dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of peptides. A database searches were performed using X!Tandem Alanine (Release 2017. 2. 1. 4) (http://www. thegpm. org/TANDEM). Enzymatic cleavage was described to be due to trypsin digestion with one possible misscleavage. Cys carboxyamidomethylation was set as static modification whereas Met oxidation, N-ter deamidation and N-ter acetylation were set as variable modifications. Identifications were performed using a user-supplied database and an internal database of standard contaminants (trypsin, keratins, BSA). Identified proteins were filtered and grouped using X!TandemPipeline v0. 2. 38 (http://pappso. inrae. fr/bioinfo/i2masschroq/)  94. Data filtering was achieved according to a peptide E value smaller than 0. 01 with a minimum of 2 peptides to identify a protein. Metabolite profiling of R.
2. 6, igraph). Known MIA genes from other species were used as bait to query the global co-expression network. Co-expressed neighbors were collected to identify potentially co-expressed new MIA genes. Deep learning classification. To predict the MIA-related genes from our gene expression atlas with ANN, we referred to our recently described protocol  99. The input dataset was the same than the one used to construct co-expression network. The H2O library (v3. 36) in R (v4. 2) was used to train a feedforward neural network with backpropagation. To define the true positive events, a number of genes were labeled as MIA as described above. For the true negative events (non-MIA related genes), we used genes predicted as conserved orthologs from the BUSCO evaluation made to assess assembly completeness.
Because these genes are strongly conserved across very diverse plant families, they are not expected to belong to the MIA pathway. We finally labeled 75 genes as MIA-related and 1908 as non-MIA related. This set was split into a training and a validation dataset following a 70/30 partition with seed 666. Once a network (seed 666) was trained, we looked at the number of MIA predicted genes using the full dataset (with data unseen by the network). The logloss was used as a stopping metric. A hyperparameter search revealed that a simple architecture containing 1 input layer (57 samples), 2 hidden layers (with 40 and 20 neurons, respectively) and an output layer containing 2 values (MIA vs non-MIA) provided the most relevant predictions. We added dropout ratios (0. 1 in the input and 0. 5 in each hidden) to improve the model generalization.
tetraphylla genomes gtf file. Next, 100,000 bp regions are scanned on either sides of the MIA annotation to search for any genes with the following pfam accessions annotated: PF03171 (2OG-Fe(II) oxygenase superfamily), PF14226 (non-haem dioxygenase in morphine synthesis N-terminal), PF00891 (O-methyltransferase domain), PF08240 (Alcohol dehydrogenase GroES-like domain), PF00067 (Cytochrome P450), PF08031 (Berberine and berberine-like), and PF00201 (UDP-glucosyl transferase). Regions with more than one gene of interest (MIA + pfam accession of interest) were recorded as a cluster of interest. Microsyntenic region with the two ADH rich clusters of R. tetraphylla (contig 414) was identified in C. roseus genome v. 2. 1 using BLASTN (v. 2. 6. 0-1  80  ) with the following parameters: blastn -outfmt 6 -task blastn -perc_identity 70 -evalue 1e -10.
Statistics and reproducibility. General information on how statistical analyses were conducted, including software and packages versions and number of replicates (3-7), are described in the relevant sections. Statistical analysis and graphical representation was performed in R 103 using several packages including ggplot2 (v. 3. 3. 5  89  ), topGO (v. 2. 44. 0 88 ), edgeR (v3. 28. 1  96  ), igraph (v1. 2. 6) and H2O library (v3. 36). Gene accession numbers. RtTHAS5 (OR514622); RtTHAS4A (OR514623); RtTHAS4B (OR514624); RtTHAS3 (OR514625); RtYOS (OR514626); RtVAS (OR514627); RtAMS (OR514628); RtGS (OR514629); MSTRG. 5534 (OR514630); MSTRG. 5531 (OR514631); MSTRG. 5530 (OR514632). Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Acknowledgements This work was supported by  EU Horizon 2020 research and innovation program  [ MIAMi  project-Grant agreement  N°814645 ];  BII Foundation  (grant nos. NNF19OC0055591  and  NNF20SA0067054 );  ARD CVL Biopharmaceutical program  of the  Région Centre-Val de Loire  [ ETOPOCentre project] ;  ANR  [project  MIACYC -ANR-20-CE43-0010 ]; and  APR-IR  of the  Région Centre- Val de Loire [BioSynNAC  project]. The authors benefited from the use of the cluster at the  Centre de Calcul Scientifique en région Centre-Val de Loire. We also thank the  "Plateforme d'Analyse Protéomique de Paris Sud-Ouest" (PAPPSO)  for proteomic analysis.
