dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantifications of at least 5 different mouse adipose tissues per group (5 pictures per mouse) were performed using Adobe Photoshop CS5. 1 software. (G and H) S6 kinase activation (G) and S636/639 IRS1 phosphorylation (H) in epididymal adipose tissues were evaluated by western blots for phospho-S6 kinase and phospho-S636/639 IRS1. Quantifications of 10 different mouse adipose tissues per group were performed using ImageJ. Where indicated, rapamycin (RAPA) was administrated (4 mg/kg) by intraperitoneal injection twice a week for the duration of the experiment. All data are mean ± SEM. *p < 0. 05; **p < 0. 01; ***p < 0. 001. n. s. , non-significant. See also Figure  S6. negative impact on the accumulation of adipose tissue (Figure  6 ), we were interested to understand how DRAM-1 might regulate the differentiation of adipocytes when cultured in isolation and, in particular, how DRAM-1 might affect the later stages of differentiation from pre-adipocytes to adipocytes.
This partial (or selective) inhibition of mTORC1 is not without precedent. Laplante et al. (2012)  showed that overexpression of the mTOR inhibitor (D) Pre-adipocyte differentiation efficiency was evaluated by western blots directed against phospho-AKT T308 and PPARɣ. AKT and ERK2 were used as loading controls. (E) Quantification of PPARɣ levels measured from 3 independent experiments was performed using ImageJ. (F) Quantification of phospho-AKT levels measured from 3 independent experiments was performed using ImageJ. (G) mTOR activation was evaluated by western blotting for phospho-S636/639 IRS1 in Dram1 CRISPR 3T3-L1 cells or NTC CRISPR cells. (H) Quantification of phospho-S636/639 IRS1 levels measured from 3 independent experiments was performed using ImageJ. (B, C, E, F, and H) Data are mean ± SEM.
Samples were loaded onto a PepMap100 trap column at a flow of 20 ml/min of RP-LC buffer A (A: 5% ACN, 0. 1% formic acid) and then separated using a PepMap C18 75-mm inner diameter 3 15-cm analytical column. Analyst software was used for data-dependant acquisition. The basic survey scan (1 s) from 400 to 1200 Da was followed by four Enhanced Precursor Ion scans (1 s). Data acquired with Q-star were exported from Analyst using the script Mascot. dll 1. 6b23(Matrix Science, London, UK). Raw data obtained from Orbitrap were processed with Raw2Msm MGF files were searched using Mascot (Matrix Science), querying UniProt (UniProt Consortium, 2010) Homo sapiens plus an in-house database containing common proteomic contaminants and the sequence of Streptavidin-FLAG-DRAM-1. Mascot was searched assuming the digestion enzyme trypsin allowing for two miscleavages with a fragment ion mass tolerance of 0.
1 Da and a parent ion tolerance of 35 ppm for Q-Star data and 10 ppm for Orbitrap data. The iodoacetamide derivative of cysteine was specified in Mascot as a fixed modification. Oxidation of methionine and Protein N-terminal acetylation were considered as variable modifications. Scaffold (version 4. 3. 2, Proteome Software) was used to summarize MS/MS based peptide and protein identifications.
Temperature at the hESI source was 50 C and spray voltage was 4500V for positive mode and /3500V for negative mode. Sheath gas was 25 arbitrary units and auxiliary gas 15 arbitrary units for both ionization modes. A capillary temperature of 325 C was used for both positive and negative ionization modes. Lock masses were used and the mass accuracy obtained for all metabolites was below 5ppm. Data were acquired with Thermo Xcalibur software. The peak areas of different metabolites were determined using Thermo TraceFinder software where metabolites were identified by the exact mass of the singly charged ion and by known retention time on the HPLC column. Commercial standards of all metabolites detected had been analyzed previously on this LC-MS system with the pHILIC column. Intracellular metabolites were normalized to protein content of the cells, measured at the end of the experiment by the Lowry assay.
Sections were then incubated with Labeled Polymer HRP anti-rabbit (cat#K4011, Dako). Incubation in DAB (3,3 0 -diaminobenzidine tetrahydrochloride, cat#3468, Dako) was used to reveal staining. Sections were mounted and observed with a Zeiss Axio Scope. A1 microscope (Zeiss) and ZEN 2012 software.
1 software. (G and H) S6 kinase activation (G) and S636/639 IRS1 phosphorylation (H) in epididymal adipose tissues were evaluated by western blots for phospho-S6 kinase and phospho-S636/639 IRS1. Quantifications of 10 different mouse adipose tissues per group were performed using ImageJ. Where indicated, rapamycin (RAPA) was administrated (4 mg/kg) by intraperitoneal injection twice a week for the duration of the experiment. All data are mean ± SEM. *p < 0. 05; **p < 0. 01; ***p < 0. 001. n. s. , non-significant. See also FigureS6.
