dataesr/scientific-paragraphs-categorization huggingface dataset:
The sample included in this study underrepresented young and deprived individuals, who were not as many to return their questionnaire (data not shown). However, the aim of this study was not to provide estimates of the physical component of health-related quality of life to be extrapolated to the general population, but rather to document variation in estimates resulting from using different multimorbidity measures. It is unlikely that this response bias has contributed to the results in any way. Prevalence of certain chronic conditions and of multimorbidity, as well as health-related quality of life, has been shown to present substantial international variations  [10, 42, 43]. However, the impact of chronic conditions on health-related quality of life seems to be quite similar across countries  [44].
Material and Methods Participants and procedure This study was conducted in collaboration with MSF and based in two training stations: Brussels and Geneva. The participants in this study were HCWs travelling to Ebola camps in Africa through MSF. The cohort included doctors, nurses, paramedical staff (e. g. microbiologists, epidemiologists, pathologists) and nonmedical staff (e. g. technicians, data managers, project leaders). The training sessions for Ebola were held once a week from 1 November 2014 to 28 February 2015. We contacted 300 potential MSF HCW by e-mail before their training session. Each participant was supplied with three different set of questionnaires, to be filled before the training (questionnaire 1), after the training (questionnaire 2) and after completion of their mission (questionnaire 3).
Data analysis Statistical analyses were conducted by SPSS 20 software (IBM SPSS, Chicago, IL, USA) and included descriptive analyses using the chi-square test, ANOVA, Student's t tests and bivariate correlations. Multiple regression analyses were performed to investigate the potential interrelationships between risk perception variables (questionnaire 1). For that purpose, we first created an optimum set of predictors by repeatedly testing and eliminating the nonsignificant variables. In addition to participants' sex and working position, the other predictors were perception about transmission, personal control, both CO measures and their two-way interactions with, respectively, sex and working position. (The interaction of sex by working position was not computed because of the small number of women among the medical staff.
Figure. Schema of the genes involved in IL-1R2 activities analysed in this study. This figure represents the potential actions and interactions of genes involved in the IL1R2 gene activities in the bronchus of an asthmatic individual. Arrows represent the interactions between genes selected. The upper panel shows the possible involvement of the receptor IL-1R1 in the TH1 and TH17 immune response. The bottom panel illustrates the actions of IL-1R2 in the TH2 immune response. The complete information about the genes selected are available in the Supplementary T able E4. Genotypes were extracted from the genome-wide data obtained in the context of the European GABRIEL asthma consortium using the 610K Quad Array from Illumina (Illumina, San Diego, CA, USA). A single SNP analysis was performed using the Family-Based Association Test (FBAT; http://www.
hsph. harvard. edu/fbat/default. html) under an additive genetic model. Analyses of gene-gene interactions including 17,040 SNP pairs were tested using UNPHASED software (v3. 1. 5) (https:// sites. google. com/site/fdudbridge/software/unphased-3-1) with the «genotype tests» option. The most frequent haplotype for each pair of SNPs was used as the reference haplotype for the interactions. Moreover, only haplotypes with a frequency ≥ 1% were examined. Analyses were first performed in the discovery SLSJ family dataset, and the most significant signals were used for replication in EGEA families (P≤0. 05 for single SNPs and P≤0. 0005 for interaction analyses (corrected P value considering the number of independent SNPs). Using the SNPSpD and the matSpD tools, the critical P value thresholds for single SNP analysis was set to P≤0.
ACKNOWLEDGMENTS The authors thank all of the families for their valuable participation in this study. The  Canada Research Chair in Genetic Determinants of Asthma , ( 2005-10 ), and the  Canada Research Chair in Environment and Genetics of Respiratory Disorders  and Allergy (2015-) held by C. L. enabled the maintenance and continuation of the SLSJ asthma study. C L. is the director of the  Asthma Strategic Group of the Respiratory Health Network , investigator of CHILD Study and is a member of the  AllerGen NCE Inc. The EGEA familial study was supported by the  French National Agency for Research  and GABRIEL (contract  018996  from the  European Commission ).
