dataesr/scientific-paragraphs-categorization huggingface dataset:
Underwater video and still images are routinely collected by ROVs during offshore energy industry operations as part of site surveys and inspections of infrastructure and are usually retained indefinitely by operating companies. Although not intended for scientific research, industry video and images can be repurposed to provide data on a range of biological, ecological and oceanographic variables in areas that are challenging for independent research  (Gates et al. , 2017). Industry video and images have already provided information on the distribution of threatened species  (Gass and Roberts, 2006) , productivity of offshore ecosystems for commercial fishes  (McLean et al. , 2017) , and anthropogenic impacts on deep-water fauna  (Jones et al. , 2012). Existing media archives could be used to investigate longerterm processes occurring in ocean ecosystems.
Collectively, the submarine cabled observatories span ocean depths from 80 to 2900 m, including approximately 1700 km of high power and high bandwidth fiber optic cables, 14 subsea terminals, and more than 30 secondary junction boxes at key experimental sites. This infrastructure provides power and communication to hundreds of seafloor instruments and state-of-the-art moorings with instrumented profilers streaming data to shore at the speed of light. The continuous, real-time, two-way communication can capture interannual and interdecadal variability, as well as document, quantify, and respond to NE Pacific transient events. The observatories also span biogeochemical and physical environments that include a continental margin strongly influenced by upwelling and expanding hypoxia (e. g. , De  Leo et al.
The Kaimei Trench Expedition (KATE), conducted along the Izu-Ogasawara and northern Mariana trenches in 2016/2017, sampled to 9760 m for practical salinity, absolute salinity, dissolved oxygen, carbonate system parameters, carbon and oxygen isotopes, and tracers of sedimentary organic and microbial nitrogen metabolism  (Kawagucci et al. , 2018). KATE data will be merged with historical hydrographic data and shared on the JAMSTEC website. Hadal KATE samples analyzed for microbial diversity and environmental metagenomics provide a basis for future integrated biological studies.
, 2018). As mentioned in Section " Ocean Bottom Pressure, " OBP is in process to become an EOV. Long-range acoustic "GPS" carried out by an underwaterarray of acoustic transducers can provide accurate, long-term, high temporal resolution float tracking (velocity EOV), longrange AUV/glider navigation (including under ice), and acoustic tomography (temperature EOV) if the platforms are equipped with acoustic receivers. The same acoustic receivers can carry out wind and rain measurements and monitor marine mammals and "soundscapes" (ocean sound EOV). Cabled (ATOC) and moored scenarios have used this mature technology for decades, with no significant impact on marine mammals. The high signal to noise ratio, no calibration, quadratic growth in data with number of instruments, and sampling at the speed of sound collectively enable high accuracy and high temporal resolution of basin-scale heat content.
Super-Deep Hydrographic Measurements Shipboard hydrographic measurements conducted at hadal depths for climate studies require the highest possible quality measurements because of the low signal-to-noise ratio. In the past, water sampling in ocean trenches used bottles equipped with reversing thermometers, or CTD observations obtained without water samples to correct the CTD data. Today, fullocean-depth (11,000 m) reference quality measurements are possible with modern conductivity-temperature-depth/oxygen (CTD/O 2 ) sensors and water sampling equipment through the use of synthetic (polyarylate) fiber coaxial cable  (Kawagucci et al. , 2018). Acquiring data of the highest possible quality requires in situ calibration of CTD/O 2 sensors. A deep-ocean standard thermometer realizes SI traceable measurements with an uncertainty of 0.
These range from simple data servers containing. csv files, to ERDDAP-based systems 9  with a stable, versioned file structure such as the ones used by the NOAA IOOS 10  , to compute-on-demand systems like the one used by the OOI 11. Further, anyone familiar with attempting to locate or convert data records that exist only in a large collection of mysterious floppy disks or tape reels is well aware of the problem of changing data storage and data delivery technology. The era of "big data" has only accelerated this issue, as more different types of data are collected, and large-volume data types such as video are increasing, that require long-term storage in a stable and accessible format. Future efforts should go into processing of data on cloud servers, using open-source software packages to enable interactive and collaborative data discovery, exploration, and manipulation (e.
