dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, for sample P3, the vesicle band was recovered from iodixanol gradient, DNA was extracted using the same protocol as viruses, and sequenced. The virome sequence data from each of the 12 samples (4 protocols x 3 biological replicates) and that of the vesicles sample were binned together for assembly of the virome (see Material and Methods section for more details). Quality filtered reads were pooled and assembled into 910 contigs of length greater than 2,500 bp, a threshold used for keeping putative small virus genomes  (22). Of these, 124 contigs were selected for further analysis based on VirSorter results and coverage criteria (see Material and Methods section). These 124 contigs were ranging in size from 2. 5 kb to 122 kb with a N50 of 24. 4 kb and were designated as the Epoisses cheese virome.
Mapping of the quality filtered reads against this virome revealed that microbial cell contamination was higher with protocol P1 and in the vesicles sample than in any of the other three protocols (Fig. 3 ). The contigs were further analyzed and separated into four classes based on PHASTER and Blast results (i. e. probable phage contigs, plasmid-derived contigs, putative plasmid-derived contigs and unclassified contigs) (Dataset S1). Principal coordinate analysis performed on the Bray-Curtis dissimilarity index calculated from the abundance table of the 124 contigs (Fig. 4A ) showed an ordination of the samples according to the VLP extraction procedure, with samples obtained with P1 and P3 being separated from samples obtained with P2 and P4. This indicated a possible impact of the chloroform treatment on the quality of the resulting virome.
Datasets produced with protocols P2, P3 and P4 were strikingly enriched for reads mapping to probable phage contigs (>99% in average) when compared to VLP fraction obtained with P1 (94% in average) (Fig. 4B ), which was largely explained by the higher proportion of unmapped reads present in P1 samples (Fig. 4F ). The relative proportion of reads mapping to the 19 plasmid-derived contigs was greatly reduced in samples treated with P3 (filtration) and even more so in samples treated with P2 (chloroform) and P4 (chloroform and filtration), when compared to P1 (Fig. 4C ), reflecting the positive impact of both filtration and chloroform treatment on the quality of the final virome. The relative proportions of reads matching putative plasmid-derived contigs (Fig. 4D ) and unclassified contigs (Fig.
Composition of the Epoisses cheese virome Three repeats of the extraction-sequencing on Epoisses cheese bought simultaneously from the same manufacturer gave highly reproducible results. Two contigs, NODE-1 and NODE-4, now named Epvir4 and 949_Epvir1, were prevalent, representing relative mean abundances of 63% and 28%, respectively. Epvir4 genome is 42 kb long. It was assigned by VirSorter as a category 2 phage (« quite sure ») but did not share similarity with already known phages. The annotation by RAST, HHpred and BLAST revealed an organization into functional modules (Fig. S1A ). Amongst the ORFs with predicted homologues in nr database, most best BlastP matches (63%) were found in the genus Glutamicibacter (formerly Arthrobacter) suggesting that the host of this abundant phage might be a Glutamicibacter species (Dataset S2).
949_Epvir1 genome (Fig. S1B ) is 122 kb long and shares 95% nucleotide identity and 95% coverage with Lactococcus lactis phage 949, a virulent phage (despite two predicted integrase genes) isolated from cheese whey in New Zealand  (23). The third most abundant contig (1. 1% of the reads), Epvir8, has a genome 41 kb in length, and shares a small 1. 1 kb region with 88% nucleotide identity with Pseudoalteromonas phage SL25  (24). Amongst the ORFs with predicted homologues, more than half (56%) were found in Pseudoalteromonas phages while another quarter (24%) were found in Vibrio phages (Dataset S2 and Fig. S1C )). All other contigs exhibited a relative abundance lower than 1%, most of them being difficult to assign.
iii) A third category of DNA content in this vesicle fraction, composed of circular contigs that might be plasmidic but with no hit in databases, might not belong to vesicles. Indeed, the relative proportions of these contigs are similar in each virome, irrespective of the chloroform treatment. These DNA fragments might be packaged by transducing phage particles, which are more resistant to chloroform than vesicles. One has to suppose that such transducing particles have a lower density than bona fide phage particles to explain their presence together with vesicles fraction. The set up of the protocol most adapted to recover viral communities from cheese rind was considerably facilitated by the use of a microscopic device called interferometer, allowing evaluating in real time the total nanoparticle counts  (38, 39).
