dataesr/scientific-paragraphs-categorization huggingface dataset:
Establishment of training and testing sets The 1,080 images after MUSICA were randomised into the training and testing sets with 918 and 162 graphs, respectively. An additional 233 radiographs without rib fractures were also added to the testing set as the joint testing group to evaluate model generalisability. In previous studies, radiographs were manually cropped into a square or to centre the objective  [23]. The current study incorporated two additional steps for segmenting the radiographs and amplifying the data. First, the input image was downscaled from 2,458×2,881 to 1,229×1,440. Second, a sliding window was used to generate subgraphs with a window size of 512×512 in steps of 256. Each image was divided into approximately 20 subgraphs (Fig. 5a ). Once the marked area was cut, the smaller area was ltered out, and training data were generated after ltering (Fig.
Acknowledgements: We appreciate the help and support of all the participants involved in the study. Authors' information: 1 Department of Radiology, The First A liated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China. 2 The  Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University , Xi'an 710054, China. 3 Department of Medical Imaging, No. 215 Hospital of Shaanxi Nuclear Industry, Xianyang, 712000, China. 4 School of Information Science and Technology, Northwest University, Xi'an, 710127, China. 5 Department of Radiology,  Shaanxi Provincial Tuberculosis Control Hospital , Xi'an, 710105, China. 6 Academy for advanced interdisciplinary studies, Peking University, Beijing, 100191, China.
Electrodeposition of Calcium Phosphate Coating The experimental setup is composed of three electrodes: the sample to be coated is placed at the cathode, a platinum counter electrode, and a saturated calomel electrode (SCE) as reference. The experiments were carried out using a potentiostat/galvanostat SI 1287 (Solartron Analytical, Farnborough Hampshire, UK) controlled by CorrWare ® software. The electrolyte solution was prepared using Ca(NO 3 ) 2 •4H 2 O and NH 4 H 2 PO 4 as calcium and phosphate sources respectively to obtain concentrations of [Ca] = 0. 042 M and [P] = 0. 025 M corresponding to a Ca/P ratio of 1. 67. The temperature of the electrolytic solution was set at 60 • C and the stirring at 200 rpm. Figure  S1  shows the experimental set. Electrodeposition was realized with a constant potential of -1.
The XRD data were collected using a Cu anticathode (wavelength = 1. 54184 Å), from 2Θ = 3 • to 80 • with a step of 0. 03 • and a counting time of 6 s. FTIR spectroscopy analysis (FTIR spectrometer iS50, Nicolet, Thermo Scientific, Thermo Electron Scientific Instruments LLC, Madison, WI, USA)) was performed in the middle infrared range and attenuated total reflectance (ATR) mode using a diamond crystal in order to analyze the CaP coatings directly on the substrate. Several points were analyzed to check the chemical composition homogeneity of the coating. Raman spectroscopy combined with a confocal microscope (LabRAM HR 800, HORIBA Jobin Yvon, Longjumeau, France) allowed for a focus on a special area. A 532 nm laser source, a 1800 grooves/mm grating, and a ×100 microscope objective were used for the acquisitions.
Acknowledgments: The authors thank the  Midi-Pyrénées Region  ( BIOACTISURF  project n • 14054394) for supporting this research work,  Nadine PEBERE  ( CIRIMAT, Fran ce ) for the provision of the electrodeposition devices, and the  Institute of Tropical Technology (ITT-Hanoï, Vietnam)  where a preliminary study was carried out in the framework of  FOCOMAT International Associated Laboratory. We also thank the  Institut National Polytechnique de Toulouse (France)  for supporting the international mobility of C. Pierre.
PNG has poor general health due to low socio-economic status and high burden of infectious diseases, such as pneumoni a, malaria, tuberculosis and meningitis  (Riley, 2009). While the gut microbiota of different regions within PNG showed higher bacterial diversity and different abundance profiles in comparison with Westernized populations  (Martinez et al. , 2015) , a comprehensive analysis of the oral microbiome for diverse ethnic groups and regions in PNG has not been performed. In this work we combined the results from two different methodologies (Figure  1 ) to characterize the microbial community from DNA extracted from PNG saliva samples: (1) qualitative results from a microbial detection array, which contains specific probes to the different microbial spe cie s, and (2) quantitative from whole genome sequencing (WGS) reads unmapped to the human genome , which were aligned to a microbial database.
