dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike the CT studies, mostly 'localized' tissues, such as oral epithelial cells, were used when assessing genotoxicity after radiography. Of the 26 studies included in this review, only 3 used blood samples for analysis. Therefore, it is less likely that the local effect of radiography was underestimated. So far, only a small number of studies investigated potential biological effects associated with CBCT examinations, and the majority rely on the MN assay. This small number of studies is most likely due to the novelty of the device (it is about 20 years old). Nevertheless, since it is very frequently used in a pediatric population, data on age-related biological effects are necessary for radiation protection. As with CT and radiographs, data on genotoxicity are not unanimous. Two studies report an increase in MN frequency following CBCT examination, whereas three studies report no induction of MN.
Acknowledgments The DIMITRA Research Group that contributed to this paper consists of  N. Belmans ,  M. Moreels ,  S. Baatout ,  B. Salmon ,  A. C. Oenning ,  C. Chaussain ,  C. Lefevre ,  M. Hedesiu ,  P. Virag ,  M. Baciut ,  M. Marcu ,  O. Almasan ,  R. Roman ,  A. Porumb ,  C. Dinu ,  H. RotaruC. Ratiu ,  O. Lucaciu ,  B. Crisan ,  S. Bran ,  G. Baciut ,  R. Jacobs ,  H. Bosmans ,  R. Bogaerts ,  C. Politis ,  A. Stratis ,  R. Pauwels ,  K. de F. Vasconcelos ,  L. Nicolielo ,  G. Zhang ,  E. Tijskens ,  M. Vranckx ,  A. Ockerman ,  C. Morena ,  E. Embrechts. B. Baselet  and  K. Tabury  helped with preparation of the manuscript. This work was supported by the  European Atomic Energy Community 's  Seventh Framework Programme   FP7/2007-2011  under grant agreement No. 60 , 4984  ( OPERRA: Open Project for the European Radiation Research Area ) and by the  Research Foundation -Flanders (FWO)  under grant agreement No.
Funding This work was supported by the  European Atomic Energy Community 's  Seventh Framework Programme   FP7/2007-2011  under grant agreement No. 604984  ( OPERRA: Open Project for the European Radiation Research Area ) and by the  Research Foundation -Flanders (FWO)  under grant agreement No. G0A09. 18N(TREASURE: Follow up of radiation dose and radiobiological effects after dental exposure to radiation). N. Belmans is supported by a doctoral SCK CEN grant.
Introduction Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) of nasopharyngeal samples is widely used to detect SARS-CoV-2 and other respiratory viruses  [1, 2]. However, collecting nasopharyngeal samples (NPS) causes patient discomfort, which may discourage patients with mild symptoms from seeking diagnostic tests. This could compromise the detection of cases and epidemiological surveillance. The French Sentinelles network monitors acute respiratory infection (ARI) in primary health care in France from epidemiological and virological data collected by general practitioners (GPs) and pediatricians. This allows to detect and characterize the circulating respiratory viruses, as SARS-CoV-2, influenza and RSV  [3]. From the beginning of the COVID-19 pandemic, Sentinelles physicians encountered difficulties conducting virological ARI surveillance for patients with suspected COVID-19 cases using NPS, due to the following two recommendations in France: systematic SARS-CoV-2 virological test (NPS) should be conducted in specific diagnosis centers and the requirement to receive the result very quickly.
Outcomes The primary outcome was the clinical performance of OS compared to NP for SARS-CoV-2 screening in symptomatic and close contact subjects. For each group (symptomatic versus close contacts) we extracted the number of individuals positive for SARS-CoV-2 in a) both NP and OS, b) those positive only with NP, c) those positive only with OS and d) those negative with both NO and OS (table  1 ). From these data, we calculated the concordance of the two types of sample: (a+d)/(a+b+c+d). We also calculated the sensitivity of the test on each type of sample. The estimation of the sensitivity of a test requires a reference diagnosis. Since NP sampling has been shown to produce false negatives by RT-PCR, sensitivities for NP and OS are defined here respectively as (a+b)/(a+b+c) and (a+c)/(a+b+c), considering as a true positive any individual To calculate the sample size in the absence of data regarding the sensitivity of OS in symptomatic subjects, we calculated the sensitivity of OS once the first 40 paired (NP-OS) samples were obtained.
