dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Under-five death, nutrition, and vaccination data. In the absence of functioning vital registration systems in crisis-affected areas where an elevated prevalence of acute malnutrition and mortality rates are of concern, mortality information is often gathered through retrospective household surveys  15. Among them are smallscale operational surveys, which are conducted by humanitarian organizations in crisis-affected areas. Small-scale surveys have been shown to be a useful source of information for humanitarian decision making  32. These surveys are publically available in the CE-DAT, www. cedat. be. For the current study, we used surveys conducted in Ethiopia between January 2009 and December 2014. The timeframe for the study was based upon data availability for the contextual factors considered in our analytical framework.
The CE-DAT mainly compiles surveys on nutrition, mortality and vaccination coverage from humanitarian organizations, health ministries and UN agencies. Most of the included surveys follow a standardized methodology for nutrition and mortality surveys, the Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology  33. Within Ethiopia, the survey process is monitored by the Emergency Nutrition Coordination Unit. The survey reports are further validated by the CE-DAT team using the established completeness checklist, prior to entering the survey results into the database  32. The following indicators were extracted from the CE-DAT: the number of under-five deaths, the number of under-five children and the U5DR; the prevalence of GAM among children 6-59 months of age, also referred to as wasting; the MCV coverage among children 9-59 months of age; the survey coverage area (the third administrative level, 'woreda') in addition to the month and year the survey took place (see the Supplementary Table  S1 ).
g. , 12-and 24-month SPEI) detects less frequent but longer-lasting droughts associated with water resource  35, 36. The different time-scales are therefore important for assessment of drought effects on child mortality, as the effects are likely to vary with the frequency and duration of droughts. Thus, in this study, the 3-month and 12-month SPEIs were used to identify the short-and long-term droughts, respectively. The SPEI data was downloaded from the Global Drought Monitor, http://sac. csic. es/spei/map/maps. html, which provides robust information about drought conditions at the global-scale with 0. 5 degree spatial resolution and monthly time resolution. The SPEI values for a specific location and month were extracted based on the starting time and the centroid of the survey coverage area.
The SPEI takes positive or negative values, typically in the range between -2. 5 and +2. 5, indicating wet-or dry-conditions, respectively  36. For ease of interpretation, we categorized the SPEI values into: no drought (SPEI > 0), mild drought (-1 < SPEI ≤ 0), moderate drought (-1. 5 < SPEI ≤ -1) and severe to extreme drought (severe drought hereafter) (SPEI ≤ -1. 5)  37. Food security and livelihood zones data. Food security encompasses availability, accessibility, utilization and stability of food for a healthy and active life  30. We obtained the food security data from the Famine Early Warning Systems Network (FEWS NET, http://www. fews. net/), which provides evidence-based analysis on early warning and food insecurity. FEWS NET uses the Integrated Food Security Phase Classification (IPC), a set of tools and procedures which serve to identify and classify the severity of food insecurity.
The IPC is based on the four pillars of food security analysis: accessibility, availability, utilization and stability, and it classifies areas with food insecurity into five phases -minimal, stressed, crisis, emergency, and famine  38. We gathered historical food security phase data from the FEWS NET quarterly food security reports. Data was available for 81 survey areas from 2009 onwards. In addition, we obtained the livelihood zone classification data (cropping, agro-pastoral and pastoral) from FEWS NET. Livelihoods are based on similarities in terms of economic activity, food source, and income and market opportunities. The food security phase and livelihood zones data of a given survey coverage area were then matched with CE-DAT data using the GPS coordinate.
Statistical analysis. For surveys that did not report the number of under-five deaths, it was calculated by multiplying the mortality rate with sample size and recall period. The datasets were merged across space and time and then random-effects meta-analysis model was fitted to summarize survey results and explore heterogeneity across independently conducted but methodologically similar small-scale surveys. Summarizing data with this approach increases statistical power to detect effects, and improve precision  39. When applied in humanitarian decision making, the analytical approach can strengthen informed response and preparedness thereby enhancing evidence-based decision making at large scale  26, 29, 32. We explored the variability across the survey estimates using the τ 2 statistic (the between-survey variance).
