dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 1: Launch the spatial planning process The objective was to develop a multi-scale vision for the marine spatial planning process through cross-sectoral engagement with stakeholders. This step encompasses the first four SCP steps  [22, 24, 25, 32] , i. e. scoping and costing the planning process; identifying and involving stakeholders; describing the context for conservation areas; and identifying conservation goals. To initiate the process, individual interviews with specialist scientists and small workshops (less than 20 participants) with relevant user groups were held to identify, collect or build spatial datasets on ecological features and human activities, for example, coral reef distribution, rocky reef location and shore angling effort. This initial step identified realistic ).
In 2012, a final stakeholder workshop was held to discuss processes for future updates to the conservation plan and strategies to implement a prioritised subset of selected proposed MPAs (50 participants). Step 2: Describe present conditions Collection of data on social and ecological systems. Individual interviews with specialist scientists and small workshops (less than 20 participants) with relevant user groups were held to identify, collect or build spatial datasets on ecological elements and human activities, for example, coral reef distribution, rocky reef location and shore angling effort. Various types of spatial data were collected or collated at different scales on the administrative, ecological and social systems. Administrative data describe the spatial distribution of management regimes in the seascape (MPAs for instance).
Ecological data described habitats (water masses, oceanographic processes, shoreline habitats, coral reefs, rocky reefs, submarine canyons and estuaries) and species distribution (fish, turtles and cetacean) (S1 Appendix). Data on social system focused mostly on human uses (mapping of activities) (S2 Appendix). Existing and proposed MPA boundaries were mapped. All these protected areas comprise coastal and marine areas between the vegetation line and the limit of the EEZ. Existing MPA boundaries were extracted from the South African Government Gazette (Marine Living Resources Act). Spatial data were collected for other protected areas that overlapped marginally with the planning domain: terrestrial protected areas (data from EKZNW), and Admiralty reserves and State Land (data provided by the KZN Provincial Planning & Development Commission).
The boundaries of six proposed MPAs were mapped with MPA proposers. Modelling spatial distributions of biodiversity elements and human activities. A range of ecological modelling techniques and tools were used to model the distribution of habitats and species (time series analysis, cluster analysis, boolean rules, etc. ). S1 Appendix describes the ecological data that were collected or modelled (biodiversity conservation elements). S2 Appendix describe the method for mapping the presence/absence or the intensity of 38 human activities that can impact marine ecosystem structure and function negatively. Step 3: Analyse present conditions Planning domain delineation. The planning domain stretched from the coastal vegetation line, delineated from aerial photography, to the EEZ (Maritime Boundaries Geodatabase -30 m is the offshore limit of the deep photic zone in this region, where the influence of waves and light attenuates  [49] , and very little macroalgae occurs below 30 m.
Depth contours were extracted from the bathymetry layer described previously. PUs smaller than 5,000 m2 were then dissolved into adjacent polygons, but keeping MPA boundaries, depth zone boundaries and the planning domain boundary intact. In addition, the three PULs were combined to form a unique "mixed-resolution" large-scale PUL required to perform the conservation assessment. The highest resolution PU taking precedence where there was overlap. This mixed-resolution PUL contained 64,189 planning units. It was not used in the site priorisation process that articulates three distinct PULs (fine-, medium-and largescale PULs). Allocating data to multi-resolution planning unit layers. Data were verified and converted into raster or vector layers in ArcGIS. Spatial layers for each biodiversity element were overlayed with the PU layer of the appropriate resolution and aerial coverage (Fig  4 ).
Targets for areas supporting semi-permanent oceanographic processes (zones with frequent fronts or gyres) were set to only 10% of their distribution, but given that these areas are important for pelagic ecosystems and are species aggregation sites, we assigned the full target to Type A MPAs. The target for turtle nesting sites was set to 80% divided into 20% for Type A and 60% for Type B protection. Step 4: Explore future scenarios and solutions Decision support system. We used MARXAN software  [69]  to identify networks of additional protected areas (i. e. reserves) that best achieved conservation targets. MARXAN was specifically designed to compute combination calculations for complex site priorisation problems. The software aims to solve the "minimum set reserve design" problem  [69]. In this study, the use of Marxan contributed to ensure an unbiased and controlled selection of planning units along the multi-scale spatial planning procedures.
