dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013; Henri et al. , 2016). High congruence between PFGE and MLST results allowed successful conversion of PFGE profiles into MLST data, thus enabling utilization of PFGE databases for investigation of population genetics, and clonal structure of L. monocytogenes  (Maury et al. , 2016; Félix et al. , 2018). In the past, attempts to correlate biofilm phenotype to serotype, origin or persistence gave conflicting results between studies and experimental conditions. Such comparisons can now be addressed at the level of the genome. Indeed, Genome-wide association study (GWAS) is a top-down approach that involves testing large numbers of genetic variants in a population of individual organisms with a given phenotype. The application of GWAS in microbiology has slowly emerged  (Falush and Bowden, 2006; Falush, 2016)  and application of GWAS to Campylobacter successfully identified genetic factors responsible for adaptation to different animal hosts  (Sheppard et al.
The bottom of plates was scanned with the Plate Reader and analyzed by BFC elements 3 R software to obtain the numeric values of each well called the BioFilm Index (BFI) ranging from 0 to 20. With this measurement, absence of biofilm in a well results in high mobility of magnetic beads and a BFI around 20. In contrast, immobilization of beads by sessile cells results in a lower BFI or zero value. At least three experiments were performed for each condition with independently grown bacterial cultures.
Pan-Genome-Wide Association Study (Pan-GWAS) This analysis was performed on the 57 strains for which the whole genome sequence data were available from a previous study  (Henri et al. , 2016). The paired-end reads of the strains used in the study are available under the ENA bio-projects PRJEB15592 and PRJEB32254 1, 2. Total biomass measured by MPA under eight different growth conditions were classified into binary phenotypes, strong or weak biofilm formers. Strains were clustered based on their MPA OD 600 values using a function hclust (RStudio version 3. 4. 2) with the complete linkage method for hierarchical clustering. Pan-GWAS was performed based on the accessory gene content of the 57 L. monocytogenes de novo assemblies. First, draft genomes were obtained using the SPAdes algorithm  (Bankevich et al.
, 2012)  after quality check of Illumina reads using FastQC  (Andrews, 2010). Then, whole genome annotation was carried out using Prokka  (Seemann, 2014)  with default parameters. Using the assemblies as an input Prokka produced GFF3-files, including sequences and annotations, which were used to extract the pangenome of the 57 L. monocytogenes isolates with  Roary (Page et al. , 2015). Finally, gene-based GWAS was performed using Scoary  (Brynildsrud et al. , 2016)  by following the instructions provided on https://github. com/AdmiralenOla/Scoary. Scoary assessed presence/absence patterns of genes in binary phenotypes and the genes with significant association (p < 0. 05) were reported. As proposed by the Scoary pipeline, population structure was accounted for in order to control the confounding population stratification.
Pairwise comparisons were performed based on the phylogenetic tree (see section "Phylogenetic Analysis") for population structure correction. Percentage of strains between strong versus weak biofilm formers carrying each gene in their genome was calculated. For example, % of a gene in strong biofilm formers was determined as follows: (number of strains carrying the gene in their genomes among strong biofilm formers) × 100/(total number of strains grouped into strong biofilm formers). Information regarding gene name, protein name, accession number and gene ontology (GO) were retrieved from the Universal Protein Resource database 3. A limited percentage of sequences (0. 79%) with no BLAST hit was excluded from further analysis. To obtain a visualized dataset of the selected genes, GO enrichment analysis (GO-slim) was performed using PANTHER Version 14.
CONCLUSION This study is the first attempt to correlate biofilm traits with persistence status, frequency and distribution of genotypes as well as genetic composition in a well-characterized collection of food-related L. monocytogenes isolates. Under our laboratory experimental conditions, persistence or prevalence did not correspond to a higher biofilm formation capacity. The current distribution of genotypes in FPE and foods may be comparable to their heterogeneous distribution in natural habitats which, unfortunately, is difficult to investigate due to the lack of appropriate databases. Biofilm productivity exhibited profound inter-strain variations depending on growth conditions which resulted in inconsistent associations between biofilm phenotype and lineages or serotypes throughout the different conditions.
Interestingly, a temperaturedependent association of genotype with biofilm production was observed asserting genotype as a better predictor of bacterial phenotypes. While salt addition enhanced biofilm production, nutrient deprivation impaired it. Importantly, a marked non-linear effect of both treatments was documented for the first time in L. monocytogenes. Supplementation of NaCl in nutrient deprived cells significantly increased biofilm maturity regardless of temperature. To better explain the observations encountered in the study, intraspecific variations in planktonic and sessile modes of growth under each condition shall be evaluated. Pan-GWAS was successfully applied on biofilm data and the module of genes were found to be comparable across different growth conditions in spite of the small number of shared genes.
