dataesr/scientific-paragraphs-categorization huggingface dataset:
Background The Carbohydrate Active Enzymes (CAZymes) are enzymes involved in the assembly, modification or deconstruction of carbohydrates  [1]. Based on amino acid sequence similarities, CAZymes are divided into several classes, including glycosyltransferases (GT)  [2, 3] , glycoside hydrolases (GH)  [4] [5] [6] , polysaccharide lyases (PL)  [7, 8] , carbohydrate esterases (CE)  [8] , and auxiliary activities (AA)  [9]  that have been stored in the CAZy database. The huge diversity of natural glycans and their complexity has boosted studies uncovering novel CAZymes. Thus, the number of CAZymes families increases exponentially by about four new GH families per year  [10]. This broad diversity has allowed their use in plenty of industrial applications as they have been described to offer attractive opportunities in a wide range.
Genome sequence and analysis The genome sequencing of C. algeriensis TH7C1 T rendered 473,434 Illumina reads with an average coverage of 34. 55x. The de novo assembly resulted in 45 contigs and a total length of 2,535,023 bp (Accession number PRJNA743054) with an overall GC content of 44. 9%. A circular genome map of C. algeriensis was constructed, showing contigs, GC content, and GC skew (Fig. 1 ). The overall genome statistics of C. algeriensis are close to those from Caldicoprobacter faecalis, Caldicoprobacter oshimai and Caldicoprobacter guelmensis (Table  1 ). Gene prediction performed with the RAST server resulted in 2720 features including 2666 protein coding sequences (CDSs) classified in 226 SEED subsystems and 53 RNA genes. Figure  2  shows the subsystem category distribution following RAST annotation.
CAZymes annotation Sequences submitted to the dbcan server allowed the automated annotation of CAZymes using the HMMER3. 0 package and the dbcan CAZyme database (see Additional file 1: Table  S1 ). This analysis resulted in 97 genes associated with glycan assembly and breakdown. The most abundant enzymatic family predicted in this genome was glycoside hydrolases with 57 CAZyme encoding genes divided into 32 different families. The highest number of glycoside hydrolases found in C. algeriensis was related to GH109 with 9 predicted encoding genes, followed by GH3 with 6 genes and GH2/ GH13 with 4 genes. GH109 family, which contains members involved in the deconstruction of galactomannans was widely represented in this genome. Interestingly, CAZymes belonging to this family had not been identified as major catalysts in previous studies highlighting biomass-degrading potential in hot spring.
They were found associated to GH genes or other CBMs. CAZyme genes prediction as well as the protein encoding genes sequences are available in supplementary (Additional files 1: Table  S1  and 2 ). Fast blast hit of CAZyme encoding genes in the CAZy database was performed by querying the genome against DIAMOND from dbcan meta-server. This analysis showed an identity between 35 and 83% with their nearest neighbors (Table  2 ).
PUL annotation and CGC prediction To examine the presence of Gram-positive polysaccharide utilization loci (gpPUL) in the genome of C. algeriensis, we used nucleotide Basic Local Alignment search tool (BLASTX) available in dbCAN-PUL. This tool uses the repository as a database to query sequences against PUL proteins in dbCAN-PUL. This analysis resulted in a huge number of sequence similarities (11,320) (see Additional file 1: Table  S2 ) including 36 CAZymes, 21 transporters (TCs) and 6 signal transduction proteins (STP). The PUL showing the highest number of hits to our query sequences is PUL0390 with a total of 10 hits. This PUL is predicted to be capable of degrading acetylated glucuronoxylan. CAZyme gene clusters (CGC) prediction via the dbCAN2 with the CGC-Finder unveiled 33 CGCs defined by the presence of at least one CAZyme, one transporter and one transcription factor encoding genes (Fig.
3  and Additional file 1: Table  S3 ). CAZymes gene labels are based on CAZyme domain assignment, TC genes were predicted by searching against the TCDB and TF genes searched against the transcription factor families in Pfam and Superfamily. Genes organization of predicted clusters is shown in Fig. 3. Results of sequence similarities were used for the determination of carbohydrate utilization ecotypes. Among  the predicted CGCs, 20 of them contain CAZymes with no similarity with proteins in the repository. Based on enzymes combination in predicted CGCs and genes similarities with those available in dbCAN-PUL database, we predict a specific polysaccharide for each cluster (Table  3 ). The determination of carbohydrate utilization ecotypes provides insight to their biotechnological potential.
