dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction In this review, a literature search was conducted on the databases Isi Web of Science, Science Direct, Scientific. net, and Google Scholar, for the period of 1980 to the present, matching the keyword "nano", the keywords zirconia, ZrO 2 , Y-TZP, and the keywords dental and dentistry. A total of 74 papers that met these requirements were found. For the purpose of this research, some preliminary considerations about the literature references and the definition of nanostructured materials need to be made. Following the definition proposed by Gleiter  [1] , nanostructured materials are solids composed of structural elements with a characteristic size (at least in one direction) of a few nanometer, typically from 1 to 100 nm. Furthermore, it has been observed that, in scientific literature, zirconia-based ceramic materials with microstructural elements (grain size, GS) greater than 100 nm (sometimes GS equal to 500-600 nm) are also often defined as "nano".
Introduction Orthognathic surgery is classically planned by integrating clinical, cephalometric, and dental data from plaster casts. Acrylic occlusal splints are manufactured to transfer the planning data to the operating room and to set the position of the jaws during the operation. 2] [3]  The recorded position of the maxilla with a facebow may be altered by the approximation of the Frankfort plane, and positioning of the face-bow on soft tissues may be difficult. The recorded position of the maxilla may also be modified when transposing to the semiadjustable articulator (cast weight, disruption of facebow). The articulator is another source of inaccuracy: its rotational axis differs from the patient's bicondylar axis, and its reference horizontal plane may differ from the recorded Frankfort plane.
Material and methods This descriptive, single-centre, observational study involved 30 patients from February 2016 -June 2017 in the Centre Hospitalier d'Annecy Genevois. It was approved by the French Sud-Est IV ethics committee on 26 January 2016 (NCT03357211). The inclusion criteria were: patients who needed a Le Fort I osteotomy, alone or associated to a mandibular osteotomy, whatever the indication; patients more than 16 years of age; and patients who consented to the collection of personal data for this study (patient or holder of parental authority for patients less than 18 of age). The exclusion criteria were: pregnancy; patients without social security cover; dependent adults; and patients who needed a maxillary disjunction. The surgical indication was confirmed after clinical examination, sagittal and frontal cephalometry, and a plaster cast study.
Planning navigation For the navigation, movements and references were also defined in the porion-nasion plane from the planning of the cephalometry. The movements were characterised by the translations (mm) and by the rotations  (o)  of the interincisal point in this plane. The planning data were manually recorded in the specific Orthopilot ® system software. At the beginning of the operation, the navigation perspective (left and right porion point and interincisal point), was defined by a calibrated pointer, with a cranial rigid body (fixed on the left superior orbital ridge) as reference. These three points were used to build the direct orthonormal reference required for tracking. The preoperative position of the maxilla was recorded with the occlusal splint holding a rigid body before any bony procedure was begun.
Statistical analysis The data collected included: sex, age, Angle's classification, type of dysmorphia (posterior vertical excess, anterior vertical excess, asymmetry), planned movements recorded for the navigation (left/right translation (Tlr), top/bottom translation (Ttb), back/front translation (Tbf), frontal rotation (Rf), axial rotation (Ra), sagittal rotation (Rs), inter-incisal vector). The navigational data were automatically recorded during the procedure in a dedicated file and collected at the end of operation on the Orthopilot ® PC. Discrepancies were calculated from the difference between the planned and measured navigational data. The norm of the interincisal vector (vector II) was calculated as follow: The accuracy of positioning was classified in nine distinct classes with three major The secondary outcome was the vector II norm (vector between planned interincisal point and the inter-incisal point used).
Materials and methods: A database of 515 harmonized thyroid CT examinations was used, of which information regarding cartilage abnormality was provided for 326. The process consisted of determining image abnormality and, from these preprocessed images, finding the best learning algorithm to appropriately characterize thyroid cartilage as normal or abnormal. CT images were cropped to be centered around the cartilage in order to focus on the relevant area. New images were generated from the originals by applying simple transformations in order to augment the database. Characterizations of cartilage abnormalities were made using transfer learning, by using the architecture of a pre-trained neural network called VGG16 and adapting the final layers to a binary classification problem.
