dataesr/scientific-paragraphs-categorization huggingface dataset:
PRALIMAP Trial Briefly, the PRALIMAP trial (Promotion de l' ALIMentation et de l'Activité Physique) was a 2x2x2 factorial cluster randomized trial assessing the effectiveness of three interventional strategies to prevent overweight (education, environment and overweight screening and care management). Data were collected at three times: at the beginnings of grades 10 (T0), 11 (T1) and 12 (T2). Every academic year, an information letter was sent to the student's parents. Parents had to sign a written refusal to collect data for their children. In addition, students at school were also given written and oral information and had fully the right not to participate. The educational and environmental strategies were managed by trained health education professionals external to the high schools. Specifically recruited for the trial, these so-called PRALIMAP monitors explained the purpose of the measurements, reassured students about the confidential nature of the data, answered any queries and confirmed the right not to participate.
Study sample The only eligibility criterion for high schools was that they be state administered (n = 124). The PRALIMAP trial group randomly selected 24 schools after stratification on department and type of education (general and technological or professional) for participation in the PRALIMAP trial. Every selected high school headmaster agreed to participate (Flow chart, Fig. 1 ). So, 5354 adolescents aged 14 -18 years were present during the inclusion data collection (T0) process, and the Lorraine Board of Education provided PRALIMAP with their socioeconomic data. Among them 2523 were completers for BMI, PA and SB data. They constituted our study sample, recruited in the PRALIMAP trial performed.
Physical activity data PA was mainly assessed using the validated French short version of the International Physical Activity Questionnaire (IPAQ) modified for adolescents  [37] [38] [39] [40] [41] , in which the adolescent entered the amount of PA performed during the 7 days before questionnaire administration. The frequency (number of days per week) and the duration (minutes/day) of PA practice for three types of activity were assessed: vigorous, moderate and walking. Vigorous physical activity referred to activities that take hard physical effort and make the adolescent breathe much harder than normal (e. g. carrying heavy loads, digging in the garden or yard, mountain biking, playing football…). Moderate physical activity referred to activities that take moderate physical effort and make the adolescent breathe somewhat harder than normal (e.
Sedentary behaviour data SB was assessed using the validated French short version of the International Physical Activity Questionnaire (IPAQ)  [37, 38, 41] , in which the adolescent entered the amount of sitting time (minutes/day) during a weekday that was one of the 7 days before questionnaire administration. The adolescent was asked to take into account the time while at school, at home, while doing course work for school and during leisure time. This might include time spent sitting at a desk, visiting friends, reading, in front of a screen or sitting or lying down to watch television. The main variables considered were total, vigorous and moderate PA, walking scores, active commuting and sport participation and sitting time.
Acknowledgements The PRALIMAP trial group thanks all those who, through their various contributions, led to the implementation of this project. PRALIMAP Trial Group Agrinier N, Angel N, Ancellin R, Aptel E, Bailly F, Barthelemy L, Bezaz D, Bonsergent E, Collin JF, De Lavenne R, Dietz E, Enrietto P, Favre E, Gentieu M, Gouault E, Helfenstein M, Hercberg S, Kurtz F, Laure P, Legrand K, Lighezzolo J, Marx P, Osbery A, Piquee MO, Renaudin P, Robert G, Schichtel A, Tessier S, Villemin E, Wuillaume M.
, 2013). HLM was performed using the same procedure already proposed by  Chuang, Lee, and Shen (2014). Each HLM analysis was conducted in a hierarchical fashion using restricted maximum likelihood estimation for mixed effects regression models with SPSS software. In order to justify the data aggregation, we calculated r wg : this index allowed us to test the adequacy of the agreement within-group of our constructs in terms of organizations. The average values of r wg from. 72 to. 84 were obtained for the four organizational cultures. In addition, we calculated the intraclass correlation (ICC1): this was. 17 for Hierarchy,. 18 for Market,. 21 for Adhocracy, and. 20 for Clan. Finally, the reliability of group mean (ICC2) was. 62 for Hierarchy,. 64 for Market,. 68 for Adhocracy, and. 66 for Clan.
