dataesr/scientific-paragraphs-categorization huggingface dataset:
After enrollment, in Senegal, children with MAM will be evaluated for cognitive development and ability, with a DMC II (Developmental Milestones Checklist) questionnaire applied by a trained social worker. Children will be followed up weekly for 12 weeks and at 6 months (M6) at the health center. At each visit, (1) flours will be given to the caretaker for the whole week, (2) weight, height, and arm circumference will be assessed, and (3) a clinical examination will be carried out, and data on safety and child's food consumption will be investigated. In the case of a missing visit or insufficient weight gain, a visit at home will be organized by health workers to collect supplementary information. At inclusion, 3 months (M3) and 6 months (M6), stool samples will be collected. After 6 months, the follow-up will be stopped except for in children involved in the psychomotor development evaluation protocol, who will be followed until age 9 months (M9).
Clinical data At inclusion, a standardized questionnaire will be conducted with the parents or legal guardians to collect general characteristics, socioeconomic and clinical data. At each visit, clinical assessment will be recorded until recovery and then at M6, data will be registered concerning undercurrent illness, signs of infections (diarrhea, vomiting, fever, cough), need to transfer to hospital, and safety and adherence to treatments (defined as completion of all 7 days of the study regimen).
Anthropometric data The children will be monitored weekly for 3 months (or until recovery if treatment fails) at the primary health center. One additional visit is planned at 6 months. At each visit, weight, length, and MUAC will be measured. Balances will be from SECA UNICEF (± 100 g). Length will be measured on children placed in an elongated position using a Fathom Infant / Child ShorrBoard for infants aged less than 2 years (130 cm with graduations of 0. 1 cm). MUAC will be measured with a UNICEF MUAC tape.
Acknowledgements The authors thank all the local staff involved in the registration of the children, the medical staff of the  Health Ministry  and the colleagues of the four French Embassies supporting the project. This is a collaborative project named  MALINEA for "MAlnutrition et INfection de l'ENfance en Afrique ". 1  Unité d'Epidémiologie des Maladies Infectieuses, Institut Pasteur Dakar, Dakar, Senegal. 2  Unité des Epidémies et des Maladies Emergentes, Institut Pasteur ,  25  Rue du  Dr. Roux , 75015 Paris, France. 3  Unité d'Epidémiologie Institut Pasteur de Bangui, Bangui, Central African Republic. 4Unité d'Epidémiologie, Institut Pasteur de Madagascar ,  BP1274 ,  101  Antananarivo, Madagascar. 5  Unité d'Epidémiologie CERMES, Niamey,, Niger. 6Department of  Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg, Denmark.
). Nevertheless, a posteriori controls based on botanical, molecular, and/or chemical aspects remain necessary to ensure correct authentication and secure sourcing. Identification as well as authentication of a vegetal raw material requires a wide range of knowledge and technical skills and have to be taken into account as early as the conception of the product. The plant species has to be the right one, and its content in active molecules should comply with specifications. The quality, efficiency, and obviously the safety of products made from plant material can be affected by a number of factors. Indeed, even subtle variations in the raw plant material can greatly influence these three aspects and also alter the results of clinical trials based on these plants or plant extracts. The Food and Drug Administration (FDA) of the United States maintains a precise, up-to-date database of plants associated in the literature with human and/or animal intoxication.
The reproductive organs together with vegetative ones, i. e. , stem, leaf, and underground organs and their modifications, are also crucial and should be carefully investigated during plant taxonomic identification. The Internet is also a good source of alternative keys to taxa based on traits other than flower organs, but one should be cautious with websites or databases not owned by recognized academic institutions. "The plant list" and "The International Plant Names Index" are reliable sources of current taxonomic data. They can be found using the websites http://www. theplantlist. org/ and http://www. ipni. org/index. html, respectively. Keep in mind that contrary to a commonly held idea, photography is often less descriptive than a drawing, which stresses the specific features of a given plant; photography should be used mainly to confirm the identification.
