dataesr/scientific-paragraphs-categorization huggingface dataset:
All non-H atoms were refined with anisotropic displacement parameters. CCDC 1829298 (2) contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif Computational Chemistry calculations were performed within the Density Functional Theory (DFT) framework. The hybrid functional used was B3LYP  [44, 45]  in conjunction with Grimme's D3 dispersion correction,  [46]  along with the triple-zeta Def2-TZVP basis set  [47]  and Stuttgart effective core potentials  [47]  for the relativistic core electrons of the Pd atom. Optimization criteria were set to tight, vibrational analysis being performed on the optimized structures. The integration grid used was of 99 radial shells and 950 angular points for each shell (99,950).
ACKNOWLEDGEMENTS This work was supported by a grant of  Ministery of Research and Innovation ,  CNCS -UEFISCDI , project number  PN-III-P4-ID-PCE-2016-0351 , within PNCDI III, by the  Babeș-Bolyai University of Cluj-Napoca  and by the  Centre National de la Recherche Scientifique (CNRS)  and the  Université de Toulouse (UPS).
The immobilized proteins were eluted in buffer B (50 mM Tris-HCl, pH 8, 150 mM NaCl, 500 mM imidazole). The loaded, flow-through, and elution fractions were analyzed by SDS-PAGE and immunodetection. For competition experiments, the complete CbpD copurification IMAC procedure for XcpY L , XcpZ M , and XcpY L together with XcpZ M was performed in quadruplicate. The total amount of XcpY L or XcpZ M proteins in the eluate fractions (in percent; compared with the total amount in the loaded fraction) was measured and quantified from immunoblots of each replicate using ImageJ software. Microsoft Excel software was used for data processing and presentation. Statistics were determined using the Student's t test function of Excel using a bilateral model and assuming equal variance.
Xcp proteins were dialyzed against IB before titration experiments. To avoid nonspecific binding of XcpY L p or XcpZ M p to the streptavidin biosensors, the biosensors were incubated with 10 ug/ml biocytin (Sigma) for 200 s. In all experiments, the response of the nonbiotinylated proteins on the free sensors was subtracted during experiment processing. The K D values were calculated using GraphPad Prism 5. 0 software on the basis of the steady-state levels of the responses in nanometers, directly related to the concentration of the Xcp protein. Effector recognition in type 2 secretion.
Bacterial two-hybrid system and statistical analysis To investigate the interaction between XcpZ M and XcpY L periplasmic domains, competent cells of strain BTH101 were cotransformed with pUT18C and pKT25 derivatives, and bacteria were grown for 48 h at 30 °C on LB plates containing Ap100, Kan50, and Sm100. Colonies were picked at random; inoculated into 600-l cultures in LB containing Amp100, Kan50, Sm100, and 0. 5 mM IPTG; and grown overnight at 30 °C. ␤-Gal activity was measured as described  (45). At least two independent experiments were performed with three randomly picked transformants. Mean values are presented as bar graphs, and error bars indicate S. D. Microsoft Excel software was used for data processing and presentation. Statistics were determined using the Student's t test function of Excel using a bilateral model and assuming equal variance.
Acknowledgments-We thank  V. Pelicic  for careful reading of the manuscript;  D. Byrne  from the protein production platform of our institute (IMM) for  BLI facility ;  N. O. Gomez  and  S. Bulot  for help with statistical analysis;  E. Bouveret  for BACTH plasmids; and  S. Rinaldi ,  O. Uderso ,  M. Ba ,  I. Bringer , and  A. Brun  for media and material preparation.
Antibiotic resistance (AR) gene families in the metagenomes We started by evaluating the quantity of different AR proteins present in the metagenomes under study. We wanted to know how does the diversity of antibiotic resistance gene families' homologues (that is, ARd, the number of different protein families) vary with the metagenome protein family richness (that is the total number of proteins families in a metagenome). To answer this question, we used a data set of environmental metagenomes issuing from diverse ecosystems and biomes, such as oceans, coral atolls, deep oceans, Antarctic aquatic environments and snow, soils, hyper saline sediments, sludge's, microbial fuel cell biofilms, and animal microbial populations  (22). In this dataset, there is a broad variation in AR gene diversity (ARd) (Fig  1A ).
