dataesr/scientific-paragraphs-categorization huggingface dataset:
As global citizens, we have the opportunity to take action to protect the Ocean and improve the delivery of Ocean information with end-toend products developed specifically to inform Ocean stewardship, policy and Ocean governance. The current evolution of the Ocean observing system has provided a transformative boost in capabilities for Ocean monitoring and reporting over the past two decades (e. g. Ref. [41, [147] [148] [149] [150] [151]. The UN Decade of Ocean Science for Sustainable Development (2021-2030) commits us to expanding Ocean observations into our uncharted and under-sampled waters, including the polar regions, the deep Ocean, shelves and coastal areas, while re-committing ourselves to the urgently needed science, data and services at the Ocean-climate nexus. The UN Decade of Ocean Science and the IPCC SROCC underscore the importance of elevating local and indigenous knowledge to strengthen the Ocean knowledge base (14.
A). An internationally agreed framework for Global Ocean Indicators is critically needed to mark the beginning of the UN Decade of Ocean Science, to ensure we close the decade with the sustainable Ocean stewardship we need for the 'Future We Want'. Policy support is indispensable in securing the sustainability of Ocean observations, local and indigenous knowledge systems, science, and capacity to deliver Ocean services. The UN 2030 Sustainable Development Agenda delivers clear guidance on the urgent global goals to be achieved by 2030: a path towards a sustainable future for all societies on the blue planet. For policy negotiations, the key ingredient is to establish an ongoing dialogue among decision makers, the private sector and information holders concerned with the Ocean. Ocean science, data and services can help to meet national and regional goals and targets of the United Nations 2030 Agenda on SDGs, the Paris Agreement, the Sendai Framework for Disaster Risk Reduction, the Convention on Biological Diversity, and the SAMOA Pathway.
The UN Decade of Ocean Science seeks 'to reverse the decline in Ocean health. ' Through stronger international cooperation, the Decade will bolster scientific research and innovative technologies to ensure science responds to the needs of society. All SDGs are interconnected. A holistic approach that embraces sustainable Ocean stewardship informed by science, data and services to support society and the economy is required to create the 'Future We Want'. Just as UNOC and the Decade of Ocean Science are intimately connected, and just as SDG14 supports the UN 2030 Agenda and the SDGs as a whole, the three tracks of Ocean, biodiversity and climate change are inseparable. The outcomes of UNOC in Lisbon and the CBD's COP15 in Kunming must converge to strengthen the delivery of greatly enhanced ambition for the outcomes of UNFCCC's COP26 in Glasgow, despite the delays invoked by the global COVID-19 pandemic.
Here, we examine past human response to sea-level rise within a broader context of environmental change by using an isolated island group that has undergone fragmentation over the past c. 12,000 years to assess the nature of cultural transitions under pressure from rising seas. We calculate rates of sea-level rise for the Isles of Scilly in northwest Europe (hereafter referred to as Scilly) from a newly developed sea-level reconstruction (Materials and Methods), which allows us to assess rates of land loss and changes in areal extents of intertidal zones due to inundation. By combining new pollen and charcoal data from 17 sediment cores, dated using radiocarbon and optically stimulated luminescence (OSL), with reconstructed population dynamics from regional archeology, we show rapid landscape changes during a period of gradual sea-level rise, coincident with a period of major cultural change.
Experimental design We undertook field studies to collect sedimentary records of past sea level and landscape changes. Recovered sediment cores were dated using OSL and radiocarbon techniques and underwent age-depth modeling. Paleoenvironmental reconstructions were based on palynological analyses (including charcoal), phytosociological classifications, and nonmetric multidimensional scaling (nMDS). Sea-level reconstructions were based on foraminiferal analyses and a combined transfer-function/indicative meaning-based approach. Bayesian inference of sea-level trends from observational data was modeled using errors-in-variables integrated Gaussian process. Sea-level trends were extended beyond observational data using glacial-isostatic adjustment (GIA) modeling. Paleogeographies were developed by combining a model of present-day bathymetry and topography with GIA model outputs and a new paleotidal range model.
