dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of genome organization and transcript variants The cDNA sequences of the four encoded S. mansoni glucose transporters were compared with genomic sequences available at the Wellcome Trust Sanger Institute Blast server (http://www. sanger. ac. uk/cgi-bin/blast/submitblast/s_mansoni) and GeneDB (http://www. genedb. org/Homepage/Smansoni). Intron-exon junctions were manually detected (5'GT and 3' AG) using sequence alignments constructed with Megalign (DNAStar Inc. ). For this analysis, the cDNA sequences for SGTP1 [Gen-Bank: L25065], SGTP2 [GenBank: L25066] and SGTP4 [GenBank: L25067] published by Skelly et al. [4]  were used. In order to verify and complete the predicted transcript sequences of SGTP2, we carried out 5' and 3' RACE (GeneRacer kit, Invitrogen, Carlsbad, USA) using the oligonucleotides: sgtp2F1 5' GTAAAACACAAT CAATGAGACAACTG 3' , sgtp2R1 5' GTAGAAAAT AACTGGATAGATGACGA 3' and sgtp2R2 5' ATGG GAAATAAAACAAAATAGAACAA 3' , based on the predicted sequence.
Glucose induced-fit docking and migration For the biophysical simulations, namely induced-fit docking and substrate migration, we used the state-of-the-art Protein Energy Landscape Exploration server (PELE)  [70]. The PELE server provides ready-made scripts for substrate binding refinement (induced-fit docking) and unconstraint substrate binding site search (migration). The many uses of PELE can be accessed at https:// pele. bsc. es/pele. wt and its algorithm is thoroughly explained in references  [70, 71]. Briefly, the PELE algorithm performs three stages. First, localized substrate perturbations are performed. Protein perturbations of the αcarbon backbone are also performed using an anisotropic network model (ANM)  [72]. Second, the amino acid side chains in proximity to the substrate are optimized using steric filters and a rotamer library  [73].
Several parameters were optimized from the readymade scripts provided by the PELE server to facilitate sampling. For the substrate binding refinement (induced-fit docking), (i) steric_tr ('100') and tries ('25') were reduced for the simulations to be less computationally expensive, (ii) translation (tra_r '2') and rotation (rot_r '0. 3') of glucose were increased to permit more exploration of the active site, (iii) minimization radius (mirad) to allow minimization of the whole system, (iv) an ANM type to '4' was added and ANM mode changed to '5' for favorable protein perturbations; and, (v) an increased number of steps (1000) was used for sufficient overall sampling. For the unconstrained substrate binding site search (migration), (i) waitfor was increased to '4' to allow sufficient sampling of the active site, (ii) translation (tra_r) was reduced to '4' to avoid large translation of glucose, (iii) an ANM type to '4' was added since the default ANM mode produced favorable protein perturbations; (iv) an increased number of steps (1000) was used for sufficient overall sampling; and, (v) a focused ANM (anmrad 15 Å) and minimization (mirad 10 Å) in proximity of the substrate was used for localized interpretation of glucose transport.
As proof of principle, we used two experimentally and structurally characterized glucose transporters: GLUT1 of H. sapiens [PDB: 4PYP] and XyIE of E. coli [PDB: 4GBZ]. The xyz coordinates of glucose from the crystal structure of E. coli XyIE were used as the starting position for all simulations (i. e. , the active site of glucose transporters). The trajectories of glucose induced-fit (>2 000) and migration (>10 000) produced by the PELE algorithm were viewed and analyzed using the Visual Molecular Dynamics program (VMD)  [76]. Clustering analyses of glucose migration was performed using VMD. The clustering parameters were set for '10' clusters with a 10 Å cut-off distance for each cluster, since all simulations showed a concentrated glucose sampling of the active site (Additional file 2).
Acknowledgments This work was supported by funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration (grant numbers  SEtTReND   241865 ,  A-ParaDDisE 602080 ). This work was also supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale (INSERM)  (to R. J. P and J. L), the  Ministère de l'Education Supérieure et de la Recherche  (to A. C. C) and  European Social Fund  and the state budget of the  Czech Republic  ( CZ. 1. 07/2. 3. 00/30. 0032  to JJV). We thank  Jacques Trolet  ( Institut Pasteur de Lille ) for technical assistance.
