dataesr/scientific-paragraphs-categorization huggingface dataset:
The lead compound among the above compounds was selected on the merit of hydrogen bonding, salt bridge as well as pi-pi interactions, 4-(6-Fluoro-4-oxychromen-2yl) benzoic acid has been found one of the best molecules from docking studies. The binding mode of the lead compound with AURKA reveals that the amino acid residues viz, Lys162, Ala213, and His280 are more important for binding, and having the best binding energy (-11. 760 kcal/mol) was noted among the ligand datasets. Then resulted Enrichment calculations is suggesting that the docking protocol is validated because the Receiver operating characteristic curve (ROC) curve shows R 2 = 0. 99. 100ns MD simulations of Aurora Kinase A and lead compounds con rm thermodynamic stability as compared to standard compounds. The molecular dynamics simulations of 100 ns were done, which shows that the mean RMSD value of 1.
Recent studies revealed that Aurora Kinase A helps in tumor development by the stimulation of epithelial-mesenchymal change, which results in the genesis of tumor cells  [6]. Therefore, Aurora kinase A being overexpressed has been linked to oncogenic changes mainly linked to centrosome ampli cation  [7]. Thus, Aurora kinase A plays a " druggable target" in cancer, which controls oncogenic pathways associated with drug resistance  [8, 9]. We have targeted Aurora kinase A with PDB ID: 2J4Z (Fig. 1 ) with small natural inhibitors with fewer side effects to inhibit tumor progression  [10] [11] [12]. The backbone structure of avones which belong to the class avonoids is 2-phenylchromen-4-one(2phenyl-1-benzopyran-4-one) (Fig. 2 )  [13] [14] [15] [16] [17]. Flavones are generally derived from natural sources, i.
e. from fruits and plants. Some of the avones show anticancer properties and can be used in cancer therapy. On the other hand, the structural properties of avones that show inhibitory effects on cancerous cell growth remain not clear  [18] [19] [20]. To make out the structural properties of avone, which exhibit the inhibition of cancer cells, we retrieve Fluoro avone derivatives as inhibitors from the database for Aurora kinase A protein. The binding of the Fluoro avones and their analogs with Aurora kinase A can be calculated by in silico docking experiments. This provides the knowledge for designing novel anticancer drugs that target Aurora Kinase A protein.
Materials And Methodology Protein crystal structure preparation Three-dimensional (3D) crystal structures of (PDB ID:2J4Z) Aurora Kinase A protein was retrieved from RCSB Protein Data Bank. The protein retrieved (PDB ID:2J4Z) has resolutions of 2. 0Å. Before performing the docking calculations, the crystal structures of Aurora kinase A were prepared at pH 7 ± 1 by the PROPKA program and used protein preparation wizard of Schrodinger (Protein Preparation Wizard: Schrodinger Release 2019-2). The OPLS3e force eld was used to optimize the crystal structure and the side chains.
Filtration Fluoro avone derivative of avones (CID: 261400) and its compounds similar (90% similar) are taken as inhibitors against Aurora Kinase A. 3882 compounds were obtained from PubChem database (https://pubchem. ncbi. nlm. nih. gov/). These compounds are further ltered using different methods. Firstly, these compounds were pushed to Entrez and a lter of Lipinski's rule of 5 was added and 3882 compounds were ltered in we have 3430 compounds. There are different lters of CANVAS of Schrodinger 2019-software, which lter compounds based on different parameters. REOS is one of the lters which sets the criteria to identify lead-like small compounds and the compounds are ltered. Filtering 3430 compounds to 2449 compounds. The next lter of CANVAS is PAINS. PAIN lter compounds with the substructure.
PAINS1, PAINS2, and PAINS3 ltered 2449 compounds to 2448 compounds. Ligand structure retrieval and its preparation Derivatives of Fluoro avone were ltered and are used as an inhibitor to inhibit the overexpression of Aurora Kinase A. The 2 dimensional (2D) chemical structures of Fluoro avone (PubChem CID: 261400) and its derivatives were retrieved from the PubChem database and was ltered. LigPrep (Schrodinger Release 2019-2) is used for the 2D structures of the ligands and was subjected to energy minimization and appropriate bond order assignment with Optimized Potentials for Liquid Simulations (OPLS3e) force eld (module of Schrodinger to get a stable conformation of the analogs of the Fluoro avone).
