dataesr/scientific-paragraphs-categorization huggingface dataset:
4a  and b ) as early as day 3. The formation of the strict anaerobic environment was similar between the Synbiotic and Reference groups. In contrast, infants from the Control and Prebiotic groups presented a delayed colonization by strict anaerobes when compared to the Synbiotic group, and this was statistically significantly different between day 3 and week 2 (Synbiotic versus Control, P < 0. 0001 and P = 0. 005; Synbiotic versus Prebiotic, P < 0. 0001 and P = 0. 002, respectively). From the Spearman's rank correlation coefficients analysis which was determined by combining the biological datasets from all 4 groups, we observed that increased abundance of Bifidobacteriaceae was positively correlated with fecal acetic acid and negatively correlated with stool pH (Fig. 5a ). The increased abundance of strict anaerobes was positively correlated with acetic acid and conversely the increased abundance of facultative anaerobes and aerobes was negatively correlated with fecal levels of acetic acid (Fig.
A synbiotic intervention prevents the establishment of a compromised microbiome in elective C-section born infants Bacterial species abundances were determined by metagenomics analysis (Supplementary Fig. 4 ). The abundance of Bifidobacterium breve was positively correlated with differential abundance and expressed genes   S11, S12 ). Additionally, metabolomic analyses of pooled fecal samples revealed lower levels of lactose and HMO   S10 ). Heat map showing the Spearman's correlation coefficients between physiological parameters and the functional group abundances (Lactic Acid Bacteria(LAB), 16S rRNA data) from day 3 till week 22. The Spearman's rank correlation coefficients were determined by combining the biological datasets from all 4 groups. d Correlations between Bifidobacterium breve/Klebsiella at metagenomics and metatranscriptomics levels.
Microbiome multi-omics data All the biological data (16S rRNA, SCFA, Lactate and pH, metagenomics, metatranscriptomics and metabolomics) were generated from the modified intention-totreat (mITT) population. The mITT population consisted of all randomized subjects who provided at least 1 baseline and post-baseline stool sample. In this study, we could only include those subjects whose fecal samples were of sufficient volume and quality for DNA extraction and 16S rRNA sequencing. However, this accounted for the vast majority of the subjects from mITT  [18]. In this study, the number of subjects per group whose faecal genomic DNA was available for 16S rRNA sequencing, was as follows: Synbiotic group (n = 44), Prebiotic group (n = 39), Control group (n = 44) and Reference group (n = 26) (Supplementary Table  S1 ).
For data analysis and interpretation, these intervention groups were labelled as Synbiotic group, Prebiotic group, Control group and Reference group. In total, bacterial community compositions were characterised by shotgun 16S rRNA sequencing of 943 faecal genomic DNA samples (Supplementary Table  S1 ). The metagenomics and metatranscriptomics data were generated from a subset of the stool samples collected at day 3 and/or day 5 (Fig. 1 ). The samples were selected randomly from 10 subjects per group from the clinical study's biobank. The metabolomics data were generated from a subset of the stool samples collected at day 3 and/or day 5 (Fig. 1 ). The samples were selected randomly from 10 subjects per group from the clinical study's biobank. The 10 samples per group were then pooled before metabolomics profiling.
Sequencing output The average numbers of sequencing quality-passed reads mapping to the 16S rRNA Greengenes global rRNA database (dated May 2013; greengenes/13_5/99_otus. fasta) are described in the Supplementary Table  S2. Reconstruction and classification of 16S rRNA amplicon sequences Read trimming from 3′ was done to remove bases with quality score ≤ 2, followed by additional removal of reads pairs shorter than 60 bp after trimming  [73] [74] [75] [76] [77]. Following read trimming, full length 16S rRNA (V3-V6 region) reconstructions were produced from the short sequencing reads using the EMIRGE amplicon (Expectation Maximisation Iterative Reconstruction of Genes from the Environment) algorithm  [75] [76] [77] [78]. The analysis method is described in the publication of Ong and colleagues  [73]  and the updated tools and pipeline are available at: https://github.
