dataesr/scientific-paragraphs-categorization huggingface dataset:
Or the dietary RS content was calculated by using the manufacturer's specifications in the various research studies. In order to consider maturational changes from weaned to finisher pigs, pig's BW at the beginning of the experiment (start BW) was used as an additional predictor variable. Likewise, the Resistant starch and intestinal fermentation length of the experimental period was considered as additional predictor variable because the microbial response to a dietary treatment may change over time. From the original dataset including all studies, sub-data sets for the individual dependent variable categories were compiled, that is, one sub-dataset each for bacterial abundances, pH values, microbial metabolites including SCFA and lactate, as well as for performance-related variables such as ADG, ADFI and ATTD.
Data for individual SCFA were analyzed as concentrations and molar proportions of total SCFA. Three studies were set as minimum requirement to quantify the combined effect size  (Lipsey and Wilson, 2001) , together with a minimum of 10 single observations (treatment means) per dependent variable as well as the respective SEM of each variable. Only dependent variables which met these minimum requirements will be presented. Absolute bacterial abundances were extracted from studies using quantitative PCR(n = 7), terminal-fragmentlength polymorphism (n = 1) and culturing (n = 2). Although being different methods of quantification, log 10 colony forming units (CFU) were previously shown to highly correlate (0. 979 to 0. 998) to log 10 gene copy numbers per gram of sample  (Hein et al. , 2001). In applying this correlation, we expressed the available bacteria data from PCR-based and culturing-based approaches in log 10 gene copies per gram intestinal digesta or feces.
Data analysis Descriptive statistics on predictor (i. e. dietary RS level, start BW and duration of the experimental period) and dependent variables (i. e. bacteria, microbial metabolites, pH, performance and digestibility) was performed using the MEANS procedure of SAS (SAS Institute Inc. , version 9. 4) similar to  Metzler-Zebeli et al. (2017). Mixed modeling of each dependent variable was performed using the MIXED procedure as previously described  (Metzler-Zebeli et al. , 2017). The slope and intercept by study, start BW, duration of experiment and dietary RS content were included as random effects and an unstructured variance-covariance matrix (type = UN) was used to avoid a positive correlation between intercepts and slopes  (St-Pierre, 2001). Breed and sex were initially included in the model but omitted in the final model as non-influential.
Database description The RS2 originated mainly from corn, potato, pea, barley and tapioca (Supplementary Material Table  S1 ). In the majority of studies included, diets were semi-purified with the test starch as the principal starchy component. The results of the descriptive statistics for the predictor and dependent variables are presented in Table  1. The mean and median dietary RS levels amounted to 28. 8% and 23. 0% of the diet (DM basis), respectively, with dietary inclusion levels ranging from 0% to 78% RS (Table  1 ). The BW of pigs at the beginning of the experimental period ranged between 4. 6 and 105 kg with a mean and median BW of 23. 7 and 30. 4 kg, respectively. Pigs were fed the test diets between 7 and 100 days with a mean and median duration of the experimental period of 28.
The model has five unknown parameters: i) K01 and K02 are equal and represent the output flow constants of the dam; ii) K03 and K04 are the output flow constants from male and female litter, respectively and iii) K31 and K42 are the output flow constants from the dam to its male and female litter, respectively. Using the isotope dilution method, the mass of dam's TBW (TBWd, in g) was calculated by dividing the amount of D 2 O injected by the D 2 O value extrapolated from the D 2 O concentration curve to the intercept with the y-axis at time 0 (Supplementary Table  S2 ). TBWd (in %) was calculated by dividing the mass of TBWd (in g) by dams' mean mass from L11 to L15. Flow constants of the model (K01, K03, K04, K31, and K42) were then determined using the SAAM II®software. As litter size (8 or 12 pups) presumably impacts D 2 O dilution in the litter, we used values of D 2 O mass instead of D 2 O concentrations for calculations in our model.
To this end, D 2 O concentration, obtained after baseline concentration deduction, was multiplied by animal mass at each day of sampling, following Equation (1): D 2 O concentration µg. g -1 * animal mass (g) = D 2 O mass (µg) (1) This calculation implicitly assumes that dams' and pups' TBW (in %) are equivalent, which is probably the case considering that suckled pups have about 75% of TBW  [29] , which is similar to suckling dams' TBW  [22]. Moreover, in order to indicate that compartment (1) and (2) represent the same individual (dam), the same data were incremented for both compartments, and K02 was forced to be equal to K01. Flow constants of the model were obtained directly with SAAM II®from a fit of plasma and urine D 2 O mass-time curves. The water flows, from mother to male litter R31 (g.
