dataesr/scientific-paragraphs-categorization huggingface dataset:
x FOR PEER REVIEW 15 of 20 decrease in the temperature at the root of nose is due to the presence of the nasal bone directly underneath the skin and not to blood capillaries  [40]. The result of detecting the inner canthus temperature and displaying it on the image along with the bounding box is implemented in MATLAB R2021a and shown in Figure  17. Now, in some cases, the inner canthi of both eyes are detected simultaneously since they both have exactly the same temperature. The process of detecting the inner canthus temperature requires around 0. 09 s. The normalized average distance between the automatically detected inner canthus point and the manually located  is calculated using Equation (  7 ), The result of detecting the inner canthus temperature and displaying it on the image along with the bounding box is implemented in MATLAB R2021a and shown in Figure  17.
Now, in some cases, the inner canthi of both eyes are detected simultaneously since they both have exactly the same temperature. The process of detecting the inner canthus temperature requires around 0. 09 s. The normalized average distance between the automatically detected inner canthus point and the manually located (d) is calculated using Equation  (7) , and found to be equal to 0. 03. The result of detecting the inner canthus temperature and displaying it on along with the bounding box is implemented in MATLAB R2021a and shown in Now, in some cases, the inner canthi of both eyes are detected simultaneously both have exactly the same temperature. The process of detecting the inner ca perature requires around 0. 09 s. The normalized average distance between the au detected inner canthus point and the manually located  is calculated using E and found to be equal to 0.
The model is set after training and testing different YOLO versions using different datasets. The multi-age dataset is a combination of our own collected dataset and the online TFW dataset with the application of several augmentation techniques (1827 images). Nevertheless, the elderly dataset is a combination of images from three different datasets: TFW, Tufts, and IRDatabase (656 images). This provides the most diverse and large thermal faces dataset in the literature, with age classification. Our model results in precision and recall equal to 100%. However, the second largest thermal face dataset found in the literature (1000 images) results in precision and recall of 98. 8% and 92. 6%, respectively, when fed to a cascade classifier after extracting HoG features  [12]. Besides the size of our used dataset, it also contains different face poses, enabling the model to identify the eye region in straight-facing images and in rotated-facing images (Figure  18 ).
This is considered a limitation in the system developed by Lazri et al. [18]. In the case of the rotated photos, the detection of the temperature of one inner canthus is sufficient to measure the person's temperature. This is because, as shown in the graph of Figure  16 , there is only a small variation between the temperatures of each inner canthus (0. 14 • C). The augmented multi-age YOLOv7 model successfully detects the eyes in a near face and a far face image, as presented in Figure  17. when fed to a cascade classifier after extracting HoG features  [12]. Besides the size of our used dataset, it also contains different face poses, enabling the model to identify the eye region in straight-facing images and in rotated-facing images (Figure  18 ). This is considered a limitation in the system developed by Lazri et al.
Elderly images gathered from three different datasets were used to train these models. In order to test the effect of dataset size on model performance, we trained one model using the original elderly dataset and another one using the augmented elderly dataset. Both trainings started from original YOLOv7 weights. Table  1  shows that the dataset augmentation increases in %mAP@. 5 by 0. 3%. This is expected, since better deep learning models are built using augmented datasets, as mentioned in  [26]. Another approach in establishing the elderly eye detection model is the application of transfer learning to the augmented multi-age YOLOv7 model. This is considered beneficial since it uses the knowledge of somehow similarly pre-trained models to train and fine-tune the new model, which leads to better performance  [33].
In order to test the effect of dataset size on model performance, we trained one model using the original elderly dataset and another one using the augmented elderly dataset. Both trainings started from original YOLOv7 weights. Table  1  shows that the dataset augmentation increases in %mAP@. 5 by 0. 3%. This is expected, since better deep learning models are built using augmented datasets, as mentioned in  [26]. Another approach in establishing the elderly eye detection model is the application of transfer learning to the augmented multi-age YOLOv7 model. This is considered beneficial since it uses the knowledge of somehow similarly pre-trained models to train and fine-tune the new model, which leads to better performance  [33]. Table  1  shows how the model trained on the original elderly dataset without augmentation, starting from the weights of YOLOv7 augmented multi-age dataset, performed better (99.
