dataesr/scientific-paragraphs-categorization huggingface dataset:
We further browsed the RNA-seq database for ATF4, the hepatic expression of which has been associated with mitochondrial dysfunction in patients with alcohol-related liver disease. 20 As shown in Fig. S5B , no changes in ATF4 expression were seen in peripheral leukocytes from patients with AD and ACLF in comparison to HIs. Moreover, we did not detect changes in hepatic ATF4 expression in patients with AD, except in 1 patient who presented with liver failure (Fig. S5C ).
6C ). PDC is critical for the decarboxylation of pyruvate into acetyl-CoA, and its downregulation is compatible with the reduction in the lactate/pyruvate ratio observed in these patients (Fig. 3E ). The repressed expression of genes involved in pyruvate decarboxylation was also seen in the volcano plot comparing changes between ACLF and AD (Fig. 6D ). PDK4 GSK3A IDH1 G6PD ENO1 HK3 HK2 PGK1 SDHB PYGL TALDO1 ALDOA FBP1 IDH3G PHKG2 PKLR PDK1 ALDOC PYGM ENO2 PRPS1 AGL DLAT PDK2 SUCLG2 ENO3 GALM SDHA GSK3B PGM1 PDP2 PDK3 RPIA PRPS2 IDH3B ACO2 PFKL MDH2 PC SDHC SUCLA2 PDHB RPE PGM3 IDH3A PDHA1 GYS1 H6PD GYS2 PHKG1 CS PGAM2 GPI PGLS IDH2 SUCLG1 BPGM SDHD TPI1 G6PC3 PCK2 ACLY OGDH UGP2 RBKS FH MDH1 ACO1 DLD GBE1 PGM2 PHKB PHKA1 DLST PCK1 PRPS1L1 PGK2 TKT FBP2 ALDOB G6PC PDPR GCK MDH1B Malate and L-glutamine are the best suited mitochondrial metabolites to discriminate ACLF from AD To reduce the dimension of our dataset and to estimate the discriminatory power of each individual metabolite, we applied partial least square discriminant analysis (PLS-DA) to calculate the variable importance in projection (VIP) scores.
26 An advantage of our study is that data were obtained in peripheral leukocytes, in which mitochondrial dysfunction is less susceptible to any confounding effect produced by potentially existing hypoxia in tissue. 11 ur results confirm that mitochondrial dysfunction is a hallmark of AD and ACLF through the use of easy-to-measure biomarkers. FGF21, which is secreted upon mitochondrial damage,  16  was significantly increased in AD and even more so in ACLF. FGF21 predicted mortality in the latter, a finding that extends those previously described in a Mexican cohort. 27 oreover, our study is the first to show that C6:0-and C8:0carnitine predict mortality in both AD and ACLF. Therefore, our results encourage the addition of markers of mitochondrial dysfunction to available scoring systems to optimize the prediction of clinical events in patients with ACLF.
Acknowledgements We gratefully thank the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI)  for the PhD funding of  Aline Delamare  and the  Univ. Bordeaux  for the temporary teaching and research assistant position of Guillaume Naulet. We thank  ANR  for its financial support (Project  ANR-20-CE06-0008 ). Pierre Waffo Teguo , from the  Institut des Sciences de la Vigne et du Vin , is gratefully acknowledged for his help with α D measurements. We thank  Estelle Morvan  from the  IECB Biophysical and Structural Chemistry Platform (BPCS) ,  CNRS UAR3033 , Inserm  US001 ,  Univ. Bordeaux , for her assistance with NMR experiments.
ToF-SIMS imaging ToF-SIMS analysis was performed using an TOF. SIMS 5 (IONTOF GmbH, Germany) with a 25 kV Bi 3 + primary analysis beam. Dried skin sections were analyzed in burst alignment, delayed extraction, positive ion mode with a total primary ion dose of 1 × 10 12 ions/cm 2 (cycle time 135 us, rastered in random mode, 1 frame / patch, 1 shot / frame / pixel, 114/57 scans). Images are 512 × 512 pixels (untreated and sunscreen formulation) and 1024 × 1024 pixels (pre-formulation) over an area of 500 × 500 μm. Images were down binned to the same spatial resolution. Spectra were recorded over a mass range of 1-1000 Da and at a mass resolution of 5000@ m/z 300. Data analysis and visualization was performed using SurfaceLab 7 (IONTOF GmbH, Germany).
