dataesr/scientific-paragraphs-categorization huggingface dataset:
The effect of system order was adjusted for by using mixed ANOVA. All items in the questionnaire were analyzed using frequencies. Thus, McNemar's test was used for paired binary data or the marginal homogeneity test for categorical data when more than two categories was used. Finally, the overall preferences (PTN or PTU) were analyzed using the sign / binomial test, where P ≥ 0. 05 indicated that equal numbers of students preferred PTU compared to PTN (and vice versa) and P < 0. 05 indicated that significantly more students preferred PTU compared to PTN (or vice versa). The sign / binomial test gives an exact Pvalue by using the binomial distribution and so it may be used even for very small sample sizes, in principle. All calculations were carried out using SPSS V20.
Methods 2. 1. Background. Nutritional care of patients whose pregnancy was monitored at the Clermont-Ferrand Centre Hospitalier Universitaire (CHU) took the form of medical checkups in the CHU's Clinical Nutrition and Obstetrics Depts. The Clinical Nutrition Dept. was responsible for preoperative and postoperative assessments. Periconceptional and prenatal assessments were carried out jointly by the Clinical Nutrition and Obstetrics Depts. The Association des Utilisateurs de Dossiers Informatisés en Pédiatrie, Obstetrique et Gynécologie (AUDIPOG) dossier is a common, electronic perinatal healthcare record intended for maternity units that collect all the data required for activities by healthcare professionals and patient monitoring systems. The individual, anonymised datasets were collected from two information sources: printed patient records at the Clinical Nutrition Dept.
Data Collected by the Researchers. Sociodemographic data included date of birth and professional activity. Anthropometric data included height (metres) and weight (kg) before the operation, at the onset of pregnancy, and at each trimester of pregnancy. Body weight was measured at each checkup to the nearest 0. 1 kg, with patients in their underwear and with their shoes off and the local scales calibrated. The patient's height and weight at the time of bariatric surgery were used to calculate their preoperative BMI (kg/m 2 ). Weight gain during pregnancy was defined as the weight on delivery less the weight at the onset of pregnancy. Medical histories were collected at the time of the bariatric surgery. Anamnestic data included medical, obstetric, and surgical histories together with information on medical treatments.
Data on the presence of cardiovascular risk factors such as high blood pressure (blood pressure greater than or equal to 140 and/or 90 mmHg, checked at least twice, WHO definition), type 2 diabetes (fasting blood glucose level greater than or equal to 1. 26 g/L, taken twice), and age at the onset of weight gain were collected from the medical histories. Obstetric history included the number and outcome of pregnancies (miscarriages, therapeutic abortion, and births) and parity. Surgical data included the date and type of surgical procedure. The types of surgical procedure included restrictive (AGB and SG) and mixed (GBP) techniques. The date of the operation was used to calculate the time that elapsed between surgery and conception. Data on surgical complications, compliance with nutritional supplementation, minimum postsurgery weight and the time taken to reach this weight, and monitoring frequency (number of medical consultations with nutritionist doctor per year) were collected during patient follow-up.
Concerning nutritional supplements, systematic supplements contain vitamins (A, B1, B2, B6, B12, C, D3, E, B5, B8, B9, and PP), calcium, magnesium, phosphorus, iron, manganese, copper, and zinc and specific supplements are prescribed in case of deficiency. Patient pregnancy-related parameters were collected at each trimester of pregnancy and included maternal weight gain, gestational age (based on the craniocaudal length recorded on the fetal ultrasound), blood pressure, proteinuria, and stage of pregnancy. Ultrasound data included craniocaudal length, biparietal diameter, abdominal circumference, estimated fetal weight, the growth percentile estimated using Hadlock's formula, and the estimated abdominal perimeter percentile. Anomalies were interpreted based on AUDIPOG criteria. All data on comorbid conditions or complications that appeared during pregnancy and postpartum were collected.