Context Data analysis took place during the Data Challenge organized during the 2018 JFR ("Journées Françaises de Radiologie"). Various subjects were proposed, but the authors opted to participate in the challenge using the detection of abnormal thyroid cartilages from CT data. The model presented here had the highest performance of all participants in predicting thyroid cartilage abnormality on an independent testing dataset. This paper addresses the deep learning algorithm methodology used in order to achieve classification of thyroid cartilages.
Database characteristics The database contained 515 images of CT examinations of the thyroid, as well as a CSV file containing a label associated with each image (normal or abnormal cartilage). Gathered from 35 different radiology services across France, one horizontal cut was available for each patient; and all the examinations were harmonized as much as possible. During the entire challenge, only 326 of the 515 images were available to the various teams. The 189 remaining images have never been shared with the participants and were used by the organizers to independently evaluate the models developed. Among the 326 images, 30 contained abnormal thyroid cartilages (9. 2%). Image files were in NIfTI format, had been resampled to have 1-mm pixels, and were recentered to all be the same size.
Image processing To focus on the most important parts of the images and feed relevant data to the algorithm, the images were centered around the cartilages using fixed coordinates (all the images were centered similarly). Three hundred twenty-six images were given as an initial database. They were divided into training and test sets containing 244 and 82 images, respectively, and transformed to JPEG format. The training set contained 24 abnormal cartilages, and the test set contained 6 to respect the initial proportions. Thus, the algorithm learned on a sample of the data; and its performance could be tested on images it had never seen, reducing possible overfitting. The model could then be evaluated before being submitted for final results. Since 244 is a very low number of images to train a deep learning algorithm, they were used to generate new ones and improve the performance and robustness of the model.
Image classification The detection of abnormality in the thyroid cartilage was achieved using transfer learning from a pre-trained convolutional neural network (CNN) algorithm devoted to image classification. Since those algorithms are extremely expensive to train from scratch in terms of computational power and size of the dataset, transfer learning enables expansion of the algorithm for specific use on a small dataset. It starts with a CNN model using pre-trained weights and retraining it with the CT scans of thyroids and the appropriate classification (normal/abnormal). The original model used in this work was the Visual Geometry Group 16 (VGG16), developed at Oxford University in 2015, which has been trained and evaluated on the ImageNet collection and composed of everyday-object pictures  [18, 19].
This model converts an input image into the probability of belonging to a class of the ImageNet categories. An image is scanned through several filters (convolutional layers), which detect the essence of each category, in order to extract the relevant features for the final classification. The preference for VGG16 was a compromise between high-performance level for image classification tasks and a light structure in comparison to more recent CNN architectures, such as Inception V3 or ResNet-50  [20, 21]. Whereas CT images of thyroid cartilages are different from the ImageNet dataset, CNN architectures such as VGG16 are able to extract the main features and shapes of a picture, whatever the content. The first layers are more generic and can be kept as-is, while deep layer weights must be retrained in order to achieve a good classification of normal/abnormal thyroid cartilage.
In the present case, those deep layers are expected to extract the features that focus on cartilage shape and abnormalities once the retraining is achieved. The VGG16 model is composed of five blocks, which combine two-dimensional (2D) convolutions, 2D pooling, and max-padding layers, finishing with a dense layer that operates the final classification through 1000 different categories proposed by the ImageNet dataset  [18, 19]. Thanks to its simplicity, the VGG16 model is well-adapted for transfer learning for a small dataset, with a relatively reasonable number of trainable parameters (approximately 14 million). To achieve the binary classification (normal/abnormal thyroid cartilage), the final dense layers of VGG16, designed for 1000 categories, are removed and replaced with a 2D global average pooling followed by two fully connected layers with random initialization.
