dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the research staff of the  Vaccinology and Immunology Research Trials Unit at the Women's and Children's Hospital, Adelaide ,  Verity Hill , and  Mary Walker  for providing assistance;  Narelle Raven  for providing technical assistance; and  S. Guillot  and  E. Njamkepo  for providing polyclonal antibodies against ptx, fha, and prn. National serosurveys used pertussis toxin provided by  Glaxo SmithKline. N. W. and H. M. have been investigators in studies supported by pharmaceutical companies, including  GlaxoSmith-Kline or Sanofi-Pasteur. H. M. has been given travel support by.
Potential interactions between hepcidin and the combined iron marker (or ferritin) in the relation with Hb were also tested. In all analyses, hepcidin was transformed by its square root to meet the criterion of a normal parameter. For covariates with less than 3% missing observations (albumin and CRP), the median value was imputed in the multivariate analysis. A missing data category was created for the combined iron marker and EPO. Statistical analyses were performed with SAS 9. 2 (SAS Institute Inc. , Cary, NC, USA) and R 2. 15 (R Foundation for Statistical Computing, Vienna, Austria, 2012).
Acknowledgments We are grateful to and would like to thank  Pr Nicole Casadevall  for her advice and the EPO measurements performed in her laboratory ( Laboratoire d'Hematologie, Hopital Saint-Antoine, Paris, France ). We are indebted and thankful to  Dr Barbara Sasu  and  Vimal Patel  who advised us and performed hepcidin measurements at  Amgen laboratories (Amgen Inc. , Thousand Oaks, CA). In order to enable hepcidin measurements, this study was labelled as an Investigator Sponsored Study (BS) by  Amgen. NephroTest Steering Committee Martin Flamant,  P. Houillier ,  Jean Philippe Haymann ,  Jean-Jacques Boffa ,  Eric Thervet ,  Francois Vrtovsnik , Benedicte Stengel (benedicte. stengel@inserm. fr).
The NephroTest CKD cohort study is supported by grants from:  Inserm GIS-IReSP AO 8113LS TGIR (BS) ;  French Ministry of Health AOM 09114 (MFr) ;  Inserm AO 8022LS (BS) ;  Agence de la Biome  ´decine  R0 8156LL  (BS),  AURA (MFr) , and  Roche   2009-152-447G  (MFr). The Nephrotest initiative was also sponsored by unrestricted grants from  F. Hoffman-La Roche Ltd. (LM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was partly funded by  La Roche Ltd. M. Froissart  has received consulting or lecture fees or research funds from  Affymax ,  Genzyme ,  Hoffmann-La Roche ,  Novartis ,  Sandoz ,  Shire ,  Takeda , and  Vifor International. M. Froissart  has been employed by Amgen since January 1, 2011, but was a full-time academic associate professor during the time of study conception and data collection.
, 2013]. Current understanding of the global abundance and size distribution of lakes comes from two sources: map compilations and statistical extrapolations based on abundance-size relationships. Map compilations, such as the Global Lake and Wetland Database (GLWD) compiled by  Lehner and Döll [2004] , are based on digitized inventories, registers, archives, and remote sensing data from various sensor types. This compilation includes most of Earth's large lakes (e. g. , > 10 km 2 ) but underrepresents small lakes that are typically omitted from maps  [Downing et al. , 2006]. To overcome this deficiency, statistical extrapolations based on power law abundance-size relationships have been used to estimate the number of small lakes not covered by mapping [e. g. ,  Downing et al. , 2006; Minns et al.
and measure Earth's lakes, including their abundance, size (i. e. , area and perimeter), geographical distribution, elevation, and morphometric characteristics such as the shoreline development index (SDI). Here we summarize the characteristics of this GLObal WAter BOdies database (GLOWABO). Overall, we answer the fundamental question "how many lakes are on Earth?" and provide the foundation for improved quantification of limnetic contributions to the biogeochemical processes at large scales, including emissions and burial of carbon.