Molecular docking The binding cavities of targeted receptors were de ned considering all residues which come within the cut off distance of 2 Å from the crystal ligands which are present in the retrieved crystal structure of Aurora kinase A receptor and also, the binding cavity information for this receptor was taken from PDBsum (www. ebi. ac. uk/pdbsum/). The prepared 2448 ligands ( ltered analogs of Fluoro avone) were subjected to docking calculations within the de ned binding cavity using the Extra Precision (XP) and Standard Precision (SP) different modes of Glide program (Glide: Schrodinger Release 2019-2) with the OPLS3e force eld. Aurora Kinase A receptor, and binding cavity of Aurora Kinase A protein forms a complex structure of the ligands and the protein by docking into the binding cavity using SP and XP mode of Glide  [21].
We further extended the data to understand the interactions of residues with ligands. Enrichment calculations "Enrichment" is the measure of known ligands ranks to a set of decoys. Enrichments of ligands is required to improve the binding a nity with speci c residues in the cavity of targeted Kinase  [22]  using DUD. E database decoys was designed for decoy bias on enrichment. For Aurora Kinase A protein, a decoy set for Aurora Kinase is generated using the DUD. E database  [23]. Decoys are computed on the basis of similar physical properties but different chemical structures. So active set of decoys are generated, but they have not been tested. So, the random 1000 drug-like decoys for Aurora Kinase and 10 best XP docked ligands and Fluoro avone molecules were docked with Aurora Kinase A (PDB ID: 2J4Z).
Molecular dynamic (MD) studies Molecular dynamic studies are used to evaluate the strength to bind and activity of Fluoro avone, and the best 2 analogs of Fluoro avone were obtained after docking studies. The full-scale molecular dynamic simulation was performed for 100ns with a complex of Fluoro avone and its 2 best analogs of Fluoro avone with Aurora Kinase A. The process of Molecular dynamic simulations using Desmond helps to measure the movements of the atoms and calculate the forces. However, Desmond considers all detailed requirements including temperature, pressure, volume system, and all the functionality used to study the complex interactions. System builder of Desmond in the Maestro program is used, the system for the protein-ligand complex is immersed in a water-lled cubic box containing 1Å spacing water molecules using an extended TIP3 (three-point water model) with periodic boundary conditions.
dat" format. This data of RMSF and RMSD values which are collected were used to plot graphs for RMSD and RMSF. The RMSD plot (Fig. 7 ) suggests that Fluoro avone, Fluoro avone Analog 1, and Fluoro avone Analog 2 are within the good range of RMSD which is acceptable where the mean RMSD value is 1. 77 Å, which indicates that the binding of the protein and the ligands is good. The RMSD graph (Fig. 8 ) shows that the complexes started gaining equilibrium from 35 ns to 100 ns within the range of RMSD 1. 3 Å to 2. 4 Å, which gives the average RMSD value of 1. 77Å Prime MM/GBSA energies for the complexes of Aurora Kinase A protein Free binding energies (ΔG Bind) are calculated by MM/GBSA. MM/GBSA states the results in terms of solvation components or VDW, hydrophobic. The Fluoro avone, Fluoro avone Analog 1, and Fluoro avone Analog 2 (CID: 261400, 82043699, 7728977), serving as ligands to Aurora Kinase A protein were submitted to ensemble-averaged Prime MM/GBSA method for a long time MD simulation.
The ensemble-average of binding free energies of the 3 complexes of protein-ligand are reported in Table  4. The binding energy is less (more negative value) which means that the complex is stronger. The data denotes that compound with CID 82043699 and compound with CID 7728977 both have the most negative binding free energy (-50. 898 ± 3. 073 and 52. 87 ± 1. 8773) which confers the binding stability of both the analogs to be more potent. Whereas another reference compound also shows favorable binding free energy with Aurora Kinase A protein, suggesting that these 3 molecules make a strong complex with the active site of Aurora Kinase A. These MM/GBSA results suggest that the ligands satisfy the prime MM/GBSA approach to achieve a stable complex with Aurora Kinase A protein. The free energies predicted from the prime MM/GBSA show that complexes are thermodynamically favorable.
