dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectra were obtained for the 700 to 4000 Da mass range, with internal calibration based on autolytic trypsin fragments characterized by (M + H) + = 842. 509 and 2211. 104 Da. For protein identification, MS/MS peak lists were extracted, converted into mzdata. xml format files and compared with the protein database, using the MASCOT Daemon (version 2. 1. 3; Matrix Science, London, UK) search engine. The searches were performed with no fixed modification, with variable modifications for methionine oxidation, and with a maximum of one missed cleavage. Only peptides matching an individual ion score > 60 were considered. Proteins with two or more unique peptides matching the protein sequence were automatically considered to give a positive identification. Fibrinogen deglycosylation. We used endoglycosidases, N-glycanase and O-glycanase to remove glycans from hFg (Prozyme, San Leandro, CA, USA).
The required sample size was estimated from a preliminary study that measured the NOTS score in an ECC group before and after GA. The mean values of the NOTSscore decreased from 1. 5 ± 1. 2 (before GA) to 0. 25 ± 0. 71 (one month after GA). Calculations with the epiR package 0. 9-30 were based on a difference of 1. 25 and a common standard deviation value of 0. 9. It indicated the need for at least 22 participants (α = 5%, β = 10%). The control group consisted of children from 2 to 6 years with healthy teeth, included between September and December 2009. These children were recruited within the research laboratory circle, as it was difficult to include children without caries in the hospital setting. The provenance of participants is not supposed to influ-ence the impact of dental health on oro-facial function and quality of life results.
Statistical analyses (student-t test, sample size calculation for unequal sample size design) were performed to calculate the adequate sample size for the control group. Consequently, 16 children were included. Medico-social and oral descriptive data are given in table  1  for both groups of children. Both groups were similar in age (t-test) and gender (Fisher exact test). Five out of the 41 children included were slightly over the age limit (71 months) to meet the ECC definition according to the American Association of Pediatric Dentistry (AAPD)  (3). Two children belonged to the control group (both were 72 months) and three to the experimental group (72, 74 and 75 months respectively). Since these three children had also multiple carious cavities in deciduous teeth, they were included in the "ECC group" in order to simplify the analysis.
Thus, to evaluate the real impact of extractions and conservative treatments provided under GA on orofacial function, the presence or absence of orofacial dysmorphology was checked using a simplified version of the Index of Orthodontic Treatment Need (IOTN). This tool specifically measured sagittal, vertical, and transversal dimension abnormalities, as well as dento-maxillary disharmony and crowding  (28). No difference was found between either group of children regarding the presence of dysmorphology (Fisher-exact test). -Data collection For the control group, data collection was carried out during a single evaluation, as the study criteria were not expected to change significantly over the study period. For the ECC group, three visits included in the regular follow-up agenda were scheduled for each child: T0 (pre-operatively), T1 (one month after GA), and T2 (3 months after GA).
Two practitioners of the Special Dental Care Unit carried out the consultations for both groups, each involving a clinical examination and an interview with the child and its parents. The data collected concerned socio-demographic information, the children's general health status, oral health information and the study evaluation criteria described further on. Both investigators met before and frequently during the course of the study to harmonize the recording method. * Evaluation criteria -Orofacial dysfunction score (NOT-S score) The main evaluation criterion was the frequency of orofacial dysfunctions evaluated by the Nordic Orofacial Test-Screening (NOT-S), which consisted of a structured interview and a clinical examination  (20). The French version of the NOT-S was used in this study (http://mun-h-center.
Each impression was prepared for scanning using the Silverfast® image analysis software. The OSC (Occlusal Scanning Control) software was then used to determine the surface area (in mm 2 ) of the Total inter-arch Functional Surface (TotFS) for which the silicon thickness ranged from 0 (perforating surface) to 500 μm (near contact surfaces)  (29). The impression of potential permanent teeth was excluded from the analysis. The number of Functional Units and Functional Surfaces was also analyzed separately for both Anterior (incisors and canines) and Posterior (molars) areas (re-e337 spectively number of Anterior and Posterior Functional Units: AntFU and PostFU; Anterior and Posterior Functional Surfaces: AntFS and PostFS). -Statistical Analysis Data were collected and analyzed using IBM®SPSS® 19 software.
