dataesr/scientific-paragraphs-categorization huggingface dataset:
Eligible child contacts were aged below 5 years old and shared the same household with a selected index case within 3 months prior to the diagnosis of the latter. The children were enrolled following the signing of written informed consent by parents or primary caregivers. Ineligible child contacts included those born after the index cases were diagnosed and initiated on TB treatment, child contacts on TB treatment, and those that were not living in the same household with the index cases before the diagnosis. Moreover, child contacts older than 5 years, including those infected with HIV, were also excluded in accordance with Rwanda's NTP policy. 2. 3. Data Collection and Management. Data were collected in twofold, from patients' records at the PHC facilities and from parents/caregivers of eligible child contacts interviewed by trained enumerators using a structured questionnaire.
Screenings conducted during this study identified all children as eligible for IPT according to the WHO recommendation  [12]. To assess the uptake of IPT, every parent/caregiver of an eligible child was asked whether the child was initiated on the IPT or not. The IPT register at PHCs assisted to verify the information given by parents/caregivers. The sociodemographic characteristics and medical history of index cases, such as a result of smear microscopy, HIV status, residential address, and telephone number, were recorded. The data collected were validated by the index cases, parents, or caregivers of selected children once they were identified to ensure their accuracy. All children underwent a history, physical, and chest X-ray (CXR) examination. Anteroposterior and lateral CXR were also performed on all children; they were read by two independent experienced general practitioners, trained in interpreting CXR and blinded to the clinical details of participants; an experienced radiologist verified all CXR to rule out any discordance.
Data Analysis. The data collected were double-entered into a Microsoft Excel worksheet and exported to Stata Software for statistical analysis after checking their consistency. The data were analyzed using descriptive and multivariate statistics. Continuous variables were dichotomized using the median as the cut-off. Categorical variables were described using frequency tables and proportionate methods. Univariate and multivariate logistic regression was performed to determine characteristics associated with IPT uptake. Where appropriate, Chi-square test or Fisher's exact test was performed to assess the association between two variables. Variables with a  value < 0. 2 in univariate analysis were included in the logistical regression model using backward stepwise method. The final model included those factors that retained statistical significance.
Chemicals and antibody Customized anti-FFAR4 antibody was produced from a rabbit immunized against a peptide designed from the second extracellular loop (179-194 amino acids) of the bovine sequence (Accession number: NP_001315586. 1). Serum from immunized rabbit underwent affinity purification and antibody concentration was measured by enzyme-linked immunosorbent assay (ELISA; Agro-Bio, La Ferté Saint-Aubin, France). This customized antibody enabled detection of a 42 kDa band by western blot in lung tissue extract (Additional file 1: Figure  S1 ). Lung tissue was used to validate the antibody, as in other species, namely mice and humans, the level of expression of FFAR4 protein is reported to be higher in this tissue than in other tissues, such as adipose tissue, intestine and liver  [42].
After three 10-min washes in TBS containing BSA (0. 1%), sections were then incubated with secondary Alexa Fluor 488-conjugated donkey anti-rabbit IgG (1:800 dilution) in TBS containing BSA (0. 1%) for 1. 5 h at room temperature. Then, GCs were washed in TBS containing BSA (0. 1%) (three 10-min washes), and incubated in TBS with 1 μg/mL Hoechst 33,258 for 15 min at room temperature to stain nuclei. Sections were then mounted using Moviol® and fluorescence was observed under a Zeiss confocal microscope LSM700 (Carl Zeiss Microscopy GmbH, Munich, Germany) using an oil 40× objective with a numerical aperture of 1. 3 or an oil 63× objective with a numerical aperture of 1. 4 and the appropriate filters. Images were captured using Zen 2012 software (black edition version 8. 0, Carl Zeiss Microscopy GmbH).
Statistical analysis Statistical analyses were performed for GC proliferation, steroidogenesis, gene expression, protein expression, GC lipid composition taking into account treatment effect and replica effect. Concerning cell signaling, the time effect and replica effect were analyzed. GC proliferation, progesterone secretion, estradiol secretion, GC lipid composition and cell signaling were compared between the groups. A one-way ANOVA was used when distribution and variance enabled performance of a parametric study (Shapiro test, Levene test, Rcmdr package), with Tukey's post hoc comparison (R package multcomp Abbrev gene abbreviation, bp product size in base pair, E primer efficiency  [46] ). A non-parametric ANOVA (permutational ANOVA) was used when distribution was not normal and variance was not homogenous (R package lmPerm  [47] ), with a Tukey's post-hoc test (R package nparcomp  [48] ), R version 3.
