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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
84 15-18 Gene denotes 2.1 Gene:6700
85 57-65 Disease denotes COVID-19 MESH:C000657245
95 188-194 Species denotes humans Tax:9606
96 399-406 Species denotes patient Tax:9606
97 1032-1038 Species denotes people Tax:9606
98 291-299 Disease denotes COVID-19 MESH:C000657245
99 390-398 Disease denotes COVID-19 MESH:C000657245
100 475-483 Disease denotes COVID-19 MESH:C000657245
101 537-545 Disease denotes COVID-19 MESH:C000657245
102 1097-1106 Disease denotes infection MESH:D007239
103 1178-1186 Disease denotes COVID-19 MESH:C000657245
111 2746-2752 Species denotes people Tax:9606
112 2850-2856 Species denotes people Tax:9606
113 2109-2117 Disease denotes COVID-19 MESH:C000657245
114 2174-2182 Disease denotes COVID-19 MESH:C000657245
115 2226-2234 Disease denotes COVID-19 MESH:C000657245
116 2499-2507 Disease denotes COVID-19 MESH:C000657245
117 2943-2951 Disease denotes COVID-19 MESH:C000657245
125 2996-3010 Species denotes COVID-19 virus Tax:2697049
126 3992-3998 Species denotes people Tax:9606
127 3083-3091 Disease denotes COVID-19 MESH:C000657245
128 3119-3127 Disease denotes COVID-19 MESH:C000657245
129 3699-3708 Disease denotes infection MESH:D007239
130 3910-3918 Disease denotes COVID-19 MESH:C000657245
131 4166-4174 Disease denotes COVID-19 MESH:C000657245
133 6095-6098 Disease denotes SOS MESH:D006504
136 7202-7207 Gene denotes OASIS Gene:90993
137 7612-7615 Disease denotes SOS MESH:D006504
140 8676-8684 Disease denotes COVID-19 MESH:C000657245
141 9073-9081 Disease denotes COVID-19 MESH:C000657245
143 9642-9656 Species denotes COVID-19 virus Tax:2697049
152 11725-11731 Species denotes people Tax:9606
153 10033-10041 Disease denotes COVID-19 MESH:C000657245
154 10206-10215 Disease denotes infection MESH:D007239
155 10412-10421 Disease denotes infection MESH:D007239
156 10692-10701 Disease denotes infection MESH:D007239
157 11311-11319 Disease denotes COVID-19 MESH:C000657245
158 11862-11871 Disease denotes infection MESH:D007239
159 11940-11948 Disease denotes COVID-19 MESH:C000657245
161 12820-12829 Disease denotes elevators MESH:D006973
163 14373-14379 Gene denotes beacon Gene:59286

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T49 0-2 Sentence denotes 2.
T50 3-13 Sentence denotes Background
T51 15-19 Sentence denotes 2.1.
T52 20-95 Sentence denotes Person-to-Place Interactions in Post COVID-19 Workplace Reopening Scenarios
T53 96-252 Sentence denotes The IoCT offers various use cases based on the digital monitoring of the physical world and humans using smart sensors that collect and deliver information.
T54 253-504 Sentence denotes For this paper, we explored different COVID-19 transmission risks in miscellaneous workplace reopening scenarios. “Close contact” with a COVID-19 patient, which mostly occurs indoors, was one of the most common methods of COVID-19 transmission [4,25].
T55 505-727 Sentence denotes Based on our literature review, COVID-19 “close contact” was defined as contact occurring within a period of time longer than 15 min, and a physical distance of less than 2 metres in cases of face-to-face interaction [25].
T56 728-901 Sentence denotes When sharing the same space, viruses can spread via air, objects, or floor even after two to three days if protective equipment is not used, or disinfection carried out [4].
T57 902-1160 Sentence denotes A digital timely proximity tracing system can effectively limit the spread of contagious diseases by collecting information about people or places that an individual (with confirmed or suspected infection) may have had close contact with or had been to [14].
T58 1161-1357 Sentence denotes If a person with COVID-19 was in close proximity to a place, those affected locations (e.g., businesses, public sites, or buses) would then be considered contaminated places (geospatial features).
T59 1358-1496 Sentence denotes This geospatial information can be used to help in closing, disinfecting, alerting, and defining appropriate safe paths and neighborhoods.
T60 1497-1683 Sentence denotes For example, if a location is popular with families or the elderly, additional facilities and organizations in the area may need to be alerted to possible exposure in their neighborhood.
T61 1684-1841 Sentence denotes Health workers will receive timely and relevant alerts to close off and disinfect the actionable list of contaminated places to prevent further transmission.
