dataesr/scientific-paragraphs-categorization huggingface dataset:
Detailed information on these cohorts can be found in www. birthcohorts. net. We included cohorts with information on pre-pregnancy BMI and weight measurements throughout pregnancy with information on the corresponding gestational age (33 cohorts). Per cohort, women were included if they had pre-pregnancy BMI to allow classification into the specific pre-pregnancy BMI groups. Therefore, all women had information on weight at 0 weeks, which refers to pre-pregnancy weight. Since the data were modeled cross-sectionally, no further restriction was applied regarding the weight measurements throughout pregnancy. Our final sample comprised 33 cohorts and 218,216 women who contributed with 679,262 gestational weight measurements, of which 218,216 at 0 weeks and 461,046 throughout pregnancy. Of these women, 9065 (4.
Maternal anthropometrics Maternal anthropometrics were measured, derived from clinical records or self-reported (cohort-specific information is shown in Additional file 1: Table  S1 ). Maternal pre-pregnancy BMI was calculated from information on height and weight before pregnancy and was categorized as underweight (< 18. 5 kg/m 2 ), normal weight (18. 5-24. 9 kg/m 2 ), overweight (25. 0-29. 9 kg/m 2 ), obesity grade 1 (30. 0-34. 9 kg/m 2 ), obesity grade 2 (35. 0-39. 9 kg/m 2 ), and obesity grade 3 (≥ 40. 0 kg/m 2 ) according to the World Health Organization criteria  [10]. Data were obtained on early, mid, and late pregnancy weight as the closest measurement to 13 weeks of gestation (range 6-19. 9 weeks of gestation), the closest measurement to 26 weeks of gestation (range 20-31. 9 weeks of gestation), and the closest measurement to 40 weeks of gestation (range 32-45 weeks of gestation).
98 kg. For each woman, the weight gain at the start of pregnancy was taken as a random draw from the Gaussian distribution with mean of 0 and variance of 0. 98 kg. The size of the measurement error was used since it is theoretically based but any variance could have been applied. We started the modeling using a Box-Cox Cole and Green distribution (Box--Cox normal), which turned out to be too strict to fit the data. Therefore, we fitted the models, separately for each maternal pre-pregnancy BMI group, by the Box-Cox t (BCT) method using the generalized additive model for location, scale, and shape (GAMLSS) package in R version 3. 3. 1  [12]. We used GAMLSS instead of quantile regression since in the latter the centiles are estimated individually and thus may cross, leading to an invalid distribution for the outcome.
The M-curve of the models for weight gain was fitted using B-splines smoothing on gestational age with specified internal breakpoints to define the splines and three degrees which is similar to a cubic spline. Cubic splines smoothing on gestational age was also used for the S-curve, L-curve, and T-curve. The models for the different maternal pre-pregnancy BMI groups were fitted with different internal breakpoints and degrees of freedom for the curves. Model specifications for each BMI group are given in Additional file 1: Table  S2. Data were modeled cross-sectionally since taking the correlation between repeated observations of the same individual into account seems to have negligible effects on the location and precision of the centiles  [13]. We tested for pre-pregnancy weight as well as cohort and country differences in the models.
This equation can be applied on data using the y2z function of the AGD package in R. The function will allow the calculation of z scores for the exact gestational age by extrapolating the parameters. For applying the equation or function, weight gain must be > 0, because the model cannot deal with negative values. In order to fit the Box-Cox t model, parameters were calculated based on weight gain + 20 kg, and thus 20 kg must be added to weight gain to be able to use our parameters. The constant of 20 kg was chosen since -20 kg is an extremely low value for weight change during pregnancy. After adding the 20 kg, weight gain must be > 0; otherwise, the equation or function using our Box-Cox t model parameters cannot be applied for the remaining ≤ 0 values  minimal until 15 weeks, after which it increased in a linear manner until term whereas in underweight, and grades 2 and 3 obese women, the median rate of weight gain was steady throughout gestation  [9].
The generalizability of these charts to other populations is not known. In the current study, we constructed gestational weight gain reference charts for 218,216 underweight, normal weight, overweight, and grades 1, 2, and 3 obese women using data from cohorts from Europe, North America, and Oceania. We observed that for all maternal pre-pregnancy BMI groups, weight gain throughout pregnancy followed a non-linear trajectory. This finding is not consistent with results of previous studies that suggested that weight gain follows a linear trajectory at least from the second half of pregnancy onwards  [7] [8] [9]. We included a large spectrum of gestational age and had a large number of participants and weight measurements available, enabling the detection of small variations in the weight gain patterns.
