dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the two anonymous reviewers for their thorough reading and constructive comments, which helped improving the manuscript. The present work was supported by the  French National Research Agency (ANR)  as part of the  OPTIMISM  project ( ANR-09-BLAN-0227-01 ), the  European Community 's  Seventh Framework Program FP7/SPACE  ( My-Ocean2 , grant  283367 ), IS-ENES2 (grant  312979 ) and  BISICLO  ( FP7 CIG 321938 ). Computational resources have been provided by the  French Institut du Développement et des Ressources en Informatique Scientifique (IDRIS). The Antarctic sea ice concentration reprocessed data set was provided by the  Ocean and Sea Ice Satellite Application Facilities (OSISAF)  and is available at http://osisaf. met. no. The MODIS image is courtesy of the online Data Pool at the  NASA Land Processes Distributed Active Archive Center (LP DAAC ),  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac.
O R B I T F I T T I N G A N D C O M PA N I O N PA R A M E T E R S As shown in Table  2 , many of the stars considered here received fewer observations than is typical or desired for planet-search efforts. However, the signals in our data are large enough as to be unambiguous, particularly for the stellar-mass companions. We first used a genetic algorithm to search a wide range of orbital periods, running for 10 000 iterations (about 10 6 possible configurations). This approach has been used in our previous work on systems for which data are sparse and/or poorly sampled (e. g. Tinney et al. 2011; Wittenmyer et al. 2011 Wittenmyer et al. , 2015)). In all cases, convergence occurred rapidly, a hallmark of a genuine signal. Again as in our previous work, we then used the best solution from the genetic algorithm as a starting point for the generalized least-squares program GAUSS- FIT  (Jefferys, Fitzpatrick & McArthur 1988) , here used to solve a Keplerian radial velocity orbit model.
A giant planet orbiting HD 155233 We have obtained 21 radial velocity measurements of HD 155233, indicating a linear trend and a periodic signal, though with significant jitter. A simple linear fit to our data yields a residual rms scatter of 24. 2 m s - 1  and χ 2 nu = 5. 23. By comparison, a planet-only model (no trend) gives an rms of 19. 1 m s -1 and χ 2 nu = 3. 65. We fit a planet+trend model, which indicates a planetary companion with P = 885 ± 63 d, e = 0. 03 ± 0. 20, and m sin i = 2. 0 ± 0. 5 M Jup (Table  10 ). That model has a residual rms of 11. 0 m s -1 and χ 2 nu = 1. 44, with a linear trend of 15. 4 ± 6. 5 m s -1 yr -1. Feng et al. (2015)  gave a discussion on estimating minimum companion masses from velocity data with such trends when no curvature is evident. Using their equation (  1 ), which assumes an 'unlucky observer' seeing an orbital period P ∼ 1.
AC K N OW L E D G E M E N T S We gratefully acknowledge the efforts of PPPS guest observers Brad Carter, Hugh Jones, and Simon O'Toole. This research has made use of NASA's Astrophysics Data System (ADS), and the SIMBAD data base, operated at CDS, Strasbourg, France. This research has also made use of the Exoplanet Orbit Database and the Exoplanet Data Explorer at exoplanets. org  (Wright et al. 2011 ).
The monographic explanation of urban growth in the Former Soviet Union has been dominant in urban geography, and we argue that it might have hidden very generic processes of urbanisation in this system of cities  [Cottineau and Slepukhina, forthcoming]. Our modelling experiment aims at testing the genericity of Soviet and post-Soviet urbanisation by simulating generic mechanisms and testing them against harmonised historical data. The database used in this study consists of open demographic, territorial and site informations on 1929 agglomerations of the Former Soviet Union between 1840 and 2010  [Cottineau, 2014b]. We analyse and compare the explaining power of generic mechanisms in a simulation of the period before  and the period after  the dislocation of the Soviet Union. The agentbased model framework of this experiment is called MARIUS 3.