Standards In order for data systems to work together today, and continue to work in the future, there must be community acceptance of standardized rules for complete, consistent metadata (e. g. , Climate and Forecast (CF), International Standards Organization (ISO), or Federal Geographic Data Committee (FGDC) standards) and the use of self-describing data formats (e. g. , NetCDF) to allow synthesis of different types of data into a single archive. Archiving in national databases (e. g. , AODN 15  ) allows for both long-term storage and accessibility, but there is a pressing need for standardized metrics, implementation of common nomenclature within and across fields, improved provenance tracking, and the application of real-time quality control. Some fields and sub-fields have settled on one or two standards (e.
Registries and Coordinated Discovery To reduce the challenges of finding deep-ocean data, particularly to non-experts, a concerted effort is needed by the deep observatory community to converge on a small number of widely used registries for data discovery, and to support standards and APIs to allow searches in a central location to query across multiple repositories. Efforts such as GEOSS Portal 16  and EMODnet 17  show the promise of this approach. To bring all ocean data toward FAIR standards will require substantial capacity development involving all stakeholders. Capacity building training programs related to ocean observing already exist and could offer opportunities to build deep oceandedicated-training under these umbrellas. (see Section "Capacity Building" below).
Community Engagement Developing a strategy for deep-ocean observation requires attracting more students, early career scientists, and the general public in related science and technology fields. This can be achieved by engaging them with deep-sea research challenges, raising awareness of deep-sea exploration and discoveries, and emphasizing the need to fill gaps in ocean/climate interaction processes, seafloor geological processes, and deep-ecosystem functions and dynamics. Citizen science has proven valuable for annotations of deep sea-imagery. Live broadcasting of scientific missions, known as telepresence, (e. g. , ROV dives) or imagery and sound recordings from cabled observatories (e. g. , Neptune Canada 18  ) have provided unprecedented public access and engagement. There is a need to identify new engagement opportunities in data management, technology innovation, and transfer of best practices, emphasizing broad disciplinary scope and interdisciplinary science.
Summary and Recommendations A DOOS is conceptualized in Figure  5  and is put forward in the following recommendations. 1. Agree upon a set of EOVs that will capture current baseline conditions and future changes in the deep ocean, its ecosystems, and the services they provide. Work with GOOS Expert Panels and the scientific community to standardize, mature and operationalize these EOVs, and advance the availability, reliability, and longevity of autonomous systems (sensors and platforms) that can make these measurements. Emphasize approaches that may be scaled up to provide nearglobal coverage. 2. Develop a means to identify new deep-ocean data needed to address key scientific and societal needs. Develop new communication tools to inform the science community and convey deep-ocean observing needs and advances across disciplines, regions, sectors and jurisdictions.
5. Work to integrate deep-ocean observing across disciplines, share skills and best practices for the use of existing instrumentation (e. g. , robotics, sensors) and for the development of new equipment (e. g. , through demonstration projects). 6. Create collaborations across the deep components of existing regional observing networks such as those in the MBON, OOI, ONC, EMSO 2020, AtlantOS, SOOS, and FRAM and integrate these with global programs (e. g. , ARGO, deep ARGO, BGC Argo, OceanSITES, GO-SHIP). 7. Build deep-ocean observing capacity by expanding existing training programs to build "deep-ocean" dedicated training under these umbrellas, while simultaneously integrating capacity building seamlessly into existing and future observing programs. 8. Facilitate use of deep-observing data by a broad range of stakeholders, particularly in the economic and international policy sectors, to achieve a healthy ocean and sustainable blue economy.
ACKNOWLEDGMENTS We thank the many people who contributed to preparation of the  DOOS Consultative Draft , the 2016 DOOS Scoping Workshop and Report, and the  DOOS Science Implementation Guide. All of their efforts and energy contributed to the ideas in this manuscript. We also thank  NASA  for its support of  Deep Ocean Observing  as critical to the planet,  Guillermo Mendoza  and  Leslie Smith  for assistance with manuscript preparation, formatting and submission, and two reviewers and the editor for their insightful comments that improved the manuscript.