The analysis was performed for the 25 children with ECC, except for the inter-arch functional contact criteria for which only 8 complete datasets were obtained (Table  1 ). This group of 8 children was called "ECC contact" subgroup and it was checked to ascertain whether it was not different from the whole ECC group regarding age, dmft index scores, ECOHIS and NOT-S scores (Student t-test, NS). For each evaluation criterion, the comparison of average values of NOT-S and ECOHIS scores, the number of Functional Units (TotFU, AntFU and PostFU) and the Functional Surfaces (TotFS, AntFS and PostFS) was made between the ECC group and the control group at each evaluation time using a t-test. A paired t-test was used to compare each evaluation criterion within the same ECC group at the different evaluation times.
At the end of the follow-up period (December 2016), some subjects were excluded for medical (death or deterioration of general health status) or mobility reasons. Out of the twenty-two subjects initially included, a complete dataset for nineteen participants was finally used for the study until T2 and, so far, eleven until T3. The required sample size was estimated from a preliminary pilot study that measured the GOHAI score before rehabilitation (T0) and one year after (T2) (eight first included subjects, to January 2012 for April 2015). The mean GOHAI values increased from 33 to 50. 37 respectively (SD = 12. 15). Calculations were based on this difference for a continuous criterion with paired values and indicated the need at least for 18 subjects (α = 5%, β = 10%, epiR package 0. 9-30).
Statistical Analysis Statistical analysis was performed using SPSS ® (IBM, v20, New York, NY, USA) software. Statistical significance was set at p < 0. 05. After checking for the normal distribution of the data, parametric tests were used to obtain the results. The mean scores of each component of the GOHAI (GOHAI-add, "functional" field, "psychosocial" field and "pain or discomfort" field) were compared before (T0), 2-6 months after treatment (T1), 1 year after treatment (T2) and 24 months and more after treatment (T3) by ANOVA test, followed by a post-hoc Student-Newmans-Keuls test (α = 0. 001). Furthermore, age (<70 years vs. >70 years), gender were tested as explanatory variables for the change in the mean GOHAI-add scores between the different times, assessed by multivariate analysis, and repeated measures procedures (dependent factors: GOHAI-add or each components of GOHAI, fixed factor: age; gender).
Evaluation of dimensional changes At 1 week and 6 months postoperatively, alginate impressions were taken and plaster models were made with dental stone (GC Fujirock EP, GC Corp. , Tokyo, Japan). The models were computationally imaged using an optical scanner (DScan version 1. 1, E. G. S solution, Lazzaro, Italy) and DentalCAD software (E. G. S solution). The 1-week and 6-month models of each patient were superimposed using software (Polyworks, Innovmetric, Quebec City, Canada), with the adjacent teeth used as the reference point (Figure  2 ). https://doi. org/10. 5051/jpis. 2017. 47. 6. 363.
The independent t-test was applied to assess the differences in age between the test and control group. The Pearson χ 2 test was performed to compare the proportions of gender, jaw positions, and right/left locations in the jaw between the test and control group. The impression data for dimensional changes in 4 subjects were lost and the mean imputation method was applied. SPSS version 23. 0 (SPSS Inc. , Armonk, NY, USA) was used for analysis.
The registered scans were then used for statistical shape modelling. Linear measurements were automatically computed on the STL files using the "meshcube" function in the Morphopackage of R (v. 3. 3. 0, R Foundation for Statistical Computing, Vienna, Austria). This function calculates the corners of the bounding box comprising the STL mesh, which can then be translated to head width, length and height measurements. Pre-operative head width and length were used to normalise the recorded surgical parameters according to head size. AP, CorToAnt and AntToPost were normalised as percentages of pre-insertion head length, while LAT was represented as a percentage of pre-insertion head width, as indicated in Eqs. 1-4: AP (%) = AP Head length (1) LAT (%) = LAT Head width (2) CorToAnt (%) = CorToAnt Head length (3) AntToPost (%) = AntToPost Head length (4).
