dataesr/scientific-paragraphs-categorization huggingface dataset:
,  31  that is protein-centered. Indeed, protein inference in metaproteomics is complex due to the large databases required as well as the multiple occurrences of a large ratio of peptide sequences. In the present study, we relied on TSMs (taxon spectra matchings) which is a novel quantitative feature within metaproteomics equivalent of the spectral count for proteins (through which a peptide spectrum matching "PSM" is used) but at each possible taxon level. Here, we successfully identified 132 genera when considering the whole metaproteomic datasets comprising 34 samples which correspond to 10 phyla, 22 classes, 47 orders and 79 families, while other studies on teeth microbiota identified less bacterial genera. For example, Li et al. identified a total of 10 phyla, 19 classes, 32 orders, 61 families, and 102 genera,  32  using the 16S rRNA gene sequencing method, while 25 samples (10 children with and 15 children without BS) were used in that study.
To perform separation, peptides were transferred to an analytical column (75 μm × 500 mm; Acclaim Pepmap RSLC, C18, 2 μm, 100 Angstrom, Thermo Fisher). A gradient consisting of 5-26% B for 192 min and 90% B for 10 min (A = 0. 1% formic acid, 2% acetonitrile in water; B = 0. 1% formic acid in acetonitrile) at 400 nL/min, 50 °C, was used to elute peptides from the reverse-phase column. For peptide identification, data-dependent acquisition was performed with a lock-mass as the internal calibrator (m/z 1222, Hexakis "1H,1H,4H-hexafluorobutyloxy" phosphazine, Agilent Technologies, Santa Clara, USA). Using Instant Expertise software (Bruker Daltonics, Massachusetts, USA), the most intense ions per cycle of 3 s were selected and then active exclusion was used (after 1 spectrum for 2 min unless the precursor ion exhibited intensity higher of 3 times than the previous scan).
All MS/MS spectra were extracted, deconvoluted with proteomics parameters (C, H, N, and O) in automatic mode (no charge state restriction) and exported to a. mgf file with deconvoluted peaks as single-charged ions by DataAnalysis software (Bruker Daltonics). 5. 6. 1. Peptide Identification. All MS/MS spectra were searched against the Nextprot database (2018-01-17) to identify proteins from Homo Sapiens and against the SwissProt database (2017-07-28) with "Bacteria" as Taxonomy by using the Mascot v 2. 6. 0 algorithm (Matrix Science, http://www. matrixscience. com/) with the following settings: (1) enzyme: trypsin, (2) variable modifications: oxidation (M) and deamidated (N,Q), (3) fixed modifications: carbamidomethyl (C), (4) missed cleavages: 2, (5) instrument type CID: ESI-QUAD-TOF, (6) peptide tolerance: 10.
0 ppm, (7) MS/MS tolerance: 0. 05 Da, (8) peptide charge: 1+, 2+ and 3+, (9) mass: monoisotopic, (10) C13: 1, (11) minimum peptide length: 5, (12) peptide decoy: ON, (13) adjust FDR [%]: 1, (  14 ) percolator: on, (15) ions score cut-off: 12, and (  16 ) ions score threshold for significant peptide IDs: 12. 6. 7. Bioinformatic Analysis. QIAGEN Ingenuity Pathway Analysis (IPA; Qiagen, Redwood City, CA) was performed on proteomic data to identify proteins involved in pathways affected by BS. IPA is a commercial tool that is based on a proprietary database to facilitate the identification of biological themes in proteomics or gene expression data. The association between proteins in the data set and canonical pathways in the Ingenuity Pathways Knowledge Base was measured as a ratio of the number of molecules from the mapping data to a pathway divided by the total number of molecules that map to the canonical pathway.
6. 7. 1. MS/MS Data Interpretation for Proteotyping Microorganisms. Each MS/MS dataset was queried with the Mascot search engine (Matrix science) against a subset of NCBI-nr downloaded on the 3rd of January, 2018 as ftp://ftp. ncbi. nlm. nih. gov/blast/db/FASTA/nr. gz, with one representative taxon per species (50,080,649 protein sequence entries from 9133 organisms including bacteria genera and Homo sapiens taxonomies). For each dataset, a cascade search was carried out: the theoretical proteomes from genera identified in the first stage were used as database for a second search round; the theoretical proteomes from species identified in the second stage were used as database for the third search round. Peptide-to-MS/MS spectrum assignation was done with the following parameters: full trypsin specificity, maximum of one missed cleavage, mass tolerances on the parent ion of 2 ppm for the first search round and 5 ppm for the second and third search rounds, 0.