Data Collection Our dataset is derived from a French online discussion forum about drugs, illness, procedures, and other information relating to general health. As mentioned in the forum charter, discussions can be read and potentially used by all. We focused initially on the titles of discussions from 2013 to 2015. The participants themselves summarize the topic or question they post on the forum. In other words, we focus on a condensed form of the concerns of the participants regarding antidepressants or anxiolytics. The data extraction step is dependent on the data source and differs according to whether it is data from a website, from patient medical records, or from qualitative interviews. In our study, health information was extracted from Web pages via a program that explores the Web of data using R packages (R Project for Statistical Computing).
Our corpus of documents was formed from discussion titles. A page contains 50 topics and each topic consists of a title, an initial message (demand), and potential responses (messages). To create this database, we implemented the following process: first, we extracted pages including lists of discussions. Links to each discussion are found on the website in a specific location. The storage address for a discussion is indicated via a URL link. By capturing all the discussion addresses, we had access to the messages stored there in Hyper Text Markup Language (HTML) format. Second, each URL and each of the discussions were analyzed to remove unnecessary information (images and advertising) and to extract the date, titles, and discussion of messages in a Microsoft Excel file. There has been discussion about the ethical concerns of analyzing data retrieved from OHCs  [33].
To minimize these concerns, we searched for and included only the title of discussions that were publicly available. We used the dataset solely for statistical analysis and reporting of aggregated information and not for investigation of specific individuals or organizations. We have no prior knowledge of the possible identities of any study participants. We did not submit our study to an ethics committee because no user was interviewed. The identity of users is protected because of the use of alias, and all usernames or potential demographic characteristics in the results were removed from the dataset.
For example, the letter "a" can have several variants and is replaced by its generic form without an accent. Then, there is a harmonization step, consisting of converting all lowercase letters to uppercase. Each sentence is finally cut off using the punctuation that defines it. Punctuation marks are deleted. Finally, the spaces between the words are used to delineate them. The next step is to parse the text and to remove noninformative elements such as numbers or link words occurring in the database. The words and codes for data extraction from the Internet may also be present, such as XML and HTML tags (< html>, </ n>. ). Finally, a list of "stop words" is predefined in the software to automatically delete the list of prepositions and articles ( what, my,. ) that are not informative and to reduce the list of words that are most relevant to the analysis.
Study Protocol Within the ToyBox-study, an intervention with a randomized cluster design was developed following the PRECEDE-PROCEED model and the Intervention Mapping protocol  [27]. The intervention consisted of a pre-and post-test design with intervention and control kindergartens in all six European countries. The ToyBox-study (www. toybox-study. eu) is registered with the clinical trials registry (clinical_trials. gov, ID: NCT02116296) and was approved by Ethical Committees in all six European countries, in line with national regulations (i. e. , the Ethical Committee of Ghent University Hospital (Ghent, Belgium), Committee for the Ethics of the Scientific Studies (KENI) at the Medical University of Varna (Varna, Bulgaria), Ethikkommission der Ludwig-Maximilians-Universität München (München, Germany), the Ethics Committee of Harokopio University of Athens (Athens, Greece), Ethical Committee of Children's Memorial Health Institute (Warsaw, Poland), and CEICA (Comité Ético de Investigación Clínica de Aragón (Zaragoza, Spain))  [22].
Sampling The time plan of the ToyBox-intervention was designed so as to account for country-specific differences with regard to the opening and closing dates of the kindergartens and the duration and timing of national holidays. After recruitment of participants started in February 2012, baseline data was collected between May and June 2012  [22]. A minimum sample of 800 children and their families and 20 kindergartens per country, resulting in a total sample of 4800 children and their families and 120 kindergartens, was initially targeted. However, in order to account for an estimated dropout rate of about 30%, a minimum total number of about 6500 children and their families were aimed to be recruited in the six participating countries. Detailed power calculations are described elsewhere  [22].