Statistical analysis Mean values and standard deviations (mean ± SD) were calculated for head volume, width, length and height measured on the 3D scans, as well as for the recorded surgical parameters. Associations between PLS shape vectors most related to differences in normalised AP, LAT, CorToAnt and AntToPost (after removing size effects) and the corresponding surgical parameter were evaluated via bivariate correlation analyses. Normality of the data was assessed using Shapiro-Wilk test. For parametric data, Pearson's r correlation coefficient was used, while Spearman's ρ was employed for nonparametric data. In order to detect influential observations in the PLS regression, Cook's distance D Cook  [55]  was calculated for each PLS regression run and when D Cook exceeded four times the mean, these data were excluded from the analysis.
Acknowledgements We would like to express our gratitude to  Maik Tenhagen ,  Özge Göktekin  and the craniofacial team at  Great Ormond Street Hospital for Children  for assisting in the patient data collection. This research was supported by the  Great Ormond Street Hospital charity  through the  FaceValue Programme Grant  (No. 508857 ),  Fondation Leducq  (Grant No. 09CVD04 ), and the  Engineering and Physical Sciences Research Council Award  ( EP/N02124X/1 ). This report incorporates independent research from the  National Institute for Health Research Biomedical Research Centre Funding Scheme. The views expressed in this publication are those of the author(s) and not necessarily those of the  NHS , the  National Institute for Health Research  or the Department of Health.
Statistical analysis Statistical tests were performed using the Statistical Package for the Social Sciences (SPSS 20. 0; SPSS Inc. , Chicago, IL, USA). Quantitative variables were described as mean ± standard deviation and qualitative variables as number (percentage). Classification criteria were applied to each patient as described in Table  1  (only the VB score was used to apply ACR/EULAR criteria because the OSS was unavailable for most patients). Taking the physician's diagnosis as the reference standard for defining cases may lead to overestimation of the diagnostic performance of a classification system that has previously been used in everyday practice, with a risk of circular reasoning. Consequently, in our primary analysis, we compared patient groups defined by the two criteria sets.
adj. massiliensis pertaining to Massilia, the name of the city of Marseille from antiquity, where this bacteria has been discovered). MALDI-TOF MS spectrum accession number. The MALDI-TOF-MS spectrum of P. massiliensis is available online (http://www. mediterranee-infection. com/article. php? laref=256&titre=urms-database). Nucleotide sequence accession number. The 16S rRNA gene sequence was deposited in GenBank under accession number LT631513. Deposit in a culture collection. Strain Marseille-P3336 was deposited in the Collection de Souches de l'Unité des Rickettsies (CSUR, WDCM 875) under number P3336.
The paradigm shift in clinical decision making Clinical decision making is by nature the core of the line of work of orthodontists and its most medical aspect  5. The medical history, the clinical examination, and the supplementary examinations are the sources of information needed to reach a diagnosis. Where can we find other information needed for clinical decision making? In the 1970s, only traditional sources of information were available, all of which had their own limitations: -The contents of the clinical manuals could be quickly rendered obsolete; -Authors ' opinions may not have been validated scientifically; -Clinical cases seen or read, could not account for the diversity of clinical solutions published in specialized journals; -The scientific press was less easily accessible, because of a lack of electronic resources (access to e-databases and online journals); -The methodological quality of the studies was inferior, and there were no systematic reviews of the literature.
We first scanned the sequences of the FusoSecretome and human proteins for the presence of domains as defined by Pfam  [52]. We identified 55 "host-like" domains in 50 FusoSecretome proteins out of 237, including several domains related to ribosomal proteins, aminopeptidases, and tetratricopeptide repeats (TPR) (Additional file 4: Table  S3 ). Interestingly, 29 of these domains are also found in known bacterial binders of human proteins  [30]. We next detected the occurrence of experimentally identified SLiMs gathered from the Eukaryotic Linear Motif (ELM) database  [53]. As linear motifs are short and degenerate in sequence, SLiM detection is prone to over-prediction  [54]. To reduce the number of false positives, we kept occurrences falling in conserved and disordered protein sequences (see the "Methods" section).
