dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Protocol Before data collection, the subject had 5-7 s to reach steady motion on the treadmill. Data collections (3 min each) were performed five times during treadmill walking: before FES (Before), three times during FES (Stim1, Stim2, and Stim3) and after FES (After). The same protocol of data collection was used in the Control group where participants walked for 40 min on the treadmill without FES. In the FES group, the experimenter increased the intensity of stimulation every 10 min (muscle by muscle, in the range 0-250 µs of impulse duration under the verbal control of the subject-every time up to the tolerant level of pain intensity). Each increase was performed during the first minute of each 3-min interval of data collection.
Data Analysis The spatial coordinates of each marker were recorded, the body being represented as an interconnected chain of rigid segments. Kinematics data were filtered with a low-pass zero-phase shift Butterworth filter with a 5 Hz cut-off frequency. Stride length, walking frequency and velocity were estimated using the body mid-point (average of left and right GT, IS coordinates). This resulting point provides a good estimation of the center of mass  (Courtine and Schieppati, 2003). The elevation angle of each segment in the sagittal plane corresponds to the angle between the projected segment and the vertical and were computed as: θ i_sagittal = tan -1 [(X id -X ip )/(Y id -Y ip )], where X and Y designate the coordinates of the proximal (p) and distal (d) markers for the ith frame of the acquisition.
Inclusion criteria We included studies that meet the following eligibility criteria: published in English; prospective design (cohort study) with individual level exposure and outcome data; examined the effect of job strain, as defined in the theoretical model by Karasek; 5 weight gain or development of obesity as an outcome; and reported either estimates of relative risk, odds ratios or hazard ratios with 95% confidence intervals (CIs), or provided sufficient results to calculate these estimates. Data synthesis and statistical analysis Meta-analysis was used to combine the estimates from the studies reported as odds ratios or relative risk. Associations of job strain with weight gain (yes/no) and becoming obese (yes/no) were analyzed separately. The basic model was adjusted for age, sex and socioeconomic status (SES) as covariates.
SES is associated with job strain (the highest prevalence of job strain is typically in low-SES jobs) and multiple health outcomes, including obesity (overweight and obesity are most common in low-SES groups). 16, 17 We also examined associations of increase and reduction in job strain and change in weight or obesity risk in studies where relevant data were available. We examined heterogeneity of study-specific estimates using the I 2 statistic; as it was low (⩽48% in all analyses), we used fixed effects analysis (for comparison, results from random effects meta-analysis are reported in Supplementary Appendix). All statistical analyses were performed using Stata (MP version 11. 2, College Station, TX, USA).
ACKNOWLEDGEMENTS MK is supported by the  Medical Research Council  ( K013351 ),  NordForsk , the  Nordic Council of Ministers  (grant  75021 ), the  National Heart, Lung and Blood Institute  ( HL36310 ), the  National Institute of Aging  ( AG034454 ), the  Academy of Finland , and an  ESRC  professorial fellowship. MV is supported by the  Academy of Finland. We thank  Mr Kimmo Kallio  for help in retrieving the literature.
CONCLUSION This experience addressing hierarchy in medicine head-on was enlightening. We are encouraged to mix patients with health care professionals in future groups. Our next research step will be to analyze data pertaining to audiotapes of doctor-patient encounters pre-and post-MBSR to determine if improvements in communication and patient care are evident for those doctors who took the course. Patients in those encounters (not in the MBSR course) have independently rated their doctors using the Rochester Communication Rating Scale, developed by Dr. Ron Epstein's team where mindful practice is taught and studied  8. Thus, we will have independent ratings of the impact MBSR has on the practice of whole person care. ■.
Methods and analysis: The realist evaluation cycle structures the protocol. To develop the theoretical basis for the evaluation, the authors conducted interviews and reviewed the literature on community health volunteers' performance, management and organisational behaviour. This led to the formulation of the initial programme theory, which links the intervention inputs (capacitybuilding strategies) to the expected outcomes (positive work behaviour) with mechanisms that point in the direction of drivers of motivation. The contextual elements include components such as organisational culture, resource availability, etc. A case study design will be adopted. We define a case as a Red Cross branch, run by a programme manager, and will select two cases at the district level in Kampala. Mixed methods will be used in data collection, including individual interviews of volunteers, participant observation and document review.
