dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION We describe the case of a 50 years old patient with schizophrenia whom underwent a non-invasive cerebellar stimulation protocol. Data from clinical and psychometric evaluations including long term verbal memory, executive, and attention functions were collected before and after the stimulation, as well as data from a cerebellar-dependent eyeblink conditioning protocol. Although we did not report changes in the positive or negative symptoms of schizophrenia before and after stimulation, there was a global improvement in psychometric measurements after stimulation. We also found an improvement in the performance in selected attention, long/short term memory, working memory, and response inhibition; cognitive domains known to be altered in patients with schizophrenia. There was a clear improvement of EBC after stimulation.
Another trial on interdisciplinary home PC model (IN-HOME PD; NCT03189459), focused on HY3-5 homebound PD patients, is currently ongoing in the USA, and will be completed in late 2020. Finally, the results of an Italian multicentre study that has explored the effect of deep brain stimulation among LSPD patients, in order to develop a decisional algorithm to rule a possible stimulation discontinuation, will be shortly published.
ACKNOWLEDGMENTS The present study has no funding. Denali ,  INC Reasearch, Lundbeck, Lupin, Merck, MundiPharma, Neuratris, Neuroderm, Novartis, ONO Pharma, Osmotica, Parexel ,  Pfizer ,  Prexton Therapeutics ,  Quintiles ,  Roche ,  Sanofi ,  Servier ,  Sunovion ,  Théranexus ,  Takeda ,  Teva ,  UCB, Vectura, Watermark Research, XenoPort, XO, Zambon ; Grant:  Agence Nationale de la Recherche (ANR) ,  CHU de Toulouse, France-Parkinson, INSERM-DHOS Recherche Clinique Translationnelle, MJFox Foundation ,  Programme Hospitalier de Recherche Clinique, European Commission  ( FP7 ,  H2020 ),  Cure Parkinson  IK; Other: Grant to participate in a symposium and contribute to the review of an article IPMDS.
Study design The OTRLSAV study is an observational, crosssectional, five-expert-centre study conducted on an exhaustive cohort of consecutive patients treated for at least 1 year with ASV for sleep apnea (ClinicalTrials. gov Identifier: NCT02429986). The protocol complied with the Declaration of Helsinki and was reviewed and approved by an independent ethics committee (Comité de Protection des Personnes "Sud Méditérannée III"; reference number 2014. 11. 04). Detailed data about the global study design, procedures, demographic or sleep characteristics have been previously published  [24]. The complementary ASVsetting analyses reported here focus on the unpublished ASV-treatment modalities of the 177-patient population. There were no ASV-setting recommendations established among the 5 centres and the 8 participating clinicians were free to choose ASV-brands and adjust ASV-settings as they saw fit.
Clinical data The clinical information collected for the analysis included age, sex, anthropometry, the apnoea-hyponoea index (AHI; determined by initial polysomnography (PSG) or respiratory polygraphy (PG)), and the Epworth Sleepiness Scale (ESS). The presence/absence of (i) a continuous positive airway pressure (CPAP) trial prior to ASV initiation, (ii) cardiomyopathy and (iii) an altered LVEF were also noted.
Device-collected data Included patients were treated either by an ASV-F device (the BiPAP autoSV Advanced Sytem One or the BiPAP auto SV Advanced Dreamstation (Philips Respironics®, Murrysville, PA, USA)), or an ASV-V device (the Resmed AirCurve 10 CS PaceWave or the ResMed S9 AutoSet CS (a device without auto-adjusting expiratory positive airway pressure (EPAP), which is similar to the S9 VPAP Adapt used in ASV mode with fixed EPAP, historically marketed in the United States), Resmed®, Sydney, Australia)). ASV data were collected using the manufacturer's software. Data downloads were performed for the 6 months preceding the inclusion date regardless of the ASVinitiation date. Settings were detailed as follows: expiratory positive airway pressure (EPAP; fixed or minimum/ maximum), inspiratory pressure support (IPS, minimum and maximum), maximum pressure, fixed backup respiratory rate (RR) or auto backup RR, and for the Philips Respironics device, the inspiratory support minimum pressurization time and slope level.
