dataesr/scientific-paragraphs-categorization huggingface dataset:
We analyzed the fluorescence using a Spark microplate reader (TECAN) with excitation and emission wavelengths of 482 and 515 nm, respectively. Fluorescence values were divided by the absorbance at 600 nm to obtain values that were proportional to the bacterial cell concentration. Graphs were prepared using Prism 8 (GraphPad Software, Inc. ). In Vitro Oxidation of CaM. We treated CaM (6 µM) with 50 and 100 µM oxidant, corresponding to one and two molar equivalents of methionine, respectively, for 30 min at 37 °C. Each sample was suspended in Laemmli SDS sample buffer and heated for 10 min at 95 °C. We resolved the samples on a 17% Tris-glycine SDS-PAGE gel at 125 V for 2. 5 h and stained the gel with Coomassie blue. We performed the repair of oxidized CaM by MsrP as described previously, using 10 mM benzyl viologen and 10 mM sodium dithionite at 37 °C for 1 h  (18).
Each sample was suspended in Laemmli SDS sample buffer and heated for 10 min at 95 °C. We resolved the samples on a 16% tricine SDS-PAGE gel (49) at 125 V for 2. 5 h. For immunoblot analysis, we transferred the resolved proteins to 0. 2-µm nitrocellulose membranes, which we then probed with anti-Lpp antibody (provided by the Jean-François Collet Lab, de Duve Institute, UCLouvain), followed by an HRP-conjugated anti-rabbit IgG secondary antibody (Promega). We imaged chemiluminescence signals using an ImageQuant Las4000 camera (GE Healthcare). Data, Materials, and Software Availability. All study data are included in the article and/or SI Appendix.
ACKNOWLEDGMENTS. We thank the members of the Ezraty and Py groups for comments on the manuscript, advice, and discussions. We thank  Eric Cascales  ( Laboratoire d'Ingénierie des Systèmes Macromoléculaires) ,  Romé Voulhoux  ( Laboratoire de Chimie Bactérienne ), and  Pauline Leverrier  ( de Duve Institute, UCLouvain ) for providing antibodies. This work was supported by grants from the  Agence Nationale Recherche  (# ANR-21-CE15-0039 NeutrOX ) and the  Centre National de la Recherche Scientifique (CNRS)  (# PICS-PROTOX ). C. A. was supported by the  Fondation pour la Recherche Médicale , (# FDT202106012778 ).
Statistical analysis Data were analyzed using SPSS version 20 (IBM corporation NY, USA). Results were subjected to variance analysis  (Pintore et al. 2002 ) and Duncan's test was employed to notice signi cant differences at the level 0. 05%. Values presented the means of three replications and expressed as the mean ± SD. For each parameter (Mortality percentage and persistence), data were subjected to two-ways ANOVA, with exposure time and treatments type as main xed factors plus their interactions. Besides, mortality and persistence data were subjected to Probit analysis to obtain the lethal concentration (LC) and lethal time (LT). In addition, correlation analyses were performed between the matrix proprieties and the e ciently of encapsulated EO. The correlation analyses (Pearson's correlation coe cient) between the encapsulation matrices and the e cacy of the encapsulated oils (mortality and persistence) were determined.
The present Resource Announcement provides the raw metabarcoding datasets, the amplicon sequence variant (ASV) tables obtained after bioinformatic processing, the metadata describing the sampling design, and the code used for bioinformatic analysis. Future analysis could use these data to identify microbial taxa enriched in asymptomatic samples and to infer microbial interaction networks  (Nearing et al. 2022; Röttjers and Faust 2018).
