dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodological tools The objective of using several data sources was to increase the research's relevance, although more exhaustively, by limiting the bias linked to the source effect of a single database. This type of research was inevitably limited in scope; the multiplication of sources, unlike conventional epidemiological surveys, makes it possible to better reflect on the desired effect  [42, 43]. The use of Google and Wikipedia sources, which are common on the internet for information purposes, makes it possible to better identify the population's searches. The use of social networks such as Twitter, the most used network, is essential because it is as representative, if not currently more so, of trends and interest of the population on specific subjects, than any of the other networks  [44, 45].
Limits Unlike other databases used in this study, Google's© did not give the absolute number of page consultations related to oral cancers  [12] [13] [14] [47] [48] [49]. Unlike a typical epidemiological investigation, populations and subpopulations could not be identified. The geographical area and inclusion period were vast and were not identical for all the databases analysed. It was not possible to know if the same internet users carried out multiple searches or performed searches from different devices. The bibliometric period studied coincided with a general worldwide increase in the number of scientific publications, which is not, however, a guarantee of quality and scientific rigor  [50] [51] [52] [53]. Public health policies were not collected exhaustively, and the interest shown in oral cancers was dependent on an internet connection.
It is not proven that all users of these platforms can be influenced by celebrities. These "star people" may therefore have less influence than supposed. It was not possible to individually analyze the data sources used, such as Twitter accounts. There may therefore be a confounding factor in the analysis between the Twitter accounts of private individuals and those of public organizations that may echo awareness campaigns. The concrete impact of policy measures on the number of screenings, consultations, diagnoses or waiting times was not considered in our work. The temporal resolution (by week that carried over to the month) at which the data were extracted and analyzed could sometimes be underestimated. Research has shown that news-related spikes in search and social media typically return to baseline after approximately three days.
Gel analysis Digital images of 2D-gels were acquired using ChemiDoc™ XRS imaging system (Bio-Rad) with internal calibration. The acquisition parameters were 300 dpi and epi white illumination. Gel analysis was performed by adopting PDQuest software (Bio-Rad) for spot detection, according to manufacturer's protocol. Spot volume normalization, in the various 2-DE maps, was carried out using the relative spot volumes (% Vol). Initially, automated spot detection was performed, followed by manual editing for spot splitting and noise removal. The gels containing the largest number of protein spots for each phase were chosen as the reference gels. All other gels were matched with the reference gel by placing user landmarks on approximately 10% of major and minor protein spots, which were visualized to assist in automatic matching.
HR-LC-MS/MS The 1D protein spots were analyzed using 6550 i-Funnel QTOF-LC-MS/MS coupled with 1260 In nity Nano pump and 1260 Cap pump along with 1260 Chip-cube (Agilent Technologies). The peptides were fractionated along with Solvent A (0. 1% formic acid in milliQ water) and Solvent B (90% acetonitrile + 0. 1% formic acid + 10% milliQ water). For MS measurements, we employed the positive-ion mode with the mass range of up to m/z 4000 with the resolution setting 60,000 at m/z 400. The proteins were identi ed by comparison with the SWISS-PROT database entries. Search parameters for MS data were, species: Homo sapiens; Protein Mass: 0-500 kDa; Protein pI: 3-10; Enzyme: trypsin; Misscleavage: 1; Mass type: monoisotopic; Charge state: MH+; precursor and product mass tolerance +/-50 and +/-100ppm, respectively; Fixed modi cation: carbamidomethylation of Cystine (C); and Variable modi cations: oxidation of methionine (M).
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  [21]  with the dataset identi er PXD004511. The ovulation (File. S1) and the postovulation protein datasets (File. S2) were obtained and listed. MALDI TOF/TOF analysis 2D protein spot was processed using an automated gel cutter and processor (Shimadzu, Xcise™). The gel spots were washed and destained with 50% ACN and 50 mM NH4HCO3 (Solvent 1), and subjected to ingel digestion with 30 μL of solvent 7 (50 μLof trypsin stock solution in 4 ml of 50 mM of NH 4 HCO 3 ) for 2 hr at 37 ºC. ZipTips (C18) were wetted and conditioned with 50% ACN and 0. 05% TFA (Solvent 5) and 0. 1% TFA (Solvent 3), respectively. Cleaved peptides bound to the C-18 resin were desalted using 0.