5%) than the mode trained starting from the original YOLOv7 weights (99. 3%). However, upon using the augmented elderly dataset, there was no difference observed in the %mAP@. 5 scores between both models. Furthermore, the results of blindfold testing are promising (99. 6%); this proved the generality of the model and its ability to perform well in real case scenarios wherein new individuals are going to be presented to the model. Our model detects the inner canthus with an acceptable normalized distance of 0. 03 with the manually detected method. Despite this shift, we are still in the region of inner canthus and managed to detect the highest temperature in that region, as presented in Section 3 (Figure  17 ). Additionally, our work could be implemented in a real-time temperature monitoring system, since we were able to detect the eyes in a video scene with a high speed, reaching 150 FPS in the trained YOLOv7 model, as shown in Figure  19.
Conclusions and Perspectives This paper presents a fast and reliable model that accurately detects the eye region after training and testing different YOLO versions using multi-age and elderly thermal image datasets. Recruiting a thermal camera-based system protects the user's privacy and is a cheaper solution than visible cameras. This proposed system also allows the comparison of size, speed and accuracy between different recently released object detection YOLO versions: YOLOv5, v6 and v7. In addition, the presence of different head poses in the training dataset and the implementation of data augmentation methods allow the model to detect the eyes and temperature of a person in different orientations. With the aim of frailty and infectious diseases detection, our approach is the first to gather images of the elderly exclusively to build a temperature detection model.
The proposed system could be considered a solution for speed, accuracy, and image condition limitations in temperature detection methods, especially during an epidemic. It also can be adapted by hospitals, retirement homes, and homes. Looking ahead, further studies will be done in order to ensure the accurate measurement of temperature by considering emissivity, distance effect, camera angle and face angle effects, as well as the impact of ambient temperature  [20]. Additionally, we aim to implement this work in real-time, due to the high speed of YOLOv7, in order to allow the installation of the system in infirmaries and elderly care homes. This will be done after implementing an alarm system which will be raised when fever is detected. Moreover, although the databases used are considered various and have inter and intra differences, it is important to include a more significant number of individuals to produce more generalized model.
Data Availability Statement: The TFW, Tufts, and IRDatabase used and analyzed in this study can be found at http://tdface. ece. tufts. edu/ (accessed on 30 September 2022), https://github. com/IS2AI/ TFW (accessed on 5 July 2022), https://github. com/marcinkopaczka/thermalfaceproject (accessed on 25 April 2022).
2. 1. 2 [33] , and ST was called using SRST2 v. 0. 2. 0  [34]  and the S. agalactiae MLST database at https://pubmlst. org/organisms/ streptococcus-agalactiae (accessed on 27 March 2023)  [19, 35]. In silico serotyping was conducted using GBS-SBG (GBS serotyping by genome sequencing)  [20]  and the method developed by Metcalf and colleagues  [36] , with confirmation using the method of Sheppard and colleagues when needed  [37]. The presence of phage integrases was determined using the method of Crestani and colleagues  [38] , whereas presence of tetracycline resistance (TcR) genes and virulence genes scpB-lmb was determined using BLAST. Panaroo  [39]  and IQtree  [40]  were used to generate a maximum likelihood phylogeny for the isolates included in this study. The geographic origin of the isolates was mapped using ggplot in RStudio using R (v4.
Funding: The collaboration culminating in this study was funded by the  Academy of Medical Sciences, UK , through a  Global Challenges Research Fund (GCRF)   Networking Grant  (to N. N. P. and R. N. Z. ); by the  University of Glasgow's GCRF Small Grants Fund , an initiative supported by an allocation of  Global Challenges Research Fund  from the  Scottish Funding Council  under agreement  SFC/AN/10/2018  (to R. N. Z. ); and by the  University of Glasgow College of Medical ,  Veterinary and Life Sciences Doctoral Training Programme 2017-2021  (to C. C. ). S. C. was supported by the  National Medical Research Council, Ministry of Health, Singapore  (grants  NMRC/CIRG/1467/2017  and  CIRG19NOV-0024 ); the  Temasek Foundation Innovates through its Singapore Millennium Foundation   Research Grant Programme ; and the  Genome Institute of Singapore (GIS)/Agency for Science, Technology and Research (A*STAR ).
