dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis For c-Fos experiments, slides were scanned using an automatic slide scanner (Axio Scan. Z1, Zeiss). Regions of interest were drawn from scanned brain sections based on the Paxinos mouse brain atlas using QuPath tools  [59]. Coronal sections were selected for the LHb, 3-5 sections between bregma -1. 22 mm and -1. 70 mm; for MeA and VMH, 3-5 sections between bregma -1. 34 mm and -1. 70 mm; and for PAG, 3-6 sections between bregma -3. 40 mm and -3. 88 mm. The number of c-Fos + nuclei in each drawn region was automatically counted using the built-in Cell Detection method. The same cell detection parameters, such as set up, nucleus, and intensity parameters, were applied to all the regions. pS6-positive cells were counted using Zen software (blue edition 3. 0, Zeiss) and the particle analyzer plug-in of Fiji  [60]  on 6-15 VNO images per animal and expressed as a proportion of the total DAPI-positive nuclei.
Statistics Statistical analyses were performed using the software Origin Pro 2021 (OriginLab Corporation, Northampton, MA, USA) and GraphPad Prism 9. 0 (GraphPad Software). Assumptions of normality were tested before conducting the following statistical tests. Student's t-test was used to measure the significance of the differences between two distributions. In case the results failed the test of normality, Mann-Whitney test was performed. Multiple groups were compared using Kruskal-Wallis one-way analysis of variance (ANOVA) with Mann-Whitney and Holm-Šídák's tests as a post hoc comparison. The probability of error level (alpha) was chosen to be 0. 05. The statistical tests used were two-sided. Unless otherwise stated, data are expressed as mean ± standard error of the mean (SEM). Specific statistics and number of samples analyzed are described in the figure captions.
Acknowledgements We thank  Chantal Porte  for technical assistance;  Daniel Schauenburg ,  Kerstin Becker ,  Lisa-Marie Knieriemen ,  Deborah Crespin , and  Aurelie Gasnier  for mouse husbandry;  Martina Pyrski  for fruitful discussions; and  Marie-Claire Blache  for imaging experiments. Source of mouse silhouettes: https:// scidr aw. io/.
Funding This work was supported by  Deutsche Forschungsgemeinschaft (DFG)  grants  Sonderforschungsbereich 894  project  A17  (FZ and TL-Z) and  Sonderforschungsbereich-Transregio TRR 152  project  P10  (FZ and TL-Z),  Agence National de la Recherche (ANR)  grant  ANR-20-CE92-0003  (PC), and  Region Centre Val de Loire  project  201900134883  (PC). A-CT was supported by a grant from the  University of Tours and Region Centre Val de Loire.
121 ] [124]  According to the abovementioned studies performed in acute pain conditions, the analgesic paracetamol/ibuprofen combination appears to be well tolerable without a significant increase in safety concerns compared to either drug alone. In the PANSAID trial, patients on paracetamol/ibuprofen combination for postoperative pain at the first 24 hours after total hip arthroplasty did not experience a significant increase in the risk of serious adverse events compared to patients on either drug alone. 120 However, a 13-week trial showed patients on paracetamol/ibuprofen combination had a higher risk of decreases in hemoglobin level (≥1 g/dl) than monotherapy, suggesting a potential risk of bleeding on long-term use of paracetamol/ibuprofen combination. 122 A population-based retrospective cohort study, using data from the government of Quebec health insurance agency databases, suggested that combining traditional NSAIDs with acetaminophen increases the risk of upper GI events beyond that of either agent alone.
125 Even if one of the limitations of this study pertains to the use of administrative databases in which the indication for which a medication is prescribed and actual drug consumption is not known, the results of these two studies warrant performing further research to establish the beneficial effect of the paracetamol/NSAIDs combinations and to investigate their safety profile for patients in need of chronic therapy. The combination of paracetamol and gabapentin is another combination that is currently under investigation. Experimental evidence showed that paracetamol had a synergistic effect on gabapentin. 126 In patients who underwent an abdominal hysterectomy, Durmus et al studied the analgesic effect of the preoperative paracetamol and gabapentin combination compared to gabapentin 1200 mg alone.