The resulting CMO configurations are then assessed to see whether they hold as plausible patterns (in realist evaluation also called 'demi-regularities') that explain how the intervention brought about the observed results. We will complete findings by combining the different data sources (observation notes, interviews and secondary document analysis) to confirm or reject initial findings and themes. The results will be a (set of ) CMO configuration(s) that provide plausible explanations for the observed outcomes and that are backed up by empirical data. Step 5: Synthesis In the last step of each case study, we will relate the retained CMO configurations to the initial PT and accept, modify or reject its clauses in order to formulate a plausible explanation of how and why specific strategies for implementation of community-based intervention involving volunteers bring about better work behaviour among volunteers.
Data collection and instruments Collected data included utility measures, socio-demographic characteristics, and clinical and treatment information. Patients' health states were assessed using Positive and Negative Syndrome Scale (PANSS)  [9, 24] , Calgary Depression Scale for Schizophrenia (CDSS)  [1] , and global assessment of functioning (GAF)  [17] , and extra-pyramidal symptoms were measured by Barnes Akathisia Scale (BAS)  [6]. The utility measures were computed from the multiattribute EuroQol EQ-5D questionnaire  [13, 23, 40] , using the British scoring formula. EQ-5D measures five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has three categories of severity: indicating no problem, some problem, and extreme problems. Patients are asked to choose the severity level for each dimension that corresponds most to his health state according to his perception.
Statistical analysis Descriptive analyses were conducted for all variables, and correlation structures were examined. In case of high correlation between explanatory variables, the relevant variables cannot be selected with respect to their statistical significance as it may result in the selection of collinear variables and estimation biases. Therefore, an alternative method was used for the variable selection: An approach similar to that of the principal components analysis (PCA) was conducted by selecting variables with the highest contribution to the variance. Contribution to the variance was assessed iteratively with removing the variable that had shown the highest contribution to the variance at the previous step. To assess the selection bias, two sub-samples were considered: a sub-sample with no missing data and a subsample with missing values in at least one of the following variables: EQ-5D, PANSS_POS, PANSS_NEG, PANSS_PSY, age, sex, ATYP, CDSS, GAF, or BAS.
Measuring predictive ability and cross-validation The predictive errors for the various models are presented in Table  4. The results for the cross-validation are reported in the same table for facilitating the comparison. Various proportions p of the observations used to construct the training set were chosen to verify the strength of the models; p = 0. 5, 0. 25, and 0. 1 were tested. RMSE and the MAE values were similar for all analyses, showing no increase depending on the training set size. It can be concluded that the models are robust and can be used to predict the utility measure for other datasets.
Data collection The self-administrated questionnaire and a newsletter were sent to each participant between June and September 2011. Each subject was assigned to an anonymous number and given 30 min during their work schedule to complete the form. The participants' supervisors were informed by mail of the survey details before the start of the study. The responsible physician collected all questionnaires in each department. Four reminders were sent between September 2011 and January 2012 by e-mail, phone call or directly to the participant at their workplace. The mean age of all respondents was significantly lower in the respondent group than in the non-respondent group. There is a significant number of nurses and aides represented in the respondent group.
Several recent studies  (Nocker and Richter-Heitmann, 2010; Rogers et al. , 2013; Chiao et al. , 2014; Cuthbertson et al. , 2014)  demonstrated that a sample pre-treatment with propidium monoazide (a chemical molecule able to penetrate exclusively into cells with an intact membrane which are considered as viable cells) modified the bacterial community profiles obtained by NGS. The second challenge is to improve the taxonomic assignment quality by establishing an accurate updated fungal database. Since the internal transcribed spacer (ITS) regions of the rDNA are widely used for fungal species identification and recently formalized as universal DNA barcode markers for fungi  (Delhaes et al. , 2012; Schoch et al. , 2012) , several projects have been conducted to develop fungal databases, such as ITSDB or UNITE for QIIME released 3   (Diaz et al.
