dataesr/scientific-paragraphs-categorization huggingface dataset:
This mapping exercise and the development of a comprehensive database of S&T networks for DRR needs to be done in order to evaluate existing S&T networks in DRR and guide their future development. While examining existing international science ''partnerships'' in DRR (for example, the International Decade for Disaster Risk Reduction),  Carabine (2015)  concludes that it is crucial to improve the existing evidence base and use of S&T, especially when taking on board and coordinating a variety of stakeholders. The main contribution of the international science partnerships to DRR is through creating scientific evidence and promoting the use of S&T for creation of evidence-based policy. We propose that a parallel can be drawn with the potential role of S&T networks in DRR, especially at the science-policy interface.
In order to share the lessons and best practices of existing successful networks, and foster cooperation between existing initiatives, we propose the creation of a comprehensive database of S&T networks for DRR. More knowledge is needed on how common challenges experienced in networks (for example, leveraging human capacity, governance, and funding) can be overcome. The robust, evidence-based good practice guidelines created will be pivotal to structuring and informing existing and future S&T networks, as well as to establish the UNISDR Science and Technology Roadmap and, ultimately, contribute to meeting the targets of the Sendai Framework.
PRE-CLUSTERING Phylogenetic methods are intended to find relationships between classes (species) of objects. But there is no multivariate classification of galaxies  (Fraix-Burnet et al. , 2015). This would be however useful since it is easier and physically more relevant to study different types of objects rather than millions of individuals. This dimension reduction is also necessary in the era of the huge databases brought by current and future telescopes. This multivariate classification is one objective of astrocladistics. However, we are limited by the size of the samples to study. There are other phylogenetic techniques that tackle this problem efficiently, but they are based on distances, and most often adapted for the specific evolutionary processes of living organisms and their traits (e.
Sometimes, some groups stand out from these trends in some parameters, such as the group 6 for the mass or group 12 for Mg. These groups will be further investigated since they could be either the results of some weird data or, more interestingly, a new peculiar species of galaxies that could lead us to a better understanding of the evolution of galaxies than simply redder colors or larger masses. The WINGS sample galaxies belong to X-ray brights clusters, which are rather evolved systems, predominantly close to a state of viral equilibrium. We find that most of the groups have a representant in all the clusters, or conversely all clusters span the entire tree. Despite the low statistics in some of the clusters, this would indicate that the classification scheme depicted on the tree in Fig.
FASAY IN DIAGNOSIS FASAY stands out as a remarkably efficient tool to identify mutations that interfere with p53 functions as it allows the rapid analysis of multiple samples and limits the sequencing costs. Indeed, only loss-of-function clones were sequenced. FASAY was originally developed in order to detect loss-of-function germinal mutations and, as such, was used in the diagnosis of Li-Fraumeni Syndrome (LFS) and Li-Fraumeni Like syndrome (LFL)  [11]. Data obtained from these analyses revealed that mutations of p53 that retain some transcriptional activity are associated with a milder family history of cancer development, a lower number of tumors and a delayed disease onset  [34]. Owing to the growing place taken by p53 in cancer research, FASAY has been used to determine the p53 status of various human tumors such as leukemia  [35] , hepatocellular carcinoma  [36]  and myelodysplastic syndromes  [37]  (see  [38]  for review).
Seventy-two hospital sites that were located in England, Wales or Scotland and were active in RESTART in June 2015 opted into PRIME. Sites were randomly allocated (using a computer-generated block randomisation algorithm, stratified by hospital location in Scotland vs. England/Wales) to one of 12 months in which the intervention would be delivered. All sites began in the control state. The intervention was delivered by a recruitment co-ordinator via a teleconference with each site. The intervention involved discussing recruitment strategies, providing software for each site to extract from their own stroke audit data lists of patients who were potentially eligible for RESTART, and a second teleconference to review progress 6 months later. The recruitment co-ordinator was blinded to the timing of the intervention until 2 months before it was due at a site.