III. 1. 3 Technical modular implementation In order to achieve a modular implementation of the model and its mechanisms, we leveraged the mixin-methods system of the Scala programming language  [Schärli et al. , 2003]. The mixins were first proposed at the beginning of the 90s in the Jigsaw programming language  [Bracha, 1992]. They are now adopted in mainstream languages such as Scala. It has been established as a powerful way of to perform type-safe dependency injection framework  [Wampler and Payne, 2009] , which is a powerful paradigm to achieve modularity. The mixin pattern allows to achieve type safe dependency injection. Mixins are therefore a suited tool to achieve modularity in model implementations, which we use to implement MARIUS in Scala language 5. The mixin-methods concept  [Steyaert et al.
, 1993, Lucas and Steyaert, 1994]  generalizes objectoriented programming to enable feature-oriented programming  [Prehofer, 1997]. It makes the definition of class hierarchy more flexible  [Steyaert and Meuter, 1995]. Variations of isolated features (such as the different update functions of city wealth and population in our model) are defined in separate modules and mixed-in with each other at the object instantiation point, also called mixin application. The advantage of mixins (or trait) over classical object-oriented programming is the possibility of defining numerous variations of several features without increasing exponentially the number of specialized class. Scala traits expressiveness can be leveraged to implement modular evolutive type-safe modelling frameworks proposing alternative model features, feature composability and the formalisation of the feature dependencies.
The implementation of alternative behaviours in several traits provides the isolation of model component implementations and explicit dependencies between these components. Each component defines free parameters that have to be set at the model instantiation site, otherwise it won't compile. In this experiment we defined each alternative model mechanism in a particular trait. The exe-cutable model has then be composed by picking the traits we wanted to test. In order to evaluate concurrently all the alternative mechanisms, we generated all the possible models (or combination of mechanisms). It was achieved using a code generation algorithm which produces all the possible models implementations by generating a Scala source code containing all the possible traits combinations, such as the one shown on Listing 3.
Acknowledgments This work has been funded by the  ERC Advanced Grant Geodivercity , the  University Paris 1 Panthéon-Sorbonne  and the  Institut des Systèmes Complexes Paris Île-de-France. The results obtained in this paper were computed on the biomed and the vo. complex-system. euvirtual organization of the  European Grid Infrastructure  (http://www. egi. eu). We thank the  European Grid Infrastructure  and its supporting  National Grid Initiatives (France-Grilles in particular)  for providing the technical support and infrastructure.
In rat tissues, numerous QTLs were found to control both H3K4me3 and H3K27me3, but 25% of these co-regulations acted in a compensating manner (e. g. , upregulating H3K27me3 together with H3K4me3, which is expected to act antagonistically on gene expression)  [13]. These observations demonstrated the existence of correlated patterns of variation. However, the extent and location of co-variation between marks remain poorly characterized. In the present study, to obtain a detailed view of intraspecies variation in histone modifications, we determined the quantitative variation of five marks at single-nucleosome resolution in Saccharomyces cerevisiae. We took advantage of three wild strains for which transcriptome variation has been extensively characterized. This new dataset allowed us to precisely map the location of variation, to investigate whether variability is focused on precise nucleosomes or spread on many consecutive ones and to determine the degree of co-variation between marks on individual nucleosomes.
In addition, we chose to profile H3K4 mono-methylation because it is distributed on a different set of nucleosomes: those located at the 3′ of gene bodies  [16, 18]. This way, all five marks cover biochemical information on the majority of the chromatin. Such a dataset should also reveal if one strain is particularly different from the two others. The strains used were BY, RM and YJM789 (hereafter called 'YJM'). BY and RM are those where H3K14ac was studied previously  [6]. YJM derives from a clinical isolate and is genetically equidistant to BY and RM, with an SNP frequency of about 0. 5%  [19, 20]. All three strains have been used extensively to study complex traits and to map genetic determinants of gene expression variation; their genomes have been fully sequenced and assembled  [19, [21] [22] [23].
