dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and tumors characteristics Forty patients were screened for the inclusion (Figure  1 ). Thirty-five high grade MFOS were included for the final analysis (n=5 excluded: other diagnosis (n=4) and missing data (n=1; histological report unavailable). Patient and tumor characteristics (n=35) are presented in Table  1  and were consistent with literature  [29]. Briefly, median age was 36. 8 years old (range 18. 5-84. 4). All patients had good general condition, 64% were male. The delay between first symptoms and diagnosis was extremely variable, with a median time to diagnosis of 15 weeks (range 5-108). The first symptom was an evolving mass syndrome in 83% of cases (Table  1 ). All tumors were developed from mandibular bone (60%), or maxillary bone (40%). Most of tumors (75%) had local development corresponding to stage IIA/B of AJCC staging system for long bone sarcoma.
Histomorphometric Analyses Histomorphometric measurements were performed using ImageJ software v. 1. 51 (NIH, Bethesda, MD, USA). To quantify the amount of new bone formation in the osteotomy site as a function of time, a minimum of four osteotomy sites were analyzed. For each osteotomy site, a minimum of six aniline blue-stained histologic sections that spanned the distance from the furcation to the apex were used to quantify new bone formation. Each section was photographed using a Leica digital image system at 20× magnification. To calculate the percentage of new bone formation, the number of aniline blue +ve pixels within an osteotomy was measured and divided by the number of the total pixels in the same osteotomy area.
Micro-Computed Tomography (µCT) Scanning and analyses followed published guidelines  [19]. Three-dimensional µCT imaging was performed at various times after surgery. In brief, samples were fixed in 4% PFA at 4 • C overnight. Then, they were transferred to 70% ethanol solution for µCT scanning before the decalcification process. A µCT tomography data-acquisition system (VivaCT 40, Scanco, Brüttisellen, Switzerland) at 10. 5-µm voxel size (70 kV, 115 µA, 300 ms of integration time) was used for scanning and reconstruction. Bone morphometry was performed using the acquisition system's analysis software (Scanco). Multiplanar reconstruction and volume rendering were carried out using Avizo (FEI, Hillsboro, OR, USA) and ImageJ v1. 51 (NIH, Bethesda, MD, USA) software, before being imported into Adobe Photoshop and Illustrator (CC2017, Adobe, San Jose, CA, USA).
Heat Transfer During Drilling The temperature produced when cutting with a conventional protocol involving multiple drills was compared to site preparation with the mini OsseoShaper. Sawbones 35 (Pacific Research Laboratory, Vashon Island, DC, USA) was used. Drills and drilling protocols used are as listed in Table  1. Thermal radiation was measured immediately after drilling via an infrared camera (SEEK CompactPRO, Seek Thermal Application, Santa Barbara, CA, USA). The drilling protocol was repeated six times in new Sawbones. Means and standard deviations were reported. The temperature distribution during drilling was also calculated in MATLAB using a finite difference method. Details of the heat transfer model are described in Reference  [5]. The differences between the conventional drill and mini OsseoShaper models can be summarized as follows: in the conventional high-speed drilling, the heat flux was applied to the drill hole's boundary where the tip of the drill was located, and the tip was moved vertically.
CT-scanned sample The data reported here involved no experimentation on human subjects, but only reprocessing of existing scan data. CT scans had been anonymized by the medical institution. The use of these data for the present purpose respected French bioethical laws and was carried out in accordance with the Declaration of Helsinki. Written consent was given by the patients for their information to be stored in the hospital database and used for research purposes. The "Comité Consultatif pour la Protection des Personnes dans la Recherche Biomédicale" (CCPPRB, French IRB) approved the use of these data for the present purpose. This study included all the patients who had received a scanner examination for diagnosis or treatment planning at the dental service of T… University Hospital from 2007 to 2010, which means 272 subjects.
