dataesr/scientific-paragraphs-categorization huggingface dataset:
Electroretinograms (ERGs) ERGs were recorded from 1 to 3-day-old flies as previously described  (Soukup et al. , 2016). Flies were immobilized on glass microscope slides, by use of double-sided tape. For recordings, glass electrodes (borosilicate, 1. 5 mm outer diameter) filled with 3 M NaCl were placed in the thorax as a reference and on the fly eye for recordings. Responses to repetitive light stimuli were recorded using Axosope 10. 7 and analyzed using Clampfit 10. 7 software (Molecular Devices) and Igor Pro 6. 37.
From day 4, KSR was gradually shifted to Neurobasal/0. 5X B27 supplement without vitamin A, 0. 5X N2, GlutaMAX, Penstrep, non-essential amino acids at the rate of 1/3 every 2 days for 4 days, and to ¼ for 3 days. SB431542, SHH-C24II, and Purmorphamine were withdrawn from the medium at day 7. FGF8b (100 ng/mL, R&D) was added to the medium from day 9 until day 16. At day 18, cells were switched to terminal differentiation medium consisting of Neurobasal-A/1xB27 supplement without vitamin A, GlutaMAX, Penstrep containing BDNF (brain-derived neurotrophic factor, 10 ng/ml; R&D), Statistics GraphPad Prism 9. 3 (San Diego, USA) was used to determine statistical significance. Datasets were tested for normal distribution using the D'Agostino-Person Omnibus and Shapiro-Wilk normality tests. Normally distributed data were tested with parametric tests: when two datasets were compared, the Student's t-test was used, and when more than two datasets were compared, a one-way analysis of variance test (ANOVA) followed by a post hoc Tukey test was used.
Stained experimental and unstained control cells were analysed using a BD Accuri C6 flow cytometer using four fluorescence detectors for each time point over a 0-48-h timecourse. 50,000 events were collected per sample. Flow cytometry analysis was carried out using the BD Accuri C6 software with AV (FITC) analysed in Filter 1 (FL1) and PI (PE) analysed in Filter 2 (FL2). To account for spectral overlap when using AV and PI stains, colour compensation was applied. For experiments analysing apoptosis, the polygonal gating tool was used to select the population of intact (healthy) cells and non-intact SDS-PAGE and western immunoblotting. ARPE-19 cell lysates were prepared for western immunoblotting by washing wells in a 6-well plate twice with dPBS and scraping wells in lysis buffer (0. 128 M β-mercaptoethanol, 40 mM tris, 10% glycerol, 1% SDS, 0.
Statistical analysis. Data generated in this study was analysed to determine statistical significance using the GraphPad Prism 8 (GraphPad Software, USA). Sample means were compared to control groups using Oneway Analysis of Variance (ANOVA) with Dunnett's or Sidak's post hoc-test. When two or more treatments were measured alone or over a time-course, comparisons were made using a Two-way ANOVA with Sidak's or Tuk-eyHSD post-hoc test. All experiments consisted of n = 3 replicates, unless otherwise stated. All data values are presented as mean ± standard deviation and, when appropriate, as fold change of control groups for each experi-.
Acknowledgements This project was funded by  Academy of Medical   Sciences Starter Grant  for Clinical Lecturers awarded to L. F. P with L. P. as academic supervisor. N. S. was supported by a  National Eye Research Centre PhD Studentship. L. F. P. was an  NIHR Clinical Lecturer at the University of Liverpool. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the  Department of Health. The funders had no role in the design, data collection, analysis, or conclusions drawn from this study.
2018-222-31). Theoretical model. A diathesis-stress model  22  was adapted for structuring of the data collection and analyses (Fig. 3 ). It was assumed that female sex constitutes a structural vulnerability with regards to depression  26. Vulnerability with regards to genetic background and personality was not included in the present model. The sports-related stressor included in the model was having recently suffered an injury, while the life stressors were being exposed to patriarchal family norms, lifetime history of physical abuse, and lifetime history of sexual abuse.