PC2 seemed to be reinforced with practice where a significant increase in its explained variance was observed. In sum, this study shows  Ko et al. 2003; Majed et al. 2012). Other studies in motor learning have focused on the dynamical properties of the DoF to examine the synergies resulting from the organization of mechanical DoF  (Mitra et al. 1998; Hong and Newell 2006). This was referred to as dynamical DoF  (Mitra et al. 1998 ) that reflects the dimension of control or in other terms "coordinative structures" or "units of coordination"  (Daffertshofer et al. 2004; Torres-Oveido and Ting 2010). The use of principal component analysis (PCA), a linear multivariate statistical method based on correlation analysis, has proven to be effective in reducing the redundancy of large kinematic datasets and extracting relevant hidden structures and regularities in the movement variance  (Daffertshofer et al.
Apparatus A VICON motion analysis system connected to eight infrared emitting cameras (Oxford Metrics, UK) was used to record kinematic data at a sampling rate of 120 Hz. Nineteen reflective markers were placed at selected landmarks (plug-in-gait model set): xiphoid process, acromio-clavicular joints, lateral epicondyles of the elbow and knee, ulnar styloid processes, anterior superior iliac spines, exterior lateral lower 1/3 surface of the thighs, lateral malleoli, second metatarsals' head and calcanei. All sessions were performed on a motorized treadmill (60 × 170 cm, 1-25 km h -1 , Valiant, Lode, The Netherlands). Heart rate was monitored throughout all sessions using a Polar belt wrapped around the chest (Polar, Kempele, Finland).
5, PTS + 1 and PTS + 1. 5 km h -1 for comparison purposes. The overall practice duration was 28 min for S1 and 32 min for the subsequent sessions. Although higher intensities were gradually introduced, the overall practice duration did not exceed 32 min to ensure participants' compliance. Indeed, the PTS + 1. 5 and PTS + 2 km h -1 intensities were introduced in 2-min trials at S1 and S3, respectively, and were then increased in duration to 4-min trials at the following sessions (respectively). In order to allow participants to progressively reach the relative goal, the repetitions of trials at the lowest intensities (i. e. , PTS and PTS + 0. 5 km h -1 ) were decreased from two to one repetition at S3 and S4 as shown in Table  1. The rest periods between trials were monitored (HR < 120 bmp) using the heart rate data to ensure enough recovery time and avoid fatigue effects.
Data acquisition and analysis In the walk-to-run transition test, the treadmill speed at which the gait transition occurred was determined by kinematic data (i. e. , presence or absence of a flight phase). Individual PTS values were defined as the average of the four measured transition speeds  (Hreljac 1995). During practice sessions, kinematic data were collected in 30-s samples and recorded at the end of each 2-min trial (1:30-2:00 min). For the 4-min trials, an additional 30-s acquisition was carried out (3:30-4:00 min). Overall, out of 310 collected acquisitions, only two were discarded -1 )] S1 [2 × 4 min (PTS)] + [2 × 4 min (PTS + 0. 5)] + [2 × 4 min (PTS + 1)] + [2 × 2 min (PTS + 1. 5)] S2 [2 × 4 min (PTS)] + [2 × 4 min (PTS + 0. 5)] + [2 × 4 min (PTS + 1)] + [2 × 4 min (PTS + 1. 5)] S3 [1 × 4 min (PTS)] + [2 × 4 min (PTS + 0.
5)] + [2 × 4 min (PTS + 1)] + [2 × 4 min (PTS + 1. 5)] + [2 × 2 min (PTS + 2)] S4 [1 × 4 min (PTS)] + [1 × 4 min (PTS + 0. 5)] + [2 × 4 min (PTS + 1)] + [2 × 4 min (PTS + 1. 5)] + [2 × 4 min (PTS + 2)] because of marker occlusions (i. e. , marker fell or displaced during acquisition). A custom MATLAB program (The MathWorks, USA) was used to compute movement variables (i. e. , angular displacement) and run PCAs. Time series of eight key angular variables were computed from the original matrix of marker positions: angle of arm sagittal plane rotation, pelvis and thorax (shoulder line) frontal plane rotations, pelvis transverse plane rotation, elbow, hip, knee and ankle joint angles. Precisely, an overall of eight body segments were used for calculations  (Majed et al. 2012 (Majed et al. , pp.
