dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009)  to characterize measurement variability according to the solvent extraction mixtures. The percentage of total detected metabolites per sample was also calculated for each mixture used for metabolites extraction, for each dataset (i. e. , MeOH fractions analyzed by GC-MS, MeOH fractions analyzed by LC-MS and CHCl 3 fractions analyzed by GC-MS) on the final matrix (after data analyses and filtering according to blanks and QC). The normality of the data distribution (%RSD and %compounds detected) was tested using the Shapiro-Wilk test but not confirmed. The non-parametric Kruskal-Wallis' test was thus used to identify differences between the percentages of RSD and metabolites detected according to the method, followed by post hoc Conover's test. To identify which significant factors were linked to the metabolites diversity, we used Permutational Multivariate Analysis of Variance using distance matrices (PERMANOVA, 9999 permutations, vegan package for R).
Principal component analysis (PCA) was used to visualize the metabolome variation according to the irradiance condition and time (ade4 package for R). Powered Partial Least-Squares-Discriminant Analysis (PPLS-DA) were used to find the maximum covariance between our data set and their class membership. Permutational tests based on cross model validation (MVA. test and pairwise. MVA. test) were applied to test differences between groups (RVAideMemoire package) and correlation circles were drown to identify discriminating compounds (RVAideMemoire package). Wilcoxon signed-rank tests were used to identify differences in normalized intensities of discriminating compounds between sampling time (t0 vs. t7 samples) and Mann-Whitney-Wilcoxon tests to identify those between light treatments (NI vs. AI).
47 and 13. 28%, respectively, Figure  2C ) while M3 gave significantly higher RSD (median RSD of 21. 12%; post hoc p < 0. 05). A higher number of metabolites were detected with M1 and M3 (96. 96 ± 4. 51 and 87. 39 ± 18. 08% of total detected metabolites, respectively; Figure  2D ) but the variability in M3 appeared superior (standard deviation of 18. 08%, four times higher compared to M1). A lower number of metabolites was detected in the same dataset with M2 (66. 95 ± 11. 23%; post hoc p < 0. 05). In the same MeOH fractions analyzed by LC-MS (Figure  2E ), low RSDs were obtained for all mixtures but M2 gave the lowest (median RSD 5. 29%) compared to M1 and M3 which were not significantly different regarding the reproducibility (median RSDs of 6. 42 and 6. 45%, respectively; post hoc p = 0.
58). A higher number of metabolites were detected with M3 (92. 2 ± 7. 8%), followed by M1 (85 ± 7. 4%; Figure  2F ). As for GC-MS analyses in these MeOH fractions, less metabolites were significantly detected in the same dataset with M2 (71. 3 ± 6. 3%). Combining results obtained with both techniques on the MeOH fractions (Figures  2G, H ), we got a higher number of metabolites detected with M1 and M3 (91 ± 8. 5 and 89. 8 ± 13. 4%, respectively) with a lower variability for M1, while not statistically supported [Figure  2H ; KW = 14, p < 0. 05, post hoc p(M1 vs. M3) = 0. 98]. Collectively, we determined that solvent mixture 1 was more appropriate to reflect the chemical diversity of MPB biofilm and was used for the light exposure experiment.
Short-Term Light Exposure Experiment CHCl 3 Fractions Analyzed by GC-MS After GC-MS data analyses and the exclusion of artifactual and irreproducible peaks (coefficient of variation > 25%), a total of 56 features were highlighted in the CHCl 3 fraction (see Supplementary Table  S1  and Supplementary Figure  S2 ). These features were tentatively annotated using NIST 2017 library and by comparison with a standard mixture for fatty acid methyl esters. Around 77% of the compounds could be annotated while 23% remain unknown. Among the annotated compounds (Table  2  and Figure 3 ), a majority of fatty acids (FA) were displayed (53. 5%), with a dominance of saturated FA (SFA; 30. 2%) followed by monounsaturated FA (MUFA; 11. 6%) and polyunsaturated FA (PUFA; 11. 6%). Other families of molecules were highlighted such as alkenes (9.
