dataesr/scientific-paragraphs-categorization huggingface dataset:
Machine learning is a general technique that has gained in popularity in the last few years  (Hastie et al. , 2009; James et al. , 2013). A machine learning algorithm can be trained to recognize a specific biological attribute once a list of example bona fide features is provided. Attributes are commonly referred to as labels in supervised learning. A machine learning problem generally consists of trying to assign labels to new objects, given a list of features on which the algorithm was trained. In the case of DNA sequences, commonly used features are GC content, oligonucleotide frequency profiles, and codon usage. Two popular tools have been developed for prediction of viral sequences in a dataset of DNA sequences  (Roux et al. , 2015; Ren et al. , 2017). VirSorter is a tool for prediction of viral contigs in metagenomic datasets, which uses alignments and similarity search in a database of known viruses  (Roux et al.
Training and Testing Datasets To build and test MARVEL, the RefSeq microbial dataset was downloaded (January 2018) and only genomes belonging to the Bacteria domain (NCBI txid: 2) and to dsDNA viruses from the Caudovirales order (NCBI txid:28883) were selected (this is the baseline dataset). Tailed phages were selected at this step as a representative group given that they constitute the majority of viruses present in most environmental samples  (Ashelford et al. , 2003; Filée et al. , 2005; Ackermann, 2007). The baseline dataset was split into two subsets according to the GenBank record date: before January 2016; and January 2016 and thereafter. This time-based division is usually applied in classifiers to simulate the use of the tool on newly isolated sequences  (Roux et al. , 2015; Ren et al.
, 2017). We refer to the before-2016 subset as the training dataset, and to the 2016-and-later subset as the testing dataset. The training dataset has 1,247 phage genomes and 1,029 bacterial genomes, and it was used to train and generate a model for prediction of phage bins. The testing dataset has 335 bacterial genomes and 177 phage genomes. Training and testing datasets have no overlap and are available in MARVEL's repository page  1. Training and testing datasets were further processed to generate mock datasets of contigs with specific lengths. For each fragment length analyzed in this study  (2, 4, 8, 12, and 16 kbp) , complete genomes were randomly fragmented in 10 contigs of the specified length that may or may not have overlap. Next, contigs belonging to the same organism were clustered to form a simulated bin.
Such characteristics require a second layer of information, which may be added by utilization of results from gene prediction programs, such as Prodigal  (Hyatt et al. , 2010)  and GeneMark  (Besemer et al. , 2001). Therefore, we evaluated phage and bacterial genomes according to six of these genomic features extracted from the baseline dataset of RefSeq complete genomes. These six features are: average gene length, average spacing between genes, density of genes, frequency of strand shifts between neighboring genes, ATG relative frequency, and fraction of genes with significant hits against the pVOGs database  (Grazziotin et al. , 2017). Average gene length was computed by adding up the length of all predicted CDSs in the genome or in the contigs in a bin (in bp) divided by the total number of predicted CDSs.
Average spacing was calculated as the mean length in bp of regions between two CDSs. Density of genes was calculated as the total number of CDSs divided by genome length measured in kbp. Frequency of strand shifts was computed by adding up the number of strand shifts between neighboring genes, and dividing by the total number of CDSs in the genome. ATG relative frequency was computed by counting the number of ATG triplets in one of the strands, in all contigs in a bin or in the complete genome, divided by the total number of 3-mers in that sequence (one strand). Finally, each CDS in a genome was searched using HMMscan  (Eddy, 2011)  against the pVOGs database of viral HMM profiles  (Grazziotin et al. , 2017)  (downloaded in January 2018); a significant hit was noted when the e-value was less than or equal to 10 -10.
The number of significant hits was divided by the total number of CDSs to generate the fraction of genes with significant hits against the pVOGs database. All values based on predicted CDSs were extracted from GenBank files as available for download in January 2018 (exploratory step) or predicted in simulated fragments by Prodigal  (Hyatt et al. , 2010)  as driven by Prokka  (Seemann, 2014). Using Python Scikit Learn libraries  (Pedregosa et al. , 2011) , we tried different machine learning approaches based on the six features listed above. Specifically: support vector machine (SVM), logistic regression, neural networks, and random forest. Classifiers were evaluated using the training set as well as k-fold cross-validation (k = 20), with the result that random forest was the best approach for our target prediction.