5 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57. 0215 Da), and oxidized methionine (+15. 9949 Da) as dynamic modification. Mascot DAT files were parsed using the Python version of Matrix Science msparser version 2. 5. 1 with function MS_peptide summary (http://www. matrixscience. com/msparser. html) for selecting Peptide-Spectrum Matches (PSMs) with a Mascot expectation value below 0. 3 for the first search round, 0. 1 for the second search round, and 0. 05 for the third search round using Mascot homology threshold (MHT), and allowing multiple PSMs per MS/MS spectrum. The raw number of PSMs per taxon (further called TSMs for Taxon-Spectrum Matches), the number of matching peptide sequences, the count of specific or unique peptide sequences and corresponding specific PSMs were calculated for the species, genus, family, order, class, phylum, and superkingdom "canonical" taxonomical levels.
Medical image-based cgFEM Normally, when creating bone-implant FE models from medical images, for example from three dimensional CT Scans, there is a laborious segmentation processing of the medical image so that the FEM model is finally created. By applying the cgFEM through the software FEAVox  [14] , the creation of FE models from medical images is made straightforwardly. Fig. 2  illustrates the main stages of the process, where a cgFEM mesh is generated from a coarsely segmented bone domain merely to reduce the amount of voxels to be considered in the FE model. Due to the Cartesian mesh structure of the cgFEM, compatible with 3D images, it is possible to directly identify a number of voxels into each finite element. The element stiffness matrix k e is integrated using the information of the voxels contained in it, each voxel with an associated material property derived from the image intensity values.
Introduction The following example is a human maxilla (upper jaw) model, obtained and segmented from a medical 512 3 voxel sized CT-Scan through the software 3D-Slicer  [26] , expressed in Hounsfield Units (HU). For the sake   of computational cost, only half of the complete maxilla is analysed, assuming symmetry conditions. As an approximation, the Hounsfield units to ρ conversion was done through a linear interpolation between the minimum and maximum HU corresponding to bone tissue, and the minimum and maximum bone density values, these being ρ min = 100 kg/m 3 and ρ = 2100 kg/m 3 , respectively. As in the previous example, the smoothing filter explained in Section 2. 2. 1 is applied to avoid checkerboard effect using a filter radius of r = 2 • √ 3d e. Fig. 10  shows a representation of the medical image used, identifying the dental anatomy, where I1 and I2 are the two incisors, C is the canine, P1 (named P from now on) the first premolar and M1 and M2 the first and second molars, respectively.
Microarray Hybridization An aliquot (100 ng) of RNA from each sample was labeled with Cyanine 3-CTP using Low-Input Quick Amp Labeling kits (Agilent Technologies, Waldbronn, Germany). Labeled cRNAs were purified and hybridized onto Agilent G4851B SurePrint G3 Human Gene Expression 8*60 K v2 microarrays™, allowing full coverage of the human transcriptome (Agilent Technologies, Waldbronn, Germany). The slides were washed and scanned on an Agilent G2505C™ microarray scanner with a 3 µm resolution and data were extracted using Agilent Feature Extraction software version 11. 0, as described previously by  Chézeau et al. (2018)   [33]. The experiments were performed according to MIAME standards  [34]. The microarray data were uploaded to the NCBI Gene Expression Omnibus database  [35] , where they are accessible under the GEO series accession number GSE203572,  [36].
Statistical and Functional Analyses of Microarray Data The data were quantile-normalized using Solo software (Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg)  [37]. The genes displaying differential expression between control and exposed groups were identified using a method based on Fold Change Rank Ordering Statistics (FCROS; Dembele and Kastner, 2014)  [38]. The genes for which the fold change (FC) for exposed vs. matched controls was at least 1. 5 in either direction and with f-values ≤|0. 01| were considered as significantly differentially expressed. The details and functional information on genes differentially expressed were obtained using the GeneCards™ database  [39]. A functional Gene Ontology (GO) analysis of the differentially expressed genes was performed using v6.