Clustering methods were performed on standardized data after mean-imputation of missing values. Ascending hierarchical classification (AHC) using Ward's method was used to determine the optimal number of clusters (k). Subsequently, k-means clustering, initialized from the barycentres of the AHC partition, was performed to divide the population into k homogeneous groups. The variables used to establish clusters were: age, sex, body mass index (BMI), initial PG/PSG Obstructive Apnea Index (OAI/h), initial PG/PSG Central Apnea Index (CAI/h), CPAP previously used before ASV treatment, presence of cardiopathy, and presence of reduced LVEF. All analyses were conducted by the Department of Research and Medical Information at the Montpellier University Hospitals using statistical software (SAS Enterprise Guide, version 7.
For each patient, the theoretical tidal volume is calculated using an 8 ml/kg formula and the ideal weight is calculated according to the Lorentz equation. Measured tidal volume is obtained by dividing the measured minute-ventilation by respiratory rate. Note that for Philips Respironics® devices, the minute-ventilation is expressed as "mean" in the manufacturer software whereas for ResMed® devices, the minute-ventilation is expressed as "median" (thus preventing direct comparisons between these device types). Data are collected using the manufacturer's software for a continuous 6-month period the crucial need for individual MV monitoring. In this regard, MV telemonitoring as part of a safety-first strategy deserves consideration, as least for patients with reduced-LVEF (like cluster-5 in the present study, which has higher MV when patients are treated with ASV-F).
Limits of the study Because real-life data describing ASV-settings and their associated flow-versus volume-controlled monitoring are sparse, our study was exploratory and descriptive in nature, without predefined hypotheses to be tested. Assessing the effects or interactions between ASVsettings, servo-controlled-flow or -volume monitoring types, and patient-phenotypes is complicated considering that the patient characteristics and the physician prescriptions are scalable. However, our study design (clinically stable and long-term treated patients, annual planned consultation corresponding to the inclusion date) limits in as much as possible such confounders. Our patients were included from March 13, 2015 to December 31, 2016. The SERVE-HF safety announcement was made on May 13, 2015. As a consequence, the ASV-data collected result from a mix of before and after SERVE-HF physician behaviors (with a majority of post-SERVE-HF inclusions).
Acknowledgements The OTRLASV investigators gratefully recognize the vital input and support of the home care providers: Agevie ( José Suarez ),  APARD Adene Group (Julien Bauchu, Frédéric Bousquet), ARARD Group (Florian Varielle) ,  Home Air Agiradom Group  ( Frédéric Gil ), LVL medical (Thierry Lefort)).
Material and Methods In this study, we included resting state EEG data collected as part of a larger study  (Giertuga et al. , 2017)  from 74 healthy controls (13±2. 3 years) and 67 ADHD children (12. 9±2. 4 years) from a total of 141 children aged 8-16 years. In this database, the ADHD children were diagnosed and classified into inattentive (n=20) and combined (n=47) subtypes in accordance with the Diagnostic and Statistical Manual of Mental Disorders (DSM) at the Public Pediatric Teaching Hospital in Warsaw, Poland  (Giertuga et al. , 2017). The ADHD Rating Scale (ADHD-RS) has been used to rate ADHD symptom intensity  (DuPaul et al. , 1998)  with an average score of (20. 3±3. 8), (9±4. 9) and (8. 5±4. 1) for inattention, hyperactivity and impulsivity, respectively. The inclusion criteria were: (1) no previous history of head injuries, somatic disorders and epilepsy, and (2) absence of loss of consciousness.
EEG data collection and preprocessing EEG data were recorded using a 64-channel EGI HydroCel Geodesic System (Electrical Geodesics Incorporation, Eugen, OR, United States) with a sampling rate of 250 Hz with respect to Cz as the reference electrode. Three one-minute eyes-closed resting-state data, each followed with one-minute eyes-open resting periods were collected from each subject. The eyes-open condition was included to prevent subjects from falling asleep during the experiment. The subjects were requested to remain fully relaxed and motion-less during the recording. Figure  1  shows the main processing pipeline including preprocessing, scalp EEG spectral and cortical source analyses. The EEG preprocessing was performed using custom MATLAB scripts, EEGlab (v2019. 0, Delorme and Makeig, 2004)  and Fieldtrip toolbox (v2019,  Oostenveld et al.