Study population. The primary study population consisted of athletes registered for the World Athletics Under 20 World Championships in Tampere 2018 and belonged to a team that mastered at least one of the World Athletics official languages. National teams unfamiliar with these languages or with a restricted team communication policy were excluded. Data collection. Data were collected using a self-administered web-based preparticipation health questionnaire (PPHQ) involving a code-based procedure for athlete anonymization. The PPHQ was translated into the seven study languages (English, Spanish, French, Portuguese, Amharic, Chinese, and Japanese). The eligible athletes were first informed about the study before the championships through their team managers. At dedicated stalls at the stadium and training arenas, the athletes were thereafter personally invited to complete the PPHQ through hand-out of leaflets containing detailed study information and the website link.
The PPHQ collected information regarding athlete sex, age, and country. Data on depression was collected using the WHO-5 instrument  49. A systematic review based on 213 papers reported WHO-5 to have high clinimetric validity and assessed it as a sensitive and specific screening tool for depression with high applicability across study fields  25. An early indicator of depression in young people is reduced well-being  25, 50. The WHO-5 employs positively formulated statements about health status instead of asking for symptoms of disease. Young competitive athletes were considered to perceive questions about depressive mood as less relevant and be more inclined to report on their well-being. The WHO-5 consists of five positively worded items that reflect the presence or absence of well-being rather than depressive symptomatology.
The athletes were asked to report the presence of these positive feelings in the last 2 weeks on a 6-point scale ranging from all of the time (5 points) to at no time (0 point). Each score is multiplied with 4 rendering a total score ranging from 0 to 100. Predisposition to depression was measured by level of well-being as measured by the crude WHO-5 score and depression caseness was defined as scoring < 50 on WHO-5  25. Sports injury data were collected by asking whether the athlete had suffered an injury that caused absence from regular participation in sports during the past year, henceforth referred to as a recent injury event. Structured follow-up questions were asked about injury cause and length of absence from sports. Sexual abuse was defined as any sexual interaction involving physical contact with person(s) of any age that was perpetrated against the victim's will, without consent or in an aggressive, exploitative, manipulative, or threatening manner.
WHO-5 crude scores were used for analyses of predisposition of depression and WHO-5 scores < 50 (yes/no) were used as cut-off level for depression caseness  25. Predisposition to depression and depression caseness were analyzed separately. Significance was tested for paths between different types of abuse [sexual abuse, physical abuse, any abuse (latent variable combing sexual and physical abuse)] and depression taking into consideration the influence of sex. Non-significant paths were stepwise removed until only significant paths remained. Path modeling was performed using Mplus software (8th edition) using Maximum likelihood estimation with robust standard errors.
Evaluations The idea is to evaluate how well, having processed a portion of a dataset with a regular tool (here TOPUP), one can use those to train a DL model that will rapidly process the rest of the dataset. Corrections from three DL registration approaches are engaged for comparison: unsupervised, transformation supervised and semi-supervised. We will perform experiments on two datasets: 1-A real dataset with TOPUP outputs as ground truths, 2-A synthetic dataset with absolute ground truths allowing to integrate TOPUP to the comparison.
Datasets We acquired anatomical and diffusion weighted data as part of an ongoing study investigating memory learning and consolidation. We have, for 60 healthy subjects, T1-weighted images and distorted EPI b=0 image pairs from opposite PED acquisition that are antero-posterior (AP) and postero-anterior (PA). Distorted EPI images have been processed through TOPUP to obtain undistorted images and associated distortion fields. In addition, simulated data were produced using DW-POSSUM  [1]  (an extension of FSL POSSUM  [6] ), a realistic Spin-Echo EPI simulator. It takes as input tissue segmentations, MR parameters associated with these tissues and a pulse sequence, and produces an EPI image by solving the Bloch equations. We obtained the tissue segmentations by processing the anatomical scans with FSL FAST, producing probability maps for grey matter, white matter and cerebrospinal fluid.