The quantitative oral microbiome data allowed us to assess differences between diverse PNG populations in light of diet and/or environmental factors, and against other worldwide populati ons. Our working hypothesis was that a specific oral microbiome signature might be found in these populations due to adaptation to their specific biodiversity, cultural lifestyle or genetic background, since their 50kya-establishment in the ancient Sahul region (New Guinea/Australia). S1 ). The saliva samples were collected with the Oragene DNA OG-500 collection kit (DNA Genotek Inc. , Ottawa, Canada), and the nucleic acids were extracted following the manufacturer's instructions. All samples were collected from healthy unrelated adult donors, all of whom provided written informed consent. In each sampling location, a full presentation of the project was made , fol l owed by discussion with each donor to ensure that they fully understood the project.
The 96-well array plate version of the Microbi ome array was used to run all the samples on the GeneTitan instrument (Thermo Fisher Scientific, Waltham, MA, USA). A volume of 20µL of DNA from all samples was used as input into the MDA, including a no-template control and a positive control for quality control. The standard manufacturer's protocol was followed for all steps of the workflow. Quality control (QC) was performed using the QC option in the Axiom™ Microbial Detection Analysis Software (MiDAS), which analyzes specific probes included in the Microbiome array to target regions of the human genome that are non-polymorphic (sites that do not vary in sequence from one individual to the next). Thi s QC analysis generates a metric termed Dish QC (DQC) and applies a threshold of DQC above 0.
95 for the samples to be considered as having passed QC. Six samples were removed from the analysis after QC: PNG237 and PNG238 (East Sepik province), PNG128, PNG514 and PNG525 (Western province), and PNG205 (East highlanders living in Port Moresby). The remaining samples (n=86) were then further analyzed with MiDAS, which analyses all targets considered positive and reaches a consensus for the microbial community present in the samples. Positive targets are the probes with signal intensity above the 99 th percentile of the random control probe intensities and with more than 20% of target-specific probes. These probe sequences are available from Thermo Fisher Scientific (Waltham, MA, USA) as part of the MiDAS software. To note, this technology is unable to provide a view of the varying prevalence of the microorganisms present in analyzed samples.
Microbiome inference from human whole genome sequences For 68 individuals, also characterized by Microbiome array, we used reads extracted from WGS from (1) previously published studies  (Brucato et al. , 2021) , and (2) from newly unpublished whole genome sequences data (Table  S1 ). The sequencing procedure has been described previously  (Brucato et al. , 2021). Briefly, sequencing libraries were prepared using the TruSeq DNA PCR-Free HT kit. About 150-bp paired-end sequencing was performed on the Illumina HiSeq X5 sequencer. We aligned the fastq files against the human reference GRCh38 (GCA 000001405. 15) using minimap2-2. 23 (H. Li, 2018)  and the unmapped reads were retrieved. For taxonomic assignment of the unmapped sequencing reads we resorted to KRAKEN 2 tool  (Wood, Lu, & Langmead, 2019)  using the Standard plus protozoa & fungi database (PlusPF; includes archaea, bacteria, viral, plasmid, human, UniVec_Core, protozoa and fungi; updated on January 27 th , 2021).
We then used Bracken  (Lu, Breitwieser, Thielen, & Salzberg, 2017)  (Bayesian Reestimation of Abundance after Classification with KRAKEN) to estimate abundance at the genus level, using the taxonomic assignments made by Kraken. Since these PNG populations are understudied, we are unable to consider possible unidentified microbiota species that may be specific to them. We therefore performed all analysis on genus level to reduce the bias caused by using the Microbiome array and the WGS databases. We applied Bracken's default threshold of 10 reads as the minimum number of reads required for a classification at the specified rank. To remove low background noise on the WGS results due to minor contaminants, we applied a threshold for which all genera below a given percentage would be removed. This threshold was determined by calculating the best correlation of alpha-diversity values obtained from the Chao1 index between each sample using the qualitative and quantitative re sul ts, applying several thresholds, from 1% to 0.
Microbial composition We created phylogenetic trees and calculated the respective abundance of the genera found i n the five groups from the qualitative results (Microbiome array). The abundance in this case refers to the percentage of samples in the group in which the specific taxon was detected. We used RStudio software (RStudio Team, 2020) to perform pairwise chi-squared tests and correction with Holm's method to see which genera were significantly different. Venn diagrams with the percentage of Bacteria and Virus were created to help to visualize the similarities and differences between the fi ve groups resorting to the webtool Bioinformatics & Evolutionary Genomics (https://bioinformatics. psb. ugent. be/ webtools/Venn/). Discrimination between the fi ve di fferent groups using quantitative data was performed with the Linear discriminant analysis Effect Size (LEfSe) algorithm  (Segata et al.
