dataesr/scientific-paragraphs-categorization huggingface dataset:
high). Patch feature extraction is based on scale-invariant feature transform algorithms. Basically, information can be extracted to predict the positions of the anatomical structure involving the specific landmarks (45, double the landmarks often detected by benchmark databases), with a decent average of 72% successful detection rate within a precision range of 2. 0 mm. is is another fine example of "outside-the-box" multidisciplinary and multinational collaborative novelty for engineering solutions in health. Likewise, the paper by D. Xu et al. entitled "Interactive Compliance Control of a Wrist Rehabilitation Device (WReD) with Enhanced Training Safety" is an inordinate illustration of different disciplines and areas of research coming together. It deals with human-robot interactive tasks (and interaction control), robot-assisted rehabilitation solutions, and physical therapy, to improve the safety and efficacy of training and compliance adaptation of interaction, as are vital for enabling robotic or robot-specific movements to better suit the varying and/or personalized requirements of individual patients.
Acknowledgments e guest editorial team would like to express gratitude to all the contributing authors and anonymous reviewers for their interest in selecting this special issue as a venue for disseminating their scholarly work and for their driven, careful, and insightful reading, appraisal, commentaries, and suggestions of the received manuscripts, respectfully. Finally, the lead guest editor is pleased to particularly thank the  BioMAT'X R&D&I Team, Faculty of Dentistry at the Universidad de los Andes  for their technical support ( FAI-UANDESINV-IN-2015-101 , CONCIYT-FONDEF ID16|10366 , and  CORFO 18COTE-89695 ).
Data collection Clinical investigators collected sociodemographic data, medical history and treatment, RA outcomes, QoL outcomes and oral hygiene habits. RA outcomes were measured by rheumatologists who were blinded to the subjects' group assignment. Full-mouth periodontal charting included CAL, PPD and bleeding on probing (BoP). Initial periodontal charting was performed before randomisation, using a constant pressure probe (Florida Probe®; Gainesville, FL, USA) with six measurements per tooth  [20]  at inclusion and after months. Moreover, investigators were trained before the launch of the study (overall inter-examiner kappa was as high as 0. 9 for CAL, PPD and BoP). Periodontal investigators could not be blinded to the subjects' group assignment during follow-up visits. Using the individual periodontal measurements, the total periodontal inflammatory burden was also estimated at the participant level, taking into account periodontitis severity, extent and activity.
Under these circumstances, it would not have been ethical to pursue inclusions, and amending the protocol to adapt to these new practices would have profoundly disrupted the analyses. The absence of significant improvement on the DAS28 after periodontal treatment could therefore be interpreted as insufficient power to demonstrate an effect. Nevertheless, even if we initially planned to enrol 16 participants per group to be able to detect a difference of 0. 6 point in DAS28-ESR between the two groups, it would be unlikely that achieving this objective would have revealed a statistically significant difference according to the absence of any quantitative difference in the 3-month change of DAS28-ESR between the two groups of 11 participants (-0. 03 [-0. 98;0. 92], p=0. 95). Lastly, although the plaque control record is important to assess participant compliance with oral hygiene instructions, we did not consider this data to be essential because the focus was on the ITT analysis.
This was in part due to increasing computing power. In the first decade of the XXIst century, AI applications spread rapidly in various fields such as speech recognition, autonomous driving, and medical applications. More recently, the continuous increase in computing power coupled to the availability of very large databases (Big data) fueled AI applications to unprecedented levels. Machine learning techniques that had been developed decades ago, such as convolutional neural networks, finally found applications in a wide variety of domains. A notable milestone for deep learning applications was the Google-Alphago project that allowed an AIpowered machine to beat the world Go champion in 2017. Nowadays, AI research involves various fields of expertise such as mathematics, computer sciences, and cognitive science amongst others.