Methods Data was drawn from the Quebec Longitudinal Study of Child Development (QLSCD; 1998 -2010). Trained research assistants measured height and weight according to a standardized protocol and conducted yearly home interviews with the child's caregiver (mother in 98% of cases). Information on several putative early life risk factors for the development of overweight were obtained, including factors related to the child's perinatal, early behavioral family and social environment. Group-based trajectories of the probability of overweight (6-12 years) were identified with a semiparametric method (n=1678). Logistic regression.
Data Availability Statement: The data underlying the findings cannot be made freely available because of ethical and legal restrictions. As outlined on the "data access page" of the Quebec Longitudinal Study of Child Development (http://www. iamillbe. stat. gouv. qc. ca/acces_an. htm), data may be obtained by interested researchers upon completion and approval of an application form, and signature of a contract and oath of confidentiality. Interested readers may contact the Research Data Access Centre of the Institut de la statistique du Québec (CADRISQ), located in Montréal (at the QICSS on the Université de Montréal campus) and in Québec City (on the.
Methods Data was drawn from the Québec Longitudinal Study of Child Development (QLSCD) from 1998 to 2010  [43]. A random sample of 2940 families was recruited, representing the Quebec population of families with a 5-month-old singleton infant in 1998 residing in most geographic areas of Quebec. This sample was reduced to 2120 due to non-response, inability to contact, or not meeting study criteria. A trained Research Assistant (RA) visited the participating families yearly from when the child was 5 months to 8 years old, then at 10 and 12 years. At each data collection, informed written consent was provided by all respondents. On behalf of the child, consent was given by a parent or legal guardian. The study was approved by the Health Research Ethics Committees of the Québec Statistics Institute and the University of Montreal.
Children were included in the trajectory analyses if height and weight had been measured at least once between 6 and 12 years. A weighting factor, calculated by QLSCD statisticians for use with the time 6 (age 5) data collection, was used in all analyses to approximate the initial QLSCD target population in terms of sociodemographic characteristics  [44]. Of the 1678 children with at least one valid BMI data point, 48. 9% had data at all 5 time points, 23. 4% had data for four time points, 12% had data for three time points, 7. 3% for two time points and 8. 2% for one time point only. When data were weighted this sample was reduced to n = 1552. Demographic characteristics of the weighted sample are listed in Table  1. The sample weights corrected for most socio-demographic characteristics for which there was differential attrition.
Each child was classified as overweight (= 1) or not (= 0) at each age (6-12 years) according to international cut-offs  [47]. We modeled group-based developmental trajectories of weight status (overweight vs. not) from 6 to 12 years, using the semi-parametric group-based approach in the SAS (version 9. 3) Proc Traj program  [56]. In the case of a binary outcome, this technique yields the probability of being overweight at each age, in each group. The model was determined without a priori hypotheses regarding the existence of distinct trajectories, their number, or shape  [56]. Fit indexes (Bayesian Information Criterions (BIC), and entropy) for a two, three and four group model were compared and a joint optimization of model quality and parsimony was sought  [56]. In general, the BIC increases as the number of trajectories does, whereas entropy decreases, such that the best model is usually identified by a balance between BIC and entropy.
Study Population Cambodia Data from the baseline measurements of a randomized controlled trial on the effects of multiple-micronutrient-fortified rice on child nutrition and morbidity were used. The trial was conducted in rural Kampong Speu province, Cambodia, in November 2012. Children from 20 randomly selected schools were included (N = 2471). All parents or caretakers were asked to sign an informed consent form. Ethical approval was obtained from the Cambodian Ministry of Health, Education and Planning and the Ethical Review board of PATH, USA.