<Figure 1> The EEG data were first inspected visually by an EEG expert using the EEGlab toolbox to identify and mark all artifacts including eye movement, eye blinking, muscular activation and movement artifacts as well as noisy/bad channels, which were replaced by interpolating their neighboring electrodes data using the Fieldtrip toolbox. The average number of interpolated electrodes was 3 (± 2. 6) per subject. The artifactual portions of the EEG recordings were excluded from further analysis. The remaining EEG data were bandpass filtered between 0. 5-30 Hz using a fourth-order Butterworth filter, and then re-referenced using a common average reference. The artifact-free filtered EEG data were partitioned into five-second epochs with 25% overlap to avoid information loss caused by windowing.
To avoid the small sample size effect, only the EEG data of participants who contributed at least twenty artifact-free segments were included in the analysis. In total, the EEG data from 41 healthy control (13±2. 3 years; 31 males/10 females) and 40 ADHD children (12. 9±2. 4 years; 32 males/ 8 females) including 10 inattentive and 30 combined were used for further analysis. On average, 37 (±9) segments for each healthy subject and 31 (±10) segments for each ADHD patient were obtained. For the EEG spectral and source analyses, twenty segments were randomly selected from the artifact-free eyes-closed segments from each subject. To test the effect of age and ADHD subtype on EEG spectra and current source densities, we grouped the patients and healthy subjects into six subgroups as listed in Table  1.
, 1995)  to account for collinearity among EEG electrodes or ROIs by controlling the number of false positives among the subset of electrodes/ROIs labelled as significant. The correction for multiple comparison was performed across all electrodes/ROIs and five dependent variables (delta, theta, alpha, beta or TBR) with an adjusted level of significance at p < 0. 05. At the source level, we used statistical nonparametric mapping method (SnPM) implemented in the LORETA-KEY package to assess age/subtype-related statistical differences in normalized current source densities (nCSD) between groups using voxel-byvoxel log of F-ratio tests with 5000 randomizations in each frequency band  (Holmes et al. , 1996). Using this approach, a nonparametric permutation procedure (with 5000 randomization) was first used to identify cortical voxels with significant differences (p<0.
, 2001)  to obtain maps of log-F-ratio statistics for corrected p < 0. 05 in each frequency band. We also compared the average nCSD between the groups in each ROI using the same statistical analysis used at the sensor level (repeated measures ANOVA, FDR corrected with p<0. 05). We further assessed linear associations between age and EEG spectra/current source densities in each frequency band using the linear regression analysis implemented in eLORETA software. We determined the critical probability threshold of the regression coefficients across all voxels/electrodes/ROIs and frequency bands with the nonparametric randomization method (5000 randomizations) based on corrections for multiple comparisons  (Nichols and Holmes, 2002; Winkler et al. , 2016). The linear regression analysis was also performed to evaluate correlations between age and EEG power in each frequency band at each scalp location and ROI.
, 2008). Both of these subgroups show elevated theta/beta power ratios. In our study, we did not investigate the presence of these subgroups as studied by  Lansbergen et al. (2011). Such a study requires individualized frequency bands instead of using fixed frequency bands  (Lansbergen et al. , 2011). Fourth, we used a standard head model (MNI152) and standard electrode positions determined according to the 10-10 international standard system for source analysis using eLORETA. It has been shown that electrode misplacement (compared to the actual positions of the electrodes during the recording) and other head modeling errors can affect the accuracy of EEG source localization methods  (Michel and Brunet, 2019). Finally, we used cross-sectional EEG data rather than longitudinal data from the same subjects.
Acknowledgements All authors contributed to the final manuscript. This research was supported by the  National Science Centre Poland Grants :  2011/01/D/NZ4/04958  for A. C. -K. and  2015/17/N/HS6/03020  for K. K. It was also partially supported by the  Iran National Science Foundation (INSF)  and  Cognitive Sciences and Technologies Council (CSTC)  under the grant numbers 97008049 and 6672. The study sponsors had no role in the study design, data collection and analysis, or preparation of the manuscript.
