dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, the annotators are asked to label each frames independently of the context, i. e. , of the surrounding frames. In other words, we consider as non-visible the cases in which we are not able to conclude while looking at the sole frame. For example, the following cases are annotated as non-visible: 1. only the tip of the clipper is present in the frames, i. e. , one cannot recognize a clipper in this frame; 2. the clipper is subject to strong motion blur, i. e. , one cannot identify the clipper or its tip; 3. the contrast is poor, i. e. , one cannot determine if the tip is occluded or not based on this single frame. This context-independent annotation strategy is designed to reflect the final use case: surgeons should not clip in case of poor visibility (e. g. , bad contrast). Using this annotation strategy, we build a new dataset containing 300 non-robotic laparoscopic cholecystectomy video recordings from the Inselspital (the University Hospital of Bern).
We sample these videos at 5FPS and annotate all frames containing at least a few pixels of the clipper. This includes frames in which one cannot recognize the clipper given this sole frame. In the following, we split the data into a "training/validation" set and a test set (frames from 29 videos). We use the training/validation set for model training and selection using a To compare annotations across annotators and also obtain a consistent test set for evaluating ClipAssistNet reliably on the end application, the 29 test videos are annotated by three different annotators (see Table  1  for a summary). The remaining videos, used in the training/validation set, are annotated by single annotators. For the cross-validation, we partition the training/validation set by selecting videos using a deterministic algorithm.
Models We opt for a Resnet50-based  [15]  neural network architecture with weights pre-trained on ImageNet. Images are resized to 224x224 pixels and normalized using the ImageNet statistics. During training, data augmentation is applied on the training images. The validation set is monitored for early stopping and model checkpointing (see Sect. 2. 2. 2 for implementation details). The tip visible/invisible labels are slightly imbalanced in our dataset (Table  1 ). Additionally, our training samples To address these peculiarities, we compare different variants of the binary cross-entropy (BCE) loss. Besides standard BCE and class-weighted BCE, we also consider a videoweighted cross-entropy loss which accounts for different frame counts per video. This video-weighted loss increases the emphasis on videos with a low number of frames, i.
These frames correspond to edge cases in the image contrast is low or the tip of the clip applier is close to the tissue. This high disagreement rate reflects the difficulty to label certain frames. In addition, annotators have an average error rate of 6. 8%. Annotator error rate is defined as the percentage of the frames a given annotator disagrees with the two other annotators. In other words, if the test set is single annotated (as in the training dataset), 6. 8% of the frame labels might be incorrect.
e. , true positive rate) of 95%. To begin with, we train a Resnet50-based neural network with a simple binary cross-entropy (BCE) loss on a single split (80/20) of the training/validation dataset. The model classifies the tip visibility on the validation set with an AUROC of 0. 89 and a specificity (at 95% sensitivity) of 61%. Looking at the saliency maps (Guided grad-CAM  [30] ) in Fig. 2 , we can see that the model learns to recognize the clipper shape. Nevertheless, the model has difficulty in edge cases, such as when the tip is slightly occluded (Fig. 2 , third row) or slightly visible (Fig. 2 , 4th row). Additionally, 45 out of the 50 highest loss-frames correspond to tip visible labels, while 42 out of the 50 lowest loss-frames are tip invisible labels. This could be due to the class imbalance toward tip invisibility in the training dataset.
We further compare the impact of different losses on the classification performance of the Resnet50-based classifier. Each loss is chosen to adapt our model to a given peculiarity of our dataset. Specifically, we try to account for: 1. class imbalance, by using class weights inversely proportional to the class presence in the training set; 2. label noise, by using the forward labeling noise correction methods presented by Patrini et al. [25] ; 3. disparity in the frame counts per videos, by setting samples' weights inversely proportional to the frame counts from the video they are issued from (see Sect. 2 for details). Implementation details are described in Sect. 2. 2. The performance improvements are measured using a fivefold cross-validation on the training/validation set. For all tracked metrics, the model performance is the highest when using the simple binary cross-entropy loss (Table  2 ).