-In the real dataset: the inputs of the model are the acquired AP and PA images, the ground truth distortion fields used at training and for evaluation are the ones from TOPUP, the ground truth images used only for evaluation are the undistorted images from TOPUP. The advantage of this dataset is that it is made of real data and all the artifacts it implies. The drawback is that TOPUP is used as ground truth and therefore cannot be included in the comparison. -In the simulated dataset: the inputs of the model are the simulated AP and PA images, the ground truth distortion fields used at training and for evaluation are the ones from TOPUP (same as for the real dataset), the ground truth images used only for evaluation are simulated non-distorted images. The advantage of this dataset is the synthetic, absolute nature of the ground truths allowing any algorithm comparison.
The drawback is that the simulation process is not able to reproduce all the artifacts induced by a real acquisition. For both, the weight maps have been computed by thresholding (binary brain mask), then dilating (3 voxels), then smoothing (Gaussian, σ = 6 mm) the T1-weighted images. We divided the same way the two datasets into training (n=40), validation (n=10) and testing (n=10) samples. Although it was shown in  [12]  that Voxelmorph-like architectures can achieve decent registration with only ten or so training subjects.
Results and Discussion Table  1  and Fig. 3  summarise the evaluation results for both real and simulated data experiments. -The real dataset experiment uses real acquired EPI images and evaluate how close to TOPUP (ground truth) the different models behave. In terms of field fidelity, supervised and semi-supervised models show a mean voxelique MSE around 0. 2 (equivalent to 0. 4 mm 2 ), way better than the unsupervised approach that shows an error about 4 times bigger with also a much higher variance. In terms of image similarity, the unsupervised and the semi-supervised approach show an error equivalent to 2/3 of the one of the supervised approach with akin variance. The semi-supervised approach performs well for both metrics whereas the other models present weaker results in one situation.
1. Fidelity (MSE: mean (std)) to ground truth distortion fields and images for the different models for real data with TOPUP as ground truth and for synthetic data. The field fidelity is expressed as a fraction of the voxel dimension (2 mm). This study show that, with proper distortion model, one can use deeplearning registration trained on some tens of subjects to rapidly correct a larger set for susceptibility-induced distortion, with results as good as TOPUP. One can use a processed subset of a dataset to include transformation supervision and improve the transformation fidelity which is a direct measure of the quality of the registration. We could have expected the semi-supervised model to perform half-way between its unsupervised and supervised counterparts in terms of field and image fidelity; it actually offers the best of both worlds.
Conclusion We presented a semi-supervised approach for the distortion correction of EPI images with opposite PED using DL-based image registration. We compared this model with an unsupervised and a supervised one, as well as a traditional algorithm: TOPUP. By leveraging ground truth distortion transformations during training, the proposed method can produce more accurate estimate of distortion fields (direct quantitative compared to unsupervised approaches at testing. It also outperforms the supervised approach for the image metric. On synthetic data, the results were similar to TOPUP but with much faster computation. The proposed model can typically be trained on a processed subsample of a dataset with an external tool to then produce distortion correction very efficiently to the rest.
Data Collection Data were collected using face-to face interviews (one interviewer and one PE), based on a pre-defined interview guide Table  2 , between April and December 2018. The three interviewers (MD, MP, and HF) had been trained to conduct interviews by a senior researcher (LF). Interviews took place in a quiet place chosen by the PE (their home or at their patient association premises). Throughout the research, the interviewers took field notes. After the interviewers gained more experience in conducting interviews, they interviewed by telephone several PEs who lived far away. Written informed consent was obtained before each interview. The interview guide was developed following a rigorous process  (Kallio et al. , 2016). It was based on a literature review and was validated by all co-authors.
Data Analysis The audio recordings were transcribed verbatim using Microsoft Word, taking into account the PEs' silences, nuances, and non-verbal communication, which were reported in the field notes. Transcripts were not handed out to the PEs. All collected data were treated as confidential. Any information that could identify participants was removed during the transcription to ensure anonymity. Three researchers (MD, MP, HF) read the transcripts several times and familiarized themselves with the field notes. Following  Braun and Clarke's inductive approach (2006) , qualitative data were analysed using reflexive thematic analysis. Data familiarization is key to thematic analysis, and in this study, the researchers personally collected and transcribed the data. Each researcher manually and individually coded the interviews, line by line, together with a senior researcher (LF).