Data processing The acquired mass spectra were processed using DataExplorer® software, and the mono-isotopic peptide masses were assigned and used in the database search. The protein identi cation was analysed against Homo sapiens protein sequence in MASCOT database search (http//www. matrixscience. com) using SWISS-PROT database entries. Modi cation of cysteine by carbamidomethylation and methionine by oxidation was allowed. The precursor and product mass tolerance were set as +/-50 and +/-100 ppm, respectively. Two or more unique peptides for each protein were taken for con rmation of the protein present in the sample.
Molecular functional ontology The prominent salivary proteins showing up during the ovulation and post-ovulation phases were further classi ed based on their cellular component, biological process, and molecular function in the UniProt database (http://www. uniprot. org/). The GO entries were used to depict the percentage of proteins through Interproscan analysis in BLAST2GO. The retrieved GO ID's of protein entries, with their enrichment values, were used to generate a scatter plot by adopting Reduce Visualize Gene Ontology (REViGO) web server (http://revigo. irb. hr). The scatter plotting was carried out with semantic x-and y-axes corresponding to the log size value and log10 P value, respectively.
2D -Gel electrophoresis The salivary protein expression pro les were assessed during pre-ovulation, ovulation, and post-ovulation phases of menstrual cycle. In Coomassie-stained 2D gels, protein spots were present in the pI range 4-7 and the molecular weight between 14-97 kDa (Figure  4A-C ). The analyses carried out during different phases using PDQuest software (Bio-Rad) showed nearly 50 spots in each phase. Particularly, there were more spots during ovulation phase, and many of them had higher densities compared to the other phases. Additionally, we found that the quantitative differences in the spots were high in 14. 5 to 21 kDa spot region of ovulatory phase proteins compared to the other phases. It was noticed that a few low molecular weight protein spots were speci cally expressed during the ovulation phase (Figure  5A ).
Identi cation of proteins from selected spots Sixteen spots with signi cant difference in intensities i. e. , 2-fold increase, were excised from the 2D gel of ovulation phase salivary proteins. The spots were subjected to trypsin digestion and subjected to mass spectrometry. The monoisotopic mass were searched in the protein database of human from MASCOT search to identify the full length sequence. Spot 1 closely matched with Cystatin-S family protein with 71% sequence coverage and eight matched peptides (Figure  6A  & B, Table  2 ). The differentially expressed spots were identi ed as : Cystatin-S, Prolactin-inducible protein, Cystatin-A,Cystatin-SN, BPI foldcontaining family A member 2, Alpha-tubulin N-acetyltransferase 1, Carbonic anhydrase 6, Protein LEG1 homolog, Hemoglobin subunit beta, and Pancreatic alpha-amylase.
More speci cally, Carbonic anhydrase is present during all phases of animals exhibiting estrous cycle. Indeed, there is a signi cant expression of Carbonic anhydrase in cattle, camel and goat  [28]. In the human, recent studies have con rmed that secretion of speci c peptides/proteins are different in the pediatric age compared to adults  [29]. The functional annotation of the identi ed proteins obtained from STRAP database showed that these proteins are associated with binding property and regulatory function. In addition, these extracellular proteins would play an important role during ovulation. It is to be noted that proteins having binding property are widely present along with volatiles in body uids and facilitate chemical communication during the estrus period  [30, 31]. A more recent report showed that the binding proteins are abundant during estrus compared to the other phases in estrous cycling mammals  [32].
Acknowledgments G. S. (lead author) thanks the  Indian Council of Medical Research (ICMR-New Delhi, India ; No. 45/5/2014-BIO/BMS ) for the  Senior Research Fellowship. G. A. thanks the  University Grants Commission, New Delhi , for the award of  UGC-BSR Faculty Fellow. Manali Jadhav  and  Pradnya Nikam  thank  SAIF ,  IIT-Bombay, Powai, Mumbai , for help in the LC-MS/MS analysis. The authors thank  PRIDE Team  for processing the data in the MS, which have been deposited with the ProteomeXchange Consortium.
