dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics All data are presented as mean ± SEM. Data were analyzed using the GraphPad Prism 6 Software (GraphPad Software Inc. , La Jolla, CA, USA). In the wild-type (+/+) ewes, comparisons of follicle numbers and sizes between the Rasa Aragonesa and the Grivette breeds were made using Mann Whitney test, and comparisons of gene expression and hormonal concentrations in follicular fluid between breeds and follicular size classes were made using two-way ANOVA. Within each ovine breed, gene expression and hormonal concentrations were compared between follicular classes and genotypes using two-way ANOVA. The ANOVA analyses were followed by Fisher's protected Least Significant Difference (LSD) test for multiple comparisons. For granulosa cell culture experiments, data were analyzed using repeated measures one-way ANOVA with the Greenhouse-Geisser correction of the degrees of freedom.
Acknowledgements We thank the staff of the slaughterhouse of the  CIRE platform at Nouzilly (France)  for the recovery of sheep ovaries. We are grateful to the technicians of the  INRA experimental unit of Langlade  who took care of the Grivette animals in France, and the staff of the experimental animal facilities of  CITA de Aragón  for taking care and providing the  Rasa Aragonesa ewes in Spain.
Funding This work was supported by grants from  France via the "Agence Nationale pour la Recherche " (http://www. agence-nationale-recherche. fr/)( ANR-12-BSV1-0034-02 , AMHAROC) and EC (FP7/2007-2013), grant  245140 , '' 3SR '',  Sustainable Solutions  for Small Ruminants (http://www. 3srbreeding. eu/), WP4  (this publication reflects only the authors' views and the EC is not liable for any use that may be made of the information contained herein). A. Estienne was supported by a  French Fellowship  from the  Région Centre and INRA.
The Mediterranean marine sponge Spongia officinalis has been reported as a rich source of secondary metabolites and also as a bioindicator of water quality given its capacity to concentrate trace metals. In this study, we evaluated the chemical diversity within 30 S. officinalis samples collected over three years at two sites differentially impacted by anthropogenic pollutants located near Marseille (South of France). Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomic profiling (C18 LC, ESI-Q-TOF MS) combined with XCMS Online data processing and multivariate statistical analysis revealed 297 peaks assigned to at least 86 compounds. The spatio-temporal metabolite variability was mainly attributed to variations in relative content of furanoterpene derivatives. This family was further characterized through LC-MS/MS analyses in positive and negative ion modes combined with molecular networking, together with a comprehensive NMR study of isolated representatives such as demethylfurospongin-4 and furospongin-1.
Untargeted Metabolomic Profiles of S. officinalis LC-MS profiling of the extracts of S. officinalis samples collected in October 2011, September 2012 and December 2013 at Cortiou and Riou was performed by LC-MS in positive ion mode. It revealed a large panel of apolar compounds (Figure  S1  in the supplementary materials). The LC-MS data were processed using XCMS Online, which allowed peak detection, automatic retention time alignment and peak matching  [17, 18]. The generated multivariate matrix consisted of 30 samples and 297 peaks, each characterized by m/z ratio and retention time. These peaks were assigned to at least 86 compounds from peak-correlation-based annotation with CAMERA  [19]  (Table  S1 ). The data matrix was submitted to multivariate statistical analysis: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA)  [20]  and sparse PLS-DA (sPLS-DA)  [21] , the latter facilitating the selection of the most discriminant variables.
Compound Annotation through LC-MS/MS and Molecular Networking Ion-dependent LC-MS/MS analyses were performed in positive and negative ion modes on representative samples of each group. Manual inspection at the MS/MS spectra revealed 57 compound MS/MS spectra (51 of good quality and six of low quality, as listed in Table  S3 ). These compounds were not detected in the blank samples. The MS/MS data on the 15 compounds involved in the metabolomic clustering (Figure  3 ) for compounds 1 and 2 revealed that most of them (1, 2, and 6 to 15) displayed similarities in their fragmentation pattern, revealing a diagnostic species at m/z 135 in positive ion mode. This suggested that they belong to the same chemical family. The LC-MS/MS data recorded in positive ion mode at three collision energies were used to generate a molecular network (Figure  4 ), using the Global Natural Products Social Molecular Networking (GNPS) workflow.