Methods and Database Contents We previously developed and validated an automated algorithm called GWEM (GeoCover TM Water bodies Extraction Method) for extracting lakes from highresolution satellite imagery  [Verpoorter et al. , 2012]. The GWEM algorithm extracts lakes from the GeoCover TM circa 2000 data set based on spectral characteristics and eliminates false detections due to mountain shadows by considering local elevations and the position of the sun at the time the of imaging. The GeoCover TM data set is mostly cloudless, minimizing false lake identification due to cloud shadows  [Verpoorter et al. , 2012]. When validated against a high-resolution map of Sweden  [Verpoorter et al. , 2012] , an area comprising nearly all difficult targets (cloud and mountain shadows, dark forest that can be mistaken as water, as well as turbid lakes, shallow lakes, and ice that are prone to be detected as land), the method yielded a performance index  [Bagli and Soille, 2004]  of 91% for lake area, and the lake count deviated by less than 3% from the lake data of the high-resolution map.
Metrics of lake shape, like the shoreline development index (SDI) can also be calculated in high accuracy based on the GWEM algorithm extractions  [Verpoorter et al. , 2012]. Detailed descriptions of the GWEM algorithm and its application to the GeoCover TM data set are found in  Verpoorter et al. [2012]. To create the GLOWABO database, we applied the GWEM algorithm to the global coverage of the GeoCover TM circa 2000 image data set. GeoCover TM circa 2000 is built from imagery of the Enhanced Thematic Mapper Plus (ETM+) sensor on board the Landsat 7 satellite collected in year 2000 ± 3 years. It is a static product, and it is delivered through https://zulu. ssc. nasa. gov/mrsid/. The Landsat imagery (about 8500 scenes) was pan sharpened to have 14. 25 m spatial resolution and is mosaicked into 882 nearly cloud-free tiles containing three spectral bands (ETM + 2, ETM + 4, and ETM + 7)  [Koeln et al.
, 1999; Lillesand and Kiefer, 1999]. When applying the GWEM algorithm, we used digital elevation data from the Shuttle Radar Topography Mission digital elevation model (SRTM-DEM) database v4. 1 from the Consortium for Spatial Information of the Consultative Group for International Agricultural research  [Jarvis et al. , 2008]. We filtered out all objects smaller than 9 pixels corresponding to a cutoff value of ~0. 002 km 2 (0. 2 ha) in size (i. e. , 9 × 14. 25 × 14. 25 m pixels) because water bodies smaller than this size are difficult to verify relative to image noise  [Verpoorter et al. , 2012]. Islands in lakes were considered land in the GLOWABO computation and coastal lagoons were not included in the analysis using a well-defined coastline polygon as a mask. We analyzed the complete global imagery with the exception of Antarctica and the glaciated areas of Greenland.
Global Abundance and Distribution of Lakes The database contains about 27 million water bodies larger than 0. 01 km 2 with a total surface area of 4. 76 × 10 6 km 2 excluding Caspian Sea (Figure  2 ). This is approximately 3. 5% of Earth's nonglaciated land surface area. About 22 million water bodies larger than 0. 01 km 2 are located between 60°N and 56°S, where elevation data are available. These lakes have a total surface area of 1. 89 × 10 6 km 2 , 1. 4% of nonglaciated land surface area. Hence, about 5 million lakes north of 60°N or south of 56°S, where there are no digital elevation data, make up a substantial fraction of the global lake area. There are about 117 million lakes greater than 0. 002 km 2 , covering a total area of 5. 0 × 10 6 km 2 , which corresponds to 3. 7% of Earth's nonglaciated land surface.
e. , the global continental interface between lakes and land (Figure  2c ). The SDI, a measure of roundness, decreases with decreasing lake size indicating that smaller lakes are rounder with less convoluted shorelines than large lakes (Figure  2d ). We compared the size distribution of lakes in the GLOWABO data set to estimates of global lake abundance based on statistical extrapolations of abundance-size relationships. Specifically, we compare our results to those of  Downing et al. [2006] , with the exception that we have adjusted estimates of their smallest size class (0. 001-0. 01 km 2 ) to have a higher minimum size (0. 002-0. 01 km 2 ) such that the estimates are directly comparable to the GLOWABO data. The total abundance of lakes > 0. 002 km 2 differs substantially between the statistical extrapolation approach (163 million) and our direct count by remote sensing (117 million).
