dataesr/scientific-paragraphs-categorization huggingface dataset:
5 g/L and 0. 3 g/L for Kisumu and Nairobi, respectively. A test of multicollinearity between log-transformed CRP and AGP (ln-CRP and ln-AGP) was assessed on the basis of a test of tolerance (>0. 1) to determine whether it was appropriate to include all variables in the model. Because the BRINDA group did not report estimates for the regression coefficients for their meta-regression of data from pregnant women, we estimated these coefficients separately for the Kisumu and the Nairobi studies. Estimates for the regression coefficients were exponentiated to express associations in the original units of measurements. Iron deficiency was determined by applying a cut-off of <15 µg/L  [2]  to inflammation-corrected ferritin concentrations. As per BRINDA recommendations, the lowest deciles of CRP and AGP were set as internal reference values to avoid over-adjustment for low levels of inflammation, and adjustments were restricted to ferritin concentrations that corresponded to the CRP or AGP exceeding their lowest decile.
Specific internal reference values were obtained for each of the groups described above. This was done on the basis of all values for CRP and AGP, as was suggested by the BRINDA group. We considered, however, that regression over censored independent variables is likely to result in biased estimators for the regression coefficients. For this reason, we also conducted an analysis of the Kisumu dataset with exclusion of observations with CRP values below the LOD. Although this truncation leads to a reduced sample size, and thus to a loss of efficiency, it has the advantages that the method is simple and that regression coefficients will be consistent, i. e. , with increasing sample size, the estimates will tend towards the true value  [12]. The categorical Correction Factor (CF) approach, as proposed by Thurnham et al.
Method 1: IRC Approach to Correct for Inflammation The results of the linear regression analysis are presented in Table  1. Results obtained for all Kisumu women and for Nairobi women were identical to those obtained with the use of the SAS macro provided by the BRINDA group. There was no evidence of multicollinearity. In Kisumu women, exclusion from the analysis of women with censored CRP values (i. e. , CRP values below the LOD) resulted in an increased magnitude of the slope between log-ferritin concentration and log-CRP (bivariate analysis: 0. 328 versus 0. 201; multivariate analysis: 0. 120 versus 0. 005), which indicates the bias that may occur due to inclusion of censored values for the independent variables. Such exclusion reduced the sample size by 11. 1% (from 470 to 418 women). In multivariate analysis, the regression coefficient for the association between log-ferritin concentration and log-CRP were less than the values obtained in bivariate analysis (Kisumu women, excluding women with CRP < LOD: 0.
7%), there is a gross underestimation of the true prevalence of iron deficiency by 24. 4% to 41. 1%. The Kenya Demographic and Health Survey of 2014 indicated that only 8% of women aged 15-49 years with a live birth in the last five years received iron supplements for the recommended 90 days or more  [18]. To prevent anemia and other pregnancy-related complications, there is an urgent need to scale up the development and monitoring of public health programs to improve the iron status of pregnant women. It is highly likely that similar trends in the prevalence of iron deficiency are the norm rather than the exception in most low and middle-income countries. The prevalence of iron deficiency is not matched by comprehensive and effective public health mitigation programs. The current analysis used two comprehensive datasets on pregnant women from two diverse settings, rural and urban.
When analyzing trial data, an important question (concerning the BRINDA method) is whether beta coefficients should be calculated within each intervention group, or whether they should be pooled for all groups. The source population of our datasets was quite unique in terms of inflammation and prevalence of Plasmodium infection. As such, specific correction factors, beta coefficients, and reference values were obtained for Kisumu and Nairobi separately, in order to take into account the differences that may exist between these groups. This increases the external validity of the findings and their general applicability. Furthermore, the datasets have a large sample size and contain high-quality laboratory analyses. The analysis also draws from primary data from two studies, each of which used a sampling scheme that was representative.
Both datasets are comparable in that they applied similar laboratory methods for measuring the biomarkers of interest, focused on the second trimester of pregnancy, and were cross-sectional in their nature, but designed with a specific aim to look at iron deficiency prevalence in pregnancy. This comparability provides for a generalizable interpretation of the findings. We have presented results of analyses of data from two markedly different populations (rural versus urban). Our data was selected on the basis of convenience (i. e. , the availability of two pregnant women data sets), and was cross-sectional in nature. Because the PIMAL study was a randomized controlled trial with a placebo arm, we applied the hemoglobin cut-off of >90g/L to avoid recruiting women who would need medical intervention into the placebo arm of the trial.
