dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and weaknesses of our study Most of the surveys which data come from were not and did not aim to be representative of French children. Potential selection bias could not be formally evaluated, since we did not have information on socio-demographic characteristics for all of them. It should however be stressed that our data cover a large part of the French territory as illustrated on S1 Fig. We also performed separate analyses for each survey, and results were consistent across studies (results shown in S2 Fig. to S4 Fig. ), suggesting that any regional or socio-demographic selection biases had little impact on the results and their interpretation. Anthropometric data were either measured or collected from health booklets or self-reported, and are therefore of heterogeneous quality.
Acknowledgments We acknowledge  Dr Corinne Bois  ( Conseil général des Hauts-de-Seine ) and  Dr Carine Dubuisson (ANSES)  for their contributions to the interpretation and discussion of the results. We thank all people who helped in providing the data;  Anne Forhan  (Inserm  U1018 ),  Jean Louis Lanoe  ( Institut national d'études démographiques ) and  Dr Carine Dubuisson  ( ANSES ) for data collection and database management of the EDEN, the ELFE pilot and the INCA 2 study, respectively; IRSA for providing data from the 11 health examination centers of IRSA.
Data Sources 1. Pregnancy Risk Assessment Monitoring System: PRAMS is a retrospective and crosssectional population based survey, which collects maternal self-reported information on attitudes and experiences before, during, and after the delivery of a live-born infant with information collected at one time-point during the postpartum period (approximately 2 to 6 months after delivery). Alaska PRAMS samples about one of every six live births through a stratified random sample of birth certificates, with stratification conducted according to maternal Alaska Native race and infant birth weight. All sampled respondents are linked to birth certificate information. The PRAMS unweighted response rates (or raw number not adjusting for Alaska's population demographics) for 2005 and 2006 were 77% and 75%, respectively.
The full methodology of PRAMS is described elsewhere  [14]. We had 2,802 mothers who participated in PRAMS in 2005 and 2006. 2. Alaska Childhood Understanding Behaviors Survey (CUBS): CUBS collects data about the health status and care of Alaskan children at 3 years of age. Mothers who completed a PRAMS survey and are still living in Alaska receive up to two copies of the CUBS survey in the mail soon after their child's third birthday. PRAMS phone respondents whose child was born in the last six months of 2006 also had the option of completing the CUBS survey by phone. The CUBS survey includes questions on the child's current height and weight, nutrition and eating habits, health care utilization and access, and child development and behaviors as previously described  [15]. CUBS unweighted response rates among women eligible in 2008 and 2009 were 49% and 50% (or 1,136 of the 2,802).
Data analysis Our outcome was obesity defined as BMI 95 th percentile (age and sex specific) at age 3 years and was calculated from the child's height and weight as self-reported by the mother on CUBS. All non-obese children, including those who were underweight (5. 2% of non-Native children and 3. 5% of Alaska Native children), were compared to obese children. We analyzed obesity by maternal Alaska Native vs. non-Native race. The terms "Alaska Native children" and "non-Native children" are based on maternal race only, as reported on the birth certificate. Risk factors for childhood obesity were assessed using t-tests for continuous variables and chi-square tests for categorical variables. Pre-pregnancy, prenatal, postnatal (from 2-8 months postpartum), and childhood predictors at age 3 years were included for evaluation of risk for obesity.
Beverage frequency variables were analyzed both ordinally (how the questions were originally asked on the CUBS questionnaire), as well as dichotomously, with the cut point used corresponding to the approximate mean or median of the variable for the population. The cut point for beverages was either 1 cup/day or >1 cup/day. Food consumption questions were analyzed dichotomously with the cut point similarly corresponding to the approximate mean, with cut-points reflective of the number of times per day the item was consumed. All food and beverage questions were based on questions that asked about consumption yesterday with the exception of the milk consumption question, which asked about usual consumption. Because of the high percentage of missing data for paternal education (10. 5%), we did not include paternal education in the multivariate models even though they were significant in bivariate <0.