FUNDING Preparation of this manuscript was supported by  NNX16AJ87A (NASA) Consortium for Ocean Leadership, Sub-Award  No. SA16-33. AC was supported by  FCT-Investigador contract  ( IF/00029/2014/CP1230/CT0002 ). LL was supported by a  NASA  subaward from the  Consortium for Ocean Leadership. AG and HR were supported by  Horizon 2020 ,  EU Project  " EMSO Link " grant ID  731036. AG, BB, DJ, and HR contributions were supported by the  UK Natural Environment Research Council Climate Linked Atlantic Section Science project  ( NE/R015953/1 ). JP was funded by the  Swiss Network for International Studies , and the  Swiss National Science Foundation  (grant  31003A_179125 ). TM was supported by  Program Investigador FCT  ( IF/01194/2013 ), IFCT  Exploratory  Project ( IF/01194/2013/CP1199/CT0002 ),  H2020   Atlas  project ( GA 678760 ), and the  H2020   MERCES  project ( GA 689518 ).
Purpose: The objectives of the International epidemiology Databases to Evaluate AIDS (IeDEA) are to (i) evaluate the delivery of combination antiretroviral therapy (ART) in children, adolescents and adults in sub-Saharan Africa, (ii) to describe ART regimen effectiveness, durability and tolerability, (iii) to examine HIV-related comorbidities and co-infections, and (iv) to examine the pregnancy-and HIV-related outcomes of women on ART and their infants exposed to HIV or antiretroviral therapy in utero or via breastmilk. Participants: IeDEA is organized in four regions (Central, East, Southern and West Africa), with 240 treatment and care sites, six data centres at African, European and US universities, and almost 1. 4 million children, adolescents and adult people living with HIV (PLWHIV) enrolled.
Progress towards these goals has been substantial, especially in the most affected regions of Eastern and Southern Africa. The latest estimates suggested that the two regions are on track and had achieved 76%, 79% and 83% of the 90-90-90 targets, respectively in 2017  [4]. West and Central Africa face difficulties with the first 90% target and only an estimated 42% of PLWHIV in these regions were aware of their status  [4]. Established more than ten years ago by the National Institutes of Health (NIH), the International epidemiology Databases to Evaluate AIDS (IeDEA) are a global cohort collaboration that collects HIV/AIDS data from HIV care and treatment programs, including in sub-Saharan Africa. The regional IeDEA data centres consolidate, curate and analyse data to evaluate the outcomes of people living with HIV/AIDS and monitor progress.
Data collection at individual and site level Since its inception, IeDEA has collected routine clinical data of PLWHIV followed under treatment, which includes socio-demographic characteristics, clinical outcomes, opportunistic events, treatment regimens, clinic visits and laboratory measurements. More recently, the IeDEA network has developed a Data Exchange Standard (DES) protocol (see www. iedeades. org) with 25 data tables, which include a total of 228 unique variables  (36 compulsory and 192  additional variables) and within-region unique patient research identifiers. Standardized data collection is supported by eight codebooks, including the Anatomical Therapeutic Chemical (ATC) classification for drugs and lists with codes for reasons for stopping treatment, for dropping out of the cohort, for mode of HIV infection, country, type and site of comorbidities, laboratory measurements and units of measurements and type of viral load assay.
The collection, management and sharing of data is facilitated by the Harmonist toolkit, a software and standards package that supports research projects through the Research Electronic Data Capture (REDCap) system  [6]. In recent years, site assessments and site surveys have been conducted on a regular basis to collect up-to-date information related to available clinical service and care models in the participating facilities. For example, a study compared the characteristics and comprehensiveness of adult HIV care and treatment programmes in sub-Saharan Africa with programmes in the Americas and Asia-Pacific region  [7]. Other studies examined the management of mental health and substance use disorders  [8] , or the diagnostic and screening practices for (drug resistant) tuberculosis in adult and paediatric patients  [9] [10] [11].
