dataesr/scientific-paragraphs-categorization huggingface dataset:
Use of NeOse Pro to Discriminate the Two Yoghurt Mix Three cultures were made with the two yogurts mix N1 and N2 with the three theoretical bacterial ratios (ST/LB, 1/99, 50/50, 99/1). Experiments were made with microfiltered half skimmed milk. NeOse Pro data were obtained after a 7 h culture step. They were successively analysed by PCA (results not shown) and HCA (Figures  6(a)-(c) ). The data were analysed separately since the discrepancy between the different ratios was not evident, as indicated later in the article. The two yogurt mix appeared clearly separated whatever the ratio considered. A REP-PCR was performed on the four bacterial strains. Figure  7  shows the agarose gel after staining. The four strains appeared clearly distinct, LB vs ST and STN1 vs STN2 or LBN1 vs LBN2. They were then supposed to lead to different results after fermentations steps.
28 molecules were found at significant levels, among them the most important were: acetaldehyde, 2,3-butanedione, ethyl acetate, 3-hydroxy 2-butanone. NeOse Pro data were crossed with the GC/MS results and analysed by PCA and CHA, the former being considered as the main parameters whereas the latter were just additive data (not shown). Out of the 28 main molecules detected by GC/MC analysis, only one-acetaldehyde-was clearly linked with the same set of sensors, whatever the bacterial mix considered (Table  3 ). Acetaldehyde was detected by sensors C19, C13, C44 and C45. However, following the mix, some other sensors could be excited as well-C4, C14, C51, C6, C46 for N1; C39 for N2. For the other molecules, no clear relation was noticeable. For example, ethyl acetate, was linked to C1 to C12, C14 to C18, C20, C21, C24 to C31, C33 to C38, C40, C41, C43, C46 to C56 sensors, for N1, and C58 to C62 sensors for N2.
Both biotic and abiotic batch tests were performed in water sampled from the BN borehole and in the presence of Opalinus Clay, stainless Steel, and (for the biotic tests) a microbial community, ail originating from the in situ BN experiment. No additional carbon source or électron donor were provided, to ensure biogeochemical conditions as close as possible to those prevailing in situ. The acquired data from the batch tests were used to develop a model to predict the behavior of sélénium in Opalinus Clay.
The flow cytometer was operated with Milli-Q water (Merck Chemicals, Overijse, Belgium) as sheath fluid. Samples were analyzed in a fixed volume mode of 50 gL, and the minimal threshold was fixed on the green fluorescence (FL1-H at a relative intensity of 1000). Data were analyzed with the R programming software version 3. 6. 3 with the package Phenoflow  [40]. At day 7 of the abiotic batch experiment, 100 gL of the samples was spread on LB agar medium (lysogeny broth medium; Thermo Fisher Scientific, Geel, Belgium) and in cubated under oxic conditions at 30 °C for 3 weeks to verify abiotic conditions. At the end of the abiotic batch experiment, cultures were diluted 1/10 in APW supplemented with 0. 2 mM NaH2P04-2H20 and 1 g Na-lactate to enable growth of sulfate reducing microorganisms. The total cell number was monitored after 18 and 101 days with flow cytometry.
The DNA concentration was determined with a Quantifluor dsDNA sample kit (Promega, Leiden, The Netherlands). High-throughput amplicon sequencing of the V3-V4 hypervariable région of the 16S rRNA gene was performed with the Illumina® MiSeq platform according to the manufac turées guidelines at BaseClear B. V (Leiden, The Netherlands). DNA sequencing data were processed using the OCToPUS pipeline (Optimized CATCh, Mothur, IPED, UPARSE> and SPAdes)  [41] , which consists of the following steps: quality filtering using the Hammer algorithm implemented in the SPAdes tool  [42] , merging reads using the make. contigs command from the open source software package Mothur (version 1. 39. 1), alignment and filtering of the merged reads following the standard operating procedure as described by the authors of the Mothur software (version 1.
39. 1), error correction using IPED  [43] , chimera identification using CATCh  [44] , and OTU (operational taxonomie unit) clustering using UPARSE (version 7. 0) with a 97% cut-off  [45]. Subsampling was performed based on the lowest number of reads obtained over the ten different samples, i. e. , a coverage of 29,061 reads. Raréfaction curves indicated that this level of subsampling adequately represented the bacterial diversity in the samples (Ligure SI, Supplementary Materials). The datasets generated and analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) repository (PRJNA717984).
