dataesr/scientific-paragraphs-categorization huggingface dataset:
The phylogenetic tree was inferred using the Neighbor-Joining method  (Saitou and Nei, 1987). The optimal tree with the sum of branch length = 4. 39003061 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (2000 replicates) are shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the p-distance method and are in the units of the number of amino acid differences per site. The analysis involved 24 amino acid sequences. All ambiguous positions were removed for each sequence pair. There was a total of 723 positions in the final dataset. Evolutionary analyses were conducted in MEGA7  (Kumar et al.
ACKNOWLEDGMENTS We thank  David Parejo  and  Victoria Palau  ( IBMCP ) for greenhouse support,  Marisol Gascón  ( IBMCP ) for technical advice in microscopy and personnel at  Servicio de Microscopía Electrónica (UPV)  for help and technical assistance. E. californica germplasm used in this study was obtained from the  National Genetic Resources Program (United States ) and Arabidopsis sty1-1 sty2-1 and 35S:: STY1  seeds from  Eva Sundberg. We acknowledge support of the publication fee by the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI).
Acknowledgments We thank the  Ethiopian Authority for Research and Conservation of Cultural Heritage (ARCCH)  for authorization to excavate in  Goda Buticha. We would like to acknowledge the efforts of  Habtewold H/Michael ,  Tilahun G/Selassie ,  Workalemahu Bekele ,  Hadis ,  Dejene Dendana Gulti , as well as the local people for their help and assistance during fieldwork. The authors thank  Mrs. M. -M. Blanc-Valleron  ( UMR 7207 CR2P CNRS/MNHN ) for her availability, and ease of access and use to her laboratory and the diffractometer, and  Sebastien Nomade  ( UMR 8212 LSCE CNRS/CEA ) for providing some radiocarbon analysis facilities. We are grateful to the  French Center for Ethiopian Studies  for logistical support. We thank the  Tourism and Culture Offices of the Eastern and Western Harerghe , the  Dire Dawa Administration , and the  Harari National Regional State  for fieldwork administrative support.
This research benefited from funding of the  National Geographic Society  (http:// nationalgeographic. org/),grants #  8110-06  and  8510-08  (ZA), of the  Wenner-Gren Foundation  (http://www. wennergren. org/),grant #  ICRG e 102 , (ZA and DP), of the  Hebrew University of Jerusalem  (http://new. huji. ac. il/en) (EH), of the  French National Research Agency  (http://www. agence-nationale-recherche. fr),projects #  ANR-09-JCJC-0123-01  (CT), #  ANR-14-CE31-0023-03 , and #  ANR-10-LABX-52 , of the  Regional Priority Program « Heritage, Resources, Governance » (HEREGO , coordinated by the  Institut de Recherche pour le Developpement) (DP) , the Doctoral School of the  Museum National d'Histoire Naturelle in Paris (France)  and  UMR CNRS 7194  (AL), the  Smithsonian NMNH  small grants, and the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  (https://ec.
In the electrical circuit, an isolated boost between the FC and the power bus is used for increasing the FC voltage and for matching the power bus. Moreover, a galvanic isolation allows protecting the FCs against any return of current during the battery charge by a deceleration of the Synchronous Motor (SM). To design this structure and to obtain real time multi-domain coupling problem (electrical and thermal for instance), the Mathematical Temporal Calculation Simulation (MTCSim © ) software is used. Developed by Segula Technologies in 2012, this software has been mainly employed to the development of new engines, energy management and components sizing. Using MTCSim © , it is possible to dimension the electrical power needs of the system to simulate the relevant autonomy issue of the global setup.
Conclusions and perspectives The paper presents a real interesting advance towards the understanding and the improvement of the multi-stack FC system. Based on the numerical simulation results via MTCsim © software. , the multi-stack FC integration is considered as an ideal solution to increase the FC vehicle efficiency thanks to the possible proper heat management and the thermal dissipation recovery. Moreover, the activation response time of each FC and the life time of each FC rise thanks to a management activation process of each/all FC. With a 25. 3% heat gain for the FC4 and 5% for the overall HEV system, the thermal management solution shows that the FCs overheat recovery is an innovative solution for improving the overall efficiency of the multi-stack FC system integrated into HEVs.