Alpha-diversity The alpha-diversity of each sample was calculated by estimating the genus and species richness, evenness, and diversity using the Phyloseq package v1. 23. 1  (McMurdie & Holmes, 2013). Alphadiversity measures were calculated differently for qualitative and quantitative results. For the qualitative results (from the Microbiome array data) we calculated Chao1  (Hughes, Hellmann, Ricketts, & Bohannan, 2001) , which measures the richness of the samples and is more suitable for presence/absence data. We created a ggplot for each alpha-diversity metric and performed pairwi se comparisons using t tests and correction with Holm's method using RStudio (RStudio Team, 2020). After alpha-diversity analysis, one sample was considered an outlier, PNG54 (Port Moresby), as its Chao1 index value falls out of the mean +/-3SD for all samples (Figure  S1 ), so it was removed from further analyses.
Reads from WGS data not mapping to the human genome were analyzed with KRAKEN 2  (Wood et al. , 2019) , resulting in an average of 18 652 535 reads matching microbial genomes per individual (ranging from 1 483 445 -44 976 955 reads). As expected, the WGS data identified a much higher number of genera (1757 genera; Table  S2 ) than with the Microbiome array data. In fact, the BRAKEN method used in the WGS inference limits the number of reads to 10, while the Microbiome array has a sensitivity of 100-1000 genome copies. Hence, when comparing the concordance in inferred genera between the two methods, these values are high for the most abundant genera (Figure  2B ; Table  S3 ): an average of 92% match filtering to the genera with abundance above 1% (28 out of the 65 samples achieving 100% match); 84% when filtering to the genera with abundance above 0.
Oral microbiome of PNG and other populations To have a broader perspective of the oral microbiome of PNG population samples, we merged the WGS results from our PNG populations with the available WGS samples from Philippines huntergatherers, Philippines traditional farmers and Western controls  (Lassalle et al. , 2018)  and proce sse d their dataset with our workflow. We generated an unrooted neighbor-joining for the eight populations (Figure  5A ) which showed that the PNG populations are closer to one another and separated from the Philippines and the Western controls. We also compared the abundance of the PNG core microbiome (Figure  5B ; Table  S5 ) obtained in this Islanders (14%) than in the remaining groups (9. 58%). This enrichment of Rothia and Neisseria appear to be limited to the New Guinea-Australia region (Figure  5B ), which might indicate a speci fi c trait of these genera on the oral microbiome, potentially influenced by environmental conditions found in this geographic region.
, 2021; Pedro et al. , 2020) , then connected into the ancient continent Sahul. A secondary gene flow linked to the Austronesian dispersal 3kya led to a gradient of Asian genetic ancestry in coastal groups of the region  (Brucato et al. , 2021; Mallick et al. , 2016). However, our limited dataset (no group from New Guinea with a predominant Austronesian genetic ancestry) and current knowledge on microbiome research, do not support a strong link between a population genetic ancestry (e. g. Austronesian) and a specific oral microbiome signature, despite some encouraging new studies  (Weyrich, 2021). It is, nevertheless, remarkable that our study revealed a specific pattern in the oral microbiome shared by New Guineans and Aboriginal Australian groups, that are genetically separated for at least 30 000 years  (Brucato et al.
This unexpected pattern of the PNG oral microbiome clearly needs further exploration. As this data was collected opportunistically, potential environmental and cultural cofactors were not controll ed for. In particular, betel nut chewing  (Hernandez et al. , 2017; Zhong et al. , 2021)  has shown to be highly associated with variation in the oral microbiome in other populations and is a common habit to all the PNG locations. The impact of this specific cultural behavior would presumably re sult i n an alteration of the global oral microbiome in all PNG populations, which would not greatly affect our results. Nevertheless, the effect of betel nut chewing in oral microbiome is an i mportant questi on and should be further explored in the PNG populations. The time of sample collection might also influence the results obtained for oral microbiome, as seasonality in sample collection should be considered in further studies, and might increase the power in discriminating between the oral microbiomes of the PNG populations  (Clemente et al.