ACKNOWLEDGMENTS We greatly acknowledge  Sophie Roussel  and  Michel-Yves Mistou  at  ANSES , Maisons-Alfort; Taran Skjerdal at  Norwegian Veterinary Institute , Oslo; and  Carole Feurer  at the  French Pork and Pig Institute, Maisons-Alfort  for kindly providing L. monocytogenes strains. We thank  Pierre Nicolas  at  INRA ,  Jouyen-Josas  for providing functional classification,  Brigitte Gaillard-Martinie  at  INRA, Saint-Genès-Champanelle  for SEM sample preparation, and  Christelle Blavignac  for her assistance with SEM technologies at the  Centre Imagerie Cellulaire Santé, Université Clermont Auvergne.
Statistical analysis 2. 4. 1. Libido "mating attempts" of young male rabbits during testing phase according to the body development traits To evaluate the libido of young male rabbits, we implemented a series of multinomial logistic regression models. Models contained two fixed effects, the batch (1 to 5) and the destination farm (A or Z) and a standardised co-variable related to the body development (LW at 0, 28, 63 and 147 days of life, and ADG for the periods 0-28, 28-63 and 63-147). We used the R-software (version 3. 5. 0) and the "multinom" function of the "nnet" package.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT  [19]  for binomial distributions. Solution provided odds values about the increase of relative probability to be classified as 1 (motile sperm above 65%) per unit of proposed LW or ADG change. Finally, as NAR data presented a Poisson distribution, data were analysed with six GLIMMIX procedures for Poisson distributions. Finally, semen production and motility data during the testing phase were also analysed with the same procedures described above, including the effect of the growth variables as main effect (4 quartile classes) instead of as a covariate, for a better presentation of the obtained results.
Acknowledgements The authors would like to thank  José Manuel Arias , owner of the selection centre El Adil Redondo S. L. , ( Carrizo de la Ribera, León, Spain ), and his team for their help, as without their collaboration this work could not have been carried out. This study was supported by the  Interministerial Commission for Science and Technology (CICYT)  of the  Spanish Government  ( AGL2017-85162-C2-1-R ).
05. Two additional filters were used to improve the identification of trimethylated Lys peptides: 1/selection of peptides with Mascot score ≥30, 2/discrimination of Lys trimethylation (mass shift of 42. 04695) and Lys acetylation (mass shift of 42. 01056) using a mass tolerance at 2 ppm. Ambiguous peptides were eliminated and spectra of interest were checked manually to confirm their sequence and the nature of modifications. Similar parameters were used to identify trimethylated Lys peptides in samples from A. halleri and A. lyrata but MS data were searched using a local database built using the A. lyrata genome resources (Alyrata_384_v2. 1 from the Joint Genome Institute)  (Hu et al. , 2011; Rawat et al. , 2015).
Plates were oriented vertically in the growth chamber and scanned (GS-800 scanner, BioRad) after 0, 3, 6, 8 and 10 days of treatment. The root length of each seedling was measured with the ImageJ software. Root length at Day 8 and root elongation rate (in cm day -1 ) between Days 3 and 8 were used as primary criteria to monitor the inhibitory effect of Cd. Also, the tolerance index (TI) to Cd was calculated for each line by dividing the primary parameter (length or elongation rate) measured in the presence of Cd by the one measured in the control condition. One hundred TI were calculated for each line by random sampling of one value in the Cd and control conditions, respectively, with replacement at each draw.
| Gene expression data mining The expression of genes coding KMTs in A. thaliana seedlings challenged with Cd was analysed using data from the literature. First, we used our genome-wide CATMA microarray analysis of roots and shoots from 4-week-old A. thaliana plants exposed to 5 or 50 μM Cd for 2, 6 or 30 hr (GSE10675)  (Herbette et al. , 2006). Next, we collected four datasets from the Gene Expression Omnibus database. They correspond to (a) 2-week-old seedlings grown in MS/2 agar plates and treated with 70 μM Cd for 2 hours (GSE90701)  (Khare et al. , 2017) ; (b) roots from 5-week-old plants grown in hydroponics and treated for 7 days with 1 μM Cd (GSE94314)  (Fischer, Spielau, & Clemens, 2017) ; (c) roots from 3-week-old seedlings grown in hydroponics and challenged with 200 μM Cd for 6 hr (GSE22114)  (Li et al.