Design and setting PRIME is a stepped-wedge cluster randomised trial (within the RESTART parallel-group RCT) investigating an intervention to boost recruitment in RESTART. At the time of inviting active hospital sites in RESTART to take part in PRIME in June 2015, there were 109 active hospital sites in the RESTART collaboration in the United Kingdom. We excluded 24 sites including two sites in Northern Ireland (where stroke audit data collection was not consistent), two sites in Scotland and two sites in England that piloted the PRIME intervention. We also excluded sites that had been activated to RESTART in 2015 to ensure that all sites taking part had a number of months of recruitment before receiving the intervention. The PRIME recruitment coordinator telephoned the RESTART co-ordinator at each of the remaining 85 sites in ascending order of site identification number to invite them to take part in a 'recruitment review' , followed by an email if required, until 72 sites agreed.
Bespoke stroke audit data exports The data sources used in PRIME, alongside other local hospital databases, are the Scottish Stroke Care Audit (SSCA; http://www. strokeaudit. scot. nhs. uk/) and the Sentinel Stoke National Audit Programme (SSNAP; https:// www. strokeaudit. org/Home. aspx). The SSCA was established in 2002 and includes all hospitals managing acute stroke in every Scottish NHS board, each of which uses the SSCA to evaluate its stroke care against national standards and to drive improvements; inpatient and outpatient data since January 2010 are available. The SSNAP has prospectively collected a minimum dataset for stroke patients in England and Wales since January 2013 to measure processes of acute care, rehabilitation, care in the community, and outcome measures at 6 months; inpatient data since January 2013 are available.
Bespoke stroke audit data exports were created for PRIME in collaboration with the SSCA and SSNAP teams to reflect the RESTART eligibility criteria as best the routinely collected data can. The exports show hospital staff, who have access to stroke audit data, details about only their own hospital's patients who might be eligible for RESTART. These data can be shared with local staff at each site on the RESTART delegation log who are authorised to also view information about patients within the clinical stroke service, with reference to local information governance procedures. Because the data will be exported locally, there will be no passage of identifiable, patient-level data to a third party such as the central RESTART trial co-ordinating team in Edinburgh. A set of instructions (Additional files 2 and 3) is provided to sites after their recruitment review, explaining how to produce the bespoke stroke audit data exports.
Study outcomes The primary outcome is the total number of patients randomised into RESTART per month per site. The RE-START trial database collects information about every randomisation in real time. The total number of people randomised per month will be quantified at each site for analysis. In PRIME, we will quantify the primary outcome at each site in the comparator period before the recruitment review occurs at that site, and we will measure the primary outcome in the intervention period after the recruitment review (Fig. 1 ). The time periods analysed are likely to be sufficient to allow enough time for the intervention to lead to recruitment of participants, and for us to be able to detect whether there is an initial surge in recruitment as a result of the recruitment review followed by a decay in the randomisation rate over time.
We will collect data on secondary outcomes from the information supplied in the questionnaires completed before, at and 6 months after the recruitment review to address the following subsidiary research questions: a. Number of sites in PRIME that routinely used stroke databases to identify potentially eligible patients before receiving the recruitment review b. Number of sites in PRIME that used the bespoke stroke audit data exports, and the frequency of their use in the 6 months after the recruitment review c. Barriers to recruitment in PRIME, identified by sites in the questionnaires completed before, at and 6 months after the recruitment review d. Barriers to using the bespoke stroke audit data exports identified by PRIME sites at the 6-month follow-up review e. Disadvantages of the bespoke stroke audit exports identified by PRIME sites at the 6-month follow-up review.
This protocol and the statistical analysis plan will be submitted for publication before all sites receive the PRIME intervention and before outcome data are known. The statistical analysis plan will be published separately from this protocol. In brief, PRIME's primary outcome of recruitment rate per site per month will be compared before and after the allocated month for the recruitment review using a negative binomial generalised linear mixed model, informed by a published method to model recruitment  [17]. The primary analysis will follow an 'as-randomised' principle, which means that the data will be analysed according to the allocated timing of the recruitment review rather than the time that the recruitment review actually occurred. In addition, for the primary analysis, all sites will be included, regardless of any site withdrawals and/or compliance with the PRIME trial procedures.
