dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The data supporting the findings of this study are available within the paper and its supplementary information files and can be obtained from the corresponding authors upon reasonable request. Three-dimensional cryo-EM density maps of the SPP1 procapsid I, SPP1 procapsid II and SPP1 mature capsids have been deposited in the Electron Microscopy Data Bank under the accession numbers EMD-4717 [https://www. ebi. ac. uk/pdbe/entry/emdb/EMD-4717], EMD-10002 [https://www. ebi. ac. uk/pdbe/entry/ emdb/EMD-10002], EMD-4716 [https://www. ebi. ac. uk/pdbe/entry/emdb/EMD-4716]. Atomic coordinates of the asymmetric units for each capsid state have been deposited in the RCSB Protein Data Bank under the accession codes 6R3B [https://www. ebi. ac. uk/ pdbe/entry/pdb/6R3B], 6RTL [https://www.
(2017)  to identify the rows and thanks to the knowledge of microplot dimensions and number of rows. Since the rough classification based on the thresholding of the Excess Green Index  (Woebbecke et al. , 1995)  image was sufficient to identify the rows (Fig. 2 ), but showed limitations to accurately estimate GF  (Jay et al. , 2015; Lati et al. , 2013) , a SVM classifier was trained to classify the RGB images. A database of 2500 soil and vegetation pixels was built, encompassing a large variability in crop state and illumination conditions. This database was randomly split into 2000 pixels used for training and 500 pixels used for validation. The trained SVM classifier showed an overall accuracy better than 95% on the validation set and was then applied to each sub-image to derive the corresponding GF.
Acknowledgments This work was supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" programs through AKER  ( ANR-11-BTBR-0007 ) and  PHENOME  ( ANR-11-INBS-012 ). Thanks a lot to  Mathieu Hemmerlé  for his help in the experiments, and the three reviewers for their valuable comment and recommendations.
The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis.
Typical success rates of 0. 9 have been achieved by experienced teams. The delivery of data from gliders in real time has become routine, with main glider data assembly centers in Europe (EGO/Coriolis 2  ), Australia (IMOS 3  ), and the USA (IOOS 4  ). Underwater gliders play a special role in observing systems designed to support regional modeling activities, because gliders generate many profiles at controlled locations. The potential for glider development was recognized quite early on, leading to the influential Autonomous Ocean Sampling Network  (Ramp et al. , 2009). Glider data are often used with models for two purposes: (1) verification, meaning to evaluate model output for fidelity to the ocean; and/or, (2) assimilation, the use of data to constrain model output  (Edwards et al. , 2015; Hayes et al.
Data Management OceanGliders targets high-impact, societally-relevant, sciencebased observing through a number of initial scientific Task Teams (OceanGliders TT). They are developed in the following section, but one Task Team in particular relates to the smooth, coordinated functioning of each TT with each other and with the rest of the GOOS: the data management TT. This team aims to address the needs of long-term observation aspects of data management, benefiting the wider community, supporting and encouraging scientists designing and executing process studies, as well as engineers developing new gliders, sensors, and computing   (Pearlman et al. , 2019)  for data collection, processing, and quality control. Data management requires metadata and its description, storage, and access. One of the benefits of coordination will be improved and sustained quality control of glider data.
Network Monitoring and Data Dissemination One main goal for glider operators is to make data publicly available and in particular to publish data in near-real time on the GTS (Global Telecommunication System) and in CF (Climate and Forecast) compliant formats for operational services. They provide their metadata and data to a Data Assembly Center (DAC) in charge of the data management and linked to a Global Data Assembly Center (GDAC) for further dissemination and archive. Three de facto GDACs are currently operating: Australia (IMOS), Europe and partners (EGO/Coriolis), and the United States (IOOS). Each GDAC has adopted similar strategies and conventions: CF-compliant NetCDF observation file formats can be uploaded by operators, and public sites and tools are provided for downloading and visualization.
Further development and sharing of best practices on data and metadata management are key for the OceanGliders Data Management Task Team. To that end, there is now a new central directory at www. oceanbestpractices. net, hosted by IODE, for OceanGliders. Glider-specific tools have been developed at the GDAC and regional/institutional (or DAC) level to complement the other elements of the GOOS. The unique trajectory character of glider data, and of the wide range of metadata can cause these tools to be quite complex. Even the familiar concepts of "cruise, " "mission, " "transect, " and "profile" do not adequately describe the nature of glider flight and programmable behavior in real time. There are too many to exhaustively list here, but notable examples include: GliderScope 6   (Hanson et al.