2007 ) included in the DADA2 pipeline. The databases used for taxonomic assignment were SILVA v138. 1  (Quast et al. 2012)  and 'UNITE All Eukaryotes' v8. 3  (Abarenkov et al. 2021)  for 16S and ITS sequences, respectively. ASV tables, taxonomic assignments, and metadata were joined in phyloseq objects using the phyloseq bioconductor package v1. , or according to their taxonomic assignment in the SILVA database, were removed. The remaining ASVs were clustered using the LULU algorithm  (Frøslev et al. 2017 ) with default parameters. ASVs that could not be assigned to a bacterial or fungal phylum were removed. ASVs present in less than 1% of the samples were removed to make sure that the data were free of sequencing artifacts and low abundant contaminants  (Cao et al. 2021). Finally, pairs of samples in which at least one of the samples had fewer than 1,000 sequences were removed.
Future Directions This Resource Announcement provides two metabarcoding datasets describing the changes undergone by foliar fungal and bacterial communities during infection by downy mildew (P. viticola) in untreated grapevines (V. vinifera L. ). These datasets will allow for community comparison within pairs of symptomatic and asymptomatic samples collected at the same time on the same leaf. Such a comparison could help unravel ecological interactions between P. viticola and the grapevine foliar microbiome.
Acknowledgments We thank all members of the  Consortium Biocontrôle  for their support for this work. We thank  Pascale Pienne  for her contribution to the sampling;  Céline Lalanne ,  Adline Delcamp ,  Christophe Boury , and all other members of the PGTB sequencing facility for their support in molecular biology; and  Gaétan Burgaud  and  Frédéric Garabetian  for kindly providing marine strains used as positive controls. We are very grateful to  Valérie Laval  and  Carole Couture  for valuable advice when developing protocols. We also thank  Frédéric Barraquand ,  Stéphane Robin ,  Charlie Pauvert , and  Andreas Makiola  for helpful discussions at the beginning of the project.
This work was supported by the  Consortium Biocontrôle within the 'BCMicrobiome' project. Additional funding was received from the  Aquitaine Region  ( Athene  project, no. 2016-1R20301-00007218 ) and the  ANR  ( VITAE  project,  20-PCPA-0010 ;  NGB  project,  ANR-17-CE32-0011 ;  Labex COTE ,  ANR-10-LABX-45 ). The  Genome Transcriptome Facility of Bordeaux  (PGTB) received grants from Investissements d'avenir and convention attributive d'aide  EquipEx Xyloforest  ( ANR-10-EQPX-16-01 ).
Availability of Data and Materials The sequence datasets have been deposited in NCBI SRA in bioproject PRJNA797225 and bioproject PRJNA797948. The biosample accession numbers are SAMN24973302 to SAMN24973835. Bioinformatic scripts, raw and filtered ASV tables in R phyloseq format, and tables showing variation in sequence counts during the bioinformatic process have been deposited in Recherche Data Gouv (https://doi. org/10. 15454/2YDSBL).
5 mg of geraniin. 1 H and 13 C NMR spectra were measured with a 600 MHz Avance II + spectrometer (Bruker), equipped with a TCI Cryoprobe 5 mm. Spectra were recorded with a 2-mm NMR capillary tube in 80 μL of 99. 96 %-d CD 3 OD solvent (δ 1 H 3. 31 ppm -δ 13 C 49. 00 ppm) at 300 K. The 1 H (600 MHz) and 13 C NMR (150 MHz) data were reported in ppm. Coupling constants were expressed in Hz where s stands for singlet, d for doublet, t for triplet, q for quartet, m for multiplet and br for broad. Proton connectivity (C, CH, CH 2 , CH 3 ) information was obtained from edited HSQC and 13 C experiments. Proton and carbon peak assignments were based on 2D NMR analyses.
Statistical analysis Survival data were analyzed using the statistical software R (version 4. 1. 0, R Core Team 2020 and Cox mixed-effects models (coxme, package "coxme," Therneau 2020). For each species, we tested whether the factor "condition" (levels: F/NSU, F/SSU, S/NSU, S/SSU, and D/NSU) included as fixed effect factor influenced the survival of ant workers. The factor "replicate" nested within the factor "colony" was included in the models as a random effect factor. Statistical models were compared to null (intercept only) models using Likelihood Ratio (LR) tests to assess the significance of the predictor. Upon significance, we performed pairwise comparisons between the different conditions (glht, package "multcomp,"  Hothorn et al. 2008 ) and p values were adjusted by Bonferroni correction.