FEA has been carried out using the Abaqus software according to published material properties. Five models have been created: the initial state of the patient, two non-surgical removal strategies using a low or high root canal enlargement, and two surgical removal strategies using a 3-or 6-mm apicoectomy. Resuls: Results of the VTP found a risk of perforation for the non-surgical strategies and a sinus proximity for surgical ones. FEA identified the lowest mechanical risk for the 3-mm apicoectomy strategy. A 3-mm apicoectomy approach was finally chosen and performed. Conclusion: this digital approach could offer a decision tool for the instrument removal by planning the treatment and predicting the mechanical impact of each strategy.
The patient tooth was scanned before any intervention to evaluate the instrument position using cone beam computed tomography (CBCT; Planmeca ProMax 3D, Helsinki, Finland) operating at 120 kV, 100 mAs, with a slice thickness of 0. 75 mm. The data were recorded under the Digital Imaging and Communication in Medicine (DICOM) format and analyzed. Two non-surgical and surgical strategies emerged from the discussions of the healthcare team, but no consensus was reached on the treatment that could ensure the best outcome. A digital approach, combining VTP and FEA  19  , was then implemented to visualize the planned treatment and predict the mechanical impact of the two removal strategies (Fig  1).
Finite element modelling and mechanical analysis Modified 3D images were then meshed with tetrahedral elements using the Computational Geometry Algorithms Library (CGAL) meshing library  20  imported in the FEA software Abaqus (Dassault Systèmes, Vélizy-Villacoublay, France; Fig. 3 ). The periodontal ligament could not be detected on the DICOM and was simulated around the root surface with a thickness of 250 m  21. The attributed material properties (Table  1 ) were referenced from the literature  [21] [22] [23]. All materials were supposed homogeneous, linear and elastic, and there was a perfect bonding between each component  16. The occlusal faces were not modelled due to x-ray artefacts. A vertical load of 150 N was distributed on the top surface of the root and the nodes of the base, and lateral faces of the bone were constrained to prevent displacement  16.
Despite the apparent value of the presented strategy, several limitations are to be highlighted. The main one is that the accuracy of CBCT is questionable in the occlusal part due to artifacts, whereas it is known that occlusal morphology influences the stress distribution in FEA  34, 35. Recent technologies such as micro CBCT  36  and the use of an intraoral scanner avoiding x-ray artefacts  37  could improve future simulations. FEA results should also be carefully interpreted due to the technical impossibility to identify patient-specific parameters such as force intensity  38  or for ligament modeling  39. Both VTP and FEA also required supplementary software and operator skills, which makes their use in routine clinical practice complex. The development of an intuitive software, marked as a medical device, will be necessary in the future to allow a wide dissemination of this technique among dental practitioners.
Acquisition and reconstruction parameters Data acquisition was performed on two CT systems from one manufacturer: a dual-layer CT (DLCT) (IQon Spectral CT, Philips Healthcare) and a clinical spectral photon-counting CT (SPCCT) prototype (SPCCT, Philips Healthcare). The DLCT system was equipped with EID, while the SPCCT system was equipped with novel PCD  [26]. Both devices were equipped with the same X-ray source and had the same source-to-isocenter and source-to-detector distances. Apart from the X-ray detection technology, the size of the detector pixels at iso-center was different between both systems, with 0. 625 × 0. 625 mm for EID and 0. 275 × 0. 275 mm for PCD. Further technical details concerning the prototype system and its performances are provided in previous studies  [27, 28]. For both aims of the current study, routine clinical CAC scoring protocols were used for data acquisition and reconstruction (Table  1 ).
General Agatston scores were determined from the resulting reconstructed images using a previously validated, in-house developed Python script (Python version 3. 7)  [29]. To discriminate calciumcontaining voxels from background material, a calcium scoring threshold of 130 HU was used. In addition, in line with the vendor-specific implementation for the Agatston score, a minimum connected area of 0. 5 mm 2 was used to include a group of voxels in the Agatston score of a specific calcification. For the used combination of field-of-view (220 mm) and reconstruction matrix (512 × 512), this results in a minimum of three connected voxels. In order to compare CAC quantification with physical volume, the volume score was also determined using the same in-house developed Python script  [30]. In addition to the CAC scores, several image quality metrics were determined.