3. 1  [49] ). Gene expression was analyzed using K-sample Fisher Pitman permutation test with a Monte Carlo approximation (Rcmdr package) with the treatment as a fixed factor and the replica as a random factor, with Tukey's post hoc comparison (R package nparcomp). Protein expression levels of PCNA, HSD3B1, StAR and CYP11A1 were analyzed using the Kruskal-Wallis test (Shapiro test, Levene test, Rcmdr package). A p-value of ≤0. 05 was considered to indicate a significant difference and 0. 05 < p ≤ 0. 10 a tendency.
The chemical DMSO alone (1/2000) was used as a negative control due to its use as a solvent for DHA and TUG-891. Total mRNA was extracted from GC and reverse-transcribed, and real-time RT-PCR was performed. The geometric mean of two housekeeping genes (RPL19-ribosomal protein L19 and RPS9-ribosomal protein S9) was used to normalize gene expression. Results of 10 independent cultures are presented as mean ± SEM. Bars with different superscripts are significantly different (p < 0. 05) phosphorylation with DHA (10 or 50 μM) or TUG-891 (1 or 10 μM) (data not shown). Only a transient, albeit significant, increase in MAPK1/3 phosphorylation was observed after 5 min treatment with TUG-891 at 50 μM (1. 6-fold increase; p = 0. 021; Additional file 7: Figure  S5C ).
Concerning data on steroidogenesis, steroid secreted in the culture medium by both platted and non-platted cells during the cell culture are measured after 48 h. Steroid concentration are normalized by protein concentration in each well, meaning by protein amount of platted cells only. We estimated that the proportion of platted cells to viable floating cells is the same in each well for a specific batch of cells. This normalization might biased the steroid results as the concentration of steroid for a viable amount of cells might be slightly overestimated due to the normalization taking into account only platted cells. Nevertheless, we believe that this normalization would not affect differences observed between conditions.
Statistical analyses Statistical analyses (Table  1 ; Figure  1  and Supplementary Tables S1, S3 and S4) were performed using the nonparametric Mann-Whitney test using the XLSTAT software due to the small data set sizes (n < 6) and high individual variability with a model that included the fixed effect of treatment and random effect of goat. Treatment effects were considered significant at P < 0. 05 and were considered to represent a tendency at P < 0. 1.
Microarray data and analysis Due to the absence of a caprine microarray, transcriptome analyses were performed using a bovine microarray as previously described in caprine studies  (Ollier et al. , 2007 and 2009; Faulconnier et al. , 2011). The microarray consisted of the Operon Bos taurus (bovine) AROS V1. 1 set (Operon Biotechnologies, Cologne, Germany) and contained 8329 unique genes plus 53 genes that were chosen for their potential importance in mammary metabolism, resulting in a focused microarray. This platform is accessible through GEO Platform accession number GPL5451. Five micrograms of total RNA from each mammary tissue sample was reverse transcribed and fluorescently labeled in the presence of either Cy3-or Cy5-dCTP (GE Healthcare Europe GmbH, Orsay, France) using the Pronto ChipShot™ Direct Labeling Kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
The labeling and hybridization were performed as previously described by Faulconnier et al. (2011). The labeling and hybridization were conducted in a dye-swap manner. Thus, eight microarrays (four independent comparisons in dye-swap) and 10 microarrays (five independent comparisons in dye-swap) were performed to allow the comparison of the transcriptomes for EL v. CTRL and ELFO v. CTRL, respectively. After hybridization, the slides were washed and scanned then statistical analyses were performed as previously described by  Faulconnier et al. (2011). For each comparison, data from the four replicated spots per oligonucleotide were averaged to eliminate technical variations. Statistical analyses were performed using the free R 2. 6 software (2006, http://www. r-project. org) with the ANOVA test, and the P values were adjusted using Benjamini-Hochberg correction  (Benjamini and Hochberg, 1995).
