dataesr/scientific-paragraphs-categorization huggingface dataset:
The best-fit ratios of the maps, derived from the T -T plots, are given in Table  4  for the high-latitude sky, with |b| > 20 • and for |b| < 20 • ; a is the best-fitting slope. Also listed in Table  4  are the Pearson correlation coefficients (r) for all the comparisons. If r 0. 9, the measured slope, a, is reliable, since there is a good linear relationship between the Commander and the WMAP templates. A first obvious difference between the Commander and WMAP MEM/MCMC models is that the AME component is systematically higher (between 2-4 times) in the Commander solution, at the expense of the synchrotron emission that is lower in Commander. This is clearer on the region closer to the Galactic plane (bottom half of Table  4 ), due to the better correlations measured there. When comparing the Commander synchrotron solution with the WMAP models that allow the synchrotron spectral index to vary over the sky, the data are not well-fitted with a single slope, since two populations are clearly present.
These runs were not used for the baseline product as they gave worse overall fits and are less physically motivated than our eventual choice; we note them here solely to qualitatively consider the effects that different models can have on the component outputs. The WMAP model that is closest to the baseline Commander solution is MCMC-e "sdcnm", although the AME component is brighter in the Commander products. Also, the Commander free-free amplitude is about 30% fainter than the corresponding WMAP fit; this has been noted before from comparison with RRL data by  Alves et al. (2010)  and Planck Collaboration Int. XXIII (2015). The main limitation in obtaining accurate components maps is the precise quantification of the synchrotron component. Fuskeland et al. (2014)  and  Vidal et al. (2015)  have shown that the polarized spectral index of synchrotron emission varies across the sky and these variations should be taken into account to obtain an accurate separation between the synchrotron and AME components; low frequency (5-20 GHz) data will be crucial for this.
18. We note that the Commander solution does not include data points above 857 GHz in its fit. In deriving the models, we exclude the 100 and 217 GHz Planck maps, since those bands contain CO emission. Using the same spectral component model described in Planck Collaboration Int. XV (2014), including colour corrections, we also perform least-squares fits (LSF) to the flux densities up to 3 THz. There are two variants of the LSF model reported in Table  5  and Fig. 18. One is fitted to data including the CMB contribution (henceforth referred to as "LSF") and the other is fitted to data post-CMB subtraction, namely flux densities from aperture photometry on CMB-subtracted maps (using the Commander CMB solution; henceforth referred to as "LSF-CMB"). We now go through each component in turn for both the LMC and SMC, considering both the morphology and the SEDs.
,  Israel et al. 2010 , Planck Collaboration XVII 2011)  has been subsumed into the dust spectral index and the lower dust temperature. The thermal dust polarization in the LMC traces the spiral structure, with the projected magnetic field running parallel to the structure, particularly in the 30 Dor region where the Planck data have a higher sensitivity due to its scan strategy. This is also where the synchrotron polarization is seen. In conclusion, we find that the Commander component maps in the regions of the LMC and SMC largely agree well with previous results and expectations, although the thermal dust properties are only representative up to 857 GHz.
54 µK to the Q and U maps, negligible compared to the noise per beam (although, of course, detectable in the angular power spectrum at low multipoles). The spectrum of the polarized dust is discussed in Planck Collaboration Int. XXII (2015)  and Planck Collaboration Int. XXX (  2016 ). Here we are interested primarily in the synchrotron component and consider the dust polarization only to the extent that the magnetic field pattern that it traces sheds light on the synchrotron features (Sect. 5. 2). Planck Collaboration Int. XIX (2015) informs our understanding of the dust polarization features. We begin by constructing a new map of polarized synchrotron emission by combining WMAP and Planck maps, which significantly increases the average S/N ratio compared with WMAP/Planck data alone. We then discuss some of the major features in the polarized sky, including the loops, spurs, filaments, and bubbles.
Acknowledgements. This paper is dedicated to the memory of the late  Professor Rodney Deane Davies CBE FRS  and  Professor Richard John Davis OBE , both of whom contributed greatly to Planck project. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www.
cosmos. esa. int/web/planck/planck-collaboration. This research was supported by an  ERC Starting (Consolidator)  Grant (no. 307209 ) and  STFC  Consolidated Grant (no. ST/L000768/1 ). We have made extensive use of the HEALPix package and the IDL astronomy library. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France.