Discussion In this work, we present a novel application of computer vision to monitor safety in laparoscopic cholecystectomies. We propose to increase the safety during clipping time by enforcing the adequate visibility of the clip applier through an intra-operative feedback. This feedback aims at ensuring that the cystic artery/duct are adequately clipped, i. e. , correctly encircled by the clipper jaws, while avoiding unintentional interaction with surrounding tissues. We approach this novel application as an image classification problem, which predicts the tip visibility of the clip applier in each frame. To this end, we prepare a new dataset tailor-made for our application and present a corresponding neural network image classifier. Our feedback is based on the assumption that enforcing the clipper tip visibility increases the safety of the clipping.
Despite all our effort, a part of subjectivity remains in the annotations of the tip visibility. Especially in case of poor contrast frames, the tip visibility is left to the appreciation of each annotator. Therefore, there subsists grey zone delimiting the tip visibility in these frames. The low agreement across annotators on triply annotated frames (18% of the test frames had no full agreement) highlights this issue. This high labelling noise could potentially impact (i) the trained model and (ii) the measured performance metrics. We mitigate the impact of the labelling noise on the performance metrics by triple labelling the frames in the test set. We could possibly further reduce the labelling error on the test set by annotating each frames a fourth or a fifth time. We train deep convolutional neural network classifiers with different losses on this dataset.
Introduction The human oral microbiome consists of a wide range of microorganisms, including bacteria, fungi, protozoa, viruses, and archaea and is thus, after the gut, the second largest human microbial community  [1]. The symbiotic relationship between the oral microbiome and host ensures oral health  [2]. The rupture of this balance and the establishment of a dysbiotic microbial community leads to oral disease, the most common being tooth decay and periodontal disease  [3] [4] [5] [6]. With the advent of next-generation sequencing and recent developments in bioinformatics, research on the diversity and the role of human oral microorganisms has increased dramatically  [7]. The expanded Human Oral Microbiome Database (eHOMD) currently includes a total of 775 microbial species, of which 57% are officially named, 13% unnamed but cultivated, and 30% are known only as uncultivated phylotypes  [8].
Genome Sequencing Information and Genome Properties The genome size of strain Marseille-Q4567 T was 3,122,925 bp long with a 38. 8% G+C content. It was assembled into 3 contigs with a mean coverage of 131. 6. It was deposited into GenBank under the accession number JAEPRJ000000000. Of the 2805 predicted genes, 2747 were protein-coding genes and 58 were RNAs (four 5S rRNA, four 16S rRNA, four 23S rRNA, 42 tRNAs, and 4 ncRNA). Genes with putative function (by COGs) were 1628 for strain Marseille-Q4567 T (Table  3 ). Finally, 1396 genes (49%) were annotated as hypothetical proteins for strain Marseille-Q4567 T. A circular map showing a complete view of the genome of strain Marseille-Q4567 T is shown in Figure  3. The mass screening for antimicrobial and virulence genes revealed the presence of lsa(C)_1 gene (92.
Comparison with Closely Related Bacterial Strains 16S rDNA-based similarity analysis of strain Marseille-Q4567 T against GenBank yielded the highest nucleotide sequence similarities of 98. 23% sequence identity with C. morbi ATCC 51271 T (GenBank accession no. NR_026248. 1). The 16S rRNA gene sequence was deposited into GenBank under the accession number MW410925. The phylogenetic tree highlighting the position of the strain relative to other closely related species is shown in Figure  4. Pathogens 2021, 10, x FOR PEER REVIEW 7 of 15.
Comparison with Closely Related Bacterial Strains 16S rDNA-based similarity analysis of strain Marseille-Q4567 T against GenBank yielded the highest nucleotide sequence similarities of 98. 23% sequence identity with C. morbi ATCC 51271 T (GenBank accession no. NR_026248. 1). The 16S rRNA gene sequence was deposited into GenBank under the accession number MW410925. The phylogenetic tree highlighting the position of the strain relative to other closely related species is shown in Figure  4. The genome of strain Marseille-Q4567 T was compared to the available genomes of nine closely related bacterial strains. The genome size of our strain (3,122,925 bp) was larger than that of H. luporum and E. oxidoreducens but smaller than that of C. caecimuris, C. morbi, R. hominis, F. fissicatena, A. cellulosilytica, F.