" Fig. 2  Disorder propensity of the FusoSecretome. SignalP-secreted proteins show a significantly higher fraction of disordered residues compared to non-secreted proteins (P value = 1. 9 × 10 -4 , Kolgomorov-Smirnov test, two-sided) In order to assess the reliability of the inferences, we evaluated the biological relevance of the putative human interactors by performing enrichment analyses of different orthogonal datasets using as a reference background all the proteins encoded by the human genome. First, human proteins experimentally identified as binders or targets of bacterial and viral proteins are over-represented among the 934 inferred human interactors of the FusoSecretome proteins (415 proteins, 1. 3-fold, P value = 1. 61 × 10 -11 ). Notably, the over-representation holds when bacterial and viral binders are considered separately (176 bacterial interactors, 1.
Functional role of the human proteins targeted by F. nucleatum Globally, the inferred FusoSecretome human interactors are involved in several processes related to pathogen infection  such as immune response and inflammation, response to stress, endocytosis as shown by the 137 significantly enriched Biological Processes Gene Ontology (GO) terms among their annotations (Table  2 , Additional file 8: Table  S6A ). Similarly, the targeted human proteins are overrepresented in 125 pathways (54 from KEGG and 71 from Reactome databases  [59, 60] ) involved in cell adhesion and signaling, extracellular matrix remodeling, immunity, response to infection, and cancer-related pathways (Table  2 , Additional file 8: Table  S6A ). These human proteins are mainly localized in the extracellular space, plasma membrane, and at cell-cell junctions that represent the main districts involved in the initial encounter between a pathogen and the host, as indicated by the over-representation of 30 Cellular Component GO terms (Table  2 , Additional file 8: Table  S6A ).
For instance, a previous study considered as erroneous the secretion prediction of ribosomal proteins  [76]. We assigned 20  For every protein, the detected Pfam domains are reported along with the list of domains and SLiMs for which at least one interaction has been inferred a Pfam entry detected in at least one protein sequence stored in the database of known bacterial virulence factors b Motif for which it was experimentally identified at least one instance in a viral or bacterial protein ribosomal proteins to the FusoSecretome. Although we cannot exclude a misprediction, ribosomal proteins can be secreted in some bacteria and be involved in host interaction  [77, 78]. Furthermore, increasing evidence shows that ribosomal proteins are moonlighting proteins with extra-ribosomal functions such as the E.
Protein sequence data The reference proteomes of Fusobacterium nucleatum subsp. nucleatum strain ATCC 25586 (Proteome ID: UP000002521) and Homo sapiens (Proteome ID: UP000005640) were downloaded from the UniProtKB proteomes portal  [88]  (April 2013). The protein sequences of known gram-negative bacteria virulence factors were taken from the Virulence Factors DataBase  [49]  (January 2014).
Identification of short linear motifs We used the SLiMSearch 2. 0 tool from the SLiMSuite  [93]  to identify occurrences of known short linear motifs from the ELM database  [53]  (downloaded in May 2013) in the F. nucleatum proteome. To select putative mimicry motifs, we applied two SLIMSearch context filters: (i) the motif must be in a disordered region (average motif disorder score >0. 2, calculated by IUPred) and (ii) must be conserved in at least one putative ortholog detected in a database of 694 proteomes of commensal/pathogen bacteria in Mammalia downloaded from UniprotKB (March 2014). Sequence alignments and conservation assessment were performed using the GOPHER program from the SLiMSuite using standard parameters  [94].
Protein interaction inference We built an interaction network between F. nucleatum putative secretome and human proteins by using interaction templates from the 3did database  [95] , which stores 6290 high-resolution three-dimensional templates for domain-domain interactions, and the iELM resource  [96, 97]  that lists 578 high-confidence motif-mediated interfaces between 191 ELM motifs and 402 human proteins. Both datasets were downloaded in August 2013. The domain-based interaction inference works as follow: given a pair of known interacting domains A and B, if domain A is detected in the F. nucleatum protein a and domain B in the human protein b, then an interaction between a and b is inferred. Analogously, for the SLiMmediated interaction inference: for a given known ELM motif m interacting with the domain C in the human protein c, if the motif m occurs in the F.