Applications in Medicine In the past decade, numerous very promising applications of AI have emerged in Medicine 2. Convolutional Neural Networks trained with massive labeled datasets proved to be very efficient in image recognition. The main reason explaining the actual superiority of AI over human doctors in select sub-specialties of medicine is that machines can be educated with hundreds of thousands of clinical cases. This exceeds by far the clinical experience of even the best specialists in any field. Meanwhile, healthcare data has become more abundant and more complex for each patient. And, the ideal of personalized medicine requires taking into account massive sets of data for each individual patient. The fear that machines could potentially replace human doctors in a near future is to be taken seriously as the momentum of change in the field of AI research is unprecedented.
The massive datasets obtained from these projects (demographic information and clinical data) help in return educate machine learning algorithms. Ultimately, this makes the programs more and more efficient and accurate. Thanks to the huge amount of data analyzed, it is likely that in a near future, AI-powered clinical studies will be statistically more powerful and accurate than "classic" clinical studiesincluding double-blind randomized controlled trials. Nowadays, medical applications of AI include -but are not limited to -Radiology 5 , Dermatology 6 , Neurology 7 , Ophthalmology 8 , Oncology 9 , Cardiology 10 , Genetics 11 , Emergency Medicine  12  , and Drug design  13.
Several different factors support this rationale: -Colossal investments are being made by the industry in research and development of AI applied to digital orthodontics and robotic surgery. Large economic returns are expected from these fields (an estimated 3,6 billion € in the field of orthodontics alone). -The worldwide development of substantial image databases is a proof of the increasing use of digital imagery and photographs in the management of orthognathic cases. Specifically, the introduction of intra oral scanners has radically altered the place of digital imaging in orthodontics and orthognathic surgery. -Real life demands from practitioners: reconstruction, tridimensional morphometrics, automated treatment planning, customized surgical set up planning, etc. At the outset, it is important to identify the type of AI that is being discussed in this instance.
Human cortical bone contains two types of tissue: osteonal and interstitial tissue. Growing bone is not well-known in terms of its intrinsic material properties. To date, distinctions between the mechanical properties of osteonal and interstitial regions have not been investigated in juvenile bone and compared to adult bone in a combined dataset. In this work, cortical bone samples obtained from fibulae of 13 juveniles patients (4 to 18 years old) during corrective surgery and from 17 adult donors (50 to 95 years old) were analyzed. Microindentation was used to assess the mechanical properties of the extracellular matrix, quantitative microradiography was used to measure the degree of bone mineralization (DMB), and Fourier transform infrared microspectroscopy was used to evaluate the physicochemical modifications of bone composition (organic versus mineral matrix).
However, there are very few data on characterization of healthy juvenile bone at the tissue level (at the micro meters length-scale)  19, 20  and none contrasting such data with a conjoined adult set of samples. The micromechanical properties of bone can be assessed by microindentation tests which is not available for juvenile bone. In adult bone, plane strain modulus was in the range of 7 to 35 GPa  [21] [22] [23] [24] [25]  and cortical bone stiffness is predominantly associated with mineral content and bone density while cortical bone toughness correlates with the quality of the collagen matrix  26. At the BSU level, axial elastic properties and hardness of bone are dependent on the degree of mineralization  6, 27. Using quantitative backscattered electron imaging in juvenile iliac crest bone biopsies, Fratzl-Zelman et al.
Statistical analysis. Statistical analysis was performed in SPSS 20. 0 (IBM, Amonk, NY, USA) using a significance level of 5%. Depending on the main parameters (E*, DMB, carbonation and crystallinity), the power analysis range from 68% to 99% in osteonal tissue and from 95% to 99% in interstitial tissue. All tests were two-tailed. Results are reported as scattergram and boxplots. Distribution of variables was tested with the Shapiro-Wilk procedure. Non-parametric tests were used to evaluate variables that were not normally distributed. The influence of the microstructure on mechanical behavior was studied using bivariate correlations that were tested by the Spearman's rank correlation test. The Mann-Whitney unpaired test was used to test for differences between juvenile and adult groups (category).
Acknowledgments e authors would like to express their great thanks to  Dr. Abdelnasir Gafer , Oral and  Maxillofacial Surgeon , and  Dr. Motaz Sayed Alhassan Osman ,  Department of Oral Rehabilitation, Faculty of Dentistry, University of Khartoum, Khartoum, Sudan , who performed the surgical and prosthodontic treatment for the patients of the study. Our thanks are also extended to  Dr. Manar Abdelrahman ,  Associate Professor in Biostatistics , for helping devise the evaluation sheets used in the study.