This final architecture was chosen in order to slowly reduce the high-dimensional space of the last convolutional layer (7x7x512) to a 2D binary classification space (Figure  2 ). To train the model, the last two convolutional blocks and the dense layers were trained, whereas the parameters of the first three convolutional blocks were fixed to the original weights of the VGG16 model  [19]. This allowed to keep the more generic features of the VGG16 model included in the first layers and adapt the model to the CT images through a limited number of trainable parameters (Figure  2 ). The framework has been implemented using Python 3. 6. The loading of NIfTI images and the preprocessing have been done using the libraries Nibabel (version 2.
Results Figure  3  shows the ROC curve of the model presented in this work applied on the 82 images (76 normal and 6 abnormal thyroid) of the test dataset. Those images were independent from the training dataset and, therefore, were never seen by the CNN architecture. Deduced from this ROC curve, the AUC value was 0. 72 while the optimal value of the specificity and the sensitivity were 64% and 83%, respectively, as per the Youden index  [22]. Despite the low volume of the test dataset and therefore the complexity of obtaining accurate statistics, a similar AUC value was recovered independently on the dataset kept by the challenge organizers, which was not shared to the participants. Indeed, applying the model on those 189 new thyroid images resulted in an AUC score of 0. 70, demonstrating the reliability of the results.
In our study, only one axial image with bone windowing was available so that key information was lacking. Using multiplanar reconstruction of the cartilage could improve the performance by providing more information. A study has recently shown the use of 3D images of knee MRIs for detection of abnormalities  [26]. However, this approach requires huge computational power and resources. In our study, the database used to train and test the model was restrained and harmonized, and the variety of the sources was unknown. This means that the model's capacity to generalize is difficult to estimate, and it might behave very erratically on completely independent data. The algorithm should also be tested on post-mortem CT examinations performed for medicolegal purposes. Indeed, in forensic sciences, the evaluation of thyroid cartilage is of great importance in determining the cause of death.
Despite its limitation, our model has the potential to be greatly improved, for example, through image segmentation to locate the cartilage on the images. This would allow the algorithm to focus on the relevant features to detect an abnormality. Nonetheless, it would require a significant amount of work to label the data and create a segmentation system based on shapes or intensity, which would then need to be evaluated. Image processing could also be used to improve image quality (e. g. , increase contrast) and therefore improve classification results. Another potential improvement to the model would be to have access to a bigger dataset: at least 1000 images would be much more efficient for the model training, by feeding the neural network with a wider type of images. Other neural network structures could also be tested, notably Inception V3 or ResNet-50  [20, 21] , which have achieved higher performance on the ImageNet classification.
However, those models are more complex with an important number of parameters (more than 25 million); therefore, developing such models makes more sense with larger databases. The algorithm is not yet able to be autonomous or match a professional's evaluation, but could act as a facilitator for radiologists presented with obvious diagnoses, allowing them to focus on more difficult cases. As opposed to replacing the radiologists, deep learning can become a tool for medical professionals to improve productivity and decrease workload and mistakes. The goal is a system whereby decisions can be utilized but are consistently evaluated and checked by the medical expert to make it progressively more efficient. This can be done through the growing collaboration between data scientists and radiologists, which has become more prevalent in many hospitals and is the basis of this project.
In conclusion, this project demonstrated the feasibility of using a deep learning algorithm to assist radiologists in detecting abnormal thyroid cartilages from CT examinations due, for instance, to tumoral invasion or traumas in the case of forensic science. Although the results presented show real potential, it is clear that the model is not yet as efficient as a radiologist. This is a consequence of the small dataset used for training the model, and is partly due to the complex shape of the thyroid cartilage resulting in very disparate CT images. To be used in clinical practice, the algorithm needs more data and improvement. This can be accomplished through a collaboration between data scientists and radiologists who understand its deficiencies and aim to make the algorithm more efficient.
3. 0), Numpy (1. 15. 2) and openCV (version 3. 4. 3. 18). The deep learning part was built with the Keras package (version 1. 0. 6) using TensorFlow in back end (version 1. 11. 0). For the CNN training,a stochastic gradient descent was used as an optimizer (learning rate = 0. 01 and momentum = 0. 9). All computations were carried out on a personal computer with a 2. 2GHz Intel Core i7. The time required for an epoch was between 5 and 8 minutes, and about 8 epochs were enough to reach the final score evaluated on the test dataset.