Landmarking We extracted from the CT data the Digital Imaging and Communications in Medicine (DICOM) file for each mandible and used Amira ® software (AMIRA, Visualization Sciences Group, Burlington, MA) for 3D reconstruction and measurement. To maximize the accuracy of the landmarking, points were manually positioned on 2D tomography and checked on 3D reconstruction (Table  2 ; Figs. 1, 2 ). These landmarks were translated into millimeter spatial coordinates (x, y, z) by the AMIRA ® software and used to measure distances and angles and to create planes. A plane is normally defined by three points. We decided for the first time to include at least four points per plane: all molar, premolar, canine and the inter-incisor point of the mandibular jaw in the occlusal plane (7-11 points instead of 3, to offer more stable values for statistical study), right and left condyles and mandibular foramina in the foramina condyle plane.
For studies using cephalometric radiographs, the MF was first located on panoramic before being transferred on the cephalometric radiograph to overcome the superimposition of contralateral mandibular structures  [21]. The occlusal plane was defined on panoramic radiographs as a hand-drawn line passing through the cusps of the molars. Movahhed used a software to draw it and calculate linear measurements  [19]. He reported different results according to sex and age: at age 9 years, the mandibular foramen remained below the occlusal plane in boys, while in girls it was above it. It may be explained by the different patterns of growth between boys and girls. Moreover, its reference line passed through the canine tip and "the most prominent point on the end-most fully erupted tooth" which differed from our calculated plane involving all the dentition.
Thanks to advances in CT scanning and 3D reconstruction software, the situation of MF can be nowadays more accurately studied, without distortion of the anatomical regions. We reported a stable vertical location of the mandibular foramen, 2-3 mm above the occlusal plane in adults and in children during the eruption of the permanent molars and at the same level as the occlusal plane in between. Our results are in line with other studies, except with one conducted in a young adult population: Zhou  [28]  reported a mandibular lingual located above the occlusal plane, but an MF below OP in 84% of the cases. The results of the Korean study of Kang  [10]  are slightly higher than those of ours, but the definition of the occlusal plane by only three landmarks located in the permanent teeth, as in Zhou study  [28] , might be less representative of the clinical reality than our adjusted occlusal plane.
Nevertheless, MF was located at a stable distance of 3. 2-3. 8 mm above the occlusal plane, both in the adult and the growth groups  [10]. Altunsoy  [29]  found that the MF was 2. 5-3. 6 mm above the occlusal plane of the molars, without statistically significant changes with age. More markedly, Blacher  [23]  reported a location of MF 9. 8 mm above OP in adults while using an occlusal plane passing only through the second mandibular molar and the MF. Al-Shayyab reported an average distance of 4. 5 mm between the MF and OP in adults  [23]. In this CBCT study, the occlusal plane was represented by the straight line of the cusps of the mandibular first molar, extended posteriorly by the software ruler. The average occlusal plane of our study might offer statistically steadier values. As the mandibular foramen seemed to follow the vertical evolution of the occlusal plane with molar eruption, the sagittal angulation of the ramus, defined by the angle between OP and FCP (plane defined by both right and left MF and condyles), stayed stable.
Preprocessing of the Data For this classification task, we had an image data base of 2000 X-ray radiographic images. Each image has a size of 2012 × 2012. These images were extracted from the patients files and are anonymized. A selection of 600 images were studied and labeled by the experts in six classes (CVS1,. , CVS6). These labeled data were divided in three sets of Training, Validation and Testing. We did different division of the data: First, we had started by 300, 200 and 100, respectively, for Training, Validation and Testing. We did some preliminary tests with this small dataset. Then, we obtained more data, 600, then, 900 and, finally, about 1900. We used these images, step by step, in different experiences and used a Cross Validation (CV) technique by division and permutation of these data sets in different experiences.
Considered Deep Learning Networks In a preliminary study, we used different Deep Learning network structures for this classification task. Several network architectures  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39]  have been proposed with success for ordinary photographic image classification. These different architectures have been proposed to deal with great problems of over fitting, vanishing or exploding gradients in the optimization parts of learning. Each one tries to provide a more efficient use of network parameters in order to offer ultimate improvement in the model efficiency in terms of computation time and accuracy. The success that brought the breakthrough in the ImageNet competition in 2012 is related to the efficient use of Graphical Processing Units (GPU), data augmentation, rectified linear units, new dropout regularization and deep network architecture, where convolution operations were repeated multiple times between max-pooling operations.