Statistical Analysis An analysis per canal and per tooth was performed. When one canal was evaluated below standard, the whole tooth was    Root Canal Filling in Preclinical Training Kharouf et al. evaluated below standard. Chi-squared test was performed to investigate the effect of the two different teaching protocols (G1 or G2) used. Data was processed using SigmaPlot (Version 11. 2; Systat Software, INC. , San Jose, California, United States). In addition, we compared the lapse to complete the RCT (LRCT) and the lapse to perform the root canal filling (LF) of both G1 and G2, using the t-test. The first species risk level has been fixed to p = 0. 05 for all tests performed.
XRD analyses were performed using a powder diffractometer (D8 Advance, Bruker, Germany) with Bragg-Brentano geometry equipped with a germanium monochromator and a Cu Kα source operating at 40 kV and 40 mA. Data sets were collected with a step size of 0. 019 ° in 2q and a counting time of 1 s per step. The diffraction patterns were compared with files from the Joint Committee on Powder Diffraction Standards for α-TCP (JCPDS No. 9-348), β-TCP (JCPDS No. 9-169) and hydroxyapatite (HA; JCPDS No. 9-432). Phase quantification was performed by comparing the ratios of the area for the most intense peak using the XRD analysis software (EVA, Bruker, Germany).
Microcomputed tomography Microcomputed tomography (micro-CT) analysis was performed on calvaria after 1 day, 4 weeks and 8 weeks using a high-resolution X-ray micro-CT system for small-animal imaging (micro-CT 1076, SkyScan, Kontich, Belgium). All samples were scanned using the same parameters (pixel size 9 µm, 50 kV, 0. 5 mm Al filter and 0. 8 ° of rotation step). Three-dimensional reconstructions were made using a software (CTVOX, Skyscan, Belgium) to evaluate the filling capacity of the BGS in the cup. Bone and biomaterial volume over total volume (BV+MV)/TV were determined in the cup owing to a software allowing to finely select the threshold (CTAN, Skyscan, Belgium) on 3 samples for each time point.
The slices were mounted on microscope glass slides (Polysine, Thermo Scientific, Germany) and stained with Masson's trichrome using an automated staining station (Microm Microtech). This staining combines haematein for cell nuclei (purple/black), fushin Ponceau for cytoplasm, muscle and erythrocytes (red) and light green solution for collagen (green/blue). The stained slices were scanned (NanoZoomer 2. 0RS, Hamamatsu Corp. Japan) and observed with the virtual microscope (NDP view software, Hamamatsu Corp). The parameters quantified in the region of interest (Figure  1 ) by histomorphometric analysis (Image J, National Institute of Health, USA) were the area percentages of biomaterial (MS/TS), bone (BS/TS) and residual tissues (RS/TS). The vertical bone regeneration in the cup was determined by calculating the percentage of newly formed bone in the free space, defined as follows: % Bone surface in free space = (BS/(BS+RS)) x 100 where: BS is the bone surface in the region of interest MS is the biomaterial surface in the region of interest TS is the total surface of the region of interest.
Ethical Approval and Informed Consent This study was carried out in accordance with the ethical committee of Sud-Est VI Clermont-Ferrand (Approval number: AU1371) and the National Commission of Informatics and Liberties, France (2116544 v 0). The National Agency for the Safety of Medicines and Health Products (ANSM) approved it on February 13, 2017 (ID-RCB ref: 2017-A00425-48). This study was registered with ClinicalTrials. gov (identification number ID: NCT03700840).
The bacterial primers used in this study are derived from sequences already published and have been adapted to the real-time PCR conditions (Supplementary Table  S1 ). The assays were performed on the Rotor-Gene Q thermal cycling system (Qiagen, Hilden, Germany) with the following program: 95 • C for 30 s, followed by 40 cycles of 10 s at 95 • C, 10 s at the appropriate annealing temperature (Supplementary Table  S1 ), and 35 s at 72 • C. For the total bacterial load and that of all species, a final melting curve analysis (70-95 • C in 1 • C steps at 5 s increments) was performed. Fluorescence signals were measured every cycle at the end of the extension step and continuously during the melting curve analysis. The resulting data were analyzed using Rotor-Gene Q Series software (Qiagen, Hilden, Germany).
