dataesr/scientific-paragraphs-categorization huggingface dataset:
e. as long as the elements of the protein family are not systematically distant from VirB4. All genes regarded as essential components of the T4SS were in this situation. Then, we applied the following procedure on the remaining HMM profiles. We compared all pairs of profiles with each other and with the ones of the Pfam 26 database (13 672 protein families) using HHsearch (34) (p < 0. 001 threshold)  (35). As common usage in comparative genomics, the pairs of significant hits were regarded as homologs. We cannot totally exclude the possibility that the very low similarity between some families could be due to convergence. We removed from further analysis the profiles whose Pfam annotation was clearly related to functions other than conjugation. We inspected the colocalization patterns of the hits of the remaining profiles.
MPF F We used the F plasmid T4SS as the model of MPF F. This system is composed of 18 proteins, some homologous to VirB components (reviewed in  (38) ). We built profiles for the three components of the core complex of the T4SS--TraB F , TraV F and TraK F (39)--that display structural similarities to the VirB10, VirB9 and VirB7 complex  (40). While these proteins do seem to have analogous roles, only the profileprofile alignments of VirB9 and TraK MPFF and TraV MPFF were significant (P-values of 7. 6×10 -6 and 8. 3×10 -05 respectively). Profile-profile comparisons of VirB10 and TraB MPFF are not significant, whereas they both exhibit the same PFAM domain (PF03743, with e-values of 4. 8×10 -56 and 1. 3×10 -39 respectively). This is because our long profiles (respectively 375 and 431 positions) only align at the region common to the much smaller profile PF03743 (187 positions).
Webservers We had previously made available a web site (now called CONJscan, http://mobyle. pasteur. fr/cgibin/portal. py#forms::CONJscan-T4SSscan or the short URL http://bit. ly/CONJscan) that allows searching for T4SS and conjugation-related protein profiles. In the present work, we increased the number of profiles available for search from 45 to 116. Our previously identified T4SS and relaxases were distributed as a simple text file. This made its analysis difficult with no intuitive or user-friendly way to search or filter the results according to different criteria. We have now created a web site, CONJdb, which allows searching and browsing our extended dataset thanks to a graphical interface. Our web site also implements the classification scheme. Finally, the graphical interface allows to performs searches by bacterial species, by T4SS class, or according to the presence or absence of T4SS components, namely the T4SS, the coupling protein and the relaxase.
This should facilitate the discrimination of T4SS dedicated to conjugation from T4SS dedicated to protein secretion. We are working on the development of a stand-alone application that will be distributed and should be of use to analyze large metagenomic datasets with local computational resources. In the future, CONJdb will be regularly updated and linked with other genomic databases. The web site is accessible at http://conjdb. web. pasteur. fr. The present release of CONJdb has the results of the analysis of 2393 chromosomes and 1813 plasmids for a total of 2262 complete prokaryotic genomes. Plasmids sequenced without the corresponding chromosome were not included. We detected 947 conjugative systems (up from 515), 1181 mobilizable elements (up from 595) and 646 T4SS lacking nearby relaxases (up from 243), for a total of 1623 T4SS.
The most recent and comprehensive T4SS database to date, SecReT4  (93) , contains 811 T4SS present on 638 different replicons. SecReT4 does not contain Cyanobacteria or Archaea T4SS. Another tool, AtlasT4SS  (94) , contains information from 70 genomes (58 Bacteria, 1 Archaea and 11 plasmids) and contains 134 clusters of orthologs. Importantly, CONJdb includes information on the T4SS class and on the putative T4SS function. Finally, because proteins are annotated using HMM profiles, instead of blastlike searches in a large databank of sequences, CONJscan and CONJdb provide standard sequence annotations. We hope this study and these web resources will be useful for scientists studying T4SSs and particularly for those engaging in the study of poorly known ones.
