dataesr/scientific-paragraphs-categorization huggingface dataset:
| Pairing sensory data with PTR-MS Figure  3  illustrates the individual projections on the RGCCA axes for PTR-TDS, TDS, PTR-TCATA, and TCATA. Each point represents an evaluation (product*subject*replicate). In addition to the visual analysis of the maps, the MANOVA F indicates that the product discrimination is better in TDS (48. 84) than in TCATA (24. 96). Furthermore, the discrimination is also better in PTR-TDS (94. 06) than in PTR-TCATA (78. 45). TDS seems to be slightly more correlated to PTR-TDS (R_1 = 0. 71, R_2 = 0. 72, RV = 0. 51) than TCATA to PTR-TCATA (R_1 = 0. 67, R_2 = 0. 64, RV = 0. 44). All RV coefficients were found highly significant (p < 0. 001). The two chemical blocks seem to be well correlated (R_1 = 0. 89, R_2 = 0. 4, RV = 0. 64), whereas the two sensory blocks were clearly less correlated (R_1 = 0.
| DISCUSSION This paper can be considered as a tutorial to analyze sensory and physico-chemical data conjointly with adapted pretreatments and a multiblock method. Furthermore, the example dataset allows to compare TDS and TCATA towards their propensity to fit PTR-MS data. This question was never addressed in the literature, although it could be of interest for both flavor chemists and sensory scientists. F I G U R E 5 Temporal dominance of sensations (TDS) and temporal check all that apply (TCATA) curves. The dotted line represents the chance line, and TDS dominance rates are significant (p = 0. 05) above the gray zone.
| Multiblock approach RGCCA is very flexible and can be used to answer a large number of multiblock problematics. Here, RGCCA was successfully used with a simple approach with four blocks. Other multiblock methodologies could have been investigated. For example, the two PTR-MS datasets could have been averaged in order to provide a "chemical" standard, then a three-block approach could have been considered. This would have led to more stability of the PTR-MS block but to the loss of the structure of the experiment. Another analysis could consist in explaining the duration of one single given descriptor (for example Cocoa) by the whole physico-chemical block. In this case, the sensory block should be constituted of the single Cocoa variable. Sparsity parameters can also be used in order to select a set of ions/variables responsible for this sensation.
A crucial question in multiblock approach is the choice of the algorithm parameters (C, g, τ j ) and also the choice of the number of components. This paper focused on an analysis with the first two components because two axes were sufficient to observe the main differences between the three products (assuming that interproduct variability is greater than intraproduct variability). These first two components are the more informative ones, but other components could also contain information and could deserve further investigations. For other datasets, the number of components should also be cautiously chosen. Cross-validation or elbow criterion strategies could be considered. The use of RGCCA allowed several blocks to be related together then correlations between blocks to be measured. It returns a physico-chemical and a sensory component having the highest covariance.
| Sensory discussion The originality of our approach in this dataset is to use chemistry as a tool to compare TDS and TCATA results. The product configurations were similar in both methods: C was found longer Cocoa/Woody, A longer Fruity, and B longer Spicy. In terms of univariate product discrimination, TCATA returns more significant descriptors than TDS and more pairs of different products that is in agreement with Ares et al. 11  However, the F statistics are clearly higher in TDS for Cocoa, Woody (discriminating C from A and B), Spicy (discriminating B from A and C), and Fruity (discriminating A from B and C) suggesting that the differences between the products were expressed differently in the two protocols: TCATA highlighted more product differences, whereas TDS highlighted stronger product differences.
20 Another possible explanation of the better discrimination in TDS could be the better discrimination in PTR-TDS (F = 94. 06) than in PTR-TCATA (F = 78. 45): The differences between TDS and TCATA could be due to real differences in the perceived discrimination during the two protocols. This hypothesis appears quite unlikely but not impossible. The results obtained in this example do not aim to be generalized to sensory analysis. It is specific to this protocol, these products, these subjects (only 16), and these descriptors. Further works would be necessary to reach conclusions on sensory protocols, and the methodology presented in this paper could be adapted to other datasets pairing sensory and physico-chemical methods (with other products, descriptors and more subjects) in this purpose.
| Further steps in pairing TDS and TCATA: Taking temporality into account The analysis conducted in this paper presents a pairing of sensory and chemical data using durations of sensory descriptors and total intensity of ions during the tasting period. This approach does not take the temporality into account and the time-by-time comparison of ions and sensations. In the example presented in this paper, it was not an issue as no sensorial sequentiality was observed. However, it should certainly be relevant with datasets being more complex and several perspectives can be considered. First, instead of considering matrices (evaluations as lines and descriptors or ions as columns), one could consider tensors (with the time as a third dimension) and the tensorial extension of RGCCA named Multiway Generalized Canonical Correlation Analysis.