Acknowledgments We wish to thank all PEs who agreed to receive us and participate in this study. We would particularly like to thank  Ange, Sylvie ,  Amandine , and  Dominique  who reviewed the work. We thank the  French Association  for the Development of structured education and the ACCEPTED MANUSCRIPT/ CLEAN COPY universities and institutes that organize this IPE program. This article is supported by the  French network of University Hospitals HUGO  (' Hôpitaux Universitaires du Grand Ouest ').
Dietary data collection and indices of dietary quality and inflammatory potential Maternal dietary assessment: Dietary information was collected by self-administered semi-quantitative (EDEN, Generation R, Lifeways, ROLO) [33e36], and non-quantitative (ALSPAC, REPRO_PL, SWS)  [30, 37, 38]  Food Frequency Questionnaires (FFQs) (except SWS which used nurse-administered FFQs). Maternal diet was assessed in pre-pregnancy in two studies (EDEN and SWS) and during pregnancy in all studies. Of note, maternal diet was assessed twice during pregnancy in SWS: early and late pregnancy measures were averaged to reflect overall pregnancy diet. Maternal dietary quality e Dietary Approaches to Stop Hypertension (DASH) score: As previously described in detail  [14] , the DASH score was adapted to the data collected within the cohorts of the ALPHABET consortium (Supplemental Table  S1 ), using eight food components (seven food groups and one nutrient) combined into a score based on cohort-quintile rankings  [39].
Scoring for each food parameter was based on its inflammatory potential on six inflammatory biomarkers including C-reactive protein, IL-1b, IL-4, IL-6, IL-10, and tumor necrosis factor-alpha. The dietary information on each participant was first linked to food consumption data sets from 11 countries around the world to estimate the average and standard deviation for each of the 45 food parameters. Z scores were calculated by subtracting the global standard average from the amount reported and dividing by the global standard deviation. To limit influence from highly skewed data each Z score was converted to proportions (i. e. , with values ranging from 0 to 1). These proportions were then "centered" on zero by multiplying each by 2 and subtracting 1  [40]. Each obtained value was multiplied by the corresponding food parameter effect score.
Data are presented alongside study weight (%), I 2 statistics, and expressed as standardized regression coefficient (b) changes in DASH and E-DII scores and their 95% confidence intervals (CI). All analyses were carried out using the statistical software Stata version 15. 1 (StataCorp, College Station, TX, USA). Abbreviations: WG, Weeks of Gestation. a Women included in these analyses. b 12,572 women had available dietary information in pre-pregnancy but only 3158 pregnant women delivered a live singleton baby and were included in the present study.
Strengths and limitations The main strengths of this study include its large sample size of >26,000 pregnant women from seven cohorts conducted in five European countries, the availability of individual data which permits to proceed to an individual participant data meta-analysis with several common covariates, and the consistency of the main results. Despite its strengths, the study has limitations. Dietary quality has been measured based on self-reported FFQs, which are subject to recall biases. However, most tools were validated within each cohort and, as highlighted by Olsen et al. [60]  (when merging dietary data from the world's two largest pregnancy cohorts), tool differences could lead to some differences in food and nutrient intake estimates. However, these variations would be systematic within each cohort and adjustment for this "cohort effect" in analyses might (at least partially) offset disparities.
sity of Bristol provide core support for ALSPAC. This publication is the work of the authors, and  Matthew Suderman  will serve as guarantor for the contents of this paper. EDEN: The EDEN study was supported by  Foundation for Medical Research, National Agency for Research (ANR) ,  National Institute for Research in Public health (IRESP: TGIR cohorte sant e 2008 program ),  French Ministry of Health (DGS) ,  French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A) , and  Human Nutrition National Research Programs, Paris-Sud University, Nestl e, French National Institute for Population Health Surveillance (InVS) ,  French National Institute for Health Education (INPES) , the  European Union   FP7  programmes (FP7/2007e2013, HELIX, ESCAPE, ENRIECO, Medall projects),  Diabetes National Research Program  (through a collaboration with the  French Association of Diabetic Patients (AFD)) ,  French Agency for Environmental Health Safety (now ANSES) ,  Mutuelle G en erale de l'Education Nationale a complementary health insurance (MGEN), French national agency for food security, French-speaking association for the study of diabetes and metabolism (ALFEDIAM).
