dataesr/scientific-paragraphs-categorization huggingface dataset:
Figures LegendsFigure 1: P. micra exists in two phylotypes with phenotypic and genetic diversity. A) Phylogenetic tree based on full length 16S rDNA sequences of 27 P. micra clinical isolates (named PMX) and 22 reference sequences from the NCBI database. Root: Finegoldia magna ATCC 29328. The infection type from which the 27 clinical isolates are derived are indicated by a color code; haemolysis and colony compaction abilities are indicated as present (Y) or absent (N). B) Adhesion capacity of P. micra ATCC 33270 (PmA) and HHM BlNa17 (PmB) to extracellular matrix proteins. Optical density at 595 nm represents measurement of bacterial adhesion. C) Adhesion capacity of PmA and PmB to different human colonic cell lines (TC7, HT-29, and HCT116) after 1 hour of co-culture. Bacterial adhesion was quantified by the analysis of fluorescent images of cells co-cultured with the two phylotypes and is reported as arbitrary fluorescent units (x100).
Acknowledgments The authors thank patients for having contributed to the present research program. We thank the bacteriology  Departments of Cochin ,  Pitié Salpétrière , and  Mondor Hospitals  from  APHP  for providing P. micra clinical isolates (particularly  Drs Asmaa Tazi ,  Alexandra Aubry  and  Biba Nebbad ) and the  Collection of Institut Pasteur  for providing reference strains. We thank  Claude Parsot  and  Shaynoor Dramsi  for their dedication and inspiration in the development of this work, and  Pamela Schnupf  for careful reading of the manuscript. We acknowledge the  URC of Henri Mondor and of St Antoine hospitals art APHP, Center for Translational Science (CRT)-Cytometry and Biomarkers Unit of Technology and Service (CB UTechS) at Institut Pasteur  for their supports. This study was performed with the financial support from the  Université Paris Est Creteil (UPEC) ,  PHRC (Vatnimad) of the French government ,  SNFGE  ( French society  of.
O'Leary, N. A. , Wright, M. W. , Brister, J. R. , Ciufo, S. , Haddad, D. , McVeigh, R. , Rajput, B. , Robbertse, B. , Smith-White, B. , Ako-Adjei, D. , et al. (2016). Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733-D745.
Quast, C. , Pruesse, E. , Yilmaz, P. , Gerken, J. , Schweer, T. , Yarza, P. , Peplies, J. , and Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590-D596.
Other characteristics Sociodemographic and medical characteristics were obtained from the existing PAQUID database. Sociodemographic variables included sex, age, education level, and living conditions. Medical variables included the presence of comorbidities (diabetes mellitus, Parkinson's disease, stroke, and myocardial infarction), number of medications used per day, psychotropic drug use, tobacco use, and dementia. At each follow-up visit every 2 or 3 years, dementia diagnosis was based on a threestep procedure: first, a neuropsychologist performed a neuropsychological evaluation at the participant'sliving place; second, participants suspected of dementia following this evaluation were examined by a neurologist or a geriatrician; and finally a definitive diagnosis of dementia, was made by a panel of independent neurologists, based on Diagnostic and Statistical Manual of Mental Disorders criteria (DSM-IV)  [24].
The association between MNA and oral health related variables, age, sex, education level, living alone, institutionalization, dementia, medication use, presence of comorbidities, or dependency was tested in univariate analyses. Variables associated with MNA in the univariate analysis with a conservative threshold of 25% were used in the multivariate analysis, along with age, which was forced in the multivariate model. Participants who did not have complete data for dependent, explanatory, and adjustment variables (N = 3) were excluded from the analysis. Statistical analyses were performed with a level of significance of 0. 05 using SAS software (version 9. 4) and R software (version 3. 2. 3).
Third, data were collected by trained examiners using validated and reproducible indices. Finally, we considered potential confounding factors while investigating the association between oral health and nutritional status. Therefore, our multivariate analysis provided a robust basis for the current knowledge. In conclusion, nutritional status and oral health are often poor in older adults. Our study highlights the importance of oral health, particularly xerostomia, associated with nutritional status, in older adults. Systematic joint screening of oral and nutritional problems in older adults could lead to improvements in their general health and quality of life. Information and training of health professionals working in institutions or who are in contact with older adults could improve professional practices, including screening and care of xerostomia.
