dataesr/scientific-paragraphs-categorization huggingface dataset:
Two-Bottle Choice Tests Tests were performed for 12 h at the beginning of the dark period in individually housed mice. Animals were food restricted during the duration of the experiment  [20]. This protocol provides behavioral data combining orosensory sensations (i. e. , oral detection and central perception) and post-ingestive cues. Mice were subjected to a choice between a control solution (0. 3% xanthan gum in water to minimize textural influences) or a 1% sucrose in control solution. At the end of the test, fluid intake was measured for each bottle and the preference (i. e. , ratio between experimental solution consumption and total intake) was calculated.
Statistics Results are expressed as Means ± SEM. The significance of differences between groups was evaluated with R software (v3. 4. 4; The R Foundation, Vienna, Austria). We first checked that the data for each group were normally distributed and that variances were equal. We then carried out either a Student's t-test or a Two-way ANOVA with the Tukey HSD post-hoc test. A principal component analysis (PCA), normalized and centered, was done with R software and the R-commander package (v2. 4. 4) on the different parameters studied.
Such a phenomenon was not observed in lean mice (Figure  1B, C ). Prebiotic supplementation elicited a slight decrease in the energy intake whatever the diet, but this effect was insignificant (Figure  1D ). According to previous data  [23] , blood glucose, plasma triglycerides and cholesterol levels were increased in DIO mice (Figure  1E-G ). Surprisingly in our hands, these systemic changes were not improved in prebiotic-treated mice. In agreement with the literature  [23] , chronic prebiotic consumption increased the caecal tissue mass and, in a lower extent, the fecal mass in caecum (Figure  2A, B ). Prebiotic supplementation is known to modify gut microbiota composition  [24]. To explore whether the bacterial signature was modified by our treatment, cecal bacterial content was studied by qPCR.
As shown in Figure  2C , prebiotic supplementation tended to increase the cecal content of total bacteria whatever the diet. When abundance of selected bacterial displaying beneficial health effect was measured, a rise of Bifidobacteria and Akkermansia abundance was found in the DIO+P group (Figure  2D, E ). We have previously described that ITF feeding promotes endogenous GLP-1 production through higher expression of proglucagon in the colon  [25, 26]. In the present study, we showed that the higher level of its expression was found in the cecal tissue from DIO+P mice (Figure  2F ). Collectively, these data demonstrate the efficiency of our prebiotic protocol on DIO mice. To verify the efficiency of this experimental design, body mass and composition, energy intake, various blood parameters, cecal tissue mass, mass of cecal content and cecal bacteria were analyzed.
Prebiotic supplementation is known to modify gut microbiota composition  [24]. To explore whether the bacterial signature was modified by our treatment, cecal bacterial content was studied by qPCR. As shown in Figure  2C , prebiotic supplementation tended to increase the cecal content of total bacteria whatever the diet. When abundance of selected bacterial displaying beneficial health effect was measured, a rise of Bifidobacteria and Akkermansia abundance was found in the DIO+P group (Figure  2D, E ). We have previously described that ITF feeding promotes endogenous GLP-1 production through higher expression of proglucagon in the colon  [25, 26]. In the present study, we showed that the higher level of its expression was found in the cecal tissue from DIO+P mice (Figure  2F ). Collectively, these data demonstrate the efficiency of our prebiotic protocol on DIO mice.
The Lower Sucrose Preference Found in DIO Mice Was Improved in Presence of Prebiotic The preference for a sweet stimulus was explored using a two-bottle choice test. Consistent with previous published data  [27] , DIO mice subjected to a long-term (12 h) two-bottle preference test showed a significant lower intake (Figure  3A ) and preference for 1% sucrose solution than C or C+P groups (Figure  3B ), suggesting a diet-induced modification of the orosensory perception of the sweet sensation. The prebiotic attenuated the effect of the DIO. Indeed, sucrose intake was higher in DIO-P group than DIO mice, this change improving their preference score to a level similar to C and C+P mice (Figure  3B ).
Protein disulfide isomerases including PDIA1 are implicated in cancer progression, but underlying mechanisms are unclear. PDIA1 is known to support vascular Nox1 NADPH oxidase expression/activation. Since deregulated reactive oxygen species (ROS) production underlies tumor growth, we proposed that PDIA1 is an upstream regulator of tumorassociated ROS. We focused on colorectal cancer (CRC) with distinct KRas activation levels. Analysis of RNAseq databanks and direct validation indicated enhanced PDIA1 expression in CRC with constitutive high (HCT116) vs. moderate (HKE3) and basal (Caco2) Ras activity. PDIA1 supported Nox1-dependent superoxide production in CRC; however, we first reported a dual effect correlated with Ras-level activity: in Caco2 and HKE3 cells, loss-of-function experiments indicate that PDIA1 sustains Nox1-dependent superoxide production, while in HCT116 cells PDIA1 restricted superoxide production, a behavior associated with increased Rac1 expression/activity.