Differences were considered significant at an adjusted P < 0. 05. The data are accessible through the GEO series accession number GSE68232. Similar changes in the expression of eight genes (ELF3, LALBA, SPP1, SMAD3, ABCG2, PPARG, CSN1S1 and ADIPQ) were observed by microarray analysis and by RT-PCR. Faulconnier, Bernard, Boby, Domagalski, Chilliard and Leroux.
Biological interpretation The identities of the differentially expressed genes (DEGs) were validated by a sequence comparison between the spotted oligonucleotides and the NCBI non-redundant database using the BLASTN algorithm (  2006 ). Gene symbols were obtained from the HUGO Gene Nomenclature Committee database (2006). The DEGs were classified according to their biological processes and their networks were studied using the following bioinformatics tools: Ingenuity Pathway Analysis (IPA: http:// analysis. ingenuity. com) and Ensembl (http://www. ensembl. org/index. html). Potential transcription factors involved in the regulation of the DEGs were searched using the network algorithms for transcription factor development in the MetaCore TM software (https://portal. genego. com).
13% of the DEGs), and cellular protein metabolism and transport (ca. 13. 5% of the DEGs) classes, both of which were altered by the EL (Figure  3a ) and ELFO (Figure  3b ) v. CTRL diets. In addition, cellular lipid metabolism and transport class comprised 2. 6% and 3. 8% of the DEGs identified in the EL (Figure  3a ) and ELFO (Figure  3b ) v. CTRL diets, respectively. Bioinformatics analyses were performed to refine the biological functions of the DEGs. We showed that the first five impacted molecular and cellular functions that were highlighted in the EL v. CTRL and ELFO v. CTRL comparisons, as found using the Ingenuity (IPA) software, included cellular growth and proliferation (Supplementary Table  S7A ). We identified that the PI3K/AKT signaling pathway was the first and second canonical pathway altered by the ELFO (Supplementary Table  S7B ) and EL (Supplementary Table  S7B ) diets, respectively.
Protein and amino acid metabolism were modified by EL and ELFO (Supplementary Table  S7C ), whereas only EL showed the lipid metabolism network among its first five altered networks. For regulatory network analyses, we used the Metacore TM software and detected two common networks, one focused on ESR1 (Figures  4a  and c ) and one on the transcription factor SP1 (Figures  4b  and d ), in the EL v. CTRL and ELFO v. CTRL DEG lists. Pathways involved in milk component synthesis and secretion Lipid metabolism and transport. Using the IPA software, we identified a lipid metabolism network (the fourth identified network) in the EL v. CTRL DEG list (Table  2 , Figure  5  and Supplementary Table  S5 ). In contrast, for the ELFO v. CTRL, the DEG list did not reveal any lipid metabolism networks, despite the presence of genes involved in lipid metabolism (Table  2 ).
We also noted common families in the two DEG lists (EL v. CTRL and ELFO v. CTRL). For example, two different members of the aldehyde dehydrogenase protein family were down-regulated (ALDH18A1 and ALDH3B1 by EL and ELFO diets, respectively, and ALDH3A2 by the EL diet) in the present study (Table  2 ). Protein metabolism and transport. Another highly represented class was the cellular protein metabolism and transport process, which contained 104 and 95 DEGs when EL and ELFO were compared with CTRL diets, respectively, including 21 common DEGs (Table  2 ). This is in line with the detection of an amino acid metabolism network using the IPA software for the EL v. CTRL and ELFO v. CTRL DEG lists (Figure  6  and Supplementary Table  S7 ). Among the 21 common DEGs, 4 (CTRB1, MST1, USP15 and USP28 were down-regulated by the EL and ELFO diets), representing nearly 20% of the common genes, belonged to the protease superfamily (Marchitelli et al.
Lipid metabolism The conclusions from the targeted approach led us to perform a global analysis. With the aim of increasing our nutrigenomics database, we used the same dedicated bovine microarray found in previous studies  (Ollier et al. , 2007 and 2009). Despite the relatively small number of genes represented on the microarray, we identified 344 and 314 DEGs in the goats fed EL and ELFO v. CTRL diets, respectively (Figure  1  and Supplementary Tables  S5  and S6 ). Bioinformatics analyses of each of the complete DEG lists obtained with EL and ELFO v. CTRL and using the IPA software identified lipid metabolism and transport process (the fourth network) as enriched (Supplementary Table  S7C ) with the EL v. CTRL list which is in line with the positive effects of the EL (v. CTRL) diet on milk fat content (+33%, Supplementary Table  S3 ).
