dataesr/scientific-paragraphs-categorization huggingface dataset:
String-based microarray data analysis The lists of hepatic gene differently expressed between Conv + OM (HFD) vs. Conv + PBS and between Conv + LM vs. Conv + PBS groups were mapped using the STRING database (http://string-db. org/). Each gene is represented by a node, and the thickness of lines between nodes illustrates the strength of interactions based on the literature and databases. RNA extraction and qPCR in liver, ileum and MLN Total RNA was extracted from frozen tissues using the miRNeasy mini kit (Qiagen, Courtaboeuf, France). For mRNA, qPCR was performed as previously described  (Serino et al, 2012b) , except for ileum and mesenteric lymph node (MLN), where 500 ng of cDNAs was amplified using the ViiA7 system (Applied Biosystems). Results were expressed as 2 ÀΔΔCt as already described  (Serino et al, 2012b)  and shown after normalization by the mean of the control values (Conv + PBS).
Statistical analysis Results are presented as means AE SEM. Statistical analyses were performed by one-way or two-way ANOVA followed by Sidak's or Dunnett's post-test, as reported or by unpaired Student's t-test, using GraphPad Prism version 7. 00 for Windows Vista (GraphPad Software, San Diego, CA). Significant values considered at P < 0. 05 or as reported. Figs 5 and 8 were analysed by a two-sided Welch's t-test; upper panels of Appendix Figs S1J/S5A-C/S11A-C/S12A-C were drawn by XLSTAT for Windows Excel.
Acknowledgements We thank the zootechnie-Rangueil INSERM/UPS  US006  CREFRE and the  Phenotypage-ANEXPLO Platform  ( US006-CREFRE, Toulouse, France ) for biochemical assays;  Cecile Canlet  and the  Platform MetaToul-AXIOM  for the NMR analyses;  Lucie Fontaine  from the histology facility of  I2MC (Toulouse, France );  Dr Jean-Philippe Pradère  from  I2MC  for helping with liver histology;  Claire Naylies  and  Yannick Lippi  for microarray fingerprints acquisition and data analysis carried out at  GeT-TRiX Genopole Toulouse Midi-Pyrénées facility. We warmly thank  Dr. Gilles Mithieux  and  Dr. Fabienne Rajas  from  Unité 1213 "Nutrition Diabète et Cerveau" Faculté de Médecine Laënnec  for kindly providing us with the antibody against G6Pase. Dr Tanti  and  Dr Cormont  are supported by the LABEX  SIGNALIFE : Grant  ANR-11-LABX-0028-01.
F. C. was supported by an  INSERM/Région Provence Alpes-Cote d'Azur doctoral fellowship  and by a grant from the  Société Francophone du Diabète ; they also thank the C3M histology and light microscopy facilities ( Nice, France ) for adipose tissue analyses and  Dr. Giovanni Marsico  from  Max Planck Institute (Dresden, GERMANY ) for the development of bioinformatics tools to measure adipocyte size. We tenderly thank  Lorette Gaffié  and  Dr. Robert Cameron  for their invaluable help with English editing. This work was supported in part by grants from  Agence Nationale pour la Recherche (ANR)  to RB and collaborators ( ANR-Bactimmunodia  and  LPS diagra) ; in part, by the  European Commission 's  Seventh Framework programme  under grant agreement No. 241913  (FLORINASH) to RB and by the  Institut Benjamin Delessert  to MS.
PDC is a research associate at  FRS-FNRS  ( Fonds de la Recherche Scientifique ),  Belgium. PDC was supported by grants from the  FRS-FNRS , the  FRFS-WELBIO  under grant:  WELBIO-CR-2012S-02R ,  Funds Baillet Latour  (Grant for Medical Research 2015), an  ERC Starting Grant  in 2013 ( European Research Council , Starting grant  336452-ENIGMO ). FC was supported by a fellowship from  INSERM/Région PACA/FEDER  and by a grant from the  Société Francophone du Diabète (SFD). The C3M group was supported by the  French National Research Agency (ANR)  through the  "Investments for the Future "  LABEX SIGNALIFE  ( ANR-11-LABX-0028-01  grant).
Data availability The microarray data from this publication have been deposited to the Gene Expression Omnibus (GEO) database https://www. ncbi. nlm. nih. gov/geo/ and assigned the identifier (accession GSE81318). The metabolomics data from this publication are available as electronic version Dataset EV1. The metagenomics data from this publication have been deposited to the ENA database http://www. ebi. ac. uk/ena and assigned the identifier PRJEB19465. Expanded View for this article is available online.