06; F(time * irradiance) = 1. 70, p(time * irradiance) = 0. 13; PPLS-DA, CER = 0. 619, p = 0. 146, Figure  4D ]. The effects of experimental conditions on the metabolomic fingerprint of MPB biofilms were finally explored in the same MeOH fraction analyzed by LC-MS. After data analyses and filtering, 2,547 features were considered in this fraction. The explained variance on axis 1-2 of the PCA was 62. 65% (Figure  4E ) and mainly due to a high intragroup variation in samples collected at t7 after exposure to higher artificial irradiance (t7 AI), as observed in the PCA for the same fraction analyzed by GC-MS. Only the time was correlated with metabolomic changes in this   S4 ) did not match with any known compounds after the construction of a molecular network with GNPS and were not unambiguously identified by annotation against ISDB, MassBank and SIRIUS 4.
CONCLUSION This paper represents the first report about the metabolomic fingerprinting of MPB biofilms from mudflat sediments using an untargeted GC-MS and LC-MS metabolomic approach and will provide a baseline for further work in this area. Of the three extraction solvent mixtures tested, we concluded that using a MeOH:CHCl 3 (1:1) mixture provided the best compromise. The proposed protocol, detailing steps from sample collection to data analyses, was successfully applied to a case study: the impact of light exposure condition on the metabolome of MPB biofilm. While no metabolomic change was recorded in the MeOH fraction according to light exposure conditions, significant variations in the metabolomic fingerprinting of MPB biofilm were highlighted in the apolar fraction, according to the light exposure or time.
Some metabolites correlated to these changes were identified and annotated. Our study demonstrated the interest of the metabolomic approach introduced here for rapid and simultaneous detection of metabolites from various groups and their respective chemical identification using available GC-MS databases. Both selected techniques are relevant to be used in combination for a broader analysis of metabolites. With its rich database, GC-MS allows a better identification of compounds and is particularly suitable for non-polar fractions. LC-MS, highly sensitive, is more appropriate for polar, weakly polar and neutral compounds  (Wang et al. , 2015). The metabolomic workflow introduced here on MPB biofilms has the potential to be adapted to further ecological studies on MPB biofilms in mudflat areas and would complete classical approaches on these biofilms.
DATA AVAILABILITY STATEMENT Metabolomics data have been deposited to the EMBL-EBI MetaboLights database (doi: 10. 1093/nar/gks1004. PubMed ) with the identifier MTBLS1378 (for LC-MS data) and MTBLS1379 (for GC-MS data). The complete LC-MS dataset can be accessed here https: //www. ebi. ac. uk/metabolights/MTBLS1378 and https://www. ebi. ac. uk/metabolights/MTBLS1379 for the GC-MS dataset.
e. the average lifetime of females or males in each relicate). The equation is given as follow: RE r ¼ l ML Where l is the total number of offspring (here viable larvae) produced per assayed females or males in the population replicate r, and ML is the recorded mean lifespan (in weeks) of males and females in the replicate r. RE values (as offspring per individuals and per mean weeks of survival in the population) of each sex and population replicate within each sex-ratio condition were used as data points for comparisons between modalities of sex-ratio. Note that female RE values are likely representative of both female and male conditions resulting from the experiment, because the female reproductive performance resulted from mating with males from their respective population. By contrast, male RE values are representative of the male condition only, because male reproductive performance was standardized by pairing it with a virgin and age-controlled female that did not experience the experimental conditions.
Starting models included sex-ratio condition, week (continuous variable for fertility, ordinal variable for immunity), their interaction, body condition and replicates treated as a random factor (REML estimates of variance component). The models presented here are those minimizing the AICc, where ΔAICc > 2 is usually considered to be good support  [61] , after comparisons of all models including predictors and their interactions, in a stepwise fashion (see Additional file 1: Table  S1 ). The analyses of reproductive effort were made using ANOVA testing the effect of sex-ratio conditions. Analyses were made using IBM® SPSS® Statistics 19, JMP v. 10. 0 and R version 3. 3. 2 (The R Foundation for Statistical Computing, Vienna, Austria, http://www. r-project. org). All the data files are available from the Dryad data base  [62].
Outcomes Raw and summarised outcome lists resulting from brainstorming rounds 1 and 2 are available at the study registration site: https:// osf. io/ 8f3aj/. Among the suggested outcomes raised during brainstorming, we set aside those that were obviously baseline characteristics; the latter sort were noted, but not subjected to prioritisation. Many outcomes were of a type associated with survival or recurrent event analysis (eg, time to first exacerbation, days alive and free from hospitalisation, exacerbation frequency), and data collection would have involved recording the beginning and end dates/times for an event (or a series of events, like exacerbations or hospitalisations). The latter types of outcomes were grouped together under the label 'event tracking', since they share common data set structures.