Statistical Analyses Descriptive statistics were performed using IBM SPSS Statistics for Windows, version 21. 0 (IBM Corp, Amonk, NY, USA). To assess the effectiveness of the intervention on total diet quality and the four subcomponents, multilevel repeated measures analyses were performed using MLWiN, version 2. 30 (Centre for Multilevel Modelling, University of Bristol, Bristol, UK). Multilevel modelling was used to take clustering of baseline and follow-up measurements of preschool children in kindergarten classes within kindergartens within countries into account. Five levels were used: time, preschooler, kindergarten class, kindergarten, and country. To get more insight in the subcomponents, multilevel repeated measures analyses were also performed for the sub-items of each subcomponent.
Strengths and Limitations The present study holds several strengths. To our knowledge, this is the first study that examined more holistic effects of an intervention focusing on specific health behaviours in European preschoolers. Another strength of this study was the large sample of preschoolers from six European countries and the cluster randomized pre-and post-test design including an intervention and control group. There are also a number of limitations to the study. The data are self-reported, which may result in social desirability. However, this was partially covered by ensuring anonymity and because of the use of a within design and misreporting being comparable over different time moments, this is not a real problem. The FFQ used in the ToyBox-study was developed based on a previously validated FFQ developed by Huybrechts and colleagues which was specifically designed for use by parents of preschool children  [30].
FE model: geometry and material properties The geometry and the mesh of both the endoscope and the surrounding cerebral layer were designed with the Hypermesh ® software  (Altair Engineering, 1985). Endoscope and brain were represented by two cylindrical parts, respectively 5. 2 mm and 80 mm of external diameters. Brain model was a hollow at its center along its principal axis, of 5. 6 mm of diameter; the endoscope part was placed along its center (Figure  1 ). The thickness of the cerebral layer of one patient and the center of rotation of the endoscope were personalized using MRI images. Incompressible hyper-elastic material constitutive law was implemented for the brain model (Table  1 ), with a Poisson coefficient of 0,495  (Mihai et al. 2015). A linear perfectly elastic material was used for the endoscope model (E = 69 GPa and ν = 0.
Data gathering Data were directly and only collected through the sensors of the ABLE exoskeleton, without the use of any external motion capture system. The ABLE joint kinematic data were measured through differential encoders placed at the joints level. For the end-point kinematics (the tip of the rod) a direct kinematic model of the robotic upper-limb was used together with a measure of the subject forearm length. Post-processing was done using Matlab environment (© The MathWorks, Inc. ). All the recorded data were passband filtered through a 4th order Butterworth filter (cutoff = 5Hz).
If these effects were statistically significant, further two-by-two comparisons were performed with the Wilcoxon test. To analyse individual results, we focused on the shoulder abduction/adduction movements. For each subject we compared data from PRE and WAS (the mean final posture of the two movements in PRE with respect to the first two movements in WAS, therefore only during the first repetition of WAS), to determine the presence of after-effects, and data from early EXP and late EXP (the mean final posture of the first two movements in EXP with respect to the last two movements in EXP), to determine the presence of adaptation. On these two intervals, we therefore computed a Student's t-test. For the PCA analysis, we computed the distance metric (Section "Principal Component Analysis") between the subspaces describing the PRE trial and all the subsequent blocks in the same mode.
Funding This work was performed within the  Labex SMART  ( ANR-11-LABX-65 ), and supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir program  under reference  ANR-11-IDEX-0004-02. TP has received a grant (number  13020430 ) by the  region Ile-de-France. These public agencies had no role in the design of the study, data collection, analysis, and interpretation of the data, and in writing the manuscript.