Modeling Geochemical modeling was undertaken using the geochemical code PHREEQC  [46]  to aid the interprétation of the experimental data. The modeling considered various processes relevant to the batch and in situ experiments: Se spéciation, anion diffusion, Se(VI) réduction kinetics, and Se(IV) sorption. Modeling was focused on the in situ experiment and considered each of these processes in turn to develop a reactive-transport model of increasing complexity. Where relevant, the effect of spécifie processes was tested against the batch experimental data as detailed below.
Se Spéciation A simplified spéciation model considering two species, Se(VI) and Se (IV), was used for the majority of the modeling to compare with the experimental data of these species. The two species were defined as separate components and master species in the PHREEQC input file. This approach enables modeling of reaction kinetics (Section 2. 7. 3) between the two main species that are confirmed to be présent by the chemical analyses. Représentation of selenite as a single species also enables the utilization of an existing Se(IV) sorption model (Section 2. 7. 4). PHREEQC calculations were also undertaken with a full aqueous spéciation model using the Thermochimie database (version 9)  [47] , which includes thermodynamic data for Se from Olin et al. [48]  to examine the hydrolysis of Se(VI) and Se(IV) and the occurrence of Ca and Mg ion pair species under Opalinus Clay conditions.
SEM-EDX analyses were performed on stainless Steel filters and clay pièces after finishing the abiotic tests. The main results of sample Ab_Se(IV)-CF_a are shown in Figure  3. More results from this and other samples are shown in a supplementary dataset (Figure  S3 , Supplementary Materials). Based on these results, Se was présent on the stainless Steel filter surface in the abiotic tests with selenite only when clay was also présent (Figure  3 ). Clear deposits of Se, more or less spherical in shape, could be observed, though the amount was limited. This is in agreement with the limited decrease in Se(IV) and total Se in the suspensions (Figure  2 ). EDX spot analysis shows that these spots were mostly containing Se. In these spots, oxygen was not found, while this was the case when performing EDX spot analysis on the stainless Steel itself (Figure  S3 , Supplementary Materials).
2 mM C and remained constant afterwards. No changes in TIC concentration and no acetate was detected in the second in situ injection test. SEM-EDX analyses were performed on the clay sample from the clay loop that was installed in the circuit of Interval 3 during both injection tests with Se(VI) (data not shown). However, due to the low amount of Se injected and the high surface area throughout the in situ experiment, Se was only found on the clay in only very limited amounts. This as well as the limitations of the SEM-EDX technique prevent a conclusive interprétation regarding the spéciation of the sorbed or precipitated Se species. The experimental Se data were split in two graphs, one for each injection test, in order to visualize the ~12 times higher initial Se(VI) concentration of the second injection test.
Acknowledgments: This work was undertaken in close coopération with Swisstopo, the operator of the  Rock Laboratory  and the management team at the  Mont Terri project. Financial support was provided by the  Mont Terri Consortium. The technical assistance of  Steven Smets ,  Veerle Van Gompel , and  Wim Verwimp  (SCK CEN) is greatly appreciated, as is the help in the conceptualization and interprétation of the work by  Hugo Moors ,  Mohamed Mysara ,  Caria Smolders , and  Natalie Leys  (SCK CEN). Finally, we thank the team from the radiochemical analysis (RCA) unit at SCK CEN, and  Kristof Tirez  and  Filip Beutels  ( VITO ) for performing the Chemical analyses.
Quantitative structure-property relationships (QSAR) Next, the drugability of the quinone-based 4c, tetrazole-based organic selenide 9, and the pseudopeptide-based organoselenium compounds 11, 12, and 15 were in silico explored using HyperChem (8. 1) software. The latter is used to estimate different pharmacokinetic and physicochemical parameters. These in turn reflect the relationship between molecular structure and attributes. Selected conformers of compounds 4c, 9, 11, 12, and 15 as well as ebselen as a reference, were faced optimization process. Configuration was started by adding hydrogen atoms, setup Molecular mechanics (MM + ), and semiempirical (AM1), then energy minimization proceeded. The method was executed with free parameters under the ''Polake-Ribiere conjugated gradient-algorithm"  (Althagafi et al.
