dataesr/scientific-paragraphs-categorization huggingface dataset:
The flares were fitted simultaneously, with the absorption free to vary overall, but tied to be the same for all flares. a Seconds since T 0. b i. e. the total fit-statistic, F = χ 2 + W. for the standard BAT energy bands; however, the BAT only collected event-mode data during the first sequence of pulses in the interval T0+56 s to T0+319 s. We therefore used the Konus-Wind data, which covers the entirety of the prompt emission. We mapped Konus-Wind band 1 (25-95 keV) to BAT bands 1 (15-25 keV) and 2 (25-50 keV) and Konus-Wind bands 2+3 (95-1450 keV) to BAT bands 3 (50-100 keV) and 4 (100-350 keV) to provide reasonable energy overlap and good statistics. We normalised the combined Konus-Wind rates to match the individual BAT band rates over the overlap time interval T0+56 s to T0+319 s, within which the pulse structure observed by BAT and Konus-Wind are identical.
3-10 keV in the rest frame. For all but GRB 130925A only the BAT event data and the XRT data are shown, k-corrected from the Swift Burst Analyser  (Evans et al. 2014)  and Light Curve Repository  (Evans et al. 2007 (Evans et al. , 2009) )  respectively. For GRB 130925A, the Konus-Wind data are also shown; these provide the data around 1000-3000 s. The similarity between the 4 ultra-long bursts can be seen, as can the difference between these and the TDE candidate Swift J1644. return to the centre of mass (e. g. Comerford et al. 2013; Milosavljević & Merritt 2001) , thus the offset does not rule out the TDE scenario. As  Levan et al. (2014)  pointed out, a bigger problem faced by the TDE scenario is that of timescales: for disruption of a main-sequence star by a 10 6 M⊙ black hole  Lodato & Rossi (2011)  predicted that the X-ray emission would show a rise or plateau lasting ∼ 100 days or more, whereas for GRB 130925A the light curve is steadily decaying by ∼ 0.
However, the similarity of their light curves with GRB 130925A tells us that the ratio of prompt-to-afterglow fluence for those GRBs must be similar to GRB 130925 23  , i. e. all four of the ultra long GRBs have afterglows which are under-luminous compared to their prompt emission, when compared with the population of normal long GRBs. We now consider specifically the lack of external shock emission in GRB 130925A. For a standard afterglow, the brightness of the external shock depends on the microphysical parameters of the shock, which cannot be constrained by the XRT limit alone. Fortunately, the radio data from  Bannister et al. (2013)  at T0+15 days, are close in time to the second-epoch HST data which give F1. 6µm ∼ 0. 6µJy  (Tanvir et al. , in preparation). From the Swift Burst Analyser  (Evans et al.
The combination of this lower density and the fact that more of the outflow is involved in dissipative internal shocks implies that ultra-long GRBs should have E afterglow /Eprompt values lower than the normal long GRBs as we have found for GRB 130925A. We have argued qualitatively that this is the case, based on Fig. 15 , but we can test this prediction in more detail. To do this, we fitted the Swift and Konus-Wind data of GRB 121027A in a manner analogous to that in Section 3. 1, and found the prompt fluence to be 1. 6×10 54 erg, while the afterglow fluence was 1. 5×10 52 erg. Comparison with Fig. 8  shows that, as predicted for the ultra-long GRBs, E afterglow /Eprompt for GRB 121027A (the cyan point) is notably lower than the population of bursts as a whole, supporting our model. We also note that, under the ICMART model  (Zhang & Yan 2010)  for prompt emission it is possible to get significant variations in the efficiency with which internal shock interactions convert the kinetic energy to radiation (e.
CONCLUSIONS GRB 130925A was an extremely long GRB at z = 0. 348, with an observer-frame duration of around 20 ks, and three main episodes of emission at E > 15 keV. Apart from its length, the properties of the prompt emission appear consistent with those of other bursts. However, the extreme duration of this burst is inconsistent with the general population, and we have ruled out observational bias as the cause of this incompatibility. The late-time X-ray data show a strong spectral evolution, which can be well modelled as dust scattering of the prompt emission. A systematic study of other GRBs shows evidence for such emission in at least 8 other objects. GRB 130925A is the most extreme example, because in addition to the dust echo, it shows no evidence for a contribution from a standard afterglow; we place a limit of E afterglow < 3.