8 of the Database for Annotation, Visualization, and Integrated Discovery (DAVID)  [40]. This tool was used to extract the biological meaning from a long list of differentially expressed genes and to identify biological functions possibly altered following exposure to rough Y-TZP discs. Genes that share similar functions are clustered based on biological processes, cellular components, or molecular functions. In this study, we mainly focused on GO BPs belonging to annotation clusters with an enrichment score (Z score) >1. 3  [41]. We also examined significantly enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) biological pathways (p < 0. 05), a collection of manually drawn pathway maps representing knowledge on molecular interactions and reaction networks  [33]. Transcript-transcript interactions were investigated using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)  [42]  database of physical and functional interactions v11.
5  [43] , as described previously by Chézeau et al. (  2019 )  [44]. The network nodes represent transcripts and the edges represent transcript-transcript associations. The line color of the network edges indicates the type of interaction. Thus, known interactions are represented as the light blue line (this color indicates the presence of database evidence) and purple line (experimental evidence); predicted interactions as the green line (neighborhood evidence), red line (fusion evidence), and blue line (co-occurrence evidence); and other interactions as the yellow line (text mining evidence), black line (co-expression evidence), and violet line (protein homology). The analysis section gives some brief statistics for the inferred network, such as the numbers of nodes and edges. A small PPI enrichment p-value indicates that the nodes are not random and that the observed number of edges is significant.
We were also interested in the 10 most downregulated transcripts in the initial list of 133 deregulated probes, namely ATHL1, EXOC7, GZMB, H19, IGFN1, LSP1, MIR143HG, NEAT1, SAPCD1, WDR90 (Table  2 ). These transcripts are involved in inflammatory and defense responses (to the virus), in the metabolic process (such as proteolysis and apoptosis), and in cell motility (cytoskeleton organization and adhesion). The second step consisted of injecting the list of 133 differentially expressed probes into the DAVID database. The analysis highlighted 11 enriched GO BPs in exposed Saos-2 cells (Z score > 1. 3) (Figure  8 ). The most enriched GO BP areas are related to the "response to metal ions", including 7 upregulated transcripts (MT2A, MT1L, MT1M, MT1X, MT1B, and MT1E) that encode MT, a family of proteins acting as antioxidants that is involved in the detoxification of heavy metals, but also in bone metabolism and remodeling  [45].
Six upregulated transcripts are involved in this pathway and encode MT: MT1A, MT1B, MT1E, MT1M, MT1X, and MT2A (Table  3 ). The second enriched metabolic pathway correlates with the immune response, more specifically antiviral-type defense involving interferons, including "influenza A", "measles", and hepatitis C" KEGG pathways with 5, 4, and 4 upregulated transcripts, respectively. These transcripts include CXCL8, CSNK2A1, IFIT1, MX1, OAS1, OAS2, and RSAD2. Finally, an analysis of the transcript-transcript interactions using the STRING database (v11. 5) highlighted 2 main independent networks (Figure  9 ). The first one linked to mineral absorption is mainly composed of transcripts encoding MT. The second related to immune response contains the largest number of transcripts that encode interferon-induced proteins, the chemokine CXCL8, a major mediator of the inflammatory response, as well as other immune and defense proteins.
Interestingly, small PPI enrichment pvalues were noticed, indicating that the nodes are not random and that the observed number of edges is significant. S1 ) was injected into STRING database v11. 5. The 2 significantly enriched KEGG pathways (determined using the DAVID database) are represented in bold. Legend: Network nodes represent proteins, edges represent protein-protein associations (red line: the presence of fusion evidence; green line: neighborhood evidence; blue line: co-occurrence evidence; purple line: experimental evidence; yellow line: text mining evidence; light blue line: database evidence; black line: co-expression evidence).
Thus, ZrO 2 and Y-TZP would be able to modulate immunity, to allow them to be recognized as "self" by cells  [59]. However, it is important to note that the rough Y-TZP discs upregulated a limited number of genes involved in immunity  (18) , the expression of which should probably normalize in the long run. In conclusion, rough Y-TZP is very well tolerated by Saos-2 cells. Then, 6 restricted transcript-transcript interaction networks were identified using the STRING database. The 2 most interesting are related to extracellular matrix remodeling, with a slight downregulation of COL7A1, COL27A1, ADAMTSL5, and ADAMTL10. MMP13, which encodes a metalloproteinase and proteins involved in bone remodeling, is also downregulated. The extracellular matrix proteins are involved in osseointegration and bone resorption, remodeling, and repair, along with the regulation of osteoblasts and osteoclasts  [60, 61].