com/ CSB5/GERMS_16S_pipeline. EMIRGE leverages 16S rRNA sequences on the SILVA database for template guided assembly of reconstructions of the 16S rRNA amplicon sequences  [77, 78]. EMIRGE prevents chimeric sequences from mapping. The reconstructed 16S rRNA sequences (at least 99% sequence similarity) are collapsed into OTUs, and Graphmap  [79]  was used to map these OTUs to the Greengenes global rRNA database (dated May, 2013; greengenes/13_5/99_otus. fasta)  [80]. OTUs were called at various taxonomic levels of identity (species, genus, family etc. )  [81]. EMIRGE assigns abundance estimates to the reconstructed 16S rRNA sequences. The relative abundance of OTUs was determined for each sample and converted to relative abundances at various taxonomic level (phylum, family, genus, species level).
Library concentrations were then normalized to 4 nM and validated by qPCR on a ViiA-7 real-time thermocycler (Applied Biosystems), using the Kapa library quantification kit for Illumina platforms (Kapa Biosystems). DNA libraries were then pooled at equimolar concentrations and sequenced on an Illumina HiSeq2500 sequencer in rapid mode at a read-length of 250 bp paired-end. RNA libraries were also pooled at equimolar concentrations and sequenced on an Illumina HiSeq2500 sequencer in rapid mode at a read-length of 100 bp paired-end. Sequencing data analysis The raw metagenomics and metatranscriptomics Illumina reads were adaptertrimmed and quality-trimmed using cutadapt-1. 8. 1  [82]  with parameter of "-q 20 --trim-n --minimum-length 30 --match-read-wildcards". Afterwards, for each pairedend read dataset, the first pair and its corresponding second pair were locally assembled/merged using FLASH 1.
2. 6  [83]  with "-m 10 -M 251 -x 0. 25" and "-m 10 -M 101 -x 0. 25" parameter for metagenomics and metatranscriptomics dataset respectively. The metagenomics dataset was mapped against the hg19 human reference genome with bowtie2 2. 2. 5  [3]  using "--very-sensitive-local" preset as its sensitivity parameter. Any reads that can't be confidently mapped against the human genome, which were generated by "--un" switch, were separated and processed as the nonhost reads. The non-host reads were then aligned against NCBI non-redundant protein database (downloaded on 3 January 2016) using Diamond version 0. 85  [84]  with default parameters. Based on these alignments, the microbial taxonomical classification was determined using the Lowest-Common-Ancestor (LCA) algorithm implemented in MEGAN6  [85]  (parameters: maxmatches = 25 minscore = 100 minsupport = 25).
The subsequent taxonomy and KEGG/SEED functional annotation were done as part of MEGAN6 processing and was based on MEGAN's gi2tax-July2016. bin and gi2keggMarch2016. bin database files. The metatranscriptomics dataset was mapped against hg19 human reference genome with bowtie2 2. 2. 5  [86]  using "--very-sensitive-local" preset as its sensitivity parameter. The non-human reads were then separated into its ribosomal RNA and non-ribosomal RNA components using sortMeRNA 2. 1  [87]. The non-ribosomal reads were then aligned against NCBI non-redundant protein database (downloaded on 3 January 2016) using Diamond version 0. 85  [4]  with default parameters. Based on these alignments, the microbial taxonomical classification was determined using the Lowest-Common-Ancestor (LCA) algorithm implemented in MEGAN6  [5]  (parameters: maxmatches = 25 minscore = 80 minsupport = 25).
The subsequent taxonomy and KEGG/SEED functional annotation were done as part of MEGAN6 processing and was based on MEGAN's gi2tax-July2016. bin and gi2keggMarch2016. bin database files. The metagenomics sequencing run produced, on average, 8. 6 million paired-end reads per sample, or 4. 3Gb data per-sample. After quality control and removal of host sequences, on average, 6,864,061 (79. 7%) pairedend reads were used for downstream metagenomics analysis. At the end of the classification analysis, on average, 88. 93% (66. 78-94. 97%) of the paired-end reads could be classified to the kingdom Bacteria. The metatranscriptomics sequencing run produced, on average, 11. 9 million paired-end reads per sample, or 2. 4Gb data per-sample. After quality control and removal of ribosomal RNA sequences, on average, 1,212,609 (10.