Earlier studies reported that these genes are organized in clusters involved in polysaccharides degradation and transport forming Gram-positive polysaccharide Utilization Loci (gpPUL)  [27]. In our study, we report for the first time the existing of CAZymes gene clusters in this group of Caldicoprobacteraceae. PULs were analyzed based on genes homology with PULs available in dbCAN-PUL database. Results showed 11,320 gene similarities in CAZymes, transporters and signal transduction proteins across all PULs in the dbCAN repository, displaying an identity between 18. 7% and 80. 7%. To further analyze carbohydrate utilization ability of C. algeriensis, we performed CAZymes gene cluster analysis via the CGC finder in dbCAN2 meta server. We obtained 33 CAZymes gene clusters. Among them, 22 CGCs including 19 GH families, were predicted to be involved in cellulose and hemicellulose hydrolysis (GH3/GH5/GH2/GH10/GH30/GH35/GH38/ GH4), glycogen degradation (debranching enzymes), (GH3/GH13_9/GH67/GH94) and starch utilization (GH13_39).
The most abundant CAZyme identified in CGCs was related to the GH109 family. Nine genes, which typically encode α-N-acetylgalactosaminidase and β-N-acetylhexosaminidase, were found in seven clusters (CGC11, 13, 16, 24, 26, 28 and 29). GH109 genes were combined to other GH families genes, GH65/13/51 and GH2 in CGC 16 and 29 respectively, supporting that synergistic action of many CAZymes is required for polysaccharides cleavage  [39]. Interestingly, analysis of GH109 genes similarities against genes from PULs available in the database, revealed no significant similarity. Thus, we suggest that C. algeriensis encodes new gene clusters not identified previously. Indeed, few studies reporting characterization of GH109 family members were performed  [20]  and CAZy database lists only 7 GH109 nagalases as characterized.
Consequently, an in depth analysis of genes belonging to this family is very important. Carbohydrates esterases are also identified in two CAZymes genes clusters (CGC2 and CGC5), related to   [44, 45]. Genes Similarity analysis has shown 11 other genes, in addition to GH109, with no homologous in PULs database, including genes belonging to GT2, GT4 and CE19 CAZymes families. Thus, the C. algeriensis genome could be a source of novel and original thermophilic enzymes with strong potential for biotechnological applications.
The Circular Genome Viewer (CGView server)  [48]  was used to construct a circular graphical map of C. algeriensis TH7C1 T. Carbohydrate-active enzyme (CAZyme) searches were performed using HMMER3. 0 package (http:// hmmer. org/) available from dbCAN (http:// csbl. bmb. uga. edu/ dbCAN/)  [49] , this search is run against Pfam Hidden Markov Models (HMMs). DIAMOND available from the dbcan CAZyme database was used for fast blast hits in the CAZy database. Polysaccharides Utilization Loci (PULs) were analyzed via the dbcanPUL meta server  [50]. CAZyme gene cluster (CGC) Finder in the database was used for carbohydrate-active enzyme clusters annotation. CGCs were defined as genomic regions containing at least one CAZyme gene, one transporter (TC) gene, and one transcription factor (TF) gene. Genome sequence has been submitted to the public genomic NCBI database under accession number PRJNA743054.
Funding This work was supported by the  Tunisian Ministry of Higher Education and Scientific Research  (contract program LBMIE-CBS, code:  LR15CBS06 ) and the  Algerian-Tunisian R&I Cooperation for the Mixed Laboratories of Scientific Excellence 2021-2024 (Hydro-BIOTECH , code  LABEX/TN/DZ/21/01 ). The high throughput sequencing, assembly and annotation work was supported by the  IBISBA1. 0H2020  project  730976  (https:// www. ibisba. eu/) at its CSIC Cell Factory node.
Histological analysis Gastrocnemius muscles were fixed in paraformaldehyde 4% during 24 h and then embedded in paraffin. Serial transverse sections of 4 μm were cut from the wider part of each sample using a Leica microtome and were mounted on glass slides (three sections per slide). Muscle sections were then stained using Gomori's method to detect reticulin fibres. 28 All stained sections were scanned (×20 objective) to measure the fibre cross-sectional area from at least 300 fibres per muscle using the Image-Pro Plus software 6. 0 (Media Cybernetics, Inc. ).