The contact angle between a water drop (1-2 µL) and the bacterial lawns was then measured under ambient conditions using a Digidrop contact angle meter (GBX Scientific Instruments, Romans-sur-Isère, France). Measurements were computed automatically by Windrop++ software. It should be noted that the measurement should be done within 3-4 s after depositing the drop, before its penetration into the bacterial layer. Contact angles were determined at 5 random points per bacterial film. Results are expressed as mean contact angle ± SD.
Statistical Analysis All values are expressed as mean ± SD for three independent experiments. The student t-test was used to calculate the significance of the differences between the mean effects of the extract and those for the associated untreated control after checking equality of variances with Levene's test (p-value < 0. 05). Statistically significant values were defined as a p-value (* < 0. 05, ** < 0. 01 or *** < 0. 001). SPSS 22. 0 software (SPSS, IBM Corporation, Armonk, NY, USA) was used in the statistical analysis.
The development of computational methods and especially the application of artificial intelligence and machine learning to cutting-edge biofilm imaging data, and its integration with heterogeneous data such as large multi-omics datasets, for instance, provide an unprecedented opportunity to explore the link between the presence of complex structural patterns and functional properties in biofilms  [26, 27]. Major advances in the deciphering of biofilm development were indeed achieved owing to such integrative approaches, particularly in our ability to consider the structural heterogeneity of biofilm, which remains a major challenge to understand biofilm organization and functions. The improvement of spectral imaging techniques, in association with a highly multiplexed fluorescence in situ hybridization (FISH) approach, enabled the highphylogenetic-resolution mapping of microbial communities  [28].
Cytokine/Growth Factor Multiplex Array A multiplex array for the simultaneous detection of 43 cytokines and growth factors (Abcam, Cambridge, UK) was employed to analyze the dental pulp MSC secretome. Conditioned media (containing the dental pulp MSC secretome) were collected from the cells following 24 hours of serum starvation. The array was performed according to the manufacturer's protocol, and membranes were imaged using a Syngene G:BOX Q18. Images were semiquantified using the ImageJ software Q19 (NIH, Bethesda, MD), image analysis program, with a microarray plugin (ImageJ bundled with 64-bit Java 1. 8. 0_172; downloaded from https://imagej. nih. gov/ij, last accessed September Q20 2021). The integrated density of each spot was measured, and the final integrated density was calculated as described by the manufacturer to allow comparison of the integrated densities of array spots over multiple membranes.
Growth Factor/Cytokine Analysis of the Dental Pulp MSC Secretome To further investigate soluble factors that could be responsible for the observed antimicrobial activity, dental pulp MSC conditioned media were analyzed by an antibody-based multiplex cytokine array. Of the 43 growth factors/cytokines analyzed by the Abcam array, 19 had an integrated density >1 in the dental pulp MSC secretome, as determined by ImageJ software (Table  3   ½T3  ). These were angiogenin, growth-regulated a protein, interferon-g, IL-6, IL-8, leptin, monocyte chemoattractant protein-1, placental growth factor, regulated upon activation normal T cell expressed and secreted, transforming growth factor-b, tissue inhibitor of matrix metalloproteinases-1 (TIMP-1), TIMP-2, thrombopoietin, vascular endothelial growth factor, ANGPT-1, ANGPT-2, plasminogen, matrix metalloproteinase-1, and urokinase plasminogen activator surface receptor.
Furthermore, to demonstrate antibacterial functionality of one of these factors (HGF) in the MSC secretome, blocking experiments in the presence of anti-HGF antibodies were undertaken. The results showed that blocking antibodies significantly reduced the antibacterial efficacy of HGF, supporting a role for HGF in contributing to the antibacterial activity of dental pulp MSC secretome and providing proof-of-principle support for the direct antibacterial efficacy of the growth factors/cytokines. The amino acid sequences of IL-6, IL-8, HGF, and ANGPT-1 were predicted to have bactericidal regions using the online tool AMPA (http://tcoffee. crg. cat/apps/ampa Q28 ), an antimicrobial sequence scanner for predicting active regions in antimicrobial proteins. 27 Although our data suggest that HGF has membrane disrupting potential that may contribute to its mode of antibacterial action, ANGPT-1, IL-6, and IL-8 do not appear to exert antibacterial effects through this mechanism of action.