Tandem Mass Spectrometry Proteotyping Tandem mass spectrometry proteotyping allows the identification of organisms based on peptide sequences established, without any a priori information, by metaproteomic query of a generalist protein sequence database  [22]. For this, proteins extracted from the sample were proteolyzed and the resulting peptides were analyzed by tandem mass spectrometry. A total of 6,731 high-resolution MS/MS spectra were acquired on peptides in a unique dataset. This is a relatively low number, because 10 times more spectra can be obtained under similar conditions for a sample rich in proteins, but the initial sample was available in a very small quantity. The spectra were queried to identify the possible organisms present in the sample at the different possible taxonomical ranks, using the same procedure as previously used for medical samples without a metagenomics-derived database  [9].
The wide taxonomical repertoire was obtained through a cascade of searches, starting from the extra-large NCBInr database and narrowing down successively to the most representative database for the specific sample. The final query resulted in peptide sequence assignment for 968 MS/MS spectra, i. e. , 14% of spectra interpreted. Taxonomical information was assigned to each of these spectra; i. e. , the list of taxa whose genome encodes a given peptide sequence was established at all possible taxonomical ranks. The results are displayed in Table  1. The distribution of the resulting 357 peptide sequences and 968 taxon-spectrum matches (TSMs) on the different branches of the Tree of Life indicated the presence of biological material originating from Eukaryota and Bacteria. A total of 301 peptides specifically found in Eukaryota theoretical proteomes and 25 peptides specific to Bacteria were identified.
Therefore, these debris and dust may comprise mainly the fungal spores that were able to detach from the organ surface, enhancing this specific type of organic biomass, and keratins brought by operators during excision, sampling and cardiotaph insertion. Another limit regarding our results depends on the mass spectrometry proteotyping approach, for which the precision regarding the identified species depends on the number of sequenced genomes available for the closely related species. For example, T. mentagrophytes is the best option amongst Trichophyton, but only 14 species have been genome sequenced so far for this genus, while 32 species have been taxonomically defined. As we have used one of the most advanced tandem mass spectrometers currently available for protein analysis, we believe that the resulting metaproteomic dataset is of high quality and could be further reanalyzed in the future once more genome sequences are available.
Acknowledgments: J. A. acknowledges the support by the  French National Agency for Research (Agence Nationale de la Recherche , grant  ANR-20-CE34-0012 ) and the  Région Occitanie  (grant  21023526-DeepMicro ) for metaproteomics technological development on the ProGénoMix platform. Ex vivo micro-CT imaging was performed at the  Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant  by the  FRM  grant  FRM DGE20111123012 ), supported by  France Life Imaging  (grant  ANR-11-INBS-0006 ).
They were inoculated into 24-well plates with a volume of 500 µl and a density of 1 × 10 5 cells per well and incubated at 37 °C and 5% CO 2 until use. The Alpha strain (20I/501Y B. 1. 1. 7 lineage) of SARS-CoV-2 that was used in all the experiments was sequenced and deposited at GISAID (https:// www. gisaid. org/) (accession number: EPI_ISL_1707039). The infectious viral titre, determined with the Reed and Muench method  16  , was expressed in TCID 50. The virus stock was used at the concentration of 10 5,5 TCID 50 /150 µl, which corresponds to the average viral concentration that can be found in a patient recently infected by SARS-CoV-2  17, 18. Design of the study and experimental protocol. All the experiments using infectious material were performed in a biosafety level 3 laboratory. Three different surfaces were selected to assess the virus inactivation ability of UV-C: semi-transparent porous cloth (disposable non-spun 20 g/m 2 gowns, ref 938481, Prop, France), semi-transparent non porous plastic (aprons, ref: 161105, Euromedis, France) and opaque non-porous stainless-steel holders (ref: Inox 316L, Cellux, France).