Particles counting using Interferometry A "home-made" interferometric light microscope (ILM)  (38)  was used to count nanoparticles (i. e. both vesicles and viruses) present in our VLP fractions as previously described  (39). Briefly, 5 µl of each sample were used to collect a stack of 200 images (CMOS camera). We developed a simple ImageJ  (47)  script that allows first background subtraction and image quality enhancement, then particles localization on each image. An average number of nanoparticles per frame was obtained. To calibrate the concentration estimates, crude lysates of phages P1, T4, T5, T7, λCI857 and φX174 were titrated on Escherichia coli MG1655, and counted with the ILM device. These counts were converted into concentrations, assuming that all particles present in the observation volume (10 -8 ml) were detected with the ILM device.
36 million paired-end reads (2 × 250 bp) per sample (2. 72 million reads total). All quality-filtered reads from the various experiments were pooled in order to proceed to only one global assembly. Therefore, reads from similar phages but present in several samples would be binned together during the assembly process. Assembly was performed using SPAdes with the meta option and increasing kmer values -k 21,33,55,77,99,127  (48). Contigs with length below 2,500 bp, unlikely to encode complete viruses, were discarded. The resulting 910 filtered contigs were first analyzed with VirSorter  (49) , which returned 92 putative viral contigs. A list of "most abundant and pertinent" contigs was constructed using three criteria: 1) detected as viral with Virsorter, after filtering out those placed in categories 3 or 6 (not so sure) with coverage below 10 (remaining 78 contigs), 2) detected as circular contig, even if not detected as viral (adds 34 contigs) 3) coverage above 100, even if not detected as viral (final list of 124 contigs).
These contigs were further analyzed using PHASTER (50) and compared to both the Viruses section of nucleotide database at NCBI and to the complete database using Blast  (51)  in order to provide a first characterization of putative phage-encoding contigs. Then, quality filtered reads (both paired and unpaired) were mapped individually for each sample against the potential viral contigs using Bowtie2 aligner with default values  (52)  in order to produce an abundance table. This table was processed with the R package Phyloseq for statistical analysis and data visualization  (53). The level of bacterial DNA contamination in the cheese viromes was estimated by detection of ribosomal DNAs among the reads using SortMeRNa software (54) and the SILVA v129 database  (55). The three most prevalent contigs present in the final virome, namely Epvir4, 949_Epvir1 and Epvir8, were further annotated using RAST  (56).
to a putative host. Nevertheless, few contigs were closely related to Lactococcus lactis phages (e. g. NODE-15, NODE-59, NODE-142), Pseudoalteromonas phages (e. g. NODE-48, NODE-276), Vibrio phages (e. g. NODE-12, NODE-116), Leuconostoc phages (e. g. NODE-18, NODE-658) and Halomonas phage (NODE-636). All of these bacterial genera are frequently detected as part of cheese surface (3). The best assignments carried out for all contigs are available in supplementary Dataset S1.
ACKNOWLEDGMENTS This work was conducted in the frame of the  Virome Access  project supported by the  French National Institute for Agricultural Research (INRA)  and the  Meta-omics and Microbial Ecosystems (MEM)  programme. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. We are grateful to  Martine  and  Claude Boccara  for their invaluable help with the interferometer device. We thank the  INRA GeT-PlaGE platform  (https://get. genotoul. fr/la-plateforme/get-plage)for sequencing the Epoisses virome and the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources and support.
ID30B is an undulator-based high-intensity, energy-tuneable (6. 0-20 keV) and variable-focus (20-200 mm in diameter) macromolecular crystallography (MX) beamline at the ESRF. It was the last of the ESRF Structural Biology Group's beamlines to be constructed and commissioned as part of the ESRF's Phase I Upgrade Program and has been in user operation since June 2015. Both a modified microdiffractometer (MD2S) incorporating an in situ plate screening capability and a new flexible sample changer (the FlexHCD) were specifically developed for ID30B. Here, the authors provide the current beamline characteristics and detail how different types of MX experiments can be performed on ID30B (http://www. esrf. eu/id30b).