Targeted fecal metabolites and pH measurements Sample preparation, SCFA, lactate and pH measurement are described in the publication of Knol and colleagues  [88]. Acetic acid was the main SCFA detected in the clinical samples as described in the clinical trial publication  [18]. Acetic acid, lactic acid and stool pH data (stool physiological parameters) were correlated with the 16S rRNA data as described in the statistical analysis section.
LC-MS data processing and analysis The raw LC-MS data obtained was processed using a XCMS-based peak finding algorithm  [89]. The QC samples were used to adjust for instrumental drift. Detected mass peaks were assigned putative metabolite identities by matching the respective masses (< 10 ppm error) with the KEGG and Human Metabolome Database (HMDB). Where possible, the identities of selected metabolites of interest were confirmed based on mass spectral comparison with available metabolite standards.
Statistical analysis Differences in bacterial community composition as determined by 16S rRNA sequencing among the four infant groups were assessed using distance-based redundancy analysis (db-RDA). The dissimilarity data based on Bray-Curtis distances were first ordinated using metric scaling and the results were then analysed using redundancy analysis. These steps were implemented using the capscale function from the vegan package in R software version 3. 4. 1  [90]. These analyses were carried out for each time point. Permutational Multivariate Analysis of Variance (PERMANOVA) was applied on the ordination object to assess if microbial community composition differences were statistically significant between every infant group pairs. The envfit function of the vegan package was used to fit the vectors representing different bacteria onto the ordination object (999 permutations).
This allowed to investigate and visualize relationships among the different bacteria groups influencing the overall microbial community structure in the dimensional space. The steps depicted above were applied to the family and genus datasets. Prior to assess differences in relative abundances of bacterial taxa between study groups, bacterial taxa with an average relative abundance < 0. 5% were removed. Non-parametric Mann-Whitney U tests were then performed at each time point on each pair of study group combination at family, genus and species level. Pairwise Wilcoxon test function from the R Stats package was used for implementation. For all statistical tests performed, adjusted Bonferroni p-values were derived to correct for multiplicity. The Shannon index was calculated to assess alpha diversity for each sample (using the diversity function from vegan R package).
The Spearman's rank correlation coefficients between bacterial groups and stool physiological parameters were calculated together with the associated significance values. Based on a significance level of 0. 05, Spearman's rank correlation coefficients were filtered and exported to GraphPad Prism 7 for graphical representation. The metagenomics and metatranscriptomics datasets (40 samples) were considered as one group regardless of the intervention group to determine Pearson correlations between key species and functional genes. All statistical analysis performed on the metagenomics and metatranscriptomics data were done with R statistical package version 3. 4. 0 using relative abundance data derived from MEGAN6 classification results. The metabolomics dataset represented four pools reflecting the intervention and reference groups, with each pool consisting of 10 random samples biologically pooled before metabolomics profiling.
Organic acids, stool pH and 16S rRNA data were similarly grouped by the intervention and reference groups; differences between group means were compared descriptively. genes which exhibit Pearson correlation coefficients > 0. 6 against the metagenomics reads assigned to the two species are mapped onto the KEGG metabolism pathway map. Figure  S6. Metabolic pathway derived from the metatranscriptomics dataset depicting correlations with Bifidobacterium breve (blue) and Klebsiella (red). KEGG genes which exhibit Pearson correlation coefficients > 0. 6 against the metatranscriptomic reads assigned to the two species are mapped onto the KEGG metabolism pathway map. Figure  S7. Correlation between Bifidobacterium breve and HMOs metabolism. Linear model plot depicting correlation between Bifidobacterium breve and Lacto-N I Galacto-N-biose metabolism (SEED data).
Danone Nutricia Research funded the clinical study and the analyses of the clinical samples using multi-omics. The Genome Institute of Singapore received an industrial alignment fund, IAF111135, from the Agency for Science, Technology and Research (A*STAR) from Singapore to analyse the clinical samples using 16S rRNA sequencing. The Bioprocessing Technology Institute (BTI) thanks the Agency for Science, Technology and Research (A*STAR) for their funding support (IAF project no. 111199) of HYS and BX for omics data analysis.
