dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants The study population included 3528 adolescents (52•3 % females) aged 12•5-17•5 years. Adolescents were excluded from the database a posteriori if they met one or more of the following exclusion criteria: age <12•5 or ≥17•5 years; no measurement of weight and/or height; completion of <75 % of the tests; participating simultaneously in another clinical trial; or contracting an acute infection during the week prior to the examination  (16). As a result of logistical problems (limited staff capacity for collecting and analysing 24 h dietary recall data), there was no nutrient intake information from Heraklion or Pècs participants, for which case they were excluded. For one purpose of the present study, we analysed the association between the adapted MDS and food and nutrient intakes without exclusion of under-reporters, resulting in 2330 adolescents (53•8 % females), and with exclusion of under-reporters, resulting in 1804 adolescents (52•6 % females).
Dietary intake assessment Dietary intake was obtained by means of 24 h dietary recalls  (19)  , an FFQ and the FCP questionnaire  (20, 21). The 24 h dietary recalls were collected via the HELENA-DIAT (Dietary Assessment Tool) software on two nonconsecutive days within a period of 2 weeks. Adolescents completed the 24 h dietary recall autonomously in the computer classroom during school time and dietary intake was referred to the day before the interview; therefore, no information on Fridays and Saturdays was available. This tool was adapted from a previous version developed and validated for Flemish adolescents  (22). This assessment tool is based on six meal occasions (breakfast, morning snacks, lunch, afternoon snacks, evening meal, evening snacks) referring to the previous day. Trained dietitians assisted the adolescents to complete the 24 h dietary recalls when needed.
Adapted KIDMED for adolescents A second index to assess the adherence to the Mediterranean diet in our European adolescents was developed based on the KIDMED score, an index developed for children and adolescents proposed by Serra-Majem et al. (6). The original KIDMED  (6)  is calculated based upon a test that includes sixteen questions which could be selfadministered or conducted by interview (paediatrician, dietitian, etc. ). The index ranged from -4 to 12, where questions denoting a negative connotation were assigned a value of -1 and those with a positive connotation, a value of þ1. In our adapted version, we used data from repeated 24 h dietary recalls, the FFQ and the FCP questionnaire. Information on the consumption of pulses, cereals or grains for breakfast, yoghurts and cheese, baked goods or pastries, dairy products, olive oil and nuts were taken from the 24 h recalls.
Statistical analysis Statistical analyses were performed using the statistical software package IBM SPSS Statistics for Windows version 21. Pearson χ 2 and t tests were used to test for differences between sexes in categorical and continuous variables, respectively. Normality was evaluated visually and based on the skewness of the data distributions. Skewness of variables on intake ranged from 0•869 (meat intake) to 9•809 (alcohol intake; due to a high number of non-consumers). Since these variables were studied in a large sample, it was considered that parametric tests were allowed without transformations. However, variables with a skewness > 3 were transformed (logarithmic transformation and square-root transformation were tested) and the multilevel analyses were repeated with the transformed data.
Strengths and limitations There are few studies investigating the use and validity of these indices in a European adolescent population. The MDS_A and KIDMED_A in the present study were not based on nutrient intakes, avoiding the limitations that coincide with the use of food composition data (such as the use of various tables in different countries with different methods of analysis used, unavailability of food items, loss of dietary information from mixed dishes, etc. ). All indices were calculated based on two self-administered, computer-assisted, non-consecutive 24 h recalls, among others. Following recommendations of the European Food Consumption Survey method (EFCOSUM), 24 h dietary recalls were preferred as these are open-ended questionnaires from which detailed information can be obtained.
In this respect, the 24 h dietary recalls were performed through the computer-assisted HELENA-DIAT software to standardize the recall procedures as much as possible. The same food composition table for conversion of food intake data to estimated nutrient intakes was used for all survey centres  (25). This identified differences in definitions, analytical methods, units and modes of expression, which were overcome. The fact that one single database of food composition was used could be a limitation due to the composition of some foods that may vary between countries and this might have reduced the correlation with some biomarkers. Another limitation may be that the FFQ has been validated only in a Belgian youth population and not in the HELENA study  (29). The present study used data from the largest European study investigating the association of lifestyle factors (such as diet) with health outcomes (like obesity and health biomarkers) among European adolescents.