Then the bucket values were normalized with the integration of the whole spectrum curves as following: Normalized bucket value Raw bucket value Whole = / spectrum integration. A table of bucket values was obtained with 64 rows corresponding to the animals and 714 columns corresponding to the buckets identified by their chemical shifts. (ii) Briefly, the metabolite method converted the 1 H-NMR spectra into metabolite relative concentration tables with the ASICS R package (R package version 4. 0. 2). 2  ASICS package performed an automatic approach to identify and quantify metabolites in complex 1 H-NMR spectra from their unique peak pattern (fingerprint;  Tardivel et al. , 2017; Lefort et al. , 2019). The metabolite database used consisted of the spectra of 176 pure metabolites described in  Tardivel et al.
(2017). A total of 80 metabolites were identified and quantified in at least one sample. The methanol was removed as it was used to extract the metabolites. Then only 41 metabolites were kept for further analyses as they were present in at least 50% of the samples at one time point. Thus, the final table of metabolite relative concentrations contained 64 rows corresponding to the animals and 41 columns corresponding to the metabolites. The bucket and metabolite relative concentration tables were analyzed with SIMCA P+ software (version 12, Umetrics, AB, Umea, Sweden) for carrying out the multivariate statistical analysis as previously described  (Mozduri et al. , 2021). Briefly, the variables were preprocessed with Pareto normalizations. Principal component analysis (PCA) was performed for finding outliers.
Y predicted values were drawn for each PLS model. The validation of the PLS model was evaluated by comparing the goodness of fit (R 2 Y and Q 2 ) of the original model with the goodness of fits of 500 models based on data where the ranks of the Y-observations have been randomly permuted, while the X-matrix (bucket or metabolite) has been kept intact. The permutation plots were drawn. The latent variables associated with interesting axes were analyzed using the variable importance in the projection (VIP) method. The variable (bucket or metabolite) with a VIP superior to 1 was considered as "important. " Then a one-by-one regression with either the LW effect or the TY effect was performed on the whole datasets. The p-values were corrected for multiple tests with the Benjamini-Hochberg correction using the R software (version 3.
6. 1) and named "BH p-value. " A variable was "significant" when the BH p-value was inferior to 0. 05 and "tended to be significant" when the BH p-value was between 0. 05 and 0. 1. For the buckets with VIP superior to 1, the corresponding metabolites were identified manually by importing the chemical shift lists into the Human Metabolome Database  (Wishart et al. , 2009). 3 All carbohydrates identified were D-carbohydrates, and all amino acids were L-amino acids. To simplify the names of the metabolites, the "D-" and the "L-" were removed before the names of the carbohydrates and the amino acids, respectively. To confirm the identification of the metabolites, the 1 H-NMR peaks of these metabolites were manually checked on the sample spectra with TopSpin software (version 4. 0, Bruker BioSpin, Rheinstetten, Germany).
Then the lists obtained by the bucket method and the metabolite method were compared with Venn diagrams. 4 ll the biomarkers identified by the bucket method and/or the metabolite method were considered as biomarkers. Network analysis based on the correlation of the biomarker RC and the variable (LW or TY) was performed with the functions pls and network of the MixOmics R package  (Lê Cao et al. , 2009; Rohart et al. , 2017 ; R package version 4. 0. 2. ). 5.
For the buckets with VIP > 1 and BH p < 0. 05, the involved metabolites were identified manually by importing chemical shift lists into the Human Metabolome Database  (Wishart et al. , 2009; See Footnote 3). They corresponded to 14 metabolites summarized in Table  1. There were seven carbohydrates identified as biomarkers for LW. For each metabolite, the number of peaks that contained at least one bucket with VIP > 1 was respectively, seven out of 10 peaks for glucose-6-phosphate (HMDB0001401), six out of eight peaks for glucuronic acid (HMDB0000127), all the two peaks for glyceric acid (HMDB0000139), all two peaks for glycogen (HMDB0000757), all the two peaks for lactate (HMDB0000190), two out of four peaks for malic acid (HMDB0000156), and 12 out of 13 peaks for maltose (HMDB0000163; Table  1 ).
Identification of Hepatic Biomarkers of Foie Gras Technological Yield First, the spectra were analyzed with the bucket method. A PCA was first performed, but no outlier was detected (not shown). The PLS scatter plot to explain TY had only one latent variable, and the parameters were cumulative R 2 X = 0. 506 and R 2 Y = 0. 514. The prediction of the model was Q 2 = 0. 471. The original R 2 Y and Q 2 -values were higher than those obtained after 500 permutations, and the regression line of Q 2 -points intersected the vertical axis below zero (-0. 114; Figure  3C ). The RMSEE and the RMSECv were close (8. 9 and 9. 2, respectively; Figure  4C ). The projection of the samples highlighted an evolution of TY with the first latent variable on the vertical axis (Figure  2C ). A total of 64 buckets had a VIP value superior to 1 (Supplementary Data 2).