Acknowledgements We thank all the participants who agreed to participate in this study. We also thank  Rodolphe Thiébaut  and  Karen Leffondré  for their methodological support,  Sebastien Marchi  for his support in conducting the research, the PAQUID psychologists for presenting our study to the participants,  Marie-Hélène Sage  for making appointments with the participants, and  Severine Martiren ,  Belinda Bastol , and  Catherine Maldonado  for data management. We also acknowledge  Colgate Palmolive  as they kindly offered 20 mL of Meridol toothpaste and a Meridol toothbrush to each participant.
Materials A total of 618 DICOM-formatted CBCT images of the head were used in this work. The images were acquired from 7 clinical centers with various scanners, image acquisition protocols, and field of views. All patient HIPAA identifiable personal information was removed from the DICOM files metadata through an anonymization process in the 3D Slicer platform  [6]. The anonymization was performed before the clinical centers shared the data for this retrospective study. The University of Michigan Institutional Review Board HUM00217585 waived the requirement for informed consent and granted IRB exemption. Patients' skin was not removed from the large field of scans; however, those files are used only for the training of the proposed machine learning model. Two open-source software packages, ITK-SNAP 3.
8  [5]  and 3D Slicer 4. 11 were used by clinical experts to perform user interactive manual segmentation of the volumetric images and common spatial orientation of the head as the ground-truth to train our deep learning models. All the 618 scans don't come with a full-face segmentation, the dataset was composed of 446 patients with mandible segmentation, 132 with the maxilla, 116 of the cranial base, 80 with the skin, and 14 patients with the cervical vertebra. The image spatial resolution varied from 0. 16 to 0. 5 mm 3 voxels. To test the robustness of the proposed method, patients with CLP were included in the dataset. Those patients have large bone defects in the jaw that varies a lot from one patient to another.
Proposed method Thanks to recent advances in deep learning, this study proposes a convolutional neural network (CNN) to extract a hierarchical feature representation for segmentation, which is robust to image degradation such as noise, blur, and contrast. Our algorithm requires Python 3. 9 and uses various libraries to perform image processing. For the post-processing and the pre-processing, we are using ITK, SimpleITK, VTK, and connected-components-3d libraries. For the data augmentation and the segmentation, we used the MONAI library which simplifies the UNETR implementation and is optimized to process medical images in Python.
Pre-processing Depending on the scanner and the image acquisition protocol, the CBCT scans are gray-scaled images with high contrast variation from one patient to another and the image spacing can be different. Among all the different spacing, 0. 4 mm 3 is the most frequent. It's also a resolution that keeps enough details of the skull structure to segment while limiting memory usage with reasonable image size. From one center to another, the manual segmentation method can change. Different labels are used and the skull structure can be filled or not. From this point, to have more consistency in the dataset, all the data go through the following pre-processing steps: • All the CBCTs and segmentations are re-sampled with a 0. 4-mm 3 isometric voxel size using respectively a linear and a nearest-neighbor interpolation function.
Training For each skull structure to segment, the patients were sorted by separated folders based on the clinical center they were coming from. The dataset was then split into 3: 70% for the training, 10% for the validation, and 20% for testing. The data was split evenly from each folder to avoid overfitting to any specific center. We used the MONAI "CacheDataset" tool to load the pre-processed data. Those datasets allow the use of transformers for data augmentations. Every time an image and its segmentation are loaded for the training, a number of N s cube samples are randomly cropped in the voxel grid. Those cubes all have the same L x × L y × L z shape to match the UNETR input size. For data augmentation (Table  1 ), random flip and 90˚rotation are applied in each direction along with a random shift in intensity and contrast for the scans.
Results We performed a 5-fold cross-validation, each fold with a different 20% portion of the available data for the test. It allows testing the models on the entirety of the dataset. The MONAI sliding window inference allows overlapping of the classifier for more precision but has a drastic impact on the computation time. During the validation step of the training, a prediction takes about 4s with 20% of overlap. To compute the metrics we used a 50% overlap to segment the test scans and it takes around 24s on GPU for each CBCT to be segmented. The prediction goes up to 1 min with an 80% overlap for even more precision. To compare the clinician experts' manual segmentation and the AMASSS automatic segmentation, we used the Area Under the Precision-Recall Curve (AUPRC Eq 8) metric for class imbalance.