Surveys and core collections Surveys and core collections took place across Scilly in 2009 and 2010 during low spring tides. An audit of intertidal peat deposits that were recorded by (i) English Heritage in the Intertidal and Coastal Peat Database, (ii) the Historic Environment Service in the Rapid Coastal Zone Assessment for the Isles of Scilly, and (iii) the Cornwall Archeological Unit in The Early Environment of Scilly  (27)  was carried out and digitized by Cardiff University  (23). The audit guided field surveys of extant deposits, which were surveyed using a Trimble 4700 RTK GPS base and rover system and converted to the British National Grid coordinate system using the Ordnance Survey (OS) National Grid Transformation OSTN02. Elevation was converted to Ordnance Datum St Mary's using OS National Geoid Model OSGM02.
Where identifiable macrofossils were unavailable for dating, humin and humic fractions of bulk organic sediments were dated following pretreatment and age determinations were calculated using weighted means  (23). Of the 70 samples sent for dating, 78 age determinations were returned from the two facilities; replicate determinations were obtained from the humin and humic fractions of organic bulk sediment and 16 samples failed during pretreatment due to low carbon yields. Age determinations were catalogued with relevant sample metadata (stratigraphy, depth, unit thickness, positioning, elevation, and error estimation) in dataset S1. For OSL dating, samples were taken from sediment monoliths under subdued red-lighting conditions. The light-exposed surface material of the sediment monoliths was removed and used for assessment of the dose rate (Gy/ka), based on measurements of finely ground material made using Daybreak detectors for thick source alpha counting and a Risø GM-25-5 beta counter.
Age-depth models were built using OxCal v4. 1 (40) using the IntCal13 calibration curve (41) with the P-sequence function and the deposition rate prior defined as log_10(k/k_0), where k_0 = 1, allowing k to take any value between 0. 01 and 100. Highest posterior density intervals throughout the sediment cores are reported in calibrated years before present (1950 CE) as modeled mean and 2 ranges (dataset S2).
Paleoenvironmental reconstructions Paleoenvironmental reconstructions were based on pollen of the sediment cores and monoliths, which were subsampled at 1-cm resolution  (23). Sediment subsamples (1 cm 3 ) were combined with Lycopodium clavatum L. as an exotic spike in 10% HCl (4 ml) to provide concentration data. Samples were then digested in 10% NaOH (40 ml) before sieving (15-to 106-m fraction), treated with 10% HF (4 ml), washed with 10% HCl, and underwent acetolysis before being stored in glycerol for counting. Pollen counts continued until 300 land-derived grains had been identified in each sample. Charcoal counts on two size fractions (>50 and <50 m) were carried out simultaneously. Pollen assemblages were assigned to clusters using Ward's hierarchical agglomerative clustering using the "rioja" software package  (42)  in R  (43).
This unsupervised data-driven approach was used to assign pollen samples to cluster groups based on the similarity of their taxa assemblages. After assigning pollen samples to clusters statistically, a phytosociological classification approach was used to identify the frequent and abundant taxa within each group based on the number of occurrences of the taxon (the frequency), the average percentage, median, and interquartile range  (26). An individual taxon's frequency is determined by calculating its number of occurrences divided by the number of samples in the cluster and assigning one of five frequency classes based on cutoff values between each group. If a taxon appears in 81 to 100% of all samples in the cluster group, it is assigned the highest frequency class. nMDS was applied to the data using the "vegan" software package  (44)  in R (  43 ) as a complementary method to summarize major variation in the dataset (fig.
S1 ). The stress of the nMDS was used as an indicator of the quality of the fit of the ordination. The results of the phytosociological classification (tables S3 to S6) were used to identify samples that had a greater influence of coastal processes. This was deemed necessary to differentiate pollen assemblages that have a greater forcing through human influence from those driven by coastal change  (26). Clusters that included key halophytes in high frequencies (e. g. , Chenopodiaceae) were screened from the dataset used to develop the land cover index for Scilly. The remaining samples were first orientated in time (x-axis age values based on age-depth modeling results of the sediment cores) and space (y-axis values based on the primary nMDS ordination axis) before regression analysis to determine a trend through the data.