05 for Alaska Native children. Due to a high percentage of respondents who were missing data on child BMI (16. 5%), we compared the percentage of selective socio-demographic factors (maternal age, paternal education, maternal education and location of residence) in those who were missing for BMI compared with non-missing participants (for BMI) (as 16. 5% was also missing for BMI). Subsequently, an analysis was conducted to compare daily consumption of milk, 100% fruit juice, soda and SSB, as well as usual type of milk child drank, based on regional residence (Anchorage, Interior, Northern/Southwest and Gulf Coast/Southeast). This analysis was conducted so as to better understand the protective association found between fruit juice consumption and obesity in multivariate analysis for Alaska Native children only and not non-Native children.
All data analysis used Stata 13. 0 using "svy" statements to allow for the sampling weights provided by PRAMS. Sampling weights are necessary in survey analysis to get the survey weighting, clustering and stratification accounted for so as to get standard errors of estimates correct. Data are expressed as means ±standard errors (SE). Specific details about the PRAMS and CUBS variables used in the analysis are included as a supplement to this article (S1 Appendix: Additional Methodological Details on PRAMS and CUBS Variables). This was primarily a hypothesis-generating study and we did not do any multiple comparisons to reduce the chance of Type II errors  [16].
Adherence to the MD is usually determined by establishing a MD score for a population based on median consumption of various food groups and then grouping subjects into quantiles based on their score above or below the median value  [248]. Upper versus lower quantile values typically vary 2-3 fold for olive oil (in populations where olive oil is commonly consumed)  [52]  and 2-3 fold for vegetable consumption  [249]. This narrative review, and the more quantitative data from RF values from the Phenol Explorer database  [9] , indicate that different cooking methods can also give rise to several-fold differences in polyphenol content between vegetables, depending on the type of polyphenol and type of vegetable. Hence, food processing practices may have a significant impact on the nutritional quality of some foods in the MD, comparable in some circumstances to differences in intake between low and high consumers.
The use of the RF database will help capture the influence of food processing on the nutritional values of some food groups within the MD. However, this database only applies to polyphenols and, as discussed in this review, there are many other influences on the nutritional value of food groups, and there are currently no databases that quantify these. In addition, there are practical difficulties in capturing a level of precision in epidemiological studies beyond "raw" and "cooked" for vegetables and "boiled" and "fried" for animal products, and this information is of limited value if it is not translated by food composition tables. In the same way, the use of herbs and spice might be evaluated by "yes" or "no" or, at the most, by frequency, but never quantitatively.
Open-fi eld task This system was an open-fi eld consisting of bright and opaque Plexiglas® walls (100 × 100 × 20 cm) and covered with a transparent Plexiglas® ceiling. Four white lights of 15 W were placed at each corner of the system. The open-fi eld session was recorded by camera, and the data were analyzed after the session, which rendered unnecessary the presence of an observer in the room during the test. The mouse lemurs were placed in the open-fi eld for free exploration for 30 min. At the end of the session, the nest box of the mouse lemur was placed in a corner of the open fi eld (the same corner for all animals) to allow it to return to the nest box with minimal stress. Because of persistent immobility, peripheral tracking and limited exploration are index of stress and anxiety in mouse lemurs when placed in a novel environment.
Measurement of brain glucose metabolism Anesthesia procedure. For MRI, PET, and autoradiography studies, anesthesia was prepared with subcutaneous injection of 0. 25 ml atropine (0. 025 mg/1 ml) 20 min before induction with isofl urane 5%; anesthesia was maintained with isofl urane 1-2%. PET data acquisition. Images were recorded on a MicroPET® Focus 220 system (resolution 474 × 474 × 796 µm , 60 min), the time testing. Blood sampling, PET, and MRI experiments were performed during the second week. Body weights were measured throughout the study and were not signifi cantly affected by dietary treatments.
MRI and PET images were analyzed with BrainVISA®, Anatomist® (http://brainvisa. info/), and PMOD 3. 3 softwares (PMOD Technologies Ltd. , Zurich, Switzerland). Brain FDG PET image were analyzed by both semiquantitative [standard uptake values (SUVs)] and quantitative [cerebral metabolic rates of glucose (CMR glu )] techniques. For each animal, VOIs were manually segmented using MRI images. In these VOIs, the PET signal (CPET) was calculated (Bq/ cc = Bq/ml) on dynamic images and on sum images of the last 30 min and then converted into SUV [SUV = CPET/(injected dose/body mass)]. SUV data were expressed in g/ml. CMR glu (µmol/min/100 g) were calculated using Patlak graphical analysis method  ( 27 ). The Patlak graphical analysis was performed with the arterial blood time-activity curve (TAC) from the brain.