Nevertheless, the multi-scale method exposed remains independant from the use of Marxan or any other spatial optimisation software. MARXAN algorithm uses a mathematical approach called simulated annealing  [70]  to identify near-optimal reserves portfolios (i. e. networks of protected areas) that minimize a cost function  [71]. The cost function in MARXAN is the sum of an overall penalty factor (a cost is allocated if a conservation target remains unachieved) with an overall planning units cost. A planning unit cost can be a measure of any of its aspects, such as its area or perimeter, the probability of being impacted by human activities, or the opportunity cost resulting from its loss  [68]. Each run of MARXAN produces a near-optimal reserve portfolio named solution. MARXAN produces two outputs: the "best solution" (i.
Whereas the ML method requires slightly more area, when compared with a multi-resolution scale-unlinked method, it promotes the ecological connectivity of the future MPA network across spatial scales. Although the monetary costs of developing and implementing a ML spatial plan compared to scale-unlinked methods were not quantified, the ecological, social and political benefits (and cost avoidance) of the ML method are likely to outweigh any additional costs that may be incurred for skills, data collection and social interactions. This study provides a useful, repeatable and transferable approach that is applicable to multi-scale spatial planning in general. It provides a methodological development that has the potential to transform conservation planning practices and policies in many marine regions worldwide.
Acknowledgments We thank  Christopher A. Lepczyk  and the two anonymous referees for their thorough reading of the manuscript and their constructive and thoughtful comments which have helped to improve the manuscript further. Authors acknowledge the  African Coelacanth Ecosystem Programme (ACEP). Erwann Lagabrielle  and  Amanda T. Lombard  acknowledge the  National Research Foundation of South Africa (NRF-SA) , the  MASMA program  funds and the assistance of  WIOMSA (Western Indian Ocean Marine Science Association). Jean M. Harris  thanks the  Pew Fellows Program in Marine Conservation  for support for this work. This study was finalized in the framework of the  MASMA MOZALINK  project: Grant  N˚: MASMA/OP/ 2013/02  ''Linking marine science, traditional knowledge and cultural perceptions of the sea in the Mozambique Channel to build tomorrow's marine management using spatial simulation tools and educational game".
More recently, theoretical work has begun to focus on incorporating curvature effects into active matter models  [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]. Despite some promising progress, the hydrodynamic description of pattern-forming non-equilibrium liquids in non-Euclidean spaces continues to pose conceptual challenges, attributable to the difficulty of formulating exactly solvable continuum models and devising efficient spectral methods in curved geometries. Aiming to help improve upon these two issues, we introduce and investigate here the covariant extension of a generalized Navier-Stokes (GNS) model  [41] [42] [43] [44]  describing incompressible active fluid flow on an arbitrarily curved surface. Focusing on a spherical 'bubble' geometry, we derive exact stationary solutions and numerically explore the effects of curvature on the steadystate flow dynamics, using the open-source spectral code Dedalus  [45].
KNAVE-II  (Shahar et al. , 2006)  is an interactive and semantic viewer of clinical data based on the use of domain ontologies. However, none of these tools is specific for pharmacovigilance. Many visualization and data-mining tools have been developed for sequence analysis. EventFlow  (Monroe et al. , 2013)  allows users to search for exact temporal patterns in a cohort and to summarize the information in the sequences. PeerFinder  (Du et al. , 2017)  is a visual interface for finding similar individuals based on their temporal sequences. OutFlow  (Gotz and Wongsuphasawat, 2012 ) is a tool to summarize and visualize the care trajectories of a large number of patients in the form of advanced Sankey diagrams. However, these tools were not developed for the specific needs of pharmacovigilance, and they lack functions for the search of approximate sequences and data processing modules.
Data Source The data used to implement and evaluate our tools came from eHOP, the CDW technology we developed at Rennes academic hospital (France)  (Cuggia et al. , 2011). Briefly, eHOP integrates all types of documents produced by the hospital information system and connected with healthcare. The eHOP CDW currently allows searching among 40 million unstructured data (e. g. , clinical narrative notes, surgical protocols, X-ray, or pathology reports) and 300 million structured elements (e. g. , ICD-10 diagnoses, Anatomical Therapeutic Chemical terminology for drug prescriptions and administration) from EHRs, and cover more than 1. 2 million patients. Some unstructured data, such as laboratory results or diagnoses, are also recorded in a structured form thanks to the corresponding terminological codes.