Statistical analysis Mean HU, SNR, and CNR were compared between DLCT and SPCCT using a Mann-Whitney U signed-rank test, with a significance level of p < 0. 05. Routine CAC protocol agreement between DLCT and SPCCT for Agatston scores was assessed using the Bland-Altman plots  [31]. Differences in CAC quantification potential between DLCT and SPCCT at reduced slice thickness were assessed on the largest calcifications (5 mm diameter and length). For each combination of slice thickness and increment, CAC scores were compared with the reference (DLCT, reconstructed IR level 0) using a Mann-Whitney U signed-rank test, with a significance level of p < 0. 05. All statistical analysis were performed with SPSS version 27 (IBM SPSS Statistics).
Oral and periodontal health assessment The two oral health questionnaires provided data on oral health factors that are linked to periodontal health, including information on oral hygiene, oral health habits, need for dental treatments, visit to the dentist, number of teeth, replacement of missing teeth, masticatory comfort, and chewing capacity. Periodontitis was explored through questions originally included in the questionnaire of the Centers for Disease Control and Prevention and the American Academy of Periodontology (CDC/AAP)  [38] , that was translated (in French) and validated against a full-mouth clinical periodontal examination  [39]. On the basis of this questionnaire, Carra et al. recently developed and validated a user-friendly score to estimate the individual probability of suffering from severe periodontitis, namely the PEriodontal Screening Score (PESS), based on five selected self-report items, including the self-evaluation of gum and teeth health (categorized into three classes: excellent-to-good, fair, and poor), the report of root planing treatments received for gum disease (yes/no), the presence of loose teeth (yes/no), the (dentist's) report of having lost bone around teeth (yes/no), and the perception that a tooth does not look right (yes/no).
Arterial hypertension assessment Incident cases of arterial hypertension were identified through annual follow-up health status questionnaires as those participants who reported a first-time diagnosis of arterial hypertension and/or use of antihypertensive therapy after the date of completion of the second oral health questionnaire (April 2012, considered as the study baseline) up to December 2019. Case ascertainment was based on the same criteria used in a previous study investigating arterial hypertension in the NutriNet-Santedatabase  [40].
Statistical analysis Descriptive analyses of the whole sample and by arterial hypertension status at follow-up were carried out with chisquared tests and Student t tests. Data are reported as frequency (percentages) or mean (SD), as appropriate. Responses such as 'I don't know' or 'not reported' were kept as separate variable categories in the analyses. The association between oral/periodontal health problems at baseline (main exposure) and the incidence of arterial hypertension (main outcome) over the follow-up period (April 2012-December 2019) was investigated by Cox proportional hazards models, providing hazard ratios and a 95% CI. In addition, we used the nonparametric Kaplan-Meier estimator. Periodontal health risk factors were modeled individually and in combination through the mPESS.
The primary time-dependent variable was duration of follow-up, censored at the date of the last follow-up, after excluding the cases of death. The measured event was the self-report of arterial hypertension diagnosis or the initiation of an antihypertensive treatment. The assumption of proportionality of the hazard model was verified by plotting arterial hypertension-free survival using the Kaplan-Meier curve and a Log-rank test was used for group comparisons between individuals with high mPESS vs. low mPESS. We had no a priori hypotheses about the association being different according to sex, therefore, no interaction tests were performed  [40]. Statistical analyses were carried out with SPSS (SPSS version 22. 0; SPSS Inc. , Chicago, Illinois, USA). A P value less than 0. 05 was considered statistically significant.
ACKNOWLEDGEMENTS The authors wish to thank  Cedric Agaesse  (dietitian);  Younes Esseddik  (IT manager);  Thi Hong Van Duong ,  Regis Gatibelza ,  Jagatjit Mohinder , and  Aladi Timera  (computer scientists);  Julien Alle  `gre,  Nathalie Arnault , and  Laurent Bourhis  (biostatisticians);  Fabien Szabo de Edelenyi, PhD  (biostatistics team supervisor); and  Nathalie Druesne-Pecollo, PhD  (operational manager) for their technical/logistic contributions to the NutriNet-Santestudy. Ethics approval and consent to participate: The study was approved by the ethics committee of the  French Institute for Health and Medical Research  (INSERM No. 0000388FWA00005831 ) and the  National Commission on Informatics and Liberty  (CNIL No. 908450  and No. 909216 ). It is registered at clinicaltrials. gov(#  NCT03335644 ).