Since then, there has been a trend to make this style of network increasingly deeper through the use of small (3 × 3) convolutional filters such as VGG architecture  [40] [41] [42] [43] [44] [45] [46] [47] [48]. Almost all these networks have been designed and tested for ordinary color images on great data bases. Our main objective in this preliminary study was to compare these networks for our particular case of radiographic images. We considered different classical networks: • Resnet: Resnet was introduced in the paper "Deep Residual Learning for Image Recognition "  [49]. There are several variants with different output sizes, including Resnet18, Resnet34, Resnet50, Resnet101, and Resnet152, all of which are available from torchvision models. As our dataset is small, we used Resnet18 that we adapted in our case for 6 classes.
• Alexnet: Alexnet was introduced in the paper "ImageNet Classification with Deep Convolutional Neural Networks"  [51]  and was the first very successful CNN on the ImageNet dataset. • VGG: VGG was introduced in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition"  [52]. Torchvision offers eight versions of VGG with various lengths and some that have batch normalizations layers. • Squeezenet: The Squeeznet architecture is described in the paper "SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0. 5 MB model size"  [53]. It uses a different output structure than the other models mentioned here. Torchvision has two versions of Squeezenet. We used version 1. 0. • Densenet: Densenet was introduced in the paper "Densely Connected Convolutional Networks"  [50].
Proposed Method, Structure and Metrics As we mentioned, ResNet, DenseNet, InceptionResNet and Xception are among the most efficient models that have achieved state-of-the-art accuracy on large image datasets such as CIFAR, ImageNet and all other classical photography images. However, in our case, we have a small database. Hopefully, these images are not too different from each other and the variability (inter and intra classes) are not as large as in the case of photography images. So, we decided to make our specific DL network, specifically adapted for our application and train it directly with our pre-processed images. What we learned in previous sections can be summarized as follows: • Almost all the pre-trained networks with feature transfer did not really work better. This is due to the fact that these networks are trained with great databases with photographic images which are more diverse than the radiographic images we have.
• The choices of pre-processing, optimization criteria, optimization algorithms and optimization of hyper-parameters are very difficult. These need experience and deep knowledge of mathematics and techniques. • We used categorical entropy as the optimization criteria, SGD or ADAM for optimization algorithm, and manually fixing the hyper-parameters using cross validation. • Many performance criteria such as general accuracy, categorical accuracy, precision, recall and many other criteria, all can be derived from the confusion matrix and can be used to measure the performances of a classification method. In this paper, we mainly showed the accuracy. • In our case, we show that when the training data are balanced, in general, the performances improve when the number of images increases. However, when the dataset is unbalanced, the situation is different.
In a second step, we increased the number of the training dataset to 600, then to 900 and finally to all available 1870 images at the time of writing this paper. When going from 360 to 600 and 900 images equally distributed in the six classes, the performances improved and then stopped improving. For the case where we used the whole available 1870 images, the improvement was not really significant due to the fact that the numbers of images in the six classes were not equally distributed and there was greater diversity. The dataset is still going to grow and we try to have equally distributed samples. During this development, we continue to develop other network structures and test other optimization algorithms. A clinically usable package is also planned within our project. This paper is an extended version of a communication at MaxEnt 2019 international workshop  [57].
The presence of disseminated spots observed on HA-M and SiHA-M suggests a low and heterogeneous coverage of the surface (respectively, 8 ± 8% and 2 ± 3%, Table  2 ). More precisely, SiHA-M presents wide and intense spots (Image 2 in Figure  4 ) when compared to HA-M for which smaller and less intense spots are observed. SiHA-T and HA-T samples show high coverage rates of 26 ± 26% and 17 ± 5%, respectively. However, the standard deviations highlight that HA-T samples are more homogeneous than Si-HA ones. Table  2. Percentages of the surface covered by organic compounds calculated from the images presented in Figure  3  and using Particle Analysis module of 4. 3 Wire software (Renishaw). The standard deviation of the mean values is determined with regard to the average of the three measurements performed on each substrate.