Total milk production of the dam was calculated by summing R31 and R42 and represented the mean milk production throughout the sampling period (L11 to L18). Milk consumption by male and female litters was obtained by dividing R31 and R42, respectively, by the number of males or females pups (4 in NP:8, LP:8 and LPF:8 groups or 6, in NP:12 and NPF:12 groups). Figure  2. Four-compartment model of water turnover. TBWd, TBWm, TBWf: total body water (expressed in g) of dam, male, and female litter respectively; K01 and K02 (in h -1 ) are equal and represent the output flow constants from the dam; K03 and k04 (in h -1 ) are the output flow constants from the male and female litters, respectively; K31 and K42: (in h -1 ) are the output flow constants from the dam to its male litter and from the dam to its female litter, respectively; R10, R20, R30, and R40 (in g.
Trigonelline Quantification in Experimental Diet, Dams' Plasma, and Milk Ten µL of the labelled internal standard: trigonelline-D3 (CIL, Sainte Foy la Grande, France) at 50 µM, and 100 µL of acetonitrile were added to 10µL of diet solution (mixed diet in water, 100 g. L -1 ), dams' plasma or milk. Samples were centrifuged for 10 min at 11,000 g, and the supernatant was injected into the LC-MS/MS using a hydrophilic interaction liquid chromatography system (Acquity H-Class®UPLC TM device, Waters) coupled with a triple quadrupole mass spectrometry detector (Xevo®TQD) with an electrospray interface. Data acquisition and analyses were performed with MassLynx®and TargetLynx®software, both versions 4. 1 (Waters). Trigonelline was separated over 6 min on a HILIC column (2. 1 × 100 mm; 1. 7 µM), (Waters) held at 45 • C with a linear gradient of mobile phase A (10 mM ammonium acetate in water) in mobile phase B (98% acetonitrile in water), each containing 0.
Acknowledgments: The authors want to thank specifically thank  Alexis Gandon  ( PhAN, Nantes, France ),  Sandrine Suzanne  and  Amandine Lefebvre  ( UTE, Nantes, France ) for their precious help during all the animal experiments. We thank  Isabelle Grit  ( PhAN, Nantes, France ) for help with every metabolic analysis. Finally, we thank the  Mass Spectrometry Core Facility of CNRH-O (Centre de Recherche en Nutrition Humaine Ouest ) and Biogenouest, particularly the Corsaire network of analytical platforms, for their support of this project.
Statistical Analysis Computations were performed using the Statistical Package for the Social Sciences (SPSS) software (version 19. 0; SPSS Inc. , Chicago, IL, USA). The data was expressed as means ± standard deviations of three replicates. Analysis of variance was done using Tukey's post-hoc test to determine the differences among means obtained for different samples. The level of significance was set to p < 0. 05 and Pearson correlation coefficients were generated to describe the relationship between phytochemical contents (TPC, TFC and TAC) and anti-fungal activity which was evaluated by measuring the inhibition zone (mm).
Using WHO field tables, WHZ was determined and used for admission and discharge. In later analysis, WHZ was calculated using the package 'zscore06'  [32]  in STATA 15 (StataCorp, College Station, TX). Children were followed up until recovery. Children missing their study visit were contacted either directly by telephone or via a community health worker and encouraged to return. Children referred did not return to trial after inpatient phase, as referral was considered a trial endpoint. Recovered children were followed up fortnightly for 12 weeks and relapses recorded. A supplementary feeding program accompanied the post-discharge follow-up, providing readyto-use supplementary foods when available.
Data collection and management Two study teams were comprised of one nurse, three measurers, one food distributor, and one supervisor per team. All team members were trained on research ethics and processes; standard operating procedures were defined, tested, and applied. Data were collected via tablets using the Open Data Kit (ODK1 software), and continuous data monitoring and cleaning were performed by a data manager under the supervision of the principal investigator. Electronic data were password protected, and field registries were kept in a locked office. Data were de-identified prior to analysis.
