dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene prevalence and abundance analysis Sequences of the GH13_18 referenced in the CAZy database in September 2018 were searched by BLASTP analysis (E value=0, identity !90 %) in the translated catalogue of 9. 9 million reference genes constructed using gut metagenome sequences of 1267 subjects from 3 continents (USA, China, Europe): 139 USA HMP samples; 760 European faecal samples from the MetaHIT project; 368 Chinese faecal samples  [54]. The microbial gene richness of GH13_18 in human gut was assessed by recovering the prevalence and occurrence frequency data  [54]  of homologous sequence of the catalogue assigned to GH13_18 from the 9. 9 gene frequency matrix in the 1267 subjects (http:// meta. genomics. cn/meta/dataTools).
Phylogenetic, synteny analysis and metabolic pathways In order to gain more insight in the phylogenetic diversity of the GH13_18 subfamily, a phylogenetic tree was reconstructed, using sequences classified in the CAZy database (Fig. 3 )  [6]. GH13_18 enzymes are distributed in diverse phylogenetic groups, such as lactic acid bacteria or cyanobacteria from different ecosystems like soil, marine and human gastrointestinal microbiota. The present phylogenetic tree reveals different clades; seven containing  MaGGoP that is interlocked between the SP and SPP enzymes  [26, 27]. Consequently, GH13_18 activity cannot be predicted from its clade status. Here, we observed that RgSPP belongs to the Clostridiae clade constituted by only gut bacteria (e. g. Blautia, Clostridium, Eubacterium) (Fig. 3 ) despite the fact that RgSPP and TtSPP, which shared the same substrate specificity, clustered in two different but close clades.
Prevalence in the human gut microbiota Among the GH13_18 sequences referenced in the CAZy database, 954 shared at least 90 % sequence identity with 63 sequences of the human gut metagenomic gene catalogue established from 1267 subjects  [54]. Their abundance and prevalence in the various cohorts are presented in Fig. 6  and values are summarized in Table  S3. By investigating the prevalence of the GH13_18 homologues in individuals, we observed that there are differences due to geographical origin rather than an impact of the health status of the individuals. Indeed, the genes from E. coli strains corresponding to the sequences V1. UC61-0_GL0056769, 656408. ECPG_02507 and 656419. ECJG_00789 of the catalogue were more abundant in the Chinese subjects, together with the gene ADQ02266. 1 (100 % identity with the sequence MH0230_GL0139113 of the catalogue) from the Bifidobacterium longum subsp.
1 from Romboutsia ilealis CRIB (98. 8 % identity with MH0345_GL0184076). These results provide new insights about the presence of homologous sequences from Romboutsia ilealis related to IBD. Indeed, Gerritsen et al. were not able to draw conclusions about the correlations between prevalence and/or abundance of Romboutsia ilealis and specific human diseases likely due to the limited number of available human datasets  [65]. The target RgSPP sequence and its homologues from Eubacterium rectale (ACR74281. 1) present about 98 % identity with the sequences MH0006_GL0203164 and NOM023_GL0014561 of the catalogue. Both sequences are the most prevalent genes of the family and were found in 67. 9 and 72. 1 % of the individuals, respectively. They are distributed in the various cohorts, to a lesser extent within the Chinese cohort, whatever their clinical status.
The number of ligands per RXP-NLC was estimated from the concentration of both RXP470. 1 pseudo peptide and lipids in solution. The RXP470. 1 concentration was determined by measuring the RXP-NLC composition in glutamate residues through amino acid dosing, while the lipid concentration was quantified by weighting lyophilized particle samples of known volume. Taken together, these data suggest a mean number of ligands of 105 ± 15 RXP470. 1/particle, corresponding to a density of about 1 ligand/ 105-140 nm 2. A maleimide/thiol coupling yield of 60% could thus be deduced. This direct quantitation of RXP470. 1 bound to the surface of the NLC was in the same range as the indirect less precise quantitation obtained by the HPLC measurement of free unbound RXP470. 1 eluted from SEC column during RXP-NLC purification (giving 49% yield for RXP grafting onto NLC).
