dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the  CESIR research group  for its collaborative support:  Fabienne Bazin  ( INSERM U657 ),  Sylvie Blazejewski  ( CIC 0005 , Bordeaux),  Anne Castot  (AFSSAPS),  Bernard Delorme (AFSSAPS) ,  Geneviève Durrieu  ( Service de pharmacologie médicale et clinique, CHU Toulouse ),  Blandine Gadegbeku  ( Univ. de Lyon, IFSTTAR) ,  Pierre-Olivier Girodet  ( CIC 0005 , Bordeaux),  Marcel Goldberg  ( INSERM U687-UVSQ ),  Bernard Laumon  ( Univ. de Lyon, IFSTTAR) ,  Dominique Lauque  ( CHU Toulouse ),  Nathalie Lecoules  ( CHU Toulouse ),  Laurence Memes  ( CIC 0005 , Bordeaux),  Louis Merle  ( CHU Limoges ),  Yvon Merlière  ( CNAM-TS ),  Jean-Louis Montastruc  ( Service de pharmacologie médicale et clinique, CRPV, INSERM U 1027 ,  Univ. de Toulouse ,  CHU Toulouse ),  Nicholas Moore  ( INSERM U657 ,  CIC 0005 , Bordeaux),  Pernelle Noize  (Inserm  U657 ),  Nathalie Orsoni  ( CHU Limoges ),  Antoine Pariente  ( INSERM U657 ,  CIC 0005 , Bordeaux),  Pierre Philip  ( Clinique du sommeil, CHU Bordeaux ),  Régis Ribéreau-Gayon  ( CHU Bordeaux ),  Louis-Rachid Salmi  ( INSERM U897 ,  Univ.
Materials and Methods The data used in  Nelson-Sathi et al. (2015)  were kindly provided by the authors. Statistical analyses were performed in R (R Core Team 2013) and ancestral gene repertoire reconstructions were performed with the Count program (Csu 00 r€ os 2010). Gene clusters were first coded into phylogenetic (presence/ absence) profiles depending on their presence or absence in archaeal genomes. Gene clusters are considered independent of each other. We used a probabilistic birth-death model of gain and loss of genes to model the evolutionary dynamic of each gene cluster and to compute probabilities of presence/ absence at each internal node of the reference tree (Csu 00 r€ os and Mikl os 2006; Csu 00 r€ os 2010). The model defines two rate parameters: a gain parameter and a loss parameter m.
Parsimonious reconstructions were also done in Count with the Wagner parsimony algorithm  (Farris 1970 ; Csu 00 r€ os 2008), which penalizes gains and losses differently. For each gene cluster, the scenario of ancestral presence/absence that minimizes the total cost of gain and loss is retained. A large range of gain and loss cost combinations was tested. All parsimony and ML estimations gave very similar results (See supplementary fig. S2 , Supplementary Material online). A Count session file that will allow users to reproduce our results is available at ftp://pbil. univ-lyon1. fr/pub/datasets/ DAUBIN/HGT_Archaea/.
Acknowledgments The authors are thankful to  Eric Alm ,  Greg Fournier ,  Andrew Roger  and  Eric Tannier  for their advice and for fruitful discussions. The authors also thank  Nelson-Sathi  et al. ( 2015) for promptly making their data available. B. B. , V. D. , C. B. -A. , and M. G. are supported by the  Ancestrome  Project  ANR-10-BINF-01-01.
Epigenetics gDNA was extracted using the NucleoSpin® Tissue kit (740952, Macherey Nagel). Global DNA methylation was quantified using the MethylFlash methylated DNA quantification kit (P-1034, Epigentek, France). Methyl DNA collection were performed using the "Methyl-Collector Ultra kit" (55005, Active Motif ). Histone 3 acetylation was quantified using the EpiQuik Tissue Acetyl-Histone H3 ChIP Kit (P-2012, Epigentek). ChIP was performed using the ChIP-IT High Sensitivity kit (53040, Active Motif ) with ChIP grade antibodies (Table  1 ). All these kits were used according to the manufacturer's instructions. Primers used in this study were designed with the primer3 software  [21] : GABARAP (F: 5′-AAAGCCAACC GTCTTTGCTA-3′ and R: 5′-GCCACTTCCCTATTCAC-CAA-3′), GABARAPL1 (MC1 F: 5′-GTCAGGCTGGTC TCGAACTC-3′ and R: 5′-CGCTCCTGAACAGCAACA TA-3′) and GABARAPL1 (MC2 F: 5′-AAGGAAACGCA GTGAGACAGA-3′ and R: 5′-AGCTGGGAGCACAAAA ACAG-3′), GABARAPL2 (F: 5′AATTCCCCAGACTTCCC CTA-3′ and R: 5′-GGTGGCGAAGAAGTTGGTTA-3′).