To explain the variability, meta-analyses were performed on subgroups based on survey-specific contextual factors or moderators (drought exposure levels, food insecurity, and livelihood zones); and subsequently a meta-regression was fitted adjusting for additional moderators, prevalence of GAM and MCV coverage. We also tested the interaction effects to assess whether the association between drought and U5DR varied by the moderators. Among the 88 surveys included in our meta-analysis, 16 (18. 2%) had zero counts for the number of under-five deaths. Using the inverse-variance methods for such data is problematic because the estimates and their variances are not defined for zero death counts. Hence, Poisson regression model for meta-analysis was used as a remedy to circumvent these issues  40.
001,0. 001) for the variance parameter, τ 2 ; and Normal(0,10 6 ) for θ and β ′s i. The Bayesian meta-analysis was performed in WinBugs version 1. 4  42  , where the Markov Chain Monte Carlo (MCMC) sampling is used to make inference. We run the Markov chain for 50,000 iterations after a start-up run of 10,000 iterations. We then assessed the convergence of the chain visually inspecting the trace-and autocorrelation-plots  41. The metafor package  43  in R statistical software  44  was used to generate the forest plot. Two meta-regression models were fitted based on the duration of droughts: Model 1 investigating the effect of short-term droughts on child mortality, while Model 2 focused on the long-term droughts, both adjusted for the survey-specific moderators. Finally, the median of the posterior distribution was calculated to provide estimates of the U5DR, death rate ratios (DRR), and 95% credible intervals (CrI) derived from the 2.
Acknowledgment This study was a collaboration research between University of South brittany (UBS),  Vannes, France and Diponegoro University (UNDIP) ,  Semarang, Indonesia under Double-Degree program  supported by the  Directorate of Higher Education (DIKTI). The author would like to sincerely thank to all members of the  Laboratoire de Biotechnologie et Chimie Marines (LBCM)  who had been very helpful and supporting during the research. A special gratitude was addressed to  Nolwenn Terme  from LBCM who helped to improve this article.
Reverse Transcription and Real-Time PCR RNA extraction, reverse transcription, and real-time PCR were performed as previously described  (Lenoir et al. , 2011). Briefly, total RNA was isolated with TriReagent (Sigma), and subsequent cDNA synthesis was performed with the High Capacity cDNA Starter Kit, (Applied Biosystems) according to the manufacturer's instructions. Real-time PCR was performed in a LightCycler 480 Instrument II (Roche) using the SYBR Green PCR mix (Roche) in accordance with the MIQE guidelines  (Bustin et al. , 2009). Relative mRNA expression levels were determined by the secondderivative maximum method with the LightCycler 480 analysis software. All samples were normalized to the threshold cycle value for cyclophilin-A (Ppia). Primer sequences used for this study are provided in Figure  S1.
Lung iron loading did not demonstrably trigger tissue damage, fibrosis, or inflammation (data not shown). To further identify the iron-positive immune cells in the alveolus, bronchoalveolar lavages (BAL) were performed. We confirmed by FACS analysis that more than 95% of the cells were AM. The AM were cytospinned and stained with Perls' Prussian blue. As shown in Figure  3C , a large number of the AM isolated from the Hepc KO mice were iron loaded, although at different levels, compared to the WT AM population. This phenotype appears to be unique to this macrophage population, as splenic or hepatic macrophages from Hepc KO mice have been shown to be iron deprived  (Lesbordes-Brion et al. , 2006). Of note, this phenotype of iron accumulation in the AM develops progressively with age, becoming prominent from 25 weeks old.
2 | METHODS REDUCE-IT (NCT01492361) is a phase 3b, international, multicenter, prospective, randomized, double-blinded, placebo-controlled, parallelgroup trial of icosapent ethyl 4 g/d (2 g twice daily with food) vs placebo (Figure). The main objective is to evaluate whether treatment with icosapent ethyl reduces ischemic events in patients at elevated CV risk concurrently treated with statins. Inclusion and exclusion criteria are listed in Table 1 and Table 2, respectively. Men or women age ≥45 years with established CVD (CV risk stratum 1, Table 1) or age ≥50 years with diabetes mellitus in combination with 1 additional risk factor for CVD (CV risk stratum 2, Table 1) were eligible for.