ACKNOWLEDGEMENTS This work made use of data supplied by the  UK Swift Science Data Centre at the University of Leicester. PAE, JPO, KW and APB acknowledge  UK Space Agency support. The Konus-WIND experiment is partially supported by a  Russian Space Agency contract ,  RFBR  grants  12-02-00032a  and  13-02-12017  ofi-m. DNB and JAK acknowledge support from  NASA  contract  NAS5-00136. This work includes observations made with the  Gran Telescopio Canarias (GTC) , installed in the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias , in the island of  La Palma. This work was partially supported by the  Spanish Ministry  project  AYA2012-29727-C03-01.
Non-parametric Kendall correlations analysis was used to identify associations between NMS spatial axes and the original concentrations of the 13 individual cytokines. A cytokine was considered to be reflected by the spatial axis if Kendall's tau (t) $0. 4. Statistical tests were conducted using SPSS Statistics version 19 software (IBM, Hampshire, U. K. ) and NMS was implemented using PC-ORD software (MJM Software Design, Gleneden Beach, U. S. A. )  [46]. Comparisons were considered to be significant at p, 0. 05. Where. 10 comparisons were made the p-value was adjusted for multiple comparisons via the sequential Bonferroni correction and comparisons that were significant post-correction were considered highly significant.
This research was supported by the  Wellcome Trust UK  (Grant no:  WT082028MA , http://www. wellcome. ac. uk/); the  University of Edinburgh Moray Endowment Fund ; the  World Health Organization  (Grant no:  RPC264 ); Cunningham Trust; and  Carnegie Trust for the Universities of Scotland  (http://www. carnegie-trust. org/). CDB was funded by a studentship from the  BBSRC  and a travel grant from the  British Society of Parasitology Garnham Expeditionary Fund. FM and NN are funded by the  Thrasher Research Fund and Schistosome Control Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
15% on the flow cell. The reads that issued from these three sequencing technologies were first assembled separately. The 454 reads were assembled into contigs and scaffolds using Newbler version 2. 8 (Roche). Illumina reads, trimmed using Trimmomatic (9), were assembled using the Spades software  (10, 11). Contigs obtained were combined by using SSpace  (12)  and Opera v. 1. 2 (13) software; GapFiller v. 1. 10 (14)  helped to reduce the set. Some manual refinements using CLC Genomics v. 5 software (CLC bio, Aarhus, Denmark) improved the genome. These analyses yielded four scaffolds of 21 contigs containing 6,379,625 bp and an estimated size, including gaps, of 6,382,840 bp, for a 66. 57% GϩC content. Noncoding genes and miscellaneous features were predicted using RNAmmer  (15) , ARAGORN  (16) , Rfam  (17) , and PFAM  (18).
Open reading frames (ORFs) were predicted using Prodigal  (19)  and functional annotation was achieved using BLASTP against the GenBank database (  20 ) and the Clusters of Orthologous Groups (COGs) database  (21, 22). The genome was shown to encode at least 81 predicted RNAs, including 3 rRNAs in a single operon, 58 tRNAs, 1 transfermessenger RNA (tmRNA), and 19 miscellaneous RNAs. Also, 5,988 genes represented a coding capacity of 5,867,145 bp for a 91. 9% coding percentage. Among these genes, 765 (12. 77%) were found to encode putative proteins and 1. 011 (16. 88%) were assigned hypothetical proteins. Moreover, 5,940 genes matched a least one sequence in the COGs database with BLASTP default parameters. Nucleotide sequence accession numbers. The M. triplex strain DSM 44626 genome sequence has been deposited at ENA under the accession numbers CCAU010000001 to CCAU010000021.
Workshop objectives included to (i) present candidate GOs sites and GOs partner organizations, (ii) discuss GOs actions, (iii) develop interactions between GOs and the broader GSC community, and (iv) give the participants time to form working groups and deliberate priorities. The first session that introduced the work of the GSC was co-organized and supported by the European Cooperation in Science and Technology (COST) Action (ES1103)  [5]. Following the main meeting, two satellite meetings were organized and supported by EU FP7 projects. The first, Marine Genomics for Users (MG4U)  [6]  was a stakeholders session considering how to translate advances in genomic research to ecosystem-based marine management actions and policy perspectives. The second satellite was a planning meeting of the Marine Biodiversity, Bioinformatics, Biotechnology (Micro B3) project  [7]  that leads Ocean Sampling Day, one of the first GOs actions.
