dataesr/scientific-paragraphs-categorization huggingface dataset:
Hybridization capture was performed on pools of both the modified and unmodified amplicons, using 100 bp bait molecules designed to target either the potato blight RXLR or pigeon mtDNA COI sequence. Quantitative real-time PCR demonstrated enrichment for the non-targeted amplicons when modified with M13 sequence, regardless of whether pigeon or potato blight amplicons were used as baits (Figure  3 ). For each capture experiment, the targeted amplicon was the most prevalent, but the artificial homology added by the flanking M13 regions enriched the proportion of each non-targeted amplicon 2. 6-15. 4%. Enrichment profiles of Illumina sequenced products yielded similar results (data not shown).
Acknowledgments The authors wish to thank  Alfred Roca ,  Pin Cui ,  NiccoloAlfano , and  Renita Danabalan  for helpful discussions, and  Paula Campos ,  Michael Martin , and  Inger Winkelmann  for providing DNA extracts of pigeon, potato blight, and squids, respectively. The authors also thank  Frank Aarstrup  and  Henrik Hasmna  for providing DNA extracts for the ureopathogenic bacteria. The authors also thank  Andaine Seguin-Orlando  of the  Centre for GeoGenetics and Susan Mbedi of the Berlin Centre for Genomics in Biodiversity Research (BeGenDiv)  for excellent next generation sequencing technical support.
The authors confirm that all data underlying the findings are fully available without restriction. All consensus sequences generated in this study have been deposited in GenBank (Accession numbers KJ530551-KJ530565). Illumina data described in this study is available at The NCBI Sequence Read Archive (SRA, http://www. ncbi. nlm. nih. gov/Traces/sra accession numbers SRP040713, and SRP041271).
The instrumentation of southern elephant seals with satellite-linked CTD tags has offered unique temporal and spatial coverage of the Southern Indian Ocean since 2004. This includes extensive data from the Antarctic continental slope and shelf regions during the winter months, which is outside the conventional areas of Argo autonomous floats and ship-based studies. This landmark dataset of around 75,000 temperature and salinity profiles from 20-140 °E, concentrated on the sector between the Kerguelen Islands and Prydz Bay, continues to grow through the coordinated efforts of French and Australian marine research teams. The seal data are quality controlled and calibrated using delayed-mode techniques involving comparisons with other existing profiles as well as cross-comparisons similar to established protocols within the Argo community, with a resulting accuracy of ±0.
1 ). While the primary motivation for this work has been to further the understanding of the influence of the oceanographic environment to the foraging success, reproductive performance and population trajectories of the seals  [5] [6] [7] [8] [9] [10]  , the unique ability of elephant seals to continuously dive to great depths (mean 590 ± 200 m, with maxima over 2,000 m) for long durations (average length of a dive 25 ± 15 min, maximum 120 min) has generated a large database of hydrographic profiles for the Southern Ocean  11, 12. Seal-derived data greatly complements other in situ data sources, such as ship-based measurements and Argo profilers. The seal contribution is particularly important in the regions south of the ACC, which are the most difficult to observe because of the seasonal presence of sea ice.
Recently, the seal data revealed the existence of very saline shelf waters in the Cape Darnley polynya (68 °E), in a region and season that is inaccessible to traditional ship-based surveys  23. Seal-based observation of this dense shelf water, together with overflows of cold, dense modified shelf water on the continental slope, was combined with moored observations of newly formed AABW downstream to provide the first evidence for a new, fourth region of AABW production. Another somewhat more modest source of AABW has also been described offshore of Casey station in the Vincennes Bay polynya  24. This data descriptor presents the database of hydrographic (i. e. , temperature and salinity) profiles obtained by instrumented seals in the Southern Indian Ocean. This database is a subset of the MEOP-CTD database (MEOP: Marine Mammals Exploring the Oceans Pole to Pole)  12  , complemented with the most recent Southern Indian Ocean deployment data.