However, there is a correlation between ARd and the protein family richness of the various metagenomes (overall correlation of 0. 754). In Fig 1A, regression lines for human gut microbiome (in blue, slope 0. 0037) and for other biomes (in black, slope 0. 0052) are shown. Nevertheless, the p-value for the difference of these slopes is 1. 4e-02, which is significant but not highly significant, does not allow, in our opinion, to draw solid conclusions from this difference. Another dataset composed by 110 human gut metagenomes, sampled from healthy individuals with ages ranging from 0. 05 to 53 years of age, spanning different regions of the world such as: US, Malawi, and the Venezuelan Amazon was also studied  (23). Virulence factor (VF) gene families in the metagenomes Bacterial virulence factors are proteins that enable pathogenic bacteria to parasitize a host, including gene products involved in adhesion and invasion, secretion, toxins, and iron acquisition systems  (24).
The environmental microbiomes of our dataset reveal a great diversity of virulence factors protein families' (VFd) densities (Fig  1B ). Since virulence may also be associated with the colonization of different types of biomes (some virulent traits are involved in adaptation to new environments, by allowing bacteria to adhere and colonize substrates, to access to resources such as iron, among other functions) besides the context of infection, one can expect different types of these genes in environmental microbiomes. Moreover, Fig  1B  shows that in human gut microbiomes, VFd versus protein family richness of the metagenomes achieves an overall correlation coefficient of 0. 669. Regression lines for human gut microbiome (in blue, slope 0. 0035) and for other biomes (black, slope 0. 0106) are shown.
90). When we draw the regression line for the human gut microbiomes from this dataset (human gut), we can see that the ratio of (residuals ARd)/(residuals VFd) is higher with slope of 0. 63 than for the case of environmental metagenomes with slope of 0. 44. The difference in slopes is significant, but not very significant, with p-value of 3e-02. Despite the fact that the values for ARd and VFd are lower in gut metagenomes (see  Fig 1) , one can witness a higher ratio ARd/VFd (larger slope) of metagenomes pertaining to the human faeces biome relatively to the slope calculated for all environmental metagenomes (Fig  3 ). This suggests that there could be a greater accumulation of ARd over VFd for this particular biome than in environmental ones. We can further distinguish different trends upon geographical localization of the human populations under study.
It has been reported that there is widespread resistance to almost all of antibiotics that are empirically used in Malawi due to the lack of routine microbiological culture and sensitivity testing  (36) , but also due to self-medication. Human gut samples from the Malawi people of the dataset used in the present study are described as having a high overall resistance potential with an over representation of cephalosporin and tetracycline resistance genes which may suggest extensive use of old, broad-spectrum antibiotics, a known problem in many developing countries  (27).
Metagenomic datasets Our human gut query cohort included 110 previously studied, and publicly available metagenomes pertaining to individuals from different regions of Venezuela, Malawi, and the US, as well as a broad age span (0. 05 to 53 years)  (23)  (dataset of the project mgp98 "Human gut microbiome viewed across age and geography (WGS)" at the MG-RAST metagenomic analysis server). All the metagenomes files were generated by the same team and project, and by using the same bioinformatics pipeline. Our environmental cohort comprised 64 previously selected, and publicly available environmental metagenomes, belonging to 12 different biomes (dataset collection of the metagenomes referred in the "Metagenomic mining for microbiologists" study)  (22). Although Delmont's team report using a dataset comprised of 77 metagenomes, there are only 70 MG-RAST accession numbers present in the article' s appendix, of which only 64 are publicly available.
Both project's metagenomes were downloaded from MG-RAST on the 29th of January 2016, under FASTA format, making use of successive calls to its Application Programming Interface (API) (42) using the respective MG-RAST's accession numbers available in the aforementioned bibliography. Each FASTA file comprised clustered protein-coding sequences, retrieved from MG-RAST' s file-formatting pipeline (550. cluster. aa90. faa files). The protein sequences enclosed in these files are clustered at 90% identity, containing non-redundant translated sequences. These proteincoding formatted FASTA files contain the translation of one representative from each cluster. Thus, the numbers of protein sequences for a given metagenome used here represent its richness.
BLAST, VFDB and Resfams For every metagenome present in our query, a BLASTP  (43)  search was performed against the VFDB database for bacterial virulence factors families (Virulence Factors of Pathogenic Bacteria VF. pfasta dataset of the Institute of Pathogen Biology, China)  (24)  and the Resfams AR Proteins database of bacterial antibiotic resistance protein families (Resfams AR Proteins -v1. 2 database of the Dantas Lab at the Washington University School of Medicine, St Louis)  (44). An alternative approach made use of the Antibiotic Resistance Genes Database (ARDB) (reference antibiotic resistance genes dataset resisGenes. pfasta of the Center for Bioinformatics and Computational Biology University of Maryland)  (45) , but several hindrances concerning its sub-classification by functional antibiotic resistance protein families made us discard the possibility of using such database to address of AR and VF protein family co-occurrence in metagenomes.