Also, automated tools such as AMASS focus on future clinical decision support systems, to improve the human-computer interface rather than interrupt the clinical workflow  [28] , and for this reason, human interaction is still required, but less time-consuming. Future work will continue to increase the databases for cervical vertebra, maxilla, and cranial base as well as add detailed anatomic structures such as the teeth roots and mandibular canals segmentation. Additional potential applications may be generalizable to other imaging modalities such as Magnetic Resonance Imaging, CT, micro CT, and ultrasound, which is been shown in recent manuscripts in the medical field  [29, 30].
Conclusion This proposal is a step towards the implementation of dentistry decision support systems, as machine learning techniques are becoming important to automatically and efficiently analyze dental images. The MONAI framework facilitated the processing of 618 CBCTs to perform fast training and data augmentation, which led to the high accuracy and robustness of the AMASSS tool. The UNETR showed high overall performance, achieving a Dice up to 0. 962 ± 0. 02 on heterogeneous CBCT images. Given its robustness and performance time, this validated free tool was implemented in 2 open-source ecosystems, a web-based clinical decision support system (the Data Storage for Computation and Integration, DSCI)  [31] , and a user-friendly 3D Slicer module Fig 6. These computer-aided diagnostic tools will aid in diagnosis and therapy planning, especially for patients with craniomaxillofacial anomalies and deformities.
It worth to note here that cell density, proliferation and metabolic activity were tested separately. This allowed to reveal that a greater number of cells (in the case of osteoclast-like cells) was not correlated to the metabolic activity usually assayed in order to test viability or proliferation. This justifies that in any case results from a metabolic activity test should be discussed with respect to the experimental settings and the other biological data to be conclusive. A drawback of carbonate substitution of HA is the thermal instability making its sintering at high temperature difficult to handle without decomposition. In order to solve this problem on the one hand and to produce bioceramics with chemistry closer to that of the natural bone mineral on the other hand, i. e. , carbonated HA with low crystallinity and highly reactive hydrated non-apatitic environments  (Combes et al.
, 2019)  or implanted onto a chick chorioallantoic membrane for 4 days on 8. 5 days old embryos (Figures  4B, E )  (Magnaudeix et al. , 2016). After having checked the biocompatibility of the SiHA bioceramics for both models, macropore colonization was first evaluated qualitatively, then quantitatively in order to rule out underlying mechanisms. Hence, we developed adapted computer-assisted image analysis methods based on the use of the NIH ImageJ software. 1 In the case of the cellular colonization, the use of multivariate statistical analysis methods (principal component analysis, PCA) helped to elucidate precisely which geometrical parameters could be involved in the cell colonization behaviour.
Objective: Multirooted teeth respond less favorably to non-surgical periodontal treatment and long-term tooth prognosis is influenced by the degree of furcation involvement (FI). Therapeutic strategy for multirooted teeth is essentially based on accurate diagnosis of the FI. The aim of this systematic review is to evaluate the accuracy of the different furcation assessment methods and to determine if radiographic help is needed to determine early stage of FI. methods: Electronic databases were searched up to March 2021. Comparative studies describing the reliability of different clinical and/or radiological furcation assessment methods were identified. Results: A total of 22 studies comparing at least 2 furcation assessment methods, among which 15 retrospective studies, 5 prospective studies, 1 randomized controlled trial and 1 case series, were included in this review.
Methods Protocol and search strategy: The results of the present systematic review were reported following the PRISMA guidelines. The following databases: MEDLINE, Cochrane Central Register of Controlled Trials and Science Direct databases were searched for publications. Two filters related to publications were applied: publications in the English language and conducted in Humans. Hand searching comprised of checking bibliographic references of included articles and related review articles. A typical search strategy, using Boolean operators and an asterisk symbol (*) as truncation, was employed to identify papers using MesH, keywords and other free terms:.