1 mbar). MALDI-TOF mass spectra were obtained on a Bruker ProFLEX III spectrometer, using dithranol as a matrix. Single clear light colorless prism-shaped crystals of 1a were obtained by slow crystallization from a toluene solution. A suitable crystal (0. 68 Â 0. 28 Â 0. 28 mm 3 ) was selected and mounted on a MITIGEN holder oil on a Bruker D8 VENTURE diffractometer. The crystal was kept at T ¼ 100 K during data collection. Using Olex2,  50  the structure was solved with the ShelXT structure solution program, 51 using the Direct Methods solution method. The model was rened with version 2014/7 of ShelXL 52 using Least Squares minimization. All non-hydrogen atoms were rened anisotropically. Hydrogen atom positions were calculated geometrically and rened using the riding model. The thiophene substituent was found disordered over two positions (75%/25%) and some EADP constraints were employed to maintain a reasonable model.
Electrochemical measurements. All electrochemical experiments were performed in DMF (dried by percolation on activated alumina) + Bu 4 NPF 6 0. 1 M, with a PGSTAT302 N (Metrohm) potentiostat connected to a PC and the data collected were analyzed using the Nova® 1. 11 Soware. Cyclic voltammetry (CV) was carried out by means of a three-electrode conguration consisting of a Pt disk (1. 6 mm in diameter, Bioanalytical Systems) as working electrode, a platinum wire as counter-electrode and an SCE electrode as reference separated from the solution by a glass chamber with a porous Vycor tip lled up with a saturated solution of Bu 4 NPF 6 in DMF. Potentials were reported versus the saturated calomel electrode, SCE. The working Pt electrode was soaked for 10 min in KOH (2 M), polished with 0. 1 mm alumina, etched for 10 min in concentrated sulfuric acid (2 M), sonicated rst 10 min in water, then in absolute ethyl alcohol.
Acknowledgements The authors acknowledge the  Agence Nationale de la Recherche  for funding through the  ANR  projects  CAP-BTX 2010  and  OUTSMART 2015  and the  MENESR  for a PhD grant (A. W. ). Financial support from the  European Union  and the  Conseil Régional de Bourgogne  through the  FABER  and the  PARI SMT 08  and CDEA programs is gratefully acknowledged. We would like to thank the  European Union  for fundings (FEDER and short term missions through the  COST action   TD1105  EuNe-tAir). Dr Yohann Rousselin  (ICMUB) is thanked for the X-ray structure determination of 1a, and  Jean-Marc Strub  ( Univ. Strasbourg)  for electrospray high resolution mass spectrometry analyses.
One of the simplest viable models for dark matter is an additional neutral scalar, stabilised by a Z 2 symmetry. Using the GAMBIT package and combining results from four independent samplers, we present Bayesian and frequentist global fits of this model. We vary the singlet mass and coupling along with 13 nuisance parameters, including nuclear uncertainties relevant for direct detection, the local dark matter density, and selected quark masses and couplings. We include the dark matter relic density measured by Planck, direct searches with LUX, PandaX, SuperCDMS and XENON100, limits on invisible Higgs decays from the a.
The secondary purpose of this paper is to provide an example global statistical analysis using the Global and Modular Beyond-Standard Model Inference Tool (GAMBIT)  [72] , for a DM model where extensive comparison literature exists. In Sec. 2, we describe the Lagrangian and parameters of the scalar singlet model, discuss our astrophysical assumptions, and define the nuisance parameters that we include in our global fit. Sec. 3 gives details of our scan, including the likelihood terms that we include for each constraint, the sampling algorithms we employ, and their settings. We present the latest status of the singlet model in Sec. 4, before concluding in Sec. 5. All input files, samples and best-fit benchmarks produced for this paper are publicly accessible from Zenodo  [73].