Each individual CTD-SRDL dataset is post-processed separately, as each tag has different technical specifications and a different life history. A standard set of tests, adapted from Argo standard quality-control procedures  34  , is first run to remove bad profiles, spikes, and outliers. Temperature and salinity adjustments are then determined, which vary from tag to tag, and they are applied identically to all profiles from a given tag. A salinity adjustment is first estimated for each tag in the form of a pressure dependent linear correction. This salinity bias is known to be primarily induced by an external field effect on the conductivity sensor, which cannot be corrected a priori because it depends on how the tag has been attached on the seal's head  27, 28  , and must therefore be corrected using delayed-mode techniques.
The error model was suggested by numerous comparisons of CTD-SRDL profiles with ship-based CTD carried out priori to the deployment  28. For CTD-SRDLs with profiles sampled in frozen areas, a temperature offset is also estimated using the local freezing temperature as a reference. Owing to the relatively short life duration of CTD-SRDLs (typically 4 to 8 months), sensor drifts are assumed to be negligible, which is why the same adjustments are applied to all profiles from a given CTD-SRDL. There are several cases of seals staying in the same region for several months, or returning to a previously visited region, which indicate that sensor drifts are indeed a minor issue for CTD-SRDL. Adjustments parameters were estimated for each CTD-SRDLs separately by comparisons of salinity measurements with available data in the World Ocean Database  35.
Because the southern ACC region (south of 55°S) is associated with a large-scale upwelling of circumpolar deep waters near the surface, the salinity at depth is very stable there, with a low natural variability highly suitable for use as a reference. Salinity data cross-comparisons between different CTD-SRDLs were also used to estimate suitable adjustments for CTD-SRDLs having no profiles available in the southern ACC region. Once calibrated, the accuracy of post-processed CTD-SRDL measurements was estimated to be ±0. 03 °C in temperature and ±0. 05 psu (practical salinity unit) or better in salinity for CTD-SRDLs built after 2007  28  (against ±0. 01 °C and ±0. 01 psu respectively for Argo profiles). The achieved accuracy is highly dependent upon availability of ship-based CTD comparisons, and the type of water masses sampled during the deployment time.
Data Records The dataset comprises profiles of temperature (°C) and practical salinity (psu) as a function of pressure (dbar). Each profile is located in space and time. It must be emphasized that the dataset of each individual CTD-SRDL has been edited and corrected separately, as a given CTD-SRDL has its own specificities in terms of data accuracy and quality of the estimated correction (see above). Data are provided following the Argo netCDF format  36. The generic netCDF standard is a selfdocumented binary format developed specifically store geo-referenced climate data (more information at www. unidata. ucar. edu), which has become very popular to store hydrographic data because it can be read and manipulated with ease with a wide variety of data processing software programs and programming languages.
The Argo netCDF format is a standard used to store Argo float data on Argo data servers, and thus it is well adapted to record hydrographic profile data. The Argo netCDF format includes a complete set of metadata for each profile, with data quality flags, the possibility to record both raw and adjusted data values, and the ability to record applied calibration equations. Most significant variables are shown in Table  2. While files in Argo netCDF format are meant to be the reference data files, data are also provided in Ocean Data View (ODV) spreadsheet format. Ocean Data View is a cross-platform software designed to manipulate and visualize ocean data  37. The ODV spreadsheet format is an ASCII format, which means that any text editor can read it. Contrary to the Argo netCDF files, ODV spreadsheet files contains only adjusted values that have been flagged as good data.
Technical Validation The surface dynamic height anomaly relative to a given reference pressure level is obtained by vertically integrating inverse density anomalies  38. It is a key quantity in physical oceanography because its horizontal gradient is directly proportional to the large-scale (geostrophic) currents if one assumes that currents are negligible at the reference pressure level. It is also a useful quantity to validate seal data, because it provides a single scalar quantity for each profile that depends on both temperature and salinity profiling data. Here we consider the dynamic height anomaly at 20 dbar relative to 500 dbar (here denoted as DH500) to validate the seal database, comparing its spatial distribution based on raw and adjusted seal data with the distribution obtained using hydrographic profiles present in the World Ocean Database (WOD) for the period 2000 to 2013 (ref.