Here, we re-analyzed this dataset to determine if the RM-specific profile of H3K14ac had persisted. We selected 529 genes having a pronounced BY/RM difference in H3K14ac ChIP-seq profile in the present study and extracted the nucleosomes located in the last two-thirds of the bodies of these genes. We then extracted from the previous dataset the log(RM/BY) ChIP-CHIP intensities at these nucleosomes, both before and after the transient perturbation (Fig. 2h ). This showed that the high H3K14 acetylation of these nucleosomes in the RM strain persisted after recovery from the epidrug treatment. The specific regulations of H3K14ac taking place in the RM strain are therefore robust to such transient perturbations. This robustness could result from genetic control. If DNA polymorphisms of the RM strain regulate the redistribution of H3K14ac, then the specific acetylation profile would be re-established after perturbation.
Genes sharing similar patterns of variation may be functionally related. To examine this possibility, we searched every cluster for enrichment in gene ontology terms among the annotations of its genes. As indicated in Table  1 , five clusters showed significant enrichments relating to amino acid catabolism, transmembrane transport, RNA metabolism, RNA translation (these genes are transcribed at high rate, Additional file 9) and oxido-reduction, respectively. Given the known participation of histone modifications in transcriptional regulations, we examined if genes displaying chromatin divergence also displayed inter-strain differences in mRNA levels. The yeast strains used here have previously been an experimental model for comparative transcriptomics. Large datasets are available that quantify mRNA divergence in several environmental conditions, including standard growth conditions similar to the ones we used here.
Pairwise genome alignments Genomes of two strains were aligned using MUMmer  [56]  and custom scripts developed in version 1. 0 of Nucl-eoMiner  [6]. This step produced a. c2c file listing all similarities and polymorphisms. We used this file to define regions that are common to both genomes, larger than 4 Kb and containing no indel greater than 30 bp. These regions of interest were called common uninterrupted regions (CUR)  [44]. All statistical analyses were then done using R (http://www. r-project. org).
PCA analysis For simplicity, only forward reads were used because reverse reads contained redundant information (dosage of the same nucleosome, at the other extremity). The matrix of coverages (columns = experiments, rows = chunks) was normalized by dividing each value by the sum of its column. PCA was performed using the prcomp() function in R. To assess the significance of the components, PCA was run three times on permuted datasets. On average, the fraction of variance explained by the first component on permuted data was 2. 3%.
Acknowledgements We thank  Jérôme Govin ,  Francesca Palladino  and  Cédric Vaillant  for discussions,  Séverine Croze  from  Viroscan3D/ProfileXpert  for technical advice on Illumina library preparations,  Lars Steinmetz  for the YJM789 strain,  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) for access to machines, the  Pôle Scientifique de Modélisation Numérique (Lyon, France)  for computer resource, developers of R and Ubuntu for their software and the four anonymous reviewers for their critical comments. This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme   FP7/2007-2013 Grant Agreement No. 281359.
Biologists produce large data sets and are in demand of rich and simple web portals in which they can upload and analyze their files. Providing such tools requires to mask the complexity induced by the needed High Performance Computing (HPC) environment. The connection between interface and computing infrastructure is usually specific to each portal. With Jflow, we introduce a Workflow Management System (WMS), composed of jQuery plug-ins which can easily be embedded in any web application and a Python library providing all requested features to setup, run and monitor workflows.
, 2012) , Snakemake  (Koster et al. , 2012) , Ruffus  (Goodstadt, 2010)  or Cosmos  (Gafni et al. , 2014)  provide a framework or a domain-specific language to developers wanting to build and run workflows. These software packages offer the flexibility and power of a high-level programming language, but they do not provide a user interface, enable component and workflow definition. JFlow combines a user friendly interface with an intuitive python API. It is, to our knowledge, the only WMS designed to be embedded in any web application, thanks to its organization as jQuery (http://jquery. com/) plug-ins.
Methods Jflow user interface gathers five jQuery plug-ins providing user oriented views. • availablewf lists all runnable workflows accessible to users, • activewf monitors all started, completed, failed, aborted and reseted workflows, • wfform presents workflow editable parameters in a form, • wfoutputs displays all outputs produced by the workflow organized per component, • wfstatus shows the workflow execution state as a list or an execution graph. The graph visualization uses the Cytoscape web JavaScript plug-in  (Lopes et al. , 2010). The plug-ins give access to multiple communication methods and events. They interact with the server side through Jflow's REST API, running under a cherrypy (http://www. cherrypy. org/) web server. The included server uses the JSONP communication technique enabling cross-domain requests.