Acknowledgments The authors acknowledge financial support from the  French  " Programme de Recherche Translationnelle en Sant e" (Call PRTS 2013 , Project #  ANR-13-PRTS-0011 , title: "VIBRATIONS"). This paper has been carried out within the  Federation Hospitalo-Universitaire (FHU) EPINEXT  thanks to the support of the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program  managed by the  French National Research Agency (ANR).
Data analysis Because the stimuli were moving exclusively along the horizontal meridian, we focused our analyses on the horizontal component of eye movements. We performed a sequence of analysis to separate periods of smooth pursuit, saccades, and blinks from the raw eye position signals. The identification of the blinks was performed by visual inspection. This procedure led to the removal of ϳ1% of eye recordings. Eye position time series were then low-pass filtered with a Butterworth (fourth order) using a cutoff frequency of 25 Hz. The resultant eye position signals were differentiated to obtain the velocity traces. The eye velocity signals were low-pass filtered with a cutoff frequency of 25 Hz to remove the noise from the numerical differentiation. The resultant eye velocity signals were then differentiated to provide the accelerations traces that we also low-pass filtered at 25 Hz to remove the noise.
A dedicated Matlab script was run to identify saccades. This identification was based on the acceleration and deceleration peaks of the eye (Ͼ1500°/s 2 ). Further visual inspection allowed to identify smaller saccades (Ͻ1°) that could not be identified automatically by our program. Based on these computations, periods of pursuit and of saccades were extracted. During the experimental (continuation) trials, we distinguished regular pursuit from the periods of target occlusion (after ϳ7 s). The first part and last part of each trial were analyzed separately. Each continuation trial was segmented into 1-s bins. The bin segmentation was set with respect to the initiation of target blanking. The first bin that followed the target blanking was named ϩ1, and the bin just preceding the target blanking was named -1.
Study selection for multi-study analysis Of all the known studies that have manipulated action selection and obtained a measure of agency, three were excluded from our analysis. One study only obtained agency judgements at the end of each block  (Wenke et al. 2010 ). Another study only had two outcomes, therefore did not require much action-outcome learning  (Damen et al. 2014). Finally, detailed trial-wise data were not available from one study  (Chambon and Haggard 2012). Furthermore, we only included the relevant data from the available studies: for Exp. 2, we only used the data for the half of participants who gave agency ratings at the end of each trial, as the other half gave block-wise ratings. Also, we excluded trials from the neutral flanker condition. For Exp. 6, we only used data from healthy participants, as the others were clinical patients.
Methods We conducted a retrospective descriptive study including all near-drowning patients admitted to the emergency and intensive care unit (ICU) of the University Hospital of Fort-de-France, Martinique, in the French West Indies over a period of four years  (2012) (2013) (2014) (2015). We included near-drowning patients regardless of their age and clinical severity; however, we excluded drowning patients taken to hospital who died on the spot. Patients were selected from among those encoded with a final diagnosis of "drowning" using the International Statistical Classification of Diseases and Related Health Problems, ICD-10. Data were collected from the selected patients' records. Severity of drowning was classified according to the four-stage classification of Van Berkel et al. [8] , adopted by the French Institute of Health Watch (InVS).
I. Introduction: Systems Approaches to Optimize Chronotherapeutics Broad interpatient variability in diseases and response to treatments has become increasingly apparent, so that personalizing medicine appears to be needed to ensure maximum treatment efficacy and minimum unwanted toxicities. However, a recent appraisal cautions the lack of consistent clinical benefits using the current personalized medicine concepts  (Tannock and Hickman, 2016). Optimizing therapeutic strategies should hence encompass both the specificities of the patient's pathology and the patient's genetics and lifestyle  (Khera et al. , 2016; Califano and Alvarez, 2017). To this end, multitype and multiscale datasets have been generated in preclinical studies in cell cultures and in laboratory animals, and in clinical investigations involving populations of patients or individual subjects  (Alvarez et al.