Statistical analysis All results are reported as mean ± SEM. A Student's t -test was used to compare the following parameters between the control and n-3 LCPUFA-supplemented groups: fatty acid content (lipids analysis), percent of successful trials, number of errors, latency in the Barnes maze, and total distance in open fi eld. The effect of treatments on SUV data was compared using a two-way ANOVA (treatment and time effect in each brain region). Because CMR glu data were not normally distributed, data were analyzed using a generalized linear model (GLM) with ␥ distribution (Shapiro-Wilk normality test: w = 0. 98; P = 0. 42). Differences were considered signifi cant with P < 0. 05. Statistical analyses were performed with GraphPad Prism 5. 0 and R 3. 0. 2 softwares. a Minor fatty acids [14:0, 15:0, 17:0, 19:0, 20:0, 22:0, 24:0, 14:1(n-5), 16:1(n-9), 20:1(n-7), 20:1(n-11), 22:1(n-7), 24:1(n-11), 24:1(n-7), 20:3(n-9), and 22:3(n-9)] are not reported because they represented <0.
, PA, USA) were used as secondary antibodies. All procedures were performed in phosphate-free solution containing 140 mM NaCl, 5 mM KCl, and 10 mM HEPES-Na, pH 7. 4. Images were acquired with a LSM 510 laser-scanning confocal microscope (Zeiss, Germany). The acquisition of A488 (pSTAT3 or STAT3) and then Cy3 (MAP2) was sequential to avoid overlap of excitation and emission of fluorescence. The optical sections were digitized (1024 × 1024 pixels) and processed using Image J software. Ten randomly chosen optical fields were analyzed from each experiment (3-15 neurons per field). For analysis of the intensity of pSTAT3 or STAT3 staining in neuronal cells, we first created a binary mask from MAP2-positive cells and then analyzed pSTAT3/STAT3 intensity in regions overlapping with the binary mask.
After rinsing in PBST-tween and incubation for 1-5 min in ECL-Plus reagent (Perkin-Elmer, Waltham, MA, USA), nitrocellulose membranes were exposed to Hyper Performance Chemiluminescence film. Subsequent to cyclin D1 detection, the membranes were re-incubated with rabbit polyclonal anti-β-actin antibody (Sigma, 1:10,000) according to the same experimental procedure to serve as a loading control. After detection of each phosphorylated protein, the bound antibodies were stripped off and the membranes were reblotted with either rabbit anti-p42/p44 ERK, anti-AKT, or anti-STAT3 polyclonal antibodies (Cell Signaling Technology, 1:1000). Immunolabeled bands were quantified using the public domain NIH Image program (National Institutes of Health, Bethesda, MD, USA). The relative amount of cyclin D1 protein was measured and expressed as the ratio over β-actin expression whereas the amounts of phosphorylated proteins were determined as a ratio over relevant total protein expression.
Cyclin D1-specific shRNA Design and Transfections The sequence of cyclin-specific shRNA of the cyclin D1 (CCND1) gene was designed by using siRNA Target Designer (www. promega. com/siRNADesigner/program/default. asp) software. Among designed sequences, the one corresponding to the coding region (233-255) was chosen for single-strand cDNA synthesis. The chosen forward sequence was: 5 ′ TTTGACCTGC GCGCCCTCCGTTTCTTTCAAGAGAAGAAACGGAGGGCG CGCAGGTCTTTTT3 ′ and the chosen reverse sequence was 5 ′ CTAGAAAAAGACCTGCGCGCCCTCCGTTTCTTCTCTTG AAAGAAACGGAGGGCGCGCAGGT 3 ′. After annealing of synthesized single strand cDNAs, the resulting shRNA was ligated into mU6pro vector (Promega) and resulting plasmid was amplified as previously described  (Efthimiadi et al. , 2012). The primers used to identify mU6pro vector containing the chosen shRNA sequence were: F1F (CATTCAGGCTGCGCAACTGTTG) and M13R2 (CACAGGAAACAGCTATGACCAT) giving rise to an amplified fragment of 810 bp.