contorta, and F. orotica (Table  4 ). The G+C content of strain Marseille Q4567 T (38. 8%) was smaller than that of all compared species except H. luporum (35. 3%), A. cellulosilytica (36. 7%), and C. morbi (37. 1%). Only bootstrap values of 70% or greater are shown. Type strains are indicated with superscript T. GenBank accession numbers of 16S rRNA indicated in parentheses. Sequences were aligned using MUSCLE with default parameters, phylogenetic inference was obtained using the Maximum likelihood method and MEGA X software  [22]. Bootstrap values obtained by repeating the analysis 1000 times to generate a majority consensus tree are indicated at the nodes. There was a total of 1392 positions in the final dataset. The genome of strain Marseille-Q4567 T was compared to the available genomes of nine closely related bacterial strains.
According to the API ZYM system, cells were positive for alkaline phosphatase, leucine arylamidase, acid phosphatase, naphthol-AS-BI-phosphohydrolase, α-galactosidase, βgalactosidase, α-glucosidase, β-glucosidase, and N-acetyl-β-glucosaminidase. The predominant cellular fatty acid was C 16:0 (64. 2%), followed by unsaturated structures C 18:1n9 (12. 5%) and C 18:2n6 (7. 8%). The genome size of strain Marseille-Q4567 T was 3,122,925 bp long with a 38. 8% G+C content. The type of strain, Marseille-Q4567 T (CSUR Q4567), was isolated from a sample of unstimulated whole saliva from a male with stable periodontitis. The sequence data of the 16S rRNA gene of Marseille-Q4567 T and the whole genome have been deposited in the GenBank database under accession numbers MW410925 and JAEPRJ000000000, respectively.
The matrix sample was then crystallized by air-drying at room temperature as previously described  [24]. Two spots were systematically created for each colony, and each isolate was characterized by at least 12 spots. The obtained spectra were imported into the BioTyper-RTC TM version 3. 0 software (Bruker Daltonics GmbH) and analyzed by standard pattern matching (with default parameter settings). Interpretation of the scores was carried out as previously reported  [23]. One purified strain, designated Marseille-Q4567 T and deposited in the Collection de Souches de l'Unité des Rickettsies under accession number Q4567, could not be identified by MALDI-TOF MS. Gram staining was carried out using standard Gram stain, and morphological characteristics were observed with a field emission scanning electron microscope (SU5000 FE-SEM, Hitachi High-Tech, HHT, Tokyo, Japan) using cultures grown on Columbia agar with 5% sheep blood (BioMérieux, Marcy l'Etoile, France) at 37 • C for 24 h.
Oxidase (MASTDISCS ® ID, Mast Group Ltd. , Bootle, Merseyside, UK) and catalase (BioMérieux, Marcy l'Etoile, France) assays were also performed. The ability of strain Marseille-Q4567 T to form spores was evaluated following thermal shock at 80 • C for 10 min. Cellular fatty acid methyl ester (FAME) analysis was performed by GC/MS. Two samples were prepared with approximately 3 mg of bacterial biomass per tube harvested from several culture plates. Fatty acid methyl esters were prepared as described by Sasser  [25]. GC/MS analyses were carried out as previously described  [26]. Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S, Perkin Elmer, Courtaboeuf, France). A spectral database search was performed using MS Search 2.
Assembly and Annotation of the Genome Sequence The assembly was performed with a pipeline incorporating different software (Velvet  [27] , Spades  [28] , Soap Denovo  [29] ), and trimmed data (MiSeq and Trimmomatic  [30]  software) or untrimmed data (only MiSeq software). GapCloser was used to reduce assembly gaps. Scaffolds < 800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The best assembly was selected using different criteria (number of scaffolds, N50, number of N). Prokka (Galaxy v 1. 14. 5) was used for prediction in the open reading frame (ORF) with the default settings  [31]. Deviations in the sequencing regions predicted by ORFs were excluded. BlastP was used to predict the bacterial proteome (E value of 1e03, coverage of 70% and percent identity of 30%) according to the Orthological Group (COG) database.