Patient and public involvement We have sought the views of the patient reference group for the Research to Understand Stroke due to Haemorrhage programme (www. RUSH. ed. ac. uk). No extra burden for patients was foreseen, given that the stroke audit data exports would be screened by each patient's hospital team before each patient's clinician invited them to participate. Attempting to recruit eligible, prevalent patients who had been discharged from hospital, but who might benefit from participation in RESTART, was seen to be a good thing. The patients reviewed and approved the wording of the letters that would be sent to invite prevalent patients to participate.
Roles and responsibilities The day-to-day running of PRIME is conducted by the recruitment co-ordinator, supported and overseen by the RESTART trial manager and chief investigator. The progress of PRIME is overseen by the team that designed the trial (RASS, CJW, AR and MD) and is discussed at the main RESTART Trial Steering Committee meetings. There is no data monitoring committee, because there is no patient participation in this trial. RESTART is jointly sponsored by NHS Lothian and the University of Edinburgh (Academic and Clinical Central Office for Research and Development; http://www. accord. ed. ac. uk/). The sponsor and funder had no role in study design; collection, management, analysis and interpretation of data; the writing of this report; or the decision to submit the report for publication.
Acknowledgements We thank the  Scottish Stroke Care Audit  and the  Sentinel Stroke National Audit Programme  for providing the data extraction tools and supporting their implementation. We thank the RESTART collaborators who took part in PRIME, as well as  Jonathan Drever  and  Ronnie Harkness  for their help with PRIME. RAP and CJW were supported by  NHS Lothian via the Edinburgh Clinical Trials Unit , and RASS was supported by an  MRC  senior clinical fellowship.
PRIME encountered delays when it was reviewed by the study sponsor and the research ethics committee with oversight of the RESTART (parent) trial, despite the opinion that we obtained from the UK Health Research Authority. If the evidence base for the most efficient ways to conduct randomised trials is to be improved, regulatory agencies should consider creating policies and guidance for the review of methodological research embedded within randomised trials in order to standardise processes and facilitate low-risk methodological research. In the meantime, we recommend that future SWATs seek sponsor review and research ethics committee approval as early as possible. The results of PRIME may be generalisable to future randomised trials of secondary prevention after stroke in the United Kingdom that could use electronic data held to identify potentially eligible participants.
Acknowledgement This work has been partially funded by the  SN2NS  project  ANR-10-BLAN-0503  and by  New-CompStar ,  COST Action   MP1304. M. H. is supported by the  Swiss National Science Foundation. T. K. is grateful for support by the  Polish National Science Center (NCN)  under grant number  UMO-2013/09/B/ST2/01560. S. T. is supported by the  Helmholtz Association (HGF)  through the  Nuclear Astrophysics Virtual Institute  ( VH-VI-417 ).
We propose a novel strategy to constrain the bottom and charm Yukawa couplings by exploiting LHC measurements of transverse momentum distributions in Higgs production. Our method does not rely on the reconstruction of exclusive final states or heavy-flavour tagging. Compared to other proposals, it leads to an enhanced sensitivity to the Yukawa couplings due to distortions of the differential Higgs spectra from emissions which either probe quark loops or are associated with quark-initiated production. We derive constraints using data from LHC Run I, and we explore the prospects of our method at future LHC runs. Finally, we comment on the possibility of bounding the strange Yukawa coupling.
To estimate these effects, we used MG5aMC@NLO and POWHEG  [67]  showered with Pythia 8. 2 and found that the corrections can reach up to 2% in the relevant p T,h region. This finding agrees with recent analytic studies of nonperturbative corrections to p T,h (see e. g. [68] ). With improved perturbative calculations, a few-percent accuracy in this observable will therefore be reachable. We study two benchmark cases. Our LHC Run II scenario employs 0. 3 ab -1 of integrated luminosity and assumes a systematic error of ±3% on the experimental side and a total theoretical uncertainty of ±5%. This means that we envision that the non-statistical uncertainties present at LHC Run I can be halved in the coming years, which seems plausible. Our HL-LHC scenario instead uses 3 ab -1 of data and foresees a reduction of both systematic and theoretical errors by another factor of two, leading to uncertainties of ±1.
