dataesr/scientific-paragraphs-categorization huggingface dataset:
Doubly Labeled Water Database Data were taken from IAEA Doubly Labelled Water (DLW) Database, version 3. 1, completed April, 2020  (16). This version of the database comprises 6,743 measurements of total expenditure using the doubly labeled water method. Of these, a total of 6,421 had valid data for total expenditure, fat free mass, fat mass, sex, and age. These 6,421 measurements were used in this analysis. This dataset was augmented with published basal expenditure measurements for n=136 neonates and infants  (31) (32) (33) (34) (35) (36)  that included fat free mass and fat mass. Malnourished or preterm infants were excluded. For sources that provided cohort means rather than individual subject measurements  (33, 36)  means were entered as single values into the dataset without reweighting to reflect sample size.
This approach resulted in 77 measures of basal expenditure, fat free mass, and fat mass for n=136 subjects. We also added to the dataset published basal and total expenditure measurements of n=141 women before, during, and after pregnancy  (37) (38) (39)  that included fat free mass and fat mass. These measurements were grouped as pre-pregnancy, 1 st trimester, 2 nd trimester, 3 rd trimester, and post-partum for analysis. In the doubly labeled water method (5), subjects were administered a precisely measured dose of water enriched in 2 H 2 O and H 2 18 O. The subject's body water pool is thus enriched in deuterium ( 2 H) and 18 O. The initial increase in body water enrichment from pre-dose values is used to calculate the size of the body water pool, measured as the dilution space for deuterium (N d ) and 18 O (N o ).
These isotopes are then depleted from the body water pool over time: both isotopes are depleted via water loss, whereas 18 O is also lost via carbon dioxide production. Subtracting the rate (%/d) of deuterium depletion (k d ) from the rate of 18 O depletion (k o ), and multiplying the size of the body water pool (derived from N d and N o ) provided the rate of carbon doxide production, rCO 2. Entries in the DLW database include the original k and N values for each subject, which were then used to calculate CO 2 using a common equation that has been validated in subjects across the lifespan  (17). The rate of CO 2 production, along with each subject's reported food quotient, was then used to calculate energy expenditure (MJ/d) using the Weir equation  (40). We used the food quotients reported in the original studies to calculate total energy expenditure from rCO 2 for each subject.
The size of the body water pool, determined from N d and N o , was used to establish FFM, using hydration constants for fat free mass taken from empirical studies. Other anthropometric variables (age, height, body mass, sex) were measured using standard protocols. Fat mass was calculated as (body mass) -(fat free mass). 2. Basal Expenditure, Activity Expenditure, and Physical Activityl Level (PAL) A total of 2,008 subjects in the database had associated basal expenditure, measured via respirometry. For these subjects, we analyzed basal expenditure, activity expenditure, and "physical activity level" (PAL). Activity expenditure was calculated as [0. 9(total expenditure) -(basal expenditure)] which subtracts basal expenditure and the assumed thermic effect of food [estimated at 0. 1(total expenditure)] from total expenditure.
Residuals for each subject were calculated as (Observed -Predicted) expenditure, and were then used to calculate adjusted expenditures as: Adjusted Expenditure = 1 + Residual / Predicted [1] The advantage of expressing residuals as a percentage of the predicted value is that it allows us to compare residuals across the range of age and body size in the dataset. Raw residuals (MJ) do not permit direct comparison because the relationship between size and expenditure is heteroscedastic; the magnitude of residuals increases with size (see Figure  S1 ). Ln-transformed residuals (lnMJ) avoid this problem but are more difficult to interpret. Adjusted expenditures, used here, provide an easily interpretable measure of deviation from expected values. An adjusted expenditure value of 100% indicates that a subject's observed total or basal expenditure matches the value predicted for their fat free mass and fat mass, based on the general linear model derived for adults.
The whole body massspecific basal expenditure [i. e. , (basal expenditure)/(body mass), or (basal expenditure)/(fat free mass)] for infants and children is therefore expected to be greater than adults' due to the greater proportion of metabolically active organs early in life adults  (21, 22, 44, 45). Similarly, reduced organ sizes in elderly subjects may result in declining basal expenditure  (21). To examine this effect of organ size on basal expenditure in our dataset, we used published references for organ size to determine the mass of the metabolically active organs (heart, liver, brain, and kidneys) as a percentage of body mass or fat free mass for subjects 0 -12 y  (22, (44) (45) (46) , 15 to 60 y  (21, 22) , and 60 to 100 y  (21, 47). We used these relationships to estimate the combined mass of the metabolically active organs (heart, liver, brain, kidneys) for each subject in our dataset.