, 2010)  and (4) 7-day-old seedlings grown in MS/2 agar plates and treated for 6 hr with 200 μM Cd (GSE35869)  (Jobe et al. , 2012). Also, we analysed curated data from the comparative transcriptomic analysis of A. thaliana and A. halleri plants challenged with 10 μM or 25 μM Cd, respectively, in hydroponics for 2 hr  (Weber, Trampczynska, & Clemens, 2006). Relative expression levels of KMT genes were retrieved from the different experiments, the ratio between Cd and control conditions were calculated from pairwise comparisons, and a non-parametric Student's t test was performed on log2 ratio to determine differentially expressed genes (DEGs, with p-value <. 05) and the threshold was set at twofold (-1 ≤ log2 fold change ≥1). Finally, we used the RNA-seq data we obtained to analyse the tolerance strategies to Cd of two metallicolous populations of A.
halleri (BioProject PRJNA388549)  (Corso et al. , 2018). In this experiment, gene expression in the I16 and PL22 populations was analysed after 10 days of treatment with 5 μM Cd in hydroponics. For the identification of DEGs, we selected genes with more than 10 read counts in any of the triplicate, applied a non-parametric t test (p-value <0. 05) in pairwise comparisons, and used a 1. 4-fold change threshold value (-0. 5 ≤ log2 fold change ≥0. 5).
| Determination of Cd by inductively coupled plasma mass spectrometry Plant samples were dehydrated at 90 C, weighed for data normalization, and digested at 90 C for 4 hr in 65% (wt/wt) ultrapure HNO 3. Mineralized samples were diluted in 0. 5% (vol/vol) HNO 3 and analysed using an iCAP RQ quadrupole mass instrument (Thermo Fisher Scientific GmbH, Germany). 111 Cadmium concentration was determined using a standard curve and corrected using an internal standard solution of 103 Rhodium added online.
| Statistical analysis Non-parametric statistical analysis was performed on our datasets, which typically contain small sample sizes (n ≤ 20) and do not meet the assumptions of parametric tests (normal distribution and homogeneity of variance, as determined using the Shapiro-Wilk and Fisher tests, respectively). Multiple non-parametric comparisons were performed with the Dunnett's many-to-one test using the nparcomp package  (Konietschke, Placzek, Schaarschmidt, & Hothorn, 2015)  and the R computing environment. The Fischer's approximation method was used and the confidence level was set at 95%.
We used a filtering procedure adapted from  Alban et al. (2014)  to identify Lys-trimethylated peptides with high confidence. Also, MS/MS data from A. lyrata and A. halleri were searched against a database built from the A. lyrata genome, and not against the A. thaliana genome, to improve the identification of Lys-methylated peptides. Using this procedure, we were able to identify Lystrimethylated peptides belonging to nine proteins in the gel bands of interest in the range 25-30 kDa (Table  1 , Table  S2  and Figure  S3 ). In root samples, where two protein bands at 26-28 kDa were strongly labelled with the trimethyl-Lys antibodies in all species and conditions (Figure  1c ), we identified Lys-trimethylated peptides belonging to the Eukaryotic Elongation factor 1A (EEF1A), the   S2  and Figure  S3 , respectively.
| Expression of A. thaliana genes coding protein Lys methyltransferases in response to Cd In A. thaliana, 48 genes coding KMTs from the SDG have been identified  (Serre et al. , 2018). Only two KMTs belonging to the SBS superfamily have been characterized yet in plants, namely, the cytosolic enzyme CaMKMT that methylates calmodulin (CaM)  (Banerjee et al. , 2013)  and the PrmA methyltransferase that modifies ribosomal protein L11 in plastids and mitochondria  (Mazzoleni et al. , 2015). Using In order to determine whether Cd could regulate the expression of KMT genes in A. thaliana, we analysed transcriptomic datasets from published works  (Fischer et al. , 2017; Herbette et al. , 2006; Jobe et al. , 2012; Khare et al. , 2017; Li et al. , 2010; Weber et al. , 2006). These datasets correspond to different conditions of stress with variations in Cd concentration (1-200 μM), treatment duration (2 hr to 7 days), growth medium (agar plates or hydroponics), and stage of development (7-day-old seedlings to 5-week-old mature plants).