, 2018) , we downloaded their published microbial metagenomic read data sets from ENA (www. ebi. ac. uk/ena; BioSample Accession nos. ERS1202862-ERS1202885) and processed their dataset with our workflow. These files were merged with our dataset to perform beta-diversity analysis and to calculate the abundances of the core microbiome. For the Australian Aboriginals and Torres Strait Islanders (Handsley-Davis et al. , 2021)  and the Batwa Pygmies populations (Nasidze et al. , 2011) , we were not able to obtain WGS raw data, as they only sequenced a region of the microbial 16S rRNA gene (V4 for the Australian Aboriginals and Torres Strait Islanders and V1 and V2 for the Batwa pygmies). For these, we retrieved the values of abundance for the core microbiome that were described and available in their publications.
WGS-based dataset (Human Microbiome Project, 2012; Lassalle et al. , 2018) with other 16S rRNAbased published studies for Australian Aboriginals and Torres Strait Islanders (Handsley-Davis e t al. , 2021) and Batwa Pygmies (Nasidze et al. , 2011). Interestingly, the Rothia genera abundance s i n the five PNG groups (average 8. 24%) are significantly higher (p-value =0. 0013) than in all the other compared groups (Philippines hunter-gatherers, Philippines traditional farmers, Batwa Pygmi es and Western Controls; average 3. 87%), except for the Australian Aboriginals and Torres Strait Islanders (8%). The above pattern is also significantly different for the Neisseria genera (p-value = 0. 0405), with high abundances in the PNG groups (average 15. 05%) and Australian Aboriginals and Torres Strait.
Acknowledgements We especially thank all of our study participants. We also thank  Mathilde André  and  Georgi Hudjashov  ( University of Tartu ) for their help in data analysis. We thank  Roxanne Tsang ,  Jason Kariwiga ,  Teppsy Beni ,  Kylie Sesuki  ( University of Papua New Guinea ),  Alois Kuaso , and  Kenneth Miamba  ( National Museum and Art Gallery, Papua New Guinea ), and  John Muke  ( Social Research Institute, Papua New Guinea ) for their help during the sampling campaigns. We acknowledge support from the  GenoToul bioinformatics facility of Genopole Toulouse Midi-Pyrénées, France ; the  LabEx TULIP, France ; and the  Centre National de Recherche en Génomique Humaine (CNRGH) ,  Evry, France. This work was supported by the  French National Research Agency (ANR ; grant  PAPUAEVOL  n°  ANR-20-CE12-0003-01 ), the  French Ministry of Foreign and European Affairs  ( French Prehistoric Mission in Papua New Guinea ), and the  French Embassy in Papua New Guinea.
Methods In this retrospective observational study, we present a series of cases clinicopathologically diagnosed as noninfectious granulomatous disorder of the upper lip. We used the term "disorder", as some of them have no, or minimal, inflammatory component. The patients were studied at the Oral Medicine Service of the Institut Universitaire du Cancer de Toulouse (Toulouse, France) and the Oral Medicine and Pathology Unit of the University of the Basque Country/EHU (Leioa, Spain). These corresponded to 8 women and 3 men, with a mean age of 56. 45 years at the time of diagnosis (range: 29-82 years). A specific clinicopathological protocol was designed for the study, where the main clinical and histopathological data were collected. All patients signed an informed consent form and their personal data were anonymised.
Statistical Analysis IBM SPSS Statistics version 26. 0 was used to analyze the data. The level of significance was set at -p-value ≤ 0. 05. The primary outcome measurement was the SBS (MPa). Kolmogorov-Smirnov tests were used to assess the normality of the distribution of the variables. Levene's test was used to assess the homogeneity of variances between groups. One-way analysis of variance (ANOVA) followed by Tukey (HSD) post-hoc tests were used to compare mean bond strength between groups. Fisher Exact tests were used to compare the type of fracture among groups.
For measuring filament length, 5x10 4 yeast cells were seeded in F12 medium (see above) into 24-well tissue culture plates containing or not containing monolayers of TR146 cells. For analysis of filamentation of 101 nrg1Δ/pTET-NRG1 , 10 μg/ml Dox was added. After 3. 5-4 hours of incubation at 37˚C and 5% CO 2 , cells were fixed in 4% PFA for 30 min at 4˚C and washed with PBS. Images were acquired using a Zeiss Axio Observer Z1 inverted phase contrast fluorescence microscope. 4 fields per well were imaged. The length of individual non-aggregated filaments was measured using the ImageJ software.