Statistical Analysis The SigmaPlot release 11. 2 (Systat Software, Inc. , San Jose, CA, USA) was used to analyze the data. The normality of the data was verified with the Shapiro-Wilk test. To determine whether significant differences existed in the antibacterial activity, pH values, solubility, and angle contact, the Kruskal-Wallis test (one-way analysis of variance on ranks) including multiple comparison procedures (Tukey Test) was used. A statistical significance level was set at α = 0. 05.
pH Analysis The pH of the solution in contact with the different types of cement was described for 72 h in Figure  1. All three types of cement demonstrated an alkaline pH for the solution in contact at 3, 24, and 72 h. CalPlus demonstrated the highest alkaline pH during 72 h compared to the two other types of cement. At 3 h, CalPlus has higher alkaline pH than ZOE (p < 0. 05). At 24 h, a significant difference was found between the three tested materials (p < 0. 05). Finally, at 72 h, ZOE has a higher pH than Bio-CP with no difference compared to CalPlus. The SigmaPlot release 11. 2 (Systat Software, Inc. , San Jose, CA, USA) was used to analyze the data. The normality of the data was verified with the Shapiro-Wilk test. To determine whether significant differences existed in the antibacterial activity, pH values, solubility, and angle contact, the Kruskal-Wallis test (one-way analysis of variance on ranks) including multiple comparison procedures (Tukey Test) was used.
Background. e Dental Emergency Unit (DEU) of the Pitié Salpêtrière Hospital receives mainly painful emergencies. is study aimed at evaluating the suppression of pain and anxiety as well as the patient's satisfaction after a visit to the DEU. Patients and Methods. A prospective study was carried out in 2019 (NCT03819036) in adult patients. Data was collected on D0 on site and then on D1, D3, and D7 by phone, during daytime. e main objective and secondary objectives were, respectively, to assess the intensity of pain on D1; the intensity of pain on D3 and D7; the evolution of anxiety on D1, D3, and D7; and the patients' satisfaction. ey were evaluated with a 0-10 numeric scale (NS) on D1, D3 and D7; mean scores were compared with nonparametric statistics (ANOVA, Dunn's test). Results. 814 patients were contacted and 581 patients included; 87 were lost to followup.
Study Design. e main objective of this study was to assess the reduction in pain 24 hours (D1) after the emergency visit (D0). e secondary objectives were to assess  (1)  the evolution of pain on D3 and D7; (2) the evolution of anxiety on D1, D3, and D7 after emergency visit; (3) the quality of reception of nursing and non-nursing staff; and (4) the perception of the quality of care. A prospective monocentric observational cohort study was carried out from 01 April 2019 to 31 June 2019 at the Dental Emergency Unit (DEU) of the Pitié Salpêtrière Hospital (GHPS) in Paris. e study was approved by an IRB (APHP180366; IDRCB: 2018-A02692-53-CPP EST) and registered at clinicaltrials. org (NCT03819036). STROBE recommendations were followed for the design of the study and the writing of the report.
Time Course of the Study. e study lasted 25 weeks and consisted of 3 one-week phases, with 2-month intervals, in order to limit selection bias. e maximum observation time of the patient was 10 days. Data collection was performed by 2 investigators (GD and MA). On D0 in the DEU, after inclusion in the study and medical history taking, the patients filled the sociodemographic and medical questionnaire and had clinical evaluation, diagnosis, and emergency care/prescription/advice according to their medical condition; before leaving, they filled the satisfaction questionnaire. On D1, D3, and D7, the evaluation questionnaires were completed by the investigators through a telephone interview. 2. 6. Data Collection. Data were collected through questionnaires during the emergency visits and telephone calls.
Sociodemographic data included social insurance status, habits of dental consultation, reason for consultation at the DEU, and how the patient attended the DEU. Medical data included medical history and actual treatments, diagnosis at the DEU, prescribed therapeutics, and further needed treatment. Efficacy measures included self-estimated pain and anxiety at the different time points of the study. Satisfaction measures included perception of the politeness of both medical and nonmedical staff, availability of nonmedical staff, care setting quality, cleanliness, waiting time, quality of medical information, and attitude toward recommending the DEU to friends.