Molecular Biology Methods Based on Genetic Fingerprints Appearing in the 1980s, and popularized by their use in scientific police work, molecular markers are based on the differences in the genetic (DNA) sequences existing between individuals and species. A molecular marker can be defined as a gene or a DNA sequence located on a chromosome that has the potential to discriminate two genotypes (individuals or species). The knowledge of the whole DNA sequence is not necessarily needed because a number of protocols are based only on the detection of differences in size or on matching of flanking sequences that do not require the knowledge of the sequence in between. Nevertheless, databases comprising the patterns obtained for different species are often indispensable for fraud detection. One very important advantage of these methods is that they require only minute quantities of a sample to be assayed and work for whatever part of the organ available, as DNA is the same in every part of a given plant, contrary to its phytochemical content.
It is a fast, reliable, and highly reproducible marker commonly used in molecular biology. The markers developed from AFLP and SSR markers are more reproducible but also more expensive, longer in their implementation, and also more technically demanding. To convert a single RAPD, AFLP, ISSR, or SSR marker into a SCAR marker, the sequence corresponding to that marker must be purified and sequenced. The nucleotide sequence should then be analyzed and its unique character confirmed by comparing known DNA sequences available in various databases to synthesize specific SCAR primers. These markers have the advantage of being codominant and are highly reproducible. Molecular biologists prefer these markers and the most recent data show that these markers are the most appropriate for the authentication of plant samples.
Metabolomic Approaches Coupling these spectroscopic methods with chemometric/metabolomic analyses begins with an initial spectroscopic measurement of authentic samples to fill a spectra database, allowing for comparisons. Once the compilated spectra are available, the signals are submitted to an alignment, a regional exclusion, a binning (gathering of the data), a normalization, and a scaling. Eventually, the use of statistical software based on HCA or PCA allows for the grouping of populations of samples on the basis of similarities within these populations. The major drawbacks of these methods are the lack of a profound understanding of the whole range of signals in the spectra and the need for expensive apparatuses and skilled people to operate them.
Conclusions The choice of technique will depend on the data available in a given domain and on the more or less processed material to be assayed, as well as the kind of adulteration or quality defects to be detected. For example, molecular markers can help to distinguish between two species and thus can detect adulterations with a wrong plant, but they are unable to provide evidence of poor content in an active molecule, the latter requiring analytical chemistry methods. The authentication methods described here require prior knowledge concerning the plants to be identified in order to be able, based on comparisons of morphological criteria, molecular markers, or phytochemical profiles, to affirm a sample's identity or its dissimilarity with the species to which it is supposed to belong. It is therefore important to have access to databases with botanical, genomic, and biochemical information.
These data must be generated, which requires extensive research. This work becomes technically and financially feasible with the appearance of technical innovations which allow for data generation at high throughput. While only 20 years ago a global consortium of laboratories and astronomical sums were needed to sequence the (small) genome of Arabidopsis thaliana (L. ) Heynh. , the first plant sequence to be published  [41] , it is now possible for only a few thousand euros to sequence any plant because there has been an exponential progress in this area. Similarly, mass spectrometry techniques have in recent years made essential progress. Generating data is not enough; they have to be accessible and allow for easy comparisons. For this purpose, it is necessary to create databases and servers containing all of these data.
Acknowledgments: This research was supported by  Cosmetosciences , a global training and research program dedicated to the cosmetic industry. Located in the heart of the cosmetic valley, this program led by  University of Orléans  is funded by the  Region Centre-Val de Loire. Duangjai Tungmunnithum  gratefully acknowledges the support of  French government via the French Embassy in Thailand  in the form of Junior Research Fellowship Program 2018.
The lack of data from Central Africa and from very low-income countries, such as CAR,  6  which suffers from long-term instability, highlights the need to improve our understanding of the spatial and temporal distribution of (sub-)serotypes in sub-Saharan Africa. 7, 8 Central African Republic is a resource-limited country in equatorial Africa (ranked 180/187 according to the Human Development Index in 2013). Here, we describe the (sub-)serotype distribution and antimicrobial susceptibility of Shigella species isolated from patients in CAR during the 2002-2013 period. Clinical isolates of Shigella were obtained between January 2002 and December 2013, from Centrafrican outpatients with diarrhea attending the Institut Pasteur in Bangui. If more than one isolate of the same (sub-)serotype and serogroup and with the same antimicrobial drug resistance phenotype was recovered from a given patient, only the first isolate was included.