Measurement of myotube size After 72 h of FAC treatment, myotubes were fixed in 4% paraformaldehyde for 10 min, washed in phosphate-buffered saline (PBS) 1×, and then permeabilized with PBS-bovine serum albumin (BSA) 2%-saponin 0. 2% for 30 min. Cells were then incubated at room temperature with an anti-pan MHC antibody (MyH, sc-20641, 1:200; Biotechnology) for 2 h and washed in PBS 1× before staining with a secondary goat anti-rabbit immunoglobulin (Alexa Fluor 488, 1:2000 dilution, Cell Signaling) for 1 h. The images were visualized with a fluorescence microscope (Axio Observer Z1, Zeiss, Germany). Myotube size was quantified by measuring diameter of 200 myotubes for each condition from 10 random fields at ×20 magnification using ImageJ software. Three diameter measurements were taken along the length of each myotube, and the mean diameter was calculated.
Overall liking scores of the bread and the three di erent pizzas were obtained either with a synthetic (hedonic question only) or with an analytical task (hedonic question plus intensity attributes). Care was taken to avoid any other changes to normal eating conditions, notably by recruiting on the spot only those customers who had spontaneously chosen pizza as part of their lunch. Liking scores of the homemade pizza were lower with the analytical task while the scores of the other two pizzas did not change significantly. Moreover, di erent rankings of the pizzas were obtained when the data were analyzed separately for each evaluation task format. The synthetic evaluation task would have led to the conclusion that the homemade pizza was the best liked and the readymade being the least liked, while the analytical evaluation task would have led to the conclusion that the "mixed" pizza would be liked significantly more than the other two.
Acknowledgments The authors would like to thank the  CROUS of Lyon (Regional Centers of University and Academic Services)  for its collaboration in providing the place and products of study. Thanks to the PhD students of the  Research Center of Paul Bocuse Institute  for their help in collecting questionnaires at the university cafeteria. This work is part of a PhD project funded by the  Société Scientifique d'Hygiène Alimentaire (SSHA).
Food-based dietary guidelines (FBDGs) are important tools for promoting healthy eating and nutrition education at the population level. Currently, more than 100 countries worldwide have developed FBDGs with majority of existing FBDGs in highincome countries. However, there are a few countries in Africa which have developed FBDGs. This review describes and compares the characteristics of existing FBDGs in Africa. Data were extracted from all existing FBDGs from African countries which have been archived in the Food and Agriculture Organization's (FAO) online repository of FBDGs. In addition, supporting documentation from other sources linked to the identified FBDGs were also reviewed. Extracted data were coded and synthesized to describe the purpose of the FBDGs, the process for developing the FBDGs, and how healthy diets were expressed in the FBDGs.
MATERIALS AND METHODS The review included all available national FBDGs from countries in the WHO Africa region. The FAO online repository of food-based dietary guidelines  [11]  was searched for relevant dietary guidelines that were added before March 1, 2020. Additional sources included supporting publications, supplementary documents, peer-reviewed and grey literature, and technical support papers from the FAO and FBDGs task teams in the Africa region. Data were extracted from any additional texts (leaflets, booklets accompanying FBDGs) that provided further information and advice that had been developed by the task teams. All guidelines reviewed met the following inclusion criteria: promoting dietary information; focussing on food consumption for a healthy population; providing recommendations expressed in simple, non-technical language, and seeking to promote health and prevent disease.
An Excel data extraction tool was created and used to collate data from each of the selected FBDGs. All data were extracted independently by two researchers (NNA and JQ) and subsequently discussed to harmonize any discordance. Data extracted included dietary guidelines with corresponding graphical images, food groups depicted in the images, the themes portrayed by the images, the format of dietary messages, and the intended audience. Data collected also included information and advice on the types and quantities of foods indicated in the guidelines. Information on other health behaviours included in the guidelines, such as alcohol intake, physical activity, smoking, and body weight control were also extracted.
ACKNOWLEDGEMENTS The review reported herein has been carried out as part of the food-based dietary guidelines development process in Ghana, which was technically and financially supported by the  Food and Agricultural Organisation (FAO)  of the  United Nations. Support for early development of this work was undertaken in the  TACLED  project, which was funded by  Global Challenges Research Fund Foundation  Award led by the  MRC  [grant number  MR/P025153/1 ].