Adjustments were defined in the statistical analysis plan development stage prior to data analysis (S3 Text). Model checking was based on residual plots and normal probability plots, when applicable. All analyses were performed using STATA 15 (StataCorp). Both intention to treat (ITT) and per protocol (PP) analyses were carried out for the main outcome and key secondary outcomes. ITT analysis included all children admitted to the study for whom an endpoint observation was available. PP analysis included children without missed visits who, according to maternal recall, consumed >50% of the daily dose at all times and excluded those who had received a wrong treatment dose or had been falsely discharged. Interactions were only investigated in ITT analyses. Interactions between treatment and age group (<12 months versus �12 months), sex, MUAC category (<115 versus �115 mm), WHZ category (<-3 versus �-3), and stunting (height-for-age z-score [HAZ] < -2 versus HAZ � -2) at admission were evaluated for the main outcome of weight gain velocity and the key secondary outcomes of recovery, LoS and height gain velocity, by means of likelihood ratio tests.
ACKNOWLEDGMENTS We would like to thank  Makoto Arita  and co-workers ( RIKEN Center for Integrative Medical Sciences, Japan ) for kindly providing the compounds 18(R)-RvE2, 18(R)-RvE3, and  18(S)-RvE3  to be included in our method. We would like to thank  Dr. Camille Oger ,  Dr. Valerie Bultel , and  Dr. Alexandre Guy  for isoprostanoid synthesis.
S4A ). These important results suggest that mature RumC1 contains four thioether bridges, two each in the N-and C-terminal regions. To identify the residues involved in each thioether bridge, four Cys to Ala mutants were designed: mRumC1-C3A, mRumC1-C5A, mRumC1-C22A, and mRumC1-C26A. All the mutants were produced and purified as described for mRumC1. To detect nonbridged cysteine residues, mutant mRumC1 samples were first treated with iodoacetamide under reducing conditions and then subjected to LC-MS/MS experiments. Analyses of the four single mutants revealed fairly complex peptide mixtures with many peptide fragments bearing alkylated cysteine residues. These data suggest that, during the maturation of mRumC1, the formation of a single thioether linkage may influence how the three other bridges form, making the structure highly labile.
Nano-LC-MS/MS analyses RumC fractions were generally injected at a concentration of 0. 1 M. Samples were diluted in 5% (v/v) ACN and 0. 1% (v/v) TFA and analyzed by online nano-LC-MS/MS (NCS HPLC, Dionex, and Q Exactive HF, Thermo Fisher Scientific). Peptides were sampled on a 300 m by 5 mm PepMap C18 precolumn and separated on a 75 m by 250 mm PepMap C18 column (Dionex). The nano-LC method consisted of a 40 min gradient at a flow rate of 300 nl/min, and MS and MS/MS data were acquired using Xcalibur (Thermo Fisher Scientific). Highly sensitive method (by increasing the ion time in the trap to 200 ms) in MS/MS was used to improve the signal to noise of the fragmented large peptides. For the analysis of the maturated forms, combined high collision dissociation energies of 20 to 27 were used to promote the breaking of both weak bonds (thioether bridge) and stronger bonds (amide bonds).
MS data analyses Data were processed automatically using Mascot Distiller software (version 2. 5. 1, Matrix Science) followed by Mascot (version 2. 6) searches using the sequences of the different peptides of interest as database. None was chosen as enzyme, and the precisions were set at 10 ppm for the peptide precursor and 20 milli mass unit for the fragments. At first instance, many different possibilities of variable modifications were tested to find out on which amino acid was observed in the dehydramino modification (-2H)  (36). Manual annotations were performed in parallel to Mascot searches to assess the positions of thioether linkages. For final searches, dehydramino (A, N, R, and K), deamidation (NQ), and ammonia loss (N) were set as variable modifications. Qual Browser and Xtract (Thermo Fisher Scientific) were used for the display and the deconvolution of the spectra.
Acknowledgments: We would like to thank the people from the AVB platform (iSm2  CNRS ,  UMR 7313 ,  Marseille ) and the "ANAXEM platform" animal facility (Micalis  INRA Jouy en Josas, France). We are indebted to  S. Rabot  ( INRA ) for providing the axenic and monoassociated rats and  A. Balvay  for skillful technical assistance. Funding: This study was supported by grants from the  French National Agency for Research ("Agence Nationale de la Recherche")  through the " Projet de Recherche Collaboratif " ( RUMBA  project,  ANR-15-CE21-0020 ), the " Investissement d'Avenir Infrastructures Nationales en Biologie et Santé" programme.