Acknowledgements Acknowledgements: Many thanks to  Christel Bierschbach ,  Adelheid Schuch ,  Anke Berchtold ,  Petra Pickert ,  Andre Spinneker ,  Ulrike Albers  and  Anke Carstensen  for their contribution to laboratory work. Financial support: The HELENA study was support by the  European Community Sixth RTD Framework Programme  (Contract  FOOD-CT-2005-007034 ). There was additional support from the  Spanish Ministry of Education  ( AGL2007-29784-E/ALI ),.
Methods subjects. We studied a prospectively enrolled series of patients with ICH that underwent MRI including fluid attenuated inversion recovery (FLAIR) and diffusion weighted imaging (DWI) within one week of the ICH between March 2009 and November 2015. MRIs were obtained as part of our clinical management protocol whenever feasible. Inclusion criteria were: (1) spontaneous ICH, and (2) MRI obtained within 7 days of the hemorrhage. Exclusion criteria were: (1) age < 18 years, (2) pregnancy, (3) ICH due to tumor, trauma, or hemorrhagic conversion of an ischemic stroke, and (4) patients or families who declined to participate in the study. Patient management was in accordance with current guidelines (Supplementary Material). Data were collected as part of a prospective observational cohort study approved by the local institutional review board (Columbia University Medical Center IRB).
This method allows a robust data-driven analysis when there are a large number of features compared to the number of observed events and/or when features are highly correlated. Models were trained on the clinical labels (conscious vs unconscious) obtained either at the time of MRI or at the time of ICU discharge. In order asses robustness of the model, we performed 5-fold cross validations repeated 500 times  24. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (95% CI). Logistic regression using elastic net regularization were computed with the Glmnet R package (for details please refer to the Supplementary Material). Differences in baseline features between patients that fulfilled inclusion criteria and those that did not were explored using Fisher's exact test for categorical and Wilcoxon-Mann-Whitney test for quantitative variables as appropriate.
Third, sedation is frequently used in the critical care setting and can confound assessments of consciousness. We minimized doses of sedation as recommended in guidelines  42  and systematically accounted for sedation given at the time of and preceding the assessment at time of MRI. Note that this limitation only applies the model trained at time of MRI since none of the patients received sedation at time of ICU discharge. Fourth, MRI based assessments of hemorrhage can be challenging as MRI signal changes are observed over time  43. Subacute hemorrhages typically appear as hypointense signal in FLAIR sequences between 2-7 days, which was within the inclusion criterion in this study. Finally, confirmatory investigations to validate our findings on an independent dataset will be necessary in future studies.
Acknowledgements This work was supported in part, by the  DANA Foundation ,  James S. McDonnell Foundation , the  NIH  under award number  R01LM011826 , and  NSF  under award number  1347119. Additional support included postdoctoral grants from  "Amicale des Anciens Internes des Hôpitaux de Paris & Syndicat des Chefs de Cliniques et Assistants des Hôpitaux de Paris" (AAIHP -SCCAHP) , " Assistance Publique -Hôpitaux de Paris" (AP-HP) , and the  Philippe Foundation. We thank the nurses, attendings, fellows, and neurology and neurosurgery residents of the  Neuroscience ICU  for their overall support of this project. We are grateful to  Dr Caroline Der Nigoghossian  for her help with sedation data.
Pour les analyses univariées des variables quantitatives, le test statistique t de Student sera utilisé pour comparer 2 groupes et une ANOVA pour la comparaison de 3 groupes ou plus (si la distribution normale est vérifiée). En ces de distribution ne suivant pas la loi normale, le test de U de Mann et Whitney sera utilisé pour comparer 2 groupes et le test de Kruskal-Wallis la comparaison de 3 groupes ou plus. Le seuil de significativité p est fixé à 0,05 et tous les tests sont bilatéraux. Les analyses ont été réalisées avec le logiciel IBM SPSS Statistics 19. L'analyse statistique des données a été autorisée par la souscommission Ethique du CHU d'Angers sous le numéro 2016/64. Résultats L'analyse a porté sur la période du 31 janvier 2011 au 12 mai 2015, correspondant à une durée de 4,27 années.
