dataesr/scientific-paragraphs-categorization huggingface dataset:
Malaria Refinements to the methodological approach and addition of substantially more data than in GBD 2015 have led to changes in estimates of both malaria mortality and morbidity estimates in the GBD 2016 iteration, with resulting changes in DALYs. Globally, predicted trends in malaria were similar to GBD 2015, rising to a peak in 2005 before steadily declining. Overall malaria DALYs are lower in GBD 2016 than in GBD 2015, reflecting mainly lower estimates outside of Africa and particularly for India. Outside of Africa, and for lower burden countries within Africa, estimates were informed for the first time by extensive subnational casereporting data from routine surveillance systems. These were subsequently adjusted to account for underreporting, misdiagnosis, and incompleteness, and then entered into a spatiotemporal geostatistical model to infer continuous surfaces of incidence rate before re aggregating them to national and subnational totals.
Diabetes We made several important improvements to the process of estimation of diabetes prevalence, including use of more data sources than GBD 2015 and development of a novel approach to standardise the definition of diabetes across different sources. In our assessment of diagnostic criteria for diabetes across different surveys, we identified more than 50 different definitions for diabetes based on various biomarkers (eg, fasting plasma glucose concentration, oral glucose tolerance test result, and glycated haemoglobin A1c concentration) and different levels of each biomarker. To standardise the definition of diabetes, we mostly focused on the surveys that had included fasting plasma glucose concentration as a diagnostic criterion and developed an ensemble model to characterise the distribution of fasting plasma glucose concentration at the population level in each age and sex group.
Then, we used the fasting plasma glucose concentration distribution to convert various definitions of diabetes into the standard case definition. Using the ensemble model, we also estimated the prevalence of diabetes on the basis of the mean fasting plasma glucose concentration in places for which we only had data for mean fasting plasma glucose concentration. These changes allowed us to be more consistent than in GBD 2015 in our estimation of the prevalence of diabetes across countries and over time. As a result, our estimates of the prevalence of diabetes globally and in most regions are slightly lower than those reported in GBD 2015. The strong relationship between SDI and diabetes remained, mirroring global rises in overweight and obesity. 69.
Cancer DALYs for cancer have changed compared with GBD 2015; this change is predominantly due to lower YLD estimates for GBD 2016 than for GBD 2015. These improvements stem from adjustments made in the modelling of MIRs to better reflect differences in MIR based on SDI in datasparse locations than in GBD 2015. In addition to stricter inclusion criteria for data used in the MIR modelling than in GBD 2015, we changed the modelling approach and used the most parsimonious model with just SDI as a predictor of MIR. 8  MIRs are used to estimate cancer incidence and prevalence from GBD cancer mortality estimates and therefore directly determine YLDs. Until cancer registry incidence data and accurate mortality statistics are widely available, validation of MIR is difficult in countries that lack these data sources.
Limitations Despite our continued methodological advancements and data enrichments, this study has limitations. First, all limitations documented in the elements of the GBD estimation process that allow for DALY and HALE estimation  [2] [3] [4] 12  will contribute to uncertainty in these summary measures. Second, these summary measures will also be influenced by data availability. Time lags in the reporting of health information by national authorities and thus their subsequent incorporation into the GBD estimation mean that some of the most recent changes in health states will not be captured. Relatedly, data deficiencies from populations in conflict zones (eg, Syria, Iraq, Yemen, South Sudan, and Afghanistan), autonomous subnational regions, and certain non geographically based subpopulations (ie, migrants, refugees, and some indigenous people) limit the precision of some of our estimated levels and trends of disease burden.
Conclusion Many improvements have been made to GBD 2016 to allow for a clearer and more nuanced picture of the changing picture of global health than in GBD 2015. Among these improvements are inclusion of new studies and subnational data (notably in India) and many substantial improvements to modelling and analyses. Prominent results of these changes include higher global DALY estimates for tuberculosis, HIV/AIDS, lower respiratory infection, and diarrhoeal disease than in GBD 2015. We have for the first time discussed our DALY exemplars, countries with the lowest ratio of observed to expected DALYs, as well as our poor performers, those with the largest ratio of observed to expected DALYs. These exemplars and poor performers suggest a need to learn lessons from those with clear health gains and implement systembased strategies for those struggling.