Fig. 2A ). PDIA1 silencing promoted lower superoxide generation in HKE3 and Caco2 cells (Fig. 1e , f), in line with our previous VSMC data  21. Contrarily, in HCT116 cells, PDIA1 silencing increased superoxide production (Fig. 1g ). These results were confirmed using the lucigenin reductase assay (Suppl. Fig. 3 ). Thus, overactivated Ras associates with switch in the pattern of PDIA1-mediated superoxide regulation. We interrogated whether ER stress would be differentially induced by PDIA1 silencing in these cells. However, ER stress marker expression was unaltered by PDIA1 silencing in HKE3 and HCT116 cells (Suppl. Fig. 2B ). Analogous effects occurred in HT29-D4 cells bearing V600E activating mutation on Ras effector Braf; PDIA1 silencing in these cells promoted increased superoxide production Fig.
1  Role of PDIA1 in oxidant generation by colon carcinoma cells with distinct levels of KRas activation. a KRas expression and activity in Caco2, HKE3, and HCT116 cells. Active KRas was pulled down with GST-RBD beads from lysates of serum-starved cells treated with EGF 25 ng/mL for 10 min. Aliquots of the lysates were blotted for total KRas and GAPDH as loading control. b RNAseq analysis of P4HB (PDIA1) gene in Caco2, HCT116, and HCT15 cells, showing mean expression in FPKM (Fragments Per Kilobase Million), P4HB fold-change expression in HCT15 vs. Caco2 (q < 0. 000001) and HCT116 vs. Caco2 (q < 0. 000001). c PDIA1 basal protein expression by western analysis, normalized for GAPDH (n = 3) and quantified using Odyssey software. d Intracellular PDIA1 titration, using Human P4HB ELISA Pair Set (SinoBiological).
Cell evasion was calculated as (T48h total evasion area-T0 initial spheroid area)/T0 initial spheroid area. PDIA1 silencing promoted decrease in cell evasion in HKE3 and no modification in HCT116 (Fig. 4c, d ). These data are consistent with results of a 2D random migration assay in HT29-D4 cells (Suppl. Table  1 ) and are in line with previous data in VSMC  21.
a Nox1, NoxA1, p67phox, NoxO1, and p47phox basal protein expression by western analysis normalized for GADPH. Nox1 immunoblot densities were quantified using Odyssey software. *P < 0. 05, ANOVA plus Tukey's multiple comparisons test (n = 3). b RhoGDIα, RhoA, and Rac1 basal protein expression by western analysis normalized for GAPDH. Rac1 immunoblot densities were quantified using Odyssey software. **P < 0. 01; ***P < 0. 005, ANOVA plus Tukey's multiple comparisons test (n = 3). c Nox1 complex scheme. d Rac1 basal activity, using Rac1 G-LISA activation assay (Cytoskeleton,inc). Test t *p < 0. 05, (n = 2). e Rac1 protein expression by western analysis after 72 h of PDIA1 silencing. Rac1 Immunoblot densities were quantified using Odyssey software **P < 0. 01; ns: not significant, Student's t-test (n = 3).
f Western analysis of NoxO1 and RhoGDIα protein expression before and after PDIA1 silencing, normalized for GAPDH. RhoGDIα immunoblot densities were quantified using Odyssey software, ****P < 0. 001; ns: not significant, Student's t-test (n = 3). g Superoxide production after 72h of PDIA1 silencing in HCT116 cells treated or not with 10 µM of NoxA1ds Nox1's peptide inhibitor. EOH 2-hidroxyethidiium relative superoxide production, si-PDI, si-RNA against PDIA1 protein; Scrmb NoxA1ds, NoxA1ds negative control peptide; NoxA1ds, Nox1 peptide inhibitor. *P < 0. 05; **P < 0. 01, ANOVA plus Tukey's multiple comparisons test (n = 3) target phosphorylation or cleavage enhanced in HCT116 vs. HKE3: Stat3, GSK3β, p70, S6-ribosomal protein, HSP27, Bad, PARP, p38, Caspase-3, and PRAS40. Except for the latter, each of these was affected to some extent by PDIA1 silencing (Suppl.