Data and sample The Demographic and Health Survey (DHS) program has collected and disseminated nationally representative data on several topics, including nutrition, health, and population trends, through more than 400 surveys in over 90 developing countries since 1985. The DHS program was funded by the US Agency for International Development (USAID) and supported by multiple donors and funds. Using face-to-face interviews at the household level, the DHS program collected data that are comparable across countries based on standard model questionnaires. In all households, women age 15-49 were eligible to participate. In this study, we merged data from the household questionnaire and the women's individual questionnaire for each country of SSA (including usually between 5,000 and 30,000 households per survey) and each available wave (typically conducted at about 5-year intervals in each country).
Likewise, anthropometric measures are only available from wave 2 to wave 7 for women aged 15 to 49. Given the small proportion of surveys including anthropometric data for men and older individuals (up to 64), we decided to focus on women younger than 50. As a result, our final sample covers 36 SSA countries from 1990 to 2019, as shown in Figure  1. For the 36 countries and 6 waves considered, once the women datasets were appended and the household member datasets merged, our final metabase comprises 1,386,760 female individuals (aged 15-64) and 8,140 households. Since our analysis focuses on female adults, we excluded pregnant and lactating women, minor women aged between 15 and 17, and women older than 49 years old to limit anthropometric biases, which reduced the number of observations to 713,085.
Furthermore, we removed female individuals with missing or extreme BMI values, i. e. , where BMI ≥ 50 kg/m² or BMI ≤ 12 kg/m². Our final restricted sample thus comprises 392,846 adult women (aged 18-49). As suggested by the DHS administration and related literature, we use DHS sample weights in all our analyses to make sample data representative of the entire population  (Benedict et al. , 2018). Specifically, because we compare DHS samples from different waves and different countries, we had to de-normalize our dataset. Hence, we created a new sample weight by multiplying the initial weight by the total number of females aged 15 to 49 in the country at the time of the survey, and then by dividing this result by the number of women aged 15 to 49 interviewed in this survey. 5 In addition, we differentiated cluster and strata variables across years and countries.
Study design and population The current analysis is based on the data from the Allada cohort study. This study was carried out in three health centres (Allada, Attogon, and Sekou) in the district of Allada, a semi-rural district located in the South of Benin. Children were born to women enrolled in a randomized clinical trial (Malaria in Pregnancy Preventive Alternative Drugs, MiPPAD, NCT00811421)  [30]. The original population comprised 961 children for whom detailed information is provided elsewhere  [31]. The biomonitoring data on BLLs at age 1 year were not available for 300 children, leaving for the present investigation 661 children with available BLLs measured at age 1. In addition to height and weight growth data, head circumference measurements were also available at age 6 years for a subset of 370 children, which was included in the present investigation.
Exposure and relevant data collection Blood lead levels were measured in whole blood collected from children aged 1-2 years old. BLLs were analyzed at the Centre de Toxicologie, Institut National de Santé Publique du Québec (INSPQ, Québec, Canada). All BLLs were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) after dilution of blood samples with a detection limit of 0. 2 µg/L. The analytical methods are described elsewhere (Bodeau-Livinec et al. , 2016). Parent characteristics studied were economic status (measured by family wealth score), maternal education, estimated preconception body mass index (BMI)  [32] , gestational age assessed by Ballard score, and gravidity. Information on family wealth at age 1 year was assessed through a checklist of material possession (such as a car, motorbike, bike, television, cow, and radio) which was later transformed into a wealth scale with scores ranging from 1 to 15  [12].
We included children with complete data on BLL at one year of age. We tested for effect modification by sex by calculating the p-value of product term between child sex and BLLs. In additional analyses, multiple logistic regression models were run to assess the independent relationship between three forms of undernutrition (stunting, wasting and underweight) and different levels of BLLs. Models were further adjusted for child weight (only for stunting), and height (only for underweight) but not both variables in the same model to avoid multi-collinearity  [29]. BLLs were compared between children with undernutrition and those without undernutrition using the Wilcoxon rank-sum test. All statistical analyses were performed using STATA 14. 1 (StataCorp. 2015. Stata Statistical Software: Release 14.