Asymptomatic people may be contagious, and sick people are contagious before, during and after clinical symptoms onset  [20]. As a result, temperature checking was not sufficient to detect virus carriers. Travelers arriving from Wuhan before January 24 were able to transmit SARS-CoV-2 throughout China and then to Thailand and other countries. In addition, on January 30, the World Health Organization (WHO) "believed that it was still possible to stop virus spread by applying strong preventive measures at the international level," but did not ban travel and trade  [21]. Travel controls and preventive measures have been gradually introduced by various countries, but too late  [22]. Q4-What is the extent of the pandemic today? COVID-19 epidemiologic data vary according to sources, such as Johns Hopkins University (JHU) coronavirus resource center or WHO situation updates.
Acknowledgments: The authors thank the  "Collège National des EnseignantS en Biologie Orale (CNESBO) " and the  "Académie Nationale de Chirurgie Dentaire (ANCD) " for their support. They also thank  Yannick Dieudonné  ( Hôpitaux Universitaires de Strasbourg, France ) for proofreading of the manuscript and  Ganesh D. Sockalingum  ( Université de Reims Champagne-Ardenne, UFR Pharmacie, EA7506-Biospectroscopie Translationnelle (BioSpecT) ) and  Martine Spina  ( Lycée Masséna, Nice, France ) for careful English language editing.
Total RNA extraction and RNA-sequencing After total RNA extraction (QIAGEN RNeasy® FFPE-kit), samples (n = 13 epithelial and n = 13 stromal, subsequently referred as E-samples and S-samples, respectively) were sequenced (NextSeq 550 Illumina®) (quality controls in Supplementary Table  1 ). Raw data were deposited at Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/ geo/) (GSE164619).
Immunohistochemistry (IHC) The HALO™ software system (Indica Labs, v. 2. 0. 1145. 19) was used to quantify immune infiltrates for T-cells (CD3, CD4, CD8, FoxP3), B-cells (CD45R0/B220), and macrophages (M1 iNOS1 and M2 CD163) in the epithelium, the underlying stroma and muscle, of both normal (N) mucosa, hyperplasia (H), dysplasia (D) and tumor (T) samples (6 replicates for each condition totalizing 24 samples for each antibody). Data were normalized by the surface analyzed (n cells/mm 2 ). In addition, to control for a potential inflammatory effect of 4-NQO, normal areas at distance of hyperplasia, dysplasia, and tumor lesions were analyzed (Supplementary Figures  S6-S11 ).
The single-sample Gene Set Enrichment Analysis (ssGSEA) projection tool from GenePattern (ssGSEA v8) was used to compute separate enrichment scores (ES) for each sample using different gene sets (TGS, EGS, IGS, LGS, and PGS) in various datasets downloaded from GEO (GSE75421; GSE9844; and GSE30784). Standard GSEA analysis (GSEA v6. 3) was performed to compute the ES of 4 762 curated gene sets (C2 gene set, MSigDB database v6. 1, default parameters) using the log 2 fold-change in tumor versus normal mucosa as the input. Seq-ImmuCC (http://218. 4. 234. 74:3200/immune/) was used for immune cell populations deconvolution and infer the relative proportions of ten major immune cells (B-cells, CD4 and CD8 T-cells, Dendritic cells, Eosinophils, Macrophages, Mast cells, Monocytes, Neutrophils, and NK cells).
M2 macrophages signatures and oral cancer risk We have evaluated the association of some M2 macrophages gene expression signatures with Oral Cancer Free Survival (OCFS) using data previously published from 86 patients with OPMD, in particular, oral leukoplakia  15  (GSE26549) (Supplementary Table  6 ). Briefly, gene expression profile was measured in 86 Oral leukoplakia patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. 16 After inclusion, the patients were randomly assigned to intervention (13cis-retinoic acid versus retinyl palmitate with or without betacarotene). OCFS was defined as the time from random assignment until diagnosis of any OSCC. Analysis of OCFS was performed in the entire intent-to-treat population and was analyzed by the Kaplan-Meier method.