A recent study in HCT116 cells suggested that Rac1 overexpression/overactivation promotes EMT through Stat3  43. Moreover, Stat3 negatively regulates expression of the epithelial phenotype marker E-cadherin in CRC  44. We investigated whether EMT could be a mechanism by which PDIA1 silencing promoted both decreased cell evasion and lower Stat3 activation in HKE3 vs. no change in HCT116 (Fig. 4 ; Fig. 5a-c ), focusing on E-cadherin protein expression. HCT116 cells expressed Fig. 4  Effects of PDIA1 silencing on spheroid growth and invasiveness. 3D cell proliferation assay: a Representative phase-contrast images of spheroid proliferation with or without PDIA1 silencing; pictures were taken at T0 and T48 h after spheroid formation on methylcellulose media. Scale bar, 500 µm. b Proliferation analysis: spheroid surface area was measured at T0 and T48 h, using ImageJ software.
Spheroid growth was calculated as T48 h spheroid area/T0 spheroid area; between 17 and 27 spheroids were analyzed for each condition. Data are mean fold ± SD, **p < 0. 01; ****p < 0. 0001 vs. HKE3 scrmb, ANOVA plus Tukey's multiple comparison test. c Effect of PDIA1 silencing on cell invasion: representative phase-contrast images of spheroid invasion in 2D fibronectin matrix (10 µM); pictures were taken at T0 and T48 h after spheroids were laid down on matrix. Scale bar, 500 µm. d Spheroid 2D invasion analysis: total spheroid expansion was measured at T0 and T48 h using ImageJ software. Spheroid expansion was calculated as (T48 h total evasion area-T0 initial spheroid area)/T0 initial spheroid area; between 12 and 17 spheroids were analyzed for each condition. Data are mean ± SD, ****p < 0.
Enrichment pathway analysis of protein interaction networks To contextualize our results, we constructed a protein-protein interaction network based upon enrichment pathway analysis, fashioned using string functional association network, in which nodes signify each protein and edges represent protein interactions. Interactions among Nox1, PDIA1, RhoGDIα, Rac1, B-Raf, KRas, GSK3β, STAT3, and E-cadherin were investigated and confronted to the database (Fig. 6a ). The network connecting these nine proteins of interest associated with more edges (16)  than those predicted by the software (4), suggesting that these proteins interact among themselves at a degree higher than that expected from an analogous stochastic group. Such an enrichment indicates that these proteins display some degree of biological connection as a group.
PathScan ® intracellular signaling array Signaling pathways associated with the effects of PDIA1 loss-of-function were investigated by the PathScan ® Intracellular Signaling Array Kit (ref. 7323), which is a slide-based antibody array optimizing the performance of sandwich immunoassays. The kit allows the simultaneous detection of 18 signaling molecules (described in Fig. 5 ), which are either phosphorylated or cleaved. Targetspecific capture antibodies were spotted in duplicate onto nitrocellulose-coated glass slides. Quantification of all spot intensities was performed using ImageJ software.
Cell migration assays spheroids Three-dimensional cell invasion assay was adapted from previously published works  71, 72. Twenty-four hours after cell transfection with PDIA1, si-RNA cells are trypsinized, counted, and re-suspended in complete medium containing 2. 4 mg/ml methylcellulose. In order to start the experiment with equivalent-size T0 spheroids, 1,500 cells per well were seeded for HKE3 vs. 1000 for HCT116. The suspension (100 µl) was added into each well of a Ubottom 96-well plate, allowing the formation of one spheroid per well. Twenty-four hours after plating (T0), spheroids were transferred to a flat-bottom 24-well plate coated with 10 µg/mL fibronectin. Pictures were taken at T0 and T48 h in a Olympus microscope, objective 2X. Invasion was quantified by measuring the area occupied by cells spheroid expansion at T0 and T48 h using ImageJ software.
Spheroid expansion was calculated as: (T48 h total evasion area-T0 initial spheroid area)/T0 initial spheroid area. For the proliferation analysis, 24 h after plating (T0), spheroids stayed on a U-bottom 96-well plate with methylcellulose media, and spheroid surface areas were measured at T0 and T48 h using ImageJ software. Spheroid growth was calculated as: T48 h spheroid area/T0 spheroid area. T0 spheroids out of mean size range were excluded from the analysis.