Usage Notes Instrumented seals are filling very important gaps in what is traditionally a very data-poor region of the world oceans, and therefore are becoming increasingly utilised by oceanographers studying the role of the Southern Ocean and Antarctica in global climate. Seal-derived data are making a growing contribution to climatologies built upon existing oceanographic databases, such as the World Ocean Database  35. The previous deployments have established an important baseline and the time series will grow and with it the confidence to assess how the Southern Ocean is changing and why  [40] [41] [42]. The accuracy of CTD-SRDL hydrographic data is also expected to increase significantly in the coming decade, as the technology improves. This will be very useful to assess ocean changes such as those associated with the melting of the Antarctic ice sheet and changing sea ice distribution, as these issues require a high level of accuracy for salinity data (ideally ±0.
Regional and global climate models will directly benefit through the augmented observational datasets at their disposal for evaluating their performance, which in turns leads to better predictions and advise from the climate science community to government and business managers. Seal-based hydrographic data can also be used to produce improved state estimates of the ocean circulation  12. The satellite remote sensing community estimating sea ice production, drift velocity and fast ice mapping will utilise seal data to evaluate and validate their products  11. Recently, it has become possible to add a fluorometer on CTD-SRDLs, which allows collecting vertical profiles of chlorophyll concentration together with hydrographic profiles  44, 45. This technology is quickly becoming a major source of information on the primary production in the iron-limited Southern Ocean.
The seal dataset is also contributing to our understanding of the use of oceanographic features by foraging southern elephant seals in the Southern Ocean, and more particularly in the Kerguelen Plateau region, and is now successfully used to improve model-derived estimates of animal movements. How to cite this article: Roquet, F. et al. A Southern Indian Ocean database of hydrographic profiles obtained with instrumented elephant seals. Sci. Data 1:140028 doi: 10. 1038/sdata. 2014. 28 (2014).
Acknowledgements The French contribution of this international study was carried out as part of activities of the  French observatory MEMO (Mammifères Echantillonneurs du Milieu Marin) , funded by the two  CNRS institutes INSU  and  INEE. The project benefited from the financial and logistic support from  CNES (TOSCA) IPEV , the  Total Foundation  and the  ANR. MEMO is closely associated to the Coriolis centre, which distributes real-time and delayed-mode products, and is part of the SOERE consortium CTD02 (Coriolis-temps différé Observations Océaniques, PI:  G. Reverdin ). Seal CTD data from the Integrated Marine Observing System (IMOS) were provided through the  Australian Animal Tracking and Monitoring System facility. IMOS is supported by the  Australian Government  through the  National Collaborative Research Infrastructure Strategy  and the  Super Science Initiative.
A reporting system for dengue surveillance in Thailand started in 1958. The national surveillance system for DHF was initiated in 1972 by the Bureau of Epidemiology (BoE), Thai Ministry of Public Health (MoPH) becoming fully operational in 1974  [16]. DF was included in the surveillance system in 1994. Reports for patients diagnosed with dengue disease are collected from hospital in-patients and hospital out-patients from health facilities nationwide, all government hospitals and some private hospitals and clinics (the reporting sites are mostly public hospitals, with a few voluntary reports from private hospitals). It is mandatory to report the confirmed cases, but not for the suspected cases (which is subject to the physicians' willingness to report). The reporting form (Form 506) is used to record demographic data -age, sex, day of onset and the address (locality) where the case occurred, categorised as municipalities ('cities' or 'suburbs') or 'other' (mostly rural) areas  [16].
