dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenomic Analyses All genomic nucleotide sequences were first annotated with PROKKA 1. 12 (Seemann, 2014) using the same parameters, i. e. , default parameters with no search of ribosomal and transfer RNA, to ensure consistent ORF detection among all sequences. At this stage, the genome sequence of O. anthropi SUBG007 was excluded from further analysis because of an excessive number of very short predicted proteins, suggesting a poor assembly quality. The remaining annotated proteomes were then used to create two datasets. Dataset 1 included all non-Brucellaceae Rhizobiales proteomes, the two Caulobacteriales proteomes, and 25 Brucellaceae proteomes representative of the family diversity. Dataset 2 included all downloaded and locally sequenced Brucellaceae proteomes as well as four other Rhizobiales proteomes to serve as outgroups.
Proteomes of Bartonella species were not selected as outgroups despite being the closest Brucellaceae's family because their highly reduced genomes would lower the number of proteins shared with the Brucellaceae and reduce the power of the analysis. Protein orthology relationships were inferred for both datasets independently using the OrthoMCL pipeline  (Li et al. , 2003)  based on all-against-all BlastP hits with an e-value of 10 -20. Among the 20,319 and 16,717 orthology groups returned, respectively 145 and 195 were retained because they included only orthologs (detected only once in each proteome) and were present in 100% and at least 95% of all proteomes, including all outgroups. We used 95% ortholog prevalence threshold for the Brucellaceae analysis because this dataset included a number of genomes originating from metagenomic data (Supplementary Table  S1 ) for which some conserved genomic regions were missing.
Proteins of each orthogroup were aligned independently using MAFFT v. 7. 310  (Katoh and Standley, 2013)  with the L-INS-I method and the resulting alignments were concatenated in a single large alignment for each dataset, of 78512 and 79,793 residues respectively, in which missing proteins in some orthogroups were replaced by tracks of gaps. To select the model of protein evolution that best fitted our two datasets, we performed a PROTTEST 3. 4. 2 preanalysis  (Darriba et al. , 2011). The program returned the LG model  (Le and Gascuel, 2008)  with a gamma model of rate heterogeneity, a proportion of invariable sites, and an empirical residue frequency as the best model for both datasets. RAxML v8. 2. 11  (Stamatakis, 2006)  was used to compute a maximum likelihood tree for each concatenated alignment, using the rapid bootstrap algorithm with the protein model returned by the PROTTEST analysis (GAMMAILGF) and 100 bootstraps.
Additional phylogenomic reconstructions were computed to test the positioning of Falsochrobactrum spp. genomes within the resulting trees. Each reconstruction was performed on the same selection of 25 Brucellaceae genomes used in Dataset 1 (and the genome of Mesorhizobium loti as an outgroup), for which F. ovis genome, F. shanghaiense genome, or none were discarded, respectively. Trees were calculated for these three combinations using each of the 145 and 195 orthogroups alignments obtained from Dataset 1 (Rhizobiales analysis) and Dataset 2 (Brucellaceae analysis), with RaxML and the same parameters as in the two first calculations. All trees were displayed and annotated using the iTOL v. 4 web interface  (Letunic and Bork, 2019).
16S rRNA Sequence Alignment Analysis The 16S rRNA sequence was extracted from each Brucellaceae genome used in Dataset 1 and were completed with 15 16S rRNA sequences from type strains of all Brucellaceae species with no genome available, downloaded from NCBI. The sequence of some other Rhizobiales were also added for comparison purposes. Pooled fasta were aligned using MUSCLE  (Edgar, 2004)  with default parameters using Ugene v. 1. 30 (Okonechnikov et al. , 2012). The alignment was manually inspected and unexpectedly similar sequence stretches found in phylogenetically distant strains were considered as hallmarks of past recombination events.
gANI Analysis All Brucellaceae annotated genomes from Dataset 2 were subjected to a pairwise gANI calculation using ANIcalculator v. 1  (Varghese et al. , 2015)  with rRNA genes excluded. As recommended in  Varghese et al. (2015) , all genome pairs with a gANI value > 96. 5 were considered belonging to the same species.