The outcome of the pregnancy was recorded (miscarriage, birth, and medical termination of pregnancy). According to the AUDIPOG formula, hypotrophy was defined by a small weight for gestational age (SGA) below the 10th percentile and severe hypotrophy by a SGA below the 3rd percentile. Concerning birth and delivery, data on the manner of onset of labour (spontaneous or induced by prostaglandins and/or oxytocics), the methods of delivery (vaginal delivery, forceps delivery, and caesarean), and maternal complications such as postpartum bleeding (blood loss greater than or equal to 500 mL within 24 hours of delivery) were recorded. Lifestyle was assessed based on alcohol and tobacco consumption at the onset of pregnancy. Neonatal data included sex, weight and size at birth, blood pH, the Apgar score at 5 minutes, neonatal complications such as shoulder difficulty or dystocia, the use of special procedures such as admission to a neonatal or intensive care unit, and gestational age expressed as weeks of amenorrhea (WA).
Gestational age at birth was used to define the level of prematurity (below 37 WA), including severe prematurity (below 32 WA) and extreme prematurity (below 28 WA). Birth weight and AUDIPOG data were used to distinguish between infants of normal weight (between the 10th and 90th percentile), small-for-gestational-age infants (below the 10th percentile), severe small-for-gestational-age infants (below the 3rd percentile), and large-for-gestationalage infants (above the 90th percentile). Apgar scores of less than 7 to 5 minutes of life and arterial blood pH below 7. 20 were considered pathological. Therapeutic abortions (TA) administered due to fetal malformations and in utero fetal deaths were also recorded.
Statistical Analyses. In the descriptive analysis, qualitative variables were described by their numbers and percentages while quantitative variables were described by their means, standard deviations, and minimum and maximum values. The Chi-Squared Independence Test was used to compare ratios when theoretical numbers were greater than 5. In cases where this condition was not checked, Fisher's exact test was used. Comparisons of quantitative variable means were performed using the Student's test (2 groups) or by variance analysis based on ANOVA tests (more than 2 groups). All tests were bilateral and the acceptable risk of error was set at 5%. The statistical analyses were performed using Stata 12. 0 software (STATACORP, Texas, USA).
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by s  National Research Foundation of Korea (NRF)  grant funded by the  Korean government (MSIP)  ( NRF-2014K1A4A7A01074642 ),  Gyeonggi-do , and by a grant from the  Bio-industry Technology Development Program  (no. 312062-05 ).
Gene deletion of the cytochrome P450-encoding gene bcbot4 The B. cinerea gene bcbot4 (GenBank: AY277723. 2) was deleted by homologous recombination. The 5′ genomic non-coding regions of bcbot4 (1442 pb) was amplified by PCR. In the same way, we obtained the 3′ regions of bcbot4 (866 pb) and the nourseothricin resistance gene (nat ). For each gene, a Knock-Out (KO) cassette consisting of the 5′ region of bcbot, the nat resistance gene and the 3′ region bcbot was generated by double-joint PCR fusion  24  as previously described for B. cinerea. 25 All the PCR primers have been reported. 19 rotoplasts from the B05. 10 strain were prepared and transformed as previously described. 19, 26 Additionally, the genetic purity of the KO transformants was checked by the absence of the bcbot4 WT allele. Information about the B.
An additional run at φ = 0°of 100 frames was collected to improve redundancy. The diffraction frames were integrated using the program SAINT, and the integrated intensities were corrected for Lorentz_polarization effects and absorption with SADABS. The structure was solved by direct methods and refined to all 3237 unique F o 2 by full matrix least-squares calculations using SHELXL-97. All the hydrogen atoms, except the hydrogens of the -OH groups, were placed at idealized positions and refined as rigid atoms. The positions of the OH hydrogens are found in the diffraction map and refined as rigid atoms. Cameroonan-7α,10β,14-triol (19). Colourless oil; [α] 20 D -6. 47 (c 9. 15 in CHCl 3 ); IR (film) ν max 3364, 2957, 2870, 1455, 1043, 756 cm -1 ; 1 H NMR (500 MHz, CDCl 3 ) δ 3. 89 (1H, s, H-7), 3.