ACKNOWLEDGMENTS This work was supported by  Cancer Research UK  ( A17196 ),  Worldwide Cancer Research  ( 16-1194 ), and  INRA  ( UMR1419   NuMeA ). We thank the  Core Services and Advanced Technologies at the Cancer Research UK Beatson Institute  ( C596/A17196 ), with particular thanks to the  Beatson Advanced Imaging Resource, Biological Services Unit, Histology and Proteomics Facility.
7, FDR ≤ 0. 05) (Figure  4A  and Supplemental Figure  5A ). Among these genes, 192 were deregulated compared to both controls, with 155 upregulated and 37 downregulated genes (Supplemental Figure  5A  and B ). We performed a C2 canonical pathway analysis from the Molecular Signatures Database (MSignDB v6. 2)  (25, 26)  which revealed that the cell cycle (mitotic) was the main modified pathway (Figure  4B ). Among the 20 more activated pathways (FDR q value from 5. 93*10 -22 to 1. 78*10 -5 ), 9 were directly related to the cell cycle (Figure  4B  and Supplemental Table  1 ). Interestingly, all deregulated genes involved in the cell cycle were upregulated (Figure  4A , Supplemental Figure  5A  and B and Supplemental Table  1 ). Deregulated genes in pathways not directly related to the cell cycle, like AURORA B, PLK1, FOXM1 or E2F, in fact frequently overlap with those of the cell cycle (Supplemental Table  1 ).
This restricted the analysis to 180 and 213 genes, respectively upregulated and downregulated in κF-DH vs both controls (Supplemental Figure  7A  and B ). We performed a C2 canonical pathway analysis from the Molecular Signatures Database (MSignDB v6. 2) and found that among the upregulated genes, the only significant pathways were related to the unfolded protein response (UPR) (Figure  5B  and Supplemental Table  2 ). Genes of the UPR pathway upregulated in F-DH PCs as compared to both controls are indicated on the MA-plots (Figure  5A ) showing that they were not only upregulated but, as expected, were also highly expressed in PCs  (29). Analyses using a classification of the differentially expressed genes by biological process ("GO biological process" from the Molecular Signatures Database) showed that the three most significant pathways were related to topologically incorrect proteins and ER stress, further suggesting that the activation of the UPR was likely due to the pathogenic LCDD LC (Figure  5C  and Supplemental Table  3 ).
Flow cytometry Intracellular staining was performed using the Intraprep™ kit (Beckman Coulter). Flow cytometry analysis were performed on a BD Pharmingen LSRFortessa® cytometer. Data were analyzed with BD FACSDiva software (BD Biosciences). Antibodies used are listed in the Supplemental Table  5  Western Blot Serum proteins were separated by reducing SDS-PAGE (10%) and transferred onto polyvinylidene difluoride membranes (Millipore). Membranes were blocked in 5% milk Trisbuffered saline (TBS), then incubated with appropriate antibodies in 3% milk TBS (supplemental Table  2 ), washed three times with TBS 0. 1 % tween and revealed by chemiluminescence (ECL, Pierce).
RNA sequencing Libraries were generated from 500 ng of total RNA using Truseq Stranded mRNA kit (Illumina). Libraries were then quantified with Qubit ds DNA HS assay kit (Invitrogen) and pooled. 4nM of this pool were loaded on a Nextseq 500 high output flowcell and sequenced on a NextSeq 500 platform (Illumina) with 2 × 75bp paired-end chemistry. Mapping was performed with STAR_2. 4. 0a versus mm10 following Encode RNA-seq options -outFilterIntronMotifs RemoveNoncanonicalUnannotated -alignMatesGapMax 1000000 -outReadsUnmapped Fastx -alignIntronMin 20 -alignIntronMax 1000000 -alignSJoverhangMin 8 -alignSJDBoverhangMin 1 -outFilterMultimapNmax 20 then a read counts table was generated with featureCounts (subread-1. 5. 0-p3-Linux-x86_64) withprimary -g gene_name -p -s 1 -M -C options. Quality control of RNAseq count data was assessed using in-house R scripts.