Conceptualization, M. N. M. and R. B. -S. ; data curation, M. K. ; formal analysis, M. N. M. , M. K. , L. K. , E. M. M. , and H. V. ; funding acquisition, M. N. M. and R. B. -S. ; methodology, M. N. M. , E. E. , L. K. , B. G. W. , F. M. K. , R. M. , E. M. M. , A. Y. D. , and H. V. ; project administration, B. G. W. and F. M. K. ; resources, A. Y. D. and R. B. -S. ; supervision, M. N. M. ; visualization, M. N. M. and M. K. ; writing-original draft, M. N. M. , E. E. , M. K. , and R. B. -S. ; writing-review and editing, M. N. M. , L. K. , B. G. W. , H. V. , and R. B. -S. Funding: The Kisumu (PIMAL) study was part of the INSTAPA project, which received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 211484. The Nairobi study (Maternal Nutrition Study-2014) was funded by Danone Nutricia Africa Overseas (DNAO).
Acknowledgments: The Kisumu (PIMAL) study was supported by the  INSTAPA  project, which received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 211484. The  Nairobi (Maternal Nutrition study) study  was supported by  Danone Nutricia Research. We thank local authorities, community volunteers, research staff, and students involved in both studies.
Author Contributions: Conceptualization, M. N. M. and R. B. -S. ; data curation, M. K. ; formal analysis, M. N. M. , M. K. , L. K. , E. M. M. , and H. V. ; funding acquisition, M. N. M. and R. B. -S. ; methodology, M. N. M. , E. E. , L. K. , B. G. W. , F. M. K. , R. M. , E. M. M. , A. Y. D. , and H. V. ; project administration, B. G. W. and F. M. K. ; resources, A. Y. D. and R. B. -S. ; supervision, M. N. M. ; visualization, M. N. M. and M. K. ; writing-original draft, M. N. M. , E. E. , M. K. , and R. B. -S. ; writing-review and editing, M. N. M. , L. K. , B. G. W. , H. V. , and R. B. -S. Funding: The Kisumu (PIMAL) study was part of the INSTAPA project, which received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 211484. The Nairobi study (Maternal Nutrition Study-2014) was funded by Danone Nutricia Africa Overseas (DNAO).
Data sources We extracted data from two series of surveys: the Demographic and Health Surveys and the World Health Surveys  [15, 16]. These are household, cross-sectional surveys that employ a multistage sampling design. The Demographic and Health Surveys collect anthropometrically measured height and weight from all respondents, as well as a series of asset indicators meant to capture personal wealth. The World Health Surveys collect mostly self-reported height and weight measurements, along with many asset indicators. From these sources, we compiled 182 surveys, representing 103 countries across different time points from 1995 to 2015. A total of 2. 24 million survey respondents aged 15-49 were included in the sample. Detailed information concerning the surveys can be found in Tables A-F and Figs A-B in S1 Appendix.
Measurements of overweight and obesity We categorized adults aged 18 and over with a BMI of 30 kg/m 2 or greater as obese and those with a BMI of 25 kg/m 2 or greater as overweight. We will refer to overweight, inclusive of obesity, as "overweight" throughout the paper. For survey respondents aged 15-17, classification was made according to the International Obesity Task Force's growth curve  [17]. Adults with missing or implausible BMI measurements (less than 12 or greater than 60) were excluded (more information is available in S1 Appendix Section 2. 6). Height and weight data in the World Health Surveys are self-reported, and some studies have shown bias in self-reporting of height and weight  [18]. We conducted sensitivity analyses to test for systematic differences between overlapping Demographic and Health Surveys and World Health Surveys and found no significant bias (Fig C  and Tables H, J , L in S1 Appendix).
Results We collected data from 182 nationally representative surveys with individual-level data for 2. 24 million respondents. for confounders, the shifting pattern was seen from the raw data. In the $0 to $5,000 category, the richest had an overweight prevalence of 35. 2% (95% uncertainty interval: 23. 4%-46. 9%) and an obesity prevalence of 11. 7% (5. 1%-18. 3%), while the poorest had an overweight prevalence of 15. 3% (3. 0%-27. 6%) and an obesity prevalence of 3. 5% (0. 0%-8. 3%). In the greater than $40,000 category, the richest had an obesity prevalence of 12. 0% (  The first line marks where the richest decile was no longer the most likely to be overweight or obese. The second line marks where the richest decile was less likely than the poorest to be overweight or obese. We evaluate the change in the gradient based on where the other percentiles are statistically significantly greater than zero (which means the wealth group has a higher chance of obesity than the 90th-100th percentile personal wealth income group).