To be available from the different jQuery plug-ins, the workflows have to be implemented using the Jflow API. A Jflow component is in charge of an execution step. Adding a component to the system requires to write a Python Component subclass. In Jflow, different solutions are available to ease component creation. To wrap a single command line, the developer can give a position or a flag for each parameter. Jflow also embeds an XML parser which allows to run genuine Mobyle (Neron et al. , 2009) components. Finally, to allow developers to integrate components from other WMS, Jflow provides a skeleton class. This class only requires to implement the parsing step. A workflow chains components. It is represented by a directed acyclic graph (DAG) where nodes represent jobs and edges links between inputs and outputs.
Conclusion Jflow is a simple and efficient solution to embed WMS features within a web application. It is, to our knowledge, the only WMS designed with this purpose. It is already embedded in RNAbrowse  (Mariette et al. , 2014)  and NG6  (Mariette et al. , 2012) , where it has been used to process more than 2000 sequencing runs on a 5000 cores HPC environment. The jQuery code in charge to build Jflow plug-ins and manage user action. When the select. activewf event is thrown from activewfdiv, a function is called with two parameters: event and workflow. The last parameter stores all the workflow's information, such as its name and its id, used in this example to update the modal box title and to build the wfstatus plug-in. (c) The status of the illumina_qc workflow with the id 26 displayed as a graph in the NG6 application.
RNA sequencing and bioinformatics RNAseq was carried out by the MGX platform (Montpellier GenomiX, www. mgx. cnrs. fr/). RNAseq libraries were constructed with the TruSeq RNA sample preparation (Low throughput protocol) kit from Illumina. A 1 mg aliquot of total RNA was used for the construction of the libraries. The first step in the workflow involves purifying the poly(A)-containing mRNA molecules using poly(T) oligo-attached magnetic beads. Following purification, the mRNA is fragmented into small pieces using divalent cations under elevated temperature. The cleaved RNA fragments are copied into first-strand cDNA using SuperScript II reverse transcriptase and random hexamer primers. This was followed by second-strand cDNA synthesis. These cDNA fragments then go through an end repair process, the addition of a single 'A' base and subsequent ligation of the adaptor.
The products are then purified and enriched with 15 cycles of PCR. The final cDNA libraries were validated with a DNA 1000 Labchip on a Bioanalyzer (Agilent) and quantified with a KAPA qPCR kit. For each sequencing lane, four or six libraries were pooled in equal proportions, denatured with NaOH and diluted to 6. 5 pM before clustering. Clustering and 100 nucleotide single read sequencing were performed according to the manufacturer's instructions. Image analyses and basecalling were performed using the HiSeq Control Software and Real-Time Analysis component (Illumina). The quality of the data was assessed using fastqc from the Babraham Institute (http://www. bioinfor matics. babraham. ac. uk/projects/fastqc/) and the Illumina software SAV (Sequence Analysis Viewer). Using the Illumina-supplied algorithm CASAVA (1.
Differential expression analysis The RPKM (reads per kilobase per million mapped reads) data are provided in Supplementaty Table  S12. However, to compare the expression levels between the species and between the growth TRs, we used the DESeq methodology  (Anders and Huber 2010)  which is more accurate than RPKM data  (Dillies et al. 2013 ; Supplementary Table  S13 ). The DESeq package is particularly adapted to experiments with biological replicates. The method is based on the negative binomial distribution, with variance and mean linked by local regression. The count data were normalized on the total number of counts, taking the variance and the mean of the biological replicates into account, and subjected to further pair-wise differential expression transcriptome analysis. For each gene, we estimated the fold change from the first to the second condition (i.