ACKNOWLEDGMENTS The authors thank the staff of the  Station d'Expérimentation Appliquée et de Démonstration sur l' oie et le Canard (Dordogne, France)  for their excellent supervision of this study, especially  Franck Lavigne  and  Cédric Mondoux. We also thank the staff of the  UMR1388 Génétique Physiologie et Systèmes d'Elevage INRAe , in particular  Michel Bouillier-Oudot  and  Caroline Molette  for their help in designing the experimental project and getting funding, and  Hélène Manse  and  Stéphane Seidlinger  for liver sampling.
The sections obtained were dyed through immersion in a solution of lactophenol cotton blue 0. 05% for two minutes. Afterward, they were rinsed with a lactophenol solution and mounted in a polyvinyl alcohol solution. Observations and recording of data were made for each of the observation times. The data on percentages of the presence for each fungus development stage of the infection were calculated in relation to the total of the observed stomata, and transformed through the arccosine function. A variance analysis was performed using the university version of the SAS© GLM procedure (SAS University Edition, SAS Institute, NC, USA) software, and a comparison of means was conducted using the SAS©LSMEANS (SAS University Edition, SAS Institute, NC, USA) procedure. The data analysis was conducted for the presence of fungal structures per observation time, and for the total of detected structures in every graft condition.
Selection of Target Genes Five genes reported as associated with the response to H. vastatrix infection in C. arabica were selected  [23, [58] [59] [60]. Furthermore, two constitutive genes for coffee  [61] [62] [63]  were selected. The oligonucleotides specific to these genes were designed using the Primer3Plus ®  [64]  software, selecting a unique sequence for each gene obtained from the NCBI database. The parameters used for designing these oligonucleotides were 20 base pairs per oligo at a 60 • C maximum melting temperature, and a > 40% G-C relation. Oligos were synthesized at the T4Oligo laboratory in Irapuato, Mexico (Table  1 ). Table  1. Primers designed for genes NBS-LRR, NDR1b, NPR1, PR1, PR5, UbiE2 and CaGAPH, used in the expression analysis for qRT-PCR.
An RNA-ISH assay with pooled probes for genes associated with disease-free survival (DFS) was developed to probe 111 PDAC tumor samples. Results: Gene expression profiling identified four subtypes of cancer cells (C1-C4) and three subtypes of cancer-adjacent stroma (S1-S3). These stroma-specific subtypes were associated with DFS (P ¼ 5. 55E-07), with S1 associated with better prognoses when paired with C1 and C2. Thirteen genes were found to be predominantly expressed in cancer cells and corresponded with DFS in a validation using existing RNA-seq datasets. A second validation on an independent cohort of patients using RNA-ISH probes to six of these prognostic genes demonstrated significant association with overall survival (median 17 vs. 25 months; P < 0. 02). Conclusions: Our results identified specific signatures from the epithelial and the stroma components of PDAC, which add clarity to the nature of PDAC molecular subtypes and may help predict survival.
Data collection Snap-frozen primary tumors from patients treated for PDAC at the Massachusetts General Hospital (Boston, MA) from 1995 to 2014 were used for this study. Each patient gave written, informed consent and the studies were conducted in accordance with the U. S. Common rule. Patients' data were entered prospectively into clinical databases approved by the Massachusetts General Hospital (Boston, MA) Institutional Review Board. Patients with unresectable pancreatic tumors, patients who received palliative surgery, and patients who received neoadjuvant chemotherapy were excluded. Only patients with resectable tumors who received neither neoadjuvant chemotherapy nor radiotherapy were included.
RNA isolation and RNA-seq Samples in extraction buffer were thawed and pooled prior to purification on picopure RNA columns (PicoPure RNA Isolation Kit, Thermo Fisher Scientific), while genomic DNA was removed via RNase-free DNase digestion (Qiagen) on the columns. Total cellular RNA from each column was eluted in 11 mL of elution buffer. Amplified cDNA was then generated by using the SMARTer Ultra-Low-input RNA Kit, version 4 (Takara Bio USA) followed by Nextera XT DNA Library Preparation kit (Illumina) for sample barcoding and fragmentation as per the manufacturer's protocols. The sequencing results have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE164665. Additional details can be found in Supplementary Materials and Methods.