GABARAPL1 expression is regulated by CREB-1 Behind epigenetic modifications of the promoter, second step of gene regulation is the recruitment of transcriptional factors (TF). Therefore, in order to characterize the mechanisms governing GABARAPL1 expression in these cells, we cloned two fragments of the GABARAPL1 promoter (-659/+241 and -336/+241) in the pGL3 luciferase reporter plasmid. As suggested by 3 different softwares (TESS http://www. cbil. upenn. edu/tess, Cister http://zlab. bu. edu/~mfrith/cister. shtml and Patch http://www. gene-regulation. com/pub/programs. html#patch) used to predict TF binding sites, several putative CRE (cAMP Response Element) elements were in GABARAPL1 promoter (Fig. 5a ). Indeed, following transfection of our constructions in MCF-7 cells, we observed a significant increase of luciferase signal in cells transfected with the-336/+241-GABARAPL1-promoter-pGL3 plasmid compared to the empty pGL3 vector suggesting the presence of functional regulatory elements in this region.
Acknowledgements We thank  Dr. Vincent Coulon  ( Montpellier, France ) for providing the pCDNA3. 1-CREB-1plasmid [60]. This work was supported by the  Université de Franche-Comté , and funding from  "BQR Jeunes chercheurs" of Université de Franche-Comté, Ministère de l'Enseignement Supérieur de la Recherche (MESR) ,  Région de Franche-Comté 2013 and Ligue Contre le Cancer  (# 007. Y-2014 ). ACT was supported by a fellowship from  Région de Franche-Comté.
Specifically, we analyzed a collection of 96 gene expression profiles (GSE60966) from microdissected brain regions of the central reward and stress pathways in a rat behavioral model of dependent and non-dependent alcohol selfadministering rats  [11, 12]. In this model, rats are chronically exposed to intermittent alcohol vapor to intoxication to induce dependence. Dependent rats in this model rapidly escalate their alcohol intake during repeated withdrawal periods and show compulsive responding for alcohol  [11] [12] [13]. In particular, dependent rats selfadminister sufficient amounts of alcohol to reach blood alcohol levels comparable to excessive drinking in alcohol use disorders humans  [14, 15] , and manifest physical and motivational (anxiety, dysphoria, and hypohedonia) signs of withdrawal during acute and protracted abstinence periods  [11] [12] [13] [14] 16, 17].
Regulatory network assembly We reconstructed a transcriptional-interactome from a dataset of 96 gene expression profiles (GEP) from eight brain regions believed to be relevant in alcohol's reinforcing properties using the Affymetrix RN230. 2 platform. Specifically, the following brain regions were microdissected and analyzed from non-dependent and dependent alcohol self-administering rats as well as age-matched alcohol naive rats: (a) mPFC, (b) shell and (c) core NAc sub-regions, (d) CeA), (e) BLA, (f ) dorsolateral and (g) ventral bed nucleus of the stria terminalis (BNST), and (h) VTA (Additional file 1: Figure  S1 ). To improve the accuracy and reproducibility of the analyses, the Cleaner algorithm  [21]  was used to normalize the GEP data. Cleaner maps individual Affymetrix probes to the most recent Refseq transcript database, thus eliminating probes mapping to multiple genes or to incorrect and/or intragenic regions  [21].
Cleaner also clusters probes into probesets, based on probe correlation across the entire GEP dataset, producing probesets that optimally monitor the expression of individual alternative gene transcripts, thus excluding low-quality and incorrectly matched probes  [21]. Cleaner identified 7,019 high-quality probesets in the Affymetrix RNU230. 2 GEP data, representing the transcripts of 6,818 expressed genes, down from the 19,456 probesets originally represented on the microarray platform. Thus, only about one-third of the monitored probes were considered to be high quality and mapped to expressed genes. The Cleaner-normalized dataset was then processed by ARACNe to produce a genome-wide transcriptionalinteractome. ARACNe first computes the Mutual Information (MI), I(TF; t), between each transcription factor, TF, and candidate target, t, in the dataset  [10].