Results Number and coverage of marine protected areas. The World Database of Protected Areas (WDPA, downloaded in June 2013)  31  identifies a total of 3,061 coastal MPAs of which 695 (23%) are MRs with a total coverage of 0. 9% of the world coastal areas (Supplementary Table  1 ). MPAs are considered marine reserves (MRs) if they satisfy at least one of the following criteria: they are fully no-take zones, they include a no-take zone or they are classified as strict nature reserves or wilderness areas (that is, IUCN categories Ia or Ib). Connectivity between marine protected areas. To estimate the probability of larval dispersal between pairs of MPAs, we simulated the release of ten thousand virtual larvae from each MPA four times a year for 6 years. Larvae were let to drift passively over a period of 30 days, corresponding to the mean pelagic duration (PLD) of most fish species  32.
Methods Marine protected areas and marine reserves. The MPA database was downloaded from the World Database on Protected Areas  31  in June 2013 and filtered before downloading to only keep protected areas known as 'marine'. From this first set containing 9,600 MPAs, we retained only MPAs that passed the following sequential filtering criteria. First, we removed MPAs located completely on land using the land ecoregion of the world polygon (www. worldwildlife. org/biomes). Second, we removed MPAs covering pelagic area totally or for most of their surface using a depth limit of 200 m derived from the world bathymetric database ETOPO1 (ref. 56). Third, we deleted MPAs designated to protect species not considered in this study (for example, birds) by inspecting the 'Designation' field of the MPA shapefile.
Fourth, in attempting to eliminate unreliable MPAs, we removed features with IUCN categories 'Not recorded' or 'Not applicable' (except national parks). The final database includes 3,061 MPAs of which 185 are classified as 'fully no-take' and 131 as 'partly no-take' (Supplementary Table  1 ). However, the fully and partly no-take designations do not coincide with the IUCN category Ia. We consider the 695 MPAs that are either in IUCN category Ia or Ib or designed at least partially no-take as effective MPAs (marine reserves, MRs). This criterion is less conservative than the one used in other studies finding a lower proportion of MRs, such as the one by Costello and Ballantine 9 who considered only MPAs in IUCN category Ia as MRs (360 MPAs), concluding that only 6% of coastal and open sea MPAs are MRs.
Exclusive economic zones. Exclusive economic zones (EEZs) are sea zones prescribed by the United Nations Convention on the Law of the Sea (1982) whereby a coastal State assumes jurisdiction over the exploration and exploitation of marine resources. EEZs extend from the coast to 200 nautical miles (370 km) off the coast. Polygons including inland waters, territorial waters and EEZs (that is, all areas where countries have exclusive rights and jurisdiction) were downloaded from the maritime boundary geodatabase 57. For both economic and food security dependency, the significant difference in area protected (d,f) is due to the difference between low and medium dependency classes (Conover post hoc test, t ¼ 2. 6, P ¼ 0. 03 and t ¼ 2. 9, P ¼ 0. 01, respectively). The significant difference in larval supply among food security dependency classes (i) is due to the differences between the high dependency class and the medium (t ¼ 2.
Connectivity among MPAs. We calculated the connection probabilities between all pairs of MPAs within the same marine biogeographic realm. The spatial position of larvae relative to MPA polygons was assessed using the function 'gContains' in the R package rgeos 0. 3-19 on the latitude and longitude of each larva. Only the coastal portion of MPAs (shallower than 100 m, from ETOPO1) was considered suitable for the recruitment of larvae. Connection probabilities were used to construct a connectivity matrix among all MPAs. Network metrics were used to characterize the global system of MPAs. In such a system, single MPAs are the nodes and connection probabilities are the edges of the network. Here, the word 'network' is used to denote a set of connected nodes (that is, a connected component), identified using the 'components' function of the R package igraph 1.
Estimates of fish biomass per unit area come from Edgar et al. 10  for a set of 121 MPAs worldwide. The environmental and socio-economic variables were collected from public databases  64, 65. The index of population pressure was calculated by fitting a quadratic kernel density surface ('heatmap' plugin in QGis) to each settlement point on a year 2000 world population density grid  66. The relationship between the log 10 of fish biomass per unit area and the 12 predictors was modelled through a boosted regression tree (BRT) with an explanatory power of R 2 ¼ 80% (Pearson correlation between observed and predicted values r ¼ 0. 90, t ¼ 22. 46, d. f. ¼ 119, Po2. 2 Â 10 À 16 ). The relative influence of the predictors is listed in Supplementary Table  3. A simplified model retaining only the first eight variables was used to predict fish biomass per unit area for the 695 MRs of this study (Supplementary Data 1).