This approach permits to cluster compounds based on the similarities of their MS/MS spectra  [26, 27]. Compounds implicated in differences observed with the metabolomic profiling were searched and annotated in priority. The network consisted of 594 nodes connected with 939 edges, 217 of them appeared as single loops. Among these nodes, 62 were detected in blank samples. One main cluster observed comprised seven nodes whose parent masses matched with 9 out of the 15 previously mentioned compounds (1 and 6 to 13, Figure  4b ). Although no library hit was found for this cluster through the GNPS workflow, the marine database MarinLit provided fruitful information for manual dereplication based on the molecular formulas proposed from high-resolution mass measurements (Table  S2 ) and MS/MS fragmentation patterns.
Compound Annotation through LC-MS/MS and Molecular Networking Ion-dependent LC-MS/MS analyses were performed in positive and negative ion modes on representative samples of each group. Manual inspection at the MS/MS spectra revealed 57 compound MS/MS spectra (51 of good quality and six of low quality, as listed in Table  S3 ). These compounds were not detected in the blank samples. The MS/MS data on the 15 compounds involved in the metabolomic clustering (Figure  4 ) for compounds 1 and 2) revealed that most of them (1, 2, and 6 to 15) displayed similarities in their fragmentation pattern, revealing a diagnostic species at m/z 135 in positive ion mode. This suggested that they belong to the same chemical family. The LC-MS/MS data recorded in positive ion mode at three collision energies were used to generate a    S5 ).
LC-MS Analyses For metabolomic profiling, LC-MS of the sponge extracts was performed on an Ultimate 3000 Micro-HPLC system (Thermo Scientific) connected to an ESI-Qq-TOF Q-STAR Pulsar mass spectrometer (Sciex) equipped with an IonSpray source. The sponge extracts were subjected to HPLC (C18 Uptisphere WTF Interchim, 150 × 1 mm, 300 Å, 5 µm, column, 40 µL/min gradient elution, 90-20% CH 3 CN/H 2 O with an isocratic 0. 1% HCOOH, over 35 min). The injection volume was 1 µL. Blank samples consisted of solvent alone, i. e. , CH 2 Cl 2 /MeOH 1:1. The MS data were collected in positive ion mode in the m/z range 250-1500. Representative samples (mix of samples from Riou and one representative sample from Cortiou 2011, 2012 and 2013) were also analyzed in positive or negative ion mode in ion-dependent acquisition mode to generate automatic MS/MS spectra on the main ions detected.
Molecular Networking and Manual Dereplication LC-MS/MS data were converted into mgf files using MassHunter®software,(Qualitative Analysis B. 07. 00, Agilent Technologies, Les Ulis, France). Converted data files were subjected to online GNPS workflow (http://gnps. ucsd. edu). Consensus spectra were generated through MS-Cluster with a parent ion mass tolerance of 0. 5 Da and a fragment ion mass tolerance of 0. 5 Da, with a minimum of 2 spectra. The networks were generated using the following settings: min pair cos: 0. 7, minimum matched fragment ion: 6, network topK: 10. Resulting networks were visualized using Cytoscape 3. 2. 0. The preferred layout was applied. Node colors were mapped based on the source files of MS/MS spectra. The edge thickness attribute was defined to reflect cosine similarity scores, with thicker lines indicating higher similarity.
Multivariate Data Analysis The profile-mode LC-MS data were processed using XCMS Online version 2. 01. 00 (https:// xcmsonline. scripps. edu/index. php) [17, 18]. Peaks were detected with the matched-filter method, using a maximal tolerated m/z deviation in consecutive scans of 30 ppm, and minimal and maximal peak widths of 10 and 60 s, respectively. The multivariate matrix generated consisted of 30 samples and 297 peaks, each characterized by a m/z ratio and a retention time. Isotopic peaks and adducts were annotated using the package CAMERA  [19]  implemented on XCMS Online, with automatic assignment of isotopes and adducts. The peak correlation based annotation obtained with CAMERA, which highlights and annotates co-eluted peaks, proposed that the 297 peaks detected correspond to at least 86 individual metabolites.
The multivariate matrix was treated using the freely available R environment version 3. 1. 1 (www. r-project. org). PCA, PLS-DA and sPLS-DA were carried out using R package MixOmics  [51]. The multivariate analyses were performed on mean-centered data. sPLS-DA was carried out for 3 components with 10 variables kept on the first component and 30 variables kept on components 2 and 3. Within the variables selected, the compounds with a maximum area of 5000 counts and not eluted in the void volume were further picked out.