Strengths and limitations Strengths of this study were the description of the pattern of weight gain throughout pregnancy in a large sample of pregnant women from 33 cohorts from Europe, North America, and Oceania. However, our chart for grade 3 obese women would have benefited from a larger sample and thus the values of selected percentiles in our chart may differ from the true values in the underlying population. We included data from cohort studies from Europe, North America, and Oceania but a large proportion of data come from Northern Europe. This suggests that our charts might be generalizable to Western populations and specifically to populations of Northern European ancestry. Further studies are needed to develop gestational weight gain charts among populations from low-to middle-income countries and of different ethnic backgrounds.
Since most studies were general population-based cohort studies, we might have an overrepresentation of the healthier population due to selective non-response in the participating cohorts. This might have underestimated the prevalence of inadequate and excessive gestational weight gain and of the adverse health outcomes. However, we observed similar findings in the full group and when we restricted our analyses to women with uncomplicated pregnancies, which suggest no strong bias due to selection in the cohorts. Also, due to the data request format within this collaboration, only one weight measurement at early, mid, and late pregnancy was obtained, when available, for each woman even if multiple weight measurements were taken during each period. This might have limited the number of weight measurements available for the creation of these charts.
For our analyses, we had available weight measurements at the start of pregnancy and subsequent weights from 8 weeks onwards. The lack of weight measurements during the beginning of pregnancy could have influenced the modeling of weight gain patterns, but we believe this is unlikely since not much variation is expected during this period. The correlation between weight at the start of pregnancy and weight at 8 weeks of gestation was 0. 99 and an intraclass correlation coefficient using an absolute agreement definition of 97. 9% was obtained through a two-way mixed effects model. Finally, we relied not only on weight data obtained by measurements and derived from clinical records but also on self-reported data, which might be a source of error. Women tend to underestimate their weight on self-report  [36].
Annesi-Maesano ,  J. Y. Bernard ,  J. Botton ,  M. A. Charles ,  P. Dargent-Molina ,  B. de Lauzon-Guillain ,  P. Ducimetière ,  M. de Agostini ,  B. Foliguet ,  A. Forhan ,  X. Fritel ,  A. Germa ,  V. Goua ,  R. Hankard ,  B. Heude ,  M. Kaminski ,  B. Larroque † ,  N. Lelong ,  J. and  Development (ZonMw) ,  Netherlands Organisation for Scientific Research (NWO) ,  Ministry of Health, Welfare and Sport and Ministry of Youth and Families. Research leading  to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ), project  EarlyNutrition  under grant agreement no. 289346 , the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 633595  (DynaHEALTH) and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  733206  ( LifeCycle Project ).
: Risco cardiometabólico na infância: desde o início da vida ao fim da infância" (Ref. FCT PTDC/DTP-EPI/3306/2014 ) and the  Unidade de Investigação em Epidemiologia-Instituto de Saúde Pública da Universidade do Porto (EPIUnit)  ( POCI-01-0145-FEDER-006862; Ref. UID/DTP/04750/2013 ). AC Santos holds a FCT Investigator contract  IF/ 01060/2015. GENESIS The study was supported by a research grant from  Friesland Foods Hellas. Gen3G Gen3G  was supported by a  Fonds de recherche du Québec en santé  ( FRQ-S) operating grant  (grant # 20697 ); a  Canadian Institute of Health Reseach (CIHR) Operating  grant (grant # MOP 115071 ); a  Diabète Québec  grant and a  Canadian Diabetes Association operating  grant (grant # OG-3-08-2622-JA ). GINIplus The GINIplus study was mainly supported for the first 3 years of the  Federal Ministry for Education, Science, Research and Technology  (interventional arm) and  Helmholtz Zentrum Munich  (former GSF) (observational arm).
The 4-year, 6-year, 10-year, and 15-year follow-up examinations of the GINIplus study were covered from the respective budgets of the 5 study centers ( Helmholtz Zentrum Munich  (former GSF),  Research Institute at Marien-Hospital Wesel, LMU Munich ,  TU Munich  and from 6 years onwards also from  IUF-Leibniz Research-Institute for Environmental Medicine at the University of Düsseldorf ) and a grant from the  Federal Ministry for Environment (IUF Düsseldorf ,  FKZ 20462296 ). Further, the 15 year follow-up examination of the GINIplus study was supported by the  Commission of the European Communities , the 7th Framework Program:  MeDALL  project, and as well by the companies  Mead Johnson and Nestlé. HUMIS European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements Early Nutrition no.