Statistical analysis Inter-group comparisons of the skin absorption quantification described in Section 2. 3. 2 were performed using GraphPad Prism software by a two-tailed unpaired Student's t-test and differences were considered statistically significant from p < 0. 05 (*), p < 0. 01 (**) or p < 0. 001 (***). Data analysis and visualization was performed with Thermo Xcalibur 2. 2 and Thermo ImageQuest (Thermo-Fisher Scientific, USA), METASPACE, Multimaging (ImaBiotech, France  [39] ) and OriginPro 2019b (OriginLab Corp. , USA).
Endogenous biomarkers MALDI imaging analysis of normal, untreated skin sections, in combination with optical images, revealed biomarkers associated with various distinct areas in the tissue, most notable in the SC and the viable epidermis (Fig. 2A ). Fig. 2  shows single ion biomarker images (colorscheme: hot) at 60% opacity, superimposed onto the optical skin images. Examples for those biomarkers are [C 26 H 55 NO 2 + H] + at m/z 414. 431 in the SC and lipid species (PE)(36:2) [C 36 H 24 N 6 + H] + at m/z 744. 553 in the viable epidermis. The annotation platform, METASPACE, identified a total of ~1200 molecular species, based on the database, Swis-sLipids (false discovery rate level 50%), in the control skin sample. Colocalization analysis using lipid species Ceramide (Cer)(d44:2) at m/z 676.
Participants having less than three of the aforementioned criteria were considered as a Control. These studies are case-control studies aiming at comparing participants with diagnosed MetS (i. e. , ≥3 criteria) vs. Control participants (<3 criteria) (Figure  7 ). To select the study samples, criteria of exclusion were similar between the Discovery and the Replication studies and included: participants diagnosed with cancer before and one year after blood draw, participants with cardiovascular events before blood draw (stroke, angina and heart failure) and participants with missing data for the cardiometabolic parameters (i. e. , waist circumference, blood pressure, TG, HDLc, fasting glucose). For the Discovery study, Case and Control participants were matched according to their sex, age (2 y classes), smoking status (never+former vs.
Statistical Analysis 4. 4. 1. Metadata Statistical Analysis Metadata associated with each participant selected in the Discovery (i. e. , PURE) and the Replication (i. e. , Nutrinet-Santé) studies included qualitative data (i. e. , sex, smoking, education, localization, season of blood draw) and quantitative data (i. e. , weight, body mass index (BMI), waist and hip circumference, systolic and diastolic blood pressure, fasting blood glucose, total cholesterol, TG, HDLc and LDLc, alternative healthy eating index (AHEI) score and plasma fatty acids) recorded or assessed at baseline. The AHEI score was calculated to estimate the quality of diet of participants and based on ten components reflecting recent dietary guidelines  [101]. Based on the cardiometabolic criteria, the metabolic score (MetS-z-score) was calculated as described in https://github.
Moreover, a special emphasis was put on the cardiometabolic parameters (i. e. , waist perimeter, systolic and diastolic blood pressure, fasting glucose, TG, HDLc and MetS-z-score) through Volcano plot representations. Cardiometabolic variables were mean centered and reduced allowing for comparison as the difference in variables expressed in standard deviation between Case and Control participants. Each variable was represented in the Volcano plot with the difference in standard deviation (SD) between Case and Control participants in the x-axis and the -log(p-value) of the Wilcoxon signed-rank test (BH-corrected) in the y-axis. Statistical analysis and graphical representations were generated by the R statistical computing environment (https://www. R-project. org/, accessed on 1 August 2022) using the "En-hancedVolcano" package (https://github.
Candidate Oxylipins Selection To leverage the case-control design of our studies and the matching of the selected participants, conditional logistic regression was used to model the association of a set of oxylipins with the odds of having a MetS diagnosis. Moreover, to overcome the highdimensional setting of our datasets and to put oxylipins unlinked to the outcome modeled aside, the Elastic-net penalization  [102]  method was applied for the initial oxylipin selection. This method relies on a penalization parameter that controls for the strength of selection by shrinking the coefficients toward zero (setting some to exactly zero, thus, performing variable selection). Contrary to methods as the LASSO penalization that tend to select only one representative from a pool of multi-collinear variables, Elastic-net penalization preserves all variables appearing linked to the outcome.
Model Construction and Validation The objective was to evaluate whether a discrimination model fitted on the Discovery study could be efficient on an independent study regarding the outcome of prediction. First and foremost, in order to merge the oxylipin dataset generated in the Discovery and the Replication studies, a harmonization of the quantitative data adjustment protocol was necessary (see Supplemental Methods). Then, a LASSO-penalized conditional logistic regression model was constructed using the participants in the Discovery study and including the oxylipins previously selected in the Discovery and in the Replication studies. The LASSO penalization was chosen to remove putative redundant oxylipins from the complete signature, with model classification performances assessed for both Discovery and Replication study participants using the AUC ROC which were computed using 10-fold cross-validation, with confidence intervals and cross-validated error rates being calculated.