Case series The inclusion criteria were the presence of mandibular miniplates for at least 6 months after fracture osteosynthesis or orthognathic surgery, a trauma recurrence on the mandible leading to a mandibular fracture identified on a computed tomography (CT) scan, and patient age of mandibular plate for less than 6 months. The demographic data (age, sex), etiology of the initial mandibular osteosynthesis (location and type of miniplates), and new fracture type (circumstances, location based on CT scan) were recorded. Thirteen patients from six French University Hospitals met the inclusion criteria. The results are summarised in Table  1. All the patients were men, and their mean age was 32 years  (range, 20-64 years). The average time between the first procedure with the mandibular miniplate placement and the mandibular fracture was 35 months (range, 6-128 months).
Sequence generation {16a} Randomization will be carried out beforehand by a computerized system using RStudio® software version 1. 4. 1103 (RStudioTeam) with R version 4. 0. 2 (RCore Team). It will be organized by an independent person. The library used is "blockrand" with the blockrand function. The script is blockrand (n = 90, levels = c ("Gr1", "Gr2", "Gr3")). This generates an allocation list in each of the groups equally and randomly.
Implementation {16c} The creation of the sequence is carried out. Enrollment is done by residents and practitioners. The assignment to a group is also carried out by the practitioners or residents who treat the patient. When a patient meets the inclusion criteria, the resident who treats him comes to look for a sealed envelope containing the ticket described above. Then, the anonymized identifier and the patient group are collected in a database separate from the rest of the study and stored in the clinical research department of the hospital.
Data collection and management Plans for assessment and collection of outcomes {18a} LSPI will only be used once per patient during the bonding appointment. It will be read with a periodontal probe on the 4 sides of each tooth from 16 to 26 (12 teeth so 48 measurements in total per patient). Then, to evaluate the plaque control, the MOP will be used at each appointment. Its evaluation is done on 3 intra-oral photographs in occlusion after removal of the orthodontic archwires. They will be calibrated using a spreader with green marks to have the same magnification. The camera used is a Nikon D7000 model with an AF-S Micro Nikkor 60-mm lens. The settings used will be manual mode, F32 aperture, shutter speed 200, 1:3 magnification. The photographs will be taken by the resident, but the posterior evaluation will be carried out by one of the two examiners, blind to the allocation of the patient to one of the three groups.
Plans to promote participant retention and complete follow-up {18b} Subjects may withdraw their consent and request to exit the study at any time and for any reason. Data prior to this withdrawal of consent will be collected unless the participant objects in writing. The investigator may temporarily or permanently interrupt the participation of a subject in the study for any reason that would best serve the subject's interests. In the event of a premature exit, the investigator must document the reasons as completely as possible. Leaving a participant's study should not change their usual care in relation to their illness. In the event of a subject lost to follow-up, the investigator will make every effort to resume contact with the person.
Data management {19} and confidentiality {27} All clinical data will be collected in an anonym/ individual case report form (CRF). The data will then be compiled in an Excel spreadsheet meeting the requirements of the French Data Protection Authority (FDPA). The Excel file including the anonymized data and that of the correspondence table will be protected by a password and saved on the secure server of the Rennes hospital. They will not be transmitted outside the establishment. The files will be accessible only to the investigators and examiners. Photo shoots will be kept in the patient's CRF. Patient demographic data (age at inclusion, gender) and date of signature of consent are collected. At each visit, the following information is noted: date, caries, plaque index (LSPI or MOP) and gingical index for each tooth, lack of hygiene, demonstration at the sink, detachment of braces, and photographs.
Statistical methods for primary and secondary outcomes {20a} The statistical analysis will have a descriptive and an inferential part. The descriptive statistical analysis of the quantitative variables will be done by giving for each variable the positional parameters (mean, median, minimum, maximum, first and third quartiles) as well as the dispersion parameters (variance, standard deviation, range, interquartile range). The Gaussian character of the data will be tested by the Shapiro-Wilk test and by QQ plot diagrams. The qualitative variables will be described by giving the contingency and proportions of each modality in the sample. For the first outcomes (comparisons of final MOP between the 3 groups) and for inflammation outcome (gingival index), analyses will be made either by an analysis of variance (Gaussian case) or by its nonparametric equivalent, namely the Kruskall-Wallis test (non-Gaussian data).