Low expressed genes were filtered out, then normalization and statistical analysis were performed using Bioconductor package DESeq2. Further filtrations excluded genes from the Y chromosome, non-coding RNA, unknown transcripts, predicted genes (including Rik and Gm RNA) and pseudogenes. Candidate differentially expressed genes were selected using a combination of FDR < 0. 05 and abs(log2FC) > 0. 7. Venn diagrams were created using Venny 2. 1 (http://bioinfogp. cnb. csic. es/tools/venny/index. html). Overlaps of differentially expressed genes with C2 Canonical Pathways and GO Biological Process gene sets from Molecular Signature Database v6. 2 were obtained on the Gene Set Enrichment Analysis (GSEA) website  (25, 26). Only the resulting top twenty gene sets from significant list (FDR < 0. 05) were considered.
Acknowledgments The authors thank the staff of the  BISCEm technical platforms at the University of Limoges (Animal facility , cell cytometry, microscopy, transgenesis and bioinformatics facilities), the  Department of Pathology of Poitiers ,  L. Magnol  and  K. Vuillier  for access to the Konelab 30 analyser and  A. Garot  for helpful discussions. This work was supported by grants from  Fondation Française pour la Recherche contre le Myélome et les Gammapathies monoclonales, Limousin committees of Ligue nationale contre le cancer, Fondation pour la Recherche Médicale ,  Agence régionale de la santé and Institut Universitaire de France. S. B. is supported by  Centre Hospitalier Universitaire Dupuytren Limoges and Plan National Maladies Rares. M. V. A. , A. B. and M. O. A were funded by fellowships from  Région Limousin  (now  Région Nouvelle Aquitaine) ,  French ministry of research and Agence de Valorisation de la recherche de l'université de Limoges.
Pineoblastoma (PB) are rare, aggressive pediatric brain tumors of the pineal gland with modest overall survival despite intensive therapy. We sought to define the clinical and molecular spectra of PB to inform new treatment approaches for this orphan cancer. Tumor, blood, and clinical data from 91 patients with PB or supratentorial primitive neuroectodermal tumor (sPNETs/CNS-PNETs), and 2 pineal parenchymal tumors of intermediate differentiation (PPTIDs) were collected from 29 centres in the Rare Brain Tumor Consortium. We used global DNA methylation profiling to define a core group of PB from 72/93 cases, which were delineated into five molecular subgroups. Copy number, whole exome and targeted sequencing, and miRNA expression analyses were used to evaluate the clinicopathologic significance of each subgroup.
Tumor, blood, and clinical data Tumor tissue, blood, and clinical data from 93 patients diagnosed with PB, related sPNETs/CNS-PNETs, or PPTID were collected from 29 centres as part of the global Rare Brain Tumor Consortium biorepository and clinical registry (rarebraintumorconsortium. ca) using procedure approved by Research Ethic Board at the Hospital for Sick Children and participating institutions (Supplementary Table  1 , online resource). All cases were diagnosed at their referring institutions. Available pathology reports and prepared slides were all reviewed by an experienced pediatric neuropathologist. Six of these cases have previously been analyzed by Affymetrix 100K singlenucleotide polymorphism (SNP) array and reported by Miller et al. [42]. DNA from frozen tissue or formalin-fixed, paraffin-embedded materials, and blood were extracted using the Qiagen AllPrep DNA/RNA Mini kit (Qiagen, Germany), and total RNA from 6 tumors was prepared with nCounter miRNA prep kit according to standard protocol.
Molecular and bioinformatic analyses Tumor DNA was analysed on the Illumina HumanMethylation450 or MethylationEPIC methylation arrays (Illumina, San Diego, CA) as described previously (www. tcag. ca) [64, 59]  and 5000-15,000 most variable probes (standard deviation >0. 3) were used for all downstream analyses (R v3. 3. 1). Tumor types were determined using unsupervised cluster analyses of methylation data against 1200 reference tumor profiles  [59]. For t-distributed stochastic neighbor embedding, default parameters were used, except for perplexity = 10 (Rtsne v0. 15, R v3. 5. 3). For hierarchal clustering, 1-Pearson correlation was used for distance measuring, with average linkage (pheatmap R package, R v3. 61). k-means clustering was performed with Euclidean for distance measuring, and average linkage (ConsensusClusterPlus R package).
Loukides for biological specimens;  C Hanzen ,  I Tennevet ,  O Langlois ,  D Frappaz  for clinical data; M This project was funded by the  Canadian Institutes of Health Research  (grant no. 137011 ) and b. r. a. i. n. child to A. H. D. A. H. is supported by the  NIH/NCI  (grant  2R01CA143167 ). B. K. L. is a  Garron Family Cancer Centre Research Fellow. AH holds a Tier 1  Canada Research Chair.
refills). This last cluster was also characterized by common free-access-to-care status and long-term disease status for asthma. This study highlighted that a majority of French asthma patients were not exposed to controllers before an asthma-related hospitalization, while 4% were exposed to higher dispensation of LABAs than of ICS. Investigating these patterns in detail may lead to practical proposals to prevent exacerbations. Drug use information in claims databases is not susceptible to poor patient recall or to failure to fill a prescribed medication, and it is a valid way to determine drug exposure in large populations, in contrast to data obtained from electronic health records ("prescribing data") that must be used with the assumption of patients' adherence to prescriptions.
