dataesr/scientific-paragraphs-categorization huggingface dataset:
Image reconstruction was performed using the NRECON ® software v1. 7. 4. 6. The CTAn ® software v1. 20. 8. 0+(64-bit) was used for image analysis and the DataViewer ® software v1. 6. 0. 0(64-bit) (orientation, VOI selection) for 3D visualization of the scanned areas. Quantitative analysis of the bone content (Tissue volume TV, Bone volume BV, and BV/TV ratio) into a standardized region of interest (VOI) slightly larger than the bone tunnel (VOI: 225 × 225 × 750 vx; 5. 85 × 5. 85 × 19. 5 mm) was determined using both 2D and 3D measurements. The calculation of other parameters included trabecular thickness (Tb. Th, µm), trabecular separation (Tb. Sp, µm) and trabeculae number (Tb. N). The results obtained for all parameters were compared with each other in the two conditions of implantation in the study, HBS versus HBS blood composite, and a control untreated selected bone of an equivalent VOI.
1 software, Zeiss, Jena, Germany). After an appropriate thresholding using the ImageJ Fiji software v1. 54b to differentiate the newly formed bone from the implanted cement, quantitative SEM analysis was made to determine the respective amount of remaining cement into a selected area. Quantitative analyses were performed with DragonFly (ORS, Montréal, QC, Canada) version 2022. 1. 0. 1231 using a deep learning module for realistic and accurate quantification of bone tissue. Dragonfly provides solutions for advanced image processing, segmentation, and quantification with higher accuracy compared to human experts executing the same task  [16].
Histological Analyses In accordance with our routine protocol  [17] , undecalcified 7 µm-thick sections of each sample were obtained using a hard tissue microtome (HistoCore Autocut R, Leica Biosystems, Nussloch, Germany) and were then mounted on an adhesive tape (Histfilm, Watson Biolab, Kobe, Japan) to avoid stripping. Sections were observed using hematoxylineosin (HE) staining, Goldner's trichrome, and Movat's pentachrome stains. The latter was perfectly adapted to distinguish mineralized bone (yellow to green), osteoid tissue (red), and cement (blue). Quantitative histological analyses were performed to determine the proportion between bone and cement into a selected area thanks to Weka Segmentation using the ImageJ Fiji software.
Microcomputed Tomography (µ-CT) Analysis Orientation and region of interest (VOI) selection with the DataViewer ® software permitted us to select a 225 × 225 × 750 vx zone, which was analyzed with the CTAn ® software. The quantitative analysis of the bone content (Tissue volume TV, Bone volume (BV), BV/TV (%) and trabecular parameters are summarized in Table  4. A control VOI in the surrounding trabecular bone was also analyzed and compared to both conditions. Micro-CT allowed us to distinguish both cements and to select a representative VOI for quantitative analysis (Figure  9 ). The HBS cement remained dense 4 months after implantation with minimal resorption whereas the HBS blood composite showed a high level of remodeling with a noticeable induced macroporosity. Grey levels from the cements were very close to those observed in the surrounding host bone.
That is the reason why results concerning tissue volume, bone volume, and the ratio BV/TV were not relevant as the CTAn ® software was not able to clearly differentiate bone from the implanted cements. In fact, the BV measurement included both the remaining cement and the newly formed bone inside the bone tunnel without any significant difference between the two types of cements. Although slightly lower for the HBS blood composite, thus suggesting a higher cement resorption of this cement, the BV/TV ratios were not significantly different between the two cements. More interestingly, trabecular parameters Tb. Th and Tb. N were similar between the newly formed bone associated with the HBS blood composite and the control untreated bone equivalent VOI. A significant difference in Tb. Th was observed between the two cement conditions and the control bone areas (p < 0.