, 2014)  or ISHAM ITS Database 4. The later ITS database was recently generated from quality controlled ITS sequences, which represent the actual sequence variation found in each species. Given that fungi represent the largest family of microorganisms with multiple names, another challenge is to prevent unnecessary nomenclatural flux, as recently proposed  (de Hoog et al. , 2014). In this context, the ISHAM ITS or UNITE databases represent a nice opportunity to create an accurate database with consensual nomenclature. Further advances in data generation and analysis are also required to minimize errors and misinterpretations, to make cross-studies and multicenter trials more feasible, and finally to link the bench and the clinic. To understand the biological function of the respiratory mycobiome in host inflammatory lung responses, its implication in CRD progression, its role in local cross-kingdom microbial interactions, as well as in the cross-talk between the intestinal and lung microbiomes, future large-scale cross-sectional and longitudinal studies must be conducted to answer these questions raised.
ACKNOWLEDGMENTS LD has received research grants from the  French Ministry of Health and Research (PHRC No. 2006/1902 ),  Lille Hospital , and the  Pasteur Institute , the association " Vaincre la Mucoviscidose" (Defeat Cystic Fibrosis ; Mucofong-ATF  N8 2006/351 ,  MucoBacMyco-RC 2012-0600697 ), and the  Pharmaceutical Division of Pfizer France  ( Nu 2006/158 ). The authors would like to thank  Nigel J. Clifford  for English editing.
Participants and sampling Interviews were performed between April and November, 2012. Participants were hepato-pancreatico-biliary (HPB) surgeons. HPB surgery is high risk, associated with a 90-day mortality rate of 3 to 5% and a severe morbidity rate of 25%, and applied mainly to treat malignancies. According to the Grounded Theory methodology, a theoretical purposive sampling technique using maximum-variation  [15]  was used (sampling as wide a range of perspectives as possible to capture the broadest set of information and experiences, guided by the emerging data); accordingly, we interviewed surgeons with different ages, experience, academic positions, and geographical locations of practice. We initially contacted 27 surgeons, and all agreed to participate. The final sample size was determined by data saturation (i.
Data collection Data were collected through face-to-face unstructured interviews, all conducted by one researcher (MO) in French. The initiating prompt was "Can you describe in detail your last two working days-from the moment you woke up to the end of the day?" This approach suited our objective of obtaining an in-depth narrative; as the surgeons recalled their activity, the interviewer frequently prompted them to expand on their feelings and thoughts. The surgeons accepted open discussion easily and never focused only on the past two days, telling also about past experience and their general point of view.
Results Participants worked at 10 different teaching hospitals throughout France, their mean age was 45 years, and 4 were women; 14 were professors of surgery (including 7 department heads), 6 were qualified surgeons with a full-time contract, 5 were chief residents in surgery (6 to 9 years in training), and 2 surgical residents (second and fourth years in residency). The median interview lasted 48 minutes (range: 23-74 min). Data analysis identified 7 main (superordinate) themes. They focused on the situations perceived by the surgeons to have significant emotional features. We organized these themes along the timeline of surgical care: emotions before, during and after surgery. For each theme we propose 3 elements, corresponding to what surgeons described as their principal emotions, the possible causes, and their coping strategies (Table  1 ).
Funding: This study was funded by a  Programme Hospitalier de Recherche Clinique grant  ( PHRC National 2011 ,  AOM 11060 ) from the  French Ministry of Health  awarded to OF and by the  Association de Chirurgie Hepato-Biliaire et de Transplantation Hépatique (ACHBT). OF granted the funding. The funders had no role in study design, data. collection and analysis, decision to publish, or preparation of the manuscript.
bkslab. org/). The URLs for most online tools in the field are listed at our website www. vls3d. com (21). We have developed an online tool in 2006  (22)  named Free ADMET Filtering for Drugs (FAF-Drugs) that was optimized in 2011 (23) (FAF-Drugs2) and that is now much further enhanced (FAF-Drugs3). It should be noted here that freely available tools most often compute data for only one compound and/or do not compute several of the rules mentioned above or, else, compute properties that are not implemented in FAF-Drugs and as such are complementary to our server.