Study conduct and data collection Healthcare professionals were selected based on their capacity to provide influenza vaccination to the different age groups, their experience in performing EPSS, and their willingness to participate and vaccinate with the study vaccines. Healthcare professionals distributed paper safety report cards to individuals (or for participants aged <18 years, their parents or legal guardians) vaccinated with IIV3, IIV3-ID, or IIV4, and recorded the vaccination information for each participant daily using an electronic data capture system (eClinicalOS, Clinical Leader, PA, US). Participants were instructed to report any suspected ARs, especially those occurring in the first 7 days, by calling a dedicated local toll-free telephone number provided on the safety report card.
Statistical analysis For each vaccine, the rates of ARs and AEIs at the end of the surveillance period were calculated as the number of participants who reported ARs or AEIs and the total number of ARs or AEIs reported as numerators and the number of safety report cards distributed as the denominator. ARs were coded with Medical Dictionary for Regulatory Activities (MedDRA) terminology (version 20. 1). AEIs were as listed in the PRAC guidance. 2 Two-sided 95% confidence intervals (CIs) were calculated as described previously  8, 9  using SAS® version 9. 4 (SAS Institute, Cary, NC, USA). For IIV3 and IIV3-ID, comparisons assessed if the current AR rates were greater or not than the upper limit of the 95% CI for those reported in the NH influenza season 2016/17. For IIV4, frequency categories were determined from the rates of ARs (very common, ≥10%; common, ≥1% to <10%; uncommon, ≥0.
Several guidelines have been established to define the surgery criteria, depending on the presence or not of "worrisome signs" and "high risk stigmata of malignancy" (i. e. , high grade dysplasia and invasive carcinoma)  [5] [6] [7] [8]. In these algorithms, the patient's performance status is taken into account to determine the surgery benefit/risk ratio. Recently, several European societies (i. e. , the European Study Group on Cystic Tumours of the Pancreas, United European Gastroenterology, European Pancreatic Club, European-African Hepato-Pancreato-Biliary Association, European Digestive Surgery, and European Society of Gastrointestinal Endoscopy) published updated evidence-based guidelines for the management of pancreatic cystic neoplasms  [9, 10]. In this study, we retrospectively analysed the clinical, imaging and pathological data of patients with IPMN who underwent surgery to evaluate: i) the diagnostic value of the relative and absolute European criteria or IPMN surgical resection; and ii) the post-operative morbidity and mortality rates.
Patients and inclusion criteria Between January 2007 and December 2017, 720 patients underwent surgery for pancreatic adenocarcinoma or IPMN. Among them, all consecutive patients with BD, MD or mixed IPMN eligible for pancreatic surgery (i. e. , resectable IPMN at imaging) and who underwent resection were included in the present study. All resected IPMN were histologically proven. This study was conducted according the French rules (Bioethics law of November 2016research category number 3), and the Comité National Informatique et Liberté (French Data Protection Authority) recommendations for the anonymous extraction and treatment of data. According to these rules, due to the study retrospective nature, it was not necessary to obtain the patients' informed consent for the analysis of their personal data.
Collected data Since January 2005, all clinical, radiological and pathological data of patients undergoing pancreatic surgery at our centre are prospectively recorded in a dedicated database. We extracted the main clinical and radiological data, including most of the criteria retained by the recent European Consensus: age, sex, medical history, circumstances of diagnosis and symptoms related to IPMN, date of diagnosis, and biological data (including hepatic enzymes, blood lipase level), as well as the results of computerized tomodensitometry (CT) (helical triple-phase CT, each pancreatic section was effectively collimated into 3-mm sections at a pitch of 1. 5), magnetic resonance cholangiopancreatography (MRCP) (1-T superconducting magnetic-resonance unit Magnetom Impact, Siemens, Erlangen, Germany, using a half-Fourier single-shot turbo spin-echo sequence), endoscopic ultrasound (EUS), and fine-needle aspiration biopsy (FNA) (Olympus, Hamburg, Germany).
