dataesr/scientific-paragraphs-categorization huggingface dataset:
5 mM of rhenium compound  [31]  (PDB id 5K1N). Interestingly, crystals soaked with a low concentration of rhenium derivatives gave structures that diffracted to 1. 7 Å resolution, showing a new wt-TTR conformation. Both structures show a Re atom that coordinates with His88 in monomer B and B', Figure  2a. The structural analysis highlighted that the tetramer is well conserved even if the monomers B and B' in the segment comprised of residues 72-94 (residues 72-90 correspond to EF-helix) are shifted, opening the thyroxin binding site B-B' while shrinking A-A' site, Figure  2a, b. This TTR conformation has never been observed before, even if its effect in the central channel is attributable to the well-known negative cooperativity of TTR  [33]. Another region of TTR, usually characterized by considerable structural variations, is located between residues 94 to 104 (FG loop).
0 ammonium sulphate and 200 mM of zinc acetate. The C-α r. m. s. d. among the structures (PDB id: 3DGD, 3GPS, 3GRB, 3GRG) is less than 0. 4 Å, indicating that they are very similar to each other, Figure  3a. The binding of Zn 2+ with ZBS1 involves the two amino acids Cys10 and His56 and it does not lead to any significant conformational change. This suggests that the allocation of Zn 2+ in ZBS1 is physiological and may prevent amyloidogenic character, Figure  3b [40]. conformation obtained in the presence of Re, where the EF-helix swings away from the T4 binding site opening the dimer B-B', may represent the tetramer conformation that is able to interact with the Aβ peptide. This consideration is strengthened by previous studies, where it has been observed that Leu82 (EF-helix) and Leu110 (strand G) are key residues for the interaction between TTR and Aβ  [35, 36].
0 ammonium sulphate and 200 mM of zinc acetate. The C-α r. m. s. d. among the structures (PDB id: 3DGD, 3GPS, 3GRB, 3GRG) is less than 0. 4 Å, indicating that they are very similar to each other, Figure  3a. The binding of Zn 2+ with ZBS1 involves the two amino acids Cys10 and His56 and it does not lead to any significant conformational change. This suggests that the allocation of Zn 2+ in ZBS1 is physiological and may prevent amyloidogenic character, Figure  3b [40]. Moreover, the occupation of ZBS1 does not produce any effect on TTR-RBP interaction (the residues of TTR involved in the binding with RBP are Arg21, Val20, Leu82 and Ile-84)  [41]. In contrast, in the presence of a higher Zn 2+ concentration, the binding of Zn 2+ to Crystals 2021, 11, 354 6 of 12 ZBS2 (Hys88, Hys90 and Glu92) and ZBS3 (Glu72, Asp74 and Hys31) Figure  3b  induces slight structural rearrangements around the α-helix at all tested pH.
These modifications are comparable with those detected in other TTR structures crystallized without metals but at acidic pH  [42]. Contrary to the effect observed for ZBS1, the involvement of ZBS2 and ZBS3 and their corresponding conformational changes affect the interaction of TTR with RBP  [40]. TTR L55P is considered as one of the most aggressive amyloidogenic variants that accelerate pathology onset. Structural studies report that apo-TTR L55P, as well as TTR L55P in complex with 2,4-dinitrolphenol (DNP), possess the typical tetrameric structure. The only detected change is local in the monomers, where, due to a disorder of the short edge strand D, an extended loop between strands C and E appears  [43, 44]. The crystal structure of TTR L55P (PDB id: 3SSG), in complex with Zn 2+ , does not show significant differences compared to apo-TTR, TTR L55P-DNP and the M-TTR-Zn 2+  [45].
Ca 2+ The calcium ion, Ca 2+ , is one of the most important metals involved in the regulation of cellular signalling pathways and tissue homeostasis. Several studies report that the dysregulation of Ca 2+ is a key factor in the triggering of neurodegenerative processes  [57]. TTR binds Ca 2+  [58] , and X-ray studies do not show any relevant structural changes in the wt-TTR tetrameric structure. TTR-Ca 2+ crystal complexes were obtained by sitting drop vapour-diffusion from a solution containing 100mM HEPES, 200 mM CaCl 2 , 28% PEG400, pH 7. 5 (PDB id: 4MRB, 200 mM Ca 2+  [59] ) and 34-40% PEG 400, 400 mM CaCl 2 , 0. 1 M HEPES, pH 7. 5 (4N85, 400 mM Ca 2+ )  [60]. The superposition of the two structures reveals that there is a common Ca 2+ site between Glu66 and Asp99 of monomer A, while a second site is detected around N-terminus portions in the crystal at higher Ca 2+ concentrations, Figure  5a, b.