Comparison of the 16S rRNA Region The numerous discrepancies revealed in the previous section between our phylogenomic analysis and 16S rRNA-based phylogenies prompted us to investigate in more detail the 16S rRNA sequence of the Brucellaceae. We constructed an alignment using the 16S rRNA sequence extracted from all Brucellaceae genomes and several outgroups from Figure  1 , completed with 16S rRNA sequences for the Brucellaceae type strains not available as genome sequences in the NCBI database. The resulting alignment show numerous likely recombined regions involving diverse Ochrobactrum and non-Ochrobactrum species (see Table  1  and Supplementary Figure  S1 ). For instance, the O. gallinifaecis ISO 196 16S rRNA sequence is highly recombined, with three independent inferred recombined regions, all three of them shared with non-Ochrobactrum species or possibly with Aquamicrobium aerolatum from the family Phyllobacteriaceae (Table  1 ).
Falsochrobactrum Species Phylogenetic Positioning To go further in the taxonomic analysis of the Brucellaceae, a new core-proteins tree was constructed, including all non-Brucella Brucellaceae genomes available in GenBank at the   S1, S2 ). The topology of this new tree is highly congruent with the topology observed for the Brucelleaceae type strains in the Rhizobiales tree, except for the position of Falsochrobactrum spp. (Figure  2  and Supplementary Figure  S2 ). Indeed, although the two Falsochrobactrum species cluster together in both topologies, they branch with maximum support as a distinct genus in the Rhizobiales tree (Figure  1 ) but within the Ochrobactrum diversity in the Brucellaceae tree (Figure  2 ). To test whether this discrepancy was caused by the set of included genomes or by the set of core proteins used, additional phylogenetic trees were constructed using the same representative Brucellaceae type strains with/without Falsochrobactrum genomes, and with each core protein dataset.
When the two Falsochrobactrum genomes are included in the analysis, both calculations provide the same topology than the original trees (see Supplementary Figures  S3A, B ). When each single Falsochrobactrum genome is included alone, the branching is similar whatever the protein dataset: outside the Ochrobactrum clade for F. ovis, and inside the Ochrobactrum clade for F. shanghaiense (see Supplementary Figures  S3C-F ). This inconsistent positioning suggests that extensive recombination events occurred in F. shanghaiense or F. ovis, or both, and questions the classification of Falsochrobactrum spp. as a new genus, at least from a genomic point of view. Unfortunately, the deep branching of these genomes within the Brucellaceae and the lack of sequenced close relatives hamper any further investigation using existing algorithm of recombination detection.
Finally, the present work provides new elements on the question of the Brucella species delineation, and its status as a genus. Species definition has been problematic for numerous bacterial taxa because of a lack of a strong and universally accepted theoretical basis  (Gevers et al. , 2005). Our finding that all Brucella species can be considered as a single species according to gANI scores is actually also supported by the approach of the Genome Taxonomy Database (GTDB 2  )  (Parks et al. , 2018). This approach is an alternative to the official prokaryotic taxonomic nomenclature in which taxonomic ranks are defined according to a strict monophyly among groups and thresholds based on corrected genomic distances. Interestingly, under the GTDB framework, Brucella remains a genus, consisting of a single species as initially proposed by  Verger et al.
and revealed that a number of genome sequences available in GenBank were incorrectly identified. Indeed, phylogenomic and gANI analyses indicate that O. anthropi FRAF13 belongs to the tritici clade, O. pituitosum AA2 to the pseudogrignonense clade, and both O. rhizospherae SJY1 and O. thiophenivorans MYb6 to the pituitosum clade (see Supplementary Figure.
DATA AVAILABILITY STATEMENT The datasets generated for this study can be found in the ncbi Biosample, SAMN12821270, SAMN12821271, SAMN12821272, SAMN12821273, SAMN12821274, SAMN12821275, SAMN1282 1276, SAMN12821277, SAMN12821278, SAMN12821279, SAMN12821280, SAMN12821281, SAMN12821282, SAMN 12821283, SAMN12821284, SAMN12821285, SAMN12821286, SAMN12821287, SAMN12821288, SAMN12821289, SAMN 12821290, SAMN12821291, SAMN12821292, SAMN12821293, SAMN12821294, SAMN12821295, SAMN12821296, SAMN 12821297, SAMN12821298, SAMN12821299, SAMN12821300, and SAMN12821301.