0 × 10 28 s À1 ), and 1P had the highest production rate (Q ≈ 6. 9 × 10 29 s À1 ). Bame et al. [1986]  performed a moment analysis of the electron data from the ICE spacecraft, which made a flyby of G-Z with a closest approach of 7800 km in the comet's plasma tail. Based on the density, speed, and temperature of the electrons, Bame et al. concluded that ICE had traveled through six distinct regions. From farthest to closest to the comet, the regions were described as pristine solar wind (7 cm À3 ; 500 km s À1 ; 2 × 10 5 K), transition region (10 cm À3 ; 400 km s À1 ; 4 × 10 5 K), sheath (10 cm À3 ; 200 km s À1 ; 3 × 10 5 K), boundary layer (50 cm -3 ; 50 km s À1 ; 1. 5 × 10 5 K), cold intermediate coma (100 cm À3 ; 0 km s À1 ; 7 × 10 4 K), and plasma tail (200 cm À3 ; 0 km s À1 ; 7 × 10 4 K).
The second day of data was measured on 15 August 2015 when the comet was ~1. 3 AU from the Sun and ~350 km from Rosetta. We note that black regions occur where no data was collected. Figure  4  shows electron DEF over the entire day of 30 October 2014. Figure  4a  shows the average DEF at each E/q step versus time, Figure  4b  shows the average DEF at each elevation step versus time, and Figure  4c  shows the average DEF at each azimuth versus time. The solid and dashed lines in Figures  4b  and 4c , respectively, show the position of the Sun and 67P in IES' field of view. The vertical, dashed lines mark the times of case studies that will be discussed in Figure  6. The black pixels in Figure  4c  are unreturned data due to a noisy anode. These observations were made when 67P was outside of the heliocentric distance where water is significantly sublimated and are representative of the electron environment around a nearly inactive comet.
e. , differential energy flux) data product on the European Space Agency's Planetary Science Archive. The raw counts returned by IES are already stored in the Planetary Science Archive. We note that the DEF of electrons above 100 eV was larger when 67P was near 3 AU. Clark et al. [2015]  found the intensity of electron DEF at lower energies continued to increase until early October and then remained at a high level to the end of their study in early January 2015. Additionally, this result strongly supports the work of  Bodewits et al. [2015] , which finds the inactive coma unexpectedly bright in emission lines OH, OI, CN, NH, and NH 2 and attributes the brightness to dissociative electron impact excitation. This hypothesis might be explored further by comparing the observed brightness in the coma at neighboring time intervals when the observed DEF of electrons was particularly high and low.
Acknowledgments Rosetta is a  European Space Agency (ESA)  mission with support by member nations and  U. S. National Aeronautics and Space Administration (NASA). The work on IES was supported, in part, by  NASA  through contract  1345493  with the  Jet Propulsion Laboratory, California Institute of Technology. We thank the teams at  Imperial College London  and  ESA  who have been responsible for the operation of IES. All of the data shown from the Rosetta mission can be found on the  European Space Agency's Planetary Science Archive  (http://www. rssd. esa. int/index. php?project=PSA&page=ftpaccess) with the exception of the kappa function fit parameters to the electron distributions, which can be requested from the author at tbroiles@swri. edu.
18 The study results showed that 80% of patients (n=363) achieved their primary goals and that the use of an electronic case report form (eCRF) was a feasible approach to data collection for the ULIS study programme. 5 The study also confirmed the validity of the GAS T-score as an overall assimilation of primary and secondary goal attainment. Clinicians were encouraged to continue to use the standardised measures that they normally applied in routine clinical practice. Although these were diverse, some relationships emerged between the use of specific measures and certain goals for treatment. For example, visual analogue scales were commonly used alongside GAS to record the reduction of pain symptoms or improved ease of care. ULIS-II formed the basis for development of a more structured approach, framing goals around recommended goal parameters for each goal area and using a limited set of standardised measures from which the subsets used were individually determined according to priority goal areas.