Cellular protein metabolism and transport We identified another highly represented class, as the cellular protein metabolism and transport process contained 104 and 95 DEGs for the EL and ELFO v. CTRL diets, respectively, including 29 DEGs in common (21 DEGs selected in Table  2 ). This finding is in line with the network identified using the IPA software from the EL v. CTRL and ELFO v. CTRL DEG lists, which suggests that these genes play a role in amino acid metabolism in mammary glands (Figure  6 ). This network is of particular interest during lactation in mammary epithelial cells that synthesize milk proteins. Notably, a large proportion of the genes involved in this network were downregulated, which could reflect a perturbation in mammary function (e. g. the non-significant trend for a 15% to 17% lower protein yield in the EL and ELFO groups).
Mammary nutri-regulome With the aim to describe the mammary nutri-regulome, we performed analyses using the Metacore TM software with the DEG lists to identify key regulators that could be modulated by the dietary treatments. Both the EL v. CTRL (Figure  4a ) and ELFO v. CTRL (Figure  4c ) analyses identified a network that is focused on ESR1 and is known to be involved in mammary development and cell differentiation  (Schams et al. , 2003). Furthermore, ESR1 is connected to the JAK/STAT pathway through its regulation by prolactin  (Frasor and Gibori, 2003). Another highlighted transcriptional network was centered on the transcription factor SP1 (Figures  4b  and d ), which is known to regulate a variety of processes including cell growth, apoptosis, differentiation and immune responses. Moreover, SP1 is a transcription factor for the SCD1 gene (Pauciullo et al.
Acknowledgments The authors thank  D. Roux  and his team for the management of the animals at the  Experimental Unit at Theix. The authors also thank  J. Rouel  for his helpful support for the experimental trial,  S. Bes  and  C. Labonne  for their technical assistance. Transcriptomics analyses were performed with the metabolism exploration platform (PFEM) at the  ARA INRA center.
Numerical solving and identification procedure This system of equations governing the coupled diffusion and production of gas in the cheese and the gas transfer and permeation in the cheese packaging headspace was solved using a dedicated algorithm "ode15s" developed in Matlab® computing software (The Mathworks Inc. , Natick, Comment citer ce document : Acerbi, F. , Guillard, V. , Saubanere, M. , Guillaume, C. , Gontard, N. (  2018  Where y pred and y exp are respectively the experimental and the simulated residual value, N is the number of experimental measurements and μ yXz is the average of the experimental values. CVRMSD enables to compare the quality of the model with other models present in literature. A good quantitative model is considered to have CVRSMD ≤ 10%.
¹³C β-carotene, metabolism, asymmetric metabolites, digestion, stomach, duodenum, gastrointestinal lumen Abbreviations: AUC = Area under the time-versus-concentration curve, BCO1 = β-carotene oxygenase 1, BCO2 = β-carotene oxygenase 2, DCM = dichloromethane, EDTA = ethylenediaminetetraacetic acid, LC-MS/MS = high performance liquid chromatography-tandem mass spectrometry, methanol = MeOH, methyl tert-butyl ether = MTBE, TRL = triglyceride-rich lipoprotein fraction This trial was registered at ClinicalTrials. gov as NCT03492593 Conclusions: β-carotene appears to be robust to digestion, with minor amounts of β-apocarotenals/carotenone formed. Absence of asymmetric 13 C β-apo-carotenals in plasma and TRL suggests lack of absorption, levels below the limit of detection, lack of stability, or further conversion during the digestive process to as yet unidentified products.
The study screened 13 healthy male subjects who met the study criteria, with the aim of 9 subjects completing the entire protocol (n=7 test meal subjects, n=2 control meal subjects). Sample size was chosen based on preliminary data obtained in a similarly designed study with lycopene (not yet published). Subjects were randomized to control or test meal by the study coordinator based upon order of enrollment. After an overnight fast, subjects arrived at the clinic, and the nasoduodenal tube was inserted and positioned either in the gastric compartment or in the duodenum. An X-ray confirmed proper tube placement. Afterward, a 0 h blood sample was drawn, and subjects were asked to consume either the liquid test meal or liquid control meal (delivering 0 g of protein, 100 g of carbohydrate, and 55 g of lipid) within 5 min.