Lifeways : The  Lifeways Cross-Generation Cohort Study  is funded by the  Irish Health Research Board  (reference  HRC/2007/13 ) and is overseen by an inter-disciplinary steering group. REPRO_PL: The REPRO_PL cohort was mainly supported by the  Ministry of Science and Higher Education, Poland  ( PBZ-MEiN-/8/2/2006 ; contract no. K140/P01/2007/1. 3. 1. 1 );by the grant  PNRF-218-AI-1/07  from  Norway through the Norwegian Financial Mechanism  within the  Polish-Norwegian Research Fund  and the  National Science Centre  under the call  JPI HDHL Nutrition and Cognitive Function  ( 2015/17/ Z/NZ7/04273 ). ROLO: ROLO study is supported by  Health Research Board Health Research Centre for Diet and Health Research Ireland and The National Maternity Hospital Medical Fund , and The European Union's  Seventh Framework Programme  ( FP7/2007e2013 ).
Acknowledgements ALPHABET: Authors acknowledge all investigators working on the ERA-HDHL  ALPHABET  Project and are grateful to all the participating families in England, France, The Netherlands, Poland, and  Republic of Ireland , who take part in this ongoing cohort study. ALSPAC: We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. Please note that the ALSPAC study website contains details of all available data is available through a fully searchable data dictionary and variable search tool (http://www. bristol. ac. uk/alspac/researchers/our-data/ ).
Funding ALPHABET: This research was supported by an award from the  European Union 's  Horizon 2020 research and innovation programme  under the  ERA-Net Cofund of the Joint Programming Initiative A healthy diet  for a healthy life (http://www. healthydietforhealthylife. eu) action number  696295  (Biomarkers for Nutrition and Health). Co-funding was provided by  Science Foundation Ireland, Ireland  (Grant Number  SFI/16/ERA-HDHL/ 3360 ), the  UK Biotechnology and Biological Sciences Research Council  ( ERA-HDHL Biomarkers: BBSRC: BB/P028179/1  and  BB/ P028187/1 ), the  National Center for Research and Development  ( ERA-HDHL/01/ALPHABET/1/2017 ), the  ZonMw The Netherlands  (no 529051014; 2017))  ALPHABET  project (no 696295; 2017), and the  French National Agency of Research  (reference AnrR16227KK).
Introduction Since December 2019, the novel coronavirus (SARS-CoV-2) pandemic emerged, causing >240 million infections and >5 million deaths and these numbers are growing steadily. [1] n the early stages of the epidemic, many children with SARS-CoV-2 were infected as part of family clusters,  [2]  and the mean incubation period is usually 2 days with a range of 2 to 10 days. Transmission takes place through respiratory droplets or contact with symptomatic cases. Furthermore, SARS-CoV-2 has been detected in the stool of some patients especially in children, so fecal-oral transmission is potentially possible. Transmission from asymptomatic cases, especially children, seems to have also an important role. According to the experience of several countries affected by the pandemic, children might experience different symptoms than do adults and usually present milder Informed consent to use recorded health data and publication was obtained from caregivers of all individual participants included in the study after the ethics committee approved the present study.
For all positive patients, records on demographics, clinical symptoms at presentation, clinical outcomes (hospitalization and mortality) and available radiological imaging, laboratory values, and SARS-CoV-2 viral loads (VLs) from nasopharyngeal swab were collected by trained personnel in an anonymized format from the hospital digital records. Hospitalization data were recorded as dichotomous for admission, continuous for days of hospitalization, categorical for reasons for hospitalization, and type of admission (levels are reported in Supplemental Digital Content 3, http://links. lww. com/MD/G876). Clinical features were recorded as present/ nonpresent and symptom clustering analysis was performed. The presence of MIS-C was assessed according to the WHO classification. [7] VL was collected as cycle threshold viral load (CT-VL) where the higher the value the lower the VL present in the sample.