Consequently, to start the docking process, each side  (compound & protein)  faced orientation steps to be ready for proceeding. First of all, each compound was configured as follows; i) adding hydrogen atoms, starting energy minimization, and then render atomic charges and potential energies. Moreover, other significant parameters were regulated by the MMFF94x force field. So, the oriented compound may be saved as a new database by MDB format. After that and regarding each selected protein, faced sequential orientation steps starting by adding H-atoms over receptor sites. Then, the receptors were connected before fixing the potential energy. The final stage concerning site-finder to fix the active receptors and dummies over the protein helix. Accordingly, the docking process can be started to complete 30 poses for each method.
Acknowledgments The author thanks  Mansoura University , the  Science and Technology Development Fund (Egypt) , the  Embassy of France (Egypt). The author thanks the  Conseil Re´gional de Bourgogne (France)  and the  Ministe`re de l'Enseignement et de la Recherche (France). This article is based upon work from  COST Action CA16112-NutRedOx (Personalized Nutrition  in aging society: redox control of major age-related diseases), supported by  COST (European Cooperation in Science and Technology).
Acknowledgments: The authors would like to thank the association " Mediterranean Nutrition and Health (NMS: Nutrition Méditerranéenne & Santé )". A. Y. received financial support from  NMS  and awarded the NMS prize in 2019. A. N. is currently post-doc fellow in the laboratory after granting at in cotutelle with the  University of Burgundy  and the  University of Tunis El Manar (Tunis, Tunisia ). We also thank the  UNESCO Chair , wine culture and tradition, Dijon, for its continuous interest.
Data variables, data sources and data collection Data encoders and/or medical staff recorded the data from the patient's clinical files, and these were entered in a digital archive (Microsoft Excel v97-2003; Microsoft, Redmond, WA, USA). Patient files were crosschecked against the digital archives, and corrections made as necessary. Laboratory testing, specifically rapid diagnostic testing for cholera and stool culture samples, was not available during the entire study period. Study variables included registration number, age, sex, place of origin, cholera test result, nutritional status and type of treatment given. The rehydration treatment plan (Table  1 ) was used as a proxy to determine the dehydration status of the patient (Plan A: no.
Analysis and statistics The electronic data were exported to and analysed using EpiData software v2. 2. 3. 187 (EpiData Association, Odense, Denmark). De-scriptive statistics were used for patient characteristics. The CFR was calculated. We analysed CFR using cross tabulation and Fisher's test and mean LOS using the Kruskall-Wallis test. Significance levels were set at 5% (P  0. 05).
28 However, in the Yemenese study the proportion of severely dehydrated children was higher, whereas we had more moderately dehydrated children. 19 ur study used both weight-for-height and MUAC to diagnose malnutrition, although MUAC is the recommended measurement of nutritional status for children with dehydration. 29 Nutritional status should ideally have been assessed using MUAC on admission, followed by a reassessment with weight-for-height on discharge. Also, as only patients below 5 years of age were routinely assessed for malnutrition in our study, complete and reliable data were not available for older children. We also found that children without dehydration stayed in the CTC longer than those who were dehydrated. This is probably due to concomitant diseases, but our data does not confirm this.
The small number of deaths, however, in our study does not allow any association between nutritional status and clinical outcome in children with cholera to be made. A strength of our study was the setting of a busy CTC that included all admissions during the outbreak. There were, however, some limitations. Our study only enrolled patients from one of three CTCs, and we are not sure if the study site was representative of the other two. Our study used both weight-for-height and MUAC to diagnose malnutrition, although MUAC is the recommended measurement for children with dehydration. 29 We were only able to fully analyse data for children below 5 years, as the CTC only routinely assessed these patients for malnutrition. The inclusion of all children aged 15 years in future studies would make analyses and conclusions more robust and could be useful for clinical care as well.
However, treatment was interrupted and ceased due to tyrosine kinase inhibitor-related side effects  [125]. Finally, should long-term follow-up from studies on ablation therapy demonstrate a potential cure, then it may provide a less invasive option to surgery, but only in cases where tumors are surgically inaccessible or where an attempted surgical excision might be too risky  [114, 115]. Burosumab, a fully human, monoclonal antibody to FGF23, recently received approval by the US Food and Drug Administration (FDA) for the treatment of patients with TIO who cannot undergo surgical removal of tumors  [100, 101]. Approval was based on two single-arm Phase II studies (NCT02304367, NCT02722798), in which burosumab was associated with improvements in serum phosphate, osteomalacia, mobility, quality of life, and fatigue  [102, 103].