Acknowledgments. We express our gratitude to Gavin Salam for drawing our attention to the quarkinitiated contributions and pointing out that deriving bounds on the strange Yukawa coupling might be feasible. We thank Chris Hays for helpful discussions concerning the ultimate experimental precision that measurements of p T spectra in Higgs production may reach at the HL-LHC, and Paolo Torrielli for prompt help with MG5aMC@NLO. We are grateful to Florian Bernlochner, Chris Hays, Gavin Salam, Giulia Zanderighi and Jure Zupan for their valuable comments on the manuscript. UH acknowledges the hospitality and support of the CERN Theoretical Physics Department. Appendix: A study of the dependence of the κc and κb bounds on the projected systematic uncertainties In this appendix we discuss in more detail the prospects of the proposed method at the HL-LHC with 3 ab -1 of integrated luminosity.
ACKNOWLEDGMENTS The outstanding support provided during preparation and realization of the experiments by the scientists of the different divisions of FERMI is greatly appreciated. The  Max-Born-Institute group  acknowledges financial support received from the  Helmholtz Virtual Institute "Dynamic Pathways in Multidimensional Landscapes"  ( VH-VI-419 ). Support from the  CNRS  through the " PEPS SASELEX " and from the  French ANR  via the " UMAMI " project is acknowledged by the coauthors from Paris. B. M. acknowledges the financial support by the  "ERC X-Five " grant.
VI. ACKNOWLEDGMENTS The authors would like to thank Marco Schioppo for careful reading of our manuscript and Elizabeth Bridge for useful discussion regarding the feasibility of strontium Rydberg atoms for electrometry. This work was done under the auspices of UK NMO Here we consider further the estimation of the uncertainty in the electric field that arises from the calculation of the Stark map. As stated earlier, at the current level of precision the dominant contribution is the uncertainty in the experimentally-measured zero-field energies of the Rydberg states. In this work, we carried out a more detailed analysis of the data set discussed in  [32]. For each Rydberg series, the experimentally available Rydberg energies are fitted with the Rydberg-Ritz formula to obtain the zero-field energy at all principal quantum numbers.
program. WB would like to acknowledge the support from the Marie Curie Initial Training Network FACT. PH and MPAJ acknowledge C Vaillant for useful discussions and the early version of the Stark map code. The experimental Stark map in Fig 1 was measured by R Hanley. They also acknowledge financial support from EPSRC grant EP/J007021/, and EU grants FP7-ICT-2013-612862-HAIRS, H2020-FETPROACT-2014-640378-RYSQ and H2020-MSCA-IF-2014-660028. Appendix A: theoretical stark map uncertainty.
χ 2 -χ 2 fitFIG. 5. 225 FIG. 5. Contour plots showing the change in χ 2 resulting from varying the electric field strength and angle β in the fitting model. Contours are shown for the datasets with(top) and without(bottom) an externally applied electric field. From the contours we can determine the uncertainty for the fitting parameters along with correlation between their values. The blue dots indicates the value for E and β that minimize χ 2. The red dashed lines indicate the ±1σ bounds for each fitting parameter based on the extrema of the contour corresponding to increase in χ 2 of 1.
The photon mass, m γ , can in principle be constrained using measurements of the dispersion measures (DMs) of fast radio bursts (FRBs), once the FRB redshifts are known. The DM of the repeating FRB 121102 is known to < 1%, a host galaxy has now been identified with high confidence, and its redshift, z, has now been determined with high accuracy: z = 0. 19273(8). Taking into account the plasma contributions to the DM from the Intergalactic medium (IGM) and the Milky Way, we use the data on FRB 121102 to derive the constraint m γ 2. 2×10 -14 eV c -2 (3. 9 × 10 -50 kg). Since the plasma and photon mass contributions to DMs have different redshift dependences, they could in principle be distinguished by measurements of more FRB redshifts, enabling the sensitivity to m γ to be improved.