Functional enrichment analyses We have compiled several gut-related disease gene sets gathering data from the literature and public repositories. Patient secretome profiling (2566 proteins) for tumor colorectal tissue samples were taken from  [100]. We retrieve 152 colorectal cancer genes from the Network of Cancer Genes database (version 4. 0,  [101] ). The list of human genes whose expression correlates with F. nucleatum abundance in colorectal cancer patients  [13]  was kindly provided by Aleksandar Kostic (Broad Institute, USA). The compendium of 163 loci associated with inflammatory bowel diseases was taken from a large meta-analysis of Crohn's disease and ulcerative colitis genome-wide association studies  [80]. The enrichment of these gut-related disease gene sets among inferred interactors was tested using a one-sided Fisher's exact test.
Human interactome building, network module detection and annotation We use the human interactome that we assembled and used in  [62, 66]. Briefly, protein interaction data were gathered from several databases (e. g. , BioGRID, InnateDB, Intact, MatrixDB, MINT, Reactome) through the PSICQUIC query interface  [104]  and from large-scale interaction mapping experiments (e. g. ,  [105] ). We kept only likely direct (i. e. , binary) interactions according to the experimental detection method  [106]  and mapped protein identifiers to UniprotKB IDs. Given the redundancy among SwissProt and TrEMBL entries, protein sequences were clustered using the CD-HIT algorithm  [107]. SwissProt/TrEMBL pairs at 95% identity were considered as the same protein: interactions of TrEMBL protein were assigned to the SwissProt protein.
As a result, we obtained a human binary interactome containing 74,388 interactions between 12,865 proteins (February 2013). We detected 855 network modules detected using the Overlapping Cluster Generator algorithm  [63]. Modules were functionally annotated by assessing the enrichment of Gene Ontology (GO) biological process and cellular component terms  [103] , and cellular pathways from KEGG  [59]  and Reactome  [60]. Enrichment P values were computed using the R package gProfileR  [102]  and corrected for multiple testing with the Benjamini-Hochberg procedure (significance threshold = 0. 025) and annotated proteins in the human interactome were used as statistical background. Similarly, the over-representation of inferred human interactors and gut disease gene sets in network modules of the human interactome was assessed using a one-sided Fisher's exact test followed by Benjamini-Hochberg multiple testing correction (significance threshold = 0.
Acknowledgements The authors would like to thank the members of the  TAGC laboratory  for fruitful discussion,  Anaïs Baudot  ( I2M, CNRS, France ) for critically reading the first draft of the manuscript,  Aleksandar Kostic  ( Broad Institute, USA ) for kindly providing the list of human genes whose expression correlates with F. nucleatum abundance in colorectal cancer patients, and  Henrik Nielsen  ( DTU Bioinformatics, Denmark ) for assistance in running SecretomeP predictions. AZ is grateful to  Coralie, Olivia, and Claire  for their constant support.
Funding The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d'Avenir" program , to CB, and was partially supported by the  French 'Plan Cancer 2009-2013' program  ( Systems Biology call ,  A12171AS ). The funding organizations had no role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.
RNA Sequencing Total RNA was extracted in quadruplicate from lower incisors at E14. 5 and 16. 5 (2 days or 4 days after RA treatment) and respective controls. The mRNA-seq libraries were prepared as detailed in the Illumina protocol (supplemental Experimental Procedures). Sequence reads mapped to the mouse reference genome mm10/NCBI37 using Tophat. Only uniquely aligned reads were retained for further analysis. Quantification of gene expression was performed with HTSeq-0. 6. 1. (see http://wwwhuber. embl. de/users/anders/HTSeq/doc/overview. html). For each transcript the resulting reads per kilobase of exon model per million mapped reads (RPKM) were converted to raw read counts, which were then added for each gene locus. Data normalization was performed as described  (Anders and Huber, 2010)  and resolved using the DESeq Bioconductor package.