Emerging Requirements Glider data management will need to encompass developments within the glider networks, the GOOS and outside of the oceanographic domain in order to anticipate future changes in global data management. The implementation of Findable-Accessible-Interoperable-Reusable (FAIR) data principles is a common theme in environmental data management and will place demands for development on glider data infrastructure  (Tanhua et al. , 2019). The integration of data from different networks within the GOOS and the implementation of new Essential Ocean Variables (EOVs) are also emerging as requirements. Furthermore, additional demands on data management will emerge with such as automated piloting (e. g. ,  Chang et al. , 2015; Smedstad et al. , 2015)  and operational glider network monitoring technologies.
ADDRESSING GLOBAL OBSERVING NEEDS The OceanGliders group met to discuss possible areas of focus and beyond the central need for improved data management, identified three key areas of focus for the developing program. These areas of interest were organized into Task Teams (TT) whose goals are to address the societal needs for ocean data and to entrain the community into discussions around the role of gliders in meeting these needs. It is expected that the mission-based TTs will organically develop by organizing the different initiatives into integrated and coordinated global efforts.
Data flow from individual glider operators to the GTS was coordinated through the U. S. IOOS Glider Data Assembly Center (DAC). The system was tested in September when 3 hurricanes were simultaneously present in the North Atlantic, each with gliders deployed in their path. This included Hurricane Florence, a category 4 storm at its peak that impacted the eastern seaboard of the US (Figure  8 ). The glider data transmitted over the GTS were used as input to the operational Ocean Heat Content maps that were used to help with NHC forecast intensity decisions. OceanGliders supports the development of sustained glider observations to address hurricane issues worldwide as well as additional ones related to extra-tropical storms. Extra-tropical storms, also referred to as mid-latitude cyclones, are large scale (>1,000 km) low pressure weather systems that occur in middle and high latitudes and are associated with frontal systems.
The wind speeds of these storms can be as high as those associated with tropical storms but their impacts last longer because of their greater spatial extent. Due to the large-scale features, extratropical storms are well-represented in atmospheric models. Hence, ocean gliders have mainly contributed to understanding the impacts of the storms on the ocean and coastal environments, particularly in terms of changes to the heat content (e. g. , rapid cooling), its feedback on storm intensity, sediment resuspension and transport processes, and ecosystem response. Ocean gliders are complementary to other storm sampling systems in their ability to relatively rapidly profile the upper ocean and transmit data to land even during the most severe storm conditions  (Domingues et al. , 2019). They provide unique datasets for studies of rapid upper ocean evolution and high-value profile data for assimilation in both operational forecast and research models before, during and after storms.
, 2017). Underwater gliders do sample the vertical structure of the ocean in an unprecedented way, with high resolution along the horizontal over long periods of time. Gliders also transmit the observational data in near real-time. This remote access to observational data that resolves the (sub)mesoscale can improve forecasting the ocean dynamics, biogeochemistry and ecosystem. The glider data is a perfect match for assimilation in regional/coastal numerical models, providing ocean state estimates at small scales with increased accuracy benefiting societal applications. Gliders can map the subsurface ocean at high resolution and provide powerful tools for monitoring previously inaccessible ecological processes. OceanGliders promotes and supports all physical, biogeochemical and biological studies focusing on these small-scale processes and encourages long-term continuation of these studies.
They can sample in extreme conditions and to increasing depths, from surface to 6,000 m depth. Gliders require pilots; however navigation is increasingly automated as a result of advances in platform reliability, community experience and piloting support tools. Glider observations require careful data processing protocols, an area that is being actively resolved, with tools and services emerging, and standard products from several deployments (e. g. , gridded sections, geostrophic currents, etc. ) that could be more accessible to non-expert users, many of which are from the OceanGliders community. Although gliders are relatively "slow" samplers, this is not an impediment to providing sampling capability at key space and time scales for the global observing system. Gliders are uniquely poised to deliver sustained and responsive observations to the GOOS in the following areas: • Connecting coast to open ocean: key for monitoring the regional effects of climate variability, and of processes (circulation, currents, upwelling) that have an impact on regional ecosystems.
VISION Our vision is for a mature sustained global glider observing network by 2030. It will not only support regional, sustained operational deployment of gliders serving the present societal needs around operational services, ocean health and climate, but also solely allow new ocean observing applications in this framework. The outstanding capacity of gliders to play a role 14 https://sustainabledevelopment. un. org/?menu=1300 as an agent of integration, across scales, from the coast to the open ocean, and from physics to biology, needs to be used to enhance the GOOS, integrating with its other components (in situ and satellites). These global glider operations will likely have different schedules of operation, carry different sensors, and serve different needs but will have a shared support system through the OceanGliders program that will allow them to work together efficiently, to govern and support the system, coordinating global glider operations and ensuring that the needs of society for ocean data are best met.