First, the large datasets and the complexity of biological information require the development of sophisticated data mining methods  55. Second, the analysis time is influenced by several factors, including the time required to collect a single 3D image (usually less than a minute), the number of images needed for representative and reliable results from a specific area (a few minutes), and the number  of facial areas to be analysed to obtain comprehensive information for the entire face (tens of minutes). While the combination of micrometric resolution and full-face in vivo microscopy represents a substantial technical challenge, recent advancements in imaging systems and computational techniques, particularly the integration of LC-OCT imaging with AI-based algorithms, hold promise for future real-time 3D visualisation of skin structures in the most relevant facial areas for aging investigations.
Conclusion The potential of in vivo 3D LC-OCT imaging to generate large datasets from extensive panels of healthy volunteers, currently comprising 100 female Caucasians, has been demonstrated. The technique high isotropic resolution and depth of analysis facilitated the computation of histological and cellular epidermal quantitative metrics using AI-based algorithms, enabling the correlation of skin microstructure variations with ageing. This initial exploratory study highlighted the technique capabilities in supporting a better understanding of the ageing process, particularly in determining skin layer thickness with micrometric precision at different facial anatomical sites (temple, cheekbone, and mandible). Additionally, the study provides further insights into the slight thickening of the stratum corneum (temple and mandible) and consistent viable epidermis thickness (temple, cheekbone) with ageing, contributing valuable data to the existing literature.
Introduction Misoprostol, a synthetic prostaglandin E1 analog, was initially developed as an anti-ulcer agent and is currently used for medical termination of pregnancy. 1, 2 2] [3]  In the World Health Organization's global pharmacovigilance database, there are seven spontaneously reported cases of epidermal necrolysis, following the use of misoprostol, reported between 1997 and 2020. 4 3] 5  Recently, a 25-year-old grand multipara Eritrean woman encountered TEN following the use of misoprostol 200 mcg tablets prescribed for termination of a pregnancy. TEN is an acute and life-threatening medical condition that is usually drug-related; although it does occur in children, infections are rarely responsible for the disease. 6 The most common precipitating medications that have been associated with TEN are non-steroidal anti-inflammatory drugs, sulfonamides, antiepileptics, and different antibiotics.
Discussion Taking the development of the cutaneous reaction, proportion of the body surface area affected and clinical characteristics of the reaction (involvement of the mucus membrane) into consideration, the case was diagnosed as TEN. Clinically, TEN begins with a prodrome of fever, malaise and influenza-like symptoms, one to three days before the development of mucocutaneous lesions. Mucous membranes are involved in nearly all cases and buccal mucosa is the frequently affected area. 8 In the early stages, skin pain may be prominent and out of proportion to clinical findings. 9 After one to three days, the skin lesions could progress to full-thickness epidermal necrosis. [3] 5  All SmPCs document only rash as a cutaneous adverse reaction. Searches made in PubMed, Google scholar, and the online drug information databases such as Martindale: the Complete Drug Reference, 10 Drugdex 11 and the SIDER side-effect resource  12  could not find any association between misoprostol and TEN.
14  The former study reported a weak correlation between fentanyl delivery rate and TEWL in excised split-thickness human cadaver skin, while the latter found that hexyl nicotinate was weakly correlated with TEWL in vivo in 21 volunteers. However, such findings are contrasted to those of Lotte et al. 36  The authors reported a significant correlation (R 2 = 0. 53) between TEWL and percutaneous absorption of caffeine. In that study caffeine was topically applied to four different anatomical sites and its concentrations were measured in human urine samples (n = 6-8). A closer look at their data shows similar ranges in TEWL values and the total amount of penetrated caffeine within 4 days for three sites (arm, abdomen, and postauricular), while for the forehead, these parameters have almost double the values (1.
A calibration curve of free curcumin in DMSO was used to quantify the curcumin content. Both free curcumin and CL were diluted in PBS with a neutral pH to analyze fluorescence emission profiles. Using a fluorescence spectrometer, fluorescence emission spectra were recorded using an excitation wavelength at 420 nm and capturing emission data within the range of 435-700 nm. This analysis provided insights into the fluorescence behavior of both free curcumin and curcumin within the liposomal formulation.