Cats Eighty domestic shorthair cats were assigned to two groups of 40 cats each, and were balanced for sex, age, weight and body condition score (BCS). Several cats were removed before the start of the study following a failure to adjust to their social group, and others had missing data owing to veterinary care that they needed during the study. Only cats with complete data were included in the analyses, resulting in final groups consisting of 31 cats in the control group (age 7. 5 ± 4. 1 years; weight 4. 4 ± 0. 8 kg; BCS 5. 7 ± 0. 8/9; 58% of BCS >5/9) and 38 cats in the test group (age 8. 3 ± 4. 0 years; weight 4. 2 ± 0. 9 kg; BCS 5. 3 ± 0. 8/9; 34% of BCS >5/9). The cats were group housed in an enriched environment. They had controlled individual food access via an electronic feeding system and ad libitum water access.
Data collection periods The feeding patterns and behaviour of both groups were assessed before the start of the treatment, 9 months after the start of the calorie restriction and, finally, 1 month after the end of the calorie restriction: reference test periods T0 (ad libitum), T + 9 months (T9; restriction) and T + 10 months (T10; ad libitum). The foods offered during the reference test periods were always the same commercial diets offered and followed the same schedule. These included different wet products (n = 6), dry (n = 6) and treat products (n = 2). Treats are not presented in the following results because the reference data were missing following a technical issue.
Recorded data The BCS and body weight of each cat were measured monthly over the entire study, except during the final ad libitum period (T10) when weighing was done on a weekly basis. The BCS scoring was always done by the same trained person who was blind to the treatments of the cats. The feeding patterns of the cats were automatically measured during each reference test period using an electronic feeding system (see the tables referred to in the 'Results' section for the complete list of feeding parameters recorded). The agonistic interactions, including physical contact, as well as avoidance, were recorded during 3 mins of food anticipation before the first meal of the day (ie, wet food) based on videos. Two days of video were collected for each group and each reference test period.
Statistical analyses Generalised linear mixed models were fitted to account for the correlated data (ie, the same cat participated in the test multiple times) to compare all feeding parameters between periods by group. A linear mixed model was also used to assess the weight evolution of the cats, treating time as continuous and including the time-BCS category interaction. No statistical analyses could be performed on the agonistic interactions data owing to the data structure. P values <0. 05 were considered to be significant, and as showing a trend if they were between 0. 05 and 0. 1. All statistical analyses were performed in R 3. 5. 2  12 and SAS 9. 4 (SAS Institute). Results of feeding patterns and agonistic interactions are presented for the test group as a whole. Indeed, nonoverweight cats of the test group showed significant changes in their feeding patterns between T0 and T9 in response to the end of ad libitum feeding that were similar to those seen in overweight calorie-restricted cats.
Acknowledgements The authors would like to thank all collaborators for contributing to various aspects of the research work presented here. Special thanks to  Drs Sébastien Feurté  and  Anne-Lise Guigon  for the determination of meal requirements and tracking; to the pet care team for the organisation of the feeding and daily setting of the calorie cut-off; to  Doug Stiebler  for technical support with the electronic feeding system and interface; to  Anaïs Sanna  and  Noémie Caël  for data handling; to  Xuemei Si  and  Hanne Vlaeminck  for statistical analysis; to  Dr Jean-François Savard  for sharing his pet welfare expertise; to  Dr Laurence Colliard  for sharing her nutrition expertise and critical review of this work; and to  Allen Dryer  for the review of this manuscript.
Data were analyzed by the manufacturer's software (Cepheid). The amount of eCFU was numbered using the calibration curve. Absence of PCR inhibitors was assessed by detection of a 110-bp-long fragment of a human housekeeping gene (β-globin)  [28]. Detection and identification of mutations associated with clarithromycin resistance (A2142G, A2142C and A2143G) were performed using a multiplex real-time Scorpion PCR assay  [29]. Each run was validated using positive (DNA extracts of sequenced strains) and negative (molecular-quality distilled water) controls. As Scorpion PCR is easier, faster and cheaper than E-test, more isolates were analyzed with Scorpion PCR  [29]. Random amplified polymorphism DNA reaction (RAPD-PCR) was carried out as previously described on a Perkin-Elmer GeneAmp PCR system 2400 thermal cycler (Perkin-Elmer Cetus, USA).