The BLAST+ executables package was downloaded on the 17th of November 2015 (ncbiblast-2. 2. 31+ version). The VFDB was downloaded on the 11th of November 2013 (31 classified FASTA files of bacterial virulence factor sub-families), and the Resfams database was downloaded on the 29th of January 2016, (123 classified FASTA files of bacterial antibiotic resistance protein subfamilies. Every protein enclosed in each of the addressed metagenomes, was used as a query in order to search for similarities to either AR or VF protein-coding traits. Hence, we aimed at retrieving the best hit (best scored alignment) that enabled us to assign an AR or VF function to each of the aforementioned metagenomic proteins. Every BLASTP search was performed with a very stringent e-value cut-off of 1e-15 (10 orders of magnitude lower than the conservative commonly used E-value of 1e-5).
Next, we filtered the resulting output files, as to only retrieve the alignment hits with >60% coverage of the query size, and whose query and subject length ratio is between 0. 75 and 1. 5. When using a combination of this very stringent e-value of 1e-15 together with coverage, and length ratio filters, we expect to only retrieve true homologues, avoiding false positives. Furthermore, from all the generated alignments between our metagenomic query cohort and the preceding databases, we computed the representative counts for the different gene families that coded for either AR or VF traits. Thus, the amounts of different classes (gene families) that are present in a given metagenome represent its diversity in terms of AR or VF traits. We have also removed hits for proteins that aligned with both antibiotic resistance and virulence factor proteins (25.
Acknowledgements We thank  Eduardo P. C. Rocha  for critical reading of the manuscript and constant interest and suggestions since the beginning of this work,  Guillaume Sapriel  for suggestions to improve the methods,  Pedro David ,  Eric Duchaud ,  Guillaume Achaz , and all the members of our teams for the fruitful discussions,  Octávio Paulo ,  Francisco Pina-Martins  and  Sophie Brouillet  for the technical help, and granting access to the servers. Conceived and designed the experiments: JP TN. Performed the experiments: PE. Analysed the data: JP FD TN. Wrote the paper: JP FD TN.
Cloning of CM3, CM16, 0. 28, Expression in P. Pastoris and Purification Amplicons corresponding to the three cDNA clones are reported in Figure  1A. The sizes of the three amplicons correspond to the expected ones calculated on the basis of the nucleotide sequence (507 bp for CM3, 432 bp for CM16 and 456 bp for 0. 28). The sizes of the three amplicons correspond to the expected ones calculated on the basis of the nucleotide sequence (507 bp for CM3, 432 bp for CM16 and 456 bp for 0. 28). As regards the 0. 28 gene, we followed indications coming from previous papers (among which Sanchez-Monge and colleagues  [17] ) reporting that the mature protein begins at position 31 (thus with SGPW-), differently from what indicated by the SignalP software used for prediction. A first attempt to separately express CM3, CM16 and 0.
e. , HisTrap HP 5 mL, Figure  2 ) yielding 0. 080, 0. 45 and 0. 32 mg/mL -1 of CM3, CM16 and 0. 28, respectively. Plants 2018, 7, 101 4 of 13 As regards the 0. 28 gene, we followed indications coming from previous papers (among which Sanchez-Monge and colleagues  [17] ) reporting that the mature protein begins at position 31 (thus with SGPW-), differently from what indicated by the SignalP software used for prediction. A first attempt to separately express CM3, CM16 and 0. 28 proteins was done in Escherichia coli Rosetta Gami strain. The coding regions of the three genes were cloned in the pOPINE vector and the transformed E. coli cells were induced with isopropyl B-D-thiogalactoside (IPTG) but the proteins were not expressed either in the soluble or insoluble fraction (data not shown). This result prompted us to use the eukaryotic P.
Plant Material and Genes Isolation Total RNA was extracted from 28 days post-anthesis kernels of the bread wheat cultivar Bobwhite using the RNeasy Plant Mini Kit (Qiagen Italia, Milan, Italy) according to manufacturer's instructions and first strand cDNA was obtained using the QuantyTect®Reverse Transcription kit (Qiagen Italia, Milan, Italy). Primers were designed using the GenBank accession numbers AY436554. 1, X17573. 1 and AJ223492. 1, respectively relative to CM3, CM16 and 0. 28. cDNAs were amplified by Reverse Transcription-PCR (RT-PCR) using the primers CM3_F/CM3_R for CM3, CM16_F/CM16_R for CM16, 0. 28_F/0. 28_R for 0. 28 (sequences are listed in Table  S1 ). High fidelity AccuPrime Taq DNA Polymerase was from ThermoFisher Scientific (Waltham, MA, USA). The PCR was carried out by repeating for 25 times the following cycle: 1 min at 94 • C; 45 s at 60 • C; 1 min at 68 • C.