Then, flow cytometry was conducted using 20,000 events for each sample (BD FACS Aria III Becton Dickinson, USA). The viable (Annexin-/PI-) and apoptotic cells (Annexin+/PI-; Annexin+/PI+) were quantified using the FlowJo software 10. 0. 7 (FlowJo LLC, USA). Early apoptosis was characterized by Annexin V+/PI-and late apoptosis was characterized by Annexin V+/PI+.
Additionally, the impact of ultrasound on the release of curcumin from CLs was tested through a release experiment, utilizing an ultrasound sonication bath operating at a frequency of 35 kHz and a power density of 1 W/cm². The release was assessed by monitoring alterations in the absorbance value corresponding to curcumin release from the liposomes. In Figure  4B , we compare the release profile of CLs induced by ultrasound (shown in the red plot) to the control fraction (represented by the blue plot), which was not subjected to post-ultrasound sonication; there was an immediate steep increase in absorbance (A424), followed by a gradual rise over the subsequent 60-minute period, contrasting with the behavior observed in the non-sonicated group that showed no immediate release. In particular, the data indicated that after subjecting it to 10 seconds of sonication, approximately 40% of curcumin was released within a 120-minute timeframe, while the control CLs exhibited a release of only about 13%.
RR acknowledges the American University of Sharjah  for the Post Doctoral Grant  2022-2023. Authors acknowledge QUWA 4 th Forum of Women in Research Forum 2023,  University of Sharjah  along with all the sponsors for the Research grant. This research work was supported by  Abu Dhabi National Oil Company (ADNOC) ,  Emirates NBD ,  Sharjah Electricity Water  & Gas.
Molecular modeling experiments After geometric optimization of the compounds MN34-20, 39, NM-049 and NM65-45 structures using Gaussian 16 at DFT B3LYP level and 6-31g orbital basis sets,  85  the docking studies were performed with AutoDock Vina version 1. 1. 2. 76 Files for the docking were prepared from 3JPV PIM1 structure after removing of water molecules and pdbqt files prepared with Chimera software. 77 Apolar hydrogen atoms were removed and Gasteiger charges were added. Docking experiments were performed using the default AutoDock Vina parameters.
Acknowledgements The authors gratefully acknowledge the financial support by the  Universitede Rennes 1  for a PhD grant (to N. M. ). We also thank  Rennes Metropole  ( Spectrum 100 PerkinElmer spectrometer ) and the  Fonds Europeen de Developpement Regional (FEDER ;  D8   Venture Bruker-AXS diffractometer ). Thermofisher is also acknowledged for generous gift of 2,2,6,6-tetramethylpiperidine. S. B. and T. R. thank  IBiSA  ( French Infrastructures en sciences du vivant: Biologie, Santeet Agronomie) and Biogenouest (Western France life science and environment core facility  network supported by the  Conseil Regional de Bretagne)  for supporting the KISSf screening facility ( FR2424 ,  CNRS  and  Sorbonne Universite). L. P. and V. T. are grateful to the  French Cancer League (Ligue contre le cancer Comite17 Charente-Maritime)  for financial support and to the  Canceropole Grand Ouest Marine Molecules, Metabolism and Cancer network (3MC)  for scientific support.
The opposite case is observed with the metabolite with an Rf value of 0. 18 on TLC 4 (green color), which is present only in predated leaves and could therefore be considered a metabolic marker for herbivores. Nuclear magnetic resonance spectroscopy 1 H NMR. The 1 H NMR data were obtained only from the organic extracts of the plants and caterpillars using DMSO-d 6 as a solvent. Solvent signals from deuterated DMSO and residual water at 2. 5 and 3. 3 ppm, respectively, were removed (Figure  1 ). The analysis shows minor differences between the spectra of healthy and predated Allamanda leaves. This result is inconsistent with hypothesis 3 (in which a plant response to aggression was observed). The 1 H NMR spectra of caterpillars' bodies and feces show large differences between the samples, and the chemical profile of healthy Allamanda leaves and the caterpillar feces is very similar.