Second, the hip, elbow, ankle and knee angles represented the absolute 3D angle between two segments, respectively; trunk and thigh, forearm and arm, foot and shank and shank and thigh. Time series were filtered with a second-order Butterworth low-pass filter (12 Hz cutoff frequency). Prior to running PCA, stance phases of angle time series were retained for analysis and rescaled to unit variance so that variables with large amplitude do not influence the determination of PCs  (Daffertshofer et al. 2004 ). For each practice trial, a PCA was performed resulting in computing 112 PCA on racewalking data (7 participants × 4 sessions × 4 speed trials) and 7 PCA on normal walking (7 participants). With the eigenvalues and eigenvectors obtained, the number of PCs required to capture a significant portion (i.
e. , 90%) of the total variance was assessed for each participant's trial. PCs were sorted in a descending order of their respective amount of variance. The percentage of total variance accounted for by each of the first three PCs was also systematically analyzed. Variables' loadings (weightings) onto each of the first three PCs were examined to have an insight on common trends explaining the role or nature of these PCs. The most representative movement variables within each PC were considered those with a relatively high loading value on a specific PC (i. e. , eigenvector components higher than |0. 4|, Stevens 2009). PCA was complemented with a correlation analysis of each of the normalized eight variables' datasets for each trial in order to assess common patterns of changes in the degree of variables' correlation.
Statistical analysis In order to assess immediate changes in gait patterns at the first practice trial (S1) compared to normal walking (S0) and after the instructions were given, all dependent variables (i. e. , total variance explained by PC1, PC2 and PC3 and the number of PCs needed to capture 90% of total variance) were compared between S1 and S0 at PTS using Wilcoxon's tests with respect to the normality of data sets verified by Shapiro-Wilk's test. The effect size values were described by the magnitude of change expressed as Cliff's delta (|r|) to give a rigorous judgment about the differences between S0 and S1. The effect of practice on gait patterns was examined by comparing variables within the four sessions at the common relative speeds (i. e. , PTS, PTS + 0. 5, PTS + 1 and PTS + 1.
5 km h -1 ) using Friedman tests (normality tested using Shapiro-Wilk's procedure). Post hoc pairwise comparisons were done when needed using Wilcoxon's tests with a Bonferroni adjustment. A correlation analysis examined the correlation coefficient between each of the eight individual normalized variables' datasets for each participant. Using Wilcoxon's tests, the mean correlation coefficients (Spearman's rho) were then compared between S0 and S1 at PTS and between early practice (S1) and late practice (S4) at the four common relative speeds. This analysis was done to identify common strategies in the control of movement reorganization. All tests were performed with Statistica 7. 1 package (Statsoft 2005) with a level of significance set at p < 0. 05.
Cell proliferation and hyperplasia resulting from sustained inflammatory response and apoptosis (programmed cell death) have been reported for asbestos  (Buder-Hoffmann et al. 2001; Heintz et al. 2010; Smith et al. 2016). Sustained inflammatory response can be triggered by repeated exposures and/or biopersistent substances at sufficient doses, as observed for CNTs and CNFs in some animal studies (Tables  3  and 6 ). Cell proliferation alone was not predictive of carcinogenesis for chemicals  (Melnick et al. 1993). The mechanism of cell proliferation was examined in more depth in this review since it is a hallmark of cancer observed for other poorly-soluble particles and fibers, and relatively few such data are available to date for CNTs and CNFs. For example, a study showing focal adenomatous hyperplasia in alveolar epithelial tissues in mice inhaling MWCNT-7  (Sargent et al.
The advantages (availability of different modes of administration and analysis of numerous tissues, assay focusing on gene mutations, correspondence with transgenic in vitro assays) and disadvantages (limited sensitivity to clastogens, spontaneous mutant frequency and cost) have been recently summarized  (Lambert et. al. 2005). WHO considers that a positive study is predictive of carcinogenicity, and a negative result in a properly conducted transgenic mutation assay demonstrates that the agent is not a gene mutagen  (WHO 2006). Genotoxicity is an important endpoint related to potential carcinogenicity. In vivo genotoxicity data on CNTs and CNFs were limited at the time of the IARC Monograph 111 meeting, and some targeted literature searches were subsequently performed for this review. A full systematic literature search was not performed; however, the following two regular, targeted literature searches were performed.