Aim: To create an irreversible pulpitis gene signature from microarray data of healthy and inflamed dental pulps, followed by a bioinformatics approach using connectivity mapping to identify therapeutic compounds that could potentially treat pulpitis. Methodology: The Gene Expression Omnibus (GEO) database, an international public repository of genomics data sets, was searched for human microarray datasets assessing pulpitis. An irreversible pulpitis gene expression signature was generated by differential expression analysis. The statistically significant connectivity map (ssCMap) method was used to identify compounds with a highly correlating gene expression pattern. qPCR was used to validate novel pulpitis genes. An ex vivo pulpitis model was used to test the effects of the compounds identified, and the level of inflammatory cytokines was measured with qPCR, ELISA and multiplex array.
Gene expression connectivity mapping (CMap) is an innovative bioinformatics platform, which uses a reference database of gene expression profiles obtained from microarray experiments in cell lines treated with various drugs. The basic concept is to use a reference database containing drug-specific gene expression profiles and compare it with a disease-specific gene signature. Theoretically, these genes may be important for the induction of the phenotype of interest, therefore, similar alteration of gene expression by any compound or drug identified would be predicted to induce the same phenotype. The technique is employed for drug repositioning approaches  (Lamb, 2007)  and further developed into the 'statistically significant connectivity map' (ssCMap) by using statistical means to control for false connections between gene signature and reference profiles  (Musa et al.
Differential expression analysis GEO databases were searched using the keywords: 'dental pulp', 'pulpitis', 'inflammation of dental pulp' and 'irreversible pulpitis'. The search identified two studies, GSE92681 and GSE77459. GSE92681 was excluded as it looked at long noncoding RNAs, which are not compatible with ssCMap. The Series Matrix Files (SMF) for GSE77459 data sets, which contain gene expression levels for all the probes used in the microarrays, were downloaded and opened in Microsoft Excel (Microsoft Corporation). The data sets  (Galicia et al. , 2016)  were for six healthy dental pulps from sound teeth and six dental pulps from carious teeth with a diagnosis of irreversible pulpitis according to the American Association of Endodontists (AAE) classification  (Levin et al. , 2009). Two sample equal variance t-tests were performed on the gene expression levels measured in healthy and dental pulps diagnosed with irreversible pulpitis with Bonferroni post hoc test to control the expected number of false-positive differentially expressed genes (DEGs).
For instance, the gene signature created is not inclusive of all differentially expressed genes as many have not been cloned or their function identified yet, therefore, a future analysis may reveal further biomarkers and more compounds as potential therapeutics for pulpitis. The limited number of GEO datasets available to be included in this study is also another limitation as likely a larger number of data sets may result in different gene signatures and consequently additional compounds. Nevertheless, the genes and the compounds identified and validated in this study are relevant to pulpitis disease pathophysiology and potential treatment.
, 2018). Together, NGS, metagenomics, and metaproteomics  (VerBerkmoes et al. , 2009; Siggins et al. , 2012; Wang et al. , 2016; Speda et al. , 2017)  provide a powerful platform to investigate the microbial communities from remote and polyextreme habitats  (Rekadwad et al. , 2017). In particular, the field of marine microbial ecology was boosted by NGS-metagenomic platform technology, showing the biodiversity of various marine environments and revealing previously unknown microbial communities  (Kodzius and Gojobori, 2015; Coutinho et al. , 2018). The large number of metagenomic datasets generated by those platforms has facilitated the identification of a huge number of metagenomes  (Kennedy et al. , 2010; Barone et al. , 2014; Ziko et al. , 2019). These microbial gene datasets helped to obtain a better understanding of adaptive mechanisms and community interactions.
Metagenomic Data and Database The high inconsistency between culturable bacteria and expected bacterial count in environmental samples indicates that we currently know only a small fraction of the bacterial diversity in nature  (Amann et al. , 1995). The single-amplified genome (SAG) strategy  (Alneberg et al. , 2018)  for retrieving genomes from samples without the need for cultivation in combination with high-throughput NGS technologies for culturable bacteria is a valuable approach for mining metagenomic data from the Red Sea and the Red Sea brine pools  (Kamanda Ngugi et al. , 2015). The speed of genome sequencing by NGS technologies is orders of magnitude higher than that of experimental testing, creating a 'bottleneck' in downstream experimental analysis  (Médigue and Moszer, 2007; Pop and Salzberg, 2008; MacLean et al.