No water nor food was available in the Desiccation treatment (black and solid line) (different letters indicate significant differences at P < 0. 05 after Bonferroni corrected post hoc comparisons) 1 3 equally well with workers of more than 20 other speciesisolated individuals or in groups in long-term experiments (data not shown, more than 200 days LP pers. com. ). Similar-sized insects can also probably be maintained using the same setup. Finally, in case the ant behavior needs to be recorded, the mesh lids could be replaced by standard lids during the video recording. We are, therefore, confident that our setup will ease future studies on isolated or small groups of individuals and will make future studies more comparable.
Acknowledgements We thank  Damien Gergonne , who performed preliminary experiments which inspired this study. We also thank the editors and anonymous reviewers for their comments that helped to improve our work. Lina Pedraza  was funded by  Deutscher Akademischer Austausch Dienst DAAD  grant no. 57381412  (Doctoral  Programmes in Germany   2018/19 ). Abel Bernadou  was supported by  Deutsche Forschungsgemeinschaft DFG  grant no. BE6684/1-1  (Research Unit " So-long"-FOR 2218 ).
Directed mutagenesis of BdNRT2A Directed mutagenesis of the conserved Ser specific to monocots (supplementary data S3) were performed using the protocol of 'QuickChange II XL Site-Directed Mutagenesis' (Stratagen). Sequence of BdNRT2A was amplified from pDONR207 containing BdNRT2A with the forward and reverse primers BdNRT2A S461D for BdNRT2A S461D and the forward and reverse primers BdNRT2A S461A for BdNRT2A S461A. The amplification products were digested with Dpn I and then cloned into E. coli One Shot TOP10. The different constructs were sequenced, and those with desired mutations were reintroduced into pMDC43 (Pro35S::GFP-BdNRT2A S461D ) and pMDC43 (Pro35S::GFP-BdNRT2A S461A ) for transitory expression in Arabidopsis mesophyll protoplasts.
, 2001)  while LATS activity was unaffected in both single and double mutants. In order to confirm that BdNRT2A and/or BdNRT2B are functional orthologs of AtNRT2. 1 and AtNRT2. 2 we searched for Brachypodium mutants affected in BdNRT2A and/or BdNRT2B. In the JGI Brachypodium collection no true T-DNA insertion mutant in BdNRT2A is available following our own testing experiments (Supplementary data S1). We used the NaN3 mutant collection from Versailles  (Dalmais et al. , 2013)  to search for point mutations in the coding sequence of BdNRT2A. We identified seven independent lines corresponding to three silent and four non-silent point mutations leading to amino acid changes (Table  1 ). The SIFT scores for three of the non-silent point mutation were high (> 0. 2) and thus with very low chance to impede the protein structure, but one mutation resulted in a stop codon (W248*) that likely resulted in a truncated protein deprived of 257 amino acids (out of 505 total amino acids) at the C-terminal.
Acknowledgements We thank  Hervé Ferry  and  Michel Burtin  for taking care of the plants at the IJPB. We thank  Rozenn Le-Hir  for her help and advices in confocal manipulation and we thank the  IJPB's Plant Observatory  technical plateforms for their support. We thank the  TILLING plateform EpiTrans, INRA , 2018. EPIgenomics and  TRANSlational Research Facility , doi: 10. 15454/1. 5572407597184844E12. We are gratefull to  Christian Meyer  for critical reading of the manuscript. This work was supported in part by  Saclay Plant Sciences  ( ANR-10-LABX-0040 ), and by  ANR NiCe  ( ANR-17-CE20-0021 ). Authors contribution SFM, LCD designed and planned the experiments at IJPB. LCD, PB, SFM, MG, TG performed or participated to the various experiments. LCD performed the oocyte 15 N uptake experiments at the  John Ines Center  under the supervision of AJM.