Scan with a CBCT Settings of CBCT are first optimized according to the object size. When selecting the clinical situation and in order to facilitate image interpretation, images deformed by artefacts, for example if large volumes of metallic materials are present, should be rejected. The segmentation could be conducted using different strategies such as gray-level threshold, a region-based technique or a deep learning method  [14, 15]. The different dental structures can be marked on the 3D image using labels and segmented with a free software depending on their Hounsfield units (3D Slicer, ITK-SNAP). The 3D-labelled image is then meshed and exported as an STL file for further educational activities (Figure  3 ).
Scan with a CBCT Settings of CBCT are first optimized according to the object size. When selecting the clinical situation and in order to facilitate image interpretation, images deformed by artefacts, for example if large volumes of metallic materials are present, should be rejected. The segmentation could be conducted using different strategies such as gray-level threshold, a region-based technique or a deep learning method  [14, 15]. The different dental structures can be marked on the 3D image using labels and segmented with a free software depending on their Hounsfield units (3D Slicer, ITK-SNAP). The 3D-labelled image is then meshed and exported as an STL file for further educational activities (Figure  3 ). The 3D file obtained from IOS or CBCT corresponds to a point cloud forming triangles.
This mesh corresponds to a network of different density and quality of triangles that can be analyzed using free software such as Meshlab (ISTI and CNR, Rome, Italy) or Blender (Blender Foundation, Amsterdam, Netherlands). An ideal mesh corresponds to fairly equilateral, or at least fairly identical, triangles. In order to be as fair as possible, it could be suggested that one removes the color during the analysis of the 3D file  [6]. The 3D file could also be repaired, closed or smoothed to improve the rendering using free software such as Meshmixer (Autodesk, San Rafael, EU) (Figure  4 ). gles. This mesh corresponds to a network of different density and quality of triangles that can be analyzed using free software such as Meshlab (ISTI and CNR, Rome, Italy) or Blender (Blender Foundation, Amsterdam, Netherlands).
An ideal mesh corresponds to fairly equilateral, or at least fairly identical, triangles. In order to be as fair as possible, it could be suggested that one removes the color during the analysis of the 3D file  [6]. The 3D file could also be repaired, closed or smoothed to improve the rendering using free software such as Meshmixer (Autodesk, San Rafael, EU) (Figure  4 ). Cleaning and repairing the 3D file of a segmented pulp using the free software Meshmixer (Autodesk, San Rafael, EU). (a) STL presenting holes (yellow arrow) and stair-steps (blue arrow) due to the resolution of the cone beam computed tomography, could be automatically smoothed and repaired using "Smooth" and "Inspector" tools in the software (b).
Re-meshing and Simplification Redefining or reducing the size of the 3D file facilitates its integration into the educational software. Indeed, a large file has a large definition and therefore a greater weight (Figure  5 ). This high definition is less useful for an educational model, where immersivity is enhanced by a fast-loading time. Once again, free software (Meshlab or Blender) can be used for this step. Finally, the 3D file could also be prepared and transformed in 3D PDF.
Re-Meshing and Simplification Redefining or reducing the size of the 3D file facilitates its integration into the educational software. Indeed, a large file has a large definition and therefore a greater weight (Figure  5 ). can be analyzed using free software such as Meshlab (ISTI and CNR, Rome, Italy) or Blender (Blender Foundation, Amsterdam, Netherlands). An ideal mesh corresponds to fairly equilateral, or at least fairly identical, triangles. In order to be as fair as possible, it could be suggested that one removes the color during the analysis of the 3D file  [6]. The 3D file could also be repaired, closed or smoothed to improve the rendering using free software such as Meshmixer (Autodesk, San Rafael, EU) (Figure  4 ).
Re-meshing and Simplification Redefining or reducing the size of the 3D file facilitates its integration into the educational software. Indeed, a large file has a large definition and therefore a greater weight (Figure  5 ). This high definition is less useful for an educational model, where immersivity is enhanced by a fast-loading time. Once again, free software (Meshlab or Blender) can be used for this step. Finally, the 3D file could also be prepared and transformed in 3D PDF This high definition is less useful for an educational model, where immersivity is enhanced by a fast-loading time. Once again, free software (Meshlab or Blender) can be used for this step. Finally, the 3D file could also be prepared and transformed in 3D PDF (Adobe, San josé, EU)  [7]  to improve visualization and enhance their spatial ability, or for 3D printing, using a "model builder" tools  [16].