Discussion and Conclusions There are 117 million lakes > 0. 002 km 2 covering 3. 7% of Earth's nonglaciated land area. These values represent the first direct, count of lakes by satellite-based remote sensing. The GLOWABO database constitutes a substantially improved answer to the question addressed by scientists for almost a century, on "how many lakes are there on Earth?"  [Halbfass, 1922; Thienemann, 1925; Downing et al. , 2006]. As previously shown at subcontinental scale  [McDonald et al. , 2012] , the size distribution of lakes reported here based on direct remote sensing is inconsistent with the prevailing view based on statistical extrapolations of the abundance of small lakes and their proportional contribution to total lake surface area  [Downing et al. , 2006; Minns et al. , 2008; Lazzarino et al.
, 2009; Hendriks et al. , 2012]. This has important implications for global-scale limnological understanding, specifically for the relative contributions of small versus large lakes to the global carbon cycle. Lake abundance is changing rapidly in some regions, and changes in the relative abundance of large and small lakes may reflect important characteristics of regional hydrologic and geomorphic processes  [Zhang et al. , 2009; Englund et al. , 2013; Liu et al. , 2013]. The GLOWABO database derives from a static remote sensing imagery and does not capture these changes. At the global-scale, cloudless data sets are composites of images taken over a multiyear periods. Because of time required to compile global cloud-free imagery, regional-scale analyses are better suited to tracking changes in lake abundance.
The influence of such changes is unknown, and the disappearance of lakes in some regions [e. g. ,  Liu et al. , 2013]  may be balanced out by the creation of both natural and man-made lakes in other regions [e. g. ,  Englund et al. , 2013]. Future development of Landsat imagery is likely to allow the study of dynamics of abundance and size of lakes at the decadal scale. The GLOWABO can also be combined with a large variety of data sets, archives, registers, or other remote sensing data in order to ameliorate the global information on lakes. For example, the Advanced Spaceborne Thermal Emission and Reflection Radiometer global digital elevation model data set may be used to fully represent the altitudinal size distribution of lakes even at high latitude where the SRTM-DEM data are missing. Our results constitute a valuable source of data on lake abundance and size distribution, to aid upscaling of biogeochemical fluxes, in particular, regarding processes that can be scaled to lake area, distribution, and perimeter.
Studies describing disabilities in RMDs are common, but they often focus on one condition, such as rheumatoid arthritis or OA  [8, 9] , and definitions used for disability vary substantially, making comparison between data difficult. The International Classification of Functioning, Disability and Health (WHO-ICF), the WHO's framework for measuring health and disability, provides a worldwide accepted framework, with specific core sets developed and validated for musculoskeletal conditions  [10] [11] [12] [13] [14]. These core sets, which are short lists of WHO-ICF categories relevant to specific diseases, serve as practical tools for clinical practice and allow for standardization of data for health information and research  [13, 15]. A population-based, self-reported disability survey, the 2008-2009 Disability-Health (DH) Survey, was conducted in France.
Disability-health (DH) survey The data for this study were from the 2008-2009 DH survey (available at http://www. cmh. ens. fr/greco/enquetes/XML/lil-0459. xml), a cross-sectional population-based survey undertaken by the French National Institute of Statistics and Economic Studies and the French Head Office of Research, Studies, Evaluation and Statistics of the Social Affairs Ministry to describe disabilities. The present work focused on the part of the survey involving the population living in a household (Figure  1 ). The DH survey methodology has been described in detail elsewhere  [2]. Briefly, a two-stage method was used. First, a preliminary filter-survey was conducted to identify people who were disabled. According to their answers to the filter survey, people were classified into four levels of presumed disability severity, from 1 (no disability) to 4 (high level of disability).
The sampling rate was higher for people living in the geographic areas that were more intensively sampled. From March to July 2008, data were collected for 39 065 subjects across the administrative departments in France. Trained investigators used the computer-assisted interview (CAPI) format to collect data from people in their homes. The questionnaire was 160 pages long and each interview lasted between 2 and 3 hours. A household member or a proxy could answer for identified survey respondents not able to answer alone. The response rate was 76. 6%, corresponding to 29 931 subjects with complete data (age range 0-106 years old). Each respondent was assigned a weight reflecting the probability of being investigated and answering the questionnaire, which allowed for estimating representative results at a national level.
