dataesr/scientific-paragraphs-categorization huggingface dataset:
However, these studies do not include any spatial view of adhesion. The transition between planktonic and sessile status of bacteria was the subject of recent work  [15]  using modeling software (MAUDE), but still without any space visualization. Some complex mathematical models also deal with the influence of bacterial elastic properties and shape, and the topography of the surface on bacterial adhesion  [16]. In most published studies, only bacterial adhesion to the surface was considered  [17]. Recently, the authors of  [18]  investigated the probability of bacteria adhering to each other and linked some specific bacterial properties, such as surface sensitivity and EPS production, to the ability of bacteria to attach either to nearby (spatial neighbors) or progeny (temporal neighbors) cells.
25-0. 75 NA, oil immersion lens to capture clear images of the spirochete. A numerical zoom of 1. 5 was applied to all acquisitions. All Syto ® 9-stained bacteria were detected using the 488 nm UV diode and a 485 to 500 nm band-pass emission filter. Biofilm stacks (123 × 123 µm) acquired at 1 µm intervals were scanned with a line average of 2. Leica software (LAS AF V. 2. 2. 1) was used for microscope piloting and image acquisition. Image analyses were performed using the Comstat2 plugin in the ImageJ software V1. 43m (National Institute of Health, Edmond, OK, USA) to estimate characteristic microscopy parameters: the biovolume (the volume occupied by the microcolonies divided by the surface end expressed in µm 3 /µm 2 ) representing the overall volume of the microcolonies, the mean thickness (µm) on all the surface and the mean thickness on bacteria (µm) without the void surface, roughness coefficient, which is calculated from the thickness distribution and is an indicator of the heterogeneity, the surface-to-volume ratio (µm 2 /µm 3 , surface of the microcolonies divided by their volume), and the maximum thickness (µm).
All the parameters are described in  [33]. After microscopic observation, the stain was removed from the µ-slides' wells and the attached 2 h cells were collected in 100 µL of PBS, centrifuged (8000× g, 10 min, 20 • C), and the pellets were stored at -20 • C for further characterization by qPCR. Three-dimensional images were generated with Imaris Viewer 9. 6 software from biofilm stack obtained using the Leica TCS-SP5 confocal laser scanning microscope.
gordonii (Figure  5A-C ), perhaps due to the higher proportion of S. gordonii (Figure  7A, B ). The microscopy results were in concurrence with the results of cell concentrations measured by qPCR which also remained unaffected by iron levels. However, the thickness (average and maximum) significantly increased at high iron concentrations (Figure  5D, E ), suggesting an effect on the initial biofilm architecture. In the case of attached dual-species S. gordonii-T. denticola cells, the biovolume (Figure  5A ) increased at 8 and 80 µM of iron compared to 0. 8 µM, in accordance with qPCR data (Figures  S3  and 7A,C ). Moreover, a significantly lower roughness coefficient and surface to biovolume ratio for dual-species S. gordonii-T. denticola sessile cells (Figure  5B, C ) can be attributed to the more homogenous distribution of these bacteria across the surface (Figure  6 ).
Methods We used a well-known approach in biomedical imaging called U-Net  [1]  to segment the endoscope and the guidewire. To cope with the difficulties related to the topology of the guidewire which is long and thin, the low contrast and signal to noise ratio, we propose specific pre-and post-processing steps to enhance this deep learning method. Endoscope segmentation. We followed the same steps of pre-processing as per the nnU-Net  [2]  approach, consisting in cropping to keep non-zero regions only, normalizing intensity and data augmentation. The image database contained 300 annotated images. Data augmentation allowed for the creation of 3200 additional ones. We applied elastic transformations in a limited range to keep medically plausible images. A first dataset of 2100 images was used for training, a second of 700 images for validation, and a third of 700 images for test.
Results The Dice score was used to evaluate the endoscope segmentation. After post-processing, an average Dice score of 0. 94 0. 05 was obtained. Furthermore, we applied segmentation activation map based on gradient  [3]  to visualize which pixels are used by the network during segmentation. The results clearly demonstrate that pixels from endoscope only are used for the prediction making. To evaluate the segmentation of the guidewire we used a stricter metric, the Hausdorff Distance (HD) which is more adapted to long, thin objects. The smaller the HD the better the segmentation. An average HD of 24. 26 pixels for native images of size 1024x1024 pixels was obtained. Qualitative results are available at this link: https://github. com/garancemartin/mie_results.
Among the respondents (102/254 students), 96% stated that ECHO is easy to use, 86% that it saves time, and 81% that it gives them a better overview of their activity. After several years of use, ECHO has solved many of the difficulties related to the use of internship paper booklets, while also providing a documentary database of the activities of our dental department. The student activity data stored in ECHO are directly accessible by faculty members and can be reused to facilitate departmental management and research and to improve patient follow-up.