All antibodies were from BioLegend. Stained cells were analysed on a FACS Gallios (Beckman Coulter), or a SP6800 Spectral Analyzer (Sony). The data were analysed with FlowJo software (FlowJo LLC). The gating of the flow cytometric data was performed according to the guidelines for the use of flow cytometry and cell sorting in immunological studies  [72]. including pre-gating on viable and single cells for analysis. Absolute cell numbers of lymphocyte populations were calculated based on a defined number of counting beads (BD Bioscience, Calibrite Beads), which were added to the samples before flow cytometric acquisition.
The collected double-strand cDNA was then amplified and indexed in a separate PCR. For the analysis of RNA-seq samples from in vitro induced hyphae, 1% of RNA was used to spike RNA samples from naive mice, which were then subjected to the same enrichment procedure described above. RNA quality, fragment size and cDNA concentration were determined using a fragment analyzer automated CE system (Advanced Analytical) and a Qubit fluorometer (Invitrogen). For the control on technical issues from the use of biotinylated baits-based technology to capture C. albicans-derived cDNA  [27]  for both isolates SC5314 and 101 as well as the use of SC5314 genome assembly for aligning sequencing data from both isolates, two duplicated RNA-seq samples from SC5314 (963bait and 964bait) and 101 (965bait and 966bait) grown in vitro were investigated.
RNA-seq data analysis The sequencing was performed by Lausanne Genomic Technologies Facility (LGTF). cDNA libraries were subjected to cluster generation using the Illumina TruSeq PE cluster kit v3 reagents and sequenced on the Illumina HiSeq 2500 system with TruSeq SBS kit v3 reagents. Sequencing data were processed analogous to a previous study  [27] : Data were processed PLOS PATHOGENS using Illumina Pipeline software (v1. 82). Purity-filtered reads were adapters-and qualitytrimmed with Cutadapt (v1. 2. 1) (Martin, 2011) and filtered for low complexity with Prinseq (v0. 20. 3) [74]. After alignment against the Candida albicans genome SC5314 version A21-s02-m09-r07 using STAR (v2. 5)  [75, 76] , htseqcount (v0. 5. 4p3) was used to summarize the number of read counts per gene locus  [77]. Genes with counts fewer than one per million in all samples were removed from the statistical analysis.
Data normalization and differential expression analysis were performed in R (v3. 2. 2), using Bioconductor packages. Normalization of read count data was performed with the R package edgeR using the TMM (trimmed mean of Mvalues) method  [78]. Normalized read count data was transformed to log2 counts per million by voom (implemented in the R package limma)  [79]. The hierarchical clustering and principal component analysis were done in R (v3. 2. 2). A linear model was built on the transformed data from the following conditions: SC_in_vitro, SC_d1, 101_in_vitro, 101_d1, 101_d3, and 101_d7 (all in duplicates). Differential expression analysis was then performed on the linear model with multiple contrasts representing the differences: (i) between the 101_in_vitro and SC_in_vitro conditions, and (ii) between the 101_d1/3/7 conditions and the SC_d1 condition.
P-values produced from the differential analysis were adjusted using the Benjamini & Hochberg correction  [80] , and adjusted p-values (FDR) < 0. 05 were considered significant. Heatmaps were generated with GENE-E (Broad Institute). GO process enrichment analysis was performed with the GO term finder integrated in the Candida Genome Database. The Gene Ontology (GO) term enrichment analysis was performed using DAVID Bioinformatics Resources 6. 8 (NIAID, NIH). The enriched GO terms were hierarchically clustered with the semantic similarity between GO terms based on the graph structure of GO (Wang measure) using the R package GOSemSim (v2. 12. 1)  [81]  and plotted with the R package ComplexHeatmap (v2. 2. 0)  [82].
RT-qPCR cDNA was generated by RevertAid reverse transcriptase (Thermo Fisher) and random nonamers. RT-qPCR was performed using SYBR Green (Roche) and a QuantStudio 7 Flex (Life Technology) instrument. All RT-qPCR assays were performed in duplicates and the relative expression levels of each gene was determined by normalization to the housekeeping gene. hG6PD was used as housekeeping gene in human and EFB1 or ACT1 in C. albicans, respectively. Using EFB1 or ACT1 as a housekeeping gene provided comparable results. The primers PLOS PATHOGENS used to detect human transcripts are listed in Table  3 , those to detect fungal transcripts are listed in Table  4.