Amplification products were purified by using the Wizard®SV Gel and PCR Clean-Up System (Promega Italia s. r. l. Milan, Italy), cloned into pGEM-T Easy Vector (Promega Italia s. r. l. Milan, Italy) and checked by nucleotide sequencing. Sequence analyses were performed using the DNAMAN software (Lynnon Biosoft, Quebec, CA) that uses the ClustalW algorithm for multiple sequence alignment. The region encoding the mature protein of each gene was inserted into the pPICZαA vector using the In-FusionTM Advantage PCR Cloning kit (Clontech Laboratories, Mountain View, CA, USA) with primers CM3_For_inf/CM3_Rev_inf, CM16_For_inf/ CM16_Rev_inf and 0. 28_For_inf/0. 28_Rev_inf (sequences are listed in Table  S1 ). The PCR was carried out by repeating for 30 times the following cycle: 10 s at 98 • C; 30 s at 60 • C; 1 min at 72 • C.
Protein Identification by Mass Spectrometry Proteins were in-gel digested by trypsin (Promega). Resulting peptides were analyzed by LC-MS/MS using an ESI-Q-Exactive Plus mass spectrometer (ThermoFisher Scientific, San Jose, CA, USA) coupled to an Ultimate 3000 RSLC nano liquid chromatography system (Dionex, Sunnyvale, CA, USA). Peptides were eluted onto a C18 column (Acclaim PepMap RSLC, 75 µm i. d. × 150 mm, 2 µm, 100 Ǻ, Dionex) with a 52 min linear gradient from 6% to 40% of mobile phase B (80% acetonitrile and 0. 1% formic acid in water) in A (0. 1% formic acid in water). Peptides were detected in a positive ion mode using Top 10 data dependent workflow (extended protocol in Nguyen et al. [33] ). Spectra were analyzed with Proteome Discoverer software (ThermoFisher, version 2. 1. 0. 81) using the following parameters: (1) protein database composed of sequences of CM3, CM16 and 028 proteins, human keratins, pig trypsin and Komagataella pastoris (NCBI Taxonomy ID 4922, 5317 entries); (2) enzyme used: trypsin (2 missed cleavages); (3) fixed modification: carbamidomethyl (Cys); (4) variable modification: oxidation (Met); (5) precursor mass tolerance: ± 10 ppm; (6) fragment mass tolerance: ± 0.
Acknowledgments:  Rémy Puppo  and  Régine Lebrun  from the  Mediterranean Institute of Microbiology Proteomic Facilities, Aix-Marseille University ,  CNRS FR3479 ,  Marseille Protéomique , IBISA labeled, are acknowledged for mass spectrometry analyses. Florence Pineau  from  INRA-BIA, Nantes (France)  is acknowledged for technical assistance in degranulation experiments.
Identification of sequence and structural variants in GJA8. The human gene GJA8 presents one isoform (NM_005267. 4), comprising of two exons, with the coding sequence (CDS) entirely contained within exon 2. Sequence variants in the CDS were detected using a combination of Next-Generation Sequencing (NGS) methods and direct sequencing: 35 patients were screened by whole exome sequencing (WES), 207 patients using different targeted NGS panels of eye development genes including GJA8, and 184 patients by Sanger sequencing, which was also used to validate NGS findings and check family segregation. Additionally, CNV data was available for 188 of these patients: 151 individuals (96 UK and 55 Spanish) had been assessed by array-based Comparative Genomic Hybridization (aCGH), with resolutions ranging from 44 kb to 244 kb, whereas for 37 Spanish individuals, CNVs were detected from NGS data using a read depth comparison approach.
A detailed description of the different methods can be found in the Supplementary Materials and Supplementary Table  1. The genomic coordinates of the sequence and structural variants are reported according to Build GRCh37/hg19. The allelic frequencies of the sequence mutations were obtained from the Genome Aggregation Database (gnomAD, http://gnomad. broadinstitute. org/) (Lek et al. , 2016). For each variant of interest, amino acid conservation across species was visually inspected using the Vertebrate Multiz Alignment & Conservation (100 Species) track from the UCSC Genome Browser. Three conservation scores were annotated using the database dbNSFP v. 3. 3  (Liu et al. , 2016) , specifically the GERP++ Rejected Substitutions (RS) score  (Davydov et al. , 2010) , phyloP 100way_vertebrate score  (Siepel et al.