Immunological and classical subtypes of OPMD We have recently identified two distinct gene expression based OPMD subtypes named immunological and classical respectively. 14 Briefly, based on genome-wide expression profiles of 86 oral leucoplakias (GSE26549)  15  and using the 2,500 most variable genes, we performed an unsupervised and unbiased clustering of these samples (discovery dataset). Two main clusters, including 42 (cluster 1) and 44 (cluster 2) OPMD, respectively, were identified. Cluster 1, showing a strong and significant enrichment of immune pathways was named 'immunological' while cluster 2, characterized by a moderate enrichment of pathways involved in xenobiotic metabolism as well as EGFR signaling, was named 'classical'. By comparing genome-wide expression profiles of immunological as well as classical OPMD, we have identified the 200 most overexpressed genes and the 200 most under-expressed genes.
Using whole genome expression profiles, E-samples and S-samples were divided into two distinct clusters (Figure  2a ). We identified two sets of genes differentially expressed between T and N in E-samples and S-samples, respectively called TGSE and TGSS that included 837 and 1,519 genes respectively (Supplementary Table  4 ). Both gene sets were divided in a UP and DOWN subsets including genes which were coordinately up-or down-regulated within a sample. The overlap between TGSE and TGSS was 373 (16%). TGSE and TGSS were able to discriminate cancer from normal samples in three independents murine (GSE75421) and human (GSE9844 and GSE30784) datasets (Figure  2b ). In summary, gene expression changes were more abundant in the stroma and different from those occurring in the epithelial compartment and the 4-NQO model is relevant to understand the dynamic changes observed during oral carcinogenesis.
M2 macrophages signatures and oral cancer risk To investigate the precise role of M2 macrophages during oral carcinogenesis, we identified 13 murine and human gene expression signatures previously reported in the literature  18- 28  (Supplementary Figure  S15a ). Surprisingly, virtually no overlap was observed between genes included in these signatures (Supplementary Figure  S15b ). We then evaluated the association of each M2 signature with OCFS using data from 86 patients with OPMD (oral leukoplakia, GSE26549, Supplementary Table  6 ). 15 ssGSEA was used to compute an ES of each individual sample. Analysis was first done in the overall population in which none of the M2 macrophages signatures was associated with oral cancer-free survival (Supplementary Figure  16 ). We then explored the signatures in the immunological (n = 42) versus classical (n = 44) subtypes of OPMD.
Discussion In this work, by carefully analyzing, in the 4-NQO model, of the epithelial and the underlying stromal compartments, using gene expression profiles as well as immunostaining and semiquantitative assessment of different immune cell populations, we report that immunological changes are observed in early stages of oral tumorigenesis. Using different data mining approaches, we identify changes associated with M2 macrophages as being the most prominent quantitatively. Intriguingly, M2 macrophages gene expression signatures were consistently associated with a decreased oral cancer-risk in patients with an immunological OPMD subtype that we have previously reported. 14 ur study shows that all immune cell populations (T-and B cells, macrophages) are increasing early during mucosal transformation.
Statistical analysis Statistical analysis was performed using a pairwise ANOVA test, a post-hoc Tukey's test, and Spearman's rank order correlation analysis. The statistical significance level was considered as P < 0. 05. Data was analyzed using PRISM 9. 1 (GraphPad, La Jolla, CA, USA). All experiments have been performed at least three times with at least triplicate samples (biological and technical replicates).
Study design: A cross-sectional questionnaire was conducted online through social networks from 9th till 17th April 2020. The targeted population was the sixth-year medical students in Syrian Universities. The fourth and fth year students were allowed to submit the questionnaire in order to use their responses as a comparison group during data analysis. The minimum required sample size was (204) based on statisticians Borg and Gall recommendations  [12]  and on the available statistics from the Higher Ministry of Education in Syria.
Data analysis: Data were anonymously imported into an EXCEL® le and saved on a secured laptop. After processing, the data were analyzed using R  [14]  software version 3. 5. 3 and the R studio environment and data visualization was obtained by seaborn,  [15]  The Shapiro-Wilk normality test, Unpaired t-test, Pearson's Chisquared test, one way analysis of variance (one way ANOVA) test, and Tukey's HSD test were used during the statistical analysis. A P-value of ≤ 0. 05 was considered signi cant. Furthermore, both medical knowledge and clinical judgment items were assessed according to ve-point scores with an added overall score of 10 points. Linear regression tting model was used to determine the factors that signi cantly affected the overall score for the nal-year medical students. The model included: university of the participant, gender, English level, need for supervision and having fears of getting infected.