(i. e. No additional sample was taken and VKA free and total concentrations were determined on an aliquot from a plasma sample, which had been taken for the biological monitoring of INR). All samples were collected before the hemodialysis session and about 12 h after the VKA intake. Plasma samples from patients eligible for inclusion in the study were recovered daily. Then, they were frozen at -20 • C in the Pharmacology-Toxicology laboratory and analyzed weekly before being destroyed. The following clinical and laboratory data were collected: age, sex, BMI, date of the start of dialysis, CKD etiology, VKA indication and dosage, INR, creatinine, calcium, phosphorus, and albumin. All data were measured the day of the INR test and were collected from our biological and clinical software (DxLab® and DxCare®, Medasys).
Statistical analysis Baseline characteristics are described for all participants. The results are expressed as the median (interquartile range, IQR) or the frequency (percentage). The normality of our data distribution was checked using the shapiro test. The Mann-Whitney test was used for intergroup comparisons. The associations between total VKA plasma concentrations and albumin levels, and free VKA plasma concentrations and INR were assessed using the Spearman correlation test. The first order risk was set at 5 % two-sided for the entire study. Statistical analyses were performed using GraphPad Prism software (version 8, GraphPad Software Inc. San Diego, CA, USA).
Stability Stability was assessed by calculating the percentage of loss of concentration in a sample stored in the test condition vs. a reference sample prepared immediately before analysis. In plasma, fluindione and warfarin were found to be stable after storage for 24 h at room temperature, between 4 and 8 • C, and at -20 • C. The same was found after one-week of storage at -20 • C (Supplementary Table  3 ). The analysis of the commercial QC Dosinaco at the four concentration levels showed a percentage of loss lesser than 20 % for both molecules after 9 months of storage at -20 • C (Supplementary Table  4 ). For VKAs free concentrations, our tests did not show a significant difference (percentage loss of concentration < 20 %) between samples for which isolation of free form was taken rapidly (less than 24 h) or was delayed (after a storage at -20 • C for 1 month) (data not shown).
Zootechnical data and faeces sample collection Pigs were weighed individually, and feed disappearance was determined per pen at day 0, 63, 81 and 97 of the trial. From these data, average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR) were calculated. Dead or culled pigs were weighed, and their estimated feed consumption was considered for FCR calculation (Supplementary Table  1 ). Faecal samples were also collected individually on the above indicated days via rectal massage from 5 pigs per pen. The same 5 pigs within each pen were sampled at each time point. Faecal samples were then stored at -20°C until analysis.
Primary analysis Raw metagenomic reads were processed using QIIME2  (Bolyen et al. , 2019)  microbiome analysis package Data were first imported in Fastq format paired-end sequence. After a quality check, DADA2  (Callahan et al. , 2016)  was used as the main process of selecting sequence variants for amplicon analysis. DADA2 used a statistical error correction model and builds ASVs (Amplicon sequence variants). In order to link each ASVs to the closest organism, SILVA database v138  (Quast et al. , 2013)  was used as the reference database for identifying the sequences. De novo phylogenetic trees used in downstream measures of diversity was also built using default QIIME2 provided methods phylogeny align-to-tree-mafft-fastree (MAFFT multiple sequence alignment program)  (Price et al. , 2010; Katoh and Standley, 2013).
selbal is distributed as a R package. https:// malucalle. github. io/Microbiome-Variable-Selection/selbal. html. To explore interaction with zootechnical variables, FactomineR  (Le et al. , 2008)  and Factoextra R  (Kassambara & Mundt, 2021)  packages were used specifically for Multiple Factor analysis (MFA). MFA is used to identify the relationships between multiple variables or factors in a dataset. It is similar to principal component analysis (PCA), but unlike PCA, which is used to identify the underlying structure of the data, MFA is used to identify the relationships between the variables or factors.
However, over the whole period of the trial (D0 to D97), no significant difference (p=1,7x10 -1 ) was observed between the two groups (Figure  3B ). Finally, the feed conversion ratio (FCR) was evaluated per pen. No significant difference was observed for the FCR between the two groups on the overall period, despite a reduction of 2. 7%, from D81 to D97 in the BG-fed group (Figures  4A, B ). All the zootechnical data have been summarized in Supplementary Table  1.