We averaged the mass-specific metabolic rates of resting muscle and other lean tissue (see above;  (21, 22) ) and assigned a value of 52. 5 kJ kg -1 d -1 to "other fat free mass", and we used a mass-specific metabolic rate of 19 kJ kg -1 d -1 for fat. Results are shown in Figure  S6. Due to the greater proportion of metabolically active organs in early life, the estimated basal expenditure from the three-compartment model follows a power-law relationship with FFM (using age cohort means, BEE= 0. 38 FFM 0. 75 ; Figure  S6B ) that is similar to that calculated from observed basal expenditure in our dataset (see Table  S2  and 7. Modeling the Effects of Physical Activity and Tissue Specific Metabolism, below). Estimated BEE from the three-compartment model produced mass-specific metabolic rates that are considerably higher for infants and children than for adults and roughly consistent with observed age-related changes in (basal expenditure)/(fat free mass) (Figure  S6C ).
Thus, changes in organ size can account for much of the variation in basal expenditure across the lifespan observed in our dataset. Nonetheless, observed basal expenditure was ~30% greater early in life, and ~20% lower in older adults, than estimated basal expenditure from the three-compartment model (Figure  S6D ). The departures from estimated basal expenditure suggest that the mass-specific metabolic rates of one or more organ compartments are considerably higher early in life, and lower late in life, than they are in middle-aged adults, consistent with previous assessments  (21) (22) (23) (24) (25). It is notable, in this context, that observed basal expenditure for neonates is nearly identical to basal expenditure estimated from the three-comparment model, which assumes adult-like tissue metabolic rates (Figure  S6B ,C,D).
S10 ). Moderate and vigorous physical activity reach a peak ~4times greater than the mean values observed for 20 -30 y men and women, far greater than the amplitude of change in adjusted total expenditure. We used adjusted total and basal expenditures to model activity expenditure and PAL over the lifespan for comparison with published accelerometry measures of physical activity. Modeling activity expenditure and PAL was preferable because our dataset has no subjects less than 3 y with measures of both total and basal expenditure, and only 4 subjects under the age of 6 y with both measures (Table  S1 ). Using values of adjusted total expenditure and adjusted BEE TEE (basal expenditure expressed as a percentage of total expenditure) for age cohorts from Table  S3  enabled us to model activity expenditure and PAL for this critical early period of development, in which both physical activity and expenditure change substantially.
The agreement, and lack thereof, between the pattern of accelerometry-measured physical activity and modeled activity expenditure and PAL must be assessed with caution. These measures are from different samples; we do not have paired accelerometry and energy expenditure measures in the present dataset. The life course pattern of accelerometry-measured physical activity, particularly total counts, is broadly consistent with that of modeled activity expenditure. However, more work is clearly needed to determine the effects of physical activity and other factors to variation in activity expenditure and PAL over the lifecourse.
location or value of change points, only the number of them. Each increase in the number of change points from 0 to 3 improved the model adj. R 2 and standard error considerably. Increasing the number of change points further to 4 or 5 did not improve the model, and the additional change points identifed by the segmented() function fell near the change points for the 3-change point model. We therefore selected the 3-change point model as the best fit for adjusted total expenditure in this dataset. Segmented regression results are shown in Table.
Acknowledgements The DLW database, which can be found at https://doubly-labelled-water-database. iaea. org/homeor https://www. dlwdatabase. org/, is generously supported by the  IAEA ,  Taiyo Nippon Sanso  and, SERCON. We are grateful to these companies for their support and especially to  Takashi Oono  for his tremendous efforts at fund raising on our behalf. The authors also gratefully acknowledge funding from the  US National Science Foundation  ( BCS-1824466 ) awarded to  Herman Pontzer. The funders played no role in the content of this manuscript.
