dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods We studied the changes in pharmaceutical sales of iodine-based drugs in France through a stratified random sample of 3004 pharmacies set up by the company Celtipharm  [10]. Through their participation in the Xpr-Soß system, the pharmacies have automatically reported their sales several times a day since 2007. Thanks to a regularly updated exhaustive database of the 22,458 active French pharmacies, a stratification to improve representativeness is performed on sales revenue (6 levels for global revenue and 4 levels per type of sales: prescribed drugs, OTC, and other type of sales), localization (5 geographic areas) and sales area (5 types, from rural to densely urban). Each stratum has a minimum of 30 pharmacies or is merged with neighboring strata. Sampling rates per strata are computed with the Neyman optimal allocation algorithm  [11].
Extrapolations from the sample have been validated with data from drug manufacturers who distribute their products directly (and only) to pharmacies. We study sales data from January 2010 to April 2012 in order to include a reference period (the year 2010) and the period surrounding the Fukushima crisis. We compiled a list of 92 products containing or claiming to contain iodine based on a search for the pattern ''iod*'' in their name or principal ingredients. From that list, a total of 58 had been sold at least once in pharmacies from our sample during the study periods. We analyzed separately sales made with a physician prescription and those made without prescriptions. Sales were observed at the national and regional levels. The number of pharmacies selling the listed products was also analyzed.
Each experiment included an isotypic control where the pSTAT5specific antibody was replaced by an IgG1-AF647 antibody (BD Biosciences). After staining for 45 min at 4uC, cells were washed once in cold PBS and fluorescence was acquired on the same day on a Facs Canto flow cytometer (BD, Franklin Lakes, NJ) using the FacsDiva V software. Flow cytometry data were analyzed with the FlowJo V8. 8. 6 software (Tree Star, Ashland, OR). The inducible cytokine response (D%pSTAT5 + ) was measured by the difference in the percentage of pSTAT5 + cells before and after cytokine stimulation.
Observing the distribution of data points in Fig. 5A  (left panel) suggested that, for comparable CD25 expression, MTregs of ICL patients had lower IL-2 responses than MTregs of healthy controls. Thus, defective IL-2 responses in ICL could not be solely explained by a decreased expression of CD25. To determine whether a high level of circulating IL-2 may lead to a desensitization of IL-2 responses, we measured IL-2 concentrations in the plasma of healthy donors and of a subset of ICL patients (n = 7). We did not detect increased IL-2 levels in the tested ICL patients (median values: 20. 6 pg/ml in HD; 11. 8 pg/ml in ICL; P$0. 05; not shown), suggesting that an Interestingly, a positive correlation was detected between IL-2 and IL-7 responses (Fig. 5B ), suggesting that common mechanisms may perturb several cytokine systems in ICL.
Acknowledgements We are grateful to  Jean-Charles Dagorn  and  Caroline Bontemps  from  Dynabio S. A. for great help in the ELISA development. The authors were supported by  Institut National de la Sant é et de la Recherche M édicale ,  Centre National de la Recherche Scientifique, Institut National du Cancer ,  Association pour la Recherche sur le Cancer ,  La Ligue Nationale contre le Cancer , and a  France-Tunisia exchange program  CMCU-Utique. M. S. was supported by  Minist ère de la Recherche et de la Technologie , S. P. by  La Ligue Nationale contre le Cancer.
Statistical analysis Allele and genotype frequencies were estimated by the direct counting method, and adherences of phenotypical proportions to expectations under Hardy-Weinberg equilibrium (HWE) were tested by the complete enumeration method using the GENEPOP 3. 4 software  [34]. Linkage Disequilibrium (LD) between CTLA-4 SNPs was evaluated by means of Lewontin's standardized coefficient D' and by a likelihood ratio test of linkage disequilibrium implemented at the ARLEQUIN software  [35]. CTLA-4 haplotype frequencies were estimated in each population sample by a coalescence-based method implemented in the PHASE v2 software  [36]. The frequency of each allele, genotype or haplotype was compared between patients and controls by the two-sided Fisher exact test, with the aid of the GraphPad Instat 3.