Also, the camkmt1 line was found significantly more tolerant to Cd than the wild type at each Ca concentration tested (Figure  6 ). Together, these data validated our screening approach and confirmed the identification of a Cd-tolerant A. thaliana mutant affected in the methylation of CaM. Then, we used inductively coupled plasma mass spectrometry (ICP-MS) to determine whether the difference in Cd-tolerance of camkmt1 could be due to changes in its capacity to take up the element from the environment. Cadmium was measured in roots and shoots of plants grown in hydroponics and challenged with various Cd concentrations (0. 2, 1 and 5 μM) for 7 days. There was no significant difference in the absorption and translocation of Cd in camkmt1 as compared to Col-0 (Figure  S6 ). Thus, the tolerance to Cd of camkmt1 was not due to changes in Cd accumulation but rather to an improved capacity to cope with the toxic element.
Data from A. thaliana samples were searched against the TAIR (2011) non-redundant database containing 35,387 sequences with the following parameters: trypsin as enzyme, 3 missed cleavages allowed, carbamidomethylation of cysteine as a fixed modification, and mono-, di-, tri-methylation of Lys, acetylation of Lys, N-terminal acetylation of the protein, deamidation of asparagine and glutamine, N-terminal pyromutamylation of glutamine and glutamate, and oxidation of methionine as variable modifications.
I G U R E 7 Molecular characterization of the A. thaliana sbs2 and sbs2b mutants. (a) Structure of the SBS2 locus. Splicing variants predicted in the Araport database are shown (https://apps. araport. org/thalemine/portal. do?externalids = AT1G63855). Thin lines represent introns, grey boxes represent exons, thick dark lines represent untranslated regions and triangles indicate T-DNA insertion sites in the sbs2 (GK-911F08) and sbs2b (SALK_037552) mutants. Primers A and B are indicated by arrows. (b) Amplification of SBS2 transcripts by PCR. PCR was performed using reverse transcribed total RNA from 10-day-old seedlings with Primers A (ATGATGACTACTACGACGACGAC) and B (CTCAATACGATCTCAACCAACTGA) for SBS2, and ACT7-F (ACATCGTTCTCAGTGGTGGTCC) and ACT7-R (ACCTGACTCATC-GTACTCACTC) for ACTIN7. PCR products were resolved by agarose gel electrophoresis.
ACKNOWLEDGMENTS This work was supported by grants from the  Région Auvergne Rhône-Alpes  (PhD to N. B. C. S. ), the  Fondation de Coopération Scientifique Rovaltain  ( PlantStressMetalPTMs  project), the  Toxicology Program of the Commissariat à l'Energie Atomique et aux Energies Alternatives , the  Department of Plant Biology and Breeding of the Institut National de la Recherche Agronomique , the  LabEx GRAL  ( ANR-10-LABX-49-01 ) and the  Fonds de la Recherche Scientifique-FNRS  ( PDR T. 0206. 13  to N. V. ). This article is dedicated to the memory of  Professor Roland Douce.
F I G U R E 7 Molecular characterization of the A. thaliana sbs2 and sbs2b mutants. (a) Structure of the SBS2 locus. Splicing variants predicted in the Araport database are shown (https://apps. araport. org/thalemine/portal. do?externalids = AT1G63855). Thin lines represent introns, grey boxes represent exons, thick dark lines represent untranslated regions and triangles indicate T-DNA insertion sites in the sbs2 (GK-911F08) and sbs2b (SALK_037552) mutants. Primers A and B are indicated by arrows. (b) Amplification of SBS2 transcripts by PCR. PCR was performed using reverse transcribed total RNA from 10-day-old seedlings with Primers A (ATGATGACTACTACGACGACGAC) and B (CTCAATACGATCTCAACCAACTGA) for SBS2, and ACT7-F (ACATCGTTCTCAGTGGTGGTCC) and ACT7-R (ACCTGACTCATC-GTACTCACTC) for ACTIN7. PCR products were resolved by agarose gel electrophoresis.