Definition of rheumatic and musculoskeletal disorders (RMDs) RMDs were self-reported. Participants were asked to identify their diseases from a list of 52 disorders, which is known to improve the accuracy of self-reporting  [16]. Among the disorders, six musculoskeletal diseases were identified: OA (knee, hip and other OA), low back pain, neck pain, inflammatory arthritis (including rheumatoid arthritis), spine deformity, and osteoporosis. The survey also collected data on co-morbidities with the same list, which included cardiovascular, neurological, psychiatric, respiratory, sensorial, endocrine, digestive, urologic, dermatologic disorders, and sequelae of injury (Table  S1 ), as a numerical variable (number of co-morbidities) or dichotomous variable (comorbidity represented at least one of these conditions).
Because the prevalence of diseases and frequencies of disability vary by age and sex, we computed AAFs by age group (#20, 20-40, 41-60, 61-80, and. 80 years old) and sex. Statistical analyses involved use of SAS 9. 2 (SAS Inst. , Cary, NC). Sampling weights were accounted for by specific SAS procedures for handling complex sample designs. AAFs were computed with use of the macro developed by Ru ¨ckinger et al. [18]  with modification to account for sample design  [2].
Discussion We show for the first time the frequency and impact of RMDs on disability at the individual and societal levels in a developed country, using data representative of the whole non-institutionalized population. Our findings highlight that RMDs are highly prevalent and have a significant impact on limitations in activities and restriction in participations, which suggests the need for more efforts to improve care and research in this field. One strength of our study is that we assessed disabilities encountered with RMDs from an individual perspective by presenting ORs, which is probably useful for patients and clinicians, as well as a societal perspective by presenting AAFs, which is more useful for a general audience and policymakers. Other strengths of this work are that our results are representative of the population living in a household and are therefore valuable to policymakers.
INTRODUCTION The biodiversity of communities is generally studied over limited temporal and spatial scales  (Grime, 1998; Duffy, 2008). Longterm and global-scale studies should serve as the foundations for our understanding of ecosystem functioning by allowing the development of ecological rules that are based on long-term historical investigations  (Balvanera et al. , 2006; Cardinale et al. , 2006). Paleodiversity investigations at a millennium scale, which may cover the current interglacial period, i. e. , the Holocene, should inform on macroecological processes that act over periods longer than what observations and monitoring are capable of providing. Widespread fossil records such as pollen could be analyzed from the perspective of biodiversity assessment  (Mitchell, 2010). Additionally, pollen records are increasingly available in the form of continental databases containing fossil pollen data at large spatial and temporal scales  (Grimm et al.
, 2007; Fyfe et al. , 2009). However, studies that are dedicated to the reconstruction of past vegetation diversity based on fossil pollen covering an area greater than that represented by a single site are rare despite increased awareness of the benefits of diversity assessment at the global scale (e. g. ,  Kier et al. , 2005). This paper aims to analyze palynological diversity patterns in boreal North America using pollen data that has been archived in the freely available database Neotoma (www. neotomadb. org). We tested the effect of ecological maturation of forest biomes on diversity structure facing climate changes. Because the early Holocene was marked by major climatic rearrangements, together with associated increased species immigration, dispersal, and reorganization processes, we would expect a higher palynological diversity at the origin immediately after deglaciation.
, 2001) , we have suggested that they can provide a geographic framework for past palynological diversity, until a framework for long-term ecological conditions, with a regional coverage, is available. Fossil pollen series held in the Neotoma database refer to sites where sediments have been extracted for pollen analyses. These pollen series were extracted from different sedimentary archives (peat, mire, lakes) and were subjected to different field or laboratory protocols, which potentially result in methodological bias. To reduce potential bias, several criteria were used to select the sites that we used for the palynological diversity reconstructions. Pollen series presenting only isolated specimens and surface samples were systematically excluded. Because chronological control is one of the keystones of paleoecological reconstructions, we selected only well-dated cores including at least one geochronological control point (i.
e. , 14 C or 210 Pb dating, tephra layer, etc. ) for every 2500 year interval at least; a minimum of five dating points were required through the Holocene, starting 11,650 calendar years before the present, "present" being AD 1950 by convention  (Walker et al. , 2009). When chronologies were expressed in radiocarbon years before present ( 14 C BP), we used a version of the Intcal04 calibration curve  (Reimer et al. , 2004) , which was smoothed using a 250-year fast Fourier transform filter, to convert uncalibrated chronologies to calendar years before present (hereafter, cal BP). This procedure limits deviations from conventional age-depth modeling methods  (Grimm, 2008). We investigated the pollen series that spanned the last 15,500 years due to the low number of pollen series showing data before this date.