Surgical data Type of resection and postoperative complications were collected for patients who underwent surgery. All surgical indications were discussed and validated by our pancreas disease multidisciplinary group  [5, 11]. In all cases, the surgical approach was surgical resection with appropriate lymphadenectomy, and frozen section analysis of the resection margins. The surgical approaches were classified as follow: distal pancreatectomy, pancreatoduodenectomy, total pancreatectomy, enucleation, and median pancreatectomy. Surgical morbidity was defined by the occurrence of grade III, IV or V postoperative complications, according to the classification by Dindo and Clavien  [12]. Grade III includes complications that require surgical, endoscopic or radiological intervention, with or without general anaesthesia.
Statistical analyses Qualitative and quantitative data (Student's-t, χ 2 , exact Fisher tests) were analysed using the GraphPad-Instat and GraphPad-Prism 6. 0a software programs (GraphPad Software Inc. San Diego, CA, USA). To identify independent predictors of malignancy, univariate and multivariate logistic regression analyses were performed using logistic regression tests with the SAS software (version 9. 4, SAS institute, Cary, NC, USA). A p-value < 0. 05 was considered significant. Due to the retrospective design of the study a post-hoc analysis was performed to evaluate the power of the study. The power analysis was performed using G-power software 3. 1. 9. 2.
Statistical analysis. Significance values were determined by applying the non-parametric Friedman test using the GraphPad Prism-6 software. All data are represented as means ± SEM (standard error of the mean). The statistical analyses compare serum vs. no serum condition, unless otherwise indicated in the figure legends. P-values >0. 05 were considered statistically not significant (ns) and the following denotations were used: ****, P≤0. 0001; ***, P≤0. 001; **, P≤0. 01; *, P≤0. 05.
Acknowledgments We thank  Olivier Diaz  and  François Pénin  for helpful discussions. We are grateful to  Ralf Bartenschlager  and  Thomas Pietschmann  for the gift of HCVcc constructs,  Mansun Law  for the AR3A antibody, and  Charles Rice  for the Huh7. 5 cells and  9E10 NS5A  monoclonal antibody. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) ANIRA-Genetic Analysis  facility for excellent technical assistance and support. We thank  Didier Décimo  for support with the BSL3 facility. We thank  Omran Allatif  for guidance with the statistical analysis.
Financial support: This work was supported by the  French "Agence Nationale de la Recherche sur le SIDA et les hépatites virales" (ANRS) , the  European Research Council  ( ERC-2008-AdG-233130-HEPCENT ) and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the " Université de Lyon", within the program  " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). SD was supported by a fellowship of the  ANRS. Author contributions: Study concept and design: SD, FLC. Acquisition of data: CG, NF, TB, MG, SD. Analysis and interpretation of data: SD, CG, NF, MG, FLC. Drafting of the manuscript: SD, FLC. Material support:  BP.
Replicate dissolved oxygen profiles across the core surface were collected behind the flat wall of the core tube before any subsampling was done. A Unisense Clark type electrode with a 100µm tip was used, and data on oxygen was obtained at typically 200µm depth intervals across the benthic interface and into the sediment. With core top water removed, porewaters were extracted from the sediment core at typically 1 to 2 cm depth intervals using Rhizon samplers  (Seeberg-Elverfeldt et al. , 2005)  that prevent oxygen contamination of the collected porewaters (c. f. Klar et al. 2017). After porewater extraction and filtration, the residual sediment was sliced using a polycarbonate sheet at 0. 5, 1 and 2 cm depth-intervals, and stored at -20° C in zip-lock bags prior to further analyses. The concentrations of Fe(II), and Fe(II) plus Fe(III) (i.