Acknowledgments: We thank  Isabelle Carle ,  Monique Lejay-Collin ,  Malika Gouali , and  Corinne Ruckly  for excellent technical assistance. Financial support: The  French National Reference  Center for Escherichia coli,  Shigella , and Salmonella is funded by the  Institut Pasteur and Sant é Publique France. The Unit é des Bact éries Pathog ènes Ent ériques belongs to the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  funded by the  French Government  as part of the  Investissement d'Avenir program  (grant no. ANR-10-LABX-62-IBEID ). Authors' addresses:  Sebastien Breurec ,  Facult é de M édecine, Universite des Antilles , Pointe-a-Pitre, France, E-mail: sbreurec@gmail. com. Clotaire Rafaï ,  Manuella Onambele ,  Thierry Frank , and  Alain Farra ,  Laboratoire de Bact ériologie ,  Institut Pasteur de Bangui, Bangui, R épublique Centrafricaine , E-mails: clotairerafai@yahoo.
Pre-and Post-harvest Phenotypic Measurements Plant height from the pot to the top of the apical meristem was measured weekly for 7 weeks on 10 plants per line. The growth rate was calculated as cm gained per day. The average leaf area of a fully expanded leaf (one leaf per plant, 10 plants) was calculated by scanning the leaf and calculating the area of the image using ImageJ software. Fruit characteristics (number of days between anthesis and orange maturity stage, and fruit weight) were calculated on at least six fruits per plant from 10 plants. Fruit weight, diameter, firmness measurements (compression and maximal force for skin rupture) and color were measured on a minimum of 20 fruits per line immediately after harvesting (ripe fruits). The weight loss of seven fruits per line, related to conservation properties, was measured after 5 days postharvest at 20 • C.
The 1 H-NMR spectra were recorded on each dried titrated extract solubilized in 0. 5 mL D 2 O with the sodium salt of (trimethylsilyl)propionic-2,2,3,3-d4 acid (TSP) at a final concentration of 0. 01% for chemical shift calibration, at 500. 162 MHz on an Avance spectrometer (Bruker BioSpin, Wissembourg, France) using a 5 mm inverse probe. Sixty-four scans of 32K data points were acquired with a spectral width of 6000 Hz, acquisition time of 2. 73 s, 90 • pulse angle and recycle delays of 25 s. The raw 1 H NMR spectral profiles have been deposited, with associated metadata, into the Metabolomics Repository of Bordeaux MeRy-B (http://services. cbib. u-bordeaux2. fr/MERYB/ public/PublicREF. php?REF=T07001).
Proteome Analysis Protein extraction, two-dimensional gel electrophoresis, and protein identification and classification were carried out as previously described  (Xu et al. , 2013). Briefly, proteins were extracted using the phenol extraction method developed by  Faurobert et al. (2007). Proteins were then separated by two-dimensional gel electrophoresis. After Coomassie colloidal staining, image analysis was performed with Samespot software (version 4. 1), and the normalized spot volumes were obtained. Of the 108 spots revealed, 49 were eliminated as not being variable. Protein identification of the 59 variable spots was performed at the proteome platform of Le Moulon (Gif-sur-Yvette) using a nano-LC-MS/MS method following a previously described procedure  (Xu et al. , 2013). The database search was run against the tomato unigene database version 4 held by the Solanaceae Genome Network (http://solgenomics.
net/; Tomato_200607_build_2) with X!Tandem software (http://www. thegpm. org/TANDEM/, version 2010. 12. 01. 1). Sequences were subsequently converted to Solycs (ITAG version 2. 4) by Blast analysis. This conversion was confirmed by re-analyzing the mass spectra or by searching for similarity between the protein peptide sequences and the ITAG2. 4 version of the genome by TBLASTN.
Microarray slides were scanned with a Genepix 4000 B fluorescence reader (Axon Instruments) using Genepix 4. 0 Pro image acquisition software. The photomultiplier tube voltage was adjusted to 640 V for Cy3 and 700 V for Cy5. Spot flagging was carried out by Genepix (missing spots) and by visual inspection of the images in order to exclude saturated and heterogeneous spots. The raw data were then submitted to LIMMA R Package v2. 12. 0  (Ritchie et al. , 2015)  for data visualization, normalization as described  (Alhagdow et al. , 2007). Background was first subtracted from the raw median intensity values using the normexp function. Two normalization algorithms were successively applied to the raw data  (Yang et al. , 2002) : print-tip loess (default parameters) as the within-array normalization, followed by a quantile normalization (default parameters) as between-array normalization.