Potential biomarker to predict Lawsonia intracellularis infection As displayed in Figure  7 , a simple ratio of two taxa might identify animals whose microbiota would be conducive to a later infection or would be an early indicator of a L. intracellularis infection not yet detectable by qPCR. Our dataset analysis led to the ratio constituted by Fibrobacter (as numerator), and Lachnospiraceae_AC2044 (as denominator), the increase of which was linked to animals infected by L. intracellularis in later times, with a good discrimination power (AUC>0. 84). Thus, a higher proportion of Fibrobacter would be preferentially associated with a faecal microbiota that would be more susceptible to L intracellularis, as opposed to a higher proportion of Lachnospiraceae_AC2044_group, which would be associated with a greater resistance to future infection.
Specialized metabolite (SM) diversification is a core process to plants' adaptation to diverse ecological niches. Here, we implemented a computational mass spectrometry-based metabolomics approach to exploring SM diversification in tissues of 20 species covering Nicotiana phylogenetics sections. To markedly increase metabolite annotation, we created a large in silico fragmentation database, comprising >1 million structures, and scripts for connecting class prediction to consensus substructures. Together, the approach provides an unprecedented cartography of SM diversity and section-specific innovations in this genus. As a case study and in combination with nuclear magnetic resonance and mass spectrometry imaging, we explored the distribution of N-acylnornicotines, alkaloids predicted to be specific to Repandae allopolyploids, and revealed their prevalence in the genus, albeit at much lower magnitude, as well as a greater structural diversity than previously thought.
Most recent advances in computational metabolomics provide a long-awaited framework to systematically explore the above-described importance of the species × tissue SM variations  (4). These capacities to explore plant chemical spaces are further propelled by platforms such as, the MassIVE database (https:// massive. ucsd. edu/) reaching 12,000 metabolomics datasets in 2022. Despite the increasing amount of data that can be generated from modern mass spectrometry (MS) instruments, the average annotation rate of most MS metabolomics studies remains at the order of a few percent of deconvoluted MS/MS features  (17). The number of computational tools to address this challenge of transforming spectral information into chemical knowledge is hence rapidly increasing and can be divided into two main approaches.
In addition, we aimed via this pluri-tissue approach to exploring tissuelevel shifts in SM class prevalence across the focal species as a mechanism of organismic-level chemodiversification. Noteworthy, amounts of leaf exudate material collected greatly differed among the focal species, with Nicotiana setchellii (2. 7 mg of exudate per g of leaf fresh weight) and Nicotiana glutinosa (2. 5 mg/g) producing the largest amounts of dried exudates collected from leaf washes (fig. S1  and table  S1 ). All methanolic extracts were analyzed using a previously established UPLC-ESI + -QTOF MS (ultraperformance liquid chromatography-positive mode electrospray ionization quadrupole time-of-flight MS) method with optimized settings for massive MS/MS data collection  (6). A total of 17,901 metabolitederived MS/MS spectra (hereafter referred to as features) were, after a data redundancy and contaminant check using a custom script, deconvoluted and considered for feature-based molecular networking (FBMN) processing with settings that were optimized to handle the species × tissue-exacerbated metabolic diversity in the dataset.
Deep metabolome annotation empowered by a multiinference approach incorporating a 1 million natural product in silico spectral database and consensus substructure computations The previous analysis indicated a critical need not only for broadly increasing feature annotations beyond CANOPUS class predictions but also for gaining structural insights into core (sub)structures underlying molecular networks' topology. As outlined in a recent review  (40) , substructure annotation provides information about functional groups, building blocks, or scaffolds within a chemical structure. This level of information is complementary to compound class prediction, most commonly addressing biosynthetic origin and/or compound physicochemical properties. To propel substructure identification in our dataset, we first optimized a multi-inference annotation pipeline (figs.