Study design The TEMSO trial (NCT00134563) was a 2-year, multinational, multicenter, randomized, double-blind, placebocontrolled, parallel-group study designed to evaluate the efficacy and safety of teriflunomide in reducing the frequency of relapses and delaying accumulation of physical disability in patients with RMS. Detailed methodology of the TEMSO study has been published previously  [11, 17]. Briefly, eligible patients were aged 18-55 years, met McDonald criteria for MS  [8] , and exhibited a relapsing clinical course, with or without progression. For enrollment, patients were required to be ambulatory [Kurtzke's Expanded Disability Status Scale (EDSS)  [5]  score B5. 5], with at least two clinical relapses within the previous two years, or at least one during the preceding year, but with no relapses within 60 days of randomization.
This post hoc analysis of the TEMSO data set extends these observations. This analysis has limitations that are worthy of further discussion. First, any post hoc analysis has inherent limitations; the study was not powered a priori to evaluate the outcomes analyzed herein and therefore, further prospective confirmation is required to support our observations. Second, there is an apparent inconsistency between the positive effects of teriflunomide treatment on certain relapse outcomes and relapse intensity, for which no Fig. 3  Adjusted annualized rates of a all hospitalizations and b emergency medical facility visits significant benefit of treatment was observed. However, relapse intensity was assessed by the investigator based on a subjective rating only. For relapses with sequelae, both a subjective assessment based on an investigator rating and a more objective definition were applied which yielded congruent effects.
Germline polymorphisms in the ABCG2 gene have been described as affecting expression, cellular localization and/or substrate recognition of the encoded protein. More than 24 sequence variations have been reported. The most studied C421A (rs2231142) nucleotide change, results in a glutamine-to-lysine substitution in the translated protein (pQ141K). Among the studies of SNPs in the ABCG2 gene, two included CML patients treated with imatinib  [20, 26]. The fi rst showed that the homozygous GG genotype of rs2231137 in ABCG2 in advanced stage CML patients was signifi cantly associated with poor major or complete cytogenetic response  [26] , although this result was not subsequently validated in an independent cohort 20. We were able to test the association between ABCG2 haplotype G-G and 1772 other ABCG2related SNPs including rs2231137 and rs2231142 (not represented in our DNA chip) by means of an open-access database.
This question will be addressed in patients included in the OPTIMimatinib clinical trial, an ongoing prospective clinical trial evaluating imatinib dose adjustment driven by imatinib trough levels (OPTIM-Imatinib, EudraCT number 2010-019568-35). In conclusion, our results demonstrate the infl uence of a constitutive ABCG2 haplotype on the response to imatinib in CP-CML patients and raise the possibility of personalizing imatinib daily doses in this population on the basis of constitutive genotyping.
Study design This study was approved by the Human Ethics Committee of the St Louis Hospital, Paris. Written informed consent was obtained from all patients prior to study participation. Analyses were performed in two independent patient cohorts; the Saint Louis exploratory cohort (SLEC) treated at the Saint Louis hospital specialized clinical trial center (CIC), and the SPIRIT validation cohort (SVC) treated at participating centers in the SPIRIT trial  [36]  (clinicaltrials. gov: NCT00219739). Data were collected at participating institutions, analyzed using the sponsor's data management systems. Access to primary clinical data was available to all authors.
ACKNOWLEDGMENTS We thank  Pr Pierre Laurent-Puig  for REPAC network coordination,  Dr Christophe Blondel  for his contribution to SNP selection and validation,  Magali Nacfer  for logistical assistance, the  FRENCH-CML Group (FI-LMC) ,  Pr Jean Jacques Kiladjian ,  Dr Delphine Rea ,  Dr Odile Maarek , and all SPIRIT investigators for patient recruitment,  Dr Maud Vanpeene  and  Dr Mélanie Letexier  for technical assistance,  Dr Anne Marfaing ,  Dr Eric Delabesse  and  Dr Marc Spentchian  who contributed to DNA sample collection,  Dr Sarah Zohar  for assistance in ERB submission and  Dr Sarah MacKenzie  (funded by  Intergroup pour la Léucemie Myeloïde Chronique)  for manuscript correction. This work was supported by  INSERM  ( Pharmacogenetic-REPAC Network ), by the Grant  U03S03  from the  ECOS-Sud program (France-Uruguay) , a Grant from the  Association Jean Bernard , www.