RNASeq data mining Experiments used: Caco2: SRR1580950, SRR4249634, SRR4249633, SRR4249636, SRR1581012, SRR4249635. HCT116: SRR1636085, SRR3228429, SRR5009474, SRR5297165, SRR1636086, SRR3228430, SRR5009521, SRR5297166, SRR1636087, SRR5009406, SRR5009538, SRR902610. HCT15: SRR1756568, DRR046626, and ERR208903. Experiments of the lineages listed were retrieved from NCBI's SRA through searches during the months of November and December 2017. Samples containing metadata information indicating any type of treatment were discarded. Samples that were included had indications that they were experimental controls or did not show any metadata indicating otherwise. Expression correlation analysis of transcripts between samples was performed using the Poisson Distance and pheatmap functions of the PoiClaClu (v.
1. 0. 2) and pheatmap (v. 1. 0. 8) R packages. Samples that had very different behavior in relation to the majority of the same cell line or similar with different strains other than their own were discarded. That is, according to the position of the sample in the hierarchical clustering procedure and the distance. Quality control was done with FastQC (v0. 11. 5) and MultiQC (v1. 0) with default settings. For sequence mapping, the HiSat2 (v2. 0. 5) aligner and the preformatted index of the reference GRCh38 release 84 of the H. sapiens genome from the Ensembl project was used, including dbSNP (b144) variants, splice site, and exon position information. For transcript assembly, StringTie (v1. 3. 1c) with strict GRCh38 annotation was used. The transcript data were tested for differential expression with the BallGown (v2.
Acknowledgements This project was supported by  FAPESP (Fundaçao de Amparo a Pesquisa do Estado de Sao Paulo) , scholarhip grants:  2013/02070-6  and  2016/00686-8  (to T. C. deB. ),  2014/20595-1 (to P. V. O. ) and  2011/50469-0  (to A. I. S. M. ); and grant  2009/ 54764-6. The authors are members of  CEPID Redoxoma-FAPESP (Fundacaõ de Amparo àPesquisa do Estado de Saõ Paulo) , grant  2013/07937-8. We were also supported by  Fundacaõ Zerbini  and ARCUS. We are grateful to  Professor Walter Kolch MD  ( University College Dublin, Belfield, Dublin 4, Ireland ) and  Helene Maccario  ( Aix-Marseille University, Marseille, France ), for the kind donation of HCT116 and HKE3 cell lines, and to  Victor Debbas  and  Maria Bertoline  ( Sao Paulo University, sao Paulo, Brazil ) for helping with specific experiments.
Study population This study was based on observational routinely-collected data from one home parenteral nutrition referral centre in Lyon, France. Health data were recorded in computerised patient records during clinical practice. In our centre, every patient, who is under home parenteral nutrition for extended period, is routinely hospitalised for nutritional assessment. All short bowel subjects, who were hospitalised for a nutritional this assessment in our unit from January 2007 to October 2016 were screened. Clinically stable patients with a short bowel syndrome for at least 1 year and on home parenteral nutrition were included. Short bowel syndrome was defined as a remnant short bowel length inferior to 200 cm with or without colon resection  1. Owing to the eating difficulties of adult patients with a paediatric short bowel syndrome, these patients were excluded from our study  15.
Nutritional assessment All included patients received a standardised nutritional assessment during a hospital stay of a few days. If a patient received several nutritional assessments, only the data from the most recent hospitalisation were collected. These nutritional assessments are routinely performed in the home parenteral nutrition referral centre of Lyon. The purpose is to ensure that the long-term home parenteral nutrition is appropriate. Nutritional assessment includes body composition analysis and anthropometry, a 3-day food diary, indirect calorimetry, blood and stool tests.
Body composition and anthropometry All anthropometric measurements were performed by medical practitioners. Weight, height, brachial circumference at the centre of the upper limb and triceps skinfold at the same site using an anthropometric calliper were determined. The device used for determination of handgrip strength changed in 2012. Thus, only handgrip strength measured with a Jamar dynamometer data after 2012 was considered. Fat-free mass and fat mass were estimated by dual X-ray absorptiometry (DEXA). Indirect calorimetry (Quark RMR -Cosmed) was performed for 30 min in fasting subjects after discontinuation of parenteral nutrition for at least six hours.
Statistical analysis Statistical analysis was performed using RStudio Version 1. 0. 136 with occasional reliance on 'prettyR', 'RVAideMemoire', 'ggplot2', 'car', 'zoo'  16. Owing to the small number of patients, non-parametric statistics were used. Data are presented as median and 95% confidence interval. All P-values below 0. 05 were considered significant. The relationship between oral energy intake and the following 24 variables were examined: age, sex, short bowel syndrome causes, bowel anatomy, remnant small bowel length, duration of short bowel syndrome, resting energy expenditure, body weight, fat-free mass, fat mass, brachial circumference, skinfold, hand strength, plasma albumin, thyroidstimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), CRP, parenteral energy intake, number of parenteral infusions per week, volume of parenteral infusion stool weight, one-hour D-xylose blood concentration and citrullinemia.