Statistical Analysis Immunoblotting intensities and enumeration data obtained from cytochemical studies were compared between experimental groups with One-Way ANOVA followed by Newman-Keuls (for cellular assays) or Fisher's PLSD (for Western blots) post-tests for multiple comparisons. Unpaired Student's t-test (Prism software, Graph-Pad, San Diego, CA, USA) was used in experiments where only two sets of values were compared.
Upon differentiation by retrieval of growth factors after 10 DIV, the three neural lineages (neurons, astrocytes, oligodendrocytes) could be identified by immunocytochemistry of phenotypic markers (βIII-tubulin, GFAP, O4 respectively, data not shown) as already reported in our previous study  (Charrier et al. , 2006). This indicated that the cells comprised in the neurospheres fulfill the criteria for bone fida adult neural stem cells  (Louis et al. , 2013). Besides, during these preliminary experiments and the set-up of optimal culture conditions, we observed that reducing EGF concentration in the culture medium to 8 ng/ml still allowed neurosphere yield of 70-75% the values obtained with 20 ng/mL EGF at 10 DIV from adult SVZ tissues. In all further experiments leptin effects on neurosphere expansion were therefore systematically assessed in the presence of 8 ng/mL EGF.
Acknowledgments We thank  Amgen (USA)  for gracious gift of murine recombinant leptin and  Pr Anne-Marie Prêt  ( University of Versailles-St-Quentin, Center for Molecular Genetics CNRS, Gif-sur-Yvette, France ) for English language revision. This study was supported by funds from  Institut National de la Recherche Agronomique (INRA) ,  Centre National de la Recherche Scientifique (CNRS) ,  Universities of Tours and Poitiers  ( Action Collaborative de Recherche Poitiers-Tours) ,  La Ligue Contre le Cancer Grand Ouest Comités de la Vienne et des Deux Sèvres  and by PhD fellowships from  French government, in Biology of Ageing (SS)  and  Neuroscience (LE, SC).
Database All programs performing IT are invited to participate in the ITR which is managed under the auspices of the Intestinal Transplant Association (ITA), a section of The Transplantation Society. The ITR is updated and presented every 2 years at the ITA's international small bowel transplant symposium. Each program is responsible for obtaining local institutional review board approval where needed. Data storage and analysis comply with Health Insurance Portability and Accountability Act requirements. The database was partially supported by unrestricted educational grants from Astellas Canada and United States from 1988 to 2011. Astellas had no input into database design, data collection, data analysis or data interpretation. The ITR collects a ''bare-bones,'' limited data set (available at http://www.
Descriptive and survival statistics Data were analyzed using the Statistical Analysis System 1 version 9. 2 (SAS Institute, Cary, NC). Patient populations were defined using standard descriptive statistics. For continuous variables, means, standard deviation, medians and interquartile ranges are presented. Categorical data are presented as frequency counts and percentages. Pediatric recipients were defined as recipients less than 18 years of age. Patient survival is calculated as overall (or all-cause) survival. For graft survival, grafts were considered to have failed on: (1) their removal date or (2) patient death date. For the sake of consistency with previous reports (3) and to take into account changes over time in recipient characteristics, surgical techniques, frequency of other organ inclusion and immune suppression practices, the outcomes of IT were examined during three eras previously defined by the Pittsburgh intestine transplant programs which is the largest single center contributor to the ITR: (1) 1985-January 11, 1995, (2) Jan 12, 1995-July 3, 2001 and (3) July 4, 2001-Present.
Multiple variable survival analysis Dependent factors. Graft survival during the last 10 years of the registry data collection was the main outcome of interest. Graft survival was examined both overall and in separate analyses, 1-year conditional survival. Independent factors. Three levels of independent factors were examined-center volume, patient-level characteristics and graft-level characteristics. Based on expert advice from clinicians contributing to the ITR, the pediatric population was arbitrarily categorized into three groups (0-2 years, 3-6 years, 7-17 years) and two adult groups (18-50 years, and 51 years or older). Graft survival was first examined using Kaplan-Meier product-limit curves for graphical representation, and univariate Cox proportional hazards models to evaluate crude associations between graft survival and site, as well as patient and graft characteristics.