Scanning procedure Although 13 of the directions in the 15-dimensional parameter space are well constrained, efficiently sampling all 15 parameters simultaneously requires sophisticated scanning algorithms. We explore this space primarily with two different scanning packages interfaced via Scan-nerBit: a differential evolution sampler Diver, and an ensemble Markov Chain Monte Carlo (MCMC) known as T-Walk  [100]. Both algorithms are the current state of the art when it comes to scaling with dimension  [100] , and thus are the natural choice for this study. Both of these algorithms are particularly well suited for multimodal distributions, and each serves a purpose in this study. T-Walk allows efficient and accurate calculation of the Bayesian posterior distribution for the target model. The package can also be used for frequentist studies if the sampling density is amplified by a judicious choice of run parameters.
Indirect detection likelihoods The lack of evidence for anomalous gamma-ray emission from dwarf spheroidal galaxies in data collected by the Fermi-LAT experiment allows stringent constraints to be placed on the DM annihilation cross-section  [42]. We use the Pass 8 analysis of the 6-year dataset, with the composite likelihood ln L exp = N dSph k=1 N ebin i=1 ln L ki (Φ i • J k ) , ( 15 ) where N dSph and N ebin are the number of considered dSphs and the number of energy bins, respectively. The partial likelihoods L ki are functions of the signal flux, and hence of the quantities Φ i and J k defined in Eqs. 12 and 13, respectively. The main results of Ref. [42]  were obtained by profiling over the J k as nuisance parameters, yielding a combined profile likelihood of ln L prof. dwarfs (Φ i ) = max J1.
J k (ln L exp + ln L J ) , ( 16 ) where ln L J = N dSph k=1 ln N (log 10 J k | log 10 Ĵk , σ k ). ( 17 ) Here the use of a log-normal distribution to describe the uncertainty on J k is a good approximation. Tabulated binned likelihoods have been provided by the Fermi-LAT experiment, and implemented in DarkBit via the gamLike package. 2 he strongest neutrino indirect detection constraints on DM-nucleon scattering currently come from the Ice-Cube search for annihilation in the Sun  [64, 106]. We access the 79-string results via the DarkBit interface to the nulike package  [63, 107] , which constructs a fully unbinned likelihood using event-level energy and angular information available in the published 79-string IceCube dataset, marginalised over detector systematics. We obtain predicted neutrino spectra at the Earth using WimpSim  [108]  yield tables contained in DarkSUSY  [105].
We have varied 13 nuisance parameters in addition to the two parameters of the scalar singlet model. We have searched this parameter space using the most modern scanning algorithms available, to provide both frequentist and Bayesian statistical interpretations. By using parallel computing resources, we have achieved this with, for example, a maximum runtime for any Diver scan presented in this paper of just 3 hr. Finally, due to the modularity and flexibility of the GAMBIT system, it will be possible to include new likelihoods and/or change parts of the calculation at any time in future, in order to quickly update the analysis to take into account new experimental developments. All input files, samples and best-fit benchmarks produced for this paper are publicly accessible from Zenodo  [73].
Acknowledgements We thank  Jim Cline ,  Andrew Fowlie ,  Tomás Gonzalo ,  Julia Harz ,  Sebastian Hoof ,  Kimmo Kainulainen ,  Roberto Ruiz ,  Roberto Trotta  and  Sebastian Wild  for useful discussions, and  Lucien Boland ,  Sean Crosby  and  Goncalo Borges  of the  Australian Centre of Excellence for Particle Physics at the Terascale  for computing assistance and resources. We warmly thank the  Casa Matemáticas Oaxaca , affiliated with the  Banff International Research Station , for hospitality whilst part of this work was completed, and the staff at  Cyfronet , for their always helpful supercomputing support. GAMBIT has been supported by  STFC (UK ;  ST/K00414X/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ), Glasgow University (UK; Leadership Fellowship), the  Research Council of Norway  ( FRIPRO 230546/F20 ), NOTUR (Norway;  NN9284K ), the  Knut and Alice Wallenberg Foundation (Sweden; Wallenberg Academy Fellowship) , the  Swedish Research Council  ( 621-2014-5772 ), the  Australian Research Council  ( CE110001004 ,  FT130100018 ,  FT140100244 ,  FT160100274 ),  The University of Sydney (Australia ;  IRCA-G162448 ),  PLGrid Infrastructure (Poland) ,  Polish National Science Center (Sonata   UMO-2015/17/D/ST2/03532 ), the  Swiss National Science Foundation  ( PP00P2-144674 ), the  European Commission Horizon   2020 Marie Skłodowska-Curie actions  ( H2020-MSCA-RISE-2015-691164 ), the  ERA-CAN+ Twinning Program  ( EU & Canada ), the  Netherlands Organisation for Scientific Research  ( NWO-Vidi 680-47-532 ), the  National Science Foundation (USA ;  DGE-1339067 ), the  FRQNT (Québec)  and  NSERC/The Canadian Tri-Agencies Research Councils  ( BPDF-424460-2012 ).