Modelling of hVKORC1 Aer searching the Protein Data Bank (PDB),  33  we retrieved four reports describing the structure of VKOR from Synechococcus sp. (bVKOR, 283 amino acids, aas) (ESI, Table  S1  ‡). The alignment of the sequences revealed a bVKOR segment (13-181 aas) having the best identity/homology (25/36%) with hVKORC1 (163 aas) (ESI, Fig. S2A ‡ ). Since the homology is weak between sequences, we rst used a set of independent bioinformatics methods for prediction of the secondary structure composition in proteins. These methods are designed on distinct concepts and algorithms and they correctly forecasted all structural elements of the experimentally characterized bVKOR (4NV5, resolution of 2. 80 Å)  11  (ESI, Fig. S1 ‡ ), thus validating the accuracy of the prediction methods. These methods were applied to predict the secondary structure of the target sequence, hVKORC1.
Molecular dynamics simulations Set-up of the system. The set-up of molecular dynamics (MD) simulations were performed using GROMACS 4. 6. 5 program package. 53 The CHARMM-36 force eld 54 modied to support DLPC lipids simulations  49  was employed. The 50 000-step energy minimization was performed on each full system using a steepest descent energy minimization. The equilibration was performed on the solvent, keeping the solute atoms (except Hatoms) restrained for 100 ps at 310 K and a constant volume (NVT). The temperature of protein, membrane and solvent (water and ions) was separately coupled to the velocity rescale thermostat,  55  a modied Berendsen thermostat, 56 with a relaxation time of 0. 1 ps. Each system has been equilibrated during 1 ns (NPT) with all non-hydrogen atoms of the protein and DLPC membrane harmonically restrained.
Principal component analysis. The collective motions of hVKORC1 were investigated by principal components analysis (PCA). For an N-atoms system, a trajectory matrix contains in each column a Cartesian coordinates for a given atom at each time step (x(t)). Fitting the coordinate data to a reference structure results the proper trajectory matrix (X). The trajectory data is then used to generate a covariance matrix (C), elements of which are dened as: 1 c ij ¼ h(x i À hx i i)(x j À hx j i)i (1) where h(x i À hx i i)(x j À hx j i)i denotes an average performed over the all the time steps of the trajectory. The principle components (PCs) are obtained by a diagonalization of the covariance matrix C. 2 C ¼ VLV T (2) This results in a diagonal matrix L containing the eigenvalues as diagonal entries and a matrix V containing the corresponding eigenvectors.
The solute and solvent dielectric coefficients were set to 16 and 80, respectively. DE MM includes DE int (covalent bonding including the bond, angle and dihedral energies), electrostatic DE elect and van der Waals DE vdw energies. The polar contribution was calculated using PB model, while the non-polar free energy estimated by solvent accessible surface area (SASA). DG bind was computed for each generated ligand$target complex over the merged trajectories containing 400 frames (and also for the individual MD simulation replicas over a trajectory containing 200 frames). We used the g_mmpbsa module of GROMACS, which combines subroutines from GROMACS and APBS  75  to calculate the enthalpy contribution (DH) to the Gibbs free energy and the per-residue energy decomposition. The energy components DE MM and the polar and non-polar components of DG sol were calculated in the bound and unbound form, and subsequently their contribution to the binding energy DR BE x of residue x (ref.
e. , 200 mM Hepes buffer (pH 7. 4) containing 150 mM KCl, 1 mM dithiothreitol, 0. 75 to 2 g L À1 of total proteins containing membrane VKORC1) in the presence of increasing amounts of vitamin K1 epoxide (from 0. 003 to 0. 2 mM) using anticoagulant concentrations from about 0. 05 to 20 Â K i. Data were tted by non-linear regression to the noncompetitive inhibition model 4 using the R-t program. K i values were obtained from at least three separate experiments performed on three different batches of protein. v ¼ V max ½S aK m þ a 0 ½S ¼ 1 a 0 V max ½S À a a 0 Á K m þ ½S (8) where a ¼ 1 þ ½I K i and a 0 ¼ 1 þ ½I K 0 i :.
Acknowledgements The authors thanks  Eric Solary  ( Institut Gustave Roussy ),  Konrad Hinsen  ( Centre de Biophysique Moléculaire, CNRS Orléans ) and  Alexey Alexandrov  ( Ecole Polytechnique ) for useful discussions. This work was performed using HPC resources from  GENCI-IDRIS  (Grant 2015(Grant ,  2016-077291)-077291 ).