Epidemiological data on DF, DHF and DSS in Thailand are disseminated from central departments in the form of weekly newsletters (the BoE Weekly Epidemiological Surveillance Report) and published online on the MoPH website within the Annual epidemiological surveillance reports (AESRs). Dengue disease laboratory diagnostics in Thailand can be ordered on an individual basis and include dengue virus isolation, viral genome detection by reverse transcription polymerase chain reaction (RT-PCR), four-fold increases of paired sera (haemagglutination inhibition) or IgM. 40 U or IgG increasing. 100 U. Virological surveillance (virus isolation and serotyping by RT-PCR) is performed by the Department of Medical Science, especially before the outbreak season, which appoints a number of hospitals from around the country to act as sentinel sites.
Methods The overall methodology, search strategy, and inclusion and exclusion criteria for this literature analysis and review are included in a protocol that was developed by a Literature Review Group (LRG). The protocol was based on the preferred reporting items of systematic reviews and meta-analyses (PRISMA) guidelines  [22]. The protocol was registered on PROSPERO, an international database of prospectively registered systematic reviews in health and social care managed by the Centre for Reviews and Dissemination, University of York (CRD42012002170: http://www. crd. york. ac. uk/PROSPERO/ display_record. asp?ID=CRD42012002170) on 23 March 2012.
Search strategy and selection criteria The LRG guided the literature analysis process, defined the search strategy, and prepared the protocol and review documents. Specific search strings for each database were designed with reference to the expanded Medical Subject Headings thesaurus, encompassing the terms 'dengue', 'epidemiology', and 'Thailand'. Different search string combinations were used for each electronic database with the aim of increasing the query's sensitivity and specificity. Searches of selected online databases (Table  S1 ) were conducted between 9 February 2012 and 28 February 2012. As stated in the protocol, studies (as well as conference materials, grey literature and official reports and bulletins) published in either Thai or English between 1 January 2000 and 28 February 2012 were included in the analysis.
Author Summary We conducted this comprehensive systematic review to determine the impact of dengue disease in Thailand for the period 2000-2011, and to identify future research priorities. Well-defined methods were used to search and identify relevant published research, according to predetermined inclusion criteria. In addition to information from studies published in the literature, the review draws largely on surveillance data from the Annual Epidemiological Surveillance Reports published by the Thailand Ministry of Public Health. The pattern of annual number of reported dengue cases over the review period was complicated by epidemic years; consequently, a trend in the number of reported cases could not be identified. It was apparent that despite a shift in age group distribution dengue from younger towards older persons, dengue in Thailand remains a predominantly childhood disease.
The seasonality and heterogeneous spatial and temporal nature of the disease were confirmed. It is clear that the nationwide passive surveillance system is a source of consistent data relating to severity, age and serotype. However, several gaps were identified that would benefit the understanding of dengue epidemiology in Thailand, such as seroprevalence data and a record of the proportion of reported cases that are hospitalized. participants or by study type were imposed, although single-case reports and studies that only reported data for the period before 1 January 2000 were excluded, as were publications of duplicate data sets, unless the articles were reporting different outcome measures. Editorials and reviews of previously published data were also excluded. Additional publications not identified by the search strategy, unpublished reports and grey literature were included if they met the inclusion criteria and were recommended by the LRG.
Sources were reviewed by the LRG to ensure they complied with the search inclusion and exclusion criteria. Following a review of the source titles and abstracts, during which duplicates were removed, the LRG performed a second review of the full text of any published sources selected to make the final selection of relevant sources to include. In an amendment to the original protocol the Literature Review Group sanctioned the extraction of surveillance data for 2011 from the MoPH Bureau of Epidemiology Surveillance Database website on 16 July 2012. We chose not to exclude articles and other data sources nor formally rank them on the basis of the quality of evidence. Whilst an assessment of study quality may add value to a literature review, we were of the view that given the expected high proportion of surveillance data among the available data sources and the nature of surveillance data (passive reporting of clinicallysuspected dengue), such quality assessment would not add value to our review.