impactjournals. com/oncotargetFrance and a grant from the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique  ( PHRC 2011 ,  11N28 ), SPIRIT ClinicalTrials. govIdentifi er:  NCT00219739. PR received research grants from  BMS  and  Novartis. JMC performs consultancy activity for Novartis.
For example, more than 600 articles are listed when the data bank PubMed is questioned about the Wingate test. The effects of training, athletic practice, diet, and pharmacological substances upon the production of maximal mechanical power will not be discussed in this paper mainly focused on the methodology and limiting factors of allout tests and  max in cycling. The influence of biochemical factors (acidosis and alkalosis, phosphate ions, etc. ) upon the results of the all-out tests probably depends on the protocol. Therefore, the bioenergetics of the all-out cycling exercises is recalled in the present review in addition to the biomechanical characteristics specific of maximal and high speed cycling. Thereafter, the different protocols are presented before the discussion of the technical and biological factors that determine the results to these tests.
Indeed, a large number of experimental force-velocity data is generally necessary to compute curvature indices. The force-velocity relationship in cranking (Figure  3 ) was first studied for cranking exercise in elite subjects practicing canoeing, kayaking, hand-ball, and boxing (Figure  4 ). The test began with a load equal to 1 kg. After 5 min of recovery, the braking force was increased by 1 kg, and the same exercise was performed again until the subjects were unable to reach a peak velocity higher than 100 rpm. The relationship between peak velocity and braking force was computed according to the least square method. The first and second bouts (1 and 2 kg) were considered as warming-up and learning exercises and were performed again at the end of the test. Therefore, the subjects generally performed 8 to 10 short all-out sprints, but the only second trials with 1 and 2 kg were taken into account in the computation of the force-velocity relationship.
The acceleration of the flywheel was computed every 50 ms from the flywheel velocity data given by a disc with 360 slots fixed on the flywheel, passing in front of a photoelectric cell (669 impulses for each pedal revolution). These all-out sprints were performed against 4 different braking forces (Figure  5 ) which corresponded to braking torques (  ) equal to  19, 38, 57, and 76  N⋅m at the crank level, that is,  equal to 2, 4, 6, and 8 kg. For each value of , the individual relationships between  and T could be described by a linear regression (Figure  6 ) and the values of  0 and  0 for each load were determined by extrapolation from these individual regressions. The relationship between  peak and  was also determined. The value of  max calculated from the usual - peak relationship ( max = 0.
The validity of the use of the force-velocity relationship for the prediction of field performances in sprint cycling has been verified in a study which compared maximal torque-and power-pedalling rate relationships estimated from the data of an inertial load test and power measured on the field  [198]. Torque was measured on the field with an SRM power transducer during 65 m all-out sprints in elite cyclists. There were no statistically significant differences between laboratory and field for  max (1791 versus 1792 W),  opt (128 versus 129 rpm), and maximum torque (266 versus 266 Nm). As a consequence, the linear regression slope of the torquepedalling rate was similar (-1. 040 versus -1. 035) in the field and laboratory tests.
5 h, 6 h, or 12 h). (a) Kinetics of blood-feeding in mites: for each time point, 10 uninfected (Uninf. ) or 10 infected (Inf. ) mites were blood-fed on ICR mice. The number of effective blood-fed mites (fed) was evaluated in each group. The experiment was repeated 6 times and pooled. Infection status and time effects were not significant (two-way analysis of variance). Results are expressed as mean ± SEM. (b) Rate of L3 infection in mites: for each time point, 10 mites from the group fed on infected jirds were blood-fed on ICR mice, and then fed and unfed mites were separated. Each individual mite was necropsied and the L3 were searched for and isolated. The experiment was repeated 6 times and the data were pooled. Feeding status was significant (two-way analysis of variance). A time effect was significant neither in "unfed" nor in "fed" group (Bonferroni's multiple comparison test).