Methods This retrospective study was performed from December 2007 to October 2015 at Limoges University Hospital, France. Patients diagnosed with breast cancer, and with complete data available regarding their surgically resected tumours, including tumour Ki67, ER, PR, HER2 and uPA/PAI-1 status, were eligible for this study. Exclusion criteria were missing data for any of the abovementioned tumour markers, macroscopic lymph node involvement, previous breast cancer, initial metastatic breast cancer or prior neoadjuvant chemotherapy. Patients with microscopic lymph node involvement or isolated cells were included, because these factors do not influence the decision to perform adjuvant chemotherapy  [14]. Clinical data were collected in accordance with French bioethics laws regarding patient information and consent.
Patient consent to the use of their data and biological material was sought prior to the commencement of medical care. Clinicopathological subtypes, as well as the risk of tumour recurrence, were defined according to the St. Gallen criteria  [1, 2]. All Ki67 staining was performed by the same pathological laboratory using the MIB1 monoclonal antibody (1:80 dilution; Dako, Glostrup, Denmark); the largest tumour area, including the most proliferative zone, was assessed. The Ki67 score was calculated as the percentage of immunostained cells. The optimal cut-off for a high versus low Ki67 score was defined as 20% (i. e. ≥ 20% staining was defined as Ki67 hi ), according to the 2015 recommendations  [15]. Quantitative evaluation of uPA and PAI-1 concentrations was performed at the Biological Oncology Laboratory (Marseille, France) using the commercially available FEMTELLE® enzyme-linked immunosorbent assay.
Statistical analyses Nominal variables were compared among groups using the chi-square test or Fisher's exact test, as appropriate. Means were compared using the nonparametric Mann-Whitney U-test for continuous variables, and the Kruskal-Wallis test was used for comparisons of ordinal variables among more than two groups. A P value <0. 05 was considered to indicate statistical significance. Statistical analyses were performed using STATVIEW® software (SAS Institute Inc. , Cary, NC, USA).
Nevertheless, uPA/PAI-1 remains a feasible, low-cost test. In the second part of this study, we evaluated the definition of recurrence risk according to uPA/PAI-1 expression combined with the St. Gallen criteria, inclusive of Ki67. Previous studies have shown that the use of uPA/PAI-1 combined with a clinicopathological parameter, such as vascular invasion, is extremely helpful for yielding prognostic data  [24]. We identified a correlation between Ki67 and uPA/PAI-1 status in grade II tumours, in agreement with Kolben et al. [25] , although we lack an explanation for this correlation. The efficacy of uPA-PAI-1 testing compared with molecular signatures for recurrence risk assessment is not clear. The West German Study Group-Plan B trial is the first study to evaluate the correlation between the Oncotype Dx® recurrence score and uPA/PAI-1 status, which were prospectively compared as risk indicators in a phase III trial setting in patients with early stage breast cancer.
IFT-particles mediate the intracellular transport: IFT-B linked to kinesin in the anterograde direction, IFT-A and dynein in the retrograde direction. The cross-sections detail ultrastructural differences between primary and motile cilium, notably the molecular machinery allowing the movement for motile cilium. Insights into Ciliary Genes. doi:10. 1093/molbev/msx146 MBE and mechanisms linked to these diseases (e. g. , causative genes are unknown for 50% of Joubert Syndrome cases  [Parisi and Glass 2013] ) and more generally, to understand ciliary processes at the molecular level. In this context, experimental studies have been undertaken to identify genes associated with cilia, notably a great number of transcriptomic and proteomic studies, in different species, tissues and conditions (indexed in the CilDb database  [Arnaiz et al.
Construction of Phylogenetic Profiles Using OrthoInspector 2. 0  (Linard et al. 2015) , we predicted orthologs in 100 eukaryotic species of the 20,193 reviewed human protein-coding genes in the SwissProt database  (February 2015; UniProt Consortium 2015). The chosen species were restricted to well annotated genomes and chosen to represent all major lineages of the eukaryote domain. Orthology predictions were used to construct a binary presence/absence matrix X p,s, where an entry (g,s) is equal to 1 if at least one ortholog of human gene g was found in species s and 0 otherwise. Each matrix row defines a gene phylogenetic profile.