Analysis and representation were performed by the R software using the "glmnet"  [104] , "pROC"  [105]  and "selectiveInference"  [106]  packages. Odds ratios of each oxylipin of the optimized signature were estimated and plotted as Circos plot  [107]   In order to assess the consistency and the complementarity of the OxyScore with the clinical criteria of metabolic syndrome, Spearman correlations were established between the OxyScore calculated for each participant in the Discovery study and the criteria of MetS (including the MetS-z-score). Correlations were considered as significant when p < 0. 05 and r ≥ 0. 5 (noteworthy link) or r ≥ 0. 7 (strong link).
Acknowledgments: The authors would like to thank  Dominique Bayle ,  Séverine Valéro ,  Nicole Hartung ,  Nadja Kampschulte ,  Elisabeth Koch ,  Laura Kutzner  and  Katharina Rund  for supporting sample preparation. This study was supported by  French ,  German ,  Danish  and Polish national research agencies (respectively,  ANR , grant N •  ANR-16-HDHL-0004-01/02 ; the  German Federal Ministry for Education and Research (BMBF) , grant  N • 01EA1702 ; the  Danish Innovation Fonden , grant N •  4203-00006B ; and the Polish national center for research and development, grant  N • ERAH. E220. 18. 001 ) in the framework of the  Oxygenate  project supported by the  European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL ; http://www. healthydietforhealthylife. eu/). Independent support was provided by  USDA CRIS  Project  2032-51530-025-00D  to JWN.
Based on Eudicotyledons BUSCO, this predicted set of genes is 90. 7% complete with a very low duplication rate (2. 5%, fig. 1B ). The combination of BLASTP and BLASTX against UniProt database and hmmscan against the PFAM database led to the functional annotation of 65. 7% of the predicted genes (21,890 of the 33,300 genes, fig. 1C , supplementary table  S1 , Supplementary Material online). To identify putative orthologs of MIA biosynthetic genes, we used functionally validated MIA pathway genes from Catharanthus roseus, V. minor, Tabernanthe iboga, Gelsemium sempervirens and Rauwolfia species to conduct BLAST searches considering hits of at least 90% coverage and 40% identity (supplementary tables S2-S3, Supplementary Material online). The most probable orthologues were then selected based on best hits and phylogeny analysis (supplementary table  S4 , Supplementary Material online).
RNA Sequencing and Assembly RNA was extracted from liquid nitrogen flash-frozen roots, young and old leaves using NucleoSpin RNA Plant and Fungi mini kit (Macherey-Nagel, Düren, Germany) and purified using RNase-free TURBO DNase set (Thermo Fisher Scientific, Illkirch-Graffenstaden, France), both according to the suppliers' instructions. RNA library construction and sequencing was performed at FGTech using Illumina NovaSeq 6,000 technology. Raw RNA-seq data have been deposited under the SRA accession numbers SRR19972991, SRR19972992, and SRR19972993. Transcriptome was assembled using CLC assembler (v. 4. 4. 1) with a word size of 60 and a bubble size of 250. De Novo Genome Assembly, Gene Model Prediction and Gene Functional Annotation The V. thouarsii genome assembly and gene model prediction were performed by Future Genomics Technologies (Leiden, The Netherlands).
Adapters were removed using porechop  (Wick et al. 2017). ONT reads were first assembled into contig using Flye assembler (v. 2. 8. 2,  Kolmogorov et al. 2019)  with the following options: -minoverlap 10,000 -i 2. Redundant contigs were removed using purge_haplotigs (v. 1. 1. 0) followed by two rounds of polishing with Illumina paired-end reads using pilon  (v. 1. 23, Walker et al. 2014). Gene modeling was performed using MAKER2 pipeline  (v. 3. 01. 02, Holt and Yandell, 2011)  using CLC assembled transcriptome as evidence. Putative function for each gene model was then assigned via a combination of similarity search  (BLASTX of predicted transcript and BLASTP of TransDecoder (v. 5. 5. 0, Haas et al. 2013)  predicted ORFs against the UniProt database) and hmmscan  (v. 3. 1b2, Finn et al. 2011 ) against the PFAM database (https://pfam.