21 This implies some technical issues as to have the same time discretization in PTR and TDS or TCATA, still dealing with breathing correction. Then, functional analysis  22, 23  also exists to take properly the temporality into account. These types of methods do not systematically require to have the same time points and could be another solution to take temporality into account. This work presents an automatic pipeline of pretreatment of PTR-MS data and the application of the RGCCA method to analyze sensory and physico-chemical data conjointly. Both pretreatment and RGCCA were successfully applied on a dataset with TCATA and TDS evaluations paired with nosespace measured by PTR-MS. TCATA and TDS gave similar results, but TDS was shown to preserve the product configuration slightly more than TCATA in this study.
The former classifications: strengths and shortcomings The HC classifications reported by authoritative textbooks and recent reviews and guidelines are based on a schema (Table  3 ) in which numbers and letters reflect the chronology of first descriptions of genotype-phenotype correlations. 51, 71, 85, 86 Four types are included, with type 2 and type 4 further subdivided into subtypes A and B. They have the advantage of being very informative from a molecular point of view and officially endorsed by OMIM (the Online Mendelian Inheritance of Man database), but also present several caveats and inconsistencies. The main limitations of the current classifications are listed below. Poor applicability in clinical practice (limitations due to costs and lack of widespread expertise) Apart from the genetic test looking for the common HFE variants, the identification of the molecular defect causing rarer forms of HC is currently offered by few laboratories, heterogeneously distributed and scattered worldwide.
19e diagnostic possibilities in rarer HC while at the same time opening enormous challenges of interpretation of the results. NGS is generally available only at referral centers and requires specific expertise to avoid misdiagnosis, with a long wait time for results (see below). Nonetheless, treatment of patients with a defined HC phenotype should not be delayed pending the result of the genetic test. Recently, NGS methods have also made it possible to estimate the global prevalence of HFE and non-HFE HC in different populations,19as summarized in Table2. Combined pathogenic allele frequency for HC genes in the 1000 Genomes Project (1000G), Exome Sequencing Project (ESP), and Exome Aggregation Consortium (ExAc) datasets.
Acknowledgments The authors thank  Paul C. Adams ,  Edouard Bardou-Jacquet ,  Patricia Bignell ,  Barbara Butzeck ,  Clara Camaschella ,  Robert Evans ,  Robert Fleming ,  Tomas Ganz ,  Olivier Lor  eal,  Giacomo Marchi ,  Elizabeta Nemeth ,  Antonello Pietrangelo ,  Alberto Piperno ,  John D. Ryan ,  Mayka Sanchez ,  Paulo Santos ,  Dorine W. Swinkels , and  Heinz Zoller  for critically reviewing and editing the manuscript.
Data Collection The clinical and biochemical data were extracted from the patient's electronic medical records from each participating hospital. The following data were collected for each patient: gender, date of birth, weight, height, body mass index (BMI), IBD type [Crohn's disease (CD) or ulcerative colitis (UC)], smoking status, disease extent based on the Montreal classification (the severity of relapse was classified into four disease activity/severity as clinical remission mild, moderate, and severe, as previously described), IBD medical treatments (including oral and local 5-aminosalicyclic acid [5-ASA], corticosteroids, antibiotics, immunosuppressants, tumor necrosis factor-α inhibitors [anti-TNF], vedolizumab, ustekinumab, and tofacitinib), previous or ongoing iron deficiency therapies (oral or intravenous, dosage, and duration), reason for hospitalization, type of hospitalization (traditional, diurnal, weekday, or following a visit), and CRP concentrations  [16].
Neonatal nutritional supplements may improve early growth for infants born small, but effects on long-term growth are unclear and may differ by sex. We assessed the effects of early macronutrient supplements on later growth. We searched databases and clinical trials registers from inception to April 2019. Participant-level data from randomised trials were included Nutrients 2022, 14, 392. https://doi. org/10. 3390/nu14020392 https://www. mdpi. com/journal/nutrients if the intention was to increase macronutrient intake to improve growth or development of infants born preterm or small-for-gestational-age. Co-primary outcomes were cognitive impairment and metabolic risk. Supplementation did not alter BMI in childhood (kg/m 2 : adjusted mean difference (aMD) -0. 11[95% CI-0. 47, 0. 25], p = 0.