This rapid distribution phase was followed by a slower decline of the plasma concentration, which led to a non-negligible portion of fluorescence remaining 24 h after injection (10%). At this time point, organs (liver, spleen, kidneys, lungs, femur bone, and brain) were harvested and tissue fluorescence was evaluated. As illustrated in Figure  4b , particles mainly accumulated in liver, which was a hallmark of mainly hepatic-biliary metabolism, while fluorescence in other organs was quite low. This biodistribution pattern was not significantly affected by RXP470. 1 functionalization, and was consistent with previously published data for this type of lipid nanoparticles  [29, 55].
This rapid distribution phase was followed by a slower decline of the plasma concentration, which led to a non-negligible portion of fluorescence remaining 24 h after injection (10%). At this time point, organs (liver, spleen, kidneys, lungs, femur bone, and brain) were harvested and tissue fluorescence was evaluated. As illustrated in Figure  4b , particles mainly accumulated in liver, which was a hallmark of mainly hepatic-biliary metabolism, while fluorescence in other organs was quite low. This biodistribution pattern was not significantly affected by RXP470. 1 functionalization, and was consistent with previously published data for this type of lipid nanoparticles  [29, 55]. For both types of particles, the ex vivo the isolated aorta harvested 24 h after animal sacrifice and exsanguination were imaged (Figure  5a ).
The amplicon PCR reaction was carried out for 30 cycles with annealing temperatures of 55 • C for 16S and 45 • C for ITS. Diluted pooled samples were loaded on an Illumina MiSeq and sequenced using a 500-cycle MiSeq Reagent Kit v2 (San Diego, CA, USA, adapted from Yergeau et al. ,  [17] ). The average size of the amplicon sequences were 293 bp and 276 bp for the 16S and ITS regions, respectively. Sequences were analyzed as described by Tremblay et al. [18]. Briefly, common sequence contaminants (i. e. , Illumina adapters and PhiX spike-in reads) were removed from raw sequences using a kmer matching tool (DUK; http://duk. sourceforge. net/). Filtered reads were assembled with the FLASH software version 1. 2. 11 [19]. The assembled amplicons were trimmed to remove forward and reverse primer sequences in each read using in-house Perl scripts.
Paired-end assembled amplicons were then filtered for quality. Sequences with more than one undefined nucleotide, an average Phred score lower than 30, or more than 10 nucleotides with a quality score of less than 10 were discarded. De novo operational taxonomic unit (OTU) generation was performed using a three step-clustering pipeline. Briefly, quality-controlled sequences were dereplicated at 100% identity. These 100% identity clustered reads were then denoised at 99% identity using dnaclust  [20]. Clusters of fewer than three reads were discarded, and the remaining clusters were scanned for chimeras using UCHIME de novo, followed by a UCHIME reference using the Broad's Institute 16S rRNA Gold reference database. The remaining clusters were clustered at 97% identity (dnaclust) to generate OTU.
Taxonomy assignment of the resulting bacterial and archaeal OTUs was performed using the RDP classifier with a modified Greengenes training set built from a concatenation of the Greengenes database (version 13_5). Taxonomy assignment of fungal OTUs was performed using the SH Qiime release 20. 11. 2016 database (version 7. 1). Hierarchical tree files were generated with in-house Perl scripts and used to generate training sets using the RDP classifier (v2. 5) training set generator's functionality  [21]. A bootstrap cutoff of 50% was applied to each query to generate final taxonomic lineages. With taxonomic lineages in hand, OTU tables were generated and normalized using edgeR's Relative Log Expression (RLE) method  [22] , as suggested by McMurdie and Holmes  [23]. Bacterial/archaeal OTU representative sequences were aligned on a Greengenes core reference alignment  [24].
Alignments were filtered to keep the V4 hypervariable region of the alignment. A phylogenetic tree was then built from this alignment with FastTree  [25]. Alpha diversity (observed OTUs) was computed from un-normalized OTU tables, rarefied at 1000 reads with 10 permutations, for both 16S and ITS. For 16S data, weighted Unifrac distances were generated using the normalized OTU table and the phylogenetic tree previously generated. For ITS data, the Bray-Curtis dissimilarity metric was generated from the normalized OTU table. Taxonomic profiles were computed from the normalized OTU tables using the QIIME software suite version 2017. 6. 0  [26, 27]. Permanova analyses with 1000 permutations were done in R using the adonis function of the vegan library. One sample from the 16S dataset (LB-LH, day 16 experimental condition) and one sample from the ITS dataset (control day 64 experimental condition) failed to give enough reads for downstream analyses and were therefore excluded from our analyses.