Pontzer et al. Daily Energy Expenditure through the Human Life Course OBSERVED EXPENDITURES Supplementary Materials: A. Contents: (MJ/d) ENERGY EXPEND. TEE BEE AEE Adult 60+ TEE) (%ADULT ADJUSTED Adj TEE Adj BEE TEE Materials and Methods FAT FREE MASS (kg) AGE (y) 1. 00 1. 50 2. 00 1. Doubly Labeled Water Database 2. 00 2. 25 2. 50 2. 75 MODEL INPUT 3. 00 Physical Activity (PA) 10 8 12 B. 2. Basal Expenditure, Activity Expenditure, and PAL AEE TEE BEE MODEL OUTPUT 1. 25 1. 50 1. 75 2. 00 Adj TEE Adj BEE TEE 0. 50 3. Predictive Models for TEE, BEE, AEE, and PAL 1. 75 6 1. 00 -0. 50 0. 00 4. Adjusted TEE, Adjusted BEE, and Adjusted BEE TEE 0. 75 1. 00 1. 50 4 1. 25 Tissue Metabolism (TM) 2 0. 75 0. 25 0. 50 0. 50 -1. 00 0 0. 00 3. 00 5. Segmented Regression Analysis 0 20 40 60 80 100 12 0 10 20 30 40 50 2.
00 0 20 40 60 80 100 2. 00 2. 25 2. 50 2. 75 C. 6. Organ Size and BEE 8 10 1. 25 1. 75 1. 50 1. 50 1. 75 7. Modeling the Effects of PA and Cellular Metabolism 4 6 0. 75 1. 00 0. 50 2 8. Physical Activity, Activity Expenditure and PAL 0. 25 0 0. 00 0 9. The IAEA DLW database consortium 20 40 60 80 100 0 AGE (y) FAT FREE MASS (kg) 10 20 30 40 50 0 20 40 AGE (y) 60 80 100 Figures S1-S10.
All primers were designed using NCBI Primer-BLAST and selected to generate amplicons with a length of 100-200 bp (Supplementary Table  2 ). The primers were synthesized by Eurogentec. Standard curves were generated to calculate the efficiency of each set. Only primer sets with an efficiency of 1. 85-2. 1 were used for qPCR. The relative mRNA levels for each assay were computed from the Ct values obtained for the target gene. The data were pooled and analyzed using RQ manager v1. 2 and DataAssist v1. 0 (Applied Biossytems, Thermo Fisher Scientific, CA, United States).
Statistics To compare the four groups (CC, CH, HC, and HH), nonparametric Kruskal-Wallis tests were performed, followed by a pairwise comparison by Dunn's multiple comparisons test (GraphPad Prism 7). P < 0. 05 was considered statistically significant. Data are expressed as: median (Q1; Q3). The first quartile (Q1) and the third quartile (Q3) correspond to 25 and 75% percent of scores, respectively. For lipid analyses on placenta, liver, and plasma, principal component analyses (PCAs) were additionally performed using the R statistical software (R Core Team, 2013). 1 The FactomineR package (dedicated to multivariate data analysis;  Lê et al. , 2008)  was included in R commander to analyze the biological relevance of observed FA changes, namely to extract important information and express this information as a set of new variables called principal components  (Rousseau-Ralliard et al.
Plasma Fetal plasma fatty acids have been evaluated, but not enough samples from female fetuses could be obtained, so only data from males are presented. However, in a previous study, we have seen that in response to high fat-fed mothers, fetal plasma fatty acid concentration increased in a sex-specific manner, with higher concentration in males only  (Tarrade et al. , 2013). In the male fetuses, plasma FA concentrations were significantly higher (p = 0. 0004) in the CH and HH groups  (1,305 [1,210; 1,335]  and 1,022 [888; 1,170] µg/ml of plasma, respectively) compared to the  CC and HC groups (531 [521; 550]  and 472 [460; 484] µg/ml of plasma, respectively), pointing out mainly the effect of the maternal diet during gestation (Figure  5A ). In addition, concentrations in HC differed from that in CC (p = 0.
The MFA combining biometric data sets and fetal liver gene expression with fetal liver (NL and PL) fatty acid concentration and profiles showed that recipient diet played a preponderant role, although a clear separation among the four groups could be observed either on the factor map (Figure  8A ) or hierarchical clustering (Figure  8B ). These data strengthened the effects observed on liver weight, (CC > HH, with HC and CH in between). The FA profiles or gene expression in HC were not significantly different from CC, when we studied these different datasets separately, however, in this MFA, this HC group appeared completely dissociated from CC, demonstrating that the fetal repercussions of the preconception environment also deserved to be studied because it affects, beyond the placenta, the fetus health as well.