A twoday GSC hackathon also followed the meeting, which focused on issues related to the GOs informatics stack. The workshop presentations as well as interviews with some of the attendees were recorded by Mediomix (http://mediomix. de/ The 15th Genomic Standards Consortium (GSC 15) meeting will be held at the National Institute of Health (NIH) in Bethesda, Maryland, 22-24 April 2013. This meeting will highlight the utilization of genome metadata standards for the enhancement of our biological understanding of microbes, the interaction between microbial genomes, human health and disease. It will include a GOs Network session (considering the potential of citizen science genomics and humans as GOs) and a GOs satellite meeting at the Smithsonian Institution (focused on marine genomic observing systems and Ocean Sampling Day).
Standards are key to accelerating science, avoiding redundancy and enabling inte-gration of data streams. The GSC runs a range of consensus-driven projects and is calling for community involvement and encouraging broader compliance. The GSC welcomes the involvement of the GOs Network as a GSC Project/Working Group and looks forward to future interactions. Among other things, the GOs Network will provide usecases for further developing GSC standards and showcases for the scientific opportunities that flow from their application. Renzo Kottmann (Max Planck Institute Bremen, Germany) then described the background and mission of the "Microbial Ecology & the Earth System" COST action. He introduced the microbial ecology workflow from field sampling to analysis and publication and the need for standards in this domain.
The next talks focused on analysis of data and included an update on the newly launched metagenomics portal (MG Portal)  [12]  at the European Bioinformatics Institute (Sarah Hunter, European Bioinformatics Institute, UK), an update on CAMERA  [13]  (Jeff Grethe, University of California San Diego, USA), and presentation of the BIOM standard as a new addition to QIIME  [14]  (Jai Ram Rideout, University of Colorado, Boulder, USA). Anna Klindworth (Max Planck Institute Bremen, Germany) then talked on the evaluation of primer and primer pairs for 16S ribosomal RNA biodiversity studies. Finally, Guy Cochrane (European Bioinformatics Institute, UK) outlined submission of MIxS-compliant data to the International Nucleotide Sequence Database Collaboration (INSDC)  [15]  and provided some insight into the challenges for those designing and operating submission services.
Dawn Field (NERC Centre for Ecology and Hydrology Wallingford, UK; University of Oxford, UK) chaired the session and gave a brief overview of the GOs Network since the initiation of the concept following GSC 11 (April 2011) when she and Neil Davies began discussing the benefits of place-based genomic research at the "L4" and "Moorea" sites  [4]. Subsequent discussions with an ever growing number of researchers pioneering DNA-based approaches at premier research sites, led to a letter published in early 2012 by 68 authors (many present at GSC 14) calling for the creation of a Genomic Observatories Network  [3]. GSC 14 offered the first chance for this group to meet and decide the future of the GOs Network. The candidate sites (potential Genomic Observatories) all represented long-term research sites with decades to a century of data thus offering unique opportunities to integrate genomic research into a range of other research activities.
Discussions then focused on the difficulties encountered in order to better coordinate and prepare for a more extensive effort. OSD is a first example of the way that coordinated research projects (e. g. , Micro B3) might leverage the GOs Network. For example, many of the sites participating in the OSD pilot were not part of the Micro B3 grant. They contributed time and resources to make the collections because of their interest in developing common standards-based genomic datasets and in sharing their experiences with others in a mutual knowledge exchange (hence their participation in the GOs Network. It was agreed that the common platform (instrument) the GOs Network could represent, especially when specifically proven and tested by initiatives such as OSD, would enable many other future projects involving coordination and comparable approaches across regions, and eventually the globe.
Day Three was dedicated to giving time for all the breakout groups to report back to the full group. The Biodiversity Genomics group, chaired by Chris Meyer (Smithsonian, USA), was deemed so central to the GOs Network that the core group of the GOs Network was tasked to develop it further. Topics covered in discussion included in particular the biology of Dark Taxa and how this presented challenges for data acquisition and analysis. Are new approaches needed in taxonomy, especially as more microbial data are generated where unknown taxa (and genes) are routinely found? In subsequent cross-discussions between the working groups it became clear that developing the science of biodiversity genomics was a general goal of the GOs Network that would require input from all, but especially the Theory and Informatics working groups.