major and L. mexicana) may be a confounder. # The duration is estimated by interrogating the patient. There is no accepted way to standardize this estimate and it is subject to recall bias. Duration of lesion should be included as an inclusion criterion. Depending on the species and the phase of clinical study, only patients with lesions lasting by patient history less than 3 or 6 months would be enrolled. # For selection of time for inclusion, provide the justification and rationale citing local data to show the natural history of CL in the study focus. While randomisation is expected to even out the distribution between treatment arms, it is possible that this factor is not similar in different arms which could influence healing rates either way, and thus reduce the power of the study, causing the study to fail to detect a difference when there was one.
The overall duration of follow-up will be based on local data on the natural history of disease, as well as practical considerations (e. g. dropout rates with longer follow-up) 4. Visists in-between the above are also encouraged, when feasible. For a unified, standardised efficacy reporting, a simple, dichotomous outcome definition as either ''cure'' or ''failure'' should be adopted, whereby ''cure'' can only be declared at the end of follow-up (Day 180-360), whereas ''failure'' can occur at any time (and will require rescue treatment). Figure  3  describes the decision-tree. ''Cure'' is defined as: N An ulcer that on Day 42-63 is completely re-epithelialised (Ø) and remains so on both Days 90 and 180-360 (no relapse) N An ulcer that on Day 45 is ,50% of the initial size and is completely re-epithelialised (Ø) on both Days 90 and 180-360 (no relapse) ''Failure'' is defined as: N An ulcer that on Day 42-63 is.
gov NCT01067443). The choice of the non-inferiority margin is very important as it governs the validity of the trial, and has also ethical implications  [44]. The objective is to avoid harmful treatment to be declared non-inferior, and to retain a treatment that brings a true benefit for the patient  [41]. The decision should be based on previous studies with the reference treatment and the minimally important effect that one wants to observe with the new treatment which would provide additional benefit for the patients. In order to identify the correct D, it has also been proposed to compare (i) the two-sided 95% confidence interval of the difference between the test and the reference treatment to (ii) a two-sided 95% CI of the difference between the reference treatment and the placebo based on historical data and meta analyses (if such data are available)  [45].
Acknowledgments The authors would like to thank all the participants to the workshop organised by  WHO/NTD  and WHO/TDR during 15 th -17 th December 2009, which provided the basis for the current paper. Specifically:  Mohammad Hossein Alimohammadian ,  Fabiana Alves ,  Abraham Aseffa ,  Afif Ben Salah ,  UrbaGonzalez ,  Lama Jalouk ,  Alejandro Llanos-Cuentas ,  Ivan Dario-Velez. We are also indebted with  Steven Senn  for reviewing the study design and statistical section and with  Pascal Launois  and  Christine Halleux  for critically reviewing the manuscript.
Super learner The discrete super learner has been proposed by Dudoit and Van der Laan et al. [13]  as a generalization of the stacking algorithms  [14] , to choose the optimal regression algorithm among a set of candidates. The underlying selection strategy relies on the choice of a loss function, which aims at evaluating the gap between the actual and the predicted outcomes for each candidate. The comparison between candidates relies on V-fold cross-validation: the dataset is divided into V mutually exclusive and exhaustive subsets of nearly equal size. At each V step of the procedure, one of the V sets serves as a validation set, while the others play the role of training sets for each candidate algorithm. Observations in the training set are used to construct the estimators, and observations in the validation set are used to assess the performance of the estimators, the so-called risk, on the basis of the chosen loss function (L2 or squared error in the present study).
At the end of the procedure, each set has served both as the training and validation sample. For each candidate algorithm, the super learner then averages the V estimated risks over the V validation sets, resulting in the so-called cross-validated risk, for each candidate algorithm. Based on their crossvalidated risks, the candidate estimators can be ranked and the optimal learner is finally applied to the entire dataset. Finally, a weighted linear combination of the candidate learners is used to build a new estimator, the so-called super learner estimator  [15]. We investigated the following algorithms: generalized additive model, generalized linear model, stepwise regression (forward and based on the Akaike information criterion (AIC)), polynomial linear model, random forest, neural network, Bayesian generalized linear model, elasticnet regularized generalized linear model, polynomial spline regression and gradient boosting.