, 2016). The large volumes of data that are thus generated across species require dedicated approaches to properly analyze each individual dataset, to handle the complexity arising from multiple data types and dimensions, and to finally translate the results into individualized therapies. The rise of genomics and the accumulation of large amounts of data and longitudinal and dense multidimensional time series have paved the way for a new systems-based approach to biology. Systems approaches are defined in this work as interdisciplinary methodologies combining mathematical, statistical, technological, experimental, and clinical expertise for the development of dedicated devices, theoretical algorithms, and clinical protocols enabling treatment individualization. Systems medicine involves the implementation of such systems biology approaches in medical concepts, research, and practice, through iterative and reciprocal feedback between clinical investigations and practice and computational, statistical, and mathematical analysis, as it has been emphasized in the Roadmap of the Coordinated Action for Systems Medicine (CaSyM) from the European Union (https://www.
, 2010; Burgess et al. , 2015; Grima et al. , 2016). Next, the least invasive way to monitor the transcriptional output of the molecular clock is to measure the expression profiles of core clock genes in peripheral blood mononuclear cells (Fig. 2 ). This approach is feasible in healthy subjects  (Boivin et al. , 2003; Takimoto et al. , 2005) , albeit atypical patterns can be found  (Teboul et al. , 2005) , and only scarce data are available in patients. Finally, plasma, saliva, or even breath circadian "omics" datasets such as transcriptome and metabolome are now available in humans  (Dallmann et al. , 2012; Martinez-Lozano Sinues et al. , 2014).
IV. Systems Approaches toward Personalized Chronotherapeutics What is meant by systems approaches could be defined by the use of mathematical and statistical methods to analyze multitype and multiscale datasets. In the context of chronotherapeutics optimization, those pluridisciplinary pipelines mostly aim at designing patient-specific drug combinations and administration schedules. We first review the different mathematical models representing the circadian control of the following: 1) intracellular pathways within a single cell and 2) the dynamics of a whole cell population, in the absence of drug. Those models can represent either diseased or healthy cells/organs, and the next step toward therapeutic optimization consists in representing the drug chronoPK-PD on these tissues. We further explain why it is crucial to base the pharmacological modeling on the cell/organ physiology as it allows for multiscale approaches ultimately leading to reliable clinical models that provide the basis for a personalization algorithm.
Hence, modeling the dynamics of the key molecular events constitutes a rational basis for treatment optimization. Thus, physiologically-based pharmacology modeling, first introduced by  Rowland (1984) , has rapidly developed over the past years, resulting in the implementation by pharmaceutical industries of dedicated clinicallyfocused software such as PKSim (Bayer) and Simcyp (Certara). Physiologically-based modeling consists in the quantitative representation of the molecular pathways involved in drug pharmacology and efficacy. These models are based on ODEs and represent in each considered physical compartment the following: 1) the concentrations of the parent drug and its metabolites over time, resulting from biochemical events such as bioactivation, detoxification, passive diffusion, or active transport; 2) the drug activity on the cells, such as pathway activation, DNA damage leading to DNA repair, cell cycle arrest, and/or cell death.
Then mouse studies serve as a basis to design the drug-specific structure of the whole-body PK-PD model, which incorporates the cellular model to represent each considered organ. Next, a model for an average patient can be Systems Chronotherapeutics obtained by keeping the structure of the mouse model and resizing the parameters for humans using physiologic literature information combined to clinical datasets in patient populations. More precisely, model parameters can be scaled from mouse to human as follows: 1) organ volumes are inferred from literature values for each species according to age and sex  (Davies and Morris, 1993; Marino, 2012) ; 2) intracellular reaction rates are kept unchanged from the preclinical models; 3) protein activities are proportionally scaled according to interspecies in vitro studies  (Davies and Morris, 1993; Satoh and Hosokawa, 1998; Maier-Salamon et al.