The regression used generalized additive models to estimate smooth, nonparametric link functions between the two variables, fit to the data using penalized regression splines in the "mgcv" software package  (45)  in R  (43)  with uncertainties determined as the variance of the SEs of the estimated trend in time. To examine biomass burning across Scilly during the Holocene, charcoal records were analyzed using the "paleofire" (v. 1. 2. 3. ) software package  (46)  in R  (43). Seventeen charcoal records from the analyzed sediment cores and monoliths (table  S1 ) were included in the analysis to create a composite charcoal curve. To facilitate intersite comparison, the 17 records were pretreated using the established protocol for transforming and standardizing individual records: (i) transforming noninflux data (e.
Sea level Sea level was reconstructed using assemblages of intertidal foraminifera from the fossil sediments as precise sea-level indicators, and subsampling was concentrated along sediment cores and monolith sections that exhibited high concentrations of Chenopodiaceae pollen. Known volumes of sediment (1 to 5 cm 3 ) were washed through 63-and 500-m mesh sieves, wet-split into equal aliquots, and counted until >100 tests were identified or until the entire sample had been counted  (23). Paleomarsh surface elevations were estimated from samples with count totals of >50 tests by applying a two-component weighted average partial least squares transfer function model with inverse deshrinking to predict sample elevations from relative abundances of foraminifera  (23). Paleomarsh surface elevation predictions from the fossil assemblages and their bootstrapped root mean squared prediction errors (RMSEPs) were corrected for tidal range differences between the training set location (Erme Estuary, South Devon) and Scilly (St Mary's tide gauge) before being converted to estimates of sea level, following: S = H -I, where the position of former sea level (S) is the elevation of the sample relative to MSL (H) minus the indicative meaning (I) of the sample, in this case, the paleomarsh surface elevation result from the transfer function (dataset S2).
The transfer function approach provided 14 precise estimates of former sea level with uncertainties calculated as 2 RMSEs of surveying, sampling, and transfer function errors. These samples all paired with precise dating (radiocarbon or OSL) constraints, which provided corresponding 2 age uncertainties. The remaining age determinations were used to develop a further 56 sea-level index points (SLIPs), 8 of which were precise and the remainder were marine-or terrestrial-limiting. Indicative meanings for these SLIPs were based on lithologies and paleoenvironmental analyses (dataset S1), and the corresponding reference water levels and indicative ranges were based on those used for the sea-level database of Britain and Ireland  (20). The 70 new index points developed in this study (dataset S1) were combined with the 20 existing limiting data points for Scilly  (26)  from the British and Irish sea-level database  (20)  to produce a new Holocene sea-level database and sea-level curve for Scilly.
Temporal trends and uncertainties in relative sea level were estimated for the Scilly region by applying an error-in-variables integrated Gaussian process (EIV-IGP) model  (47)  to the combined relative sea-level dataset. This method incorporates sample-specific vertical and temporal uncertainty and the uneven distribution of data points through time. Uncertainties reported in the text from the EIV-IGP model are a mean with 95% credible interval. Tidal range Tidal range changes through the Holocene were simulated for Scilly using a paleotidal model of the northwest European shelf seas (48). The Regional Ocean Modeling System was used to develop a threedimensional tidal model, configured with latitude/longitude grid spacing of 1 to 2 km. Tidal elevation amplitudes were output at 1-ka intervals from 21 ka to present day, with boundary forcing for the paleotidal runs derived from a global paleotidal model  (49).
Glacial isostatic adjustment Glacial isostatic adjustment models were used to derive relative sea-level predictions for the study region and extend sea-level histories beyond the extent of the observational data. The glacial isostatic adjustment model used builds upon Bradley et al. (50). The model was run at a spherical harmonic degree of 512° to provide ~35-km resolution across the study region and considers two different global icesheet models, Bradley2017 (20) and ICE5G  (51) , which prescribed the evolution of major ice sheets from ~122 ka to present day. Sensitivities to the choices of Earth rheology were investigated by generating relative sea-level predictions for a range of Earth model parameters with  2 misfits calculated by  2 = 1 ─ (N -1)  i=1 N ( ( RSL i p -RSL i obs ) ─  i ) 2 where N is total number of precise SLIPs; RSL i p and RSL i obs are the predicted and observed relative sea-level data defined at given latitude, longitude, and time; and  i is 2 sigma errors on the observed SLIP (dataset S1).