Discussion The surface of inorganic bioceramics can be functionalized by adsorption or by covalent grafting of organic biomolecules. In the latter case, most of functionalization protocols are based on specific An image segmentation method, the "Particle Analysis" module of the Wire software (version 4. 3, Renishaw, Wotton-under-Edge, UK), was used to estimate the surface covered by the organosilane. Images were generated from the bicolor (black and red) Raman maps for which the well-depth was fixed at 10%, thus allowing the segmentation of the image domains. The surface coverage (in percentages) was then estimated by dividing the obtained area by the total surface of the analyzed samples (22. 5 × 10 3 µm 2 ). The mean values (± standard deviation) correspond to the average of the three measurements performed on each substrate.
Atomic Force Microscopy AFM images of samples surface were obtained in air using an Agilent 5500 LS AFM (newly Keysight, Santa Rosa, CA, USA) in contact mode (AppNano ACT). The tip radii were smaller than 10 nm and the surface topography was obtained on an area of 50 × 50 µm 2. The set-point applied by the cantilever corresponded to a maximum of 1. 2 V and the speed was 1. 2 lines per second. The resulting images were treated using the Gwyddion software (2. 4 version, Free data analysis software, http://gwyddion. net/). The surface topography was measured and the root-mean-square roughness (RMS) was calculated for each surface as a function of N and M, which designate the definition of the image, i. e. , 512, and the number of lines, i. e. , 512, respectively. The RMS is given by the standard deviation of the z-values (amplitude) determined for each point of coordinates (x,y) in the image (Equation (  1 )).
Contact Angle and Surface Free Energy Measurements Contact angle was measured with a Dynamic Contact Angle Measuring Instrument GBX DGD-MCAT (Digidron Goniometer GBX PixeLINK, Ireland). Drops (having a volume of 4. 4 µL) were deposited automatically on polished and silanized pellet surfaces (previously cleaned with an argon stream) using the sessile drop method. The pictures of the drops were recorded by a camera coupled to a light microscope and analyzed using Digidrop software in polynomial mode. Measurements were made at room temperature after a time delay (20 s) to ensure the droplets equilibrium. Each contact angle value was the average of twenty measurements made on two different regions of ten pellets. In order to determine the surface free energy (γ SL ) of the samples, three solvents having different surface tension were used: distilled water (H 2 O), ethylene glycol (HO(CH 2 ) 2 OH, purity: 99.
Acknowledgments: The authors thank the  French Région Limousin  and the  French Agence Nationale de la Recherche  in the scope of the  LabEx SigmaLim  ( ANR-10-LABX-0074-01 ) for their financial support. The authors are grateful to  Valérie Coudert  and the  Service commun de Caractérisation des Matériaux de Limoges (CARMALIM)  for AFM acquisitions. The authors also thank  Pr. Pascal Tristant  for his assistance in contact angle measurements and  Emeline Renaudie  for her assistance in elaboration of calcium phosphate ceramics and their functionalization.
Materials and Methods Our study dates from 2016. A cross-sectional study with self-administered questionnaires was conducted among dentists of Eastern France operating outside health care institutions. A letter explaining the context and some terms necessary for an understanding of the study was distributed with the questionnaire. The participants had the opportunity to contact a researcher (C. C. ) in case of doubt during the administration of the questionnaire. In our study, 870 questionnaires of practitioners' knowledge and practices about dental unit water quality were sent to dental offices from a database created using the yearbooks of dentists and orthodontists. We asked dentists to complete these questionnaires and return them to us by mail. The questionnaire was composed of two sections.