The selected data sources were collated and summarized using a data extraction instrument developed as a series of Excel (Microsoft Corp. , Redmond, WA) spreadsheets. Data from literature reviews of previously published peer-reviewed studies and pre-2000 data published within the search period were not extracted. The original data sources and the extraction tables were made available to all members of the LRG for review and analysis. In view of the expected heterogeneity of eligible studies in terms of selection and number and classification of cases, a meta-analysis was not conducted; a narrative synthesis of our findings is presented. For the purposes of the analysis, we defined national epidemics as those years in which the number of cases was above the 75 th percentile for the period.
Throughout the review period, relatively more cases of severe dengue (DHF and DSS) were reported in individuals aged between 5 and 14 years compared with those aged 15 years or older. In particular, DSS was less common in individuals aged 15 years or older. Data from cohort studies indicate that many children in the 5-14 years age group may be experiencing a second infection of dengue, which could account for the high incidence of severe disease in this age group  [41]. Lower incidence rates of severe disease in older age groups could be due to reduced exposure to infection or reduced severity of disease in individuals experiencing their third or fourth infection  [43]. Over the review period, approximately 70% of deaths due to dengue disease reported to the MoPH were in patients younger than 15 years.
Typically, the highest CFRs were seen either in young children aged 0-4 years or in older adults aged 55-64 years, a trend that likely reflects the susceptibility of the young and old to more adverse consequences of dengue disease and its clinical management, as well as the risk associated with comorbidities in older adults  [44]. However, the number of reported cases in those aged 55 years and above is small compared to the other age groups. Individuals aged over 65 years had the lowest reported incidence rate of dengue overall, and the only case fatalities in this age group were recorded in 2001, 2005 and 2010. Comparable regional data for age-related distribution of dengue were not recorded in the studies selected for this review. Individual studies suggest a pattern similar to that seen nationally, with younger age groups more likely to contract dengue than adults and the elderly  [37] [38] [39].
DENV serotype distribution At the time of this review, comprehensive regional DENV serotype data for Thailand from the MoPH AESRs were only available for the period 2005-2010. These serotype data show a broadly similar pattern in each region, with a reduction in the proportion of DENV-1 and an increase in the proportion of DENV-2 isolates over that period (Figure  7 )  [28] [29] [30] [31] [32] [33]. DENV-4 peaked during 2005 and 2006 and then declined, but remained in circulation in the Central region throughout this 5-year period, albeit at a decreasing percentage of all dengue disease cases (4. 6-6. 2% during 2008-2010, compared with 10. 4% in 2007 and 46. 1% in 2005). By contrast, in 2009 and 2010, DENV-4 was not isolated in samples from the North or Northeast regions and was reported at ,2% in the South region.
Strengths and limitations of the review This literature review presents the epidemiology of dengue disease in Thailand over the period 2000-2011. A key strength of this survey and analysis is that it describes the epidemiological data from a national aspect rather than from limited study site data. In addition, the review protocol aimed to minimize potential exclusions of valuable data sources including MoPH data, as well as searching for relevant books, unpublished data, abstracts and dissertations. More than 600 data sources were screened and the selected sources were subjected to a comprehensive data extraction method to capture the data, which adds strength to this review. However, by its very nature, this literature review captured mainly publicly available data and studies and is, therefore, subject to publication bias; the data presented here should be interpreted accordingly.
Acknowledgments The authors take full responsibility for the content of this contribution but would like to thank  Graham Joint  ( Communigen Ltd ) for assisting with data extraction and preparation of manuscript drafts. The authors would like to thank  Sopon Lamsirithaworn  ( Ministry of Public Health, Thailand ),  Leon Ochiai  ( Sanofi Pasteur ),  Gee Marsh  ( Sanofi Pasteur ),  Danaya Chansinghakul  ( Sanofi Pasteur) , and  Sunate Chuenkitmongkok  ( Sanofi Pasteur ) for their critical comments and helpful suggestions on this manuscript. In particular the authors are grateful to  Dr Sujarti Jatanasen  for reviewing and contributing to the description and discussion relating to the Thailand National Surveillance System.