ARACNe-inferred targets of the Nr3c1 TF (that is, the Nr3c1 regulon) were found to be significantly enriched in genes differentially expressed following shRNA-mediated silencing of this TF (P <0. 004 by GSEA analysis  [23] , Figure  3A  and B ). Promoters of ARACNe-inferred Nr3c1/GR targets were found to be significantly enriched in canonical Nr3c1/GR binding sites (Figure  3C ; P <0. 05), compared to randomly selected promoters from non-ARACNe predicted targets. Binding site enrichment analysis was performed using the position-specific-scoring-matrix for Nr3c1/GR (MA0013. 1) downloaded from the JAS-PAR database  [24]. The position-specific-scoring-matrix was scored using the maximum-likelihood method outlined by Conlon et al. [25]. Taken together, these independent analyses suggest that the ARACNe-inferred regulon is highly enriched in bona-fide, physical Nr3c1/ GR targets.
Results showed enrichment of the genes in the Nr3c1/GR regulon among the genes whose expression is affected by siRNA-mediated downregulation of Nr3c1/GR in M213 cells (GSEA result: NES = 1. 28, P <0. 004). (C) Sequence logo of Nr3c1/GR binding site from JASPAR database  [24]. had significant correlation with Nr3c1/GR activity, as shown by the heatmap in Additional file 1: Figure  S7. Additionally, Nr3c1/GR activity was significantly correlated with nine TfcoTFs (Ncoa3, Ncor2, Smarca4, Trim28, Nfkb1, Hopx, Smarcad1, Nr3c2, Pou2f1), as shown in Additional file 1: Figure  S8.
Gene expression profiles Gene expression profiles were collected using the Affymetrix Rat Genome 230 2. 0 GeneChip® system (31,099 probe sets). Expression measurements were normalized with gcrma (Wu 2004), which adjusts for background intensities in Affymetrix array data, including adjusting for optical noise and non-specific binding. All array files were processed and normalized using R version 2. 15. 1. All gene expression profiles generated in this study are publically available from GEO at  [50]  (project accession # GSE60966).
Cleaner The normalized gene expression profiles were analyzed using the Cleaner Algorithm  [21]. The Cleaner Algorithm performs probe-remapping and probe-correlation analyses for assembly of informative, transcript-specific probeclusters in Affymetrix expression microarrays. After applying Cleaner, the roughly 31,099 probesets that were originally on the microarray platform were reduced to 7,019 highlycorrelated probesets that mapped to 6,715 unique genes. Cleaner R package (version 1. 01) was applied to the expression profiles after normalization using R version 2. 15. 1. The Cleaner-normalized gene expression profile used in our study is available for download from figshare (http://dx. doi. org/10. 6084/m9. figshare. 1154010).
Signaling molecule classification To identify signaling molecules (Sigs), we selected the rat genes annotated in the GO Biological Process database as: 'signal transduction' and in the GO Cellular Component database as GO:0005622 -'intracellular' or GO:0005886 -'plasma membrane'. This produced a final list of 2,842 genes, from which 1,100 were present on the Cleanermapped expression profile.
Single-sample MARINa and activity correlation analysis The relative activity of each transcription co-factor (Tfco) and signaling molecule was inferred for each sample using a version of the MARINa algorithm  [9]  modified to allow signature analysis on a sample-by-sample basis. Singlesample signatures were calculated by differential expression analysis of the sample's gene expression profile and the average of all gene expression profiles in the same dataset. This analysis allows the inference of the relative activity of each transcriptional regulator or signaling molecule in each sample, based on the relative change in expression of its ARACNe-inferred targets. Specifically, we define the relative activity of a given regulator in a specific sample as the normalized enrichment score (NES) computed by MARINa, based on its gene expression signature.
Additional files Additional file 1: Tables  S1, S2. The number of MR candidates in each region and signature, as well as the overlap between regions. Table  S3. The MARINa results for Nr3c1/GR in all eight profiled regions, for both alcohol exposed (NaivevsNonDep) and addicted (NonDepvsDep) signatures. Figure  S1. The results of unsupervised hierarchical clustering of the microarray dataset included in this study. Figures  S2, S3. The results of hierarchical clustering of MR candidates that were statistically significant after pvalue integration using Stouffer's method for the alcohol exposed (NaivevsNonDep) and addicted (NonDepvsDep) signatures. Figures S4, S5   Additional file 2: Excel spreadsheet showing ARACNe-predicted targets for the top 25 MR candidates in the alcohol addicted (NonDepvsDep) signature.