NOVEL ASSESSMENT METHODS ULIS-III will use the Upper Limb Spasticity Index (ULS Index) to record patient-centred goals and standardised outcomes directed by priority goal areas. This is a battery of assessments designed to capture the diversity of presentation, goals and outcomes inherent in populations of patients with upper limb spasticity, while targeting measurement on the priority areas of treatment for that individual. While the goal-directed approach to selection of targeted standardised measures is novel in this context, the measures themselves are wellvalidated tools and are suitable for use in routine practice. ULIS-III will also employ some new tools including: ▸ The Upper Limb Focal Spasticity Therapy Recording Schedule (ULSTR), which records the number, duration and types of therapies related to specific goals; ▸ The Spasticity-Related Quality-of-Life Tool (SQoL-6D), which records health utility data and is designed specifically to assess aspects of quality of life that are likely to be affected by spasticity.
Ethics and dissemination ULIS-III is conducted in compliance with the Declaration of Helsinki, the Council for International Organizations of Medical Sciences International Ethical Guidelines for Epidemiological Studies  35  and the guidelines for Good Pharmacoepidemiology Practices. 36 thics approval will be sought at each site according to local legislation. All patients must provide written informed consent to participate in the study and to allow their medical data to be collected and analysed, or patient participation must be validated in accordance with local policy/guidelines. An electronic data capture system (Viedoc 4, PCG Solutions AB, Uppsala, Sweden) will be used by the investigators to collect data in an electronic format; data will then be anonymised and transmitted to the sponsor for analysis.
Health economic evaluation Healthcare resource utilisation, including direct clinical costs, indirect costs associated with spasticity and concomitant medications, will be assessed at baseline and at every subsequent visit. These will include assessment of changes in concomitant treatments, such as medications associated with spasticity and pain, changes in employment status and maintenance of healthcare, for example, nurse or caregiver. ULSTR data will be used to determine healthcare resource use associated with upper limb spasticity-related physical therapy. Utility data (quality-adjusted life-years (QALYs)) for the subgroups of patients in native English-speaking countries will be derived using data from the EQ-5D-5L and SQoL-6D assessment tools.
Primary outcome Patient-centred goal attainment Cumulated GAS T-score, defined per patient as the mean of the individual GAS T-scores for all cycles through 24 months (in predefined goal categories) derived using: ▸ GAS-eous tool ▸ Goal-related standardised measures within the ULS Index  discontinue follow-up within the 2-year period, as spasticity can be a self-limiting condition. End-of-study visits or telephone interviews will be conducted 24 months after recruitment to ascertain the patient's condition and reasons for discontinuing follow-up, if this was the case. All primary and secondary goals and outcome assessment data will be documented in eCRFs at each visit. To ensure data consistency and accuracy, all amendments to eCRF data once entered will be automatically tracked and a reason for change will be required.
Planned statistical analysis All statistical evaluations will be performed using the Statistical Analysis System (SAS V. 9 or later; SAS Institute, Inc, Cary, North Carolina, USA) according to a preplanned analysis strategy. No formal treatment comparisons will be made; however, results will be reported descriptively with 95% CIs and p values presented for exploratory purposes only. A p value of <0. 05 will be considered to indicate statistical significance.
Health economics analysis Estimated direct and indirect healthcare costs associated with upper limb spasticity will be presented descriptively based on assigning unit costs derived from established sources of unit cost data to the healthcare resources used. The sources of unit cost data will be determined at a data review meeting. Cost-effectiveness ratios for cost per response will be based on patients' Disability Assessment Score achievement, which is defined as an improvement of ≥1 point in the DAS score in the principal target of treatment at the end of the study compared with visit 1, with separate analyses performed for each domain. GAS outcomes will also be evaluated for these subgroups. In addition, analyses will be performed on the US and Australian subgroups to evaluate cost per QALY and cost-utility.