2% (w/v) agarose gels (Promega, Charbonnières-les-Bains, France) in 1X TAE buffer at 120 V for 1. 33 h and then visualized with Midori Green staining (Nippon Genetics, Düren, Germany). RAPD band patterns were analyzed using the BioNumerics software (version 6. 6; Applied Maths) and 1-3 representative isolates (82 LAB and 48 yeast isolates) of each band pattern were selected for sequencing. Selected LAB isolates were identified by 16S rRNA gene sequencing  [27]  using the primers 16S-F (5 -CCGAATTCGTCGACAACAGAGTTTGATCCTGGCTCAG-3 ) and 16S-R (5 -CCCGGGATCCAAGCTTACGGCTACCTTGTTACGACTT-3 ) while yeast identification was performed by sequencing of the D1/D2 region of the 26S rRNA gene  [28]  with NL1 (5 -GCATATCAATAAGCGGAGGAAAAG-3 ) and NL4 (5 -GGTCCGTGTTTCAAGACGG-3 ) primers. PCR products were verified by agarose gel electrophoresis (0.
8% m/v in 1X TAE buffer), and were sequenced by Eurofins sequencing platform (Eurofins Genomics, Ebersberg, Germany) using the same primers. The sequences were assembled into contigs using DNA baser software and the sequences were compared with the GenBank database using the "Basic Local Alignment Search Tool" (BLAST) (https://www. ncbi. nlm. nih. gov/ BLAST, accessed on 1 December 2021) for assignment to the closest known relatives.
PCR and Metagenetic Sequencing Conditions PCR was carried out using the primers S-D-bact-0341-b-S-17 (5 -CCTACGGGNGG CWGCAG-3 ) and S-D-BAct-0785-a-A-21 (5 -GACTACHVGGGTATCTAATCC-3 ) to amplify the V3-V4 regions of the bacterial 16S RNA  [30]. For fungi, ITS3f (5 -GCATCGATGAA GAACGCAGC-3 ) and ITS4_Kyo1 (5 -TCCTCCGCTTWTTGWTWTGC-3 ) primers were used to amplify the ITS2 region  [31]. Amplifications and sequencing steps were successively performed in the same run at the Genome Quebec sequencing platform (McGill University, Montreal, QC, Canada) using Illumina Miseq PE300 230 technology to generate 2 × 300 bp reads. The sequences obtained in this study were submitted to DDBJ\EMBL\GenBank under the accession numbers provided in Tables  S6  and S7.
Bioinformatic and Phylogenetic Analysis Sequence data were analyzed using the FROGS pipelines (Find Rapidly OTU with Galaxy Solution) with the Galaxy platform (https://galaxy. migale. inra. fr/, accessed on 26 March 2020). Paired-end reads were first merged using Flash, then reads were clustered into OTUs using the SWARM algorithm, with an aggregation distance of 3. Chimera removal was performed using the UCHIME method on the VSEARCH tool. Prior to taxonomic affiliation, an abundance filter was applied to screen sequence clusters; sequences with relative abundance below 5 × 10 -5 were excluded. Finally, assignment was performed using the 16S SILVA Pintail100-138 and ITS UNITE Fungi 8. 2 databases for 16S and ITS data, respectively  [32]. Multi-affiliations were corrected using the NCBI website.
Biodiversity and Statistical Analysis Alpha and beta diversity analyses were performed using the QIIME 2 pipeline  [33]  and the Calypso software tool v8. 84  [34]  after normalization to the lowest sequence number in a sample and total sum normalization of count data combined with square root transformation (Hellinger transformation). Principal Coordinate Analysis (PCoA) based on Bray-Curtis distances was performed to compare biodiversity distribution at the OTU level between different categories (milk origin, geographical region, preparation method). Statistical differences between groups were evaluated using an Adonis test. Furthermore, the linear discriminant analysis (LDA) effect size (LEfSe) algorithm was applied to identify the representative bacterial and fungal taxa of each fermentation stage as well as of each collected sample.
The MS was operated within a mass range of m/z 29-206 and detection was by ionization impact at 70 eV  [38]. Volatile compounds were identified by comparison to retention indexes, mass spectral data of standards, and from the NIST 2008 Mass Spectral Library data (Scientific Instrument Services, Ringoes, NJ, USA). GC-MS data were processed as described by Pogačić et al. [38]. Volatile compounds were quantified using the abundance of one selected mass fragment (m/z) in arbitrary units. Differences between volatile profiles for each sample were investigated by comparing the abundance of identified compounds using principal component analysis under R software. Hierarchical clustering of the identified compounds was performed in order to investigate their abundance and dynamics in each sample using Ward's minimum variance linkage and the Euclidian distance method with R software.