Figure3. Graph shows ROC deduced from the model applied on the 82 images of the test dataset (76 negative and 6 positive labels). The optimal value (according to the maximum value of the Youden index, illustrated by the continuous grey vertical line) of the sensitivity and specificity are 83% and 64%, respectively. The dashed line represents the ROC curve of a pure random model. The value of the area under the ROC curve (AUC) is 0. 72.
Characterisation techniques. A Bruker D8 powder diffraction spectrometer, with monochromatic Cu Kα radiation (0. 1541 nm) was used for ascertaining the crystalline nature of the synthesized fluorapatite mineral powder. The diffractometer step size was 0. 065° and the 2θ scanning range was from 10° to 60°, such that data were collected over a period of approximately 25 min with a scan speed of 0. 014° s -1. Scanning electron microscopy (SEM, a Hitachi SU8230 1-30 kV cold field emission gun) was used to investigate the size and shape of the synthesized crystals and also for characterising the porosity in chitosan scaffolds. The microscope was equipped with an Oxford Instruments 80 mm 2 SD detector for energy dispersive X-ray (EDX) spectrometry with Aztec processing software to enable compositional analysis both of the mineral powders and the scaffolds.
Acknowledgements The authors acknowledge the sponsors of this work; the  EU-Marie-Curie-IF (PRe-FActo)  and  Medical Technologies Innovation and Knowledge Centre  (phase 2 -Regenerative Devices), funded by the  EPSRC  under grant number  EP/N00941X/1. Also, the authors would like to acknowledge  Mr. Mohammed Javed  for the laboratory support and  Mr. John Harrington  and  Mr. Stuart Micklethwaite  for SEM support. Raw data of this work can be found at https://doi. org/10. 5518/717.
Statistical Analysis Collected data were analyzed with SigmaPlot release 11. 2 (Systat Software, Inc. , San Jose, California, United States). The proportions of voids and sealer areas corresponding to one of two treatments GuttaCore (GC) or Thermafill (TH) were compared by Kruskal-Wallis one-way analysis of variance on ranks, with statistical significance set at α =0. 05.
Image processing and parameters quantification The high-resolution reconstructed datasets were visualized using ImageJ and CTvox, and topographies typically contained more than 2100 slices along the post axis, occupying more than 80-90 Gigabytes each. Example 3D reconstructions are shown in Figs. 1 and 2 with corresponding high-resolution cross-sectional and longitudinal slices shown in Fig. 2. For data analysis, n = 13 artefact-free reconstructions were quantified so as to compare integrity, interface architecture, gaps and internal voids (the 14th PCE-CT scan failed due to unrecoverable data loss). The 3D datasets were reduced to produce 5 µm thick slices using the minimum intensity projection of 8 neighbouring sub-micrometer slices (Z project function of ImageJ). Slices 100 µm apart were then compared within the cervical area of each tooth, at increasing distances below the apical margins of the core build-up.
Slices at identical depths of all teeth were selected for further processing for both intra-and inter-tooth comparisons. Analysis included measurements of the canal, cement and post perimeters as well as the cross-sectional areas, all performed by a single evaluator (AS). The total length of observed interfacial gaps between root and restoration materials was recorded. The length of remnants of endodontic sealer (AH Plus) on the canal perimeter was also measured. Data were used to derive four measures of quality: % canal area filled with post and with cement, % interfacial gaps and % canal perimeter covered by remnants of sealer.
2018). Geometric measurements performed on each slice along the axis of the filling reveal values for area and length estimates of the canal, filling and discontinuities at the interfaces. Average values as well as any trends in the data such as the expected taper in the cervical canal perimeter/area from the most coronal to apical region, are given in Table  S1. The measurements were used to derive the percentage of the filled canal cross-section and percentage of imperfections (gaps and sealer presence) observed at the dentine-cement interfaces, as depicted in Figure  3. The results reveal expected increases in the canal areas filled by the post (Fig. 3a ) and decreases in canal area filled by cement (Fig. 3b ). The trend in the data and ratios of cement to canal areas with increasing depths are shown in Figure  S4.