The trees associated with the correlation plot were obtained by hierarchical clustering with complete linkage. The difference between the two groups of caries risk relative to age, gender, mouth location, interdental space, BOIB, PI and GI were tested with chi-square tests. Kruskal-Wallis tests were performed to compare the mean counts for the different bacterial species relative to each clinical characteristic. All statistical analyzes and associated plots were performed using the R environment (R Core Team, 2015), specifically the lme4 package  [32] , to estimate the mixed model.
Statistical analysis The primary outcome was the CHILD-OIDP score. The statistical analyses were performed on participants for whom the CHILD-OIDP and the potentially associated factors were recorded exhaustively. Data were expressed in medians (1st; 3rd quartile), minimum and maximum for non-Gaussian continuous variables and in numbers (percentages) for categorical variables. Bivariate and multivariate analyses were carried out to identify factors associated with the CHILD-OIDP using quantile regression. Beta coefficient and 95% confidence intervals were calculated. All the data were analyzed using SAS software (version 9. 4; SAS Institute, Cary, NC, USA). All the statistical tests were bilateral with a significance level of 5%, which means that a p-value< 5% was considered significant.
Qualitative methodology At the end of the questionnaire, a free space was left for the patient to add a verbatim if they wished. No specific question was asked; the order was simply to express freely what they wanted to say about the disease, the care course, their life or their children's life. An inductive approach using thematic analysis was used to manually analyze the verbatim answers  [13] [14] [15]. This method allowed organizing thematic and to identify and organize patterns. The objective was to precisely analyze the whole dataset. Topics were chosen for their prevalence and their importance in relation with the research question.
Results Five expertise centres participated in the study. Two specialized in maxillo-facial surgery and three in oral surgery. A list of 1168 patients was obtained from the centres. A sample was obtained after eliminating patients not living at the declared address, and by stratifying patients as a function of age, gender and disease type (cleft versus dental rare RD). The final analysis was performed on 440 children between 6 and 17 years old (Fig. 1 ). Overall, 108 patients did not receive the questionnaire because their address had changed, leading to the inclusion of 132 patients in the survey. Complete data (people whose score was calculable and who respected inclusion criteria) were available for 110 patients. The sample included 49 boys (44. 5%) and 61 girls (55. 5%). Ages ranged from 6 to 17 years old and 75 were between 6 to 12 years old (68.
Qualitative analysis The verbatim responses were analyzed for qualitative appreciation of patient illness. 36% (40/110) of the patient answered. Thematic analysis was used to analyze the interview data  [16]. All the verbatim responses can be found in the Additional file 1. The topics addressed in these verbatim responses can be classified according to the following topics (Fig. 2 ): patients reported a lack of information on treatment and the course of care, and a lack of listening from medical and non-medical staff. Insufficient psychological care of children and their parents was reported. They also reported complicated relations between care in public hospital and care in private establishments (Topic 1). Patients and their parents reported feelings of anxiety, loneliness and difficulties in facing the gaze of others.
Acknowledgements The authors wish to thank the members of the  Head, Neck  and  Teeth French Rare Diseases Network : all the medical and non-medical staff in competence and reference centres. The authors wish also to thank the members of the  URC research lab in Robert Debré Hospital, Paris :  Béatrice Andriss ,  Adyla Yacoubi ,  Thérèse Kolta ,  Pierre Coste.
Funding One funding/research grant was received to support this study from University Paris-Diderot (Action structurante Dental faculty Garancière). Strasbourg Patients' enrollment was possible through the EU-funded project (ERDF) A27 "Oro-dental manifestations of rare diseases", supported by the RMT-TMO Offensive Sciences initiative, INTERREG IV Upper Rhine program, and the INTERREG V RARENET program.