Grey levels from the cements were very close to those observed in the surrounding host bone. That is the reason why results concerning tissue volume, bone volume, and the ratio BV/TV were not relevant as the CTAn ® software was not able to clearly differentiate bone from the implanted cements. In fact, the BV measurement included both the remaining cement and the newly formed bone inside the bone tunnel without any significant difference between the two types of cements. Although slightly lower for the HBS blood composite, thus suggesting a higher cement resorption of this cement, the BV/TV ratios were not significantly different between the two cements. More interestingly, trabecular parameters Tb. Th and Tb. N were similar between the newly formed bone associated with the HBS blood composite and the control untreated bone equivalent VOI.
Sulci depth Measurements of parieto-occipital sulcus, cingulate sulcus and calcarine sulcus depth were performed offline using HOROS™ Project medical image viewer software version 3. 3. 6 (LGPL-3. 0; horosproject. org) for T2-weighted sequences with a 3. 5-mm slice thickness. Laterality was assessed by determining the fetal position in utero. Anatomic planes for measurements were assessed using the ISUOG guidelines for sonographic examination of the fetal CNS  29, 30. Measurement of sulcal and Sylvian fissure depth (in mm) in both hemispheres was carried out by tracing a perpendicular line from the midline towards the border of the specific sulcus as described previously by Hahner et al. 27. To ensure a 90 • angle, lines were traced using the HOROS software perpendicular lines tool. A straight line was traced from the frontal bone to the occipital bone in the axial plane and vertically from the vault to the skull base in the coronal plane.
Statistical analysis Data were compared between healthy fetuses and fetuses with CMV infection, separately for those with no abnormalities seen on US/MRI (unaffected) and those with mild CNS and/or extra-CNS abnormalities seen (mildly affected). Quantitative variables were assessed for normality using the Shapiro-Wilk test, and normally distributed variables were compared using the t-test and expressed as mean ± SD. Non-normally distributed quantitative variables were compared using the Mann-Whitney U-test and expressed as median and interquartile range (IQR: 25 th -75 th percentiles). Qualitative variables were compared using the χ-square or Fisher's exact test. To correct for fetal head size, sulcal, insular and SF depth and SFA measurements were adjusted by BPD. For quantitative brain variables, linear regression analysis adjusted for ipsilateral ventricular width and gestational age at MRI was performed.
ACKNOWLEDGMENTS We are indebted to the  Radiology services of Hospital Clínic de Barcelona and H ôpital Louis-Mourier  and their technicians. This project has been partially funded with support of the  Erasmus + Programme  from the  European Union  (Framework Agreement number:  2013-0040 ). This publication (communication) reflects the views only of the authors, and the  Erasmus Mundus Commission  cannot be held responsible for any use that may be made of the information contained therein. Additionally, the research leading to these results has received funding from  'la Caixa' Foundation  ( LCF/PR/GN14/10270005 ),  Cerebra Foundation  for the  Brain Injured Child  ( Carmarthen, Wales, UK ) and AGAUR 2017 SGR grant No  1531. A. L. M. -E. and A. H. -V. have received financial support from the  Secretaría Nacional de Ciencia y Tecnología de Panam á (SENACYT)  grant No.
Background/Objectives: The mainstay treatment of cow's milk allergy (CMA) is to remove cow's milk proteins from children's dietary intake. In this context, dietary intake of children with CMA should be particularly checked and monitored. The objective of this study was to assess the applicability, usability, and accuracy of a new dietary intake (DI) assessment online tool (Nutri-cate© online application) for managing CMA in children. Subjects/Methods: This study used a pre-existing database of DI from the Nutricate© online application. DIs from 30 CMA children were used to compare micro/macronutrients (energy, protein, calcium, and iron intakes) calculated by Nutricate© and NutriLog© as the reference method. Comparisons were performed using the Pearson correlation analysis and the Bland-Altman plot.