INTRODUCTION Knowledge on population intakes of energy and macronutrients is essential for monitoring trends as well as for nutritional interventions. In particular in childhood, a balanced diet is of great importance to ensure optimum growth and development and to prevent childhood obesity. 1  However, assessment of longterm dietary information ('usual intakes') is challenging. 3] [4]  As young children lack the cognitive skills for valid self-reports, dietary assessment is even more problematic in this age group. The age of 8-12 years is considered as transition period in which children develop the abilities for valid self-reports. 5 Dietary data in children younger than 8 years are commonly assessed through parental proxies, which may result in additional reporting errors for example due to unobserved meals.
Dietary data Dietary intake was assessed using the computerised 24-HDR 'SACINA' (Self-Administered Children and Infants Nutrition Assessment). This 24-HDR was recently validated by comparison of reported energy intakes with objective measures of energy expenditure. 36 The SACINA is based on the previously designed and validated 'YANA-C' developed for Flemish adolescents and further adapted to European adolescents in the HELENA study. 37, 38 It is structured according to six meal occasions (breakfast, morning snack, lunch, afternoon snack, dinner, evening snack) embedded in questions related to a range of chronological daily activities. Standardised photographs are displayed on the screen to assist portion size estimation. Proxies, mainly the parents, completed the 24-HDR under supervision of fieldwork personnel in about 20-30 min.
Except for Cyprus, where school ends at 1:05 pm, school meals were additionally assessed by means of direct observation. Teachers and school kitchen staff were interviewed by trained survey personnel and data were documented using special documentation sheets including portion sizes. School meal data were merged with the parentally reported 24-HDR data to enhance the completeness of dietary recalls. The 24-HDRs were assessed on nonconsecutive days over the whole week and over the complete IDEFICS assessment period. The assessment procedure in Hungary slightly deviated from the other study centres. Here, dietary information was recorded on documentation sheets and entered into the SACINA programme afterwards. ] [41] [42] [43]  Estonia combined the Norwegian and Finnish FCTs,  44, 45  Hungary used the German FCT,  41  Cyprus included foods from the German and Swedish FCTs.
41, 42 If nutrient information of single food items was missing in the country-specific FCTs, nutrient information was added using values available from other countries' FCTs. Missing quantities for single food items as well as obviously implausible data entries were imputed by country, food group and age-specific median intakes (0. 01% of the entries) to avoid excessive recall exclusions. Incomplete interviews were excluded, for example, if the proxy did not know about at least one main meal or in case of missing school meal information. According to the study protocol, it was envisaged to assess repeated 24-HDR data in ~20% of the sample. In the final database, one recall day is available in 9560 children and a second one in 2518 children where the second recalls were used to correct for the day-to-day variation.
Estimation of usual intakes Usual intake distributions for energy and macronutrients were estimated based on the validated National Cancer Institute (NCI)-Method, which is one of the most sophisticated methods for this purpose. Details are given in Tooze et al. 19, 46  In brief, the NCI-Method calculates estimates of usual intake based on non-linear mixed regression models. In addition to the correction for within-person errors in the dietary data, the statistical model includes a random effect to account for person-specific errors: in a preparatory step, initial adjustments are made and the data are transformed using a one-parameter Box-Cox transformation with a positive real-valued power parameter such that the transformed data are approximately normally distributed. In the second step, the variance is decomposed into within-and between-person variance and the distribution of usual intake is estimated by correcting for the variance inflation.
Finally, the data are back-transformed to the original scale. It is assumed that 24-HDRs form an unbiased estimator of usual intake on the original scale. The NCI-Method is superior to other methods as it, for example, allows the inclusion of covariates like age, sex or additional FFQ information and is able to provide estimates for subpopulations. It further accounts for nuisance effects like the day of the week. Moreover, it enables the estimation of usual intakes even if repeated 24-HDR data are only available in a subgroup, whereas other methods require at least two 24-HDRs for the whole study population. The NCI-Method has been implemented in SAS macros, which were downloaded from the website http://riskfactor. cancer. gov/diet/usualin takes/macros. html (date of download: 19 september 2012).