Precision values for the samples are approximately ±0. 5-2% for crystalline materials and ±10-20% (of the amount present) for total clay. Scanning electron microscope work used a Carl Zeiss LEO1450VP Scanning Electron Microscope (SEM) fitted with an Oxford Instruments EDS system, and an X-Act Silicon Drift Detector (10mm 2 area) using the AZtec Energy software system (v. 3. 1). Acid volatile sulfide (AVS) and pyrite (PY) were determined in the sediment following the approach of  Canfield et al. (1986)  using a sequential acid (6M HCl) reflux, and then acidified chromous chloride reflux, to convert the solid phase sulfides to hydrogen sulfide that is collected as silver sulfide (Ag 2 S) in a silver nitrate solution. The two sulfide pools in the sediment are calculated after gravimetric measurement of the Ag 2 S formed from the known mass of sediment added.
Acknowledgements This project was funded through Work Package 3 of the UK Shelf Sea Biogeochemistry Programme (NE/K001973/1 and NE/K001787/1), jointly funded by the Natural Environmental Research Council (NERC) and the Department for Environment, Food and Rural Affairs (Defra). The views expressed are those of the authors and do not necessarily represent those of NERC or Defra. W. B. H. was further supported a NERC Fellowship (NE/K009532/1). The samples for this project were collected with the excellent support of captains, crew and NMF staff on the RRS Discovery. We are particularly grateful to fellow researchers within the SSB programme for providing assistance at sea. We express special thanks to Carolyn Harris for analysing nutrient samples during cruise DY034 and Malcolm Woodward for collating these data, Ross Williams for XRD work, David Chatelet (Biological imaging unit, University of Southampton) for help with ImageJ, Shir Akbari for POC/PIC analysis, and Matt Cooper for ICP-OES analyses.
In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of sourceresolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e. g. , sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants.
Recent signal processing advances, however, allow studying source-resolved electroencephalography (EEG) dynamics during walking  [22] [23] [24] [25] [26]  and other actions, an approach termed Mobile Brain/Body Imaging (MoBI)  27. In this report, we present a multimodal dataset from 20 healthy young participants that allows to study coordination of steps to the timing and rate of the auditory pacing stream as well as executive function in gait adaptation. Participants were instrumented with high-density EEG (108 channels), surface electromyography (EMG) with electrodes placed on the tibialis anterior muscle of both legs, pressure sensors on the heel to measure heel strikes, and goniometers measuring joint angles of ankle, knee, and hip. Participants walked on a treadmill at a constant speed while attempting to step in synchrony with an auditory pacing stream and were required to adapt their step length and rate to shifts in tempo of the pacing stimulus (e.
g. , to unexpected shifts to a faster or slower pacing tempo). To our knowledge this is the first published dataset featuring EEG recordings during a dynamic gait adaptation task requiring synchronization of steps to auditory cues and one of only three other public EEG-MOBI datasets recorded during walking  [28] [29] [30]. We have used this dataset to investigate top-down inhibitory control in gait adaptation modeling source-resolved oscillatory cortical dynamics and event-related potentials time locked to cue tones and heel strikes following cue rate shifts  31, 32. These data could be used to support further studies of gait adaptation, error processing, and auditory-motor synchronization during walking, analyses that might give further insights into the underlying cortical mechanisms of auditory rhythmic cue training.
This has significant importance for the field of gait rehabilitation in the elderly and Parkinson's disease  [12] [13] [14] [15]. The multimodal nature of the dataset allows for investigation of relationships between EEG and EMG during walking, including corticomuscular coherence, and joint analysis of movement parameters and EEG. The dataset contains data from 20 subjects, 18 of which have sufficiently clean EEG data for meaningful analysis, and 16 have kinematic data (not crucial for EEG analysis since heel strike markers come from pressure sensors, whose records are present for all subjects). The number of subjects in our study is relatively high compared to other EEG studies of walking with fewer subjects, in which significant effects have been demonstrated. These include studies using fewer than 15 participants that have demonstrated significant power modulations relative to the gait cycle  23, 25, 26, 33, 34  and significant effects of visual feedback during walking  33.