help and support with the field experiments, as well as  T. Randrianarisoa ,  N. Vazeux-Blumental ,  H. Belcram ,  A. Postec ,  L. Malicet-Chebbah ,  B. Rouger ,  E. Forst ,  G. Van Frank ,  E. Blanc ,  A. Merbhène ,  M. Turbet-Delof ,  I. Goldringer ,  A. Hospital ,  M. Colas ,  F. Ammar ,  A. Sidik Meite ,  K. Ménard  and  N. Tinomme. The authors also thank  T. Moittie  ( Asur Plant Breeding ),  M. Balduz  ( Lemaire Deffontaines ),  C. Duquet  and  M. Prevost  ( Limagrain Europe ),  C. Michelet  ( RAGT2n ) and  S. Caiveau  ( Syngenta France SAS ) for providing the seeds. This work was supported by the doctoral school FIRE of the  Learning Planet Institute  and the  BAP department  from  INRAE  ( PerfoMix  project).
Acknowledgements We thank the gardeners and the stakeholders of the urban parks of  Marseille  for helping us during fieldwork. We particularly appreciated the help of  Patrick Bayle ,  Catherine Stenou  and  Josette Sakakini. We thank  David Genoud ,  Eric Dufrêne  and  Matthieu Aubert  for species identification. We are also grateful to  Léa Chalvin & Rémy Roques  for their help during fieldwork.
The cattle tracing databases set up over the past decades in Europe have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are parts of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we developed an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high-to low-prevalence herds. The algorithm was coupled with a generic computational model describing infection spread within and between herds, based on data extracted from the French cattle movement tracing database (BDNI). This model was used to simulate a wide array of infections, with either a recent outbreak (epidemic) or an outbreak that occurred five years earlier (endemic), on which the performances of the rewiring algorithm were explored.
Introduction Following bovine spongiform encephalopathy and classical swine fever epidemics in the 1990s, the European Union initiated the mandatory identification and registration of cattle in Europe  (EU, 2000). This decision led to the creation of national identification databases, such as the cattle tracing system in the United Kingdom  (Kao et al. , 2006 , Vernon, 2011) , the French national bovine identification database (BDNI)  (Rautureau et al. , 2011 , Dutta et al. , 2014) , the Italian national bovine database  (Natale et al. , 2009 , Bajardi et al. , 2011)  and the database of the Swedish board of agriculture  (Nöremark et al. , 2009 (Nöremark et al. , , 2011)). These animal tracing systems have enabled the monitoring of infectious livestock diseases and the development of strategies to prevent their spread  (Gilbert et al.
, 2005 , Moslonka-Lefebvre et al. , 2016 , Beaunée et al. , 2017) , since animal trade is a major transmission pathway between herds. Indeed, commercial exchanges are not only recorded comprehensively, but also controlled by farmers, unlike animal mobility in the wild. These databases, whose reliability has increased over time since their creation  (Green and Kao, 2007) , are therefore powerful tools for simulating infectious diseases in cattle  (Ezanno et al. , 2020)  and assessing the impact of livestock movements on epidemics  (Ezanno et al. , 2021). The information provided by these commercial animal movements can be used as a basis for representing comprehensively the demographic processes and trades between cattle farms located in a given region, using a metapopulation framework  (Liu et al.
To do this, we developed a rewiring algorithm in conjunction with a simulation model. The simulation model coupled demography based on a dataset from the French cattle tracing system (BDNI) with a SIRS epidemiological model. The use of such a generalist epidemiological model made it possible to simulate a wide range of epidemiological settings, only by varying its parameters. The algorithm worked by preventing movements from high-to low-prevalence herds, while maintaining the number of animals bought and sold by each farm. It was based on an edge-level criterion: the estimated difference in prevalence between the herd of origin and the herd of destination of the movement considered. The following presents the cattle movement network concerned by this study, as well as the model and the algorithm.
Cattle movement network The dataset used for this study corresponded to an extraction from the French national bovine identification database (BDNI). It concerned all cattle herds in Brittany (a French region) that sold or bought at least one animal during the year 2014. This set of 21,548 herds is referred to as the 'metapopulation' thereafter. The database also included commercial exchanges as well as demographic events, all of which were referred to as 'movements' hereafter. Three types of commercial exchanges were considered: (i) 'internal movements' had an origin and a destination among the herds in the dataset, (ii) 'imports' had only a destination in the dataset and (iii) 'exports' had only an origin in the dataset. They represented respectively 64%, 16% and 20% of the commercial exchanges involving at least one herd of the dataset.