Assays were run in antagonist mode. Cyproterone acetate or IRC117539 were incubated up to 5 h with 5 nM AR LBD or 5 nM AR T887A-LBD, 500 nM fluorescein D11FxxLF, 5 nM Tb anti-GST antibody, and 5 nM dihydrotestosterone. Curves were generated using XLfit software from IDBS.
Acknowledgements Marion Huchet , an important contributor to this study passed away during the revision process. We acknowledge her work as, sadly, she cannot be an author of this publication. The authors wish to thank  Luce Bruetschy ,  Denis Carr  é,  Christelle Charnet ,  Sophie Chaumeron ,  Raphael Delille ,  Anne Espirito ,  Shari Dini Mohamed , and Fr éd éric Scaerou at IPSEN. We thank the imaging core facility of the  Institut Universitaire d'H ématologie  for microscopy analyses. The core facility is supported by grants from the  Association Saint-Louis ,  Conseil Regional d'Ile-de-France , and the  Ministère de la Recherche. We thank  Dr Nathan Lack  ( Vancouver Prostate Center, Vancouver and Koç University, Istanbul ) for prostate cancer cell lines. This work was supported by a fund from IPSEN, which covered US salary and partly by a  European Molecular Biology Organization (EMBO)   Small Grant  (to  U Sahin ,  2019 ).
Isolation and growth conditions The SN16 strain was isolated from the stool of an 87-year-old patient admitted to Timone Hospital in Marseille in September 2015. The patient had a cognitive impairment that was accompanied by a loss of weight. The isolated bacterial strain could not be identified by Matrix Assisted Laser Desorption Ionization -Time of Flight Mass Spectrometry (MALDI-TOF MS). The screening was performed on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany) as previously reported  [6]. Spectra obtained (Fig. 1 ) were imported and analysed using the BIOTYPER 3. 0 software against the Bruker database, that was continually incremented with the MEPHI database  [1]. The stool sample was pre-incubated for 5 days in an anaerobic blood culture vial (Becton-Dickinson, Pont de Claix, France) enriched of 5% sheep's blood and filter-sterilized rumen at 37°C.
Fatty acid methyl ester (FAME) analysis by Gas Chromatography/ Mass Spectrometry (GC/MS) Cellular fatty acid methyl ester (FAME) analysis was performed by GC/MS as described by Sasser  [7]. GC/MS analyses were carried out as described previously  [8]. Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK). The major fatty acid was hexadecanoic acid (41%). The most abundant fatty acids were saturated (65%). Minor amounts of unsaturated, branched and other saturated fatty acids were also described (Table  1 ).
Strain identification In order to classify this bacterium, the 16S rRNA gene was amplified using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and sequenced using the Big Dye ® Terminator v1. 1 Cycle Sequencing Kit and 3500xLGenetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France) as previously described  [9]. The 16S rRNA nucleotide sequence was assembled and corrected using the CODONCODE ALIGNER software (http://www. codoncode. com). Strain Marseille-P2264 T exhibited a 95. 6% 16S rRNA similarity with Longibaculum muris strain MT10-315-CC-1. 2-2 (GenBank Accession number NR_144615. 1), the phylogenetically closest species with standing in nomenclature (Fig. 3 ). We consequently proposed to classify this strain as new genus called Massilimicrobiota within the Firmicutes phylum and Massilimicrobiota timonensis SN16 T is the type species.
Genome sequencing Genomic DNA was extracted using the EZ1 biorobot with the EZ1 DNA tissue kit (Qiagen, Hilden, Germany) and then sequenced on a MiSeq sequencer (Illumina Inc. , San Diego, CA, USA) with the Nextera Mate Pair sample prep kit and Nextera XT Paired End (Illumina), as previously described  [8]. The assembly was performed using a pipeline containing several software (VELVET  [10] , SPADES  [5, 11]  and SOAP DENOVO  [12] , on trimmed (MISEQ and TRIMMOMATIC  [13]  software) or untrimmed data (only MISEQ software). GAP-CLOSER was used to reduce assembly gaps. Scaffolds <800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The best assembly was selected using different criteria (number of scaffolds, N50, number of N). The genome of strain Marseille-P2264 T was 2,849,574 bplong with a 31.