Height for Age Height measurements were performed to the nearest 0. 1 cm by trained investigators using standard procedures. Age in months was calculated from the children's birth date, retrieved via interviews and verified by school records and birth certificates (Cambodia). Height for age z-scores were calculated according to the WHO 2007 reference curves, using the WHO macro for SPSS  [19]. Stunting was defined as height for age z-score below -2 SD. For analyses where age or height for age as continuous covariates were not linearly associated with the dependent variable, data were categorized. Cutoffs were chosen so that three categories of approximately equal group size were made. Because age and height for age ranges differed between both populations, the categories were defined differently per population.
Statistical Analysis Analyses were done using SPSS software version 21 (IBM, NY, USA). Hair zinc followed a skewed distribution, therefore the data for this variable were natural log-transformed for regression analysis and expressed as median and interquartile range for descriptive analysis. The variable STH infection refers to the presence of any STH infection, 'zinc' refers to zinc concentration and 'height for age' refers to height for age z-score in all analyses. For statistical testing, linear regression analysis was performed with height for age z-scores, plasma zinc or the natural logarithm of hair zinc as continuous dependent variables. Covariates of each analysis are specified in the table footnotes. In the analyses of associations between zinc and STH infection with height for age, age was included as a continuous covariate and inflammation categories were included as categorical covariate for the plasma zinc data.
Methods The substances covered in this assessment are the IS currently authorised in Europe, after scientific review: aspartame  [1] , acesulfame potassium (K)  [2] , cyclamic acid and its salts  [3] , steviol glycosides  [4] , neohesperidin dihydrochalcone  [5] , neotame  [6] , saccharine and its salts  [7] , sucralose  [8] , aspartame-acesulfame salt  [2]  and thaumatin  [5]. The search strategy aimed to find both published and unpublished studies. A three-step search strategy was utilised in this review. An initial search of Medline (Table  1 ), Cochrane Database of Systematic Reviews and Psychinfo was undertaken, followed by analysis of the text words contained in the title and abstract, and of the index terms used to describe the article. A second search using all identified keywords and index terms diet was then undertaken across all included databases.
Thirdly, the reference list of all identified articles was searched for additional studies. No language restriction was applied. Moreover, food industries and consumer associations were contacted to know if they were aware of unpublished studies. Types of studies were broad and unrestrictive to capture as much data as possible. Meta-analysis, randomised controlled, quasi experimental, cohort, casecontrol and cross-sectional studies were accepted. In vitro, ex vivo, and animals studies have been excluded. Whatever its design and quality, a single study is never able to establish the causality between the exposure and the disease, in this case between IS consumption and its effects on health. In our review, we used part of Hill's criteria of causation to assist in the assessment of the causal relationships.
Data in adults A meta-analysis covering studies undertaken before 2006 along with around ten randomised experimental studies were identified to address these points. The meta-analysis of 15 randomised experimental studies  [9]  assessed the effects of aspartame consumed alone or with other (unspecified) IS on food and energy intake during the course of a day in adults. These measurements covered a limited number of subjects (less than 30) and highly variable time periods of a few days to 16 weeks. The main inclusion criterion for studies in this metaanalysis was the measurement of food intakes for at least 24 hours, to assess the full extent of any compensatory effects of the various meals consumed over the day. The authors conclude that consuming aspartame as a sugar substitute results in a decrease of daily energy intake by 220 Kcal on average.
Data in children Four RCT studies focusing on the relationship between IS consumption and body composition were identified. In three of these studies, changes in weight and BMI did not differ between IS consumers and non-consumers  [33] [34] [35]. These studies focused on overweight or obese children and had methodological limitations. The fourth study, of good methodological quality, examined the effects of consuming 250 mL/day of an artificially sweetened drink, compared to the same amount of sugarsweetened drink, in 641 normal-weight children (aged four to 12 years), who were regular consumers of sugarsweetened drinks, for 18 months  [36]. This study showed a significant decrease in the BMI z-score (the most relevant criterion to assess changes in corpulence in growing children) in the group that consumed artificially sweetened drinks.