Statistical Analysis The effect of the incubation time on the fermentation parameters and the differences between the inoculation treatments were analyzed using a mixed-effect model with temporal pseudoreplication [28] using R version 3. 3. 3 [29]. The nmle package was used by the mixed-effect model  [30]. The fixed-effect formula was the tested parameter-treatment, while the random-effects formula was ~day|silo. The following fermentation parameters were individually tested by the model: pH, lactic acid, acetic acid, and the ratio of lactic acid to the sum of acetic acid and ethanol (LA/AAEtOH). A linear model was used in the preparation of some figures using the equation y~ns(x 3 ) from the splines package of R (version 3. 4. 0). The confidence interval boarding the trend lines was computed using the predictdf function of the package from t-based data and from standard error results of the glm function.
This observation means that inoculation favors a faster succession to a potentially more stable silage, with fewer active species. In addition, a discriminant analysis (Figure  1C ) was performed using the complete OTU dataset, after exclusion of the fresh samples. The results of the analysis showed that microbial succession followed similar trends in LB-LH and LH, but was more distinct between LB and Control. The comparison of the relative abundance of Lactobacillales could explain part of the observed difference (see Figure  3  and the heatmap Figure  S2  in the Supplementary Materials). Considering that the discriminant analysis includes the effects of both time and treatment, understanding the links to a specific parameter is not easy. (Figure  3 ), several OTUs affiliated as Lactobacillus were more abundant in LB, including Lb.
Two values were generated for each individual OTU by pairwise comparison between treatments. The variation in the number of individual OTUs between two treatments was represented as logFC (fold change), and the difference in the number of individual OTUs was represented as logCPM (count per million). Although the computation was performed for each opening, only the results from the last opening are discussed here (Figure  3 ). To understand the dynamic for individual OTUs, the sequences were manually compared to the NCBI database in order to annotate the phylogenic identification. Additional analyses of the contrasts using this manual annotation are presented in the Supplementary Materials in the form of two tables (Tables  S1  and S2 ), comparing the identity of the main OTUs that increased or decreased between the different treatments.
Results from a PERMANOVA analysis show that the treatments, opening periods, and the interaction treatments:openings were significant (p < 0. 001) for the ITS amplicons. The observed variation between samples was related to the inoculation treatment as well as the time of incubation. After 64 days of fermentation, specific patterns were visible between treatments. The differences were related to the presence of certain yeast genera. An OTU affiliated as Dipodascus was present in LB and in LH in nearly 28. 3 ± 2. 9% and 21. 4 ± 1. 0%, respectively, of total sequences. Further analysis of the sequence of that OTU using a BLASTn search of GenBank (release 222. 0) led to closer identity with the genus Geotrichum. Both genera belong to the family Dipodascaceae and taxonomic revisions are underway  [51] , as Geotrichum may be the anamorphic state of species of the genera Dipodascus and Galactomyces  [52].
Main changes in fungal diversity using the UPGMA dendrogram generated for analysis of the heatmap (see Figure  S4 ). After 64 days of fermentation, specific patterns were visible between treatments. The differences were related to the presence of certain yeast genera. An OTU affiliated as Dipodascus was present in LB and in LH in nearly 28. 3 ± 2. 9% and 21. 4 ± 1. 0%, respectively, of total sequences. Further analysis of the sequence of that OTU using a BLASTn search of GenBank (release 222. 0) led to closer identity with the genus Geotrichum. Both genera belong to the family Dipodascaceae and taxonomic revisions are underway  [51] , as Geotrichum may be the anamorphic state of species of the genera Dipodascus and Galactomyces  [52]. Interestingly, the OTU associated with Dipodascus was almost absent from LB-LH and Control samples at day 64 (0.