Introduction ID30B was the last of the facilities to be constructed and commissioned as part of the UPBL10 project of the ESRF Phase I Upgrade Program and is the most recent addition to the Joint Structural Biology Group (JSBG) portfolio of beamlines at the ESRF  (Mueller-Dieckmann et al. , 2015). The UPBL10 project was conceived to replace and supersede the highly automated and successful ESRF ID14 beamline complex  (Wakatsuki et al. , 1998) , and comprises two fixedenergy macromolecular crystallography (MX) beamlines [ID30A-1  (Bowler et al. , 2015)  and ID30A-3], one tuneable MX beamline (ID30B), and a biological small-angle scattering (bioSAXS) beamline (BM29;  Pernot et al. , 2013)  all located around the ESRF straight section ID30  (Theveneau et al. , 2013). One of the initial concepts of UPBL10 was to develop one branch (ID30A) of a canted undulator setup as a Massively Automated Sample Selection Integrated Facility (MASSIF) for automated sample evaluation to provide an efficient pipeline of well characterized samples for more tailored data collection on other more dedicated MX beamlines, including ID30B to be situated on the second branch.
5 mm (Fig. 4a ). An AXAS-A X-ray fluorescence detector (KETEK, Munich, Germany) on a retractable pneumatic table has also been fully integrated into the MD2S. An additional digital I/O card was installed in the MD2S control electronics rack for direct detector control thus allowing a gated triggering of the PILATUS3 6M detector. This has enabled the development of fast continuous mesh scanning data collection protocols (see movie S1 of the supporting information). The MD2S control software runs on a Windows PC and is based on the JLib library (EMBLEM Technology Transfer GmbH, Heidelberg, Germany; http://software. emblem. de). The MD2S can be controlled using a graphical user interface (GUI) or remotely by a socket server using the Exporter protocol of JLib. The Prosilica GC655C GigE onaxis video camera of the MD2S is read using the LIMA generic library for high-throughput image acquisition  (Homs et al.
If a sample is detected as too far from predefined specifications, the sample transfer can be refused and returned to the dewar or placed in a special receptacle in the HCD for later recovery. The high-level software control of the FlexHCD system on ID30B runs on a Windows PC and is based on the JLib library software suite (EMBLEM Technology Transfer GmbH, Heidelberg, Germany; http://software. embl-em. de), encompassing a StaubCom server for robotic movements, a WAGO controller for HCD carousel rotations, a proxy sense card for sample holder detection, and all image acquisition and processing software. The FlexHCD software can be controlled using a dedicated GUI or remotely by a socket server using the Exporter protocol of JLib.
Beamline control software The individual motors required to align the optical elements, move the experimental table and detector translation table as well as open and close the millisecond fast shutter are all controlled at the lowest level by ESRF-developed IcePAP electronics  (Janvier et al. , 2013). All pneumatic devices on ID30B are driven via WAGO-I/O-System 750 control modules (WAGO Contact SAS, Roissy, France), which are also used to monitor thermocouples. The scattering foil diodes on ID30B are read via Keithley picoammeters. All XBPM Basler acA1300-30gm GigE cameras and the MD2S sample camera on ID30B can be accessed via the LIMA generic library  (Homs et al. , 2011)  and viewed in a web browser. All motors, apart from those of the MD2S diffractometer and FlexHCD sample changer, are controlled on a higher level using BeamLine Instrumentation Support Software (BLISS), the new ESRF system for experiment control, which is a Python-based open source project [https:// gitlab.
esrf. fr/bliss/bliss (Guijarro et al. , 2017) ]. A number of pseudo motors, such as slit gaps and offsets, table height, translation, roll, pitch and tilt are configured in BLISS, which also provides procedures for the automatic alignment of the experimental environment to the X-ray beam. All the beamline thermocouples and diodes are configured to be read using BLISS, which additionally provides, via a unified web-based application shell, a graphical scanning functionality to facilitate beamline alignment, develop automated alignment routines, and diagnose beamline-specific problems. As for all the ESRF MX beamlines, MxCuBE, the Macromolecular Crystallography Customized Beamline Environment software [http://mxcube. github. io/mxcube (Gabadinho et al. , 2010) ], provides the user interface to control instruments and procedures required for a diffraction experiment.