For each PCR tube representing one bacterial strain, the mix was made as follows: 1 µL DNA, 15. 75 µL deionized water, 5 µL Q5 buffer reaction 5X (New England Biolabs, Ipswich, England), 0. 5 µL dNTPS 10 mM, 1. 25 µL Forward primer 10 µM (5 -GAGTTTGATCCTGGCTCAG-3 ), 1. 25 µL reverse primer 10 µM (5 -TGGACTACCAGGGTATCTAAT-3 ) and 0. 25 µL Highfidelity Taq polymerase (New England Biolabs, Ipswich, UK). PCR was performed with the following amplification program: 98 • C for 5 min, 30 cycles at 98 • C for 10 s, 63 • C for 30 s and 72 • C for 30 s, and 72 • C for 2 min. The resulting PCR products of approximately 806 bp were then purified before sequencing, according to the method of Sanger et al. [18]. The sequences were then determined using the BLASTN program with the standard databases (http://www.
These regions were amplified and sequenced by Illumina MiSeq, as described in our previous study  [12]. All amplicons were purified, quantified and then pooled together to constitute the library, which was then sequenced with an Illumina MiSeq protocol generating 300 bp paired-end reads. The resulting sequences were analyzed consecutively using DADA2  [19]  and FROGS  [20]  pipelines. The pre-processing steps were carried out using DADA2 package version 1. 14. 1 in R program version 3. 6. 1. This resulted in Amplicon Sequence Variants (ASVs) tables. Taxonomic affiliations using DAIRYdb database version 2. 0  [21]  for the 16S rRNA gene and UNITE database version 8. 2  [22]  for ITS2 sequences were conducted using FROGS version 3. 2. 2 [20]. Multi-affiliations were then checked using the affiliation Explorer  [23].
Bacterial and fungal ASVs tables were rarefied while creating the Phyloseq Objects with FROGSSTAT Phyloseq Import Data (Galaxy Version 3. 2. 2) before assessing the samples' diversities. Species diversity within each sample was estimated by the α-diversity indices "richness", and Shannon using FROGGSTAT Phyloseq Alpha Diversity tool (Galaxy Version 3. 2. 2). Bray-Curtis dissimilarity indices were used to estimate species diversity between the samples. These indices were calculated using FROGGSTAT Phyloseq Beta Diversity tool (Galaxy Version 3. 2. 2). Hierarchical clustering of the fermented milks of both jars, based on the Ward linkage method and Bray-Curtis matrix, was then applied and visualized using the FROGSSTAT Phyloseq Sample Clustering tool (Galaxy Version 3. 2. 2).
Microbial Dynamics and Species Identification throughout Fermentation Bacterial compositions analyzed by DNA metabarcoding showed that raw milk samples were dominated by the bacterial genera Enterobacter (22. 84% of total sequences), Chryseobacterium (16. 00%), Acinetobacter (11. 46%), Mycoplasma (9. 44%) and Enterococcus (8. 72%). Concerning all the fermented milk samples, the most abundant bacterial genera were Lactobacillus (62. 73% of total sequences), followed by Lentilactobacillus (17. 87%), Lactococcus (7. 78%), Citrobacter (2. 17%) and Enterobacter (2. 02%). Some species of Lentilactobacillus genus could not be identified and provided several options when blasted into the actual databases: kefiri/otakiensis/sunkii or hilgardii/diolivorans. The three bacterial species, Lactobacillus kefiranofaciens, Lentilactobacillus sp.
and Lactococcus lactis, were present in almost all the samples but with varying abundances. It was noticeable that the bacterial composition of raw milks differed from that of the fermented milks (Figure  6 ). The 10 most abundant bacterial species within the raw milks and fermented milks of each jar are illustrated in Figure  6. composition of raw milks differed from that of the fermented milks (Figure  6 ). The 10 most abundant bacterial species within the raw milks and fermented milks of each jar are illustrated in Figure  6. As for the culture-dependent method, a total of 146 bacterial isolates were collected from the raw and fermented milks from both jars and were identified based on the current databases. Lentil. kefiri was the dominant species in both jars throughout the production process.