Gene expression data analysis Prior to analysis, the log 2 -transformed RNA-seq data was filtered out to exclude poorly expressed genes (defined as a median expression of 0 across all samples) and poorly variant genes (defined as a SD below 0. 5). Data filtering resulted in 12,828 and 13,108 genes for cancer and stromal cells, respectively. Unsupervised analysis to identify robust sample clusters on RNA-seq data was performed with non-negative matrix factorization (NMF) using the R package "ConsensusCluster-Plus" with parameters defined as a maximum of six clusters, 1,000 iterations, using 60% of samples and the hierarchical cluster algorithm with the hclust R function using Pearson distance and average linkage  (14). The number of optimal clusters was chosen to have the higher cophenetic correlation coefficient among the number of evaluated clusters, resulting in four robust clusters of gene expression for both cancer and stromal cells.
Supervised analyses to identify gene expression signatures comparing cancer with stromal cells and comparing survival within both cancer and stromal LCM subgroups were done using a moderated t test with empirical Bayes statistic included in the limma R package. The FDR was assessed to correct the multiple testing hypothesis using qvalue with the q-value R package  (15). The methods used to analyze the ontology of the underlying gene expression signatures are based on gene ontology (GO) biological processes. These methods, specifically, Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://david. abcc. ncifcrf. gov/) and gene set variation analysis (GSVA), were used to assess the relative enrichment of gene sets at the sample level via an unsupervised, nonparametric approach  (16).
Gene expression data analysis of public datasets To assess the gene expression signatures identified in this study, we gathered clinicopathologic and high-throughput gene expression data from clinical pancreatic carcinoma samples found in 15 previously reported datasets made publicly available through the following international database repositories: National Center for Biotechnology Information Genbank Gene Expression Omnibus, ArrayExpress, European Genome-phenome Archive, and The Cancer Genome Atlas (Supplementary Table  S1 ). The Ontario Institute for Cancer Research, funded by the Government of Ontario, is acknowledged as the source of data used in this study. Detailed information concerning Institutional Review Board and Ethical Committee approval and patients' consent for all 15 studies was provided in the corresponding.
publications. The complete dataset contained 1,288 samples profiled using whole-genome DNA microarrays (Affymetrix or Agilent) and RNA-seq (Illumina), including 963 surgically resected primary cancer samples. Each dataset was normalized as previously described previously  (18). In addition, the datasets were normalized separately, using robust multichip average (RMA) for Affymetrix microarray raw data and quantile normalization for the available processed data  (19). Normalization was done in R using the Bioconductor associated package. When multiple probes mapped to the same GeneID, the one with the highest variance in each dataset was retained. Finally, the previously normalized RNA-seq data were log 2 transformed. We defined the molecular subtypes of all pancreatic samples in each dataset separately as defined in the original publications: the three Collisson and colleagues subtypes were classical, quasi-mesenchymal, and exocrine-like, the two Moffitt and colleagues epithelial subtypes were basal-like and classical, and the four Bailey and colleagues subtypes were squamous, pancreatic progenitor, immunogenic, and ADEX  (5) (6) (7).
The gene expression signature of this study was then applied to each public dataset separately using the nearest centroid method with the Pearson correlation representing distance. On the basis of the genes of each signature and within the associated dataset, centroids were computed as the mean expression value of each group of interest and samples of each dataset were classified to the closest centroid. Multiple component analysis (MCA) was used to construct a lowdimensional visual representation of associations between the LCMderived cancer groups with consensus molecular subtypes. MCA computes relationships within a series of pairwise comparisons of categorical variables with decomposition in singular values as a generalization of the principal component analysis. The top two components with the highest percentages of inertia were chosen to define the factorial plan to use.
negative), tumor grade, and tumor-and stroma-specific GES (short-term vs. long-term survival). Variables with a P <0. 10 were tested in multivariate analysis. All statistical tests were two sided at the 5% level of significance. Statistical analysis was performed using the survival package (version 2. 30) in the R software (version 2. 15. 2; http:// www. cran. r-project. org/).