The statistical analysis used some innovative tools such as SuperLearner  [35]  for prediction and TMLE  [16, 17]  for estimation. The idea behind super learning is to optimize the prediction performances, accepting the fact that we do not know anything about the true shape of the underlying data distribution, so that every kind of parametric regression model would be biased. SuperLearner allows us to use a large library of candidate regression algorithms, parametric of data-adaptive, to honestly evaluate their prediction performance, and to build a new, tailored algorithm that is a combination of the best candidates. As expected from theory, we found that SuperLearner outperformed each candidate algorithm included in its library. From such results, we expect SuperLearner to do the best possible job to estimate the overall probability distribution of the outcome in our dataset.
Methods The CREDIT study design, site and participant selection process and participant baseline characteristics have been reported previously  [6]. Briefly, the study involved 314 centres in 12 countries, 10 in Europe, plus centres in Canada and Japan. Eligibility criteria were: men and women with T2DM, aged >40 years, who had started insulin therapy (any type or supplier) within 12 months prior to study entry and who had an HbA1c measurement within the 3 months before starting insulin. As a non-interventional study, there was no fixed study visit schedule, and insulin choice, dosage, titration, funding and concomitant oral agent therapy were according to usual local practice. Data were gathered in routine clinical practice, with the treating physicians asked to report updated participant data on a 6-month cycle.
Data at the '1-year' follow-up represents that provided during the 9-18 months after beginning insulin. Ethical approval according to local regulations was obtained for all study sites. Conduct of the study adhered to standards of data collection for clinical trials, according to the Declaration of Helsinki. Written informed consent was obtained from all participants before commencement of data collation. To assess glucose control-related outcomes, we studied HbA1c, fasting plasma glucose (FPG), postprandial plasma glucose (PPPG), symptomatic hypoglycaemic episodes (overall), nocturnal hypoglycaemic events, body weight increase and switching to alternative insulin therapy.
Statistical Analysis Data are analysed and reported by descriptive statistics using sas software (v 9. 2; Cary, NC, USA). Propensity score-matched cohorts were used to assess blood glucose control outcomes (as above) by pairs of insulin regimens: basal, mealtime, basal + mealtime and premix (giving six possible pairings). No distinction was made between analogue and human insulins. The propensity score analysis was performed by one of the authors (N. F. ). Proc GLIMMIX in sas (v 9. 2) was used to develop propensity scores used as the basis for the selection of propensity score-matched cohorts. Each propensity score included the following baseline explanatory variables which could potentially be related with treatment allocation: age, sex, time since diagnosis of diabetes, weight, BMI, baseline HbA1c, use of biguanides, dipeptidyl peptidase (DPP)-4 inhibitors, glinides, glucagon-like peptide (GLP)-1 receptor agonists, sulfonylureas, thiazolidinediones and geographical region; we also included the number (count) of glucoselowering drugs (other than insulin), α-adrenergic blockers, angiotensin 2 receptor blockers/ACE inhibitors, anticoagulants, antiplatelet agents, β-adrenergic blockers, calcium channel blockers, diuretics, fibrates, statins, vasodilators (including nitrates).
As a result the greater drops in HbA1c in two of the comparisons may simply have been due to higher starting levels or to residual confounders in the datasets. In these circumstances it is probably unwise to conclude anything about the apparent differences in relative risk of hypoglycaemia. Our study has limitations. Lack of the strict site monitoring that usually occurs in RCTs may have resulted in missed or inaccurate information, for example of hypoglycaemia where recall is known to be a problem. Although non-interventional and with retrospective entry of baseline data, we cannot exclude the possibility that investigators started insulin on occasion in the knowledge that the patient could be entered into the study. Nevertheless, clearly the baseline HbA1c level shows that insulin was indicated in the overwhelming majority.