Contributors Competing interests: None declared. Ethics approval: Ethics Committee of the Canton of Bern, Switzerland. Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: Investigators wishing to work with IeDEA data should contact the regional data centres (see www. iedea. org) and send a concept sheet for the analyses they are interested in performing and the variables that would be required. See www. iedeades. org for list of variables. Those wishing to work with the data must sign a data-use agreement. Open Access: This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4. 0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
Box: Strengths and limitations of this study • An important strength of the IeDEA cohort collaboration in sub-Saharan Africa is its large size, which allows analyses of outcomes of antiretroviral therapy (ART) in children, adolescents and pregnant and postpartum women, across diverse settings. • The data reflect routine care across a wide range of real-world settings during the scale up of ART in sub-Saharan Africa and thus provide a valuable platform to conduct operational and clinical research and to study temporal trends and the impact of changes in guidelines and other interventions. • The development of a standardised Data Exchange Standard protocol has contributed to increase data quality, and data have been enriched by linkage to cancer registries, vital registries and administrative databases.
: FC, PMG, and ME conceptualised the study. FC and CHDO performed statistical analyses. FC and ME wrote the first draft of the paper. CHDO, KA, AJ, EB, SB, FD, MAD, SND, KM, BSM, DN, KWK, CY, PMG and ME contributed to interpreting the data and to the writing and revising of the manuscript. Funding: The International Epidemiology Databases to Evaluate AIDS (IeDEA) is supported by the U. S. National Institutes of Health's National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, the National Institute on Drug Abuse and Alcoholism, the National Institute of Diabetes and Digestive and Kidney Diseases, the Fogarty International Center, the National Library of Medicine, and the Office of the Director: Central Africa, U01AI096299; East Africa, U01AI069911; Southern Africa, U01AI069924; West Africa, U01AI069919.
Boundaries and internal CTCF sites act cooperatively to form TADs. To investigate the role of CTCF in maintaining the TAD structure at this locus, we generated mice with a series of alleles in which the four CTCF sites at the TAD boundary were deleted, followed by a consecutive deletion of five further sites within the TADs. cHi-C was performed from E12. 5 limb buds to visualize the effects on TAD architecture and to quantify the contacts within the TADs (intra-TAD) and between the TADs (inter-TAD). We also produced virtual 4C interaction profiles from the cHi-C data to assess contact changes of Sox9 and Kcnj2 in these alleles (see Methods). Deletion of the four CTCF sites that constitute the Sox9-Kcnj2 TAD boundary (ΔBor) resulted in a moderate increase of contacts between TADs, but the two TADs remained largely separate (Fig.
Acknowledgements This study was supported by grants from the  Deutsche Forschungsgemeinschaft  ( KR3985/7-3  and  MU 880/16-1  to S. M. ). We thank  J. Fiedler  and  K. Macura  (transgenic unit) and  M. Hochradel  (sequencing core facility) of the MPIMG,  A. Stiege  and  U. Fischer  for help with cloning and cell culture, and  N. Brieske  for in situ hybridizations. We also thank  M. Robson ,  F. Martinez-Real  and all members of the  Mundlos laboratory  for input and discussions.
Database search Pest information, on hosts and distribution, was retrieved from the European and Mediterranean Plan Protection Organization (EPPO) Global Database (EPPO, 2019) and relevant publications. Data about the import of commodity types that could potentially provide a pathway for the pest to enter the EU and about the area of hosts grown in the EU were obtained from EUROSTAT (Statistical Office of the European Communities). The Europhyt database was consulted for pest-specific notifications on interceptions and outbreaks. Europhyt is a web-based network run by the Directorate General for Health and Food Safety (DG SANTE) of the European Commission, and is a subproject of PHYSAN (Phyto-Sanitary Controls) specifically concerned with plant health information. The Europhyt database manages notifications of interceptions of plants or plant products that do not comply with EU legislation, as well as notifications of plant pests detected in the territory of the MS and the phytosanitary measures taken to eradicate or avoid their spread.
The following potential pathway of entry of C. sepedonicus into the EU is currently not regulated: • infected host plant debris in soil adhering to agricultural machinery and implements, footwear and vehicles originating in infested third countries. As of December 2018, there were 183 interceptions of C. sepedonicus in the Europhyt database (Figure  2 ), all of them on S. tuberosum. About 40% of the interceptions had the Netherlands as destination (most of them until 2000). About 50% of the interceptions originated in Germany (most of them until 2000), with another 30% of the interceptions originating in Poland (most of them since 2004). Is the pest able to enter into the EU territory? If yes, identify and list the pathways! Yes, mainly by the movement of infected planting material, such as seed tubers.