These include calls to the underlying BLISS control system for the execution of procedures such as energy changes, alignments and various scans; loading and unloading of samples by the FlexHCD; and MD2S data collection protocols using the Exporter protocol of JLib. The PILATUS3 6M detector control software runs on a dedicated Linux computer and is triggered for data collection via the LIMA generic library PILATUS implementation, sending commands to the Dectris camserver. For automated data acquisition protocols, as already described for MASSIF-1  (Svensson et al. , 2015) , we use the Beamline Expert System (BES), a customized version of Passerelle EDM (https://supportsquare. io/portfolio/ products/) running on a central computing cluster. Mean-while, all experimental planning and final data collection parameters are stored in the ISPyB database for MX with results of automatic data processing  (Monaco et al.
Results 3. 1. Data collection at cryogenic temperature On ID30B, diffraction data are routinely collected at 100 K from samples mounted in SPINE standard pins [see http://www. spineurope. org for details  (Cipriani et al. , 2006) ]. For example, Thaumatococcus daniellii thaumatin was crystallized and cryoprotected as previously described  (Nanao et al. , 2005). Diffraction data were subsequently collected from a single crystal using a radiation damage optimized strategy calculated by BEST  (Bourenkov & Popov, 2010)  as implemented in EDNA  (Incardona et al. , 2009). This data set was processed with XDS  (Kabsch, 2010)  and AIMLESS  (Evans et al. , 2011)  using an automatic data processing pipeline  (Monaco et al. , 2013)  and the results downloaded from ExiMX. The structure was further refined using REFMAC  (Murshudov et al.
In situ diffraction experiments The ID30B in situ data collection setup (x2. 4. 2) is compatible both with standard and raster-based data collections between 6 and 20 keV. While the total rotation range is limited due to the restricted sample environment, complete data sets can be collected from large single crystals with high-symmetry space groups lying in favourable orientations. In the case of smaller, lower-symmetry crystals the most successful approach is to create a complete data set from multiple partial data sets collected from different crystals. To validate our in situ setup a number of data sets of thaumatin crystals grown in Crystal-Direct 2  (Cipriani et al. , 2012; Zander et al. , 2016)  (using a flux of $8 Â 10 10 photons s À1 with a 20 mm 2 cannon aperture; 20 ms exposure per 0.
Conclusions and discussion ESRF beamline ID30B has been in full user operation, including remote access, since June 2015 and, at the time of writing, data collected at the beamline has resulted in 83 PDB depositions and 62 publications. A significant amount of time during the first two years was dedicated to the optimization of the experimental setup including commissioning of the FlexHCD sample changer and the in situ plate holder. The FlexHCD sample loading/unloading time is currently < 20 s, and <12 /14 s using the IRELEC flipping gripper and SPINEplus single/double gripper, respectively. This equates to a sample-to-sample exchange time, when all preparative (changing the MD2S to a sample transfer configuration, and moving the detector to a safe distance as well as protecting its sensor area with a cover) and diagnostic (multiple images of the grippers are taken and processed during loading/unloading cycles) steps are included, of <60 s, and <50 /40 s using the IRELEC flipping gripper and SPINEplus single/double gripper, respectively.
In 2017 the FlexHCD processed 13166 and 3253 samples from SC3 and Uni-puck storage containers, respectively, with overall rates of sample loss and user intervention of <0. 01% and <1%. We are currently exploring ways to further improve the speed, reliability and automatic error recovery of the FlexHCD. Users now routinely execute automated MXPress data collection protocols and we expect beamline productivity to increase as users take more advantage of these. Moreover, we will soon complement the currently available workflows with automated experimental phasing data collection protocols, some based on the use of the MK3. Other instrument developments will include the evaluation of a CrystalDirect 2 harvester  (Cipriani et al. , 2012; Zander et al. , 2016)  recently installed on the beamline for feasibility studies, the development of a plate-changing tool for the Flex robot, and use of the REX nozzle changer to facilitate dehydration experiments  (Sanchez-Weatherby et al.