Statistics Statistical analyses were performed using R software, version 3. 1. 0 (45). Residual plots were visually inspected to confirm assumptions of homoscedasticity and normality. Bioavailability of the 13 C β-carotene in the TRL fraction was baseline-corrected, and area under the time-concentration curve (AUC) calculated using trapezoidal approximation. To ensure the same level of 13 C β-carotene was absorbed by the same subject during both gastric and duodenal sampling, a paired t-test of 13 C β-carotene AUC in TRL was performed (P < 0. 05 was considered statistically significant). The relationship between time and digesta type on digesta analyte concentration ( 13 C β-carotene itself, or the ratio of the 13 C β-apo-carotenals/carotenone to 13 C βcarotene in the same sample), was modeled with mixed-effects regression using the lme4 package  (46).
ACKNOWLEDGEMENTS This work was supported by a grant from the  Olga Triballat Foundation. Rachel Kopec  received support from the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship  (under grant agreement n°  267196 ). The authors do not report any potential conflicts of interest. R. E. K. , C. C. V. , P. B. designed research, R. E. K. , M. N. , B. G. , and M. C. conducted research, R. E. K. analyzed data and performed statistical analyses, R. E. K. wrote paper, R. E. K. , C. C. V. and P. B. have primary responsibility for final manuscript content. All authors have read and approved the final manuscript.
Supported by: This work was supported by grants from the  PACA (Provence-Alpes-Côte d'Azur) Region  and the  Olga Triballat Foundation. Rachel Kopec  received the support of the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship  (under grant agreement n°  267196 ).
This step is especially informative for S. mansoni, a digenetic organism, with life cycle stages and protein expression specific to both the mollusc and human hosts. While the human genome has been extensively annotated and made generally available, pathogen genome and expression annotation can be more elusive. Pathogens such as Plasmodium falciparum have been sequenced and extensively annotated  19. In comparison, the S. mansoni genome, while much larger than that of P. falciparum (11,809 S. mansoni proteins vs 5,628 P. falciparum proteins), was only recently sequenced and assigned a full set of accession identifiers in GeneDB  20. The sequencing effort is ongoing and there is limited annotation of corresponding proteins and structural information available. Thus, it is challenging even to cross-reference S.
mansoni proteins described in various reports, particularly in those published prior to full genome sequencing  21. Additionally, life cycle stage annotation is difficult to access or even absent in most databases. The best source of annotation is directly from primary references in literature. Most primary reference accessions were often embedded in portable document format (pdf) files and other file formats that make extraction challenging. Furthermore, the context of an extracted accession and the life cycle stage to which it applies must be isolated from each reference and verifiable for accuracy and further study. We address these challenges by designing a simple natural language processing engine (NLP) that accomplishes data extraction, accuracy, verifiability, and correlation between disperse data sets required to construct the Biological Context Filter.
mansoni and human proteins with similarity to solved complex structures. (Table  1 ). We applied three network-level filters to prune the initial predictions (Fig. 1 ). The first Network Filter removed interactions where the templates are used for more than 1% of the total predictions. These include templates based on promiscuous domains and sequences that can present as promiscuous due to similarity or other reasons. Promiscuous domains, while present in many interacting complexes lack specificity and are overrepresented in the predicted data set, making them less desirable as vaccine candidate antigens. For example, a domain in the crystal structure of HIV Capsid Protein (p24) bound to FAB13B5, Protein Data Bank (PDB 1E6J), is a frequently used template in potential interactions. Fab (fragment antigen binding) regions, like FAB13B5, are immunoglobin proteins which form the paratope.
This filtering results in the remaining 34,164 interactions (Table  1 ). Next, the Biological Context Filter isolates potential interactions according to various life cycle stages of S. mansoni and likelihood of in vivo occurrence in human tissues. In the first step of the Biological Context Filter, interactions passing the Network Filter were enriched with data from a simple natural language processing (NLP) algorithm that automatically identified S. mansoni proteins expressed in different pathogen life cycle stages from literature and databases using listed nomenclature and functional information (Materials & Methods) (Table  2 ). However, there is limited life cycle stage expression information using database annotation. The NLP algorithm was designed to address this limitation and accomplished the following: (1) characterization of 12,720 S.