The BRT analysis was performed using the functions 'gbm. step', 'gbm. simplify' and 'gbm. predict' of the R packages gbm 2. 1. 1 and dismo 1. 1-1. Larval supply from marine reserves. We calculated the connection probabilities from the 695 MRs to the 289 EEZs of the same marine biogeographic realm. The spatial position of larvae relative to EEZ polygons was assessed using the function 'gContains' in the R package 'rgeos' on the latitude and longitude of each larva. Only the coastal portion of EEZs (shallower than 100 m, from ETOPO1) was considered suitable for the recruitment of larvae. Larval density (LD i ) for a given EEZ was calculated as: LD i ¼ P j e ij B j A j A i ; where e ij is the fraction of larvae released in MR j that seed EEZ i, B j is the fish biomass per unit of area in MR j, A j is the coastal surface area (areas shallower than 100 m, from ETOPO1) of MR j and A i is the coastal surface area of EEZ i.
The product B j A j is the predicted fish biomass in MR j and was used to account for differential larval production among MRs. The LD i values were indexed by subtracting the minimum from the raw values and dividing them by the maximumminimum deviation and multiplying them by 100, so as to obtain values bounded between 0 and 100. Differences in LD i among countries grouped by their level of fishery dependency were tested using a Kruskal-Wallis test. Post hoc differences between pairs of groups were tested using a Conover test; P values are adjusted using the Benjamini-Hochberg method. The analyses are conducted with the R package PMCMR 4. 1. Fisheries dependency. The fisheries dependency assesses the importance of a coastal country's small-scale fisheries in terms of their contributions to a national economy, employment and food security.
These are the socio-economic indices that are the most explicitly highlighted in the literature  1, 5, 36, 37. While additional dimensions like recreation and tourism are important too  67  , we lack quantitative assessment at the global scale and evidence that these dimensions are directly related to larval seeding. We thus quantify fishery dependency following the methodology proposed and applied by Barange et al. 1  and building on data obtained from the Sea Around Us project  68. We focused only on coastal small-scale fisheries, because fisheries outside EEZs and in the high-seas are less likely to benefit from coastal MRs. The economic indicator was calculated as the ratio of landed value of small-scale marine fishing (artisanal and subsistence fishing in the Sea Around Us database) to the country's gross domestic product.
We average these data over five years and use the most recent available values (2006-2010 in the Sea Around Us database). The employment indicator was calculated as the ratio of the number of smallscale marine fishers to the national economically active population. Contributions were obtained from statistics of the United Nations Food and Agriculture Organization and International Labour Organization and from published literature  5, 69, 70. Data on small-scale fisheries provided by member countries typically lead to an underestimation of employment  5. The extrapolations made by Teh and Sumaila 5 compensate for the underestimation of small-scale fisheries, but occasionally appear to overestimate employment  1. Therefore, we took the average value of the extrapolated data of Teh and Sumaila  5  and the reported data from available country-level studies  69, 70.
Acknowledgements Fish biomass data were kindly provided by  Graham Edgar. We thank  Alicia Dalongeville  and  Ana S. Rodrigues  for their comments on previous versions of this manuscript. This work was funded by the  'Fondation pour la Recherche sur la Biodiversite' and the 'Fondation Total ' through the  FISHCONNECT and SEACONNECT projects. U. R. S. thanks OceanCanada  ( SSHRC 895-2013-1009 ).
Statistical analyses All analyses were performed using SAS V. 9. 4. Data are presented as mean±SD or n (%). Variations over time in maternal energy expenditure were analysed using paired t-tests with adjustment for multiple comparisons. Comparisons were restricted to consecutive measures (ie, first trimester vs prepregnancy, second vs first trimesters and third vs second trimesters). Multiple regression modelling was used to estimate the effect of physical activity exposures on infant's birth weight. To assess the risk variation of LGA or SGA as a function of energy expenditure, relative risk (RR) estimates were calculated separately for each outcome using log-binomial regression. We also verified if there were statistical interactions with prepregnancy BMI category (<18. 5, 18. 5-24. 9, 25-29.