This increasing demand on health systems is ubiquitous across time, location, GBD region, and SDI, posing a huge opportunity for healthcare innovators in prevention and treatment for morbidity reduction. Our analysis of exemplars and poor performers is also indicative of various practices that can hasten or slow the epidemiological transition and warrant deep investigation. Institute of Mental Health of the National Institutes of Health (award R01MH110163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Bill & Melinda Gates Foundation or the National Institutes of Health. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with licence number SLN20143170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law 2000.
Katherine B Gibney  is supported by an  NHMRC   Early Career Fellowship. Shifalika Goenka  is supported by the  Bernard Lown Scholars   in Cardiovascular Health Program ,  Harvard School of Public Health  ( 2015-17 ) and a  Wellcome Trust  (Grant No  096735/B/11/Z ). Amador Goodridge  acknowledges support from  Sistema Nacional de Investigación (SNI) de Panamá and Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). Simon I Hay  is funded by grants from the  Bill & Melinda Gates Foundation  ( OPP1106023 ,  OPP1119467 ,  OPP1093011 , and  OPP1132415 ). Shariful Islam  received postdoctoral research fellowship from  The George Institute for Global Health  and career transition grants from  High Blood Pressure Research Foundation of Australia ; the  Ministry of Education Science and Technological Development of the Republic of Serbia  has cofinanced Serbian parts of this GBD related contribution throughout the Grant  OI 175 014 ; publication of results was not contingent upon Minsitry's prior censorship or approval.
Ai Koyanagi 's work was supported by the  Miguel Servet  contract financed by the  CP13/00150  and  PI15/00862  projects, integrated into the National R + D + I and funded by the  ISCIII-General Branch Evaluation and Promotion of Health Research -and the  European Regional Development Fund (ERDFFEDER). Tea Lallukka is supported by the  Academy of Finland  (Grants # 287488  and # 294096 ). Cheru T Leshargie  would like to thank  Debremarkos University  for the arrangement of internate service to communicate for tasks such as registering and communicating with GBD organiser and to download the manuscript and other supportive document, send on comments, and sending the coauthor form for the GBD. Miriam Levi  acknowledges institutional support received from  CeRIMP ,  Regional Centre for Occupational Diseases and Injuries, Local Health Unit Tuscany Center, Florence, Italy.
Toni Meier would like to acknowledge institutional support from the "Competence Cluster of Nutrition and Cardiovascular Health (nutriCARD)-JenaHalleLeipzig. Philip Mitchell's research is supported by an Australian NHMRC Program Grant number 1037196. Ulrich Mueller has received financial support from the German National Cohort Study (grant 01ER1511D). Charles Newton is supported by the Wellcome Trust, UK. Olanrewaju Oladimeji is a Senior Research Specialist at the Human Sciences Research Council (HSRC), South Africa and Doctoral Candidate at the University of KwaZuluNatal (UKZN), South Africa; we acknowledge the institutional support by leveraging on the existing organizational research facilities at HSRC and UKZN. AO was supported by intensificacion ISCIII FEDER funds and RETIC REDINREN. Richard Osborne was funded in part through an Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship #APP1059122.
MO is supported by U54HG007479 from the NIH, USA. Charles Parry would like to acknowledge support from the South African Medical Research Council. Norberto Perico acknowledges that work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group supported by the International Society of Nephrology (ISN). Konrad Pesudovs is support by Flinders University. William Petri is support by NIH grant AI043596; Kazem Rahimi is funded by an NIHR Career Development Fellowship and is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) and the Oxford Martin School. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of GBD Genitourinary Disease Expert Group supported by the International Society of Nephrology (ISN).