Similar findings about suitability of random forest classifiers in bioinformatics have also been reported  (Boulesteix et al. , 2012; Zhang et al. , 2017). The relative weight of each feature on a given dataset was calculated by the ID3 implementation of random forest  (Quinlan, 1986). Features with low gain of information were removed from the final model, in order to simplify feature extraction in the final version of the tool. The following features were selected as more informative: gene density, strand shifts, and fraction of genes with significant hits against pVOGs database (see section "Results"). We then extracted these three informative features from a complete training set of 8 kbp simulated bins, and a random forest classifier was trained to be MARVEL's prediction core. The random forest model was trained with 50 initial tree estimators and leaf pruning; other parameters were set to their default values.
Tests With Real Metagenomic Data From Composting Samples A dataset of Illumina raw reads from composting samples generated by our group  (Antunes et al. , 2016)  was used to test MARVEL's performance in real metagenomic data. Five samples were extracted from a composting unit, and whole community DNA was extracted to generate shotgun metagenomic reads; this dataset contains mostly bacterial sequences. Raw reads for all five samples were cross-assembled with metaSpades  (Nurk et al. , 2017)  generating a set of contigs. Metabat2  (Kang et al. , 2015)  was used for binning with parameters: -m 1500 -s 10000. Other parameters remained with their default values. Resulting bins were evaluated regarding quality and the presence of Bacterial and Archaeal marker genes using CheckM  (Parks et al. , 2015).
Pipeline Implementation MARVEL was coded in Python 3 and uses Prokka  (Seemann, 2014)  and HMMscan  (Eddy, 2011)  as important dependencies. As input, MARVEL requires a directory with metagenomic bins in FASTA format; it generates a results directory containing bins predicted as phages. An auxiliary script was made available to generate bins from Illumina paired-end reads using standard tools and methods  (Breitwieser et al. , 2017).
RESULTS As mentioned, we tested six different genomic features; the three best features for our target prediction were gene density, strand shifts, and fraction of significant pVOGs hits. The relative weights of each feature (based on gain of information) according to the ID3 implementation on both training and testing datasets are: genes density: 0. 32, strand shifts: 0. 31, pVogs hits: 0. 37. Figure  1  shows results for two of these features on the baseline dataset; numerical results for all three features are shown in Table  1. In Supplementary Figure  S1  we also present a PCA analysis of the three selected features. For a given length, simulated bins were randomly subsampled and given as input to MARVEL in five replicates. Predictions were performed for each simulated bin and results are shown in    S1.
Identification of Novel Phage Genome Candidates From Composting Samples Reads from composting samples were assembled and binned, generating 209 bins. These bins were given as input to MARVEL, which classified 58 bins as phage genomes (Supplementary Table  S2 ). These 58 bins ranged in length from 10 to 236 kbp (averaging 27 kpb), which are in the expected range of phage genomes  (Mahmoudabadi and Phillips, 2018). We submitted the 209 bins to CheckM. Out of the 58 bins predicted as phages, only one presented bacterial marker genes. This bin contains a CDS predicted to code for a member of the MerR family of transcriptional activators (pfam00376). All other 57 bins were classified by CheckM as "root, " meaning that they had no hits against the set of bacterial marker genes used by CheckM. The potential novelty of the sequences in these 58 bins can be evaluated by observing the number of CDSs in each  bin with significant pVOGs hits: the observed range was [14-60]% (Supplementary Table  S2 ).
Such efficiency helps ensure competitive superiority of phage genes over host genes and is essential for phage control of host transcription/translation machinery and cellular resources  (Mrázek and Karlin, 1998; Miller et al. , 2003). MARVEL's results have also indicated that the pVOGs database of HMM profiles is comprehensive enough to capture the signal of conserved phage proteins, such as DNA polymerases, helicases, and terminases. These proteins were often identified in the newly discovery genomes, which is in agreement with previous reports from the literature  (Rohwer and Edwards, 2002; Comeau et al. , 2007). In sum, gene density, strand shifts and pVOGs hits combined as features in a machine learning approach allow more accurate and more sensible prediction of phage genomes compared to other features reported in the literature.