WEB SERVER FAF-Drugs3 is user-centered as it has a new user-friendly interface with new graphical windows that facilitate the analysis of the compounds online. The FAF-Drugs3 web server is an easy-to-use service consisting of a set of seven object-oriented Python modules embedded in the RPBS' Mobyle framework  (25). Mobyle is a centralized workspace for the end-user and an on-the-fly program results pipelining. This portal also allows users to open a personal session upon registration (not mandatory) where data are stored. Each compound processed by FAF-Drugs3 is represented as a molecular object importing methods from the OpenBabel toolkit through its Python wrapper Pybel which allows to access to the OpenBabel C++ library  (26). Furthermore, FAF-Drugs3 Python modules act as generators of methods belonging to this chemical object and needed for the filtering steps.
The Mobyle frontend submits all the FAF-Drugs3 processes to an 800 core cluster running on Debian Operating System and managed by SUN Grid Engine. In the front-page, the user is invited to upload a file and to select the type of filtering computations that should be carried out. As illustrated in Figure  1 , FAF-Drugs3 performs the following main procedures: (i) an input data curation stage that removes large molecules and compounds containing some types of inorganic atoms and a desalting procedure, a structure normalization step and the removal of duplicates, (ii) the computation of several physicochemical properties and rules and the filtering of the small molecules, (iii) the detection of substructures such as potential toxic groups, aggregators and PAINS and (iv) the output section where the results are reported and from where files can be downloaded.
Output Once the FAF-Drugs3 process is finished, the user is redirected to the result pages. All data can be downloaded and two key web-based interfaces are offered (see Figure  1 ). The first one can be defined as a summary result page where users can find links for downloading all filtered compound files Accepted. sdf (compounds with no structural alerts and satisfying the physicochemical filter), Intermediate. sdf (lowrisk structural alerts), Rejected. sdf (the molecules that do not pass the selected or user-defined physicochemical filter which include a high-risk structural alert and/or exceed the threshold of occurrence of low-risk structural alerts) and PAINS. sdf (molecules flagged due to the presence of some chemical groups that belong to the PAINS category). Several tabulated files are also available: results.
Curation procedure A data curation procedure is applied with several chained steps: (i) Removal of large compounds (i. e. molecules with more than 120 heavy atoms), of compounds that contain an atom other than H, C, N, O, F, P, S, Cl, Br, I, B and isotopes. (ii) A desalting procedure, which was optimized in order to correctly detect the salt part of a mixture. We collected 211 most used salts in medicinal chemistry and designed the corresponding SMARTS strings. It is important to note that if users only need to curate a small compound library (without computing other properties), then the Bank-Cleaner service (http://fafdrugs3. mti. univ-paris-diderot. fr/links) can be used. To illustrate this initial step, we processed the third release of the NCI Open Database (September 2003) that contains 260 071 structures.
Physicochemical descriptors In order to generate a compound collection with acceptable physicochemical properties, FAF-Drugs3 computes 17 descriptors: MW, number of rigid and rotatable bonds, log P (with XLOGP3 (29)), log D (using the ChemAxon package (www. chemaxon. com)), tPSA  (30) , number of RO5 HBA and HBD (3) (HBA and HBD are detected according to the publication with an in-house SMARTS definition depicting, respectively, the number of N and O atoms and the number of OH and NH), number of heavy atoms, heteroatoms/carbon atoms ratio, number of rings, maximum ring size, number of stereocenters and Fsp3  (11) , number of charges and the formal charge of the molecule. In order to correctly compute descriptors depending on the charged state of the groups, a protonation procedure, as implemented in ChemAxon, is used.
e. when its log D (pH7. 4) + number of Aromatic Rings < 5 (the log D is computed with the ChemAxon's Calculator Plugins Academic package (www. chemaxon. com)). Our web server, along with descriptor calculations, computes also several other rules: the GSK 4/400  (9)  and Pfizer 3/75  (10). In addition, FAF-Drugs3 embeds the freely available Eli-Lilly open drug discovery medicinal chemistry software  (12). This package was developed to flag, accept or reject compounds according to the presence of difficult chemical groups and of some other properties including their resemblance to known drugs. A compound that passes this filter can enter the open drug discovery program offered by the company. Furthermore, if the users are interested in preparing a compound collection potentially enriched in inhibitors of protein-protein interactions, we have previously reported a decision tree model named PPI-HitProfiler (44) that analyzes the 3D shape of a compound and search for a critical number of multiple bonds, this tool is now implemented online.