T62 1842-2079 Sentence denotes Moreover, organizations at each location can be provided with forms to send to their staff, customers, or visitors informing them of possible contamination, and requesting that they complete a contact trace survey for health authorities.
T63 2080-2392 Sentence denotes Three important questions in COVID-19 risk evaluation are: “Who was in close contact with the COVID-19 positive person?” and “What places did the COVID-19 positive person visit, and who else visited those places after that?” The first question can be automatically queried using person-to-person contact tracing.
T64 2393-2493 Sentence denotes The second question addresses the main focus of this paper which is person-to-place contact tracing.
T65 2494-2705 Sentence denotes If a COVID-19 positive person was in close proximity to a place, those affected locations (geospatial features such as businesses, or public sites, or buses) are considered to be potentially contaminated places.
T66 2706-2822 Sentence denotes This information is useful for advising people on safety measures, self-quarantine, and for issuing cleaning alerts.
T67 2823-2959 Sentence denotes As shown in Figure 1, both people and places, as well as duration of contact, play an important role in the pattern for COVID-19 spread.
T68 2960-3099 Sentence denotes The primary step when assessing the COVID-19 virus transmission scenarios is determining the factors affecting the risk of COVID-19 spread.
T69 3100-3182 Sentence denotes According to [26], COVID-19 risk prevention and control depend on population flow.
T70 3183-3444 Sentence denotes For this research, the epidemic risk status is assessed based on three levels of personnel flow strategies: (1) Staying at home, temperature monitoring, and traffic control; (2) Wearing a mask and restricting gatherings; and (3) Strengthening health management.
T71 3445-3709 Sentence denotes Additionally, based on existing studies from both the World Health Organization (WHO) [27] and the Government of Canada website [28], the number of active virus particles in a place is considered to be the most critical factor in determining the risk of infection.
T72 3710-3867 Sentence denotes Virus particles live for different lengths of time which vary depending on several factors, especially with regards to the composition of different surfaces.
T73 3868-4057 Sentence denotes To identify and limit the risk pattern of COVID-19, a range of use cases can be considered that utilize the interactions of people, places, and available sensor information for a workplace.
T74 4058-4195 Sentence denotes The following table (Table 1) sums up the office cleaning use case using person-to-place scenarios for post COVID-19 workplace reopening.
T75 4197-4201 Sentence denotes 2.2.
T76 4202-4249 Sentence denotes Interoperability Using the OGC SensorThings API
T77 4250-4300 Sentence denotes Interoperability is a major challenge for the IoT.
T78 4301-4409 Sentence denotes The real potential of IoT lies in the “systems of IoT systems” rather than with disparate IoT silos [29,30].
T79 4410-4592 Sentence denotes An interoperable IoT system of systems provides a uniform way for sharing, finding, and accessing IoT sensing and tasking capabilities, the Internet, and different applications [31].
T80 4593-4725 Sentence denotes Interoperability requires layers of standards in order to address the heterogeneity issues amongst sensors, data, and networks [32].
T81 4726-4850 Sentence denotes Data and sensor interoperability refer to the ability to exchange and understand data formats, protocols, and sensor models.
T82 4851-5045 Sentence denotes Network interoperability has no value if the bits and bytes are delivered but cannot be interpreted, i.e., if the data being exchanged over the network cannot be understood by machines a priori.
T83 5046-5253 Sentence denotes Further, various levels of interoperability include synthetic, semantic, and cross-domain interoperability which mean the standardization of conceptual models, practices, and policies from disparate systems.
T84 5254-5482 Sentence denotes The OGC SensorThings API (OGC STA) [22,25] is an OGC and United Nation’s International Telecommunication Union Telecommunication (ITU-T) standard that defines a data model and an API for IoT sensing and tasking interoperability.
T85 5483-5602 Sentence denotes The OGC STA is part of the well-established OGC Sensor Web Enablement (SWE) suite of open international standards [23].
T86 5603-6012 Sentence denotes SWE standards are in use by many large-scale organizations such as the Department of Homeland Security (DHS) [33], National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), United States Geological Survey (USGS) [34], Natural Resource Canada (NRC), the World Meteorological Organization (WMO), and many others, including private sector companies [29,35,36].
T87 6013-6199 Sentence denotes The previous generation of SWE standards, such as the Sensor Observation Service (SOS), are heavyweight when it comes to running applications in edge devices with limited resources [37].
T88 6200-6446 Sentence denotes OGC STA represents the new generation of SWE standards that was specifically designed for IoT applications and is thus efficient and lightweight, e.g., it uses the REpresentational State Transfer pattern (RESTful) and the efficient JSON encoding.