The following are available online at www. mdpi. com/2218-1989/7/2/27/s1, FigureS1: Overlay of the total ion chromatograms of the extracts of S. officinalis, FigureS2: PCA analysis of not-normalized versus normalized dataset, FigureS3: VIP scores on the first three components of the PLS-DA, FigureS4: Score and loading plots of sPLS-DA, FigureS5: 1 H NMR spectrum of demethylfurospongin-4 (1), FigureS6:13 C NMR spectrum of demethylfurospongin-4 (1), Figure S7: LC-MS/MS spectra of the of compounds 3, 4 and 5, Figure S8: LC-MS/MS spectra of compounds 6, 13, 14 and 15, Figure S9: LC-MS/MS spectra of the compounds 7 and 8, Figure S10: LC-MS/MS spectra of compounds 9 and 10, Figure S11: LC-MS/MS spectrum of compound 11, Figure S12. 1 H NMR spectrum of furospongin-1 (11), Figure S13: DEPT NMR spectrum of furospongin-1 (11), Figure S14: 1 H NMR spectrum of furofficin (2), Figure S15: 1 H-1 H COSY spectrum of furofficin (2), Figure S16: HSQC spectrum of furofficin (2), Figure S17: HMBC spectrum of furofficin (2), Figure S18: NOESY spectrum of furofficin (2), Figure S19: 1 H NMR spectrum of spongialactam A (12a), Figure S20: 1 H-1 H COSY spectrum of spongialactam A (12a), Figure S21: HSQC spectrum of spongialactam A (12a), Figure S22: HMBC spectrum of spongialactam A (12a), Figure S23: NOESY spectrum of spongialactam A (12a), Figure S24: 1 H NMR spectrum of spongialactam B (12b), Figure S25: 1 H-1 H COSY spectrum of spongialactam B (12b), Figure S26: HSQC spectrum of spongialactam B (12b), Figure S27: HMBC spectrum of spongialactam B (12b), Figure S28: NOESY spectrum of spongialactam B (12b), Table.
Acknowledgments: This work is part of  Cléa Bauvais 's Ph. D. thesis, supported by a grant from the doctoral school ED 387: Interdisciplinary approach to living organisms ( University Pierre & Marie Curie, Paris, France ). We acknowledge for their finding support the  European Union   7th Framework Programme  ( Bluegenics FP7-KBBE-2012-6 ) under grant agreement No. 311848  and the ATM grant from the  MNHN  ( 2012-2014 ). We thank the  "Plateforme analytique du Muséum"  for access to the NMR and mass spectrometry facilities. The 400 MHz and 600 MHz NMR spectrometers used in this study were funded jointly by the  Région Ile-de-France , the  Muséum national d'Histoire naturelle (Paris, France ) and by  CNRS (France). We are grateful to  UMR CNRS 8076 ,  Châtenay-Malabry  ( Alexandre Maciuk , and  Mehdi Beniddir ) for access to mass spectrometry facilities and fruitful discussions.
Supplementary Materials: The following are available online at www. mdpi. com/2218-1989/7/2/27/s1, FigureS1: Overlay of the total ion chromatograms of the extracts of S. officinalis, FigureS2: PCA analysis of not-normalized versus normalized dataset, FigureS3: VIP scores on the first three components of the PLS-DA, FigureS4: Score and loading plots of sPLS-DA, FigureS5: 1 H NMR spectrum of demethylfurospongin-4 (1), FigureS6:13 C NMR spectrum of demethylfurospongin-4 (1), Figure S7: LC-MS/MS spectra of the of compounds 3, 4 and 5, Figure S8: LC-MS/MS spectra of compounds 6, 13, 14 and 15, Figure S9: LC-MS/MS spectra of the compounds 7 and 8, Figure S10: LC-MS/MS spectra of compounds 9 and 10, Figure S11: LC-MS/MS spectrum of compound 11, Figure S12. 1 H NMR spectrum of furospongin-1 (11), Figure S13: DEPT NMR spectrum of furospongin-1 (11), Figure S14: 1 H NMR spectrum of furofficin (2), Figure S15: 1 H-1 H COSY spectrum of furofficin (2), Figure S16: HSQC spectrum of furofficin (2), Figure S17: HMBC spectrum of furofficin (2), Figure S18: NOESY spectrum of furofficin (2), Figure S19: 1 H NMR spectrum of spongialactam A (12a), Figure S20: 1 H-1 H COSY spectrum of spongialactam A (12a), Figure S21: HSQC spectrum of spongialactam A (12a), Figure S22: HMBC spectrum of spongialactam A (12a), Figure S23: NOESY spectrum of spongialactam A (12a), Figure S24: 1 H NMR spectrum of spongialactam B (12b), Figure S25: 1 H-1 H COSY spectrum of spongialactam B (12b), Figure S26: HSQC spectrum of spongialactam B (12b), Figure S27: HMBC spectrum of spongialactam B (12b), Figure S28: NOESY spectrum of spongialactam B (12b), Table.