, Ltd. (Shanghai, China). Brie y, the 16S rRNA gene sequencing data was ltered and trimmed, and further classi ed into operational taxonomic units (OTUs) within a 0. 03 difference (equivalent to 97% similarity). A representative set of sequences was then generated and was assigned taxonomy using the SILVA database (Release115 http://www. arb-silva. de). Analyses for rarefaction curves, and calculation of richness estimators and diversity indices were performed using the MOTHUR program. Taxonomic community structure and phylogeny were analyzed through visualization of the data sets of the microbial diversities and abundances of different samples.
A HP-5MS fused-silica capillary column was utilized to separate the derivatives. Helium was used as a carrier gas at a constant ow rate through the column. The samples were analyzed in a random sequence. The acquired GC/MS data were imported to SIMCA Statistical Analysis (version 13. 0, Umetrics AB, Umeå, Sweden), where multivariate statistical analysis including principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were performed. The differential metabolites were determined by the combination of the Variable importance in the projection (VIP) value (> 1) of PLS-DA model and the P values (< 0. 05) from two-tailed Student's t test on the normalized peak intensities. Fold change was calculated as a binary logarithm of the average normalized peak area ratio between the two groups.
1 M, pH 7. 4) and 0. 5 mM of TMSP-d4. Samples were added to a 5-mm NMR tube for immediate acquisition. 1D proton nuclear magnetic resonance ( 1 H-NMR) spectra acquisition was performed using the Inova Agilent NMR spectrometer (Agilent Technologies Inc. , Santa Clara, CA, USA) operating at a frequency of 600 MHz, equipped with a triple resonance cold probe at a constant temperature of 298 K (25 • C). A total of 256 scans were collected with 32-k data points over a spectral width of 8000 Hz. An acquisition time of 4 s and 1. 5 s relaxation delay was incorporated between scans, during which a continual water pre-saturation radiofrequency field was applied. After data acquisition, spectroscopic data pre-processing was performed. Manual spectral processing, which included Fourier transform, phasing correction, baseline correction, water region deletion, shim correction, apodisation (line broadening with lb~0.
3) and referencing control were applied before the profiling was performed. The identification and quantification of the metabolites were made by computerassisted manual fitting using the Chenomx RMN Suite software (Chenomx Inc. , Edmonton, AB, Canada). To avoid bias, samples were randomly profiled blindly to the evaluator, and identified metabolites fit each spectrum and double-checked by another evaluator. Also, the metabolites found were crosschecked consulting the Chenomx and The Human Metabolome Database (http://hmdb. ca (first accessed on 1 February 2021). The resulting sample profiles consisting of each metabolite and its concentration were presented in millimoles per litre/mg of liver tissue and analysed.
After that, the membranes were probed with secondary antibodies conjugated with peroxidase, and bands were visualised using a chemiluminescent reagent (ThermoFisher Scientific, Waltham, MA, USA). The membrane images were captured using an image system (Amersham Imager 600, GE Healthcare, Uppsala, Sweden), and band volume quantitation was quantified by Gel-Pro Analyzer software (Media Cibernetics, Inc. , Rockville, MD, USA).
Statistical Analysis The data analyses were performed by two-way ANOVA, followed by Bonferroni post-hoc test to correct multiple comparisons. We direct the discussion with the following comparisons: WC vs. WW, to extract the tumour effects in the weanling animals; AC vs. AW, to extract the tumour effects in the young adult animals; and WW vs. AW, to extract the age-related differences in the tumour-bearing animals. The student's t-test was applied to comparisons between WW vs. AW for cachexia indexes and relative tumour weight. Data are expressed as means ± standard deviation (SD) and considered significant when p ≤ 0. 05 (Graph Pad Prism software, version 5. 0, San Diego, CA, USA).
The K IS values were lower than K I , indicating that curcuminoid 5 acts as a competitive-uncompetitive mixed-II type inhibitor  [21, 23]. These data indicated that the affinity of the inhibitor for the enzyme-substrate complex was stronger than that for the free enzyme. Cosmetics 2021, 8, x FOR PEER REVIEW 7 of 10 curcuminoid 5 acts as a competitive-uncompetitive mixed-II type inhibitor  [21, 23]. These data indicated that the affinity of the inhibitor for the enzyme-substrate complex was stronger than that for the free enzyme. In addition, the inhibitory type of curcuminoid 17 on mushroom tyrosinase using Ltyrosine substrate was determined by the same methods. Amongst them, the double-reciprocal plots of the enzyme inhibited by curcuminoid 17 are shown in Figure  3. The results showed that curcuminoid 17 could bind, not only with free enzyme, but also with the enzyme-substrate complex.
The competitive effect was stronger than the uncompetitive effect, meaning that curcuminoid 17 inhibited the free enzyme more than the enzyme-substrate complex. Cosmetics 2021, 8, x FOR PEER REVIEW 7 of 10 curcuminoid 5 acts as a competitive-uncompetitive mixed-II type inhibitor  [21, 23]. These data indicated that the affinity of the inhibitor for the enzyme-substrate complex was stronger than that for the free enzyme. In addition, the inhibitory type of curcuminoid 17 on mushroom tyrosinase using Ltyrosine substrate was determined by the same methods. Amongst them, the double-reciprocal plots of the enzyme inhibited by curcuminoid 17 are shown in Figure  3. The results showed that curcuminoid 17 could bind, not only with free enzyme, but also with the enzyme-substrate complex. The values of both Km and Vmax decreased with increasing inhibitor concentrations and the KI values were lower than KIS, indicating that 17 acts as a competitive-uncompetitive mixed-I type inhibitor  [23, 24].
Data Availability Statement: Not applicable. Acknowledgments: This work was supported by the Junior Research Fellowship Program, French Embassy, Thailand, the Center of Excellence for Innovation in Chemistry (PERCH-CIC), Ministry of Higher Education, Science, Research and Innovation, and The Research Unit in Synthetic Compounds and Synthetic Analogues from Natural Product for Drug Discovery (RSND), Burapha University. Supports from Ramkhamhaeng University and Burapha University are gratefully acknowledged. Conflicts of Interest: The authors declare no conflict of interest.
Mass spectrometry data interpretation The recorded MS/MS spectra were searched for peptide matches against a database comprising the 3796 annotated proteins from S. bentonitica BII-R7 (UniProt database proteome ID UP000175905)  (Sánchez-Castro et al. , 2017). Peak lists were generated using MASCOT DAEMON software, version 2. 5. 1 (Matrix Science). Search parameters were set as follows: trypsin as the proteolytic enzyme and a maximum of 2 missed cleavages, 5 ppm mass tolerance for the precursor and 0. 02 Da mass tolerance for the MS/MS signals, fixed modifications for carbamidomethylation of cysteine residues, and glutamine and asparagine deamidation and methionine oxidation as variable modifications. Mascot results were parsed using IRMa software, version 1. 31. 1c (Dupierris et al. , 2009).
Peptide-to-spectrum matches were assigned with a p value of 0. 05, and proteins were validated with the detection of at least two different peptide sequences. Comparative analysis of protein abundances based on total spectral counts were performed by calculating the T-Fold and t-test p-values (Excel formula, Microsoft) previously developed for the PatternLab software  (Carvalho et al. , 2008) , and following standard normalization. The proteins were classified according to their T-Fold and p-values. Only proteins with T-Fold ≥1. 5 or ≤-1. 5 and with a p-value ≤0. 05 (defined here as the "Blue class") were retained. The estimated detection of the spectral counts and their abundances were visualized by grouping them into a hierarchical cluster tree using the R package pvclust (n = 2000 bootstrap), utilizing the war.
D method and spearman distance, to establish the similarity/dissimilarity between the samples. The Pearson correlation coefficient was calculated using the R package reshape2. Proteins that passed a cut-off of -0. 9 and 0. 9 were used to build an interaction network in Cytoscape, version 3. 8. 2 using the Betweenness Centrality of each node. MS proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository  (Perez-Riverol et al. , 2019)  with the dataset identifier PXD023459 and project DOI 10. 6019/PXD 023459.
General overview of shotgun proteomics The nanoLC-MS/MS analysis of the 18 samples yielded 1,151,245 MS/MS recorded spectra, of which 682,785 could be assigned to peptide sequences of S. bentonitica BII-R7 proteome with a p-value ≤0. 05. The high assignment rate (60%) indicated good quality MS/MS spectra. The spectra were assigned to 12,150 unique peptide sequences, which represented 1922 proteins validated by at least two independent peptides matches. The entire experimental dataset corresponded to ~50% of the predicted proteome of the strain. Hierarchical clustering analysis was used to determine the similarity of the inter-sample protein profiles. Se (IV)-treated and untreated samples grouped into two different clusters, which in turn could be divided into three subgroups corresponding to each growth phase (Fig.
Acknowledgements This work was supported by the grants obtained by M. P-C: ( FPU 15/ 04284 ; " Formación de Profesorado Universitario ") and  EST 18/00610  ("Ayudas a la movilidad para estancias breves y traslados temporales") from  Spanish Ministry (Ministerio de Universidades ). Funding was also provided by grants  CGL2014-59616-R; RTI2018. 101548. B. I00  to  M. L. M. Funding  for open access charge: University of Granada / CBUA. The authors thank  Concepción Hernández-Castillo  and  María del Mar Abad Ortega  for their assistance with microscopy at the  University of Granada ("Centro de Instrumentación Científica"). We also thank  Dr. Kenneth McCreath  for editorial support.
Randomisation A permuted block randomisation (1:1 ratio) was made using an Interactive Web Response System. The randomisation list was generated by an external statistician and kept confidential at the sponsor's premise to ensure allocation concealment. Subjects were automatically assigned to the Test or Control group. The allocation was blinded to investigator and sponsor. An independent access was provided to the technician responsible for product preparation. 2. 9. Statistical Analysis 2. 9. 1. Clinical Outcomes Analyses were performed using SAS 9. 4 software on the Full Analysis Set (FAS) population. The primary and secondary outcomes were described by product group and by visit. Due to the low number of AAD observed, no statistical tests were performed on related outcomes. A descriptive analysis of the number of days with GI-symptoms and of GSRS scores (total, individual, and by symptom dimension scores and their change from V2) was performed.
Gut Microbiota Statistical analyses were performed, and graphs were plotted with R software (version 3. 6. 0). Total bacterial count (as log 10 -transformed), alpha diversity, Shannon index ITS/16S ratio were analyzed as change from baseline using linear mixed model (nlme 3. 1-140) as described above or using Mann-Whitney or Wilcoxon signed rank test when normality was rejected. Beta-diversity was analyzed using PERMANOVA (vegan 2. 5-6) and pairwise group mean dispersions using Tukey HSD. Quantitative Microbiome Profiling (QMP) was done as described by Vandeputte et al. [10]. Differential analyses were performed with DESeq2 (version 1. 24. 0) on 16S based QMP and ITS datasets. Fold changes were evaluated with the Wald test (FDR adj. p < 0. 1). p-values were adjusted for multiple testing with the Benjamini-Hochberg procedure (p adj ).
Gut Mycobiota Response to Hp Treatment and Product Intervention We monitored fungal dynamics across the study. While there was no change in ITS Shannon index in response to Hp treatment (Figure  4A ), we observed a transient increase in the fungi to bacteria (ITS/16S) Shannon ratio at V4 (p adj = 5. 79 -03 ) which then reverted to baseline at V7 (p adj = 0. 864), with a trend for a lower ITS/16S ratio (p = 0. 08) in the Test group (Figure  4B ). The abundance of Candida was transiently enriched following 14-day Hp treatment (p adj = 1. 81 -25 ), followed by a recovery to baseline level (V2 to V7, p adj = 0. 467), with no difference between groups (Figure  4C ). Additional data are provided in Supplementary Materials and Figure  S6. indicates a higher fold-change and blue a lower fold-change in Test group (C) Heatmap of differentially abundant genera between follow-up (V7) and baseline (V2) which behave differently in the two groups.
Gut Mycobiota Response to Hp Treatment and Product Intervention We monitored fungal dynamics across the study. While there was no change in ITS Shannon index in response to Hp treatment (Figure  4A ), we observed a transient increase in the fungi to bacteria (ITS/16S) Shannon ratio at V4 (padj = 5. 79 -03 ) which then reverted to baseline at V7 (padj = 0. 864), with a trend for a lower ITS/16S ratio (p = 0. 08) in the Test group (Figure  4B ). The abundance of Candida was transiently enriched following 14-day Hp treatment (padj = 1. 81 -25 ), followed by a recovery to baseline level (V2 to V7, padj = 0. 467), with no difference between groups (Figure  4C ). Additional data are provided in Supplementary Materials and Figure  S6 ).
From V6 to V7, two weeks after product consumption ceased, the results showed a trend of stabilization (for major SCFA) or an increase (for minor SCFA) of the concentrations in the Test group and a systematic increase of both in the Control group. This resulted in a similar non statistically different levels of all SCFA concentrations between groups at V7. As compared to baseline, concentrations at V7 were mostly lower, with a significant statistical difference for major SCFA in the Test group and for minor SCFA in both groups (Figure  5  and Supplementary Figure  S7 ). Serum contents of SCFA showed no difference between groups at any timepoint between V2 and V6 (data not shown). Fecal calprotectin was quantified in 73 subjects (35 and 38 subjects in Test and Control groups, respectively).
This resulted in a similar non statistically different levels of all SCFA concentrations between groups at V7. As compared to baseline, concentrations at V7 were mostly lower, with a significant statistical difference for major SCFA in the Test group and for minor SCFA in both groups (Figure  5  and Supplementary Figure  S7 ). Serum contents of SCFA showed no difference between groups at any timepoint between V2 and V6 (data not shown). Fecal calprotectin was quantified in 73 subjects  (35 and 38  subjects in Test and Control groups, respectively). Calprotectin concentration evolved similarly in both groups with a significant increase from baseline (V2) at the end of Hp treatment (p < 0. 0001) (Mean (SD): 68. 1 (44. 8) and 55. 4 (37. 3) µg/g at V4, in Test and Control, respectively), followed by a decrease at V6 (p < 0.
, 2011). Good feeding tolerance was defined as the ability of the preterm infant to safely ingesting and digesting the prescribed enteral feeding without complications associated with aspiration, infection, and gastrointestinal dysfunction  (Shulman et al. , 1998). In group 1, three babies developed the disease within the first 3 weeks of life (hereafter called early-onset NEC), while the remaining three developed NEC 6-8 weeks after birth (hereafter called late-onset NEC). Each baby belonging to group 1 NEC was matched with two babies, the first one belonging to group 2 FI and the second one to control group 3 GDT. In order to reduce uninformative variations that could interfere with the identification of relevant information encoded in the experimental spectral dataset, controls were selected according to matched gestational age (± 1 week), birth weight (± 200 g), and postnatal age at the time of urine sampling (± 7 days).
Proton Nuclear Magnetic Resonance Spectroscopy Analysis The analysis was conducted at 300K by using a Varian UNITY INOVA 500 spectrometer (Agilent Technologies, CA, United States) operating at 499. 839 MHz. A standard 1-D pulse sequence NOESY was used with water suppression. For each urine spectrum, a total of 128 scans were collected in 64k data points over a spectral width of 6,000 Hz using a relaxation delay of 2 s, an acquisition time of 1. 5 s, and mixing of 0. 1 s. Before Fourier transformation, the free induction decay was multiplied with 0. 3 Hz exponential line broadening spectra. All spectra were phased, and baseline corrected using MestReNova (Version 8. 1, Mestrelab Research SL, Santiago de Compostela, Spain). The chemical shift scale was set by assigning a value of δ 0. 00 ppm to the internal standard TSP signal.
After correction for misalignments in chemical shift, primarily due to pH-dependent signals and deleting the regions containing the water and TSP signals, the NMR spectra were binned into 0. 0025 ppm buckets over a chemical shift range of 0. 5-9. 5 ppm. Bins were normalized to the sum of total spectral area to compensate for the overall concentration differences and used as a dataset (97 x 4,423) for multivariate analysis. The assignment of the metabolites in the 1 H NMR spectra was performed according to literature data  (Diaz et al. , 2016; Scalabre et al. , 2017) , the Human Metabolome database, available at http://www. hmdb. ca (Wishart et al. , 2018) , and Chenomx NMR suite 8. 1 software (evaluation version, Chenomx, Edmonton, Canada). All the samples were analyzed simultaneously.
Data Processing and Statistical Analysis For multivariate statistical analysis, data were Pareto scaled  (Misra, 2020). Multivariate statistical analysis of the NMR dataset consisted of principal component analysis (PCA), orthogonal projection to latent structures (OPLS) regression, and orthogonal projection to latent structures discriminant analysis (OPLS-DA) supported by the SIMCA software (version 16. 0, Umetrics, Umeå, Sweden). OPLS was used in the case of a continuous Y-matrix (i. e. , multiple time points) and OPLS-DA for identifying discriminant metabolites in a pairwise comparison between case and control groups. The quality of OPLS and OPLS-DA models were evaluated through the following parameters: the cumulative values of total Y explained variance, i. e. , goodness of fit (R 2 Y), and the Y predictable variation, i.
, 2005). The univariate statistical analysis was performed by the GraphPad Prism Statistics software package, version 8. 1. 2 (GraphPad Prism Software Inc. , San Diego, CA, United States), to measure the Pearson's correlation coefficient between metabolites and postnatal time and compare the variation in the abundance of discriminant metabolites between groups. The magnitude of variation was evaluated by calculating the effect size (ES) adjusted for small sample number  (Berben et al. , 2012). Effect sizes were classified small between 0. 2 and 0. 5, medium between 0. 5 and 0. 8, and large when greater than 0. 8. The non-parametric Mann-Whitney U test was used for the univariate statistical approach; a pvalue ≤ 0. 05 was considered statistically significant.