Several studies also showed that consuming IS before a meal resulted in increased secretion of GLP1 (Glucagon-like peptide), a gastrointestinal hormone that usually increases insulin secretion, slows down gastric emptying and reduces glucagon secretion  [53]. This increase in GLP1 may be induced by IS activating sweetness receptors, as suggested by data in rats  [54]. When considering short-and medium-term effects, several studies assessed the effect of regular IS intake (one to three times per day, for a few days to several weeks), in capsule form or in beverages, on maintaining blood sugar control (glucose and insulin concentrations measured after a night of fasting, glycated haemoglobin HbA1c). For type 2 diabetics, the consumption of sucralose  [55]  or aspartame  [56] [57] [58]  for periods of up to 18 weeks did not change fasting glucose levels) compared to sucrose or a placebo.
The second study, undertaken in the United States for seven years, reported an increase in the incidence of T2D in subjects consuming more than one artificially sweetened beverage per day in a model with adjustment for the primary confounding factors (HR (95 % CI) 1. 67  [1. 27-2. 20 ]. The third study, which reported an increased incidence of T2D in subjects consuming more than one artificially sweetened beverage per week (HR (95 % CI) 1. 70  [1. 13-2. 55] , focused on a limited-sized Japanese population not representative of the general population, monitored for seven years. It is important to underline the heterogeneity of these data, particularly in terms of the characteristics of the populations and the monitoring periods (from seven to 24 years). Furthermore, in these studies, the consumption of artificially sweetened beverages was recorded when the subjects were first included, often through self-administered frequency questionnaires, with no updating of dietary data over time.
Effects on cancer The relationship between IS consumption and cancer in humans was assessed in 55 scientific studies. Thirty-nine of these studies involved the urinary tract and 32 focused exclusively on bladder cancer. The other studies assessed the relationship between IS consumption and the risk of brain cancer (four studies), digestive system cancer (six studies) or other cancers (five studies). Except in the studies focusing on bladder the IS in question were not identified by the authors. The relationship between saccharine consumption and bladder cancer was the most commonly studied, given that data were available in rodents  [70]. The results of studies in humans are conflicting. Based on the analysis of data in humans, it is not possible to determine a relationship (whether for saccharine or for the other studied IS), since the studies did not adjust their results for major confounding factors such as exposure to chemical pollutants.
There are also very few data on the long-term impact of IS on food preferences. Therefore, it appears necessary to study the effects of IS consumption on dietary choices. In most of the cohort studies, the consumption of artificially sweetened beverages was reported only when the subjects were first included, and subsequent consumption was not taken into account. These studies assessed only the consumption of artificially sweetened beverages, and not the total consumption of artificial sweeteners. It is also difficult to distinguish between the effects of the various IS consumed alone and their effects when combined with other IS. Future cohort studies should be capable of taking into account qualitative and quantitative changes in the consumption of artificially sweetened products and adapted dietary questionnaires, to accurately and specifically assess IS consumption.
Acknowledgment The present review is based on the opinion of the  French Agency for Food, Environmental and Occupational Health & Safety  on the assessment of the nutritional benefits and risks related to intense sweeteners. The opinion and the full expert report are available on Anses' website: https://www. anses. fr/sites/default/files/documents/NUT2011sa0161RaEN. pdfhttps://www. anses. fr/en/documents/NUT2011sa0161Ra. pdfThe authors thank  Veronique Rabenda  and  Delphine Nicolet  for their valuable help with the bibliographic research.
Statistical analysis All statistical analyses were performed using the MIXED procedure of the SAS software package (version 9. 3, SAS Inst. Inc. , Cary, NC, USA). Data of diet composition, animal performance, milk and mammary FA profiles, as well as mammary mRNA abundances of candidate genes were analysed by a one-way ANOVA with a statistical model that included the fixed effect of the diet. Two-way ANOVA was applied to data of FA composition and gene mRNA abundance of AT, the statistical model including the fixed effects of the diet, the adipose site and their interaction. In both cases, animals were nested within the treatment and used as the error term to contrast the effect of SO supplementation. Differences between means were evaluated using the 'pdiff option' of the 'LS means' statement of the MIXED procedure.