Following the meeting it was agreed that the GOs Network would take forward its activities under 5 key umbrella areas: Science, Informatics, Technologies, Policy, and Governance. The Theory & Modeling group would fall under Science, Biocode Commons under Informatics, ABS under the broader topic of Policy (including data management policies and the social/cultural challenges of applying biodiversity genomics to achieve policy goals, such as 'good environmental status'). Governance would be formalized into the leadership of the GOs Network. The Technologies thematic area was added to cover Bioarchiving (handling and preserving samples from field collection to ex situ repositories) as well as the rapid evolution of molecular analytical approaches, such as in situ ecogenomic sensor platforms. This structure provides ample coverage of topics and will allow the GOs Network to grow areas as required.
Building a Reference Model for Bio-Collections Hackathon The first hackathon built a reference model for bioCollections, using Protégé  [36]  to model this within the basic formal ontology framework  [37]  with the resulting product posted to the Biocode Commons Google Code site  [38]. Primary contributions to this ontology included definitions for types of sampling processes, including, for instance, identification process, observing process, sub-sampling process (a complete list of processes can be found by examining the ontology file posted on the Google Code site. In addition to the ontology output we proposed expanding the Darwin Core "Basis of Record" to include DNAExtract and Subsampled Tissue in order to align Darwin Core with what we discussed. Several further actions were identified that could be addressed at future events.
Field Information Management Systems Hackathon: The second hackathon dealt with "Methods linking data at the point of sampling & collection" as part of EuroMarine  [39]. The focus was on advancing technological developments to consistently track samples and contextual data to link multidisciplinary data from the point of collection. The two goals were to (i) track samples using online databases synchronized with offline field/lab applications (spreadsheets) that are compatible with data collection across the fields encompassed in EuroMarine and (ii) enable linking multidisciplinary data from the point of collection using for example geo-references and supporting structured storage and electronic exchange, thus reducing the need for retrospective text and data mining. This hackathon was a follow up of the EuroMarine Task 4.
Acknowledgments This work was supported in part by the  US National Science Foundation  through the  research coordination network  award  RCN4GSC ,  DBI-0840989  and in part by a grant from the  Gordon and Betty Moore Foundation , and travel grants of  COST Action   ES1103. The stakeholder session was supported by the  European Union 's  Seventh Framework Programme  ( FP7 /2007-2013)  under grant agreement no  266055 , and the  Marine Genomics  for Users EU  FP7  project (Coordination and support action, call  FP7-KBBE-2010-4 ) grant no. 266055. We thank  Eppendorf and Biomatters Ltd. for their sponsorship of the meeting. We are grateful to the  University of Oxford's Zoology Department and Oxford's e-Research Centre  for hosting us. We would like to thank all the members of the  GSC  and new participants in the  GOs Network  for their insightful contributions to the meeting.
Vectors The EMBL Data Library accession number of the sequence of P. ostreatus arabinofuranosidase  [9]  reported in this paper is HE565356. The vector pSAL4-poabf used for recombinant expression of PoAbf in S. cerevisiae was prepared by cloning poabf cDNA in pSAL4 (in EcoRI/HindIII) under control of copper-inducible CUP1 promoter  [10]. The vector pPICZ-abf used for recombinant expression of PoAbf in P. pastoris had been previously prepared cloning poabf cDNA in pPICZB (in EcoRI/XbaI) under control of methanolinducible AOX1 promoter  [9]. It was used as template for sitedirected mutagenesis experiments (below) to prepare the vector for recombinant expression of the selected random mutant PoAbf (F435Y/Y446F) in P. pastoris.
A 3D structure model of PoAbf was obtained by homology detection and structure prediction by HMM-HMM (Hidden Markov Models) comparison to localise the two mutations Y435F and F446Y (Fig. 5 ). Five 3D structures of GH51 family proteins are available: PDB: 1QW9 from Geobacillus stearothermophilus showing 8% sequence identity (id. ) to PoAbf; PDB: 2C7F from Clostridium thermocellum (6% id. ); PDB: 2VRQ from Thermobacillus xylanilyticus (14% id. ); PDB: 2Y2W from Bifidobacterium longum (14% id. ); and PDB: 3S2C from Thermotoga petrophila (14% id. ). They have been used as templates to calculate a model for PoAbf, using HHpred and Modeller  [12, 13]. Although each template shares very low sequence identity with PoAbf, this enzyme is expected to adopt the same fold as the GH51 members, since all they belong to the same GH family, even though with distant homologies  [14].