Dough behavior during fermentation Fermentation time is the time required to tripling the initial volume of the dough. The dough fermentation time and expansion rate was determined where a small amount of dough was placed in a cylindrical flask and a displacement transducer (VRVT050/E/TM, Penny & Giles, UK) was introduced into the flask which was directly connected with the data logger. Data logger records the change in height of the dough with respect to time during fermentation. The volume expansion of dough was supposed to be equal to CO 2 produced by the yeast during fermentation assuming that no CO 2 gas escaped from the dough (closed dough porosity, and negligible mass exchange with the ambiance). From these values, a graph showing the volume of CO 2 produced during fermentation as function of time was obtained.
Bread crumb pore size distibution The bread was sliced (Sofraca, France) after storing at 4°C for a period of 24 h. The pore size distribution in the bread crumb was analysed by scanning the bread slices with the flat bed scanner (HP Scanjet G4010-HP, USA). This scanner delivers the scanned image instantaneously. The scanned image was analysed by using imageJ® software (version 1. 51j8). Initially, the software was calibrated then the image was duplicated and converted into 8 bit gray scale image. A mask for the image was created for applying threshold. Thresholding is one of the most important step, in which the gray scale image was segmented and converted into binary image. The major advantage of obtaining this binary image is to reduce the complexity of the data and to simplify the process.
Auto local threshold method provided in the software was used for image segmentation. Morphological filters such as erosion and dilation, opening and closing were used for structuring the pore area. Morphological reconstruction is another major part of image processing which was used for constructing an image without altering the original shape of the pores present in the bread slice. Then, particle analysis was performed for analyzing the bread crumb structure. The selected parameters measured in the particle analysis were the pore area and total pore count. The range of pore size and circularity of the pores to be analysed was fixed and the analysed values were exported to Microsoft Office Excel (version 12. 0. 6612. 1000), to generate primary information of the pores. Atlast, image calculator performed arithmetic and logical operations between the original and duplicate images to deliver a final labelled RGB image in the new window.
The crystal structure of Cu-bound Hcc1 The copper-loaded soluble domain of Hcc1 crystallized under aerobic conditions in the space group P2 1 (Table  1 ). X-ray diffraction data were phased by molecular replacement and the crystal structure was refined to 2. 66 Å. The final atomic model contains three equivalent copies of Hcc1 in the asymmetric unit, with an average Root-mean-square deviation of atomic positions (RMSD) value of 0. 1 Å among 157 alpha-carbons in pairwise comparisons. The structure was deposited in the PDB database with accession number 6N5U. Hcc1 adopted a typical Sco fold, i. e. , a thioredoxin/peroxiredoxin fold with four alpha helixes and nine beta strands (Fig. 3A ), comparable to the Sco variants from human (PDB ID 2GGT,  [34] ), yeast (PDB ID 2B7J,  [46] ), and B. japonicum (PDB ID 4WBR for the copper bound species and 4WBJ for the apo form), with RMSD values within the range 0.
78-1. 05 Å(Fig. 3C-E ). The comparison of Hcc1 and B. subtilis Sco (PDB ID 1XZ0,  [41] ) revealed a higher RMSD (1. 62 Å), mainly due to divergent conformations of the Sco loop and the cysteine-containing motif in the latter (Fig. 3F ). The characteristic Sco loop, comprising Hcc1 residues 117-131 (see the Experimental procedures for details on the numbering scheme used in this work), is fully defined in two of the three protein molecules in the asymmetric unit. For all monomers, additional electron density was evident at the base of the Sco loop, i. e. the canonical copper-binding site in Sco proteins. This feature was interpreted as a bound copper ion, consistent with the measured protein:metal stoichiometry. The copper ion is coordinated by the S atoms of C42 (d~2. 4Å) and C46 (d~2. 3Å) (from loop L3) and the N atom from H131 (d~2.
Rather, the present crystal structure of AtSco1 disclosed the presence of a tri-coordinated copper ion, with a distorted trigonal planar coordination sphere comprising the three ligands of the conserved CxxxCx n H motif. This geometry is consistent with a Cu 1+ ion [60] and agrees with EXAFS (Extended X-Ray Absorption Fine Structure) data for Cu +1 -Sco from S. cerevisiae  [23] and B. subtilis [61]. Instead, the geometry is not consistent with the expected four-coordinate, distorted tetragonal copper site expected for Cu +2 -Sco [44,45,61]. A crystal structure of copper-bound Sco from B. japonicum is available (PDB ID 4WBR), annotated as a Cu +2 Sco form. Indeed, this structure displays electron density compatible with a water molecule acting as a fourth ligand of the metal ion. Thus, this structure provides details for a Cu 2+ site in Sco proteins.