R/maanova Differential expression and transcriptome comparisons were carried out using the R/maanova package [https://www. bioconductor. org/packages/release/bioc/html/maanova. html (R Development-  Core-Team, 2015) ]. Means and standard errors were calculated for each gene in each line. A simple model taking into account the genotype (line) was used. A matrix of comparisons was used for the pairwise comparison of the transcriptome datasets first of each transgenic line with WT and then with each other. For each comparison, a threshold for the FDR corrected p-value of 0. 001 was applied. Following initial tests, we added a sub-hypothesis to the comparison matrix to test whether the AO-GLD transcriptome and the AO-MDHAR transcriptomes were inverted i. e. , the wild-type expression was the average of the gene expression in the two transgenic lines being compared.
Weighted-Gene-Correlation Network Analysis Weighted gene correlation network analysis (WGCNA) was carried out using the normalized expression data for the 7633 genes with a "trait" file including the 22 metabolites and 40 proteins using the WGCNA package in "R"  (Langfelder and Horvath, 2008). No outliers were detected after sample clustering so the whole dataset was used. A soft threshold of 14 and minimum module size of 25 were set. Networks were visualized using Cytoscape version 3. 2. 0 (Cline et al. , 2007)  (www. cytoscape. org) using the node and edge output file from the WGCNA package with a threshold set to 0. 52 (to fix a number of nodes and edges suitable for visualization). The dataset used contained the 182 differentially expressed genes (differentially expressed in the pairwise comparisons of the three types of transgenic lines), the 22 metabolites and 40 proteins.
Gene Ontology Gene ontology (GO) enrichment on the whole transcriptome signatures generated from pairwise comparisons of the lines was carried out using the R package "goseq"  (Young et al. , 2010)  with the corrected p-value false detection rate set to 5%. The package requires the log of fold-change and p-value from the R/maanova analysis. No length correction was required as the data was from microarrays. Tomato gene ontology for the 182 differentially expressed genes was carried out using the online tools available from the Boyce Thompson Institute, Cornell University (http://bioinfo. bti. cornell. edu/cgi-bin/MetGenMAP/ home. cgi, Boyle et al. , 2004).
The Subset of 182 Genes Defines a Regulatory Network with Several Hub Genes Including Heat-Shock Transcription Factors, Chaperones, and Ribosomal Proteins The 182 core gene set was used in network analysis with the metabolites and the protein dataset. A topological overlap matrix using the WGCNA script was produced and an edge file for network visualization using Cytoscape. The network visualized with a degree-sorted circle layout, is shown in Figure  5  with the top eleven hub genes. The most highly connected genes included two or three ribosomal proteins, an observation which is compatible with those above, and two heat-shock proteins (including DnaJ) and a heat-shock transcription factor.
The Transcriptomes Show a Signature Common to Stress Responses in Arabidopsis, Particularly Mitochondrial Stress but No Correlation with Hydrogen Peroxide Signaling Our tomato transcriptome data was compared using ROSMETER, a bioinformatic tool which unites transcriptome data from different stress experiments or mutants in Arabidopsis linked to specific ROS production in specific organelles (see Materials and methods for more information). Its use is based on the hypothesis that the chemical identity and sub-cellular localization of ROS leave a specific imprint on the transcriptomic response  (Rosenwasser et al. , 2013). We used the RNAi AO and RNAi MDHAR datasets to compare with the Arabidopsis transcriptomes. Our first observation is that independently of the Polyphenolic compounds were assayed in the pericarp tissue (3 biological replicates of 30 fruits per pool) of the lines by HPLC-DAD.
A comparison of the means was carried out using a Kruskal Wallis test with correction (Dunn). Different letters indicate significant differences (5% significance level). (B) Boxplots showing raw data for three differentially accumulated metabolites in the transgenic lines and wild-type: alanine, dehydroascorbate and chlorogenic acid. A comparison of the means was carried out using a Kruskal Wallis test with correction (Dunn). Different letters indicate significant differences (5% significance level). stress and/or organelle, the transcriptome responses are inversed when these two lines are compared (Figure  6 ). The particular conditions to be highlighted include mitochondrial stress: in this case, the application of rotenone gives a strong negative correlation with the RNAi MDHAR and a positive correlation with RNAi AO 3 h after application.