, 2015). However, to date, there is only one public database compiling the available Red Sea genomic data: the INtegrated Data Warehouse of MIcrobial GenOmes (INDIGO 1 ). INDIGO aims to be a dedicated database of metagenomic information of microorganisms from the Red Sea. It contains fully annotated genomes of bacterial and archaeal species isolated from the Red Sea. INDIGO uses the Automatic Annotation of Microbial Genomes (AAMG) algorithm, which comprises different annotation methods, in combination with profile and pattern matching (PPM). This algorithm combination aims to reduce the level of uncertainty in gene annotation and therefore lowers the likelihood of false-positive enzyme function annotation  (Grotzinger et al. , 2014). Furthermore, INDIGO derives and combines information from several databases, such as the CDD (Conserved Domains Database)  (Lu et al.
, 2020) , GO (Gene Ontology)  (Ashburner et al. , 2000; Consortium T. G. O. , 2020) , InterPro  (Hunter et al. , 2009) , KEGG (Kyoto Encyclopedia of Genes and Genomes)  (Kanehisa and Goto, 2000) , and UniProt  (Consortium T. U. , 2020) , to provide as much information as possible  (Alam et al. , 2013). Having such a database is necessary for a systematic screening of the Red Sea in search of extremophilic enzymes. However, a wider input and contribution network is necessary to keep the collection up-to-date and growing.
Nonetheless, to date, only two Red Sea brine pool enzymes have been crystallized, namely, 5,6-dihydroxy NADPH-bound ADH/D1 alcohol dehydrogenase (PDB code: 5YVM)  (Grotzinger et al. , 2018)  and γ-carbonic anhydrase CA_D (PDB code: 6SC4)  (Vogler et al. , 2020). Consequently, most structural studies of Red Sea enzymes still rely on structure-based homology models (Figure  2 ). These models reveal that most Red Sea brine pool protein surfaces consist largely of negative charges, which enables higher salt tolerance by coordinating a water shell around the protein structure. The homotrimers ADH/A1a and γ-CA_D and the homodimers ADH/D1 and MerA exhibit positive electrostatic potential cores, which function as multimerization contacts (Figure  2 ). The only exceptions are EstATII and ATII-ABL, which show a higher positive electrostatic potential distribution on the surface.
However, even this framework cannot capture the dynamics of this marine ecosystem that are continuously changing and will only provide a snapshot of the microbial profiles. Several databases are known for holding metagenomic data isolated from Red Sea brine pools. However, only a few are regularly updated. Thus, metagenomic data are already available, yet they are not compiled in one place. This makes a systematic screening of Red Sea (extremo)enzymes almost impossible. A dedicated metagenomic annotation database would accelerate and simplify the search for novel robust industrially applicable enzyme variants from Red Sea brine pools. Metagenomic annotated data are a good starting point for selecting potential industrially applicable enzyme variants. However, suitable genetic screening techniques for direct gene or gene cluster screening in the Archaea expression systems are still missing.
The chemical composition of the NBrHAp composite thin films was examined by X-ray photoelectron spectroscopy (XPS; SPECS GmbH, Berlin, Germany) with Al Kα monochromatic radiation (1486. 6 eV). All recorded XPS spectra were analyzed using Spectral Data Processor v. 2. 3 (SDP) software. The experimental conditions were in agreement with previous studies  [35]. The vacuum in the analyzer chamber was p ~3 × 10 -9 Torr. The X-rays were emitted by an anti-cathode of Al, U = 12. 5 kV, with filament emission current I = 20 mA. The XPS recorded spectrum involved an energy window w = 20 eV with the resolution R = 20 eV, and with 400 recording channels. The NBrHAp composite thin films morphology was investigated by scanning electron microscopy (SEM) using a Hitachi S4500 apparatus (Hitachi, Tokyo, Japan).