S2 and S5). Briefly, feature spectra were first queried against an in-house Nicotiana attenuata SM MS/MS database (entries resulting from the analysis of purified SMs) and the GNPS library, the resulting hits being referred respectively to as annotation levels 1 to 2 according to the Metabolomics Standard Initiative nomenclature  (41). Interrogation of these two experimental spectral databases provided hits for 4% of the MS/ MS features (Fig. 3A ). Level 3 of the annotation nomenclature regrouped class-based annotations mostly derived from manual inspection of network-level hits (5%). To circumvent limitations in the chemical space covered by these two experimental databases, we conducted spectral interrogations in parallel against in silico predicted MS/MS spectral libraries using both molDiscovery that predicts MS spectra of small molecules on the fly and scores their probabilistic modeling  (24)  and a combination of CFM-ID and MatchMS.
The above-described multiquery approach of the 17,901 features from our dataset retrieved annotations for 57% of these features, with 9% hits for priority levels 1 to 3 (Fig. 3A ). To maximize structural insights that could be gained from this deep annotation, we lastly computed the top most common substructures [referred to as network consensus structure (NCS)] based on feature annotations for each of the FBMN molecular networks that did not contain any level 1 to 2 annotations. Consensus structure computational prediction relies on an algorithmic approach that uses hits obtained from in silico MS/MS spectral databases (see description in Materials and Methods and "Code availability" section). The NCS strategy is illustrated in Fig. 3 (C  and D ) with top NCS hits for network #486 whose MS/MS features were initially classified as amino acids and derivatives by CANOPUS.
The latter can typically be approached through MS2LDA, an unsupervised method to extract common patterns of mass fragments and neutral losses, referred to as mass motifs, from collections of fragmentation spectra  (22). From this analysis, we retained 76 mass motifs that best depicted the structural diversity within our dataset as confirmed by hierarchical clustering (resulting in clusters of covarying mass motifs) and mapping of enriched CANOPUS predictions for each mass motifs (motif-level propagation of CANOPUS predictions) (Fig. 5A  and fig. S9 ). In analogy to the critical role of conserved domain/motif inferences in protein structure-activity studies, mass motif inference offers a dimensionality reduction perspective on recurrent fragmentation patterns derived from particular substructures.
With this workflow, we notably shed light on the structural diversity and phylogenetics distribution of NANNs, a gain-of-function defensive innovation previously thought to have evolved with Repandae allopolyploids speciation  (38). A major challenge in MS metabolomics remains to reach broad structural annotation ("deep metabolome annotation") and substructure discovery beyond chemical class predictions and the dereplication of previously identified SMs, which is the most frequent outcome of molecular networking-based data exploration. In particular, with the use of heterogeneous computational annotation tools and that of querying highly diverse experimental and in silico MS/MS database comes the inherent difficulty of systemically prioritizing and/or merging the minimal set of most reliable annotations collected from these inferences.
MolNetEnhancer has been developed to more efficiently combine outputs from molecular networking, MS2LDA as well as in silico and chemical classification tools  (45). However, substructure discovery from MolNetEnhancer outputs is strongly hampered by the scarcity of annotated motifs in the Mass2Motifs database embedded into MS2LDA and by the fact that many of these correspond to relatively unspecific fragmentations (e. g. , water, methyl, and hexose losses). Only 24 of the 76 mass motifs retained for further analysis had partial annotation hints in the MS2LDA Mass2Motifs database (data S5). To improve substructure discovery and annotation, we implemented two complementary approaches. On the one hand, we propagated CANOPUS predictions at mass motif level (MP-CANOPUS) by computing frequencies in "superclass/subclass/most specific class," and combined this information with mass motif coregulation analysis (Fig.
5 ). The second approach implemented for substructure analysis involved advanced maximum common substructure calculations to integrate annotations from multiple tools on a network (NCS) or motif level (MCS). Overall, we obtained 349 NCS or 303 MCS predictions for the whole dataset (data S3 and S6). Maximum common substructure computation for substructure prediction had been used in one of our previous studies to cluster candidate structures obtained by the MetFrag searches among coregulated herbivory-induced metabolites  (46)  and is also one of the processing steps within the Network Annotation Propagation tool of the GNPS web platform  (47). Together, we advocate that the NCS/MCS approach implemented here has three main advantages: (i) It is an efficient mean of summarizing common substructure within the diversity of outputs from database queries as SMILES strings, (ii) it can used as input to reveal substructures statistically associated with intense chemodiversification in a given species, and (iii) it provides structural guidance during the manual interpretation of mass motifs or molecular network.