Statistical analysis Statistical analysis was performed using Med-Calc @ 12. 1. 4. 0 (MedCalc Software® -Mariakerke -Belgium). Normality of distribution was assessed using the Kolmogorov-Smirnov test. Non-parametric data were expressed as median [IQR]. Friedman's test was used to detect significant variations for MAP, HR and PTA between time-points within groups. Kruskal-Wallis test was used to compare MAP, HR and PTA variations between groups. For each group, ΔPTA calculated over 1 min at each time-point was used to perform a Receiver Operating Characteristics (ROC) curve to detect the performance of ∆PTA to predict an increase of MAP and/or HR within 5 min. A p-value < 0. 05 was considered statistically significant.
Sequence Data Processing Reads were cleaned and analyzed using Mothur v. 1. 39. 5 following the Miseq standard operating procedure 1  (accessed April 2018). First, feces and eggshell sequences were analyzed together. Reads from each sample set were combined using the make. contigs command. Sequences containing polymers or ambiguity were rejected using screen. seqs and identical sequences were merged with the unique. seqs command. The remaining sequences were aligned using the Silva reference files, release 128 2  and the chimeras were removed. Feces and eggshell sequences were segregated using remove. groups. Sequences originating from the control eggshell sample were removed from the eggshell sequences. The new eggshells dataset and the feces dataset were merged into merge_dataset for the remaining analysis using the merge.
Alpha-and Beta-Diversities For alpha-diversity analysis, the species diversity within a sample, the number of observed OTUs, Inverse Simpson's and Shannon even indices were calculated using a subsample with the size of the smallest library with 1,000 iterations; feces and eggshell samples were treated separately. The results were compared between groups using Student's t-test (unpaired and paired) and Kruskal-Wallis test with a significance level of 0. 05. These statistical analyses were run on GraphPad Prism 8 (GraphPad Software, LaJolla, CA, United States). For betadiversity analysis, a measure of similarity between sample pairs, a distance matrix with the similarity values for all pairwise comparisons (t = Day 0 and t = 4 weeks, flocks 1 to 12, feces and eggshells) was created using the Jaccard index based on shared or distinct species, and the Yue & Clayton index which includes the species proportions of both the shared and distinct species.
The different groups were statistically compared using the Analysis of molecular variance (AMOVA) with a significance level of 0. 05 and visualized using 2D non-metric multidimensional scaling (NMDS). Biomarkers associated with feces or eggshells were highlighted using the linear discriminant analysis effect size (LEfSe). Finally, OTUs rarely observed in the final merge_dataset (nseqs = 1) were removed using the remove. rare command and a Venn diagram was generated to reveal the OTUs shared among groups. Command lines used in Mothur are available at https://github. com/CRSV. Raw sequences can be found on CNBI SRA database under accession number PRJNA602334.
Fecal Microbiota The number of DNA sequences for the 96 feces samples ranged from 39,680 to 105,243 with an average of 62,661 sequences per sample, and a total of 5,876,676 sequences. The vast majority of sequences, 99. 8%, were of bacterial origin, whereas 0. 2% were from Archaea. A total of 22 and 20 different phyla were revealed based on searches against the Silva and RDP databases, respectively (Supplementary Table  S2 ). For all feces samples, the number of phyla ranged from 7 to 15 with an average of 12 per sample according to RDP, and from 7 to 18 with an average of 14 per sample according to Silva. Four phyla, Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes, showed a relative abundance > 1% of the total fecal microbiota according to each database (Figure  1A ). According to both databases, these results are nearly identical.
Likewise, a total of 169 and 193 families were found according to Silva and RDP databases, respectively (Supplementary Table  S2 ). For all feces samples, the number of families ranged from 56 to 91 with an average of 71 according to RDP, and from 60 to 104 with an average of 80 according to Silva. Families with a relative abundance > 1% according to each database are shown in Figure  2A. Again, according to both databases, these results show a high degree of similarity, except for the presence of Clostridium_sensu_stricto_1 (3%) revealed by Silva and Bacteria_unclassified (1%) by RDP. Genera relative abundance by sample at the genus level was also described at both sampling times (Supplementary Table  S4 ).