289346  and by funds from the  Norwegian Research Council 's  MILPAAHEL programme , project no. 213148. INMA-Sabadell This study was funded by grants from the  Instituto de Salud Carlos III  ( Red INMA G03/176 ) and the  Generalitat de Catalunya-CIRIT  ( 1999SGR 00241 ). INMA-Valencia This study was funded by  Grants  from  UE  ( FP7-ENV-2011 cod 282957  and  HEALTH. 2010. 2. 4. 5-1 ),Spain:  ISCIII  ( G03/176 ;  FIS-FEDER: PI09/02647 ,  PI11/ 01007 ,  PI11/02591 ,  PI11/02038 ,  PI13/1944 ,  PI13/2032 ,  PI14/00891 ,  PI14/01687 , and  PI16/1288 ;  Miguel Servet-FEDER   CP11/00178 ,  CP15/00025 , and  CPII16/ 00051 ), and  Generalitat Valenciana: FISABIO  ( UGP 15-230 ,  UGP-15-244 , and  UGP-15-249 ). INMA-Gipuzkoa This study was funded by grants from the  Instituto de Salud Carlos III  ( FISFIS PI06/0867 ,  FIS-PS09/0009 )  0867 , Red  INMA G03/176 ) and the  Departamento de Salud del Gobierno Vasco  (2005111093 and  2009111069 ) and the  Provincial Government of Guipúzcoa  ( DFG06/004  and  FG08/001 ).
INMA-Menorca This study was funded by grants from the  Instituto de Salud Carlos III  ( Red INMA G03/176 ). KOALA Data collection for the KOALA study from pregnancy up to age 1 year was financially supported by grants from  Royal Friesland Foods (Leeuwarden) ;  Triodos Foundation (Zeist) ;  Phoenix Foundation; Raphaël Foundation; Iona Foundation; Foundation for the Advancement of Heilpedagogie  (all in the Netherlands).
Study Participants Women were eligible if they were 18-80 years of age and had a known pathogenic BRCA1 or BRCA2 mutation. Of the cohort participants, 94% were tested in family clinics and 6% were tested in a research setting, and it was unknown whether or when they opted for a clinical test. We defined two subcohorts for the analyses: (1) a prospective cohort comprising women unaffected with BC at baseline, for whom reproductive history data from baseline and, if collected, follow-up questionnaires were combined (2276 BRCA1 and 1610 BRCA2 mutation carriers); and (2) a retrospective cohort comprising both unaffected and affected women at baseline, for whom only data from the baseline questionnaire were used (5707 BRCA1 and 3525 BRCA2 mutation carriers). The kConFab study women were included only in the prospective cohort.
Data Collection The baseline and follow-up questionnaires collected detailed information on known or suspected risk factors for BC, including reproductive and medical history and surgical interventions. We collected family history of cancer either from the baseline questionnaire or from pedigrees provided by the genetic counselling centers. We collected information on cancer occurrences, which were confirmed by medical records including pathology records, or through linkage to cancer registries for 92% of all cases. The overall response to the follow-up questionnaires was 73% (1). Information on vital status was obtained from municipal, death, or cancer registries or from relatives. Participants provided written informed consent, and each study was approved by a relevant research ethics committee.
Combined Cohort Analyses We also conducted a combined analysis using both retrospective and prospective data. We modeled time from birth to the date of diagnosis of any cancer, RRM, last follow-up questionnaire, last information from external source, age 80 years, loss to follow-up or death, whichever came first, with timedependent weights as described by Antoniou et al. (25)  for the retrospective period and weights equal to one for the prospective period. Statistical analyses were performed using SAS 9. 4.
kConFab and IBCCS). MBT and NA drafted the initial manuscript. The complete writing group consisted of MBT, NA, KK, MAR, and DFE. MBT, YL, and NA performed and are responsible for the statistical analyses, and the complete analysis group additionally consists of JLH, HO, ACA, CE, RM, KK, MBT, YL, MAR, DFE, and NA. TMM and MRB are the international database managers. MAR coordinated the collaborative study, and DEG, DFE, NA, CN, MBT, JLH, and  MAR  initiated and coordinated the original studies. CN, BB, VM, FE, LG, EL, AH, CB, DGE, DE, JC, KRO, LI, MA, PJM, CJD, JCO, MGEMA, MK, SB, IA, EMJ, MD, MF, SAM, AO, TC, AJ, JS, CFS, YT, EO, MN, LF, AMG, BA, KO, and RKS invited patients and collected data. All authors read and approved the final manuscript. The funders had no role in the design of the study; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication.