The numbers of repeated recalls needed for the variance decomposition were too small in case of Belgium, Estonia, Cyprus and Sweden such that the estimation procedures did not converge. All models considered nuisance effects (day of the week, interview sequence) and were adjusted for age and sex-when not stratifying by age and sex. Furthermore, all models were run assuming the within-person variances between the subgroups to be equal. The analyses were performed using the statistical software package SAS (version 9. 2; SAS Institute, Cary, NC, USA).
However, this result may also depend on the rather small degree of misreporting observed in the study at hand. Application of the NCI-Method as well as (to a smaller extent) exclusion of misreports led to narrower intake distributions and hence corrected for the variance inflation such that the data presented in Tables  4  and 5  are likely to reflect the true population distributions of intake to a satisfactory degree. Still it should be kept in mind that the true intake distributions remain unknown because of the lack of objective validation data. Data on intake distributions in European children corrected for the day-to-day variation as well as for misreporting are rarely published. Huybrechts and De Henauw 53 calculated mean and median usual energy and nutrient intakes in Belgian pre-school children accounting for the mentioned problems but percentile estimates were not reported.
The enKid Study conducted in 718 children and adolescents from Northern Spain was the only study identified that showed two percentiles (25th and 75th) apart from the mean adjusting for the variance inflation in dietary data and excluding UnR. 54 In that study, for all participants aged 2-24 years, the same cutoff value for the identification of UnR was applied, that is, differences in energy intake due to age and sex were not accounted for. This may result in misclassifications especially in young children; furthermore, OvR were not identified. Comparison of our results with other studies on dietary intakes in children is difficult due to differences in dietary assessment methods, underlying FCTs, study populations, statistical estimation procedures as well as in the final presentation of results (mainly only mean/median intakes reported).
ACKNOWLEDGEMENTS This work was done as part of the IDEFICS study (http://www. idefics. eu)and the  I. Family Study  (http://www. ifamilystudy. eu/). We gratefully acknowledge the financial support of the  European Community  within the  Sixth RTD Framework Programme  Contract No. 016181  ( FOOD ) for the IDEFICS study and within the  Seventh RTD Framework Programme  Contract No. 266044  for the I. Family study. We are grateful to the  Volkswagen Foundation  that financially supported the production of this supplement. ADD receives grant support from  IWT , the agency for innovation by science and technology. LL received grant support from  VR, FORTE, FORMAS  (research councils in Sweden).
Statistical analyses All data will be entered into a customized Access database (Microsoft, Redmond, Wash, USA). Analyses will be performed using Stata, version 12. All data will be analyzed by intention to treat. The tests were two-sided, with a type I error set at α = 0. 05 (except for multiple comparisons when appropriate). Baseline characteristics (GPs and their patients) will be presented as the mean ± standard deviation (SD) or the median (interquartile range) for each randomization group for continuous data and as the number of patients and associated percentages for categorical parameters. Hierarchical linear regression models (mixed models) with levels for practice, individuals within practices, and repeated measurements per individual have been used to estimate effects of the intervention on number of joints smoked per month post-baseline time points.
These models (intercept and slope as random effects) included an interaction between randomization group and time point, and adjusted for number of joints smoked per month at baseline, age of first consumption, gender, CAST at baseline, socioeconomic status, and GP's characteristics. Intraclass correlation coefficients are presented by arm. The secondary analyses will compare changes on between groups with random effects models: method of consumption, supply method, perception of the effects on health, professional life, social life, and driving under the influence of alcohol will be also compared. This data will be collected through a self-administered questionnaire that the patient will complete and leave at the GP's surgery. To assess the problem caused by missing data (GP and patients), estimation methods developed by Verbeke and Molenberg are proposed.
Ethical considerations The protocol received the approval of the Comité de Protection des Personnes (Commission for Public Safety) SUD-EST VI of Clermont-Ferrand on March 5th, 2010. The study is conducted in compliance with the regulations on patient confidentiality (Comité consultatif sur le traitement de l'information en matière de recherche dans le domaine de la Santé (Advisory Committee on Data Processing for Matters of Research in the Field of Healthcare) and Commission Nationale de l'Information et des Libertés (National Commission for Data Protection) agreements). Inclusion is voluntary and subject to strict anonymity and medical confidentiality. The description of this design follows the CONSORT recommendations for reporting on trials (http://www. consortstatement. org). The trial has been registered on http:// clinicaltrials.