In this respect, our study led to the curation of 76 mass motifs (data S5). Such an effort is important to empower supervised search of mass motifs, which is already possible in MS2LDA and which will be greatly facilitated with the recent release of the MS2QUERY tool  (48). A very important delivery of our work is the development and public sharing of the 1M-DB which is, to the best of our knowledge, the largest in silico spectral database. This approach resulted into fivefold more hits (annotation of 57% of the total features) than experimental spectral database interrogation alone. Data of the 1M-DB can currently be accessed and interrogated from the GNPS platform. The size of this dataset can represent a challenge for MatchMS-based queries, which can nonetheless be locally implemented with reasonable computing capacity with the parallelized script (see the "Code availability" section) provided with our study.
It is therefore foreseeable that the efficiency of the interrogation of the 1M-DB will strongly benefit from up-to-date optimization of MatchMS parallelization as part of future version releases. Multiple tools have been developed in recent years to produce hypothetical MS/MS spectra  (23) (24) (25) (26) 49). A more recent development in this area is that of QCxMS, a computational tool that generate high-quality fragmentation spectra from molecules based on molecular dynamics calculations  (26). This program is currently too much computationally demanding and could not be transposed to the scale of this study, besides the computation of MS/MS predictions for the 429 structures of the Jassbi database and using a limited number of fragmentation trajectories (Zenodo link: https://doi. org/ 10.
5281/zenodo. 8123590). One promising direction for improving the confidence of such in silico fragmentation-based annotation is exemplified by the recently developed COSMIC workflow that incorporates a confidence score consisting of kernel density P value estimation from a decoy library and a support vector machine algorithm  (50). With the increasing quality of MS/MS predictions, one interesting perspective could be to extract mass motifs from them and thus directly infer fragment substructures produced from known structure in silico decomposition. In terms of structural information, the SM metabolic cartography generated in this study goes far beyond to a recently published chemotypic classification of the Nicotiana genus that mostly consisted in the dereplication of primary metabolites such as steroids and only a few SMs  (51).
Plant material, growth conditions, and treatment Nicotiana species with their origin and associated accession numbers are summarized in table  S1. Seeds of all Nicotiana species were directly germinated on soil, with the exception of N. attenuata, for which smoke-induced seed germination was established as described previously  (8). For all species, glasshouse growth conditions were as described previously  (8). Six-to eightweek-old elongated plants were used for all metabolomics analyses. To analyze the regulatory function of jasmonate signaling on metabolomics-inferred specialized metabolism classes, petioles of two elongated plants were treated with either 20 μl of lanolin paste containing 150 μg of MeJA (Lan + MeJA) or with 20 μl of pure lanolin (Lan) according to Heiling et al. (6). Leaf samples were harvested 72 hours after treatment, flash-frozen in liquid nitrogen, and stored at -80 °C until use.
Feature-based molecular networking of UPLC-QTOF MS data Raw data were converted to the. mzML format using MSConvert (version 3. 0. 21112-b41ef0ad4)  (69). The resulting data files were then processed with the Batch Mode (see "Code availability" section, script S10) of MZMine 2. 53  (70)  and exported for FBMN analysis in the GNPS environment  (18, 19)  and for spectral analyses in Sirius. The resulting. mgf and. csv files were further filtered to exclude redundant nonbiologically informative MS/MS features using developed Python scripts S11 and S12 (see "Code availability" section). The m/z signals that appear >5 times (±3 ppm) with a retention time coefficient of variation greater than 10% were discarded. This filtering step excluded 11,580 features (of a total of 29,481 retrieved from the MZMine-based processing), a vast majority of those corresponded to redundant features detected at high level in solvent blanks.