Acknowledgements This work was supported by grant from the  Ministero dell'Universitae della Ricerca Scientifica -Industrial   Research Project  ''Integrated agro-industrial chains with high energy efficiency for the development of eco-compatible processes of energy and biochemicals production from renewable sources and for the land valorization (EnerbioChem)''  PON01_01966 , funded in the frame of Operative National Programme Research and Competitiveness 2007-2013 D. D. Prot. n. 01/Ric. 18. 1. 2010.
Acknowledgements The reviewers  George D. Stanley ,  Jr. ( University of Montana, Missoula, USA ) and  Bogusław Kołodziej  ( Jagiellonian University, Kraków, Poland ) are thanked for their helpful suggestions and comments, which improved this manuscript. Thanks to  Vincent Huault  ( Université de Lorraine, Nancy, France ) for suggesting the new genus name. This contribution is a part of an international collaboration aimed at evaluating palaeontological renewal through time and space as well as palaeoecologic and palaeogeographic evolution of coral/reefal communities spanning the latest Triassic to Early Jurassic times (research funded by the  National Swiss Science Foundation  grant  200021-130238  to RM).
To investigate answers to these questions, we conducted an extensive spectroscopic survey of 83 main belt S-type asteroids (Appendix Table  1 , Appendix Fig. 2 ) and members of 3 S-type families as it was recently established unambiguously  (Nakamura et al. 2011 ) that these asteroids encompass the parent bodies of OCs. In parallel, we also obtained for the first time spectral measurements for a representative number (53) of UOCs (Appendix Table  2 ) as those were lacking in current databases (e. g. , RELAB; http://www. planetary. brown. edu/relab/).
Telescopic Observations To explore the mineralogical composition of our asteroid sample, we utilized the SpeX instrument  (Rayner et al. 2003)  over the near-infrared 0. 8-2. 5 micron range on the NASA Infrared Telescope Facility (IRTF) on Mauna Kea, Hawaii. This wavelength range covers the diagnostic absorption bands (at 1 and 2 µm) due to olivine and pyroxene, which are characteristic for both OCs and S-type asteroids (see A1 for a brief description of the observation protocol and the data reduction procedure). Combining our new near-infrared measurements with available visible wavelength spectra  (Bus 1999)  gives for the first time a nearly complete spectral database of main-belt S-types with D>60 km (95% or 54/56; accounting for >85% of all S-type mass). Previous near-IR measurements were acquired for 32 main belt S-types  (Gaffey et al.
Laboratory measurements While spectral data for more than 60 equilibrated ordinary chondrites (EOCs) have existed within the RELAB database for a long time (http://www. planetary. brown. edu/relab/), similar data have been quasi inexistent for type 3 UOCs [there were spectra for only 11 UOCs: Suwahib -Buwah (H3. 7), Dhajala (H3. 8), ALHA77214 (L3. 4), Hallingeberg (L3. 4), Khohar (L3. 6), Mezo-Madaras (L3. 7), Hedjaz (L3. 7), Bishunpur (LL3. 1), Krymka (LL3. 1), Chainpur (LL3. 4), Parnallee (LL3. 6)]. Considering that the range of properties within petrologic type 3 (subtypes 3. 0-3. 9) is as great as that from types 4-6  (Sears et al. 1980) , it is clear that there is a major lack of data within the RELAB spectral database. In order to reduce the current gap in data between UOCs and EOCs, we obtained a large number of Antarctic samples (http://curator.
jsc. nasa. gov/antmet/) with the specific goal of measuring the spectral properties of UOCs. As UOCs are compositionally diverse, we asked for a representative sample (53 meteorites in total) for H, L and LL UOCs (i. e. the 3. 0-3. 9 continuum needing to be well covered for H, L and LL -See Appendix Table  2 ). The meteorites were sieved to grain sizes in the 0-45 micron range and spectra were subsequently collected at the RELAB facility (http://www. planetary. brown. edu/relab/) over the 0. 3-2. 6 micron range. The spectra are available in the RELAB database.