Materials and Methods ECHO was developed following the analysis of a Unified Modeling Language (UML) model of the care-related (medical, administrative, and educational) procedures and data recorded as part of the pre-existing processes at the end of each care session. This new information system was designed to meet the following requirements: 1) to facilitate the quantitative and qualitative recording of all student activities; 2) to help monitor the completion of mandatory procedures; and 3) to allow for the validation of students' essential clinical skills. It also had to meet the requirements classically expected of computer registries, namely: a) to ensure short training time with easily navigable screens; b) to be web-based; c) to include drop-down menus and logic checks; d) to comply with General Data Protection Regulation and internet security standards; h) to allow for printing out summary data; i) to be linked to but not act as a substitute for electronic dental records (EDRs)  [5].
ECHO was registered with the Data Protection Officer (formerly named CIL -Commission Informatique et Liberté) of Timone Hospital under #2018-01. Its back-end was developed in PHP/MySQL with the open source MVC framework Laravel  [6] , and its front-end is based on the opensource framework Bootstrap  [7]. The database was designed using Laravel schema designer  [8] , which allows automatic generation of models and migrations tables for the Laravel framework. ECHO is hosted on an Apache server managed by the digital services department of Timone Hospital. In the months following the launch of ECHO, a qualitative evaluation of dental student perceptions of this information system was conducted using an anonymous online Google Form questionnaire. The questionnaire was available from 2018/03/13 to 2018/03/27.
Results According to the analysis of the UML model of care-related procedures and data, the pre-existing process of student evaluation was characterized by redundancy between administrative and educational data. Indeed, each type of data was collected separately on different media (i. e. , administrative data on paper forms and academic data in the internship booklet). ECHO was designed to centralize both types of data in a unified computerized process. After each session of care, the procedures performed are entered by the student in ECHO and validated by the practitioner/teacher who supervised the session of care. The practitioner/teacher assigns a qualitative grade and an autonomy grade for each procedure (from A to C); he/she can also provide a general appreciation of the care session in the free text field.
Each procedure is assigned a different weight in the overall grade. Students' skills acquisition is assessed based on the repeated performance of procedures. The possibility of creating non-billable procedures (including educational procedures), which are not listed in the national procedure codification system, allows for the evaluation of skills that are not necessarily linked to technical procedures. Figure  1  presents the simplified UML class diagram of ECHO. ECHO is linked to the hospital information system via dedicated web services, which facilitates the management of user rights access and helps to avoid data entry errors in terms of patient identity. Thanks to this link, users can directly launch and access patients' EDRs from ECHO without extra navigation. Thus, data entry can be performed on ECHO and then in patients' EDRs in a streamlined workflow.
Failure rate Data from the 6-year period was extracted from the clinic's DMI (Implantable Medical Devices) traceability software managed by the Pharmacy of the hospital. The following parameters were recorded by a hospital practitioner qualified in oral surgery: -Chronology of failure: date of implant placement and removal, date of diagnosis of the implant failure. -Type of failure: Poor osseointegration, loss of osseointegration, occlusal dysfunction, infection. The medical and dental history determined the early or late character of the implant failure. Occlusal analysis was only indicated for late failures after crown loading.
Skeletons (also called centerlines) of the extracted canals were computed using Thermo Scientific Avizo Software (v. 2019. 3, Thermo Fischer Science Inc. , MA, United States). The Euclidean distance to the closest surface is stored at each point of the skeleton. This is used to estimate the average pore thickness. From each segment of each skeleton, the angles theta and phi have been computed in order to quantify the spatial orientation of the canal system. Theta, the angle with respect to the long axis (z), can be between Frontiers in Bioengineering and Biotechnology | www. frontiersin. org 0 °and 90 °. In the plane perpendicular to z (x, y plane), phi can have values from -90 °to 90 °. An angle of 0 °indicates that the segment is parallel to the x-axis and thus to the bone surface (Figure  2 ).
Fluid Simulation The regions (VOIs) used for the analysis of the IPS orientation by analyzing the skeletons (described in Section 2. 4) were further used as input for a fluid simulation. In addition to the calculation of the absolute permeability via the Absolute Permeability Experiment Simulation module of Avizo software, the tensor of the absolute permeability was calculated via the Absolute Permeability Tensor Calculation module (Thermo Scientific Avizo Software, XLab Module, Version 2020. 2). Absolute permeability is defined as the measure of the ability of a porous material to transmit a single-phase liquid in units of darcy (d), with 1d = 0. 987 μm 2. With λ 1 , λ 2 and λ 3 being the permeability along X, Y and Z, respectively. Through Darcy's law, the fluid and flow were computed. To numerically estimate the absolute permeability, the Stokes equations are applied  (Bortel et al.