Study design and participants The RESCUE study (restored enterohepatic signaling: chyme reinfusion therapy) is a prospective study with a pre-and post-intervention design conducted at Rennes University Hospital (CHU Rennes) and Clinique Saint Yves, Rennes, France (a tertiary referral center for intestinal rehabilitation of patients prior to restorative surgery) from January 2017 to July 2018. The study was registered at clinicaltrials. gov (NCT02990195). Eligible participants were adult IF patients aged 18 years or older with a temporary double enterostomy Accepted Article intended to receive CR as primary care. Further inclusion criteria were a minimum of 25 cm of healthy distal small intestine that was suitable for CR. We excluded patients with mental disability, pregnancy or lactation, hepatocellular carcinoma, auto-immune disorders, blood coagulation disorders, or shock of any cause.
Data collection and definitions Data were collected and managed using a customized Access Database (Microsoft, Redmond, WA, USA). The following items were recorded: gender, age, height, body weight, body mass index (BMI), underlying diseases, afferent small intestinal length (from duodeno-jejunal flexure to the proximal stoma), efferent small intestinal length (from distal stoma to terminal part of the remnant efferent small intestine), resected length, efferent small intestinal anatomy, energy (kcal/kg) and protein (g/kg) intake (parenteral, oral, and enteral) adjusted by body weight, and duration of CR and PN. We measured proximal stoma or fecal output daily in all patients before and after CR initiation, respectively. Percent weight loss was assessed as follows:. None of the patients was diagnosed with ascites during CR ℎ  (%) = 100 * ( ℎ - ℎ)  ℎ treatment.
Serum liver tests (alanine aminotransferase, ALT; aspartate aminotransferase, AST; alkaline phosphatase, ALP; γ-glutamyl transferase, GGT and total bilirubin), albumin and C-reactive protein (CRP) were evaluated by routine assay at the Department of Clinical Biochemistry at Maastricht University Medical Center (Maastricht, The Netherlands). For baseline comparisons of TBS, FGF19 and C4 levels, own published data on twelve healthy controls (mean age 55 years, 33% female) was used. 23.
5 system (Roche, Basel, Switzerland). qRT-PCR data was analyzed using LinRegPCR software,  24  and gene expression levels were normalized to the geometric mean of 4 reference genes (36B4, PSMB4, REEP5, and HPRT1). Employed primer sequences are provided in Supplementary Table  S1. Paired ileal biopsies were available from seven of twelve patients. In remaining patients, (paired) biopsies could not be gathered for logistic reasons (n = 2), last-minute patient refusal (n = 1), or transcript data was excluded from analyses due to a non-enterocytic transcript signature (n = 2).
Statistical analysis Initially, twenty patients were planned for enrollment in this study. A planned interim-analysis was performed after enrollment of two thirds of patients (n=13). One patient was not included in the study after enrollment because he received enteral nutrition as enteroclysis into the downstream small bowel, before starting the protocol. On the interim-analysis (n = 12), the primary endpoint (FGF19 level) had significantly changed (P <. 005) justifying early termination of the study. For blood analyses, five out of seventy-two time Accepted Article points were missing (6. 9%). Multiple data imputation was used to handle the missing data. The mean of day -3 and day 0 measurements were taken as baseline values. Data are expressed as mean ± standard deviation (SD), median (interquartile range), or frequency (percentage) where appropriate.
Graphical data are expressed as mean ± 95% confidence interval (CI). Differences between IF patients and healthy controls were evaluated by Mann-Whitney U test. Correlations were evaluated by the Pearson (r) or Spearman's (ρ) correlation coefficient dependent on data distribution. For continuous variables, changes during CR were evaluated by paired-samples t test or Wilcoxon matched-pairs signed ranks sum test dependent on the data distribution. Categorical variables were analyzed using Fisher's exact test. To evaluate longitudinal changes, ANOVA or Friedman's test for repeated measurements, corrected using Dunn's multiple comparisons test, were used. Linear mixed models were performed to identify whether the FGF19 or C4 response predicts the evolution of markers for intestinal absorptive function (CIT) and liver injury (ALP and GGT).