These results are somewhat unexpected based on evidence from the fiber and nanoscale particle paradigms. However, it must be mentioned that variables considered by the authors were endpoints measured in BALF extracted from the lungs of animals exposed by inhalation or instillation: increase of PMNs, macrophages, LDH, and total protein, which represent only some of the potential biological responses to CNTs. Closer examination of these associations may reveal reasons for the findings, such as that a given mass dose of shorter CNTs contains a higher concentration of reactive free ends. In addition, extension of such databases to examine pre-cancer endpoints could provide useful insights on the role of physico-chemical properties, assuming the data across studies are sufficient for metaanalysis.
2008 Shvedova et al. , 2014;; Kato et al. 2013; Snyder-Talkington et al. 2013a , 2016; Vesterdal et al. 2014b; Poulsen et al. 2015; (Table 4). Some studies were excluded following examination (as described in Tables S-1 and S-3). A number of in vitro geno-toxicity studies in human lung or mesothelial cells were added to this review (Tables S-2 and S-4) (as described in Section "In vitro (cellular) responses associated with direct genotoxicity-Measurement of genotoxicity"). Further discussion in this review on key events and mechanisms of carcinogenesis provides additional relevant references since the monograph meeting. Quantitative estimates of total deposited doses in rodent studies and workers are compared (Sections S-1-S-3 in online supplementary material). Finally, the currently available evidence and data gaps (Tables  6  and 7 ) are described in this critical review.
However, the IARC classifications are based on hazard and do not consider dose (unless the mechanisms at the doses in the experimental animal studies are not likely to operate in humans). Most of the rodent cancer studies of MWCNs were based on IP injection to deliver the dose directly to the peritoneum (vs. pleura). An inhalation study in mice showed that MWCNT-7 is a cancer promoter (of adenocarcinoma) in mice following a single IP injection of MCA  (Sargent et al. 2014 ) (Table  3 ). Inadequate number of animals in some dose groups or controls reduced the statistical validity of results in some of those studies (Tables  2  and 3 ). Data are lacking on the dose of MWCNTs that would reach the pleura with chronic inhalation exposure at relatively low concentrations, such as have been measured in the workplace.
For example, the  IARC (2006)  guidance could be extended to include more specific criteria on evaluation of mechanistic evidence of carcinogencity. Such guidance would be especially useful given the increasing trend toward alternative testing strategies and reduction in chronic bioassays. In addition, dose-response data sufficient to develop and test predictive models are needed to provide the evidence basis for reliably interpreting the mechanistic data in the absence of chronic bioassay data. Although recognizing certain data limitations, some coauthors consider that the collective evidence is sufficiently strong to consider all types of CNTs to be potentially carcinogenic to humans (as discussed in IARC Monograph 111) (IARC, in press). Occupational exposure studies have shown that airborne CNTs are inhalable and respirable; studies in human respiratory tract replicas, and in rodent studies, show that CNTs and CNFs can be deposited and retained in the respiratory tract; some studies in rodents have shown that MWCNT can translocate to the pleura.
The establishment of these relationships could strengthen the evidence basis to evaluate other CNTs or CNFs that have not been studied in animals. Such quantitative analyses could provide estimates of relative potency and support the development of occupational health guidance. However, due to the varying nature of these structures, and the variety of experimental designs, it remains a challenge to obtain a large enough database needed for valid statistical inference. Experimental factors need to be accounted for in any meta-analysis in order to reliably characterize the dose-response relationships for CNTs and CNFs from different studies. Experimental factors include the animal species/strain and gender, route of exposure, number of dose groups, animals per dose group, material preparation procedures, dose levels, and dose rates.