The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis.
We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.
The representation of these systems involves relationships linking multiple interdependencies between genetic and epigenetic modifications that lead to cancer development. Relational databases that focus on various features of cancer pathways have appeared with increasing frequency [e. g. , MethyCancer  (He et al. , 2008) , Catalogue of Somatic Mutations in Cancer (COSMIC)  (Forbes et al. , 2015) ]. While the relational data approach has proved useful for management of structured data, issues remain in moving forward. For example, (i) integration of multiple data types in relational databases is nontrivial as it involves redefinition of data schema where new information follows a different structure; (ii) in exploration of cancer-related hypotheses, different concepts may need to be linked.
Using highly connected information (specific to malignant systems) can be inefficient for queries, which associate (join) data stored in a number of different tables with correspondingly slow response times. The graph database approach has demonstrated capability in facilitating not only both (i) and (ii) but also in (iii) exploration of context, characterized by wide diversity. In a graph database, concepts are represented by nodes and their associations by edges. Thus, the approach provides a more natural way of representing highly interconnected data, with exploration of stored content benefitting additionally from the use of different graph algorithms. A key feature of graph databases is that traversal exploration is permitted [i. e. , nodes can be accessed from neighbor nodes by means of edge connections (relationships)].
This gives a major advantage in terms of performance in comparison to relational databases, where such associations require loops on multiple table indexes. Examples of graph database frameworks include AllegroGraph  (AllegroGraph url, n. d. ) , Sparksee (Sparksee url, n. d. ), FlockDB  (FlockDB url, n. d. ) ,  InfiniteGraph (InfiniteGraph url, n. d. ) , OrientDB  (OrientDB url, n. d. ), and Neo4j (Neo4j url, n. d. ). Neo4j is a well-established framework for ''property graphs,'' fundamental to graph databases specialized for directional relationships (directed edges) and ''multirelational graphs'' (with nodes linked by multiple different edges). Additional information on concept and relationships can be stored as properties (or attributes) of the nodes and edges. The Neo4j framework uses Cypher, a declarative query language (similar to SQL), to perform data interrogation.
g. ,  Johnson et al. (2014)  and  Henkel et al. (2015) ]. In  Henkel et al. (2015) , authors describe a Neo4j-based framework that facilitates identification, comparison, and ranking of in-silico models (encoded in SBML and CELL-ML standard formats and stored in major specialized resources such as BioModels Database) that correspond to specific categories. In this study, we present a Neo4j graph database developed for the management of genetic-epigenetic interdependencies in CRC development. We provide Cypher query examples on the way in which the graph database can be applied to CRC initiation to identify (i) genetic-epigenetic modifications and (ii) molecular phenomena observed and reported in the specialized literature. In addition, we explore path connections associated with the highest ''incidence score''; the score is computed as a product of the conditional probabilities of relationships between molecular events, with the highest scores associated with the most plausible pathways.
Data collection The EpiGeNet framework has been developed using a graph database approach by integrating statistical data on molecular interdependencies observed in CRC development, mined from a manually curated and annotated database, StatEpigen  (Barat and Ruskin, 2010; StatEpigen url, n. d. )  (note: reference date for data integration from StatEpigen into EpiGeNet is November 30, 2015). In StatEpigen, information is structured by simple and conditional relationships between genetic and epigenetic events. Data on hyper/hypomethylation, mutation, histone modifications, loss of heterozygosity, and gene expression are included (for healthy phenotype) with 1 CpG islands are genomic regions (of length ‡ 200 bp) with the percentage of CG dinucleotides > 50%. additional detail on polyps, adenoma, carcinoma, or metastasis (for CRC development in aberrant cases).