MALDI MS and MS/MS Analyses. Samples were analyzed from CHCA or SA matrix deposits, in positive ion mode the Rapiflex instrument from Bruker Daltonics ©. A pulsed Nd:YAG laser at a wavelength of 355 nm was operated at a 66. 7 Hz frequency with a laser focus of 29%. Data were acquired with the Flex Control software (version 4. 1, Bruker Daltonics © ). Spectra were integrated with the Flex Analysis software (version 4. 0, BrukerDaltonics © ), the centroid algorithm was used to assign peaks. An acceleration voltage of 25. 0 kV (IS1) was applied for a final acceleration of 21. 95 kV (IS2) and a lense voltage of 9. 6 kV. The reflectron mode was used for the ToF analyser (voltages of 26. 3 kV and 13. 8 kV). The delayed extraction time was 30 ns. Acquisitions were performed using a reflector detector voltage of 1.
722 kV. MS data were processed with the Flex Analysis software (version 4. 0, Bruker Daltonics © ). External calibration was performed with commercial peptide mixture (Calibration peptide standard II, protein I, Bruker Daltonics, Wissembourg, France). Fragmentation experiments were performed under laser induced dissociation (LID) conditions with the LIFT cell voltage parameters set at 19. 0 kV (LIFT 1) and 3. 7 kV (LIFT 2) for a final acceleration of 29. 5 kV (reflector voltage) and a pressure in the LIFT cell around 4 x 10 -7 mbar. The precursor ion selector was set manually to the first monoisotopic peak of the molecular ion pattern for all analyses. MS/MS data were processed with the Flex Analysis software (version 4. 0, Bruker Daltonics © ). Mass lists were generated according to the following parameters: SNAP as peak detection algorithm, S/N threshold 3.
Statistical analyses. Data were expressed as mean±SEM. Dissociation constant of F-LEAP2 was determined from saturation curves of the specific binding. GTPγS binding experiments and imaging studies were analyzed by ANOVA with Tukey´s post-test. Food intake studies were analyzed by one-way ANOVA test for comparison of different mean values. Statistical analysis was performed using GraphPad Prism 6. 0 software (GraphPad Software, Inc). Differences were considered statistically significant when p<0. 05.
After 15 min, mouse brains were obtained, as described above, and the F-LEAP2 signal was estimated in the ARC. In this case, the ARC of mice pretreated with fulllength LEAP2 or ghrelin showed significantly lower F-LEAP2 labeling, as compared to the ARC of mice pretreated with vehicle (Figure  5 ). Overall, these data indicated that F-LEAP2 retained specificity to GHSR in mice. 3. 6. Bioactivity assessment. Since LEAP2 inhibits ghrelin-induced food intake, the bioactivity of F-LEAP2 was estimated by assessing its ability to impair the orexigenic effects of ghrelin in mice (In vivo experiment 3). ICV-injected LEAP2 or F-LEAP2 did not affect food intake (not shown), while ICV-injected ghrelin potently increases food intake, as it is well established. In mice pretreated with the LEAP2 variants, ICV-injected F-LEAP2 induced ~40% reduction of ghrelin-induced food intake, in a similar fashion to that seen for full-length LEAP2 (Figure  6 ).
Protocol Design This randomized double-blind controlled trial was authorized by the ethics committee (Comité de Protection des Personnes Sud-Est 6, Clermont-Ferrand, France) and the French state authority (Agence Nationale de Sécurité du Médicament). It was registered on EudraCT under ref. 2012-005658-52 and on clinicaltrials. gov as NCT01893385. At the inclusion visit the volunteers gave fully informed written consent, and then blood samples were taken to determine serum Vit-D levels and the biological parameters required to validate eligibility criteria: blood cell count, and usual plasma and urinary levels of calcium, phosphorus, creatinine, liver enzymes (AST, ALT), glucose, and total proteins. Based on serum Vit-D data, the volunteers were grouped as follows: (i) persons with a serum Vit-D level greater than or equal to 30 ng/mL: these individuals were excluded, and advised to accept an influenza vaccine in autumn; (ii) persons with a Vit-D level below 30 ng/mL: these individuals were randomly assigned to one of two groups: (1) a supplemented group (D) receiving six Vit-D doses (Uvedose R 100,000 IU, 1 vial/15 days, Crinex Lab.