Open access † †These outcomes can be biased by competing risks, namely mortality. Care should therefore be taken when aggregating them, and the complement (eg days alive and without the outcome in question) thus considered to avoid this bias. Alternatively, competing-risk analyses may be required. COPD, chronic obstructive pulmonary disease. a simpler treatment failure definition based uniquely on treatment recurrence as a secondary outcome (table  3 ); this will enable comparisons with national database prescription information in France where the future trial will take place (https:// osf. io/ 8f3aj/). In addition to the individual elements composing a composite measure of treatment failure, their use should also be juxtaposed with how patients' preferences in terms of end-of-treatment options are locally taken into account.
16 The hospital episode data collected during eo-Drive will provide the means for a comparison with the latter by using each day a patient is alive and out of the hospital as a cumulative recurrent event plotted against time. 17 terestingly, though asked to consider an eosinophilguided corticoid therapy context, simultaneous changes in blood eosinophilia did not reach consensus as a priority outcome, but were classified as non-essential secondary outcomes (online supplementary additional file 2), suggesting that changes in this biomarker were not considered as essential for interpretation of results. This may be because experts were strictly considering the evaluation of treatment efficacy, without exploring associated potential mechanisms. It may also suggest that certain experts do not consider eosinophilia to be a pertinent mechanism of disease for many COPD patients.
Data availability statement Data are available in a public, open access repository. The eo-Delphi protocol, questionnaires, and item lists are available via the Open Science Framework at DOI:10. 5281/zenodo. 3531475,https:// osf. io/ 8f3aj/. The ranking dataset supporting the conclusions of this article is available in the Zenodo repository, DOI: 10. 5281/zenodo. 3531475; http:// zenodo. org/ record/ 3531475.
Recruitment and survey participants The survey was conducted in compliance with data protection legislation. Clinical Research Ethics Committee or Independent Review Board approval was not required for this exploratory patient satisfaction survey. All respondents provided informed consent to participate. They were made aware that the research was sponsored by a pharmaceutical company interested in the treatment of CD. People with a diagnosis of CD (self-report) were invited to participate in the survey via the Carenity social media platform for people living with chronic disease. Patient associations (e. g. , Dystonia Europe, the Dystonia Medical Research Foundation or AMADYS) also shared the survey with their members via email and/or newsletters and social media. The survey was hosted on the Carenity patient community platform.
Acknowledgements This patient survey was sponsored by  Ipsen Pharma. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. The authors wish to thank  Dystonia Europe , the  Dystonia Medical Research Foundation , AMADYS and all the patients who participated in this survey. We thank  Anita Chadha-Patel PhD  of  ACP Clinical Communications Ltd (Hertfordshire, UK ) for providing medical writing support, which was funded by  Ipsen (Paris, France)  in accordance with Good Publication Practice guidelines.
Clinical and tumour pathological features are summarized in Table  S1. For RNA extraction, samples were obtained from patients who had a first surgery and no pre-operative therapies. IDH1 mutation was established by immunohistochemistry against the IDH1 R132H epitope with at least 80% of positive cells in both DLGG and foci for each tumour. IDH1 mutation was then confirmed by sequencing of the IDH1 gene exon 4. 1p/19q mutation was identified by molecular detection of loss of heterozygosity using polymorphic markers to screen both whole 1p and 19q chromosome arms as described in  10. ATRX mutation was established by loss of nuclear staining with IHC using ATRX polyclonal antibody (SIGMA Ref HPA001906). To explore the expression of ETNPPL by immunofluorescence in the non-tumoral human, we used a cortical sample removed during neurosurgical approach for meningioma resection.
Incubations without primary antibody or with antibodies against GFP were used as negative controls to avoid misinterpretation due to the presence of autofluorescent compounds (lipofuscin). To check ETNPPL antibody specificity, the blocking peptide (Novus NBP1-91655PEP) was incubated with the antibody (ratio of 10:1 peptide/antibody) at room temperature for one hour before the staining. equipment and settings. Bright field images were taken with a Nikon Eclipse microscope and a NIH-elements software using standard settings. Fluorescent images were taken with a Zeiss apotome Axio Imager 2 equipped with a Zeiss ZEN software. Main settings were: binning 2 × 2, apoptome mode: 5. Quantifications were obtained by two investigators (N. L. , M. A. ) on image captures and with ImageJ software manual cell counting tool.