First, a list of genes directly or indirectly linked to cilia was created by adding to our positive gene set all genes interacting with a high confidence level (>0. 7) with at least two ciliary genes in the version ten of the STRING database  (Szklarczyk et al. 2015). From the 674 canonical pathways of the Reactome database  (Croft et al. 2014) , 68 pathways were retained containing no genes (for small pathways of <50 genes) or not more than a single gene (for larger pathway) from the extended ciliary gene set described above. Genes belonging to the 68 pathways unrelated to cilia constitute our negative set of 971 genes. Analyses of Phylogenetic Profiles and Prediction of Ciliary Genes by Three Independent Methods Knowledge-Guided Score Knowledge-guided prediction of ciliary genes was performed under the hypothesis that these genes were differentially distributed among ciliated and nonciliated species within the following eukaryotic lineages: Stramenopiles, Archaeplastida, Alveolata, Excavata, Amoebozoa, and Fungi.
Hierarchical Clustering of Genes The pairwise distance between two phylogenetic profiles x, y was estimated by the complement of the Pearson correlation coefficient dr ¼ 1 À corr(x,y) using the R package amap. Based on this pairwise distance, hierarchical clustering of all 20,193 phylogenetic profiles was performed using the Ward algorithm  (Ward 1963) , as implemented in the R function hclust. Gene clusters were defined as branches from the resulting dendrogram, using the dynamic tree-cutting algorithm  (Langfelder et al. 2008).
Species Profile Clustering Clustering of phylogenetic profiles for the 274 ACG (Ancestral Ciliary Genes) was performed for 97 of the 100 eukaryotic species. Anolis carolinensis, Cricetulus griseus, and Schistosoma japonicum were excluded from the analysis due to errors in their genome annotation. Bootstrapped hierarchical clustering was realized in R, using the package pvclust  (Suzuki and Shimodaira 2006 ) with binary distances, the Ward algorithm  (Ward 1963 ) and 10,000 bootstrap replicates. Approximately Unbiased (AU) P values  (Shimodaira 2004)  were computed using multiscale bootstrapping. Clusters corresponding to branches with AU ! 0. 9 were considered to be significant.
Acknowledgments This work was supported by the  Agence Nationale de la Recherche  ( BIPBIP :  ANR-10-BINF-03-02 ;  ReNaBi-IFB :  ANR-11-INBS-0013 ); the  Fondation pour la Recherche Medicale  ( DBI20131228569 ); and Institute funds from the  CNRS , the  Universitede Strasbourg  and the  Facultede Medecine de Strasbourg. We thank  Dr Severine B€ ar  ( University of Strasbourg ) for providing the hTERT-RPE1 cells, and the  BICS and BISTRO bioinformatics platforms for informatics support.
Background: In 1989, the concept of human gene therapies has emerged with the first approved human gene therapy trial of Rosenberg et al. Gene therapies are considered as promising therapies applicable to a broad range of diseases. Objective: The objective of this study was to review the descriptive data on gene therapy clinical trials conducted worldwide between 1989 and 2015, and to discuss potential success rates of these trials over time and anticipated market launch in the upcoming years. Methods: A publicly available database, 'Gene Therapy Clinical Trials Worldwide', was used to extract descriptive data on gene therapy clinical trials: (1) number of trials per year between 1989 and 2015; (2) countries; (3) diseases targeted by gene therapies; (4) vectors used for gene delivery; (5) trials status; (6) phases of development.
Methods The data used in the article were extracted from an interactive publicly available database, 'Gene Therapy Clinical Trials Worldwide', provided by the journal of Gene Medicine  [14]. At the time of writing, data were last updated in February 2016. The database presented information on individual gene therapy trials performed worldwide, including: trial country; principal investigator; disease category; indication; vector used; gene transferred; gene type; clinical phase; trial status; and the year trial approved/ initiated. The sources of the data of this database were official agency sources (Research Administration and Compliance [RAC], Gene Therapy Advisory Committee [GTAC]), the published literature, presentations at conferences, and from information provided by investigators or trial sponsors.
Cells were stored in YEPD broth supplemented with glycerol (Sigma-Aldrich, 20% v/v final concentration) at -80 °C. PCR amplification of FLO1, FLO5 and FLO8 genes. Primers used in this study are listed in Supplementary Table  S1. Based on the S288C genome, primers were designed using the Unipro UGENE software 1. 19. 0 to selectively amplify the entire sequence of FLO1, FLO5 and FLO8 genes. To develop oligos for FLO1, FLO5 and FLO11, repeated regions in the long intragenic repeats (>40 nt) were identified using the EMBOSS ETANDEM software26 with the threshold score set at 20. Primer specificity was checked with a BLAST analysis of all FLO genes from strain S288C. Total genomic DNA was extracted using the Wizard ® Genomic DNA Purification Kit (Promega Corporation, Charbonnières-les-Bains, Lyon, France), according to the manufacturer's instructions.