Assembly Completeness Assessment Assembly quality was assessed using the stat program from BBMap tool  (v. 38. 94, Bushnell, 2014). Complementary quality metrics were obtained from merqury  (v. 1. 3, Rhie et al. 2020). Briefly, 20-mers database was constructed from Illumina short-reads using count function from meryl  (v. 1. 3, Koren et al. 2017). K-mer survival rate was then used to estimate base-level consensus quality score (QV). K-mer completeness was evaluated considering the fraction of reliable k-mers in read database also found in the assembly. Genome and gene models completeness were assessed by applying Benchmarking Universal Single-Copy Orthologs  (BUSCO v. 5. 2. 2, Simão et al. 2015)  with default settings using a plant-specific database of 2,326 singlecopy orthologs (eudicots_odb10).
Transposable Elements Prediction and Annotation Extensive de novo TE annotator  (EDTA v. 1. 9. 5, Ou et al. 2019 ) was used to identify and annotate transposable element (TE). Sensitive option using RepeatModeler (v. 2. 0. 1,  Smit and Hubley, 2015)  was used to identify remaining TEs. Classification consistency was evaluated using evaluate option. alluniRefprexp082813 curated database was used to perform TE annotation.
Whole-Genome Duplication Analysis To infer whole-genome duplication (WGD) events, transcript sequences of V. thouarsii, V. minor  (Stander et al. 2022) , Arabidopsis thaliana  (Lamesh et al. 2012) , Catharanthus roseus  (Franke et al. 2019) , Mytragyna speciosa  (Brose et al. 2021) , Solanum lycopersicum  (Hosmani et al. 2019) , Camptotheca acuminata  (Kang et al. 2021) , Calotropis gigantea  (Hoopes et al. 2018) , G. sempervirens  (Franke et al. 2019) , Ophiorrhiza pumila  (Rai et al. 2021) , and P. somniferum  (Guo et al. 2018)  were input to the DupPipe pipeline  (Barker et al. 2010). For each dataset, discontiguous MegaBLAST  (Ma et al. 2002 , Zhang et al. 2004 ) was used to identify duplicated gene pairs (40% sequence similarity over 300 bp). For each gene pair, the open reading frame was infer from the NCBI's plant RefSeq protein database (May 21, 2021) using BLASTx (v.
2. 6. 0-1, Camacho et al. 2009). Only the best hit sequence was retained (sequence similarity threshold: 30% over 150 aa). DNA sequence alignment against its best hit homologous protein sequence and its translation was performed using GeneWise  (Birney et al. 2004 ). Resulting amino acid sequences for each gene pair were aligned using  MUSCLE (v. 3. 6, Edgar, 2004). This alignment further guided nucleic acid alignment using  RevTrans (v. 1. 4, Wernersson and Perdersen, 2003). Codeml's F3 × 4 model from PAML package (v. 4. 9,  Yang, 1997)  was used to calculate substitutions per synonymous site (Ks) and thus determine divergence times between gene pairs.
Acknowledgements We thank access and support to the CCSC computing resources (Cascimodot Federation, CNRS, Orléans). We acknowledge funding from the  EU   Horizon 2020 research and innovation program  ( MIAMi project-Grant  agreement  N°814645 ),  ARD-CVL Biopharmaceutical program  of the  Région Centre Val de Loire  ( ETOPOCentre  project ) , and  ANR  (project  MIACYC - ANR-20-CE43-0010 ). The authors benefitted from the use of the cluster at the  Centre de Calcul Scientifique en région Centre-Val de Loire.
Data Availability The annotated genome has been deposited in the NCBI database under the Bioproject accession number PRJNA860765. RNAseq raw reads have been deposited in the NCBI database under the SRA accession numbers SRR19972991, SRR19972992, and SRR19972993. Genome annotation predicted transcripts and proteins, and transcript expression abundances are available on figshare: https://doi. org/10. 6084/m9. figshare. 20223093. v1.
GBEFIG FIG. 1. -The annotated Voacanga thouarsii genome. (A) Voacanga thoaursii flowers (i) and the molecular structure of two main MIA: tabersonine (ii) and voacamine (iii) which results in the combination of a vobasine (1) and an ibogain (2). (B) BUSCO scores of genome and annotated genes. (C) Functional annotation of genes using SwissProt, Pfam, KEGG, and GO databases. (D) Transposable element proportion and classification. TIR: terminal inverted repeat, LTR: long terminal repeat, other LTR: LTR containing retrotransposons except for Gypsy and Copia, non-LTR: retrotransposons without LTR sequence, non TIR: DNA transposons without TIR sequence. (E) Synonymous substitution (Ks) rate distribution plot for V. thouarsii orthologs compared with other eudicots. (F) Phylogenetic tree of V. thouarsii and 10 other species including three Apocynaceae (purple: C.