8% G+C content. The degree of genomic similarity of strain SN16 T with closely related species was estimated using the ORTHOANI software  [14]. OrthoANI values among closely related species (Fig. 4 ) ranged from 60. 81% between Clostridium spiriforme and Massilimicrobiota timonensis to 82. 21 % between Clostridium saccharogumia and Clostridium cocleatum. When Massilimicrobiota timonensis was compared to these closely related species, values ranged from 60. 81% with Clostridium spiriforme to 62. 62% with Eggerthia catenaformis.
Nucleotide sequence accession number The 16S rRNA gene and genome sequences were deposited in GenBank under Accession number LN998062 and NZ_UYXN00000000. 1, respectively. Description of Massilimicrobiota gen. nov Massilimicrobiota (mas. si. li. mi. cro. bio'ta N. L. fem. n. , combination of Massilia, the Latin name of Marseille, and microbiota, in reference to the human intestinal flora from which the type strain was isolated). Description of Massilimicrobiota timonensis strain SN16 T gen. nov. , sp. nov. Massilimicrobiota timonensis (ti. mo. nen'sis. L. masc. adj. , timonensis from Timone, the name of the university hospital in Marseille, France where the strain type was isolated). The characteristics of the species are detailed in Table  1. The type strain is SN16 T ([ CSUR P2264 = DSM101840).
Acknowledgements The authors thank  Catherine Robert  for sequencing the genome,  Aurelia Caputo  for submitting the genomic sequence to GenBank and  Magdalen Lardière  for English reviewing. We also thank  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Taku Sakazume ,  Yusuke Ominami ,  Hishada Akiko  and the  Hitachi team of Japan (Hitachi High-Technologies Corporation, Science & Medical Systems Business Group   24-14 , Nishishimbashi 1-chome, Minato-ku, Tokyo  105-8717 Japan ) for the collaborative study conducted together with the  IHU Méditerranée Infection , and for the installation of a TM4000 microscope at the  IHU Méditerranée Infection.
Material and methods Three patients from the same family presenting with GDD were included into the study. Clinical history and imaging data (X-rays and CT-scans) were collected retrospectively. Diagnosis was based on clinical data  [1]  and on genetic screening for ANO5 mutations  [4]. Results from mutation screening were compared to all the ANO5 point mutations reported to date in the literature  [1, 2, [4] [5] [6] [7] [8] [9] [10] [11].
Fluorescence images of the whole spheroid were recorded using 4× objective. The analysis of images was performed with ImageJ (NIH, Bethesda, MD, USA) software. To estimate the dye penetration profile in spheroid, the special macros was proposed  [33]. Briefly, the spheroid area was divided into 100 concentric rims with a linearly decreasing diameter. After that, the mean intensity of pixels in each rim was calculated. The final profiles were plotted as mean ± standard deviation from different cryo-sections (n = 4-9).
Parameters such as the Henry's law constant, air parameters or the temperature dependence needed for applying an Arrhenius equation were not considered as inputs, as they are often directly related to one or several inputs I 1 to I 7. Simulated data were taken to cover all the possibilities in the SLS process. A total number of 68,644 data were generated, lied on the fact that the corresponding dissolution time of these data varied from 20 s to 1200 s, which is of interest in the SLS process. If it is lower than 20 s, the dissolution process is quite quick and improvement of the SLS process appears less important. If it is higher than 1200 s, the dissolution process is too long so that it appears not interesting to be studied. A summary of the input and outputs values are presented in Table  1.
Datasets In general, the training and testing datasets are required for constructing and validation of AI models. Data were divided into 2 groups at a ratio of 70/30. A total number of 48,051 training data (70%) were used to construct the prediction models, whereas 20,593 testing data (30%) were used for evaluation and validation of the models. The 70/30 ratio for generating the datasets in this study has been done as suggested by Khorsheed and Mohammad  [35] , and Leema et al. [36]. Description of two AI algorithms in this study will be briefly presented in the next section.
Ensemble Bagged Trees (EDT Bagged) EDT Bagged is a hybrid model which is a combination of bagging algorithm and decision trees  [44]. Out of these methods, bagging method uses the bootstrap sampling to optimize the input training data for learning the decision trees  [45]. This process can be carried out in several steps such as (i) it takes a bootstrap sample from the original training dataset, (ii) it fits the decision trees with the extracted dataset, (iii) it then finds the best fit models with the optimized dataset, (iv) it thereafter uses each of the fitted models with the optimize dataset to predict the results, and (v) it finally averages the prediction results of all the models  [46]. EDT Bagged can significantly improve the prediction accuracy as bootstrap aggregation used in bagging method can reduced the variance in individual prediction methods like decision trees.