Introduction The heterogeneous family of soft-tissue sarcomas (STS) encompasses rare malignant tumors that arise from mesenchymal tissue. The histological and molecular analyses of these tumors First, we examined whether KL expression has a clinical relevance in terms of survival for LPS patients. A Kaplan Meier analysis of 140 primary human LPS samples profiled on gene expression microarrays (GSE30929) revealed a significant difference in survival times between the two groups dividing patients according to KL expression level in tumors (Figure  1A ). In the same cohort, we compared histotypes and noticed that the KL expression is higher in WDLPS compared to other tumors (Figure  1B ). We next focused on DDLPS because of their relatively high prevalence and chemoresistance. microarrays (GSE30929) revealed a significant difference in survival times between the two groups dividing patients according to KL expression level in tumors (Figure  1A ).
In the same cohort, we compared histotypes and noticed that the KL expression is higher in WDLPS compared to other tumors (Figure  1B ). We next focused on DDLPS because of their relatively high prevalence and chemoresistance. Since KL is expressed in normal adipose tissue  [26] , we investigated whether its expression is significantly altered in DDLPS tumors. For that purpose, we used free publicly available data from the GENT database, which provides processing and normalization of datasets produced by different laboratories (Figure  1C ). The data pointed out that KL is significantly downregulated in the 61 DDLPS samples (GSE21050) analyzed by a gene expression microarray as compared to the 49 samples of normal adipose tissue (GSE13506). As for now, unfortunately, there is no available dataset on KL expression in tumors at the protein level.
These results altogether show that KL mRNA expression is downregulated in DDLPS, compared to normal tissue and well-differentiated tumors, and that a lower expression is associated with a poorer prognosis for LPS patients. This turns Klotho into a potential clinical biomarker in high-grade LPS and suggests that its loss could be a selective advantage for tumor development. We hypothesized that this might be related to its action on IGF-1R signaling  [27]. Figure  1. Klotho (KL) expression has a prognostic value for liposarcoma patients and is downregulated in dedifferentiated liposarcomas (DDLPS) tumors (A) Kaplan-Meier curves of the overall survival in 140 liposarcoma patients divided into high-and low-risk groups according to the expression level of KL in tumors (high or low, cut-off = mean) determined by microarrays (dataset ID: GSE30929).
Higher KL expression is significantly associated with a better survival for liposarcomas Figure  1. Klotho (KL) expression has a prognostic value for liposarcoma patients and is downregulated in dedifferentiated liposarcomas (DDLPS) tumors (A) Kaplan-Meier curves of the overall survival in 140 liposarcoma patients divided into high-and low-risk groups according to the expression level of KL in tumors (high or low, cut-off = mean) determined by microarrays (dataset ID: GSE30929). Higher KL expression is significantly associated with a better survival for liposarcomas (LPS) patients (Log-rank test, p < 0. 001). (B) A boxplot of KL mRNA expression in dedifferentiated (DDLPS, n = 40), myxoid round cell (MRCLPS, n = 28), pleomorphic (PLPS, n = 20), and well-differentiated (WDLPS, n = 52) liposarcomas profiled on a gene expression microarray (dataset ID: GSE30929).
KL expression is significantly (** p < 0. 01 and *** p < 0. 001) higher in WDLPS tumors compared to other histotypes. (C) A boxplot of KL mRNA expression in adipose tissue (n = 49) and DDLPS tumors (n = 61) profiled on gene expression microarray (GSE13506 and GSE21050, respectively) and normalized by GENT database. KL expression is significantly (p < 0. 001) reduced in DDLPS tumors compared to adipose tissue. On the boxplots, the line in the middle of the box is the median; the whiskers are drawn down to the 10th percentile and up to the 90th. Points below and above the whiskers are represented as individual points. Since KL is expressed in normal adipose tissue  [26] , we investigated whether its expression is significantly altered in DDLPS tumors. For that purpose, we used free publicly available data from the GENT database, which provides processing and normalization of datasets produced by different laboratories (Figure  1C ).