PCR amplification reactions were performed in a total volume of 25 μL. Each reaction mixture contained 20 ng template DNA, 10 X Ex Taq ™ buffer (Ex Taq ™ , TAKARA BIO INC. ) containing 4. 5 mM MgSO 4 , 4 μL of 10 mM dNTPs, 20 pmol each of the amplification primers and 0. 20 μL Takara ex Taq DNA Polymerase. PCR was performed using a TRIO Thermal Cycler (Biometra, Göttingen, Germany). PCR products were visualised on a 1% agarose gel and acquired using the Kodak GL 100 imaging system (Fisher Scientific, Illkirch-Graffenstaden, France). Gel conversion, normalisation and analysis were carried out using Fingerprinting II Informatix ™ Software (Bio-Rad, Milan, Italy). PCR products were quantified with a NanoDrop 1000 (Thermo Fisher Scientific, Villebon-sur-Yvette, France), and purified by NucleoSpin Gel and PCR clean-up kit (Macherey Nagel SARL, Sète, France) for sequence analysis.
The N-terminal sequence of FLO1 (primers FLO1 A, FLO1 BF and FLO1 BR) and FLO5 (primers FLO5-AF and FLO5-AR, FLO5-BF and FLO5-BR) genes was amplified using the KAPA HiFi PCR Kits (Kapa Biosystems, Nanterre, France), according to the manufacturer's instructions. PCR products were sequenced (Eurofins MWG GmbH, Martinsried, Germany) and the resulting sequences translated using the «Expasy Translate Tool» (http:// www. expasy. org/tools/dna. html) program. Alignments were made using program Clustal omega (http://www. ebi. ac. uk/Tools/msa/clustalo). Ploidy level of S. cerevisiae strains. Strain ploidy level was assessed using SYTOX ®  Green (Invitrogen)  fluorescence and flow cytometry according to Delobel and Tesnière  65  , using an Accuri C6 TM flow cytometer. A single colony was selected and grown overnight in liquid YEPD with shaking at 150 rpm.
The optical density at 600 nm was measured immediately and at 30 s intervals for 3 min using a spectrophotometer (Spectronic Model 601, Spectronic Instruments, Rochester, NY, USA). Three individual transformants were used to generate the data with regard to the expression of FLO5 truncations, while only one transformant for FLO1 deletion. All analyses were performed in triplicate. Invasive growth assay. Invasive growth assay was performed as previously described by Tofalo et al. 29. A fresh culture of each strain was isolated on YEPD plates with a toothpick and incubated for 3 days at 28 °C and then for 2 days at room temperature. Deionized water was used to wash all cells off the agar surface, leaving subsurface cells that had actually invaded the agar. Plates were photographed before and after washing.
Acknowledgements This work is part of the project named " Produzione di Vini spumanti di qualità a partire dai vitigni autoctoni " funded by the  Abruzzo Region  within the  Rural Development Programme 2007/2013. We are grateful to  Martine Pradal  for providing technical assistance in the development of the experiments. We apologize for the omission of relevant references due to space restrictions. We also thank the reviewers for their suggestions and  Philippe Chatelet  for his useful suggestions on the text.
The results from some of these experiments will be covered. In order to gather all the data from our own experiments, and compare it to previously published data, we created an online database (CSF-PR 2. 0) where 87 dataset containing quantitative CSF data from Multiple sclerosis (MS), Alzheimer's and Parkinson's disease were included. This database was used as a starting point for selecting most promising biomarker candidates for which PRM assays now are under developed. The process of using the database to select biomarker candidates and developing the PRM-assays towards absolute quantification quality will be described.
Lipidomics data were extracted, using the workflow4metabolomics Galaxy platform, combined to normalization of batch effects by fitting regression models to quality control samples. Factor growth effect, encompassing time and mother, was assessed by an in-home multivariate statistical analysis, AoV-PLS combined to linear discriminant analysis, in the scope of biomarkers selection. The predictive ability of these biomarkers on the postnatal weight growth trajectory of the preterm infants was evaluated by multiple linear regression method, taking into account confounding factors such as infants' birth weight, gestational age, total parenteral feeding and mother's body mass index. Several relevant lipids biomarkers were annotated using online database searches on LIPID-Maps and an in-house reference databank.