Search Strategies We searched Ovid MEDLINE, Embase, Cochrane Library Central Registry of Controlled Trials, and Cochrane Database of Systematic Reviews from inception to 1 April 2019. We also searched for eligible ongoing trials in Current Controlled Trials (www. controlled-trials. com), Clinical Trials (www. clinicaltrials. gov), the Australian and New Zealand Clinical Trials Registry (www. anzctr. org. au), and WHO ICTRP Search Portal (https://apps. who. int/ trialsearch/) on 30 January 2021 (Search Table  S10 ).
Secondary outcomes were (1) Growth assessments: weight (raw data and z scores), length/height (raw data and z scores), head circumference (raw data and z scores), Ponderal Index, BMI, body composition (fat mass, fat-free mass, measured by bioimpedance or (Dualenergy X-ray absorptiometry) DXA, skinfold thickness, or other methods); (2) Bone development: bone mineral content, volumetric bone mineral density, bone fractures; (3) Nutrition: feeding tolerance; intake (protein, energy); appetite; breastfeeding and duration.
Data Synthesis and Statistical Analysis Trialists provided de-identified data, which were recoded as required, verified, and checked for consistency with published data. Each final trial dataset was verified by the original trialists before analysis individually using IPD-MA pre-specified variables and outcomes, and the results were returned to the trialists for verification. The individual trial datasets were then combined for IPD meta-analysis. There was no imputation for missing data. We used a one-stage approach to analyse each outcome so that the IPD from all eligible trials were included in a single random effects model. Binary outcomes were analysed using log-binomial regression models, and data were reported as Relative Risk (RR) with 95% CIs and associated 2-sided p-values. We changed the algorithm to encourage the models to converge where specified.
Continuous data were analysed using linear regression models, and data were reported as mean differences (MD) with 95% CIs and associated 2-sided p-values. All models included adjustment for pre-specified confounders. The analyses of IPD were adjusted for sex, gestational age, and birth weight z-scores. We explored the effects of infant sex by presenting data separately for each sex as prespecified subgroups and by testing a treatment by identifying sex interaction terms within the model. Statistical analyses were performed using SAS (v. 9. 4, SAS Institute, Cary, NC, USA). We validated the one-stage model using a two-stage approach in RevMan 5. 3  [21].
Search Results We identified 7288 records after de-duplication. Title and abstract screening left 271 records for full-text screening. We excluded 62 records that did not meet the inclusion criteria (Table  S11 ). The remaining 99 potentially eligible trials (209 records) were included, among which 44 trials had published post-discharge data, and 55 trials did not. We attempted to contact the authors of all 99 trials. The authors of 21 trials agreed to share their IPD. Of these, 19 trials with 2473 infants had post-discharge growth outcome data, and an additional 17 trials with 1808 infants for which IPD data were not available had published growth outcomes (Figure  1 ). Details of the studies are described in Table  1. Included studies were of variable methodological quality (Table  S1 ).
Our IPD-MA has some limitations. We identified 42 eligible trials with published post-discharge growth outcomes. However, due to local ethics restrictions, missing datasets or investigators declining to share data, 25 of these trials could not be included. To address this issue, we conducted sensitivity analyses combining both IPD and AD. Most of these analyses gave results consistent with the analysis of IPD only, except for the growth outcomes in infancy, where supplementation appeared to improve early growth in infancy only in the analyses of combined IPD and AD. Furthermore, a post-hoc calculation showed that the analysis of IPD alone (338 children) had sufficient power to detect a 0. 55 difference in BMI in childhood with 80% power and alpha of 0. 05, 5% type I error. Therefore, it is unlikely that clinically important effects of supplementation were missed.
Acknowledgments: We would like to acknowledge  Alan Lucas ,  Mary Fewtrell , and  Atul Singhal  for assisting with individual trial data and interpreting the findings,  Julie Brown  for help with developing the search strategies and study selection,  Robyn May  for help with establishing contacts with the  ESSENCE IPD-MA collaborators ,  Mariana Muelbert  and  Laura Galante  for help with translating the  ESSENCE IPD-MA documents , and  Gordon Liu  for help with assessing the quality of the included studies.