The first claim to measure a redshift was made for FRB ments could in principle be used to constrain m γ. However, the identification of the host galaxy of FRB 150418 has subsequently been challenged  [21] , and is now generally not accepted  [22]. Our interest in the possibility of using FRBs to constrain m γ has recently been revived, however, by the observation of repeated emissions from FRB 121102  [22]. These have permitted precise localisation of its host galaxy, which has made possible a precise determination of its redshift, z = 0. 19273(8)  [23]. This redshift determination makes it possible, in turn, to use data on FRB 121102 to provide a robust constraint on m γ , as we discuss in this paper. The dispersion measure (DM) is related to the frequency-dependent time lag of an FRB by ∆t DM = 415 ν 1 GHz -2 DM 10 5 pc cm -3 s.
OCT System and Data Acquisition An ultrahigh-speed spectral domain OCT system was used to perform en face Doppler OCT measurements of total retinal blood flow. The same system is described by Choi et al. 28  and, therefore, is only briefly summarized. The system uses a commercially available superluminescent diode (Superlum, SLD-371) with a full-width-at-half-maximum bandwidth of 55-nm and an 840nm central wavelength. A line scan camera (Basler Sprint spL4096-140 km) with 832 illuminated pixels was used to achieve an imaging speed of 244,000 A-scans per second. The axial resolution and total imaging range, in tissue, were 5. 7 μm and 1. 5 mm, respectively. OCT power incident on the cornea was 2. 5 mW, and the system sensitivity was 99 dB. Total retinal blood flow was calculated using a technique described previously.
Measures Baseline information was captured at the enrollment visit. Data for months 3, 6, and 12 were collected by a hard copy questionnaire that was mailed to participants. GI events during baseline were identified as those that occurred during the 6-month period prior to enrollment. Month 3 GI events encompassed those that occurred through the first 3 months of the study, while month 6 GI events were captured after month 3 through month 6, and month 12 GI events were captured after month 6 through month 12. Persistence was defined as continuing with original therapy without switch or discontinuation. At months 3 and 6, persistence was determined by asking the patient if they had discontinued or switched their OP treatment in the previous 3 months; month 12 persistence was determined by asking the patient if they had discontinued or switched in the previous 6 months.
Falls and fractures Patterns of falls and fractures among OP patients were reported at baseline and at each follow-up period. Among new users, falls were more common during the study (46. 9% had at least one fall between baseline and month 12) than during the 12 months prior to study enrollment (37. 4%). It is possible that the difference in frequency may reflect recall bias during the pre-study period, since minor falls may not be highly memorable, but the data do not allow us to explore this further. A total of 49. 4% of new users reported a prior OP-related fracture at baseline, but only 0. 8% experienced a fracture between baseline and month 12. These patterns were also observed among experienced users: falls prior to study enrollment occurred in only 21. 4% of experienced users, but 38.
ACKNOWLEDGMENTS We thank  Dr. M. Ho ¨rning ,  K. Tsuji ,  Dr. K. Hasegawa , and  Prof. T. Asada  for helpful suggestions and discussions. We also thank  Panasonic Co. , Fujifilm Co. ,  Tamai Kasei Co. , Ltd. , JMS Co. , Ltd. , and  SAROUTE Co. , Ltd. for their support with the analysis and cell transport. This work was supported by the  Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (B)  ( 15H03948 ,  26289065 ,  23310087 ,  26286027 ),  Grants-in-Aid for Scientific Research (C)  ( 15K08270 ), and  Grants-in-Aid for JSPS Fellows  ( 26. 04046 ). The research is (partially) supported by the  Japan Agency for Medical Research and Development (AMED) program , the  European Commission  (contract no. 604263  [Neuroscaffolds]), and the  French National Research Agency (ANR ; contract number  ANR-12-RPIB-0015  [Cardiac Patch]).