Point mutations identified in GJA8. Screening of the GJA8 coding region in our cohort of 426 individuals with AMC detected 10 missense variants in 11 unrelated families (Tables  1  and 2 ). For each missense variant, the amino acid conservation across species is shown in the Supplementary Fig. 1. Taking into account the segregation patterns, the frequency of the variants in public databases of unaffected individuals (Table  2 ), in silico predictions of functional effects and previous reports from the literature (Table  2 ), as suggested by  (Richards et al. , 2015) , six of these variants (p. (Thr39Arg), p. (Trp45Leu), p. (Asp51Asn), p. (Phe70Leu), p. (Gly94Arg) and p. (  Val97Gly )) were considered likely causative, giving a frequency of 1. 6% of independent individuals with AMC conditions (7/426) carrying likely pathogenic GJA8 sequence variants.
1-q21. 2, characterised by the presence of numerous segmental duplications (SDs), which make the region susceptible to recurrent rearrangements. To investigate whether structural variants affecting GJA8 were present in our cohort of families with AMC, we examined a subset of 188 unrelated individuals for whom copy number information was available from aCGH and/or NGS data. As such, the samples for which CNV data were generated were not chosen according to any selection criteria applied across the total cohort, and therefore they effectively represented a randomly-selected subset of independent AMC cases. This resulted in the identification of 1q21 microdeletions in five families (Fig. 2 , Table  1 ). The first microdeletion was identified in a proband (family 12, Fig. 2b ) with bilateral coloboma of the iris and choroid, mild dysmorphic features (broad forehead, narrow palpebral fissures, depressed nasal root and low set ears), scoliosis, genu valgum and gastroesophageal reflux.
These variants were bioinformatically predicted damaging and have not been reported in unaffected individuals according to public databases. Interestingly, three of these changes (p. (Thr39Arg), p. (Trp45Leu) and p. (Asp51Asn)) have been previously described in families with cataracts  (Sun et al. , 2011; Ma et al. , 2016; Javadiyan et al. , 2017; Mohebi et al. , 2017; Ma et al. , 2018)  and a fourth (p. ( Gly94Arg )) in a proband with sclerocornea and lens abnormalities  (Ma et al. , 2018). Given the rarity of these variants, the identification of the same missense changes in unrelated affected individuals strongly supports a causative relationship between these variants and eye developmental disorders. In particular, p. (Asp51Asn) had been reported as a de novo mutation in a patient with bilateral microphthalmia, congenital cataracts and sclerocornea  (Ma et al.
Timing of bipolarization was assessed in oocytes expressing EB3-GFP imaged at 10 min intervals. Statistical analyses were performed using Graphpad Prism 5. 0 software. ns, non-significant (p> 0. 05); * p<0. 05; ** p<0. 01; *** p<0. 001; **** p<0. 0001. Deconvolution of the stack of images has been performed as described in the Methods section.
PCR-based screening of HpyAII R-M system Fourty nine clinical H. pylori isolates from patients diagnosed with gastric cancer, duodenal ulcer, or non-ulcer dyspepsia and twenty-nine isolates from healthy adult volunteers (HVs) of both sexes who had no symptoms of gastritis or dyspeptic syndromes like abdominal pain were analyzed. PCR was carried out to detect R. hpyAII, M1. hpyAII and M2. hpyAII genes using primers 1 to 6 (Supplementary Table  S1 ). PCR-based screening was done in the presence of positive and negative controls for each gene. Distribution of R. hpyAII, M1. hpyAII and M2. hpyAII genes in completely sequenced H. pylori strains was analyzed using NCBI BLAST tool. Nucleotide sequences were submitted as a query in NCBI BLAST tool, and H. pylori (taxid: 210) was selected as the database for the analysis.
In silico genome analysis for GAAGA/TCTTC sites distribution The complete genome sequence of the 26695 strain of H. pylori, annotated by  (14)  was used in this study. Intergenic regions of the genome were identified based on the given annotations. Intergenic region in the vicinity of the transcription start sites (50 nucleotides upstream) were screened for distribution of 5 TCTTC 3. To check the distribution of 5 TCTTC 3 sites in the gene body and the possible promoter region spanning 200 bp upstream of start codon till the stop codon of the gene was screened using Python scripts. Trends in the frequency and spatial distribution of 5 TCTTC 3 repeats were also analyzed. Control intergenic sequences was generated by randomly shuffling 10 000 existing intergenic sequences from the whole genome, and analyzing the frequency and spatial distribution of 5 TCTTC 3 sites.