Due to the multilevel nature of the data (multiple grafts within multiple patients within site), generalized multivariable Cox regression models with time to graft failure as the outcome variable were used that accounted for the within patient correlation using three models. First, a site-level model was calculated that included only site-level volume to determine the crude hazards of this volume size. Next, a full model was calculated that included site volume, patient-level characteristics added in simultaneously and cross-level interaction terms between the patient-level characteristics and site volume. When no meaningful cross-level interactions were found in the full model, suggesting that there were no differential effects of site volume on graft failure by patient characteristics, the final models were run without interaction terms.
RESULTS The ITR database currently includes patient information from 82 contributing centers that provided data on 2887 transplants in 2699 patients who were transplanted on or before February 2, 2013. Based on the authors' knowledge of global intestine transplant activity, we believe that the ITR has captured data on more than 95% of the world's cases. The 10 programs contributing the largest number of patients to this report include: (presented in alphabetical order) Clarian Transplant Center (Indianapolis), Georgetown University Hospital (Washington, DC), Mount Sinai Hospital (New York), SickKids Hospital (Toronto), UCLA (Los Angeles), University of Miami (Miami), University of Nebraska (Omaha), University of Paris (Paris) and UPMC (Pittsburgh). These centers fully participated in every cycle of data collection since the inception of their programs.
Hospital stays have significantly decreased over time to a median duration of 44 days (Table  1 ; p < 0. 001). Since 2009 (when we started using electronic ITR data entry and obtained more reliable immune suppression data), most patients (72%) were induced with either an IL-2 blocker, an antilymphocyte product or Campath 1. The majority of current survivors (92%) are taking tacrolimus as maintenance immune suppression. Fifteen percent of the current survivors are also taking an mTOR inhibitor. As previously reported  (1, 2, 16) , in those patients with at least 6 months of follow-up after transplantation, 67% had stopped parenteral nutrition (PN) (25% required partial or full PN while 8% reported the status was unknown). The rate of freedom from any parenteral fluid or nutrition support was approximately 5% higher in patients who received a colon segment with their graft (p < 0.
Discussion The ITR is truly a unique database that epitomizes collegiality and cooperation among transplant centers worldwide. From its inception, the creators realized that,     IT as a new surgical modality, would suffer from the fact that a small number of centers would perform a relatively low volume transplant thus limiting the interpretation of individual center experiences. Only by pooling these experiences, could real trends effecting techniques and outcomes be elucidated. Once created, the ITR has been immensely successful with voluntary contribution of nearly all cases performed worldwide for almost 3 decades. Herein represents the latest report from this ITR in nearly a decade with several important trends and findings reported. The designation of IT as the standard of care for the treatment of irreversible intestinal failure (failed PN) in the United States in 2000  (17, 18)  stimulated significant growth in IT case volumes, first in the United States and later Europe and South America (Figure  1 ).
The current ITR database does not include HLA matching, DSA or details of immune suppression treatment. However, this information will be added in the next round of data collection (see below). Learning how to prevent graft loss after 1 year requires careful study of complex long-term interactions between the unique biology of the intestine graft, differences in immune suppression regimens and socioeconomic risk factors  (33, 45). Accomplishing these tasks is particularly difficult in the field of IT. Randomized trials are nearly impossible due to their complexity, the tendency for most centers to provide individually tailored patient care; the expense and time required to recruit sufficient numbers of study participants; possible inadequate subset representativeness; limited funding; and the small case volumes at most sites.
Currently the ITR is unable to study these issues because it only collects ''bare-bones'' patient data. To address these limitations, the ITR has recently formed a Scientific Committee that aims to enhance knowledge generation by expanding the data collected by the ITR. Starting this year, the intestine transplant community will be asked to provide detailed information on HLA matching, DSA and the comparative effectiveness of different immune suppression strategies to determine how these factors contribute to long-term outcomes. Eventually, we hope this information will be used to design a randomized registry trial, which has been identified as an efficient and effective but relatively inexpensive technique to study treatment options in challenging real world settings  (59). As with other registry reports, this paper has several limitations.