Statistical Analysis. A descriptive analysis of the sample was carried out. Pain and anxiety scores were grouped into 3 classes: absent or mild, from 0 to 4; moderate, from 5 to 7; and severe, from 8 to 10. Statistical analysis of the evolution of pain and anxiety scores on D0, D1, D3, and D7 was carried out with GraphPad Prism 5 software using ANOVA followed by Dunn's posttests. Correlations between the type of treatment and perceived pain as well as the type of treatment and perceived anxiety were searched with Pearson correlations (R 2 ). Contingency analyses were performed with Chi 2 test. Level of significance was set at 95%.
Limitations. A potential selection bias is related to language. e patients who do not speak French enough to understand the protocol and questionnaires have been excluded. ey might belong to a lower socioeconomic status and health coverage. is study evaluated the DUE only during working days and daytime. Out-of-hours periods have been evaluated in an ancillary study  [87]. e same study has also been performed during COVID-19 time  [88]. is study focused on daytime emergencies and did not evaluate out-of-hours periods. erefore, an ancillary study was planned (NCT04352881) but interrupted by the COVID-19 pandemics. During COVID-19 time, an additional study (NCT04354272) performed with the same methodology was launched in order to compare pain anxiety and efficacy of the DEU during the two periods.
Acknowledgments e authors would like to thank collectively all the nonmedical staff for their involvement in the DEU and all the medical staff with special regard for  Randy Roy le Doux ,  Anas AlCheikh ,  Andrea Matera ,  Mathilde Baydoun ,  Jessica Quittard ,  Pascale Harley ,  Nicolas Girard ,  Maud Sarric , and  Paul Rustom , for their strong commitment to the  DEU  for many years. e authors would also like to thank the  URC  of the GHPS for their invaluable help in the administrative part of the study, especially  Laura Wakselman. e research was performed with recurrent funding allocated to the LabNOF.
Cationic Polymerization and IPN Synthesis Typical epoxide function conversion-time profiles for Cu1 and Cu2-based photoinitiating systems are given in Figure  6  and the data are gathered in Table  3. In fact, the cationic polymerization of the epoxide functions was carried out under air and upon irradiation at 405 nm. Indeed, the cationic polymerization is insensitive to oxygen. As expected, copper complexes alone and the additives alone were not able to initiate the CP in these irradiation conditions. The addition of Iod salt or Iod/NVK into the formulation containing the PI induced good photopolymerization profiles, i. e. , the combination Cu/Iod/NVK (0. 1%/2%/3% w/w/w) is very efficient to produce polymer materials in terms of Rp and final epoxy function conversion compared to Cu/Iod (0.
Surface characterization Topography. The roughness of the used surfaces was assessed before and after the coating process at different length scales by using profilometry and atomic force microscopy (AFM). For profilometry (S neox, SensoFar), a 20 × objective (field of view: 845 ×707 µm) was used to scan 5 samples in interferometric mode. The raw data was processed by a 5 mm cut-off filter and separating the waviness from the roughness. For AFM analysis (MFP 3D, Asylum Research), 5 samples were scanned using an AC240TS cantilever in contact mode at a scan angle of 90 • , a scan rate of 0. 75 Hz, and a scan-area of 20 µm × 20 µm. Wettability. Surface energy (γ S ) was calculated from linear regression of a series of contact angles (θ) using deionized water, diiodomethane, formamide, and glycerol according to the OWRK-model stated in the supplementary information  [37].
The PCR product was sent to Genoscreen (France) for primer removal and for further sequencing using an automatic DNA sequencer (model 3730xl; Applied Biosystems). The nucleotide sequences were aligned with the closest relative sequences of representatives in the GenBank database by means of BLASTN (Basic Local Alignment Search Tool), an algorithm using the CLUSTALW program  (Thompson et al. , 2002)  and available at the National Center for Biotechnology Information (NCBI). They were subsequently deposited in the NCBI database (refer to Table  S2  accession numbers). DNA sequences blasted in NCBI were able to lead to both Pseudostichoccus monallantoides and Diplosphaera sp. , and the microscopic observations of the cultures clearly displayed distinct algae morphologies (Fig. 2 ).