The first section consisted of questions to report the dental equipment used at the dental office (units, implantology and surgical motors, ultrasound scaler generators) and the means used to guarantee the water quality (flushing, filter, water softening, existence of an independent bottle reservoir, disinfection protocol). The second section explored the characteristics of each dental chair present in the dental office (brand, model, age, maintenance, feed water, disinfection products). Participation was voluntary and without any compensation. Anonymity was guaranteed at the phase of data analyses. Ethical approval was not required. Regarding data analysis, blank questionnaires returned to us were excluded, whereas those that were totally or only partially completed were included in the study.
Method for the Evaluation of the RSBDH A member of the RSBDH board undertook the internal evaluation of the Network, with the help of a steering committee, in collaboration with the Network coordinator, and with the academic support of the Centre for Clinical Research in Dentistry of the University Clermont Auvergne. Data from 2013 to 2015 were obtained and analysed. The founding documents of the Network, the Network statutes and rules of procedure were consulted, as were all minutes of meetings of the Board and Executive Board, the general assemblies, end-of-year reports, and financial statements. In addition, all documents pertaining to the recruitment of patients, of dental practitioners, of institutions, and the website were searched. Additional documents, such as the specifications for the website and patient database, the organisational flowchart etc.
Data analysis Data entry was duplicated and errors were corrected before analysis. The statistical analyses were performed using the Statistical Package Stata software version 13. Relationships between caries increment, retention rates, carious status, sealing process and socioeconomic variable (measured by an ecological variable, the region) were explored rst using bivariate analysis. Chi-squared (with Rao-Scott correction), F Fisher-Snedecor, ANOVA or Student t-tests were used depending of the variables being considered. Carious increment was explained using a mixed multiple random-effects regression. Mixed models were used including the examiner parameter as random effect  [17] [18] [19] [20]. Factors signi cantly associated with carious increment in bivariate analyses but also non-signi cant parameters considered as being relevant were integrated in the analyses.
This procedure has to be considered a local and site-specific analysis. During the application of the beam, two-dimensional SEM images as datasets can be taken simultaneously. This implementation shows less falsification or structural damages of the specimen surface after the specimen slicing/cutting procedure. Based on both sectioning procedures, SEM is able to reveal a surface layer of monoclinic grains and intergranular micro cracks for very small depths (1-2 µm) at the onset of transformation. At the other bound, the width of a monoclinic surface layer can be measured in depth, even if it exceeds the penetration depth of XRD or Raman spectroscopy, i. e. , at distances higher than 20-50 microns, where XRD and Raman are no longer relevant. Finally, SEM of the fractured surface of an implant that failed during dynamic loading or fractured at the static loading test can be used for fracture analyses.
Ceramics 2019, 2 573 applied stress during sample preparation. However, this procedure allows for a rather huge field of view available for SEM imaging or even Raman mapping. Moreover, a focused ion beam (FIB) can be used for sectioning the specimen at specific desired locations  [13]. This procedure has to be considered a local and site-specific analysis. During the application of the beam, two-dimensional SEM images as datasets can be taken simultaneously. This implementation shows less falsification or structural damages of the specimen surface after the specimen slicing/cutting procedure. Based on both sectioning procedures, SEM is able to reveal a surface layer of monoclinic grains and intergranular micro cracks for very small depths (1-2 µm) at the onset of transformation. At the other bound, the width of a monoclinic surface layer can be measured in depth, even if it exceeds the penetration depth of XRD or Raman spectroscopy, i.
e. , at distances higher than 20-50 microns, where XRD and Raman are no longer relevant. Finally, SEM of the fractured surface of an implant that failed during dynamic loading or fractured at the static loading test can be used for fracture analyses. applied stress during sample preparation. However, this procedure allows for a rather huge field of view available for SEM imaging or even Raman mapping. Moreover, a focused ion beam (FIB) can be used for sectioning the specimen at specific desired locations  [13]. This procedure has to be considered a local and site-specific analysis. During the application of the beam, two-dimensional SEM images as datasets can be taken simultaneously. This implementation shows less falsification or structural damages of the specimen surface after the specimen slicing/cutting procedure.