This was expected using FFPE samples but the RNA were still suitable for labelling and hybridization on DNA chips according to the Affymetrix technical department. After amplification and labelling with an Affymetrix WT Pico Kit, cDNA were hybridized on Human Gene 2. 1 ST chips. Arrays CEL files were processed with Affymetrix Expression Console software (GC-RMA (Robust Multiarray Average) algorithm) and Affymetrix Transcriptome Analysis Console (TAC 3. 1. 0. 5) softwares. Array CEL files were also analyzed in parallel using R software 3. 6. In that case, files were preprocessed by RMA algorithm from the Oligo R Package version 1. 48. 0. Then Limma package version 3. 40. 6 was used to identify genes differentially expressed. Genes identified both by Transcriptome Analysis Console 3. 1. 0. 5 and Limma algorithms and with a p-value ≤ 0.
05 (unpaired tests) and a fold change ≥1. 3 (DLGG/foci) were considered as differentially expressed (Table  1 ). The raw data that support the findings of our study are openly available at the functional genomics data Gene Expression Omnibus (GEO: GSE130149). RT-qPCR. cDNA synthesis was performed using 1-5 μg of total RNA with random hexamers and reverse transcriptase (Promega, GoScript). Quantitative RT-qPCR was performed in triplicate for each tumour sample using the KAPA SYBR PCR kit (Sigma Aldrich KK4600) with a LightCycler 480 apparatus (Roche). 1 μL of cDNA was used for each reaction. Primers are listed in Table  S2. Expression values were calculated using the 2 -ΔΔCT method and normalized using the housekeeping gene RPLP0. comparative genomic hybridization array (cGHa). The LGG85 cell line was established from a male patient diagnosed with a grade-IV IDH1-mutated glioma with culture conditions used for Gli4 and Gli7 glioblastoma cell lines  11.
Array CGH profiling for the LGG85 line and from the original tumour were performed with the Human Agilent Sureprint G3 8 60 K Microarray Kit (Agilent Technologies, USA). LGG85 DNA was labelled with cyanin 5 (Cy5) while reference DNA from a male subject was labelled with cyanin 3 (Cy3). Sample and reference DNAs were pooled and hybridized for 24 hours at 67 °C on the arrays. The fluorescence was read by the Agilent SureScan Microarray scanner and the Cy5 /Cy3 ratios were converted into log2 transformed values with the Agilent Cytogenomics software. DLRS (Derivative Log Ratio Spread), gRepro, rSignalIntensity and LogRatioImbalance were 0. 2, 0. 009, 450. 68 and -0. 1 respectively. A single copy loss was associated with a value of the log2 below -0,8 (log2 (1/2) = -1) since a single copy gain was associated with a value of the log2 above 0,5 (log2 3/2) = 0,58).
Statistical analysis and countings. All statistical tests used and sample sizes (n) are indicated in the figure legends. Tests were performed using the GraphPad Prism software (v8. 2. 1). Significances: ****, ***, **, * represent p < 0. 0001, p < 0. 001, p < 0. 01 and p ≤ 0. 05 respectively. For cell countings, at least 5 independent fields of 10,000 μm² were counted. The effect of ETNPPL overexpression in Gli4, Gli7 and LGG85 cell was performed in 3 independent experiments.
Glioma cells the etnppL lipid metabolism enzyme. Lipid metabolism is altered in cancer cells  15. The expression of a lipid metabolic enzyme called Ethanolamine-Phosphate Phospho-Lyase (ETNPPL, also known as AGXT2L1) was found reduced in foci (Table  1 ). ETNPPL is involved in the catabolism of phosphoethanolamine (Fig. S4A ), a key compound for the synthesis of phosphatidylethanolamine which is a major phospholipid of the cellular membrane  16. ETNPPL is mainly expressed in brain and in liver according to NCBI gene database (Fig. S4B )  17. In human brain, its expression appears to be restricted to mature astrocytes (Fig. S4C )  18. Little is known about ETNPPL (13 publications) and a potential link with brain tumours has not been established. This prompted us to explore the expression and role of ETNPPL in the human brain and gliomas.
ETNPPL antibody quality was indicated by the presence of a band at the expected size (55 kDa) on a western blot (WB) performed with DLGG extracts (Fig. 4A ). These results confirmed the astrocytic expression of ETNPPL as suggested by RNA expression databases. We then analyzed ETNPPL expression in diffuse low-grade gliomas samples. By WB, ETNPPL was detected as a single band in 5 of the 6 analyzed grade II tumours (Figs. 4A  and S8A ). By IHC and reminiscent of what was observed in the normal brain, ETNPPL was detected either in the cytoplasm or the nuclei of the cells in one grade II astrocytoma (Fig. 4B ). DLGG can contain normal and mutated cells so it was important to establish that ETNPPL + cells were tumoral. This was addressed by performing double IF for ETNPPL and IDH1 R132H (1 oligodendroglioma) and ATRX (2 astrocytomas).