Most sedimentary series do not entirely recover the last 15,500 years, but generally start to accumulate pollen and sediments after deglaciation, which was not synchronous across North America  (Dyke, 2004). Finally, we only analyzed pollen data for terrestrial vegetation including trees, shrubs, upland herbs, and vascular cryptogams. For simplification, the term "pollen" is extended to include spores of vascular cryptogams. The taxonomic resolution that was taken into account varied for different taxa; species level information was used when available. Numerous pollen taxa were included at the genus or family level, but this depended on specific determinations. However, taxonomy was homogeneous among all pollen series. Before calculating diversity and to ensure equal time resolution between sites, pollen counts were averaged over 1000-year consecutive time windows that were centred on millennia, For example, 1000 cal BP represented the time window 500-1500 cal BP (noted as 1 ka afterwards for simplification).
ACKNOWLEDGMENTS We gratefully acknowledge the working group of the  Neotoma Paleoecology Database  (http://www. neotomadb. org), and Neotoma data contributors. Financial support was provided to OB by the  Forest Complexity Modeling program (FCM, NSERC). The research was conducted within the framework of the  International Associated Laboratory (LIA France-Canada   CNRS-UM2-EPHE-UQAT-UQAM-UQAC ) entitled MONTABOR (Forêts montagnardes et boréales: chrono-écologie et aménagement écosystémique durable). CC also benefited from a  Centre national de la recherche scientifique (CNRS)  mobility status at the  Centre d'étude de la forêt (CEF, Montreal, Canada ) and a visiting grant from  CEF , where OB and YB are affiliated. We warmly thank  Julie Aleman  for early discussion and comments, and we are indebted to  Professors Richard Field  and the Frontiers referees for useful comments on an earlier version of this manuscript.
p:/ / \V\V•\-. ,•. cisg. law. pacc. edu/. The Cl. SG is uniquely important to the regulation of commerce, particularly when something goes wrong. \Xlho cares about a contract when everything goes right? \'V'hcn goods are destroyed or if the contract is somehow breached, then you have the Convention to look to. The C1SG is a unigue convention because its interpretation relies greatly and increasingly upon a Common Law style of interpretation. Not that the judges in Switzerland just take the word of U. S. judges, lawyers or academics on how to inteq)tet the convention; of course not. Instead, la\vyers from around the "\VOrld must make arguments based on precedent. For many years, however, precedent was unavailable or available only in limited circles. To address this short-coming•we at Pace La\v School have for several years collecting an internet database.
Some of you "\Vill probably contribute to it. Pace's ClSG database collects thousands over arbitral, judicial and academic interpretations of this key UN convention. These interpretations arc used by administrators and judges to interpret the convention. These interpretations are essential precisely because they enjoy some form of value as controlling precedent-i. e. for common lmv style interpretation and use of what o1ben;vise looks like a code. \\lith the CISG database, la"''yers around the \vorld have equal opportunity to access the precedent upon which to base their arguments. It is an important tool for leveling the playing field. But the field '\vould remain tilted if \VC did not also provide non-U. S. la\vycrs "\Vith the opportunity to develop the skills requjred to argue from precedent.
In the sensorimotor system defined in the previous section, M is 18-dimensional and S is 6-dimensional. f corresponds to the transformation defined section 2. 1 and illustrated Figure  4 , and has a Gaussian noise with a standard deviation of 0. 01. By collecting (m, s) pairs through vocal experiments, the agent learns the joint probability distribution defined over the entire sensorimotor space SM (therefore 24-dimensional). This distribution is encoded in a Gaussian Mixture Model (GMM) of 28 components, i. e. , a weighted sum of 28 multivariate normal distributions 3. Let us note G SM this GMM. It is learnt using an online version of the Expectation-Maximization (EM) algorithm  (Dempster et al. , 1977)  proposed by  Calinon (2009)  where incoming data are considered incrementally. Each update is executed once each sm_step(= 400) vocalizations are collected.