Initial patient data have been collected for 79 patients across 13 sites in three countries. Of these 79 patients, 42 set primary goals only, 24 also set one secondary goal and 13 set two secondary goals in addition to a primary goal. Goal setting data were quality checked and centres were given a rating based on the WHO ICF and SMART guidelines, as described above. 18 As shown in table 4, 11/13 centres achieved the highest rating (A++). Goals were organised into two domains and subdivided into six key goal areas. The most common areas for primary goals were passive function (30/79 (38. 0%)) followed by active function (17/79 (21. 5%)) and pain (17/79 (17. 7%)). Although the percentages varied slightly, the total 129 goals, including secondary goals, represented a broadly similar pattern. Only two primary goals did not fit into these six key goal areas-these addressed improvement of cosmesis ( perception of body image) and the facilitation of therapy.
DISCUSSION The ULIS programme sets in train a methodology that is practical for use in routine clinical practice and in future studies. This methodology will enable studies to expand their clinical data set to one of sufficient size to elucidate important findings, including which patients are most likely to benefit from what types of treatment and how best to capture the outcomes. ULIS-III will build on previous studies that have used goal attainment to evaluate outcomes from upper limb spasticity interventions. 5 6 38 It will expand the patient cohort and capture the benefits of treatment in real-life clinical practice over several treatment cycles. ULIS-III will use a more structured approach to outcome measurement incorporating both GAS and standardised measures in a targeted way that is feasible to implement in clinical practice.
Acknowledgements This work was supported by  Ipsen Pharma. The authors thank all the investigators and patients who have participated in the ULIS programme and, in particular,  Steve De Graaff  and  Ian Baguley ,  Australia. They  also acknowledge the work of the  ULIS-III  project managers at  Ipsen ,  Isabelle Gueguen  and  Elodie Blouquit. In addition, the authors would like to acknowledge the local coordinating investigators involved in training local investigators:  Ian Baguley ,  Mario Nunzio Manca ,  Tae Mo Chung ,  Andrzej Friedman ,  Alexis Schnitzler ,  Jorge Hernandez Franco ,  Svetlana Khatkova  and  Raymond Rosales. They  would also like to acknowledge the editorial assistance of  Watermeadow Medical. Ipsen Pharma  provided financial support for this assistance. Financial support for manuscript preparation was also provided through the  Dunhill Medical Trust.
The mission features a medium size satellite, a set of space and ground instruments designed to detect, locate and follow-up GRBs of all kinds, a anti-Sun pointing strategy allowing the immediate follow-up of SVOM GRBs with ground based telescopes, and a fast data transmission to the ground. The satellite (see Figure  1 ) carries two wide field high energy instruments: a coded-mask gamma-ray imager called ECLAIRs, and a gamma-ray spectrometer called GRM, and two narrow field telescopes that can measure the evolution of the afterglow after a slew of the satellite: an X-ray telescope called MXT and an optical telescope called VT. The ground segment includes additional instrumentation: a wide angle optical camera (GWAC) monitoring the field of view of ECLAIRs in real time during part of the orbit, and two 1-meter class robotic follow-up telescopes (the GFTs).
2014). The MXT detector is made of 256 x 256 Si pixels of 75 µm side and has an expected energy resolution of 75 eV at 1 keV. MXT will be operated in the energy range of 0. 2-10 keV, will have an effective area of 45 cm 2 at 1 keV, and a field of view of 64×64 arc minutes. Despite the smaller effective area with respect to XRT on board Swift at 1 keV (about 120 cm 2 ) MXT will be able to fully characterize the GRBs light curves (as shown in Figure  3 ). In fact with a sensitivity of 7×10 -13 erg cm -2 s -1 in 10 4 s, MXT will detect the afterglows of more than 90% of SVOM GRBs. Indeed its sensitivity is well adapted to early GRB afterglow observations, and its PSF of about 4 arc min will allow to significantly reduce the ECLAIRs error boxes. Simulations based on the Swift-XRT database show that the localization accuracy for the MXT is ∼13" for 50% of the bursts, 10 minutes after the trigger (statistical uncertainty only).