T89 6447-6546 Sentence denotes The OGC SensorThings API follows the ODATA (Open Data Protocol) for managing the sensing resources.
T90 6547-6805 Sentence denotes As a result, it has a REST-like API and supports the Hypertext Transfer Protocol (HTTP) create, read, update, and delete operations (i.e., GET, POST, PATCH, DELETE) and ODATA query options (select, expand, filter, orderby, top, skip) for data retrieval [38].
T91 6806-7007 Sentence denotes In addition to supporting HTTP, the OGC SensorThings API has an extension for supporting Message Queuing Telemetry Transport (MQTT) for the creation and real-time retrieval of sensor Observations [39].
T92 7008-7108 Sentence denotes The OGC STA enables interoperability for two layers: (1) Service interface, and (2) Data model [40].
T93 7109-7322 Sentence denotes With regards to the service interface layer, the STA defined a RESTful pattern, based on the OASIS OData standard, that allowed different STA services to exchange and filter entities defined by the STA data model.
T94 7323-7498 Sentence denotes As for the data model aspect, the STA data model was based on the International Organization for Standardization (ISO) and OGC Observation and Measurement standard model [41].
T95 7499-7629 Sentence denotes As a result, the data model can interoperate and is backward compatible with the OGC Sensor Observation Service (SOS) Web service.
T96 7630-7701 Sentence denotes The following UML diagram describes the entities of the STA data model.
T97 7702-7791 Sentence denotes In the OGC STA, every Thing can have zero or more locations in space or time ((Figure 2).
T98 7792-7852 Sentence denotes Furthermore, each Thing can have zero or more “Datastreams”.
T99 7853-7947 Sentence denotes A Datastream is a collection of “Observation” entities grouped by the same “ObservedProperty”.
T100 7948-8111 Sentence denotes An Observation is an event performed by a “Sensor”, that is a process producing a result with a value that estimates the ObservedProperty of a “FeatureofInterest”.
T101 8112-8198 Sentence denotes The OGC STA provided an interoperable framework with which to build the proposed IoCT.
T102 8199-8361 Sentence denotes STA’s O&M-based data model and query functions have been shown to work for a very wide range of IoT systems from simple weather stations to complex drone systems.
T103 8362-8498 Sentence denotes By using the OGC STA, we were able to develop an IoCT that interconnects heterogeneous IoT devices, data, and applications over the Web.
T104 8499-8698 Sentence denotes In order to deal with the pandemic’s fast-changing requirements, IoT developers need an established working architecture that will work not only for today, but also for future, COVID-19 applications.
T105 8699-8942 Sentence denotes In addition, healthcare applications are often near real-time and need to be scalable and performant, i.e., able to accommodate a very large number of devices that are sending high frequency data simultaneously without sacrificing performance.
T106 8943-9099 Sentence denotes The goal for the IoCT is to build an interoperable foundation for future expansion and integration using various existing and new COVID-19 IoT applications.
T107 9101-9105 Sentence denotes 2.3.
T108 9106-9146 Sentence denotes Interior Space Modelling Using IndoorGML
T109 9147-9204 Sentence denotes Indoor spaces differ from outdoor spaces in many aspects.
T110 9205-9357 Sentence denotes Basic concepts, data models, and standards of spatial information need to be redefined in order to meet the requirements of indoor spatial applications.
T111 9358-9471 Sentence denotes The proper representation of indoor spaces is a key issue for indoor spatial information modelling and analytics.
T112 9472-9626 Sentence denotes In recent years, the topic of 3D geospatial indoor modelling has been the focus of attention for location-based services and indoor navigation [42,43,44].
T113 9627-9937 Sentence denotes As the risk of COVID-19 virus transmission is higher in indoor environments, indoor space modelling is an important topic that facilitates the interoperability between different indoor and outdoor data collection methods and builds a consistent framework for collaborative research and development of the IoCT.
T114 9938-10073 Sentence denotes Aggregating different sensor observations for each room is essential for estimating the room’s COVID-19 risk and cleaning requirements.
T115 10074-10171 Sentence denotes The visualization of Interior Space Risk State is another task which requires interior modelling.
T116 10172-10399 Sentence denotes In order to represent the risk of infection and to identify which specific areas of a building require the most cleaning, the status of individual floors should be able to be viewed separately from other floors in the building.
T117 10400-10586 Sentence denotes The risk of infection for various parts of each floor should be represented in a map with different ways of representing the data that is necessary for determining the risk in each area.