Statistical analysis Any samples that exhibited cohesive or mixed failures were excluded from the statistical analysis. 20, 21 The data analysis was performed with SigmaPlot, v. 11. 2 (Systat Software Inc. , San Jose, USA). The one-way analysis of variance (ANOVA) test was applied in the comparisons of the μTBS data of the 3 groups. The χ 2 test was performed to compare the effects of the 3 different etching protocols used on the mode of failure. In all tests, a statistical significance level of α = 0. 05 was adopted.
Collected data included: clinical and biological characteristics, treatments received for LGLe or pSS and patients' outcome. Disease activity was measured using the EULAR Sjögren Syndrome Disease Activity Index (ESSDAI),  5  with active disease defined as an ESSDAI ≥5. Since neutropenia was considered as a feature of LGLe, it was not included in the calculation of the ESSDAI. LGLe were defined on flow cytometry results, as monoclonal, oligoclonal or polyclonal, expansions of T-cells or NK-cells harbouring an LGL phenotype (different combinations of CD57, CD56 and CD16 markers), representing ≥10% of peripheral lymphocytes. In patients with T-LGLe, Vβ repertoire analysis of the T-cell receptor was performed to assess clonal rearrangement. When possible, STAT3 mutational status of LGL cells by direct sequencing was also performed.
The chemical analysis of specimens was performed with an EDAX Octane Plus spectrometer (AMETEK Materials Analysis Division, Mahwah, NJ 07430, USA) for 10 zones of 443 μm 2 for each sample. After that, a mesiodistal section was made in the middle of every dentin wafer with wire saw (Walter EBNER, Le Locle, Switzerland) and prepared for chemical analysis (EDX). Compositional analysis was performed using the linescanning mode of the micro-EDX in order to investigate the depth of demineralization in dentin thickness. All the samples were considered for the SEM and EDX analyses. Data analysis was performed with Sigma Plot (release 11. 2, Systat Software, Inc. , San Jose, CA, USA). The one-way analysis of variance test was applied in the comparisons of the mean calcium (Ca) mass percentages for the dentin etched with the different acids.
Funding This research was undertaken in the framework of the  LONGLIFE  project (http://www. longlife-project. eu. ) funded by the  European Community 's  Seventh Framework Program  ( FP7/2007-2013 ) under the grant agreement n. 280741 and the  SISCERA  project (http:// siscera-project. eu. ) also funded by  E. U. ( H2020-FTIPilot-2016 , grant agreement n. 737954).
ETHICS STATEMENT All animal experiments were performed in agreement with the EU Directive 2010/63, the European Convention for the (A) ALS3, SAP2 and SAP6 gene expression in cellular fractions from tongue homogenates of infected mice treated with 10 μl of saline, FLZ (4 mg/ml), IY, or GI (both at 100 mg/ml) at 1, 2, and 3 days post-infection was evaluated after 6 days of infection. Tongue homogenates were centrifuged, cellular fractions were lysed, and total RNA was extracted and retro-transcribed into cDNA. ALS3, SAP2, and SAP6 genes were detected as described in section "Materials and Methods. " Data for ALS3, SAP2, and SAP6 genes are the mean ± SEM from one experiment with n = 3 mice/group. # p < 0. 05 FLZ or compound-treated infected mice vs. saline-treated infected mice. (B) Candidacidal activity of peritoneal neutrophils from infected mice, treated as above described, was evaluated after 6 days of infection.
Data are the mean ± SEM from two different experiments each with n = 3 mice/group. *p < 0. 05 FLZ or compound-treated infected mice vs. saline-treated infected mice. Reactive oxygen species (ROS) of the peritoneal neutrophils (4 × 10 6 /ml) were evaluated after 6 days of infection in the presence of C. albicans (2 × 10 6 CFU/ml). Quantification of total ROS production was determined by calculation of area under curve (AUC). Data are the mean ± SEM from one experiment with n = 4 mice/group. *p < 0. 05 FLZ or compound-treated infected mice vs. saline-treated infected mice.