Toxicophores and molecules interfering with biological assays Structural alerts here include several known toxicophores, which are chemical moieties directly and/or indirectly linked to toxicity, and also substructures or molecules interfering with biological assays. Searches for such molecules or toxicophores were already performed in FAF-Drugs2 but we have introduced several changes and optimized the process. Regarding toxicophores, a survey of the literature was made in order to optimize the definition of the SMARTS strings used for the search as compared to the FAF-Drugs2 version. We gathered a consensus list of 154 documented toxicophores (see Supplementary Table  S1  for the name of the chemical groups and corresponding SMARTS codes), i. e. if there are no or very little published data on the groups, it is not implemented in FAF  (2, 4, 8, (12) (13) (14) (15) (16) 18, (46) (47).
1 H-NMR and 13 C-NMR spectra were recorded at 300/500 and 125/75 MHz, respectively, at 298 K unless otherwise stated. Chemical shifts are given in ppm (δ) and are referenced to the internal solvent signal or to TMS used as an internal standard. Multiplicities are declared as follow: s (singlet), brs (broad singlet), d (doublet), t (triplet), q (quadruplet), dd (doublet of doublet), ddd (doublet of doublet of doublet), dt (doublet of triplet), m (multiplet), AB = AB quartet, ABX = ABX system. Coupling constants J are given in Hz. Carbon multiplicities were determined by DEPT135 experiment. Diagnostic correlations were obtained by two-dimensional COSY, HSQC and NOESY experiments. Infrared spectra (IR) were recorded on a Perkin-Elmer FT-IR system (Villebon-sur-Yvette, France) using a diamond window Dura SamplIR II and the data are reported in reciprocal centimeters (cm ´1).
Representative Procedure for Gold Catalyzed-Cyclization of Allenol 7 To a solution of chlorophosphonite-gold complex 6a (3. 8 mg, 0. 005 mmol) in PhMe (0. 6 mL) at rt was added AgOTs (1. 4 mg, 0. 005 mmol) and the resulting solution was stirred for 10 min. The allenol 7 (50 mg, 0. 2 mmol) in PhMe (0. 8 mL) was then added and the solution was stirred for 1 h until TLC indicated full conversion. It was then directly purified on silica gel (eluent: EtOAc/heptane, 1/8), providing 8 (45 mg, 0. 18 mmol, 90%) as a colorless oil, which data were in full agreement with the literature  [60]. The enantiomeric excess was determined to be 9% by chiral HPLC (Chiralcel AD-H, eluent heptane/i-PrOH, 99. 5/0. 5).
Acknowledgments: G. M. and A. F. thank the  Institut de Chimie des Substances Naturelles  for financial support. X. G. thanks warmly  Pr A. J. Burke  and the organizing committee of the International Symposium on Synthesis and Catalysis 2015 (ISySyCat 2015, held in Evora, Portugal) and the  Editorial Office of Molecules  for the opportunity to publish this paper.
RESULTS In all 14 Romanian cases, dA-AL-I was detected in DNA samples obtained from the phenol/chloroform extraction method (range: 0. 7-26. 8 adducts per 10 8 DNA bases; Figure  2  and Supplementary Table  1 ) and below the limit of quantification (0. 3 adducts per 10 8 DNA bases) in all 15 non-Romanian cases. Only one Romanian sample was below the detection threshold when using DNA samples obtained from the Autopure extraction protocol. Thus, our data show that dA-AL-I is stable towards tissue storage in RNAlater and largely survives extensive DNA processing. Consistent with previous studies, dA-AL-II was below the limit of quantification in all subjects  (Yun et al, 2015). The load of A:T4T:A mutations in tumours was significantly higher in the 14 Romanian samples with detectable dA-AL-I than in the 15 other samples (Wilcoxon rank-sum test Po10 À 5 when comparing both the numbers and the proportions of A:T4T:A mutations).