ACK N OWLED G M ENTS This work was financially supported by the European project: African Food Revisited by Research (AFTER, http://www. after-fp7. eu), FP7 245-025 and the Regional Council of Reunion Island  (FEADER 2008 -2013 Qipa). The authors are grateful to Thierry Goli, Michel Rivier, and Alice Le Gal for the PAH methods and Charlene Desby and Julien Ricci for their assistance in analysis.
| Data analysis All formal statistical analyses were conducted in R v3. 5. 0 (R Core Team, 2018). Statistical analyses were performed using generalized linear (mixed effects) models (GLM or GLMM, respectively). The appropriate distribution families were determined using a distribution fit (R package MASS;  Venables & Ripley, 2002). Minimal models were identified by step-wise reduction. Significance of terms was established with likelihood ratio tests (LRT). We validated the final models by inspecting the model outputs and diagnostic plots.
| Colony strength Number of adult bees was analyzed separately for the observation hives and the hives in the nine nonfocal orchards because of the differences in their setup (e. g. , observation hives had reduced entrances). In the nonfocal orchards, the four nucleus colonies were cohoused in two standard hive boxes. The numbers of bees per hive for the cohoused colonies were averaged prior to analysis, because there was considerable drifting of bees between directly adjacent colonies. The effect of treatment (covered vs. uncovered), day (continuous variable representing the number of days since introduction of hive into the orchard, scaled to 0-1 range), and their interaction on pretrial and posttrial counts of adult bees in hives was tested with a negative binomial GLMM (R package lme4;  Bates, Maechler, Bolker, & Walker, 2015).
| Foraging behavior The number and duration of trips individual bees made away from their colonies in the two focal orchards were calculated using a custom R script (see Supporting Information Section S3 for details). Trip duration was defined as the period between two consecutive detections at the outer RFID reader. Using the density distribution of log10-transformed trip lengths, we define three distinct types of trips: short trips <6 min, foraging trips, and overnight trips (Supporting Information Figure  S2 ). Only foraging trips were included in our analyses, as the other trips may serve a different purpose. For example, short trips may be defecation or orientation flights, which typically last between 2. 5 and 5 min  (Degen et al. , 2015). Overnight trips were infrequent (5% of trips in the covered orchard only) and could also.
| Pollination service delivery Honeybee abundance (bees per 1,000 flowers) was compared in the covered and uncovered focal orchards using a GLMM with a normal distribution and a log link function. The full model consisted of treatment, consecutive day of the trial (both defined the same way as for the colony strength model), their interaction, and random intercepts for quadrat ID and time of day at which the survey was performed (9:00, 11:00, 13:00, and 15:00; coded as ordered factor). The five highest bee counts (outliers) were removed from the dataset to improve fit. The number of honeybee visits received by flowers in focal orchards was compared using a negative binomial GLM (R package MASS;  Venables & Ripley, 2002). The visita- F I G U R E 2 Estimated number of adult honeybees in (a) 16 observation hives at two focal orchards (eight covered and eight uncovered) and (b) 18 hives at nine nonfocal kiwifruit orchards (four covered and five uncovered orchards).
Between 2 and 6 November, groups of test flowers (bagged prior to opening to prevent insect visitation) were unbagged and observed until a honeybee visited (n = 24 and 23 in uncovered and covered orchards, respectively). The visited flower was then labeled with the duration of the visit and rebagged along with an unvisited (control) flower. Any resulting fruit was harvested when mature (20 and 23 March). Missing fruit was not included in the dataset, as fruit may have been absent for reasons other than pollination failure. Seeds per fruit were counted (see Supporting Information Section S2). During the experiment, orchard managers blew milled kiwifruit pollen up into the canopy at a low rate (75 g/ha) in the covered focal orchard only. While pollen was not directly blown onto our bagged test flowers, more pollen was available to be moved by bees onto test flowers from surrounding flowers.
For this reason, in our analysis we included single visit data collected from two additional orchards using the same methods; a covered orchard, which did not have pollen applied artificially and visited in the same year as the current study (n = 19), and an uncovered orchard sampled in 2010 (n = 18; Goodwin, Evans, Cross, Janke, & Jacob, 2017). To assess pollination in the focal orchards, 20 open-pollinated, one-day-old flowers were marked in the middle and edge of the orchard, on three days (120 flowers total per orchard). All flowers were open to visits by bees before and after being marked. Missing fruit was not included in the dataset. The numbers of seeds were recorded for all harvested fruit.
1 H NMR spectra post-acquisition treatments All 1 H NMR spectra were aligned thanks to TMPS signal (δ = 0 ppm). An exponential weighting function corresponding to 0. 3 Hz line broadening was applied before applying Fournier transformation using Topspin® software (version 3. 2, Bruker Biospin, Germany). Phase correction was performed manually to have the greatest reproducibility  (Bharti & Roy, 2012). Baseline correction was performed automatically with MestReNova® NMR software (version 11. 0. 3, Mestrelab Research, Spain).