, 2011)  and in the blastocyst stage were observed near to term  (Tarrade et al. , 2013). Offspring postnatal phenotype was characterized by predisposition to develop metabolic syndrome, such as excess adiposity and hypertension  (Picone et al. , 2011; Dupont et al. , 2014). However, since maternal exposure to highfat diet extended from puberty to the end of gestation, it was not possible to dissociate the effect of each period of exposure on the feto-placental phenotype, i. e. , an early effect, affecting the gametogenesis of rabbits from a pure gestational effect of this exposure. In this study, the fetoplacental effects of maternal exposure to a high-fat diet were investigated for each window of vulnerability and a combination of the two. Thereby, data Relative gene expression, obtained by RT-qPCR (2 -Ct ) of genes encoding ATP-binding cassette A1 and subfamily G member 1 for the transport of cholesterol (ABC-A1 and ABC-G1), adipophilin (Adipo), differentiation cluster glycoprotein 36 (CD36), fatty acid synthase (FAS), fatty acid transport protein 4 (FATP4), 3-hydroxy-3méthylglutaryl-coenzyme A(HMG CoA), low-density lipoprotein receptor (LDL-R), nuclear receptors Liver X receptors (LXRα), peroxisome proliferator-activated receptor gamma (PPARγ), retinoic X receptor alpha (RXRα), solute carrier family 2 (facilitated glucose transporter) member 1 known as GLUT-1 (SLC2A1), GLUT-3 (SLC2A3), sodium-coupled neutral amino acid transporters 1, 2, and 4 (SLC38A1, SLC38A2, and (SLC38A4), and sterol regulatory element-binding protein 2 (SREBP2) in the placenta 28 dpc.
The values were normalized with DataAssists Software, using two reference genes coding for 4E translation initiation factor 2 (EIF4E2) and ribosomal protein L18 (RPL18). Statistically, Data were analyzed by Kruskal-Wallis test followed by, if applicable (i. e. , if significant at p < 0. 05), pairwise Dunn's multiple comparisons test to explore pre-conception exposure (embryo origin) and in utero exposure (recipient diet during gestation). The data are represented by Median [Q1; Q3] as the median, the first and third quartiles. Medians in a row without a common letter differ, p < 0. 05. CC, control embryo transferred in control recipient does; CH, control embryo transferred in HD-fed recipient does; HC, embryo from HD-fed donor transferred in control recipient does; HH, embryo from HD-fed donor transferred in HD-fed recipient does.
The values were normalized with DataAssists Software, using two reference genes coding for 4E translation initiation factor 2 (EIF4E2) and ribosomal protein L18 (RPL18). Statistically, the data were analyzed by Kruskal-Wallis test followed by, if applicable (i. e. , if significant at p < 0. 05), pairwise Dunn's multiple comparisons test to explore preconception exposure (embryo origin) and in utero exposure (recipient diet during gestation). The data are represented by Median [Q1; Q3] as the median, the 1st and 3rd quartiles. Medians in a row without a common letter differ, P < 0. 05. CC, control embryo transferred in control recipient does; CH, control embryo transferred in HD-fed recipient does; HC, embryo from HD-fed donor transferred in control recipient does; HH, embryo from HD-fed donor transferred in HD-fed recipient does.
ACKNOWLEDGMENTS We are grateful to the staff of the  UE 1298 SAAJ (Experimental unit of Sciences de l'Animal et de l'Aliment de Jouy)  for housing the rabbits and for all the help given in the animal experimental procedures. We thank the  MIMA2 platform (Microscopy and Imaging Facility for Microbes, Animals and Foods platform INRAE , 2018, https://doi. org/10. 15454/1. 5572348210007727E12 ), particularly  CIMA (Chirurgie et Imagerie Médicale  chez l'Animal, https://www6. jouy. inra. fr/mima2/Equipements/Imagerie-sur-l-Animal) for the access to surgical room and equipment. Moreover, we thank all the students and persons who participated in this project from near and far, from the  BREED Unit. We are members of  COST Actions BM1308  "Sharing advances on large animal models (SALAAM)".
Participants We studied all consecutive participants enrolled during the first wave of the pandemic (between March and June 2020) in the quasiexperimental GERIA-COVID study within the geriatric acute care unit of the University Hospital of Angers, France  [3]. A first analysis of 14-day mortality was previously published on a subsample of this cohort (the patients included between March and May 2020, at the time of writing the first report)  [3]. Inclusion criteria for the present analysis were as follows: 1) COVID-19 diagnosed with RT-PCR and/or chest CT-scan; 2) data available on the serum measures and on the usual treatments; 3) vital status available 3 months after the diagnosis of COVID-19. Ninety-seven patients with COVID-19 were consecutively admitted into the unit during the study period and recruited in the GERIA-COVID study.