Intestinal morphometry and enzyme activity analysis Villous and crypt sizes were determined on haematoxylineosin-stained jejunum sections. A minimum of 15 welloriented villus and 10 well-oriented crypts were measured under a light microscope (Nikon Eclipse E40, Nikon Instruments, France) using an image analysis software (NIS-Elements AR 3. 0, Nikon Instruments). The specific activities of lactase and aminopeptidase N were determined in jejunum mucosa as previously described  [28]. The protein concentration was measured according to the method of Lowry  [29].
Intestinal RNA extraction and microarray description Total RNA was extracted and quantified from individual jejunum samples as previously described  [30]. The porcine microarray (8 × 60 K, GPL16524, Agilent Technology) used consisted in 43,603 probes derived from the 44 K Agilent-026440 porcine specific microarray (V2, for 71% of total probes), 3773 probes from immune system, 9532 probes from adipose tissue and 3768 probes from muscle tissue, as already reported  [25]. After quality control and quantity normalization steps, fluorescence signal data from 63 microarrays containing 39,446 spots were available for further statistical analysis and log2-transformed. Annotation was based on  Voillet et al. (2014)  data  [25]. Nevertheless, all differentially expressed probes (DEPs) were double-checked and some probes were manually annotated by sequence blasting against NCBI or Ensemble (release 84) databases.
Statistical analysis Analyses were performed using the R software version 3. 1. 3 [32]. Blood parameters, jejunum morphometry and enzyme activities were analyzed by a two-way analysis of variance (ANOVA) with gestational age (d90 and d110), genotype (LW, MSLW, LWMS and MS) and the interaction between gestational age and genotype as main fixed effects. Normality of data and homogeneity of variance were confirmed by Shapiro and Bartlett tests, respectively; data were Box-Cox transformed when their distribution was not normal. Models and procedures used to analyze transcriptomic data have been described in Voillet et al. [25]. A mixed linear model was applied to microarray data using two fixed effects (gestational age and fetal genotype), their interaction, and the sow as random effect, then compared to a reduced model by F-type test to identify DEPs for gestational age and/or fetal genotype.
Functional enrichment analysis and pathway analysis Functional enrichment analysis of DEPs in purebred LW and MS was supplied by GeneCoDis 3. 0 software  [33]  focused on Gene ontology (GO) Biological Process (BP), using the Homo sapiens genome (Ensembl 65 release) as reference. Functional enrichment analysis was applied on DEPs of the complete model with absolute log2-fold change ≥0. 5 (corresponding to an absolute fold change 1. 4) between day 90 and day 110. To set the statistical enrichment of a particular biological function, a hypergeometric test was used. All P-values were adjusted using the FDR approach (FDR < 0. 01). DEPs of the complete model in purebred LW and MS that meet the conditions described above (adjusted P value ≤0. 01 and absolute log2-fold change ≥0. 5) were subjected to Ingenuity® Pathway Analysis  [34]  (IPA; Ingenuity Systems, Qiagen, USA) for literature-based biological analysis.
Multivariate integration and regression analysis Multivariate statistics were used to select highly relevant genes that may predict phenotypic variable variation. We assessed the 'uni-directional' relationships between microarray dataset and phenotypic variables. The dataset used here including all expressed probes. For this purpose, the probes were considered as predictors of phenotypic variables using the sparse partial least square regression model (sPLS) implemented in mixOmics R package  [35]. sPLS regression is related to classical PLS regression but it proceeds with additional variable selection that reduces noisy data and increases the predictive performance of the model, especially when the number of variables is far greater than the number of samples  [36]. The number of genes selected in the first four components was tuned by Q2 criterion (> 0.
Influence of parental genotype on gene expression Effects of paternal genotype or maternal genotype on intestinal maturation were detected by reciprocal crossing. Surprisingly, no probe was influenced by paternal genotype. Only 28 DEPs (corresponding to 18 unique differentially expressed genes, DEGs) were found as affected by maternal genotype in interaction with gestational age (FDR < 0. 05, Additional file 3: Table  S3 ). Principal component analysis (PCA) analysis of the 28 DEPs illustrated the moderate effects of the maternal genotype (PC1 = 44. 2%, PC2 = 12. 1% and PC3 = 10. 0% of the total variance; Fig. 3a  and b ). However, PC1 mainly separated data from purebred MS fetuses at day 90 from those of purebred LW fetuses, PC2 separated data from fetuses from MS sows at day 110 from those of fetuses from LW sows, and PC3 separated data from fetuses from LW sows at day 90 from those of fetuses from MS sows at day 90.