Paleogeographies Paleogeographies for Scilly were developed using three sea-level histories: (i) the newly developed probabilistic relative sea-level curve (the 50% confidence interval) for 7. 5 to 0 ka, (ii) the relative sea-level output for the Bradley(71p350) glacial isostatic adjustment model, and (iii) the relative sea-level output for the ICE5G(71p350) glacial isostatic adjustment model. Bathymetry data for the Isles of Scilly and surrounding waters were downloaded from the Channel Coastal Observatory (www. channelcoast. org; accessed June 2018). Separate ascii files of Lidar data, collected between 18 March 2014 and 18 June 2015, were collated and gridded in ArcGIS, with a grid resolution of 30-m offshore, increasing in resolution to 1. 5 m in the intertidal zone and on land. Bathymetry and Lidar data were both referenced to present-day MSL.
The data compilations comprise 1410 archeological radiocarbon dates from Devon and Cornwall and 1424 dates from Brittany and Normandy, collated from over 150 databases and published resources (dataset S3; see "Data and materials availability" for database citations). Dates with analytical (e. g. , low carbon yields), sampling (e. g. , evidence of contamination), and provenance (e. g. , environmental associations rather than archeological) issues were screened from both compilations, which resulted in final dataset sizes of n = 920 (Devon and Cornwall) and n = 1214 (Brittany and Normandy). The "rcarbon" software package  (53)  in R  (43)  was used to calibrate the remaining dates with the IntCal13 calibration curve  (40)  and then cluster dates from same sites that occurred within 50 year bins (i.
S3 ). A dataset (n = 2411) of recorded archeological monuments from Scilly (dataset S3) was obtained from the Cornwall and Scilly Historic Environment Record (heritagegateway. org. uk). Dataset metrics included classifiers (e. g. , identifiers and locators), monument types (e. g. , structures, settlements, cairns, entrance graves, boundary and field markers, findspots, middens, and wrecks), and inferred relative ages. The relative ages associated monuments with known (e. g. , Bronze Age), or "windows" (e. g. , Medieval to Modern) of known, archeological epochs, which were subsequently used to assign calendar dates to the monuments, based on established archeological period timings for southwest Britain  (22). The index was developed using a probabilistic approach by assigning the likelihood of a monument occurring within its temporal range to 1 and summing probabilities of occurrence for all monuments in 200-year bins through the Holocene (fig.
The Study We conducted a retrospective analysis of all reports of undiagnosed diseases in the ProMED-mail registry that were published during January 1, 2007-June 14, 2018 (Figure  1 ). ProMED-mail staff provided all the archives for undiagnosed diseases and unknown diseases relative to humans, animals, and plants. We also collected data directly from Disease Outbreak News on the WHO website (https://www. who. int/csr/ don/archive/country) for the period of the study; these data are also open access and disseminated as specified by Article 11 of the International Health Regulations (2005). From ProMED-mail reports, we collected data on case location (WHO zone), source of information, date of publication, number of cases, geographic distribution (i. e. , regional, national, or international) of cases, affected population, and final diagnosis.
Zika Virus Testing and Outcomes during Pregnancy, Florida, USA, 2016 • Sensitivity and Specificity of Suspected Case Definition Used during West Africa Ebola Epidemic • Nipah Virus Contamination of Hospital Surfaces during Outbreaks, Bangladesh, 2013-2014 • Detection and Circulation of a Novel Rabbit Hemorrhagic Disease Virus, Australia • Drug-Resistant Polymorphisms and Copy Numbers in Plasmodium falciparum, Mozambique, 2015 • Increased Severity and Spread of Mycobacterium ulcerans, Southeastern Australia • Emergence of Vaccine-Derived Polioviruses during Ebola Disease Outbreak, Guinea, 2014-2015 • Characterization of a Feline Influenza A(H7N2) Virus • Japanese Encephalitis Virus Transmitted Via Blood Transfusion, Hong Kong, China • Changing Geographic Patterns and Risk Factors for Avian Influenza A(H7N9) Infections in Humans, China • Pneumonic Plague in Johannesburg, South Africa, 1904 • Dangers of Noncritical Use of Historical Plague Databases • Recognition of Azole-Resistant Aspergillosis by Physicians Specializing in Infectious Diseases, United States • Melioidosis, Singapore, 2003-2014 • Serologic Evidence of Fruit Bat Exposure to Filoviruses, Singapore, 2011-2016 • Expected Duration of Adverse Pregnancy Outcomes after Zika Epidemic • Seroprevalence of Jamestown Canyon Virus among Deer and Humans, Nova Scotia, Canada • Postmortem Findings for a Patient with Guillain-Barré Syndrome and Zika Virus Infection • Rodent Abundance and Hantavirus Infection in Protected Area, East-Central Argentina • Two-Center Evaluation of Disinfectant Efficacy against Ebola Virus in Clinical and Laboratory Matrices • Phylogeny and Immunoreactivity of Human Norovirus GII.