Stefano Tyrovola's work was supported by the Foundation for Education and European Culture (IPEP), the Sara Borrell postdoctoral programme (reference no CD15/00019 from the Instituto de Salud Carlos III (ISCIII Spain) and the Fondos Europeo de Desarrollo Regional (FEDER). Job F M van Boven's work was supported by the University Medical Center Groningen, University of Groningen, The Netherlands. Ronny Westerman would like to acknowledge that this work is on behalf of the German National Cohort funded by the German Ministry of Education and Research. Lijing L Yan is partly supported by the National Natural Sciences Foundation of China grants (71233001 and 71490732). Marcel Yotebieng is partially supported by the NIAID U01AI09629901 and the NICHD R01HD087993. All other authors declare no competing interests.
Background and Aims: Based on 19 nuclear simple sequence repeat markers and parental analysis, we aimed to identify and propose the pedigree of different accessions held at the Estación Experimental Agropecuaria Mendoza of the Instituto Nacional de Tecnología Agropecuaria germplasm collection. The results were compared with data recorded in large, international databases. Methods and Results: We identified 37 different cultivars, of which 18 were original and not previously identified. The parentage analysis showed that European cultivars, such as Muscat of Alexandria, Muscat à Petits Grains, Listán Prieto, Mollar Cano and Malbec, were involved in natural crossings resulting in different South American cultivars. Conclusions: Many of the cultivars identified here represent unique individuals based on their genotype.
Molecular analysis For each accession studied, young leaves were sampled and frozen at À80°C. The DNA was extracted according to  Laucou et al. (2011) , using a plant mini kit (DNeasy plant mini kit, Qiagen, Hilden, Germany). Two independent samples were analysed for each vine. A set of 19 microsatellites was selected to genotype the accessions, including the set of eight microsatellite markers previously chosen for the screening of grapevine cultivars by the European Vitis database  (This et al. 2004) , and the Vitis International Variety Catalogue (VIVC): VVS2  (Thomas and Scott 1993) , VVMD5, VVMD7, VVMD27, VVMD28, VVMD32  (Bowers et al. 1996 (Bowers et al. , 1999)) , VrZAG62, VrZAG79  (Sefc et al. 1999). Eleven additional markers previously used to assess grapevine diversity were also included: VVMD21  (Bowers et al.
Data analysis The SSR data were used to estimate different genetic parameters with the software CERVUS v3. 0. 7  (Kalinowski et al. 2007 ). The alleles number per locus (N a ), the range of allele size (bp), the frequency of null alleles (F na ), the expected (H e ) and observed heterozygosity (H o ), polymorphic information content (PIC) and average non-exclusion probability for identity of two siblings (NE-SI) were estimated. The loci data from the core set of eight SSR proposed by the VIVC were used to estimate the differences in allele size between our dataset and reference data for the same loci recorded in the international catalogue, which contains genotypes from all over the world  (Lacombe et al. 2013). After standardisation of SSR allele size, we identified the different accessions by comparing our microsatellite genotypes with the data contained in the VIVC database.
In a second step, all the SSR profiles were compared with the Institut National de la Recherche Agronomique (INRA) database generated from the French reference collection of INRA Domaine de Vassal  (Laucou et al. 2011) , which comprises more than 2500 different accessions. Once the identity of each accession was established, the relationship among genotypes and parentage analysis was established using the 19 SSR markers analysed. We compared the profile of each accession to verify synonyms and homonyms, and parentage analysis was performed with CERVUS v3. 0. 7  (Kalinowski et al. 2007). Parent assignment was based on loci matching and logarithm of the odds (LOD) scores comparing the 19 microsatellites data for each tested accession with its putative parents. The LOD score, which is the natural logarithm of the likelihood, was calculated separately for each candidate parent and for the three genotypes together.
Accession identity/original genotypes The 50 different V. vinifera accessions were grouped into 37 different genotypes after the analysis (Table  3 ). Data of all microsatellites analysed are presented in Tables  S1  and S2. The comparison of the eight reference loci with the VIVC database revealed that 19 genotypes (nine European and ten local cultivars) corresponded to previously genotyped cultivars already recorded in the database, whereas the 18 remaining corresponded to unreported genotypes with no homology in the VIVC database nor in that of the INRA Domaine de Vassal. Because no homology was found with those databases, and based on historical data and morphological observation, it is highly probable that many of these unreported genotypes had a local origin. Another possibility, although less strong, is that they were introduced from Europe, where they were lost thereafter.