Moreover, the relative weights of these three features are approximately the same, suggesting that our model is robust, and should perform well even when a new phage genome has few or no hits to the pVOGs database. In terms of performance metrics, MARVEL has high F1 scores and accuracy for all bin lengths analyzed, but especially for bins composed of contigs 4 kbp long and longer (Figure  2 ). True positive rates were particularly high for all fragment lengths. As already mentioned, VirFinder and VirSorter do not in general have good recall values, as opposed to specificity, for which their performance is usually very good  (Roux et al. , 2015; Ren et al. , 2017). Altogether, these performance results in simulated data suggest that MARVEL is effectively able to predict dsDNA phage genomes in metagenomic bins.
, 2014; Paez-Espino et al. , 2017) , suggesting that this limitation may not be serious. The use of MARVEL in one dataset of real data, with contigs having widely varying lengths, yielded promising results, resulting in 57 potentially novel phage sequences. Upstream processing such as assembly and binning are two major factors that also influence MARVEL's performance. Chimeric contigs, as well as poorly binned bins may generate noisy data, which will certainly increase the number of erroneous predictions. Therefore, it is important to choose thresholds and parameter values to ensure quality of upstream processing  (Mavromatis et al. , 2007; Roux et al. , 2017). There are several tools available for assembly and binning which generate good quality contigs and bins  (Kang et al. , 2015; Li et al.
, 2016; Wu et al. , 2016; Nurk et al. , 2017). We emphasize, however, that assessing quality of viral bins is not an easy task. CheckM is a tool for assessing marker genes, contamination and completeness of metagenomic bins, but unfortunately only bacterial and archaeal datasets of marker genes are available  (Parks et al. , 2015). Our results (Table  2 ) show that MARVEL and VirSorter have comparable running times, while VirFinder is much faster than either. For all programs, wall time was under an hour for what we believe are realistic-sized datasets.
Since the Larmor radius of CRs deflected by the turbulent magnetic field is much smaller than the Galaxy size, CR propagation can be assimilated to a random walk, and can be described by a diffusion process from the rigidity of a few GV to tens of TV. In general, numerical algorithms are required to solve the full phenomenological transport equation (e. g. , GALPROP  [12] , DRAGON  [4] , or PICARD  [7] ), allowing to deal with any geometry and dependence for the transport parameters (diffusion, convection, reacceleration). An alternative approach is to restrict the geometry and dependences to cases for which the transport equation can be solved semi-analytically  [8]. This method is faster, which is an advantage to run extensive scans over the transport parameter space. In the following, we briefly present the semi-analytical models implemented in USINE and the code structure.
USINE The USINE code calculates the galactic flux of any CR species, solving the transport equation (2. 1) in a 1D and 2D two-zone model. All input ingredients are controlled by an (USINEformatted) ASCII parameter file, including the list of propagated CRs, their parents, the ISM description, the formula for the transport and source parameters, the files for the interaction crosssections and CR data to use (USINE relies on the CRDB 1 'USINE export' format  [10] ), Solar modulation model and its parameters, etc. The first version of USINE was written in 1999 in C. The second version was written between 2003 and 2005 in C++ and interfaced with ROOT  2. The current version (V3) is based on a complete rewriting of V2 in order to provide a more generic and flexible code, whose structure is illustrated in Fig.
Conclusion USINE is a C++/ROOT code dedicated to the propagation of GCR nuclei and anti-nuclei as well as electrons and positrons. Although USINE was developed for two zones (thin disc and thick halo) 1D and 2D models, it could be easily extended to more complex modelling. A lot of efforts were dedicated towards flexibility (the USINE classes for the propagation ingredients are designed to support 3D and time dependence), simplicity of the inputs (ASCII files for cross-sections, CR data, and initialisation and description of the free parameters of the study), and comparisons to data (import from CRDB, plots, and additional free parameters easily handled in the minimisation). In the context of AMS-02 data and the many young researchers entering the field, USINE should prove a fast and useful tool to learn or better understand the richness of the physics of GCRs.