Introduction HIV-exposed uninfected (HEU) children have higher morbidity and mortality compared with HIV unexposed uninfected children. Despite the fact that malnutrition contributes to about half of all infant deaths below 5 years of age in low-income and middle-income countries and that growth impairment has been reported in the HEU population, the spectrum of growth disorders associated with HIV and antiretroviral therapy (ART) exposure during the in utero and perinatal periods is yet to comprehensively summarised among the global HEU population. This protocol for a systematic review and meta-analysis aims to critically synthesise data concerning the prevalence of underweight, stunting and wasting at different ages in the global HEU population. Methods and analysis Medline, EMBASE, Cochrane Library, TOXLINE, WHO Global Index Medicus and the Web of Science will be searched for relevant articles published between 1 January 1989 and 1 December 2017 without language restriction.
MEthOds And AnAlysIs design This will be a systematic review and meta-analysis of the published literature. The Centre for Reviews and Dissemination guidelines  13  will be used for the methodology of this review and the review will be registered in the PROS-PERO International Prospective Register of systematic reviews. For studies with several publications of their findings over time, we will retain in the review the one that has the best quality and the most appropriate regarding our objective. For the meta-analysis, studies with a documented measure of the effect of in utero/postnatal HIV and ART exposure on growth comparing HEU and HUU groups will be included. For studies without reported measures of effect, these will be computed if the provided data are adequate.
data extraction and management A tool for data abstraction will be developed and the following data will be extracted: 1. Details about the author: name and journal. 2. Characteristics of the study: design, country, period when the study was conducted, year of publication, duration of follow-up, sample size and study endpoints. 3. Characteristics of participants: Age, sex, class of maternal ART, term of delivery, low birth-weight, mode of infant feeding (no BF, mixed feeding, exclusive BF), duration of BF, cotrimoxazole prophylaxis and growth references used. 4. Outcome measures at different time points: weight-forage, length-for-age, weight-for-length, BMI for age and growth velocity. Two investigators will independently extract data and any disagreements will be reconciled through discussion or by a third investigator as necessary.
Assessing the methodological quality of included studies and risk of bias The quality assessment tool for Observational Cohort and Cross-sectional studies of the National Heart, Lung and Blood Institute (NHLBI) will be used to assess the quality of observational cohort studies 14 (online supplementary file 2), whereas for intervention studies, the Effective Public Health Practice (EPHPP) tool  15  (online supplementary file 3) will be used by two investigators independently. Agreement between the two investigators will be measured by Cohen's kappa statistic. For the NHLBI tool, the score will be graded as good, fair and poor whereas for the EPHPP tool, it will be graded as strong, moderate and weak. data synthesis and analysis Prevalences of underweight, stunting and wasting in the HEU and HUU groups will be recalculated based on the information provided by individual studies and ORs will be computed at each age point.
Open access following heterogeneity levels: absence, weak, moderate and high. For each risk factor, the combined measure of relative risk will be estimated from a random-effect model if I 2 ≥25% and a fixed-effect model if I 2 <25%. If substantial heterogeneity is observed, a meta-regression will be used to identify the characteristics of the studies (eg, study quality, study location, sample size, adjusting of confounders or not, ART agents and regimens, duration and mode of BF, maternal socioeconomic status and so on) that may explain the observed heterogeneity. All data will be analysed using Stata V. 14. A forest plot will be presented for each factor studied in the studies found. The qualitative synthesis will be used in cases where data extracted is insufficient to perform quantitative synthesis or when the included studies significantly differ in design, setting and outcome measures.
1. Type of studies: all observational cohort studies and randomised controlled trials that have data on any growth parameter of interest, either as main or secondary outcomes. Only comparative studies with HEU and HUU children will be included in the meta-analysis. 2. Type of participants: adequately defined HEU (children exposed in utero to HIV and combination ART of any class, who are declared HIV negative by either DNA PCR between 6 weeks and 18 months OR appropriate HIV serological tests above 18 months) and HUU children (children born of mothers with documented HIV-negative serological test), aged 0-5 years.
1. Type of studies: all observational cohort studies and randomised controlled trials that have data on any growth parameter of interest, either as main or secondary outcomes. Only comparative studies with HEU and HUU children will be included in the meta-analysis. 2. Type of participants: adequately defined HEU (children exposed in utero to HIV and combination ART of any class, who are declared HIV negative by either DNA PCR between 6 weeks and 18 months OR appropriate HIV serological tests above 18 months) and HUU children (children born of mothers with documented HIV-negative serological test), aged 0-5 years. All ART exposure will be considered. 3. Outcome measures: prevalence of, stunting, wast- ing and underweight; growth velocity or studies that have enough data to compute the outcomes of interest at yearly time intervals, available from 12 to 60 months.