This accession corresponds to the accession Criolla Chica No. 2 also present in the Vassal collection (see online database http://bioweb. supagro. inra. fr/collections_vigne). The accessions Quebranta and Quebranta del Perú, Mollar Negra and Negra Mole corresponded to Quebranta and Mollar Cano, respectively. The accessions Blanca Oval and Blanca Oval Cuyana were identical for 18 loci, with only one allele different in the VVMD32 marker (Table  S1 ). Similarly, the accessions Cereza and Cereza Italiana were identical for 17 loci, with differences in one allele in the markers VrZAG79 and VVIb01, the loci being heterozygous in one accession and homozygous in the other. These two cultivars did not present any ampelographic differences, excepting that Cereza Italiana presents lighter berry coloration than Cereza.
Acknowledgements This research was funded by projects  PRET 1251101 ,  PNFRU 1105062  and  REDGEN 1137021  of the  Instituto Nacional de Tecnología Agropecuaria (INTA), Argentina. Authors are grateful to  Dr Javier Ibañez  from  Instituto de Ciencias de la Vid y el Vino (Logroño, Spain ) for valuable discussion about software use and parentage analysis. Mention of trade names or proprietary products is for the convenience of the reader only and does not constitute endorsement or preferential treatment by  INTA.
3†Cultivar name adopted here. ‡No simple sequence repeat data are available in the VIVC database for these cultivars. ARG, Argentina; ESP, Spain; F, female; FR, France; GRE, Greece; H, hermaphrodite; M, male; ND, non-determined; PER, Peru; T, table grape; VIVC, Vitis International Variety Catalogue; W, winegrape; np, cultivar not present in the VIVC.
Passive satellite data have brought a new vision of the space-time distribution of CH 4 atmospheric mole fractions that complements that from the surface observations, albeit with comparatively lower precision and accuracy. The use of SCIAMACHY to infer CH 4 emissions in global inversions necessitates the use of accurate radiative transfer models in the retrieval of XCH 4 , which account for scattering by cirrus and aerosol. As a result of the loss of a SCIAMACHY detector at the end of 2005, instrumental noise increased, resulting in XCH 4 retrieval uncertainties increasing up to 40 ppb  [38, 40, 47]. Although GOSAT retrievals still present significant biases  [31, 48] , these data represent an important improvement both for random and residual systematic errors, the latter being now estimated to be only 4-6 ppb on individual column soundings  [48].
To mitigate the risk of the spaceborne pump laser development for MERLIN, the conceptual design will make use of the so-called "Future Laser System" FULAS development, which is a cooperation between ESA and DLR to advance spaceborne laser technology  [71]. This active method does not require reflected sunlight, and enables measurements to be made in all seasons at all latitudes, during daylight and night. The differential approach, the size of the laser spot (~120 m at the surface along the track), and the selective sampling guarantees low systematic errors (target < 3. 7 ppb) and measurements in broken cloud condition, with almost no contamination by aerosol or water vapor. MERLIN observations promise to enhance the effective constraints brought by satellite data on methane surface emissions once assimilated into atmospheric modelling systems.
In the presence of optically thick clouds, the Lidar beam can reach the surface when gaps between clouds occur due to the near-nadir view and the small Lidar ground spot. MERLIN can also provide XCH 4 measurements above dense stratiform clouds to be used as a reflective target instead of the surface target. As MERLIN has its own transmitter, its observations will produce data at high latitudes, in winter and at night. MERLIN XCH 4 will complement and enhance the existing observation systems for GHG monitoring, complementing surface-based, balloon, aircraft and passive satellite observations. In addition to XCH 4 , the MERLIN products will also include information on secondary climate-relevant parameters, for example, cloud top heights, surface retro-reflectance and, potentially, canopy height.