Timeline We designed the timeline based on simple graphic semiology rules. The visual variables and data types that highlight qualitative or quantitative differences were based on Bertin's work  (Jacques, 2018). This work proposes rules for data visualization according to visual variables (forms, colors, surface, etc. ). These rules allow highlighting the data qualitative and quantitative differences. The development of this timeline was inspired by the timeline proposed by C. Plaisant's group in the Inspired EHRs ebook. We used the VCM language (Inspired EHRs, 2018) to efficiently represent the data elements in the timeline. This graphical language, which was developed by J. B. Lamy, represents the main medical concepts (symptoms, treatments, antecedents of diseases) with icons and also proposes their alignment with ICD-10 classification codes (Figure  1 ).
Here, we used this alignment function to display the ICD-10 data from the PMSI database (Programme de médicalisation des systèmes d'information; medicalized information system program) in the timeline. We developed the two prototype graphical user interfaces (GUIs) as web applications using the JavaScript d3. js  (Lamy et al. , 2010)  and JQuery libraries for the graphical and interactive aspects. We designed the prototype components as a web service written in Java to be weakly coupled to the CDW data source (eHOP for the prototypes).
Timeline Description The application provides access to heterogeneous clinical data information, including the form of free text (hospitalization reports, discharge letters, etc. ), coded using different terminologies (e. g. , the International Classification of Diseases, 10th revision, ICD-10, medical diagnosis codes), and structured data (laboratory results, prescriptions and drug administrations). Each data type has a corresponding visual representation. The prototype used a web interface to allow interactive navigation. Real patient data were used to design and evaluate for the prototype assessment. Figure  2  is a screen mock-up to show how the information is displayed in the timeline. To the best of our knowledge, this application includes new features. First of them, the possibility of aggregating structured data.
Sequence Analyzer Description We developed an application to visualize sequences in a patient cohort. Unlike other visualization tools, such as PeerFinder and OutFlow, our tool manages the entire process of data visualization, from CWD data processing to screen display. The sequence extraction interface is used to build a cohort of patient sequences and to choose the methods for data processing (Figure  4 ). To build a cohort, the user first creates a list of concepts by choosing the codes relating to a concept in a tree structure that gathers several medical terminologies ([1] on Figure  4 ). Therefore, a laboratory code and a diagnosis code can be part of the same concept. Once the list of concepts is established (INR and VKA in the example in Figure  4 ), the user clicks on the "view sets" button  ([2]  on Figure  4 ).
Implementing VCM logos makes it easier to identify diagnostics. Data aggregation features allow information to be synthesized to minimize cognitive load by avoiding redundant information. Other aggregation axes could be evaluated. An aggregation between semantically and/or temporally close data could also be considered for example. This would include data that are very close in time and very similar, like repeated ICD-10 codes for chronic diseases such as diabetes. A more document oriented visualization could be proposed. Indeed, it turned out that in use the return to the text document is systematic to verify the veracity of the information.
Sequence Analyzer Advantages and Limits Our modified Smith Waterman algorithm has several advantages: it allows searches for similar sequential patterns. The modifications we have made allow us to take into account the time dimension of the data. Other tools (e. g. , EventFlow) can search for patterns with distant events, but only exact matches. Another original feature of the tool is that it supports all the visualization steps: from searching for sequences in the database, through data processing and transformation, to onscreen display. All these steps are under user control. However, in our case study we found that the CDW data processing step was essential for the sequence search: the sequences, and thus the patterns founded by algorithms, will vary depending on the choices made by the user.
Data source GeoSentinel is a global clinician-based sentinel surveillance system that monitors travel-related illness and other conditions among international travelers. It was established in 1995 as a collaboration between the Centers for Disease Control and Prevention (CDC) and the International Society of Travel Medicine. GeoSentinel currently consists of 70 specialized travel and tropical medicine clinical sites in 31 countries  [13]. GeoSentinel's data collection protocol has been reviewed by the CDC's National Center for Emerging and Zoonotic Infectious Diseases and is classified as public health surveillance and not human subjects research. Additional ethics clearance was obtained by participating sites, as required by their respective institutions.