Designing selective ERb ligands could help reducing ER related side effects besides exerting the desired estrogenic activity. Niu et al. (55) , succeeded in building machine leaning models with high accuracies (77. 10 to 88. 34%) and AUCs greater than 0. 8 that performed equally on an external validation dataset composed of ERb selective agonists. It is to note that the negative data used for this study consisted of generated decoys rather than selective ERa ligands. Another particularity we could notice for ER models, was the predominance of Quantitative Structure Activity Relationship (QSAR) models based on machine learning (ML) algorithms. The most used ML algorithms were random forest, SVM and Naïve Bayes. Other computational approaches comprise molecular docking and pharmacophore modelling.
S. Environmental Protection Agency (EPA). The 25 international research teams forming the CoMPARA consortium used a common training set of 1,746 chemicals compiled from a data set of 11 ToxCast/Tox21 HTS in vitro assays to generate 91 predictive QSAR models for AR binding, agonist, and antagonist activity predictions. The resulting models were evaluated using non-overlapping curated literature data. Finally, all predictions were combined into consensus models with an average accuracy of 80%. In complement to the CoMPARA project, several computational LB models, obtained using deep learning and QSAR methods, were described in the literature. SB methods such as docking and MD were also used, albeit to a lesser extend because of a lack of AR structural data. In fact, there is no AR antagonist-bound crystal structure yet available in the PDB which can impair the prediction of AR antagonist molecules and the development of AR SB models.
In the other SB study, the RXR DUD-E data set  (121)  was used with a single docking approach to select the RXRa structure. Overall good performance in predicting RXR ligands was obtained with an AUC value close to 0. 8 and an early enrichment factor value EF1% of 8. 6. The third study presents a RXRa agonist ligands prediction model developed using a LB deep learning approach  (100). The model was trained on the Tox21 database and was associated with an AUC value similar to those obtained in the previous study with the docking approach.
6 i. e. the ratio of positive hits among all the database compounds. Three other groups built QSAR models to identify potential activators of PXR. The first one  (84) , achieved performances of R² = 0. 64 and was applied to identify 16 novel activators of PXR. Dybdahl et al. (83) , built a model associated with a sensitivity of 82% and a specificity of 85% that was used to identify potential PXR activators in a database of environmental chemicals. Interestingly, these molecules were also linked to cause adverse effects such genotoxicity or teratogenicity for examples.
For this reason, only two projects among the reviewed ones were dedicated to the identification of LXRb selective agonists whereas the remaining two others did not make any distinction. Both projects used a virtual screening workflow based on a combination of LB and SB methods. In the first article, Chen et al. (67)  built a selective LB model using an association of Kohonen maps and stepwise multiple regression. The model relied on the structures of newly reported dual agonists and was associated with R 2 equal to 0. 837 and 0. 843 for train and test set respectively. This model was then used to perform predictions of potential selective ligands from the ZINC database falling within the applicability domain of the model. A promising compound was found with a predicted pEC50 = 7. 0 for LXRb and pEC50 = 6.
TR are thus largely studied in the EDCs context  (139, 140). In this review, 10 models dedicated to TR are listed, including 3 models specifically developed for TRb and 4 tools to predict potential EDCs that include SB  (12, 104, 105)  and LB (19, 100) models for a panel of NRs and not only TR. It is to note that these LB initiatives trained their model using the TR Tox21 dataset and that the associated performance was among the lowest of all studied receptors. Among the TR focused studies, Zhang et al. (89)  presented a virtual screening protocol combining ensemble docking and MM-GBSA rescoring to identify TRb ligands. This protocol was developed and evaluated using the TRb DUD-E data set and was associated with an AUC value of 0. 865 and an EF10% value of 7. 418. This protocol was then applied in a prospective virtual screening of an indoor dust contaminants inventory but as it was developed using the TRb DUD-E dataset, it could also be applied to identify potential TRb binders for therapeutic applications.
Identifying RORgt modulators emerges as a promising therapeutic strategy for all these conditions. However, until very recently, the known RORgt modulators were all sharing the same scaffold and efforts have been made towards the discovery of new RORgt modulators with original scaffolds. Two articles  (98, 99)  described SB in silico approaches to identify potent inverse agonists of RORgt that can be used in auto-immune diseases treatment. In the first article, docking and negative image-based methods (NIB) were both first evaluated retrospectively with RORgt experimental data extracted from the ChEMBL database to define docking score threshold and cutoff similarity value, respectively. These 2 methods were then used in parallel to screen a collection of more than 100000 molecules commercially available.