Principal Component Analysis (PCA). A total of 24 chromatograms were acquired using HPLC-EI-MS, 12 from the aqueous fraction and 12 from the organic fraction (three biological replicates for each species). To identify differences in the metabolites or even in their concentration in the samples, the set of 24 samples was analyzed, obtained from XCMS and used for data analysis. Two spreadsheets were obtained after data processing of the HPLC-MS analysis, with 168 and 412 variables from the organic and aqueous fractions, respectively (retention time m/z). In the PCA analysis, two plots were generated, namely score and loading plots. The first one shows the sample groupings, whereas the second one indicates the contribution of each variable to these samples. The PCA analysis.
Principal Component Analysis (PCA). A total of 24 chromatograms were acquired using HPLC-EI-MS, 12 from the aqueous fraction and 12 from the organic fraction (three biological replicates for each species). To identify differences in the metabolites or even in their concentration in the samples, the set of 24 samples was analyzed, obtained from XCMS and used for data analysis. Two spreadsheets were obtained after data processing of the HPLC-MS analysis, with 168 and 412 variables from the organic and aqueous fractions, respectively (retention time m/z). In the PCA analysis, two plots were generated, namely score and loading plots. The first one shows the sample groupings, whereas the second one indicates the contribution of each variable to these samples. The PCA analysis was Plants 2023, 12, 520 9 of 17 focused on the detection of any inherent pattern within the data.
As observed in Figure  7 , samples of healthy and predated Allamanda leaves are closely related, with small differences from each other, when compared with the caterpillars' bodies and feces. Further, when all samples are compared, the predated and not predated leaves from Allamanda are very similar to each other, and different from the other samples. In addition, the caterpillar body extracts present a great difference. In Figure  8  (loadings plot), the variables that influence the grouping observed are presented. biological replicates for each species). To identify differences in the metabolites or even in their concentration in the samples, the set of 24 samples was analyzed, obtained from XCMS and used for data analysis. Two spreadsheets were obtained after data processing of the HPLC-MS analysis, with 168 and 412 variables from the organic and aqueous fractions, respectively (retention time m/z).
15 mg of each extract was solubilized in 650 µL of DMSO and transferred to NMR tubes. 1D 1H nuclear magnetic resonance spectra were recorded with a BRUKER Avance 300 MHz spectrometer equipped with a BBO probe and automatic tube changer. Chemical shifts (δ) were expressed in ppm relative to tetramethylsilane (TMS) taken as the external reference, with internal calibration performed on the solvent signal. All spectra were processed using Topspin 2. 1 software. The classical 1D proton with a 90 • pulse width was performed. The spectra were acquired using 256 scans and 2 dummy scans of 32 K data points with a spectral width of 5411. 255 Hz. High performance liquid chromatography coupled with mass spectrometry (HPLC-MS). Triplicates of each extract were made and dissolved in 750 µL of methanol (LCMS grade) and 750 µL of ultra-pure water (ElgaPurelab Classic).
The mixtures were sonicated in the ultrasonic bath then filtered on 13 mm and 0. 45 µm PTFE filters and placed in amber vials. The analysis was performed on a Waters (Milford, MA, USA) Alliance e2695 liquid chromatographic system, equipped with a Waters 2996 photodiode array detector (PDA), coupled with a orthogonal quadrupole mass spectrometer (Micromass ZQ, Manchester, UK). The systems were controlled by MassLynx v. 4. 1 software (Micromass, Manchester, UK). The mass was equipped with an electrospray ionization ESI (Waters) source; the ionization was performed in positive mode. A double detection was carried out via mass spectrometry in ESI (range 71 to 1200 Da) and using a PDA diode array detector (UV detection between 210 and 400 nm). The analytical column was an XTERRA MS C18 column (2.
1 V. Cyclic Voltammetry (CV). Cyclic voltammograms were recorded at 298 ± 1 K in a conventional three electrode cell using a platinum wire auxiliary electrode and an Ag/AgCl (3M NaCl) reference electrode. Measurements were carried out with CH I660 equipment using 0. 10 M potassium phosphate buffer at pH 7. 0 as a supporting electrolyte. The working electrode was prepared by evaporating 50 µL of an ethanol (EtOH) suspension of the extract of interest, ground leaves or insect samples, under air on a glassy carbon electrode (GCE, BAS MF 2012, geometrical area 0. 071 cm 2 ). In order to mimic the natural environment, no degasification of the electrolyte was performed. Principal Component Analysis (PCA). The raw data from HPLC-MS were exported as netCDF files, using DataBridge software (Waters, USA), and pre-processed using XCMS Online  [82, 83]  for feature detection, retention time correction and alignment of metabolites detected on HPLC-MS analysis.