OceanGliders will support global standards and best practices to ensure that the operations and the data delivered can be monitored at the global scale. Improved data interfaces and standardized data will ensure quality-controlled data are easily found and effectively used. By 2030 one should be able to effortlessly, perhaps unknowingly, find and acquire qualitycontrolled physical, biogeochemical and biological data from gliders alongside an already huge range of earth observations and use them to address scientific, commercial, or policy initiatives. Attaining our vision would ensure that the value of observations to society will never be lost, indeed, will increase over time as they are used and reused and in new ways not originally imagined. Here, we have identified three key areas for the OceanGliders program to focus on: Boundary Currents, Storms and Water Transformation.
ACKNOWLEDGMENTS The editorial team would like to recognize the support of the global glider community to this paper. Our requests for data and information were met with enthusiasm and welcome contributions from around the globe, clearly demonstrating to us a point made in this paper that there are many active and dedicated teams of glider operators and users. We should also acknowledge the support that OceanGliders has received from the WMO/IOC JCOMM-OCG and JCOMMOPS that have allowed this program to develop, encouraging us to articulate a vision for the role of gliders in the GOOS. We acknowledge support from the EU Horizon 2020 AtlantOS project funded under grant agreement No. 633211 and gratefully acknowledge the many agencies and programs that have supported underwater gliders: AlterEco, ANR, CFI, CIGOM, CLASS Ellet Array, CNES, CNRS/INSU, CONACyT, CSIRO, DEFRA, DFG/SFB-754, DFO, DGA, DSTL, ERC, FCO, FP7, and H2020 Europen Commission, HIMIOFoTS, Ifremer, IMOS, IMS, IOOS, IPEV, IRD, Israel MOST, JSPS, MEOPAR, NASA, NAVOCEANO (Navy), NERC, NFR, NJDEP, NOAA, NRC, NRL, NSF, NSERC, ONR, OSNAP, Taiwan MOST, SANAP-NRF, SENER, SIMS, Shell Exploration and Production Company, Sorbonne Université, SSB, UKRI, UNSW, Vettleson, Wallenberg Academy Fellowship, and WWF.
Materials and Methods This retrospective, Health Insurance Portability and Accountability Act-compliant study was approved by the Institutional Review Board of our hospital, and a waiver of informed consent was obtained. [16]. We retrospectively searched the electronic medical record database at our Institution, a tertiary academic center, for consecutive patients operated because of bowel obstruction and imaged with CT between March 1, 2013, and October 31, 2017. The decision to treat patients with bowel obstruction was made as a consensus by a panel composed of an emergency physician, an abdominal surgeon, and a radiologist. Our search yielded an initial target population of 76 consecutive patients who were considered eligible for inclusion in the study. Subjects were excluded for (a) lack of intravenous contrast agent administration prior to surgery (n = 55) and (b) time from CT exam to surgery greater than 2 months (n = 3).
CT Imaging Analysis. CT exams were reviewed by two radiologists first separately and then in consensus, using a clinical picture archiving and communication system (AGFA Impax, Mortsel, Belgium). The radiologists were blinded to the purpose and the design of the study, the clinical history, clinical report, and surgery findings. To minimize recall bias, all technical and personal data were removed from the images and datasets were randomized prior to all reading sessions. Two reading sessions separated by 4 weeks were performed-one session showing the unenhanced CT images alone and the other session showing both the unenhanced and the contrast-enhanced images. During each reading session, readers were asked to identify the etiology and the level of the obstruction (i. e. , small or large bowel obstruction).
The order by which unenhanced only and unenhanced plus enhanced images were presented to the readers during the two reading sessions was randomly assigned. The surgical diagnosis of the etiology was considered the reference standard. 2. 4. Statistical Analysis. Data were collected in an Excel database (Microsoft Corporation, Redmond, WA, USA), and statistical analysis was performed using GraphPad (San Diego, CA, USA) software. The sensitivity and specificity of unenhanced CT and enhanced CT were calculated. Comparisons between groups were performed using the McNemar test, Fisher's exact test, or chi-square test, as appropriate. A P value < 0. 05 was considered statistically significant. Cohen's kappa statistics were applied for measuring agreement between two readers.
EFSA was asked to update the 2015 EFSA risk assessment on Xylella fastidiosa for the territory of the EU. In particular, EFSA was asked to focus on potential establishment, short-and long-range spread, the length of the asymptomatic period, the impact of X. fastidiosa and an update on risk reduction options. EFSA was asked to take into account the different subspecies and Sequence Types of X. fastidiosa. This was attempted throughout the scientific opinion but several issues with data availability meant that this could only be partially achieved. Models for risk of establishment showed most of the EU territory may be potentially suitable for X. fastidiosa although southern EU is most at risk. Differences in estimated areas of potential establishment were evident among X. fastidiosa subspecies, particularly X.
on the design of effective detection and control strategies given that the pathogen may have spread during the asymptomatic period. A literature review was undertaken using the EFSA Update of the Xylella spp. host plant database (EFSA, 2018a) which was filtered to include all papers which included artificial inoculation and which included information on the time of first symptom expression and which quantified the number of individuals. The data from these studies were categorised by X. fastidiosa subspecies and host plant. The absence of data on the time of symptom development for all individual hosts led to the application of two modelling approaches (parametric and non-parametric) to determine the distribution of the asymptomatic period for each combination of host-subspecies. The asymptomatic period of X.