ACKNOWLEDGMENTS We thank  Roustem Khazipov ,  Rosa Cossart, Valerie Crepel ,  Tarek Deeb , and  Isabel Del Pino  for useful comments and discussions on a previous version of the manuscript;  Delphine Gonzales ,  Nathalie Aubailly , and all of the personnel of the  Animal Facility of the NeuroCentre Magendie  for animal care;  Katy Lecorf  for help regarding virus preparation; the UMR5293 vector core facility for producing adeno-associated virus (AAV) vectors; and the laboratory of  S. M. Sternson  ( Howard Hughes Medical Institute ) for providing the chemical compound PSEM-89S. This work was performed thanks to the following funding sources:  Inserm  (X. L. ),  CNRS  (X. L. ),  Region Nouvelle-Aquitaine  (X. L. ), the  Japanese Society for the Promotion of Science (JSPS)  (X. L. and H. H. ),  Agence Nationale de la Recherche (ANR)  (X.
Study design and population The Effect of Language and Confusion on Pain During Peripheral Intravenous Catheterization (KTHYPE) study is a randomised, controlled, parallel-group, single-blind, multicentre, international clinical trial conducted in three hospitals: Rennes University Hospital (Rennes, France); Saint-Gr egoire Private Hospital (Saint Gr egoire, France); and Cliniques universitaires Saint-Luc, Universit e catholique de Louvain (Brussels, Belgium). The Rennes University Hospital Institutional Review Board reviewed and approved this clinical investigation (No. ID-RCB 2015-A01353-46), which was registered at https://clinicaltrials. gov/ct2/show/NCT02662322 (NCT02662322). The study was approved by all local ethics committees (No. 2015-A01353-46e2016 01 07). Adult patients >18 yr of age requiring a 20G PIVC on the dorsal surface of the hand before a scheduled surgery were included.
Data collection At each participating centre, data were collected and entered into the electronic web-based case report form by two investigators (NF and FR) blinded to the allocation group, under the supervision of the trial site investigators. Data collection was monitored by trained clinical research associates. Data collected by the anaesthesiologist researcher were anthropometric (age, gender, and BMI), type of surgical procedures according to the classification of the American College of Cardiology/American Heart Association,  25  standard of education, and socio-economic category. To ensure a blinded evaluation, the presence or not of spontaneous patient arm, face withdrawal, smile, or an unprompted vocalisation or comments were also recorded by the anaesthesiologist researcher. The subjects were asked to quantify their pain, anxiety, and comfort on 11-point NRS (0¼no pain to 10¼worst imaginable pain experience, 0¼no anxiety to 10¼worst imaginable anxiety, and 0¼no comfort to 10¼best imaginable comfort) just before (for anxiety and comfort only) and within 3 min after the completed PIVC.
Statistical analysis was performed with SAS version 9. 4 (SAS Institute, Cary, NC, USA). All data analyses were performed by researchers blinded to the group allocation. Normal distribution was assessed by descriptive statistics and histograms. Quantitative variables are presented as mean (SD) for continuous variables and as n (%) for categorical variables. An analysis of study outcomes was performed by one-way analysis of variance for continuous variables, and c 2 test or Fisher's exact test for categorical variables. In cases of statistical significance, two-by-two comparisons were performed with a Bonferroni adjustment threshold for multiple testing. A sensitivity analysis taking into account the standard of education was also performed for the primary outcome using twoway analysis of variance, in which the groupestandard of education interaction was tested.
Increased incidence rates of narcolepsy type 1 (NT1) after the 2009-2010 H1N1 influenza pandemic (pH1N1) have been reported world-wide. While some European countries found an association between the NT1 increase and H1N1 vaccination Pandemrix, reports from Asian countries suggested the H1N1 virus rather than Pandemrix to be linked with the increase of new NT1 cases. We analyzed the number of de-novo NT1 cases in the last two decades until 2016 using the European Narcolepsy Network (EU-NN) database. Using robust data-driven modelling approaches we confirmed the peak of NT1 incidence in 2009-2010 pH1N1 and identified a new peak in 2013 that is age-specific for children/adolescents. Most of these de-novo cases showed a subacute disease onset consistent with an immune-mediated type of narcolepsy, which is most likely not related to Pandemrix vaccination that was used in 2009-2010, but may have been triggered by some new epidemiological event in Europe.
European Medicines Agency recommended restricting use of Pandemrix but the H1N1 virus has still circulated after 2009-2010 pH1N1. Currently only limited data after 2010 (i. e. , only until 2012 or 2013) were available from some countries  9, 14, 15. In a report from the Finnish Department of Health and Welfare the incidence started to decrease significantly after the second year since Pandemrix vaccination  16. This means that a possible later increase in incidence (after end of 2011) may be due to H1N1 influenza virus or some other agent that have been used after 2011. The European narcolepsy network (EU-NN) is an association of leading European sleep centers. It launched the first prospective European web-based database for narcolepsy and related disorders which allows collection, storage and dissemination of data on narcolepsy in a comprehensive and systematic way  13.