The dataset was created with 12 samples from each organic and aqueous extract fraction (3 samples from each manipulation (see Table  1 )). Peak Plants 2023, 12, 520 13 of 17 detection was performed using cent Wave peak detection (∆m/z = 10 ppm; minimum peak width, 5 s; maximum peak width, 20 s) and mzwid = 0. 015, minfrac = 0. 5, and bw = 5 were used for the retention time alignment. The processed data (csv file) were further exported to MetaboAnalyst 4. 0  [84]. All data variables were scaled by the pareto method prior to PCA. PCA is an unsupervised method commonly used to identify patterns between multivariate samples  [85, 86]  and was recently employed on the chemical variability of Allamanda cathartica extracts  [87].
Sampling campaigns All information related to the wastewater collection and sites (collection dates, flow rates and population) can be found in the Supplemental Files (Table  S3 ). As part of an ongoing global surveillance program, the number of countries (and sites) has increased from 8 (12) in the first iteration to 10 (25) and finally 16 (47). Data pertaining to the first two campaigns have been previously published (2019-20)  (Bade et al. , 2021)  and 2020-21  (Bade et al. , 2022). The population covered has increased from five to more than 17 million inhabitants. All samples were collected at the end of Decemberearly January, coinciding with the New Year period. The first sampling campaign was performed before the COVID-19 pandemic, the second during the pandemic and the third when restrictions were ended/eased or presented only in a few countries.
For quantification purposes, both transitions needed to be present, while the ion ratio (within 20%) as well as retention time had to compare with the standard (within 2%). If only one transition was present, the compound was deemed at above the limit of detection (LOD) but below the limit of quantification (LOQ). For calculation purposes, this was given as the midpoint between the LOD and LOQ. As no analyte-specific internal standards were used for this method, quantification was based on the peak area ratios between native and surrogate internal standards compared to an external calibration curve. All data were acquired and processed with SCIEX OS. Further details about the analytical methodology are provided in the Supplemental Files.
Calculations For each compound, a calibration curve of up to 11 points was constructed from 0. 1 to 10,000 ng/L. Concentrations were calculated using the isotope dilution method and processed using SCIEX OS or Multiquant 3. 0. 2. As labelled internal standards were not available for the NPS in this work, a surrogate internal standard was utilised (Table  S4 ). The flow rates and population data provided by each collaborating laboratory (Table  S3 ) were then used to calculate excreted mass loads  (Table S4 for 2022-23; (2019 ( -20 (Bade et al. , 2021) )  and 2020-21  (Bade et al. , 2022) ).
Acknowledgements The authors sincerely thank the wastewater treatment plant operators for their help in collecting samples and the support provided by the lab staff who performed the extraction of the samples. The authors also thank  SCIEX  for the use of their instrument and  Liam O'Brien  for his statistical advice. The  University of Iceland  wants to particularly acknowledge Efla hf. and Veitur ohf. for their assistance with sample collection. SLU acknowledges the support of  Inga Haalck ,  Oscar Götlind  and  Gabriel Persson  for sample collection. IGE acknowledges the support of  Nelly Laforet  and  Myriam Millet-Perrin  for sample collection. The  Queensland Alliance for Environmental Health Sciences, The University of Queensland , gratefully acknowledges the financial support of the  Queensland Department of Health.
R. B. is the recipient of an  Australian Research Council   Discovery Early Career Award  (project number  DE220100381 ), and J. M. is the recipient of an  Australian Research Council Australian Laureate Fellowship  (project number  FL200100028 ) funded by the  Australian Government. N. R acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No. 896141. L. B. acknowledges the grant  RYC2020-028936-I  funded by  MCIN/AEI/ 10. 13039/501100011033  and by " ESF Investing in your future ". E. G-L. acknowledges the financial support of the  Madrid Government (Comunidad de Madrid-Spain)  under the Multiannual Agreement with Complutense University in the line Program to Stimulate Research for Young Doctors in the context of the  V PRICIT (Regional Programme of Research and Technological Innovation)  (project number  PR65/19-22432 ).