To date, there are three validly published studies that reported the safety of use and the beneficial role of A. muciniphila in obese humans as a probiotic  (12, 23, 24) , while two clinical trials are in progress to evaluate the effects of the use of A. muciniphila in obese patients with type 2 diabetes and in hyperglycemic adults (NCT04797442/NCT05114018). The purpose of this review is to summarize what is currently known about A. muciniphila in terms of both the phenotypical and the genotypical characteristics, as well as its culture methods. We will also discuss its relationships with many human diseases. And most importantly, we will discuss the already established human trials and those that are still in progress focusing on and its possible use as a promising probiotic.
Lactobacillus plantarum, Oral 13 individuals Obesity Increase in the abundance (157) Streptococus thermophiles, supplementation (6 of A. muciniphila after the Lactobacillus acidophilus, weeks) intervention Lactobacillus rhamnosus, Bifidobacterium lactis, Bifidobacterium longum, and Bifidobacterium breve. Clinical Prebiotics oat b-glucans 5 gr of oat ß-glucan 40 participants with Type 2 Follow up on A. NCT04299763 trials in (12 weeks) type 2 diabetes diabetes muciniphila levels in fecal progress mellitus microbiota using (qPCR) dietary fiber formulation supplementation 20 healthy x Explore the change in A. NCT03785860 with 15g/day fiber participants muciniphila gut abundance. powder (1 month) Acetate Supplementation 10 patients on stable Depression/ Encourage the growth of A. NCT05022524 with Acetate (Apple dose of antipsychotic anxiety muciniphila Cider Vinegar) (5 medication for months).
muciniphila. to improve liver cancer effect of immunotherapy Lactobacillus Bifidobacterium V9 46 Non-small Cell Cell Lung NCT05094167 Lung Cancer Cancer Patients receiving immunotherapy fecal microbiota capsules x 20 participants with Advanced 1-Selection of donor of NCT04924374 Advanced Lung lung cancer fecal microbiota based on Cancer Treated their fecal abundance in F. With prausnitzii, B. longum, A. Immunotherapy muciniphila and Fusobacterium spp. 2-Manipulating the microbial populations to enhance the efficacy of immunotherapy. DS-01 (microbial consortia 2 capsules daily (12 100 men or women Irritable Evaluate changes of A. NCT04598295 consisting of 24 strains across 12 weeks) with IBS with bowel muciniphila and other species) constipation syndrome species A. muciniphila orally given A.
60 overweight/obese Obesity/Type Evaluate the effects of A. NCT04797442 muciniphila WST01 and drug naïve type 2 diabetes muciniphila WST01 strain strain powder with 2 diabetes patients in overweight or obese maximum live patients with T2D. bacteria of 5*10^10 CFU/g (12 weeks) A. muciniphila Daily oral dose of 98 hyperglycaemic Dysglycaemia demonstrate the efficacy of NCT05114018 pasteurized A. healthy adults pasteurized A. muciniphila muciniphila (120 (pAKK) in improving days) insulin sensitivity.
In situ high-pressure visible absorbance measurements High-pressure data were analysed using two approaches: The first approach was based on studying the HP-induced disappearance of the fine structure of the peak at 560 nm. To perform such analysis, it was necessary to determine the λ start of the beginning of the peak at 560 nm. λ start corresponded to the local minimum in the 2 nd derivative spectrum. The specific area of the fine structure of the peak at 560 nm (A s ) in the basic spectra was determined in the interval between λ start and λ end , where λ end represented the end of the fine structure of the 560 nm band. The absorbance at λ start and λ end was the same, and it has been used as a baseline to calculate A s. The total area (At) was calculated similarly to As but without baseline subtraction.