The 10 most abundant fungal species within the raw and fermented milks of each jar are illustrated in Figure  7. As for the culture-dependent method, a total of 146 bacterial isolates were collected from the raw and fermented milks from both jars and were identified based on the current databases. Lentil. kefiri was the dominant species in both jars throughout the production process. It was either accompanied by Lb. kefiranofaciens or Lentilactobacillus diolivorans, or both at the same time. The identified bacterial isolates selected from the initial raw milk RM-D0 and at some points throughout fermentation (D0, D2, D4, D7, D10, D47 and D100) are presented in Figure  S2. Concerning the fungal communities, regardless of the jar, the most abundant fungal genera were Geotrichum (55. 54% of total sequences), followed by Pichia (20.
Acknowledgments: The authors would like to thank  Abla Majed , the labneh Ambaris producer where the study was conducted. The authors would like to also thank  Loubnah Belahcen  for her assistance with the sensory analysis data treatment. Sylvain Santoni  ( AGAP, University of Montpellier ,  CIRAD ,  INRAE ,  Montpellier SupAgro , 34000 Montpellier, France) is also appreciated for helping with the Illumina MiSeq DNA metabarcoding sequencing.
Despite large omics datasets, the establishment of a reliable gene annotation is still challenging for eukaryotic genomes. Here, we used the reference genome of the major fungal wheat pathogen Zymoseptoria tritici (isolate IPO323) as a case study to develop methods to improve eukaryotic gene prediction. Four previous IPO323 annotations identified 10,933 to 13,260 gene models, but only one third of these coding sequences (CDS) have identical structures. To resolve these discrepancies and improve gene models, we generated full-length transcripts using long-read sequencing. This dataset was used together with other evidence (RNA-Seq transcripts and protein sequences) to generate novel ab initio gene models. The selection of the best structure among novel and existing gene models was performed according to transcript and protein evidence using InGenAnnot, a novel bioinformatics suite.
These particularities of fungal genomes require specific training of ab-initio prediction software and development of fungal-specific pipelines  [7] -  [15]. Long-read sequencing is now used to provide full genome assemblies, reducing drawbacks due to genome fragmentation into contigs. Experimental transcript evidence has also been improved using transcripts assembled from RNA-seq short reads, providing large transcript datasets for gene annotation/curation. Iso-Seq longread sequencing now provides full-length transcript sequences that bypass problems observed with the assembly of RNA-Seq short reads such as chimeric transcripts covering adjacent genes  [16]. Iso-Seq also provides transcript isoforms allowing the identification of alternative start, stop and splicing events. Nevertheless, RNA-Seq reads are still required to quantify the relative abundance of Iso-Seq transcript isoforms, since Iso-Seq is not quantitative and could reveal rare transcripts likely resulting from errors of the transcriptional machinery  [17].
Indeed, it was identified by translating all possible ORFs from the region, and its overall structure (start, stop, two introns) was only predicted using infection-related RNA-seq data. Clearly, the complete coding potential of this genome still has not been identified despite the four thorough annotations that have been developed over the past dozen years. To address this problem, we established a novel strategy to annotate a compact eukaryotic genome using Z. tritici as a case study. For this process we generated a large set of full-length cDNA sequences using PacBio Iso-Seq long reads  [33] ,  [34]. We also developed a novel suite of tools, InGenAnnot, to compare genes models predicted by different ab initio software and to select the best gene model according to transcript (RNA-Seq, Iso-Seq) and protein evidence.