Acknowledgments We would like to thank  Prof. Willem de Vos  for critically reading the final manuscript and giving his valuable advice. We would like to thank the  GIS (Genome Institute of Singapore)  platforms for their support during the project in the areas of scientific and research computing (led by  Chih Chuan Shih ), research pipeline development (led by  Andreas Wilm ) and the next generation sequencing platform (led by  Wendy Soon ). We would like to acknowledge  Seong Soo Lim  and  Hans Heilig  for support in preparation of 16S rRNA sequencing libraries.
Availability of data and materials Some of the datasets used and/or analysed during the current study are available in the supplementary information documents, additional datasets are available from the corresponding author on reasonable request. Raw 16S rRNA data are available at https://www. ebi. ac. uk, accession number is PRJEB44790. Shotgun Metagenomics and Metatranscriptomics data are available athttps://www. ncbi. nlm. nih. gov, accession number is PRJNA726032.
Data Sources We collected all available data on cholera, past or present WASH programs, population census, and socio-environmental information at the village level for Madarounfa district (Niger) for the year 2018 over different sources (Internet, public reports available locally, and professionals from the Health district and Hydraulic and Sanitation district). Usually, data are compiled at a national or provincial level. In the case of this study, most of the data were collected directly at the district level for village level type of information and had to be compiled by ourselves. The sources of data included the Madarounfa district report on CLTS for 2018; line-listing of cholera cases from health centers of the Health District of Madarounfa in 2018; REF2017-REGMI database from the "Ministry of Hydraulic and Sanitation" providing information on water infrastructures, water coverage, and GPS data; MADA-POP-2017-25CSI providing population data from 2017 based on projection from last census; Community Development Plans (CDP-2018-2022) from Gabi, Safo, and Sarkin Yamma providing socio-environmental information.
Finally, distances between villages and between surface water and villages were measured directly through the dedicated tools proposed by the Google Earth website to see if these factors could be associated with cholera transmission. The data were collected in December 2018 in Madarounfa district from the Maradi Province. Field visits in the studied areas were also carried out between December 2018 and April 2019 to cross-check information through observational methods. Finally, other sources of information such as JMP 2018  (23)  or "Niger cholera factsheet" (from Regional Cholera Platform 1  ) was used to compare the area of study to the national context.
Village Stratification The study sample collects data on 125 villages including 41 villages from the Gabi Commune, 26 villages from the Safo Commune, and 58 from the Sarkin Yamma Commune for a total population targeted by the study of 135,680 persons in 2018. The last census in Safo reports 79,024 inhabitants, 101,704 inhabitants in Gabi, and 44,704 inhabitants in Sarkin Yamma, as 60. 2% of the population of those three targeted communes were considered due to lack of data in some villages. Three communes over the five existing ones of Madarounfa district were included in this study.
Data Compilation for the Study A final database was created for the study compiling all the available data for each village therefore including the following variables: (1) number of inhabitants; (2) number of cholera cases from the 2018 outbreak; (3) prevalence of cholera per 1,000 inhabitants from the 2018 outbreak; (4) water coverage at the village level; (5) sanitation coverage per village;  (6)  distance between each village and the nearest cholera-affected village;  (7)  distance between a village and the nearest surface water; (8) availability of road access to the village. With the exact GPS location of the 125 studied villages (from REF2017-REGMI database of the "Ministry of Hydraulic and Sanitation"), it was possible to measure the distances between villages and the nearest affected villages as well as the nearest surface water points (often a river).
28 (95% confidence interval from 1. 05 to 1. 55; P < 0. 0001) for villages with full water coverage but no sanitation. Once full CLTS is reached, prevalence of cholera drops significantly regardless of water coverage of the village. In the dataset, the prevalence rate of cholera was 14. 9 per 1,000 inhabitants in villages without water or sanitation (81 cases of cholera over 5,435 inhabitants), and only 0. 4 per 1,000 inhabitants in villages with full access to sanitation and water (eight cases over 22,741 inhabitants).
With the above discussion in mind, we must underline possible limitations of this study, starting from the crosssectional nature of the data collection process. Indeed, the data were collected within a fixed time frame as a result of the cholera outbreak and therefore cannot provide a more general overview of the phenomenon as, for example, a longitudinal study would. Secondly, given the complexity of the hurdle model, which requires a considerable number of parameters to estimate, the sample size of 125 villages (for a population of 135,680 persons) is not exactly large, thereby relying more on the asymptotic approximation of the test statistics in the model (and possibly affecting the power of the analysis). Despite this, we still observed significant factors affecting the response variable.