Serum Antibody Quantification Vaccine response was assessed at two points (V2 and V3) by measuring hemagglutination inhibition (HAI) antibody (Ab) titers against the influenza vaccine antigens. The HAI test was performed in microplates by incubating serum with the 2013-2014 influenza reference strains (H1N1, H3N2, and Yamagata), following the WHO procedure. Inter assay quality control was performed with reference antisera as positive controls. Erythrocyte controls allowed adjustments in incubation time and were performed on each plate. Each field isolate antigen and the control antigens have been tested with a negative serum control. The HAI Ab titer was defined as the highest dilution of serum inhibiting the agglutination of guinea pig erythrocytes (Charles River Lab. ). In accordance with the European Agency for the Evaluation of Medicinal Products' guidelines  (35) , data was expressed in 3 ways: geometric mean titer (GMT) with a 95% confidence interval; seroconversion rate (percentage of subjects achieving at least a 4-fold increase, or an increase from >10 to 40 in HAI Ab titer for seronegative subjects); and seroprotection rate, i.
Statistical Analysis Data are expressed as mean ± SEM. Statistical analysis was performed using GraphPad Prism R 5. 03 for Windows (GraphPad Software Inc. , San Diego, CA, USA). Vit-D supplementation and period effects were analyzed by twoway ANOVA followed by a Bonferroni post hoc test. Differences within groups were determined by a paired Student t-test or a Wilcoxon matched-pairs signed-ranks test. Differences between groups were tested by an independent Student t-test or a Mann-Whitney U-test. Differences were considered statistically significant at p < 0. 05. The relationship between serum Vit-D and cathelicidin data was assessed using a Pearson correlation (significant threshold: p < 0. 05).
ACKNOWLEDGMENTS We authors thank all the volunteers who agreed to participate in the trial. They are grateful to the  Clinical Investigation Center  ( Prof C. Dubray ,  Dr. G. Pickering ,  S. Boulliau ,  F. Girond ,  N. Macian ,  S. Eschalier ), the hospital pharmacy for product delivery,  C. Blavignac  ( Cell Imagery Center ), and  L. Monbrun  ( Genotoul Anexplo, Toulouse ) for cytokine analysis, and  Prof B. Lina  ( Hôpital de la Croix-Rousse, Lyon ) for HAI titer assays. They also thank  L. Slimani  and  S. Rougé  for their technical assistance.
Bioplex pro Mouse cytokine 23-plex assay. A cytokine 23-plex kit (BioRad, CA, USA, Cat #M60009RDPD) was used according to manufacturer's instructions to measure the concentrations of eotaxin, G-CSF, GM-CSF, IFN-γ, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-17A, KC MCP-1 (MCAF), MIP-1α, MIP-1β, RANTES, and TNF-α in supernatants from BMM. The fluorescent signal intensity was measured using Bioplex 200 system. Cytokine concentrations were determined using standard curves generated using Bioplex manager software (V4. 1).
RNA extraction and quantitative real-time PCR (qRT-PCR). Total RNA from BMM was isolated and purified with a Rneasy Mini kit according to the manufacturer's instructions (Qiagen, Hilden, DE, USA). cDNA from total RNA was synthesized (50-100 ng RNA/20 μl) using a QuantiTect Reverse transcription kit according to the manufacturer's instructions (Qiagen). qRT-PCR was performed using the Taqman Fast Advanced Master Mix (Applied Biosystems, Foster City, CA, USA) and was run for the following gene using the probe provided by Thermofisher: acid phosphatase 5 (ACP5) (Mn00475698_m1). qRT-PCR assays were performed in duplicates on an Applied Biosystems QuantStudio 5 Real-Time PCR system. The data were analyzed using QuantStudio 5 software V1. 4. The gene expression levels were normalized to β-actin for BMM samples respectively and expressed relative to unstimulated controls following the 2-ΔΔCT method.
5-gauge needle at a point on the midline of the skull between the ears and eyes, as we have described previously  10. The dose of P. gingivalis (5 × 10 8 ) was injected. In the treatment group, 1 mg of Kava-205Me was concurrently injected. Mice were euthanized 4 days post injection. The size of the lesion (area in square millimeters) resulting from the injection in each animal was analyzed using ImageJ software.