While the CCD for the blue channel is a normal thinned back-illuminated one, a deep-depleted one is adopted for the red channel to obtain high sensitivity at long wavelength. The Quantum Efficiency (QE) of the red-channel CCD at 0. 9µm is over 50%, which enables VT to have the capability of detecting GRBs with the redshift larger than 6. 5. The field of view of VT is about 26 × 26 , which can cover the error box of ECLAIRs in most cases. Both CCDs have a pixel size of 13. 5µm × 13. 5µm, corresponding to spatial resolutions of 0. 77 arc second. This ensures the GRB positioning accuracy to be greatly improved by VT from several arc minutes (ECLAIRs) and tens of arc-seconds (MXT) to a level of sub-arc second. In order to promptly provide the GRB alerts with the sub-arc second accuracy, VT will do some data processing on board.
The data will then be relayed to the Operation Center, which will send SVOM alerts to the internet via the GCN and VO Event networks (http://gcn. gsfc. nasa. gov/), and to the ground instruments GWAC and the GFTs. SVOM can also perform target of opportunity observations (with MXT and VT for instance), with a delay of few hours, which depends on the availability of uplink communication with the satellite. SVOM will try to select high-z GRB candidates by analysing MXT and VT data, and multi-band photometry data from GFTs. If a GRB is detected by MXT in the soft X-rays, but not by VT in the optical band, it will be selected as a candidate of high-z or an optically dark GRB. Ground telescopes with NIR capabilities are encouraged to follow up these candidates as soon as possible to try to measure their redshift.
POLAR -Space-Borne Gamma-Ray Bursts Compton Polarimeter Until recent years, only a few polarization measurement results on Gamma-Ray Burst (GRB) prompt emissions have been published  (Yonetoku et al. 2011) , while in the same time more and more hard X-ray/Gamma-ray polarization measurement instruments have been put into operation or under development. All these efforts will accumulate a larger observation database on GRB's polarization. POLAR  (Produit et al. 2005 ) is a novel space-borne Compton polarimeter dedicated for the precise measurement of the polarization of GRB's prompt emission, which is expected to be on-board the Chinese space laboratory "Tiangong-2 (TG-2)" with a scheduled launch date in late 2016. The future detailed measurement of the polarization of GRBs will lead to a better understanding of its radiation region geometry and emission mechanisms.
Acknowledgements The authors are grateful to the  International Space Science Institute of Beijing (ISSI-Beijing) , its executive director  Prof. M. Falanga  and all the staff for hosting and funding the workshop " Gamma-Ray Bursts: a tool to Explore the Young Universe" held in Beijing from April 10  to 15 2015. W. Yuan  and  C. Zhang  thank  R. Willingale ,  J. P. Osborne  and  P. O'Brien  for their contribution to the  EP  project and acknowledge support of the " Strategic Priority Research Program on Space Science"  (Grant number No. XDA04061100 ) of the  Chinese Academy of Sciences. B. Cordier  and  D. Götz  acknowledge financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). Sun acknowledges support from the  973 program 2014CB845802  and  NSFC 11503028.
Double-stranded RNAs extraction and pyrosequencing Double-stranded RNAs (dsRNAs) were extracted from fresh leaves of GF305 peach grafted with each of the APV sources following the protocol described by Gentit et al. [15]  with the modification introduced by Candresse et al. [16]. After a random reverse transcription step and a whole-genome amplification, the fragments were analyzed by 454 pyrosequencing, following the strategy reported by Candresse et al. [16]. After demultiplexing, the reads were assembled using the CLC Genomics Workbench 7. 0 (http://www. clcbio. com) and annotated by BlastX and BlastN comparison with GenBank, using a 10 -3 e-value cut-off. The scaffolding and ordering of the contigs for each viral isolate were facilitated by mapping the contigs on reference viral genomes.
Sequence and phylogenetic analyses Analysis of 454 pyrosequencing sequence data was performed as described by Candresse et al. [16]  using the CLC Genomics Workbench 7. 0. Multiple alignments of nucleotide or amino acid sequences were performed using the ClustalW program as implemented in MEGA version 6. 0  [19]. Phylogenetic trees were reconstructed using the neighbor-joining technique with strict nucleotide or amino acid distances and randomized bootstrapping for the evaluation of branching validity. Genetic distances (p-distances calculated on nucleotide or amino acid identity) were calculated using MEGA version 6. 0. The RDP4 program  [20]  was used to search for potential recombination events in the APV genomic sequences obtained in this study.