Acknowledgments The authors, IARC Mechanisms Subgroup members, gratefully acknowledge the  IARC Secretariat  and Monograph 111 Working Group members for their scientific review and input on the mechanistic evidence sections that we drafted for the monograph. This manuscript is based on, and updates, the material that we originally prepared for the carbon nanotubes section of the monograph, and it is also informed by the review comments and discussions before and during the monograph meeting. We would especially like to thank the  IARC/WHO  Secretariat members  Dr. Yann Grosse ,  Dr. Kathryn Guyton , and  Dr. Kurt Straif  for their insightful review comments on an earlier draft of this paper. Special thanks go to the  IARC  staff for their administrative support during the monograph meeting, and to  Ms Suzette Smiley-Jewell ,  University of California, Davis , and  Ms Ellen Galloway ,  NIOSH , for editorial assistance on this article.
A V8i VICON eight-camera (Mcam2) motion capture system (Oxford Metrics Ltd. , UK) with 64 analog channels was used to record the movement of the foot markers and to detect the position of the obstacle. Kinematic and kinetic data were collected simultaneously at a rate of 500 Hz. Data acquisition and stimulus display were controlled by a custommade program written in Matlab TM (R2009b, The MathWorks Inc. , Natick, MA, USA).
Data Analysis Kinematic and force plate data were low-pass filtered using a Butterworth filter with a 15 Hz  (Mickelborough et al. , 2000)  and a 10 Hz  (Caderby et al. , 2014)  cut-off frequency, respectively. The ML (yP) and AP (xP) coordinates of the center of pressure were computed from force plate data as follows: yP = Mx + Fy × dz Fz (1) xP = -My + Fx × dz Fz (2) where Mx and My are the moments around the AP and ML axes, respectively; Fy, Fx and Fz are the ML, AP and vertical ground reaction forces, respectively; and dz is the distance between the surface of the force plate and its origin. Instantaneous acceleration of the center of mass along the AP and ML axes was determined from the ground reaction force according to Newton's second law. Center of mass velocity and displacement were computed by successive numerical integrations of center of mass acceleration using integration constants equal to zero, i.
The MOS corresponded to the difference between the ML boundary of the base of support (BOS ymax ) and the ML position of the ''extrapolated center of mass'' at swing foot contact (YcoM FC ). Thus: MOS = BOS ymax -YcoM FC (7) Because kinematic data showed that participants first landed on the force plate with the swing heel or the swing toe, BOS ymax was estimated using the ML position of the swing heel or metatarsal marker at foot contact. The ML distance between the position of the swing foot marker at foot contact (heel or toe) and the position of the stance metatarsal marker at t 0 represented step width, and was representative of the size of the ML base of support. Based on the study by  Hof et al. (2005)  and the results from our previous studies  (Caderby et al. , 2014; Yiou et al. , 2016) , the ML position of the extrapolated center of mass at foot contact (YcoM FC ) was calculated as follows: YcoM FC = yM FC + y M FC ω 0 (8) where yM FC and y'M FC are respectively the ML center of mass position and velocity at foot contact, and ω 0 is the eigen frequency of the body, modeled as an inverted pendulum and calculated as follows: ω 0 = g l (9) where g = 9.
objectives To synthesise evidence on the implementation, costs and cost-effectiveness of demand generation interventions and their effectiveness in improving uptake of modern contraception methods. methods A Cochrane systematic review was conducted. Searches were performed in electronic databases (MEDLINE, EMBASE) and the grey literature. Randomised controlled trials, cluster randomised trials and quasi-experimental studies, including controlled before-after studies (CBAs) and cost and cost-effectiveness studies that aimed to assess demand interventions (including community-and facility-based interventions, financial mechanisms and mass media campaigns) in low-and middleincome countries were considered. Meta-analyses and narrative synthesis were conducted. results In total, 20 papers meeting the inclusion criteria were included in this review.
Literature search With the support of a medical librarian, two investigators (LB, AZ) conducted multiple searches in EMBASE (CINAHL, Ovid Medline), MEDLINE, the Cochrane Database of Systematic Reviews and the grey literature (Google Scholar, Social Care Online) and screening references from 1970 to 2015 (April, 2015) using Mesh terms (see Appendix S1). Searches began with the 1970s because that was when most national voluntary family planning programs were implemented to reduce population growth in developing countries; 1970-1990 is considered the 'reproductive revolution' era in LMICs, with the exception of sub-Saharan Africa  [12]. Demographers consider the 'reproductive revolution' to be the period when average fertility decreased significantly in LMICs while the prevalence of contraceptive use increased significantly  [13].