As mentioned previously, expression of IDH1 R132H assessed by IF in the astrocytomas was too weak to be quantified accurately. In the oligodendroglioma (Figs. 5A, C , S6A,B), we found that >95% of ETNPPL + cells also expressed the mutated form of IDH1 R132H. Figure  5B-C  shows that in the 2 astrocytomas, most of ETNPPL + cells have lost ATRX expression (>95% of cells). We also examined the ETNPPL expression in gliomas cells using DLGG primary cultures (Fig. 5D ). In the two explored cultures, most of IDH1 R132H + cells (>90%, 150 cells counted) co-expressed ETNPPL. In these 2 patients, the protein was mainly in the cytoplasm. The ETNPPL expression in diffuse low-grade glioma cells was also validated using a second antibody (HPA072938) directed against another part of the protein (Table  S2 ).
Foci and high-grade gliomas have a reduced expression of ETNPPL. Microarray and qPCR anal- yses indicate that ETNPPL RNA is reduced in the foci (Table  1 , Fig. 2A ). First, we validated this at the protein level using IHC in seven DLGG containing foci. Figure  6A ,B shows a significant reduction of the percentage of ETNPPL + cells in the foci even if its expression is highly variable between tumours. Second, we also explored glioma databases for the expression of ETNPPL in different tumour grades. All three databases (TCGA, Rembrandt and NCBI dataset GDS1962) showed a sharp reduction of ETNPPL RNA in high-grade tumours compared to low-grade tumours (Fig. S4D )  14, 19, 20. We confirmed this decrease by using IHC in a glioblastoma tumour. Result presented on Fig. 6C  shows the absence of ETNPPL in this tumour.
This is consistent with glioma database analyses showing that ETNPPL expression is inversely correlated to STAT3 and MKI67 (Fig. S4F ) whose expression are higher in foci and glioblastomas. In addition, Kaplan-Meier analysis shows that patients with low expression of ETNPPL have lower overall survival (Fig. S4G ). These observations suggested that this enzyme may oppose glioma cells proliferation. We demonstrated this hypothesis by overexpressing ETNPPL in 3 glioblastoma cell cultures (Gli4, Gli7, LGG85). Gli7 and LGG85 cells were sensitive to ETNPPL overexpression which reduced their growth while no effect was detected in Gli4 cells. These glioblastoma-derived cultures have different types of mutations. Notably, Gli4 cells have lost one copy of the NF1 gene (unpublished data) which characterizes the mesenchymal subtype of glioblastomas  41.
Acknowledgements This work was supported by grants from  ARC ,  INCA-GSO, ARTC-SUD. N. Leventoux , was supported by an  ARTC-SUD  fellowship. We thank  Marie-Pierre Blanchard ,  Amélie Sarrazin  and  Christophe Duperray  from  MRI RIO biocampus  facilities for their technical assistance as well as RHEM facility and  CHU of Montpellier, Centre de Ressources Biologiques du CHU de Montpellier (CRB) ,  Collection Neurologie , F-34285 Montpellier, France. Affymetrix microarrays were processed in the  Microarray Core Facility  ( V. Pantesco ) and the  Platform Bioinformatics and Biomarkers  ( Bio2M ) at the  Institute of Regenerative Medicine and Biotherapies -Montpellier Hospital. We also want to thank:  Professor Steven Sloan  who kindly authorized us to use the ETNPPL graph on supplemental Fig.
princeton. edu/bcontrol). Rats performed an average of 694 trials per session (range: 335-1188), one session per day lasting 60-90 min, 6 days per week, during 9 months. Rats were trained using an automated training protocol that had several stages and lasted between 2 and 3 months (depending of the animal). The data presented in this study were taken from the period after training yielding an average of 56,506 valid trials per rat. A first group of n = 10 rats (Group 1) were trained in the frequency discrimination version of the task, in which the correlated sequence of trials was present from the training (see below). A subset of three rats from this group were also trained in a random time-out version of the task where the duration of the after-error timeout was randomly chosen between 1, 3, or 5 s.