Eggshell Microbiota The number of DNA sequences from the 168 lab wipes ranged from 546 to 6,216 with an average of 1,233 sequences per lab wipe and a total of 207,225 sequences. Most sequences, 99. 6%, were bacteria, and 0. 1% were Archaea. A total of 26 and 29 different phyla were identified based on the Silva and RDP databases, respectively (Supplementary Table  S2 ). For all samples, the number of phyla ranged from 6 to 20 with an average of 11 per sample according to RDP, and from 8 to 22 with an average of 14 per sample according to Silva. The phyla with a relative abundance > 1% of the total eggshell microbiota according to each database are shown in Figure  1B. 6 and 5 phyla were revealed, based on the Silva and RDP databases, respectively. Of these, four phyla, Firmicutes, Actinobacteria, Proteobacteria and Bacteroidetes, showed similar relative abundances according to both databases.
The main differences were observed for the relative abundance of Tenericutes (3%) and Bacteria_unclassified (1%) according to Silva and an absence of Tenericutes and a relative abundance of Bacteria_unclassified (6%) when using RDP. A total of 344 and 257 families were found according to the Silva and RDP databases, respectively (Supplementary Table  S2 ). For all samples, the number of families ranged from 48 to 118 with an average of 72 families according to RDP, and from 49 to 104 with an average of 82 families according to Silva. Families with a relative abundance > 1% according to each database are shown in Figure  2B. Several differences were revealed according to each database, not only in the relative abundance of some families but also in the presence or absence of specific families, most notably the presence of Mollicutes_RF9_fa, Enterococcaceae, and Bacillaceae according to Silva and the presence of Porphyromonadaceae, Bacteroidales_unclassified, Actinomycetales_unclassified, Firmicutes_unclassified, Peptostreptococcaceae, and Bacillaceae_2 according to RDP.
ACKNOWLEDGMENTS These results were obtain during Sandrine Trudeau master at the  University of Montreal  (Trudeau, 2019). Her memoire containing a first non-edited version of the current manuscript, can be freely access here: http://hdl. handle. net/1866/22620. We thank  Ludovic Lahaye  and  Benoît Lanthier  from  JEFO Nutrition Inc. for their collaboration, and  La Coop  fédérée for their participation.
Introduction: Phthalates, a group of ubiquitous industrial chemicals, have been widely used in occupational settings, mainly as plasticizers in a variety of applications. Occupational exposure to different phthalates has been studied in several occupational settings using human biomonitoring (HBM). Aim: To provide a comprehensive review of the available literature on occupational exposure to phthalates assessed using HBM and to determine future data needs on the topic as part of the HBM4EU project. Methods: A systematic search was carried out in the databases of Pubmed, Scopus, and Web of Science for articles published between 2000 and September 4, 2019 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 22 studies on the occupational HBM of phthalates was considered suitable for review.
, 2017). In the framework of the EU HBM initiative known as HBM4EU (www. hbm4eu. eu), a state-of-the-art review of occupational HBM data on phthalate exposure was performed. The aim of this review was to identify the industry fields/work tasks that may result in the highest exposure to phthalates. In addition, we provide an overview of available knowledge on occupational HBM of phthalate exposure, with particular attention given to the following: i) the specific biomarkers used (old and newer phthalates); ii) the (current) biomonitoring levels of workers in different occupational settings; iii) the observed links between biomarkers of exposure and health effects; and iv) the future needs in occupational HBM of phthalates in Europe.
Methods According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was carried out in the databases of PubMed, Scopus, and Web of Science for articles published between 2000 and September 4, 2019. Initial search terms were developed to identify studies in occupational settings that use HBM of phthalates (metabolites) as well as HBM studies/surveillance programs that incorporate the monitoring of phthalate exposure. One search strategy was developed and adapted to each database (PubMed, Scopus, and Web of Science). In the databases, we used a combination of free text terms, and the search strategy was developed with low specificity to the advantage of high sensitivity: broad search terms included phthalate, workplace, worker, and occupation.