A Transcriptional Signature Is Observed and Is Correlated to Silencing of the Ascorbate Metabolizing Enzymes The transcriptomes revealed a switch linked to the activity of ascorbate oxidase on one side of the switch and the enzymes MDHAR/GLD acting in the other direction, always with wildtype gene expression in the middle. There is a tendency for genes to be up-regulated when ascorbate oxidase is downregulated. The transcriptome inversion is also seen in datasets from Arabidopsis provided by the ROSMETER tool. The switch seen between AO and MDHAR RNAi lines is capable of distinguishing the conditions found in some of the ROSMETER transcriptome data, although there are limits to this analysis because correlations can be based on a small number of genes (a minimum of 45 according to the original paper).
The ROSMETER analysis does not reveal links with hydrogen peroxide metabolism because correlations between our lines and the public data are inversed when comparing ascorbate peroxidase and catalase mutants, both of which in theory should contain more hydrogen peroxide as a result of decreased detoxification. The strong response to rotenone is interesting because rotenone is an inhibitor of mitochondrial complex I, which is also where the galactonolactone dehydrogenase protein is located  (Schimmeyer et al. , 2016). Also in the paper by  Rosenwasser et al. (2013) , rotenone-induced stress is also positively correlated with developmental senescence, a process which has parallels with fruit ripening. The data seem to indicate the existence of a signal affecting transcription closely linked to GLD activity and the redox state of the ascorbate pool.
A topological overlap matrix was generated using the WGCNA script for the 182 core genes, the 22 metabolites and the 40 proteins. The edge file produced was imported into Cytoscape for network visualization. The network was visualized with a degree-sorted circle layout. The node sizes and node label sizes are proportional to the degree of connectivity of each node as fixed by Cytoscape. The color scale goes from blue (least connected nodes) to orange (most connected nodes). The hub genes are described in the table which gives the most highly connected genes following visualization of the topology overlap matrix, generated by the WGCNA "R" package, with Cytoscape. The ITAG2. 4 identifier (Solyc) with the annotated function is shown with the degree of association as defined by Cytoscape. The transcriptome data (fold change between AO and MDHAR RNAi lines with the FDR corrected p-value on the transcript levels) for each gene are also shown.
expression between the lines and the associated p-value (-log10 transformed) are shown in Figure 4. The AO-MDHAR and AO-GLD comparisons generated the highest number of differentially expressed genes (comparisons 4; 290 genes and comparison 6; 330 genes). The comparisons WT = (transgenic X + transgenic Y)/2 are comparisons 5, 8, and 9. If the hypothesis is true then the p-values become non-significant and the -log10 of the pvalues gives low values which is the case for the comparisons 5 [WT = (AO + MDHAR)/2] and 8 [WT = (AO + GLD)/2] but not the case for the comparison 9 [WT = (GLD + MDHAR)/2]. The whole dataset including means, standard error, p and Fvalues and the fold-change with p-values for each comparison is available in Supplementary Table.
ACKNOWLEDGMENTS We acknowledge valuable help from  Noé Fernandez  from  SGN  for converting the TOM2 probes to Solyc identifiants and  Shilo Rosenwasser  for help with application of the ROSMETER tool to tomato data. We would also like to thank  Manu Botton  for help with the plant culture and pre-harvest phenotyping,  Joël Chadoeuf  for advice on statistical models in R maanova,  Camille Croset  for polyphenol analysis,  Marine Palau  for contributing to the proteome analysis and  Christopher Sauvage  for advice on the R scripts. The metabolome analyses were performed at  Bordeaux Metabolome facility-MetaboHUB. We thank  Daniel Jacob  for developing and maintaining the MeRy-B database. A version of the abstract was published at http://qualityfruit. inptoulouse. fr/_attachments/preliminary-program-article/Book%2520of%2520Abstracts%2520COST%2520Porto2016%2520 %252814out16%2529.