GA, maternal prepregnancy BMI, weight gain, paternal weight, third trimester sleep score and physical activity were treated as continuous variables. Age was categorised as follows: <25, 25-35 and >35 years; education was dichotomised based on high school completion (yes/no); parity was categorised as 0, 1 or !2; ethnic groups were defined as Caucasian, multiethnic or other; drug use and smoking were categorised as current users, former users or nonusers; history of diabetes and of hypertension prior to pregnancy were dichotomised (yes/no). In order to limit overadjustment, gestational weight gain and physical activity in subsequent trimesters were tested as potential mediators using a SAS Macro. 29 o reduce potential bias associated with missing data, a multiple imputation procedure  30 31  based on the missing at random (MAR) assumption was applied, since all items used to calculate energy expenditure scores were not always answered.
Indeed, composite scores of energy expenditure had non-observation rates of 18%-19%, 28%-44% and 33%-46% for the first, second and third trimesters, respectively. Twenty-five (25) imputed datasets were generated, and estimates from the 25 analysis replicates were combined to obtain a valid statistical inference. 31 Baseline characteristics and pregnancy outcomes were presented using the non-imputed dataset. To determine whether deviation from the MAR assumption could have biassed inferences, sensitivity analyses were performed. In addition to performing the analyses with the raw dataset, the following procedure was applied: (1) various percentiles q j (eg, j=10%, 25%, 50%, 75% and 90%) of energy expenditure in the raw dataset were calculated; (2) missing energy expenditure components were replaced by the q j values and (3) the imputed data were used for confounders.
Sensitivity analyses Sensitivity analyses using the raw dataset (without missing data, n=809-821) resulted in consistent findings for the associations between first trimester sports and exercise and birth weight (À2. 66 g per MET * hours/week, p=0. 075), and between first trimester vigorous exercise and birth weight in women with pre-eclampsia (À25. 72 g per MET * hours/week, p=0. 007). When replacing missing physical activity components by predetermined percentiles, the association between sports and exercise during the first  trimester and birth weight remained similar, as did the association between first trimester vigorous exercise and birth weight in the pre-eclampsia stratum (see online supplementary tables 1 and 2).
However, data derived from the PPAQ correlate with acceptable accuracy with objective data obtained by accelerometry during pregnancy,  19 20  despite a possible overestimation. 46 To establish the precise effects of various physical activity intensities and levels on birth weight, future cohorts should nevertheless use objective measures of physical activity such as accelerometry. As with any observational studies, the observed associations may not represent cause-effect relationships. However, our results are consistent with previous meta-analyses of randomised trials  1 4  while providing additional information regarding the impact of exercise intensity and timing on the association with birth weight. Finally, our dataset had a relatively high proportion of missing values, with missing physical activity data increasing over time.
Physical activity participation from the Pregnancy Physical Activity Questionnaire Mean of the 25 imputed datasets. †Different from preceding period at p<0. 0001 (with Bonferroni correction). zDifferent from preceding period at p<0. 05 (with Bonferroni correction). xExcluding women not working/studying/doing voluntary work (n=1563-1749). MET, metabolic equivalent of task.
Acknowledgements This project was conducted as part of the research programme of the  Integrated Research Network in Perinatology of Quebec and Eastern Ontario (IRNPQEO). The authors thank all the recruiting centres involved in this study, and  Dr Alexandre Bureau , professor at the  Department of Social and Preventive Medicine, Universit e Laval , for statistical support and revision of the statistical methods and interpretation. Contributors WDF is the principal investigator for the 3D Birth Cohort and IM was in charge of the physical activity assessment. WDF, IM, EB and FA supervised data collection. IM, MB and JC developed the study hypotheses, and planned and performed data analyses. MB and IM wrote the original draft, and all the authors (BCG, WDF, EB, FA, JC) commented on the draft, contributed to the interpretation of the findings and had final approval of the submitted version.