• Zika Virus Testing and Outcomes during Pregnancy, Florida, USA, 2016 • Sensitivity and Specificity of Suspected Case Definition Used during West Africa Ebola Epidemic • Nipah Virus Contamination of Hospital Surfaces during Outbreaks, Bangladesh, 2013-2014 • Detection and Circulation of a Novel Rabbit Hemorrhagic Disease Virus, Australia • Drug-Resistant Polymorphisms and Copy Numbers in Plasmodium falciparum, Mozambique, 2015 • Increased Severity and Spread of Mycobacterium ulcerans, Southeastern Australia • Emergence of Vaccine-Derived Polioviruses during Ebola Disease Outbreak, Guinea, 2014-2015 • Characterization of a Feline Influenza A(H7N2) Virus • Japanese Encephalitis Virus Transmitted Via Blood Transfusion, Hong Kong, China • Changing Geographic Patterns and Risk Factors for Avian Influenza A(H7N9) Infections in Humans, China • Pneumonic Plague in Johannesburg, South Africa, 1904 • Dangers of Noncritical Use of Historical Plague Databases • Recognition of Azole-Resistant Aspergillosis by Physicians Specializing in Infectious Diseases, United States • Melioidosis, Singapore, 2003-2014 • Serologic Evidence of Fruit Bat Exposure to Filoviruses, Singapore, 2011-2016 • Expected Duration of Adverse Pregnancy Outcomes after Zika Epidemic • Seroprevalence of Jamestown Canyon Virus among Deer and Humans, Nova Scotia, Canada • Postmortem Findings for a Patient with Guillain-Barré Syndrome and Zika Virus Infection • Rodent Abundance and Hantavirus Infection in Protected Area, East-Central Argentina • Two-Center Evaluation of Disinfectant Efficacy against Ebola Virus in Clinical and Laboratory Matrices • Phylogeny and Immunoreactivity of Human Norovirus GII.
Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline.
Another particularity is that these molecules are stable in extracellular environments: for example, they are resistant to pH and heat changes. Nowadays, the use of microarrays, real-time polymerase chain reaction (PCR) and next generation sequencing (NGS) technologies and the creation of databases give us the opportunity to study miRNAs as cancer biomarkers. Several studies have exploited the biomarker properties of miRNAs for cancer detection and classification, using machine learning techniques  [16] [17] [18] [19] [20]. These works typically analyze thousands of different miRNAs, amounts that would make it impossible for medical experts to manually validate the results or to obtain novel insights. Furthermore, employing thousands of miRNAs in machine learning approaches translates into operating in feature spaces of thousands of dimensions, which nurtures the usual issues linked to the curse of dimensionality.
Therefore, in addition to enhancing the interpretability of results, determining small, actionable subselections of features warrants approaches that are insensitive to biases emerging from batch effects (due to processing data from multiple studies, for example), from the use of sets of classifiers that vary in terms of their strengths and weaknesses or just from the nature of their technical foundations. Finding the smallest subset of circulating miRNAs that can identify the presence of cancer or the type of tumor is therefore of utmost practical importance. In this work, we propose a new methodology to reduce the number of significant circulating miRNAs needed by machine learning techniques to detect and identify cancer types using 16 miRNA datasets from clinical trials. The technique relies on a heterogeneous ensemble of classifiers to provide more robust results than single algorithms or even homogeneous ensembles.