Aknowledgments MB acknowledges support from the  European Research Council (ERC)  under the  EU Seventh Framework Program  (FP7/2007-2013)/ ERC Starting Grant  (agreement n. 278234  NewDark  project led by  M. Cirelli ). DM acknowledges support from the  " Investissements d'avenir, Labex ENIGMASS ", and by the  French ANR , Project  DMAstro-LHC ,  ANR-12-BS05-0006.
The sedimentation became dominated by carbonates with limestonemarl alternations. A further regressive phase recorded by oolitic deposits across Europe marks the end of the Early Jurassic. The red data (TOC and HI) are from this study (black data after Hermoso and Pellenard  [33] ). The ages are from Ogg and Hinnov  [34]. The interval division is linked to the organic content and to the marine vs. terrigenous organic matter ratio.
The sedimentation became dominated by carbonates with limestonemarl alternations. A further regressive phase recorded by oolitic deposits across Europe marks the end of the Early Jurassic. The red data (TOC and HI) are from this study (black data after Hermoso and Pellenard  [33] ). The ages are from Ogg and Hinnov  [34]. The interval division is linked to the organic content and to the marine vs. terrigenous organic matter ratio. [33] ). The ages are from Ogg and Hinnov  [34]. The interval division is linked to the organic content and to the marine vs. terrigenous organic matter ratio.
Dataset The study area covers a 300 × 300 km zone extending over what was the proximal and distal parts of the Paris Basin during the Toarcian (Figure  3 ). The studied time interval spans for the Lower-Middle Toarcian from the base of Tenuicostatum biozone (182. 7 Ma) to Bifrons biozone (179. 7 Ma) (Figure  4 ). The calibration of the numerical stratigraphic model was based on data from literature, 2 cross-sections (Figure  5 ) and 7 wells (Figure  6 ), including structural setting, biostratigraphy, sedimentary characterization and mineralogical/geochemical analysis (wireline logging, carbologs analyses, and more than 300 Rock-Eval analyses). Geosciences 2018, 8, x FOR PEER REVIEW 5 of 17.
Dataset The study area covers a 300 × 300 km zone extending over what was the proximal and distal parts of the Paris Basin during the Toarcian (Figure  3 ). The studied time interval spans for the Lower-Middle Toarcian from the base of Tenuicostatum biozone (182. 7 Ma) to Bifrons biozone (179. 7 Ma) (Figure  4 ). The calibration of the numerical stratigraphic model was based on data from literature, 2 cross-sections (Figure  5 ) and 7 wells (Figure  6 ), including structural setting, biostratigraphy, sedimentary characterization and mineralogical/geochemical analysis (wireline logging, carbologs analyses, and more than 300 Rock-Eval analyses).
Conclusions The balance between a high primary productivity and the factors favoring organic matter preservation represents an important part of the debate that aims to determine the conditions for the development of high organic carbon contents in sediments. The main goal of this study was to analyze the processes controlling organic matter distribution and heterogeneity through a numerical quantitative approach coupled to a sensitivity analysis applied to the Lower-Middle Toarcian succession of the Paris Basin. A Global Sensitivity Analysis was performed on oxygen mixing rate, primary productivity and diffusive transport of organic matter with CougarFlow© on 3D numerical meta-models built with the software DionisosFlow© Organic-Rich Sediment. The results and associated interpretations allow us to draw several important conclusions: (1) The primary productivity has a strong impact on oxygen level in shallow epicontinental basins.
Acknowledgments: DionisosFlow© is developed in the framework of the  IFP Energies Nouvelles  project  DORS ("Dionisos Organic-Rich Sediment "). We are grateful to  Didier Granjeon ,  Jean-François Deronzier ,  Jean-Pierre Barde  and  Laurent Riquier  for constructive discussions. We also thank  BeicipFranlab  for support with DionisosFlow©. We wish to thank four anonymous reviewers for their constructive remarks.