Background: Recent short-term clinical trials in patients with Duchenne Muscular Dystrophy (DMD) have indicated greater disease variability in terms of progression than expected. In addition, as average life-expectancy increases, reliable data is required on clinical progression in the older DMD population. Objective: To determine the effects of corticosteroids on major clinical outcomes of DMD in a large multinational cohort of genetically confirmed DMD patients. Methods: In this cross-sectional study we analysed clinical data from 5345 genetically confirmed DMD patients from 31 countries held within the TREAT-NMD global DMD database. For analysis patients were categorised by corticosteroid background and further stratified by age. Results: Loss of ambulation in non-steroid treated patients was 10 years and in corticosteroid treated patients 13 years old (p = 0.
INTRODUCTION TREAT-NMD, an "EU funded network of excellence" founded in 2007, has pioneered the formation of national and global patient registries in the field of rare neuromuscular diseases. In Duchenne muscular dystrophy (DMD), TREAT-NMD has facilitated the establishment of standardised national patient registries in multiple countries worldwide via the use of a standardised mandatory data set collected by each registry  [1]. These national registries have combined data in an effort to create the TREAT-NMD global database for DMD  [2] , a unique global resource containing mutational information for over 7000 DMD patients and clinical data from over 5000 DMD patients from 31 countries. Data derived from registries and natural history studies are complementary. Registries involve much wider population cohorts and may demonstrate country-specific differences usually not captured by natural history (observational) clinical studies.
Patients and study design In this cross-sectional study, we analysed clinical data from 5345 genetically confirmed DMD (identification of a pathogenic dystrophin mutation leading to lack of expression of function dystrophin protein) patients from 31 countries, held within the TREAT-NMD global DMD database. Standardised specific data based on the TREAT-NMD mandatory and highly encouraged items were collected via the national TREAT-NMD patients registries. Anonymised data from these registries  [13]  were transferred to the global DMD-database via a secure File Transfer Protocol in November 2013 in order to provide a single anonymised cohort of DMD phenotypes, clinical data and outcomes. Patient organisations and patient advocacy groups were involved and consulted when the study was designed.
Patient groups were represented in the European Neuromuscular Centre (ENMC) workshops, and TREAT-NMD Global Database Oversight Committee (TGDOC) meetings. In some cases, national DMD registries are run by patient organisations, so these directly contributed to the data collection. Patient organisations were also involved in publicising information about the registry and assisting with patient recruitment. The results are disseminated to the patients via registry newsletters and TREAT-NMD newsletters. For analysis patients were categorised by corticosteroid background (yes, no, past and unknown use) and then further stratified by age (under the age of 20 and 20 years and older). Age stratification was carefully chosen to reduce selection bias in the analysis of the group of patients under the age of 20 years.
Statistical analysis In the group of patients under the age of 20 years we used Turnbull estimator to analyse the time to reach disease milestones (wheelchair dependence, the need for scoliosis surgery, the need for ventilation and cardiomyopathy). Turnbull analysis was chosen because actual event time was not collected (intervalcensored). Turnbull analysis is a generalization of the Kaplan-Meier estimator and allows the analysis of interval-censored data  [14]. Turnbull estimates were computed in the software R (www. r-project. org). In the group of patients aged 20 years and older the number of deceased patients was considered significantly and increasing with time, but an exact number was not known. Therefore Turnbull or Kaplan Meier estimator could not be used. Instead we used chisquare analysis to analyse the effect of corticosteroids on disease milestones above the age of 20 years.
Patients The global TREAT-NMD DMD registry contains 7149 DMD patients. For one quarter of the records (25. 2%: n = 1804) incomplete clinical information was provided, leaving 5345 patients suitable for in-depth clinical analysis. 84. 4% (n = 4509) of the patients were under the age of 20 years and 15. 6% (n = 836) were 20 years and older. (Table  S1. in the Supplementary Appendix shows an overview of the number of patients in the database per age group and the number of patients analysed per clinical milestone). Country specific differences in patient populations were observed in the dataset. Generally, long-standing registries (in existence for more than 5 years) had higher numbers of older patients than newer registries. The oldest patient populations in the database come from Japan and the Netherlands, with as high as 7% of their registry population being older than age 30.