Validation of differential gene expression by qRT-PCR analysis To validate microarray results, seven differentially expressed genes of interest were selected to perform qRT-PCR. As shown in Table  4 , the Pearson's correlations between expression profiles of these genes measured by microarray and PCR was greater than 0. 8 for all genes except CIRBP. The similarity of results between qRT-PCR and microarray confirmed the reliability of our microarray data (Additional file 10: Figure  S3 ).
Discussion The use of a relevant experimental design (eight conditions in a two by four factorial design) leading to Fig. 6  Network 1 (carbohydrate and lipid metabolism, energy production) in MS at day 110 compared to day 90 using IPA. Genes in red and green were up-regulated or down-regulated in the study dataset, respectively. A higher color intensity means the higher degree of up or down-regulation, respectively. Genes in white were from the knowledge base contrasting situations in terms of late fetal maturation associated with the selection of DEPs (FDR-adjusted P value <0. 01) highlighted the impact of complex relationships between gestational age and fetal genotype on maturity of the intestine. No paternal genotype effect occurred while that of maternal genotype was moderate and mainly observed at 90 days of gestation.
Integration of blood and intestinal phenotypic variables and intestinal transcriptome To get insight into the relationships between phenotype items and gene expression and to identify the key variables which can explain the biological outcomes of interest, sPLS was used to combine the phenotypic and microarray datasets without considering the fixed effects of fetal genotype and gestational age. Our correlation analysis revealed strong relationships between jejunum gene expression patterns and three phenotypic variables (blood fructose level, intestinal lactase activity and intestinal villous height). Piglet genotype did not modify the gestational age-induced increase in lactase activity, whereas it tended to modify the gestational age-induced changes in villous height (increase between day 90 and day 110) and blood fructose level (decrease between day 90 and day 110).
Funding This project received financial support from  French National Agency of Research  ( PORCINET  project,  ANR-09-GENM005 ). YY is supported by the  China Scholarship Council  ( CSC)-INRA joint PhD program. VV is a PhD fellow supported by the  INRA GA and PHASE departments and the region Midi-Pyrénées. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements This work was supported by the  CNRS, INSERM  and  Univ Montpellier , and by grants from the  Agence National de la Recherche  ( ANR-12-BSV2-0015 ;  ANR-13-RPIB-0009 ), the  Fondation Recherche Medicale  ( FRM DEQ 20130326522 ). TCM was supported by a funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no  627227. Compliance with ethical standards.
No change to gamma settings was applied. For neuron quantitation, TUBB3-positive cells were counted and the neuronal yield was calculated as % of the population from the ratio between TUBB3positive cells and Hoechst-stained cells (i. e. whole cell population). For quantification of protein amounts, regions of interest were set for each cell in the images based on cell boundaries and mean fluorescence intensities were measured. The background fluorescence was subtracted for each image and values were then normalized. For each time point or treatment condition, at least 10 images per experiment were recorded for both neuron counting and protein level quantification experiments. Data are shown as means ± SD from 3 independent experiments.
Cell viability assay Cell density was quantified to assess cytotoxicity following the different treatments. For each treatment condition, proliferating cells were seeded on glass coverslips pre-coated with poly-D-lysine and laminin at the same cell concentration. Cell differentiation was induced up to 3 days when the cell confluency reached ~80%. Then, cells were loaded with 2 μM Hoechst 32258 for 10 min followed by 2 washing steps with PBS. Coverslips were fixed and mounted as described above and images of nuclei were acquired with LSM 710 NLO confocal microscope. Nuclei were counted using Fiji/ImageJ software to determine the residual cell density. The nuclear labelling reports the number of cells in the population. Cells were treated with AA, LiCl, RuR or NAC as aforementioned. Images were acquired for proliferating cells treated for 24 h with AA, and after 72 h of differentiation.