Survey of GeoSentinel travelers with a potential rabies exposure We analyzed records submitted to GeoSentinel of international travelers with exposure to a potentially rabid animal. Demographics and travel history, place of exposure, and animal involved in the exposure were recorded. Travelers who started RPEP before (Group A) and at (Group B) presentation to a GeoSentinel clinic during September 2014-July 2017 were included. Among travelers in the former group, data were collected and assessed for type of exposure  [3] , rabies PrEP, occurrences where RPEP with or without RIG should have been administered, and differences between international travelers who received RIG in the country of exposure and those who did not (relative risk). We excluded records if they were not travelrelated or the exposure country was unascertainable.
Although a single dose of PrEP should confer some protection, in the event of a potential rabies exposure, WHO recommends full RPEP including RIG if indicated  [17]. Given the travel and tropical medicine specialization of GeoSentinel sites, these data may not be representative of all international travelers who receive RPEP or of travelers to a particular country or region. GeoSentinel data are not generalizable, so risk estimates of a particular illness cannot be calculated. Additional information (i. e. , urban or rural exposure, activity during exposure, type of animal exposure, and pre-travel rabies immunization receipt) was collected only on those travelers in Group A; data on whether RIG was provided or offered at GeoSentinel clinic visits were not collected. Information collected from travelers who started RPEP outside a GeoSentinel clinic was based upon patient report and clinical description of the WHO exposure category and is subject to observer and recall bias.
Acknowledgments Members of the  GeoSentinel network: Emilie Javelle, Marseille, France; Natasha Hochberg, Boston, USA; Nicholas Beeching, Liverpool, UK; Cedric Yansouni, Montreal, Canada; Andrea Boggild, Toronto, Canada; Udomsak Silachamroon, Bangkok, Thailand; Prativa Pandey, Kathmandu, Nepal; Joseph Torresi, Melbourne, Australia; Sapha Barkati, Montreal, Canada ;  Phyllis Kozarsky & Henry Wu, Atlanta, USA ;  Scott Benson ,  Salt Lake City, USA; Albie de Frey, Johannesburg, South Africa ;  Rainer Weber , Zurich, Switzerland;  Shuzo Kanagawa & Yasuyuki Kato ,  Tokyo, Japan; Christophe Rapp & Cecile Ficko, Paris, France; Francois Chappuis, Geneva, Switzerland; Poh-Lian Lim, Singapore; Phi Truong Hoang Phu, Ho Chi Minh City, Vietnam; Yukihiro Yoshimura & Natsuo Tachikawa, Yokohama, Japan; Emmanuel Bottieau & Joannes Clerinx, Antwerp, Belgium; Rogelio Lopez-Velez & Francesca Norman, Madrid, Spain; Hugo Siu & Luis Manuel Valdez, Lima, Peru; Perry van Genderen, Rotterdam, the Netherlands ;  Jan Hajek ,  Wayne Ghesquiere ,  & Yazdan Mirzanejad ,  Vancouver ,  Canada ;  Jean Haulman & David Roesel, Seattle, USA ;  Carsten Schade Larsen & Christian Wejse, Aarhus, Denmark ;  William Stauffer & Pat Walker ,  St.
Quantification of fluorescence was performed by ProScanArray Express software (PerkinElmer) employing an adaptive circle quantification method from 50 μm (minimum spot diameter) to 300 μm (maximum spot diameter). Average RFU values with local background subtraction of four spots and standard deviation of the mean were reported using Microsoft Excel and GraphPad Prism software. Incubation with C-type Lectins. Cy3 labeled C-type lectins were diluted in incubation buffer (25 mM Tris•HCl, 150 mM NaCl, 4 mM CaCl 2 , pH 7. 5 containing 0. 5% bovine serum albumin (BSA), and 0. 005% Tween-20). C-type lectin solutions (200 μL per array) were used to incubate individual wells on a glycan array slide at 4 °C for 18 h. Arrays were washed with incubation buffer without BSA and with water and dried in a slide spinner.