Short-and long-range spread modelling The probability of spread was assessed using two distinct modelling approaches due to the epidemiological processes that occur at different spatial scales and the computational burden that would be encountered by capturing these processes in a single epidemiological model with sufficient detail. Models were thus developed for short-range spread (e. g. within an orchard) and long-range spread (e. g. regional scale) separately. The short-range spread model is a fully mechanistic epidemiological model describing the spatialtemporal pattern of disease spread and assessing the effectiveness of eradication measures for new outbreaks in a free area. Based on available data from spread of X. fastidiosa in olive orchards in Apulia and the results of an EFSA expert knowledge elicitation (EKE), the model was parametrised to describe the epidemics of the disease at high spatial resolution in homogeneous host plant orchards characterised by a regular planting distance.
Thanks to the structure and the flexibility of the epidemiological model a wide range of conditions can be explored parameterising the model to account for different scenarios to support risk assessment and the design of emergency plans. The long-range spread model describes the spread of a pest through a landscape of susceptible hosts and was parameterised primarily based on X. fastidiosa subsp. pauca spread in Italy. The model simulates spread by coupling a generic epidemiological model with a dispersal-kernel model, similar to approaches used to successfully model spread for a range of different pest types from fungal to vector-borne disease. Available data on the epidemiology and spread of X. fastidiosa in Apulia, Italy, were used to parameterise the model but the influence of changing the epidemiological and landscape parameters were also assessed.
Summary of the emergency phytosanitary measures relevant for spread analysis Decision (EU) 2015/789 sets up the EU emergency measures to prevent the introduction into and the spread within the Union EU of X. fastidiosa. Since its adoption, the EU Decision has been updated on several occasions based on new scientific and technical developments. The regulated plants for planting susceptible to X. fastidiosa are defined as 'Specified plants', while some of them as 'Host plants'. Specified plants, including host plants, are all plants for planting listed in Annex I of the EU Decision (i. e. plant species known to be susceptible X. fastidiosa worldwide). Host plants are instead plants for planting belonging to genera and species listed in the Commission database of host plants having been found to be susceptible to X.
, 2017)  and pest information and distribution was obtained from the update of the Xylella spp. host plant database  (EFSA, 2018a)  and from local data sets provided by National Plant Protection Organizations with X. fastidiosa outbreaks in the EU (Osservatorio Fitosanitario Regione Puglia, Italy; Servicio de Sanidad Vegetal, Generalitat Valenciana, Spain; Bureau de la Sant e des V eg etaux, Minist ere de l'Agriculture et de l'Alimentation, France). The length of the asymptomatic period was assessed using a literature review of the EFSA update of the Xylella spp. host plant database (EFSA, 2018a) which was filtered to include all papers which included artificial inoculation and which included information on the time of first symptom expression and which quantified the number of individuals.
The data from these studies were categorised by X. fastidiosa subspecies and host plant. The absence of data on the time of symptom development for all individual hosts led to the application of two modelling approaches (parametric and non-parametric) to determine the distribution of the asymptomatic period for each combination of host-subspecies. It should be noted that in this Scientific Opinion SRS and long-range spread were analysed separately. This was due to the differing biological mechanisms driving spread at these two spatial scales and the impracticalities for the necessary level of detail, and associated computational burden, to be handled by a single model capturing processes at both spatial scales. The 'short-range' model essentially models spread processes occurring within a large orchard (i.
Data and methodology Pest information and distribution was obtained from the EFSA Update of the Xylella spp. host plant database (EFSA, 2018a) and from the local data sets provided by the Regional Authorities and/or National Plant Protection Organizations of Italy, France and Spain (i. e. Osservatorio Fitosanitario Regione Puglia, Italy; Servicio de Sanidad Vegetal, Generalitat Valenciana, Spain; Conselleria de Medi Ambient, Agricultura i Pesca del Govern de les Illes Balears, Spain; Bureau de la Sant e des V eg etaux, Minist ere de l'Agriculture et de l'Alimentation, France). The potential for establishment of X. fastidiosa in the EU was modelled using two different approaches: identification of climate types suitable for X. fastidiosa occurrence based on the K € oppen-Geiger climate classification  (Peel et al.