T118 10587-10806 Sentence denotes Moreover, in order to visualize trajectories for contact tracing and to quickly identify the location of infection spreading behavior within an indoor space, the buildings should be visible as a 3D construct on the map.
T119 10807-11161 Sentence denotes Therefore, in order to analyze the IoCT multi-sensors system and visualize it in indoor scenarios, an interoperable 3D building modelling standard, such as the “CityGML” Level of Detail 4 [45], “OGC IndoorGML” [46], building construction standards (e.g., “Building Information Modelling” (BIM), or “Industry Foundation Classes” (IFC) [47]), is necessary.
T120 11162-11246 Sentence denotes The main concern for using those models is their fit and how often they are updated.
T121 11247-11350 Sentence denotes Construction features of indoor spaces are not a major focus of COVID-19 workplace reopening scenarios.
T122 11351-11488 Sentence denotes Instead, the aggregation of sensors in each room, and the connectivity between the rooms, is fundamental for risk assessment and tracing.
T123 11489-11551 Sentence denotes Thus, the OGC IndoorGML is used for the IoCT indoor modelling.
T124 11552-11749 Sentence denotes According to Ryoo et al. [43], the OGC IndoorGML can be used more effectively than CityGML or any other geometric representations of space for analyzing the trajectories of people inside buildings.
T125 11750-11888 Sentence denotes This allows for more accurate appraisal of the types of intersection of trajectories, contact, and exposure for infection risk evaluation.
T126 11889-12143 Sentence denotes Applications such as cleaning risk assessments for COVID-19 workplace reopenings that need to operate efficiently together with indoor scales, various sensors, and objects that are moving and changing over time would benefit from using the OGC IndoorGML.
T127 12144-12302 Sentence denotes 3D geometry can be included in an IndoorGML document, and the overlap with other standards (e.g., OGC CityGML) can be addressed by adding external references.
T128 12303-12436 Sentence denotes There were no specific standards in the field of indoor geospatial modelling until the OGC standard IndoorGML was introduced in 2014.
T129 12437-12598 Sentence denotes The OGC IndoorGML intentionally focused on modelling indoor spaces using connected dual graphs for navigation purposes whilst considering various semantics [46].
T130 12599-12731 Sentence denotes OGC IndoorGML standard specifies an open data model and Extensible Markup Language (XML) schema for indoor spatial information [46].
T131 12732-12870 Sentence denotes Indoor space is comprised of connected constructs such as rooms, corridors, stairs, and elevators, all of which can be considered “Cells”.
T132 12871-13059 Sentence denotes This sets it apart from other standards in the field of 3D modelling, such as CityGML or IFC, as they model the building features (e.g., walls, windows) instead of the indoor space itself.
T133 13060-13134 Sentence denotes They also do not consider the connectivity and semantics of indoor spaces.
T134 13135-13310 Sentence denotes As shown in Figure 3, the nodes of the IndoorGML graph in this paper are considered the smallest organizational or structural units for the building and are called Cells [19].
T135 13311-13426 Sentence denotes Every Cell has an identifier (e.g., room number) and a location (x, y, z) to provide more precise location details.
T136 13427-13674 Sentence denotes Cells are connected and have a common boundary with other cells but do not overlap with them. “Geometric” features and “Topological” relationships, such as adjacency and connectivity, amongst indoor cells can be defined by an IndoorGML graph [48].
T137 13675-13895 Sentence denotes The topological relationships in IndoorGML are explicitly described using the xlink concept of XML provided by Geography Markup Language (GML) and the referencing is realized with the use of href attributes (xlink:href).
T138 13896-14063 Sentence denotes Semantics are also an important characteristic of the Cells in the IndoorGML. “Semantics” allow us to define cells which can be important for cleaning risk assessment.
T139 14064-14175 Sentence denotes For example, the most commonly used areas are public rooms, corridors, and doors, and thus present higher risk.
T140 14176-14297 Sentence denotes For this paper, an indoor space is represented as a topographic cellular space comprised of rooms, corridors, and stairs.
T141 14298-14405 Sentence denotes At the same time, it is also represented as different cellular spaces with beacon or camera coverage Cells.
T142 14406-14581 Sentence denotes Each semantic interpretation layer creates a different indoor model, and each model forms a separate dual graph layer (e.g., connectivity, sensor) for the same cellular space.
T143 14582-14704 Sentence denotes This multi-layered space model (Figure 3), is an aggregation of the space layers and inter-layer connections or relations.
T144 14705-14793 Sentence denotes The Indoor GML for the implementation of the structure space model is shown in Figure 4.