The generalization of 3D files should be approached with caution, because it is still emerging in the field of education. The prime limitation in following such a protocol is the need for educators with experience in non-dental software, that may impose collaborations with other universities or training. Moreover, 3D files can sometimes lack an entertaining aspect and be too complex for students to manipulate  [30] , and therefore require the development of more user-friendly interphase. As the resources for the development of these educative tools are not always known, a dental faculty network, open-access publications or educational congresses could be relevant strategies to share tips and tools, such as 3D PDF files  [7] , free software to interact and paint on 3D files (Paint 3D, Microsoft, Redmond, EU) or Blender (Figure  6d ,e), free web-based platforms (Metaciel, Université Clermont Auvergne, Clermont-Ferrand, France)  [31] , or open source codes of applications  [9].
Would it be possible to add a couple of lines describing what this method is? Within the RasCAL program the Monte Carlo error is calculated by randomising the parameters within the set limits and calculating the best fit to the data, this is performed 100 times and the error is calculated from a histogram of obtained values. The manuscript has been updated to read: "Errors were calculated using the bootstrap error analysis in the RasCAL program, where random parameter values are generated and a fit performed 100 times to generate confidence intervals. The parameter errors were then used to calculate the error in adsorbed amount, assuming the maximum and minimum possible values. " Page 9 Line 21 -Page 10 Line 2 2-Page 10, Line 4: "The SLDs [. ] were _ALL_ fixed according to the literature values (Table  1 ).
The surface for reflectivity was the most stable 10 face, the crystallographic plane found at the surface of the natural rhomb. Attempts were made to 11 cleave the surface rather than polish, however, it was not possible to obtain an area large enough 12 for reflectivity studies (roughly 20 cm 2 ) without introducing large steps, causing the cells to leak. 13   The roughness of the polished crystals was assessed by X-ray reflectivity on a Rigaku SmartLab 14 X-ray diffractometer (ISIS, Rutherford Appleton Laboratory, Didcot, UK). This measurement 15 gave an approximate indication of the quality of the surface (data shown in supporting 16 information, Figure  S1 ). Crystals, which had an X-ray roughness less than 15 Å, were then used 17 in neutron reflectivity experiments. 18.
6° and 2. 0°, to cover the Q 17 range of interest (0. 006-0. 2 Å -1 ). To ensure no contribution from the edges of the sample, a 18 constant beam footprint of 30 x 45 mm 2 was maintained on the sample surface. The dQ/Q 19 resolution varied between 1. 5-10% over the Q range measured, during data fitting a value of 4% 20 resolution was used. The data reduction was performed using the COSMOS software. 30.
ANALYSIS OF REFLECTIVITY DATA 8 The shape of the reflectivity curve at a single interface is determined by the surface roughness 9 and the change in neutron refractive index upon moving from one medium to the next through 10 the surface. Neutron reflection theory is outlined elsewhere. 31 A particularly powerful aspect of 11 neutron reflection is the seemingly random nature of neutron scattering lengths across the 12 periodic table and between isotopes. The scattering length density (SLD) of a material is 13 calculated using equation 2 where N is the number density of atoms in a material and b i is the 14 scattering length of each nucleus. 15.
3 The measured reflectivity profile is then analysed by comparison of the experimental data with a 4 model structure. The general approach is to constrain the structural model as much as possible to 5 fit the data. If a single layer is found not be sufficient to fit the experimental data, adsorbed 6 layers can be broken into several smaller blocks/layers to describe adsorbed structures in more 7 detail. In some cases a mathematical description of the SLD profile is appropriate, such as 8 exponential or Gaussian. 32 In all of these cases, the total reflected intensity from adsorbed layers 9 is calculated using the matrix method developed by Abeles and used in reflectivity fitting 10 programs such as RasCAL. 33 1 The polymer segment density distribution normal to the surface was modelled in several ways, 12 such as discrete layers, exponential decrease from the surface, or a Gaussian distribution.