60 rosés wines were divided into two sets, both containing 30 wines from 3 different regions of France (Bordeaux, Languedoc and Provence, 10 of each). They were analyzed in triplicate using an original and fast UPLC-high resolution MS method with a similar approach previously used for red wines  (2). The goal of this 6 min shotgun analytical method was to give a rapid mass spectrometry fingerprint of these wines and be able to identify specific ions. Statistical analyses were performed after 1) peak extraction in each spectrum, 2) peak alignment between all the 60 rosés wines and 3) specific peaks selection by applying a genetic algorithm 4) validation. The discrimination between the 3 regions was applied onto the described datasets and the chosen solution -involving only 8 molecules including 5 polyphenols-produced good classification results of 97% on the training set, 87% for the 2-fold-cross-validation and 80% for validation.
Le développement des approches sans filtre de masse pour interpréter les spectres MS/MS est actuellement un domaine de recherche très dynamique. Cependant, l'utilisation de ces logiciels est encore aujourd'hui freinée par la difficulté et le temps nécessaire à l'interprétation complexe des résultats. Nous proposons une démarche et quelques représentations graphiques originales pour faciliter ce processus d'interprétation. The mining of Data-Independent Acquisition (DIA) data usually requires the use of an MS/MS spectral library. Peptide identification and quantification highly depend on the quality and the completeness of this library. Typically, libraries are generated using Data-Dependent Acquisition (DDA) methods, which produce incomplete datasets due to the semi-stochastic nature of the parent ion selection.
Omics data produce high-dimensional multivariate databases usually with a higher number of variables than samples and high biological variability that may impact the data. They also frequently involve complex experimental designs that should be considered during the data analysis to maximize their efficiency and accuracy. However, in practice, part of this information is often ignored and the analysis is resumed to a one-response (O)PLS-DA modelling. To be able to extract meaningful information from these data, one solution lies in a combination of multivariate dimension reduction methods and statistical modeling. This methodology allows to understand the effect of each experimental factor on the multivariate response and quantify or test their statistical significance. Two popular approaches, namely ASCA and APCA combine the strengths of ANOVA and PCA in this context.
Nevertheless, among the challenges associated to that strategy, it is first necessary to reliably assign identities to the detected features. Secondly, the large datasets obtained require a clever data analysis strategy to maximize the amount of useful information recovered from them. This is especially relevant when data originate from several analytical methods and when multiple experimental factors are varied simultaneously. These two issues can be tackled by combining the use of an in-house developed library of standards and a recently proposed approach of multivariate decomposition in PLS-based discrimination for structured experiments, i. e. AMOPLS (ANOVA Multiblock OPLS). The most reliable metabolite identification can only be achieved by matching at least two orthogonal properties of an unknown compound to those of a reference standard, under the same analytical conditions and in the same lab.
This result is in agreement of the literature which describe ABT-737 is supposed to target proteins located in mitochondria membrane. The workflow has demonstrated the possibility for the localization as well as semi-quantification of drug delivered into single cells using a TOF-SIMS. Imaging mass spectrometry (IMS) can be used for probing and analyzing in situ the spatial arrangement of a wide range of molecules, in particular metabolites. However, no algorithms exist for automated molecular annotation. A high-resolution imaging dataset for one tissue section has 10. 000. 000 individual ion images and manual interpretation covers up to 0. 01% data only. This lack of global understanding of data is a bottleneck. Our consortium has developed a big data analysis platform to answer this question.
Based on efficient algorithms and big data technologies, this platform is able to translate millions of ion images to hundreds of molecular images. Hundreds of datasets provided to us by the IMS community, from various species, organs, conditions, and IMS technologies, have been processed using the METASPACE metabolite annotation engine  (Palmer et al. , 2016, Nature Methods). We performed FDR-controlled annotation of all these datasets against databases considering various adducts. We stored all metabolite and lipid annotations and indexed them for fast access to have a custom search engine for metabolite images. METASPACE is an open-source platform for spatial metabolomics. High-throughput molecular annotation with a controlled level of confidence makes possible the extraction of molecular knowledge from different datasets in a reproducible way, enabling comparisons between replicates, users, and labs.