The Nutricate© tool usability was assessed via a System Usability Scale questionnaire (SUSq). Results: Correlation coefficient between the levels of micro/macronutrients obtained by Nutrilog© and Nutricate© software were highly significant (p = 0. 0001) and were well-correlated (R coefficient > 0. 6), indicating a very good concordance between the two methods. This observation was reinforced by the Bland-Altman plot, indicating the absence of proportional or fixed bias for energy, protein, calcium, and iron intakes. The mean SUSq score obtained was 81 ± 14, which is considered to be an excellent score. Conclusions: Nutricate© online application is a reliable method to assess micro/macronutrient (energy, protein, calcium, and iron intakes) intake in CMA children. Applicability and usability of this new dietary intake assessment online tool is excellent.
Sample The Nutricate© web application was launched online in January 2020. Since the launch, the dietary intake data of 226 children with cow's milk allergy were entered by either the PCPs or the parents. For the purpose of this study, between November 2020 and May 2022, the dietary intake data entered by volunteer parents of 30 children with cow's milk allergy were used. The time lapse between the age at diagnosis and this present analysis was, on average, 40. 4 ± 24. 5 months. All children and their parents participating in this study were screened and included by the same PCP (FV). Given this research was not interventional or meant to improve biological or medical conditions, but rather relied on using a pre-existing database, the present study was therefore not considered interventional research, according to French regulatory requirement ("Jardé" French law).
In this context, this study did not require any approval from an ethics committee  [21]. This study was, however, declared and approved by the French commission in charge of the safeguard of personal data protection (Commission Nationale de l'Informatique et des Libertés) under the reference CNIL Nutricate_Kids HCL 21_5238/2020. Dataset related to anthropometrics and clinical phenotypes (other allergies and symptoms) of the 30 participants with cow's milk allergy were anonymously collected by one PCP (FV) on a secure Excel file. An audit of the complete dataset was performed before statistical analysis.
Participants' Anthropometric Characteristics Body weight was measured by using an electronic scale to the nearest 0. 1 kg while ensuring the participant was wearing light clothes without shoes. Height was measured, without shoes, by using a standard physician's scale to the nearest 0. 1 cm. Body mass index (BMI) was calculated by taking the participant's weight in kilograms divided by the square of the height in meters (kg/m 2 ). Z-score for weight-for-age, height-for-age, and BMI-for-age were classified according to the WHO's classification  [22]  by using an anthropometric Z-score calculator R package  [23]. Anthropometric characteristics and clinical phenotypic data related to the 30 children with cow's milk allergy are presented on Table  1. Participants were well-balanced according to gender, and Z-scores were close to the normality.
Nutricate© Software Nutricate© is a user-friendly web application developed by Nutricia (LS) by adhering to guidelines related to the data collection of dietary information, with the goal of estimating the levels of some nutrient intakes  [24]. The use of subject-based computerized food data intake was previously used in the HELENA study  [25]. The Nutricate© web application collects dietary intake data (infant formula, nutritional supplements, and foods and beverages) based on the 24-hour dietary recall (24HDR) method. Parents were asked to report the daily average of food intake over a one-week period prior to the collection date. The 24HDR collection frame was presented based on a standardized meal sequence of breakfast, lunch, and dinner (Figure  1a ). This 24HDR data collection frame was previously used by  Thompson et al.
[26]. Each meal sequence presented pictures of increasing portion size (Figure  1b ) using the same process developed by Hercberg et al. [27]  containing pictures of several foods and beverages  [27, 28]. After 24HDR completion and validation by parents, the Nutricate© web application automatically generated a password. This password allowed access to an individual dietary report with specific macro/micronutrient intake values (Table  2 ). The quantities of each food and beverage category reported in the Nutricate© software was automatically linked to the French food composition database (CIQUAL/ANSES food table; https://ciqual. anses. fr/ accessed on the 10 July 2020). In order to present a short list of foods, some foods were merged into one category (presented in Supplementary Table  S1  with their correspondence in the French food composition database).
For instance, all animal-protein-rich foods (i. e. , white/red meat, fish, ham, and egg) were merged into one category. The repartition of all animal-protein-rich foods was computed according to one category, similar to the repartition used in the INCA study of French infants (0-6 years)  [29]. In this context, Supplementary Table  S1  presents correspondence between some created, merged food categories and their macronutrient contents. Supplementary Table  S1  presents present data for animal-protein-rich foods, starch, vegetables, and fruits. asked to report the daily average of food intake over a one-week period prior to the collection date. The 24HDR collection frame was presented based on a standardized meal sequence of breakfast, lunch, and dinner (Figure  1a ). This 24HDR data collection frame was previously used by Thompson et al.