Impacts Direct losses due to infection by C. sepedonicus are due to the destruction of the vascular tissue, wilting of the plant, and rotting of the tubers. These losses can be very high, with losses of 41-56% reported in inoculated studies  (Hukkanen et al. , 2005). Historic losses in the absence of regulatory control measures have been considerable  (EPPO, 1997; Van der Wolf et al. , 2005a). There are many (  157 ) recent (all since 2012) reports of C. sepedonicus (all, when the information is available, on S. tuberosum) in the Europhyt database of plant disease outbreaks, with about 60% reports from Lithuania and a further 20% from Romania. About 40% of the reports were made in 2018, with a further 27% in 2016 and another 15% in 2015. Is the pest able to spread within the EU territory following establishment? How? Yes, mainly by the movement of infected tubers.
Abstract. 1. Introduction. 1. 1. Background and Terms of Reference as provided by the requestor. 1. 1. 1. Background. 1. 1. 2. Terms of reference. 1. 1. 2. 1. Terms of Reference: Appendix 1. 1. 1. 2. 2. Terms of Reference: Appendix 2. 1. 1. 2. 3. Terms of Reference: Appendix 3. 1. 2. Interpretation of the Terms of Reference. 2. Data and methodologies. 2. 1. Data. 2. 1. 1. Literature search. 2. 1. 2. Database search. 2. 2. Methodologies. 3. Pest categorisation. 3. 1. Identity and biology of the pest. 3. 1. 1. Identity and taxonomy. 3. 1. 2. Biology of the pest. 3. 1. 3. Intraspecific diversity. 3. 1. 4. Detection and identification of the pest. 3. 2. Pest distribution. 3. 2. 1. Pest distribution outside the EU. 3. 2. 2. Pest distribution in the EU. 3. 3. Regulatory status. 3. 3. 1. Council Directive 2000/29/EC.
Data analysis One isolate per patient was eligible for calculating PNTM isolation rate, which refers to the total number of PNTM divided by the total number of pulmonary specimens received in a year (annual rate) or during the study period (overall isolation rate). For patients with multiple PNTM isolations, we mostly took into consideration the very first isolate. PNTM isolation prevalence for the study period, or for a given year, was calculated as the total number of PNTM positive cultures in a particular region divided by the population estimate and expressed per 100,000 population. In this study, we used the 2014 estimates of the Tunisian population, the most recent official estimates available: Tunis (2,643,695 inhabitants), Bizerte (568,219 inhabitants), and Zaghouan (176,945 inhabitants).
25 U HotStarTaq polymerase (Qiagen), 1 μl each primer (10 pM), 33 μl nuclease-free water and 5 μl DNA template. Negative controls consisting of PCR mixture without DNA template were included in each PCR run. The PCR products were visualized by gel electrophoresis, treated with Shrimp Alkaline Phosphatase (SAP) and Exonuclease I (ExoI) (Sigma-Aldrich,USA), and sequenced in both directions using the BigDye Terminator sequencing kit (Applied Biosystems, Villebon-sur-Yvette, France) with an ABI PRISM 3100 automatic sequencer (ABI, USA). The sequences generated in the present study were deposited to the GeneBank. Their accession numbers are provided in Additional file 2. Nucleotide sequences were processed using BioEdit (BioEdit software, version 7. 0. 5), and aligned with the GenBank database (NCBI) using the Basic Local Alignment Search Tool (BLAST).
2a  and Fig. 2b , respectively. Since we ensured full coverage of the regions of Bizerte and Zaghouan during the time period 2002-2011, one can reliably estimate their respective mean annual prevalence of PNTM isolation, which were found to be 0. 3/100,000 and 0. 1/100,000, yielding an overall annual PNTM isolation prevalence of 0. 2/100, 000. It is worth noting that, during this time period, only one PNTM isolate was recovered from the 383 processed specimens from Zaghouan. Initial classification of the cultured bacteria as NTM was based on their acid fastness and biochemical characteristics, and next confirmed by PCR targeting the recA intein (data not shown). Among the 30 cultured PNTM isolates, 9 (30. 0%) were slow growers. Phenotypic characteristics of the isolated PNTMs are shown in Table  1.