The CAST score (Cannabis Abuse Screening Test) is only assessed at the start of the study, as it has the sole objective of detecting problematic use of cannabis  [28, 29] , but is not suited to measuring the progression of consumption. It will enable comparison of the progression of consumption in adolescents who had an initial score which revealed harmful use and the others. The primary and secondary criteria are collected during the preceding month, then at 3, 6, and 12 months. Collection during the preceding month was chosen in order to limit memorization bias, while maintaining a sense of overall consumption, as certain patients do not consume cannabis regularly. Any adolescents who consume cannabis are eligible, on the condition that they smoke at least one joint per month. These levels of consumption may appear highly dispersed, but the European School Survey Project on Alcohol and other Drugs and the Survey on Health and Consumption administered during the Defence Preparation Day data described correlations between quantity consumed and certain behaviours (without causal links to date), e.
Acknowledgments We wish to thank all of the patients who agreed to participate in the study. The study relies on the investigating General Practitioners who agreed to participate in the study. We wish to thank the Departments of General Medicine that collaborated on the project:  Grenoble, Saint-Etienne and Montpellier. We wish to thank the members of the Scientific Committee, consulted at the key stages of the study:  Bruno Falissard Psychiatrist ,  PUPH de Santé Publique , South Paris Faculty; Unit Director INSERM U669 (Adolescent mental health);  Amine Benyamina Psychiatrist, PH, Hôpital Paul Brousse (APHP) , Director of the Addiction Treatment Functional Unit; Jean-Pierre Lebeau General Practitioner, MCA,  Department of General Medicine of the Faculty of Tours , Co-editor in chief of the journal Exercer;  Luc Martinez General Practitioner ,  Department of General Medicine of the Faculty of Paris VI, Vice President of the French Society of General Medicine; Nicolas Authier Psychiatrist -Pharmacist ,  MCU-PH, Addictovigilance Centre of Clermont-Ferrand.
The cattle were of 4 breeds; 6 were meat animals, and 1 was a dairy animal. All 7 animals came from 2 foci, 1 in central France (n = 4) and 1 in southwestern France (n = 3); the diameters of these foci were 130 and 160 km, respectively (Figure  2 ). None of the animals originated from the same herd, but in each focus area, 2 animals came from herds <10 km apart. Three animals had lived on another farm, but in each case, the farm was in the same focus area. We conducted molecular analyses of fresh cyst tissue samples from the 2 patients and the 7 cattle using PCRbased DNA sequencing of 2 genes, the mitochondrial cytochrome c oxidase 1 (cox1) and ATPase subunit 6 (ATP6), as previously described  (10, 11). Samples from all cattle and the female patient showed the same sequences of the cox1 gene (GenBank accession no.
KC430087) and 100% identity with reference E. ortleppi gene sequences available in the GenBank database, determined by using BLAST software (http://www. ncbi. nlm. nih. gov/blast). The sample from the male patient, however, had a different cox1 sequence (GenBank accession no. KJ624625), with 1 substitution. All cattle and human lesions had the same ATP6 sequence (GenBank accession no. KC430091), showing 100% identity with a reference sequence from GenBank (accession no. DQ318953). All findings were consistent with E. ortleppi infection.
Acknowledgments We thank the  General Directorate for Food  and the  Departmental Directorate for Social Cohesion and Protection of the Population  for organizing the nationwide slaughterhouse survey. We are also grateful to  Florence Grenouillet ,  Vanessa Hormaz ,  Jean-Marc Boucher , and  Carine Peytavin de Garam  for their excellent technical support and to  Lois Rose ,  Carolyn Engel-Gautier ,  Dana Pottratz ,  Jean-Baptiste Aupet , and  Michel Gaudron  for their editorial assistance. Dr Grenouillet  is a microbiologist at the  Besançon University Hospital. His areas of interest include epidemiology, optimization of diagnosis, and follow-up of echinococcosis and invasive fungal diseases.