Ethics The study was conducted in accordance with the ethical standards set forth in the Helsinki Declaration  (1983). No participant or relatives objected to the use of anonymized clinical and biological data for research purposes. Ethics approval was obtained from the Ethics Board of the University Hospital of Angers, France (2020/100). The study protocol was also registered on ClinicalTrials. gov (NCT04560608) and declared to the French National Commission for Information Technology and civil Liberties (CNIL; ar20-0087v0).
Acknowledgments The authors wish to thank the  GERIA-COVID study group. GER-IA-COVID study group :  Cédric Annweiler 1 , Marine Asfar 1 , Mélinda Beaudenon 1 , Jean Barré 1 , Antoine Brangier 1 , Mathieu Corvaisier 1 , Guillaume Duval 1 , Jennifer Gautier 1 , Mialy Guenet 1 , Jocelyne Loison 1 , Frédéric Noublanche 1 , Marie Otekpo 1 , Hélène Rivière 1 , Guillaume Sacco 1 , Romain Simon 1. Affiliations: 1:  Department of Geriatric Medicine, University Hospital, Angers, France. The authors have listed everyone who contributed significantly to the work in the Acknowledgments section. Permission has been obtained from all persons named in the Acknowledgments section. There was no compensation for this contribution.
Cell viability test and IC 50 determination After 48h of treatment with selenofolate or folate, cell viability was measured using the Cell Titer-Glo® Luminescent Cell Viability Assay (Promega, Catalogue #G7570). Luminescence was read in a CLARIO star® (BMG LABTECH) multi-mode microplate reader. Viability levels were calculated in comparison to the untreated cells and subjected to dose-response curve fitting analyses in GraphPad Prism 8. 3. 0 Software (GraphPad Inc, USA) to calculate the IC 50.
Statistical analysis Data were analysed via one-way ANOVA and Nested t-test using GraphPad Prism 8. 3. 0 software (GraphPad Inc. USA) with α set to 0. 05. Homogeneity of variance was ascertained using Brown-Forsythe test. The Tukey-Kramer HSD posthoc test was performed to determine the source of significance at p 0. 05.
A Phase II clinical trial showed that patient survival benefited from Vintafolide treatment compared to historical controls  [18]. Despite the early success of Vintafolide, the Phase III clinical trial of Vintafolide in combination with Doxil® was suspended in 2015 (NCT01170650) showing no life-extension benefit. Our results show that treating cancer cells with selenofolate in vitro is an effective way to inhibit the growth of all cancer cells. Selenofolate did not exert apoptotic cell death in IGROV1 cells, whereas its antiproliferative effects are apparent (Figure  6 ). Upon analysis of these results, one might consider the following hypotheses. Selenium compounds forming selenides (RSe-) are known for their redox activity causing oxidative stress in cancer cells leading to cell death  [2].
4-imidazolon-2-yl]-ornithine (MG-H1), was estimated using country-specific dietary questionnaires linked to an AGEs database. Cause-specific hazard ratios (HR) and their 95% confidence intervals (CI) for associations between dietary AGEs and risk of hepatocellular carcinoma (HCC), gallbladder and biliary tract cancers were estimated using multivariable Cox proportional hazard regression. After a median follow-up time of 14. 9 years, 255 cases of HCC, 100 cases of gallbladder cancer and 173 biliary tract cancers were ascertained. Higher intakes of dietary AGEs were inversely associated with the risk of HCC (per 1 SD increment, HR-CML = 0. 87, 95% CI: 0. 76-0. 99, HR-CEL = 0. 84, 95% CI: 0. 74-0. 96 and HR-MH-G1 = 0. 84, 95% CI: 0. 74-0. 97). In contrast, positive associations were observed with risk of gallbladder cancer (per 1 SD, HR-CML = 1.
| Identification and follow-up of cases In most countries, cases were identified through population cancer registries (Denmark, Italy, the Netherlands, Norway, Spain, Sweden and the United Kingdom). In other countries such as France, Germany and Naples (Italy), a combination of methods was used to identify cases including health insurance records, cancer and pathology registries and an active follow-up of study participants. Cancer incidence data were coded according to the 10th revision of the International Statistical Classification of Diseases, Injuries and Causes of Death and the second revision of the International Classification of Diseases for Oncology. Only first incident cases were included according to topographical codes: HCC (C22. 0), intrahepatic bile duct (C22. 1), extrahepatic bile duct (C24.