Exclusion criteria for the GERD group were: patients with missing teeth or with prosthesis (fixed or removable), making impossible BEWE score evaluation. Inclusion criteria for the non-GERD control group were: given informed consent, patients without GERD or any other digestive disease, referred to dental checkup. Exclusion criteria for the control group were: history of symptoms of gastrointestinal disorders. All data were analyzed using statistic SPSS version 21 software. Continuous variables were tested for normality by the Kolmogorov-Smirnov test. Continuous data were characterized by median and 25 and 75 percentiles; nominal variables were characterized by frequency and percents. Continuous variables were compared by Mann-Whitney test. Comparison of nominal variables was performed by chi 2 test.
In our patients, the use of acid fruit or drinks was associated with DE. The use of antacid, as GERD patients do, had a protective effect against DE. Mouthwash consumption was included in the questionnaire due to the positive influence on tooth protection related to biguanides action (polyhexamethylene biguanide -PHMB and chlorhexidine -CHX) on the enzymatic degradation of the demineralized organic matrix. In our study, DE risk for patients using mouthwash was not statistically lower than in patients who did not use mouthwash (p=0. 839). Lower pH values in GERD patients compared with a control group, confirm the higher risk for DE in this condition. Our data showing higher prevalence of DE in GERD are important for preventive measures. Dental examination should be introduced in the investigation protocol of every GERD patient in the gastroenterology services.
Three-dimensional superimpositions All CT scans were processed with Meshlab TM software (ISTI [Italian National Research Council], Rome, Italy) to eliminate artifacts caused by the orthodontic appliance and to prepare the images for the superimpositions. The 3D superimpositions were performed with the bestfit method using only a local maxillary superimposition considering the maxilla base as a fixed surface to assess dental movement alone. This local superimposition was performed in accordance with the study by Treil et al. ,  20  which used the infraorbital foramen as a fixed point with the best-fit method. The superimposition was performed with the Cloudcompare TM v2. 6. 0 software (Research and Development Electricity Institute EDF, Paris, France). The color palette was changed a second time to shades of grey to enhance visualization of TADs and tooth movement.
All studies were registered with ClinicalTrials. gov (NCT00496197, NCT00548262, NCT00537329, NCT00689338, and NCT00805740). Patients initiated the treatment with a 200-mg loading dose of intravenous (IV) anidulafungin on day 1 of the study, followed by a maintenance dose of 100 mg IV once daily. In all but one of the studies (A8851022 [NCT00805740]), patients could be switched to an oral azole after ≥ 5 days (A8851011 [NCT00496197], A8851015 [NCT00548262], A8851016 [NCT00537329]) or ≥ 10 days (A8851019 [NCT00689338]) of IV treatment, according to pre-specified criteria in each protocol. Antifungal treatment (IV anidulafungin plus subsequent oral azole if used) was maintained for ≥ 14 days after the last positive Candida culture and resolution of symptoms.
Patients who had received > 48 h of prior antifungal therapy had the presence of confirmed or suspected Candida osteomyelitis, endocarditis or meningitis, had prosthetic devices at infection sites that could not be removed within 24 h of study entry or who had previously failed treatment for the current episode of Candida infection were excluded. From the pooled database, only patients with peritoneal fluid or hepatobiliary tract as sites of infection, as reported by the investigators in the case report form (CRF), were included in this analysis.
Statistical analysis Efficacy and safety analyses were for descriptive purposes, and no hypotheses for efficacy endpoints were tested. Evaluation of the data comprised primarily of summary descriptive statistics. Success rates for global response were estimated with exact 95% confidence intervals (CI) for binomial proportion (Clopper-Pearson method). In efficacy analyses, indeterminate or missing was considered failure. Multivariate logistic regression was used to calculate odds ratios and 95% CI to identify factors that were significantly related to treatment failure in the MITT population. Risk factors for developing candidiasis were included in the protocols of all studies. Available patient characteristics data included in the multivariate analysis were age, baseline Acute Physiology and Chronic Health Evaluation (APACHE) II score, body mass index and baseline Candida species (mono-vs polypathogenic infections).