Methods. We took advantage of the separation of positional errors of Gaia SSO observations into a random and systematic component. We treated the Gaia observations in an alternative way by collapsing up to ten observations that correspond to a single transit into a single so-called normal point. We implemented this input procedure in the Gaia SSO short-term processing pipeline and the OpenOrb software. Results. We validate our approach by performing extensive comparisons between the independent observation and normal point input methods and compare them to the observed positions of previously known asteroids. The new approach reduces the ephemeris uncertainty by a factor of between three and ten compared to the situation where each point is treated as a separate observation. Conclusions. Our new data treatment improves the sky prediction for the Gaia SSO observations by removing low-weight orbital solutions.
Introduction As of 2018, ESA's astrometric cornerstone mission Gaia, is in constant whole-sky scanning operation mode  (Gaia Collaboration et al. 2016). Primarily focusing on stellar astrometry with unprecedented precision and coverage, Gaia also contributes substantially to asteroid science (e. g. Mignard et al. 2007; Tanga et al. 2016). The primary contribution of Gaia to solar system research is very-high precision astrometry of known solar-system objects (SSOs), distributed with Gaia data releases, and calibrated with use of Gaia's subsequent iterative reference frames (e. g. Gaia  Collaboration et al. 2018a ). The first distribu-Corresponding author, email: fedorets@iki. fi tion of Gaia SSO data occurred as part of Gaia Data Release 2, and is described by Gaia  Collaboration et al. (2018b).
2018 ) also provides off-ecliptic asteroid astrometry. In the case of potential asteroid discoveries, the available positional data and errors are of 'quick-look' quality (unlike for the iterative solutions of Gaia data releases), and follow-up observations are urgently required. The required quick response time comes at the expense of reduced calibration data quality. An astrometric Gaia observation of an SSO is recorded on an array of consecutive charge-coupled devices (CCDs) -the sky mapper, which detects the object, and nine astrometric fields, which monitor the sky position and motion of the object. Two additional CCDs -the blue and red photometers -do not contribute to astrometry. Initially, the two recorded positions are along-scan and across-scan, with respect to Gaia's on-sky movement.
2016). This pipeline is run on a daily basis at the Data Processing Centre CNES (DPCC, Toulouse, France). The orbital solutions not corresponding to any known SSOs identified previously in the workflow are treated as new asteroid candidates, and are fed to the followup network (FUN) software. In the follow-up network software, SSO orbits are propagated to ephemerides for a set of different epochs, and these are then dispatched to ground-based observers via a web-based tool. The follow-up observations are coordinated at the Institut de mécanique céleste et de calcul des éphémérides (IMCCE, Observatoire de Paris, France). In an effort to discover new asteroids, a daily processing chain of Gaia asteroid data has been established  (Tanga et al. 2016)  within the Gaia Data Processing and Analysis Consortium (DPAC).
Software implementation Software-wise, the normal-point treatment has been implemented in two instances: first, it has been integrally embedded in Gaia's short-term processing pipeline. It is also available optionally as a separate input file format for the versatile OpenOrb orbital software package  (Granvik et al. 2009). The normal point treatment pertains to the input data only, and thus is applicable to any flavour of orbital ranging methods. In this particular case, random-walk ranging is used. Although the orbit sampling phase is identical, the burn-in phase is implemented in different ways. The discrepancy is intentional, underlining the specializations of two different codes. The Gaia code, written in Java, is implemented as a part of the chain with the final goal to distribute a proposed cloud of sky positions of newly discovered asteroids for ground-based observers.
Numerical simulations and discussion To validate our approach within the DPAC processing pipeline, we performed a number of orbit computations for known asteroids. We selected a sample of asteroids with different numbers of transits and observational timespans. The details of the selected asteroids are described in Table  2. The minimum number of transits bundled together is three: bundles with two transits, although abundant, have shown to produce too wide ephemeris distributions for follow-up purposes, and are therefore omitted from the short-term processing. For each asteroid, we performed orbital inversion using random-walk statistical ranging with the Gaia Java code using two input data models. The old model, treats each data point as a separate observation and the error covariance is the sum of the systematic and random error components, and the new one collapses points within a transit to a normal point, and the number of observations corresponds to the number of transits.