Statistics Normal distribution was tested graphically and statistically (Shapiro-Wilk test). Descriptive/explorative statistics and statistical hypothesis tests (ANOVA, Kruskal-Wallis test, Mann-Whitney test, t-test) with post-hoc testing (Bonferroni) were performed using IBM SPSS Statistics (version 25. 0, released in 2017, IBM Corp. , Armonk, NY, United States). p-values ≤ 0. 05 were considered statistically significant. No statistical analyses were performed for the arrangement of the IPS investigation due to the limited number of samples investigated (n = 3 pairs).
Surface Extraction The high spatial resolution of the CT scans allowed the exact representation of the vessel pores by a surface mesh (Supplementary Figure  SA2A ). Binning (x5) of the data shows that a lower resolution leads to a loss of small connections between vessels, such that a precise structure representation is not possible anymore (Supplementary Figure  SA2B ). The surface geometry of the complex threedimensional structure of the unbinned bone masks for all six VOIs is represented by triangle meshes (Supplementary Figure  SA3 ). The present study compared the patient-specific 3D-morphology of alveolar bone and iliac bone to further decipher the difference in graft morphology. At the time of bone augmentation, patient-matched biopsies were retrieved from the edentulous region of the alveolar bone and the iliac crest and were subjected to synchrotron radiation analysis.
3). All CT images were evaluated using DICOM viewing software (OsiriX v4. 1. 2, Geneva, Switzerland) in a bone-window and multiplanar reconstruction images were generated in the sagittal, dorsal and transverse planes. The size and bone density (Hounsfield units, HU) of both cystic cavities were quantified at each examination. Regions of interest (ROI) were defined in all three planes for each DC that were centred on the cystic cavities and avoided the mandibular cortices and roots of adjacent teeth (►Fig. 4). The mean HU values across both ROI were recorded at each examination (►Table 1). The bone density (HU) of "normal mandible" was calculated by defining a similar ROI in approximately the same location in a CT of another young, adult Boxer without any mandibular pathology. To obtain a base reference value of the biomaterial used  in this case at the time of implantation, a sample of pure DBM allograft was scanned and its mean HU value was similarly calculated.
Experimental Section The synthesis of the SGs is presented in detail in the Supporting Information. CQ, BisGMA, and TEGDMA were obtained from Sigma-Aldrich. Iodonium salt (speedcure 938) and ethyldimethylaminobenzoate (speedcure EDB) were gifts from Sartomer-Lambson (UK). The photopolymerization kinetics were followed by real-time fourier transform infrared spectroscopy (FTIR) spectroscopy (JASCO 6600) as presented in the previous works. [14, 15] The UV-vis spectra were recorded with a JASCO spectrophotometer (V730). The molecular modeling data (frontier orbitals, UV-vis spectra, and triplet state energy level) were obtained using the Gaussian 09 software at UB3LYP/6-31G* level. [21].
For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloudcomputing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.
However, these studies have poor statistical power due to the reduced population size, since they adopt either a single cohort analysis approach in  [11] [12] [13]  or a small-scale but straightforward analysis typically involving no more than four cohorts in  [14, 15]. The reduced quality and the structural heterogeneity of the existing cohort databases along with the lack of data curation pipelines obscure the development of robust AI models and the detection of biomarkers. According to the literature, the existing platforms and tools for data curation focus on the development of qualitative approaches, such as, the ExeTera software  [16]  which provides data filtering options based on semantic information, and the dementias platform UK (DPUK), where emphasis is given on the definition of standard data quality criteria  [17].
Regarding data harmonization, the existing tools are semi-automated and diseasespecific, focusing on the extensive collaboration of the clinician with the technical experts. The DataSHaPER  [18]  uses a DataSchema as a reference model to harmonize heterogeneous data structures through the manual definition of elements and rules for terminology mapping across biobanks. Furthermore, the BiobankConnect software  [19]  points particular emphasis on the application of lexical matching to identify lexically matched terminologies across biobanks. The SORTA tool  [20]  utilizes ontologies to align terminologies with conceptual similarity across diverse ontologies and particularly in biobanks. Apart from data harmonization, the lack of GDPR compliant and cross border data sharing mechanisms has a direct effect on the statistical power of the cohort studies.
The conventional data integration strategy, where patients' data from different clinical centers are integrated into a centralized database is not always feasible neither viable due to legal violations and security compromise attempts that will expose the patient data. The euroCAT platform  [21, 22]  offers a distributed learning framework for the development of multi-centric models through the installation of local servers on the hospital's premises. The PHT platform  [23]  adopts a similar methodology for distributed analysis through the training of distributed logistic regression models with adequate performance. In another study, four cohorts were analyzed in a distributed manner yielding lymphoma classification models with more than 85% sensitivity and specificity but with reduced statistical power  [15].