Figure1Escalation of alcohol intake induced by chronic, intermittent alcohol vapor exposure in rats. (A) Responding for alcohol by the animals used for the microarray dataset in the gene network analysis. A total of 96 gene expression profiles from eight brain regions (mPFC, BLA, dorsolateral and ventrolateral BNST, CeA, core and shell of NAc, VTA) from four rats per group in this experiment were used to reconstruct the transcriptional regulatory network, or transcriptional-interactome. White bars show the average operant responding for ethanol over the last 11 self-administration sessions prior to induction of dependence (Baseline) for both the rats exposed to alcohol vapor to induce dependence and non-dependent rats exposed to air in the same apparatus for control. Operant responding during acute withdrawal after 4-week exposure to chronic, intermittent alcohol vapor (or air for control) is shown by the right hand bars: black bar = dependent rats and grey bar = non-dependent rats.
Acknowledgements Supported by  NIH  grants  AA017371 ,  AA020960 ,  DA013821 ,  GM085659  (PPS);  AA021667 (PPS/AC );  CA121852-09 ,  CA1968426 ,  HL111566  (AC). We are grateful to  Dr. Ronald A. Bush  of  NIH/NIDCD  for the AAV expressing Psip1 and control AAV.
They are not dynamic and are not coupled to a model that dynamically models groundwater recharge and surface water levels. Therefore, the simulation of groundwater-surface water interactions is very limited, and neither capillary rise nor groundwater pumping or groundwater recharge by irrigation return flows is considered. At a continental scale,  Maxwell et al. (2015)  showed the possibility of setting up an integrated hydrological model that simulates surface and subsurface flow at a high spatial resolution (1 km). The model solves surface and subsurface flow simultaneously and is constructed entirely of available datasets including topography, soil texture and hydrogeology. However, the steady-state simulation did not take into consideration runoff generation, transient dynamics or human activities such as groundwater pumping that affect the quantity of surface water fluxes and groundwater recharge  (Do ¨ll et al.
Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge. The aim of this study was to create a neuroanatomical ontology, called "Human Connectomics Ontology" (HCO), in order to represent macroscopic gray matter regions connected with fiber bundles assessed by diffusion tractography and to annotate MRI connectomics datasets acquired in the living human brain.
First a neuroanatomical "view" called NEURO-DL-FMA was extracted from the reference ontology Foundational Model of Anatomy (FMA) in order to construct a gross anatomy ontology of the brain. HCO extends NEURO-DL-FMA by introducing entities (such as "MR_Node" and "MR_Route") and object properties (such as "tracto_connects") pertaining to MR connectivity. The Web Ontology Language Description Logics (OWL DL) formalism was used in order to enable reasoning with common reasoning engines. Moreover, an experimental work was achieved in order to demonstrate how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets. Indeed, neuroimaging datasets of five healthy subjects were annotated with terms of the HCO and a multi-level analysis of the connectivity patterns assessed by diffusion tractography of the right medial Brodmann Area 6 was achieved using a set of queries.
Axioms can be expressed in the OWL 1 ontology language standard (Web Ontology Language, defined by the W3C), and especially the OWL DL 2 sublanguage, based on Description Logics 1 OWL, http://www. w3. org/TR/owl-ref/ 2 OWL DL, http://www. w3. org/TR/owl-ref/#OWLDL (DL)  (Baader et al. , 2003). OWL DL provides a good compromise between expressivity and computational complexity  (and decidability). Moreover, it allows reasoning on formal knowledge and infering automatically new axioms using description logic reasoning engines such as FaCT++ 3. Ontology-based systems and reasoning engines are particularly relevant in the human connectomics realm as they provide the capability to apprehend consistently multi-scale knowledge, to describe heterogeneous data with semantic annotations, and finally to facilitate data querying, sharing and interoperability.
An important and complementary field of research in neuroimaging concerns the development of digital atlases providing both a template brain and neuroanatomical labels in a conformed space. Individual brain datasets are aligned to the atlas using volumetric or surface-based registration approaches in order to propagate the neuroanatomical labels of the atlas to brain regions. A great number of in vivo neuroimaging datasets are currently annotated using brain atlases such as the Talairach Atlas  (Talairach and Tournoux, 1988) , the Montreal Neurological Institute (MNI) atlas  (Tzourio-Mazoyer et al. , 2002) , or the atlases embedded in software tools such as Freesurfer 4   (Fischl et al. , 2004; Desikan et al. , 2006)  or the JHU white matter tractography atlas 5   (Wakana et al. , 2007; Hua et al.