Note: the baseline data extracted from StatEpigen use the gene symbols (HUGO notation); full gene names used in this article are provided in Supplementary Table  S1 , where the gene symbol-full name mapping was resolved using the ''Retrieve/ID mapping'' tool from the UniProt database  (Consortium, 2015). The simple relationships represent the probability of single molecular event occurrence at a specific oncogenesis stage; for example, from StatEpigen  (Barat and Ruskin, 2010) , P APC hyperMeth CpG ð Þ ; adenoma ð Þ = 0:459; (1) is the probability of hypermethylation at CpG islands in APC promoter = 0. 459 in colon adenoma for the available data set. Simple relationships have general form giving values (val1) P Ge ð Þ; s ð Þ= val1; (2) where G = gene symbol, e = molecular event for gene G, s = oncogenesis stage.
Data model In EpiGeNet, molecular events of conditional relationships are represented by the MolecularEvent nodes. The node identifier (key) is given by a pairwise (Gi ei) list; the relationship between molecular events is represented by an edge connecting the two MolecularEvent nodes. The edge type is determined by phenotype information (healthy phenotype or aberrant stages, including polyps, adenoma, carcinoma, metastasis) and edge direction by the conditionality of the relationship. Information on gene symbol and event type is stored as attributes of the MolecularEvent node, and the probability value is stored as a property of the edge (denoted as ''CondProbValue''). Details on the experiments, providing information on the conditional relationship, are also stored as edge properties (attributes); specifically, these include the ''TestedSampleNo'' attribute (number of samples of the experimental analysis) and the ''Pmid'' attribute (the PubMed identifier of the publications describing the experiments).
1 ); this edge has attributes, Pmid = 18716850  (Dhir et al. , 2008)  and TestedSampleNo = 25. Data from StatEpigen were filtered to include only results where both molecular events are present in the conditional relationships and information on the tested sample number is given. In cases where different studies have reported the same conditional/simple relationships, but with different probability values, these data were combined to give a single conditional/simple relationship with probability value calculated from a weighted arithmetic mean (expectation) based on the initial probability values and tested sample numbers. The data model is represented in Figure  1 , and details on the number of edges corresponding to the phenotypes are given in Table  1.
Availability of the database The framework described above was developed predominantly in JAVA (eclipse) using the Neo4j 2. 3. 1 functionality. It is available for noncommercial purposes, and the code files developed to populate the Neo4j graph database using the StatEpigen data are freely available (see the first Reference). The queries described further can be explored (see first Reference for url). The edge type is given by the phenotype [i. e. , healthy, aberrant (polyps, adenoma, carcinoma, or metastasis)], and the edge direction is indicated by the conditionality of the event relationship. Relationship probability value is stored as an edge attribute. Information on gene name and event type is stored as node attributes. Event types are genetic or epigenetic signals, including hyper/hypomethylation, mutation, histone modifications, gene expression, and loss of heterozygosity.
Identification of molecular conditional relationships observed in adenoma, but not reported for polyps phenotype (according to StatEpigen data) Identification of abnormal molecular modifications, specific to adenoma (from empirical StatEpigen sources) together with corresponding polyps phenotype, may facilitate understanding of mechanisms leading to CRC initiation. In Listing 1, we use the Cypher language to address this question and to query events that are exclusively related to adenoma with no polyps phenotype information (based on StatEpigen curation). Listing 1. Cypher query to identify molecular conditional relationships, observed in adenoma, but not matched with the polyps phenotype (integrated data basis-StatEpigen curation): The results, for Listing 1, include a set of 43 conditional relationships between genetic-epigenetic events in adenoma, with multiple interdependencies between signals affecting MGMT, APC, TP53, KRAS, and CDKN2A (P14 and P16) genes.