Results Inactivation of SARS-CoV-2 on non-porous surfaces. Data obtained on non-porous surfaces (apron or stainless-steel supports) showed an efficiency of UV-C on the inactivation of viral replication using an infectious concentration of 1. 5 × 10 5. 5 50% tissue culture infective doses (TCID 50 ) per150 µl, from 3 s of irradiation (2. 5 mJ/cm 2 for stainless-steel, 3. 4 mJ/cm 2 for apron). Indeed, in cell culture, no cytopathic effect was observed regardless of the irradiation time applied to the two supports. The supernatants were also tested with PCR technique: a rise in cycle threshold (C T ) value is representative of a decrease in concentration of viral genomic material sensitive to the primers. For the apron samples, C T ranged from 28. 1 to 32. 6 for irradiation times of 3 to 30 s respectively (Fig.
Nevertheless, the data from viral culture prove that the virus is well inactivated. On the gown sample, a minimum of 1 min treatment was shown to be necessary for generating no signal in PCR. However, a minimal irradiation time of 3 s, and enabling reduction of over 6Log 10 of the viral load, seems adequate for the decontamination of most objects. The research was conducted using Alpha strain. Results are expected to be equivalent with other strains. In the present study, the apparent UV-C sensitivity was shown to be much higher than previously reported (Table  2 ), with Z value > 0. 5820 m 2 /J. This value is at least 3 times higher than reported by Biasin et al. 4  , Storm et al. 9  , Ma et al. 14  and Martínez-Antón et al. 21. Experimental conditions of these studies are very different, as they are all using a nearly planar illumination: Storm et al.
in genome No of pyrimidine doublets No of purine/pyrimidine/pyrimidine SARS-CoV-2/human/USA/USA-WA1/2020 GenBank: MT246667. 1 15,086 7597 7676 Alpha SARS-CoV-2/human/USA/GA-CDC-4201216-001/2021 GenBank: 0K045638. 1 15,071 7590 7668 Difference to USA-WA1 0. 10% 0. 09% 0. 10% Delta SARS-CoV-2/human/Colombia/CO-SAN-UW21120626151/2021 GenBank: 0N150700. 1 15,014 7554 7601 Difference to USA-WA1 0. 48% 0. 57% 0. 98% Difference to Alpha 0. 38% 0. 47% 0. 87% XBB. 1. 16 SARS-CoV-2/human/THA/ MTM_01_682/2023 GenBank: 0Q946922. 1 14,950 7519 7612 Difference to USA-WA1 0. 90% 1. 03% 0. 83% Difference to Alpha 0. 80% 0. 94% 0. 73%.
05% Tween-20 (Sigma Aldrich) in saline for 20 min and stained for 30 min using a 50 μL Sytox Green (Life Technologies) in 1X PBS buffer pipetted over each contaminated side of the PDMS. The PDMS was imaged by fluorescence microscopy (Olympus BX63, Japan) using 20X and 50X air objectives, and a GPF filter (λ ex /λ em 395/470 nm). Image analysis, image filtering, and cell counts were performed with the Olympus cellSens imaging software. Surface characterization. Surface roughness and topography maps were obtained using a non-contact three dimensional Optical profilometer (Bruker Contour GT-K, Tucson, AZ, USA). After calibrating the system scanner, the sample was placed on the microscope stage and the camera was adjusted to focus on the surface microstructure by raising or lowering the z-axis until the appearance then disappearance of two sets of fringes.
Methods This retrospective multicenter clinical study was carried out in full compliance with the Declaration of Helsinki of the World Medical Association, with the ISO 14155:2020, the Regulation EU 2017/745, Law #2016-41, Law #2019-774, and registered on the HDH & INDS platform. This study included patients treated for a single or multiple implant-supported rehabilitation with sandblasted and acid-etched dental implants (KONTACT TM S, Biotech Dental, France) in five private dental centers between 2016 and March 2019 by six experienced surgeons. The data collection ended in 2021, ensuring a minimum of one-year follow-up for each patient. Implants included in this study were sandblasted and acid-etched implants in titanium grade 4 (T60 4B) with a diameter and length of 3. 0 mm and 5. 4 mm and 6 mm and 16 mm, respectively.