The data pointed out that KL is significantly downregulated in the 61 DDLPS samples (GSE21050) analyzed by a gene expression microarray as compared to the 49 samples of normal adipose tissue (GSE13506). As for now, unfortunately, there is no available dataset on KL expression in tumors at the protein level. These results altogether show that KL mRNA expression is downregulated in DDLPS, compared to normal tissue and well-differentiated tumors, and that a lower expression is associated with a poorer prognosis for LPS patients. This turns Klotho into a potential clinical biomarker in high-grade LPS and suggests that its loss could be a selective advantage for tumor development. We hypothesized that this might be related to its action on IGF-1R signaling  [27].
Compared to corresponding conditions with IB115-empty vector cells, KL-overexpression significantly (p < 0. 001) enhanced cell death induced by gemcitabine, thapsigargin, and ABT-737. Dose-response curves were interpolated as standard slopes. The data shown summarize the results obtained in at least three independent experiments and correspond to median and IQR. Cell death of IB115 cell lines was measured by TMRM-staining and flow cytometry. IB115 cell lines were pretreated for 1 h with indicated concentrations of BMS-7548074 and then treated for 72 h with (H) 10 nM thapsigargin (TG) or (I) 100 nM gemcitabine. Compared to non-pretreated IB115-empty vector cells, BMS-7548074 significantly increased the TG and gemcitabine-induced cell death in a dose-dependent manner. Histograms sum up (H) four and (I) three independent experiments.
KL-Overexpression Sensitizes DDLPS to ER Stress by Disrupting Intracellular Calcium Homeostasis Klotho might sensitize cells to TG by affecting the SERCA expression. According to gene expression microarray analysis, SERCA1 and SERCA3 genes are not (or only a few) transcribed while the SERCA2 gene is highly expressed in IB115 cells (Supplemental Figure  S4 ). Furthermore, in adipose tissue, no SERCA1 protein is detected and SERCA3 is only slightly expressed according to the Human Protein Atlas database. In contrast, SERCA2 is upregulated in LPS compared to normal tissues and high SERCA2 expression is associated with high malignant LPS such as DDLPS  [39, 40]. Through western blotting, we found that Klotho does not affect the expression of SERCA2 (Figure  4A ). Long-term exposition to TG induces ER stress and activation of the Unfolded Protein Response (UPR), which encompasses several pathways that activate both cell death and survival mechanisms.
(H,I) Cell death was measured with a TMRM-staining analyzed by flow cytometry after 72 h incubation of IB115 cell lines, which were pretreated during 1 h with indicated concentrations of larixyl acetate and then treated with (H) 20 nM and 10 nM TG for the IB115-empty vector and IB115-KL cells, respectively (in order to have nearly similar cell death rates), or (I) 100 nM gemcitabine. Histograms sum up (H) four and (I) two independent experiments. Data shown correspond to medians (IQR).
Cell Proliferation A total of 2500 cells were plated into 24-well plates in four replicates. Culture medium was changed at days 0-3-5 (0. 5 mL per well). After trypsinization, the cell number was evaluated by flow cytometry (FACS Calibur, BD Biosciences, San Jose, CA, USA) based on their morphological features (FSC/SSC). Data acquisition and analysis were performed with the BD PlateManager and BD CellQuestPro software.
Mitochondrial Potential Loss Assay (Tetramethylrhodamine, Methyl Ester, TMRM) A total of 5000 cells were seeded into 96-well plates in at least three replicates. The day after, the culture medium was replaced by drug-containing medium (0. 2 mL per well). After 72 h of incubation, 200 nM TMRM dye (Fluka, brand of ThermoFisher Scientific, Waltham, MA, USA) and 20 µM verapamil (Sigma-Aldrich, St. Quentin Fallavier, France) were added into medium. Plates were incubated at 37 • C, 5% CO 2 for 30 min. Then, supernatants were collected, cells were trypsinized and resuspended in their respective supernatants. TMRM-staining was analyzed by flow cytometry (FACS Calibur, BD Biosciences, San Jose, CA, USA). Data acquisition and analysis were performed with the BD PlateManager and BD CellQuestPro software.
Cell population was determined on a FSC/SSC dot plot. Then, the region corresponding to cells that lost their mitochondrial electric potential (TMRM-negative cells) was selected on a SSC/FL2 dot plot diagram. The number of TMRM-negative cells is shown as a percentage of the analyzed cell population. Dose-response curves were interpolated with Prism6 v6. 01 (GraphPad Software Inc. , La Jolla, CA, USA) software as standard slopes.