e. , to maximize the number of situations in which an aerosol estimate can be performed. Our work began with the development of the MT method described in  [4]  for the VENµS satellite mission, which will offer a two-day repetitivity over 100 sites. In order to apply this technique to the Sentinel-2 mission, which has a longer revisit period of five days with two satellites, and to improve its estimates when the surface reflectance varies quickly, MS criteria have been added to the MT method. As a result, a hybrid method, using both temporal and spectral approaches for retrieving the AOT, has been developed. These methods have been introduced in the Multi-Sensor Atmospheric Correction and Cloud Screening software, developed as a prototype version at the Centre d'Etudes Spatiales de la Biosphère (CESBIO) and as an operational version at the Centre National d'Etudes Spatiales (CNES).
They will be used to produce Level 2A products, which, according to Sentinel-2 denomination  [16] , are ortho-rectified images expressed in surface reflectance. This software is being used within the VENµS satellite ground segment and within the French THEIA land data center (http://www. theia-land. fr). After a description of the satellite missions and datasets used in Section 2, this paper details the MT and the MS algorithms and their combination in Section 3 and, then, compares their performances in Section 4.
The Remote Sensing Instrument (RSI) on-board FormoSat-2 provides eight-meter resolution images in four visible and NIR bands. However, for cost reasons, we did not purchase datasets with a repetitivity of one day, but with a repetitivity of three to ten days. The LandSat program is the longest-running Earth-observation satellite program  [21]. It was developed by NASA and USGS. The first LandSat satellite flew in 1972 and was followed by six additional ones. In this study, we used LandSat-5 and 7, launched in 1984 and 1999, respectively. The repeat cycle for these two satellites is 16 days; however, an eight-day repetitivity can be reached combining images from both satellites. The Thematic Mapper (TM) instrument has been installed on-board LandSat-5, while LandSat-7 is equipped with an improved version: the Enhanced Thematic Mapper Plus (ETM+).
Both instruments acquire images in the nadir direction in the VNIR, the short wave infrared (SWIR) and the thermal infrared (TIR) ranges, at a resolution of 30 m, except for TIR, for which the resolution is 120 m for LandSat-5 and 60 m for LandSat-7 (see Table  1 ). In May 2003, the LandSat-7 Scan Line Corrector (SLC) failed, degrading the acquired images from then on. For this reason, we used data acquired before this breakdown, to keep a repetitivity close to that of Sentinel-2. Since April 2013, LandSat-8 data have been available, but have not been involved in this study, because of the reduced repetitivity compared to the period when both LandSat-5 and LandSat-7 were available and working nominally. However, the multi-sensor atmospheric correction and cloud screening (MACCS) spectro-temporal processor is applicable to LandSat-8 and currently used to process surface reflectance products over France at the THEIA Land Data Center.
Datasets In order to test our algorithm, several LandSat-5/7 and FormoSat-2 time series were used. FormoSat-2 images were purchased by CNES for the preparation phase of the VENµS mission, over sites of interest for our laboratory. They were orthorectified using the algorithms detailed in  [22]. The absolute calibration of the sensor was obtained using the desert sites method  [23] , which uses stable and uniform desert sites to transfer the calibration of a well-calibrated instrument (such as MODIS) to other optical spaceborne sensors. LandSat orthorectified images were acquired via the United States Geological Survey (USGS). As it is not possible to get dense time series from LandSat-5 and 7 over Europe, we chose several sites in the United States, where LandSat-5 and 7 acquisitions are most frequent.
In addition, in the case of LandSat, we only used images acquired before the SLC breakdown in 2003. For LandSat, the sites containing the American sun-photometers of Boulder, Columbia, Fresno/Corcoran, Howland, Rogers Dry Lake/University of California Los Angeles (UCLA) and Stennis were selected. For FormoSat-2, time series above La Crau/Avignon, Seysses/LeFauga, Ras El Ain and Yaqui AERONET sites were used (Table  2 ). Although we used very different sites in different climatic zones, the sites we selected never had an aerosol AOT above 0. 7 for the remote sensing data acquisition dates and even 0. 4 for LandSat time series in stable conditions (the notion of stable conditions is explained in Section 4).