16  Implants are considered failed if they need to be removed and replaced. Cumulative implant survival was evaluated at 12, 18, 24, and 48 months with a Kaplan-Meier analysis performed with IBM SPSS Version 27. 0 (Armonk, NY: IBM Corp). The last implant follow-up (50 months) was used as the time for censoring for Kaplan-Meier analysis. The primary stability was evaluated as the maximal insertion torque at the time of implantation. A minimal insertion torque of 15 N. cm was considered in this study to establish sufficient primary stability. Moreover, the marginal bone loss was measured from the radiographs during the follow-up. Descriptive statistics (means and standard deviations) were performed with Microsoft Excel (Version 2104). To investigate the effect of bone quality, the survival rate was evaluated for each bone type and compared.
Electron spin resonance -spin trapping (ESR-ST) The test solution was made by dissolving the PI/Iod or PI/EDB in tert-butylbenzene (Iod = 2 mg/mL, EDB = 2 mg/mL and PI = 0. 2 mg/mL). After nitrogen saturation at room temperature, the solution was irradiated using a light source of LED@405nm. N-tert-butylbenzonitrone (PBN) was used as a spin trap (PBN = 2 mg/mL). The free radicals formed can be observed by an X-band spectrometer (Bruker EMXplus). The ESR spectral simulation was performed by the PES WINSIM software. [50].
Methods: This protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Protocols) checklist. Literature will be searched in the following databases: PubMed, Cochrane Library, Web of Science, CINAHL, Embase, and PsycINFO. We will perform additional searches on Google, Google Scholar, and ProQuest Dissertations & Theses Global, screen the references of the included studies to capture additional relevant studies, and contact the authors of studies if we need more details. We will consider studies using qualitative, quantitative, and mixed methods. There will be no restrictions on the publication date and dental setting. We will include studies published in French, English, and Portuguese. Two independent reviewers will select the study, extract data, and assess methodological quality using the Mixed Methods Appraisal Tool's checklist.
Outcomes We will include studies with different synonyms related to teledentistry implementation such as adoption, usage, uptake, readiness, and intent to its use. We will include studies reporting factors that could influence teledentistry implementation as reported by DHCPs; for instance, knowledge, intention, behavior, self-efficacy, satisfaction, skills, social influences, environmental context, and resources. We will include studies where data are collected using a variety of techniques, including individual interviews, focus groups, or questionnaires. We will include studies using validated or nonvalidated measurement tools to collect data. In this review, we will consider an enabler as any circumstance, factor, or variable that positively influences teledentistry implementation. A barrier will refer to any circumstance, factor, or variable preventing teledentistry implementation.
Information Sources and Search Strategy A health science information specialist will assist us in developing the literature search strategy in PubMed and will translate it after validation and revision in other electronic databases. A second health science information specialist will review the initial search strategy with the Peer Review of Electronic Search Strategies Tool  [45]. The main concepts considered in the search strategy are "teledentistry," "telemedicine oral health," or "remote dental care," "remote oral care," "virtual dental care," or "virtual oral care" and "dental health care providers," "health personnel," or "dentistry. " The electronic search will be carried out to identify all relevant studies from the following databases: PubMed, Cochrane Library, Web of Science, CINAHL, Embase, and PsycINFO.
Data Extraction Two reviewers will extract the data independently using an Excel (Microsoft Corp) data extraction sheet. A calibration exercise will be independently performed by both reviewers on 5% of the included studies. The data extraction sheet will be modified and revised as necessary during the process of extracting data. Consistent with the abovementioned steps, data charting will be considered an iterative process as the review develops. Any disagreements that arise between the reviewers will be resolved through discussion and consensus, or with the consultation of a third reviewer. The draft extraction form is provided in Multimedia Appendix 2. The following data will be extracted:.
• Characteristics of the included studies (eg, title, main author, year of publication, country of publication, language of publication, aims, funding, type of publication, study design, dental care settings, use of a theoretical framework, name of the theoretical framework, methods of data collection, limitations, and conclusions) • Participant characteristics (eg, profile, sample size, sex, gender, age, general dentist or specialist, type of specialty, type of practice, and region of practice) • Characteristics of teledentistry (eg, synchronous or asynchronous) • Study outcomes (eg, knowledge, practices, beliefs, self-efficacy, time, compliance, behavior, teledentistry adoption and use, training, reimbursement, and equipment). We will contact the authors if we need additional information on the study.