Acknowledgements We are grateful to  Loes Nijssen  for laboratory assistance. We thank all the staff from the  Dept. of Digestive Surgery, the 'Direction de la Recherche Clinique et de l'innovation' and the "Centre de Ressources biologiques" of Rennes University Hospital  for their help in the performance of the study. We are very grateful to  Mr. Michel Fournier , former director of the  Société Labodial  and creator of the Enteromate, which for 30 years was the only device available for chyme reinfusion.
α/β; GCDCA-3S, glycochenodeoxycholate-3-sulfate; Financial Support: This work was supported by the  ESPEN   Research Fellowship   2015-16  ( Kiran Koelfat ), The Dutch Society of Gastroenterology 2014-15 (Kiran Koelfat) and The  Netherlands Organization for Scientific Research   NWO 022. 003. 011 ( Kiran Koelfat ). Xinwei Chang  is supported by the  China Scholarship Council  ( 201707040095 ).
Recombinant GSs and BRSs Produced by E. coli In April 2021, 790 sequences were recorded in the GH70 family of CAZy database (compared to only 264 in November 2015  [48] , see http:/www. cazy. org/GH70, accessed on 24 July 2021). However, less than 60 sequences of biochemically characterized enzymes are listed (Table  1 ). This number is very small, and many new and original enzymes are likely to be discovered in the future. To date, the database records 40 enzymes classified as dextransucrases, α-1,6 glucosyltransferases or α-1,6/α-1,3-glucosyltransferases (representing 77% of listed GH70 sucrose active enzymes); seven as mutansucrases or α-1,3 glucosyltransferases (13. 5%); two as reuteransucrases (3. 8%); two as alternansucrases (3. 8%) and only one as a branching sucrase (1. 9%) (Figure  1 ).
Finally, two enzymes containing two catalytic domains both featuring the characteristics found in GH70 family have been described to date. In these enzymes, one domain shows GS activity and the other one BRS activity. This is the case of the DSR-E enzyme from Ln. citreum NRRL B-1299 in which the catalytic domains 1 and 2 (CD1 and CD2) code for a dextransucrase activity and an α-1,2 branching activity, respectively  [77]. Similarly, the recently discovered gtfZ glucansucrase from Lb. kunkeei DSM 12361 also possesses a dextransucrase domain and an additional domain dedicated to dextran branching via α-1,3 osidic linkages  [78]. Table  1. List of GH70 characterized sucrases recorded in the CAZy database in April 2021 (in bold) as well as few others reported in the literature. Enzymes are classified according to their specificity: R for reuteransucrases, M for mutansucrases, D for dextransucrases, A for Alternansucrases, BRS for branching sucrases.
[158]  and the six 3D structures available (red: domain V; yellow: domain IV; green: domain B; blue: domain A; purple: domain C). PDB IDs: 3KLK (glucansucrase GTF180-∆N from Lb. reuteri 180,  [158] ), 3AIB (mutansucrase GTF-SI-catalytic-core from Streptococcus mutans,  [157] ), 4AMC (reuteransucrase GTFA-∆N from Lb. reuteri 1213,  [160] ), TTQ (branching sucrase ∆N123-GBD-CD2 from Ln. citreum NRRL B-1299,  [19, 153] ), 5NGY (dextransucrase DSR-M∆2 from Ln. citreum NRRL B-1299,  [23] ), 6HTV (alternansucrase, ASR∆2 from Ln. citreum B-1355,  [124] ). The catalytic domain A comprises the structural elements constituting the catalytic (β/α)8 barrel, at the top of which is found the catalytic triad (Figure  5 ). The nucleophile, acid/base and TSS are positioned between strand β4 and helix α4, strand β5 and helix α5, and strand β7 and helix α7, respectively.
Enzyme Engineering for Man-Made α-Glucans, Oligosaccharides and Glucoconjugates The number of GS and BRS sequences available in the databases is increasing incredibly and in parallel their biochemical characterization is also expanding. In this context, what is the benefit of generating artificial catalysts, and if there were good reasons for doing so, what would they be? First, biochemical characterization is expanding at a much slower pace than the acquisition of biochemical data, which remains rather limited compared to the potential offered by these enzymes. Second, extensive profiling of linkage and acceptor specificities is limited only to few representatives. Third, even if GS and BRS promiscuity is broad, glucosylation yields can sometimes be rather low due to competition with natural reactions.