Thus, the existence of a platform which adopts a federated data management strategy that avoids the installation of local servers in the hospitals' premises and enables the sharing of sensitive data from multiple cohorts with heterogeneous structure remains a crucial challenge. Towards this direction, we present the HarmonicSS platform, a highly scalable and GDPR compliant cloud computing infrastructure which offers beyond the state-of-the-art services for federated data storage, curation, and harmonization, as well as, trustworthy and explainable federated AI (Artificial Intelligence) modeling workflows, which are in line with the EU data protection regulations for novel infrastructures, data spaces, data platforms and AI tools  [24, 25]. Τhe platform was developed under the HarmonicSS EU funded project (HARMONIzation and integrative analysis of regional, national and international Cohorts on primary Sjögren's Syndrome (pSS) towards improved stratification, treatment and health policy making)  [7, [9] [10] [11] [12] 14, 15]  and removes the need for the installation of local servers or any type of software in each site through the adoption of a federated data management platform which supports a large family of federated AI algorithms yielding interpretable and explainable AI models.
Overview The HarmonicSS platform includes a wealth of harmonized cohort databases on top of which the core modules operate. The main architectural components (or core modules) of the Har-monicSS platform have been designed according to a hierarchical, top-down approach (Fig. 1 ) and have been grouped into three layers, namely: (i) the input layer, (ii) the cohort data management layer, and (iii) the cohort data analytics layer. The main users of the HarmonicSS platform are the data provider and the data pro-cessor. The data provider interacts with the data management layer which is located on the top of the architecture and includes: (i) the data sharing assessment module, and (ii) the data sharing management module. On the other hand, the data processor interacts with the data analytics layer which is located at the bottom of the architecture and includes: (i) the cohort data harmonization module, (ii) the federated AI analytics module, and (iii) the visual analytics and user interfaces module.
Depending on the type of functionality each layer offers, the modules can be also grouped into two main categories, namely the cohort data governance modules and the cohort data analytics modules. The former includes the data sharing assessment and data sharing management modules, whereas the cohort data analytics modules include the cohort data harmonization, federated AI analytics, and visual analytics and user interfaces modules. The data sharing assessment module ensures the GDPR (General Data Protection Regulation) compliance of the cohort data through the evaluation of the legal and ethical documents of the data providers who are interested in sharing their data with the HarmonicSS private cloud repositories. Once the GDPR compliance is ensured, a data curation workflow is applied to enhance the quality of the cohort data in terms of accuracy, relevance, and completeness.
The data sharing management module offers a ''handshaking" process which controls the access of the data analytics services into the private cloud databases. The cohort data harmonization module includes a series of mechanisms for the alignment of the heterogeneous structures of the curated cohort data according to the pSS reference model using semantic matching methods for cohort data transformation and ontology alignment based on a knowledge base. The federated AI analytics module supports the training of supervised machine learning algorithms across the federated cloud databases towards the construction of explainable and trustworthy AI models which are validated on a series of federated and harmonized testing cohort databases. The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module.
The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services, including 3D visualization methods based on graph theory and query-based descriptive statistics for subgroup analysis, as well as, prospective data entry forms which have been properly designed to enable the inclusion of prospective patient data automatically interlinked with the corresponding harmonized retrospective data on the federated databases. The HarmonicSS federated database management system is compatible with HL-7 (Health Level 7) data exchange protocols, including the FHIR (Fast Healthcare Interoperability Resources)  [26]  which enhances its adaptability and enables the interlinking of the HarmonicSS data with existing FHIR database management systems. In addition, the harmonized cohort data meet the principles of findability, accessibility, interoperability, and usability (FAIR)  [27]  and thus can serve as an interconnection hub with EOSC (European Open Science Cloud) initiatives and European data spaces for hosting and processing to support research in autoimmune diseases.
Secure sharing of the cohort data Upon the GDPR compliance of the DPIA and DPA documents, the pseudonymized patient data are uploaded into secure private data-bases within the Greek Research and Technology Network (GRNET) cloud infrastructure. The NextCloud  [32]  file hosting service was used to develop the federated database management system which was built on top of the cloud infrastructure to provide private cloud databases (and private cloud spaces, as well) for each data provider, as well as, to ensure the secure access of the federated data analytics services to each private cloud database. Through the NextCloud  [32] , the services of the data sharing assessment module can be easily integrated into any cloud infrastructure that fulfills the legal and ethical criteria for data sharing.