ACKNOWLEDGMENTS We gratefully acknowledge the populations of Madarounfa District Niger. We kindly thank  UNICEF  for providing support to collect the data. We would also like to especially thank  Ms. Aicha Arzika Issoufou  from the  Health Surveillance and Epidemic Response Division ,  Mr. Abdoul Karimou Sakola , director of the  Madarounfa Department of Hydraulics and Sanitation ,  Mr. Yaou Zoumbeye , head of  Sanitation Division of Maradi ,  Mr. Habibou Jika , Hygiene and Sanitation technician of the Madarounfa Medical District, and  Mr. Mahaman Nourou Sanoussi , WASH Officer at  UNICEF Maradi office. We gratefully acknowledge  Jolly Ann Maulit  from  UNICEF WCARO in Dakar  for their relevant comments and editions.
(b) The effect of dihydroretinoid treatments on sucrose preference was compared to Veh and NS control mice. (c) Social preference monitored throughout the entire period of stress protocol was represented as mean and compared to non-treated mice. Error bars represent standard error. Statistical differences were indicated as: *, p < 0. 05; **, p < 0. 01; ***, p < 0. 001 and for comparison with 50% of chance level $, p < 0. 05; $$, p < 0. 01; $$$, p < 0. 001. this paradigm  (Yohn et al. , 2019). From the epidemiological point of view, identification of endogenous RXR-ligand and its precursor of nutritional origin as involved in control of stress responses is also meaningful for understanding environmental factors like nutrition in stress related disorders  (Marx et al. , 2021). In particular, our recent data indicate that 9CDHROL is an endogenous and physiological precursor of 9CDHRA in mammals and can be considered as new type of vitamin A or even a new vitamin category, vitamin A5/X  (Krężel et al.
Behavioral procedures All behavioral tests were carried out in the Institute Clinique de la Souris (http://www. ics-mci. fr/) according to standard operating procedures. Whereas some mice were tested only for despair behaviors in the forced swim paradigm (Fig. 1c  and d ) all the other mice were tested in sucrose preference and forced swim tests according to the timeline depicted in Fig. 2  (note that non-treated Rbp1-/-and Rxrγ-/-mice and their respective control groups [Fig. 1a  and b ] were not subjected to CSDS). Each mouse was tested only once in each type of task in order to avoid confounding effects of multiple exposures to the same test. All studied cohorts, their behavioral experience and representation of corresponding data within specific figures were listed and explained in table shown in supplementary data as Fig.
Acknowledgments We thank  Brigitte Schuhbaur  for mouse genotyping and  Marta Wietrzych-Schindler  for help in behavioral analyses. We acknowledge financial support from the  University of Strasbourg Institute for Advanced Study  (W. K. ), LabEx  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  as part of the  program Investissements d'Avenir   ANR-10-IDEX-0002-02  (A. P-D. , A. K. , W. K. ),  Spanish MINECO  ( SAF 2016-77620-R-FEDER; PID 2019-107855RB-I00 ) and  Xunta de Galicia (Consolidación GRC   ED431C 2017/61  from  DXPCTSUG ;  ED-431G/02 INBIOMED-FEDER  " Unha maneira de facer Europa ") (B. V. , R. A. , A. R. dL. ). A. K. was supported by  PhD fellowship of French Goverment from French Embassy in Poland.
In West Africa, where over 95% of the world's yams are produced,  6  pounded yam is the preferred consumption form. It is prepared by pounding the cooked tuber and kneading it into a sticky paste. 7 n this respect, D. rotundata is usually preferred to D. alata owing to its ease of dough formation when pounded. 7 However, several studies have shown that some genotypes of D. alata have the ability to form good dough, comparable to that of D. rotundata. 8, 9 1] [12] [13]  Sensory quality evaluations of new genotypes are mostly carried out using sensory panels. It is a laborious and lengthy process that lacks a high-throughput phenotyping method. For this reason, the organoleptic qualities of new varieties are assessed at the end of the breeding process. By that time, only a few genotypes remain and the chance of identifying high quality varieties is low.