[26]. Each meal sequence presented pictures of increasing portion size (Figure  1b ) using the same process developed by Hercberg et al. [27]  containing pictures of several foods and beverages  [27, 28]. After 24HDR completion and validation by parents, the Nutricate© web application automatically generated a password. This password allowed access to an individual dietary report with specific macro/micronutrient intake values (Table  2 ). The quantities of each food and beverage category reported in the Nutricate© software was automatically linked to the French food composition database (CIQUAL/ANSES food table; https://ciqual. anses. fr/ accessed on the 10th July 2020). In order to present a short list of foods, some foods were merged into one category (presented in Supplementary Table  S1  with their correspondence in the French food composition database).
The Nutricate© web application was initially designed by a dietician (LS) and internal project managers, software engineers, and informatic programmers/developers. In this context, the use of the System Usability Scale questionnaire is very relevant to test the usability, agility, and comfort of the new Nutricate© web application. There are many benefits to using the System Usability Scale questionnaire because this questionnaire is very easy to administer to participants, can be used on small sample sizes with reliable results (10 parents in this study), and its validity allows for the differentiation between usable and unusable systems. Moreover, the questions are short, highly comprehensive, easily customizable, and easily administered via simple survey internet tools. In addition to this questionnaire, the education levels (EL) of parents participating in this study were assessed by using the International Standard Classification of Education (ISCED) (http://www.
24-Hour Recall Questionnaire as a Reference Dietary intake data for each of the 30 study participants with cow's milk allergy, collected using the Nutricate© web application, were extracted as a list of each individual's dietary report (example Figure  2 ). Each dietary report was then entered into the Nutrilog© software (version 2. 31; https://nutrilog. com; accessed on 15 December 2022). Nutrilog© is a professional nutrition web application that uses the CIQUAL/ANSES French Food Composition Database (Agence Nationale de SEcurité Sanitaire de l'alimentation, de l'Environnement et du travail/French National Agency of Health, Safety of Nutrition, Environment, and Employment; https://ciqual. anses. fr; accessed on 15 December 2022). Nutrilog© was used as the reference method in this study  [32].
It provided information about the individual food profile, and the food composition table was integrated into the software database containing the CIQUAL food table, France 2012, and USD SR24; USA 201 was provided by the supplier. The Nutrilog© was used to compare the micro/macronutrient outputs. The differences between the two methods were calculated as follows: Nutrilog©-Nutricate©. The 95% upper limit (UL) and lower limit (LL) of agreement (SD 1. 96) are depicted as a long, dashed line. The full line indicates the mean difference and zero.
Statistical Analysis As it was a pilot study, no formal sample size calculation was performed. The sample size was arbitrarily set, with thirty participants considered satisfactory enough to detect differences between the two methods (Nutrilog© and Nutricate©). Similarly, data from eleven children were considered to compute usability metrics. Continuous variables were expressed as means (standard deviation, SD), and categorical variables were expressed as numbers (percentage). The data normality was checked using the Kolmogorov-Smirnov test and through the visual inspection of the histograms. The concordance of total energy and nutrient estimates between both collection modes (Nutrilog© and Nutricate©) were tested by Spearman correlation statistics. Bland-Altman plots were used to reinforce statistics and to evaluate bias between both methods  [33].
The use of subject-based computerized food data intake was used more recently in the HELENA study. Moreover, it was shown that electronic records are more complete than paper-based records  [37]. This study shows that Nutricate© is an easy tool to use and an accurate online application for managing children with cow's milk allergy. Subjects analyzed in this study were from the typical children with cow's milk allergy population, with their anthropometric measurements (weight, height, etc. ) being similar to those seen in most previously published studies  [34, 38]  and with the food groups being similarly well-balanced, as seen previously  [39]. Moreover, the levels of macronutrient intake (% carbohydrates, % proteins, and % lipids) computed from Nutrilog© (Supplementary Table  S2 ) were similar to those observed in other cow's milk allergy studies  [34, 38, 40].