Acknowledgements The authors gratefully acknowledge members of the  ESRF-EMBL Joint Structural Biology Group  and other  ESRF  support groups for all their help in this project. We thank past beamlines 1258 McCarthy et al. ID30B beamline for crystallography experiments at the  ESRF and present ESRF directors ( Sine Larsen ,  Harald Reichert ,  Serge Perez ,  Bauke Dijkstra  and  Jean Susini ) and  EMBL-Grenoble management (Stephen Cusack)  for their support in this project. We also acknowledge the invaluable support of  Arinax  (particularly  Ralf Siebrecht  and  Bernard Lavaut ),  Sandor Brockhauser  (European XFEL) for STAC software assistance and  Ivars Karpics  ( EMBL-Hamburg ) for help with the integration of the plate screening option in MxCuBE.
Solvent 33. 0 22. 4 30. 7 Diffraction data https://doi. org/10. 15785/SBGRID/544 https://doi. org/10. 15785/SBGRID/546 https://doi. org/10. 15785/SBGRID/547 PDB code 6fj6 6fj8 6fj9 Solvent 26. 6 27. 6 23. 5 Diffraction images https://doi. org/10. 15785/SBGRID/541 https://doi. org/10. 15785/SBGRID/542 https://doi. org/10. 15785/SBGRID/543 PDB code 6fid 6fj2 6fj4.
Since 2013, the French Animal Health Epidemic Intelligence System (in French: Veille Sanitaire Internationale, VSI) has been monitoring signals of the emergence of new and exotic animal infectious diseases worldwide. Once detected, the VSI team verifies the signals and issues early warning reports to French animal health authorities when potential threats to France are detected. To improve detection of signals from online news sources, we designed the Platform for Automated extraction of Disease Information from the web (PADI-web). PADI-web automatically collects, processes and extracts English-language epidemiological information from Google News. The core component of PADI-web is a combined information extraction (IE) method founded on rule-based systems and data mining techniques. The IE approach allows extraction of key information on diseases, locations, dates, hosts and the number of cases mentioned in the news.
We evaluated the combined method for IE on a dataset of 352 disease-related news reports mentioning the diseases involved, locations, dates, hosts and the number of cases. The combined method for IE accurately identified (F-score) 95% of the diseases and hosts, respectively, 85% of the number of cases, 83% of dates and 80% of locations from the disease-related news. We assessed the sensitivity of PADI-web to detect primary outbreaks of four emerging animal infectious diseases notifiable to the World Organisation for Animal Health (OIE). From January to June 2016, PADI-web detected signals for 64% of all primary outbreaks of African swine fever, 53% of avian influenza, 25% of bluetongue and 19% of footand-mouth disease. PADI-web timely detected primary outbreaks of avian influenza and.
To avoid having to manually draw up IE rules, we introduce a method where rules such as R 1 , R 2 and R 3 are automatically discovered in a dataset of news where all candidates are prelabelled as correct or incorrect. We describe this dataset in the Evaluation protocol for information extraction section. Automatic rule discovery. Our automatic rule discovery is based on frequent itemset discovery, a widely known data mining technique  [19, 20]. This technique can reveal correlations in a large volume of data by providing elements that frequently occur together in the dataset. In order to use the algorithms for frequent itemset discovery, we first transform every candidate into a set of elements that describe the candidate and context in which it occurs in the news. We detail how elements that describe each candidate are constructed based on the example in S 1 : • Elements related to the word.
Each candidate in the labelled dataset is thus associated with a set of elements that describe its context. The automatic rule discovery algorithm is executed for each candidate class. For example, the following rule is automatically discovered in the class which corresponds to the correct number of infected animals: (killed, -1 to -3), (position, PAR1). This rule states that when the word "killed" is found in one of the three words preceding a number (i. e. a candidate for a number of cases) in the first paragraph of the document, then this number likely belongs to the class of correct numbers of cases. Each rule is also associated with its confidence value, which is the probability that a candidate complying with this rule belongs to its corresponding class. For instance, the previous rule (killed, -1 to -3), (position, PAR1) has a confidence of 83%, which means that 83% of candidates complying with this rule are correct numbers of cases.