Simple Summary: The development of a widely accepted and comparable animal cancer registration system lacks standardized animal cancer coding. The GIVCS group have developed a comparative coding system for canine neoplasms-Vet-ICD-O-canine-1-compatible with the human ICD-O-3. 2 and consistent with the currently recognized classification schemes for canine tumors. This system comprises 335 topography codes and 534 morphology codes allowing the collection of consistent epidemiologic canine cancer data and offering a robust framework for comparative oncology studies. Abstract: Cancer registries are fundamental tools for collecting epidemiological cancer data and developing cancer prevention and control strategies. While cancer registration is common in the human medical field, many attempts to develop animal cancer registries have been launched over time, but most have been discontinued.
Morphologic List The list included the ICD-O-3. 2 code, the Vet-ICD-O-canine-1 code, the morphologic group (e. g. , epithelial neoplasms, adenomas, adenocarcinomas), the veterinary term (name/s of the tumor according to the current veterinary classification schemes), the level (preferred, synonym, related), and the topography (for entities related to a specific topography). Out-of-date terminology was retained for some tumor entities to allow the correct coding of retrospective and historical data and assigned the [obs] (obsolete) descriptor.
Discussion This study provides a reference system for coding canine tumors for cancer registration purposes and for facilitating comparative oncology studies, epidemiologic investigations, and tumor mapping. The uniformity and standardization offered by the Vet-ICD-O-canine-1 aids in overcoming the inconsistencies and lack of coordination inherent to previous initiatives of veterinary cancer registration. Adhering to the present coding system will enable a reliable comparison of registry data from different countries and allow for largescale epidemiologic studies on canine cancer. The Vet-ICD-O-canine-1 is consistent with the ICD-O-3. 2 and most morphological terms and codes overlap fully, as expected given the well-described histologic similarities between human and canine neoplasms. However, the new coding system considers the peculiarities of some canine tumors, resulting in 126 new codes for entities that cannot be reliably coded with the ICD-O-3.
However, in the presence of a recurrence or a metastatic lesion of a sebaceous epithelioma, the behavior code should be changed to /3 or /6, respectively. As another example, carcinoma spreading to the lung from an unknown site of origin should be coded as C34. 6 (lung) or 8010/9 (carcinoma/unknown if primary or metastatic). A metastatic carcinoma in an intra-abdominal lymph node would be coded as C77. 2, 8010/6 (carcinoma NOS, metastatic). If a malignant neoplasm is suspected to be metastatic and not primary, in the absence of clinical information, or if clinical data support a metastatic condition, the behavior code /9 should be used. If the behavior is unclear or not stated, it should be coded according to the Vet-ICD-O-canine-1. Table  4  describes the morphology code matrix concept that should be applied to veterinary coding systems.
Material and Methods This cross sectional observational retrospective cohort study was approved by an IRB (CPP, protocol ABCD, INDS: TPS 1106180), registered in the national data protection agency (CNIL), and followed the STROBE recommendations for observational studies. It complied with the ethical principles of the Helsinki declaration and Good Clinical Practice. Anonymity of participants was respected throughout the course of the study. -Patient selection The COFP consultation of the dental service of the Pitié-Salpêtrière Hospital (GHPS) is a clinical setting for either inpatients or outpatients, mainly referred by dentists, physicians (GP or specialists) for complex pain. Clinical records from 2015 to 2020 were screened between September 2019 and June 2020 according to the following inclusion and exclusion criteria.
Electron spin resonance spin trapping experiments Electron spin resonance spin trapping (ESR-ST) experiments were performed by X-band spectrometry (Bruker EMXplus), and upon irradiation with a light source (LED@405 nm). For the capture of the generated free radicals, a Ntert-butyl-α-phenylnitrone (PBN) solution with certain concentration (in nitrogen-saturated tert-butylbenzene) were formulated at room temperature. Lastly, the simulations of ESR spectra were carried out with the help of PEST WINSIM software. [15, 34].