Induction of arthritis and scoring. Six-weeks-old, pathogen-free DBA1/BO male mice were obtained from Taconic Laboratories. Arthritis was induced by two consecutive intraperitoneal injections of ArthritoMab (AB) antibody cocktail (Chondrex, WA, USA). 3. 5 mg were injected at baseline and a second injection of 1 mg was done at day 4. A sample of 5 mice/group was considered based on our previous data  18  and all animals were randomly allocated in each group. For P. gingivalis injected groups, 3 intraperitoneal injections of 5 × 10 8 bacteria/100 μl were administered. Mice were euthanized after 35 days by CO 2 inhalation. All four paws were evaluated by a reviewer blinded to the treatment group to score arthritis using a visual qualitative assessment scoring as follows: (0) no paw swelling, (1) mild swelling, (2) moderate swelling, (3) severe swelling.
DATA SUMMARY 1. The Ruminococcus gnavus E1 sucrose 6 F -phosphate phosphorylase (SPP) sequence is available from GenBank under accession number FQ790378 (see Fig. S5 , available in the online version of this article). 2. All sequences, from the glycoside hydrolase GH13_18 subfamily used in the present study have been extracted from the CAZy database, URL: http://www. cazy. org. 3. Data used for the prevalence and abundance study were provided by the MetaHIT project (http://www. metahit. eu). The human intestine is colonized by a complex, diverse and dynamic community of microorganisms, the so-called microbiome, which is in permanent interaction with the host  [1]. It is now well established that the gut microbiome is of considerable interest for health. It is indeed involved in food degradation and assimilation, formation of bile salts, protection against pathogens, integrity of epithelial layer and immunity (for review see  Sekirov et al.
5 µM each primer. The sequences of the primers corresponding to the genes in mice were designed using the Universal Probe Library assay design centre (Roche) and are reported in Table  S1. Thermal cycling conditions were as follows: 5 min denaturation at 94 C; followed by 45 cycles of 10 s at 94 C, 10 s at 60 C and 10 s at 72 C. Data were collected using the Light Cycler 480 software (Platform AVB, iSm2, Marseille, France). Cycle thresholds were normalized to rpoB or gyrB levels and fold changes were calculated against the normalized control of induction and untreated values when applicable. The relative quantification of glycosyltransferase and gene expression was performed using the comparative DDCt method  [45]. Each sample was treated in triplicate to ensure statistical significance of the analysis.
In silico analysis The gene model from locus RUGNEv3_61221 of Ruminococcus gnavus E1 (GenBank accession no. FQ790378. 1) was downloaded from the Microbial Genome Annotation and Analysis Platform (MaGE, accessible via https://www. genoscope. cns. fr/) and translated to the corresponding protein sequence (GenBank accession no. CCA61958. 1), herein referred to as RgSPP. Eighteen hundred other amino acid sequences of bacterial GH13_18 members were extracted from the public version of the CAZy database  [6]. The RgSPP sequence was submitted to the Phyre2 server  [46]  in order to generate a 3D structural model and visualized using PyMOL software (PyMOL Molecular Graphics System, version 2. 0; Schrödinger). The SignalP 4. 1 server was used to determine the presence and location of protein signal peptide cleavage sites in the RgSPP amino acid sequence  [47].
Multiple alignment of characterized GH13_18 members was performed using MAFFT EINSI advanced mode  [48]. The secondary structure of AAO33821 from Bifidobacterium adolescentis based on its solved crystal structure (PDBID: 1R7A) was analysed using ESPript (http://espript. ibcp. fr) [49]. Where present, signal sequences and additional modules were removed to isolate the catalytic modules for bioinformatics analysis. A multiple sequence alignment was produced using the Muscle program  [50] , and the evolutionary history was inferred using the neighbour-joining method  [51]. The evolutionary distances were computed using the Poisson correction method  [52]  and are in the following units: the number of amino acid substitutions per site. The analysis involved 1065 amino acid sequences. All ambiguous positions were removed for each sequence pair.
There was a total of 987 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 software  [53]. Cluster analysis was based on the neighbour-joining method with the closely related bacterium Ruminococcus gnavus E1 as the out-group root. Synteny blocks were analysed by the MaGE platform, which allowed the comparison of coding sequencespredicted from the genomic DNA of Ruminococcus gnavus E1 to those predicted from genomic DNA present in the PkGDB (Prokaryotic Genome DataBase) and the National Center for Biotechnology Information RefSeq database (a collection of raw sequences from whole-genome sequencing). Beyond a simple sequence comparison, this interface allows the analysis of the synteny between two chromosomes.