Instead, in our crystal structure of Cu-bound AtSco1, the absence of a fourth copper ligand in all molecules of the asymmetric unit, supports the presence of a tricoordinated Cu +1 metal site. A comparison of Cu +1 bound AtSco1 with Cu +2 -Sco from B. japonicum and human Ni +2-Sco1 (PDB ID 2GGT)  [34]  reveals that the conformation of the loop containing the cysteine ligands is conserved, despite the differences in the oxidation state or the identity of the metal ion (Fig. 3 ). Moreover, in Ni +2 human Sco2 the two cysteine ligands are oxidized forming a disulfide bridge. We conclude that the geometry of the metal site is constrained by the protein scaffold, and the main difference between the Cu +1 and Cu +2 forms is the presence of a fourth ligand. Given that Sco proteins are presumed to transport and deliver Cu +1 in vivo, the distorted trigonal geometry lacking a fourth ligand may provide a higher accessibility for copper, facilitating metal transfer.
Crystallization, data collection and structure determination Crystallization screenings were carried out using the sitting-drop vapor diffusion method and a Gryphon (Art Robbins Instruments) or Mosquito (TTP Labtech) nanoliter-dispensing crystallization robot. Following optimization, crystals of Cu 2+ -Sco1 soluble domain grew after 15 days from a 12 mg/ml protein solution, by mixing 1 µl of protein solution and 1 µl of mother liquor (100 mM Hepes, 23 % w/v PEG 3350, 10 % v/v Tacsimate, at pH 7. 5), in a hanging drop setup with 1 ml mother liquor in the reservoir, at 20 ºC. Single crystals were cryprotected in mother liquor containing 30% glycerol as cryoprotectant and flash-frozen in liquid nitrogen. X-ray diffraction data were collected at the synchrotron beamline MASSIF-3 (ESRF, Grenoble, France), at 100 K, using wavelength 0.
Nuclear Magnetic Resonance NMR experiments were carried out on a 700-MHz Bruker Avance III spectrometer equipped with a triple resonance inverse (TXI) probe. All experiments were carried out at 298 K using standard techniques. Backbone resonance assignments for Cu +1 Cu +1 -CoxII** were obtained by analyzing HSQC, HNCO, HN(CA)CO, HNCA, HN(CO)CA, CBCA(CO)NH, and HNCACB experiments acquired on a uniformly labeled 13 C and 15 N sample prepared in 100 mM KCl and 100 mM potassium phosphate buffer at pH 6. 0. The backbone 15 N and 1 H assignments were transferred from pH 6. 0 to 7. 0 by following the position of the N-H cross peaks in HSQC spectra in a pH titration. All NMR data were processed using the TopSpin TM NMR suite and analyzed using CcpNmr  [67].
Diffraction data were processed using XDS[62]and scaled with Aimless from the CCP4 program suite[63]. The crystal structure of AtSco1 soluble domain was solved by molecular replacement using the program Phaser [64] and the atomic coordinates of human Sco1 residues 135-300 from chain A in PDB ID 2GGT as search probe. The structure was refined by iterative cycles of manual model building with Coot [65] and refinement with Buster [66]. Cu +1 atoms were manually placed in mFo DFc sigma-A-weighted electron density maps employing COOT and the resulting model was refined as described above. The final structure was validated through the Molprobity server (http://molprobity. biochem. duke. edu). It contained 98% of residues within favoured regions of Ramachandran plot, with no outliers. Figures were generated and rendered with Pymol version (Schrödinger, LLC).
Argentina- France ECOS exchange program  ( ECOS A15B01 ) to AJV and PMA. This work was funded by the grant  PICT 2012-1285 , from the  Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT, Argentina ). MNL and AJV are staff members of the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina ). MEL, MM and EG are recipient of fellowships from  CONICET. The NMR spectrometer was supported with grants from  ANPCyT. This work also received support from the  Argentina-France ECOS exchange program  ( ECOS A15B01 ) to AJV and PMA. There are no conficts of interest to report. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright.