dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are thankful to  Dr Sylvain Trépout  ( Institut Curie, Orsay, France ) for assistance in cryoTEM experiments,  Ms Stéphanie Denis  ( Institut Galien Paris Saclay, Châtenay-Malabry, France ) for training in cell culture, and to  Prof. Nereide Stela Santos-Magalha ẽs  ( Immunopathology Laboratory Keizo Asami, Federal University of Pernambuco, Recife, Brazil ) and  Dr François-Xavier Legrand  ( Institut Galien Paris Saclay, Châtenay-Malabry, France ) for helpful discussions.
Half-maximal inhibitory concentration values (IC 50 ) were determined using ICEstimator software (http:// www. antimalarial-icestimator. net). The amodiaquine survival assay (AQSA) was based on a similar assay for piperaquine  [21]. Tightly synchronized ring-stage parasites (0-3 h postinvasion) were exposed to 200 nM monodesethyl-amodiaquine for 48 h and maintained for a further 24 h in drug-free medium. Live parasites were then enumerated microscopically from Giemsa-stained thin blood films on examination of ≥ 10 000 erythrocytes. Parasite survival following exposure to mono-desethyl-amodiaquine was determined as a percentage relative to untreated controls.
Amodiaquine Resistance and Association With Molecular Markers Investigation of potential molecular markers associated with amodiaquine resistance used an expanded data set including culture-adapted P. falciparum isolates from this study plus 34 culture-adapted clinical isolates collected from sentinel sites between February 2017 and February 2018 (n = 10 Kampong Speu, n = 13 Mondulkiri, n = 3 Pursat, n = 8 Ratanakiri). Pfcrt and Pfmdr1 were sequenced using whole genome sequencing with Illumina paired-reads sequencing, according to published protocols  [19, 20]. After processing, data were integrated into the Whole-genome Data Manager (version 2. 0)  [19, 20]. Single-nucleotide polymorphisms (SNPs) were investigated using Phen2gen software  [20].
Statistical Analysis Data were analyzed with Excel StatX and Graphpad Prism (version 8. 3. 0). ACPR was evaluated using Kaplan-Meier survival curves and associated 95% confidence intervals (95% CI) and compared using the log-rank test (Mantel-Cox). IC 50 and AQSA values versus clinical outcome were compared using the Mann-Whitney test. Resistance thresholds for IC 50 and AQSA parasite survival were determined with receiver operating characteristic (ROC) analysis. Kruskal-Wallis tests were used to identify significant differences in Pfcrt-Pfmdr1 haplotype AQSA parasite survival results. Significant P values were <. 05.
Notes Author contributions. R. L. , P. R. , F. A. , and B. W. contributed to the concept and design of the study. M. M. -K. , C. M. , N. K. , S. K. , S. Ke, C. K. , N. Kl, R. E. , S. C. , and B. I. were involved in data acquisition and M. M. -K. , R. L. , C. M. , N. K. , D. M. B. , M. D. B. , P. R. , F. A. , and B. W. in data analysis and interpretation. All authors critically reviewed the manuscript, approved the final version, and take full responsibility for the publication. Acknowledgments. Naomi Richardson of Magenta Communications Ltd developed a first draft of this article from the statistical output, collated author contributions, provided graphic services, and was funded by the World Health Organization. Disclaimer. The funding source was not involved in the design and conduct of the study, the interpretation of the results, and the development of this publication.
To visualize the SOCE, cells were placed into the quartz cuvette and 0. 5 EGTA was added just before the beginning of the recordings. To induce the SOCE, the cells were treated with 1 µM thapsigargin (TG) during 10 min in Ca 2+ -free HBS to induce Ca 2+ release from the ER and the opening of SOCE channels  [7, 45]. Then, 1 mM CaCl 2 was added to measure the change in [Ca 2+ ] cyt subsequently to Ca 2+ influx  [45]. In the dose-response experiments, different concentrations of 2-APB and its analogs were added 30 s prior to Ca 2+ readdition  [7, 9]. In experiments with PHA, cells were placed in the cuvette, and 0. 9 mM CaCl 2 was added to reach a 1 mM extracellular concentration. Free Ca 2+ concentrations were calculated using the Bad4 software  [46].
This has resulted in a number of recent omics studies identifying potential biomarkers of DKD  8  , but not in novel drugs yet. This could be attributed to the fact that potential targets need to be transformed into drugs which often takes a substantial amount of time and effort. In addition, most studies employed genomic and transcriptomic strategies which do not necessarily reflect changes in proteins that are the major drug targets  9. In the present work we adopted a bioinformatic strategy of drug repurposing based on the analysis of protein signatures, aiming at finding new drugs that would complement the widely used RAAS blockers in DKD treatment. To this end, we established the glomerular protein signature of DKD in a mouse model of type I diabetes treated with ACEi versus control untreated animals; this signature was then compared with a database of thousands of molecular signatures of potential bioactive compounds (Connectivity MAP: https ://porta ls.
broad insti tute. org/cmap/). Connectivity Map (CMap) algorithms enable data-driven studies on drug repositioning  10, 11. Specifically it allows to compare a query gene signature to a differential gene expression database built by treating human cell lines with a wide range of chemical compounds. The analysis output is a list of compounds ranked according to their relevance to the query gene signature (positive or negative enrichment). CMap has been applied successfully in several studies including ours aiming at identifying candidate drugs for repurposing  12, 13. Here, we used the CMap resources and identified the sesquiterpene lactone parthenolide that was subsequently analyzed for its in vivo capacity to reduce the development of DKD in the type I diabetes mouse model.
Here we show that treatment with an orally bioavailable analog of parthenolide, DMAPT, potently reduces urinary ACR in Ins2Akita mice, verifying in vivo   The classification is based on the score of negative enrichment (the closer to -1 the better) of the small molecules with a n ≥ 4. Abbreviations: "Mean", is the arithmetic mean of the connectivity scores for those signatures; "n" is the number of signatures of a given compound available in the Cmap database; "enrichment" indicates the degree of matching between a query signature (here the RI-DKD-GPs signature) and the reversed signature of a given compound; "p" (permutation p) estimates the likelihood that the enrichment would be observed by chance; "specificity" provides a measure of the uniqueness of the matching between a compound and the CVD signature; "percent non-nul" measures the support for the connection between a set of compound signature and the query signature based upon the behavior of the individual signature in that set.
Each sample (5 µl) was loaded onto a C18 precolumn (300 μm inner diameter × 5 mm; 5 µm particule size; 100 Å pore size; Dionex) at 20 μl/min in 5% acetonitrile, 0. 05% trifluoroacetic acid. After 5 min of desalting, the precolumn was switched online with the analytical C18 column (75 μm inner diameter × 50 cm; 3 µm particule size; 120 Å pore size inhouse; packed with Reprosil C18) and was equilibrated in 95% solvent A (5% acetonitrile, 0. 2% formic acid) and 5% solvent B (80% acetonitrile, 0. 2% formic acid). Peptides were eluted using a 5-50% gradient of solvent B over 110 min at a flow rate of 300 nl/min. The mass spectrometer was operated in a data-dependent acquisition mode with Xcalibur software. Survey MS scans were acquired in the Orbitrap on the 300-2000 m/z range with the resolution set at 60,000.
The 20 most intense ions per survey scan were selected for CID fragmentation and the resulting fragments were analyzed in the linear ion trap (LTQ). A dynamic exclusion of 60 s was used to prevent repetitive selection of the same peptide. Each sample was injected once for MS analysis. Protein identification and quantification from raw nanoLC-MS/MS data. Raw nanoLC-MS/MS files were processed with the MaxQuant software (version 1. 5. 2. 8) for database search with the Andromeda search engine and for quantitative analysis  39. Data were searched against "Mus musculus" entries in the Swiss-Prot protein database (UniProtKB/Swiss-Prot protein knowledgebase release 2013_06; 16,641 sequence entries of Mus musculus). Carbamidomethylation of cysteine was set as a fixed modification whereas oxidation of methionine and protein N-terminal acetylation were set as variable modifications.
Specificity of trypsin digestion was set for cleavage after K or R and two missed trypsin cleavage sites were allowed. The precursor mass tolerance was set to 20 ppm for the first search and 4. 5 ppm for the main Andromeda database search. The mass tolerance in MS/MS mode was set to 0. 8 Da. Minimum peptide length was set to 7 amino acids and minimum number of unique peptides was set to 1. Andromeda results were validated by the target-decoy approach using a reverse database at both a peptide and protein FDR of 1%. For label-free relative quantification of the samples, the "match between runs" option of MaxQuant was enabled with a time window of 3 min to cross-assign the MS features detected in the different runs. Data processing and statistical analysis. Protein entries identified as potential contaminants from the 'protein-Groups.
The mean proportion of missing values over the whole analytic run was 2. 1% (line 2,435, column S, Table  S1 ). Proteins with a p value of less than 0. 05 (T-test) were considered as significantly varying between two groups. Proteomic data availability. The mass spectrometry proteomics raw data have been deposited to the Pro-teomeXchange Consortium via the PRIDE  40  partner repository with the dataset identifier PXD018728. pathway enrichment analysis. Pathway enrichment analysis of up-and down-regulated proteins was using the Gene Set Enrichement Analysis (GSEA) software package from the Molecular Signatures Database (MSigDB) (https ://www. gsea-msigd b. org)  10, [41] [42] [43]  using "Hallmarks gene sets" and "Canonical Pathways" as Compute Overlaps. MSigDB is a joint project of UC San Diego and Broad Institute.
Acknowledgements The work was supported by grants from: the  Agence Nationale de la Recherche  (Grant  IN2DN ,  ANR-12-BSV1-0035-01 ), the  "Fondation pour la Recherche Médicale " (Grant No. DEQ20170336759 ); AV, HM, and JPS are members of the  EUTox consortium. The work was also supported in part by the  Région Midi-Pyrénées, European funds (Fonds Européens de Développement Régional, FEDER) ,  Toulouse Métropole , and by the  French Ministry of Research  with the  Investissement d' Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  Project,  ANR-10-INBS-08 ).
Glycyrrhizin (GLR) is triterpenoid saponin used for the treatment of various of liver and skin diseases. GLR blocks the nucleocytoplasmic translocation of the protein HMGB1, thereby inhibiting its extracellular release and its functions as a damage-associated molecular pattern molecule. Here we have analyzed the interaction with HMGB1 of GLR and its three main metabolites mono-glucuronyl-glycyrrhetinic acid (MGA), glycyrrhetinic acid (GA) and a sulfate metabolite of GA (Cpd 3). We determined the optimized binding configurations (starting from crystallographic data for HMGB1) of the bound 18α-and 18β-epimers to compare the potential energy of interaction (ΔE) and free energy of hydration (ΔG). The molecular modeling shows that GLR and MGA can form much more stable complexes with HMGB1 than the non-glycosylated molecules GA and Cpd 3.
In silico molecular docking procedure The 3D structure of HMG proteins was retrieved from the Protein Data Bank (www. rcsb. org) under the PDB codes 1HME (HMG box motif in the B domain of HMG1)  [29]  and 2YRQ (solution structure of the tandem HMG box domain from Human High mobility group protein B). Docking experiments were performed with the GOLD software (Cambridge Crystallographic Data Centre, Cambridge, UK). Within the binding site, side chains of amino acids have been considered as rigid. The structure of the various ligands has been optimized using a classical Monte Carlo conformational searching procedure as described in the BOSS software  [30]. Ligands are defined as flexible during the docking procedure. For each ligand, up to 30 poses (100 for the very flexible molecules) that are energetically reasonable were kept while searching for the correct binding mode of the ligand.
The decision to keep a trial pose is based on ranked poses, using the PLP fitness scoring function (which is the default in GOLD version 5. 3 used here). In addition, an empirical potential energy of interaction ΔE for the ranked complexes is evaluated using the simple expression ΔE(interaction) = E (complex) -(E(protein) + E(ligand)). For that purpose, the Spectroscopic Empirical Potential Energy function SPASIBA and the corresponding parameters were used  [31, 32]. Molecular graphics and analysis were performed using the Discovery Studio 2020 Client software, Dassault Systemes Biovia Corp.
Interaction of GLR and its metabolites with HMG Box B To begin with, we used the crystallographic structure of the HMG box motif in the B domain of HMG1 (PDB code: 1HME)  [29]. It is an L-shaped small protein of 77 amino acids with three alpha-helices defining two arms and a single cysteine residue. Each compound was docked onto the HMG box and the molecular docking calculations permitted to determine and compare the potential energy of interaction (ΔE) and free energy of hydration (ΔG). The results are collated in Table  2  and illustrations of the drug-HMG box complexes are shown in Fig. 4. Among the 8 compounds evaluated, 18α-GLR is by far the best HMG binder. It forms much more stable complexes with the HMG box than the corresponding β epimer. The difference of ΔE is important between the two epimers and the difference is even more important when we compared the ΔG values (Table  2 ).
Interaction of GLR with HMG Box A: influence of the protein disulfide bridge Next, we investigated the binding to Box A of HMGB1 and in this case, we specifically analyzed the influence of the disulfide bridge between cysteine residues C30 and C52 in the box on the interaction with 18α-GLR vs. 18β-GLR. It is known that these two cysteine residues readily form an intramolecular disulfide bridge under mild oxidative stress  [43]. The molecular model was constructed starting for the native tandem structure, with two boxes A-B (173 amino acids, PDB code: 2YRQ) and then cut to keep only amino acids sequence from Gly-1 to Glu-91 encompassing Box A (Fig. 6 ). Thus, we delimited a "native SH Box A" unit containing two cysteine residues at C30 and C52 in the thiol form, with a -SH to -SH distance of 5.
5 kcal/mol for the 18β-GLR/Box B complex vs. -24. 8 kcal/mol for the 18β-GLR/Box A S-S bridged complex). A better interaction with Box A than Box B was also suggested previously in a modeling study with GLR and synthetic derivatives  [44]. But we found little difference between the two C18 epimers of GLR in terms of binding energy (ΔE). Both 18α-GLR and 18β-GLR form roughly comparable complexes with the constructed Fig. 3. Human HMGB1 protein with its two DNA-binding domains (Boxes A and B) and the acidic tail. The human protein contains 215 amino acids with three cysteine residues (C23, C45 and C106); the P53, TLR4 and RAGE binding sites are indicated (adapted from  [1] ). Below is shown the conformation of the HMG tandem (173 amino acids) with the A and B boxes (PDB entry 2YRQ). In this case, the three cysteine residues are at positions C30, C52 and C113.
The fact that higher doses of GA compared to GLR are needed to reduce HMGB1 levels agrees with our calculations. GLR is a much more potent HMG sequestering agent than GA. Similarly, the sulfated metabolite Cpd 3 appears as a weak HMG binder, thus suggesting that its toxic effects (pseudoaldosteronism) are unrelated to HMGB1 binding. Most pharmacological studies with GLR or GA are performed with the racemic mixture, or with one isomer only. For example, Shetty et al. [58]  showed that 18a-GA down-regulates the expression of proinflammatory cytokine/growth factor genes HMGB1, IL-6 and IL-8 in prostate cancer cells, whereas Cavone et al. [38]  showed that 18b-GA inhibits immune activation triggered by HMGB1 in the tear fluid during Fig. 4. Molecular models of the interaction between GLR or its metabolites and the HMG box B of HMG1 (PDB code: 1HME).
It is an interesting drug candidate. -Complexes between 18α/β-GLR and the A Box of HMGB1 are much better stabilized when the Box is itself stabilized with an intramolecular disulfide bridge between the C30 and C52 cysteine residues. GLR (and MGA) forms stable complexes with the thiol-free Box A (cysteines C30 and C52) but the stability of the drug-protein complexes is significantly reinforced when the two cysteine residues are linked by a disulfide bridge (cystine C30-C52). The data generated significantly extends our knowledge of the structure-activity relationship of GLR derivatives as HMGB1 ligands. The 3D-structure modeling provides an explanation for the differences in the activity of GLR and GA, in terms of HMGB1 recognition. These calculations now provide a solid basis for further experimental studies with GLR and MGA as HMGB1 ligands.
Scanning electron microscope low vacuum mode Surface morphologies of hydrogels were characterized using a Quanta scanning electron microscope (FEI-Japan). The low-vacuum mode used allows the observation of surface topography of biomaterials without sputter coating while protecting them from electron beam damage. The maximal resolution attained could be lower than 5 nm, using an electron beam spot size of 7 nm. A large field detector (LFD) was used to execute this analysis. Square-shaped samples (9 mm Â 9 mm) were inserted in the scanning electron microscope chamber, and tests were performed at a relative humidity of 50% and a temperature of 25 C. Using partial vacuum, a regular pressure of 60 mbar was created. Images were taken from a 10-mm distance with an acceleration voltage of 15 kV and analyzed using 'xT microscope server' software.
X-ray photoelectron spectroscopy X-ray photoelectron spectroscopy (XPS) were carried out using the KRATOS Axis Ultra X-ray photoelectron spectrometer and performed with monochromatic incident radiation Al-K α X-ray and a photon energy hv of 1486. 6 eV. XPS is an effective technique to characterize the surface of the hydrogels and provide adequate information on the possible interactions in the hydrogel. The test was performed at 150 W and a base pressure of 10 À9 mbar. Photoelectron data were recorded with survey scans at 1100-0 eV at an analyzer pass energy of 160 eV and narrow scans at an analyzer pass energy of 20 eV. The binding energies were corrected by setting the C 1s spectral to 284. 6 eV.
(Scale bar ¼ 300 μm). 3D ¼ three-dimensional; SEM ¼ scanning electron microscopy; GelMA ¼ gelatin methacryloyl. S¼ μ different locations of the film surface (Fig. 2b ). The maximal loading force was 6 nN. Elasticity maps and the corresponding histograms (statistic distribution) were estimated from the analysis of the approach curves as per the model used by Dimitriadis et al  [34] : F ¼ 4E 3ð1 À ν 2 Þ R 1=2 δ 3=2 Â 1 þ 1; 133χ þ 1; 283χ 2 þ 0; 769χ 3 À 0; 0975χ 4 Ã (1) where δ is the indentation depth, ν is the Poisson coefficient, and R is the colloid radius. The Dimitriadis correction for finite thickness is defined by the χ parameter: χ ¼ ffiffiffiffiffi ffi Rδ p h ( 2 ) where h is the sample thickness. The FVI or force maps were analyzed by means of an automatic Matlab algorithm, described by Polyakov et al  [35].
Scratch assay Keratinocytes were cultured in a 24-well plate until a confluent cell layer was formed. Then, a 100-μm-wide scratch was formed using a P200 pipette tip, and the hydrogels with and without nanoliposomes were placed inside culture inserts (a pore size of 8 μm). Microscopic images were taken over a period of 24 h to identify the migration rate of keratinocytes and analyzed using Image J software. The coverage percentage was evaluated by measuring the scratch surface directly after scratching and after 24 h.
Acknowledgments R. K. and J. B. contributed equally to this work. The authors are grateful to the  European Commission  for the  Erasmus Mundus grant  to  R. K. ( Erasmus Mundus External Window 'ELEMENT' Program ). The  ANRT French agency  (project No  312/2012 ) is also acknowledged for financial support to G. B. M. K. E. acknowledges support from the  French Ministry of Higher Education, Research and Innovation. J. M.
JECFA evaluated both substances (JECFA Nos. 1324 and 1575, respectively) as part of the group "Aliphatic and alicyclic hydrocarbons" and "Epoxides", respectively, at the 63rd  (JECFA, 2004 ) and 65th  (JECFA, 2006)  meetings, respectively, and determined that they presented no safety concern at current levels of intake. In the United States, the Flavor and Extract Manufacturers Association (FEMA) Expert Panel evaluated both materials as flavoring ingredients, and assigned them "Generally Recognized as Safe" (GRAS) status under their conditions of use (FEMA Nos. 2252 and 4085, respectively)  (Adams et al. , 2011). The European Food Safety Authority (EFSA) also reviewed all available data for both materials (FL-Nos. 01. 007 and 16. 043, respectively) and requested additional toxicological data for the derivation of a no-observed-adverse-effect level (NOAEL) before a final evaluation could be completed  (EFSA, 2009 (EFSA, , 2011)).
The studies described herein, sponsored by the flavor and fragrance industries, were conducted according to OECD testing guidelines and submitted to EFSA in response to these requests. Additional endpoints of reproductive function were included in both studies due to lack of relevant data in the broader database for these substances. EFSA finalized the evaluations of the safety of β-caryophyllene  (2015)  and βcaryophyllene epoxide (2014) concluding that there were no remaining safety concerns for either substance, and determined the NOAEL to be 222 mg/kg bw/day for β-caryophyllene and 109 mg/kg bw/day for βcaryophyllene epoxide. EFSA also concluded that the NOAELs provided adequate margins of safety (MOS) for these and structurally similar substances relative to estimated daily intakes in Europe, according to the EFSA safety evaluation procedure for flavorings  (EFSA, 2014 (EFSA, , 2015)).
Clinical pathology Significance was evaluated at a probability value of p < 0. 05. Males and females were analyzed separately. Initially, Levene's test  (Levene, 1960)  for homogeneity and Shapiro-Wilk test  (Shapiro and Wilk, 1965)  for normality were performed; and if not significant, an ANOVA with Dunnett's test  (Dunnett, 1964 (Dunnett, , 1980) )  was conducted. If the preliminary test was significant, transforms of the data to achieve normality and variance homogeneity were used. The order of transforms attempted was log, square root, and rank-order. If the log and square root transforms failed, the rank-order was used.
Sperm analysis Means and standard deviations for sperm motility, epididymal sperm count, homogenization-resistant spermatid (HRS; from the testis) count, and morphology data were calculated for each control and treatment group. Data were compared across groups using a nonparametric analysis of variance  (Kruskal and Wallis, 1952). If a significant difference occurred (p < 0. 05), the Mann-Whitney U test  (Mann and Whitney, 1947)  was used for pair-wise comparisons of each treated group to the control group.