1 -a 10 -j 2 or -j 4). The Trinity script inchworm_transcript_splitter. pl (version 2. 8. 5)  [44]  was used to split the transcripts with nonuniform coverage based on the Jaccard clip method. Clipped transcripts were extracted with homemade scripts and clustered with Stringtie and associated bam files to obtain transcripts per million (TPM) counts. All libraries were concatenated into one gff file without merge to avoid loss of information by fusion of small transcripts into larger ones due to the large number of genes in the Z. tritici genome with overlapping untranslated regions (UTRs). Processing of Iso-Seq sequences Iso-Seq raw data were processed with the Iso-Seq V3. 2 pipeline from PacBio generating polished Circular Consensus Sequences (CCS). CCS were then mapped to the Z. tritici IPO323 genome with Gmap (2019-01-31)  [45]  and unmapped, low-mapping-quality (≤0) or multi-mapped CCS were filtered out.
The CupCake package (v10. 0. 0, https://github. com/Magdoll/cDNA_Cupcake) filtered the isoforms, removing the less-expressed and degraded transcripts using the following tools: collapse_isoforms_by_sam. py, get_abundance_post_collapse. py, filter_by_count. py, filter_away_subset. py. Readthrough transcripts were removed using the previous annotations (MPI, JGI, CURTIN, RRES) with BEDTools intersect  [46]  with an an overlap of 100% for full coding sequences (CDS) (-F 1. 0) and the same strand (-s)) of at least 2 CDS. Transcripts mapped on the mitochondrial genome were filtered out as well. Subsequently, all libraries were processed with chain_samples. py from CupCake and clustered for stringent selection. Splicing junctions obtained by STAR (SJ. out. tab files) from Illumina RNA-Seq libraries were used to filter out isoform transcripts with unsupported junctions.
Gene prediction and selection of the best gene models Two gene predictors, Eugene v1. 6. 1  [10] , and LoReAn v2. 0  [47] , handling long-read transcript sequences as evidence, were used to perform new annotations. Eugene was launched with the provided fungal parameters (WAM fungi matrix) and trained with a dataset of proteins from four genomes of species phylogenetically related to Z. tritici: Cercospora beticola (GCF_002742065. 1_CB0940_V2); Ramullaria collo-cygni (GCF_900074925. 1_version_1); Zasmidium cellare (GCF_010093935. 1_Zasce1); and Sphaerulina musiva (GCF_000320565. 1_Septoria_musiva_SO2202_v1. 0). Gene structures were predicted with assembled transcripts from RNA-Seq and a dataset of Dothideomycetes proteins obtained from Uniprot without Zymoseptoria sequences to avoid inference with gene models to be improved.
Filtered Iso-Seq transcripts were used as strongly weighted evidence in model prediction with the parameter "est_priority=2". LoReAn was launched in fungus mode with the Augustus retraining mode using the same Dothideomycetes Uniprot dataset without Zymoseptoria sequences and the same Iso-Seq transcript dataset used for Eugene. RNA-Seq data were used as a merged mapping file (BAM) by the pipeline to assemble transcripts and detect splicing sites. The new and previous gene datasets cleaned for TEs with ingenannot filter were annotated for annotation edit distance (AED)  [48]  scores using ingenannot aed with a fungal protein dataset without any Zymoseptoria species, selected Iso-Seq and RNA-Seq transcripts. AED were computed on gene models only with "--aed_tr_cds_only" to avoid bias between datasets with or without UTR annotations and with "--penalty_overflow 0.
UTRs were inferred in two passes with the ingenannot utr_refine. First, after deleting all previously annotated UTRs and inferring new coordinates from a filtered set of Iso-Seq transcripts. Second, by inferring UTRs with a filtered set of RNA-seq assembled transcripts, considering only transcripts with no UTRs from the first step. Both sets were established with the ingenannot isoform_ranking for filtering and ranking UTR isoforms based on RNA-Seq evidence. Gene models from each annotation were compared using their AED scores with ingenannot aed_compare and specific/shared gene models were identified using ingenannot compare. BUSCO  [49]  analyses with ascomycota_odb10 were performed to evaluate the completeness of datasets. Functional annotation and prediction of secreted proteins Functional annotations of genes obtained using Interproscan 5.