The current EU-NN database includes data of 994 NT1 patients from 26 sleep centers diagnosed from 1980s to 2018. In this study, we use a data-driven approach to compare the numbers of NT1 patients presenting symptoms before, during and a few years post the 2009-2010 pH1N1 in EU-NN database. Giving that the EU-NN database is of good quality control  13, 17  , i. e. , the structure and data acquisition is standardized according to EU-NN regulations and the data are collected from most of the leading European sleep centers guaranteeing reliable diagnosis of NT1, we believe that this study can deepen our knowledge of the etiology of pH1N1 or vaccination associated narcolepsy in 2009-2010. Using the EU-NN database we aim to 1) test whether we could replicate the Chinese findings of increased NT1 including those cases that haven`t been vaccinated with Pandemrix in 2009-2010 in a pan-European database; 2) test whether the increased NT1 peak was unique for the 2009-2010 influenza season or repeated over time, compatible with the so called multiple hit hypothesis.
Methods Each center of EU-NN has obtained ethical approval for publishing the patients' data for scientific purpose by a national Institutional Review Board before entering patients (please refer to the section 'Data access policy, ethics and security specifications' of reference  13  ). The scientific review committee of EU-NN has approved the study protocol. All methods are in accordance with the relevant guidelines and regulations. All patients have provided their informed consent to be entered into the EU-NN database and their data can be used for scientific studies. Patients with NT1 were diagnosed according to the third edition of International Classification of Sleep Disorders (ICSD-3). Patients with daily periods of the irrepressible need to sleep or daytime lapses into sleep occurring for at least 3 months and the presence of at least one of the following: 1.
Cataplexy and mean sleep latency of 8 minutes or less and at least 2 sleep onset rapid eye movement periods (SOREMPs) on a multiple sleep latency test (MSLT). A SOREMP on the preceding nocturnal polysomnography may replace one of the SOREMPs on the MSLT. 2. Cerebrospinal fluid (CSF) hypocretin-1 concentration, measured by immunoreactivity, is after conversion to Stanford values either 110 pg/mL or less, or less than one third of mean values obtained in normal subjects using the same standardized assay. The following criteria were used exclude NT1 patients from the EU-NN database for our analysis: 1. Patients with an indefinite diagnosis. The EU-NN database contains a variable on certainty of clinical diagnosis. The clinicians were asked rate their diagnosis certainty on a 3-level basis (probable, possible and definite).
However, ARIMA cannot use the data after the 2009 pH1N1 episode to predict the numbers in 2009-2011. We therefore used LOESS (locally estimated scatterplot smoothing methods)  18  , another model that allows us to exploit the entire dataset, both before and after 2009-2010 pH1N1 to predict the numbers of cases in 2009-2011. Similarly as the aforementioned analyses done with ARIMA models, we then predicted the numbers of NT1 onset in 2009-2011 using the LOESS models and calculated the ratios between the real and predicted numbers of patients and their 95% predictive CIs. In addition, we divided the database into two subgroups, children and adolescent cases (age of starting EDS ≤18 yrs, n=256, f: 127, m: 129) and adult cases (age of starting EDS >18 yrs, n=252, f: 103, m: 149), and repeated the LOESS modeling in the two subgroups to further investigate whether the increased numbers of NT1 in 2009-2011 were age-specific.
Discussion Using data-driven modelling approaches, we analyzed the changes in the number of the new NT1 cases (i. e. , the onset of EDS) in the last two decades using the European Narcolepsy Network (EU-NN) database. As a major result we identified a new peak of NT1 incidence in 2013 that is age-specific for children. In addition we confirmed the peak in 2009-2010 pandemic H1N1 influenza that has already been identified in China and in some individual European countries. We consider our results as robust since we used a more sensitive model to analyze the data and, for the first time, took the delayed diagnosis into account, which was a major bias of previous studies. Several other aspects of our findings, in particular the agespecificity, the subacute disease onset of these de-novo children cases and the restrained use of Pandemrix after 2010 allow us to argue for a new epidemiological event triggering the increased NT1 cases in 2013.