Our new model takes into account the configuration of the local magnetic field, the morphology of HI structures, and the formation of the two well-known molecular clouds with regions with ongoing star formation, the Ophiuchus and Lupus molecular complexes. The HI data also provide evidence that high-velocity clouds (HVCs) are probably associated with this region and interact with the bubble. Our analysis is organised as follows: the data are presented in Section 2, the region is described in detail in Section 3, structures seen in polarisation are described in section 4, a detailed.
0 km s -1 and covers all Milky Way velocities between V LS R -400 km s -1 and 500 km s -1. The X-ray maps are taken from the ROSAT all-sky survey R5 band (0. 56-1. 21 keV)  (Snowden et al. 1997). The maps have a pixel size of 12 × 12 arcmin. Finally, the radio polarisation data are from the S-band Polarization All Sky Survey (S-PASS;  Carretti et al. (2013) ;  Carretti et al. , in preparation). S-PASS is a single-dish polarimetric survey of the entire southern sky at 2. 3 GHz, performed with the Parkes 64 m Radio Telescope, and its S-band receiver has a beam width FWHM = 8. 9 arcmin. Final maps were convolved with a Gaussian window of FWHM = 6 arcmin to a final resolution of 10. 75 arcmin.
(2004) , robust constraints on the uncertainties were retained by  Kirsten et al. (2015)  for their calculations. In our analysis, we tested if the runaway star ζ Oph, without any association with the pulsar B1929+10, might have been located near the centre of the bubble. In this case, ζ Oph could have been part of a BSS process with an unknown companion. To calculate the orbit of the star, we used the python software galpy 1 with the MWPotential2014 model for the Galactic potential  (Bovy 2015). Normal distributions of the star properties were used to calculate the different possible previous orbits and the bubble distance using the assumed distance of 139 ± 10 pc. Uncertainties on measurement were set as the standard deviations for the normal distributions. The list of the runaway properties is summarised in Table  1.
The systemic radial velocity of the binary system is controversial, however. Hoogerwerf et al. (  2001 ) used a radial velocity of +66 ± 10 km s -1 , which is the same as provided by the SIMBAD search database. Jilinski et al. (2010)  in their dynamical study of runaway stars produced through the BSS process in the Sco-Cen, used the radial velocity -10. 3±6. 9 km s -1 derived by  Bakış et al. (2008)  by averaging systemic velocities obtained from measured separated He I and Mg II lines. Figure  14  shows the closest trajectory for a radial velocity of ∼ 57 km s -1 (H2001) and a bubble distance of ∼ 138 pc. With these parameters, the star system past orbit was at ∼ 4. 7 pc around 2. 8 Myr ago. The orbit using the radial 1 http://github. com/jobovy/galpy velocity derived by  Bakış et al. (2008)  (B2008) does not converge inside the bubble.
Conclusion A careful inspection of HI data and radio polarised emission in the Ophiuchus and Lupus complex regions allowed us to identify a young Galactic bubble located at the interface between the Galactic halo and the Galactic plane. This bubble probably expands inside the larger HI shell surrounding the USco OB subgroup that has previously been identified by de Geus (1992). Its distance is estimated at ∼ 140 pc. The bubble may have been created after a supernova explosion, for which the runaway binary system V716 Cen could be the member of an original triple system. The shock of the supernova would have swept the remaining material of the early USco molecular cloud and formed the young Lupus molecular cloud complex that is located at the western boundary of the bubble. The fragmented structure of Lupus allows some of the hot X-ray gas filling the bubble to escape at higher Galactic latitude.
Parts of the Ophiuchus molecular complex, the Ophiuchus northern molecular clouds, called the Oph ridge in this model, may have the same origin. The magnetic properties of the bubble were inspected through radio synchrotron emission at 2. 3 GHz from the S-PASS data and the dust polarisation at 353 GHz from the Planck survey. The region is dominated in polarisation intensity by the GCS. This structure may be the result of shocked material following the supernova explosion at the origin of the HI cavity. The shock in this denser area of the bubble could have compressed the magnetic field and aligned its direction perpendicularly to the direction of the shock front propagation. However, other works have reported evidence that the GCS sits on the Sagittarius arm or beyond at ∼ 2 kpc or farther.