Our results also showed that the duration of short bowel syndrome is not associated with oral energy intake at least in the included patients, which are on parental nutrition for at least one year. The main limitation of our study is its retrospective design. This limitation is related to the difficulty of collecting prospective data in short bowel syndrome patients owing to the low population size. To limit the bias of recruitment and ensure consecutive recruitment during the study period, we screened all patients with a nutritional assessment in our unit. We analysed only fat-free mass data assessed by DEXA to ensure the homogeneity of the results. Oral energy intake determination was based on a 3-day diary and one concern is therefore possible misreporting of oral energy intake. Nevertheless, good agreement between objective and subjective assessment of oral energy intake in the short bowel syndrome population has been previously shown  7.
Funding: This work was supported by the national  SERAPIS program  ( Optimization of the Sulphur Fertilization: Development of Innovative Fertilizers and Indicators of Sulphur Nutrition in Plants). The SERAPIS program is coordinated by the private company TIMAC AGRO INTERNATIONAL-ROULLIER Group and benefits from financial support from the  Région Basse-Normandie ,  Région Bretagne ,  Région Pays-de-la-Loire ,  FEDER Basse-Normandie ,  Fonds Unique Interministériel ,  Roullier ,  Angers Loire Métropole  (grant number  12P03057 ). This work was also supported by a grant to  Philippe D'Hooghe  from the  French Ministry of Research. Research in S. K. 's lab is funded by the  Deutsche Forschungsgemeinschaft  ( EXC 1028 ).
The bottom plots in each panel represent the cumulative reductions in reactivation required to meet the targets calculated as ωðtÞ ¼ 1 À ωðtÞ=ωð2002Þ. Clearance of infection upon successful treatment: θ = 1. Other parameters as in Table  1. Model described by Eqs. (  1 )-(  5 ), and R 0 given by (6)  datasets should be interrogated in search for answers, but this is potentially due to especially intense social protection programs implemented over recent decades in Brazil  [21] [22] [23] [24] [25]  , leading to improved health conditions in population segments classically more at risk for TB. The parameters that have been most commonly varied to explain incidence trends in modeling studies are rates of successful treatment (τ) and mean effective contacts (β)  26. For completion and comparability with other studies we conceive additional scenarios where the observed declines in incidence are attributed to decays in τ and ω (Supplementary Fig.
Acknowledgements The  Bill and Melinda Gates Foundation  is acknowledged for its support through grant project number  OPP1131404. M. G. M. G. and J. F. O. received additional support from  Fundação para a Ciência e a Tecnologia  ( IF/01346/2014 ), and M. G. M. G. and D. A. from the  European Union 's  Horizon 2020 research and innovation programme  under grant No  733174  (IMPACT TB).
C K-edge NEXAFS spectra collection The micro-spectroscopic data were collected on the scanning transmission X-ray microscope end-station of the HERMES beamline (SOLEIL synchrotron), a particularly well-adapted beamline for carbon K-edge investigations. 20, 21 The X-ray beam was focused to 38 nm using a 30 nm Fresnel zone plate and the transmission signal was collected using a photomultiplier tube. The C1s→π* transition in the NEXAFS spectra of a PC71BM sample  22  at 284. 43 eV was used for energy calibration. The stacks of images were collected on fields of view from 8 µm to 15 µm large, scanning the sample with 50 nm steps in both horizontal and vertical directions at each energy increment over the energy range of C K-edge. Each stack was composed of 165 images, covering the energy range between 270 eV and 350 eV, with a sampling step set of 1 eV from 270 eV to 280 eV, 0.
C K-edge NEXAFS data analyses The stack of images was analysed using aXis2000 (http:// unicorn. mcmaster. ca/aXis2000. html)  23 and Mantis (http://spectromicroscopy. com)  24  software. The images were aligned using the alignment routine of the Stack Analyze program (Jacobsen/Stony Brook). The transmission images were then converted into optical density (OD) scales according to the following equation: OD = -ln(I/ I 0 ) = σt = µρt, where I 0 represents the incident X-ray flux measured on a free area of the sample holder, I represents the measured transmitted intensity at the pixel of interest, σ is the linear absorption coefficient, t is the thickness, µ is the mass absorption coefficient and ρ is the density. An image taken before the edge is considered as background and subtracted from the others.