CD spectroscopy measurements: Thermal unfolding of R-PE Thermal unfolding of R-PE was studied by measuring ellipticity at 222 nm. Experimental data were normalised between 0 and 100 % using the equation: 𝑤 (% 𝑜𝑓 𝑢𝑛𝑓𝑜𝑙𝑑𝑒𝑑 𝑅 -𝑃𝐸) = 𝜃 𝑠𝑡𝑎𝑟𝑡 -𝜃 𝑇 𝜃 𝑠𝑡𝑎𝑟𝑡 -𝜃 𝑒𝑛𝑑 (𝟖) where θ start , θ end and θ T , represents ellipticity in mdeg at 32 °C, 95 °C, and a given temperature, respectively. R-phycoerythrin unfolding data, obtained by far-UV CD spectroscopy, were fitted into a two-transition model, as described previously  (Greenfield, 2006) , with slight modifications: -1)-ln() () 𝑦 = 𝑘 1 𝑘 1 + 1 * In all parameters, 1 and 2 in subscripts correspond to the first and second transition during R-PE unfolding, respectively. C, k, T, T m , and H represent R-PE concentration (7. 5x10 -8 M), unfolding constant, the variable temperature in Kelvin, melting temperature in Kelvin and enthalpy of unfolding in cal/mol, respectively.
Statistical analyses Statistical analyses were performed using the software RStudio Desktop ® v. 1. 2 (RStudio Inc. , Boston, MA, USA). To test for differences in the extractive contents among branches, knots and bark wood from AF and FC walnut trees, the Kruskall-Wallis test was applied to each potentially explicative factor and response variable. When an effect of the factor on a studied variable was detected, Wilcoxon test was used to investigate the nature of this effect and the significance of the differences between the groups of modalities in the descriptive variables. NIRS spectra were first transformed using a Standard Normal Variate (SNV) correction  (Naes et al. 2004 ) to reduce the effect of irregularities of surface and the intra spectrum variability (correction of the light dispersion).
The second derivative was then computed using the Savitzky Golay algorithm with a smoothing range of 11 data points and a third degree polynomial  (Savitzky & Golay 1964). The use of this derivative allows to separate overlapping peaks and correct the baseline deviation of spectra. Mathematical corrections were applied using the package "prospectr" v. 0. 1. 3 in the R environment. Principal component analysis (PCA) was applied through the package "FactoMineR" (v. 1. 34) on the transformed NIRS spectra to detect possible differences in chemical composition between samples and the presence of groups within the tested samples (raw samples of AF and CF branch, knots and bark). NIRS models were developed using the Partial Least Squares -Discriminant Analysis regression method (PLS-DA) using the package "mdatools" v.
Acknowledgements This work is part of the  AGROBRANCHE  ( 2018-2021 ) project ("Study of the valuation of branches in agroforestry for bio-based materials and green chemistry "), supported by the  French Environment & Energy Management Agency (ADEME). The authors gratefully acknowledge the  GDR 3544 "Science du Bois"  for its financial support of the STSM attributed to RD to carry out the LC-MS analyses at the  LERMaB, University of Lorraine-Nancy (France).
Since the oral route is obviously preferable for the comfort of patients in human treatment (specially to old, demented, multi-medicated patients), absorption of TDMQ20 by the gastrointestinal tract was evaluated by comparison of blood concentrations after oral and IV administration, and the ability of TDMQ20 In order to document the pharmacological data on this lead molecule, we investigated the pharmacokinetics of TDMQ20 in rats and evaluated the concentration of TDMQ20 within the brains of treated rats. Since the oral route is obviously preferable for the comfort of patients in human treatment (specially to old, demented, multi-medicated patients), absorption of TDMQ20 by the gastrointestinal tract was evaluated by comparison of blood concentrations after oral and IV administration, and the ability of TDMQ20 to reach the brain, its target organ, was evidenced by comparison between blood and brain concentrations after oral administration.
LC-MS/MS Method A triple-quadrupole mass spectrometer (Thermo Fisher, Waltham, MA, USA) was connected to a Thermo Fisher Ultimate 3000 HPLC system (Thermo Fisher, USA). Data collection was controlled by Thermo Scientific Xcalibur software. Samples were separated on a Hypersil GOLD C18 column (50 × 2. 1 mm, 1. 9 µm) at 40 • C. The mobile phase was a gradient of (A) 0. 1% formic acid aqueous solution and (B) methanol. The gradient elution was as follows: from 0 to 1 min, A/B = 90/10, then a linear gradient from A/B = 90/10 at 1 min to A/B = 10/90 at 3 min, followed by 3 min (from 3 to 6 min) at A/B = 10/90 (Table  S1 ). Flow rate: 0. 3 mL/min. Injected volume: 1 µL. All analytes were quantified after optimization of MRM mass spectrometry, with detection in positive ion mode, with selected parameters and considered fragmentations detailed in Tables  S2  and S3 , respectively.