Following this objective, the MERLIN mission plans to deliver these products within two days of acquisition, as it is currently implemented for the GOSAT data used within CAMS. MERLIN will also be a scientific and technological demonstrator. On the scientific side, if XCH4 has the expected performances in terms of systematic errors, MERLIN will provide the first very lowbiased space-based GHG data. Such low-biased data will be highly useful to the scientific community, especially for atmospheric inversions. This will in turn facilitate the evolution of verification systems to test the GHG emissions reported at the country level, that is, independent data complementary to the country declarations to the UNFCCC based on inventories. Until now, only country-based declarations from inventories have been used to officially report GHG emissions.
Following this objective, the MERLIN mission plans to deliver these products within two days of acquisition, as it is currently implemented for the GOSAT data used within CAMS. MERLIN will also be a scientific and technological demonstrator. On the scientific side, if XCH 4 has the expected performances in terms of systematic errors, MERLIN will provide the first very low-biased space-based GHG data. Such low-biased data will be highly useful to the scientific community, especially for atmospheric inversions. This will in turn facilitate the evolution of verification systems to test the GHG emissions reported at the country level, that is, independent data complementary to the country declarations to the UNFCCC based on inventories. Until now, only country-based declarations from inventories have been used to officially report GHG emissions.
The elevation correction can be calculated assuming hydrostatic equilibrium: p sur f = p NWP exp [ SSE -Z NWP -R•T NWP (p) g] (4) where T NWP (p), p NWP , and Z NWP are the modelled vertical temperature profile, surface pressure and the surface elevation of the NWP analyses for the grid box in which the measurement is located. R denotes the gas constant for air. SSE is an ancillary data product that results from knowledge of the orbit height, instrument pointing, and measurement of the distance to the target (r T ) given by laser ranging methods. The geophysical auxiliary parameters (surface pressure, temperature profiles and water vapour profiles) are provided by (NWP) centres. Absorption cross sections of methane and other "interfering gases" for the desired temperature and pressure ranges are derived from line-by-line calculations using updated spectroscopic databases in combination with numerical models for the calculation of the integrated weighting function  [26].
The combined use of different data sets (including in situ observations) in a single model parameter optimization framework offers the perspective of applying the concept of carbon cycle data assimilation (e. g. ,  [93] ) to CH 4. Such a combined use of different data sets in a consistent manner will certainly be challenging, but potential inconsistencies will provide directions for improvements. The methodology proposed for the MERLIN observations is well demonstrated by ground-based and airborne measurements. It involves the measurement of the DAOD using Lidar signals at two distinct frequencies in the vicinity of a CH 4 absorption line originating either from ground or cloud top as the target. The measured DAOD values are converted to XCH 4 by means of auxiliary meteorological data from NWP and spectroscopic information.
Level 1 processing comprises vertically resolved Lidar data up to a height of 40 km at three different vertical sampling rates, as well as the generation of DAOD products on different horizontal averaging intervals (baseline is 50 km). Level 2 processing yields XCH 4 and level 3 products will consist of corresponding maps using an original Kalman-filtering algorithm. The full suite of the existing, high accuracy and reliable techniques, including flask sampling and passive remote sensing (e. g. , TCCON), will be used for validation of the MERLIN products as long as the mission is operating. Of particular importance for campaign-based validation are balloon-borne and airborne in situ sensors, as well as airborne remote sensors such as CHARM-F, the DLR's IPDA Lidar instrument operated onboard the German research aircraft HALO.
1% on XCH 4 ). The situation is quite different for the calculated systematic error budget of 3. 1 ppb where several contributors show similar errors. However, the largest impact of 2. 3 ppb arises from assumed detector non-linearity. Uncertainty reductions obtained here for the aggregated boreal regions (+53% on average) indicates that MERLIN observations will further constrain flux estimates at high northern latitudes, contrary to most passive missions. A successful MERLIN mission will provide the first low-bias, global, spaceborne IPDA Lidar data for an anthropogenic greenhouse gas, allowing for the significant improvement of our knowledge of the global CH 4 budget. The MERLIN mission will open the path for future active remote sensing missions dedicated to the monitoring of greenhouse gases from space.