The mean that the System Usability Scale questionnaire obtained was 81 ± 14, a score considered to be excellent  [43] ; this means that the Nutricate© web application is considered appropriate for the targeted population. The completion of dietary intake was considered to be fast and easy by the parents participating in this study; it was, however, interesting to note their very high level of education (data not shown), which could explain their positive feedback regarding the tool usability. This being said, the small sample size of this dataset, particularly for the System Usability Scale questionnaire score, is considered a limitation in this study. However, the strengths of this study lie on the use of solid statistical methods and a validated System Usability Scale questionnaire tool.
Conclusions Nutricate© is a validated web application used to collect dietary intake data from children with cow's milk allergy. Nutricate© online dietary intake collection is automatically linked to the food data composition database, allowing for a dietary report to be immediately generated. Additionally, the tool is very user-friendly; it can be used at any time and any place, which is convenient for the parents of children with cow's milk allergy. This has the added benefits of relieving the burden of healthcare professionals by removing the need for interviewers and of data processors by removing the need to analyze dietary intake, hence saving both time and money. Most importantly, this tool shows the potential to improve the adherence of healthcare professionals to the cow's milk allergy healthcare system.
Supplementary Materials: The following supporting information can be downloaded at: www. mdpi. com/xxx/s1, Table  S1 : Ratio of each food categories used to merge into one food category and their correspondence in macro-micro nutrient according to food composition database; Table  S2 : Macronutriments repartition intake (n=30). Author Contributions: L. B. and P. A. conducted statistical analysis and drafted the initial manu-.
I. INTRODUCTION B ENEFITING from constant interest from both industry and academia, stochastic analysis evolved significantly over the past years. Various techniques are currently available, allowing designers to evaluate the impact of system uncertainties for various circuit applications, packaging, and interconnects. Historically, the most common approach to stochastic analysis was Monte Carlo (MC). Featuring several improved versions, the technique is straightforward. Data are collected via repeated simulation and allows various metrics to be computed, quantifying the performance and robustness of a system subject Mihai Telescu and Noel Tanguy are with the University of Brest, CNRS, Lab-STICC, F-29238 Brest, France (e-mail: mihai. telescu@univ-brest. fr; noel. tanguy@univ-brest. fr). Riccardo Trinchero, Nastaran Soleimani, and Igor Simone Stievano are with the Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy (e-mail: riccardo.
VI. CONCLUSION The main novelty of this article is the stochastic time-domain mapping approach. For a given eye pattern, the inner eye is mapped using a suitable polygonal approximation. After an initial training stage, a parametric surrogate model is built, predicting the effect of system uncertainties on the shape of this polygon. This allows designers to effectively compute the metric of their choice or use eye masks in a stochastic context. The procedure was demonstrated in the case of a flexible data link for wearable applications, with uncertain electrical and geometrical parameters, using machine learning regression to build the surrogate model. Data were collected by simulating the link in a differential configuration operating at 2 Gbps. Three types of regressions were used, showing that for the problem at hand, a vector formulation of the so-called KRR is the preferable choice.
The FRAP for each food item was obtained from the database developed by Pellegrini et al. (Pellegrini et al. , 2003 (Pellegrini et al. , , 2006)). More details on this calculation in the E3N cohort can be found in  Mancini et al. (Mancini et al. , 2018). For the dietary inflammatory index, the adapted dietary inflammatory index (ADII) was estimated by the method developed by  van Woudenbergh et al. (van Woudenbergh et al. , 2013) , with the updated weights of Shivappa et al. (Shivappa et al. , 2014). The detailed methodology to adapt this score to the E3N cohort is presented elsewhere  (Laouali et al. , 2019).