Corticosteroid use Corticosteroid use within the registry was recorded in four main categories: "corticosteroids yes" (reflecting current corticosteroid use, dose unspecified), "corticosteroids no" (reflecting never used corticosteroids) and "past corticosteroid use"(dose, duration of past use and stopping reasons not specified). Almost half of the study population (49. 7%: n = 2658) were currently using steroids, 37. 7% (n = 2015) had never used corticosteroids and 9. 8% (n = 522) reported past steroids used. For the remaining 2. 8% (n = 150) patients the use of corticosteroids was unknown. Corticosteroid use varied with age; younger patients reported more corticosteroid use than older patients. Current care standards recommend commencing corticosteroid use at around 4-6 years of age  [15] ; and this was reflected within the database.
Until the age of 14 years corticosteroid use within the database increased in an age dependent manner, with patients aged 6-8, 9-11 and 12-14 reporting the greatest corticosteroid use (57%, 74% and 64% respectively) (Fig. 1 ). Corticosteroid use notably decreased after the age of 14 as ambulation decreased. In the database, population 20 years and older, only 15. 2% (n = 127) of patients reported current corticosteroid use. This was further reduced in the population aged 30 years and older where only 4. 4% (n = 6 of 135) reported current corticosteroid use. The early use of corticosteroids has only been common practice since 2004, and DMD patients born before 1998 are less likely to have ever used corticosteroids. (Table  S2. in the supplementary index provides an overview of corticosteroid use per age group within the database).
Cardiomyopathy Cardiomyopathy was reported in 12. 0% (n = 639) of registry patients. In 66. 5% (n = 425) diagnosis was based on a reduced ejection fraction on  echocardiogram. In the remaining 34. 5% (n = 214) the diagnostic methods were not available. Prevalence of cardiomyopathy in the database increased with age with 6. 7% (n = 302) of patients aged 20 and under reporting cardiomyopathy and 40. 4% (n = 337) of patients aged 20 and above having cardiomyopathy. Until the age of 20 no significant effect of corticosteroids on the development of cardiomyopathy was observed (p = 0. 94). Importantly, no trend for a negative effect of the use of corticosteroids was seen (Figure  S1C. in the Supplementary Appendix).
Corticosteroids and clinical milestones in patients aged 20 years and older Whilst the majority of patients in the DMD global database were less than 20 years old, there was a sizable cohort of patients aged 20 and over 15. 6% (n = 836). 33. 9% (n = 283) of patients aged 20 years and older underwent scoliosis surgery. As observed in the younger patients group, the frequency of scoliosis surgery was lower in the corticosteroid treated population (χ 2 4. 4 p < 0. 0001) (Fig. 4A ). Assisted ventilation was reported in 50. 4% (n = 420) of patients over the age of 20 years. 4. 8% (n = 20) of these patients reported using corticosteroids, 16. 9% (n = 70) reported past use, 75. 5% (n = 318) reported never having used corticosteroids, and for 2. 6% (n = 11) corticosteroid use was unknown. Chi-square testing of this group showed that corticosteroid use is associated with significantly less use of assisted ventilation (χ 2 7.
g. adverse events forms) and agreed user roles (e. g. patient reported/clinician reported data) inbuilt in national registries to allow comprehensive, Good Clinical Practice (GCP) compliant data collection. Data collected in this way can contribute to better understanding of the course of the disease, disease management, and safety and efficacy profile of new therapies, and ultimately may lead to new policies and disease management protocols. The infrastructure provided by TREAT-NMD and the standardized, common data set collected by the TREAT-NMD national registries has ultimately facilitated the formation of the TREAT-NMD global database for DMD  [2]. This is a collection of more than 7000 genetically confirmed DMD patients worldwide of which we have clinical data, which passed quality control for 5345 patients.
ACKNOWLEDGMENTS This work was supported by TREAT-NMD operating grants,  FP6 LSHM-CT-2006-036825 ,  20123307 UNEW FY2013  and  AFM 16104. Further support came from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 305444  ( RD-Connect ) and 305121 (Neuromics) and  Medical Research Council UK  (reference  G1002274 , grant  ID 98482 ). The authors would like to acknowledge the families of those living with Duchenne muscular dystrophy who have been instrumental in the formation of the DMD national registries. We also acknowledge former and current members of the  TREAT-NMD office at the Institute of Genetic Medicine in Newcastle including Stephen Lynn ,  Emma Heslop  and  Rachel Thompson. We also extend our thanks to the members of the TREAT-NMD executive committee:  Hanns Lochmüller ,  Annemieke Aartsma-Rus ,  Anna Ambrosini ,  Filippo Buccella ,  Kevin Flanigan ,  Eric Hoffman ,  Janbernd Kirschner ,  Eugenio Mercuri ,  Ichizo Nishino ,  Kathy North ,  Jes Rahbek  and  Thomas Sejersen.