Acknowledgements We are grateful to  Douglas Baxter  for providing data on individuals from his study on Zn isotope composition in blood (Stenberg et al. , 2005). Many thanks to  Jeroen Sonke  and  Franck Vanhaecke  for very helpful discussions. We also thank  Francis Camoin  from the  Etablissement Français du Sang in Lyon  for providing the blood samples, and  Michelle Gigou  and  Christian Bre  ´chot from the  Hepatobiliary Center  ( U785 INSERM ) of the  Paul Brousse Hospital  for providing the liver samples. Axelle Zacaı ¨and  Jean-Michel Brazier  are thanked for their help during sample preparation. Funding for reagents and isotope analyses was supported by the  Bullukian Foundation  and the  Merieux Foundation. We also thank the  Max Planck Society  for salary support. Finally, we would like to express our gratitude to  Geraldine Fahy  and  Carin Molenaar  for the English editing of the different drafts.
7 C of column temperature. The system was calibrated with molecular weight standards: dextrans of 1000 Da, maltoheptaose DP7, maltohexaose DP6, maltopentaose DP5, maltotetraose DP4, nystose, 1-kestose, sucrose and fructose. ChemStation with Agilent OpenLAB software were used for controlling the system operation and data analysis, according to the methodology described by Moreno et al. [1].
Commercial and laboratory extracted and purified agave fructans in powder Experimental features Comparison between two mass analytical techniques to determine the size distribution of agave fructans. Data source location Agave fructans of Agave tequilana Webber var. azul from Jalisco, M exico Data accessibility Data is provided with this article Related research article Specifications table Subject area Chemistry More specific subject Molecular weight distribution of polysaccharides by two techniques area Type of data Table and figures How data was Agave fructans samples of different molecular weight obtained commercially and previously fractionated in acquired laboratory. The HPLC-SEC system consisted of a 1220 Infinity LC System HPLC coupled with a refractive index detector (Agilent, Alpharetta GA, USA).
Literature search The public search generators PubMed, Google Scholar, Web of Science (Core collections), and Scopus were used to search for literature. Research articles which investigated the effect of RS2 from purified or natural sources on the bacterial abundance and intestinal fermentation and were published in scientific journals between the years 2000 and February 2017 were considered for data extraction. Adequate articles were identified by applying the following search terms in different combinations: RS, high-amylose starch, slowly digestible starch, pig, piglet, swine, gastrointestinal tract and individual segments, gut/intestine, fermentation, microbial metabolites, total and individual SCFA (or volatile fatty acids), lactic acid, neutral and anionic forms of fermentation acids, bacteria, microbiota, microflora and microbiome.
Construction of database The construction of the database was similar to that previously described  (Metzler-Zebeli et al. , 2017). Of the 35 identified articles, 24 were eligible for the present metaanalysis by meeting a sufficient number of quality criteria (Supplementary Material Table  S1 ). Quality assessment criteria included information about dietary composition, RS level and source (purified concentrate or natural source), details on pig (breed, age, BW, age, sex, production stage), housing condition, number of pigs within treatment groups, duration of the experimental period, experimental design including randomization of treatment groups, description of statistical analysis, and intra-study error (if standard deviation was provided, it was converted into standard error), as well as bacterial abundances, microbial metabolites (i.
e. SCFA and lactate) and pH values in digesta of stomach, ileum, cecum, proximal, mid and distal colon and feces (rectum). In addition, average daily feed intake (ADFI), average daily weight gain (ADG) and ATTD data were extracted. Only in vivo studies were included in the present meta-analysis. The main information extracted from each research article is presented in Table  1. When the dietary concentration of RS was not provided, the RS amount in the diet was estimated using published values for similar RS2 sources. In general, different analytical methodologies, including different in vitro α-amylase digestion approaches (e. g. Champ, 1992; Morales et al. , 1997)  or total starch and RS assay kits (e. g. Megazyme assay kits), the official AOAC method  (2002) , and the determination of RS according to  Englyst and Cummings (1984) , were applied to determine the dietary RS concentration.