, 2008). Recently, FMA provided a mapping between several terminologies used in brain atlases, such as Freesurfer or the JHU white matter tractography atlas, and the corresponding neuroanatomical concepts defined in FMA  (Nichols et al. , 2014). This effort facilitates the use of FMA as a reference and pivotal terminology for the annotation of brain segmentation results such as cortical or subcortical gray matter regions, and different white matter fiber bundles. The main contribution of this paper was to create a generic neuroanatomical ontology called "Human Connectomics Ontology" 6  (HCO) in order to represent macroscopic regions defined on MRI datasets connected via fiber bundles assessed by diffusion tractography in the living human brain. Grounded on the FMA reference ontology, HCO was expressed in OWL and used the OWL DL sublanguage in order to be processable by usual reasoning engines.
The latter provide highly optimized implementations of reasoning algorithms to process and correctly answer arbitrarily complex queries, such as those involving, e. g. , transitive part-whole and spatial relationships. Moreover, an experimental work was achieved in order to show how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets: a multi-level analysis of the connectivity pattern of the right medial Brodmann Area 6 (BA6) reconstructed by diffusion tractography was achieved, using a set of queries on annotated neuroimaging datasets of five healthy subjects. The medial BA6 region is a cortical region defined using a set of cytoarchitectural criteria  (Zilles and Amunts, 2010)  and is part of the medial frontal cortex located on the midline surface of the hemisphere just in front of the primary motor cortex.
Requirements and design Competency questions  (Neuhaus and Vizedom, 2013)  are more and more used to specify the domain that an ontology should cover. Therefore, we designed a set of competency questions pertaining to a use case inspired by the medial BA6 connectivitybased parcellation  (Johansen-Berg et al. , 2004) , in order to assess how the Human Connectomics Ontology (HCO) can support hypothesis-driven analysis of connectomics datasets at different levels of granularity: • Which gray matter parts of the right superior frontal gyrus have a connectivity pattern passing through the corticospinal tract or through some gray matter parts of the right precentral gyrus? • Which gray matter parts of the right superior frontal gyrus have a connectivity pattern passing through some gray matter parts of the right medial parietal cortex or through some gray matter parts of the right inferior frontal cortex? • Which gray matter parts of the right superior frontal gyrus have a connectivity pattern passing through the corticospinal tract or through some gray matter parts of the right precentral gyrus or through some gray matter parts contiguous with the right precentral gyrus? • Which anatomical white matter fiber bundles connect some gray matter parts of the right superior frontal gyrus to some gray matter parts of the right temporal lobe? In order to meet these requirements, a neuroanatomical ontology module, called "NEURO-DL-FMA, " was first constituted in order to annotate gross anatomy of the brain (i.
In order to reuse nearest neighbor topology knowledge represented in the FMA, all the entities related using the fma:attributed_continuous_with object property of type fma:Continuous_with_relation were included in our view. Finally, only the following three object properties and their inverse (if exists) were kept: fma:constitutional_part, fma:regional_part, fma:attributed_continuous_with. This view was achieved using a web service implementation developed by the University of Washington's Structural Informatics Group 7. In this implementation, the FMA (OWL Full 3. 2. 1 version) is embedded in a MySQL 8  relational database. This web service based on Apache Jena 9  accepts VSparQL queries allowing portions of the FMA to be extracted by recursively following complex pathways within the ontology graph  (Shaw et al.
Finally, a translation into OWL DL was necessary in order to enable the subsequent use of reasoning engines. This was achieved using a local java program based on the OWL API package 11. All classes and object properties of the view were included in the ontology. As object properties were expressed at the individuals' level in the view, all object properties were translated at the classes' level using existential restrictions. Figure  3  depicts an example of translation of the fma:Precentral_gyrus entity from the view expressed in OWL Full (cf. part 1 of the figure) into NEURO-DL-FMA expressed in the OWL DL formalism (cf. part 2). In the view, entities such as fma:Precentral_gyrus appear both as a class and as an individual (cf. part 1). While part-whole relationships such as fma:constitutional_part or fma:regional_part_of were expressed at the individuals' level in the view (cf.