Acknowledgements This study was supported by the  Spanish Ministry of Economy and Competitiveness  ( AGL2011-23700 ), the  Council of Castile and Leon  ( CSI023U13 ), and the  INRA  ( UMR1213 ; France). T. Castro-Carrera was granted a predoctoral fellowship from the  Spanish National Research Council  (CSIC;  JAE Programme ) supported by  European Social Fund. The authors gratefully acknowledge the staff from the  Instituto de Ganadería de Montaña (Spain)  for their help in the fieldwork and in feed and lipid analyses, and  S. Bes  from  INRA (France ) for assistance in RNA analyses.
Data from French children, born between 1981 and 2007, included in studies in different parts of France, have been pooled for this study. Our first aim was to investigate the position of this population according to both the WHO growth charts and the French reference curves. The growth of exclusively breastfed and never breastfed children were also compared, according to the WHO growth charts. Finally, the prevalences of overweight, obesity and risk of overweight were compared using the WHO growth charts, the French references and the International Obesity Task Force (IOTF) definitions.
Background on the WHO growth charts In 2006, the WHO published new growth charts (www. who. int/childgrowth/en). [3, 4]  The charts from birth to 5 years were derived from growth data of children from six countries (Brazil, Ghana, India, Norway, Oman, and the United-States), who participated in the WHO-Multicenter Growth Reference Study (MGRS) between 1997 and 2003. [3]  Children included in this study were exclusively or predominantly breastfed for at least 4 months and breastfeeding continued to at least 12 months of age, with complementary foods introduced by 6 months of age; mothers had not smoked during or after pregnancy. The WHO decided to extend the curves to 19 years (weight curves are available only up to 10 years), in order to evaluate the growth of school-aged children and adolescents.
[4]  These extended reference charts were based on existing growth data from North-American children born during the 1960s and the 1970s. These data were combined with the MGRS data to provide smooth curves. In 2014, the Canadian Pediatric Endocrine Group extended the weight curve to 19 years from the same datasets and the same methods than the WHO (http://www. growthcharts. ca). [8]  Throughout this article, we will employ WHO growth charts to refer to the 2006 WHO growth charts and its 2014 extension in Canada.
Children studied Children born between 1981 and 2007 who had been included in 12 French studies from the general population were analyzed (Table  1  and 2 ). As described in Table  1  and 2   [13]  and PELAGIE  [14] ) collected data during the period from birth to 5 years of age. A total of 4830 children (2289 girls and 2541 boys) living in 5 distinct areas of France (Table  1  and S1 Fig. ) and born between 1981 and 2007 were included (96% had at least two measurements). Overall, 52940 pairs of weight and height measurements were used.
, depending Information on breastfeeding was available for 2202 children born between 2003 and 2007 from two of these studies (EDEN and the ELFE pilot). A total of 1265 children (592 girls and 673 boys) were exclusively breastfed, 937 (447 girls and 491 boys) were never breastfed. The number of corresponding weight and height measurements was 17174 and 12236 pairs, respectively. In order to cover the age range from 5 to 18 years, we included data from three longitudinal studies (ELANCE,  [10]  FLVS  [13]  and ICAPS  [16] ) and five cross-sectional studies (ENNS,  [15]  INCA 2,  [17]  data from InVS/DGESCO,  [18]  IRSA,  [19]  and PNNS en Aquitaine  [20] ) (Table  2 ). There were 29211 pairs of weight and height measurements from 22457 children (11244 girls and 11213 boys) born between 1981 and the 2004 (6% had at least two measurements).
Given the distinct WHO growth charts, we separated the periods, from birth to 5 years and from 5 to 18 years. The same methods were used to compare z-score curves of exclusively breastfed and never breastfed children, based on the WHO growth charts, focusing on the birth to 24 months, period the most susceptible to differ according to breastfeeding status. The prevalences of overweight (including obesity) and obesity were calculated in both boys and girls according to the WHO growth charts  [3, 4]  and the French references  [1]  for the various age ranges. The same age groups were used for the IOTF definition,  [23]  from two years onwards. All analyses used SAS software (version 9. 3; SAS Institute, Cary, NC).