Introduction and aims: Areal bone mineral density (aBMD), although useful for fracture prediction, obscures the true 3 dimensional changes in cancellous and cortical mass and volume by integrating mass over a planar area of interest. Methods: Proximal femur structure was measured in 719 female Caucasians aged 20 to 89 years using  QCT (Khoo et al. , Osteoporos Int 2009 : 20: 1539). CT scanner performance and protocols were characterized using a Mindways QA phantom and QA scans and mineral mass estimates normalized by CT-scanner specific field-uniformity correction using Mindways BIT software (Mindways Software Inc. , Austin, Texas USA). Three regions of interest were defined using an algorithm derived from the DXA approach, the femoral neck (FN), trochanter (TR) and intertrochanter (IT). Voxels with a density greater than 350 mg/cm 3 were classified as cortical.
Data Analysis Following the assessment process, the raw score for each child was converted into a standard score according to a scale, based on the child's age. The scale was divided into a series of two-month age intervals; the mean standard score was 100, with a standard deviation of 15. Descriptive analyses were then performed to describe the sample. A one sample t-test was used to compare the mean standard scores of the Canadian sample and those provided in the Beery VMI. An independent sample t-test was used to compare the mean standard scores between the boys and girls participating in this study. Data analyses were performed using SPSS software (version 19. 0).
Histological analysis After excision, the left myocardial ventricle was rapidly fixed in 10% formalin, and paraffin-embedded. Sections (7-μm) were processed for hematoxylin and eosin (H & E), and van Gieson's staining (Elastica van Gieson Kit, Merck, Darmstadt, Germany) to detect collagen deposition (red color) indicative of cardiac fibrosis  [21]. The stained sections were visualized under light microscopy (Vanox-S, Olympus, Tokyo, Japan), and photographed with a digital camera (U-PMTVC, Olympus, Tokyo, Japan). The cross-sectional area of cardiomyocytes was measured using the ImageJ 1. 30v Software system in at least 10 different areas for each section. Results were obtained from 2-4 sections per rat for a total of 5 rats selected at random from the various treatment groups.
Statistical analysis All data are results of 6 to 8 in vivo experiments, and are presented in figures as means ± SD (bars). An analysis of variance (ANOVA) followed by Tukey's multiple comparisons test was performed to analyze the modulating effect of sorafenib on the development of LVH following aortic banding. Statistical analyses were performed using PRISM 6 GraphPad software (GraphPad, La Jolla, CA, USA). Pvalues <0. 05 were considered significant.
The disadvantage of this methodology is that to distinguish between idiomatic and non-idiomatic usages of the MWE it relies on the MWE's known idiomatic meaning, and this information is usually not available. In addition, this approach fails when only idiomatic or only literal usages of the MWE are overwhelmingly frequent. Although these approaches are in line with ours, they require lexical semantic resources (e. g. , a database that determines semantic similarity among words) and syntactic resources (parsers) that are unavailable for many languages. Our approach only requires morphological processing and a bilingual dictionary, which are more readily available for several languages. Note also that these approaches target a specific syntactic construction, whereas ours is appropriate for various types of MWEs.
To further assess the quality of the results, we performed a human evaluation on the English data set. We first produced the results in the BN set-up, and then sorted both the (predicted) positive and the (predicted) negative instances by their PMI. We randomly picked 100 instances of both lists, at the same positions in the ranked lists, to constitute an evaluation set. We asked three English-speaking annotators to determine whether the 200 expressions were indeed MWEs. The annotation guidelines are given in Appendix A. Comparing the three annotators' labels, we found out that they agreed on 141 of the 200 (70. 5%). This should probably be taken as an upper bound for the task. We then computed the majority label and compared it with our predicted label. Exactly 142 of the predicted labels were annotated as correct; that's an accuracy of 71%.